types of research studies cohort

Cohort Study: Definition, Designs & Examples

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BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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A cohort study is a type of longitudinal study where a group of individuals (cohort), often sharing a common characteristic or experience, is followed over an extended period of time to study and track outcomes, typically related to specific exposures or interventions.

In cohort studies, the participants must share a common factor or characteristic such as age, demographic, or occupation. A “cohort” is a group of subjects who share a defining characteristic.

Cohort studies are observational, so researchers will follow the subjects without manipulating any variables or interfering with their environment.

This type of study is beneficial for medical researchers, specifically in epidemiology, as scientists can use data from cohort studies to understand potential risk factors or causes of a disease.

Before any appearance of the disease is investigated, medical professionals will identify a cohort, observe the target participants over time, and collect data at regular intervals.

Weeks, months, or years later, depending on the duration of the study design, the researchers will examine any factors that differed between the individuals who developed the condition and those who did not.

They can then determine if an association exists between an exposure and an outcome and even identify disease progression and relative risk.

Retrospective

A retrospective cohort study is a type of observational research that uses existing past data to identify two groups of individuals—those with the risk factor or exposure (cohort) and without—and follows their outcomes backward in time to determine the relationship.
In a retrospective study , the subjects have already experienced the outcome of interest or developed the disease before starting the study.
The researchers then look back in time to identify a cohort of subjects before developing the disease and use existing data, such as medical records, to discover any patterns.

Prospective

A prospective cohort study is a type of longitudinal research where a group of individuals sharing a common characteristic (cohort) is followed over time to observe and measure outcomes, often to investigate the effect of suspected risk factors.

In a prospective study , the investigators will design the study, recruit subjects, and collect baseline data on all subjects before they have developed the outcomes of interest.

The subjects are followed and observed over a period of time to gather information and record the development of outcomes.

Determine cause-and-effect relationships

Because researchers study groups of people before they develop an illness, they can discover potential cause-and-effect relationships between certain behaviors and the development of a disease.

Provide extensive data

Cohort studies enable researchers to study the causes of disease and identify multiple risk factors associated with a single exposure. These studies can also reveal links between diseases and risk factors.

Enable studies of rare exposures

Cohort studies can be very useful for evaluating the effects and risks of rare diseases or unusual exposures, such as toxic chemicals or adverse effects of drugs.

Can measure a continuously changing relationship between exposure and outcome

Because cohort studies are longitudinal, researchers can study changes in levels of exposure over time and any changes in outcome, providing a deeper understanding of the dynamic relationship between exposure and outcome.

Limitations

Time consuming and expensive.

Cohort studies usually require multiple months or years before researchers are able to identify the causes of a disease or discover significant results. Because of this, they are often more expensive than other types of studies. Retrospective studies, though, tend to be cheaper and quicker than prospective studies as the data already exists.

Require large sample sizes

Cohort studies require large sample sizes in order for any relationships or patterns to be meaningful. Researchers are unable to generate results if there is not enough data.

Prone to bias

Because of the longitudinal nature of these studies, it is common for participants to drop out and not complete the study. The loss of follow-up in cohort studies means researchers are more likely to estimate the effects of an exposure on an outcome incorrectly.

Unable to discover why or how a certain factor is associated with a disease

Cohort studies are used to study cause-and-effect relationships between a disease and an outcome. However, they do not explain why the factors that affect these relationships exist. Experimental studies are required to determine why a certain factor is associated with a particular outcome.

The Framingham Heart Study

Studied the effects of diet, exercise, and medications on the development of hypertensive or arteriosclerotic cardiovascular disease, in a longitudinal population-based cohort.

The Whitehall Study

The initial prospective cohort study examined the association between employment grades and mortality rates of 17139 male civil servants over a period of ten years, beginning in 1967. When the Whitehall Study was conducted, there was no requirement to obtain ethical approval for scientific studies of this kind.

The Nurses’ Health Study

Researched long-term effects of nurses” nutrition, hormones, environment, and work-life on health and disease development.

The British Doctors Study

This was a prospective cohort study that ran from 1951 to 2001, investigating the association between smoking and the incidence of lung cancer.

The Black Women’s Health Study

Gathered information about the causes of health problems that affect Black women.

Millennium Cohort Study

Found evidence to show how various circumstances in the first stages of life can influence later health and development. The study began with an original sample of 18,818 cohort members.

The Danish Cohort Study of Psoriasis and Depression

Studied the association between psoriasis and the onset of depression.

The 1970 British Cohort Study

Followed the lives of around 17,000 people born in England, Scotland, and Wales in a single week of 1970.

Frequently Asked Questions

1. are case-control studies and cohort studies the same.

While both studies are commonly used among medical professionals to study disease, they differ.

Case-control studies are performed on individuals who already have a disease (cases) and compare them with individuals who share similar characteristics but do not have the disease (controls).

In cohort studies, on the other hand, researchers identify a group before any of the subjects have developed the disease. Then after an extended period, they examine any factors that differed between the individuals who developed the condition and those who did not.

2. What is the difference between a cross-sectional study and a cohort study?

Like case-control and cohort studies, cross-sectional studies are also used in epidemiology to identify exposures and outcomes and compare the rates of diseases and symptoms of an exposed group with an unexposed group.

However, cross-sectional studies analyze information about a population at a specific point in time, while cohort studies are carried out over longer periods.

3. What is the difference between cohort and longitudinal studies?

A cohort study is a specific type of longitudinal study. Another type of longitudinal study is called a panel study which involves sampling a cross-section of individuals at specific intervals for an extended period.

Panel studies are a type of prospective study, while cohort studies can be either prospective or retrospective.

Barrett D, Noble H. What are cohort studies? Evidence-Based Nursing 2019; 22:95-96.

Kandola, A.A., Osborn, D.P.J., Stubbs, B. et al. Individual and combined associations between cardiorespiratory fitness and grip strength with common mental disorders: a prospective cohort study in the UK Biobank. BMC Med 18, 303 (2020). https://doi.org/10.1186/s12916-020-01782-9

Marmot, M. G., Rose, G., Shipley, M., & Hamilton, P. J. (1978). Employment grade and coronary heart disease in British civil servants. Journal of Epidemiology & Community Health, 32(4), 244-249.

Rosenberg, L., Adams-Campbell, L., & Palmer, J. R. (1995). The Black Women’s Health Study: a follow-up study for causes and preventions of illness. Journal of the American Medical Women’s Association (1972), 50(2), 56-58.

Samer Hammoudeh, Wessam Gadelhaq and Ibrahim Janahi (November 5th 2018). Prospective Cohort Studies in Medical Research, Cohort Studies in Health Sciences, R. Mauricio Barría, IntechOpen, DOI: 10.5772/intechopen.76514. Available from: https://www.intechopen.com/chapters/60939

Setia M. S. (2016). Methodology Series Module 1: Cohort Studies. Indian journal of dermatology, 61(1), 21–25. https://doi.org/10.4103/0019-5154.174011

Zabor, E. C., Kaizer, A. M., & Hobbs, B. P. (2020). Randomized Controlled Trials. Chest, 158(1). https://doi.org/10.1016/j.chest.2020.03.013

Further Information

Cohort Effect? Definition and Examples
Barrett, D., & Noble, H. (2019). What are cohort studies?. Evidence-based nursing, 22(4), 95-96.
The Whitehall Studies
Euser, A. M., Zoccali, C., Jager, K. J., & Dekker, F. W. (2009). Cohort studies: prospective versus retrospective. Nephron Clinical Practice, 113(3), c214-c217.

Research Methodology

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

Quantitative study designs: Cohort Studies

Quantitative study designs.

Introduction
Cohort Studies
Randomised Controlled Trial
Case Control
Cross-Sectional Studies
Study Designs Home

Cohort Study

Did you know that the majority of people will develop a diagnosable mental illness whilst only a minority will experience enduring mental health? Or that groups of people at risk of having high blood pressure and other related health issues by the age of 38 can be identified in childhood? Or that a poor credit rating can be indicative of a person’s health status?

These findings (and more) have come out of a large cohort study started in 1972 by researchers at the University of Otago in New Zealand. This study is known as The Dunedin Study and it has followed the lives of 1037 babies born between 1 April 1972 and 31 March 1973 since their birth. The study is now in its fifth decade and has produced over 1200 publications and reports, many of which have helped inform policy makers in New Zealand and overseas.

In Introduction to Study Designs, we learnt that there are many different study design types and that these are divided into two categories: Experimental and Observational. Cohort Studies are a type of observational study.

What is a Cohort Study design?

Cohort studies are longitudinal, observational studies, which investigate predictive risk factors and health outcomes.
They differ from clinical trials, in that no intervention, treatment, or exposure is administered to the participants. The factors of interest to researchers already exist in the study group under investigation.
Study participants are observed over a period of time. The incidence of disease in the exposed group is compared with the incidence of disease in the unexposed group.
Because of the observational nature of cohort studies they can only find correlation between a risk factor and disease rather than the cause.

Cohort studies are useful if:

There is a persuasive hypothesis linking an exposure to an outcome.
The time between exposure and outcome is not too long (adding to the study costs and increasing the risk of participant attrition).
The outcome is not too rare.

The stages of a Cohort Study

A cohort study starts with the selection of a group of participants (known as a ‘cohort’) sourced from the same population, who must be free of the outcome under investigation but have the potential to develop that outcome.
The participants must be identical, having common characteristics except for their exposure status.
The participants are divided into two groups – the first group is the ‘exposure’ group, the second group is free of the exposure.

Types of Cohort Studies

There are two types of cohort studies: Prospective and Retrospective .

How Cohort Studies are carried out

Adapted from: Cohort Studies: A brief overview by Terry Shaneyfelt [video] https://www.youtube.com/watch?v=FRasHsoORj0)

Which clinical questions does this study design best answer?

What are the advantages and disadvantages to consider when using a cohort study, what does a strong cohort study look like.

The aim of the study is clearly stated.
It is clear how the sample population was sourced, including inclusion and exclusion criteria, with justification provided for the sample size. The sample group accurately reflects the population from which it is drawn.
Loss of participants to follow up are stated and explanations provided.
The control group is clearly described, including the selection methodology, whether they were from the same sample population, whether randomised or matched to minimise bias and confounding.
It is clearly stated whether the study was blinded or not, i.e. whether the investigators were aware of how the subject and control groups were allocated.
The methodology was rigorously adhered to.
Involves the use of valid measurements (recognised by peers) as well as appropriate statistical tests.
The conclusions are logically drawn from the results – the study demonstrates what it says it has demonstrated.
Includes a clear description of the data, including accessibility and availability.

What are the pitfalls to look for?

Confounding factors within the sample groups may be difficult to identify and control for, thus influencing the results.
Participants may move between exposure/non-exposure categories or not properly comply with methodology requirements.
Being in the study may influence participants’ behaviour.
Too many participants may drop out, thus rendering the results invalid.

Critical appraisal tools

To assist with the critical appraisal of a cohort study here are some useful tools that can be applied.

Critical appraisal checklist for cohort studies (JBI)

CASP appraisal checklist for cohort studies

Real World Examples

Bell, A.F., Rubin, L.H., Davis, J.M., Golding, J., Adejumo, O.A. & Carter, C.S. (2018). The birth experience and subsequent maternal caregiving attitudes and behavior: A birth cohort study . Archives of Women’s Mental Health .

Dykxhoorn, J., Hatcher, S., Roy-Gagnon, M.H., & Colman, I. (2017). Early life predictors of adolescent suicidal thoughts and adverse outcomes in two population-based cohort studies . PLoS ONE , 12(8).

Feeley, N., Hayton, B., Gold, I. & Zelkowitz, P. (2017). A comparative prospective cohort study of women following childbirth: Mothers of low birthweight infants at risk for elevated PTSD symptoms . Journal of Psychosomatic Research , 101, 24–30.

Forman, J.P., Stampfer, M.J. & Curhan, G.C. (2009). Diet and lifestyle risk factors associated with incident hypertension in women . JAMA: Journal of the American Medical Association , 302(4), 401–411.

Suarez, E. (2002). Prognosis and outcome of first-episode psychoses in Hawai’i: Results of the 15-year follow-up of the Honolulu cohort of the WHO international study of schizophrenia . ProQuest Information & Learning, Dissertation Abstracts International: Section B: The Sciences and Engineering , 63(3-B), 1577.

Young, J.T., Heffernan, E., Borschmann, R., Ogloff, J.R.P., Spittal, M.J., Kouyoumdjian, F.G., Preen, D.B., Butler, A., Brophy, L., Crilly, J. & Kinner, S.A. (2018). Dual diagnosis of mental illness and substance use disorder and injury in adults recently released from prison: a prospective cohort study . The Lancet. Public Health , 3(5), e237–e248.

References and Further Reading

Greenhalgh, T. (2014). How to Read a Paper : The Basics of Evidence-Based Medicine , John Wiley & Sons, Incorporated, Somerset, United Kingdom.

Hoffmann, T. a., Bennett, S. P., & Mar, C. D. (2017). Evidence-Based Practice Across the Health Professions (Third edition. ed.): Elsevier.

Song, J.W. & Chung, K.C. (2010). Observational studies: cohort and case-control studies . Plastic and Reconstructive Surgery , 126(6), 2234-42.

Mann, C.J. (2003). Observational research methods. Research design II: cohort, cross sectional, and case-control studies . Emergency Medicine Journal , 20(1), 54-60.

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http://orcid.org/0000-0003-4308-4219 David Barrett 1 ,
Helen Noble 2
1 Faculty of Health Sciences , University of Hull , Hull , UK
2 School of Nursing and Midwifery , Queen’s University Belfast , Belfast , UK
Correspondence to Dr David Barrett, Faculty of Health Sciences, University of Hull, Hull HU6 7RX, UK; D.I.Barrett{at}hull.ac.uk

https://doi.org/10.1136/ebnurs-2019-103183

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statistics and research methods

In 1951, Richard Doll and Austin Bradford-Hill commenced a ground-breaking research project by writing to all registered doctors in the UK to ask about their smoking habits. The British Doctors Study recruited and followed-up over 40 000 participants, monitoring mortality rates and causes of death over the subsequent years and decades. Even by the time of the first set of preliminary results in 1954, there was evidence to link smoking with lung cancer and increased mortality. 1 Over the following decades, the study provided further definitive evidence of the health risks from smoking, and was extended to explore other causes of death (eg, heart disease) and other behavioural variables (eg, alcohol intake).

The Doctors Health Survey is one of the largest, most ambitious and best-known cohort studies and demonstrates the value of this approach in supporting our understanding of disease risk. However, as a method, cohort studies can have much wider applications. This article provides an overview of cohort studies, identifying the opportunities and challenges they present to researchers, and the role they play in developing the evidence base for nursing and healthcare more broadly.

Cohort studies are a type of longitudinal study —an approach that follows research participants over a period of time (often many years). Specifically, cohort studies recruit and follow participants who share a common characteristic, such as a particular occupation or demographic similarity. During the period of follow-up, some of the cohort will be exposed to a specific risk factor or characteristic; by measuring outcomes over a period of time, it is then possible to explore the impact of this variable (eg, identifying the link between smoking and lung cancer in the British Doctors Study.) Cohort studies are, therefore, of particular value in epidemiology, helping to build an understanding of what factors increase or decrease the likelihood of developing disease.

Though the most high-profile types of cohort studies are usually related to large epidemiological research studies, they are not the only application of this method. Within nursing research, cohort studies have focused on the progress of nurses through their education and careers. Li et al —as part of the European NEXT study group—recruited almost 6500 female nurses who, at the time of recruitment, had no intention to leave the profession. The study followed the cohort up for a year, identifying that 8% developed the intention to leave nursing, often due to issues such as poor salary or limited promotion prospects. 4

Usually, cohort studies should adopt a purely observational approach. However, some research is labelled as a cohort study while exploring the effectiveness of specific interventions. For example, Lansperger et al explored nurse practitioner (NP)-led critical care in a large university hospital in the USA. They collected data on all patients who were admitted to the intensive care unit over a 3-year period. Patients from this cohort were cared for by teams led by either doctors or NPs, and outcomes (primarily 90-day mortality) were monitored. By comparing the groups, the researchers established that outcomes were similar regardless of whether patient care was led by a doctor or an NP. 5

Strengths and weaknesses of cohort studies

Cohort studies are an effective and robust method of establishing cause and effect. As they are usually large in size, researchers are able to draw confident conclusions regarding the link between risk factors and disease. In many cases, because participants are often free of disease at the commencement of the study, cohort studies are particularly useful at identifying the timelines over which certain behaviours can contribute to disease.

However, the nature of cohort studies can cause challenges. Collecting prospective data on thousands of participants over many years (and sometimes decades) is complex, time-consuming and expensive. Participants may drop out, increasing the risk of bias; equally, it is possible that the behaviour of participants may alter because they are aware that they are part of a study cohort. The analysis of data from these large-scale studies is also complex, with large numbers of confounding variables making it difficult to link cause and effect. Where cohort (or ‘cohort-like’) studies link to a specific intervention (as in the case of the Lansperger et al study into nursing practitioner-led critical care 5 ), the lack of randomisation to different arms of the study makes the approach less robust than randomised controlled trials.

One way of making a cohort study less time-consuming is to carry it out retrospectively. This is a more pragmatic approach, as it can be completed more quickly using historical data. For example, Wray et al used a retrospective cohort study to identify factors that were associated with non-continuation of students on nursing programmes. By exploring characteristics in five previous cohorts of students, they were able to identify that factors such as being older and/or local were linked to higher levels of continuation. 6

However, this retrospective approach increases the risk of bias in the sampling of the cohort, with greater likelihood of missing data. Retrospective cohort studies are also weakened by the fact that the data fields available are not designed with the study in mind—instead, the researcher simply has to make use of whatever data are available, which may hinder the quality of the study.

Reporting and critiquing of cohort studies

When reporting a cohort study, it is recommended that STROBE guidance 7 is followed. STROBE is an international, collaborative enterprise which includes experts with experience in the organisation and of dissemination of observational studies, including cohort studies. The aim is to STrengthen the Reporting of OBservational studies in Epidemiology. The STROBE checklist for cohort studies - available at https://www.strobe-statement.org/fileadmin/Strobe/uploads/checklists/STROBE_checklist_v4_combined.pdf - includes detail related to the introduction/methods/results/discussion of the study.

Critical appraisal of any cohort study is essential to identify the strengths and weaknesses of the study and to determine the usefulness and validity of the study findings. Components of critical appraisal in relation to cohort studies include evaluation of the study design in relation to the research question, assessment of the methodology, suitability of statistical methods used, conflicts of interest and how relevant the research is to practice. 8–10

Cohort studies are the cornerstone of epidemiological research, providing an understanding of risk factors for disease based on findings in thousands of participants over many years. Disease prevention guidelines used by nurses and other healthcare professionals across the globe are based on the evidence from high-profile studies, such as the British Doctors Study, the Framingham Heart Study and the Nurses’ Health Study. However, cohort studies offer opportunities outside epidemiology: in nursing research, the approach is useful in exploring areas such as factors that influence students’ progression through their programme or nurses’ progression through their career.

This approach to research does bring with it some important challenges—often related to their size, complexity and longevity. However, with careful planning and implementation, cohort studies can make valuable contributions to the development of evidence-based healthcare.

Colditz GA ,
Philpott SE ,
Hankinson SE
Galatsch M ,
Siegrist J , et al
Landsperger JS ,
Semler MW ,
Wang L , et al
Aspland J ,
Barrett D , et al
von Elm E ,
Altman DG ,
Egger M , et al
Rochon PA ,
Gurwitz JH ,
Sykora K , et al
Critical Appraisal Skills Programme

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Commissioned; internally peer reviewed.

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Case-control and Cohort studies: A brief overview

Posted on 6th December 2017 by Saul Crandon

Introduction

Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence . These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies (1). Although these studies are not ranked as highly as randomised controlled trials, they can provide strong evidence if designed appropriately.

Case-control studies

Case-control studies are retrospective. They clearly define two groups at the start: one with the outcome/disease and one without the outcome/disease. They look back to assess whether there is a statistically significant difference in the rates of exposure to a defined risk factor between the groups. See Figure 1 for a pictorial representation of a case-control study design. This can suggest associations between the risk factor and development of the disease in question, although no definitive causality can be drawn. The main outcome measure in case-control studies is odds ratio (OR) .

Figure 1. Case-control study design.

Cases should be selected based on objective inclusion and exclusion criteria from a reliable source such as a disease registry. An inherent issue with selecting cases is that a certain proportion of those with the disease would not have a formal diagnosis, may not present for medical care, may be misdiagnosed or may have died before getting a diagnosis. Regardless of how the cases are selected, they should be representative of the broader disease population that you are investigating to ensure generalisability.

Case-control studies should include two groups that are identical EXCEPT for their outcome / disease status.

As such, controls should also be selected carefully. It is possible to match controls to the cases selected on the basis of various factors (e.g. age, sex) to ensure these do not confound the study results. It may even increase statistical power and study precision by choosing up to three or four controls per case (2).

Case-controls can provide fast results and they are cheaper to perform than most other studies. The fact that the analysis is retrospective, allows rare diseases or diseases with long latency periods to be investigated. Furthermore, you can assess multiple exposures to get a better understanding of possible risk factors for the defined outcome / disease.

Nevertheless, as case-controls are retrospective, they are more prone to bias. One of the main examples is recall bias. Often case-control studies require the participants to self-report their exposure to a certain factor. Recall bias is the systematic difference in how the two groups may recall past events e.g. in a study investigating stillbirth, a mother who experienced this may recall the possible contributing factors a lot more vividly than a mother who had a healthy birth.

A summary of the pros and cons of case-control studies are provided in Table 1.

Table 1. Advantages and disadvantages of case-control studies.

Cohort studies

Cohort studies can be retrospective or prospective. Retrospective cohort studies are NOT the same as case-control studies.

In retrospective cohort studies, the exposure and outcomes have already happened. They are usually conducted on data that already exists (from prospective studies) and the exposures are defined before looking at the existing outcome data to see whether exposure to a risk factor is associated with a statistically significant difference in the outcome development rate.

Prospective cohort studies are more common. People are recruited into cohort studies regardless of their exposure or outcome status. This is one of their important strengths. People are often recruited because of their geographical area or occupation, for example, and researchers can then measure and analyse a range of exposures and outcomes.

The study then follows these participants for a defined period to assess the proportion that develop the outcome/disease of interest. See Figure 2 for a pictorial representation of a cohort study design. Therefore, cohort studies are good for assessing prognosis, risk factors and harm. The outcome measure in cohort studies is usually a risk ratio / relative risk (RR).

Figure 2. Cohort study design.

Cohort studies should include two groups that are identical EXCEPT for their exposure status.

As a result, both exposed and unexposed groups should be recruited from the same source population. Another important consideration is attrition. If a significant number of participants are not followed up (lost, death, dropped out) then this may impact the validity of the study. Not only does it decrease the study’s power, but there may be attrition bias – a significant difference between the groups of those that did not complete the study.

Cohort studies can assess a range of outcomes allowing an exposure to be rigorously assessed for its impact in developing disease. Additionally, they are good for rare exposures, e.g. contact with a chemical radiation blast.

Whilst cohort studies are useful, they can be expensive and time-consuming, especially if a long follow-up period is chosen or the disease itself is rare or has a long latency.

A summary of the pros and cons of cohort studies are provided in Table 2.

The Strengthening of Reporting of Observational Studies in Epidemiology Statement (STROBE)

STROBE provides a checklist of important steps for conducting these types of studies, as well as acting as best-practice reporting guidelines (3). Both case-control and cohort studies are observational, with varying advantages and disadvantages. However, the most important factor to the quality of evidence these studies provide, is their methodological quality.

Song, J. and Chung, K. Observational Studies: Cohort and Case-Control Studies .Â Plastic and Reconstructive Surgery. Â 2010 Dec;126(6):2234-2242.
Ury HK. Efficiency of case-control studies with multiple controls per case: Continuous or dichotomous data .Â Biometrics .Â 1975 Sep;31(3):643â€“649.
von Elm E, Altman DG, Egger M, Pocock SJ, GÃ¸tzsche PC, Vandenbroucke JP; STROBE Initiative.Â The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Â Lancet 2007 Oct;370(9596):1453-14577. PMID: 18064739.

Saul Crandon

No Comments on Case-control and Cohort studies: A brief overview

Very well presented, excellent clarifications. Has put me right back into class, literally!

Very clear and informative! Thank you.

very informative article.

Thank you for the easy to understand blog in cohort studies. I want to follow a group of people with and without a disease to see what health outcomes occurs to them in future such as hospitalisations, diagnoses, procedures etc, as I have many health outcomes to consider, my questions is how to make sure these outcomes has not occurred before the “exposure disease”. As, in cohort studies we are looking at incidence (new) cases, so if an outcome have occurred before the exposure, I can leave them out of the analysis. But because I am not looking at a single outcome which can be checked easily and if happened before exposure can be left out. I have EHR data, so all the exposure and outcome have occurred. my aim is to check the rates of different health outcomes between the exposed)dementia) and unexposed(non-dementia) individuals.

Very helpful information

Thanks for making this subject student friendly and easier to understand. A great help.

Thanks a lot. It really helped me to understand the topic. I am taking epidemiology class this winter, and your paper really saved me.

Happy new year.

Wow its amazing n simple way of briefing ,which i was enjoyed to learn this.its very easy n quick to pick ideas .. Thanks n stay connected

Saul you absolute melt! Really good work man

am a student of public health. This information is simple and well presented to the point. Thank you so much.

very helpful information provided here

really thanks for wonderful information because i doing my bachelor degree research by survival model

Quite informative thank you so much for the info please continue posting. An mph student with Africa university Zimbabwe.

Thank you this was so helpful amazing

Apreciated the information provided above.

So clear and perfect. The language is simple and superb.I am recommending this to all budding epidemiology students. Thanks a lot.

Great to hear, thank you AJ!

I have recently completed an investigational study where evidence of phlebitis was determined in a control cohort by data mining from electronic medical records. We then introduced an intervention in an attempt to reduce incidence of phlebitis in a second cohort. Again, results were determined by data mining. This was an expedited study, so there subjects were enrolled in a specific cohort based on date(s) of the drug infused. How do I define this study? Thanks so much.

thanks for the information and knowledge about observational studies. am a masters student in public health/epidemilogy of the faculty of medicines and pharmaceutical sciences , University of Dschang. this information is very explicit and straight to the point

Very much helpful

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Cohort Studies

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This chapter summarizes the basic features of cohort studies, a type of observational epidemiology study that some have also called longitudinal, or prospective, though these terms also apply to other epidemiological designs. A cohort study evaluates both the risk and the rate of disease or disease-related outcomes in a population that is characterized in terms of relevant risk factors or exposures, placed under observation, and followed for some time until disease develops or not. In contrast to its classical counterpart, the case-control study (cf. chapter “Case-Control Studies” of this handbook), cohort studies can relate multiple diseases to the exposure or exposures identified. On the other hand, cohort studies are frequently restricted to a limited number of exposures and potential confounders that can be included in the study, especially if historical data are used.

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PW Statistical Consulting, Laxou, France

Pascal Wild

Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

Anthony B. Miller

Division of Cardiovascular Sciences National Heart, Lung, and Blood Institute, Bethesda, MD, USA

David C. Goff Jr.

Institute for Public Health and Nursing Research, Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany

Karin Bammann

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Wolfgang Ahrens

Iris Pigeot

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Wild, P., Miller, A.B., Goff, D.C., Bammann, K. (2023). Cohort Studies. In: Ahrens, W., Pigeot, I. (eds) Handbook of Epidemiology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6625-3_6-1

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DOI : https://doi.org/10.1007/978-1-4614-6625-3_6-1

Received : 04 September 2022

Accepted : 05 September 2022

Published : 08 June 2023

Publisher Name : Springer, New York, NY

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Introduction to Epidemiological Studies

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1 Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece. [email protected].
2 Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece.
PMID: 29876887
DOI: 10.1007/978-1-4939-7868-7_1

The basic epidemiological study designs are cross-sectional, case-control, and cohort studies. Cross-sectional studies provide a snapshot of a population by determining both exposures and outcomes at one time point. Cohort studies identify the study groups based on the exposure and, then, the researchers follow up study participants to measure outcomes. Case-control studies identify the study groups based on the outcome, and the researchers retrospectively collect the exposure of interest. The present chapter discusses the basic concepts, the advantages, and disadvantages of epidemiological study designs and their systematic biases, including selection bias, information bias, and confounding.

Keywords: Bias; Case-control study; Cohort study; Confounding; Information bias; Observational studies; Selection bias; Study design.

Publication types

Case-Control Studies
Cohort Studies
Cross-Sectional Studies
Epidemiologic Research Design*
Follow-Up Studies

Library databases
Library website

Evidence-Based Research: Evidence Types

Introduction.

