research into type 2

A promising new pathway to treating type 2 diabetes

This year marks the 100th anniversary of the discovery of insulin, a scientific breakthrough that transformed Type 1 diabetes, once known as juvenile diabetes or insulin-dependent diabetes, from a terminal disease into a manageable condition.

Today, Type 2 diabetes is 24 times more prevalent than Type 1. The rise in rates of obesity and incidence of Type 2 diabetes are related and require new approaches, according to University of Arizona researchers, who believe the liver may hold the key to innovative new treatments.

"All current therapeutics for Type 2 diabetes primarily aim to decrease blood glucose. So, they are treating a symptom, much like treating the flu by decreasing the fever," said Benjamin Renquist, an associate professor in the UArizona College of Agriculture and Life Sciences and BIO5 Institute member. "We need another breakthrough."

In two newly published papers in Cell Reports , Renquist, along with researchers from Washington University in St. Louis, the University of Pennsylvania and Northwestern University, outline a new target for Type 2 diabetes treatment.

Renquist, whose research lab aims to address obesity-related diseases, has spent the last nine years working to better understand the correlation between obesity, fatty liver disease and diabetes, particularly how the liver affects insulin sensitivity.

"Obesity is known to be a cause of Type 2 diabetes and, for a long time, we have known that the amount of fat in the liver increases with obesity," Renquist said. "As fat increases in the liver, the incidence of diabetes increases."

This suggested that fat in the liver might be causing Type 2 Diabetes, but how fat in the liver could cause the body to become resistant to insulin or cause the pancreas to over-secrete insulin remained a mystery, Renquist said.

Renquist and his collaborators focused on fatty liver, measuring neurotransmitters released from the liver in animal models of obesity, to better understand how the liver communicates with the brain to influence metabolic changes seen in obesity and diabetes.

"We found that fat in the liver increased the release of the inhibitory neurotransmitter Gamma-aminobutyric acid, or GABA," Renquist said. "We then identified the pathway by which GABA synthesis was occurring and the key enzyme that is responsible for liver GABA production -- GABA transaminase."

A naturally occurring amino acid, GABA is the primary inhibitory neurotransmitter in the central nervous system, meaning it decreases nerve activity.

Nerves provide a conduit by which the brain and the rest of the body communicate. That communication is not only from the brain to other tissues, but also from tissues back to the brain, Renquist explained.

"When the liver produces GABA, it decreases activity of those nerves that run from the liver to the brain. Thus, fatty liver, by producing GABA, is decreasing firing activity to the brain," Renquist said. "That decrease in firing is sensed by the central nervous system, which changes outgoing signals that affect glucose homeostasis."

To determine if increased liver GABA synthesis was causing insulin resistance, graduate students in Renquist's lab, Caroline Geisler and Susma Ghimire, pharmacologically inhibited liver GABA transaminase in animal models of Type 2 diabetes.

"Inhibition of excess liver GABA production restored insulin sensitivity within days," said Geisler, now a postdoctoral researcher at the University of Pennsylvania and lead author on the papers. "Longer term inhibition of GABA-transaminase resulted in decreased food intake and weight loss."

Researchers wanted to ensure the findings would translate to humans. Kendra Miller, a research technician in Renquist's lab, identified variations in the genome near GABA transaminase that were associated with Type 2 diabetes. Collaborating with investigators at Washington University, the researchers showed that in people with insulin resistance, the liver more highly expressed genes involved in GABA production and release.

The findings are the foundation of an Arizona Biomedical Research Commission-funded clinical trial currently underway at Washington University School of Medicine in St. Louis with collaborator Samuel Klein, co-author on the study and a Washington University professor of medicine and nutritional science. The trial will investigate the use of a commercially available Food and Drug Administration-approved inhibitor of GABA transaminase to improve insulin sensitivity in people who are obese.

"A novel pharmacological target is just the first step in application; we are years away from anything reaching the neighborhood pharmacy," Renquist said. "The magnitude of the obesity crisis makes these promising findings an important first step that we hope will eventually impact the health of our family, friends and community."

• Liver Disease
• Chronic Illness
• Diseases and Conditions
• Hormone Disorders
• Diet and Weight Loss
• Personalized Medicine
• Diabetes mellitus type 1
• Diabetes mellitus type 2
• Stem cell treatments
• Liver transplantation
• Sports medicine

Story Source:

Materials provided by University of Arizona . Original written by Rosemary Brandt. Note: Content may be edited for style and length.

Journal Reference :

• Caroline E. Geisler, Susma Ghimire, Stephanie M. Bruggink, Kendra E. Miller, Savanna N. Weninger, Jason M. Kronenfeld, Jun Yoshino, Samuel Klein, Frank A. Duca, Benjamin J. Renquist. A critical role of hepatic GABA in the metabolic dysfunction and hyperphagia of obesity . Cell Reports , 2021; 35 (13): 109301 DOI: 10.1016/j.celrep.2021.109301

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Type 2 Diabetes Research At-a-Glance

The ADA is committed to continuing progress in the fight against type 2 diabetes by funding research, including support for potential new treatments, a better understating of genetic factors, addressing disparities, and more. For specific examples of projects currently funded by the ADA, see below.

Greg J. Morton, PhD

University of Washington

Project: Neurocircuits regulating glucose homeostasis

“The health consequences of diabetes can be devastating, and new treatments and therapies are needed. My research career has focused on understanding how blood sugar levels are regulated and what contributes to the development of diabetes. This research will provide insights into the role of the brain in the control of blood sugar levels and has potential to facilitate the development of novel approaches to diabetes treatment.”

The problem: Type 2 diabetes (T2D) is among the most pressing and costly medical challenges confronting modern society. Even with currently available therapies, the control and management of blood sugar levels remains a challenge in T2D patients and can thereby increase the risk of diabetes-related complications. Continued progress with newer, better therapies is needed to help people with T2D.

The project: Humans have special cells, called brown fat cells, which generate heat to maintain optimal body temperature. Dr. Morton has found that these cells use large amounts of glucose to drive this heat production, thus serving as a potential way to lower blood sugar, a key goal for any diabetes treatment. Dr. Morton is working to understand what role the brain plays in turning these brown fat cells on and off.

The potential outcome: This work has the potential to fundamentally advance our understanding of how the brain regulates blood sugar levels and to identify novel targets for the treatment of T2D.

Tracey Lynn McLaughlin, MD

Stanford University

Project: Role of altered nutrient transit and incretin hormones in glucose lowering after Roux-en-Y gastric bypass surgery

“This award is very important to me personally not only because the enteroinsular axis (gut-insulin-glucose metabolism) is a new kid on the block that requires rigorous physiologic studies in humans to better understand how it contributes to glucose metabolism, but also because the subjects who develop severe hypoglycemia after gastric bypass are largely ignored in society and there is no treatment for this devastating and very dangerous condition.”

The problem: Roux-en-Y gastric bypass (RYGB) surgery is the single-most effective treatment for type 2 diabetes, with persistent remission in 85% of cases. However, the underlying ways by which the surgery improves glucose control is not yet understood, limiting the ability to potentially mimic the surgery in a non-invasive way. Furthermore, a minority of RYGB patients develop severe, disabling, and life-threatening low-blood sugar, for which there is no current treatment.

The project: Utilizing a unique and rigorous human experimental model, the proposed research will attempt to gain a better understanding on how RYGB surgery improves glucose control. Dr. McLaughlin will also test a hypothesis which she believes could play an important role in the persistent low-blood sugar that is observed in some patients post-surgery.

The potential outcome: This research has the potential to identify novel molecules that could represent targets for new antidiabetic therapies. It is also an important step to identifying people at risk for low-blood sugar following RYGB and to develop postsurgical treatment strategies.

Rebekah J. Walker, PhD

Medical College of Wisconsin

Project: Lowering the impact of food insecurity in African Americans with type 2 diabetes

“I became interested in diabetes research during my doctoral training, and since that time have become passionate about addressing social determinants of health and health disparities, specifically in individuals with diabetes. Living in one of the most racially segregated cities in the nation, the burden to address the needs of individuals at particularly high risk of poor outcomes has become important to me both personally and professionally.”

The problem: Food insecurity is defined as the inability to or limitation in accessing nutritionally adequate food and may be one way to address increased diabetes risk in high-risk populations. Food insecure individuals with diabetes have worse diabetes outcomes and have more difficulty following a healthy diet compared to those who are not food insecure.

The project: Dr. Walker’s study will gather information to improve and then will test an intervention to improve blood sugar control, dietary intake, self-care management, and quality of life in food insecure African Americans with diabetes. The intervention will include weekly culturally appropriate food boxes mailed to the participants and telephone-delivered diabetes education and skills training. It will be one of the first studies focused on the unique needs of food insecure African American populations with diabetes using culturally tailored strategies.

The potential outcome: This study has the potential to guide and improve policies impacting low-income minorities with diabetes. In addition, Dr. Walker’s study will help determine if food supplementation is important in improving diabetes outcomes beyond diabetes education alone.

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• Open access
• Published: 05 October 2023

Precision subclassification of type 2 diabetes: a systematic review

• Shivani Misra   ORCID: orcid.org/0000-0003-2886-0726 1 , 2   na1 ,
• Robert Wagner   ORCID: orcid.org/0000-0002-6120-0191 3 , 4 , 5   na1 ,
• Bige Ozkan   ORCID: orcid.org/0000-0003-2745-1189 6 , 7 ,
• Martin Schön   ORCID: orcid.org/0000-0002-9224-4189 4 , 5 , 8 ,
• Magdalena Sevilla-Gonzalez   ORCID: orcid.org/0000-0001-6135-9998 9 , 10 , 11 ,
• Katsiaryna Prystupa   ORCID: orcid.org/0000-0003-3368-1028 4 , 5 ,
• Caroline C. Wang 6 ,
• Raymond J. Kreienkamp   ORCID: orcid.org/0000-0002-1683-323X 10 , 12 , 13 , 14 ,
• Sara J. Cromer 10 , 11 , 12 , 13 ,
• Mary R. Rooney   ORCID: orcid.org/0000-0002-5607-4848 6 , 15 ,
• Daisy Duan   ORCID: orcid.org/0000-0002-4392-3206 16 ,
• Anne Cathrine Baun Thuesen   ORCID: orcid.org/0000-0002-8639-9117 17 ,
• Amelia S. Wallace   ORCID: orcid.org/0000-0002-1466-3791 6 , 15 ,
• Aaron Leong 10 , 11 , 12 , 18 ,
• Aaron J. Deutsch   ORCID: orcid.org/0000-0001-6750-5335 10 , 11 , 12 , 13 ,
• Mette K. Andersen   ORCID: orcid.org/0000-0001-8227-1469 17 ,
• Liana K. Billings   ORCID: orcid.org/0000-0001-7991-3010 19 , 20 ,
• Robert H. Eckel 21 ,
• Wayne Huey-Herng Sheu 22 , 23 , 24 ,
• Torben Hansen   ORCID: orcid.org/0000-0001-8748-3831 17 ,
• Norbert Stefan   ORCID: orcid.org/0000-0002-2186-9595 5 , 25 , 26 ,
• Mark O. Goodarzi   ORCID: orcid.org/0000-0001-6364-5103 27 ,
• Debashree Ray   ORCID: orcid.org/0000-0002-0979-2935 15 , 28 ,
• Elizabeth Selvin   ORCID: orcid.org/0000-0001-6923-7151 6 , 15 ,
• Jose C. Florez 10 , 11 , 12 , 13 ,
• ADA/EASD PMDI ,
• James B. Meigs 10 , 11 , 18   na2 &
• Miriam S. Udler   ORCID: orcid.org/0000-0003-3824-9162 10 , 11 , 12 , 13   na2  

Communications Medicine volume  3 , Article number:  138 ( 2023 ) Cite this article

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• Body mass index
• Type 2 diabetes

Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.

We searched PubMed and Embase for publications that used ‘simple subclassification’ approaches using simple categorisation of clinical characteristics, or ‘complex subclassification’ approaches which used machine learning or ‘omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.

Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

Plain language summary

In people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they make. We looked at recent publications describing research in this area to see whether it is possible to separate people with type 2 diabetes into different subgroups and, if so, whether these groupings were useful for patients. We found that it is possible to group people with type 2 diabetes into different subgroups and being in one subgroup can be more strongly linked to the likelihood of developing complications over others. This might mean that in the future we can treat people in different subgroups differently in ways that improves their treatment and their health but it requires further study.

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Introduction

Type 2 diabetes is a global health problem posing substantial burdens on human health 1 . The diagnosis of type 2 diabetes is based on elevated blood glucose coupled with the absence of clinical features indicating alternative subtypes, such as type 1, monogenic, pancreatic or medication-induced diabetes 2 . A diagnosis of type 2 diabetes is generally the default or can be arrived at through exclusion of other types. Traditionally, most type 2 diabetes care guidelines have advocated treatment choice based on cost-effectiveness and side effects of specific medications, which have no relationship to underlying pathophysiology in the individual. More recent guidelines have suggested differential glucose-lowering therapies on the basis of higher body mass index (BMI) (favouring use of glucagon-like peptide analogue, GLP-1) or presence or absence of cardiovascular and/or renal disease and/or heart failure (favouring GLP-1 and/or sodium-glucose co-transporter 2, SGLT-2 inhibitors) 3 .

There is considerable heterogeneity in the clinical characteristics of patients with type 2 diabetes. Clinicians recognise that differences in degree of obesity or body fat distribution, age, dyslipidaemia or presence of metabolic syndrome can influence prognosis in diabetes and can be important considerations in treatment and management 4 , 5 , 6 . There is increasing awareness that type 2 diabetes heterogeneity may reflect differences in the underlying pathophysiology, environmental contributors, and the genetic risk of affected individuals. The mechanisms leading to the development of type 2 diabetes may differ from one individual to another and this could impact treatment and outcome.

Accurate characterisation of the heterogeneity in type 2 diabetes may help individualise care and improve outcomes. This goal has been realised in part for monogenic diabetes, where treatments can be tailored to genetic subtype to deliver precision care achieving better outcomes than standard care 7 . Given the complex pathophysiology and genetics of type 2 diabetes, applying precision medicine approaches is challenging. Critical to this endeavour is a better understanding of specific subtypes.

There are many studies of type 2 diabetes subtypes. The literature reflects diverse approaches based on the presence or absence of one or more simple clinical features or biomarkers and, more recently, sophisticated methods that deploy machine learning (ML) or use omics data. Classification approaches such as clustering methods to categorise this heterogeneity show inter-cluster differences in progression to complications or need for insulin treatment. These approaches consider clinical features at diagnosis 8 or clinical information combined with genetic data to characterise disease heterogeneity 9 , 10 . Simpler approaches are more easily implemented across all resource settings, while complex approaches may have greater precision in classifying heterogeneity. The breadth and scope of the evidence in favour of type 2 diabetes subclassification have not to date been thoroughly examined.

The Precision Medicine in Diabetes Initiative (PMDI) was established in 2018 by the American Diabetes Association (ADA) in partnership with the European Association for the Study of Diabetes (EASD). The ADA/EASD PMDI includes global thought leaders in precision diabetes medicine who are working to address the burgeoning need for better diabetes prevention and care through precision medicine 11 . This Systematic Review is written with the ADA/EASD PMDI as part of a comprehensive evidence evaluation in support of the 2nd International Consensus Report on Precision Diabetes Medicine 12 .

In this systematic review for the PMDI we aimed to provide a critical assessment of the evidence to date for type 2 diabetes subclassification using (i) simple approaches based on categorisation of clinical features, biomarkers, imaging, or other parameters, and (ii) complex subclassification approaches that use ML incorporating clinical data and/or genomic data. We aimed to identify areas where further research is needed with the goal to improve patient and health system outcomes in type 2 diabetes care.

Our analysis shows that many simple approaches to subclassification have been tried but none have been replicated and most are not associated with meaningful clinical outcomes. However, a more complex stratification, using machine learning applied to clinical variables, yielded reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches, however, require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

This systematic review was written and conducted in accordance with our pre-established protocol (PROSPERO ID CRD42022310539) and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (PRISMA) 13 . We systematically reviewed papers to address two research questions devised by an expert working group: 1) What are the main subtypes of type 2 diabetes defined using simple clinical criteria and/or routinely available laboratory tests (simple approaches), and 2) What subphenotypes of type 2 diabetes can be reproducibly identified using ML and/or genomics approaches (complex approaches)? Subsequently, we refer to the first question as simple approaches and the second question as complex approaches . The quality of each paper was reported, and the aggregate of data evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) system 14 .

Study eligibility criteria

We included English-language original research studies of all design types that analysed populations with prevalent or new-onset type 2 diabetes and attempted in some way to stratify or subgroup patients with type 2 diabetes. We used broad terms to identify stratification studies and all approaches to stratification (the exposure) were included (supplementary table  1 ). We excluded studies examining risk for the development of type 2 diabetes, use of glycaemic control (e.g. HbA1c strata) alone to stratify, studies of stratification in types of diabetes other than type 2 diabetes, and review articles or case reports.

For simple approaches the exposure was defined as any of the following; a routine blood or urine biomarker that was widely available in most clinic settings; a blood or urine biomarker that might not be routinely available now but could have the potential to become easily accessible; any routinely available imaging modality; any physiological assessment that could be undertaken in an outpatient setting or results from routinely available dynamic tests. The stratification approach was either a cut-off or categorisation based on one or more of the above or if an index, ratio, trend or other analysis was undertaken, it could be calculated without complex mathematics. Finally, all outcomes were accepted for example clinical characterisation of subgroups, association with specific biomarkers and association with complications or mortality.

For complex approaches, the exposure used was defined as any of the inputs for the simple approach outlined above and/or any form of genetic data. However, unlike the simple approach, the stratification approach either deployed ML approaches or used other complex statistical approaches for stratification. All outcomes were accepted, as above, for simple.

Literature search and selection strategy

PUBMED and EMBASE databases were searched from inception to May 2022 for relevant articles using a strategy devised by expert health sciences librarians ( supplementary methods ). We undertook independent searches for each systematic review question. From both searches, each abstract and subsequently, full text paper, was screened by two independent team members for eligibility. In addition to the initial exclusion criteria, at the full-text review stage, we further excluded studies where exposures were not clearly defined and/or if the data on outcomes of the stratification were not available in results or supplementary material. We also excluded studies where the only stratification modality was a measure of glycaemic control, as this itself provides the diagnosis of type 2 diabetes. In cases of disagreement between two reviewers, a third reviewer made the final decision. The process involved group-based discussions to resolve disagreements to ensure all decisions were made on the same grounds.

Data extraction

Data were manually extracted from each full-text paper by individual team members and cross-checked by an independent team member at the data synthesis stage. We extracted relevant data on study design (observational or clinical trial), analysis design (cross-sectional or prospective), study population characteristics, stratification method and results (exposure), outcomes, and study quality assessment. For population characteristics, we extracted data on whether the type 2 diabetes population was new-onset or prevalent, the sample size, ethnicity and gender, the duration of diabetes (for cross-sectional analysis) and duration of follow-up (for longitudinal follow-up). For exposures, we extracted the approach to stratification and the number and nature of subgroups identified. For outcomes, we documented the type of outcome studied and the findings according to stratified subgroup.

Data synthesis

Following full-text data extraction, we undertook a qualitative analysis of exposures (measures used to stratify individuals) for each systematic review question. For simple sub-classification approaches, we extracted the details of stratification criteria in each paper ( supplementary methods ), then categorised the exposure as blood/urine test, imaging, age). After data extraction, these exposures were further refined into subcategories based on common emerging themes (e.g., use of pancreatic autoantibodies, BMI categories, measures of beta-cell function, use of lipid profiles). For complex approaches, the exposure included both the input clinical and/or genetic data used and the ML approach to analysis (e.g., k-means, hierarchical clustering, latent-class analysis), deployed. In both reviews, outcomes were heterogeneous, so we broadly categorised them where possible. Due to the variability in exposures and outcomes, it was not possible to undertake formal meta-analyses of any outcome. All coding, categorisation and thematic synthesis was undertaken and agreed upon by at least three members of the research team.

Quality assessment

The GRADE system was used to assess the quality of the studies extracted 13 . At least two members assessed whether study exposures and outcomes were clearly defined, valid and reliable, and whether confounders were appropriately accounted and adjusted for. Disagreements were resolved by discussion between the joint first and senior authors during group discussion. Assessors evaluated study limitations, consistency of results, imprecision, and reporting bias to assign study-specific and overall GRADE certainty ratings as very low, low, moderate and high 15 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Search and screening for simple and complex systematic review questions

The first question examined simple stratification approaches using clinical variables that may reveal type 2 diabetes heterogeneity. A total of 6097 studies met the inclusion criteria and were screened (Fig.  1A ). Of these, 183 studies were included for full text data review, of which 132 studies were subsequently excluded. The most common reasons for exclusion at the full-text review stage were studies conducted in populations without prevalent or incident type 2 diabetes, study designs that used ML approaches or stratification approaches that used HbA1c or diabetes medications. In total, 51 “simple approach” studies underwent full-text data extraction.

A This shows the flow diagram for simple approaches to subclassification and B Complex approaches.

The second question aimed to identify papers with complex approaches, mostly ML-based strategies, to identify subgroups of patients with type 2 diabetes (Fig.  1B ). A total of 6639 studies were screened, of which 106 were found eligible for full-text review. The most common reasons for exclusion were study populations not comprising participants with type 2 diabetes or classification approaches not using ML. In total, 62 ‘complex’ studies underwent full-text data extraction.

Use of simple approaches to subclassify type 2 diabetes

Description of extracted studies.

The 51 studies using simple type 2 diabetes subclassification approaches incorporated 1,751,350 participants with prevalent or new-onset type 2 diabetes. Among them, 39% (20/51) of studies included participants of white European ancestry, 43% (22/51) incorporated exclusively participants from non-white European ancestries and 17% (9/51) included mixed ancestry groups (Supplementary Data  1 ). The majority of the studies (78%, 40/51) were conducted in populations with prevalent type 2 diabetes, and 22% (11/51) in new-onset type 2 diabetes. Approximately half the studies had a prospective design (25/51), the remaining half had a cross-sectional (26/51) design. For longitudinal studies, study follow-up periods ranged from <1 year to 22 years.

Studies included a wide range of exposures (Fig.  2 ) based on routine clinical measurements with standard cut-offs or groupings. These included assessment of individual routine clinic-based measurements (e.g., levels of BMI, or biomarker variability over time) or composite stratification incorporating two or more tiers of criteria (e.g. groupings combining one or more biomarkers or anthropometric measurements) including both routine and non-routine but clinically available tests, including oral glucose tolerance tests (OGTT) which, while a glycaemic test, also indirectly measures insulin resistance. The associations of stratified exposure characteristics were investigated with various outcomes: 1) measures of glycaemia, 2) clinical characteristics, 3) measures of diabetes progression such as time-to-insulin treatment or development of microvascular complications and 4) cardiovascular outcomes and/or mortality.

