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Autism Spectrum Disorder

What is asd.

Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. Although autism can be diagnosed at any age, it is described as a “developmental disorder” because symptoms generally appear in the first 2 years of life.

According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) , a guide created by the American Psychiatric Association that health care providers use to diagnose mental disorders, people with ASD often have:

• Difficulty with communication and interaction with other people
• Restricted interests and repetitive behaviors
• Symptoms that affect their ability to function in school, work, and other areas of life

Autism is known as a “spectrum” disorder because there is wide variation in the type and severity of symptoms people experience.

People of all genders, races, ethnicities, and economic backgrounds can be diagnosed with ASD. Although ASD can be a lifelong disorder, treatments and services can improve a person’s symptoms and daily functioning. The American Academy of Pediatrics recommends that all children receive screening for autism. Caregivers should talk to their child’s health care provider about ASD screening or evaluation.

What are the signs and symptoms of ASD?

The list below gives some examples of common types of behaviors in people diagnosed with ASD. Not all people with ASD will have all behaviors, but most will have several of the behaviors listed below.

Social communication / interaction behaviors may include:

• Making little or inconsistent eye contact
• Appearing not to look at or listen to people who are talking
• Infrequently sharing interest, emotion, or enjoyment of objects or activities (including infrequent pointing at or showing things to others)
• Not responding or being slow to respond to one’s name or to other verbal bids for attention
• Having difficulties with the back and forth of conversation
• Often talking at length about a favorite subject without noticing that others are not interested or without giving others a chance to respond
• Displaying facial expressions, movements, and gestures that do not match what is being said
• Having an unusual tone of voice that may sound sing-song or flat and robot-like
• Having trouble understanding another person’s point of view or being unable to predict or understand other people’s actions
• Difficulties adjusting behaviors to social situations
• Difficulties sharing in imaginative play or in making friends

Restrictive / repetitive behaviors may include:

• Repeating certain behaviors or having unusual behaviors, such as repeating words or phrases (a behavior called echolalia)
• Having a lasting intense interest in specific topics, such as numbers, details, or facts
• Showing overly focused interests, such as with moving objects or parts of objects
• Becoming upset by slight changes in a routine and having difficulty with transitions
• Being more sensitive or less sensitive than other people to sensory input, such as light, sound, clothing, or temperature

People with ASD may also experience sleep problems and irritability.

People on the autism spectrum also may have many strengths, including:

• Being able to learn things in detail and remember information for long periods of time
• Being strong visual and auditory learners
• Excelling in math, science, music, or art

What are the causes and risk factors for ASD?

Researchers don’t know the primary causes of ASD, but studies suggest that a person’s genes can act together with aspects of their environment to affect development in ways that lead to ASD. Some factors that are associated with an increased likelihood of developing ASD include:

• Having a sibling with ASD
• Having older parents
• Having certain genetic conditions (such as Down syndrome or Fragile X syndrome)
• Having a very low birth weight

How is ASD diagnosed?

Health care providers diagnose ASD by evaluating a person’s behavior and development. ASD can usually be reliably diagnosed by age 2. It is important to seek an evaluation as soon as possible. The earlier ASD is diagnosed, the sooner treatments and services can begin.

Diagnosis in young children

Diagnosis in young children is often a two-stage process.

Stage 1: General developmental screening during well-child checkups

Every child should receive well-child check-ups with a pediatrician or an early childhood health care provider. The American Academy of Pediatrics recommends that all children receive screening for developmental delays at their 9-, 18-, and 24- or 30-month well-child visits, with specific autism screenings at their 18- and 24-month well-child visits. A child may receive additional screening if they have a higher likelihood of ASD or developmental problems. Children with a higher likelihood of ASD include those who have a family member with ASD, show some behaviors that are typical of ASD, have older parents, have certain genetic conditions, or who had a very low birth weight.

Considering caregivers’ experiences and concerns is an important part of the screening process for young children. The health care provider may ask questions about the child’s behaviors and evaluate those answers in combination with information from ASD screening tools and clinical observations of the child. Read more about screening instruments   on the Centers for Disease Control and Prevention (CDC) website.

If a child shows developmental differences in behavior or functioning during this screening process, the health care provider may refer the child for additional evaluation.

Stage 2: Additional diagnostic evaluation

It is important to accurately detect and diagnose children with ASD as early as possible, as this will shed light on their unique strengths and challenges. Early detection also can help caregivers determine which services, educational programs, and behavioral therapies are most likely to be helpful for their child.

A team of health care providers who have experience diagnosing ASD will conduct the diagnostic evaluation. This team may include child neurologists, developmental pediatricians, speech-language pathologists, child psychologists and psychiatrists, educational specialists, and occupational therapists.

The diagnostic evaluation is likely to include:

• Medical and neurological examinations
• Assessment of the child’s cognitive abilities
• Assessment of the child’s language abilities
• Observation of the child’s behavior
• An in-depth conversation with the child’s caregivers about the child’s behavior and development
• Assessment of age-appropriate skills needed to complete daily activities independently, such as eating, dressing, and toileting

Because ASD is a complex disorder that sometimes occurs with other illnesses or learning disorders, the comprehensive evaluation may include:

• Blood tests
• Hearing test

The evaluation may lead to a formal diagnosis and recommendations for treatment.

Diagnosis in older children and adolescents

Caregivers and teachers are often the first to recognize ASD symptoms in older children and adolescents who attend school. The school’s special education team may perform an initial evaluation and then recommend that a child undergo additional evaluation with their primary health care provider or a health care provider who specialize in ASD.

A child’s caregivers may talk with these health care providers about their child’s social difficulties, including problems with subtle communication. For example, some children may have problems understanding tone of voice, facial expressions, or body language. Older children and adolescents may have trouble understanding figures of speech, humor, or sarcasm. They also may have trouble forming friendships with peers.

Diagnosis in adults

Diagnosing ASD in adults is often more difficult than diagnosing ASD in children. In adults, some ASD symptoms can overlap with symptoms of other mental health disorders, such as anxiety disorder or attention-deficit/hyperactivity disorder (ADHD).

Adults who notice signs of ASD should talk with a health care provider and ask for a referral for an ASD evaluation. Although evaluation for ASD in adults is still being refined, adults may be referred to a neuropsychologist, psychologist, or psychiatrist who has experience with ASD. The expert will ask about:

• Social interaction and communication challenges
• Sensory issues
• Repetitive behaviors
• Restricted interests

The evaluation also may include a conversation with caregivers or other family members to learn about the person’s early developmental history, which can help ensure an accurate diagnosis.

Receiving a correct diagnosis of ASD as an adult can help a person understand past challenges, identify personal strengths, and find the right kind of help. Studies are underway to determine the types of services and supports that are most helpful for improving the functioning and community integration of autistic transition-age youth and adults.

What treatment options are available for ASD?

Treatment for ASD should begin as soon as possible after diagnosis. Early treatment for ASD is important as proper care and services can reduce individuals’ difficulties while helping them build on their strengths and learn new skills.

People with ASD may face a wide range of issues, which means that there is no single best treatment for ASD. Working closely with a health care provider is an important part of finding the right combination of treatment and services.

A health care provider may prescribe medication to treat specific symptoms. With medication, a person with ASD may have fewer problems with:

• Irritability
• Repetitive behavior
• Hyperactivity
• Attention problems
• Anxiety and depression

Read more about the latest medication warnings, patient medication guides, and information on newly approved medications at the Food and Drug Administration (FDA) website  .

Behavioral, psychological, and educational interventions

People with ASD may be referred to a health care provider who specializes in providing behavioral, psychological, educational, or skill-building interventions. These programs are often highly structured and intensive, and they may involve caregivers, siblings, and other family members. These programs may help people with ASD:

• Learn social, communication, and language skills
• Reduce behaviors that interfere with daily functioning
• Increase or build upon strengths
• Learn life skills for living independently

Other resources

Many services, programs, and other resources are available to help people with ASD. Here are some tips for finding these additional services:

• Contact your health care provider, local health department, school, or autism advocacy group to learn about special programs or local resources.
• Find an autism support group. Sharing information and experiences can help people with ASD and their caregivers learn about treatment options and ASD-related programs.
• Record conversations and meetings with health care providers and teachers. This information may help when it’s time to decide which programs and services are appropriate.
• Keep copies of health care reports and evaluations. This information may help people with ASD qualify for special programs.

How can I find a clinical trial for ASD?

Clinical trials are research studies that look at new ways to prevent, detect, or treat diseases and conditions. The goal of clinical trials is to determine if a new test or treatment works and is safe. Although individuals may benefit from being part of a clinical trial, participants should be aware that the primary purpose of a clinical trial is to gain new scientific knowledge so that others may be better helped in the future.

Researchers at NIMH and around the country conduct many studies with patients and healthy volunteers. We have new and better treatment options today because of what clinical trials uncovered years ago. Be part of tomorrow’s medical breakthroughs. Talk to your health care provider about clinical trials, their benefits and risks, and whether one is right for you.

To learn more or find a study, visit:

• NIMH’s Clinical Trials webpage : Information about participating in clinical trials
• Clinicaltrials.gov: Current Studies on ASD  : List of clinical trials funded by the National Institutes of Health (NIH) being conducted across the country

Where can I learn more about ASD?

Free brochures and shareable resources.

• Autism Spectrum Disorder : This brochure provides information about the symptoms, diagnosis, and treatment of ASD. Also available  en español .
• Digital Shareables on Autism Spectrum Disorder : Help support ASD awareness and education in your community. Use these digital resources, including graphics and messages, to spread the word about ASD.

Federal resources

• Eunice Kennedy Shriver National Institute of Child Health and Human Development  
• National Institute of Neurological Disorders and Stroke  
• National Institute on Deafness and Other Communication Disorders  
• Centers for Disease Control and Prevention   (CDC)
• Interagency Autism Coordinating Committee  
• MedlinePlus   (also available en español  )

Research and statistics

• Science News About Autism Spectrum Disorder : This NIMH webpage provides press releases and announcements about ASD.
• Research Program on Autism Spectrum Disorders : This NIMH program supports research focused on the characterization, pathophysiology, treatment, and outcomes of ASD and related disorders.
• Statistics: Autism Spectrum Disorder : This NIMH webpage provides information on the prevalence of ASD in the U.S.
• Data & Statistics on Autism Spectrum Disorder   : This CDC webpage provides data, statistics, and tools about prevalence and demographic characteristics of ASD.
• Autism and Developmental Disabilities Monitoring (ADDM) Network   : This CDC-funded program collects data to better understand the population of children with ASD.
• Biomarkers Consortium - The Autism Biomarkers Consortium for Clinical Trials (ABC-CT)   : This Foundation for the National Institutes of Health project seeks to establish biomarkers to improve treatments for children with ASD.

Last Reviewed:  February 2024

Unless otherwise specified, the information on our website and in our publications is in the public domain and may be reused or copied without permission. However, you may not reuse or copy images. Please cite the National Institute of Mental Health as the source. Read our copyright policy to learn more about our guidelines for reusing NIMH content.

Autism Spectrum Disorder

The  National Institute of Mental Health (NIMH) , a component of the National Institutes of Health ( NIH ), is a leading federal funder of research on ASD . 

What is autism spectrum disorder? 

Autism spectrum disorder (ASD) refers to a group of complex neurodevelopment disorders caused by differences in the brain that affect communication and behavior. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)—a guide created by the American Psychiatric Association used to diagnose health conditions involving changes in emotion, thinking, or behavior (or a combination of these)—people with ASD can experience: 

• Challenges or differences in communication and interaction with other people
• Restricted interests and repetitive behaviors
• Symptoms that may impact the person's ability to function in school, work, and other areas of life 

ASD can be diagnosed at any age but symptoms generally appear in early childhood (often within the first two years of life). Doctors diagnose ASD by looking at a person's behavior and development. The American Academy of Pediatrics recommends that all children get screened for developmental delays and behaviors often associated with ASD at their 18- and 24-month exams.  

The term “spectrum” refers to the wide range of symptoms, skills, and levels of ability in functioning that can occur in people with ASD. ASD affects every person differently; some may have only a few symptoms and signs while others have many. Some children and adults with ASD are fully able to perform all activities of daily living and may have gifted learning and cognitive abilities while others require substantial support to perform basic activities. A diagnosis of ASD includes Asperger syndrome, autistic disorder, childhood disintegrative disorder, and pervasive developmental disorder not otherwise specified that were once diagnosed as separate disorders.  