Not all evidence is the same, and appraising the quality of the evidence is part of evidence-based practice research. The hierarchy of evidence is typically represented as a pyramid shape, with the smaller, weaker and more abundant research studies near the base of the pyramid, and systematic reviews and meta-analyses at the top with higher validity but a more limited range of topics.

Several versions of the evidence pyramid have evolved with different interpretations, but they are all comprised of the types of evidence discussed on this page. Walden's Nursing 6052 Essentials of Evidence-Based Practice class currently uses a simplified adaptation of the Johns Hopkins model .

Evidence Levels:

Level I: Experimental, randomized controlled trial (RCT), systematic review RTCs with or without meta-analysis

Level II: Quasi-experimental studies, systematic review of a combination of RCTs and quasi-experimental studies, or quasi-experimental studies only, with or without meta-analysis

Level III: Nonexperimental, systematic review of RCTs, quasi-experimental with/without meta-analysis, qualitative, qualitative systematic review with/without meta-synthesis (see Daly 2007 for a sample qualitative hierarchy)

Level IV : Respected authorities’ opinions, nationally recognized expert committee or consensus panel reports based on scientific evidence

Level V: Literature reviews, quality improvement, program evaluation, financial evaluation, case reports, nationally recognized expert(s) opinion based on experiential evidence

Systematic review

What is a systematic review.

A systematic review is a type of publication that addresses a clinical question by analyzing research that fits certain explicitly-specified criteria. The criteria for inclusion is usually based on research from clinical trials and observational studies. Assessments are done based on stringent guidelines, and the reviews are regularly updated. These are usually considered one of the highest levels of evidence and usually address diagnosis and treatment questions.

Benefits of Systematic Reviews

Systematic reviews refine and reduce large amounts of data and information into one document, effectively summarizing the evidence to support clinical decisions. Since they are typically undertaken by a entire team of experts, they can take months or even years to complete, and must be regularly updated. The teams are usually comprised of content experts, an experienced searcher, a bio-statistician, and a methodologist. The team develops a rigorous protocol to thoroughly locate, identify, extract, and analyze all of the evidence available that addresses their specific clinical question.

As systematic reviews become more frequently published, concern over quality led to the PRISMA Statement to establish a minimum set of items for reporting in systematic reviews and meta-analyses.

Many systematic reviews also contain a meta-analysis.

What is a Meta-Analysis?

Meta-analysis is a particular type of systematic review that focuses on selecting and reviewing quantitative research. Researchers conducting a meta-analysis combine the results of several independent studies and reviews to produce a synthesis where possible. These publications aim to assist in making decisions about a particular therapy.

Benefits of Meta-Analysis

A meta-analysis synthesizes large amounts of data using a statistical examination. This type of analysis provides for some control between studies and generalized application to the population.

To learn how to find systematic reviews in the Walden Library, please see the Levels of Evidence Pyramid page:

Levels of Evidence Pyramid: Systematic Reviews

Guidelines & summaries

Practice guidelines.

A practice guideline is a systematically-developed statement addressing common patient health care decisions in specific clinical settings and circumstances. They should be valid, reliable, reproducible, clinically applicable, clear and flexible. Documentation must be included and referenced. Practice guidelines may come from organizations, associations, government entities, and hospitals/health systems.

ECRI Guidelines Trust

Best Evidence Topics

Best evidence topics are sometimes referred to as Best BETs. These topics are developed and supported for situations or setting when the high levels of evidence don't fit or are unavailable. They originated from emergency medicine providers' need to conduct rapid evidence-based clinical decisions.

Critically-Appraised Topics

Critically-appraised topics are a standardized one- to two-page summary of the evidence supporting a clinical question. They include a critique of the literature and statement of relevant results. They can be found online in many repositories.

To learn how to find critically-appraised topics in the Walden Library, please see the Levels of Evidence Pyramid page:

Levels of Evidence Pyramid: Critically-Appraised Topics

Critically-Appraised Articles

Critically-appraised articles are individual articles by authors that evaluate and synopsize individual research studies. ACP Journal Club is the most well known grouping of titles that include critically appraised articles.

To learn how to find critically-appraised articles in the Walden Library, please see the Levels of Evidence Pyramid page:

Levels of Evidence Pyramid: Critically-Appraised Articles

Randomized controlled trial

A randomized controlled trial (RCT) is a clinical trial in which participants are randomly assigned to either the treatment group or control group. This random allocation of participants helps to reduce any possible selection bias and makes the RCT a high level of evidence. Having a control group, which receives no treatment or a placebo treatment, to compare the treatment group against allows researchers to observe the potential efficacy of the treatment when other factors remain the same. Randomized controlled trials are quantitative studies and are often the only studies included in systematic reviews.

To learn how to find randomize controlled trials, please see our CINAHL & MEDLINE help pages:

CINAHL Search Help: Randomized Controlled Trials
MEDLINE Search Help: Randomized Controlled Trials

Cohort study

A cohort study is an observational longitudinal study that analyzes risk factors and outcomes by following a group (cohort) that share a common characteristic or experience over a period of time.

Cohort studies can be retrospective, looking back over time at data that has already been collected, or can be prospective, following a group forward into the future and collecting data along the way.

While cohort studies are considered a lower level of evidence than randomized controlled trials, they may be the only way to study certain factors ethically. For example, researchers may follow a cohort of people who are tobacco smokers and compare them to a cohort of non-smokers looking for outcomes. That would be an ethical study. It would be highly unethical, however, to design a randomized controlled trial in which one group of participants are forced to smoke in order to compare outcomes.

To learn how to find cohort studies, please see our CINAHL and MEDLINE help pages:

CINAHL Search Help: Cohort Studies
MEDLINE Search Help: Cohort Studies

Case-controlled studies

Case-controlled studies are a type of observational study that looks at patients who have the same disease or outcome. The cases are those who have the disease or outcome while the controls do not. This type of study evaluates the relationship between diseases and exposures by retrospectively looking back to investigate what could potentially cause the disease or outcome.

To learn how to find case-controlled studies, please see our CINAHL and MEDLINE help pages:

CINAHL Search Help: Case Studies
MEDLINE Search Help: Case Studies

Background information & expert opinion

Background information and expert opinion can be found in textbooks or medical books that provide basic information on a topic. They can be helpful to make sure you understand a topic and are familiar with terms associated with it.

To learn about accessing background information, please see the Levels of Evidence Pyramid page:

Levels of Evidence Pyramid: Background Information & Expert Opinion
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Next Page: CINAHL Search Help
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Types of clinical trials

Medical research studies involving people are called clinical trials.

There are two main types of trials or studies - interventional and observational.

Interventional trials aim to find out more about a particular intervention, or treatment. A computer puts people taking part into different treatment groups. This is so that the research team can compare the results.

Observational studies aim to find out what happens to people in different situations. The research team observe the people taking part, but they don’t influence what treatments people have. The people taking part aren’t put into treatment groups.

There are different types of trials within these two groups. This page has information about

Pilot studies and feasibility studies

Prevention trials, screening trials, treatment trials, multi-arm multi-stage (mams) trials, cohort studies.

Case control studies

Cross sectional studies

Pilot studies and feasibility studies are small versions of studies which are sometimes done before a large trial takes place.

Feasibility studies are designed to see if it is possible to do the main study. They aim to find out things such as whether patients and doctors are happy to take part, and how long it might take to collect and analyse the information. They don’t answer the main research question about how well a treatment works.

Pilot studies are small versions of the main study. Pilot studies help to test that all the main parts of the study work together. They may also help answer the research question. Sometimes the research team include the information collected during the pilot study in the results of the main study.

Prevention trials look at whether a particular treatment can help prevent cancer. The people taking part don't have cancer.

These trials can be for the general population or for people who have a higher than normal risk of developing a certain cancer. For example, this could include people with a strong family history of cancer.

Screening tests people for the early signs of cancer before they have any symptoms. As with prevention trials, screening trials can be for the general population. Or they can be for a group of people who have a higher than normal risk of developing a certain cancer.

Researchers may plan screening trials to see if new tests are reliable enough to detect particular types of cancer. Or they may try to find out if there is an overall benefit in picking up the cancer early.

For trials that compare two or more treatments, you are put into a treatment group at random. This is a randomised trial. They are the best way to get reliable information about how well a new treatment works. We have more information about randomisation .

A multi arm trial is a trial that has:

several treatment groups as well as

Multi-arm multi-stage (MAMS) trials have the same control group all the way through. The other treatment groups can change as the trial goes on. As these trials are more complex there are a number of treatments that people might have.

The research team may decide to stop recruiting people to a particular group. This could be because they have enough people to start looking at the results. Or because early results show the treatment isn’t working as well as they’d hoped.

The researchers may add new treatment groups as new drugs become available to look at. This means they don’t have to design and launch a brand new trial each time they want to research a new treatment. So it helps get results quicker.

The Stampede trial for prostate cancer is an example of a MAMS trial.

Observational studies Cohort studies, case control studies and cross sectional studies are all types of observational studies.

A cohort is a group of people, so cohort studies look at groups of people. A cohort study follows the group over a period of time.

A research team may recruit people who do not have cancer and collect information about them for a number of years. The researchers see who in the group develops cancer and who doesn’t. They then look to see whether the people who developed cancer had anything in common.

Cohort studies are very useful ways of finding out more about risk factors. But they are expensive and time consuming. They can be used when it wouldn’t be possible to test a theory any other way.

Case control studies

Case control studies work the opposite way to cohort studies. The research team recruits a group of people who have a disease (cases) and a group of people who don't (controls). They then look back to see how many people in each group were exposed to a certain risk factor.

Researchers want to make the results as reliable as possible. So they try to make sure the people in each group have the same general factors such as age or gender.

Case control studies are useful and they are quicker and cheaper than cohort studies. But the results may be less reliable. The research team often rely on people thinking back and remembering whether they were exposed to a certain risk factor or not. But people may not remember accurately, and this can affect the results.

Another issue is the difference between association and cause. Just because there is an association between a factor and a disease, it doesn’t mean that the factor causes the disease.

For example, a case control study may show that people with a lower income are more likely to develop cancer. But it doesn’t mean that the level of income itself causes cancer. It may mean that they have a poor diet or are more likely to smoke.

Cross sectional studies

Cross sectional studies are carried out at one point in time, or over a short period of time. They find out who has been exposed to a risk factor and who has developed cancer, and see if there is a link.

Cross sectional studies are quicker and cheaper to do. But the results can be less useful. Sometimes researchers do a cross sectional study first to find a possible link. Then they go on to do a case control or cohort study to look at the issue in more detail.

Oxford Handbook of Clinical and Healthcare Research (1st edition) R Sumantra, S Fitzpatrick, R Golubic and others Oxford University Press, 2016

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Types of Research Studies

Epidemiology studies.

Epidemiology is the study of the patterns and causes of disease in people.

The goal of epidemiology studies is to give information that helps support or disprove an idea about a possible link between an exposure (such as alcohol use) and an outcome (such as breast cancer) in people.

The 2 main types of epidemiology studies are:

Observational studies ( prospective cohort or case-control )

Randomized controlled trials

Though they have the same goal, observational studies and randomized controlled trials differ in:

The way they are conducted
The strengths of the conclusions they reach

Observational studies

In observational studies, the people in the study live their daily lives as they choose. They exercise when they want, eat what they like and take the medicines their doctors prescribe. They report these activities to researchers.

There are 2 types of observational studies:

Prospective cohort studies

Case-control studies.

A prospective cohort study follows a large group of people forward in time.

Some people will have a certain exposure (such as alcohol use) and others will not.

Researchers compare the different groups (for example, they might compare heavy drinkers, moderate drinkers, light drinkers and non-drinkers) to see which group is more likely to develop an outcome (such as breast cancer).

In a case-control study, researchers identify 2 groups: cases and controls.

Cases are people who already have an outcome (such as breast cancer).
Controls are people who do not have the outcome.

The researchers compare the 2 groups to see if any exposure (such as alcohol use) was more common in the history of one group compared to the other.

In randomized controlled trials (randomized clinical trials), researchers divide people into groups to compare different treatments or other interventions.

These studies are called randomized controlled trials because people are randomly assigned (as if by coin toss) to a certain treatment or behavior.

For example, in a randomized trial of a new drug therapy, half the people might be randomly assigned to a new drug and the other half to the standard treatment.

In a randomized controlled trial on exercise and breast cancer risk, half the participants might be randomly assigned to walk 10 minutes a day and the other half to walk 2 hours a day. The researchers would then see which group was more likely to develop breast cancer, those who walked 10 minutes a day or those who walked 2 hours a day.

Many behaviors, such as smoking or heavy alcohol drinking, can’t be tested in this way because it isn’t ethical to assign people to a behavior known to be harmful. In these cases, researchers must use observational studies.

Patient series

A patient series is a doctor’s observations of a group of patients who are given a certain treatment.

There is no comparison group in a patient series. All the patients are given a certain treatment and the outcomes of these patients are studied.

With no comparison group, it’s hard to draw firm conclusions about the effectiveness of a treatment.

For example, if 10 women with breast cancer are given a new treatment, and 2 of them respond, how do we know if the new treatment is better than standard treatment?

If we had a comparison group of 10 women with breast cancer who got standard treatment, we could compare their outcomes to those of the 10 women on the new treatment. If no women in the comparison group responded to standard treatment, then the 2 women who responded to the new treatment would represent a success of the new treatment. If, however, 2 of the 10 women in the standard treatment group also responded, then the new treatment is no better than the standard.

The lack of a comparison group makes it hard to draw conclusions from a patient series. However, data from a patient series can help form hypotheses that can be tested in other types of studies.

Strengths and weaknesses of different types of research studies

When reviewing scientific evidence, it’s helpful to understand the strengths and weaknesses of different types of research studies.

Case-control studies have some strengths:

They are easy and fairly inexpensive to conduct.
They are a good way for researchers to study rare diseases. If a disease is rare, you would need to follow a very large group of people forward in time to have many cases of the disease develop.
They are a good way for researchers to study diseases that take a long time to develop. If a disease takes a long time to develop, you would have to follow a group of people for many years for cases of the disease to develop.

Case-control studies look at past exposures of people who already have a disease. This causes some concerns:

It can be hard for people to remember details about the past, especially when it comes to things like diet.
Memories can be biased (or influenced) because the information is gathered after an event, such as the diagnosis of breast cancer.
When it comes to sensitive topics (such as abortion), the cases (the people with the disease) may be much more likely to give complete information about their history than the controls (the people without the disease). Such differences in reporting bias study results.

For these reasons, the accuracy of the results of case-control studies can be questionable.

Cohort studies

Prospective cohort studies avoid many of the problems of case-control studies because they gather information from people over time and before the events being studied happen.

However, compared to case-control studies, they are expensive to conduct.

Nested case-control studies

A nested case-control study is a case-control study within a prospective cohort study.

Nested case-control studies use the design of a case-control study. However, they use data gathered as part of a cohort study, so they are less prone to bias than standard case-control studies.

All things being equal, the strength of nested case-control data falls somewhere between that of standard case-control studies and cohort studies.

Randomized controlled trials are considered the gold standard for studying certain exposures, such as breast cancer treatment. Similar to cohort studies, they follow people over time and are expensive to do.

Because people in a randomized trial are randomly assigned to an intervention (such as a new chemotherapy drug) or standard treatment, these studies are more likely to show the true link between an intervention and a health outcome (such as survival).

Learn more about randomized clinical trials , including the types of clinical trials, benefits, and possible drawbacks.

Overall study quality

The overall quality of a study is important. For example, the results from a well-designed case-control study can be more reliable than those from a poorly-designed randomized trial.

Finding more information on research study design

If you’re interested in learning more about research study design, a basic epidemiology textbook from your local library may be a good place to start. The National Cancer Institute also has information on epidemiology studies and randomized controlled trials.

Animal studies

Animal studies add to our understanding of how and why some factors cause cancer in people.

However, there are many differences between animals and people, so it makes it hard to translate findings directly from one to the other.

Animal studies are also designed differently. They often look at exposures in larger doses and for shorter periods of time than are suitable for people.

While animal studies can lay the groundwork for research in people, we need human studies to draw conclusions for people.

All data presented within this section of the website come from studies done with people.

Joining a research study

Research is ongoing to improve all areas of breast cancer, from prevention to treatment.

Whether you’re newly diagnosed, finished breast cancer treatment many years ago, or even if you’ve never had breast cancer, there may be breast cancer research studies you can join.

If you have breast cancer, BreastCancerTrials.org in collaboration with Susan G. Komen® offers a custom matching service that can help find a studies that fit your needs. You can also visit the National Institutes of Health’s website to find a breast cancer treatment study.

If you’re interested in being part of other studies, talk with your health care provider. Your provider may know of studies in your area looking for volunteers.

Learn more about joining a research study .

Learn more about clinical trials .

Learn what Komen is doing to help people find and participate in clinical trials .

Updated 12/16/20

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Open access
Published: 22 May 2024

Feasibility and acceptability of a cohort study baseline data collection of device-measured physical behaviors and cardiometabolic health in Saudi Arabia: expanding the Prospective Physical Activity, Sitting and Sleep consortium (ProPASS) in the Middle East

Abdulrahman I. Alaqil ORCID: orcid.org/0000-0003-0458-2354 1 , 2 , 3 ,
Borja del Pozo Cruz ORCID: orcid.org/0000-0002-9728-1317 2 , 4 , 5 ,
Shaima A. Alothman ORCID: orcid.org/0000-0003-2739-0929 6 ,
Matthew N. Ahmadi ORCID: orcid.org/0000-0002-3115-338X 7 , 8 ,
Paolo Caserotti 2 ,
Hazzaa M. Al-Hazzaa ORCID: orcid.org/0000-0002-3099-0389 6 , 9 ,
Andreas Holtermann ORCID: orcid.org/0000-0003-4825-5697 3 ,
Emmanuel Stamatakis 7 , 8 &
Nidhi Gupta 3

BMC Public Health volume 24 , Article number: 1379 ( 2024 ) Cite this article

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Physical behaviors such physical activity, sedentary behavior, and sleep are associated with mortality, but there is a lack of epidemiological data and knowledge using device-measured physical behaviors.

To assess the feasibility of baseline data collection using the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS) protocols in the specific context of Saudi Arabia. ProPASS is a recently developed global platform for collaborative research that aims to harmonize retrospective and prospective data on device-measured behaviors and health. Using ProPASS methods for collecting data to perform such studies in Saudi Arabia will provide standardized data from underrepresented countries.

This study explored the feasibility of baseline data collection in Saudi Arabia between November and December 2022 with a target recruitment of 50 participants aged ≥ 30 years. Established ProPASS methods were used to measure anthropometrics, measure blood pressure, collect blood samples, carry out physical function test, and measure health status and context of physical behaviors using questionnaires. The ActivPal™ device was used to assess physical behaviors and the participants were asked to attend two sessions at (LHRC). The feasibility of the current study was assessed by evaluating recruitment capability, acceptability, suitability of study procedures, and resources and abilities to manage and implement the study. Exit interviews were conducted with all participants.

A total of 75 participants expressed an interest in the study, out of whom 54 initially agreed to participate. Ultimately, 48 participants were recruited in the study (recruitment rate: 64%). The study completion rate was 87.5% of the recruited participants; 95% participants were satisfied with their participation in the study and 90% reported no negative feelings related to participating in the study. One participant reported experiencing moderate skin irritation related to placement of the accelerometer. Additionally, 96% of participants expressed their willingness to participate in the study again.

Based on successful methodology, data collection results, and participants’ acceptability, the ProPASS protocols are feasible to administer in Saudi Arabia. These findings are promising for establishing a prospective cohort in Saudi Arabia.

Peer Review reports

Global data from 2023 indicate that an estimated 27.5% of adults do not meet physical activity guidelines and have poor physical behaviors (e.g., physical activity, sedentary behavior, and sleep) that are linked with an increased risk of morbidity and mortality [ 1 , 2 , 3 , 4 ]. Sufficient physical activity and sensible sedentary times are associated with better health outcomes (e.g., cardiovascular health, mental health, and physical function) [ 1 , 2 ]. Despite this fact, 50–90% of Saudi Arabian adults perform low or insufficient daily physical activity; about 50% spend at least five hours per day sitting [ 5 ]. Furthermore, around 33% of the population experiences sleep durations of less than 7 h per night [ 6 ]. These trends could be a reason why non-communicable diseases account for 73% of mortality and cardiovascular diseases account for 37% of all deaths among Saudi Arabian adults [ 7 ]. However, there have been few studies in Middle Eastern countries, and the evidence that links between physical behaviors and health outcomes is under-represented in Saudi Arabia [ 1 ].

Furthermore, within Saudi Arabia, the few studies exploring this connection often rely on self-reported physical behaviors that often do not provide the most accurate picture [ 5 , 8 , 9 , 10 , 11 ]. This lack of data necessitates studies that incorporate measurements from devices that directly track these behaviors among Saudi Arabian adults, which aligns with recent guidance from the World Health Organization (WHO) on the necessity of incorporating device-measured physical behaviors into future studies to explore their relationships with various health aspects [ 1 , 12 ]. By employing such a method, we can gain more precise insights into the dose-response relationships between different physical behaviors and various health outcomes among Saudi Arabian adults.

The Prospective Physical Activity, Sitting, and Sleep Consortium (ProPASS) is an initiative that aims to explore how thigh-based accelerometry measurement of physical behaviors influences a wide range of health outcomes. This initiative operates on a global scale and aims to harmonize data from both retrospective and future studies [ 13 ]. To fulfill the aim, ProPASS is developing methods for collecting prospective data and processing, harmonizing, and pooling data from previous and future studies [ 14 ]. To date, the methods of the ProPASS consortium have been used to harmonize data from large-scale epidemiological studies, such as the 1970 British Birth Cohort, the Australian Longitudinal Study on Women’s Health [ 15 ], and Norway’s Trøndelag Health Study (HUNT) [ 16 , 17 ]. As such, this study seeks to determine if the ProPASS methodologies will be effective in the context of data collection within Saudi Arabia. This will be beneficial because it will help to standardize the measurement of physical behaviors, enhance harmonization across studies, and create more a representative and valid understanding of the associations between physical behaviors and health globally, including under-represented countries such as Saudi Arabia.

This paper describes the feasibility of baseline ProPASS data collection in Saudi Arabia with prospectively harmonized data with the main resource. This feasibility study of baseline data collection will serve as a framework for a future cohort study that will investigate the associations between device-measured physical behavior (e.g., physical activity, sedentary behavior, and sleep) and cardiometabolic health in Saudi adults.

The study was approved by the Institutional Review Board at Princess Nourah Bint Abdul Rahman University, Riyadh, Saudi Arabia (IRB 22–0146), and was carried out in accordance with the principles of the Declaration of Helsinki.

Study design and procedures

Participants were informed about the study’s aims and asked to read and sign the consent form before any measurements were taken. After agreeing to participate, they were asked to attend two sessions at the Lifestyle and Health Research Center (LHRC) at the Health Sciences Research Center of Princess Nourah Bint Abdulrahman University. During the first visit, each participant’s anthropometric measurements (e.g., height, weight, waist circumference), blood pressure and heart rate, blood samples, and handgrip strength were measured. Next, the participants completed questionnaires on demographic information, dietary habits, self-rated health, self-reported smoking status, and the Global Physical Activity, Sedentary Behaviors, and Sleep behavior questionnaires. At the end of the first visit, the researcher attached the ActivPAL™ accelerometer device to their thigh which they were asked to wear for seven consecutive days. Participants were also provided with a diary to record their waking and sleeping hours [ 18 ]. On the 8th day of study, the participants were asked to attend the LHRC for session two where they returned the device and were interviewed (see Fig. 1 ).

Demonstration and summary of the study procedure

Participants and eligibility

The study aimed to recruit a total of 50 Saudi adults aged ≥ 30 years, which is generally considered a common sample size for feasibility studies [ 19 , 20 ]. The eligibility criteria were: (1) Saudi nationals (2), resident in Riyadh, and (3) aged ≥ 30 years old. The exclusion criteria were: (1) having a current medical condition that forces them to be chair-bound or bedridden for more than half of their waking hours (2), being allergic to plasters or adhesives (3), being allergic to low-density polyethylene (4), having a skin condition that would prevent them from wearing the monitor, and (5) those who may need to pass through a metal detector/security checkpoint during the duration of the study. The study’s aims, protocol, and procedures were clearly described to all participants before any measurements were taken.

Recruitment

Participant recruitment was carried out over the month of November 2022. Participants were recruited from different locations across Riyadh, Saudi Arabia, by using electronic flyers on social media (e.g., Twitter, WhatsApp) that provided information about the study and the researcher’s contact details. Prospective participants who were interested in joining the study were asked to provide their contact information via a link to Google Forms featured in the study description. The participants who initially expressed interest but later decided not to join were invited to share their reasons for non-participation through a physical or telephonic meeting.

Measurements based on ProPASS methodology

The current study employed the ProPASS method and protocol for new cohort studies that seek to join ProPASS prospectively [ 14 , 21 ]. All measurements were taken by researchers that were well-trained in the ProPASS protocol and methods. Blood pressure and hand grip strength measurements were taken three times, and the mean average was then calculated; all other measurements were taken only once.

Anthropometric measurements

Height (to the nearest 0.1 cm) and weight (to the nearest 0.1 kg) were measured with a stadiometer (SECA 284; Seca, Hamburg, Germany), and scale (SECA 284; Seca, Hamburg, Germany), respectively. Waist circumference (to the nearest 0.1 cm) was measured midway between the lower rib margin and the iliac crest at the end of a gentle expiration [ 22 ]. Body mass index (BMI) was calculated using the standard calculation (height in meters squared/body weight in kilograms).

Blood pressure and heart rate

Blood pressure was taken after resting for five minutes in a sitting position. Blood pressure was taken three times with one minute between measurements and the average reading was recorded [ 23 ]. Blood pressure and heart rate were measured using a Welch Allyn Connex 7300 Spot Vital Signs Monitor, which provides a high degree of accuracy [ 24 ]. Mean arterial pressure (MAP) was then calculated (MAP = 1/3 * SBP + 2/3 * DBP in mm Hg) using the average of both the SBP and DBP values [ 25 ].

Blood samples

Non-fasting finger-prick (capillary) blood samples (40 µL) were collected for analysis after warming the finger for five minutes. A drop of blood was taken directly from the heated finger to be analysed for blood glucose, triglycerides, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. A previously validated CardioChek PA analyser (CardioChek PA Blood Analyser, UK) was used to analyse the blood samples [ 26 , 27 ].

Medication use

Participants’ medication use was evaluated by the question: Do you currently use any prescription medicines ? If the answer was yes, the participants were asked which medications they use, such as medication for high blood pressure, high cholesterol, asthma, COPD, anxiety, depression, thyroid problems, allergies. They were also asked whether the medication was in the form of tablets, or nasal sprays, whether the medication was anti-inflammatory, chemotherapeutic, urological, birth control, or neurological, and the age at which the participants had begun using the medication.

Familial disease history

Familial disease history was assessed by the question: Do your parents, siblings or children have, or have they ever had, some of the following diseases before the age of 60 ? The responses included asthma, hay fever/nasal allergies, chronic bronchitis, emphysema or COPD, anxiety or depression, myocardial infarction (heart attack), diabetes, stroke or brain hemorrhage, and cancer. The responses were yes, no , and I don’t know .

Chronic health status

Participants’ chronic disease status and/or long-term health issues were assessed by the question: Have you had, or do you have any of the following diseases? The responses included angina, myocardial infarction (heart attack), heart failure, peripheral vascular disease, atrial fibrillation, stroke/brain hemorrhage, thrombosis, pulmonary embolism, asthma, COPD or emphysema, diabetes, hypothyroidism (low metabolism), hyperthyroidism (high metabolism), cancer, migraine, psoriasis, kidney disease, arthritis (rheumatoid arthritis), Bechterew’s disease, gout, mental health problems, osteoporosis, sleep apnea, arthrosis, nerve disease, hearing/ear disease, eye disease, and infection. Those who replied yes were asked a follow-up question: How old were you when you had it for the first time?

Mobility limitations

The questionnaire was based on three questions on performance-based measures of mobility, which had already been translated and culturally adapted into Arabic [ 28 ]. These three questions are valid and reliable tools to identify the early indications of disability and can be used as indicators to identify those at high risk of future disability [ 29 ]. Self-reported mobility was assessed via the following questions: (1) Do you have difficulty in walking 2.0 km? (2) Do you have difficulty in walking 0.5 km ? and (3) Do you have difficulty in walking up one flight of stairs? The five response options were: (1) able to manage without difficulty (2), able to manage with some difficulty (3), able to manage with a great deal of difficulty (4), able to manage only with the help of another person, and (5) unable to manage even with help.