The figure summarises simple approaches that have been taken to subclassify type 2 diabetes and complex approaches. HbA1c glycated haemoglobin, BMI body mass index, GAD-65 glutamic acid decarboxylase-65 antibodies.

Description of categorised subgroups

Simple approaches to classification included use of lipid profiles ( n  = 8), BMI ( n  = 6), pancreatic beta-cell related measures ( n  = 6), pancreatic autoantibodies ( n  = 6), age at diagnosis ( n  = 2), OGTT data ( n  = 4), cardiovascular measures ( n  = 3), other biomarkers in urine or blood and alternative approaches ( n  = 5) (Table  1 ).

Different categories of triglycerides, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, atherogenic small dense lipoproteins with and without features of metabolic syndrome were used to stratify type 2 diabetes in eight studies. Cardiovascular disease (CVD) outcomes were assessed in 3/8 of the studies 16 , 17 , 18 which showed that a more atherogenic metric of the specific lipid exposure (e.g., higher LDL cholesterol) was associated with a greater frequency of CVD outcomes. Other outcomes included pulse wave velocity 19 or clinical characteristics; age, BMI, presence of metabolic syndrome in specific subgroups.

The six studies assessing pancreatic autoantibodies focused on glutamic acid decarboxylase 65 (GAD-65) levels. Studies used positive versus negative status or high versus low titre, and one study sub-stratified by age. Outcomes included time-to-insulin treatment 20 , 21 , associations with other clinical characteristics such as lipid profiles, BMI and blood pressure 22 , 23 , 24 and measures of beta-cell function. There was no consistency in study design and most were observational with low to moderate evidence grade; two studies showed that GAD-65 positivity was associated with faster time-to-insulin treatment 20 , 21 .

Patients with type 2 diabetes were stratified according to their BMI in six studies, either by BMI alone ( n  = 5) or BMI in combination with HbA1c. The number of BMI categories varied between two and six in the identified studies. The association between BMI and glycaemic outcomes (change in HbA1c from baseline) was assessed in four studies either as primary or secondary outcomes 6 , 25 , 26 . We graded the quality of evidence as very low to moderate, and no consistency of effect was observed across all studies. In one secondary analysis of a randomised control trial, higher BMI at baseline was associated with faster progression to adverse renal outcomes, however, this was not replicated in any other study 27 .

Age at diagnosis was assessed as a stratification tool in two studies; younger age (mean age 33 years) was associated with higher rates of proliferative retinopathy in an observational study with 12 months follow-up versus older age (mean 50 years) 4 . In a second study, patients aged 60–75 versus those >75 years had a high risk of CVD and mortality when stratified by cholesterol levels 6 . Neither study was replicated to confirm findings.

Four studies used results from oral glucose tolerance tests (OGTT) as exposures. The specific stratification approach applied to OGTT profiles was different in each study and based on cut-offs of fasting glucose levels, glucose gradients after stimulation and responses to different drug treatments. Outcomes included clamp-derived insulin sensitivity and differences in the shape of glucose profiles between youths and adults 28 .

Measures of estimated beta-cell function were assessed in six studies including C-peptide levels and homoeostasis model assessment-2 indices for beta-cell function (HOMA2-B) or insulin resistance (HOMA2-IR), which require measurement of fasting insulin and glucose levels. C-peptide was defined using variable cut-offs. Outcomes included clinical phenotype data, response to medication, and microvascular or macrovascular complications. For example, hyperinsulinaemia and higher urine C-peptide were independently associated with cardiovascular disease.

Other exposure variables included less routine biomarkers, pulse wave velocity, ketosis/ketoacidosis and other disease indices, but these were each single studies precluding grouping. All data are summarised in Table  1 .

Use of complex approaches to subclassify type 2 diabetes

There were 62 studies of complex/ML approaches to type 2 diabetes subclassification in a total of 793,291 participants (Table  2 ). Over half of the studies included non-European ancestry in relevant proportions (>20%). Only ~30% (19 out of 62) of the studies analysed participants with new-onset diabetes. Mean diabetes duration ranged from recent onset (within 1 year) to over 36 years. Most data were from observational studies (46 out of 62), with some post-hoc analyses of clinical trials (10), survey data (4) and mixed study types (2). Half of the studies had prospective design (31 out of 62) with a mean follow-up duration ranging from 1 year to 11.6 years. K-means clustering was the most applied ML approach (30 out of 62). Eight studies used established centroids 8 to assign participants to clusters. Two studies decomposed combinations of genetic variants and their association with clinical and laboratory phenotypes into genotype-phenotype clusters by using Bayesian non-negative matrix factorisation.

Description of the categorised subgroups

Following the seminal work by Ahlqvist et al. 8 , multiple studies used the variables derived at time of diabetes diagnosis: age, HbA1c, BMI, HOMA2-B, HOMA2-IR and GAD-65 antibody (Table  2 ). The majority of these studies employed C-peptide-based homoeostasis model assessment indices (HOMA, or its updated variant, HOMA2, using fasting insulin and glucose), as surrogates for insulin resistance (HOMA2-IR) and insulin secretion (HOMA2-B). In different contexts and populations, 22 studies replicated identification of the four non-autoimmune diabetes subtypes first described by Ahlqvist et al. 8 : severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). The subset of studies including measurements of GAD antibody also identified the fifth cluster, severe autoimmune diabetes (SAID). Associations of these subtypes with clinical outcomes, including glycaemia, microvascular and macrovascular outcomes, and death, were replicated in 12 studies (Table  3 ).

Thirteen additional papers used variations of the original set of variables from Ahlqvist et al. 8 by substituting HOMA with C-peptide, adding lipid traits, e.g. HDL-cholesterol, or approximating the clusters from different/simplified variable sets by applying advanced statistical learning approaches such as self-normalising neural networks. These approaches identified some type 2 diabetes subgroups resembling the clusters from Ahlqvist et al. and also novel subgroups related to the additional variables (Fig.  3 ). Several of the novel subgroups were associated with clinical outcomes. However, these findings have not been replicated in other studies (Table  2 ).

Clustering variables denoted in blue are consistent across the different studies, those in black are unique to the particular study outlined. A greyed-out box indicates that the indicated diabetes cluster was replicated from the Ahlqvist study, a dark blue box indicates a new diabetes cluster. GAD, glutamic decarboxylase antibody; BMI, body mass index; HDL, high-density lipoprotein cholesterol; HOMA2-IR/B, homoeostasis model assessment-2 insulin resistance/beta cell function. SAID, severe autoimmune diabetes; SIDD, severe insulin-deficient diabetes; SIRD, severe insulin resistant diabetes; MOD, mild obesity-related diabetes; MARD, mild age-related diabetes.

Additional papers ( n  = 27) assessed various sets of phenotypic inputs for ML approaches. Grouped into five categories of inputs, studies identified many subtypes and associations with clinical outcomes, however, they all lacked replication (Table  2 ). Four papers applied complex ML methods to a set of less than ten clinical variables such as systolic blood pressure, waist circumference, BMI, fasting plasma glucose, and age at diabetes diagnosis, and resulting subgroups were variably associated with outcomes, such as mortality. Eleven studies used a larger set of more than ten clinical features as inputs for classification, including data from electronic health records 29 , 30 , and identified subgroups variably associated with clinical outcomes, including risk of cardiovascular disease. Two other studies specifically employed cardiovascular traits, including ECG 31 and echocardiographic 32 for ML algorithm inputs, and each identified subgroups with different associations with risk of cardiovascular disease. Finally, four studies involved inputs of change of glycaemic variables (HbA1c trajectories, glycaemia during a mixed meal test, continuous glucose monitoring features) 33 , 34 , 35 , one study focused on fasting GLP-1, GIP and ghrelin levels 36 , and two studies focused on behavioural traits such as novelty seeking, harm avoidance, and hospital anxiety and depression scale.

Human genetic risk information is rapidly penetrating clinical medicine. Two sets of papers utilised genomic data to identify diabetes subtypes, either in the form of inherited common genetic variation 10 , 37 or gene expression data from muscle biopsies 38 (Table  2 ). The first approach clustered genetic variants with clinical traits associated with type 2 diabetes to identify subsets of variants predicted to act in shared mechanistic processes. Using these sets of genetic variants, process-specific or partitioned polygenic scores were constructed in individuals with type 2 diabetes and were associated with differences in clinical features and prevalence of metabolic outcomes, with replication across multiple cohorts. The muscle gene expression study has not been replicated. Overall, half of the studies had cross-sectional designs, and the other half involved prospective follow-up (Table  2 ).

For simple approaches, of the 51 studies assessed, 55% were quality graded as very low-, or low-GRADE certainty, 45% had moderate certainty and none achieved high certainty. For complex approaches, around 70% of the studies had moderate evidence certainty. In both approaches, the majority of the studies had moderate or lower GRADE certainty on account of the (1) study design not addressing precision medicine objectives (not an RCT testing differential treatment effects in subclassified type 2 diabetes groups), (2) lack of a meaningful clinical outcome (i.e. although subgroups of type 2 diabetes were found, the measured outcome had little clinical significance because the study was not designed to study this) (3) Confidence in the findings were low due to small sample sizes, lack of replication or lack of diversity of studied subgroups and (4) the potential for bias was large due to lack of adjustment for possible confounders.

Summary of findings

This systematic review analysed two broad approaches to the subclassification of type 2 diabetes to identify clinically meaningful subtypes that may advance precision diagnostics. We found many simple stratification approaches using, for example, clinical features such as BMI, age at diagnosis, and lipid levels, but none had been replicated and many lacked associations with clinical outcomes. Complex stratification models using ML approaches with and without genetic data showed reproducible subtypes of type 2 diabetes associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

Simple approaches to subclassification included urine and blood biomarkers, anthropometric measures, clinical data such as age at diagnosis, surrogate beta-cell metrics derived from blood C-peptide or insulin along with other less diabetes-related biomarkers such as bilirubin levels or pulse wave velocity. Approaches to subclassification were diverse. Some studies dichotomised continuous variables based on clinical cut-points. Other studies used a composite exposure (two or more criteria each with cut-points) or analysed changes in continuous variables over time e.g. change in eGFR over time.

The study designs, specific cut-offs and outcomes were heterogenous, and no studies met high-quality GRADE certainty. No study evaluating a simple approach to type 2 diabetes subtyping has been adequately reproduced, although some studies identified biologically plausible subgroups. For example, subclassifications derived using BMI, beta-cell function, lipid profiles and age appeared to be associated with some outcomes which could be helpful in clinical practice. These potential subclassifications need to be replicated in better-designed studies (see section on additional supporting literature). Other evidence not included in our systematic review (either due to the study population including people without diabetes or the analysis was only performed in people with the exposure without a comparison group), support the role of simple variables in stratifying diabetes; for example, younger age at diagnosis is reproducibly associated with worse cardiorenal outcomes in a number of studies 39 .

Machine learning approaches yielded some reproducible subtypes of type 2 diabetes using a variety of clinical and genetic variables. The best-replicated subtypes were the clusters first described by Ahlqvist et al. 8 , which were replicated in 22 studies, including ~88,000 individuals of diverse ancestry. There also was replication of genetic subtypes of type 2 diabetes from Udler et al. 10 with associations with clinical features seen in multiple cohorts across almost 454,000 individuals 36 . However, the latter associations involved small absolute effects with unclear clinical utility for individual patient management, and studies were restricted to individuals of European ancestry. While there was replication of the clusters from Ahlqvist et al. across studies, the generated clusters appeared to be dependent on the characteristics of the underlying populations, especially factors such as distribution of ancestry, age, duration of diabetes, anthropometric trait variability as in BMI, and the variety of variable terms included in learning models. Nevertheless, at least some of the resulting subtypes appeared to be robust to differences in specific ML method, input variables, and populations (Fig.  3 ).

Many of the input variables for the complex ML subtyping approaches were also used in studies involving simple approaches to subclassification, recapitulating the biological plausibility of specific clustering variables in defining type 2 diabetes subtypes. One study directly compared a simple clinical approach to the clustering approach from Ahlqvist et al. 8 and found that simple single clinical measures analysed in a quantitative (rather than categorical) framework could better predict relevant clinical outcomes, such as incidence of chronic kidney disease and glycaemic response to medications 40 . Thus, further research is needed to determine whether assigning a patient to one of the clusters from Ahlqvist et al. 8 offers additional clinical benefit beyond evaluation of simple clinical measures and also beyond current standard of care. For example, high quality randomised controlled trial evidence is needed to demonstrate that knowledge of a patient’s clinical or genetic cluster membership could meaningfully guide treatment and/or clinical care and improve outcomes.

Study quality

No studies included in our systematic review had above moderate certainty of evidence. Some strengths of included studies were the large sample sizes, the diversity of variables considered, and inclusion of both prevalent and new-onset cases of type 2 diabetes. However, the varied study designs and lack of replication limits our ability to draw firm conclusions about the most effective approaches to subclassification. Most variables used for subclassification capture momentary metabolic states, which limits their long-term utility as cluster assignment is likely to change over time 41 , 42 . Most studies were retrospective analyses of established cohorts, and there were, at the time of the search, no data available involving subtype-stratified clinical trials or real world implementation of approaches. Finally, most studies focused on European-ancestry populations, and the clinical value of these approaches may vary across different ancestries. While East Asian ancestries had representation in some studies, research in Black, South Asian and Hispanic populations remains sparse. This is particularly important, as four out of five people with type 2 diabetes come from marginalised groups or live in low- or middle-income countries. Future precision diagnostic interventions should address and narrow inequalities.

Additional supporting literature

Since our literature search was conducted, four new publications have advanced our understanding of type 2 diabetes subclassification.

Two recent studies applied ML approaches to stratify diabetes heterogeneity, both considering continuous approaches rather than with discrete clusters 43 , 44 . Nair et al. used a non-linear transformation and visualisation of nine variables onto a tree-like structure 44 and with replication in two large datasets. This approach linked underlying disease heterogeneity to risk of complications; those at risk of cardiovascular disease had a different phenotype to those with microvascular complications and to drug response and demonstrated associations of gradients across the tree using genetic process-specific scores from Udler et al. 10 Wesolowska-Andersen et al. performed soft-clustering from 32 clinical variables which yielded 4 diabetes archetypes comprising a third of the study population. The remaining study population was deemed as mixed-phenotype. This study has not been replicated 43 . A third study re-identified the genetic subtypes and their clinical associations from Udler et al. 45 .

Additionally, one of the first clinical trials to assess precision medicine approaches for diabetes management was published after our literature search. The TriMaster Study tested dichotomised BMI and eGFR strata in a three-period crossover trial using three pharmacologic interventions with the primary hypothesis being stratum-specific differences in HbA1c 46 . Participants with obesity (BMI > 30 kg/m 2 ) showed a glycaemic benefit on pioglitazone versus sitagliptin and participants with lower eGFR (60–90 ml/min/1.73 m 2 ) responded with lower HbA1c to sitagliptin as compared to canagliflozin. In a secondary analysis, drug-choice corresponding to patient preferences yielded lower glycemia than a random allocation, suggesting that listening to patients is critical in informing therapeutic decisions 47 . Ramifications of this study are limited by the non-comparable pharmacologic doses used, and the primary focus on glycaemia which may not be indicative of long-term therapeutic success and/or prevention of complications. Yet these studies have generated higher quality evidence linking type 2 diabetes heterogeneity to treatment and disease outcomes. It remains to be seen if these can be replicated in other ancestries and translated into ‘usable products’ for healthcare professionals.

It is worth noting that ketosis-prone type 2 diabetes, an established type 2 diabetes subtype, was not captured adequately in our systematic review: only one study included ketosis-prone type 2 diabetes as an exposure 48 . Study designs for ketosis-prone type 2 diabetes were usually analyses of cohorts with diabetic ketoacidosis at presentation with type 1 diabetes as the outcome, rather than as an exposure in people with type 2 diabetes. Since our search was designed to identify studies stratifying type 2 diabetes, this literature was not captured. Like many other ‘simple’ criteria for classification, the characteristics of people with diabetic ketoacidosis at presentation of type 2 diabetes have been studied, but with few prospective studies that have been replicated 49 .

Age at diagnosis as a simple approach to stratification also did not feature strongly in our search results. The body of literature that outlines higher risk of microvascular or macrovascular complications in early-onset type 2 diabetes has focussed on comparing people with type 2 diabetes to those without diabetes in different age groups 39 , 50 or studied cohorts of early-onset cases in isolation 51 and, thus, would not have been captured in our search strategy. Recent epidemiological studies have compared outcomes between early and late age onset strata 52 , 53 showcasing higher risks of cardiorenal outcomes with early age at onset, but these were retrospective analyses of health record databases, potentially confounded by age-related risk of complications and duration of diabetes. To move forward, prospective studies stratifying different interventions (e.g., tighter treatment targets or better cardiovascular risk reduction) in those diagnosed at younger age, are needed.

Findings in context

We found that simple features have not been precisely and reproducibly evaluated to a high enough standard to subclassify type 2 diabetes into subtypes. This is not surprising, as many studies were not necessarily conducted for the purpose of ‘precision diagnosis’, but rather as studies of clinical phenotypes spanning a time period that preceded the current research focus on precision medicine. It is important to re-emphasise that many of the simple clinical criteria studied, do have other bodies of evidence supporting associations with outcomes, like age -at -diagnosis. While these studies have set the scene, the field needs more robust evidence.

‘Complex’ methods for diabetes subclassification have shown better reproducibility, have been linked to a variety of meaningful clinical outcomes more consistently, and more recently have been able to demonstrate differential treatment responses related to stratification.

What do these findings mean for a precision medicine approach to type 2 diabetes diagnosis? Ideally, subclassification strategies should be deployed at diagnosis of type 2 diabetes on the basis of measured clinical characteristics such that people in different subgroups of type 2 diabetes could be treated differently. One key question is whether such efforts would cost-effectively improve clinical outcomes, compared to the current standard of care. However, another more fundamental question is whether subclassification approaches at diagnosis alone are enough? For example, another approach may be to iteratively subclassify longitudinal disease trajectories. Such an approach is supported by studies that have shown cluster-based assignments of type 2 diabetes at diagnosis are not robust and may change over time 54 . It may be argued that subclassification at one-time point is overly simplistic and should be regularly reviewed based on trajectory.

Irrespective of the subclassification approach studied, they need replication in independent datasets, assessment in diverse populations, in people with both new-onset and prevalent diabetes, and investigation using prospective data, ideally in the form of randomised clinical trials. Clinical trials of treatment approaches tailored to diabetes subtypes will be necessary to understand the clinical benefits of clinical subtyping. Ideally, sub-phenotyping should lead to benefits for patients in real-world clinical settings. Conducting these studies will be challenging due to the necessity for extensive follow-up, large sample sizes, and substantial resource requirements. There is a pressing need for innovative strategies to generate high-quality evidence on treatment options tailored to specific diabetes subtypes in diverse populations. These data will be critical to determine generalisability of findings and amenability for clinical translation including in resource-constrained settings.

Clinical applicability

The current evidence supports distinguishable subtypes of type 2 diabetes and that these subtypes are associated with variation in clinical outcomes. However, the very low to moderate quality of existing studies and the need for replication in ancestry-diverse studies make it difficult to identify a strongly evidence-based, universally applicable approach.

The most clinically valuable methods are likely to be those that are easy and inexpensive to implement. For more complex approaches, computer decision support tools will need to be developed and assessed for feasibility and utility. Although the evidence supporting complex approaches has leap-frogged the evidence in favour of more simplified approaches, there is still likely a place for simple approaches that can be more accessible at diverse clinical interfaces. Meanwhile how cluster assignment could be translated into actionable data for the individual remains unclear; will for example, a given person with type 2 diabetes exist in a distinct subgroup with associated outcomes or will the subtype of type 2 diabetes have associated probabilities or risks of certain outcomes? While stratifying people with type 2 diabetes into discrete subtypes might result in information loss, compared to continuous risk modelling 40 , discrete clusters might inform clinical decisions 42 .

Limitations

The limitations of this review reflect the limitations of the literature. To manage the breadth of literature analysed in this systematic review, focussed on genomic data and did not include proteomic or metabolomic data as these are potentially more premature for clinical use. We also did not include studies on participants at risk of type 2 diabetes, although we recognise that a body of evidence is emerging to stratify type 2 diabetes incidence risk using multiple approaches that are similar to those for established type 2 diabetes. Since we focused on studies that attempted to subgroup type 2 diabetes, we also did not capture analyses of independent cohorts with a particular type 2 diabetes phenotype at baseline, for example, studies of young people with type 2 diabetes or those with ketosis-prone type 2 diabetes, as outlined.

Next steps and recommendations

Future research should aim to identify and validate clinically useful and cost-effective methods for type 2 diabetes subclassification that can be applied across diverse populations. Such research will involve replication of a given approach in independent datasets, including from diverse ancestral populations, to ensure generalisability that doesn’t widen health disparities. For simple stratification approaches, there is still much that can be done—agreement on standardised study designs for precision diagnostics studies could be a first step. For ML requiring real-time computation, the development of strategies to overcome local resource constraints in implementing these methods could be explored.

In this first systematic review of the evidence underpinning type 2 diabetes diagnostic subclassification, multiple approaches were identified. Among them are strategies that used simple criteria based on fundamental categorisation of mostly routine measures, and complex approaches with multi-trait or genetic inputs that required ML or other computation. While simple approaches are more easily deployed, the study designs and level of evidence currently limits any firm conclusions regarding the utility of such approaches. The clinical variables and data incorporated into ‘complex’ approaches have yielded reproducible subclassifications and a growing body of evidence supports clinically meaningful associations of subtypes with outcomes and treatment responses. This is a rapidly evolving field with higher quality evidence emerging. It will be crucial to develop interventions that target diverse populations and be feasible in all resource settings to prevent widening existing inequalities in the precision medicine era of diabetes care.

Data availability

The extracted data from full-text articles included in this systematic review are available in Supplementary Data  1 .