In addition to differences or challenges with behavior and difficulty communicating and interacting with others, early signs of ASD may include, but are not limited to: 

• Avoiding direct eye contact
• Delayed speech and language skills
• Challenges with nonverbal cues such as gestures or body language
• Showing limited interest in other children or caretakers
• Experiencing stress when routines change 

Scientists believe that both genetics and environment likely play a role in ASD. ASD occurs in every racial and ethnic group, and across all socioeconomic levels. Males are significantly more likely to develop ASD than females.  

People with ASD also have an increased risk of having epilepsy. Children whose language skills regress early in life—before age 3—appear to have a risk of developing epilepsy or seizure-like brain activity. About 20 to 30 percent of children with ASD develop epilepsy by the time they reach adulthood.  

Currently, there is no cure for ASD. Symptoms of ASD can last through a person's lifetime, and some may improve with age, treatment, and services. Therapies and educational/behavioral interventions are designed to remedy specific symptoms and can substantially improve those symptoms. While currently approved medications cannot cure ASD or even treat its main symptoms, there are some that can help with related symptoms such as anxiety, depression, and obsessive-compulsive disorder. Medications are available to treat seizures, severe behavioral problems, and impulsivity and hyperactivity. 

How can I or my loved one help improve care for people with autism spectrum disorder?

Consider participating in a clinical trial so clinicians and scientists can learn more about ASD and related conditions. Clinical trials are studies that use human volunteers to look for new or better ways to diagnose, treat, or cure diseases and conditions. 

All types of volunteers are needed—people with ASD, at-risk individuals, and healthy volunteers—of all different ages, sexes, races, and ethnicities to ensure that study results apply to as many people as possible, and that treatments will be safe and effective for everyone who will use them.

For information about participating in clinical research visit NIH Clinical Research Trials and You . Learn about clinical trials currently looking for people with ASD at Clinicaltrials.gov .

Where can I find more information about autism spectrum disorder?  The following resources offer information about ASD and current research: American Academy of Pediatrics   Centers for Disease Control and Prevention (CDC) Eunice Kennedy Shriver National Institute of Child Health and Human Development   Interagency Autism Coordinating Committee (IACC) National Center for Advancing Translational Sciences   National Institute on Deafness and Other Communication Disorders   National Institute of Environmental Health Sciences   The National Task Group on Intellectual Disabilities and Dementia Practices (NTG) Additional organizations offer information, research news, and other resources about ASD for individuals and caregivers, such as support groups. These organizations include: Association for Science in Autism Treatment     Autism National Committee (AUTCOM)     Autism Network International (ANI)     Autism Research Institute (ARI)   Autism Science Foundation     Autism Society     Autism Speaks, Inc.  

Research Findings from the Study to Explore Early Development

Researchers working on the Study to Explore Early Development (SEED) have recently published many studies reporting on important findings related to autism spectrum disorder (ASD). The scientific findings for all SEED studies published to date are summarized below.

Many additional studies are underway.  We will provide summaries of those studies in the future.

SEED investigators have prepared several reports detailing the study methods and describing the children enrolled in the SEED sample. These reports provide foundational information about SEED for other researchers, policymakers, and clinicians.

Community-based service use in preschool children with autism spectrum disorder and associations with insurance status

Rubenstein E, Croen L, Lee LC, Moody, E, Schieve LA, Soke GN, Thomas K, Wiggins L, Daniels J

Research in Autism Spectrum Disorders, 2019

This study examined the association between insurance status and community-based services received outside of school among preschool-aged children with a prior autism spectrum disorder (ASD) diagnosis. Children eligible for autism-related special education services are required by law to receive individualized ASD services in school (“Individuals with Disabilities Education Improvement Act of 2004,” 2004). However, additional community-based services such as behavioral therapy, speech therapy, and occupational therapy are often needed. The Study to Explore Early Development (SEED) provides important information, not available in previous studies, on the use of community-based services by insurance status in preschool-aged children. In this report, insurance status was categorized as private insurance alone, public insurance alone, both private and public insurance, or uninsured. The results showed that about 35% of the children with a prior ASD diagnosis had public insurance alone and 51% had private insurance alone. In addition, 13% had both types of insurance, while few children (1%) were uninsured. The most commonly received services were speech therapy and occupational therapy. Nearly 40% of children received no community-based services at all. After adjusting for sociodemographic variables, insurance status was not associated with the number of different types of community-based services received. However, children with public insurance alone were the least likely to receive behavioral therapy and the most likely to receive psychotropic medication. These findings suggest that many preschool-aged children do not receive community-based services, and the receipt of certain important services varies by insurance type. Increasing access and availability for evidence-based services, especially for children with public insurance only, may improve service use and outcomes for children with ASD.

Assessment of demographic and perinatal predictors of non-response and impact of non-response on measures of association in a population-based case control study: findings from the Georgia Study to Explore Early Development

Schieve LA, Harris S, Maenner MJ, Alexander A, Dowling NF

Emerg Themes Epidemiol., 2018

This report describes characteristics of those who did or did not participate in and complete Georgia SEED between 2007 and 2012. The population (POP) sample was recruited from birth certificates. The autism spectrum disorder (ASD) and developmental disability (DD) groups were recruited from health and special education sources servicing children with developmental disabilities. Children who enrolled in the study were given a comprehensive evaluation to confirm case status. Using birth certificate data, demographics of mothers and children who completed SEED were compared with those who were invited but did not complete the study. Those who did not complete the study included those not located, those located but who declined to participate, or those enrolled in SEED but who dropped out before completing most study steps. Researchers found that all mothers who completed the study tended to be older and had more education than mothers who did not complete the study; yet, they were similar on other demographic factors, such as race/ethnicity and marital status.  Mothers in the ASD group, in particular, were more like to have more education than potential ASD cases invited but who did not complete the study. In two risk factor analyses, associations between the ASD and POP groups were not influenced by the differences in the characteristics of those who participated and completed the study. Assessment of non-response has not yet been done for the other SEED sites. For GA SEED, these findings suggest that differences in participation and completion rates do not appear to affect the study results examined. The information in this report is useful to other researchers conducting epidemiologic studies, especially those seeking to enroll large, diverse population-based samples.

Demographic and Operational Factors Predicting Study Completion in a Multisite Case-Control Study of Preschool Children

Bradley CB, Browne EN, Alexander AA, Collins J, Dahm JL, DiGuiseppi CG, Levy SE, Moody EJ, Schieve LA, Windham GC, Young L, Daniels JL

American Journal of Epidemiology , 2018

This report describes study completion among 3,769 families who enrolled in the first phase of SEED between 2007 and 2011. Families were asked to complete multiple steps for SEED, including phone interviews, filling out forms, participating in an in-person visit to check a child’s development, and providing biological specimens (such as cheek swabs and blood). Researchers found that completion was generally 70% or higher for each study step and 58% of participants completed all key study steps. Researchers found that completion rates varied by families’ demographic characteristics and also the distance they had to travel to the study clinic.  This information is important in helping researchers understand the SEED data already collected and in planning future SEED phases. These study findings also inform researchers on possible ways to improve participation in other future studies.

Demographic Profile of Families and Children in the Study to Explore Early Development (SEED): Case-control Study of Autism Spectrum Disorder.

DiGuiseppi CG, Daniels JL, Fallin DM, Rosenberg SA, Schieve LA, Thomas KC, Windham GC, Goss CW, Soke GN, Currie DW, Singer AB, Lee LC, Bernal P, Croen LA, Miller LA, Pinto-Martin JA, Young LM, Schendel DE.

Disability and Health Journal, 2016

This is one of two reports that describe the characteristics of children enrolled in SEED. This report focuses on sociodemographic characteristics. SEED successfully enrolled a highly diverse sample of participants, including minorities and low socioeconomic status families. The SEED population sample represents racial, ethnic, and demographic diversity in the United States. SEED improves upon other ASD risk factor studies in that it does not rely on administrative data sources, which lack many important details of both child development and maternal risk factors. Nor does it rely on small samples from only a few clinics or schools. SEED collects detailed data in a large and diverse sample.  This provides unique opportunities for researchers to learn more about how socioeconomic characteristics relate to risk factors for ASD and health outcomes in children with ASD.

Autism Spectrum Disorder Symptoms among Children Enrolled in the Study to Explore Early Development (SEED).

Wiggins LD, Levy SE, Daniels J, Schieve L, Croen LA, DiGuiseppi C, Blaskey L, Giarelli E, Lee LC, Pinto-Martin J, Reynolds A, Rice C, Rosenberg CR, Thompson P, Yeargin-Allsopp M, Young L, Schendel D.

Journal of Autism and Developmental Disorders, 2015

This is one of two reports that describe the characteristics of children enrolled in SEED. This report focuses on developmental characteristics. Children enrolled in SEED are divided into four groups: three with children who have varying types of developmental delays and disabilities, including ASD, and one with children from the general population. The report describes how various facets of children’s development vary across these four groups and highlights the many needs of children with ASD and other developmental disabilities.

Using standardized diagnostic instruments to classify children with autism in the Study to Explore Early Development.

Wiggins LD, Reynolds A, Rice CE, Moody EJ, Bernal P, Blaskey L, Rosenberg SA, Lee LC, Levy SE.

This report describes the SEED process for determining whether a child enrolled in the study will be classified as an ASD case. This classification is based on an in-person assessment given by trained SEED clinicians. Children enrolled in the study are screened for autism symptoms by asking their mothers to respond to a brief questionnaire.  Children with an indication of possible autism symptoms are assessed further during an in-person visit.  Clinicians give these children a more in-depth developmental evaluation known as Autism Diagnostic Observation Schedule and ask their mothers or other caregivers to participate in an interview known as the Autism Diagnostic Interview – Revised. Besides providing clinicians with information to determine a child’s ASD classification, these assessments provide valuable information on ASD-specific behaviors and traits, allowing researchers to better understand the different characteristics among children with ASD.

The Study to Explore Early Development (SEED): a multisite epidemiologic study of autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) network.

Schendel DE, Diguiseppi C, Croen LA, Fallin MD, Reed PL, Schieve LA, Wiggins LD, Daniels J, Grether J, Levy SE, Miller L, Newschaffer C, Pinto-Martin J, Robinson C, Windham GC, Alexander A, Aylsworth AS, Bernal P, Bonner JD, Blaskey L, Bradley C, Collins J, Ferretti CJ, Farzadegan H, Giarelli E, Harvey M, Hepburn S, Herr M, Kaparich K, Landa R, Lee LC, Levenseller B, Meyerer S, Rahbar MH, Ratchford A, Reynolds A, Rosenberg S, Rusyniak J, Shapira SK, Smith K, Souders M, Thompson PA, Young L, Yeargin-Allsopp M.

Journal of Autism and Developmental Disorders, 2012

This report describes SEED methods. SEED is one of the largest studies investigating genetic and environmental risk factors for autism spectrum disorder (ASD) and child health and behavioral traits associated with ASD. SEED enrolls preschool-aged children with ASD and other developmental disabilities and children from the general population in six sites across the United States. SEED methods focus on enrolling families from diverse populations in each area. A key strength of SEED includes the collection of in-depth information on child development, which allows researchers to more rigorously classify children into various study groups (ASD, other developmental disabilities, or population controls) than what is done in many other ASD research studies.  In SEED, researchers use standardized assessment tools to determine a children’s final study group and to assess specific behavioral traits among children with ASD. Another key strength is the collection of comprehensive data on child health and potential risk factors for ASD. SEED’s large and diverse sample of study participants allows researchers to analyze data in greater detail than most other ASD studies and answer many important questions about ASD.   Top of Page

Maternal Psychiatric Conditions, Treatment with SSRIs, and Neurodevelopmental Disorders

Ames JL, Ladd-Acosta C, Fallin MD, Qian Y, Schieve LA, DiGuiseppi, C, Lee LC, Kasten EP, Zhou G, MPH, MD, PhD, Pinto-Martin J, Howerton E, Eaton, CL, Croen LA, PhD

Biological Psychiatry, 2021

A study published online in Biological Psychiatry looked at whether psychiatric conditions during pregnancy, like depression, and the use of selective serotonin reuptake inhibitors (SSRIs) were associated with autism spectrum disorder (ASD) among the children of mothers who were treated. The study found ASD was more common among children of mothers who had psychiatric conditions during pregnancy. However, among the subset of children whose mothers had psychiatric conditions, ASD was not more common among those treated with SSRIs. The authors conclude that this study provides evidence that maternal psychiatric conditions during pregnancy, but not the use of SSRIs, are associated with increased risk of ASD. These findings have implications for clinical decision-making regarding the continuation of SSRI treatment during pregnancy.