Dietary habits

The dietary habits questionnaire was translated and culturally adapted into Arabic [ 28 ]. The questionnaire assessed the dietary habits of the participants was adapted from the Survey of Health, Aging, and Retirement in Europe (SHARE), which has been demonstrated to be a valid and reliable tool for assessing diet [ 30 ]. The questionnaire focused on the consumption of dairy products, legumes, eggs, meat, fruit and vegetables.

Self-rated health

A set of valid and reliable questions adapted from Idler et al.’s (1997) questionnaire was used to assess participants’ self-rated health by asking them to rate their health status using the following questions: (1) In general, would you say your health is…: Excellent; Very good; Good; Fair; Poor; (2) Compared to one year ago, how would you rate your health in general now?: Much better now than one year ago; Somewhat better now than one year ago; About the same; Somewhat worse now than one year ago; Much worse now than one year ago [ 31 , 32 ].

Smoking habits

Self-report questions on smoking behavior were adapted from the UK Biobank questionnaire and were used to assess participants’ present and past smoking habits including at what age they began smoking. the number of cigarettes smoked per day, the type of tobacco used, the duration of smoking, and, among former smokers, the age when smoking ceased [ 33 ].

Physical behaviours

Physical behaviors such as physical activity, sedentary behavior, and sleep were measured by using (1) self-reported and (2) device-based measures:

Self-report measures

Physical activity was measured on a self-report basis via the Global Physical Activity Questionnaire (GPAQ) which was translated into Arabic and previously validated [ 34 ]. In addition, the Sedentary Behavior Questionnaire (SBQ), which had already been translated into Arabic [ 28 ], was used to subjectively assess participants’ sedentary behavior time [ 35 ]. Lastly, the Pittsburgh Sleep Quality Index was used to assess sleep quality and sleep disturbances over a one-month period [ 36 ].

Device-based measures

Physical behaviors were measured by wearing a thigh-worn accelerometer device (an ActivPAL™ Micro4, PAL technologies, Glasgow, Scotland) that participants wore continuously for 24 h for seven full days [ 37 ]. The Activpal™ device was sealed with a nitrile sleeve and attached with a medical waterproof 3 M Tegaderm transparent dressing on the front of the right mid-thigh on the muscle belly by a well-trained member of researcher team. The ActivPAL™ monitor is a valid and reliable measure of time spent walking [ 38 ], sitting, and standing time in healthy adults [ 39 ]. In addition, the participants were asked to fill in a recording sheet that included a sleep diary (times that the participant went to and got out of bed), as well as, the dates and times when the accelerometer fell off or was removed.

Physical function

Physical function was objectively measured using a digital hand-grip strength dynamometer (Takei Hand Grip Dynamometer 5401-C, Japan) via three successive hand-grip assessments for each hand (left and right); the mean value for each hand was then recorded. The instrument can measure hand-grip values from 5 to 100 kg; the minimum unit of measurement is 0.1 kg. The tool is a good health outcomes predictor [ 40 , 41 ].

Data collection evaluation of feasibility

Overall, the study evaluated feasibility in two main stages where feedback from the first six participants was used to resolve any unforeseen issues in the protocol implementation on the remaining participants. Any changes to the procedure were documented.

The current study evaluated the feasibility of Saudi adults’ participation based on the following constructs: (1) recruitment capability (2), acceptability and suitability of study procedures, and (3) resources and ability to manage and implement the study. Table 1 outlines the feasibility constructs, measures, outcome definitions, and methods employed. In evaluating feasibility, the current study followed the recommendations for a feasibility study as reported by Orsmond and Cohn, 2015 [ 42 ].

Overall, the study collected data on the feasibility constructs via tracking the registration, equipment availability, and time spent on various tasks performed (for example training researchers, performing various tasks like attaching the sensor) and completion rate (such as tracking diary entries, questionnaire entries and number of days with accelerometer data), via personal contacts (for information on barriers and facilitators of participation), via processing sensor data, and via interviews after the measurement (for example obtaining information on potential issues during measurement and willingness to participate).

Participant interviews after measurement

After the completion of the study, face-to-face semi-structured interviews were conducted with all participants who had completed the 7-day study period. The aim of these interviews was to collect comprehensive feedback regarding participants’ experiences with the study protocol, with the goal of capturing additional insights that was not captured by other feasibility measures. Some examples of such measures were motivations for joining the study, their expectations prior to participation, and their levels of satisfaction with the study procedures. A detailed interview guide is described in Appendix A [ 28 , 43 , 44 ].

Statistical analysis

Descriptive analysis summarized participants’ demographics, anthropometric measurements, health status, clinical measurements, physical behaviors characteristics, and interview questions responses. The continuous variables were characterized using mean ± standard deviations (SD), while categorical variables were presented using frequencies accompanied by percentages (%). The recruitment rate was calculated by the number of participants who participated and signed the consent form / total number of participants who registered in the study (see Fig. 2 ). Additional analyses were performed to compare participants who reported burden with those who reported no burden of participation (see supplementary materials). T-tests and Chi-square tests were employed for this comparison. IBM’s Statistical Package for the Social Sciences (SPSS) (version 27 SPSS, Inc. Chicago, Illinois) was used to conduct the qualitative analysis. The raw data of ActivPAL were analyzed by using the ActiPASS software (ActiPASS © 2021 - Uppsala University, Sweden).

Recruitment and study participant’s diagram

A total of 75 participants initially volunteered to participate. Ten participants were excluded from the study as they did not meet the inclusion criteria ( n = 8) or could not be contacted ( n = 2). In addition, 11 participants withdrew their interest in participating for various reasons: (1) excessive distance between the location of the study (LRHC) and their residence ( n = 3) (2), hesitant about joining the study ( n = 1) (3), believed that the ActivPAL™ device would interfere with his/her health ( n = 1) (4), believed that the ActivPAL™ device would interfere with their regular exercise routine ( n = 2) (5), had family and work commitments ( n = 3), and (6) claimed that the timing was unsuitable ( n = 1). Out of a total of 54 participants who had agreed to participate in the study, 48 participants from Riyadh, Saudi Arabia, attended and completed the consent form. However, four of those participants provided incomplete data (i.e., they completed the questionnaires only and did not wear an ActivPAL™ device). Therefore, a total of 44 participants out of 75 potential participants (59%) successfully completed the study (wore an ActivPAL™ device and completed all questionnaires). See Fig. 2 for the study’s recruitment flow.

Participants

Of the 48 participants, nearly half were female (47.9%). On average, the participants were 37 ± 7.3 years old, had a BMI of 28.3 ± 5.6, and a waist circumference of 86.9 ± 16.4 cm. Most participants were married, had college degrees, were employed as office workers and professionals, had never smoked, and did not use any medication (see Table 2 ). A total of 87.5% of participants had a family history of disease; 85.4%, 95.8%, and 89.6%, reported having no difficulty walking 2 km, 500 m, and up one flight of stairs, respectively. Approximately 48% of participants rated their health as very good , while 39.6% reported their health as about the same compared to one year ago . In terms of dietary habits, nearly half the participants reported consuming dairy products every day, 25% consumed legumes and eggs 3 to 6 times a week, 56.3% consumed meat every day, and 45.8% consumed fruits and vegeTables 3, 4, 5 and 6 times a week.

Table 3 presents the primary variables of the study: including average systolic, diastolic, and mean arterial pressure values of 121.13 ± 11.81 mmHg, 79.26 ± 8.92 mmHg, and 93.15 ± 9.20 mmHg, respectively. The mean resting heart rate was 74.3 ± 12.66. Furthermore, the non-fasting blood profile of the sample was analyzed and showed the following values: total cholesterol: 177.89 ± 33.79 mg/dL; HDL-cholesterol: 50.96 ± 13.02 mg/dL; triglycerides: 123.94 ± 68.92 mg/dL; LDL-cholesterol: 103 ± 29.89 mg/dL; TC/HDL-cholesterol ratio: 3.71 ± 1.11; LDL/HDL-cholesterol ratio: 2.19 ± 0.81; non-HDL-cholesterol: 127.06 ± 33.51 mg/dL; non-fasting glucose: 102.98 ± 35.36 mg/dL. Table 3 provides an overview of the participants’ physical activity related behaviors.

Feasibility evaluation

The following results highlight the approaches taken by the current study to assess the feasibility of baseline data collection using ProPASS methodology specifically in the context of Saudi Arabia.

The evaluation of the feasibility of the study protocol was conducted in two stages, initially involving six participants, whose feedback was used to refine and improve the protocol implementation for the remaining participants. Of the six selected participants, three were female. In the pre-evaluation, only two minor issues were encountered; (1) accessing the lab outside of working hours (16:00–22:00) as most participants were unable to attend during the day (07:00–16:00) due to work commitments. This issue was resolved in all subsequent data collection points by receiving approval for extended lab hours; (2) obtaining the required number of ActivPAL™ devices from the technical coordinator due to miscommunication and high demand by other researchers. To prevent further issues, the author obtained 30 devices in advance for the feasibility evaluation.

Recruitment capability

The recruitment rate was used to measure the feasibility of recruitment methodology to collect baseline ProPASS data; the results showed that 64% ( n = 48) of participants signed the consent form and attended the LRHC lab (see Fig. 2 ). After screening the eligibility criteria, out of a total of 75 participants, 65 met the study criteria, and 11 were excluded from participating due to the reasons as detailed in Fig. 2 . As Fig. 2 illustrates, although 54 participants scheduled an appointment for the study, only 48 (64%) attended and signed the consent form. In the final stage of the recruitment process, around 59% ( n = 44) of participants completed all the required measurements for the study.

Acceptability and suitability of study procedures

The adherence rate (i.e., the extent to which participants adhered to the outlined procedures in terms of the number of days with valid accelerometry data) was 5.7 days. Furthermore, participants provided sleep diary entries for 85.4% of days. All questionnaires were completed with a 100% response rate.

To assess the study’s time demands on participants, the length of time participants needed to complete all measurements was mean time of 25 min (23 min to complete the questionnaires and two minutes to attach the sensor). Additionally, the completion rates for the registered participants who completed all the required measurements (i.e., accelerometer measurement, diary registration, and questionnaires) was 91.6%. (See Table 4 ).

Resources and ability

The final feasibility outcomes (i.e., having the required resources and ability to manage and implement the study) are presented in Table 5 . This objective was assessed based on four domains: skin irritation, equipment availability, training requirements, and accelerometer loss (see Table 5 ). The first domain revealed that three participants experienced skin irritation during the study; of these, two participants had mild symptoms, such as itchiness and discomfort that lasted for the first three days but did not lead to their withdrawal from the study. However, one participant reported moderate irritation resulting in red skin which required them to withdraw from the study. The second domain, equipment availability, indicated that all the necessary equipment was available 100% of the time. The third domain was training requirements, and the researchers required four hours of training on how to use it correctly. Finally, in the accelerometer loss domain, the study recorded four failed devices out of 30 that did not generate data for seven days.

Participant interview after measurement

After completing the study, all participants were interviewed around five primary themes: (1) motivation and expectations of participation (2), participant satisfaction (3), the burden of participation (4), willingness to participate again , and (5) perception of time usage (see Fig. 3 ).

Interview outcomes of participant’s experience with the study protocol

To determine the participants’ motivations for and expectations about joining the study, they were asked: What made you want to join this study? The results showed that 90% of participants were interested in learning about their physical behaviors and health status; 43% participated in supporting the researcher, and 14% reported that the final report attracted them to participate (see Fig. 3 a and the example of final report in supplementary material). Participant satisfaction was assessed via two questions: (1) What was your overall experience of participating in the study? and (2) Was it as you expected? The findings indicated that 62% of participants were satisfied that the study was as expected, 33% were more satisfied than expected, and 5% were unsatisfied and found the study below their expectations (see Fig. 3 b).

Regarding the overall burden of participation, 76% of participants reported that it was no burden , 5% reported that it was a burden , and 14% believed it was somewhat burdensome (see Fig. 3 c). Additionally, 79% of participants expressed their willingness to participate again in the future (see Fig. 3 d). Finally, regarding time usage, 67% of participants found it easy to complete the seven-day study without any concerns (see Fig. 3 h).

The feasibility of the baseline ProPASS data collection methodology was evaluated among Saudi adults who participated in this study. The findings revealed that the methodology was both feasible and acceptable, paving the way for large-scale prospective cohort research in Saudi Arabia. This research marks the first attempt to establish a prospective cohort study in Saudi Arabia using established ProPASS methods [ 13 , 15 ] and protocols. Conducting such a cohort study in Saudi Arabia is crucial due to the country’s high prevalence of non-communicable diseases that are mostly due to poor physical behaviors (e.g., lack of physical activity, sedentary behavior, and sleep) [ 7 ], due to recent enormous economic growth accompanied by technological transformations and urbanization [ 11 ].

The first aspect of feasibility evaluated of the baseline ProPASS data collection methodology was the capability to recruit participants. The findings indicated that the recruitment rate was 64% which is similar to prior studies [ 46 , 47 ]. One study indicated that a recruitment rate of at least between 20 and 40% is required to be deemed feasible [ 48 ]. Thus, the recruitment rate in the current study seems acceptable for creating a future cohort using ProPASS methods in Saudi Arabia. Additionally, in the current study, the refusal rate was only 15% which is significantly lower than in previous studies [ 45 , 49 ] where refusal rates ranged from 50 to 66%. One reason for the low refusal rate in the current study is that the recruitment was material specifically designed to motivate Saudi participants to join the study by indicating that the study would provide data and insight into their current state of health. For example, the results of the semi-structured interviews illustrated that 90% of participants joined the study because they wanted to know about their physical behaviors and health status (see Fig. 3 ). This result also indicates that our recruitment material might be suitable for ensuring high participation in the future cohort study.

The second aspect of feasibility for the baseline ProPASS data collection methodology that was evaluated in this study was the acceptability and suitability of the study procedures. Previous studies have shown that in order to obtain reliable estimates of adults’ habitual physical activity, it is necessary to record accelerometer data for 3–5 days [ 50 , 51 ] to gather valid data to perform analysis and provide information about the habitual physical behaviors. A recent study indicated that distributing accelerometers in person was associated with a high proposition of participants consenting to wear an accelerometer and meeting minimum wear criteria [ 21 ]. Our study was able to collect an average six days of valid data which was sufficient to obtain representative descriptions of the participants’ physical behaviors [ 52 ]. There were high general adherence rates for participant diary entries, questionnaires completion, and adherence to the study protocol, indicating that the ProPASS methods could be feasibly implemented with a larger study population. The study also assessed the time commitment necessary to complete the questionnaires and attach the ActivPAL™ devices to participants’ thighs. Completing the questionnaires took approximately 23 min (SD = 8). Prior studies have indicated that shorter questionnaires (e.g., 20 min) yield a higher response rate from participants, a finding that was consistent with our study [ 53 , 54 ]. Additionally, attaching the sensor to the participant’s thigh took about two minutes. These findings indicate that participation in this study was not burdensome, which was confirmed by the interviews that showed that 95% of participants felt that participating in the study (i.e., filling out all questionnaires and wearing the ActivPal™ device for 7 days) was not a burden. Overall, ProPASS methods appear to be less burdensome, well-suited, and readily accepted by participants.

The third aspect of feasibility for the baseline ProPASS data collection methodology was the availability of resources and the ability to manage and execute the study. As we aim to create a new cohort adhering to global (ProPASS) standards, protocol training was vital to obtain quality outcomes as per the ProPASS protocol. As a result, the protocol training took around four hours which was similar to a prior study [ 45 ]. In terms of the availability of resources, all essential equipment was always accessible. The study also considered skin irritation as an important factor. One study noted that 38% of participants stopped using ActivPal™ due to skin irritation from PALstickies or Tegaderm dressings [ 55 ]; another reported one discontinuation due to irritation associated with a Tegaderm dressing [ 56 ]. In the current study, there were three reported irritations, with two having mild initial discomfort that eventually subsided. One participant left the study due to moderate irritation. Nonetheless, it is important to note that the data collection occurred during colder winter periods (average 20 degrees Celsius). It is possible that instances of skin irritation could be more pronounced during Saudi Arabia’s hot summer season, characterized by temperatures of approximately 40 degrees Celsius. Future studies should investigate the feasibility of using devices and tape suitable for summer temperatures. In addition, the current study also had a low accelerometer failure rate: only four accelerometers failed to record, which is similar to previous studies [ 57 , 58 ]. All ActivPal™ devices were returned at the end of the study during visit two, ensuring that the ProPASS method is suitable to be used in future cohorts in Saudi Arabia.

Strengths and limitations of Study

This study represents the first of its kind to utilize device-based measures for assessing physical behaviors among adults in Saudi Arabia. The device-based measure has been shown to provide useful information about physical behaviors when compared to using self-report questionnaires [ 16 ]. Furthermore, it marks the initial examination of the ProPASS consortium method in the Middle East, particularly in Saudi Arabia. Nevertheless, the current study has certain limitations including recruiting among relatively young participants, presumably without any medical conditions and with postgraduate qualifications. This may limit the generalization of the findings to the entire population. The acceptability of the study in other age groups and among individuals with lower educational backgrounds is yet to be studied. In addition, the feasibility of the baseline ProPASS data collection methodology study was conducted during winter, which might have influenced the observed levels of physical behaviors in our sample. Similarly, the study was unable to evaluate the feasibility of utilizing 3 M Tegaderm dressings in hot summer months. Lastly, it’s important to note that our study employed a relatively small sample size; nonetheless, this size is considered acceptable for feasibility studies.

The baseline ProPASS data collection methodology and protocol for a future cohort study are both feasible and acceptable for implementation within the context of Saudi Arabia. This feasibility study represents the first step toward establishing a prospective ProPASS cohort study to examine the association between physical behaviors and cardiometabolic health among Saudi Arabian adults.

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Abbreviations

The Prospective Physical Activity, Sitting and Sleep consortium

Physical activity, sedentary behavior, and sleep

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Acknowledgements

The authors would like to express gratitude to all participants for their involvement in the study. Additionally, we extend our appreciation to the research assistants (Rasil Alhadi, Ragad Alasiri, and Khalid Aldosari) who assisted in the data collection. Finally, we would like to thank the LHRC, Princess Nourah Bint Abdulrahman University for providing their site for collecting the data.

This research was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. GrantA353]. The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.

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Conceptualization: AIA, NG, ES, and BdCMethodology: AIA, NG, ES, HMA, and BdCInvestigation: AIAData collection: AIAInterpretation of the findings: AIA, HMA, ES, NG, AH, PC, MNA, and BdCDrafting the paper: AIAReviewing and editing the draft: AIA, ES, HMA, BdC, SAA, PC, MNA, AH, and NGAll authors critically read, revised the draft for important intellectual content, approved the final version of the manuscript to be published, and agreed to be accountable for all aspects of the work.

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Alaqil, A.I., del Pozo Cruz, B., Alothman, S.A. et al. Feasibility and acceptability of a cohort study baseline data collection of device-measured physical behaviors and cardiometabolic health in Saudi Arabia: expanding the Prospective Physical Activity, Sitting and Sleep consortium (ProPASS) in the Middle East. BMC Public Health 24 , 1379 (2024). https://doi.org/10.1186/s12889-024-18867-2

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v.61(1); Jan-Feb 2016

Methodology Series Module 1: Cohort Studies

Maninder singh setia.

From the Department of Epidemiology, MGM Institute of Health Sciences, Navi Mumbai, Maharashtra, India

Cohort design is a type of nonexperimental or observational study design. In a cohort study, the participants do not have the outcome of interest to begin with. They are selected based on the exposure status of the individual. They are then followed over time to evaluate for the occurrence of the outcome of interest. Some examples of cohort studies are (1) Framingham Cohort study, (2) Swiss HIV Cohort study, and (3) The Danish Cohort study of psoriasis and depression. These studies may be prospective, retrospective, or a combination of both of these types. Since at the time of entry into the cohort study, the individuals do not have outcome, the temporality between exposure and outcome is well defined in a cohort design. If the exposure is rare, then a cohort design is an efficient method to study the relation between exposure and outcomes. A retrospective cohort study can be completed fast and is relatively inexpensive compared with a prospective cohort study. Follow-up of the study participants is very important in a cohort study, and losses are an important source of bias in these types of studies. These studies are used to estimate the cumulative incidence and incidence rate. One of the main strengths of a cohort study is the longitudinal nature of the data. Some of the variables in the data will be time-varying and some may be time independent. Thus, advanced modeling techniques (such as fixed and random effects models) are useful in analysis of these studies.

Introduction

Cohort studies are important in research design. The term “cohort” is derived from the Latin word “ Cohors ” – “a group of soldiers.” It is a type of nonexperimental or observational study design. The term “cohort” refers to a group of people who have been included in a study by an event that is based on the definition decided by the researcher. For example, a cohort of people born in Mumbai in the year 1980. This will be called a “birth cohort.” Another example of the cohort will be people who smoke. Some other terms which may be used for these studies are “prospective studies” or “longitudinal studies.”

In a cohort study, the participants do not have the outcome of interest to begin with. They are selected based on the exposure status of the individual. Thus, some of the participants may have the exposure and others do not have the exposure at the time of initiation of the study. They are then followed over time to evaluate for the occurrence of the outcome of interest.

As seen in Figure 1 , at baseline, some of the study participants have exposure (defined as exposed) and others do not have the exposure (defined as unexposed). Over the period of follow-up, some of the exposed individuals will develop the outcome and some unexposed individuals will develop the outcome of interest. We will compare the outcomes in these two groups.

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Object name is IJD-61-21-g001.jpg

Example of a cohort study

Examples of Cohort Studies

Framingham cohort study ( https://www.framinghamheartstudy.org/index.php ).

This cohort study was initiated in 1948 in Framingham. Framingham, at the time of initiation of the cohort, was an industrial town 21 miles west of Boston with a population of 28,000. This Framingham Heart Study recruited 5209 men and women (30–62-year-old) in the study to assess the factors associated with cardiovascular disease (CVD). The researchers also recruited second generation participants (children of original participants) in 1971 and the third general participants in 2002. This has been one of the landmark cohort studies and has contributed immensely to our knowledge of some of the important risk factors for CVD. The investigators have published 3064 publications using the Framingham Heart Study data.

Swiss HIV cohort study ( http://www.shcs.ch/ )

This cohort study was initiated in 1988. It was a longitudinal study of HIV-infected individuals to conduct research on HIV pathogenesis, treatment, immunology, and coinfections. They also work on the social aspects of the disease and management of HIV-infected pregnant women. The study started with a recruitment of individuals ≥16 years. The cohort was gradually expanded to include the Swiss Mother and Child HIV Cohort Study. The cohort has provided useful information on various aspects of HIV and published 542 manuscripts on these aspects.

The Danish cohort study of psoriasis and depression (Jensen, 2015)

This is another large cohort study that evaluated the association between psoriasis and onset of depression. The participants in the cohort were enrolled from national registries in Denmark. None of the included participants had psoriasis or depression at baseline. The outcome of interest was the initiation of antidepressants or hospitalization for depression. The authors compared the incidence rates of hospitalization for depression in psoriasis and reference population. The psoriasis group was further classified as mild and moderate psoriasis. The authors found that psoriasis was an independent risk factor for new-onset depression in young people. However, in the elderly, it was mediated through comorbid conditions.

We have presented examples of some large cohort studies. It will be worthwhile to read the design and conduct of these studies, and it will help the readers understand the practical aspects of conducting and analyzing cohort studies.

Types of Cohort Studies

Prospective cohort study.

In this type of cohort study, all the data are collected prospectively. The investigator defines the population that will be included in the cohort. They then measure the potential exposure of interest. The participants are then classified as exposed or unexposed by the investigator. The investigator then follows these participants. At baseline and during follow-up, the investigator also collects information on other variables that are important for the study (such as confounding variables). The investigator then assesses the outcome of interest in these individuals. Some of these outcomes may only occur once (for example, death), and some may occur multiple times (for example, conditions which may recur in the same individual – diarrhea, wheezing episodes, etc.).

Retrospective cohort study

In this type of cohort study, the data are collected from records. Thus, the outcomes have occurred in the past. Even though the outcomes have occurred in the past, the basic study design is essentially the same. Thus, the investigator starts with the exposure and other variables at baseline and at follow-up and then measures the outcome during the follow-up period.

Sometimes, the direction may not be as well defined as prospective and retrospective. One may analyze retrospective data on a group of people well as collect prospective data from the same individuals.

Examples of prospective and retrospective cohort studies

Our objective is to estimate the incidence of cardiovascular events in patients with psoriasis. We have decided to conduct a 10-year study. All the individuals who are diagnosed with psoriasis are eligible for being included in this cohort study. However, one has to ensure that none of them have cardiovascular events at baseline. Thus, they should be thoroughly investigated for the presence of these events at baseline before including them in the study. For this, we have to define all the events we are interested in the study (such as angina or myocardial infarction). The criteria for identifying psoriasis and cardiovascular outcomes should be decided before initiating the study. All those who do not have cardiovascular outcomes should be followed at regular intervals (predecided by the researcher and as required for clinical management). This will be a prospective cohort study.

Our objective is to assess the survival in HIV-infected individuals and the factors associated with survival. We have clinical data from about 430 HIV-infected individuals in the center. The follow-up period ranges from 3 months to 4 years, and we know that 33 individuals have died in this group. We decide to perform the survival analysis in this group of individuals. We prepare a clinical record form and abstract data from these clinical forms. This design will be a retrospective cohort study.

Outcomes in a Cohort Study

A cohort study may have different types of outcomes. Some of the outcomes may occur only once. In the above mentioned retrospective study, if we assess the mortality in these individuals, then the outcome will occur only once. Other outcomes in the cohort study may be measured more than once. For instance, if we assess CD4 counts in the same retrospective study, then the values of CD4 counts may change at every visit. Thus, the outcome will be measured at every visit.

Strengths of a Cohort Study

Temporality: Since at the time of entry into the cohort study, the individuals do not have outcome, the temporality between exposure and outcome is well defined
A cohort study helps us to study multiple outcomes in the same exposure. For example, if we follow patients of hypercholesterolemia, we can study the incidence of melasma or psoriasis in them. Thus, there is one exposure (hypercholesterolemia) and multiple outcomes (melasma and psoriasis). However, we have to ensure that none of the individuals have any of the outcomes at the baseline
If the exposure is rare, then a cohort design is an efficient method to study the relation between exposure and outcomes
It is generally said that a cohort design may not be efficient for rare outcomes (a case-control design is preferred). However, if the rare outcome is common in some exposures, then it may be useful to follow a cohort design. For example, melanoma is not a common condition in India. Hence, if we follow individuals to study the incidence of melanoma, then it may not be efficient. However, if we know that, theoretically, a particular chemical may be associated with melanoma, then we should follow a cohort of individuals exposed to this chemical (in occupational settings or otherwise) and study the incidence of melanoma in this group
In a prospective cohort study, the exposure variable, other variables, and outcomes may be measured more accurately. This is important to maintain uniformity in the measurement of exposures and outcomes. This is also useful for exposures that may require subjective assessment or recall by the patient. For example, dietary history, smoking history, or alcoholic history, etc. This may help in reducing the bias in measurement of exposure
A retrospective cohort study can be completed fast and is relatively inexpensive compared with a prospective cohort study. However, it also has other strengths of the prospective cohort study.

Limitations of a Cohort Study

One major limitation of a prospective cohort design is that is time consuming and costly. For example, if we have to study the incidence of cardiovascular patients in patients of psoriasis, we may have to follow them up for many years before the outcome occurs
In a retrospective cohort study, the exposure and the outcome variables are collected before the study has been initiated. Thus, the measurements may not be very accurate or according to our requirements. In addition, the some of the exposures may have been assessed differently for various members of the cohort
As discussed earlier, cohort studies may not be very efficient for rare outcomes except in some conditions.

Additional Points in Cohort Studies

Multiple cohort study.

Sometimes, we may be interested to compare the outcomes in two or more groups of individuals. Thus, we may have a multiple cohort study. It is important the exposure, outcome, and other variables should be measured similarly in both the study and the comparison group.

Measurement of exposure and outcome

Since the individuals are included in the study based on the exposure status, this has to be well defined and accurate. The outcomes also have to be well defined and measured similarly in all the participants. If you have more than one group in the cohort (as in multiple cohorts or reference population), you should ensure that the follow-up protocols are similar in all the groups.

Question: What if there is an error in measuring the exposure or the outcome?

It is quite possible that individuals participating in a cohort study may not be correctly classified – some exposed individuals may be classified as unexposed and the other way round. If the misclassification of the exposure or the outcome is random or nondifferential, then the two groups will be similar and the estimates from the study will be biased towards the null. Thus, we will underestimate the association between the exposure and the outcome. If, however, the misclassification is differential or nonrandom, then the estimates may be biased toward the null, away from the null, or may be an appropriate estimate.