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Acknowledgements

No specific funding was received to undertake this body of work. The authors acknowledge individual and institutional funding as follows: S.M. has a personal award from Wellcome Trust Career Development scheme (223024/Z/21/Z) and holds Institutional funds from the NIHR Biomedical Research Centre Funding Scheme; B.O. is supported by American Heart Association grant (20SFRN35120152); M.S.G. is supported by the American Diabetes Association (9-22-PDFPM-04) and NIH (5UM1DK078616-14); R.J.K. is supported by NIGMS T32GM774844 and Pediatric Endocrine Society Rising Star Award; SJC is supported by a Junior Faculty Development Award from the American Diabetes Association (7-21-JDFM-005); D.D. is supported by NIH grant K23DK133690; A.C.B.T., M.A. and T.H. acknowledge that The Novo Nordisk Foundation Center for Basic Metabolic Research is supported by and unrestricted grant from the Novo Nordisk Foundation (NNF18CC0034900); A.W. is supported by NIH/NHLBI grant T32HL007024; A.L. is supported by grant 2020096 from the Doris Duke Foundation and the American Diabetes Association Grant 7-22-ICTSPM-23; A.J.D. is supported by NIH/NIDDK grant T32DK007028; W.H.H.S. obtained funding from MOST, Taiwan (MOST 107-2314-B-075A-001 -MY3 and by MOST 109-2321-B-075A-001). M.G. is supported by the Eris M. Field Chair in Diabetes Research and NIH grant P30-DK063491; D.R. is supported by NIH/NIDDK grant R21DK125888, and other grants from the NIH; E.S. is supported by NIH/NHLBI grant K24 HL152440 and other grants from the NIH; J.C.F. is supported by NIH K24 HL157960; J.B.M. reports funding from NIH U01 DK078616, R01 HL151855; M.U. is supported by an NIH K23DK114551. The ADA/EASD Precision Diabetes Medicine Initiative, within which this work was conducted, has received the following support: The Covidence licence was funded by Lund University (Sweden) for which technical support was provided by Maria Björklund and Krister Aronsson (Faculty of Medicine Library, Lund University, Sweden). Administrative support was provided by Lund University (Malmö, Sweden), University of Chicago (IL, USA), and the American Diabetes Association (Washington D.C., USA). The Novo Nordisk Foundation (Hellerup, Denmark) provided grant support for in-person writing group meetings (PI: L Phillipson, University of Chicago, IL).

Author information

These authors contributed equally: Shivani Misra, Robert Wagner.

These authors jointly supervised this work: James B. Meigs, Miriam S. Udler.

Authors and Affiliations

Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Shivani Misra

Department of Diabetes and Endocrinology, Imperial College Healthcare NHS Trust, London, UK

Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany

Robert Wagner

Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf’m Hennekamp 65, 40225, Düsseldorf, Germany

Robert Wagner, Martin Schön, Katsiaryna Prystupa & Martin Schön

German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany

Robert Wagner, Martin Schön, Katsiaryna Prystupa, Norbert Stefan, Martin Schön & Norbert Stefan

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Bige Ozkan, Caroline C. Wang, Mary R. Rooney, Amelia S. Wallace, Elizabeth Selvin & Bige Ozkan

Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, MD, USA

Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia

Martin Schön

Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA

Magdalena Sevilla-Gonzalez & Magdalena Sevilla-Gonzalez

Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA

Magdalena Sevilla-Gonzalez, Raymond J. Kreienkamp, Sara J. Cromer, Aaron Leong, Aaron J. Deutsch, Jose C. Florez, James B. Meigs & Miriam S. Udler

Department of Medicine, Harvard Medical School, Boston, MA, USA

Magdalena Sevilla-Gonzalez, Sara J. Cromer, Aaron Leong, Aaron J. Deutsch, Jose C. Florez, Sara J. Cromer, Magdalena Sevilla-Gonzalez, Tinashe Chikowore, Aaron J. Deutsch, Aaron Leong, Camille E. Powe, Jose C. Florez, James B. Meigs, Miriam S. Udler, James B. Meigs & Miriam S. Udler

Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA

Raymond J. Kreienkamp, Sara J. Cromer, Aaron Leong, Aaron J. Deutsch, Jose C. Florez & Miriam S. Udler

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

Raymond J. Kreienkamp, Sara J. Cromer, Aaron J. Deutsch, Jose C. Florez, Jordi Merino, Raymond J. Kreienkamp, Aaron J. Deutsch, Jose C. Florez, Miriam S. Udler & Miriam S. Udler

Department of Pediatrics, Division of Endocrinology, Boston Children’s Hospital, Boston, MA, USA

Raymond J. Kreienkamp & Raymond J. Kreienkamp

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Mary R. Rooney, Amelia S. Wallace, Debashree Ray, Elizabeth Selvin & Caroline C. Wang

Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Anne Cathrine Baun Thuesen, Mette K. Andersen, Torben Hansen, Jordi Merino, Anne Cathrine B. Thuesen, Christoffer Clemmensen, Mariam Nakabuye & Ruth J. F. Loos

Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St 16th Floor, Boston, MA, USA

Aaron Leong & James B. Meigs

Division of Endocrinology, Diabetes and Metabolism, NorthShore University Health System, Skokie, IL, USA

Liana K. Billings

Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA

Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine, Aurora, CO, USA

Robert H. Eckel

Institute of Molecular and Genomic Medicine, National Health Research Institute, Miaoli County, Taiwan, ROC

Wayne Huey-Herng Sheu

Division of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan, ROC

Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan, ROC

Wayne Huey-Herng Sheu & Wayne Huey-Herng Sheu

University Hospital of Tübingen, Tübingen, Germany

Norbert Stefan & Norbert Stefan

Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany

Norbert Stefan

Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Mark O. Goodarzi

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Debashree Ray

Division of Preventative Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

Deirdre K. Tobias & Vanessa Santhakumar

Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Deirdre K. Tobias, Zhila Semnani-Azad, Marta Guasch-Ferré & Paul W. Franks

Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA

Jordi Merino, Sara J. Cromer, Raymond J. Kreienkamp, Aaron Leong, Camille E. Powe, Jose C. Florez, Marie-France Hivert & Miriam S. Udler

Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden

Abrar Ahmad, Monika Dudenhöffer-Pfeifer, Hugo Fitipaldi, Hugo Pomares-Millan, Maria F. Gomez & Paul W. Franks

Department of Obstetrics and Gynaecology, the Rosie Hospital, Cambridge, UK

Catherine Aiken

NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK

Departments of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada

Jamie L. Benham

Department of Molecular Genetics, Madras Diabetes Research Foundation, Chennai, India

Dhanasekaran Bodhini

Division of Pediatric Endocrinology, Department of Pediatrics, Saint Louis University School of Medicine, SSM Health Cardinal Glennon Children’s Hospital, St. Louis, MO, USA

Amy L. Clark

Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, Devon, UK

Kevin Colclough, Alice Hughes, Kashyap Amratlal Patel, Katherine Young, Angus G. Jones, Elisa de Franco, Sarah E. Flanagan, Andrew McGovern, John M. Dennis, Andrew T. Hattersley & Richard Oram

CIBER-BBN, ISCIII, Madrid, Spain

Rosa Corcoy

Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain

Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain

Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA

Sara J. Cromer, Raymond J. Kreienkamp, Magdalena Sevilla-Gonzalez, Aaron J. Deutsch, Camille E. Powe, Jose C. Florez & Miriam S. Udler

Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA

Jamie L. Felton, Linda A. DiMeglio, Carmella Evans-Molina, Arianna Harris-Kawano, Heba M. Ismail, Dianna Perez, Gabriela S. F. Monaco & Emily K. Sims

Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA

Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA

Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA

Ellen C. Francis

University Hospital Leuven, Leuven, Belgium

Pieter Gillard & Chantal Mathieu

Department of Nutrition, Université de Montréal, Montreal, QC, Canada

Véronique Gingras

Research Center, Sainte-Justine University Hospital Center, Montreal, QC, Canada

Department of Pediatrics, Erasmus Medical Center, Rotterdam, The Netherlands

Romy Gaillard

Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK

Eram Haider, Robert Massey, Adem Y. Dawed & Ewan R. Pearson

Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA

Jennifer M. Ikle & Anna L. Gloyn

Stanford Diabetes Research Center, Stanford School of Medicine, Stanford University, Stanford, CA, USA

University of Florida, Gainesville, FL, USA

Laura M. Jacobsen

Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Anna R. Kahkoska

Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland

Jarno L. T. Kettunen & Tiinamaija Tuomi

Folkhalsan Research Center, Helsinki, Finland

Jarno L. T. Kettunen

Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland

Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Lee-Ling Lim

Asia Diabetes Foundation, Hong Kong SAR, China

Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China

Lee-Ling Lim, Claudia Ha-ting Tam, Chuiguo Huang, Gechang Yu, Yingchai Zhang & Ronald C. W. Ma

Departments of Pediatrics and Clinical Genetics, Kuopio University Hospital, Kuopio, Finland

Jonna M. E. Männistö

Department of Medicine, University of Eastern Finland, Kuopio, Finland

Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Niamh-Maire Mclennan, Rebecca M. Reynolds & Robert K. Semple

Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA

Rachel G. Miller & Tina Costacou

Metabolic Disease Unit, University Hospital of Padova, Padova, Italy

Mario Luca Morieri

Department of Medicine, University of Padova, Padova, Italy

Department of Orthopedics, Zuyderland Medical Center, Sittard-Geleen, The Netherlands

Jasper Most

Departments of Pediatrics and Medicine, University of Chicago, Chicago, IL, USA

Rochelle N. Naylor

Department of Medicine, Johns Hopkins University, Baltimore, MD, USA

Scott J. Pilla, Sarah Kanbour, Sudipa Sarkar & Nestoras Mathioudakis

Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA

Scott J. Pilla

Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA

Sridaran Raghaven

Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA

Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway

Pernille Svalastoga, Ingvild Aukrust, Janne Molnes & Pål Rasmus Njølstad

Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway

Pernille Svalastoga & Pål Rasmus Njølstad

Eastern Health Clinical School, Monash University, Melbourne, VIC, Australia

Wubet Worku Takele, Gebresilasea Gendisha Ukke & Siew S. Lim

Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China

Claudia Ha-ting Tam, Chuiguo Huang, Gechang Yu, Yingchai Zhang & Ronald C. W. Ma

Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China

Claudia Ha-ting Tam & Ronald C. W. Ma

Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA

Mustafa Tosur & Maria J. Redondo

Division of Pediatric Diabetes and Endocrinology, Texas Children’s Hospital, Houston, TX, USA

Mustafa Tosur, Marzhan Urazbayeva & Maria J. Redondo

Children’s Nutrition Research Center, USDA/ARS, Houston, TX, USA

Mustafa Tosur

Stanford University School of Medicine, Stanford, CA, USA

Jessie J. Wong & Korey K. Hood

Internal Medicine, University of Manitoba, Winnipeg, MB, Canada

Jennifer M. Yamamoto

Department of Diabetology, APHP, Paris, France

Chloé Amouyal

Sorbonne Université, INSERM, NutriOmic team, Paris, France

Department of Nutrition, Dietetics and Food, Monash University, Melbourne, VIC, Australia

Maxine P. Bonham & Gloria K. W. Leung

Monash Centre for Health Research and Implementation, Monash University, Clayton, VIC, Australia

Mingling Chen

Health Management Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China

Feifei Cheng

MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Tinashe Chikowore

Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA

Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Department of Women and Children’s health, King’s College London, London, UK

Sian C. Chivers & Sara L. White

Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Dana Dabelea, Kristen Boyle & Wei Perng

Section of Adult and Pediatric Endocrinology, Diabetes and Metabolism, Kovler Diabetes Center, University of Chicago, Chicago, IL, USA

Laura T. Dickens

Department of Pediatrics, Riley Hospital for Children, Indiana University School of Medicine, Indianapolis, IN, USA

Linda A. DiMeglio

Richard L. Roudebush VAMC, Indianapolis, IN, USA

Carmella Evans-Molina

Biomedical Research Institute Girona, IdIBGi, Girona, Spain

María Mercè Fernández-Balsells

Diabetes, Endocrinology and Nutrition Unit Girona, University Hospital Dr Josep Trueta, Girona, Spain

Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA

Stephanie L. Fitzpatrick

University of California at San Francisco, Department of Pediatrics, Diabetes Center, San Francisco, CA, USA

Stephen E. Gitelman

Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia

Jessica A. Grieger, Nahal Habibi, Kai Liu, Maleesa Pathirana & Alejandra Quinteros

Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia

Jessica A. Grieger, Nahal Habibi, Maleesa Pathirana & Shao J. Zhou

Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 1014, Copenhagen, Denmark

Marta Guasch-Ferré

Division of Endocrinology and Diabetes, Department of Pediatrics, Sanford Children’s Hospital, Sioux Falls, SD, USA

Benjamin Hoag

University of South Dakota, School of Medicine, E Clark St, Vermillion, SD, USA

Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Randi K. Johnson & Maggie A. Stanislawski

Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA

Randi K. Johnson

Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK

Angus G. Jones, Andrew T. Hattersley & Richard Oram

Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK

Robert W. Koivula, Katharine R. Owen & Paul W. Franks

Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA

UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA

Ingrid M. Libman

Center for Translational Immunology, Benaroya Research Institute, Seattle, WA, USA

S. Alice Long

Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

William L. Lowe Jr.

Department of Pathology & Molecular Medicine, McMaster University, Hamilton, ON, Canada

Robert W. Morton

Population Health Research Institute, Hamilton, ON, Canada

Robert W. Morton, Russell de Souza & Diana Sherifali

Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Tuborg Havnevej 19, 2900, Hellerup, Denmark

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Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa

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Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

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Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA

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Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinology, KU Leuven, Leuven, Belgium

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School of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK

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Department of Obstetrics, Gynecology, and Reproductive Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

Camille E. Powe

Sanford Children’s Specialty Clinic, Sioux Falls, SD, USA

Rashmi Jain

Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA

Rashmi Jain & Kurt Griffin

Centre for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark

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Institute for Sports and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark

Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA

AMAN Hospital, Doha, Qatar

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Divsion of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan, ROC

Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA, USA

Cate Speake

Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

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Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark

University of Newcastle, Newcastle upon Tyne, UK

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Section of Genetics and Epidemiology, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA

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Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, The Netherlands

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Department of Endocrinology, University Hospitals Leuven, Leuven, Belgium

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Sorbonne University, Inserm U938, Saint-Antoine Research Centre, Institute of Cardiometabolism and Nutrition, Paris, 75012, France

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Walter and Eliza Hall Institute, Parkville, VIC, Australia

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Department of Medicine, University of Melbourne, Parkville, VIC, Australia

Deakin University, Melbourne, VIC, Australia

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Department of Epidemiology, Madras Diabetes Research Foundation, Chennai, India

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School of Agriculture, Food and Wine, University of Adelaide, Adelaide, Australia

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Institut Cochin, Inserm U, 10116, Paris, France

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Cécile Saint-Martin

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Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

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Department of Epidemiology and Biostatistics, University of California San Francisco, California, USA

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Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

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Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

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Department of Clinical and Organizational Development, Children’s Memorial Hospital, Chicago, IL, USA

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American Diabetes Association, Arlington, VA, USA

Chandra Gruber

College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

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Faculty of Medicine and Health Sciences, Global Health Institute, University of Antwerp, 2160, Antwerp, Belgium

Department of Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA

Emily Mixter & Louis H. Philipson

School of Nursing, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

Diana Sherifali

Division of Endocrinology, Metabolism, Diabetes, University of Colorado, Boulder, CO, USA

Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Dublin, Ireland

John J. Nolan

Department of Endocrinology, Wexford General Hospital, Wexford, Ireland

Division of Endocrinology, NorthShore University HealthSystem, Skokie, IL, USA

Department of Medicine, Prtizker School of Medicine, University of Chicago, Chicago, IL, USA

Department of Genetics, Stanford School of Medicine, Stanford University, Stanford, CA, USA

Anna L. Gloyn

Faculty of Health, Aarhus University, Aarhus, Denmark

Maria F. Gomez

Departments of Pediatrics and Medicine and Kovler Diabetes Center, University of Chicago, Chicago, USA

Siri Atma W. Greeley

Sanford Research, Sioux Falls, SD, USA

Kurt Griffin

University of Washington School of Medicine, Seattle, WA, USA

Irl B. Hirsch

Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA

Marie-France Hivert

Department of Medicine, Universite de Sherbrooke, Sherbrooke, QC, Canada

Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea

Soo Heon Kwak

Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA

Lori M. Laffel

Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

Ruth J. F. Loos

Broad Institute, Cambridge, MA, USA

James B. Meigs

Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Department of Diabetes & Endocrinology, Imperial College Healthcare NHS Trust, London, UK

Department of Diabetology, Madras Diabetes Research Foundation & Dr. Mohan’s Diabetes Specialities Centre, Chennai, India

Viswanathan Mohan

Department of Medicine, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand

Rinki Murphy

Auckland Diabetes Centre, Te Whatu Ora Health New Zealand, Auckland, New Zealand

Medical Bariatric Service, Te Whatu Ora Counties, Health New Zealand, Auckland, New Zealand

Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK

Katharine R. Owen

Metabolic Research Laboratories and MRC Metabolic Diseases Unit, University of Cambridge, Wellcome-MRC Institute of Metabolic Science, Cambridge, UK

Susan E. Ozanne

Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA

Toni I. Pollin

Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, MI, USA

Rodica Pop-Busui

AdventHealth Translational Research Institute, Orlando, FL, USA

Richard E. Pratley

Pennington Biomedical Research Center, Baton Rouge, LA, USA

Leanne M. Redman

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

Robert K. Semple

Yale School of Medicine, New Haven, CT, USA

Jennifer L. Sherr

Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia

Arianne Sweeting

Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, NSW, Australia

Kaiser Permanente Northwest, Kaiser Permanente Center for Health Research, Portland, OR, USA

Kimberly K. Vesco

Clinial Research, Steno Diabetes Center Copenhagen, Herlev, Denmark

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Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

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Contributions

In this manuscript, SM and RW contributed equally as first authors. J.B.M. and MSU jointly supervised the work. B.O., M.S. and M.S.-G. contributed equally as second authors. Review Design: S.M., R.W., B.O., M.S., M.S.G., K.P., C.C.W., R.J.K., S.J.C., M.R.R., D.D., A.C.B.T., A.S.W., A.L., A.J.D., M.K.A., L.K.B., R.H.E., W.H.H.S., T.H., N.S., M.O.G., D.R., E.S., J.C.F., A.D.A./E.A.S.D. P.M.D.I. J.B.M. and M.S.U. Systematic Review Implementation: S.M., R.W., B.O., M.S., M.S.G., K.P., C.C.W., R.J.K., S.J.C., M.R.R., D.D., A.C.B.T., A.S.W., A.L., A.J.D., M.K.A., L.K.B., R.H.E., W.H.H.S., T.H., N.S., M.O.G., D.R., E.S., J.C.F., J.B.M. and M.S.U. Full-text data extraction: S.M., R.W., B.O., M.S., M.S.G., K.P., C.C.W., R.J.K., S.J.C., M.R.R., D.D., A.C.B.T., A.S.W., A.L., A.J.D., M.K.A., L.K.B., R.H.E., W.H.H.S., T.H., N.S., M.O.G., D.R., E.S., J.C.F., J.B.M. and M.S.U. Data synthesis: S.M., R.W., B.O., J.B.M., M.U., M.S., K.P., C.W., M.S.G., J.C.F., R.K., M.R.R., A.S.W. Manuscript writing: S.M., R.W., J.B.M. and M.U. Manuscript Review: S.M., R.W., B.O., M.S., M.S.G., K.P., C.C.W., R.J.K., S.J.C., M.R.R., D.D., A.C.B.T., A.S.W., A.L., A.J.D., M.K.A., L.K.B., R.H.E., W.H.H.S., T.H., N.S., M.O.G., D.R., E.S., J.C.F., J.B.M. and M.S.U. Project Management: S.M., R.W., A.D.A./E.A.S.D. P.M.D.I., J.B.M. and M.U.

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Correspondence to Shivani Misra .

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Competing interests.

The authors declare the following conflicts of interest. S.M. has investigator-initiated funding from DexCom, has received speaker fees (donated to institution) from Sanofi for a scientific talk over which she had full control of content and serves on the Board of Trustees for the Diabetes Research & Wellness Foundation (UK); R.W. declares lecture fees from Novo Nordisk, Sanofi and Eli Lilly. He served on an advisory board for Akcea Therapeutics, Daiichi Sankyo, Sanofi, Eli Lilly, and NovoNordisk; S.J.C. reports a close family member employed by a Johnson & Johnson company; R.H.S. reports fees from Novo Nordisk and Amgen; L.K.B. has received consulting honoraria from Bayer, Novo Nordisk, Sanofi, Lilly, and Xeris; W.H.H.S. reported as Advisor and/or Speaker for AstraZeneca, Bayer HealthCare, Boehringer Ingelheim Pharmaceuticals., Daiichi-Sankyo, Eli Lilly and Company, Merck Sharp & Dohme, Mitsubishi Tanabe Pharma Corporation, Novartis Pharmaceuticals, Novo Nordisk, Pfizer, Sanofi-Aventis, Takeda Pharmaceutical Company; N.S. is Senior Associate Editor of Diabetes and has received speaking honoraria from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer and Sanofi for scientific talks over which he had full control of content; M.G. has served on an advisory board for Nestle Health Science; E.S. is a Deputy Editor of Diabetes Care and a member of the editorial board of Diabetologia and receives payments from Wolters Kluwer for chapters and laboratory monographs in UpToDate on measurements of glycemic control and screening tests for type 2 diabetes; J.C.F. has received speaking honoraria from AstraZeneca and Novo Nordisk for scientific talks over which he had full control of content; J.B.M. is an Academic Associate for Quest Inc. Diagnostics R&D; M.U. reports an unpaid collaborator with AstraZeneca. All other authors have no disclosures. A.L. reports a close family member employed by Merck & Co., Inc.

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Misra, S., Wagner, R., Ozkan, B. et al. Precision subclassification of type 2 diabetes: a systematic review. Commun Med 3 , 138 (2023). https://doi.org/10.1038/s43856-023-00360-3

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Toward an Improved Classification of Type 2 Diabetes: Lessons From Research into the Heterogeneity of a Complex Disease

Affiliations.

• 1 Section of Diabetes and Endocrinology, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA.
• 2 Texas Children's Hospital, Houston, TX 77030, USA.
• 3 Division of Diabetes, Endocrinology and Metabolism, Baylor College of Medicine, Houston, TX 77030, USA.
• PMID: 34291809
• PMCID: PMC8787852
• DOI: 10.1210/clinem/dgab545

Context: Accumulating evidence indicates that type 2 diabetes (T2D) is phenotypically heterogeneous. Defining and classifying variant forms of T2D are priorities to better understand its pathophysiology and usher clinical practice into an era of "precision diabetes."

Evidence acquisition and methods: We reviewed literature related to heterogeneity of T2D over the past 5 decades and identified a range of phenotypic variants of T2D. Their descriptions expose inadequacies in current classification systems. We attempt to link phenotypically diverse forms to pathophysiology, explore investigative methods that have characterized "atypical" forms of T2D on an etiological basis, and review conceptual frameworks for an improved taxonomy. Finally, we propose future directions to achieve the goal of an etiological classification of T2D.

Evidence synthesis: Differences among ethnic and racial groups were early observations of phenotypic heterogeneity. Investigations that uncover complex interactions of pathophysiologic pathways leading to T2D are supported by epidemiological and clinical differences between the sexes and between adult and youth-onset T2D. Approaches to an etiological classification are illustrated by investigations of atypical forms of T2D, such as monogenic diabetes and syndromes of ketosis-prone diabetes. Conceptual frameworks that accommodate heterogeneity in T2D include an overlap between known diabetes types, a "palette" model integrated with a "threshold hypothesis," and a spectrum model of atypical diabetes.