Maternal Pre-Pregnancy Weight and Gestational Weight Gain in Association with Autism and Developmental Disorders in Offspring

Susana L. Matias, Michelle Pearl, Kristen Lyall, Lisa A. Croen, Tanja V. E. Kral, Daniele Fallin, Li-Ching Lee, Chyrise B. Bradley, Laura A. Schieve, Gayle C. Windham

Obesity, 2021

A study published online explored whether obesity in mothers prior to pregnancy or weight gain during pregnancy was associated with autism spectrum disorder (ASD) or other developmental disorders in their children.  Mothers classified as having severe obesity (body mass index ≥35 kg/m) prior to pregnancy had a significantly higher risk of having children with ASD and other developmental disorders. The largest amounts of weight gain during pregnancy were associated with ASD, particularly among male children. Since pre-pregnancy weight and weight gain during pregnancy are regularly measured and potentially modifiable, these findings could assist targeting high-risk mothers for early interventions.

Infection and Fever in Pregnancy and Autism Spectrum Disorders: Findings from the Study to Explore Early Development

Croen LA, Qian Y, Ashwood P, Ousseny Z, Schendel D, Pinto-Martin J, Fallin D, Levy S, Schieve LA, Yeargin-Allsopp M, Sabourin KR

Autism Research, 2019

This study evaluated the associations between a child having autism spectrum disorder (ASD) or other developmental disabilities (DD), and whether the child’s mother had an infection during her pregnancy. The Study to Explore Early Development’s (SEED’s) detailed data on type and timing of a mother’s infection and whether the mother also had a fever allowed researchers to conduct a more in-depth analysis on this topic than had been done previously. Study findings showed that overall maternal infections during pregnancy were common, occurring in approximately 60% of women in this study, and were not associated with having a child with ASD or DD. Certain infections – those that occurred in the second trimester and were accompanied by fever (7% of mothers) – were associated with ASD in children. These study findings add to other studies of risk factors that highlight the potential association between maternal health during pregnancy and ASD.

Neonatal jaundice in association with autism spectrum disorder and developmental disorder

Cordero C, Schieve LA, Croen LA, Engel SM, Siega-Riz AM, Herring AH, Vladutiu CJ, Seashore CJ, Daniels JL

Journal of Perinatology, 2019

This study examines the association between a child having jaundice just after birth and autism spectrum disorder (ASD) and other developmental disorders (DDs). Jaundice is a yellow discoloration of the skin and eyes that occurs in some newborns because of a build-up of bilirubin, a substance that forms when blood cells are broken down. While most jaundice lasts only a short time, high levels of bilirubin can affect the developing brain. The Study to Explore Early Development’s (SEED’s) detailed data on the health of mothers and their children allowed researchers to conduct a more in-depth analysis on this topic than had been done previously. Study findings showed that among children who had been born too early (or preterm), newborn jaundice was associated with both ASD and other DDs. However, among children born on time, newborn jaundice was not associated with either ASD or other DDs. This study highlights the association between newborn health and ASD and other DDs.

Early Life Exposure to Air Pollution and Autism Spectrum Disorder: Findings from a Multisite Case-Control Study

McGuinn LA, Windham GC, Messer LC, Di Q, Schwartz J, Croen LA, Moody EJ, Rappold AG, Richardson DB, Neas LM, Gammon MD, Schieve LA, Daniels JL

Epidemiology, 2020

This study used Study to Explore Early Development (SEED) data to examine the association between autism spectrum disorder (ASD) and exposure to air pollutants during key periods of brain development. Particulate matter (PM), or tiny particles of air pollution, and ozone are common air pollutants. Previous studies have found an association between ASD and exposure to these air pollutants; however, previous studies have been unable to look at exposure to these air pollutants in relation to key periods of brain development or account for potential differences in pollutants in regions of the United States. This study looked at air pollutant exposure among participants living in six different areas of the United States (located in California, Colorado, Georgia, Maryland, North Carolina, and Pennsylvania) during several critical periods: 3 months before pregnancy, each trimester of pregnancy, the entire pregnancy, and the first year of life. Study findings showed an association between air pollution and ASD by period of exposure; ASD was associated with ozone exposure during the third trimester and with PM exposure during the first year of life. These findings support previous studies of a positive association between ASD and potential exposure to air pollution during the late prenatal period and early postnatal period. Further investigation into these findings may be helpful in increasing our understanding of these association

Air pollution, neighborhood deprivation, and autism spectrum disorder in the Study to Explore Early Development

McGuinn LA, Windham GC, Messer LC, Di Q; Schwartz J, Croen LA, Moody EJ, Rappold AG, Richardson DB, Neas LM, Gammon MD, Schieve LA, Daniels JL

Environmental Epidemiology, 2019

This study used Study to Explore Early Development (SEED) data to examine whether the association between autism spectrum disorder (ASD) and early exposure to air pollution is modified by neighborhood deprivation.  Previous research, including studies using SEED data, have found an association between ASD and exposure to particulate matter (PM), or tiny particles of air pollution, during the first year of life; however, these studies did not look at different measures of neighborhood deprivation, which may also be associated with ASD and are often geographically correlated with air pollution. This study went beyond prior studies by combining data on pollution, roadway proximity, and neighborhood deprivation at the census tract level in six different areas of the United States. Study findings showed that the association between ASD and PM exposure during the first year of life was stronger for children living in neighborhoods of high deprivation, as compared to neighborhoods of moderate or low deprivation. Confirmation of these preliminary findings may be useful in future studies.

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Association Between Breastfeeding Initiation and Duration and Autism Spectrum Disorder in Preschool Children Enrolled in the Study to Explore Early Development

Soke GN, Maenner M, Windham G, Moody E, Kaczaniuk J, DiGuiseppi C, Schieve LA

Autism Res, 2019

This study compared breastfeeding initiation and duration among children with autism spectrum disorder (ASD) to children from the general population without ASD. SEED’s large sample size and diverse sample allowed researchers to conduct a more thorough assessment than previous studies. Study findings showed mothers of children with ASD were just as likely as mothers of children from the general population to initiate breastfeeding. However, among mothers who initiated breastfeeding, those who had children with ASD were less likely than those who had children without ASD to continue breastfeeding for longer than 6 months. The reasons for shorter breastfeeding duration among children with ASD are unclear. A longer duration of breastfeeding might protect a child from developing ASD, but it is also possible that early discontinuation of breastfeeding is related to underlying developmental conditions in children with ASD, such as child irritability, sensory, or health issues. To better understand why the duration of breastfeeding might be shorter among mothers of children with ASD compared to those without ASD, future studies should consider evaluating the reasons women discontinue breastfeeding.

Maternal diabetes and hypertensive disorders in association with autism spectrum disorder

Cordero C, Windham GC, Schieve LA, Fallin MD, Croen LA, Siega-Rizf AM, Engel SM, Herring AH, Stuebe AM, Vladutiu CJ, Daniels JL

This study examined associations between a child having autism spectrum disorder (ASD) or other developmental disabilities (DDs) and whether the child’s mother had diabetes or hypertension during pregnancy. Diabetes and hypertension are among the most common complications experienced by women during pregnancy. SEED’s large sample size and detailed data about the health of mothers and their children allowed researchers to conduct a more in-depth analysis on this topic than previous studies. Study findings showed that having hypertension during pregnancy was associated with both ASD and other DDs in children compared with not having hypertension during pregnancy. Diabetes during pregnancy was not associated with ASD, but was associated with other DDs. This study highlights the relationship between maternal health during pregnancy and children with ASD and other DDs.

Maternal Pre-pregnancy Body Mass Index (BMI) and Gestational Weight Gain in Relation to Autism Spectrum Disorder (ASD) and Other Developmental Disorders in Offspring

Windham GC, Anderson M, Lyall K, Daniels JL, Kral TV, Croen LA, Levy SE, Bradley CB, Cordero C, Young L, Schieve LA

This study examined the relationship between mother’s body mass index (BMI) before pregnancy, mother’s weight gain during pregnancy, and associations with ASD and other developmental disabilities (DDs). Although previous studies have reported an association between higher maternal BMI and ASD, having this information, along with weight gain during pregnancy in SEED, allowed researchers to conduct a more in-depth analysis on this topic than previous studies. Study findings show an association between higher pregnancy weight gain and having a child with ASD, and this association was even stronger when the mother was overweight or obese before becoming pregnant. On the other hand, while maternal BMI before pregnancy was associated with having a child with a DD, mother’s weight gain during pregnancy was not. This study highlights the possible effects of maternal weight on child having ASD or DDs and the importance of maintaining a heathy weight before and during pregnancy.

Brief Report: Maternal Opioid Prescription from Preconception through Pregnancy and the Odds of Autism Spectrum Disorder and Autism Features in Children

Rubenstein E, Young JC, Croen LA, DiGuiseppi C, Dowling NF, Lee LC, Schieve L, Wiggins LD, Daniels J

Journal of Autism and Developmental Disorders, 2018

This study examined possible associations between prescription of opioid medications just before and during pregnancy and ASD and other developmental disabilities (DDs). Currently, the information available on this topic is very limited. SEED collects detailed information about mothers’ health histories, including prescribed medication, which allowed researchers to conduct this exploratory analysis. Illicit opioid use was not included in this analysis. The study findings show that approximately 8% of mothers reported receiving an opioid prescription just before or during pregnancy; of these mothers, the majority (76%) received only one prescription. The most common reasons for opioid prescriptions were migraine headaches, injury, and back pain. Mothers who were prescribed opioids just before becoming pregnant were more likely to have a child with ASD or a child with DDs and some autism symptoms. Researchers were limited by small sample sizes; thus, they were not able to conduct a detailed assessment of whether the associations found were related to the medication itself, the reason the mother took the medication, or some other unknown factors that may be associated with opioid use. This study is among the first to assess possible associations between prescription of opioids just before or during pregnancy and ASD and other DDs. More research is needed to understand how opioid use before and during early pregnancy may impact a child’s development.

Family History of Immune Conditions and Autism Spectrum and Developmental Disorders: Findings from the Study to Explore Early Development

Croen, LA, Qian Y, Ashwood, P, Daniels JL, Fallin D, Schendel D, Schieve LA, Singer AB, Zerbo O

Autism Research, 2018

This study examined the relationship between autism spectrum disorder (ASD) and other developmental disorders (DDs) and having a family history of conditions related to immune system functioning. Such conditions include asthma, allergies, and autoimmune disorders such as eczema or psoriasis. Previous studies have suggested some association, but the results about specific conditions varied. SEED’s large sample size and detailed data on specific types of immune disorders allowed researchers to conduct an in-depth analysis on this topic and examine the associations with ASD alongside associations with other DDs. The study findings show that maternal history of eczema or psoriasis and asthma are associated with both ASD and other DDs in children. Researchers also found that children with ASD are more likely to have eczema or psoriasis and allergies than children without ASD. Autoimmune disorders were not notably increased among children with other DDs. This study highlights the relationship between maternal health before and during pregnancy and ASD and other DDs, and provides researchers more information about the health of children with ASD.

Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Andrews SV, Sheppard B, Windham GC, Schieve LA, Schendel DE, Croen LA, Chopra P, Alisch RS, Newschaffer CJ, Warren ST, Feinberg AP, Fallin MD, Ladd-Acosta C

Molecular Autism, 2018

In this study, researchers used SEED data and data from another study of children and adolescents with and without ASD to learn more about how genes are regulated in children with ASD. Many genes are turned on or off by a process called “methylation.”  Although methylation does not change a person’s actual genes (or genetic code), methylation helps different types of cells do their specific jobs by affecting which genes are turned on and which genes are not. The researchers examined children’s DNA to look for differences in the methylation of genes between children with and without ASD. Previous studies of methylation in relation to ASD were limited by small sample sizes. This study is one of the largest so far to look broadly at methylation patterns in children with and without ASD. The study showed several potential differences in methylation between children in the two groups. Some of the differences suggest links to brain function, and they were consistent with results from previous studies. These findings provide clues as to how genes might be related to ASD in children.

Associations Between the 2nd to 4th Digit Ratio and Autism Spectrum Disorder in Population-Based Samples of Boys and Girls: Findings from the Study to Explore Early Development.

Schieve LA, Tian L, Dowling N, Croen L, Hoover-Fong J, Alexander A, Shapira SK.

This study examined associations between ASD and the ratio of children’s index (2nd) finger length to their ring (4th) finger length. The ratio of finger lengths (or digit ratio) has been linked to the level of sex hormones a child was previously exposed to during pregnancy. Researchers study digit ratios because they rarely have direct measurements of fetal exposure to hormones.  Study findings in boys showed that digit ratio was associated with ASD, but only in certain subgroups, such as children who had ASD and also a birth defect or genetic syndrome. This suggests the association might not have been related to hormone levels, but might instead be explained by genetics.  Study findings in girls showed that digit ratio was associated with ASD and that the association was not limited to certain subgroups of children.  There has been little past study of the association between digit ratio and ASD, particularly in girls.  The findings in this report suggest that hormone exposures during pregnancy might be related to ASD in girls, but many gaps remain in our understanding of the underlying reasons for this association and further research is needed.