Follow-up of the study participants is very important in a cohort study and losses are an important source of bias in these types of studies. Some patients are lost to follow-up in large cohorts; however, if the proportion is very high (>30%), then the validity of the results from this study are doubtful. This loss to follow-up becomes all the more important if it is related to the exposure or outcome of interest. For example, in our prospective study, majority of the patients who were lost to follow-up had severe psoriasis at the baseline, then we will get biased estimates from the study. Thus, managing follow-ups and minimizing losses are an important component of the design of a cohort study.

Nested case-control study

This is a specific type of study design nested within a cohort study. In this, the investigator will match the controls to the cases within a specific cohort. The exposure of interest will be assessed in these selected cases and controls. For example, our hypothesis is that there is a biological marker that in present/elevated (to begin with) in individuals who develop cardiovascular events in psoriatic patients. It is expensive to assess this marker in all patients. Thus, we select all those who develop the outcomes (cases) in our cohort and a sample of individuals who do not develop the outcomes (controls). An important aspect, however, is that we should have stored the biological material that we have collected at baseline, and the biological marker should be assessed in this sample. This procedure maintains the temporal strength of the cohort study.

Cohort studies will help us to estimate the cumulative incidence and incidence rate.

Cumulative incidence

We follow 10,000 psoriatic patients for 10 years. Of these, 50 have a cardiovascular event. Thus, the cumulative incidence will be 50/10,000 or 0.005. This measure is a proportion. Thus, the cumulative incidence will be 0.5% or 5/1000.

Incidence rate

We follow-up 10,000 psoriatic patients for 10 years. Of these, 50 have a cardiovascular event.

How do we calculate the incidence rate?

Let us assume that all the cardiovascular events occurred at the end of the 2 nd year. Our outcome of interest was the first cardiovascular event. Thus, at the end of the 2 nd year, 50 individuals have the outcome.

The total time contributed by these 50 individuals is 50 × 2 years = 100 person years (PY) - (A).

The total time contributed by the rest of the cohort is (10,000 − 50) × 10 = 99,500 PY - (B).

Thus, the total person time is A + B = 99,600.

The incidence rate is 50/99,600 or 0.000502. As it is obvious from the term, this measure is a rate (compared with cumulative incidence which was a proportion). Thus, the incidence rate of first cardiovascular event in psoriatic patients is 0.502/1000 PY or 5.02/10,000 PY.

Other analysis

Other methods such as logistic regression, Kalpan–Meier curves, cox-regression, Poisson regression, lognormal regression may be useful in cohort studies. These are relatively advanced analyses and should be discussed with a statistician.

Fixed and random effects models

One of the main strengths of a cohort study is the longitudinal nature of the data. Some of the variables are time varying (such as blood pressure), and some may be time independent (such as sex). The fixed and random effects models are useful to handle longitudinal data. The random effects model provides both between- and within-individual variance and is useful for time-dependent and time-independent variables. These models are used in linear outcomes (such as body mass index) or categorical outcomes (such as presence/absence of psoriasis). These are advanced modeling techniques and should be discussed with a statistician.

Some Practical Points

Project management.

The investigator should remember that conducting a large-scale prospective cohort study requires proper project management.

Follow-up of participants

The investigator should devise strategies to ensure proper follow-up of individuals at the designated time intervals. A computer program should be put in place at the start of the prospective study. The program should indicate the number of participants due for a visit every day. If the individual does not visit for the next week, a reminder should be sent to the individual. This can be performed through texting or a phone call to the individual. Some investigators hire field workers or outreach workers to ensure follow-up of study participants.

It is important that we include only patients with permanent addresses in the area for long-term cohort studies. Details about the stay (permanent address, temporary address, and duration of residence in the current address) should be a part of the inclusion criteria.

Data management

The investigator should prioritize data management in these studies. The data entry program should be installed at the start of the project. In addition, data entry and cleaning should be done as soon as data are collected. This will help us to identify the lacunae in the existing data, loss of follow-ups, and missing data points.

Missing data

It is very important to address missing data in cohort studies. There are statistical methods to handle missing data in studies – such as complete case analysis, available case analysis, single imputation, or multiple imputations. The investigator should work with a statistician to address missing data in the dataset. These methods should also be described in the statistical analysis section of the manuscript.

In a cohort study, participants who do not have the outcome at baseline are followed over time to estimate the incidence of the outcome. In this type of design, the temporality between the exposure and outcome is well defined. The studies may be prospective, retrospective, or a mixture of both. Prospective cohort studies may be time consuming and expensive. Losses during follow-up are an important source of bias in cohort studies; thus, measures to ensure follow-up of participants should be included in the design of a prospective cohort study. Advanced modeling techniques are useful to analyze longitudinal data and are preferred in cohort studies.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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Outdoor air pollution and risk of incident adult haematologic cancer subtypes in a large US prospective cohort

W. Ryan Diver ORCID: orcid.org/0000-0002-5418-9000 1 , 2 , 3 ,
Lauren R. Teras ORCID: orcid.org/0000-0003-2419-8536 3 ,
Emily L. Deubler 3 &
Michelle C. Turner ORCID: orcid.org/0000-0002-6431-1997 1 , 2 , 4

British Journal of Cancer ( 2024 ) Cite this article

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Cancer epidemiology
Haematological cancer
Risk factors

Outdoor air pollution and particulate matter (PM) are classified as Group 1 human carcinogens for lung cancer. Pollutant associations with haematologic cancers are suggestive, but these cancers are aetiologically heterogeneous and sub-type examinations are lacking.

The American Cancer Society Cancer Prevention Study-II Nutrition Cohort was used to examine associations of outdoor air pollutants with adult haematologic cancers. Census block group level annual predictions of particulate matter (PM 2.5 , PM 10 , PM 10-2.5 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), sulfur dioxide (SO 2 ), and carbon monoxide (CO) were assigned with residential addresses. Hazard ratios (HR) and 95% confidence intervals (CI) between time-varying pollutants and haematologic subtypes were estimated.

Among 108,002 participants, 2659 incident haematologic cancers were identified from 1992–2017. Higher PM 10-2.5 concentrations were associated with mantle cell lymphoma (HR per 4.1 μg/m 3 = 1.43, 95% CI 1.08–1.90). NO 2 was associated with Hodgkin lymphoma (HR per 7.2 ppb = 1.39; 95% CI 1.01–1.92) and marginal zone lymphoma (HR per 7.2 ppb = 1.30; 95% CI 1.01–1.67). CO was associated with marginal zone (HR per 0.21 ppm = 1.30; 95% CI 1.04–1.62) and T-cell (HR per 0.21 ppm = 1.27; 95% CI 1.00–1.61) lymphomas.

Conclusions

The role of air pollutants on haematologic cancers may have been underestimated previously because of sub-type heterogeneity.

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Ambient air pollution is an established risk factor for multiple diseases and has been recognised as a Group 1 human carcinogen by the International Agency for Research on Cancer (IARC) since 2013 [ 1 ]. The primary evidence for carcinogenicity was from studies of lung cancer. Evidence for an association with haematological malignancies was insufficient with mixed results in studies of leukaemias and lymphomas combined and there were a limited number of informative studies for the evaluation.

Studies of ambient air pollution in adults have not consistently identified positive relationships with haematologic cancers. A recent prospective study based on US National Health Interview Survey data reported significant positive associations of average residential census tract PM 2.5 (fine particulate matter; <2.5 μm in diameter) and Hodgkin lymphoma, non-Hodgkin lymphoma (NHL), and leukaemia mortality [ 2 ]. However, findings from other large-scale prospective cohort studies of mortality in the US [ 3 ] and Denmark [ 4 ] have not reported evidence for associations with PM 2.5 or other ambient air pollutants. However, a recent pooled study of low-level air pollution in Europe showed associations of NO 2 with leukaemia and PM 2.5 with lymphoma [ 5 ]. Findings from case-control studies [ 6 , 7 , 8 ] have been equally inconsistent though some positive associations were reported among sub-populations.

A major limitation of prior studies is the lack of research on detailed subtypes of haematologic cancers. It is well established that they are a heterogeneous group of diseases which often have distinct risk factors [ 9 ]. For example, research on cigarette smoking [ 9 , 10 , 11 , 12 ] and secondhand smoke [ 13 , 14 ] has identified associations with Hodgkin lymphoma, peripheral T-cell lymphoma, follicular lymphoma, marginal zone lymphoma, and acute myeloid leukaemia, while observing no associations or even inverse associations with other haematologic cancer subtypes. It is plausible that ambient air pollution may also be associated with some subtypes of haematologic cancer, but not others. This may, in part, explain why previous studies have not consistently observed associations between ambient air pollution and adult haematologic cancers.

In addition, the largest studies to date have been studies of haematologic cancer mortality [ 2 , 3 ] which makes it unclear whether observed associations are related to the diagnosis of haematologic cancers or rather the likelihood of survival. It is unclear whether associations with less fatal haematologic cancers have been missed. This is particularly important for haematologic cancers since the survival rates vary strongly by sub-type [ 15 ].

In this study, we will uniquely examine associations of residential ambient air pollutants (PM 2.5 , PM 10 , PM 10-2.5 , ozone(O 3 ), nitrogen dioxide (NO 2 ), sulfur dioxide(SO 2 ), and carbon monoxide(CO)) with histologic subtypes of haematologic cancers using the Cancer Prevention Study-II Nutrition Cohort, a large prospective study of US men and women with linked data on air pollutants and 25 years of follow-up time. We will extend upon previous mortality-based studies in the overall Cancer Prevention Study-II (CPS-II) here for the first time and examine time-varying average ambient air pollution exposures and cancer incidence endpoints [ 3 ].

Study population

Subjects in this analysis were selected from the CPS-II Nutrition Cohort, a prospective study of cancer incidence and mortality in 184,184 men and women from the United States, described in detail elsewhere [ 16 ]. Briefly, the Nutrition Cohort is a sub-cohort of the approximately 1.2 million subjects in CPS-II, a prospective study of mortality established by the American Cancer Society in 1982. Participants in the larger study were recruited nationally and completed a four-page questionnaire at enrolment that included residential addresses. CPS-II participants from 21 states with population-based state cancer registries were invited to participate in the Nutrition Cohort in 1992. The goals of this sub-cohort were to obtain updated information on dietary and other exposures and to identify incident cases of cancers. Participants completed a 10-page mailed questionnaire that included information on demographic, medical, behavioural, environmental, occupational, and dietary factors. Follow-up questionnaires were sent to cohort members every 2 years beginning in 1997 through 2017 to ascertain cancer diagnoses and update residential addresses. Responses to follow-up surveys were received from at least 87% of living participants after multiple mailings. All aspects of the CPS-II Nutrition Cohort study have been approved by the Emory University Institutional Review Board. Written informed consent is received from participants to obtain medical records. At the time of each mailed survey, participants are informed that their identifying information is used to link with cancer registries and death indexes.

This analysis excluded subjects from the CPS-II Nutrition Cohort who were lost to follow-up ( n = 6190), reported a personal history of cancer other than non-melanoma skin cancer at baseline in 1992 ( n = 22,870), had poor quality address linkage ( n = 41,225), whose address included a PO Box or “Care of” ( n = 5383), or reported a diagnosis of cancer in the first survey interval that could not be verified ( n = 513). The final analytic cohort included 108,002 men and women.

This analysis includes 2659 subjects with haematologic cancers diagnosed between the date of enrolment (1992/1993) and June 30, 2017. Most cases ( n = 1907) were identified by self-report of cancer on the follow-up surveys and subsequently verified by medical record abstraction or linkage with state cancer registries. An additional 752 cases were identified as haematologic cancers through automated linkage of the entire cohort with the National Death Index, 78% of these were subsequently verified by linkage with the state cancer registries.

Lymphoid neoplasm subtypes were defined using the Interlymph Pathology Working Group guidelines [ 17 ], based on the 2008-revised WHO classification of tumours of haematologic and lymphoid tissues [ 18 ]. The International Classification of Disease for Oncology, Second and Third Edition (ICD-O-2 and ICD-O-3) was used to define subtypes with at least 50 cases and included: diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukaemia/small lymphocyte lymphoma (CLL/SLL), follicular lymphoma, multiple myeloma, marginal zone lymphoma, mantle cell lymphoma, and T-cell lymphoma. Myeloid leukaemias were divided into acute myeloid leukaemia (AML), and chronic myeloid leukaemia (CML) subtypes.

Ambient air pollution data was obtained from the Centre for Air, Climate and Energy Solutions (CACES) for ambient particulate (PM 2.5 , PM 10 ) and gaseous (O 3 , CO, SO 2 , NO 2 ) air pollutants at high spatial resolution (estimates at Census block group centroids). Briefly, the CACES modelling approach employed a 3-stage process for each pollutant and year: (1) forward stepwise selection of a subset of ~300 geographic covariates (e.g. land use, roads); (2) partial least squares (PLS) dimension reduction of the selected covariates to obtain ~2–3 composite variables; and 3) universal Kriging employing the composite variables obtained from the PLS. The use of PLS leverages predictive information from a large number of geographic covariates with less concern for model overfitting while also limiting the impact of geographic covariate outliers. Importantly, each pollutant model was developed using the same unified framework. The CACES database includes estimates for O 3 , SO 2 , NO 2 for the years 1979–2015, PM 10 for the years 1988–2015, CO for the years 1990–2015, and PM 2.5 for the years 1999–2015 [ 19 ].

Pollutants were linked to the US Census block group of the participants residential addresses. In CPS-II, address data was first collected in 1982, and then updated continuously from 1997–2015. Therefore, address data from 1982 was used for the years 1992–1996 until updating began in 1997. Participants were assigned an average pollutant level for each year based on their address during that year. In a calendar year when participants changed address, the value given for that year was a weighted average based on the number of months at each address. PM 2.5 values from 1991–1998 were estimated based on the average ratio of PM 10 -PM 2.5 for each census block group from 1999–2015, as has been done previously [ 20 ]. After the estimation of earlier PM 2.5 , data was available for all six pollutants beginning in 1991 (the year prior to the start of follow-up) through 2015. The coarse fraction of PM 10 was calculated by subtracting PM 2.5 from PM 10 . Data for 2015 was used for the year 2016 for all pollutants.

Statistical analysis

In this analysis, ambient air pollutant concentrations at the residences were modelled using yearly time-varying average exposures. Person-years of follow-up for each participant were calculated from the completion of the CPS-II Nutrition Cohort questionnaire in 1992/1993 to date of (1) diagnosis of haematologic cancer; (2) diagnosis of cancer other than haematologic cancer; (3) death occurring between the last returned survey and next mailed survey; 4) return of last questionnaire; 5) last questionnaire the participant was known to be cancer free if they reported haematologic cancer that could not be verified; or 6) end of follow-up on June 30, 2017.

Cox proportional hazards regression [ 21 ] was used to compute multivariable-adjusted hazard ratios (HR) and 95% confidence intervals (CI) for the association between each ambient air pollutant and haematologic cancer sub-type incidence. The time scale for the models was assessed in days of follow-up. At each event time of an incident haematologic cancer sub-type diagnosis, a risk set was formed, consisting of all included participants who were not censored, and an average air pollutant exposure was constructed for each member of the risk set from 1991, the year prior to the start of follow-up, to the calendar year prior to the event year based on their residence over follow-up time. Therefore, all exposure data is estimated in the pre-diagnosis period in the statistical models. The HRs were estimated for units representing the distance from the 5 th percentile to the mean of each pollutant that subjects were exposed to during follow-up. The proportional hazards assumption was assessed visually and statistically using the cumulative sum of the martingale residuals [ 22 ] to identify potential changes in associations over time. Descriptive statistics were calculated showing the distributions of the pollutants, correlations between pollutants, and presenting the mean values by covariate categories.

All models were stratified on single-year of age, and additionally adjusted for sex (male, female), race (white, black, other), education (high school or less, some college, college graduate), marital status (single, married, other), continuous body mass index (BMI), BMI squared, smoking status (never, quit 30+ years, quit 20 to <30 years, quit 10 to <20 years, quit <10 years, current smoker), continuous cigarettes/day and year smoked with squared terms in current smokers, started smoking before age 18 (no, yes), secondhand smoke exposure (h/week), ACS diet score (low, medium, high) [ 23 ], alcohol use (non-drinker, <1, 1–2, 2+, and missing drinks/day), an occupational dirtiness index to identify workplace PM 2.5 exposure [ 24 ], and any regular exposure (no/yes/missing) to one of six industrial exposures (asbestos, chemicals/acids/solvents, coal/stone dust, coal tar/pitch/asphalt, formaldehyde, or diesel engine exhaust). Smoking variables were updated time-dependently. Census tract level ecologic covariates from US Census in 1990 and 2000 and the American Community Survey in 2010 were included for median household income, percent college-educated, percent of the population that is African American or Other Race, unemployment rate, and poverty rate and were updated throughout follow-up to account for updated census information over time and residency changes. All P -values are two-sided.

Effect modification by sex, smoking status, and region was assessed using a likelihood ratio statistic to compare models with and without multiplicative interaction terms. A p -value of <0.05 was used to define statistical significance. Alternative modelling using different covariates (models minimally adjusted for age and sex, models without ecologic variables), exposure assessment based on fixed exposures averaged from 1992–2015, alternate PM 2.5 exposure data from previous mortality research in CPS-II [ 3 ], and two-pollutant models were also conducted. Statistical analysis was conducted using SAS (version 9.4) and R (version 4.2.0). The programme code is available upon request.

The distribution of air pollutant values at baseline among participants is shown in Table 1 . Values in the CPS-II study population are consistent with those observed in the U.S. [ 19 ] with average levels below those of the current national ambient air quality standards (NAAQS) [ 25 ]. However, there are areas with exposures greater than the current NAAQS. There were high correlations during follow-up for NO 2 and CO ( r = 0.74–0.80), and moderate correlations for other pollutants including PM 2.5 and PM 10 ( r = 0.54–0.72), SO 2 and O 3 ( r = 0.50–0.57), NO 2 and PM 10 ( r = 0.41–0.64) (Supplementary Table S1 ).

Participants included in this analysis were mostly white (97%) with an average age of 63 years in 1992 and were roughly equally divided among men and women (Table 2 ). Never-smokers accounted for 43% of the population, although only 9% of participants were currently smoking at baseline. The largest number of participants (62%) lived in the Midwest and Northeastern parts of the United States, however, there was substantial representation from the South (18.3%) and West (19.7%) as well. A majority of subjects did not move from their original census block group over follow-up time (63%) and only 4% had more than three different census block groups during the 25 years of the study.

Only modest differences in air pollution exposure at baseline were observed by participant characteristics (Table 2 ). There was some indication of higher PM 10 values for older participants, while the inverse was seen for O 3 and SO 2 . Non-white participants generally lived in areas with higher levels of all pollutants. We did not observe large differences in pollutant exposure by lifestyle factors such as smoking, alcohol, and BMI. Subjects living in urban areas had higher pollutant exposures. There were also regional differences in residential air pollution exposures.

There was generally no clear association of ambient air pollution with NHL and myeloid leukaemia overall (Table 3 ). There were some positive associations between several air pollutants and Hodgkin lymphoma, with a statistically significant association observed for NO 2 (HR per 7.2 ppb = 1.39, 95% CI 1.01–1.92).

When examining more detailed NHL subtypes (Table 3 ) there were some positive associations of residential particulate matter with marginal zone and mantle cell lymphoma. There was a statistically significant association between PM 10-2.5 and mantle cell lymphoma (HR per 5 μg/m 3 = 1.43; 95% CI 1.08–1.90). Among the gaseous pollutants, there were some positive associations observed with NO 2 and CO; associations with marginal zone lymphoma (NO 2 HR per 7.2 ppb = 1.30, 95% CI 1.01–1.67; CO HR per 0.21 ppm = 1.30, 95% CI 1.04–1.62) and T-cell lymphoma (CO HR per 0.21 ppm = 1.27, 95% CI 1.00–1.61) were statistically significant. Other NHL subtypes were generally not associated with any other pollutants. There were also some inverse associations observed, including of both PM 10 and NO 2 with CLL/SLL.

Alternative adjustment with a minimal set of covariates (age and sex) or without the ecologic covariates were similar (Supplemental Table 2 ), as were models that used fixed (non-time varying) air pollutant estimates for comparison (Supplemental Table 3 ).

Some differences by sex were observed (Figs. 1 and 2 ; Supplemental Tables 4 and 5 ). PM 2.5 was associated with a higher risk of Hodgkin lymphoma (HR per 4.1 μg/m 3 = 1.73; 95% CI 1.06–2.82) in women. The association was not present in men, and the test for interaction was statistically significant (p-int = 0.02). CO was associated with an increased risk of follicular lymphoma (HR per 0.21 ppm = 1.23; 95%CI 1.02–1.49) in women, but not men.

Hazard ratios for PM 2.5 , PM 10 , and PM 10- 2.5 are shown in three panels with separate indicators for men (blue) and women (red) with 95% confidence intervals shown as gray lines. Confidence limits that extend beyond the scale are indicated by arrowheads. Units for the HRs: PM 2.5 (4.1 µg/m 3 ), PM 10 (6.7 µg/m 3 ), and PM10-2.5 (5 µg/m 3 ).

Hazard ratios for NO 2 , O 3 , SO 2 , and CO are shown in four panels with separate indicators for men (blue) and women (red) wiith 95% confidence intervals shown as gray lines. Confidence limits that extend beyond the scale are indicated by arrowheads. Units for the HRs: NO 2 (7.2 ppb), O 3 (9.9 ppb), SO 2 (2.3 ppb), and CO (0.21 ppm).

Results in never-smokers are shown in Table 4 . The statistically significant positive association between PM 2.5 and the risk of Hodgkin lymphoma that was observed in women was also present in never-smokers. The association of NO 2 and risk of Hodgkin lymphoma was also elevated (HR per 7.2 ppb = 1.58, 95% CI 0.96–2.61), but no longer statistically significant in never-smokers. Associations with air pollutants and other subtypes in never-smokers were generally in the same direction as the models including all subjects, but some results were no longer statistically significant.

There were no strong regional differences in PM 2.5 associations with haematologic cancers (Supplemental Table 6 ). Statistically significant associations were largely unchanged in selected two-pollutant models (Supplemental Table 7 ).

We conducted an analysis of ambient air pollutants and the risk of incident haematologic cancers and found significant positive associations with some subtypes. Higher coarse particulate matter exposure was positively associated with mantle cell lymphoma risk, while fine particulate matter was associated with Hodgkin lymphoma in women and never-smokers. Among gaseous pollutants, increased NO 2 was associated with Hodgkin lymphoma. We also observed an increased risk of marginal zone lymphoma with higher levels of CO, and similar associations with follicular lymphoma and T-cell lymphoma that were limited to women. Several patterns emerged as the main findings of this study. Ambient air pollutants were generally associated with a higher risk of Hodgkin lymphoma, CO was positively associated with multiple NHL subtypes, and some previously unstudied NHL subtypes were found to be associated with ambient air pollution. This suggests that ambient air pollution may play a larger role in haematologic cancer risk than previously observed.

Most previous research on outdoor air pollutants and haematologic cancers has focused on broad groups that do not account for aetiologic heterogeneity. Common groups examined include “leukaemia” [ 2 , 3 , 4 , 5 , 6 , 8 , 26 , 27 ], “leukaemia and lymphoma” [ 28 ], “haematologic cancer” [ 29 ], or “non-Hodgkin lymphomas (NHL)” [ 2 , 3 , 5 ]. Of the prospective cohorts, one large US study of PM 2.5 and fatal cancers found increased risks of leukaemia and NHL [ 2 ], as did a pooled European cohort of NO 2 with leukaemia and PM 2.5 with lymphoma [ 5 ]. However, another large US study found no associations with PM 2.5 , NO 2 , or O 3 for the same sites [ 3 ]. A large cohort study of cancer risk in Denmark also found no association with nitrogen oxides (NOx) and leukaemia or NHL [ 4 ], nor did a smaller US cohort study of total suspended particles with leukaemias and lymphomas[ 28 ]. Large registry-based case-control studies of leukaemia in Denmark identified significant associations with PM 2.5 [ 6 , 27 ], while ecologic studies found associations with some pollutants, but not others [ 29 , 30 ]. Given the differences we observed by haematologic cancer sub-type, the inconsistent or null findings from these studies may be explained by these broad groupings of all lymphomas or leukaemias together, thus masking associations with one or more subtypes.

The observed associations with particulate matter and Hodgkin lymphoma in this study are consistent with previous research. PM 2.5 was positively associated with Hodgkin lymphoma with the strongest findings in women and never-smokers, though there were only 54 total observed cases. A population-based case-control study in Denmark [ 7 ] found no overall association with Hodgkin lymphoma, but there was an elevated risk of the classical Hodgkin lymphoma sub-type for PM 2.5 per 5 μg/m 3 (HR = 1.21, 95%CI 0.96–1.54). Low-level exposure to PM 2.5 in Europe [ 5 ] showed an elevated although non-significant association with Hodgkin lymphoma (HR per 5 μg/m 3 = 1.31, 95%CI 0.79–2.16). A large US cohort study of fatal cancer [ 2 ] identified a statistically significant association of 10 μg/m 3 of PM 2.5 with a higher risk of Hodgkin lymphoma (HR = 4.18, 95%CI 1.20–14.60). There was a weakly elevated but non-significant association between PM 2.5 and fatal Hodgkin lymphoma in the larger CPS-II mortality cohort [ 3 ] (HR per 4.4 μg/m 3 = 1.12 95% CI 0.82–1.54). In sensitivity analyses using PM 2.5 exposure data from the previous mortality study, there were similar elevated HRs for Hodgkin lymphoma as observed here (Supplemental Table 8 ). Our findings in women were not examined in other studies, but the stronger association in never-smokers was also seen in the one study that evaluated smoking [ 2 ]. The PM 2.5 and Hodgkin lymphoma association may be more apparent in never-smokers if there was residual confounding due to cigarette smoking. This may also explain the observed stronger associations in women since half are never-smokers compared to only 32% of men, although results may also be due to chance. Overall, there is some emerging evidence for an association between PM 2.5 and Hodgkin lymphoma.

We observed some associations with particulate matter and haematologic subtypes that are not as well studied in the literature. Our finding of an association between coarse particulate matter and the risk of mantle cell lymphoma has not been previously reported. To our knowledge, studies of potential risk factors for mantle cell lymphoma are relatively few, and suggested risk factors remain unconfirmed [ 31 ]. A study in Denmark found that PM 2.5 was associated with an increased risk of the AML sub-type [ 6 ], which we did not observe. The novel association with particulate matter and mantle cell lymphoma requires additional follow-up.

Gaseous pollutants have not been well studied in relation to haematologic subtypes, and we observed some novel findings. In this study, we observed an association between NO 2 (an indicator of vehicular traffic emissions) and an increased risk of Hodgkin lymphoma. In a previous mortality analysis in CPS-II, there was a positive, but imprecise association of NO 2 and fatal Hodgkin lymphoma (HR per 6.5 ppb = 1.16, 95% CI 0.87–1.53, n = 125 deaths). Among other studies of Danish adults [ 7 ] and Swiss children [ 32 ] there was no association with NO 2 . A recent meta-analysis suggested an association of a threshold effect at higher levels of NO 2 exposure with acute lymphocytic leukaemia (ALL) in children [ 33 ], but no association with PM 2.5 or PM 10 . In a US case-control study, children diagnosed with ALL were more likely to have mothers living in areas with higher levels of CO based on traffic [ 34 ]. However, ALL is relatively rare in adults, and we were unable to examine it in this population. The statistically significant positive associations for CO with marginal zone lymphoma, T-cell lymphoma, and women with follicular lymphoma are supported by other epidemiologic evidence related to tobacco smoke and have biological plausibility.

Epidemiologic research on tobacco smoke is informative for associations with air pollutants because tobacco smoke is a meaningful source of particulate matter and CO [ 35 ]. The large Interlymph consortium evaluated NHL risk factors by sub-type and found more years of cigarette smoking to be associated with increased risk of follicular, marginal zone, mantle cell, and T-cell lymphomas while other subtypes had associations with smoking in inverse directions [ 9 ]. Studies on secondhand smoke also showed increased associations with follicular lymphoma [ 13 , 14 ]. There is also evidence suggesting that the tobacco smoking associations with follicular lymphoma are stronger in women [ 10 ], similar to what we observed with CO. Increases in circulating oestrogens related to smoking [ 36 ] can lead to poor precursor B-cell differentiation and an accumulation of non-cycling cells that could develop into NHL [ 37 ]. If this is being driven by CO, then this pathway may be more relevant for women with existing oestrogen levels that are already higher than men. Studies of tobacco smoke show support for the observed associations with CO exposure with specific NHL subtypes in this study.