Conclusion: The heterogeneity of T2D demands an improved, etiological classification scheme. Excellent phenotypic descriptions of emerging syndromes in different populations, continued clinical and molecular investigations of atypical forms of diabetes, and useful conceptual models can be utilized to achieve this important goal.

Keywords: LADA; atypical diabetes; cluster analysis; ketosis-prone diabetes; monogenic diabetes; palette model; pediatric diabetes; spectrum model.

© The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: [email protected].

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Insulin-inhibitory receptor research offers hope for type 2 diabetes therapy

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Research targeting the insulin-inhibitory receptor, or inceptor, unveils promising avenues for beta cell protection, offering hope for causal diabetes therapy.

A novel study in mice with diet-induced obesity demonstrates that the knock-out of inceptor enhances glucose regulation , prompting its further exploration as a drug target for type 2 diabetes treatment.

These findings , led by Helmholtz Munich in collaboration with the German Center for Diabetes Research, the Technical University of Munich, and the Ludwig-Maximilians-University Munich, drive advancements in diabetes research. They have been published in Nature Metabolism .

Targeting inceptor to combat insulin resistance in beta cells

Insulin resistance, often linked to abdominal obesity, presents a significant health care dilemma in our era. More importantly, the insulin resistance of beta cells contributes to their dysfunction and the transition from obesity to overt type 2 diabetes.

Currently, all pharmacotherapies, including insulin supplementation, focus on managing high blood sugar levels rather than addressing the underlying cause of diabetes: beta cell failure or loss. Therefore, research into beta cell protection and regeneration is crucial and holds promising prospects for addressing the root cause of diabetes, offering potential avenues for causal treatment.

With the recent discovery of inceptor, the research group of beta cell expert Prof. Heiko Lickert has uncovered an interesting molecular target. Upregulated in diabetes, the insulin-inhibitory receptor inceptor may contribute to insulin resistance by acting as a negative regulator of this signaling pathway. Conversely, inhibiting the function of inceptor could enhance insulin signaling—which in turn is required for overall beta cell function, survival, and compensation upon stress.

In collaboration with Prof. Timo Müller, an expert in molecular pharmacology in obesity and diabetes, the researchers explored the effects of inceptor knock-out in diet-induced obese mice. Their study aimed to determine whether inhibiting inceptor function could also enhance glucose tolerance in diet-induced obesity and insulin resistance, both critical pre-clinical stages in the progression toward diabetes.

Removing inceptor improves blood sugar levels in obese mice

The researchers delved into the effects of removing inceptor from all body cells in diet-induced obese mice. Interestingly, they found that mice lacking inceptor exhibited improved glucose regulation without experiencing weight loss , which was linked to increased insulin secretion in response to glucose.

Next, they investigated the distribution of inceptor in the central nervous system and discovered its widespread presence in neurons. Deleting inceptor from neuronal cells also improved glucose regulation in obese mice. Ultimately, the researchers selectively removed inceptor from the mice's beta cells, resulting in enhanced glucose control and a slight increase in beta cell mass.

Research for inceptor-blocking drugs

"Our findings support the idea that enhancing insulin sensitivity through targeting inceptor shows promise as a pharmacological intervention, especially concerning the health and function of beta cells," says Timo Müller.

Unlike intensive early-onset insulin treatments, utilizing inceptor to enhance beta cell function offers promise in alleviating the detrimental effects on blood sugar and metabolism induced by diet-induced obesity. This approach avoids the associated risks of hypoglycemia-associated unawareness and unwanted weight gain typically observed with intensive insulin therapy.

"Since inceptor is expressed on the surface of pancreatic beta cells, it becomes an accessible drug target. Currently, our laboratory is actively researching the potential of several inceptor-blocking drug classes to enhance beta cell health in pre-diabetic and diabetic mice. Looking forward, inceptor emerges as a novel and intriguing molecular target for enhancing beta cell health, not only in prediabetic obese individuals but also in patients diagnosed with type 2 diabetes," explains Heiko Lickert.

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Type 2 Diabetes

Healthy eating is your recipe for managing diabetes.

About 38 million Americans have diabetes (about 1 in 10), and approximately 90-95% of them have type 2 diabetes. Type 2 diabetes most often develops in people over age 45, but more and more children, teens , and young adults are also developing it.

What Causes Type 2 Diabetes?

Insulin is a hormone made by your pancreas that acts like a key to let blood sugar into the cells in your body for use as energy. If you have type 2 diabetes, cells don’t respond normally to insulin; this is called insulin resistance . Your pancreas makes more insulin to try to get cells to respond. Eventually your pancreas can’t keep up, and your blood sugar rises, setting the stage for prediabetes and type 2 diabetes. High blood sugar is damaging to the body and can cause other serious health problems, such as heart disease ,  vision loss , and kidney disease .

Symptoms and Risk Factors

Type 2 diabetes  symptoms often develop over several years and can go on for a long time without being noticed (sometimes there aren’t any noticeable symptoms at all). Because symptoms can be hard to spot, it’s important to know the  risk factors and to see your doctor to get your blood sugar tested if you have any of them.

Testing for Type 2 Diabetes

A simple blood test will let you know if you have diabetes. If you’ve gotten your blood sugar tested at a health fair or pharmacy, follow up at a clinic or doctor’s office to make sure the results are accurate.

Managing Diabetes

Unlike many health conditions, diabetes is managed mostly by you, with support from your health care team (including your primary care doctor, foot doctor, dentist, eye doctor, registered dietitian nutritionist, diabetes educator, and pharmacist), family, and other important people in your life. Managing diabetes can be challenging, but everything you do to improve your health is worth it!

You may be able to manage your diabetes with healthy eating and being active, or your doctor may prescribe insulin, other injectable medications, or oral diabetes medicines to help manage your blood sugar and avoid complications . You’ll still need to eat healthy and be active if you take insulin or other medicines. It’s also important to keep your blood pressure and cholesterol close to the targets your doctor sets for you and get necessary screening tests.

You’ll need to check your blood sugar  regularly. Ask your doctor how often you should check it and what your target blood sugar levels should be. Keeping your blood sugar levels as close to target as possible will help you prevent or delay diabetes-related complications.

Stress is a part of life, but it can make managing diabetes harder, including managing your blood sugar levels and dealing with daily diabetes care. Regular physical activity, getting enough sleep, and relaxation exercises can help. Talk to your doctor and diabetes educator about these and other ways you can manage stress.

Make regular appointments with your health care team to be sure you’re on track with your treatment plan and to get help with new ideas and strategies if needed.

Whether you were just diagnosed with diabetes or have had it for some time, meeting with a diabetes educator is a great way to get support and guidance, including how to:

• Develop a healthy eating and activity plan
• Test your blood sugar and keep a record of the results
• Recognize the signs of high or low blood sugar and what to do about it
• If needed, give yourself insulin by syringe, pen, or pump
• Monitor your feet, skin, and eyes to catch problems early
• Buy diabetes supplies and store them properly
• Manage stress and deal with daily diabetes care

Ask your doctor about diabetes self-management education and support services and to recommend a diabetes educator, or search the Association of Diabetes Care & Education Specialists’ (ADCES) nationwide directory  for a list of programs in your community.

Type 2 Diabetes in Children and Teens

Childhood obesity rates are rising, and so are the rates of type 2 diabetes in youth. More than 75% of children with type 2 diabetes have a close relative who has it, too. But it’s not always because family members are related; it can also be because they share certain habits that can increase their risk. Parents can help prevent or delay type 2 diabetes by developing a plan for the whole family:

• Drinking more water and fewer sugary drinks
• Eating more fruits and vegetables
• Making favorite foods healthier
• Making physical activity more fun

Healthy changes become habits more easily when everyone makes them together. Find out how to take charge family style with these healthy tips .

Get Support

Tap into online diabetes communities for encouragement, insights, and support. The American Diabetes Association’s Community page and ADCES’s Peer Support Resources  are great ways to connect with others who share your experience.

• Living With Diabetes
• Managing Diabetes: Medicare Coverage and Resources [PDF - 1 MB]
• Diabetes: What Is It? Your Health with Joan Lunden and CDC
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• Open access
• Published: 31 March 2024

Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED)

• Jan Novak 1 ,
• Katerina Jurkova 1 ,
• Anna Lojkaskova 1 ,
• Andrea Jaklova 1 ,
• Jitka Kuhnova 2 ,
• Marketa Pfeiferova 3 ,
• Norbert Kral 3 ,
• Michael Janek 1 ,
• Dan Omcirk 1 ,
• Katerina Malisova 4 ,
• Iris Maes 5 ,
• Delfien Van Dyck 5 ,
• Charlotte Wahlich 6 ,
• Michael Ussher 6 , 7 ,
• Steriani Elavsky 8 ,
• Richard Cimler 2 ,
• Jana Pelclova 4 ,
• James J. Tufano 1 ,
• Michal Steffl 1 ,
• Bohumil Seifert 3 ,
• Tom Yates 9 , 10 ,
• Tess Harris 6 &
• Tomas Vetrovsky 1  

BMC Public Health volume  24 , Article number:  927 ( 2024 ) Cite this article

Metrics details

The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour.

The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients ( n  = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews ( n  = 7) to optimise the intervention components; and (d) piloting ( n  = 10) to refine the intervention to its final form.

The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients’ weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence.

Conclusions

The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care.

Peer Review reports

The global prevalence of type 2 diabetes and prediabetes has risen steadily, posing significant public health challenges. In 2021, the global diabetes prevalence was estimated to be 10.5%, with an additional 9.1% of adults having impaired glucose tolerance, which places them at high risk of type 2 diabetes [ 1 , 2 ].

Physical activity (PA) is a cornerstone in the management of (pre)diabetes [ 3 , 4 ]. Regular PA improves glycaemic control, aids in weight management, and reduces cardiovascular risk factors [ 5 , 6 , 7 ]. Furthermore, reducing and interrupting prolonged sitting improves markers of metabolic health [ 8 , 9 , 10 ]. Despite these well-documented benefits, a significant proportion of individuals with (pre)diabetes remain insufficiently active [ 11 , 12 ]. For example, a recent accelerometry study from Denmark found that 63.2% and 59.5% of participants with diabetes and prediabetes, respectively, did not adhere to the WHO recommendations of weekly minutes of moderate-to-vigorous PA, compared with 49.6% of participants without (pre)diabetes [ 13 ]. Therefore, interventions that can effectively promote and sustain PA in this population are critically needed.

Mobile health (mHealth) technologies have emerged as promising tools for delivering PA interventions [ 14 , 15 , 16 ]. The ubiquity of smartphones and wearable devices offers a unique opportunity to provide personalised, context-sensitive, and scalable just-in-time adaptive interventions (JITAIs), which use data from wearable sensors to intervene when it is most relevant for the patient [ 17 , 18 ]. Despite the potential of mHealth, its application in diabetes care faces several challenges. These include ensuring user engagement, tailoring the intervention to individual needs and preferences, and integrating the technology seamlessly into daily life [ 19 , 20 , 21 ]. Additionally, there is a need to address the digital divide, as not all (pre)diabetes patients may have access to or be comfortable with using advanced technologies [ 22 ]. Therefore, designing mHealth interventions that are accessible, user-friendly, and effective in promoting sustained behaviour change is essential.

Building upon the potential of mHealth technologies in diabetes care, general practitioners (GPs) within primary care emerge as crucial players in this landscape. GPs are at the forefront of managing (pre)diabetes, especially in guiding patients towards healthier behaviours, including increased PA and reduced sedentary lifestyles [ 23 , 24 ]. Despite their pivotal role, GPs often encounter time constraints, limiting their capacity for extensive behavioural counselling [ 25 , 26 , 27 ]. Here, mHealth interventions, when delivered in primary care, offer a valuable extension of GPs’ efforts [ 28 , 29 ]. These tools can enhance patient support in a time-efficient manner, aligning with the individualised care approach essential in diabetes management. This approach not only addresses some of the key challenges of mHealth, such as user engagement and personalisation, but also capitalises on the trusted patient-GP relationship to enhance the effectiveness of these interventions [ 30 , 31 ]. Consequently, integrating mHealth tools into primary care practices represents a significant step towards more effective and sustainable management of (pre)diabetes.

As a practical response to these insights, the ENERGISED trial has been designed to evaluate the effectiveness of an innovative mHealth intervention in primary care for patients with (pre)diabetes, focusing on increasing PA and reducing sedentary behaviour. The rationale and study protocol for this trial has been described previously [ 32 ]. Briefly, this 12-month pragmatic, multicentre, randomised controlled trial aims to recruit 340 patients from 21 general practices, leveraging routine health check-ups for recruitment. The trial comprises a six-month lead-in phase, where the mHealth intervention is supported by human phone counselling, followed by a six-month fully automated maintenance phase. The mHealth intervention is compared against an active control group: participants in both groups receive brief PA advice from their GP, supplemented with a Fitbit activity tracker for self-monitoring. The primary outcome is the change in average ambulatory activity, measured in steps per day via a wrist-worn accelerometer.

This paper aims to describe the participatory development and piloting of the mHealth intervention and its final version to be evaluated in the ENERGISED trial, complementing the previously published trial protocol [ 32 ]. Our decision to employ a participatory approach was driven by the recognition that the success of mHealth interventions, particularly in the context of physical activity and sedentary behaviour change, hinges on their relevance and adaptability to the end-users’ daily lives and challenges [ 33 , 34 ]. This approach aligns with contemporary best practices in intervention design [ 35 ], which advocate for the active involvement of potential users to ensure interventions are not only effective in theory, but also embraced and utilised in practice [ 36 ]. By involving patients with prediabetes and type 2 diabetes in the development process, we aimed to ensure that the intervention was grounded in the real-world experiences and needs of those it seeks to support [ 37 ], thereby enhancing its potential for a significant and lasting impact and scalability to a broad population of (pre)diabetes patients within primary care.

Methods and results

The mHealth intervention was developed according to the ‘mHealth development and evaluation framework’, which includes active participation of the target audience in focus groups and interviews [ 38 , 39 , 40 ]. This framework encompasses four sequential phases: (a) conceptualisation, (b) formative research, (c) pre-testing, and (d) piloting.

We present a combined overview of the methods and results for each phase, providing a cohesive narrative that aligns the development process with the corresponding outcomes, rather than separating out methods and results. We then present the finalised intervention, as implemented in the ongoing ENERGISED randomised controlled trial.

Participants

All participants involved in the intervention’s development were patients with (pre)diabetes who fulfilled the ENERGISED trial eligibility criteria (Additional file 1), recruited by collaborating GPs from their practices in Prague, Czech Republic.

The Ethics Committee of the General University Hospital in Prague (No. 49/20) provided study approval, and all participants provided informed consent.

Phase 1: conceptualisation

To reach a consensus on the key conceptual aspects of the intervention, the multidisciplinary team employed an informal decision-making process. This team comprised GPs (BS, MP, NK, TH), PA researchers (DVD, JP, MS, TY)– some of whom have extensive expertise with diabetes patients (TH, TY)– as well as psychologists and behavioural scientists (MU, SE, CW), and IT experts (JK, RC). The perspectives of GPs were deemed particularly crucial, as they are the primary agents tasked with the intervention implementation in real-world settings and they have day-to-day experience of consultations addressing physical inactivity with their (pre)diabetes patients. Engaging GPs early in the intervention development process was vital for identifying and overcoming potential barriers to implementation, such as time constraints and integration into existing workflows, while leveraging facilitators like the trusted GP-patient relationship and the GPs’ unique insights into patient needs and preferences [ 25 , 31 ]. This approach aligns with prior research indicating that the early involvement of key stakeholders, especially those directly impacted by the intervention’s implementation, significantly enhances the feasibility and acceptability of health interventions [ 41 ]. The four GPs involved in the conceptualisation phase represented a diverse cross-section of practice settings, including both rural (MP) and urban (BS, NK) environments, and brought a range of experiences, with years of practice varying from recently qualified (MP) to over 30 years of experience (BS, TH). This diversity ensured a broad spectrum of insights into the challenges and opportunities of implementing the intervention across different healthcare contexts. The team included both male and female GPs, with three from the Czech Republic—where the intervention is to be implemented—to ensure the intervention’s relevance to the local healthcare system. Additionally, we included a GP from the UK (TH) with additional experience of delivering physical activity trials in primary care to incorporate an external perspective. This helped to enrich the intervention’s development, with broader insights into its potential applicability and scalability beyond the initial setting.The process began with individual team members thoroughly reviewing the latest evidence in their respective fields related to physical activity, diabetes management, behaviour change theories, mHealth technologies, and interventions related to all these areas, including our prior research [ 40 , 42 , 43 , 44 , 45 ]. Following this, a series of meetings were convened, where team members presented their findings and proposed elements for the intervention’s design. During these meetings, facilitated discussions were held to integrate the diverse perspectives of the team, whilst considering resource and time constraints. The discussions were structured around several key conceptual aspects: underpinning theory and behaviour change techniques (BCTs); mode of physical activity and intervention goals; intervention components; and the required IT solution. The outcome of this process was a document, drafted by one of the researchers (TV), which outlined the agreed-upon key conceptual aspects forming the foundation of the intervention. This document, accompanied by a rationale for each aspect, was reviewed and approved by the entire team, guiding the subsequent phases of intervention development.

Theoretical underpinning and behaviour change techniques

The mHealth intervention was underpinned by the theory of self-regulation, a psychological framework that emphasises the role of self-directed processes in guiding one’s behaviour towards achieving personal goals [ 46 ]. The intervention thus incorporates a range of self-regulatory BCTs, such as self-monitoring, goal setting, and feedback [ 47 ], to which we have allocated the same numerical codes in brackets as per Michie et al. taxonomy [ 48 ].

Self-monitoring (2.3) stands as a cornerstone of self-regulation, allowing patients to track their progress and gain insights into their PA patterns. A wealth of evidence indicates that self-monitoring can significantly increase PA levels [ 45 , 49 ] and reduce sedentary behaviour [ 21 , 43 ]. Goal setting (1.1) and regular goal review (1.5) further complement self-monitoring by providing patients with clear, tangible targets to strive for and a framework to evaluate their progress. Goal-setting is the key component of self-regulation [ 50 ] and one of the most potent behaviour change techniques in increasing PA [ 16 , 51 ]. A recent meta-analysis estimated that setting a specific goal was associated with an increase of approximately 600 steps/day [ 42 ]. Action planning (1.4) and coping planning (1.2) aid in translating these goals into daily routines, helping patients identify specific activities, times, and contexts in which they can incorporate more PA. Action planning has been identified as one of the most frequently used BCTs in the general population [ 51 ] and patients with diabetes [ 52 ]. Furthermore, combining action planning, coping planning, and self-monitoring was more effective in increasing PA and reducing sedentary behaviour than using these BCTs alone [ 21 ]. Feedback on behaviour (2.2) serves as a continuous loop of reinforcement, allowing patients to understand where they are excelling and where there’s room for improvement. In a review of mHealth interventions to influence PA and sedentary behaviour, approximately half (46%) utilised feedback on behaviour [ 53 ], which is also commonly used in interventions targeting diabetes patients [ 52 ]. Providing information about health consequences (5.1) highlights the tangible health benefits of increased PA and the health risks of sedentary behaviour. This technique aims to enhance motivation and drive behavioural change, especially in patients with chronic conditions [ 54 , 55 ], including (pre)diabetes [ 52 ]. Lastly, prompts and cues (7.1) play a crucial role in nudging patients towards increased PA and reduced sedentary behaviour in real-time. While not commonly used in traditional PA interventions, prompts are massively utilised by mHealth interventions, which facilitate easy implementation of timely reminders or suggestions, often based on real-time wearable sensor data [ 53 , 55 , 56 ].

Collectively, these BCTs form the backbone of our intervention, each contributing uniquely to fostering a sustained increase in PA and a decrease in sedentary behaviour among our target population.

Mode of physical activity and intervention goals

We identified walking as the primary mode of PA for the intervention due to its accessibility, low cost, established benefits for metabolic health [ 57 , 58 ], and safety [ 59 ]. This choice is grounded in the understanding that walking can be seamlessly integrated into daily routines, making it a sustainable option for most individuals [ 60 ], including patients with (pre)diabetes [ 57 , 61 ]. Besides, walking can be easily quantified as a daily step count and self-monitored using pedometers or activity trackers.

Goal setting is pivotal to the intervention; thus, we developed a set of recommended patient goals including: (a) increasing daily step count; (b) enhancing walking cadence; and (c) interrupting prolonged bouts of sitting.

The consensus was to advise patients to boost their daily step count by at least 3,000 above their baseline, a common goal in behavioural interventions [ 59 , 62 , 63 ]. This increment equates to approximately 30 min of walking, assuming a pace of 100 steps per minute—a heuristic estimate for a moderate-intensity threshold [ 64 ]. This represents more than 150 min of moderate-intensity PA each week, in line with the WHO’s guidelines for adults with chronic conditions [ 65 ]. Recognising the significance of patient autonomy, if patients find the 3,000-step increase challenging, they can propose a more feasible goal, ensuring that the goal feels personally meaningful rather than externally imposed [ 66 ]. To offer added flexibility in planning, the daily step target will be translated into a weekly goal by multiplying by seven, in line with WHO guidelines providing weekly rather than daily goals [ 65 ].

To ensure that patients achieve at least moderate-intensity levels, they will be recommended to aim for a cadence of at least 100 steps per minute [ 64 ], initially in short durations, and gradually extending these periods to make this cadence habitual. For example, patients can monitor their step count for 5 min, trying to achieve at least 500 steps, ultimately aiming for 3,000 steps in 30 min [ 63 , 67 , 68 ]. However, if the 100 steps per minute benchmark proves challenging, they can elevate their cadence as much as comfortably possible [ 65 ].

Lastly, given the positive effect of interrupting prolonged sitting bouts on metabolic markers in (pre)diabetes patients [ 8 , 9 , 10 ], they will be urged to break up sitting every 30 min for at least 3 min, during which they should either walk, preferably at moderate intensities, or perform simple exercises, such as chair squats, calf raises, or walking in place.