Autism Spectrum Disorder and Birth Spacing: Findings from the Study to Explore Early Development (SEED).

Schieve LA, Tian LH, Drews-Botsch C, Windham GC, Newschaffer C, Daniels JL, Lee LC, Croen LA, Danielle Fallin M.

Autism Research, 2017

This study examined whether the amount of time between pregnancies was associated with ASD or other developmental disabilities in children. SEED’s detailed data on ASD subgroups and other developmental disabilities allowed researchers to conduct a more in depth analysis on this topic than those that have been done previously. The study findings show that both shorter and longer time periods between births are associated with having a child with ASD. Children conceived less than 18 months after their mother’s previous birth and children conceived 60 or more months after their mother’s previous birth were more likely to have ASD than children conceived between 18 to 59 months after their mother’s previous birth. The relationship was stronger in children with more severe ASD symptoms. Also, the association between birth spacing and ASD appeared to be unique to ASD, as there was no association found between birth spacing and having children with other developmental disabilities. The association between birth spacing and ASD was not explained by unplanned pregnancy, an underlying fertility disorder in the mother, or high blood pressure or diabetes during pregnancy. The findings from this study can help healthcare providers counsel their patients on pregnancy spacing.

Prenatal Alcohol Exposure in Relation to Autism Spectrum Disorder: Findings from the Study to Explore Early Development (SEED).

Singer AB, Aylsworth AS, Cordero C, Croen LA, DiGuiseppi C, Fallin MD, Herring AH, Hooper SR, Pretzel RE, Schieve LA, Windham GC, Daniels JL.

Paediatric and Perinatal Epidemiology, 2017

This study examined associations between alcohol use just before and during pregnancy and ASD or other developmental disabilities (DDs). Previous studies have shown that high levels of alcohol use in pregnancy are associated with child developmental effects, such as decreased intellectual ability, hyperactivity, learning difficulties, and autism-like traits. This study investigated whether lower levels of alcohol use before and during pregnancy were associated with developmental outcomes. Most mothers of children in SEED reported no or low levels of alcohol use before or during their pregnancies.  In fact, nearly all mothers reported no alcohol use in the second month of pregnancy or later (93-98% depending on month). Therefore, a main focus of the study was on alcohol use in the three months prior to pregnancy or the first month of pregnancy. The study findings show that modest alcohol use during these four months was not associated with increased risk for either ASD or other DDs.  Although this study did not find an association between ASD or other DDs and modest alcohol use before or during pregnancy, women who are pregnant or planning to become pregnant should continue to follow recommendations to avoid alcohol use because of other known effects on infant and child health.

Maternal and Paternal Infertility Disorders and Treatments and Autism Spectrum Disorder: Findings from the Study to Explore Early Development.

Schieve LA, Drews-Botsch C, Harris S, Newschaffer C, Daniels J, DiGuiseppi C, Croen LA, Windham GC.

Journal of Autism and Developmental Disorders, 2017

This study examined associations between ASD and whether, prior to becoming pregnant, a child’s mother had a condition that might have affected her ability to get pregnant (i.e., infertility). The study also looked at whether the mother had received any medical treatments to help her become pregnant or to prevent miscarriage during early pregnancy. SEED’s detailed data on specific types of infertility disorders and treatments allowed researchers to conduct a much more in depth analysis on this topic than those that have been done previously. The study findings show that several infertility disorders in the mother — including blocked tubes, uterine conditions such as fibroids, endometriosis, and polycystic ovarian syndrome — are associated with ASD in children. However, treatments for infertility or to prevent miscarriage were not associated with ASD.  While the reasons for the associations with infertility conditions could not be studied, possible explanations include increased inflammation during pregnancy or problems with the mother’s immune system. The findings from this study add to studies of other risk factors highlighting the relationship between maternal health before and during pregnancy and ASD.

Pleiotropic Mechanisms Indicated for Sex Differences in Autism.

Mitra I, Tsang K, Ladd-Acosta C, Croen LA, Aldinger KA, Hendren RL, Traglia M, Lavillaureix A, Zaitlen N, Oldham MC, Levitt P, Nelson S, Amaral DG, Hertz-Picciotto I, Fallin MD, Weiss LA.

PLOS Genetics, 2016

In this study, researchers used SEED data and data from other studies to investigate sex-specific genetic effects for ASD. The findings indicate involvement of genes on the X chromosome. These findings help us better understand how ASD might differ in girls and boys.

Presence of an Epigenetic Signature of Prenatal Cigarette Smoke Exposure in Childhood.

Ladd-Acosta C, Shu C, Lee BK, Gidaya N, Singer A, Schieve LA, Schendel DE, Jones N, Daniels JL, Windham GC, Newschaffer CJ, Croen LA, Feinberg AP, Daniele Fallin M.

Environmental Research, 2016

This study examined how environmental exposures, such as smoking during pregnancy, may impact gene regulation in children. Gene regulation is the process by which genes in a cell are turned on or off, and it is important for child development. Like other studies, researchers found that smoking during pregnancy affected gene regulation in children. However, while other studies have assessed these effects in children at the time of birth, the SEED sample provided an opportunity to look at gene regulation in older children. This study showed that the same pattern of gene effects was present in older children whose mothers had smoked in pregnancy as had been previously observed in newborns. These findings suggest that smoking during pregnancy may have lasting effects on child health and development.

Maternal Exposure to Occupational Asthmagens During Pregnancy and Autism Spectrum Disorder in the Study to Explore Early Development.

Singer AB, Windham GC, Croen LA, Daniels JL, Lee BK, Qian Y, Schendel DE, Fallin MD, Burstyn I.

Journal of Autism and Developmental Disorders, 2016

This study examined whether ASD was associated with the mother’s workplace exposure to certain chemicals or other substances during pregnancy. Because previous studies have shown associations between maternal asthma and allergy and ASD, researchers were particularly interested in exposure to substances that are known to trigger asthma symptoms, called asthmagens.  Examples of asthmagens include latex, certain drugs and chemicals such as dyes, and some cleaning products. The findings show that mothers of children with ASD had been exposed to slightly higher levels of workplace asthmagens than mothers of children in the general population. However, the difference was small and could have been due to chance. Many gaps remain in our understanding of how environmental exposures might impact the risk for ASD, and further research is needed.   Top of Page

Many Young Children with Autism Who Use Psychotropic Medication Do Not Receive Behavior Therapy: A Multisite Case-Control Study

Lisa D. Wiggins, PhD, Cy Nadler, PhD, Steven Rosenberg, PhD, Eric Moody, PhD, Nuri Reyes, PhD, Ann Reynolds, MD, Aimee Alexander, MS, Julie Daniels, PhD, Kathleen Thomas, PhD, Ellen Giarelli, PhD, and Susan E. Levy, MD, MPH

Pediatrics, 2021

A study published online in The Journal of Pediatrics explored the rates of psychotropic medication use among preschool-aged children (ages 2-5 years) with autism spectrum disorder (ASD).  While there are no medications to treat core symptoms of ASD, some medications may treat co-occurring symptoms such as attention problems, anxiety, aggression, and self-injurious behaviors.  However, The American Academy of Pediatrics recommends behavior therapy before medication is tried. In the study sample, 37 of 62 (59.7%) children with ASD who used psychotropic medications did not receive the behavior therapy prior to receiving medications.  Pediatricians are an important resource for children and families and can help facilitate behavioral treatment for children with ASD and other behavioral and developmental disorders.

Gastrointestinal Symptoms in 2- to 5-Year-Old Children in the Study to Explore Early Development

Reynolds AM, Soke GN, Sabourin KR, Croen LA, Daniels JL, Fallin MD, Kral TVE, Lee LC, Newschaffer CJ, Pinto-Martin JA, Schieve LA, Sims A, Wiggins LD, Levy SE

Journal of Autism and Developmental Disorders, 2021

This study compared gastrointestinal (GI) symptoms in 2,461 preschool children aged 30–68 months with autism spectrum disorder (ASD) to children with other developmental disabilities (DDs) and children from the general population (POP). Previous studies have shown that GI symptoms are common among children with ASD, but those studies have been limited by small sample sizes and lack of standard measures and comparison groups. Researchers used information from the Study to Explore Early Development (SEED)—including detailed information on GI symptoms, developmental level, and other problems such as anxiety (worry), aggression, and problems related to sleep and attention—to fill these gaps. Parents were asked to complete a detailed questionnaire on GI symptoms and a stool diary for their child. Based on these two instruments, 50.4% of children with ASD had GI symptoms, compared to 42.6% of children with other DDs and 30.6% of POP children. Among children with ASD, researchers also compared children who had lost skills they had previously developed (developmental regression) with those who had not lost previously developed skills and found that more children with developmental regression had GI symptoms (42.9%) than those without regression (31.8%).  Across all three study groups, GI symptoms were related to problems with sleep, attention, anxiety, and aggression.  These findings suggest that GI issues may be more common among children with ASD and are an important healthcare need to address.

Pica, Autism, and Other Disabilities

Fields VL, Soke GN, Reynolds A, Tian LH, Wiggins L, Maenner M, DiGuiseppi C, Kral TVE, Hightshoe K, Schieve LA

This study examined pica in preschool-aged children with autism spectrum disorder (ASD), other developmental disabilities (DDs), and children from the general population (POP). Pica is when a person eats non-food items with no nutritional value—such as paper, hair, paint, or dirt—which can result in medical problems. Previous research on pica in children with ASD and other DDs has been limited by small, non-representative samples, and has lacked a general POP comparison group. Researchers from the Study to Explore Early Development (SEED) examined pica in 4,739 preschool children aged 30–68 months with ASD, other DDs, and from the general population (POP).  Children with ASD and other DDs were further classified according to whether they had co-occurring intellectual disability (ID), and among children in the DD group, whether they had some ASD characteristics, for a total of 6 subgroups (ASD without ID, ASD with ID, DD with ASD characteristics, DD with ASD characteristics and ID, DD without ASD characteristics and with ID, and DD without ASD characteristics and without ID). Study results found that 23.2%, 8.4%, and 3.5% of children in the ASD, DD, and POP groups, respectively, had pica. Within the ASD group, pica was reported in 28.1% of children with ID and 14.0% of children without ID. Within the DD group, pica was reported in 26.3% of children with both ID and some ASD characteristics, 12.0% with some ASD characteristics but without ID, 9.7% with ID but without ASD characteristics, and 3.2% with neither ID nor ASD characteristics. These results show that pica may be common in young children with ASD, ASD characteristics, and/or ID, and suggest that young children in these groups can benefit from careful monitoring and safety precautions to prevent pica.  Parent prevention measures can include closely monitoring children, keeping items out of reach, using childproof locks, finding activities that occupy children’s attention, and informing other caregivers of concerns.

Mapping the Relationship Between Dysmorphology and Cognitive, Behavioral, and Developmental Outcomes in Children with Autism Spectrum Disorder

Tian LH, Wiggins LD, Schieve LA, 1, Yeargin-Allsopp M, Dietz P, Aylsworth AS, Elias ER, Julie E. Hoover‑Fong JE, Meeks NJL, Souders MC, Tsai ACH, Zackai EH, Alexander AA, Dowling NF, Shapira SK

Autism Research, 2020

This study looked at whether having more unusual physical traits (dysmorphic features (DFs)) was related to developmental problems and focused on children with autism spectrum disorder (ASD) compared to children from the general population (POP). Previous studies only looked at whether children with ASD and developmental problems had DFs; these studies did not always include a group of children without ASD. In this study, researchers used information from 881 preschool-aged children 2–5 years old enrolled in the Study to Explore Early Development (SEED). The study included an in-person physical examination where photographs, measurements, and hand scans were taken; these items were reviewed by clinical geneticists to determine the number of DFs in each child. This enabled researchers to ask whether a greater number of DFs was related to more developmental problems. The study found that children with ASD and ID had more language, movement, and learning issues as the number of DFs increased. Children with ASD but without ID had more movement and learning issues as the number of DFs increased. These relationships were not observed in the POP group. These findings suggest that DFs may be linked to the cognitive (learning and memory) problems of children with ASD. Additional studies on groups of children with ASD who do or do not have ID could help explain the findings.