There are plausible mechanisms for the observed associations with air pollutants and haematologic cancers. CO directly interacts with the haematologic system including binding with haemoglobin to form carboxyhemoglobin which can induce hypoxia in tissues. CO is also involved in the co-regulation of oxidative stress and reduces apoptosis which could be associated with a higher risk of cancer, however, it also has weak anti-inflammatory and antiproliferative effects [ 38 ]. In animal studies, mice exposed to urban air were found to have a statistically significant increase in micronuclei frequency in lymphocytes that was positively correlated with CO and PM 2.5 [ 39 ]. Human studies on ambient CO as a carcinogen have primarily been on lung cancer, however, this study and supporting animal and mechanistic work suggest that future work should examine its role in haematologic cancers. For Hodgkin lymphoma, PM 2.5 has been associated with inflammation [ 40 ] which may play a role in the reactivation of the Epstein–Barr virus and the development of Hodgkin lymphoma [ 41 ]. Beyond direct effects, it is also possible that the measured pollutants in this study are markers for established carcinogens such as polyaromatic hydrocarbons (PAHs) or dioxins [ 42 ]. The burning of fossil fuels and biomass are sources of pollutants included in the analysis (PM 2.5 , NO 2 , SO 2 , and CO) as well as pollutants like PAHs and dioxins that were not. Therefore, associations with CO may represent a better assessment of exposure to these types of pollutant sources. There was some suggestion of inverse associations with pollutants and CLL/SLL in this and other studies [ 4 , 8 ], but we did not identify any strong biological reasons for these findings indicating they may be due to chance.

The strengths of this study include its large nation-wide prospective design with the ability to examine multiple subtypes of incident haematologic cancer with time-varying air pollution exposure estimates. We are not aware of other studies of air pollution that have comprehensively examined the subtypes of NHL, and some new potential associations have been observed here which should be examined in further work. The updating of air pollution data over-time allowed us to examine pre-diagnosis pollutants ensuring appropriate temporality, and to account for location changes. Additionally, data was linked to census block groups which are fine areas of geospatial resolution. Detailed individual-level data allowed for control of confounders and the large sample size allowed for stratification by important factors such as sex and smoking status which was also updated over follow-up time.

This study is limited in its racial and ethnic diversity, and we are not able to observe whether air pollution plays a role in known haematologic cancer rate differences by race. The study population is also older, and it is possible we are missing important associations in a younger adult group. Approximately half of all lymphoid malignancies are diagnosed after 65, however, particular subtypes like Hodgkin lymphoma and precursor lymphoid leukaemia/lymphoma are primarily diagnosed in early adulthood or childhood [ 15 ]. Despite the study’s large size overall, when examining the rarer subtypes, we remain limited in statistical power and may be missing important associations, or results that were observed may be due to chance. Additionally, many air pollutants are correlated and it is possible observed associations are the effect of a mixture of multiple pollutants, which was not addressed in the current study. Mixtures of air pollutants are an important area of future research.

In conclusion, this study identified several novel associations of air pollutants with incident haematologic cancer subtypes. The sub-type-specific findings may explain mixed associations found with larger groupings of haematologic cancers in previous research. It will be important for future studies to replicate these findings and may require pooled efforts to have an adequate sample size for the rarer subtypes. These findings suggest that the role of air pollutants in the risk of haematologic cancers may have been underestimated previously because subtypes were not accounted for.

Data availability

The data underlying the findings of this study are restricted by the Emory University Institutional Review Board, which approved the consent forms. Data are available from the American Cancer Society by following the ACS Data Access Procedures ( https://www.cancer.org/content/dam/cancer-org/research/epidemiology/cancer-prevention-study-data-access-policies.pdf ) for researchers who meet the criteria for access to confidential data.

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Acknowledgements

The authors express sincere appreciation to all Cancer Prevention Study-II participants, and to each member of the study and biospecimen management group. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the Centres for Disease Control and Prevention’s National Programme of Cancer Registries and cancer registries supported by the National Cancer Institute’s Surveillance Epidemiology and End Results Programme. The authors assume full responsibility for all analyses and interpretation of results. The views expressed here are those of the authors and do not necessarily represent the American Cancer Society or the American Cancer Society—Cancer Action Network.

The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II cohort. MCT is funded by a Ramón y Cajal fellowship (RYC-2017–01892) from the Spanish Ministry of Science, Innovation and Universities and co-funded by the European Social Fund. We acknowledge support from the grant CEX2018-000806-S funded by MCIN/AEI/ 10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Programme.

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W. Ryan Diver: conceptualisation, methodology, formal analysis, visualisation, writing—original draft. Lauren R. Teras: writing—review and editing. Emily L. Deubler: data curation, writing—review and editing. Michelle C. Turner: conceptualisation, methodology, writing—review and editing, supervision.

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The Cancer Prevention Study-II study protocol was approved by the institutional review boards of Emory University (IRB00045780), and those of participating registries as required. This research was conducted in accordance with the Declaration of Helsinki.

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Diver, W.R., Teras, L.R., Deubler, E.L. et al. Outdoor air pollution and risk of incident adult haematologic cancer subtypes in a large US prospective cohort. Br J Cancer (2024). https://doi.org/10.1038/s41416-024-02718-3

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Ge Chen 1 ,
Zhengmin (Min) Qian 2 ,
Junguo Zhang 1 ,
Shiyu Zhang 1 ,
http://orcid.org/0000-0002-7003-6565 Zilong Zhang 1 ,
Michael G Vaughn 3 ,
Hannah E Aaron 2 ,
Chuangshi Wang 4 ,
Gregory YH Lip 5 , 6 and
http://orcid.org/0000-0002-3643-9408 Hualiang Lin 1
1 Department of Epidemiology , Sun Yat-Sen University , Guangzhou , China
2 Department of Epidemiology and Biostatistics, College for Public Health and Social Justice , Saint Louis University , Saint Louis , Missouri , USA
3 School of Social Work, College for Public Health and Social Justice , Saint Louis University , Saint Louis , Missouri , USA
4 Medical Research and Biometrics Centre , Fuwai Hospital, National Centre for Cardiovascular Diseases, Peking Union Medical College , Beijing , China
5 Liverpool Centre for Cardiovascular Science , University of Liverpool and Liverpool Heart and Chest Hospital , Liverpool , UK
6 Department of Clinical Medicine , Aalborg University , Aalborg , Denmark
Correspondence to Dr Hualiang Lin, Department of Epidemiology, Sun Yat-Sen University, Guangzhou, Guangdong 510080, China; linhualiang{at}mail.sysu.edu.cn

Objective To examine the effects of fish oil supplements on the clinical course of cardiovascular disease, from a healthy state to atrial fibrillation, major adverse cardiovascular events, and subsequently death.

Design Prospective cohort study.

Setting UK Biobank study, 1 January 2006 to 31 December 2010, with follow-up to 31 March 2021 (median follow-up 11.9 years).

Participants 415 737 participants, aged 40-69 years, enrolled in the UK Biobank study.

Main outcome measures Incident cases of atrial fibrillation, major adverse cardiovascular events, and death, identified by linkage to hospital inpatient records and death registries. Role of fish oil supplements in different progressive stages of cardiovascular diseases, from healthy status (primary stage), to atrial fibrillation (secondary stage), major adverse cardiovascular events (tertiary stage), and death (end stage).

Results Among 415 737 participants free of cardiovascular diseases, 18 367 patients with incident atrial fibrillation, 22 636 with major adverse cardiovascular events, and 22 140 deaths during follow-up were identified. Regular use of fish oil supplements had different roles in the transitions from healthy status to atrial fibrillation, to major adverse cardiovascular events, and then to death. For people without cardiovascular disease, hazard ratios were 1.13 (95% confidence interval 1.10 to 1.17) for the transition from healthy status to atrial fibrillation and 1.05 (1.00 to 1.11) from healthy status to stroke. For participants with a diagnosis of a known cardiovascular disease, regular use of fish oil supplements was beneficial for transitions from atrial fibrillation to major adverse cardiovascular events (hazard ratio 0.92, 0.87 to 0.98), atrial fibrillation to myocardial infarction (0.85, 0.76 to 0.96), and heart failure to death (0.91, 0.84 to 0.99).

Conclusions Regular use of fish oil supplements might be a risk factor for atrial fibrillation and stroke among the general population but could be beneficial for progression of cardiovascular disease from atrial fibrillation to major adverse cardiovascular events, and from atrial fibrillation to death. Further studies are needed to determine the precise mechanisms for the development and prognosis of cardiovascular disease events with regular use of fish oil supplements.

Health policy
Nutritional sciences
Public health

Data availability statement

Data are available upon reasonable request. UK Biobank is an open access resource. Bona fide researchers can apply to use the UK Biobank dataset by registering and applying at http://ukbiobank.ac.uk/register-apply/ .

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjmed-2022-000451

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Findings of the effects of omega 3 fatty acids or fish oil on the risk of cardiovascular disease are controversial

Most previous studies focused on one health outcome and did not characterise specific cardiovascular disease outcomes (eg, atrial fibrillation, myocardial infarction, stroke, heart failure, and major adverse cardiovascular events)

Whether fish oil could differentially affect the dynamic course of cardiovascular diseases, from atrial fibrillation to major adverse cardiovascular events, to other specific cardiovascular disease outcomes, or even to death, is unclear

WHAT THIS STUDY ADDS

In people with no known cardiovascular disease, regular use of fish oil supplements was associated with an increased relative risk of atrial fibrillation and stroke

In people with known cardiovascular disease, the beneficial effects of fish oil supplements were seen on transitions from atrial fibrillation to major adverse cardiovascular events, atrial fibrillation to myocardial infarction, and heart failure to death

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICY

Regular use of fish oil supplements might have different roles in the progression of cardiovascular disease

Further studies are needed to determine the precise mechanisms for the development and prognosis of cardiovascular disease events with regular use of fish oil supplements

Introduction

Cardiovascular disease is the leading cause of death worldwide, accounting for about one sixth of overall mortality in the UK. 1 2 Fish oil, a rich source of omega 3 fatty acids, containing eicosapentaenoic acid and docosahexaenoic acid, has been recommended as a dietary measure to prevent cardiovascular disease. 3 The UK National Institute for Health and Care Excellence recommends that people with or at high risk of cardiovascular disease consume at least one portion of oily fish a week, and the use of fish oil supplements has become popular in the UK and other western countries in recent years. 4 5

Although some epidemiological and clinical studies have assessed the effect of omega 3 fatty acids or fish oil on cardiovascular disease and its risk factors, the findings are controversial. The Agency for Healthcare Research and Quality systematically reviewed 37 observational studies and 61 randomised controlled trials, and found evidence indicating the beneficial effects of higher consumption of fish oil supplements on ischaemic stroke, whereas no beneficial effect was found for atrial fibrillation, major adverse cardiovascular events, myocardial infarction, total stroke, or all cause death. 6 In contrast, the Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial (REDUCE-IT) reported a decreased risk of major adverse cardiovascular events with icosapent ethyl in patients with raised levels of triglycerides, regardless of the use of statins. 7 Most of these findings, however, tended to assess the role of fish oil at a certain stage of cardiovascular disease. For example, some studies restricted the study population to people with a specific cardiovascular disease or at a high risk of cardiovascular disease, 8 9 whereas others evaluated databases of generally healthy populations. 10 All of these factors might preclude direct comparison of the effects of omega 3 fatty acids on atrial fibrillation events or on further deterioration of cardiovascular disease. Few studies have fully characterised specific cardiovascular disease outcomes or accounted for differential effects based on the complex disease characteristics of participants. Hence, in this study, we hypothesised that fish oil supplements might have harmful, beneficial, or no effect on different cardiovascular disease events in patients with varying health conditions.

Most previous studies on the association between fish oil and cardiovascular diseases generally focused on one health outcome. Also, no study highlighted the dynamic progressive course of cardiovascular diseases, from healthy status (primary stage), to atrial fibrillation (secondary stage), major adverse cardiovascular events (tertiary stage), and death (end stage). Clarifying this complex pathway in relation to the detailed progression of cardiovascular diseases would provide substantial insights into the prevention or treatment of future disease at critical stages. Whether fish oil could differentially affect the dynamic course of cardiovascular disease (ie, from atrial fibrillation to major adverse cardiovascular events, to other specific cardiovascular disease outcomes, or even to death) is unclear.

To deal with this evidence gap, we conducted a longitudinal cohort study to estimate the associations between fish oil supplements and specific clinical cardiovascular disease outcomes, including atrial fibrillation, major adverse cardiovascular events, and all cause death in people with no known cardiovascular disease or at high risk of cardiovascular disease for the purpose of primary prevention. We also assessed the modifying effects of fish oil supplements on the disease process, from atrial fibrillation to other outcomes, in people with known cardiovascular disease for the purpose of secondary prevention.

The UK Biobank is a community based cohort study with more than half a million UK inhabitants aged 40-69 years at recruitment. 11–13 Participants were invited to participate in this study if they were registered with the NHS and lived within 35 km of one of 22 Biobank assessment centres. Between 1 March 2006 and 31 July 2010, a baseline survey was conducted, based on a touch screen questionnaire and face-to-face interviews, to collect detailed personal, socioeconomic, and lifestyle characteristics, and information on diseases. 11–13

We excluded patients who had a diagnosis of atrial fibrillation (n=8326), heart failure (n=2748), myocardial infarction (n=11 949), stroke (n=7943), or cancer (n=48 624) at baseline; who withdrew from the study during follow-up (n=1299); or who had incomplete or outlier data for the main information (n=11 748). Because we focused only on a specific sequence of progression of cardiovascular disease (ie, from healthy status to atrial fibrillation, to major adverse cardiovascular events, and then to death), we excluded 1983 participants with other transition patterns. The remaining 415 737 participants were included in this analysis ( figure 1 ).

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Flowchart of selection of participants in study. The count of diagnosed diseases does not equate to the total number of individuals, because each person could have multiple diagnoses

Determining use of fish oil supplements

Information on regular use of fish oil supplements was collected from a self-reported touchscreen questionnaire during the baseline survey. 14 15 Each participant was asked whether they regularly used any fish oil supplement. Trained staff conducted a verbal interview with participants, asking if they were currently receiving treatments or taking any medicines, including omega 3 or fish oil supplements. Based on this information, we classified participants as regular users of fish oil supplements and non-users.

Follow-up and outcomes

Participants were followed up from the time of recruitment to death, loss to follow-up, or the end date of follow-up (31 March 2021), whichever came first. Incident cases of interest, including atrial fibrillation, heart failure, stroke, and myocardial infarction, were identified by linkage to death registries, primary care records, and hospital inpatient records. 11 Information on deaths was obtained from death registries of the NHS Information Centre, for participants in England and Wales, and from the NHS Central Register Scotland, for participants in Scotland. 11 Outcomes were defined by a three character ICD-10 (international classification of diseases, 10th revision) code. In this study, atrial fibrillation was defined by ICD-10 code I48, and major adverse cardiovascular events was determined by a combination of heart failure (I50, I11.0, I13.0, and I13.2), stroke (I60-I64), and myocardial infarction (I21, I22, I23, I24.1, and I25.2) codes.

We collected baseline data on age (<65 years and ≥65 years), sex (men and women), ethnic group (white and non-white), Townsend deprivation index (with a higher score indicating higher levels of deprivation), smoking status (never, previous, and current smokers), and alcohol consumption (never, previous, and current drinkers). Data for sex were taken from information in UK Biobank rather than from patient reported gender. Baseline dietary data were obtained from a dietary questionnaire completed by the patient or by an interviewer. The questionnaire was established for each nation (ie, England, Scotland, and Wales) to assess an individual's usual food intake (oily fish, non-oily fish, vegetables, fruit, and red meat). Diabetes mellitus was defined by ICD-10 codes E10-E14, self-reported physician's diagnosis, self-reported use of antidiabetic drugs, or haemoglobin A1c level ≥6.5% at baseline. Hypertension was defined by ICD-10 code I10 or I15, self-reported physician's diagnosis, self-reported use of antihypertensive drugs, or measured systolic and diastolic blood pressure ≥130/85 mm Hg at baseline. Information on other comorbidities (obesity (ICD-10 code E66), chronic obstructive pulmonary disease (J44), and chronic renal failure (N18)) was extracted from the first occurrence (UKB category ID 1712). Information on the use of drugs, including antihypertensive drugs, antidiabetic drug, and statins, was extracted from treatment and drug use records. Biochemistry markers were measured immediately at the central laboratory from serum samples collected at baseline. Binge drinking was defined as consumption of ≥6 standard drinks/day for women or ≥8 standard drinks/day for men. Detailed information on alcohol consumption and binge drinking in the UK Biobank was reported previously. 16

Statistical analysis

Characteristics of participants are summarised as number (percentages) for categorical variables and mean (standard deviation (SD)) for continuous variables. Comparisons between regular users of fish oil supplements and non-users were made with the χ 2 test or Student's t test.

We used a multi-state regression model to assess the role of regular use of fish oil supplements in the temporal disease progression from healthy status to atrial fibrillation, to major adverse cardiovascular events, and subsequently to death. The multi-state model is an extension of competing risks survival analysis. 17–19 The model allows simultaneous estimation of the role of risk factors in transitions from a healthy state to atrial fibrillation (transition A), healthy state to major adverse cardiovascular events (transition B), healthy state to death (transition C), atrial fibrillation to major adverse cardiovascular events (transition D), atrial fibrillation to death (transition E), and major adverse cardiovascular events to death (transition F) (transition pattern I, figure 2 ). The focus on these six transitions rather than on all possible health state transitions was preplanned and evidence based. If participants entered different states on the same date, we used the date of the theoretically previous state as the entry date of the latter state minus 0.5 days.

Numbers of participants in transition pattern I, from baseline to atrial fibrillation, major adverse cardiovascular events, and death

We further examined the effects of regular use of fish oil supplements on other pathways. For example, we divided major adverse cardiovascular events into three individual diseases (heart failure, stroke, and myocardial infarction), resulting in three independent pathways (transition patterns II, III, and IV, online supplemental figures S1–S3 ). All models were adjusted for age, sex, ethnic group, Townsend deprivation index, consumption of oily fish, consumption of non-oily fish, smoking status, alcohol consumption, obesity, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, chronic renal failure, and use of statins, antidiabetic drugs, and antihypertensive drugs.

Supplemental material

We conducted several sensitivity analyses for the multi-state analyses of transition pattern A: additionally adjusting for setting (urban and rural), body mass index (underweight, normal, overweight, and obese), and physical activity (low, moderate, and high) in the model; adjusting for binge drinking rather than alcohol consumption; additionally adjusting for other variables of dietary intake (consumption of vegetables, fruit, and red meat); calculating participants' entry date into the previous state with different time intervals (0.5 years, one year, and two years); excluding participants who entered different states on the same date; excluding events occurring in the first two years of follow-up; restricting the follow-up date to 31 March 2020 to evaluate the influence of the covid-19 pandemic; and the use of the inverse probability weighted method to deal with biases between the regular users and non-users of fish oil supplements. Also, we conducted grouped analyses for sex, age group, ethnic group, smoking status, consumption of oily fish, consumption of non-oily fish, hypertension, and drug use, to examine effect modification. The interactions were tested with the likelihood ratio test. All analyses were carried out with R software (version 4.0.3), and the multi-model analysis was performed with the mstate package. A two tailed P value <0.05 was considered significant.

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. Participants were involved in developing the ethics and governance framework for UK Biobank and have been engaged in the progress of UK Biobank through follow-up questionnaires and additional assessment visits. UK Biobank keeps participants informed of all research output through the study website ( https://www.ukbiobank.ac.uk/explore-your-participation ), participant events, and newsletters.

A total of 415 737 participants (mean age 55.9 (SD 8.1) years; 55% women), aged 40-69 years, were analysed, and 31.4% (n=1 30 365) of participants reported regular use of fish oil supplements at baseline ( figure 1 ). Table 1 shows the characteristics of regular users (n=130 365) and non-users (n=285 372) of fish oil supplements. In the group of regular users of fish oil supplements, we found higher proportions of elderly people (22.6% v 13.9%), white people (95.1% v 94.2%), and women (57.6% v 53.9%), and higher consumption of alcohol (93.1% v 92.0%), oily fish (22.1% v 15.4%), and non-oily fish (18.0% v 15.4%) than non-users. The Townsend deprivation index (mean −1.5 (SD 3.0) v −1.3 (3.0)) and the proportion of current smokers (8.1% v 11.4%) were lower in regular users of fish oil supplements. Online supplemental table S1 provides more details on patient characteristics and online supplemental table S2 compares the basic characteristics of included and excluded people.

View inline

Baseline characteristics of study participants grouped by use of fish oil supplements

Over a median follow-up time of of 11.9 years, 18 367 participants had atrial fibrillation (transition A) and 17 826 participants had major adverse cardiovascular events (transition B); 14 902 participants died without having atrial fibrillation or major adverse cardiovascular events (transition C). Among patients with incident atrial fibrillation, 4810 developed major adverse cardiovascular events (transition D) and 1653 died (transition E). Among patients with incident major adverse cardiovascular events, 5585 died during follow-up (transition F, figure 2 ). In separate analyses for individual diseases (transition patterns II, III, and IV, online supplemental figures S1–S3 ), in patients with atrial fibrillation, 3085 developed heart failure, 1180 had a stroke, and 1415 had a myocardial infarction. During follow-up, 2436, 2088, and 2098 deaths occurred in patients with heart failure, stroke, and myocardial infarction, respectively.

Multi-state regression results

Table 2 shows the different roles of regular use of fish oil supplements in transitions from healthy status to atrial fibrillation, to major adverse cardiovascular events, and then to death. For individuals in the primary stage (healthy status), we found that the use of fish oil supplements had a harmful effect on the transition from health to atrial fibrillation, with an adjusted hazard ratio of 1.13 (95% CI 1.10 to 1.17, transition A). The hazard ratio for transition B (from health to major adverse cardiovascular events) was 1.00 (95% CI 0.97 to 1.04) and for transition C (from health to death) was 0.98 (0.95 to 1.02).

Hazard ratios (95% confidence intervals) for each transition, for different transition patterns for progressive cardiovascular disease by regular use of fish oil supplements

For individuals in the secondary stage (atrial fibrillation) at the beginning of the study, regular use of fish oil supplements decreased the risk of major adverse cardiovascular events (transition D, hazard ratio 0.92, 95% CI 0.87 to 0.98), and had a borderline protective effect on the transition from atrial fibrillation to death (transition E, 0.91, 0.82 to 1.01). For transition F, from major adverse cardiovascular events to death, after adjusting for covariates, the hazard ratio was 0.99 (0.94 to 1.06, transition pattern I, table 2 ).

We divided major adverse cardiovascular events into three individual diseases (ie, heart failure, stroke, and myocardial infarction) and found that regular use of fish oil supplements was marginally associated with an increased risk of stroke in people with a healthy cardiovascular state (hazard ratio 1.05, 95% CI 1.00 to 1.11), whereas a protective effect was found in transitions from healthy cardiovascular states to heart failure (0.92, 0.86 to 0.98). For patients with atrial fibrillation, we found that the beneficial effects of regular use of fish oil supplements were for transitions from atrial fibrillation to myocardial infarction (0.85, 0.76 to 0.96), and from atrial fibrillation to death (0.88, 0.81 to 0.95) for transition pattern IV. For patients with heart failure, we found a protective effect of regular use of fish oil supplements on the risk of mortality (0.91, 0.84 to 0.99) (transition patterns II, III, and IV, table 2 ).

Stratified and sensitivity analyses

We found that age, sex, smoking, consumption of non-oily fish, prevalent hypertension, and use of statins and antihypertensive drugs modified the associations between regular use of fish oil supplements and the transition from healthy states to atrial fibrillation ( online supplemental figure S4 ). We found that the association between regular use of fish oil supplements and risk of transition from healthy states to major adverse cardiovascular events was greater in women (hazard ratio 1.06, 95% CI 1.00 to 1.11, P value for interaction=0.005) and non-smoking participants (1.06, 1.06 to 1.11, P value for interaction=0.001) ( online supplemental figure S4 ). The protective effect of regular use of fish oil supplements on the transition from healthy states to death was greater in men (hazard ratio 0.93, 95% CI 0.89 to 0.98, P value for interaction=0.003) and older participants (0.91, 0.86 to o 0.96, P value for interaction=0.002) ( online supplemental figures S5 and S6 ). The results were not substantially changed in the sensitivity analyses ( online supplemental table S3 ).

Principal findings

Our study characterised the regular use of fish oil supplements on the progressive course of cardiovascular disease, from a healthy state (primary stage), to atrial fibrillation (secondary stage), major adverse cardiovascular events (tertiary stage), and death (end stage). In this prospective analysis of more than 400 000 UK adults, we found that regular use of fish oil supplements could have a differential role in the progression of cardiovascular disease. For people with a healthy cardiovascular profile, regular use of fish oil supplements, a choice of primary prevention, was associated with an increased risk of atrial fibrillation. For participants with a diagnosis of atrial fibrillation, however, regular use of fish oil supplements, as secondary prevention, had a protective effect or no effect on transitions from atrial fibrillation to major adverse cardiovascular events, atrial fibrillation to death, and major adverse cardiovascular events to death. When we divided major adverse cardiovascular events into three individual diseases (ie, heart failure, stroke, and myocardial infarction), we found associations that could suggest a mildly harmful effect between regular use of fish oil supplements and transitions from a healthy cardiovascular state to stroke, whereas potential beneficial associations were found between regular use of fish oil supplements and transitions from atrial fibrillation to myocardial infarction, atrial fibrillation to death, and heart failure to death.

Comparison with other studies

Primary prevention.

The cardiovascular benefits of regular use of fish oil supplements have been examined in numerous studies but the results are controversial. Extending previous reports, our study estimated the associations between regular use of fish oil supplements and specific clinical cardiovascular disease outcomes in people with no known cardiovascular disease. Our findings are in agreement with the results of several previous randomised controlled trials and meta-analyses. The Long-Term Outcomes Study to Assess Statin Residual Risk with Epanova in High Cardiovascular Risk Patients with Hypertriglyceridaemia (STRENGTH) reported that consumption of 4 g/day of marine omega 3 fatty acids was associated with a 69% higher risk of new onset atrial fibrillation in people at high risk of cardiovascular disease. 20 A meta-analysis of seven randomised controlled trials showed that users of marine omega 3 fatty acids supplements had a higher risk of atrial fibrillation events, with a hazard ratio of 1.25 (95% CI 1.07 to 1.46, P=0.013). 21 The Vitamin D and Omega-3 Trial (VITAL Rhythm study), a large trial of omega 3 fatty acids for the primary prevention of cardiovascular disease in adults aged ≥50 years, however, found no effects on incident atrial fibrillation, major adverse cardiovascular events, or cardiovascular disease mortality among those treated with 840 mg/day of marine omega 3 fatty acids compared with placebo. 10 22

One possible explanation for the inconsistent results in these studies is that adverse effects might be related to dose and composition. Higher doses of omega 3 fatty acids used in previous studies might have had an important role in causing an adverse effect on atrial fibrillation. 21 One study found that high concentrations of fish oil altered cell membrane properties and inhibited Na-K-ATPase pump activity, whereas a low concentration of fish oil minimised peroxidation potential and optimised activity. 23 In another study, individuals with atrial fibrillation or flutter had higher percentages of total polyunsaturated fatty acids, and n-3 and n-6 polyunsaturated fatty acids, on red blood cell membranes than healthy controls. 24

In terms of composition of omega 3 fatty acids, a recent meta-analysis showed that eicosapentaenoic acid alone can be more effective at reducing the risk of cardiovascular disease than the combined effect of eicosapentaenoic acid and docosahexaenoic acid. 25 Similar outcomes were reported in the INSPIRE study, which showed that higher levels of docosahexaenoic acid reduced the cardiovascular benefits of eicosapentaenoic acid when given as a combination. 26 Another possible explanation is that age, sex, ethnic group, smoking status, dietary patterns, and use of statins and antidiabetic drugs by participants might modify the effects of regular use of fish oil supplements on cardiovascular disease events. Despite these differences in risk estimates, our findings do not support the use of fish oil or omega 3 fatty acid supplements for the primary prevention of incident atrial fibrillation or other specific clinical cardiovascular disease events in generally healthy individuals. Caution might be warranted when fish oil supplements are used for primary prevention because of the uncertain cardiovascular benefits.

Secondary prevention

Our large scale cohort study assessed the role of regular use of fish oil supplements on the disease process, from atrial fibrillation to more serious cardiovascular disease stages, to death, in people with known cardiovascular disease. Contrary to the observations for primary prevention, we found associations that could suggest beneficial effects between regular use of fish oil supplements and most cardiovascular disease transitions. No associations were found between regular use of fish oil supplements and transitions from atrial fibrillation to death, or from major adverse cardiovascular events to death.