Intervention components

The mHealth intervention consists of text messages implementing various BCTs, some triggered ‘just in time’ based on Fitbit activity tracker data. To tailor the mHealth intervention to individual patients and to facilitate its adoption, patients will be initially supported with regular phone counselling. GPs initiate the mHealth intervention during routine health check-ups and provide patients with the Fitbit tracker and brief PA advice. Given that self-monitoring using a simple activity tracker has been consistently demonstrated to be effective in increasing PA levels [ 45 , 49 ] and that providing PA advice by GPs is considered a standard of care [ 69 , 70 ], it was deemed unethical to withhold these components from control group participants. Therefore, in the ENERGISED randomised controlled trial [ 32 ], Fitbit and brief advice will also be provided to the control group participants. Additionally, this approach enables us to isolate the net effect of the mHealth intervention beyond the activity tracker effect [ 42 ].

mHealth interventions typically use smartphone apps or text messages [ 71 ]. As (pre)diabetes is associated with older age and lower socioeconomic status [ 72 ], a notable segment of (pre)diabetes patients may be unfamiliar with app usage or might not possess a smartphone. Therefore, to ensure the broad accessibility of the intervention, we opted to convey the mHealth component through simple text messages. Text messages have been successfully used in various health interventions [ 73 ], including those promoting PA [ 28 , 71 ]. A recent meta-analysis of mHealth interventions found higher effectiveness of interventions including text messaging, suggesting that it can be explained by their higher intrusiveness when compared with smartphone apps’ notifications [ 16 ].

Up until now, most messaging interventions use fixed content that is neither individualised nor adapted to fluctuations in patients’ PA. Furthermore, these messages are typically sent out at pre-defined times that do not respect the ever-changing context of individual patients [ 28 , 71 ]. Leveraging the latest technological advancements, messages can be delivered just in time and adapted to the immediate context and needs of patients [ 74 ]. This precision is achieved by utilising data from sensors, such as those embedded in Fitbit trackers, which offer real-time insights into a patient’s activity patterns [ 75 ]. Just-in-time adaptive interventions (JITAIs) have recently been shown as effective in enhancing PA across diverse populations [ 17 , 18 , 76 ]. Examples of just-in-time messages include prompts to increase walking pace triggered when the patient is actively walking or prompts to interrupt sitting when the patient has been sedentary for over 30 min. While the full potential of such intervention can be only realised with a smartphone plus mobile data plan, we’ve ensured inclusivity by accommodating patients with only a basic cell phone with text messaging capabilities. Such patients will receive an adapted version of the mHealth intervention with no just-in-time messages, but equalised in terms of the number and types of messages delivered. This inclusive approach ensures that the intervention is suitable for a diverse range of participants, including older individuals and those from lower socioeconomic backgrounds.

IT solution

To power the mHealth intervention, we have adapted the HealthReact system, developed at the University of Hradec Kralove [ 32 ] and compliant with rigorous data governance standards. HealthReact serves as a comprehensive platform to collect, integrate, and evaluate sensor data, particularly from devices like the Fitbit tracker. This seamless integration facilitates the triggering of just-in-time text messages based on real-time Fitbit recorded data. Researchers can select from a broad spectrum of just-in-time triggers that can be tailored to cater to individual patients’ needs. Moreover, the system provides options to set specific parameters governing the delivery of text messages, for instance, regulating the total number of daily messages, defining the minimum interval between two consecutive messages, specifying the time window during which messages are triggered, and setting the likelihood that a triggered message is actually dispatched. This level of granularity ensures that the intervention remains adaptive and patient-centric while also ensuring that participants receive an optimal number of messages.

Phase 2: formative research

Focus groups were conducted at the premises of two general practices participating in the ENERGISED trial, led by a male PA researcher with PhD and MD degrees (TV) who had no previous relationship with the participants. These focus groups comprised pre(diabetes) patients conveniently sampled from the practices by the respective GP: 7 patients (3 women, age range 53 to 66 years) from the first practice and 7 patients (1 woman, age range 63 to 78 years) from the second. The GPs welcomed the participants, then left and were not present during the focus groups that lasted 55 and 70 min, respectively. As a token of appreciation, participants were given a 20-EUR voucher.

The objective of the focus groups was to refine the key conceptual aspects developed in the previous phase, ensuring the intervention is tailored specifically to the needs and preferences of patients with (pre)diabetes. The topic guide (Additional file 2) included questions about participants’ preferred PA, patterns of sedentary behaviour, and their experiences with using activity trackers and mobile apps.

The focus groups were audio recorded and transcribed verbatim by an independent transcriber. Analysis used thematic analysis with systematic data coding to identify significant patterns and themes. A female qualitative researcher with a PhD degree (KJ) thoroughly read the transcripts, generated initial codes and grouped the codes into potential themes using NVivo software. Themes were reviewed and refined by a second researcher (TV). The analysis was both inductive, driven by the patients’ accounts, and deductive, shaped by conceptual aspects identified in phase 1.

The formative research provided a nuanced understanding of the preferences and challenges faced by individuals with (pre)diabetes regarding PA. These insights informed the customisation of our intervention. Unfortunately, individual participants could not be identified from these focus group transcriptions, so the individual age and gender of those providing quotes cannot be given in this section.

• Behaviour change techniques

Goal setting and regular review were supported by the focus group discussions. The participants’ acknowledgement of the motivational impact of setting and achieving PA targets aligns with our intervention’s emphasis on goal setting: “My friend uses a smartwatch to monitor his steps. He’ll notice if he’s only at 8,000 steps and say, ‘I need to reach at least 10,000 steps today,’ and then he’s up and off to achieve it.” This quote illustrates the motivational power of personal goals for behaviour change, a central element of our intervention design.

Feedback on behaviour emerged as a crucial BCT. Participants expressed a preference for feedback that was both affirming and instructive. One participant looked forward to positive reinforcement: “A text message that praises my day’s efforts in the evening and offers encouragement for the next day would be welcome.” Another participant emphasised the importance of reflective feedback to inform future actions: “I’d like an evening summary that evaluates my day, suggesting what I should start or continue doing the next day.” These insights support our intervention’s strategy of providing text messages with tailored feedback to help patients understand their progress and plan subsequent activities.

The concept of social comparison as a BCT elicited mixed reactions. Some participants saw value in a competitive edge: “They have a friendly competition over who was more active, who ran the most, who cycled the most. It certainly motivates.” This suggests that for some, comparing activities with others can be a strong motivator. Conversely, another perspective emphasised self-referenced progress: “I believe that self-comparison is key to personal progress, especially at this age.“, indicating a preference for personal benchmarks over external competition. Given these divergent views, we decided not to include social comparison in our intervention to avoid the potential negative effects of competition and to focus on individual self-improvement, which aligned with our goal of fostering intrinsic motivation.

The focus groups highlighted the importance of understanding the health consequences of PA: “I’m aware that we should all be more active and that I need to lose weight.” This acknowledgement supports the inclusion of educational text messages to inform patients about the health implications of their PA behaviours.

Walking as the primary mode of PA

The focus group discussions provided strong support for walking as the central PA in our intervention. Participants frequently cited walking as a preferred and accessible form of exercise. One participant’s experience highlighted that despite physical health barriers, walking was still seen as a manageable activity to increase: “I do walk and try to maintain a fast pace, but with the weight I’ve gained, even a quick 200-meter walk to the bus leaves me struggling to breathe.” Another participant maintained their walking routine despite unfavourable weather: “My dog ensures we go out for a walk every morning at seven, no matter if it’s raining, snowing, or freezing. We usually walk for half an hour, covering almost the entire block.” This comment not only illustrates the practicality of walking as an exercise that can be integrated into daily life but also shows how external motivation, such as pet ownership, can help overcome environmental barriers like bad weather. These insights collectively affirmed the choice of walking as the primary mode of PA for our intervention.

mHealth and wearables

The formative research phase underscored the potential of mHealth to engage patients with (pre)diabetes in managing their PA. The focus group participants expressed a general openness to using mobile technologies, with many indicating a willingness or interest in using mobile phones or wearables to support their PA goals. One participant articulated a positive stance towards technology: “That would be ideal for me; I’m quite fond of this technology.“, while another highlighted the need for simplicity: “I would be excited to use a pedometer. I’m considering purchasing one, provided it’s not too complex to use.” These insights validate our decision to employ mHealth as a key intervention component, ensuring that it is both accessible and user-friendly.

Just-in-time prompts

The concept of just-in-time prompts was well-received by the focus group participants: “When I’m sitting, and my watch alerts me, it prompts me to stand up, so I do.” This feedback validates our decision to incorporate just-in-time prompts into the intervention, utilising them as immediate nudges towards increased PA and reduced sedentary behaviour.

• Phone counselling

The focus group discussions revealed a strong preference for personalised support, which reinforces the inclusion of phone counselling in our intervention. One participant expressed a desire for external motivation: “I would certainly value being more physically active, but it’s something I need to push myself to do, or else have someone else encourage and guide me.” Another participant echoed this sentiment, highlighting the importance of assistance in initiating a healthier lifestyle: “I know I should engage in it, and I would be really grateful for any help I can get to do so.” These statements underscore the value of human interaction in motivating patients to engage in PA and the essential role of counselling in supporting behaviour change.

In summary, the formative research underscored a clear preference for interventions that are not only personalised but also flexible, ensuring they can be adapted to the individual needs and circumstances of those with (pre)diabetes.

Phase 3: pre-testing

In this phase, we utilised the conceptualisation refined in phase 2 to craft various types of text messages, each incorporating different BCTs. Each type had several specific examples, along with suggestions on how these messages would be triggered. A male PhD student (JN) contacted by phone the seven patients from the second focus group and invited them for face-to-face semi-structured interviews; all invited patients accepted the invitation and participated in the interviews. The interviews were conducted in the researcher’s office and lasted between 25 and 40 min. Participants were given a 20-EUR voucher.

The aim of these interviews was to gather feedback on the sample messages, which would then be used to refine and optimise the messages in alignment with the patients’ preferences and needs. To facilitate this, patients were presented with these sample messages (Table  1 ), prompting their immediate, think-aloud reactions. The interviews were audio recorded, transcribed verbatim, and subjected to thematic analysis using the same process and involving the same researcher (KJ) as in phase 2. However, unlike in phase 2, only deductive analysis was employed with the themes corresponding to the different types of messages.

Building on the insights from formative research, we developed a series of text messages tailored to leverage specific BCTs (Table  1 ). The types of messages were as follows:

Walk faster: just-in-time prompts to increase walking pace

Participants responded positively to prompts that encouraged a faster walking pace while they were actually walking. The just-in-time nature of these messages was generally deemed crucial for their effectiveness. One participant expressed enthusiasm for the motivational aspect (Male, 63 years): “Certainly, if it provides motivation, I’d strive to reach the ‘excellent, keep going’ point. That’s the purpose of our walks, to be meaningful.”

Stand up: just-in-time prompts to interrupt sitting

Text messages to interrupt prolonged sitting were seen as potentially very effective. Participants valued the reminder to break their sedentary behaviour, for example, during work hours (Female, 63 years): “I can see myself doing more during work. It would fit well with my routine. But it’s challenging to remember to stand up, so I’d welcome that notification.”

Goal review: an interim review of the patient’s weekly step goal on friday evening

The text message with an interim review of weekly step goals was met with positive feedback, as participants valued the opportunity to reflect on their activity levels. One participant appreciated the self-monitoring aspect, recognising it as a tool for self-improvement (Male, 63 years): “It’s a useful overview. It shows what you’ve accomplished and what’s left to do, giving you a chance to catch up.” This feedback underscores the importance of such notifications in enabling patients to identify when they are falling short of their weekly targets, providing them with the motivation to increase their efforts in the remaining days of the week.

Feedback and encouragement: Sunday evening feedback on the patient’s weekly performance and encouragement for the upcoming week

Most participants valued the text message with feedback on their weekly performance, seeing it as a motivator for the upcoming week (Male, 69 years): “It’s beneficial to have a weekly summary… I can aim to meet or exceed it in the next week,” suggesting that reflective feedback can inspire continued or increased effort. Yet, not everyone was persuaded by the numbers, with one participant stating (Male, 65 years): “My wife might mention, ‘We’ve walked 4,000 steps,’ and I’d respond, ‘That’s irrelevant to me.’ I walk as much as I need, whether it’s 500 steps or 5,000,” indicating a preference for intuitive rather than quantified activity. Despite such views, the consensus leaned towards the usefulness of weekly performance feedback, affirming its inclusion in the intervention.

Action plan reminder: reminders of the action plan adapted to specific plans of each patient

Participants were instructed to suggest their own plans for how and when they wished to incorporate walking into their daily routine (e.g., walking a dog at 7 a.m. or walking home from work at 4 p.m.). The Action Plan Reminder message was then tailored to their individual plans. The desire for tailored messages was evident, with participants suggesting integration with daily routines (Female, 63 years): “If it’s aligned with a regular activity, like brushing teeth in the morning and taking a shower in the evening, then it’s possible to set messages for those times. For instance, I walk my dog every evening at seven; that would be a perfect time for a reminder.” This feedback reinforced the importance of personalising the intervention.

While most message types received positive feedback from participants, the reception of just-in-time messages suggesting an extension of walking distance was mixed. Some participants found them motivating (Female, 63 years): “Absolutely. An extra block would be manageable.” However, others expressed neutral or negative views (Male, 67 years): “I could extend my walk through the village and back. But with heavy shopping, I don’t know,” and (Male, 65 years): “Walking around the house… no, that feels odd.” Consequently, we decided to exclude this type of prompt from the intervention.

Finally, determining the optimal frequency of text messages was crucial to maintaining motivation without causing annoyance. Participants’ preferences varied widely, with some expressing indifference (Male, 65 years): “I don’t know, I don’t care,” while others specified a range (Male, 69 years): “Ideally one or two a day and no more than 10 a week,” and some were open to frequent prompts (Female, 63 years): “Even 6 to 7 a day wouldn’t bother me.” Through this feedback, it became apparent that about 10 notifications per week would be the upper limit to ensure the messages remained a welcome nudge rather than a nuisance.

Phase 4: piloting

We developed a pilot version of the mHealth intervention and tested it with patients who had prediabetes or uncomplicated type 2 diabetes, were not on insulin therapy, and were regular mobile phone users, meeting the ENERGISED trial eligibility criteria (detailed in Additional file 1). Recruitment was conducted by collaborating GPs as outlined in the ENERGISED trial protocol [ 32 ]. In brief, we compiled a list of all patients with (pre)diabetes from participating general practices’ computerised medical records. A random selection of 24 patients was then made from these lists, and GPs introduced the study to all eligible patients opportunistically during routine health check-ups. This process resulted in the recruitment of 10 male patients, 4 with prediabetes and 6 with diabetes, aged between 40 and 76 years. The patients were equipped with the Fitbit Inspire 2 activity tracker [ 77 ] and instructed to maintain their typical PA for one week, using the tracker to establish their baseline steps. Subsequently, a researcher contacted them by phone, assisting them in synchronising their tracker with a Fitbit account accessible to the researchers. During this call, a daily step goal was negotiated, and opportunities for integrating walking into their daily routines were discussed, similarly as described in the Final mHealth intervention section. The information collected from this conversation was instrumental in setting up and tailoring the pilot mHealth intervention.

The pilot phase lasted two weeks, during which we monitored the number of text messages patients received. After these two weeks, the same researcher reached out to the patients for brief semi-structured interviews to gather feedback on the intervention’s usability and any potential areas for improvement. Specifically, we asked patients about the frequency, timing, and content of the messages. Patients’ responses and comments were noted during and immediately after the call and were systematically categorised by one of the researchers (TV), who also counted the number of responses per category. Patients participating in the pilot were allowed to keep the Fitbit tracker.

During the pilot, patients received an average of 9.2 ± 10.6 text messages weekly. Five felt the frequency of messages was excessive, and two that they were sometimes sent too closely together. Three patients found the timing of the just-in-time prompts to be ill-suited; for instance, some received prompts to increase their walking cadence after completing their walk. Three participants also expressed inconvenience with receiving prompts to interrupt sitting during work hours when it wasn’t feasible. Lastly, five patients felt that the message content was repetitive.

To address these issues, we implemented several intervention refinements. Specifically, we adjusted the probability of dispatching certain text messages and reduced the maximum number of just-in-time daily prompts (Table  2 ). This ensures that most patients will receive between three to six messages weekly, with only occasional weeks exceeding ten messages. Additionally, we fine-tuned the parameters for triggering just-in-time prompts related to walking cadence, minimising the likelihood of sending a prompt once walking had finished. However, this adjustment potentially leads to infrequent prompts for patients who engage in minimal walking. To address concerns about receiving prompts to interrupt sitting during work hours, we restricted these prompts to a window between 4 pm and 8 pm. Lastly, to diversify the content and reduce repetitiveness, we crafted multiple text variations for each message type.

Final mHealth intervention

The final intervention comprises six types of text messages, each embodying different BCTs. The individual types, examples of text messages, BCTs utilised, triggering rules, and probability of their dispatch are detailed in Tables  1 and 2 . The detailed implementation of the intervention within primary care settings is thoroughly described in the previously published ENERGISED trial protocol [ 32 ].

Walk Faster and Stand Up messages are triggered just in time by specific physical behaviour events as detected by Fitbit sensors: 5 min with a step count ranging from 60 to 100 (allowing for one outlier minute below and two above the range) between 8 am and 8 pm for Walk Faster, and 30 min with zero steps while detecting heart rate (to confirm wear) between 4 and 8 pm for Stand Up messages. To prevent overwhelming patients, the frequency of these messages is capped at one per day for Stand Up and two per day for Walk Faster (with a minimum interval of 60 min between them).

Action Plan Reminder messages are triggered according to individual participants’ routines once or more times per week. Goal Review, Feedback and Encouragement, and Health Education messages are triggered at predetermined times once a week, separated throughout the week. Goal Review is triggered on Friday evenings between 8 and 10 pm, allowing participants time over the weekend to catch up. Feedback and Encouragement messages are triggered on Sunday evenings between 6 and 8 pm, offering a review of the past week and motivation for the week ahead. Health Education messages are triggered on Tuesdays between 6 and 8 pm.

Each message’s dispatch is determined by a randomisation algorithm, which decides with a given probability (Table  2 ) whether the message is actually dispatched to the patient. For instance, the 50% probability of the Goal Review means that the message is triggered every Friday but only dispatched every other week on average. This randomisation not only further limits the weekly text message count but also facilitates the future evaluation of each message’s immediate impact on objectively measured PA levels using a micro-randomised design.

For each message type, there are various text versions from which one is randomly selected (Table  1 ). Furthermore, the content of Action Plan Reminder messages is tailored to each participant’s individual plans. Goal Review and Feedback and Encouragement messages are also personalised, reflecting each participant’s step count from recent days.

Of note, all standard notifications and prompts typically delivered by the Fitbit wearable and its accompanying app are deliberately deactivated for both intervention and control participants to ensure that they do not interfere with our intervention.

Adapted intervention

For optimal functioning of the intervention, patients require a smartphone compatible with the HealthReact and Fitbit apps (Android 9.0 or iOS 15.0 and later as of November 2023), along with a mobile data plan for continuous internet connectivity. These patients comprise Group A. For participants lacking such resources, the intervention is modified based on the reliability of their Fitbit data syncing:

Group B: Participants in this group who don’t sync their data continuously throughout the day but do sync regularly every day, usually in the afternoon and evening hours (often those without a mobile data plan but with a reliable Wi-Fi connection at home), receive additional time-based Walk Faster messages. This approach compensates for their lack of just-in-time Walk Faster messages due to their syncing patterns.

Group C: Participants who either sync irregularly or not at all (including those with basic cell phones instead of smartphones) are provided additional time-based Walk Faster and Stand Up messages to make up for the absence of just-in-time Walk Faster and Stand Up messages. Furthermore, in these cases, Goal Review and Feedback and Encouragement messages cannot be personalised due to missing recent step count data. Therefore, they receive non-personalised messages that remind them to review their goals and provide encouragement.

Importantly, the adapted intervention is equalised in terms of the number and types of messages delivered. This equalisation is achieved by triggering the adapted time-based Walk Faster and Stand Up messages for Groups B and C only once per day, with the probability of these messages being dispatched set at 15%.

Procedures and counselling

During the baseline visit, all patients (intervention and control) receive a Fitbit Inspire 2 activity tracker from their GP, along with brief PA advice complemented by an educational leaflet and a prescription for PA. Additionally, patients are instructed to maintain their usual PA levels for one more week while wearing the Fitbit to establish their baseline steps.

Approximately one to two weeks later, intervention patients are contacted by phone by a counsellor who assists them in setting individual goals and devising an action plan (e.g., walking a dog for 30 min on three specific days of the week). The counsellor then inputs this information into the HealthReact system to tailor the Action Plan Reminder messages and enable personalisation of the Goal Review and Feedback and Encouragement messages.

In subsequent calls at months 1 to 6 (lead-in phase) to intervention patients, the counsellor supports patients in reviewing their step goals and action plans, employing various BCTs to facilitate goal achievement. During these calls, the counsellor can adjust the mHealth intervention to adapt to the changing needs of the patients. For instance, if patients consistently achieve their step goal, the counsellor may challenge them to increase it. The counsellor also assists patients with technical issues related to the intervention.

From month 7 onwards (maintenance phase), patients no longer receive phone counselling but continue to receive text messages for an additional six months, until month 12, as previously described.

Intervention monitoring

The phone counsellors will review regular weekly reports of their patients’ Fitbit syncing patterns (Fig.  1 ). Should a patient’s syncing reliability decline, they initially receive a text message from the phone counsellor, prompting regular syncing. If this reminder proves ineffective, the counsellor addresses the issue in the subsequent scheduled call. Persistent syncing challenges may necessitate reassigning the patient to a group with a lower syncing requirement (e.g., from Group A to Group B, or Group B to Group C). Conversely, if patients in Group C or Group B demonstrate improved syncing consistency, surpassing their current group’s requirements, they are upgraded to a more appropriate group (Group B or Group A, respectively). This dynamic approach ensures each participant benefits from the most effective version of the intervention, tailored to their specific mobile phone capabilities and internet access.

A sample of the weekly report of a patient’s Fitbit syncing pattern. The vertical green lines represent individual Fitbit syncs. The compact green area signifies regular syncing (approx. every 15 min). The hatched area marks time periods from 4 to 8 pm when just-in-time Stand Up text messages are triggered. This specific patient would be classified as Group B: irregular syncing, mostly in the afternoon and during weekends, probably only when connected to the Wi-Fi at home. Despite the irregular sync, the patient would likely receive several just-in-time Stand Up messages per week (assuming she spent 30 min sitting), but hardly any just-in-time Walk Faster messages. Hence, her classification as Group B, which receives adapted Walk Faster messages independent of Fitbit data

When developing mHealth interventions, a participatory approach involving patients is critical to enhance the intervention’s relevance and ensure its adaptability to real-world settings [ 78 ]. The participatory approach also demonstrated its value in the development of our mHealth intervention. Initially, key components such as walking as the primary mode of physical activity, the provision of activity trackers, and the implementation of just-in-time prompts were conceived during the first conceptualisation phase, which relied on published evidence and expert opinions. However, it was the subsequent involvement of patients in the development process that truly affirmed and refined these components. For instance, feedback from participants underscored the importance of walking for its accessibility and potential for seamless integration into daily routines. Another example is the inclusion of phone counselling support, which was particularly valued for its personal touch and ability to facilitate the initial adoption of the intervention.