Expressive Dominant Versus Receptive Dominant Language Patterns in Young Children: Findings from the Study to Explore Early Development

Reinhartsen DB, Tapia AL, Watson L, Crais E, Bradley C, Fairchild J, Herring AH, Daniels J

Journal of Autism and Developmental Disorders, 2019

This study examined language skills in children with autism spectrum disorder (ASD), children with other developmental disabilities (DD), and typically developing children from the general population (POP). Previous research has shown that children typically understand more vocabulary and complex language than they can express. However, some studies on the language patterns of children with ASD suggest they may be better at expressing than understanding language. Researchers used information from the Study to Explore Early Development (SEED) to categorize 2,571 children aged 30–68 months according to whether they understood or expressed language better or had similar language skills in both areas.  Study findings showed that all three groups of children were better able to understand than express language.  However, 23.6% of children in the ASD group were better at expressing language, as compared to 11.5% of children in the DD group and 10.8% of children in the POP group. Children in the ASD group who were better at expressing than understanding language typically had noticeable problems understanding language and were younger, had lower nonverbal cognitive skills, and had more serious social symptoms of ASD. These findings highlight the need to consider the type of language deficits when designing clinical interventions or treatment programs for children with ASD.

Wandering Among Preschool Children With and Without Autism Spectrum Disorder

Wiggins LD, DiGuiseppi C, Schieve L, Moody E, Gnakub Soke, Giarelli E, Levy S

Journal of Developmental and Behavioral Pediatrics, 2020

This study describes wandering in children ages 4–5 years with a confirmed autism spectrum disorder (ASD) diagnosis, children with a previous but unconfirmed ASD diagnosis (DDprevASD), children with other developmental disabilities (DD), and children from the general population (POP). Wandering, or leaving a supervised space or care of a responsible person, is common among toddlers who are exploring their environment and learning to be independent. Wandering typically becomes much less common after 4 years of age; however, some studies suggest that wandering may be more common among children with ASD than children with other DD and could compromise child safety and increase parental stress. In this study, researchers described 3,896 parent reports of wandering among their 4–5-year-old children enrolled in the Study to Explore Early Development (SEED) between 2007 and 2016. The researchers also examined the relationship between a child’s likelihood to wander and certain behavioral, developmental, and other factors. Study findings showed that wandering in children aged 4–5 years was reported in 60.4% of children with ASD, compared with 41.3% of children with DDprevASD, 22.3% of children with DD, and 12.4% of children in the POP group. Findings also showed that mood, anxiety, attention, and oppositional problems were all associated with wandering behavior, independent of ASD status. These results provide important information for parents and providers on the occurrence of wandering among children with and without ASD and associated conditions (such as anxiety and attention problems) that may place children at increased risk for wandering from safe environments. Moreover, these results may facilitate discussions between parents and providers about safety, prevention, and interventions that may improve the lives of children who wander and their families.

Injury-related treatments and outcomes in preschool children with autism spectrum disorder: Study to Explore Early Development (SEED)

DiGuiseppi C, Sabourin KR, Levy SE, Soke GN, Lee LC, Wiggins L, Schieve LA

This study examines the parent-reported treatments and outcomes of medically attended injuries among children with autism spectrum disorder (ASD) living in six different areas (located in California, Colorado, Georgia, Maryland, North Carolina, and Pennsylvania) in the United States in 2003–2006, compared to children with other developmental disabilities (DDs) and children from the general population (POP). The Study to Explore Early Development’s (SEED’s) in-depth data on the health of preschool children aged 2–5 years provided researchers with key information on these injuries. For each reported injury, parents were asked whether the injury resulted in loss of consciousness, an emergency department (ED) visit, hospitalization, surgery, or long-term behavior change. Parents were also asked if their child received any medication or injections for each medically attended injury reported. Study results showed that 30% of children in SEED had at least one medically attended injury. Of those children, 83% had at least one injury-related ED visit or hospitalization. Children with ASD were more likely than children from the POP group to have had a surgical procedure for an injury. Children with ASD were also less likely than those with DDs to receive medication or injections to treat injuries. These differences may be a result of characteristics of the child or injury or may reflect the clinicians’ ability to provide certain treatments or judge the severity of the child’s pain due to challenging behaviors associated with ASD. Further research may aid in understanding the differences in treatments prescribed to children with ASD compared to those prescribed to children with DDs or from the general population.

Early life influences on child weight outcomes in the Study to Explore Early Development

Kral TV, Chittams J, Bradley CB, Daniels JL, DiGuiseppi CG, Johnson SL, Pandey J, Pinto-Martin JA, Rahai N, Ramirez A, Schieve LA, Thompson A, Windham G, York W, Young L, Levy SE

Autism, 2019

This study examined overweight and obesity at age 2–5 years in children with and without autism spectrum disorder (ASD) or other developmental disorders (DDs). Obesity rates among U.S. children have increased markedly in recent decades and children with ASD have previously been shown to be at particularly high risk for obesity. SEED’s large sample and detailed data on children with ASD and other DDs allowed researchers to conduct a more in depth analysis on this topic than done previously. Study findings show that children born to mothers who were overweight/obese before becoming pregnant, or gained more weight than recommended during their pregnancies, were more likely to be overweight or obese between the ages of 2–5 years compared with children born to mothers who were underweight or normal weight prior to pregnancy and gained the recommended amount of weight during their pregnancies. These findings were similar for children with ASD, children with other DDs, and children without DDs. However, children with ASD were more likely than children in the other groups to have rapid weight gain in infancy; rapid weight gain was also associated with increased risk for being overweight or obese between ages 2–5 years. This study highlights the importance of maintaining a heathy weight before and during pregnancy and fostering healthy growth during infancy, among all children, including those with and without ASD.

Sleep Problems in 2- to 5-Year-Olds with Autism and Other Developmental Delays

This study assessed sleep problems, such as difficulties going to sleep or staying asleep through the night, in preschool-aged children with ASD, in comparison to children with other developmental disabilities (DDs) and children in the general population. SEED’s large sample and detailed data on preschoolers allowed researchers to conduct a more in-depth analysis on this topic than in previous studies. Study findings show that children with ASD and children with other DDs who have some ASD symptoms have more sleep problems than children with DDs without ASD symptoms and children in the general population. Even when researchers used a conservative definition to classify children as having sleep problems, 47% of children with ASD and 57% of children with other DDs who had some ASD symptoms were reported to have sleep problems, compared to 29% of children with DDs but no ASD symptoms and 25% of children in the general population. Sleep is important for development in young children. Addressing sleep problems among children with ASD and children with other DDs who have ASD symptoms is an important component of healthcare needs in this population.

A Novel Approach to Dysmorphology to Enhance the Phenotypic Classification of Autism Spectrum Disorder in the Study to Explore Early Development

This study used data from SEED to develop a new method to systematically classify certain physical features in children. The purpose of this system is to evaluate dysmorphology, which is the assessment of physical features that do not follow the typical pattern of growth and development. Children with multiple dysmorphic features often have an underlying genetic condition or had early pregnancy exposures that affected their development during the pregnancy.  The SEED dysmorphology classification method is more comprehensive than that used in previous studies. The findings from this study indicate that approximately 17% of children with ASD have a high number of dysmorphic features, and hence, meet the criteria for classification as dysmorphic. In contrast, approximately 5% of children from the general population control group met the criteria for classification as dysmorphic. Some, but not all, of the dysmorphology differences between children with and without ASD were explained by previously recognized and diagnosed genetic conditions and birth defects, which both occur more commonly in children with ASD. This is the first report of dysmorphology among children with ASD in a diverse U.S. population.

Relationship of Weight Outcomes, Co-occurring Conditions, and Severity of Autism Spectrum Disorder in the Study to Explore Early Development

Levy SE, Pinto-Martin JA, Bradley CB, Chittams J, Johnson SL, Pandey J, Alison Pomykacz A, Ramirez A, Reynolds A, Rubenstein E, Schieve LA, Shapira SK, Thompson A, Young L, Kral TV

Journal of Pediatrics, 2018

This study examined overweight and obesity among children with ASD, other developmental disabilities (DDs), and children from the general population. Study findings show that children with ASD or DDs were more likely to be overweight or obese than children from the general population. The proportion of children who were either overweight or obese was 28% in those with ASD, 25% in children with another DD, and 20% in children in the general population. Children with ASD or DDs were also more likely to have birth defects, medical disorders, seizure disorders, attention-deficit/hyperactivity disorder (ADHD), and psychiatric disorders than children from the general population. After controlling for these co-occurring conditions, the association between ASD and overweight or obesity was not changed, but the association between overweight and obesity and other DDs was reduced. In addition, among children with ASD, those with moderate or severe symptoms of ASD were more likely to be overweight or obese than children with mild ASD symptoms. Addressing overweight and obesity among children with ASD and other DDs is an important component of healthcare needs in this population.

Infections in Children with Autism Spectrum Disorder: Study to Explore Early Development (SEED)

Sabourin KR, Reynolds A,  Schendel D, Rosenberg S, Croen L, Pinto-Martin JA, Schieve LA, Newschaffer C, Lee LC, DiGuiseppi C

This study evaluated the association between early childhood infections and ASD and other developmental disabilities (DDs). SEED’s large sample size allowed researchers to conduct a more in-depth analysis on this topic than previous studies. The study findings show that children with ASD were more likely than children with other DDs and children from the general population to have had an infection in the first 28 days of life (early infection). Overall, 4.9% of children with ASD, 4.2% of children with other DDs, and 2.2% of children in the general population had an early infection recorded in their medical records. Children with ASD were also more likely to have an infection in the first 3 years of life than children in the general population, but children with ASD had similar rates of infection during their first 3 years as children with other DDs. This study highlights that ASD is associated with infections very early in the child’s life.

Brief Report: Self-Injurious Behaviors in Preschool Children with Autism Spectrum Disorder Compared to Other Developmental Delays and Disorders.

Soke GN, Rosenberg SA, Rosenberg CR, Vasa RA, Lee LC, DiGuiseppi C.

This study assessed self-injurious behavior, or SIB, among preschool-aged children with ASD in comparison to children with other developmental disabilities (DDs). The study showed that SIB is common in two groups of preschool-aged children – those with ASD and those for whom some autism-related symptoms are reported by their mother or other caregiver, even though they didn’t meet the criteria to be classified as an ASD case.  SIB was much less common in children with other DDs whose mother or caregiver did not report autism-related symptoms. These findings suggest that clinicians working with young children with DDs consider screening for SIB, even in children who do not have an ASD diagnosis.

Associations between Parental Broader Autism Phenotype and Child Autism Spectrum Disorder Phenotype in the Study to Explore Early Development.

Rubenstein E, Wiggins LD, Schieve LA, Bradley C, DiGuiseppi C, Moody E, Pandey J, Pretzel RE, Howard AG, Olshan AF, Pence BW, Daniels J.

Autism, 2018

This study assessed how the variation in developmental features among children with ASD was related to their parents’ own autism-related traits.  The presence of autism traits in family members of children with ASD is commonly referred to as the “broader autism phenotype” or BAP. The study findings show that if one or both parents have traits consistent with BAP, the child’s ASD is more likely to fall within a certain clinical presentation than if neither parent has traits consistent with BAP.  This clinical presentation in the child is characterized by average nonverbal abilities, mild language and motor delays, and increased frequency of other co-occurring developmental difficulties such as anxiety, depression, aggression, and attention difficulties.  The findings reported in this study could help better our understanding of the genetics of ASD.

The Prevalence of Gluten Free Diet Use among Preschool Children with Autism Spectrum Disorder.

Rubenstein E, Schieve L, Bradley C, DiGuiseppi C, Moody E, Thomas K, Daniels J.

This study estimated the proportion of children with ASD who had been on a gluten free diet. Altogether, 20% of preschool-aged children with ASD were currently or previously using a gluten free diet. In contrast, only 1% of children in the general population control group were using a gluten free diet. Children with ASD who also had gastrointestinal problems or had previously had a developmental regression were more likely to use a gluten free diet. This study demonstrates that gluten free diets are commonly used among children with ASD. More research is needed on the effectiveness of a gluten free diet in managing both gastrointestinal and behavioral symptoms related to ASD.

Injuries in Children with Autism Spectrum Disorder: Study to Explore Early Development (SEED).

DiGuiseppi C, Levy SE, Sabourin KR, Soke GN, Rosenberg S, Lee LC, Moody E, Schieve LA.

This study evaluated injuries in preschool-aged children with and without ASD and other developmental disabilities (DDs). Parents of children were asked whether their child had ever had an injury that required medical attention, and what types of injuries had occurred. The study findings showed that injuries were common in all groups of children and there was little difference between groups. Parents reported injuries for 32% of children with ASD, 28% of children with other DDs, and 30% of children in the general population. The most common injuries were open wounds and fractures and the most common reason for injuries was falls. While there was a slight difference in injuries between children with ASD and other DDs, further study found that this was largely explained by a higher level of attention problems in the children with ASD.