Consistent with our hypothesis, the Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico (GISSI) Prevenzione study reported an association between administration of low dose prescriptions of n-3 polyunsaturated fatty acids and reduced cardiovascular events in patients with recent myocardial infarction. 27 A meta-analysis of 16 randomised controlled trials also reported a tendency towards a greater beneficial effect for secondary prevention in patients with cardiovascular disease. 28 Why patients with previous atrial fibrillation benefit is unclear. These findings indicate that triglyceride independent effects of omega 3 fatty acids might in part be responsible for the benefits in cardiovascular disease seen in previous trials. 29–31 No proven biological mechanism for this explanation exists, however, and the dose and formulation of omega 3 fatty acids used in clinical practice are not known.

For the disease process, from cardiovascular disease to death, our findings are consistent with the results of secondary prevention trials of omega 3 fatty acids, which have mostly shown a weak or neutral preventive effect in all cause mortality with oil fish supplements. The GISSI heart failure trial (GISSI-HF), conducted in 6975 patients with chronic heart failure, reported that supplemental omega 3 fatty acids reduced the risk of all cause mortality by 9% (hazard ratio 0.91, 95% CI 0.833 to 0.998, P=0.041). 32 Zelniker et al showed that omega 3 fatty acids were inversely associated with a lower incidence of sudden cardiac death in patients with non-ST segment elevation acute coronary syndrome. 33 A meta-analysis found that use of omega 3 supplements of ≤1 capsule/day was not associated with all cause mortality, but among participants with a risk of cardiovascular disease, taking a higher dose was associated with a reduction in cardiac death and sudden death. 28 Individuals who might benefit the most from fish oil or omega 3 fatty acid supplements are possibly more vulnerable individuals, such as those with previous cardiovascular diseases and those who can no longer live in the community. How fish oil supplements stop further deterioration of cardiovascular disease is unclear, but the theory that supplemental omega 3 fatty acids might protect the coronary artery is biologically plausible, suggesting that omega 3 fatty acids have anti-inflammatory and anti-hypertriglyceridaemia effects, contributing to a reduction in thrombosis and improvement in endothelial function. 34–41 Nevertheless, the effects of omega 3 fatty acids vary according to an individual's previous use of statins, which might partly explain the different effects of fish oil supplements in people with and without cardiovascular disease.

Many studies of omega 3 fatty acids, including large scale clinical trials and meta-analyses, have not produced entirely consistent results. 21 25 42 Our study mainly explored the varied potential effects of regular use of fish oil supplements on progression of cardiovascular disease, offering an initial overview of this ongoing discussion. Our findings suggest caution in the use of fish oil supplements for primary prevention because of the uncertain cardiovascular benefits and adverse effects. Further studies are needed to determine whether potential confounders modify the effects of oil fish supplements and the precise mechanisms related to the development and prognosis of cardiovascular disease events.

Strengths and limitations of this study

The strengths of our study were the large sample size, long follow-up period, which allowed us to analyse clinically diagnosed incident diseases, and complete data on health outcomes. Another strength was our analytical strategy. The multi-state model gives less biased estimates than the conventional Cox model, and distinguished the effect of regular use of fish oil supplements on each transition in the course of cardiovascular disease.

Our study had some limitations. Firstly, as an observational study, no causal relations can be drawn from our findings. Secondly, although we adjusted for multiple covariates, residual confounding could still exist. Thirdly, information on dose and formulation of the fish oil supplements was not available in this study, so we could not evaluate potential dose dependent effects or differentiate between the effects of different fish oil formulations. Fourthly, the use of hospital inpatient data for determining atrial fibrillation events could have excluded some events triggered by acute episodes, such as surgery, trauma, and similar conditions, resulting in underestimation of the true risk because undiagnosed atrial fibrillation is a common occurrence. 43 Fifthly, most of the participants in this study were from the white ethnic group and whether the findings can be generalised to other ethnic groups is not known. Finally, our study did not consider behavioural changes in populations with different cardiovascular profiles because of limited information, and variations in outcomes for different cardiovascular states merits further exploration.

Conclusions

This large scale prospective study of a UK cohort suggested that regular use of fish oil supplements might have differential roles in the course of cardiovascular diseases. Regular use of fish oil supplements might be a risk factor for atrial fibrillation and stroke among the general population but could be beneficial for disease progression, from atrial fibrillation to major adverse cardiovascular events, and from atrial fibrillation to death. Further studies are needed to determine whether potential confounders modify the effects of oil fish supplements and the precise mechanisms for the development and prognosis of cardiovascular disease events.

Ethics statements

Patient consent for publication.

Consent obtained directly from patients.

Ethics approval

The UK Biobank study obtained ethical approval from the North West Multicentre Research ethics committee, Information Advisory Group, and the Community Health Index Advisory Group (REC reference for UK Biobank 11/NW/0382). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

This study was conducted with UK Biobank Resource (application No: 69550). We appreciate all participants and professionals contributing to UK Biobank.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Data supplement 1
Data supplement 2

GYL and HL are joint senior authors.

Contributors HL supervised the whole project and designed the work. GC and HL directly accessed and verified the underlying data reported in the manuscript. GC contributed to data interpretation and writing of the report. ZQ, SZ, JZ, ZZ, MGV, HEA, CW, and GYHL contributed to the discussion and data interpretation, and revised the manuscript. All authors had full access to all of the data in the study and had final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. HL is the guarantor. Transparency: The lead author (guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Funding This work was supported by the Bill and Melinda Gates Foundation (grant No INV-016826). Under the grant conditions of the foundation, a creative commons attribution 4.0 generic license has already been assigned to the author accepted manuscript version that might arise from this submission. The funder had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from Bill and Melinda Gates Foundation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Volume 14, Issue 5
Protocol for a multicentre prospective exploratory mixed-methods study investigating the modifiable psychosocial variables influencing access to and outcomes after kidney transplantation in children and young people in the UK
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http://orcid.org/0000-0001-6090-1650 Ji Soo Kim 1 , 2 ,
http://orcid.org/0000-0002-4769-1211 Jo Wray 3 ,
Deborah Ridout 4 ,
Lucy Plumb 5 , 6 ,
Dorothea Nitsch 5 , 7 ,
Matthew Robb 8 ,
Stephen D Marks 1 , 2
1 Paediatric Nephrology , Great Ormond Street Hospital for Children NHS Foundation Trust , London , UK
2 NIHR Great Ormond Street Hospital Biomedical Research Centre , London , UK
3 Centre for Outcomes and Experience Research in Children's Health, Illness and Disability , Great Ormond Street Hospital for Children NHS Foundation Trust , London , UK
4 Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health , UCL , London , UK
5 UK Renal Registry , Bristol , UK
6 Population Health Sciences , University of Bristol Medical School , Bristol , UK
7 Non-communicable disease epidemiology , London School of Hygiene & Tropical Medicine , London , UK
8 Statistics and Clinical Studies , NHS Blood and Transplant , Bristol , UK
Correspondence to Dr Ji Soo Kim; jisoo.kim{at}nhs.net

Introduction Kidney transplantation is the preferred therapy for children with stage 5 chronic kidney disease (CKD-5). However, there is a wide variation in access to kidney transplantation across the UK for children. This study aims to explore the psychosocial factors that influence access to and outcomes after kidney transplantation in children in the UK using a mixed-methods prospective longitudinal design.

Methods Qualitative data will be collected through semistructured interviews with children affected by CKD-5, their carers and paediatric renal multidisciplinary team. Recruitment for interviews will continue till data saturation. These interviews will inform the choice of existing validated questionnaires, which will be distributed to a larger national cohort of children with pretransplant CKD-5 (n=180) and their carers. Follow-up questionnaires will be sent at protocolised time points regardless of whether they receive a kidney transplant or not. Coexisting health data from hospital, UK renal registry and National Health Service Blood and Transplant registry records will be mapped to each questionnaire time point. An integrative analysis of the mixed qualitative and quantitative data will define psychosocial aspects of care for potential intervention to improve transplant access.

Analysis Qualitative data will be analysed using thematic analysis. Quantitative data will be analysed using appropriate statistical methods to understand how these factors influence access to transplantation, as well as the distribution of psychosocial factors pretransplantation and post-transplantation.

Ethics and dissemination This study protocol has been reviewed by the National Institute for Health Research Academy and approved by the Wales Research Ethics Committee 4 (IRAS number 270493/ref: 20/WA/0285) and the Scotland A Research Ethics Committee (ref: 21/SS/0038). Results from this study will be disseminated across media platforms accessed by affected families, presented at conferences and published in peer-reviewed journals.

Renal transplantation
Paediatric transplant surgery
Paediatric nephrology
Health Services Accessibility
Quality of Life
Social Support

https://doi.org/10.1136/bmjopen-2023-078150

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STRENGTHS AND LIMITATIONS OF THIS STUDY

Prospective, longitudinal study design allows for a more detailed understanding of delays in transplantation associated with different psychosocial factors.

Combination of qualitative and quantitative data enables in-depth exploration of how psychosocial factors influence access to transplantation and outcomes thereafter.

Involvement of the majority of UK paediatric nephrology units ensuring representation of paediatric chronic kidney disease-5 population

Limited follow-up timeline will capture only short-term to medium-term rather than long-term outcomes.

Utilisation of interpreters in qualitative interviews increases involvement of non-English-speaking families; however, the lack of validated translations of questionnaires may limit involvement of non-English-speaking families in capturing quantitative data.

Introduction

Around 1000 children (aged 0–17 years) with stage 5 chronic kidney disease (CKD-5) in the UK receive kidney replacement therapy (KRT) in the form of either peritoneal dialysis, haemodialysis or kidney transplantation. 1 2 Kidney transplantation is the gold-standard therapy for reducing mortality and improving outcomes for children. 3 Minimising time on dialysis in favour of transplantation has been shown to reduce CKD-5-related complications and morbidity. 4 Compared with dialysis, transplantation is also presumed to improve patients’ health-related quality of life (HR-QoL). 5 Furthermore in the UK, for every year that the patient’s kidney transplant functions, transplantation is three times more cost-effective than dialysis for the National Health Service (NHS). 6

However, not every child with CKD-5 can access kidney transplantation. There are approximately 193–217 prevalent children on dialysis each year. 7 8 Annually only 130–160 paediatric kidney transplants are performed in the UK. 9 There appears to be variation in transplantation access, practice and outcomes between UK paediatric nephrology units. 1 2 9 A cross-sectional survey conducted by the British Association for Paediatric Nephrology examined reasons for paediatric kidney transplantation delay in the UK. The survey showed that psychosocial factors make up 19% of the barriers, although specific factors were not identified in this study. 10 Compared with paediatrics, considerable research has been done in adult patients, regarding psychosocial factors implicated in transplant access. Formalised pretransplant and post-transplant psychosocial assessments have been widely researched for adult solid organ transplant recipients. 11–14 The UK-wide study, ‘Access to Transplantation and Transplant Outcome Measures’ specifically investigated psychosocial barriers in adult kidney transplant recipients. 15 Researchers found inequities in transplant access, in spite of a universal healthcare system, based on socioeconomic status, education level, health literacy and racial background. 16 17 In terms of outcomes, they found no difference in post-transplant HR-QoL between living and deceased donor recipients and that recipient expectations influenced post-transplant recovery. 15–19 However, for the UK children, it is less clear what these ‘psychosocial factors’ are and how they influence unit-specific decisions, access to kidney transplantation and outcomes. 10 20 21 We acknowledge the recent progress being made in exploring these psychosocial factors in some countries. 22–24 However, psychosocial studies for children with CKD-5 are still limited by retrospective or cross-sectional design or by single-centre or small study cohorts. There are no UK studies that prospectively explore how these factors impact paediatric kidney transplantation access over time.

Aims and objectives

This study aims to prospectively evaluate the psychosocial factors that are actual or perceived barriers to paediatric kidney transplantation which may be associated with poor transplant outcome. We anticipate these psychosocial factors to be broad at an individual, family and societal level and that they will encompass mental health and social determinants of health as defined by Marmot et al : ‘the conditions in which people are born, grow, live, work and age’ 25 —we hypothesise that psychosocial factors implicated in transplant access can be quantified using formal, validated measures and that these factors may influence outcomes in the short-term period following transplantation.

This will be achieved through the following research objectives:

Describe the psychosocial factors perceived by clinicians as barriers to kidney transplantation.

Describe current interventions (if any) implemented to address these psychosocial factors.

Explore the experiences and beliefs of children and their families regarding any psychosocial challenges or facilitators in accessing a kidney transplant.

Quantify the identified psychosocial factors implicated in CKD management.

Measure the prevalence of identified psychosocial factors (positive and negative) in the national cohort of patients being pre-emptively worked up for transplant, on dialysis or listed for transplant.

Examine these psychosocial factors regarding their association with time to transplant and their changes following transplantation.

Synthesise findings from each phase to inform recommendations about which psychosocial factors are potential barriers or facilitators to accessing kidney transplantation.

Findings synthesised from this study will inform the development of a complex intervention to improve uptake of kidney transplantation in children with CKD-5 who would benefit most from one.

Methods and analysis

Study design overview.

This prospective study has three phases, commencing with a sequential exploratory mixed-methods design, followed by a sequential explanatory mixed-methods design ( figure 1 ). 26 This approach was chosen to first gain new insights by becoming familiar with the range of potential psychosocial factors and then longitudinally observing the influence of these factors on kidney transplantation access and outcomes for children. 27 Phase 1 will consist of exploratory interviews with purposively selected participants. These interviews will inform which questionnaires will be distributed at baseline and follow-up to the wider cohort of children with CKD-5 and their carer(s) in phase 2 stage A. Using the interviews to inform questionnaire selection will ensure questionnaires are relevant to, and resonate with, families. Participant families with outlier findings from phase 2 stage A will then be invited for explanatory interviews in phase 2 stage B. Finally, in phase 3, the qualitative and quantitative data from the previous phases will be analysed together to develop an integrated understanding of psychosocial factors that influence access to kidney transplantation.

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Flow diagram of sequential mixed-method study design. CYP, children and young people; NHS, National Health Service.

Patient and public involvement

All elements of the study design were codeveloped and approved by our Research Partner Family and study steering group. Our Research Partner Family (a young person and parent dyad) and steering group members have lived experience of CKD, dialysis and kidney transplantation in childhood either as a patient or carer. The Great Ormond Street Hospital Young Persons Advisory Group has also approved the research question and study design. The Young Persons Advisory Group consists of children affiliated with Great Ormond Street Hospital for Children NHS Foundation Trust as a patient or family member of a patient and therefore who have the lived experience needed to advise researchers designing studies involving children.

This study aims to include children, carer and staff participants from all 13 UK paediatric nephrology units, of which 10 units offer kidney transplantation surgery on site and 3 units which do not offer transplantation surgery at their own site but offer shared transplant care with nearby units.

In phase 1 and phase 2 stage A, children (aged 0–17 years inclusive) with CKD-5 on chronic dialysis, being worked up for pre-emptive kidney transplantation or on the waiting list for deceased donation (active or suspended) or awaiting living donation, will be invited to participate with their carer(s). Where appropriate, children aged 5 years and above will be invited to directly participate. For children aged under 5 years or those who are unable to assent or consent, their carers will be consulted and offered an opportunity to participate through proxy measures. Members of the multidisciplinary team (MDT) involved in pretransplantation workup at their local unit (eg, paediatric nephrologists, transplant surgeons, nurse specialists, social workers, family therapists, play therapists and members of the psychosocial team) will be invited for interviews in phase 1 only. Patients or carer(s) who are unable to give informed consent or patients who have been deemed too unwell to participate or had a recent acute hospital admission in the last 14 days will not be approached to participate in the study.

Phase 1 interviews

A purposive sampling matrix will be used to maximise the diversity of views. Criteria for sampling will include age, sex and ethnicity of children, modality of KRT, whether the kidney unit offers transplantation surgery or not and role in the MDT. Completed interviews will be analysed in parallel with ongoing recruitment. Participant recruitment will stop when no new themes are generated from the data.

Analysis of these interviews will contribute towards selecting questionnaires for use in phase 2 stage A.

Phase 2 stage A questionnaires

In this phase, we will aim to recruit every child with CKD-5 who meets the inclusion criteria at pretransplant baseline with an anticipated recruitment rate of 60%–70%.

At the time of study design, the predicted annual numbers across all 13 UK paediatric nephrology units as per UK Renal Registry (UKRR) reports were as follows 1 2 :

Number of dialysis patients on the transplant waiting-list in a year; n=70.

Number of patients starting dialysis in a year; n=125.

Number of patients not on dialysis but pre-emptively on the transplant waiting list; n=50.

Transplant rate; 130–160 patients per year.

Based on these numbers, the predicted maximum number of eligible participants in a 1-year period would be 245. We chose a 2-year period for our sample size calculation, which would be a population size of 490, to include children who experience delays and receive their transplant after 1 year (see figure 2 ). We plan to recruit 180 patients and after allowing for a 20% drop-out, we expect to include 141 families. Assuming a prevalence of pertinent psychosocial factors in the national cohort of patients being pre-emptively worked up for transplant, on dialysis or listed for transplant of 15%, then with 141 families we will be able to estimate this with 5% precision. At the time of sample size calculation, there were no paediatric studies specifically measuring this nor studies encompassing all psychosocial factors. Therefore, we used the assumed prevalence of 15% based on a study measuring psychological distress in potential adult transplant candidates. 28

Flow diagram illustrating predicted number of CYP who are eligible for transplantation and receive one across 2 years. CYP, children and young people.

We expect some overlap in the cohort between phase 1 and phase 2 stage A and appreciate some families may develop research fatigue. Families who already took part in phase 1 will be asked whether they would like to also take part in phase 2 stage A or prefer to opt-out.

Phase 2 stage B

Results from phase 2 stage A will be reviewed for negative and positive cases. Families who have outlier findings, in terms of their answers to the validated questionnaires, will be invited to interview in phase 2 stage B, with the aim of reviewing and refining themes to develop additional theoretical explanation for the influence of psychosocial factors on transplant access and outcomes. The exact parameters to define outliers and sample size will be dependent on findings from phase 2 stage A.

Outcomes measured

Using a Topic Guide ( online supplemental material 1 ), qualitative data will be collected in phase 1 to address research objectives 1–4 through semistructured, in-depth interviews undertaken by the investigator (JSK). This format was chosen over focus groups to enable participation of younger or less verbal children and avoid further inconveniencing families through multiple research appointments by having separate interviews for young children and focus groups for carers. The family interview format will be based on the child’s decision to either interview with or separately from their carers, depending on which setting they find more comfortable. To ensure no participant is unfairly excluded from interviews due to English not being their first language, interpreters will be present to support their participation. The same principle of minimising communication barriers will be applied to younger children or young people who may prefer communicating through other creative outputs such as drawing or Talking Mats. The research goals for family interviews will include exploring what families feel their life is like now living with CKD-5, what good HR-QoL looks like and what they believe delays or enables how soon they receive a kidney transplant. Similarly, research goals for MDT member interviews will include exploring what the professionals think matters most to families whose child has CKD-5 when it comes to a good HR-QoL and what they, as professionals, believe impacts how soon children access a kidney transplant in terms of psychosocial factors.

Supplemental material

The Topic Guide has been developed from the investigator’s systematic literature review on the subject matter and in consultation with the Young Persons Advisory Group and the Research Partner Family. 29 To minimise any inconvenience in joining the study, all participants will be interviewed using their preferred modality (telephone, video-call or face-to-face consultations) in keeping with COVID-19 safety recommendations. 30 Interview time with young children will be kept to less than half an hour to minimise interview fatigue. All interviews will be audio or video recorded depending on participant preference.

Participants may become distressed as they reflect on their experiences due to the sensitive nature of some of the interview topics around their mental health or transplant delays. Therefore, the participant and JSK will agree on a ‘stop signal’ before commencing the interview for use should they feel uncomfortable. If the participant uses the ‘stop signal’, they will be offered a break. The participant and JSK will then discuss whether they would like to continue, reschedule or withdraw from the interview altogether. Once the interview recording stops, there will be an opportunity to discuss the participant’s feelings and, if appropriate, they will be signposted to their local support services.

The acceptability of existing validated age-appropriate questionnaires that measure outcomes relevant to the preliminary themes will be discussed with the steering group. Potential validated questionnaires that capture the preliminary themes will be identified from the systematic literature review and a wider search of the literature. These questionnaires will be checked in terms of their psychometric properties such as internal consistency (Cronbach alpha of at least 0.7) and test–retest reliability and aspects such as availability of the measures, respondent type (parent, child or other respondent) and age range for which the questionnaire has been validated, to enable the most appropriate questionnaire to be chosen to measure each theme. The selected questionnaires will then be discussed with the steering group, considering the language of the questionnaire, acceptability and level of burden for the participant. If the list of questionnaires is too onerous for the participating family, a consensus will be reached with the steering group on which preliminary themes and therefore which questionnaires should be prioritised. Once the final list of questionnaires is agreed on, these will be submitted to the Health Research Authority for final approval.

Phase 2 stage A

In phase 2 stage A, research objectives 5–6 will be addressed by measuring the following primary outcome variables: HR-QoL, psychosocial functioning and time taken to receive a kidney transplant since the date confirming CKD-5. To understand changes in psychosocial factors over time, their associations with health burden or short-term allograft deterioration must be accounted for, Therefore, we will measure the following secondary outcome: the child’s estimated glomerular filtration rate (eGFR) over time. The eGFR will be calculated with their height and serum creatinine using the CkiD U25 formula. 31 32

Questionnaires measuring HR-QoL and psychosocial functioning, selected from phase 1, will be distributed to a larger, national cohort of children with CKD-5 and their carers at their pretransplant baseline. Follow-up questionnaires will be sent post-transplant at 3, 6 and 12 months later or 12 months after their first questionnaire if they still have not received a kidney transplant in that time frame. These follow-up time points were chosen to reflect the initial period of post-transplant adaptation, which is comparable with similar studies in adults and children at the time of protocol-writing. 33–35 For children who have not received a transplant, a 12-month interval was advised by our steering group to avoid distress triggered by frequent questionnaires reminding them of their non-transplanted state. As families are more likely to participate if they can choose which questionnaire modality is most suited to their lifestyle, participant families will be offered either paper or online questionnaires. 36 Health morbidity and coexisting disease data, including their underlying primary kidney diagnosis, as described in table 1 , will be mapped to each questionnaire time point.

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Coexisting health data to be collected and mapped against questionnaire time points

An additional questionnaire (see online supplemental material 2 ) has been codesigned with the steering group to retrieve information on the participating family’s demographic background. Data on medication burden will also be collected due to its’ possible confounding effect HR-QoL as described in current literature. 37

The Interview Topic Guide for phase 2 stage B has been designed with flexibility since the interview will depend on the nature of the outlier findings from phase 2 stage A.

Data analysis plan

To address research objectives 1–4, JSK will thematically analyse interview transcripts, following the approach of Braun and Clarke. 38 Deidentified transcripts will be managed using NVivo software. 39 For the purposes of qualitative rigour, JSK will maintain a reflexivity journal after interviews and throughout data analysis, to ensure potential researcher biases and insights are noted. Maintaining a reflexivity journal is a gold-standard practice in qualitative research, which increases the credibility and deepens the understanding of the findings by describing the context in which data were collected and analysed. 40 41 A sample of transcripts will be examined by JW and discussed with JSK. Throughout all stages of data collection and analysis, findings will be shared and discussed by JSK, JW and SM.

Participant demographics will be described using descriptive statistics. It will also be used to address research objective 4 by describing the prevalence of psychosocial factors.

Research objective 6 will be addressed through the following:

First, descriptive statistics will again be used to describe how participants’ psychosocial functioning and HR-QoL variables change before and after transplantation at each data collection time point.

Second, to describe the impact waiting to access a kidney transplant and receiving one has on psychosocial functioning and HR-QoL over time, repeated measure analysis of variance will be undertaken.

Third, the association between clinical, demographic and psychosocial factors with accessing a kidney transplant will be measured using logistic regression modelling and Kaplan-Meier statistics.

Fourth, the association of clinical, demographic and psychosocial factors with failing transplant allograft in short-term follow-up between 1 and 2 years post-transplantation will be assessed. Where the outcome variable is the child’s eGFR, linear regression modelling will be used. Where the outcome variable defines a failing post-transplant kidney as reaching an eGFR equivalent to stage IV CKD (eGFR 15–29 mL/min/1.73 m 2 ), logistic regression modelling and Kaplan-Meier statistics will be used.

Separately, the level of agreement between child and carer responses will be assessed. Choice of statistical tests will depend on the type of data, for example, Cohen’s kappa for binary data, weighted kappa for ordinal data or interclass correlation coefficients for continuous data.

Where relevant, all statistical analyses will be adjusted for (but not limited to) child’s age, gender, ethnicity, socioeconomic status and KRT status at baseline.

Interview data will be analysed using thematic analysis underpinned by the same principles as in phase 1.

An integrative analysis of data from phases 1 and 2 will be undertaken to address research objective 7 and create a conceptual model of how psychosocial factors influence transplant access and outcomes. The quantitative and qualitative data will be reviewed together to understand where they converge, complement or diverge from the other dataset or have findings that are novel. A final lay report will be cowritten with the steering group to ensure it is meaningful, relevant and accessible to families whose child has CKD.

This study protocol has been peer reviewed by the National Institute for Health Research Academy and has been approved under IRAS number 270493 by the Wales Research Ethics Committee 4 (ref: 20/WA/0285) and the Scotland A Research Ethics Committee (ref: 21/SS/0038).

Coercive pressure of joining the study will be minimised. Research participants will not be paid for participation. Participant information sheets will indicate that there will be no added benefit or disruption to their medical care.

Informed written consent will be obtained from all participants aged ≥16 years old and written assent from participants aged 5–15 years old. Consent will be obtained for all research activities including interviews, questionnaires and retrieving health information from national databases and hospital records.

Participant confidentiality will be upheld by fully adhering to the Data Protection Act. Participant identifiers will be handled with appropriate pseudonymisation and all data will be kept on General Data Protection Regulation (GDPR) compliant encrypted devices or stored on hard drives with restricted access with the relevant encryption and password protection. The only instance where confidentiality will be breached is if a participant discloses information that has direct implications for child or adult safeguarding. Potential participants will be made aware of this as part of informed consent. All interviews will be digitally recorded, transcribed verbatim and have identifiable data redacted. Audio files will be transcribed either by JSK or by a third-party interview transcription company (Take Note). To minimise data handling breaches, all engagement with Take Note will only be through their secure web platform. All video files will be transcribed only by JSK to remove third-party involvement in the deidentification process. Once deidentified, only quotes that cannot lead to participant identification will be used in reports. Care will be taken in reporting findings to ensure individuals cannot be identified by their role, diagnosis, gender, age or geographic locality.

Dissemination

Research participants will be updated about the findings through newsletters. Lay summaries approved by the steering group will be disseminated across charity websites accessed by children and families affected by CKD. To ensure that professionals who work with families affected by CKD are being reached, findings will be disseminated widely in relevant peer-reviewed journals and at national and international conferences. Finally, if appropriate, a dissemination strategy will be cocreated between JSK and the steering group for other professionals (eg, teachers) who may encounter vulnerable families with CKD and need early referral for psychosocial intervention.

Ethics statements

Patient consent for publication.

Not applicable.

Acknowledgments

We would like to thank the Great Ormond Street Hospital Young Person’s Advisory Group and members of our Research Partner Family Steering Group—Emma Beeden, Katy Beeden, Angela Watt and Heather Davis from KDARS (Kidney Disease and Renal Support) for kids, for their expertise, support and advice. We would also like to thank members of the National Kidney Federation and Kidney Care UK for their continued support of this study. Finally, we would like to thank the British Association for Paediatric Nephrology, whose support and contribution towards this study has been instrumental to its launch.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Data supplement 1
Data supplement 2

X @dr_jowray

Collaborators British Association for Paediatric Nephrology

Contributors JSK wrote the study protocol and coordinated the entire manuscript. DR contributed towards sample size calculation and statistical methods of study protocol. LP contributed towards statistical analysis of protocol and advised regarding UK Renal Registry involvement. DN contributed towards statistical analysis of protocol and advised regarding UK Renal Registry involvement. MR contributed towards statistical analysis of protocol and advised regarding NHS Blood and Transplant involvement. JW supervised JSK, study protocol development with regular input to initial drafts and final manuscript approval. SM supervised JSK, study protocol development with regular input to initial drafts and final manuscript approval.

Funding This work is supported by the National Institute for Health Research Academy, as a Doctoral Fellowship, grant number NIHR300727.