Moreover, the participatory phases enabled us to refine the intervention based on patient suggestions, leading to significant enhancements. The frequency of text messages, the customisation of Action Plan Reminder messages, and the individualisation of Feedback and Encouragement and Goal Review messages were all adjusted to better meet the needs and preferences of the target population. Additionally, to maintain engagement and avoid monotony, we introduced variations in the message content based on patient feedback.

Conversely, the participatory approach also led to the exclusion of certain features initially considered for inclusion. Based on patient feedback, we decided against incorporating social comparison elements and suggestions for extending walking distances, as they were not favoured by the participants. This iterative process of inclusion and exclusion highlights the strength of involving patients directly in the development of health interventions.

Furthermore, some patient suggestions introduced specific limitations, such as the decision to deliver Stand Up messages only from 4 pm to 8 pm to minimise interference with work routines. While this decision was made to enhance the practicality of the intervention, it also illuminated the nuanced balance between customisation and efficacy.

These examples illustrate how the participatory approach not only validated the initial conceptualisation of the intervention but also led to its substantial refinement. This process ensured that the final intervention was not only grounded in evidence but also resonant with the needs and preferences of the target population.

Study strengths

Applying the ‘mHealth development and evaluation framework’, including active participation of the target audience, to the development of our intervention endowed it with several strengths, essential for its potential success.

First, we identified walking as the primary mode of PA due to its accessibility and potential for seamless integration into daily routines [ 57 ]. Recognising that merely accumulating steps might be insufficient for significant health benefits [ 59 ], we emphasised walking cadence to reach a threshold indicative of moderate PA. Additionally, our intervention focuses on interrupting prolonged periods of sitting, a behaviour particularly detrimental to patients with (pre)diabetes [ 8 , 9 , 10 ].

Second, central to our intervention are mHealth technologies and wearable activity trackers, which offer sustainable solutions scalable to a broad population of (pre)diabetes patients within primary care [ 79 ]. Just-in-time prompts designed to increase walking cadence and interrupt prolonged sitting can be particularly effective, as they deliver timely, context-specific nudges [ 17 , 18 ]. To broaden accessibility, including for those with limited technology literacy, the mHealth intervention is delivered in the form of text messages [ 16 , 71 ]. Furthermore, we developed an adapted version of the intervention for patients without a mobile data plan or those with only basic cell phones, ensuring inclusivity.

Third, the involvement of patients in the intervention’s development highlighted the importance of tailoring and personalisation. Consequently, most text messages were designed to be individualised for each patient. For instance, Action Plan Reminder messages can be customised according to each participant’s specific routines and preferences. Furthermore, Feedback and Encouragement and Goal Review messages leverage individual goals and real-time performance data from Fitbit to provide a personalised experience for each participant. To maintain engagement and avoid monotony, we produced several variations of the message content and carefully regulated the number of messages per week to prevent intervention fatigue.

Finally, phone counselling during the lead-in phase of the intervention plays a pivotal role [ 42 ]. This personal touch not only facilitates the initial adoption of the intervention but also provides necessary support and guidance, ensuring participants are comfortable and engaged with the technology and the overall program. This combination of technological innovation and human interaction was instrumental in creating an effective, patient-centric intervention to enhance PA in (pre)diabetes patients.

Study limitations

Our study has limited generalizability due to reliance on a small sample of patients with (pre)diabetes who participated in the development of the mHealth intervention. Additionally, the gender imbalance in our participant group, with a predominance of male participants, further constrains generalizability. This selective group may not fully represent the broader population of all patients with (pre)diabetes, especially those less inclined to use technology-based solutions. Furthermore, while efforts were made to enhance accessibility, the intervention’s reliance on text messaging and wearable technology presupposes a certain level of technological literacy that may not be universally present.

Furthermore, our development process, guided by the ‘mHealth development and evaluation framework’, did not involve patient partners in the initial conceptualisation phase, which relied on published evidence and expert opinions [ 38 , 39 , 40 ]. This approach was chosen to establish a strong evidence-based foundation for the intervention. However, we acknowledge this as a limitation, recognising the value of patient involvement from the earliest stages of intervention development.

Additionally, while our multidisciplinary research team engaged in comprehensive discussions to reach a consensus on the key conceptual aspects of the intervention, we did not employ any structured approach, such as a Delphi method, in phase 1. The absence of this formal consensus method may have limited the systematic integration of diverse expert opinions and could be considered a limitation of our methodology. In addition, the absence of a structured approach precluded detailed reporting of individual team members’ specific feedback in phase 1, thereby diminishing the transparency of findings derived from this consultative process.

Another limitation relates to the use of Fitbit wearables. While they are affordable and user-friendly, Fitbit devices only sync with their server approximately every quarter of an hour. Consequently, the data triggering the just-in-time prompts can be delayed by up to 15 minutes (assuming a constant internet connection), leading to prompts that are ‘not-quite-in-time,’ as detailed in the ‘Phase 4: piloting’ section. To mitigate this, our intervention only considers data immediately preceding the sync. However, this workaround potentially results in missed triggers, especially for patients who engage in minimal walking, leading to less frequent delivery of Walk Faster messages.

Lastly, based on patient feedback, we opted to deliver Stand Up messages only from 4 pm to 8 pm to minimise negative interference with participants’ work routines and prevent annoyance. However, restricting prompts to interrupt sitting to this specific time frame may limit the efficacy of the intervention, as it doesn’t address prolonged sitting during a significant portion of the day.

The development of our mHealth intervention, rooted in a participatory design approach, underscores the importance of involving patients in creating behavioural interventions tailored to their specific needs. The incorporation of just-in-time prompts, which leverage real-time data from wearable devices, represents a significant advancement in delivering personalised and context-sensitive PA interventions for patients with (pre)diabetes. Should this approach prove effective in the ongoing ENERGISED randomised controlled trial within a primary care setting, it could significantly aid GPs in guiding patients towards increased PA and reduced sedentary lifestyles. The integration of mHealth tools offers a promising solution for GPs to overcome time constraints and enhance their capacity for behavioural counselling, leveraging the trusted patient-GP relationship. In doing so, GPs can provide continuous, personalised guidance, crucial for the management of (pre)diabetes and potentially adaptable for other health conditions in routine primary care settings.

Data availability

The datasets generated during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

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This work was supported by the Czech Health Research Council, Ministry of Health of the Czech Republic (grant number NU21–09–00007). The funding source had no role in the conceptualisation, design, data collection, analysis, decision to publish, or preparation of the manuscript.

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Jan Novak, Katerina Jurkova, Anna Lojkaskova, Andrea Jaklova, Michael Janek, Dan Omcirk, James J. Tufano, Michal Steffl & Tomas Vetrovsky

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Contributions

BS is the principal investigator of the ENERGISED trial. TV, BS, MS, JJT, RC, TH, TY conceived the study design and secured the funding. All authors contributed to the conceptualisation (phase 1) of the intervention. JN, KJ, CW, MU, AJ, AL, TV devised the qualitative part of the study. BS, NK recruited the patients for the focus groups and interviews. JN, TV, AJ, AL conducted the focus groups and interviews. KJ, JN, AL, AJ, JK analysed the data. RC, JK developed the HealthReact system powering the intervention. JN, KJ, TV, TH drafted the manuscript. All authors reviewed and approved the final manuscript.

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Correspondence to Tomas Vetrovsky .

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The study protocol has been approved by the Ethics Committee of the General University Hospital, Prague (No. 49/20), and the study was conducted in compliance with the principles of the Declaration of Helsinki. Informed consent to participate in the study was obtained from participants.

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Novak, J., Jurkova, K., Lojkaskova, A. et al. Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). BMC Public Health 24 , 927 (2024). https://doi.org/10.1186/s12889-024-18384-2

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New study into Type 2 diabetes treatment yields promising results

KINGSTON, Jamaica – New research has yielded promising results for more effective treatment and management options for patients suffering from Type 2 diabetes.

The study, titled ‘Combined Supplementation of S-Nitro glutathione and Glutathione Improves Glycaemic Control in Type 2 Diabetic Rats’, was led by a master’s graduate from the Faculty of Medical Sciences at the University of the West Indies (UWI) Mona campus, Amarley Wright.

It focuses on the use of the antioxidant Glutathione, combined with another substance known as Nitric oxide, to significantly lower blood sugar levels in diabetic rats.

“So, these are some promising results, and it highlights the possible role that this combination treatment could play in improving the lives of diabetic patients,” Wright said.

Diabetes is a disorder in which an individual develops an abnormally high blood sugar level due to inadequate or lack of insulin production by the pancreas or the inability of the body to respond properly to the hormone.

Insulin is needed to control the amount of glucose (sugar) in the blood.

Wright said Type 2 diabetes represents between 90 and 95 per cent of all diabetes cases globally, with 11.6 per cent of Jamaicans currently living with the condition.

“More than likely, each one of us knows somebody with diabetes. This is the reason why my research is of major importance. There are millions of people worldwide living with diabetes, and in Jamaica, the Economic and Social Survey showed that diabetes was one of the three main causes of death for both men and women in 2021,” he noted.

Common symptoms associated with diabetes include excessive thirst, extreme hunger, frequent urination, fatigue, and blindness.

Wright stated that the major test used to diagnose Type 2 diabetes is the Oral Glucose Tolerance Test, also known as the OGTT.

“This test involves an overnight fast. Thereafter, blood is taken from the patient and a fasting blood sugar level is measured. Then, they’re given fluids to drink, which contain glucose, and their blood sugar level is measured one hour and two hours afterwards. Normally a reading that is greater than or equal to 200 milligrams per decilitre indicates diabetes,” he explained.

In patients with diabetes, there is the development of a phenomenon called ‘oxidative stress’ where bad compounds in the body, such as free radicals become present. Good compounds known as antioxidants help fight against these bad compounds.

For Wright’s research, a major antioxidant, Glutathione, is combined with another substance known as Nitric oxide to form S-Nitro glutathione (GSNO).

“These two compounds are the focus of my work. So, I administered these compounds in Type 2 diabetic rats,” he said.

Wright’s research revealed that, among other things, Glutathione on its own was effective in significantly reducing the blood-sugar levels of the diabetic rats, which were administered the compound.

His research findings also showed an increase in the insulin concentration for the rats, which were treated as part of the study when compared to those that were left untreated.

Wright added that further work needs to be done “in terms of evaluating the toxicity as well as other biomedical parameters that can be measured so that we can know more about the mechanism that these compounds work by”.

While clinical trials on the use of the combination of compounds as treatment for diabetes in humans have begun in other jurisdictions such as India, Jamaica currently has no such programme.

Wright said his research has the potential to change that reality.

“We could engage the clinical transitional research unit from the Faculty of Medical Sciences at the University of the West Indies, Mona, to see how this could be done. We could also seek further information from them about what other drug tests we need to do to see if we can push this forward to clinical trials.

“Overall, we want to improve the quality of life for diabetic patients. We see that diabetes is not only a topical issue it is a growing one. There is expected to be an increase in the prevalence of diabetes as well,” Wright pointed out.

He added that the healthcare expenditure associated with treating diabetes is also an area of concern, in terms of insulin and other medications associated with the condition that afflicted patients have to purchase.

“So, we’re talking about effective treatment, and we see that these compounds, administering them together, could lead to a possible pharmaceutical option for treating Type 2 diabetes,” he said.

For his research, Wright received the award for ‘Best Student Oral Presentation’ at the 14th Annual National Health Research Conference held in November 2023.

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There is unequivocal evidence that Earth is warming at an unprecedented rate. Human activity is the principal cause.

• While Earth’s climate has changed throughout its history , the current warming is happening at a rate not seen in the past 10,000 years.
• According to the Intergovernmental Panel on Climate Change ( IPCC ), "Since systematic scientific assessments began in the 1970s, the influence of human activity on the warming of the climate system has evolved from theory to established fact." 1
• Scientific information taken from natural sources (such as ice cores, rocks, and tree rings) and from modern equipment (like satellites and instruments) all show the signs of a changing climate.
• From global temperature rise to melting ice sheets, the evidence of a warming planet abounds.

The rate of change since the mid-20th century is unprecedented over millennia.

Earth's climate has changed throughout history. Just in the last 800,000 years, there have been eight cycles of ice ages and warmer periods, with the end of the last ice age about 11,700 years ago marking the beginning of the modern climate era — and of human civilization. Most of these climate changes are attributed to very small variations in Earth’s orbit that change the amount of solar energy our planet receives.

The current warming trend is different because it is clearly the result of human activities since the mid-1800s, and is proceeding at a rate not seen over many recent millennia. 1 It is undeniable that human activities have produced the atmospheric gases that have trapped more of the Sun’s energy in the Earth system. This extra energy has warmed the atmosphere, ocean, and land, and widespread and rapid changes in the atmosphere, ocean, cryosphere, and biosphere have occurred.

Earth-orbiting satellites and new technologies have helped scientists see the big picture, collecting many different types of information about our planet and its climate all over the world. These data, collected over many years, reveal the signs and patterns of a changing climate.

Scientists demonstrated the heat-trapping nature of carbon dioxide and other gases in the mid-19th century. 2 Many of the science instruments NASA uses to study our climate focus on how these gases affect the movement of infrared radiation through the atmosphere. From the measured impacts of increases in these gases, there is no question that increased greenhouse gas levels warm Earth in response.

Scientific evidence for warming of the climate system is unequivocal.

Intergovernmental Panel on Climate Change

Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that Earth’s climate responds to changes in greenhouse gas levels. Ancient evidence can also be found in tree rings, ocean sediments, coral reefs, and layers of sedimentary rocks. This ancient, or paleoclimate, evidence reveals that current warming is occurring roughly 10 times faster than the average rate of warming after an ice age. Carbon dioxide from human activities is increasing about 250 times faster than it did from natural sources after the last Ice Age. 3

The Evidence for Rapid Climate Change Is Compelling:

Global Temperature Is Rising

The planet's average surface temperature has risen about 2 degrees Fahrenheit (1 degrees Celsius) since the late 19th century, a change driven largely by increased carbon dioxide emissions into the atmosphere and other human activities. 4 Most of the warming occurred in the past 40 years, with the seven most recent years being the warmest. The years 2016 and 2020 are tied for the warmest year on record. 5 Image credit: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

The Ocean Is Getting Warmer

The ocean has absorbed much of this increased heat, with the top 100 meters (about 328 feet) of ocean showing warming of 0.67 degrees Fahrenheit (0.33 degrees Celsius) since 1969. 6 Earth stores 90% of the extra energy in the ocean. Image credit: Kelsey Roberts/USGS

The Ice Sheets Are Shrinking

The Greenland and Antarctic ice sheets have decreased in mass. Data from NASA's Gravity Recovery and Climate Experiment show Greenland lost an average of 279 billion tons of ice per year between 1993 and 2019, while Antarctica lost about 148 billion tons of ice per year. 7 Image: The Antarctic Peninsula, Credit: NASA

Glaciers Are Retreating

Glaciers are retreating almost everywhere around the world — including in the Alps, Himalayas, Andes, Rockies, Alaska, and Africa. 8 Image: Miles Glacier, Alaska Image credit: NASA

Snow Cover Is Decreasing

Satellite observations reveal that the amount of spring snow cover in the Northern Hemisphere has decreased over the past five decades and the snow is melting earlier. 9 Image credit: NASA/JPL-Caltech

Sea Level Is Rising

Global sea level rose about 8 inches (20 centimeters) in the last century. The rate in the last two decades, however, is nearly double that of the last century and accelerating slightly every year. 10 Image credit: U.S. Army Corps of Engineers Norfolk District

Arctic Sea Ice Is Declining

Both the extent and thickness of Arctic sea ice has declined rapidly over the last several decades. 11 Credit: NASA's Scientific Visualization Studio

Extreme Events Are Increasing in Frequency

The number of record high temperature events in the United States has been increasing, while the number of record low temperature events has been decreasing, since 1950. The U.S. has also witnessed increasing numbers of intense rainfall events. 12 Image credit: Régine Fabri,  CC BY-SA 4.0 , via Wikimedia Commons

Ocean Acidification Is Increasing

Since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30%. 13 , 14 This increase is due to humans emitting more carbon dioxide into the atmosphere and hence more being absorbed into the ocean. The ocean has absorbed between 20% and 30% of total anthropogenic carbon dioxide emissions in recent decades (7.2 to 10.8 billion metric tons per year). 1 5 , 16 Image credit: NOAA

1. IPCC Sixth Assessment Report, WGI, Technical Summary . B.D. Santer et.al., “A search for human influences on the thermal structure of the atmosphere.” Nature 382 (04 July 1996): 39-46. https://doi.org/10.1038/382039a0. Gabriele C. Hegerl et al., “Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method.” Journal of Climate 9 (October 1996): 2281-2306. https://doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2. V. Ramaswamy, et al., “Anthropogenic and Natural Influences in the Evolution of Lower Stratospheric Cooling.” Science 311 (24 February 2006): 1138-1141. https://doi.org/10.1126/science.1122587. B.D. Santer et al., “Contributions of Anthropogenic and Natural Forcing to Recent Tropopause Height Changes.” Science 301 (25 July 2003): 479-483. https://doi.org/10.1126/science.1084123. T. Westerhold et al., "An astronomically dated record of Earth’s climate and its predictability over the last 66 million years." Science 369 (11 Sept. 2020): 1383-1387. https://doi.org/10.1126/science.1094123

2. In 1824, Joseph Fourier calculated that an Earth-sized planet, at our distance from the Sun, ought to be much colder. He suggested something in the atmosphere must be acting like an insulating blanket. In 1856, Eunice Foote discovered that blanket, showing that carbon dioxide and water vapor in Earth's atmosphere trap escaping infrared (heat) radiation. In the 1860s, physicist John Tyndall recognized Earth's natural greenhouse effect and suggested that slight changes in the atmospheric composition could bring about climatic variations. In 1896, a seminal paper by Swedish scientist Svante Arrhenius first predicted that changes in atmospheric carbon dioxide levels could substantially alter the surface temperature through the greenhouse effect. In 1938, Guy Callendar connected carbon dioxide increases in Earth’s atmosphere to global warming. In 1941, Milutin Milankovic linked ice ages to Earth’s orbital characteristics. Gilbert Plass formulated the Carbon Dioxide Theory of Climate Change in 1956.

3. IPCC Sixth Assessment Report, WG1, Chapter 2 Vostok ice core data; NOAA Mauna Loa CO2 record O. Gaffney, W. Steffen, "The Anthropocene Equation." The Anthropocene Review 4, issue 1 (April 2017): 53-61. https://doi.org/abs/10.1177/2053019616688022.

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Header image shows clouds imitating mountains as the sun sets after midnight as seen from Denali's backcountry Unit 13 on June 14, 2019. Credit: NPS/Emily Mesner Image credit in list of evidence: Ashwin Kumar, Creative Commons Attribution-Share Alike 2.0 Generic.

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New Aspects of Diabetes Research and Therapeutic Development

Both type 1 and type 2 diabetes mellitus are advancing at exponential rates, placing significant burdens on health care networks worldwide. Although traditional pharmacologic therapies such as insulin and oral antidiabetic stalwarts like metformin and the sulfonylureas continue to be used, newer drugs are now on the market targeting novel blood glucose–lowering pathways. Furthermore, exciting new developments in the understanding of beta cell and islet biology are driving the potential for treatments targeting incretin action, islet transplantation with new methods for immunologic protection, and the generation of functional beta cells from stem cells. Here we discuss the mechanistic details underlying past, present, and future diabetes therapies and evaluate their potential to treat and possibly reverse type 1 and 2 diabetes in humans.

Significance Statement

Diabetes mellitus has reached epidemic proportions in the developed and developing world alike. As the last several years have seen many new developments in the field, a new and up to date review of these advances and their careful evaluation will help both clinical and research diabetologists to better understand where the field is currently heading.

I. Introduction

Diabetes mellitus, a metabolic disease defined by elevated fasting blood glucose levels due to insufficient insulin production, has reached epidemic proportions worldwide (World Health Organization, 2020 ). Type 1 and type 2 diabetes (T1D and T2D, respectively) make up the majority of diabetes cases with T1D characterized by autoimmune destruction of the insulin-producing pancreatic beta cells. The much more prevalent T2D arises in conjunction with peripheral tissue insulin resistance and beta cell failure and is estimated to increase to 21%–33% of the US population by the year 2050 (Boyle et al., 2010 ). To combat this growing health threat and its cardiac, renal, and neurologic comorbidities, new and more effective diabetes drugs and treatments are essential. As the last several years have seen many new developments in the field of diabetes pharmacology and therapy, we determined that a new and up to date review of these advances was in order. Our aim is to provide a careful evaluation of both old and new therapies ( Fig. 1 ) in a manner that we hope will be of interest to both clinical and bench diabetologists. Instead of the usual encyclopedic approach to this topic, we provide here a targeted and selective consideration of the underlying issues, promising new treatments, and a re-examination of more traditional approaches. Thus, we do not discuss less frequently used diabetes agents, such as alpha-glucosidase inhibitors; these were discussed in other recent reviews (Hedrington and Davis, 2019 ; Lebovitz, 2019 ).

Pharmacologic targeting of numerous organ systems for the treatment of diabetes. Treatment of diabetes involves targeting of various organ systems, including the kidney by SGLT2 inhibitors; the liver, gut, and adipose tissue by metformin; and direct actions upon the pancreatic beta cell. Beta cell compounds aim to increase secretion or mass and/or to protect from autoimmunity destruction. Ultimately, insulin therapy remains the final line of diabetes treatment with new technologies under development to more tightly regulate blood glucose levels similar to healthy beta cells. hESC, human embryonic stem cell.

II. Diabetes Therapies

A. metformin.

Metformin is a biguanide originally based on the natural product galegine, which was extracted from the French lilac (Bailey, 1992 ; Rojas and Gomes, 2013 ; Witters, 2001 ). A closely related biguanide, phenformin, was also used initially for its hypoglycemic actions. Based on its successful track record as a safe, effective, and inexpensive oral medication, metformin has become the most widely prescribed oral agent in the world in treating T2D (Rojas and Gomes, 2013 ; He and Wondisford, 2015 ; Witters, 2001 ), whereas phenformin has been largely bypassed due to its unacceptably high association with lactic acidosis (Misbin, 2004 ). Unlike sulfonylureas, metformin lowers blood glucose without provoking hypoglycemia and improves insulin sensitivity (Bailey, 1992 ). Despite these well known beneficial metabolic actions, metformin’s mechanism of action and even its main target organ remain controversial. In fact, metformin has multiple mechanisms of action at the organ as well as the cellular level, which has hindered our understanding of its most important molecular effects on glucose metabolism (Witters, 2001 ). Adding to this, a specific receptor for metformin has never been identified. Metformin has actions on several tissues, although the primary foci of most studies have been the liver, skeletal muscle, and the intestine (Foretz et al., 2014 ; Rena et al., 2017 ). Metformin and phenformin clearly suppress hepatic glucose production and gluconeogenesis, and they improve insulin sensitivity in the liver and elsewhere (Bailey, 1992 ). The hepatic actions of metformin have been the most exhaustively studied to date, and there is little doubt that these actions are of some importance. However, several of the studies remain highly controversial, and there are still open questions.