Homogeneous Subgroups of Young Children with Autism Improve Phenotypic Characterization in the Study to Explore Early Development.

Wiggins LD, Tian LH, Levy SE, Rice C, Lee LC, Schieve L, Pandey J, Daniels J, Blaskey L, Hepburn S, Landa R, Edmondson-Pretzel R, Thompson W.

This study used a complex computer program to assess the wide range of developmental characteristics among children with ASD.  Researchers identified four subgroups of children within the ASD group: 1) children with mild language delay and average cognitive functioning, but increased cognitive rigidity (or difficulty changing behaviors); 2) children with significant developmental delay, below average cognitive functioning, and repetitive motor behaviors; 3) children with general developmental delay, below average cognitive functioning, and moderate to highly severe autism symptoms; and 4) children with mild language and motor delays, but increased cognitive rigidity and high rates of problem behaviors. This study shows how information on developmental characteristics can be studied using advanced statistical methods to better understand ASD.  This information might also be useful in understanding children’s future health and development.

Self-injurious Behaviors in Children with Autism Spectrum Disorder Enrolled in the Study to Explore Early Development.

Autism, 2017

This study assessed self-injurious behavior, or SIB, among children with ASD. SIB includes head-banging, hair-pulling, arm-biting, scratching, and hitting oneself. SIB is usually mild, but can be severe in some children and may result in injuries requiring medical care. Children with severe SIB may miss out on educational and social activities. This study showed that in the SEED sample, about 28% of preschool-aged children with ASD displayed SIB currently, and 47% had previously displayed SIB. Researchers found SIB was more common in children with low adaptive behavior scores and gastrointestinal, sleep, and behavioral problems. While its causes are not completely understood, identifying SIB early is helpful because it may reduce the likelihood of more severe SIB later.   Top of Page

In addition to research on ASD risk factors and on the developmental characteristics and health outcomes of children with ASD or other developmental disabilities, the wealth of data collected in SEED has allowed researchers to address critical gaps in our understanding of the performance of various ASD screening and assessment tools and to contribute to the development of genetic laboratory tests.

Temperament Similarities and Differences: A Comparison of Factor Structures from the Behavioral Style Questionnaire in Children with and Without Autism Spectrum Disorder

Barger B, Moody EJ, Ledbetter C, D’Abreu L, Hepburn S, Rosenberg SA

Journal of Autism and Developmental, 2019

This study assessed the performance of the Behavioral Style Questionnaire (BSQ), a commonly used measure of temperament, in children aged 2–5 years with and without autism spectrum disorder (ASD). The BSQ contains 100 questions designed to measure nine different behavioral tendencies, or temperaments, that affect how well children respond to their environment. Previous research has suggested that the BSQ may function differently for children with ASD compared with typically developing children. As such, researchers used Study to Explore Early Development (SEED) data to compare the behavioral tendencies the BSQ identified among children diagnosed with ASD and among children from the general population. Study findings showed that the BSQ did not identify the behavioral tendencies that it was originally designed to measure. Moreover, while the BSQ measured certain behavioral tendencies similarly among children with ASD and children from the general population, for other behavioral tendencies it did not. One behavioral tendency, “Negative Social Interactions”, was unique among children with ASD, and was not found among children from the general population. These findings suggest that more research may help us better understand how the BSQ performs in different groups of children, including children with ASD.

ASD Screening with the Child Behavior Checklist/1.5-5 in the Study to Explore Early Development

Levy SE, Rescorla LA, Chittams JL, Kral TJ, Moody EJ, Pandey J, Pinto-Martin JA, Pomykacz A, Ramirez A, Reyes N, Rosenberg CR, Schieve LA, Thompson A, Young L, Zhang J, Wiggins L

J Autism Dev Disord., 2019

This study assessed the performance of a general developmental assessment tool, known as the Child Behavior Checklist (CBCL), as a screening tool for autism spectrum disorder (ASD) symptoms in preschool-aged children. The CBCL is a broad-spectrum checklist that includes 99 items completed by a parent or a caregiver. Researchers in this study were interested in a subset of 13 items related to pervasive developmental problems. Previous research on this topic produced inconsistent results. SEED’s large sample of children with and without ASD and other developmental disabilities (DDs) allowed for a more thorough assessment. The study results showed that scores from the 13-item subscale were significantly different for children in the ASD group and the DD with ASD features group, compared to children in the DD without ASD features group and the population control group. These findings suggest that this CBCL subscale was effective at identifying children with ASD features needing further evaluation and supports its use as an ASD screening tool. The findings are particularly noteworthy because the CBCL is already widely used by schools and health professionals to screen for other developmental issues such as attention, anxiety, and depression.

DSM-5 criteria for autism spectrum disorder maximizes diagnostic sensitivity and specificity in preschool children

Wiggins LD, Rice CE, Barger B, Soke GN, Lee LC, Moody E, Edmondson-Pretzel R, Levy SE

Soc Psychiatry Psychiatr Epidemiol, 2019

The Diagnostic and Statistical Manual of Mental Disorders (DSM) specifies standardized criteria for diagnosing individuals with autism spectrum disorder (ASD) and other conditions. Criteria for diagnosing ASD were revised between the fourth (DSM-IV-TR) and the fifth edition of the manual (DSM-5). The purpose of this study was to compare DSM-IV-TR and DSM-5 definitions of ASD using information from comprehensive developmental evaluations completed with preschool children enrolled in the Study to Explore Early Development (SEED). This study was important because it compared DSM-IV-TR and DSM-5 definitions of ASD by evaluating children at a time when they often are first diagnosed, using both criteria in a single clinic visit. Study findings showed that DSM-5 criteria had the best balance between identifying true ASD cases, while ruling out children with other developmental disorders, when compared to DSM-IV-TR criteria. Researchers also found good agreement between DSM-5 and DSM-IV-TR for autistic disorder and moderate agreement for a less stringent definition of ASD known as pervasive developmental disorder not otherwise specified (PDD-NOS). These findings support the DSM-5 criteria for ASD in preschool-aged children.

Bayesian Correction for Exposure Misclassification and Evolution of Evidence in Two Studies of the Association between Maternal Occupational Exposure to Asthmagens and Risk of Autism Spectrum Disorder

Singer AB, Fallin MD, Burstyn I

Current Environmental Health Reports, 2018

In this study, researchers used SEED data and data from another study of children with and without ASD to assess how potential errors in coding the data for certain risk factors might influence the findings of epidemiologic studies. Researchers often want to study the effects of certain exposures during pregnancy but may not have the exact data they need. It is rare to have biologic measurements of the chemicals women were exposed to during pregnancy.  Therefore, studies often rely on related information to classify study participants as “likely exposed” or “not exposed” to certain chemicals. For example, studies often use information on a person’s job — such as type of job and industry where the person worked — to estimate possible chemical exposures from their workplace. In this study, researchers used a statistical method to address the possibility that certain job coding schemes could result in errors when evaluating associations between workplace exposures and ASD. They propose a way researchers might use this method in future studies to assess, and possibly correct, exposure classification errors.

Influence of Family Demographic Factors on Social Communication Questionnaire Scores.

Rosenberg SA, Moody EJ, Lee LC, DiGuiseppi C, Windham GC, Wiggins LD, Schieve LA, Ledbetter CM, Levy SE, Blaskey L, Young L, Bernal P, Rosenberg CR, Fallin MD.

This study assessed how the responses to a standardized questionnaire to screen for autism symptoms varied by family demographic characteristics. The study findings indicate that test performance was different in families with an indication of low versus higher socioeconomic status. These findings are important for both researchers and clinicians using autism screening questionnaires; they should be mindful that these tools might perform differently in various sociodemographic groups of children and their parents.

The Broader Autism Phenotype in Mothers is Associated with Increased Discordance Between Maternal-Reported and Clinician-Observed Instruments that Measure Child Autism Spectrum Disorder.

Rubenstein E, Edmondson Pretzel R, Windham GC, Schieve LA, Wiggins LD, DiGuiseppi C, Olshan AF, Howard AG, Pence BW, Young L, Daniels J.

This study assessed whether parents who have autism traits reported their children’s potential autism symptoms in a similar way as parents without an indication of autism traits. The findings indicate that parents with autism traits report more autism traits in their children compared to parents without autism traits, but parent reports do not always match clinician assessments based on observed behaviors in the child. It is possible that parents with some autism traits are more adept at identifying subtle characteristics of autism in their child. Another possible explanation for the study findings is that questions on various child behaviors could be interpreted differently by parents with and without autism traits. Further study is needed. The findings reported in this study could help better our understanding of developmental assessment results in young children.

Screening for Autism with the SRS and SCQ: Variations across Demographic, Developmental and Behavioral Factors in Preschool Children.

Moody EJ, Reyes N, Ledbetter C, Wiggins L, DiGuiseppi C, Alexander A, Jackson S, Lee LC, Levy SE, Rosenberg SA.

This study assessed and compared the performance of two standardized questionnaires to screen for autism symptoms. The accuracy of each questionnaire varied depending on the child’s level of developmental functioning and family sociodemographic traits. For example, the instruments were less accurate when children had high levels of challenging behaviors or lower levels of developmental functioning. Test performance also varied in families with indication of lower versus higher socioeconomic status. These findings are important for both researchers and clinicians using autism screening questionnaires; they should be mindful that these tools perform differently in various sociodemographic groups of children and their parents.

Brief Report: The ADOS Calibrated Severity Score Best Measures Autism Diagnostic Symptom Severity in Pre-School Children.

Wiggins LD, Barger B, Moody E, Soke GN, Pandey J, Levy S.

This report describes SEED methodology for assessing autism symptom severity among children with ASD. Measuring a child’s autism symptoms is often challenging because many children with ASD also have other developmental conditions. This can make it difficult to separate a child’s social and communication challenges from the child’s other developmental delays or conditions. Researchers evaluated several measures of autism severity and found that the Autism Diagnostic Observation Schedule (ADOS) calibrated severity score best measured the severity of core autism symptoms in a way that did not include symptoms of other developmental conditions. Because of findings from this study, the ADOS calibrated severity score will be used in other SEED research to help scientists better understand how the severity of autism symptoms relates to ASD risk factors and health outcomes.

Cross-tissue Integration of Genetic and Epigenetic Data Offers Insight into Autism Spectrum Disorder.

Andrews SV, Ellis SE, Bakulski KM, Sheppard B, Croen LA, Hertz-Picciotto I, Newschaffer CJ, Feinberg AP, Arking DE, Ladd-Acosta C, Fallin MD.

Nature Communications, 2017

In this study, researchers used SEED data and data from other studies to learn more about genetics and genetic regulation in children with ASD. While it is well-understood that genetics are related to ASD, many unanswered questions remain, such as how certain genes are turned on or off. The information from this study provides insights about how certain genes might be related to ASD.

“Gap Hunting” to Characterize Clustered Probe Signals in Illumina Methylation Array Data.

Andrews SV, Ladd-Acosta C, Feinberg AP, Hansen KD, Fallin MD.

Epigenetics & Chromatin, 2016

This study assessed new laboratory approaches to analyzing information on genetics collected through SEED. The findings contribute to the growing literature on how genes and environmental factors might interact in a way that increases the risk for ASD. While this study does not directly study these interactions, researchers describe and demonstrate how new laboratory approaches could help identify genetic associations.   Top of Page

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What is Autism?

Autism is a developmental disorder with symptoms that appear within the first three years of life. Its formal diagnostic name is autism spectrum disorder. The word “spectrum” indicates that autism appears in different forms with varying levels of severity. That means that each individual with autism experiences their own unique strengths, symptoms , and challenges. 

Understanding more about ASD can help you better understand the individuals who are living with it. 

How autism spectrum disorders are described

Psychiatrists and other clinicians rely on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) to define autism and its symptoms. The  DSM-5 definition  recognizes two main symptom areas:

• Deficits in social communication and interaction
• Restricted, repetitive behaviors, interests, or activities

These symptoms appear early in a child’s development—although diagnosis may occur later. Autism is diagnosed when symptoms cause developmental challenges that are not better explained by other conditions.

The definition of autism has been refined over the years. Between 1995 and 2011, the DSM-IV grouped Asperger’s Syndrome and Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS) with autism. Asperger’s syndrome was an autism spectrum disorder marked by strong verbal language skills and, often, high intellectual ability. PDD-NOS was a more general diagnosis for people who did not fit clearly into the other two categories. 