Competing interests JSK is the National Institute for Health Research Fellowship grant recipient, which funds this study. LP reports grants from the National Institute for Health Research and Kidney Research UK. She is also the paediatric research lead for the UK Renal Registry.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

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Diet Review: MIND Diet

Overhead View of Fresh Omega-3 Rich Foods: A variety of healthy foods like fish, nuts, seeds, fruit, vegetables, and oil

Finding yourself confused by the seemingly endless promotion of weight-loss strategies and diet plans? In this series , we take a look at some popular diets—and review the research behind them.

What Is It?

The Mediterranean-DASH Diet Intervention for Neurodegenerative Delay, or MIND diet, targets the health of the aging brain. Dementia is the sixth leading cause of death in the United States, driving many people to search for ways to prevent cognitive decline. In 2015, Dr. Martha Clare Morris and colleagues at Rush University Medical Center and the Harvard Chan School of Public Health published two papers introducing the MIND diet. [1,2] Both the Mediterranean and DASH diets had already been associated with preservation of cognitive function, presumably through their protective effects against cardiovascular disease, which in turn preserved brain health.

The research team followed a group of older adults for up to 10 years from the Rush Memory and Aging Project (MAP), a study of residents free of dementia at the time of enrollment. They were recruited from more than 40 retirement communities and senior public housing units in the Chicago area. More than 1,000 participants filled out annual dietary questionnaires for nine years and had two cognitive assessments. A MIND diet score was developed to identify foods and nutrients, along with daily serving sizes, related to protection against dementia and cognitive decline. The results of the study produced fifteen dietary components that were classified as either “brain healthy” or as unhealthy. Participants with the highest MIND diet scores had a significantly slower rate of cognitive decline compared with those with the lowest scores. [1] The effects of the MIND diet on cognition showed greater effects than either the Mediterranean or the DASH diet alone.

How It Works

The purpose of the research was to see if the MIND diet, partially based on the Mediterranean and DASH diets, could directly prevent the onset or slow the progression of dementia. All three diets highlight plant-based foods and limit the intake of animal and high saturated fat foods. The MIND diet recommends specific “brain healthy” foods to include, and five unhealthy food items to limit. [1]

The healthy items the MIND diet guidelines* suggest include:

3+ servings a day of whole grains
1+ servings a day of vegetables (other than green leafy)
6+ servings a week of green leafy vegetables
5+ servings a week of nuts
4+ meals a week of beans
2+ servings a week of berries
2+ meals a week of poultry
1+ meals a week of fish
Mainly olive oil if added fat is used

The unhealthy items, which are higher in saturated and trans fat , include:

Less than 5 servings a week of pastries and sweets
Less than 4 servings a week of red meat (including beef, pork, lamb, and products made from these meats)
Less than one serving a week of cheese and fried foods
Less than 1 tablespoon a day of butter/stick margarine

*Note: modest variations in amounts of these foods have been used in subsequent studies. [9,10]

This sample meal plan is roughly 2000 calories, the recommended intake for an average person. If you have higher calorie needs, you may add an additional snack or two; if you have lower calorie needs, you may remove a snack. If you have more specific nutritional needs or would like assistance in creating additional meal plans, consult with a registered dietitian.

Breakfast: 1 cup cooked steel-cut oats mixed with 2 tablespoons slivered almonds, ¾ cup fresh or frozen blueberries, sprinkle of cinnamon

Snack: 1 medium orange

Beans and rice – In medium pot, heat 1 tbsp olive oil. Add and sauté ½ chopped onion, 1 tsp cumin, and 1 tsp garlic powder until onion is softened. Mix in 1 cup canned beans, drained and rinsed. Serve bean mixture over 1 cup cooked brown rice.
2 cups salad (e.g., mixed greens, cucumbers, bell peppers) with dressing (mix together 2 tbsp olive oil, 1 tbsp lemon juice or vinegar, ½ teaspoon Dijon mustard, ½ teaspoon garlic powder, ¼ tsp black pepper)

Snack: ¼ cup unsalted mixed nuts

3 ounces baked salmon brushed with same salad dressing used at lunch
1 cup chopped steamed cauliflower
1 whole grain roll dipped in 1 tbsp olive oil

Is alcohol part of the MIND diet?

Wine was included as one of the 15 original dietary components in the MIND diet score, in which a moderate amount was found to be associated with cognitive health. [1] However, in subsequent MIND trials it was omitted for “safety” reasons. The effect of alcohol on an individual is complex, so that blanket recommendations about alcohol are not possible. Based on one’s unique personal and family history, alcohol offers each person a different spectrum of benefits and risks. Whether or not to include alcohol is a personal decision that should be discussed with your healthcare provider. For more information, read Alcohol: Balancing Risks and Benefits .

The Research So Far

The MIND diet contains foods rich in certain vitamins, carotenoids, and flavonoids that are believed to protect the brain by reducing oxidative stress and inflammation. Although the aim of the MIND diet is on brain health, it may also benefit heart health, diabetes, and certain cancers because it includes components of the Mediterranean and DASH diets, which have been shown to lower the risk of these diseases.

Cohort studies

Researchers found a 53% lower rate of Alzheimer’s disease for those with the highest MIND diet scores (indicating a higher intake of foods on the MIND diet). Even those participants who had moderate MIND diet scores showed a 35% lower rate compared with those with the lowest MIND scores. [2] The results didn’t change after adjusting for factors associated with dementia including healthy lifestyle behaviors, cardiovascular-related conditions (e.g., high blood pressure, stroke, diabetes), depression, and obesity, supporting the conclusion that the MIND diet was associated with the preservation of cognitive function.

Several other large cohort studies have shown that participants with higher MIND diet scores, compared with those with the lowest scores, had better cognitive functioning, larger total brain volume, higher memory scores, lower risk of dementia, and slower cognitive decline, even when including participants with Alzheimer’s disease and history of stroke. [3-8]

Clinical trials

A 2023 randomized controlled trial followed 604 adults aged 65 and older who at baseline were overweight (BMI greater than 25), ate a suboptimal diet, and did not have cognitive impairment but had a first-degree relative with dementia. [9] The intervention group was taught to follow a MIND diet, and the control group continued to consume their usual diet. Both groups were guided throughout the study by registered dietitians to follow their assigned diet and reduce their intake by 250 calories a day. The authors found that participants in both the MIND and control groups showed improved cognitive performance. Both groups also lost about 11 pounds, but the MIND diet group showed greater improvements in diet quality score. The authors examined changes in the brain using magnetic resonance imaging, but findings did not differ between groups. [10] Nutrition experts commenting on this study noted that both groups lost a similar amount of weight, as intended, but the control group likely improved their diet quality as well (they had been coached to eat their usual foods but were taught goal setting, calorie tracking, and mindful eating techniques), which could have prevented significant changes from being seen between groups. Furthermore, the duration of the study–3 years–may have been too short to show significant improvement in cognitive function.

The results of this study showed that the MIND diet does not slow cognitive aging over a 3-year treatment period. Whether the MIND diet or other diets can slow cognitive aging over longer time periods remains a topic of intense interest.

Other factors

Research has found that greater poverty and less education are strongly associated with lower MIND diet scores and lower cognitive function. [11]

Potential Pitfalls

The MIND diet is flexible in that it does not include rigid meal plans. However, this also means that people will need to create their own meal plans and recipes based on the foods recommended on the MIND diet. This may be challenging for those who do not cook. Those who eat out frequently may need to spend time reviewing restaurant menus.
Although the diet plan specifies daily and weekly amounts of foods to include and not include, it does not restrict the diet to eating only these foods. It also does not provide meal plans or emphasize portion sizes or exercise .

Bottom Line

The MIND diet can be a healthful eating plan that incorporates dietary patterns from the Mediterranean and DASH , both of which have suggested benefits in preventing and improving cardiovascular disease and diabetes , and supporting healthy aging. When used in conjunction with a balanced plate guide , the diet may also promote healthy weight loss if desired. Whether or not following the MIND diet can slow cognitive aging over longer time periods remains an area of interest, and more research needs to be done to extend the MIND studies in other populations.

Healthy Weight
The Best Diet: Quality Counts
Healthy Dietary Styles
Other Diet Reviews
Morris MC, Tangney CC, Wang Y, Sacks FM, Barnes LL, Bennett DA, Aggarwal NT. MIND diet slows cognitive decline with aging. Alzheimer’s & dementia . 2015 Sep 1;11(9):1015-22.
Morris MC, Tangney CC, Wang Y, Sacks FM, Bennett DA, Aggarwal NT. MIND diet associated with reduced incidence of Alzheimer’s disease. Alzheimer’s & Dementia . 2015 Sep 1;11(9):1007-14.
Dhana K, James BD, Agarwal P, Aggarwal NT, Cherian LJ, Leurgans SE, Barnes LL, Bennett DA, Schneider JA. MIND diet, common brain pathologies, and cognition in community-dwelling older adults. Journal of Alzheimer’s Disease . 2021 Jan 1;83(2):683-92.
Cherian L, Wang Y, Fakuda K, Leurgans S, Aggarwal N, Morris M. Mediterranean-Dash Intervention for Neurodegenerative Delay (MIND) diet slows cognitive decline after stroke. The journal of prevention of Alzheimer’s disease . 2019 Oct;6(4):267-73.
Hosking DE, Eramudugolla R, Cherbuin N, Anstey KJ. MIND not Mediterranean diet related to 12-year incidence of cognitive impairment in an Australian longitudinal cohort study. Alzheimer’s & Dementia . 2019 Apr 1;15(4):581-9.
Melo van Lent D, O’Donnell A, Beiser AS, Vasan RS, DeCarli CS, Scarmeas N, Wagner M, Jacques PF, Seshadri S, Himali JJ, Pase MP. Mind diet adherence and cognitive performance in the Framingham heart study. Journal of Alzheimer’s Disease . 2021 Jan 1;82(2):827-39.
Berendsen AM, Kang JH, Feskens EJ, de Groot CP, Grodstein F, van de Rest O. Association of long-term adherence to the mind diet with cognitive function and cognitive decline in American women. The journal of nutrition, health & aging . 2018 Feb;22(2):222-9. Disclosure: Grodstein reports grants from International Nut Council, other from California Walnut Council, outside the submitted work.
Chen H, Dhana K, Huang Y, Huang L, Tao Y, Liu X, van Lent DM, Zheng Y, Ascherio A, Willett W, Yuan C. Association of the Mediterranean Dietary Approaches to Stop Hypertension Intervention for Neurodegenerative Delay (MIND) Diet With the Risk of Dementia. JAMA psychiatry . 2023 May 3.
Liu X, Morris MC, Dhana K, Ventrelle J, Johnson K, Bishop L, Hollings CS, Boulin A, Laranjo N, Stubbs BJ, Reilly X. Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) study: rationale, design and baseline characteristics of a randomized control trial of the MIND diet on cognitive decline. Contemporary clinical trials . 2021 Mar 1;102:106270. Disclosure: several corporations generously donated mixed nuts (International Tree Nut Council Nutrition Research and Education Foundation), peanut butter (The Peanut Institute), extra virgin olive oil (Innoliva-ADM Capital Europe LLP), and blueberries (U.S. Highbush Blueberry Council). These items will be distributed to those participants who are randomized to the MIND diet arm.
Barnes LL, Dhana K, Liu X, Carey VJ, Ventrelle J, Johnson K, Hollings CS, Bishop L, Laranjo N, Stubbs BJ, Reilly X. Trial of the MIND Diet for Prevention of Cognitive Decline in Older Persons. New England Journal of Medicine . 2023 Jul 18.
Boumenna T, Scott TM, Lee JS, Zhang X, Kriebel D, Tucker KL, Palacios N. MIND diet and cognitive function in Puerto Rican older adults. The Journals of Gerontology: Series A . 2022 Mar;77(3):605-13.

Last reviewed August 2023

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http://orcid.org/0000-0001-7412-1188 Blair Hamilton 1 , 2 ,
http://orcid.org/0009-0005-9553-3081 Andrew Brown 2 ,
http://orcid.org/0009-0007-0957-5002 Stephanie Montagner-Moraes 2 ,
http://orcid.org/0000-0001-9483-3262 Cristina Comeras-Chueca 3 ,
http://orcid.org/0000-0001-8609-2812 Peter G Bush 2 ,
http://orcid.org/0000-0002-8526-9169 Fergus M Guppy 4 ,
http://orcid.org/0000-0001-6210-2449 Yannis P Pitsiladis 5 , 6
1 School of Sport and Health Sciences , University of Brighton , Brighton , UK
2 School of Applied Sciences University , Brighton , UK
3 Health Sciences Faculty , Universidad San Jorge , Zaragoza , Spain
4 Heriot-Watt University , Edinburgh , UK
5 Department of Movement, Human and Health Sciences , University of Rome ‘Foro Italico’ , Rome , Italy
6 Department of Sport, Physical Education and Health , Hong Kong Baptist University , Hong Kong , Hong Kong SAR
Correspondence to Professor Yannis P Pitsiladis, Department of Sport, Physical Education and Health, Hong Kong Baptist University, Hong Kong, Hong Kong SAR; ypitsiladis{at}hkbu.edu.hk

Objective The primary objective of this cross-sectional study was to compare standard laboratory performance metrics of transgender athletes to cisgender athletes.

Methods 19 cisgender men (CM) (mean±SD, age: 37±9 years), 12 transgender men (TM) (age: 34±7 years), 23 transgender women (TW) (age: 34±10 years) and 21 cisgender women (CW) (age: 30±9 years) underwent a series of standard laboratory performance tests, including body composition, lung function, cardiopulmonary exercise testing, strength and lower body power. Haemoglobin concentration in capillary blood and testosterone and oestradiol in serum were also measured.

Results In this cohort of athletes, TW had similar testosterone concentration (TW 0.7±0.5 nmol/L, CW 0.9±0.4 nmol/), higher oestrogen (TW 742.4±801.9 pmol/L, CW 336.0±266.3 pmol/L, p=0.045), higher absolute handgrip strength (TW 40.7±6.8 kg, CW 34.2±3.7 kg, p=0.01), lower forced expiratory volume in 1 s:forced vital capacity ratio (TW 0.83±0.07, CW 0.88±0.04, p=0.04), lower relative jump height (TW 0.7±0.2 cm/kg; CW 1.0±0.2 cm/kg, p<0.001) and lower relative V̇O 2 max (TW 45.1±13.3 mL/kg/min/, CW 54.1±6.0 mL/kg/min, p<0.001) compared with CW athletes. TM had similar testosterone concentration (TM 20.5±5.8 nmol/L, CM 24.8±12.3 nmol/L), lower absolute hand grip strength (TM 38.8±7.5 kg, CM 45.7±6.9 kg, p = 0.03) and lower absolute V̇O 2 max (TM 3635±644 mL/min, CM 4467±641 mL/min p = 0.002) than CM.

Conclusion While longitudinal transitioning studies of transgender athletes are urgently needed, these results should caution against precautionary bans and sport eligibility exclusions that are not based on sport-specific (or sport-relevant) research.

Data availability statement

Data are available on reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bjsports-2023-108029

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WHAT IS ALREADY KNOWN ON THIS TOPIC

There is currently a lack of laboratory data on strength, power and V̇O 2 max from transgender athlete populations.

WHAT THIS STUDY ADDS

This research compares laboratory measures of strength, power and V̇O 2 max of transgender male and female athletes to their cisgender counterparts.

Transgender women athletes demonstrated lower performance than cisgender women in the metrics of forced expiratory volume in 1 s:forced vital capacity ratio, jump height and relative V̇O 2 max.

Transgender women athletes demonstrated higher absolute handgrip strength than cisgender women, with no difference found relative to fat-free mass or hand size.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICY

This study provides sport governing bodies with laboratory-based performance-related data from transgender athletes.

Longitudinal studies are needed to confirm if these results are a direct result of gender affirmation hormone therapy.

Sports-specific studies are necessary to inform policy-making.

Introduction

Transgender athletes can experience conflict between the gender that they were assigned and their experienced gender. 1 The question of integrating transgender athletes into their affirmed gender categories is becoming more prominent, with sports’ governing bodies using varied approaches, from bans on transgender women in the female category 2 requiring the reduction of testosterone in the female category for some time 3 to self-identification into the athletes chosen category. 4

As part of gender affirmation hormone therapy (GAHT), some transgender women undergo testosterone suppression (target ≤1.8 nmol/L 5 ) coupled with oestrogen supplementation (target 400–600 pmol/L 5 ), while some transgender men undergo testosterone supplementation (National Health Service (NHS, UK) target 15–20 nmol/L, 6 Endocrine Society Target 11–34.7 nmol/L 7 ). Testosterone is known to impact sporting performances, with differences in circulating testosterone concentration between cisgender men (CM) and women proposed to explain most of the laboratory-measured differences in sports performance. 8 9 GAHT of transgender men and women alters the body composition of transgender athletes via testosterone-mediated effects on fat-free mass 8 and oestrogens on subcutaneous fat distribution 9 and maintenance of muscle mass. 10 An often-held assumption against transgender women athletes competing in the female category of sport is that transgender women have benefited from a high testosterone concentration from assigned male-at-birth puberty until the administration of GAHT that cannot be mitigated 11 and that cisgender women competitors are unable to achieve similar benefits naturally. 12 To date, this assumption has yet to be tested and confirmed in transgender athlete cohorts. The low serum testosterone concentrations from an assigned female-at-birth puberty would hypothetically not give transgender men the competitive advantages of higher testosterone concentrations over CM, and this viewpoint is reflected in the current inclusion sports policies for transgender men. 2

Lab-derived data on a cohort of transgender athletes, as requested in article 6.1b of the International Olympic Committee Framework On Fairness, Inclusion And Non-Discrimination based on Gender Identity and Sex Variations, 4 must be generated to better inform a decision-making process. 13 Therefore, the primary aim of this study was to compare cardiorespiratory fitness, strength and body composition of transgender women and men athletes to that of matched cisgender cohorts.

Study design

This cross-sectional study involved a single visit to the laboratory at the School of Applied Sciences, University of Brighton, UK. Each participant arrived at ~9:00 hours. after an overnight fast and departed from testing at ~15:00 hours. The complete study design can be found in the study protocol, available as a preprint. 14

Recruitment

Following ethical approval (ref: 9496), 75 (19 CM, 12 transgender men, 23 transgender women and 21 cisgender women) participants were recruited through social media advertising on Meta Platforms (Facebook and Instagram, Meta Platforms, California, USA) and X (Twitter, California, USA). Following the initial response, all participants were provided with the participant information sheet by email at least 7 days before being invited to travel to the laboratory, with further oral information about the study procedures and written informed consent provided on their visit to the laboratory.

Participants and eligibility criteria

Participants were required to participate in competitive sports or undergo physical training at least three times per week. Following written consent, participants were asked to record their last four training sessions and self-rate their training intensity for each session on a scale of 1–10 (10=maximum intensity). The mean of the four sessions was recorded to represent the athletes’ training intensity. The transgender athletes must have completed ≥1 year of GAHT, voluntarily disclosed during consent and verified during blood test analysis. The full inclusion/exclusion criteria can be found in the study protocol, available as a preprint. 14 Two cisgender women and one transgender man could not provide blood samples and were consequently excluded from all analyses as their endocrine profiles could not be verified. Furthermore, two transgender women and one cisgender woman were excluded from all analyses due to testosterone concentrations exceeding recommended female testosterone concentrations (2.7 nmol/L 15 ).

Laboratory assessments

Blood sampling and analysis.

Prior to venous blood sampling, haemoglobin concentration ((Hb)) was sampled via the third drop of a Unistik 3 Comfort lancet (Owen Mumford, Woodstock, UK) finger prick capillary blood sample analysed immediately using a HemoCue 201+ (HemoCue AB, Ängelholm, Sweden). Capillary blood was used for (Hb) analysis for practical reasons such as ease of use. It is important to note that the HemoCue 201+used in the present study is expected to yield higher (Hb) values than venous blood. 16 After capillary sampling, one 10 mL whole venous blood sample was collected from an antecubital vein into a BD serum tube (Becton, Dickinson and Company, Wokingham, Berkshire, UK) for serum extraction. Once collected, the tubes were left at room temperature (18°C±5°C) for 1 hour and then stored in a fridge (3°C±2°C) for up to 4 hours before being centrifuged (PK 120 centrifuge, ALC, Winchester, Virginia, USA) using a T515 rotor at 1300G for 10 min at 4°C, before storage at −80°C until analysis. Before analysis, the samples were stored between −25°C and −15°C, thawed at room temp until liquid, vortexing to remix samples, centrifuged at 2876G for 8 min to remove any precipitant and then analysed for participant’s testosterone and oestradiol concentrations on an immunoassay analyser (Roche Cobas 8000 e801, Roche Diagnostics, Burgess Hill, UK).

Body composition and bone mass

Participants’ body mass was measured (OMRON Healthcare, Kyoto, Japan) while participants were lightly dressed, representing clothed body mass. Body composition and bone mass were measured by DXA (Horizon W, Hologic, Massachusetts, USA). Each participant underwent a whole-body, a proximal-femur and a lumbar spine scan. The participant was asked to lie on the scan bed, and the first author (BH) performed all participant placement and scanning for the three scans. Due to inbuilt assumptions of body fat percentage for the head and scanning bed area imitations, whole-body less head data are reported for the whole-body scan. Body mass index (BMI), Fat Mass Index (FMI) and Fat-Free Mass Index (FFMI) were calculated by taking the appropriate mass value and dividing it by height (m 2 ).

Lung function

Lung function was measured using a Vitalograph Alpha spirometer (Vitalograph, Kansas, USA) with an antibacterial filter and a nose clip on the bridge of the participant’s nose. Each participant was asked to perform the flow-volume-loop spirometry to test forced vital capacity (FVC), forced expiratory volume in 1 s (FEV 1 ) and peak expiratory flow. The test was repeated until a trend of declining performance occurred. The highest numeric value for each metric obtained during a test with the correct procedure was then recorded. The FEV1:FVC ratio was used to assess the presence of obstructed lung function.

Strength was measured using a handgrip dynamometer (TAKEI 5401, TAKEI Scientific Instruments, Japan). The participants’ hand sizes were also measured around the metacarpophalangeal joints of both hands prior to testing. Each hand was tested three times in sequential order of left-right to allow each hand to rest; the mean scores were taken from the three attempts for each hand.

Lower body power

Lower body power was measured with the countermovement jump on a JUM001 Jump Mat (Probotics, Alabama, USA). During the test, if the participant went beyond 45° of countermovement or the hands came off the hips, the test would be declared void for that attempt. After recording three legitimate attempts, the mean scores were recorded.

Cardiopulmonary exercise testing

Cardiopulmonary exercise testing was performed using a 95T Engage Treadmill ergometer (Life Fitness, Illinois, USA) and a COSMED QUARK (COSMED, Rome, Italy). All V̇O 2 max tests were conducted and analysed by the first author (BH) to avoid interinvestigator variability. 17 The ramp protocol of Badawy and Muaidi treadmill V̇O 2 max testing 18 was used for each V̇O 2 max test, involving gradual increases in speed every 3 min at a 1% incline. One cisgender man and two cisgender women were excluded from the analysis as they did not meet the required respiratory exchange ratio of ≥1.1 to classify the test as maximal (cisgender men (CM), n=18, transgender men (TM), n=11; cisgender women (CW) n=16; transgender women (TW), n=21).

Statistical analysis

Data meeting the assumptions of normality and homogeneity of variance were analysed using a one-way analysis of variance along with Bonferroni post hoc corrections for pairwise comparisons. Data not meeting the parametric assumptions were compared using a Kruskal-Wallis ANOVA with Dwass-Steel-Critchlow-Fligner post hoc test for multiple comparisons, with an alpha level of 0.05 for both types of analysis. Statistical analysis and presentation are consistent with the checklist for statistical assessment of medical papers statement 19 found in online supplemental files 1–3 at Hamilton et al , The Strength, Power and Aerobic Capacity of Transgender Athletes: A Cross-Sectional Study (Internet). OSF; 2023. Available from: osf.io/a684b.

Supplemental material

Equity, diversity and inclusion statement.

The author group consists of early (n=3) and senior researchers (n=3) from different disciplines and universities (n=3). Two authors are members of a marginalised community; the lead early-career author is a transgender woman, and one of the junior authors is a woman from the global south. Our study population included male and female transgender athletes from within the UK participating in competitive sports in comparison with cisgender male and female athletes participating in competitive sports; thus, findings may not be generalisable to global athlete populations.

Participant characteristics

Our investigation encompassed a diverse cohort of athletes, with endurance sports representing 36% of the athlete cohort, team sports representing 26% and power sports representing 38%. No cisgender or transgender athletes were competing at the national or international level. No significant differences were found in age (F (3–66) =1.9, p=0.14), training intensity score (χ 2 (3) =1.2, p=0.76) or length of GAHT between transgender men and transgender women (F (1–32) =0.5, p=0.48, table 1 ).

View inline

Significant differences were found in height (F (3–66) =21.3, p<0.001), with CM being taller than transgender men (t (66) =3.8, p=0.002, table 1 ). Transgender women were also taller than transgender men (t (66) =3.3, p=0.01) and cisgender women (t (66) =6.5, p<0.001, table 1 ).

Significant differences were found in clothed mass (F (3–66) =10.6, p<0.001), with transgender women found to be heavier than cisgender women (t (66) =5.6, p<0.001, table 1 ).

BMI was also significantly different between the groups in this Study (F (3–66) =3.6, p=0.02). Transgender women athletes demonstrated higher BMI than cisgender women (t (66) =2.9, p=0.03, table 1 ), with no further differences observed.

Blood measures

There was a significant gender effect on testosterone concentration (F (3–66) =80.6, p<0.001). CM (20.5±5.8 nmol/L) exhibited significantly higher total testosterone concentration than transgender women (0.7±0.5 nmol/L, t (66) = 11.1, p<0.001, figure 1A ). Transgender men (24.8±12.3 nmol/L) had elevated total testosterone concentration compared with transgender women (t (66) =11.3) and cisgender women (0.9±0.4 nmol/L, t (66) =10.9, both p<0.001, figure 1A ). There was also a significant gender effect on oestradiol concentration (F (3−66) =7.6, p<0.001), with transgender women (742.4±801.9 pmol/L) showing higher oestradiol concentration than CM (104.3±24.8 pmol/L, t (66) =4.4 p<0.001), cisgender women (336.0±266.3 pmol/L, t (66) =2.7, p=0.045) and transgender men (150.2±59.4 pmol/L, t (66) =3.4, p=0.01, figure 1B ).

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Blood measures. (A) testosterone; (B) oestradiol; (C) haemoglobin. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. CM, cisgender men; CW, cisgender women; TM, transgender men; TW, transgender women.

Transgender women’s total testosterone concentration (0.7±0.5 nmol/L) falls within the recommendations for GAHT of ≤1.8 nmol/L, 5 and oestradiol concentrations (742.4±801.9 pmol/L) exceed the target of 400–600 pmol/L 5 for GAHT. Transgender men’s testosterone concentration (24.8±12.3 nmol/L) exceeds the NHS target of 15–20 nmol/L 6 for GAHT, although not the Endocrine Society target of 11–34.7 nmol/L. 7

Differences were reported in (Hb) concentration (F (3–66) =3.3, p=0.03), although a post hoc Bonferroni analysis showed no differences between the various groups (CM 142.8±12.5 g/L; transgender men, 143.3±19.5 g/L; transgender women, 131.3±14.2 g/L; cisgender women, 133.3±12.7 g/L; figure 1C ).

DXA assessment

There was a significant gender effect on percentage fat mass (F (3–66) =6.6, p<0.001), with CM having a lower percentage fat mass than transgender women (t (66) =−4.4, p<0.001, table 2 ), with no other differences observed. A significant gender effect was also found on absolute fat mass (F (3–66) =6.6, p<0.001), with transgender women having more absolute fat mass than CM (t (66) =3.8, p=0.002, table 2 ) and cisgender women (t (66) =3.9, p=0.002, table 2 ). FMI measures revealed a gender effect (F (3–66) =5.2, p=0.003), with transgender women found to have a higher FMI than CM (t (66) =3.7, p=0.002, table 2 ) and cisgender women (t (66) =2.8, p=0.04, table 2 ). Android to gynoid ratio analysis (F (3–66) =10.7, p<0.001) revealed cisgender women had a lower ratio than transgender men (t (66) =−2.9, p=0.03, table 2 ), and transgender women (t (66) =−4.0, p=0.001, table 2 ).

Body composition, BMD data, handgrip strength, lower anaerobic power and cardiopulmonary exercise testing

Fat-free mass

There was a significant gender effect on absolute fat-free mass (F (3–66) =24.6, p<0.001), with CM having significantly more absolute fat-free mass than transgender men (t (66) =3.5, p=0.01, table 2 ). Cisgender women had less absolute fat-free mass than transgender men (t (66) =−3.5, p=0.01, table 2 ) and transgender women (t (66) =−6.6, p<0.001, table 2 ). No gender-based effects were found when comparing transgender women athletes to cisgender women athletes, or transgender men athletes to CM athletes in the measures of FFMI (F (3–66) =3.7, p=0.02, table 2 ), percentage of fat-free mass (F (3–66) =2.4, p=0.08, table 2 ) or appendicular FFMI (F (3–66) =5.1, p=0.003, table 2 ).