One of the first reported specific molecular targets of metformin was mitochondrial complex I of the electron transport chain. Inhibition of this complex results in reduced oxidative phosphorylation and consequently decreased hepatic ATP production (El-Mir et al., 2008 ; Evans et al., 2005 ; Owen et al., 2000 ). As is the case in many other studies of metformin, however, high concentrations of the drug were found to be necessary to depress metabolism at this site (El-Mir et al., 2000 ; He and Wondisford, 2015 ; Owen et al., 2000 ). Also controversial is whether metformin works by activating 5′ AMP-activated protein kinase (AMPK), a molecular energy sensor that is known to be a major metabolic sensor in cells, or if not AMPK directly, then one of its upstream regulators such as liver kinase B2 (Zhou et al., 2001 ). Although metformin was shown to activate AMPK in several excellent studies, other studies directly contradicted the AMPK hypothesis. Most dramatic were studies showing that metformin’s actions to suppress hepatic gluconeogenesis persisted despite genetic deletion of the AMPK’s catalytic domain (Foretz et al., 2010 ). More recent studies identified additional or alternative targets, such as cAMP signaling in the liver (Miller et al., 2013 ) or glycogen synthase kinase-3 (Link, 2003 ). Other work showed that the phosphorylation of acetyl-CoA carboxylase and acetyl-CoA carboxylase 2 are involved in regulating lipid homeostasis and improving insulin sensitivity after exposure to metformin (Fullerton et al., 2013 ).

Although there are strong data to support each of these pathways, it is not entirely clear which signaling pathway(s) is most essential to the actions of metformin in hepatocytes. Metformin clearly inhibits complex I and concomitantly decreases ATP and increases AMP. The latter results in AMPK activation, reduced fatty acid synthesis, and improved insulin receptor activation, and increased AMP has been shown to inhibit adenylate cyclase to reduce cAMP and thus protein kinase A activation. Downstream, this reduces the expression of phosphoenolpyruvate carboxykinase and glucose 6-phosphatase via decreased cAMP response element-binding protein, the cAMP-sensitive transcription factor. Decreased PKA also promotes ATP-dependent 6-phosphofructokinase, liver type activity via fructose 2,6-bisphosphate and reduces gluconeogenesis, as fructose-bisphosphatase 1 is inhibited by fructose 2,6-bisphosphate, along with other mechanisms (Rena et al., 2017 ; Pernicova and Korbonits, 2014 ).

More recent work has shown that metformin at pharmacological rather than suprapharmacological doses increases mitochondrial respiration and complex 1 activity and also increases mitochondrial fission, now thought to be critical for maintaining proper mitochondrial density in hepatocytes and other cells. This improvement in respiratory activity occurs via AMPK activation (Wang et al., 2019 ).

Although the liver has historically been the major suspected site of metformin action, recent studies have suggested that the gut instead of the liver is a major target, a concept supported by the increased efficacy of extended-release formulations of metformin that reside for a longer duration in the gut after their administration (Buse et al., 2016 ). An older, but in our view an important observation, is that the intravenous administration of metformin has little or no effect on blood glucose, whereas, in contrast, orally administered metformin is much more effective (Bonora et al., 1984 ). Recent imaging studies using labeled glucose have shown directly that metformin stimulates glucose uptake by the gut in patients with T2D to reduce plasma glucose concentrations (Koffert et al., 2017 ; Massollo et al., 2013 ). Additionally, it is possible that metformin may exert its effect in the gut by inducing intestinal glucagon-like peptide-1 (GLP-1) release (Mulherin et al., 2011 ; Preiss et al., 2017) to potentiate beta cell insulin secretion and by stimulating the central nervous system (CNS) to exert control over both blood glucose and liver function. Indeed, CNS effects produced by metformin have been proposed to occur via the local release of GLP-1 to activate intestinal nerve endings of ascending nerve pathways that are involved in CNS glucose regulation (Duca et al., 2015 ). Lastly, several papers have now implicated that metformin may act by altering the gut microbiome, suggesting that changes in gut flora may be critical for metformin’s actions (McCreight et al., 2016 ; Wu et al., 2017 ; Devaraj et al., 2016 ). A new study proposed that activation of the intestinal farnesoid X receptor may be the means by which microbiota alter hyperglycemia (Sun et al., 2018 ). However, these studies will require more mechanistic detail and confirmation before they can be fully accepted by the field. In addition to the action of metformin on gut flora, the production of imidazole propionate by gut microbes in turn has been shown to interfere with metformin action through a p38-dependent mechanism and AMPK inhibition. Levels of imidazole propionate are especially higher in patients with T2D who are treated with metformin (Koh et al., 2020 ).

In summary, the combined contribution of these various effects of metformin on multiple cellular targets residing in many tissues may be key to the benefits of metformin treatment on lowering blood glucose in patients with type 2 diabetes (Foretz et al., 2019 ). In contrast, exciting new work showing metformin leads to weight loss by increasing circulating levels of the peptide hormone growth differentiation factor 15 and activation of brainstem glial cell-derived neurotropic factor family receptor alpha like receptors to reduce food intake and energy expenditure works independently of metformin’s glucose-lowering effect (Coll et al., 2020 ).

B. Sulfonylureas and Beta Cell Burnout

The class of compounds known as sulfonylureas includes one of the oldest oral antidiabetic drugs in the pharmacopoeia: tolbutamide. Tolbutamide is a “first generation” oral sulfonylurea secretagogue whose clinical usefulness is due to its prompt stimulation of insulin release from pancreatic beta cells. “Second generation” sulfonylureas include drugs such as glyburide, gliclazide, and glipizide. Sulfonylureas act by binding to a high affinity sulfonylurea binding site, the sulfonylurea receptor 1 subunit of the K(ATP) channel, which closes the channel. These drugs mimic the physiologic effects of glucose, which closes the K(ATP) channel by raising cytosolic ATP/ADP. This in turn provokes beta cell depolarization, resulting in increased Ca 2+ influx into the beta cell (Ozanne et al., 1995 ; Ashcroft and Rorsman, 1989 ; Nichols, 2006 ). Importantly, sulfonylureas, and all drugs that directly increase insulin secretion, are associated with hypoglycemia, which can be severe, and which limits their widespread use in the clinic (Yu et al., 2018 ). Meglitinides are another class of oral insulin secretagogues that, like the sulfonylureas, bind to sulfonylurea receptor 1 and inhibit K(ATP) channel activity (although at a different site of action). The rapid kinetics of the meglitinides enable them to effectively blunt the postprandial glycemic excursions that are a hallmark (along with elevated fasting glucose) of T2D (Rosenstock et al., 2004). However, the need for their frequent dosing (e.g., administration before each meal) has limited their appeal to patients.

The efficacy of sulfonylureas is known to decrease over time, leading to failure of the class for effective long-term treatment of T2D (Harrower, 1991 ). More broadly, it is now widely accepted that the number of functional beta cells in humans declines during the progression of T2D. Thus, one would expect that due to this decline, all manner of oral agents intended to target the beta cell and increase its cell function (and especially insulin secretion) will fail over time (RISE Consortium, 2019 ), a process referred to as “beta cell failure” (Prentki and Nolan, 2006 ). Currently, treatments that can expand beta cell mass or improve beta cell function or survival over time are not yet available for use in the clinic. As a result, treatments that may be able to help patients cope with beta cell burnout such as islet cell transplantation, insulin pumps, or stem cell therapy are alternatives that will be discussed below.

C. Ca 2+ Channel Blockers and Type 1 Diabetes

Strategies to treat and prevent T1D have historically focused on ameliorating the toxic consequences of immune dysregulation resulting in autoimmune destruction of pancreatic beta cells. More recently, a concerted focus on alleviating the intrinsic beta cell defects (Sims et al., 2020 ; Soleimanpour and Stoffers, 2013 ) that also contribute to T1D pathogenesis have been gaining traction at both the bench and the bedside. Several recent preclinical studies suggest that Ca 2+ -induced metabolic overload induces beta cell failure (Osipovich et al., 2020 ; Stancill et al., 2017 ; Xu et al., 2012 ), with the potential that excitotoxicity contributes to beta cell demise in both T1D and T2D, similar to the well known connection between excitotoxicity and, concomitantly, increased Ca 2+ loading of the cells and neuronal dysfunction. Indeed, the use of the phenylalkylamine Ca 2+ channel blocker verapamil has been successful in ameliorating beta cell dysfunction in preclinical models of both T1D and T2D (Stancill et al., 2017 ; Xu et al., 2012 ). Verapamil is a well known blocker of L-type Ca 2+ channels, and, in normally activated beta cells, it limits Ca 2+ entry into the beta cell (Ohnishi and Endo, 1981 ; Vasseur et al., 1987 ). This would be expected to, in turn, alter the expression of many Ca 2+ influx–dependent beta cell genes (Stancill et al., 2017 ), and the evidence to date suggests it is likely that verapamil preserves beta cell function in diabetes models by repressing thioredoxin-interacting protein (TXNIP) expression and thus protecting the beta cell. This is somewhat surprising given the physiologic role of Ca 2+ is to acutely trigger insulin secretion; this process would be expected to be inhibited by L-type Ca 2+ channel blockers (Ashcroft and Rorsman, 1989 ; Satin et al., 1995 ).

Hyperglycemia is a well known inducer of TXNIP expression, and a lack of TXNIP has been shown to protect against beta cell apoptosis after inflammatory stress (Chen et al., 2008a ; Shalev et al., 2002 ; Chen et al., 2008b ). Excitingly, the use of verapamil in patients with recent-onset T1D improved beta cell function and improved glycemic control for up to 12 months after the initiation of therapy, suggesting there is indeed promise for targeting calcium and TXNIP activation in T1D. Use of verapamil for a repurposed indication in the preservation of beta cell function in T1D is attractive due its well known safety profile as well as its cardiac benefits (Chen et al., 2009 ). Although the long-term efficacy of verapamil to maintain beta cell function in vivo is unclear, a recently described TXNIP inhibitor may also show promise in suppressing the hyperglucagonemia that also contributes to glucose intolerance in T2D (Thielen et al., 2020 ). As there is a clear need for increased Ca 2+ influx into the beta cell to trigger and maintain glucose-dependent insulin secretion (Ashcroft and Rorsman, 1990 ; Satin et al., 1995 ), it remains to be seen how well regulated insulin secretion is preserved in the presence of L-type Ca 2+ channel blockers like verapamil in the system. One might speculate that reducing but not fully eliminating beta cell Ca 2+ influx might reduce TXNIP levels while preserving enough influx to maintain glucose-stimulated insulin release. Alternatively, these two phenomena may operate on entirely different time scales. At present, these issues clearly will require further investigation.

D. GLP-1 and the Incretins

Studies dating back to the 1960s revealed that administering glucose in equal amounts via the peripheral circulation versus the gastrointestinal tract led to dramatically different amounts of glucose-induced insulin secretion (Elrick et al., 1964 ; McIntyre et al., 1964 ; Perley and Kipnis, 1967 ). Gastrointestinal glucose administration greatly increased insulin secretion versus intravenous glucose, and this came to be known as the “incretin effect” (Nauck et al., 1986a ; Nauck et al., 1986b ). Subsequent work showed that release of the gut hormone GLP-1 mediated this effect such that food ingestion induced intestinal cell hormone secretion. GLP-1 so released would then circulate to the pancreas via the blood to prime beta cells to secrete more insulin when glucose became elevated because these hormones stimulated beta cell cAMP formation (Drucker et al., 1987 ). The discovery that a natural peptide corresponding to GLP-1 could be found in the saliva of the Gila monster, a desert lizard, hastened progress in the field, and ample in vitro studies subsequently confirmed that GLP-1 potentiated insulin secretion in a glucose-dependent manner. GLP-1 has little or no significant action on insulin secretion in the absence of elevated glucose (such as might typically correspond to the postprandial case or during fasting), thus minimizing the likelihood of hypoglycemia provoked by GLP-1 in treated patients (Kreymann et al., 1987 ). Although not completely understood, the glucose dependence of GLP-1 likely reflects the requirement for adenine nucleotides to close glucose-inhibited K(ATP) channels and thus subsequently activate Ca 2+ influx–dependent insulin exocytosis. Besides potentiating GSIS at the level of the beta cell, glucagon-like peptide-1 receptor (GLP-1R) agonists also decrease glucagon secretion from pancreatic islet alpha cells, reduce gastric emptying, and may also increase beta cell proliferation, among other cellular actions (reviewed in Drucker, 2018 ; Muller et al., 2019).

Intense interest in the incretins by basic scientists, clinicians, and the pharma community led to the rapid development of new drugs for treating primarily T2D. These drugs include a range of GLP-1R agonists and inhibitors of the incretin hormone degrading enzyme dipeptidyl peptidase 4 (DPP4), whose targeting increases the half-lives of GLP-1 and gastric inhibitory polypeptide (GIP) and thereby increases protein hormone levels in plasma. GLP-1R agonists have been associated with not only a lowering of plasma glucose but also weight loss, decreased appetite, reduced risk of cardiovascular events, and other favorable outcomes (Gerstein et al., 2019; Hernandez et al., 2018; Husain et al., 2019; Marso et al., 2016a; Marso et al., 2016b ; Buse et al., 2004). Regarding their untoward actions, although hypoglycemia is not a major concern, there have been reports of pancreatitis and pancreatic cancer from use of GLP-1R agonists. However, a recent meta-analysis covering four large-scale clinical trials and over 33,000 participants noted no significantly increased risk for pancreatitis/pancreatic cancer in patients using GLP-1R agonists (Bethel et al., 2018).

Ongoing and future developments in the use of proglucagon-derived peptides such as GLP-1 and glucagon include the use of combined GLP-1/GIP, glucagon/GLP-1, and agents targeting all three peptides in combination (reviewed in Alexiadou and Tan, 2020 ). Although short-term infusions of GLP-1 with GIP failed to yield metabolic benefits beyond those seen with GLP-1 alone (Bergmann et al., 2019 ), several GLP-1/GIP dual agonists are currently in development and have shown promising metabolic results in clinical trials (Frias et al., 2017 ; Frias et al., 2020 ; Frias et al., 2018 ). At the level of the pancreatic islet, beneficial effects of dual GLP-1/GIP agonists may be related to imbalanced and biased preferences of these agonists for the gastric inhibitory polypeptide receptor over the GLP-1R (Willard et al., 2020 ) and possibly were not simply to dual hormone agonism in parallel. Dual glucagon/GLP-1 agonist therapy has also been shown to have promising metabolic effects in humans (Ambery et al., 2018 ; Tillner et al., 2019 ). Oxyntomodulin is a natural dual glucagon/GLP-1 receptor agonist and proglucagon cleavage product that is also secreted from intestinal enteroendocrine cells, which has beneficial effects on insulin secretion, appetite regulation, and body weight in both humans and rodents (Cohen et al., 2003 ; Dakin et al., 2001 ; Dakin et al., 2002 ; Shankar et al., 2018 ; Wynne et al., 2005 ). Interestingly, alpha cell crosstalk to beta cells through the combined effects of glucagon and GLP-1 is necessary to obtain optimal glycemic control, suggesting a potential pathway for therapeutic dual glucagon/GLP-1 agonism within the islets of patients with T2D (Capozzi et al., 2019a ; Capozzi et al., 2019b ). Although the early results appear promising, more studies will be necessary to better understand the mechanistic and clinical impacts of these multiagonist agents.

E. DPP4 Inhibitors

Inhibition of DPP4, the incretin hormone degrading enzyme, is one of the most common T2D treatments to increase GLP-1 and GIP plasma hormone levels. These DPP4 inhibitors or “gliptins” are generally used in conjunction with other T2D drugs such as metformin or sulfonylureas to obtain the positive benefits discussed above (Lambeir et al., 2008 ). DPP4 is a primarily membrane-bound peptidase belonging to the serine peptidase/prolyl oligopeptidase gene family, which cleaves a large number of substrates in addition to the incretin hormones (Makrilakis, 2019 ). DPP4 inhibitors provide glucose-lowering benefits while being generally well tolerated, and the variety of available drugs (including sitagliptin, saxagliptin, vildagliptin, alogliptin, and linagliptin) with slightly different dosing frequency, half-life, and mode of excretion/metabolism allows for use in multiple patient populations (Makrilakis, 2019 ). This includes the elderly and individuals with renal or hepatic insufficiency (Makrilakis, 2019 ).

Although hypoglycemia is not a concern for DPP4 inhibitor use, other considerations should be made. DPP4 inhibitors tend to be more expensive than metformin or other second-line oral drugs in addition to having more modest glycemic effects than GLP-1R agonists (Munir and Lamos, 2017 ). Finally, meta-analysis of randomized and observational studies concluded that heart failure in patients with T2D was not associated with use of DPP4 inhibitors; however, this study was limited by the short follow-up and lack of high-quality data (Li et al., 2016 ). Thus, the US Food and Drug Administration (FDA) did recommend assessing risk of heart failure hospitalization in patients with pre-existing cardiovascular disease, prior heart failure, and chronic kidney disease when using saxagliptin and alogliptin (Munir and Lamos, 2017 ).

F. Sodium Glucose Cotransporter 2 Inhibitors

A recent development in the field of T2D drugs are sodium glucose cotransporter 2 (SGLT2) inhibitors, which have an interesting and very different mechanism of action. Within the proximal tubule of the nephron, SGLT2 transports ingested glucose into the lumen of the proximal tubule between the epithelial layers, thereby reclaiming glucose by this reabsorption process (reviewed in Vallon, 2015 ). SGLT2 inhibitors target this transporter and increase glucose in the tubular fluid and ultimately increase it in the urine. In patients with diabetes, SGLT2 inhibition results in a lowering of plasma glucose with urine glucose content rising substantially (Adachi et al., 2000 ; Vallon, 2015 ). These drugs, although they are relatively new, have become an area of great interest for not only patients with T2D (Grempler et al., 2012 ; Imamura et al., 2012 ; Meng et al., 2008 ; Nomura et al., 2010 ) but also for patients with T1D (Luippold et al., 2012 ; Mudaliar et al., 2012 ). Part of their appeal also rests on reports that their use can lead to a statistically significant decline in cardiac events that are known to occur secondarily to diabetes, possibly independently of plasma glucose regulation (reviewed in Kurosaki and Ogasawara, 2013 ). Although the long-term consequences of their clinical use cannot yet be determined, raising the glucose content of the urogenital tract leads to an increased risk of urinary tract infections and other related infections in some patients (Kurosaki and Ogasawara, 2013 ).

Another recent concern about the use of SGLT2 inhibitors has been the development of normoglycemic diabetic ketoacidosis (DKA). Despite the efficacy of SGLT2 inhibitors, observations of hyperglucagonemia in patients with euglycemic DKA has led to a number of recent studies focused on SGLT2 actions on pancreatic islets. Initial studies of isolated human islets treated with small interfering RNA directed against SGLT2 and/or SGLT2 inhibitors demonstrated increased glucagon release. These studies were complemented by the finding of elevations in glucagon release in mice that were administered SGLT2 inhibitors in vivo (Bonner et al., 2015 ). Insights into the possible mechanistic links between SGLT2 inhibition, DKA frequency, and glucagon secretion in humans may relate to the observation of heterogeneity in SGLT2 expression, as SGLT2 expression appears to have a high frequency of interdonor and intradonor variability (Saponaro et al., 2020 ). More recently, both insulin and GLP-1 have been demonstrated to modulate SGLT2-dependent glucagon release through effects on somatostatin release from delta cells (Vergari et al., 2019 ; Saponaro et al., 2019 ), suggesting potentially complex paracrine effects that may affect the efficacy of these compounds.

On the other hand, several recent studies question that the development of euglycemic DKA after SGLT2 inhibitor therapy may be through alpha cell–dependent mechanisms. Three recent studies found no effect of SGLT2 inhibitors to promote glucagon secretion in mouse and/or rat models and could not detect SGLT2 expression in human alpha cells (Chae et al., 2020 ; Kuhre et al., 2019 ; Suga et al., 2019 ). A fourth study demonstrated only a brief transient effect of SGLT2 inhibition to raise circulating glucagon concentrations in immunodeficient mice transplanted with human islets, which returned to baseline levels after longer exposures to SGLT2 inhibitors (Dai et al., 2020 ). Furthermore, SGLT2 protein levels were again undetectable in human islets (Dai et al., 2020 ). These results could suggest alternative islet-independent mechanisms by which patients develop DKA, including alterations in ketone generation and/or clearance, which underscore the additional need for further studies both in molecular models and at the bedside. Nevertheless, SGLT2 inhibitors continue to hold promise as a valuable therapy for T2D, especially in the large segment of patients who also have superimposed cardiovascular risk (McMurray et al., 2019; Wiviott et al., 2019; Zinman et al., 2015).

G. Thiazolidinediones

Once among the most commonly used oral agents in the armamentarium to treat T2D, thiazolidinediones (TZDs) were clinically popular in their utilization to act specifically as insulin sensitizers. TZDs improve peripheral insulin sensitivity through their action as peroxisome proliferator-activated receptor (PPAR) γ agonists, but their clinical use fell sharply after studies suggested a connection between cardiovascular toxicity with rosiglitazone and bladder cancer risk with pioglitazone (Lebovitz, 2019 ). Importantly, an FDA panel eventually removed restrictions related to cardiovascular risk with rosiglitazone in 2013 (Hiatt et al., 2013 ). Similarly, concerns regarding use of bladder cancer risk with pioglitazone were later abated after a series of large clinical studies found that pioglitazone did not increase bladder cancer (Lewis et al., 2015 ; Schwartz et al., 2015 ). However, usage of TZDs had already substantially decreased and has not since recovered.