However, the DSM-5 no longer recognizes Asperger’s syndrome or PDD-NOS as separate diagnoses. Individuals who would previously have received either of these diagnoses may now receive a diagnosis of autism spectrum disorder instead. 

Autism symptoms and behaviors

Individuals with autism may present a range of symptoms, such as: 

• Reduced eye contact
• Differences in body language
• Lack of facial expressions
• Not engaging in imaginative play
• Repeating gestures or sounds
• Closely focused interests
• Indifference to temperature extremes

These are just a few examples of the symptoms an individual with autism may experience. Any individual could have some, all, or none of these symptoms. Keep in mind that having these symptoms does not necessarily mean a person has autism. Only a qualified medical professional can diagnose autism spectrum disorder. 

Most importantly, an individual with autism is first and foremost an individual. Learning about the symptoms can help you start to understand the behaviors and challenges related to autism, but that’s not the same as getting to know the individual. Each person with autism has their own strengths, likes, dislikes, interests, challenges, and skills, just like you do. 

How autism is diagnosed

There is no known biological marker for autism. That means that no blood or genetic test can diagnose the disorder. Instead, clinicians rely on observation, medical histories, and questionnaires to determine whether an individual has autism. 

Physicians and specialists may use one or several of the following screening tools : 

• Modified Checklist for Autism in Toddlers , Revised (M-CHAT), a 20-question test designed for toddlers between 16 and 30 months old. 
• The Ages and Stages Questionnaire (ASQ) , a general developmental screening tool with sections targeting specific ages used to identify any developmental challenges a child may have. 
• Screening Tool for Autism in Toddlers and Young Children (STAT) , an interactive screening tool, comprising 12 activities that assess play, communication, and imitation. 
• Parents’ Evaluation of Developmental Status (PEDS)  is a general developmental parent-interview form that identifies areas of concern by asking parents questions.  

The American Academy of Pediatrics encourages autism screening for all children at their 18 and 24-month well-child checkups. Parents and caregivers can also ask their pediatrician for an autism screening if they have concerns. In rare cases, individuals with autism reach adulthood before receiving a diagnosis. However, most individuals receive an autism diagnosis before the age of 8.

Prevalence of autism

For many years, a diagnosis of autism was rare, occurring in just one child out of 2,000. One reason for this was the diagnostic criteria. Autism was not clearly defined until 1980 when the disorder was included in the DSM-III. Before that time, some cases of autism spectrum disorder may have been mistaken for other conditions. 

Since the ’80s, the rate of autism has increased dramatically around the world. In March 2020, the US Federal Centers for Disease Control announced that  1 in every 54 children  in the United States is affected by autism. 

Although autism is more likely to affect boys than girls, children of all genders have been diagnosed with ASD. Several recent studies investigate the impact of race, ethnicity, and socioeconomic  disparities on the diagnosis of autism spectrum disorder. 1,2,3,4

A short history of autism

Researchers have been working on autism and autism-like disorders since the 1940s. At that time, autism studies tended to be small in scale and used varying definitions of the disorder. Autism was also sometimes lumped in with other conditions.

Focused research into ASD became more common in the 1980s when the DSM-III established autism as a distinct diagnosis. Since then, researchers have explored the causes, symptoms, comorbidities, efficacy of treatments, and many other issues related to autism. 

Researchers have yet to discover a cause for autism. Many of the ideas put forth thus far have been disproven. Likely a combination of genetic , neurological , and environmental factors are at work, which is the case with many psychiatric disorders and conditions. 

Autism Prognosis

Autism is a lifelong condition, and a wide variety of treatments can help support people with ASD. The symptoms and comorbidities—conditions occurring in the same individual—are treatable. Early intervention delivers the best results. Parents and caregivers should seek out the advice of a qualified medical professional before starting any autism treatment. 

Advances in understanding autism, its symptoms, and comorbidities have improved outcomes for individuals with autism. In recent years, more children with autism have attended school in typical classrooms and gone on to live semi-independently. However, the majority remain affected to some degree throughout their lifetime. 

Co-occurring conditions

When a person has more than two or more disorders, these conditions are known as comorbidities. Several comorbidities are common in people with autism. 

These include: 

• Gastrointestinal and immune function disorders
• Metabolic disorders
• Sleep disorders

Identifying co-occurring conditions can sometimes be a challenge because their symptoms may be mimicked or masked by autism symptoms. However, diagnosing and identifying these conditions can help avoid complications and improve the quality of life for individuals with autism. 

Autism in pop culture

Movies and books featuring characters with autism have helped bring autism spectrum disorder into the public consciousness. Some have ignited controversy; others have increased the public’s general understanding of autism. A few have done both. At ARI, we hope that people will rely on evidence-based research to understand autism spectrum disorder better.   

Learn more about autism spectrum disorder by watching one of our expert-led webinars . They help you learn about ASD from clinicians, researchers, and therapists who research autism and support individuals with ASD. 

• Donohue MR, Childs AW, Richards M, Robins DL. Race influences parent report of concerns about symptoms of autism spectrum disorder. Autism . 2019;23(1):100-111. doi:10.1177/1362361317722030
• Durkin MS, Maenner MJ, Baio J, et al. Autism Spectrum Disorder Among US Children (2002-2010): Socioeconomic, Racial, and Ethnic Disparities. Am J Public Health . 2017;107(11):1818-1826. doi:10.2105/AJPH.2017.304032
• Newschaffer CJ. Trends in Autism Spectrum Disorders: The Interaction of Time, Group-Level Socioeconomic Status, and Individual-Level Race/Ethnicity. Am J Public Health . 2017;107(11):1698-1699. doi:10.2105/AJPH.2017.304085
• Yingling ME, Hock RM, Bell BA. Time-Lag Between Diagnosis of Autism Spectrum Disorder and Onset of Publicly-Funded Early Intensive Behavioral Intervention: Do Race-Ethnicity and Neighborhood Matter?. J Autism Dev Disord . 2018;48(2):561-571. doi:10.1007/s10803-017-3354-3

Motor Skills and Executive Function in Autism

autismAdmin 2024-03-01T12:48:58-05:00 May 8th, 2024 | Back to School , Early Intervention , Educational Therapies , Executive Function , Health , Parenting , Sensory , Social Skills , Webinar |

Free webinar at 1 p.m. Eastern time (US), Wednesday, May 8, 2024 Learn about emerging research on the relationship between the development of motor skills and executive function in autistic children.

Editorial – Addressing delays: proactive parent-led interventions during waiting periods

Melanie Glock 2023-12-06T14:19:00-05:00 December 6th, 2023 | News |

The wait for an autism diagnosis and subsequent intervention can be highly stressful for many families, especially when access to needed health and educational services also hinges on the approval of

Prenatal exposure to cannabis may increase likelihood of autism

Melanie Glock 2023-08-29T16:53:04-05:00 August 29th, 2023 | News |

Cannabis use during pregnancy may alter placental and fetal DNA methylation (the process of turning genes “on” and “off”) in ways that increase the likelihood of autism spectrum disorder (ASD) or

New multi-national study adds to evidence linking alterations of the gut microbiome to autism

Melanie Glock 2023-08-29T16:27:41-05:00 August 29th, 2023 | News |

Strong new evidence linking alterations of the gut microbiome to autism spectrum disorders (ASD) comes from a new multi-national study by James Morton and colleagues. In the study, researchers in North

Sleep problems in infancy associated with ASD, autism traits, and social attention alterations

Melanie Glock 2023-07-20T18:49:05-05:00 July 20th, 2023 | News |

A new study from the United Kingdom indicates that sleep problems in infancy may help to predict later social skills deficits, autism traits, and autism diagnoses in children. Jannath Begum-Ali and

Preemptive therapy prior to autism diagnosis may be highly cost-effective

Melanie Glock 2023-07-17T16:01:07-05:00 July 17th, 2023 | News |

Preemptive therapy for infants who display early symptoms of autism may be highly cost-effective, according to a new study from Australia. Leonie Segal and colleagues based their economic analysis on a 2021

Privacy Overview

• Patient Care & Health Information
• Diseases & Conditions
• Autism spectrum disorder

Autism spectrum disorder is a condition related to brain development that impacts how a person perceives and socializes with others, causing problems in social interaction and communication. The disorder also includes limited and repetitive patterns of behavior. The term "spectrum" in autism spectrum disorder refers to the wide range of symptoms and severity.

Autism spectrum disorder includes conditions that were previously considered separate — autism, Asperger's syndrome, childhood disintegrative disorder and an unspecified form of pervasive developmental disorder. Some people still use the term "Asperger's syndrome," which is generally thought to be at the mild end of autism spectrum disorder.

Autism spectrum disorder begins in early childhood and eventually causes problems functioning in society — socially, in school and at work, for example. Often children show symptoms of autism within the first year. A small number of children appear to develop normally in the first year, and then go through a period of regression between 18 and 24 months of age when they develop autism symptoms.

While there is no cure for autism spectrum disorder, intensive, early treatment can make a big difference in the lives of many children.

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• Children’s Book: My Life Beyond Autism

Some children show signs of autism spectrum disorder in early infancy, such as reduced eye contact, lack of response to their name or indifference to caregivers. Other children may develop normally for the first few months or years of life, but then suddenly become withdrawn or aggressive or lose language skills they've already acquired. Signs usually are seen by age 2 years.

Each child with autism spectrum disorder is likely to have a unique pattern of behavior and level of severity — from low functioning to high functioning.

Some children with autism spectrum disorder have difficulty learning, and some have signs of lower than normal intelligence. Other children with the disorder have normal to high intelligence — they learn quickly, yet have trouble communicating and applying what they know in everyday life and adjusting to social situations.

Because of the unique mixture of symptoms in each child, severity can sometimes be difficult to determine. It's generally based on the level of impairments and how they impact the ability to function.

Below are some common signs shown by people who have autism spectrum disorder.

Social communication and interaction

A child or adult with autism spectrum disorder may have problems with social interaction and communication skills, including any of these signs:

• Fails to respond to his or her name or appears not to hear you at times
• Resists cuddling and holding, and seems to prefer playing alone, retreating into his or her own world
• Has poor eye contact and lacks facial expression
• Doesn't speak or has delayed speech, or loses previous ability to say words or sentences
• Can't start a conversation or keep one going, or only starts one to make requests or label items
• Speaks with an abnormal tone or rhythm and may use a singsong voice or robot-like speech
• Repeats words or phrases verbatim, but doesn't understand how to use them
• Doesn't appear to understand simple questions or directions
• Doesn't express emotions or feelings and appears unaware of others' feelings
• Doesn't point at or bring objects to share interest
• Inappropriately approaches a social interaction by being passive, aggressive or disruptive
• Has difficulty recognizing nonverbal cues, such as interpreting other people's facial expressions, body postures or tone of voice

Patterns of behavior

A child or adult with autism spectrum disorder may have limited, repetitive patterns of behavior, interests or activities, including any of these signs:

• Performs repetitive movements, such as rocking, spinning or hand flapping
• Performs activities that could cause self-harm, such as biting or head-banging
• Develops specific routines or rituals and becomes disturbed at the slightest change
• Has problems with coordination or has odd movement patterns, such as clumsiness or walking on toes, and has odd, stiff or exaggerated body language
• Is fascinated by details of an object, such as the spinning wheels of a toy car, but doesn't understand the overall purpose or function of the object
• Is unusually sensitive to light, sound or touch, yet may be indifferent to pain or temperature
• Doesn't engage in imitative or make-believe play
• Fixates on an object or activity with abnormal intensity or focus
• Has specific food preferences, such as eating only a few foods, or refusing foods with a certain texture

As they mature, some children with autism spectrum disorder become more engaged with others and show fewer disturbances in behavior. Some, usually those with the least severe problems, eventually may lead normal or near-normal lives. Others, however, continue to have difficulty with language or social skills, and the teen years can bring worse behavioral and emotional problems.

When to see a doctor

Babies develop at their own pace, and many don't follow exact timelines found in some parenting books. But children with autism spectrum disorder usually show some signs of delayed development before age 2 years.

If you're concerned about your child's development or you suspect that your child may have autism spectrum disorder, discuss your concerns with your doctor. The symptoms associated with the disorder can also be linked with other developmental disorders.

Signs of autism spectrum disorder often appear early in development when there are obvious delays in language skills and social interactions. Your doctor may recommend developmental tests to identify if your child has delays in cognitive, language and social skills, if your child:

• Doesn't respond with a smile or happy expression by 6 months
• Doesn't mimic sounds or facial expressions by 9 months
• Doesn't babble or coo by 12 months
• Doesn't gesture — such as point or wave — by 14 months
• Doesn't say single words by 16 months
• Doesn't play "make-believe" or pretend by 18 months
• Doesn't say two-word phrases by 24 months
• Loses language skills or social skills at any age

There is a problem with information submitted for this request. Review/update the information highlighted below and resubmit the form.