Bone mineral density

No differences in whole-body bone mineral density (BMD) (F (3–66) =4.6, p=0.01), femoral neck BMD (F (3–66) =1.0, p=0.39, table 2 ), total proximal femur BMD (F (3–66) =1.5, p=0.22, table 2 ) or total lumbar spine BMD (F (3–66) =0.4, p=0.78, table 2 ) were found between transgender athletes and cisgender athletes ( table 2 ).

Lung function data for all groups can be found in table 2 . FEV 1 had an effect of gender (F (3–66) =14.7, p<0.001), with CM having greater FEV 1 than transgender men (t (66) = 4.5, p<0.001, figure 2A ). Transgender women also had greater FEV 1 than cisgender women (t (66) =4.2, p<0.001, figure 2A ) and transgender men (t (66) =2.9, p=0.03, figure 2A ). There was a similar effect of gender on FVC (F (3–66) =21.6, p<0.001, figure 2B ), with CM having greater FVC than transgender men (t (66) =5.2, p<0.001, figure 2B ). Transgender women also had greater FVC than cisgender women (t (66) =5.6, p<0.001, figure 2B ) and transgender men (t (66) =4.0, p=0.001, figure 2B ). A significant effect of gender was also seen on the FEV 1 :FVC ratio (F (3–66) =3.3, p=0.03 figure 2C ), with transgender women showing a reduced FEV 1 :FVC ratio compared with cisgender women (t (66) =−2.8, p=0.04, figure 2C ) with no differences observed in transgender or CM. Peak expiratory flow (F (3–66) =5.5, p=0.002) had a minor gender-based effect, with cisgender women having lower peak expiratory flow than transgender women (t (66) −3.0, p=0.02, figure 2D ).

Lung function measures. (A) Forced rxpiratory volume in 1 s (FEV 1 ); (B) forced vital capacity (FVC) (C) modified Tiffeneau-Pinelli Index (FEV 1 :FVC); (D) peak expiratory flow (PEF). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. CM, cisgender men; CW, cisgender women; TM, transgender men; TW, transgender women.

Handgrip strength

Handgrip strength data can be found in table 2 . Absolute right handgrip strength was significantly different between the groups (F (3–66) =10.5, p<0.001), with CM having greater absolute right handgrip strength than transgender men (t (66) =2.9, p=0.03, figure 3B ). Transgender women also had greater absolute right handgrip strength than cisgender women (t (66) =3.2, p=0.01, figure 3B ). Absolute left handgrip was significantly different between the groups (F (3–66) =8.6, p<0.001). However, no differences were found between transgender and cisgender athletes ( figure 3A ). There was no effect on the right (F (3–66) =0.8, p=0.53, figure 3F ) or left-hand grip strength (F (3–66) =1.0, p=0.39, figure 3E ) relative to fat-free mass, nor was there any gender effect on the right (F (3–66) =1.6, p=0.20, figure 3D ) or left-hand grip-strength (F (3–66) =2.1, p=0.11) relative to hand size.

Absolute and relative handgrip strength (GS) measures. (A) Absolute strength (right hand); B) Absolute strength (left hand) (C) relative strength to hand size (right hand); (D) relative strength to hand size (left hand); (E) relative strength to fat-free mass (FFM) (right hand); (F) relative strength to fat-free mass (left hand). *p<0.05, ***p<0.001, ****p<0.0001. CM, cisgender men; CW, cisgender women; TM, transgender men; TW, transgender women.

Lower body anaerobic power

Lower body anaerobic power data are shown in table 2 . Gender had a significant effect on absolute countermovement jump height (F (3–66) =7.2, p<0.001), with CM having greater absolute jump height than transgender women (t (66) =4.5, p<0.001, figure 4A ). A significant effect of gender was found in countermovement jump height relative to fat-free mass (F (3–66) =10.1, p<0.001, figure 4B ), with transgender women found to have lower countermovement jump height relative to fat-free mass than both cisgender women (t (66) =−5.3, p<0.001) and transgender men (t (66) =–3.2, p=0.01, figure 4B ).

Absolute and relative anaerobic power measures. (A) Absolute CMJ height; B) Relative CMJ height to fat-free mass (FFM); (C) absolute peak power; (D) relative peak power to FFM; (E) absolute average power; (F) relative average power to FFM. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. CM, cisgender men; CMJ, Counter Movement Jump; CW, cisgender women; TM, transgender men; TW, transgender women.

There was a significant difference in absolute peak power (F (3–66) =8.7, p<0.001), with cisgender women having reduced peak power compared with transgender men (t (66) =−3.3, p=0.01) and transgender women (t (66) =−3.6, p=0.004, figure 4C ). Peak power relative to fat-free mass had a more negligible gender effect (F (3–66) =4.2, p=0.01), with no difference in peak power relative to fat-free mass found between transgender and cisgender athletes ( figure 4D ).

There was a significant gender effect of absolute average power (F (3–66) =5.9, p=0.001), with cisgender women having reduced absolute average power compared with transgender men (t (66) =–3.1, p=0.02, figure 4E ). There was no effect of gender on average power relative to fat-free mass (F (3–66) =2.6, p=0.06, figure 4F ).

Cardiopulmonary exercise testing data are shown in table 2 . A significant effect of gender was found on absolute V̇O 2 max (F (3–62) =14.1, p<0.001) with CM having greater absolute V̇O 2 max than transgender men (t (66) =3.8, p=0.002, figure 5A ) and transgender women (t (66) =4.3, p<0.001, figure 5A ). Relative V̇O 2 max to body mass also showed a significant gender effect (F (3–62) =9.8, p<0.001) with transgender women having lower relative V̇O 2 max than CM (t (66) =–5.3, p<0.001, figure 5B ) and cisgender women (t (66) =−3.3, p=0.01, figure 5B ). No significant gender effect was found on the measure of V̇O 2 max relative to fat-free mass (F (3–62) =2.0, p=0.12).

Absolute and relative cardiopulmonary exercise testing measures. (A) Absolute V̇O 2 max; (B) relative V̇O 2 max to body weight; (C) absolute anaerobic threshold (AT); (D) anaerobic threshold (%V̇O 2 max); (E) relative anaerobic threshold relative to body mass; (F) AT relative to at-free mass (FFM). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. CM, cisgender men; CW, cisgender women; TM, transgender men; TW, transgender women.

Gender affected the absolute anaerobic threshold (F (3–62) =14.1, p<0.001), with cisgender (3924±628 mL/min) men having a higher absolute anaerobic threshold than transgender men (3089±546 mL/min, t (66) =4.2, p<0.001, figure 5C ), and transgender women (3122±438 mL/min, t (66) =4.8, p<0.001, figure 5C ). No significant gender effect was found on the measure of anaerobic threshold as a percentage of V̇O 2 max (F (3–62) =0.8, p=0.51, figure 5D ). A gender effect was also seen on the anaerobic threshold relative to body mass (F (3–62) =10.7, p<0.001), with transgender women (38.3±6.6 mL/kg/min) showing a lower relative anaerobic threshold than both cisgender women (47.2±6.1 mL/kg/min, t (66) =–3.3, p=0.01, figure 5E ) and CM (52.2±9.5 mL/kg/min, t (66) =−5.4, p<0.001, figure 5E ). CM also showed a higher relative anaerobic threshold than transgender men (42.1±9.9 mL/kg/min, t (66) =3.3, p=0.01, figure 5E ). Anaerobic threshold relative to fat-free mass also had a small gender effect (F (3–62) =3.2, p=0.03), with transgender women (60.8±12.2 mL/kg FFM /min) having a lower anaerobic threshold relative to fat-free mass than CM (71.2±13.3 mL/kg FFM /min, t (66) =−2.8, p=0.045, figure 5F ).

The results presented in this study provide valuable insights into laboratory-based performance-related metrics of gender-diverse athletes participating in competitive sports. Given the primary aim of GAHT, 20 it is noteworthy that although this study is cross-sectional in design, transgender women’s oestradiol was higher than that of cisgender women ( figure 1B ). The presence of outliers affecting transgender women’s oestrogen concentration ( figure 1B ) is evident. This underscores that transgender women in this cohort of athletes exhibit a distinct endocrine profile from CM and share a similar endocrine profile with cisgender women, whom many transgender women aim to integrate into a sporting category. One of the most noticeable disparities between gender groups was in height and mass ( table 1 ), with CM and transgender women being taller and heavier than their cisgender and transgender counterparts ( table 1 ). Body composition measures (fat mass % and fat-free mass %, table 2 ) between transgender women and cisgender women found no difference. However, transgender women are, on average as a cohort taller and heavier.

In this cohort, the average difference in haemoglobin (Hb) between cisgender women and CM athletes was 7% ( figure 1C ), lower than previously described (12% 8 ). Notably, the (Hb) profiles of all the athlete groups were not significantly different, concurring with earlier research 21 and contradicting research in sedentary populations. 22 (Hb) is crucial in O 2 transport 23 and vital for endurance sports performance, 24 with O 2 delivery to the tissues a limiting factor in V̇O 2 max attainment. 25 The lack of differences in (Hb) is consistent with the lack of observed difference in absolute V̇O 2 max between transgender women, transgender men and cisgender women in this cohort. However, as cardiac output, the most crucial variable influencing V̇O 2 max 25 was not assessed in the present study, a more comprehensive mechanistic explanation for the similar maximal aerobic capacity between groups cannot be provided.

No differences in BMD were observed between transgender and cisgender women athletes in this study ( table 2 ), despite prior research hypothesising that transgender women athletes have a significant BMD advantage over cisgender women. 11 The sample size for each gender was n<30 participants and may be insufficient to characterise BMD differences reliably. Exercise has been shown to have a protective effect on BMD in CM 26 and CW, 27 and our results suggest a protective effect of exercise in transgender women, given that there is evidence of low BMD in transgender women with low weekly sports activity. 28 Nevertheless, the results suggest the complexity of bone health in athlete populations and the need for a more comprehensive assessment to understand the long-term impact of GAHT on transgender athletes’ BMD.

The differences observed in body composition in this population ( table 2 ) indirectly show the potential role of androgens in body composition, owing to the role of oestradiol in fat accumulation 29 and transgender women’s oestradiol concentrations ( figure 1B ) and fat mass ( table 2 ) being greater than all other groups. Body composition differences may have implications for sports that prioritise exercise economy, 30 defined as the average V̇O 2 relative to body mass between submaximal intensities, 31 as athletes with a higher fat mass percentage will present with a lower exercise economy owing to the increased O 2 cost of exercise. 32 The android-to-gynoid ratio analysis ( table 2 ) suggests that hormone therapy ( figure 1A,B ) influences differences in fat distribution patterns. However, fat distribution patterns of the present transgender female athlete cohort ( table 2 ) do not reach ratios previously reported in cisgender female populations (0.8). 33 Understanding these variations is essential for evaluating performance in sports where body composition is a determining factor, for example, weightlifting or boxing.

Cisgender women had lower absolute fat-free mass than transgender men and transgender women ( table 2 ). When analysing absolute fat and fat-free mass data ( table 2 ), these results can be affected by sample size and/or athlete diversity limitations. A purposefully designed future study with height-matched and sport-matched cisgender and transgender female athletes is crucial to understanding differences in these parameters, as they are influenced by height disparities ( table 1 ) and variations in sampled sports.

FVC, FEV 1 and FEV 1 :FVC ratio are higher in athletes than in the normal sedentary control individuals, 34 and there is no difference in all three metrics between aerobic athletes and anaerobic athletes. 35 Therefore, the lung function differences observed in figure 2A,B may be attributed to factors such as skeletal size benefiting lung capacity and function, 36 with transgender women’s FVC results ( figure 2B ) suggesting gender-affirming hormone care did not impact changing lung volumes owing to the GAHTs lack of effect on skeletal stature. 11 Transgender women showed a significantly reduced FEV 1 :FVC ratio compared with cisgender women ( figure 2C ). The FEV 1 :FVC ratio has been used as a screening index for identifying obstructive lung conditions globally, 37 as a lower FEV 1 owing to obstruction of air escaping from the lungs will reduce the FEV 1 :FVC ratio. Transgender women’s results ( figure 2C ) suggest obstructed airflow in the lungs 38 when compared with cisgender women. However, this observation of transgender women is unlikely to be pathological (<0.70), 39 as seen in chronic obstructive pulmonary disease.

Nevertheless, this reduced airflow could potentially lead to exercise-induced dyspnoea, resulting in performance limitations 40 in comparison to cisgender women. When comparing both the CM and transgender women athletes’ groups with identical heights (1.8 m, table 1 ), while both groups exhibit similar FVC, transgender women demonstrate a lower FEV 1 , leading to a reduced FEV 1 :FVC ratio compared with CM, although not significant. If there were a significant difference between CM and transgender women, our preliminary hypothesis would have attributed this divergence to testosterone suppression in transgender women. However, comparing transgender women to cisgender women who do not share similar height and or exhibit a comparable FVC, the observed differences become more complex to interpret. The possibility arises that factors beyond hormonal influences, such as varying levels of aerobic training, may contribute to the significant difference found in the FEV 1 :FVC ratio between transgender women and cisgender women. Further longitudinal investigation is required to elucidate if the causation underlying these pulmonary function disparities is indeed testosterone suppression.

Strength results in figure 3 disagree with previous literature in a non-athlete transgender cohort using the same methodology that showed transgender women and CM had significantly different absolute and relative hand grip strength. 41 Our results showed no differences in absolute strength between transgender women and CM and no difference in relative handgrip between any of the groups in this study ( figure 3 ). These results highlight the differences between athlete and sedentary populations. However, the results relative to hand size also concur with the notion that greater handgrip strength is caused by greater hand size, 42 as there were no differences in results between the four groups when normalised for hand size ( figure 3C,D ). Therefore, investigations with more accurate measures of strength are warranted in transgender athletes.

Transgender women presented lower absolute jump height than CM and lower relative jump height, normalised for fat-free mass, than transgender men and cisgender women ( figure 4 ). These results in this study cohort suggest that transgender women lack lower body anaerobic power compared with the other groups. Transgender women’s higher absolute peak power than cisgender women ( figure 4C ), coupled with higher fat mass potentially driven by higher oestradiol concentrations ( figure 1B ), suggest that transgender women had more inertia to overcome during the explosive phase of the countermovement jump, which may lead to decreased performance. However, when normalised for fat-free mass ( figure 4D ), transgender women’s peak power was lower than that of cisgender women, showing that this cohort also lacks peak power relatively, indicating that the higher fat mass may not be the primary contributing factor. Further investigations are warranted to find the causation of this poor lower anaerobic power performance in transgender women.

The lack of differences in anaerobic threshold (%V̇O 2 max, figure 5D ) suggests that the athletes in this study had a similar fitness status, which is an essential underlying finding given that CM showed greater absolute V̇O 2 max than all groups ( figure 5A ), with no differences between transgender women and cisgender women found, and transgender women exhibited lower relative V̇O 2 max compared with both CM and women ( figure 5B ). In this cohort, the finding of no statistical difference in absolute V̇O 2 max between transgender women and cisgender women contrasts the idea that transgender women’s bigger lung size ( table 2 ) is an inherent respiratory function advantage over cisgender women. 11 Both the absolute and relative V̇O 2 max differences between groups contradict one previous study in non-athlete transgender populations that found transgender women had higher absolute V̇O 2 peak and no difference in relative V̇O 2 peak compared with cisgender women. 41 This contradictory finding further highlights population differences between non-athlete and athlete cohorts while also contradicting literature hypothesising that there would be a baseline gap in aerobic capacity between transgender women and cisgender women. 11 The results in this athlete cohort warrant further research to elucidate the mechanisms behind this deviation, as they may be metabolic, as transgender women also exhibited a lower relative anaerobic threshold ( figure 5E ). The findings in table 2 reveal notable disparities in fat mass, fat-free mass, laboratory sports performance measures and hand-grip strength measures between cisgender male and transgender female athletes. These differences underscore the inadequacy of using cisgender male athletes as proxies for transgender women athletes. Therefore, based on these limited findings, we recommend that transgender women athletes be evaluated as their own demographic group, in accordance with the principles outlined in Article 6.1b of the International Olympic Committee Framework on Fairness, Inclusion and Non-Discrimination based on Gender Identity and Sex Variations. 4

Study limitations

The limitations of this study primarily relate to its cross-sectional design, making it challenging to establish causation or examine if the performance of athletes changes as a result of undergoing GAHT. Longitudinal studies are needed to examine how GAHT, and other factors impact athletes’ physiology and performance over time. Additionally, the composition of the study cohort may not fully represent the diversity of athletes in elite sports from worldwide populations. Athletes from various sporting disciplines and performance levels were included, and the athlete training intensity was self-reported. Therefore, the results may suffer from selection and recall bias. 43 The results may not apply to all levels or ages of athletes, specifically as this research did not include any adolescent athletes competing at the national or international level. The athletes participating in the present study represented a variety of different sports, and this would have undoubtedly impacted the results of the study as different sports stress different training and sports modalities. Exercise type, intensity and duration all have an impact on physiological responses and overall laboratory performance metrics. 44 The subgroups of sports that emerged were also too dissimilar to allow meaningful subgroup analysis. The complexity and difficulty of this area of activity means that while this study provides a starting point for understanding the complex physiology in GAHT and athletic performance, this study does not provide evidence that is sufficient to influence policy for either inclusion or exclusion. However, this is the first study to assess laboratory-based measures of performance in transgender athletes, and this opens up interesting avenues for replication and extension into the longitudinal effects of GAHT on athletic performance.

Future research should include more extensive and diverse samples to enhance the generalisability of findings or smaller, more specific cohorts to hone in on a particular sports discipline. However, such studies may be complex due to the low numbers of transgender athletes. The recruitment method of this study also provided a limitation as social media advertising was used rather than recruitment from a clinical provider. Social media recruitment leaves this study open to sample bias as social media advertising, although great for recruiting hard-to-reach participants for observational studies, 44 45 does not represent a clinical population in 86% of comparisons. 44 As the participants were not recruited from a clinic, this also means that the gender-affirming treatment of the transgender athletes was not controlled. For example, different testosterone suppression methods have different efficacies, 46 and future studies should consider differences in the prescribed GAHT to participants. Lastly, the participants were not screened by a clinician before participation, and any medical conditions were self-reported in the physical activity readiness questionnaire (PAR-Q). This method of medical reporting leaves the data open to self-reporting bias, which can mislead descriptive statistics and causal inferences 47 as participants’ cognitive processes, such as social desirability, can alter participants’ responses. 48 Therefore, it is recommended to use a clinic to screen and recruit participants to avoid such bias in a longitudinal study of transgender athlete sports performance.

Conclusions

This research compares transgender male and transgender female athletes to their cisgender counterparts. Compared with cisgender women, transgender women have decreased lung function, increasing their work in breathing. Regardless of fat-free mass distribution, transgender women performed worse on the countermovement jump than cisgender women and CM. Although transgender women have comparable absolute V̇O 2 max values to cisgender women, when normalised for body weight, transgender women’s cardiovascular fitness is lower than CM and women. Therefore, this research shows the potential complexity of transgender athlete physiology and its effects on the laboratory measures of physical performance. A long-term longitudinal study is needed to confirm whether these findings are directly related to gender-affirming hormone therapy owing to the study’s shortcomings, particularly its cross-sectional design and limited sample size, which make confirming the causal effect of gender-affirmative care on sports performance problematic.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and ethical approval for this study has been granted by the School of Applied Sciences Research Ethics Committee of the University of Brighton, Brighton, UK (Ref: 9496). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

We thank Associate Professor Ada Cheung of the Department of Medicine (Austin Health) at the University of Melbourne, Australia for her valuable review of this work prior to publication.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Data supplement 1
Data supplement 2
Data supplement 3

X @BlairH_PhD

Contributors BH, FMG and YPP designed the study. Material preparation, reporting and critical revision of the work were performed by BH, PGB, FMG and YPP. Data collection was performed by CC-C, AB, SM-M and BH. BH wrote the first draft of the manuscript, and all authors critically revised subsequent versions until all authors could approve the final manuscript. YPP is the guarantor.

Funding The study has been funded by a research grant awarded by the International Olympic Committee, Lausanne, Switzerland.

Competing interests YPP is a member of the IOC Medical and Scientific Commission, which recently published articles and framework documents on the topic. BH and FMG have recently published articles on the topic on behalf of the International Federation of Sports Medicine (FIMS). All authors declare no further conflict of interest or competing interests.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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A prospective cohort study is a type of longitudinal research where a group of individuals sharing a common characteristic (cohort) is followed over time to observe and measure outcomes, often to investigate the effect of suspected risk factors. In a prospective study, the investigators will design the study, recruit subjects, and collect ...
Cohort Studies: Design, Analysis, and Reporting
Abstract. Cohort studies are types of observational studies in which a cohort, or a group of individuals sharing some characteristic, are followed up over time, and outcomes are measured at one or more time points. Cohort studies can be classified as prospective or retrospective studies, and they have several advantages and disadvantages.
Cohort study
A cohort study is a particular form of longitudinal study that samples a cohort (a group of people who share a defining characteristic, typically those who experienced a common event in a selected period, such as birth or graduation), performing a cross-section at intervals through time. It is a type of panel study where the individuals in the panel share a common characteristic.
LibGuides: Quantitative study designs: Cohort Studies
Cohort studies are longitudinal, observational studies, which investigate predictive risk factors and health outcomes. They differ from clinical trials, in that no intervention, treatment, or exposure is administered to the participants. The factors of interest to researchers already exist in the study group under investigation.
Cohort Studies: Design, Analysis, and Reporting
Cohort studies can be either prospective or retrospective. The type of cohort study is determined by the outcome status. If the outcome has not occurred at the start of the study, then it is a prospective study; if the outcome has already occurred, then it is a retrospective study. 4 Figure 1 presents a graphical representation of the designs of prospective and retrospective cohort studies.
Cohort Studies: Design, Analysis, and Reporting
Design, Analysis, and Reporting. Cohort studies are types of observational studies in which a cohort, or a group of individuals sharing some characteristic, are followed up over time, and outcomes are measured at one or more time points. Cohort studies can be classified as prospective or retrospective studies, and they have several advantages ...
What are cohort studies?
Cohort studies are a type of longitudinal study —an approach that follows research participants over a period of time (often many years). Specifically, cohort studies recruit and follow participants who share a common characteristic, such as a particular occupation or demographic similarity. During the period of follow-up, some of the cohort ...
Case-control and Cohort studies: A brief overview
Introduction. Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence. These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies (1). Although these studies are not ranked as highly as ...
Cohort Studies
Introduction. This chapter summarizes the basic features of cohort studies, a type of observational epidemiology study that some have also called longitudinal, or prospective, though these terms also apply to other epidemiological designs. A cohort study evaluates both the risk and the rate of disease or disease-related outcomes in a population ...
Introduction to Epidemiological Studies
The basic epidemiological study designs are cross-sectional, case-control, and cohort studies. Cross-sectional studies provide a snapshot of a population by determining both exposures and outcomes at one time point. Cohort studies identify the study groups based on the exposure and, then, the researchers follow up study participants to measure ...
Evidence-Based Research: Evidence Types
A cohort study is an observational longitudinal study that analyzes risk factors and outcomes by following a group (cohort) that share a common characteristic or experience over a period of time. Cohort studies can be retrospective, looking back over time at data that has already been collected, or can be prospective, following a group forward ...
Types of clinical trials
Cohort studies, case control studies and cross sectional studies are all types of observational studies. Cohort studies. A cohort is a group of people, so cohort studies look at groups of people. A cohort study follows the group over a period of time. A research team may recruit people who do not have cancer and collect information about them ...
Types of Research Studies
The 2 main types of epidemiology studies are: Observational studies ( prospective cohort or case-control) Randomized controlled trials. Though they have the same goal, observational studies and randomized controlled trials differ in: The way they are conducted. The strengths of the conclusions they reach.
Feasibility and acceptability of a cohort study baseline data
The findings revealed that the methodology was both feasible and acceptable, paving the way for large-scale prospective cohort research in Saudi Arabia. This research marks the first attempt to establish a prospective cohort study in Saudi Arabia using established ProPASS methods [13, 15] and protocols.
Methodology Series Module 1: Cohort Studies
It is a type of nonexperimental or observational study design. The term "cohort" refers to a group of people who have been included in a study by an event that is based on the definition decided by the researcher. For example, a cohort of people born in Mumbai in the year 1980. This will be called a "birth cohort.".
MELODY: A prospective non-interventional multicenter cohort study to
In most studies, the patient perspective, addressing e.g. discomfort and pain, has not been evaluated. The aim of MELODY (Methods for Localization of Different Types of Breast Lesions) is to evaluate different imaging-guided localization methods with regard to oncological safety, patient-reported outcomes, and surgeon and radiologist satisfaction.
Outdoor air pollution and risk of incident adult haematologic ...
Briefly, the Nutrition Cohort is a sub-cohort of the approximately 1.2 million subjects in CPS-II, a prospective study of mortality established by the American Cancer Society in 1982.
Regular use of fish oil supplements and course of cardiovascular
Objective To examine the effects of fish oil supplements on the clinical course of cardiovascular disease, from a healthy state to atrial fibrillation, major adverse cardiovascular events, and subsequently death. Design Prospective cohort study. Setting UK Biobank study, 1 January 2006 to 31 December 2010, with follow-up to 31 March 2021 (median follow-up 11.9 years). Participants 415 737 ...
Vegetables and Fruits
Eat plenty every day. A diet rich in vegetables and fruits can lower blood pressure, reduce the risk of heart disease and stroke, prevent some types of cancer, lower risk of eye and digestive problems, and have a positive effect upon blood sugar, which can help keep appetite in check. Eating non-starchy vegetables and fruits like apples, pears ...
Protocol for a multicentre prospective exploratory mixed-methods study
Methods Qualitative data will be collected through semistructured interviews with children affected by CKD-5, their carers and paediatric renal multidisciplinary team. Recruitment for interviews will continue till data saturation. These interviews will inform the choice of existing validated questionnaires, which will be distributed to a larger national cohort of children with pretransplant ...
Magnesium
Type 2 diabetes. Magnesium assists enzymes that regulate blood sugar and insulin activity. Prospective cohort studies show an association of diets low in magnesium with an increased risk of type 2 diabetes. [4] However, the results are mixed in clinical trials of magnesium supplements for people with diabetes, some finding an improvement in insulin sensitivity when correcting a magnesium ...
MIND Diet
The MIND diet recommends specific "brain healthy" foods to include, and five unhealthy food items to limit. [1] The healthy items the MIND diet guidelines* suggest include: 3+ servings a day of whole grains. 1+ servings a day of vegetables (other than green leafy) 6+ servings a week of green leafy vegetables. 5+ servings a week of nuts.
Strength, power and aerobic capacity of transgender athletes: a cross
Objective The primary objective of this cross-sectional study was to compare standard laboratory performance metrics of transgender athletes to cisgender athletes. Methods 19 cisgender men (CM) (mean±SD, age: 37±9 years), 12 transgender men (TM) (age: 34±7 years), 23 transgender women (TW) (age: 34±10 years) and 21 cisgender women (CW) (age: 30±9 years) underwent a series of standard ...

Cohort Study: Definition, Designs & Examples

Retrospective

Prospective

Determine cause-and-effect relationships

Provide extensive data

Enable studies of rare exposures

Can measure a continuously changing relationship between exposure and outcome

Limitations

Require large sample sizes

Prone to bias

Unable to discover why or how a certain factor is associated with a disease

The Framingham Heart Study

The Whitehall Study

The Nurses’ Health Study

The British Doctors Study

The Black Women’s Health Study

Millennium Cohort Study

The Danish Cohort Study of Psoriasis and Depression

The 1970 British Cohort Study

Frequently Asked Questions

2. What is the difference between a cross-sectional study and a cohort study?

3. What is the difference between cohort and longitudinal studies?

Further Information

Quantitative study designs: Cohort Studies

Cohort Study

What is a Cohort Study design?

Cohort studies are useful if:

The stages of a Cohort Study

Types of Cohort Studies

How Cohort Studies are carried out

Which clinical questions does this study design best answer?

What are the pitfalls to look for?

Critical appraisal tools

Real World Examples

References and Further Reading

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Strengths and weaknesses of cohort studies

Reporting and critiquing of cohort studies

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Case-control and Cohort studies: A brief overview

Introduction

Case-control studies

Cohort studies

The Strengthening of Reporting of Observational Studies in Epidemiology Statement (STROBE)

Saul Crandon

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Introduction to Epidemiological Studies

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