Although concerns regarding edema, congestive heart failure, and fractures persist with TZD use, there have been several studies suggesting that TZDs protect beta cell function. In the ADOPT study, use of rosiglitazone monotherapy in patients newly diagnosed with T2D led to improved glycemic control compared with metformin or sulfonylureas (Kahn et al., 2006). Later analyses revealed that TZD-treated subjects had a slower deterioration of beta cell function than metformin- or sulfonylurea-treated subjects (Kahn et al., 2011). Furthermore, pioglitazone use improved beta cell function in the prevention of T2D in the ACT NOW study (Defronzo et al., 2013; Kahn et al., 2011). Mechanistically, it is unclear if TZDs lead to beneficial beta cell function through direct effects or through indirect effects of reduced beta cell demand due to enhanced peripheral insulin sensitivity. Indeed, a beta cell–specific knockout of PPAR γ did not impair glucose homeostasis, nor did it impair the antidiabetic effects of TZD use in mice (Rosen et al., 2003 ). However, other reports demonstrated PPAR-responsive elements within the promoters of both glucose transporter 2 and glucokinase that enhance beta cell glucose sensing and function, which could explain beta cell–specific benefits for TZDs (Kim et al., 2002 ; Kim et al., 2000 ). Furthermore, TZDs have been shown to improve beta cell function by upregulating cholesterol transport (Brunham et al., 2007 ; Sturek et al., 2010 ). Additionally, use of TZDs in the nonobese diabetic (NOD) mouse model of T1D augmented the beta cell unfolded protein response and prevented beta cell death, suggesting potential benefits for TZDs in both T1D and T2D (Evans-Molina et al., 2009 ; Maganti et al., 2016 ). With a now refined knowledge of demographics in which to avoid TZD treatment due to adverse effects, together with genetic approaches to identify candidates more likely to respond effectively to TZD therapy (Hu et al., 2019 ; Soccio et al., 2015 ), it remains to be seen if TZD therapy will return to more prominent use in the treatment of diabetes.

H. Insulin and Beyond: The Use of “Smart” Insulin and Closed Loop Systems in Diabetes Treatment

Due to recombinant DNA technology, numerous insulin analogs are now available in various forms ranging from fast acting crystalline insulin to insulin glargine; all of these analogs exhibit equally effective insulin receptor binding. Most are generated by altering amino acids in the B26–B30 region of the molecule (Kurtzhals et al., 2000 ). The American Diabetes Association delineates these insulins by their 1) onset or time before insulin reaches the blood stream, 2) peak time or duration of maximum blood glucose–lowering efficacy, and 3) the duration of blood glucose–lowering time. Insulin administration is independent of the residuum of surviving and/or functioning beta cells in the patient and remains the principal pharmacological treatment of both T1D and T2D. The availability of multiple types of delivery methods, i.e., insulin pens, syringes, pumps, and inhalants, provides clinicians with a solid and varied tool kit with which to treat diabetes. The downsides, however, are that 1) hypoglycemia is a constant threat, 2) proper insulin doses are not trivial to calculate, 3) compliance can vary especially in children and young adults, and 4) there can be side effects of a variety of types. Nonetheless, insulin therapy remains a mainstay treatment of diabetes.

To eliminate the downsides of insulin therapy, research in the past several decades has worked toward generating glucose-sensitive or “smart” insulin molecules. These molecules change insulin bioavailability and become active only upon high blood glucose using glucose-binding proteins such as concanavalin A, glucose oxidase to alter pH sensitivity, and phenylboronic acid (PBA), which forms reversible ester linkages with diol-containing molecules including glucose itself (reviewed in Rege et al., 2017 ). Indeed, promising recent studies included various PBA moieties covalently bonded to an acylated insulin analog (insulin detemir, which contains myristic acid coupled to Lys B29 ). The detemir allows for binding to serum albumin to prolong insulin’s half-life in the circulation, and PBA provided reversible glucose binding (Chou et al., 2015 ). The most promising of the PBA-modified conjugates showed higher potency and responsiveness in lowering blood glucose levels compared with native insulin in diabetic mouse models and decreased hypoglycemia in healthy mice, although the molecular mechanisms have not yet been determined (Chou et al., 2015 ).

An additional active area of research includes structurally defining the interaction between insulin and the insulin receptor ectodomain. Importantly, a major conformational change was discovered that may be exploited to impair insulin receptor binding under hypoglycemic conditions (Menting et al., 2013 ; Rege et al., 2017 ). Challenges in the design, testing, and execution of glucose-responsive insulins may be overcome by the adaptation of novel modeling approaches (Yang et al., 2020 ), which may allow for more rapid screening of candidate compounds.

Technologies have also progressed in the field of artificial pancreas design and development. Currently two “closed loop” systems are now available: Minimed 670G from Medtronic and Control-IQ from Tandem Diabetes Care. Both systems use a continuous glucose monitor, insulin pump, and computer algorithm to predict correct insulin doses and administer them in real time. Such algorithm systems also take into account insulin potency, the rate of blood glucose increase, and the patient’s heart rate and temperature to adjust insulin delivery levels during exercise and after a meal. In addition, so-called “artificial pancreas” systems have also been clinically tested, which use both insulin and glucagon and as such result in fewer reports of hypoglycemic episodes (El-Khatib et al., 2017 ). These types of systems will continue to become more popular as the development of room temperature–stable glucagon analogs continue, such as GVOKE by Xeris Pharmaceuticals (currently available in an injectable syringe) and Baqsimi, a nasally administered glucagon from Eli Lilly.

I. Present and Future Therapies: Beta Cell Transplantation, Replication, and Immune Protection

1. islet transplantation.

The idea to use pancreatic allo/xenografts to treat diabetes remarkably dates back to the late 1800s (Minkowski, 1892 ; Pybus, 1924 ; Williams, 1894 ). Before proceeding to the discovery of insulin (together with Best, MacLeod, and Collip), Frederick Banting also postulated the potential for transplantation of pancreatic tissue emulsions to treat diabetes in dog models in a notebook entry in 1921 (Bliss, 1982 ). Decades later, Paul Lacy, David Scharp, and colleagues successfully isolated intact functional pancreatic islets and transplanted them into rodent models (Kemp et al., 1973 ). These studies led to the initial proof of concept studies for humans, with the first successful islet transplant in a patient with T1D occurring in 1977 (Sutherland et al., 1978 ). A rapid expansion of islet transplantation, inspired by these original studies led to key observations of successfully prolonged islet engraftment by the “Edmonton protocol” whereby corticosteroid-sparing immunosuppression was applied, and islets from at least two allogeneic donors were used to achieve insulin independence (Shapiro et al., 2000 ). More recent work has focused on improving upon the efficiency and long-term engraftment of allogeneic transplants leading to more prolonged graft function (to the 5-year mark) and successful transplantation from a single islet donor (Hering et al., 2016; Hering et al., 2005 ; Rickels et al., 2013 ). Critical to these efforts to improve the success rate was the recognition that the earlier generation of immunosuppressive agents to counter tissue rejection was toxic to islets (Delaunay et al., 1997 ; Paty et al., 2002 ; Soleimanpour et al., 2010 ) and that more appropriate and less toxic agents were needed (Hirshberg et al., 2003 ; Soleimanpour et al., 2012 ).

Certainly, islet transplantation as a therapeutic approach for patients with T1D has been scrutinized due to several challenges, including (but not limited to) the lack of available donor supply to contend with demand, limited long-term functional efficacy of islet allografts, the potential for re-emergence of autoimmune islet destruction and/or metabolic overload-induced islet failure, and significant adverse effects of prolonged immunosuppression (Harlan, 2016 ). Furthermore, although islet transplantation is not currently available for individuals with T2D, simultaneous pancreas-kidney transplantation in T2D had similar favorable outcomes to simultaneous pancreas-kidney transplantation in T1D; therefore, islet-kidney transplantation may eventually be a feasible option to treat T2D, as patients will already be on immunosuppressors (Sampaio et al., 2011 ; Westerman et al., 1983 ). An additional significant obstacle is the tremendous expense associated with islet transplantation therapy. Indeed, the maintenance, operation, and utilization of an FDA-approved and Good Manufacturing Practice–compliant islet laboratory can lead to operating costs at nearly $150,000 per islet transplant, which is not cost effective for the vast majority of patients with T1D (Naftanel and Harlan, 2004 ; Wallner et al., 2016 ). At present, the focus has been to obtain FDA approval for islet allo-transplantation as a therapy for T1D to allow for insurance compensation (Hering et al., 2016; Rickels and Robertson, 2019 ). In the interim, the islet biology, stem cell, immunology, and bioengineering communities have continued the development of cell-based therapies for T1D by other approaches to overcome the challenges identified during the islet transplantation boom of the 1990s and 2000s.

2. Pharmacologic Induction of Beta Cell Replication

Besides transplantation, progress in islet cell biology and especially in developmental biology of beta cells over several decades raised the additional possibility that beta cell mass reduction in diabetes might be countered by increasing beta cell number through mitogenic means. A key method to expand pancreatic beta cell mass is through the enhancement of beta cell replication. Although the study of pancreatic beta cell replication has been an area of intense focus in the beta cell biology field for several decades, only recently has this seemed truly feasible. Seminal studies identified that human beta cells are essentially postmitotic, with a rapid phase of growth occurring in the prenatal period that dramatically tapers off shortly thereafter (Gregg et al., 2012 ; Meier et al., 2008 ). The plasticity of rodent beta cells is considerably higher than that of human beta cells (Dai et al., 2016 ), which has led to a renewed focus on validation of pharmacologic agents to enhance rodent beta cell replication using isolated and/or engrafted human islets (Bernal-Mizrachi et al., 2014 ; Kulkarni et al., 2012 ; Stewart et al., 2015 ). Indeed, a large percentage of agents that were successful when applied to rodent systems were largely unsuccessful at inducing replication in human beta cells (Bernal-Mizrachi et al., 2014 ; Kulkarni et al., 2012 ; Stewart et al., 2015 ). However, several recent studies have begun to make significant progress on successfully pushing human beta cells to replicate.

Several groups have reported successful human beta cell proliferation, both in vitro and in vivo, in response to inhibitors of the dual specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A). These inhibitors include harmine, INDY, GNF4877, 5-iodotubericidin, leucettine-42, TG003, AZ191, CC-401, and more specific, recently developed DYRK1A inhibitors (Ackeifi et al., 2020 ). Although DYRK1A is conclusively established as the important mediator of human beta cell proliferation, comprehensively determining other cellular targets and if additional gene inhibition amplifies the proliferative response is still in process. New evidence from Wang and Stewart shows dual specificity tyrosine phosphorylation-regulated kinase 1B to be an additional mitogenic target and also describes variability in the range of activated kinases within cells and/or levels of inhibition for the many DYRK1A inhibitors listed above (Ackeifi et al., 2020 ). Interestingly, opposite to these human studies, earlier mouse studies from the Scharfmann group demonstrated that Dyrk1a haploinsufficiency leads to decreased proliferation and loss of beta cell mass (Rachdi et al., 2014b ). In addition, overexpression of Dyrk1a in mice led to beta cell mass expansion with increased glucose tolerance (Rachdi et al., 2014a ).

Although important differences in beta cell proliferative capacity have been shown between human and rodent species, there are also significant differences in the mitogenic capacity of beta cells from juvenile, adult, and pregnant individuals. This demonstrates that proliferative stimuli appear to act within the complex islet, pancreas, and whole-body environments unique to each time point. For example, the administration of the hormones platelet-derived growth factor alpha or GLP-1 result in enhanced proliferation in juvenile human beta cells yet are ineffective in adult human beta cells (Chen et al., 2011 ; Dai et al., 2017 ). This has been shown to be due to a loss of platelet-derived growth factor alpha receptor expression as beta cells age but appears to be unrelated to GLP-1 receptor expression levels (Chen et al., 2011 ). Indeed, the GLP-1 receptor is highly expressed in adult beta cells, and GLP-1 secretion increases insulin secretion, as detailed previously; however, the induction of proliferative factors such as nuclear factor of activated T cells, cytoplasmic 1; forkhead box protein 1; and cyclin A1 is only seen in juvenile islets (Dai et al., 2017 ). Human studies using cadaveric pancreata from pregnant donors also showed increased beta cell mass, yet lactogenic hormones from the pituitary or placenta (prolactin, placental lactogen, or growth hormone) are unable to stimulate proliferation in human beta cells despite their ability to produce robust proliferation in mouse beta cells (reviewed in Baeyens et al., 2016 ). Experiments overexpressing mouse versus human signal transducer and activator of transcription 5, the final signaling factor inducing beta cell adaptation, in human beta cells allows for prolactin-mediated proliferation revealing fundamental differences in prolactin pathway competency in human (Chen et al., 2015 ). Overcoming the barrier of recapitulating human pregnancy’s effect on beta cells through isolating placental cells or blood serum during pregnancy may result in the discovery of a factor(s) that facilitates the increase in beta cell mass observed during human pregnancy.

Mechanisms that stimulate beta cell proliferation have also been discovered from studying genetic mutations that result in insulinomas, spontaneous insulin-producing beta cell adenomas. The most common hereditary mutation occurs in the multiple endocrine neoplasia type 1 (MEN1) gene. Indeed, administration of a MEN1 inhibitor in addition to a GLP-1 agonist (which cannot induce proliferation alone) is able to increase beta cell proliferation in isolated human islets through synergistic activation of KRAS proto-oncogene, GTPase downstream signals (Chamberlain et al., 2014 ). Interestingly, MEN1 mutations are uncommon in sporadic insulinomas, yet assaying genomic and epigenetic changes in a large cohort of non-MEN1 insulinomas found alterations in trithorax and polycomb chromatin modifying genes that were functionally related to MEN1 (Wang et al., 2017 ). Stewart and colleagues hypothesized that changes in histone 3 lysine 27 and histone 3 lysine 4 methylation status led to increased enhancer of zeste homolog 2 and lysine demethylase 6A, decreased cyclin-dependent kinase inhibitor 1C, and thereby increased beta cell proliferation, among other phenotypes. They also proposed that these findings help to explain why increased proliferation always occurs despite broad heterogeneity of mutations found between individual insulinomas (Wang et al., 2017 ).

Although factors that induce proliferation are continuing to be discovered, there are drawbacks that still limit their clinical application. Harmine and other DYRK1A inhibitors are not beta cell specific, nor have all their cellular targets been determined (Ackeifi et al., 2020 ). Targeting other pathways to induce human beta cell proliferation such as modulation of prostaglandin E2 receptors (i.e., inhibition of prostaglandin E receptor 3 alone or in combination with prostaglandin E receptor 4 activation) showed promising increases in proliferative rate yet suffers from the same lack of specificity (Carboneau et al., 2017 ). Induction of proliferation may also come at the expense of glucose sensing as in insulinomas, which have an increased expression of “disallowed genes” and alterations in glucose transporter and hexokinase expression (Wang et al., 2017 ). A further untoward consequence that must be avoided is the production of cancerous cells through unchecked proliferation. Finally, increasing beta cell mass through low rates of proliferation may increase the pool of functional insulin-secreting cells in T2D, but without additional measures, these beta cells will still ultimately be targeted for immune cell destruction in T1D.

3. Beta Cell Stress Relieving Therapies

Metabolic, inflammatory, and endoplasmic reticulum (ER) stress contribute to beta cell dysfunction and failure in both T1D and T2D. Although reduction of metabolic overload of beta cells by early exogenous insulin therapy or insulin sensitizers can temporarily reduce loss of beta cell mass/function early in diabetes, a focus on relieving ER and inflammatory stress is also of interest to preserve beta cell health.

ER stress is a well known contributor to beta cell demise both in T1D and T2D (Laybutt et al., 2007 ; Marchetti et al., 2007 ; Marhfour et al., 2012 ; Tersey et al., 2012 ) and a target of interest in the prevention of beta cell loss in both diseases. Preclinical studies suggest that the use of chemical chaperones, including 4-phenylbutyric acid and tauroursodeoxycholic acid (TUDCA), to alleviate ER stress improves beta cell function and insulin sensitivity in mouse models of T2D (Cnop et al., 2017 ; Ozcan et al., 2006 ). Furthermore, TUDCA has been shown to preserve beta cell mass and reduce ER stress in mouse models of T1D (Engin et al., 2013 ). Interestingly, TUDCA has shown promise at improving insulin action in obese nondiabetic human subjects, yet beta cell function and insulin secretion were not assessed (Kars et al., 2010 ). A clinical trial regarding the use of TUDCA for humans with new-onset T1D is also ongoing ( {"type":"clinical-trial","attrs":{"text":"NCT02218619","term_id":"NCT02218619"}} NCT02218619 ). However, a note of caution regarding use of ER chaperones is that they may prevent low level ER stress necessary to potentiate beta cell replication during states of increased insulin demand (Sharma et al., 2015 ), suggesting that the broad use of ER chaperone therapies should be carefully considered.

The blockade of inflammatory stress has long been an area of interest for treatments of both T1D and T2D (Donath et al., 2019 ; Eguchi and Nagai, 2017 ). Indeed, use of nonsteroidal anti-inflammatory drugs (NSAIDs), which block cyclooxygenase, have been observed to improve metabolic control in patients with diabetes since the turn of the 20th century (Williamson, 1901 ). Salicylates have been shown to improve insulin secretion and beta cell function in both obese human subjects and those with T2D (Fernandez-Real et al., 2008; Giugliano et al., 1985 ). However, another NSAID, salsalate, has not been shown to improve beta cell function while improving other metabolic outcomes (Kim et al., 2014 ; Penesova et al., 2015 ), possibly suggesting distinct mechanisms of action for anti-inflammatory compounds. The regular use of NSAIDs to enhance metabolic outcomes is also often limited to the tolerability of long-term use of these agents due to adverse effects. Recently, golilumab, a monoclonal antibody against the proinflammatory cytokine tumor necrosis factor alpha, was demonstrated to improve beta cell function in new-onset T1D, suggesting that targeting the underlying inflammatory milieu may have benefits to preserve beta cell mass and function in T1D (Quattrin et al., 2020). Taken together, both new and old approaches to target beta cell stressors still remain of long-term interest to improve beta cell viability and function in both T1D and T2D.

3. New Players to Induce Islet Immune Protection

Countless researchers have expended intense industry to determine T1D disease etiology and treatments focused on immunotherapy and tolerogenic methods. Multiple, highly comprehensive reviews are available describing these efforts (Goudy and Tisch, 2005 ; Rewers and Gottlieb, 2009 ; Stojanovic et al., 2017 ). Here we will focus on the protection of beta cells through programmed cell death protein-1 ligand (PD-L1) overexpression, major histocompatibility complex class I, A, B, C (HLA-A,B,C) mutated human embryonic stem cell–derived beta cells, and islet encapsulation methods.

Cancer immunotherapies that block immune checkpoints are beneficial for treating advanced stage cancers, yet induction of autoimmune diseases, including T1D, remains a potential side effect (Stamatouli et al., 2018 ; Perdigoto et al., 2019 ). A subset of these drugs target either the programmed cell death-1 protein on the surface of activated T lymphocytes or its receptor PD-L1 (Stamatouli et al., 2018 ; Perdigoto et al., 2019 ). PD-L1 expression was found in insulin-positive beta cells from T1D but not insulin-negative islets or nondiabetic islets, leading to the hypothesis that PD-L1 is upregulated in an attempt to drive immune cell attenuation (Osum et al., 2018 ; Colli et al., 2018 ). Adenoviral overexpression of PD-L1 specifically in beta cells rescued hyperglycemia in the NOD mouse model of T1D, but these animals eventually succumbed to diabetes by the study’s termination (El Khatib et al., 2015 ). A more promising report from Ben Nasr et al. ( 2017 ) demonstrated that pharmacologically or genetically induced overexpression of PD-L1 in hematopoietic stem and progenitor cells inhibited beta cell autoimmunity in the NOD mouse as well as in vitro using human hematopoietic stem and progenitor cells from patients with T1D.

As mentioned above, islet transplantation to treat T1D is limited by islet availability, cost, and the requirement for continuous immunosuppression. Islet cells generated by differentiating embryonic or induced pluripotent stem (iPS) cells could circumvent these limitations. Ideally, iPS-derived beta cells could be manipulated to eliminate the expression of polymorphic HLA-A,B,C molecules, which were found to be upregulated in T1D beta cells (Bottazzo et al., 1985 ; Richardson et al., 2016 ). These molecules allow peptide presentation to CD8+ T cells or cytotoxic T lymphocytes and may lead to beta cell removal. Interestingly, remaining insulin-positive cells in T1D donor pancreas are not HLA-A,B,C positive (Nejentsev et al., 2007; Rodriguez-Calvo et al., 2015 ). However, current differentiation protocols are still limited in their ability to produce fully glucose-responsive beta cells without transplantation into animal models to induce mature characteristics. Additionally, use of iPS-derived beta cells will still lead to concerns regarding DNA mutagenesis resulting from the methods used to obtain pluripotency or teratoma formation from cells that have escaped differentiation.

Encapsulation devices would protect islets or stem cells from immune cell infiltration while allowing for the proper exchange of nutrients and hormones. Macroencapsulation uses removable devices that would help assuage fears surrounding mutation or tumor formation; indeed, the first human trial using encapsulated hESC-derived beta cells will be completed in January 2021 ( {"type":"clinical-trial","attrs":{"text":"NCT02239354","term_id":"NCT02239354"}} NCT02239354 ). Macroencapsulation of islets prior to transplantation using various alginate-based hydrogels has historically been impeded by a strong in vivo foreign body immune response (Desai and Shea, 2017 ; Doloff et al., 2017 ; Pueyo et al., 1993 ). More recently, chemically modified forms of alginate that avoid macrophage recognition and fibrous deposition have been successfully used in rodents and for up to 6 months in nonhuman primates (Vegas et al., 2016 ). Indeed, Bochenek et al. ( 2018 ) successfully transplanted alginate protected islets for 4 months without immunosuppression in the bursa omentalis of nonhuman primates demonstrating the feasibility for this approach to be extended to humans. It remains to be seen if these devices will be successful for long-term use, perhaps decades, in patients with diabetes.

III. Summary

Although existing drug therapies using classic oral antidiabetic drugs like sulfonylureas and metformin or injected insulin remain mainstays of diabetes treatment, newer drugs based on incretin hormone actions or SGLT2 inhibitors have increased the pharmacological armamentarium available to diabetologists ( Fig. 1 ). However, the explosion of progress in beta cell biology has identified potential avenues that can increase beta cell mass in sophisticated ways by employing stem cell differentiation or enhancement of beta cell proliferation. Taken together, there should be optimism that the increased incidence of both T1D and T2D is being matched by the creativity and hard work of the diabetes research community.

Abbreviations

Authorship contributions.

Wrote and contributed to the writing of the manuscript: Satin, Soleimanpour, Walker

This work was supported by the National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) [Grant R01-DK46409] (to L.S.S.), [Grant R01-DK108921] (to S.A.S.), and [Grant P30-DK020572 pilot and feasibility grant] (to S.A.S.), the Juvenile Diabetes Research Foundation (JDRF) [Grant CDA-2016-189] (to L.S.S. and S.A.S.), [Grant SRA-2018-539] (to S.A.S.), and [Grant COE-2019-861] (to S.A.S.), and the US Department of Veterans Affairs [Grant I01 BX004444] (to S.A.S.). The JDRF Career Development Award to S.A.S. is partly supported by the Danish Diabetes Academy and the Novo Nordisk Foundation.

https://doi.org/10.1124/pharmrev.120.000160

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