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Autism spectrum disorder has no single known cause. Given the complexity of the disorder, and the fact that symptoms and severity vary, there are probably many causes. Both genetics and environment may play a role.

• Genetics. Several different genes appear to be involved in autism spectrum disorder. For some children, autism spectrum disorder can be associated with a genetic disorder, such as Rett syndrome or fragile X syndrome. For other children, genetic changes (mutations) may increase the risk of autism spectrum disorder. Still other genes may affect brain development or the way that brain cells communicate, or they may determine the severity of symptoms. Some genetic mutations seem to be inherited, while others occur spontaneously.
• Environmental factors. Researchers are currently exploring whether factors such as viral infections, medications or complications during pregnancy, or air pollutants play a role in triggering autism spectrum disorder.

No link between vaccines and autism spectrum disorder

One of the greatest controversies in autism spectrum disorder centers on whether a link exists between the disorder and childhood vaccines. Despite extensive research, no reliable study has shown a link between autism spectrum disorder and any vaccines. In fact, the original study that ignited the debate years ago has been retracted due to poor design and questionable research methods.

Avoiding childhood vaccinations can place your child and others in danger of catching and spreading serious diseases, including whooping cough (pertussis), measles or mumps.

Risk factors

The number of children diagnosed with autism spectrum disorder is rising. It's not clear whether this is due to better detection and reporting or a real increase in the number of cases, or both.

Autism spectrum disorder affects children of all races and nationalities, but certain factors increase a child's risk. These may include:

• Your child's sex. Boys are about four times more likely to develop autism spectrum disorder than girls are.
• Family history. Families who have one child with autism spectrum disorder have an increased risk of having another child with the disorder. It's also not uncommon for parents or relatives of a child with autism spectrum disorder to have minor problems with social or communication skills themselves or to engage in certain behaviors typical of the disorder.
• Other disorders. Children with certain medical conditions have a higher than normal risk of autism spectrum disorder or autism-like symptoms. Examples include fragile X syndrome, an inherited disorder that causes intellectual problems; tuberous sclerosis, a condition in which benign tumors develop in the brain; and Rett syndrome, a genetic condition occurring almost exclusively in girls, which causes slowing of head growth, intellectual disability and loss of purposeful hand use.
• Extremely preterm babies. Babies born before 26 weeks of gestation may have a greater risk of autism spectrum disorder.
• Parents' ages. There may be a connection between children born to older parents and autism spectrum disorder, but more research is necessary to establish this link.

Complications

Problems with social interactions, communication and behavior can lead to:

• Problems in school and with successful learning
• Employment problems
• Inability to live independently
• Social isolation
• Stress within the family
• Victimization and being bullied

More Information

• Autism spectrum disorder and digestive symptoms

There's no way to prevent autism spectrum disorder, but there are treatment options. Early diagnosis and intervention is most helpful and can improve behavior, skills and language development. However, intervention is helpful at any age. Though children usually don't outgrow autism spectrum disorder symptoms, they may learn to function well.

• Autism spectrum disorder (ASD). Centers for Disease Control and Prevention. https://www.cdc.gov/ncbddd/autism/facts.html. Accessed April 4, 2017.
• Uno Y, et al. Early exposure to the combined measles-mumps-rubella vaccine and thimerosal-containing vaccines and risk of autism spectrum disorder. Vaccine. 2015;33:2511.
• Taylor LE, et al. Vaccines are not associated with autism: An evidence-based meta-analysis of case-control and cohort studies. Vaccine. 2014;32:3623.
• Weissman L, et al. Autism spectrum disorder in children and adolescents: Overview of management. https://www.uptodate.com/home. Accessed April 4, 2017.
• Autism spectrum disorder. In: Diagnostic and Statistical Manual of Mental Disorders DSM-5. 5th ed. Arlington, Va.: American Psychiatric Association; 2013. http://dsm.psychiatryonline.org. Accessed April 4, 2017.
• Weissman L, et al. Autism spectrum disorder in children and adolescents: Complementary and alternative therapies. https://www.uptodate.com/home. Accessed April 4, 2017.
• Augustyn M. Autism spectrum disorder: Terminology, epidemiology, and pathogenesis. https://www.uptodate.com/home. Accessed April 4, 2017.
• Bridgemohan C. Autism spectrum disorder: Surveillance and screening in primary care. https://www.uptodate.com/home. Accessed April 4, 2017.
• Levy SE, et al. Complementary and alternative medicine treatments for children with autism spectrum disorder. Child and Adolescent Psychiatric Clinics of North America. 2015;24:117.
• Brondino N, et al. Complementary and alternative therapies for autism spectrum disorder. Evidence-Based Complementary and Alternative Medicine. http://dx.doi.org/10.1155/2015/258589. Accessed April 4, 2017.
• Volkmar F, et al. Practice parameter for the assessment and treatment of children and adolescents with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry. 2014;53:237.
• Autism spectrum disorder (ASD). Eunice Kennedy Shriver National Institute of Child Health and Human Development. https://www.nichd.nih.gov/health/topics/autism/Pages/default.aspx. Accessed April 4, 2017.
• American Academy of Pediatrics policy statement: Sensory integration therapies for children with developmental and behavioral disorders. Pediatrics. 2012;129:1186.
• James S, et al. Chelation for autism spectrum disorder (ASD). Cochrane Database of Systematic Reviews. http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD010766.pub2/abstract;jsessionid=9467860F2028507DFC5B69615F622F78.f04t02. Accessed April 4, 2017.
• Van Schalkwyk GI, et al. Autism spectrum disorders: Challenges and opportunities for transition to adulthood. Child and Adolescent Psychiatric Clinics of North America. 2017;26:329.
• Autism. Natural Medicines. https://naturalmedicines.therapeuticresearch.com. Accessed April 4, 2017.
• Autism: Beware of potentially dangerous therapies and products. U.S. Food and Drug Administration. https://www.fda.gov/ForConsumers/ConsumerUpdates/ucm394757.htm?source=govdelivery&utm_medium=email&utm_source=govdelivery. Accessed May 19, 2017.
• Drutz JE. Autism spectrum disorder and chronic disease: No evidence for vaccines or thimerosal as a contributing factor. https://www.uptodate.com/home. Accessed May 19, 2017.
• Weissman L, et al. Autism spectrum disorder in children and adolescents: Behavioral and educational interventions. https://www.uptodate.com/home. Accessed May 19, 2017.
• Huebner AR (expert opinion). Mayo Clinic, Rochester, Minn. June 7, 2017.

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Understanding autism spectrum disorder

According to the American Psychiatric Association's Diagnostic and Statistical Manual (DSM-5), autism spectrum disorder (ASD) is a neurodevelopmental disorder with a wide range of conditions and severities characterized by repetitive behaviors, challenges in social interaction, and communication difficulties. 1

ASD is a spectrum condition, which means that although all individuals with ASD share some difficulties, their condition will affect them in different ways. Some people need significant support in their daily lives, while others can live independently and have specialized talents or skills. 2 Early autism diagnosis is crucial for timely intervention and support. 3

This article will explore autism spectrum disorder, including its characteristics and challenges, and the benefits of raising awareness.

Characteristics and symptoms of ASD

People with ASD can experience a wide range of difficulties that can make life more challenging. As aforementioned, many of the characteristics and symptoms associated with ASD revolve around communication difficulties, restricted and repetitive behavior patterns, and sensory issues. 4 Here’s a closer look at these behaviors and sensory issues.

Social interaction difficulties

A core feature of ASD is difficulty with social interaction and communication . People with ASD may have a complete lack of verbal communication, or they may not understand social cues, such as body language or tone of voice, in others. They may face challenges when trying to engage in conversations, struggle with eye contact, and have difficulty forming and maintaining relationships. 5

Restricted and repetitive behavior patterns

Individuals with ASD often display repetitive behaviors or have very focused interests. These behaviors can include repeating certain actions, having a profound interest in specific topics, and adhering strictly to routines to the point where any disruption is very upsetting. Repetitive movements, such as rocking or flapping hands, are also common. These behaviors may be comforting and help them manage anxiety or cope with overwhelming sensory stimulation. 6

Sensory sensitivities

People with ASD may also have increased or reduced sensitivity to sensory input from any of the senses—sight, sound, touch, taste, and smell. They might have specific food preferences based on texture or be uncomfortable with certain clothing materials. These sensory sensitivities can affect an individual's ability to engage in everyday activities. Sensory-friendly environments and accommodations can significantly improve the quality of life for someone with ASD. Many organizations now host sensory-friendly events specifically for people with these challenges. 7

Diagnosis and assessment

Diagnosing ASD relies on observing a child’s development and behavior. Because it presents on a spectrum, signs and symptoms can vary greatly from person to person. Early diagnosis can help individuals with ASD access early intervention measures that improve developmental and adaptive skills. 8

Early signs of ASD in young children include:

• Limited eye contact 8
• Delayed speech and language skills 8
• Lack of social responsiveness 8
• Repetitive behaviors 8
• Unusual sensory interests or sensitivities 8

There’s no single medical test for ASD. Instead, diagnosis is based on observation of the child’s behavior and developmental history. The process typically includes a developmental screening, which is a short test to tell if children are learning basic skills when they should or if there are delays.  If this test raises concerns, a child may need a comprehensive diagnostic evaluation, which is a thorough developmental history and observation of behaviors by a multidisciplinary team, possibly including a pediatric neurologist, psychologist, or psychiatrist. The evaluation may also involve hearing and vision screenings, genetic testing, and neurological testing. 9

Supporting individuals with ASD

People with autism can benefit from support at different stages of development. Early intervention capitalizes on the brain’s remarkable plasticity during early development. These strategies often include therapies such as speech therapy, occupational therapy, and applied behavior analysis (ABA), which are tailored for each child. Early interventions help children with ASD develop important skills, including speech, social interaction, and self-care, which foster their long-term growth and independence. 10

When a child with ASD turns three, they may be eligible for special education services through the local school district. During their school years, they may have an individualized education plan that includes adapting teaching methods and classroom environments to accommodate the diverse needs of students with ASD. Strategies such as using visual aids, providing structured routines, and incorporating interests-based learning can improve engagement and understanding. 11

Communication and social skills training can help people with ASD improve both verbal and non-verbal communication, leading to better interaction with peers and adults. Social skills training often includes role-playing, social stories, and group activities designed to teach appropriate social behaviors and emotional understanding. Through consistent and comprehensive support across these areas, individuals with ASD can achieve improved outcomes and a higher quality of life. 12

Autism advocacy and acceptance

Autism advocacy and acceptance promote understanding, respect, and inclusion for individuals with ASD. These efforts emphasize the importance of recognizing that autism is a natural part of human diversity. Advocates work to dismantle stereotypes and misconceptions about autism, promoting a society where individuals with ASD are appreciated for their unique strengths and capabilities. 13

There are many advocacy organizations and initiatives, such as the Autism Society, Autism Speaks, and the Autistic Self Advocacy Network. These groups champion the rights of individuals with autism, pushing for policy changes, providing resources and support for families, and raising public awareness through campaigns and community events. Their work helps secure better educational, social, and employment opportunities for people with ASD. 14

Advocates also encourage people to recognize the neurological differences among people, including those with autism, as natural variations within the human population. Celebrations of neurodiversity aim to shift the narrative from one of deficit to one of difference, highlighting the distinctive contributions and perspectives that individuals with ASD bring to the world. Through raising awareness, the movement for autism acceptance is fostering a more inclusive and understanding society. 15

Become an advocate for people with autism

If you’d like to make a positive impact in the lives of people with autism spectrum disorder, you can develop the skills you need through the online master’s in autism spectrum disorder program from the University of Kansas School of Education and Human Sciences.

KU’s top-notch faculty are experts in research, teaching, and mentorship. They can help you learn to understand and influence policy in your school or organization and throughout your community. With KU’s #2 online master’s in special education programs, you can earn your degree entirely online at a pace that suits your schedule. 16

When you finish, you’ll be well-situated to help people with disabilities live full and rewarding lives.

Contact a KU admissions outreach advisor today to learn more.

• Retrieved on February 15, 2024, from www.cdc.gov/ncbddd/autism/hcp-dsm.html
• Retrieved on February 15, 2024, from psychiatry.org/patients-families/autism/what-is-autism-spectrum-disorder
• Retrieved on February 15, 2024, from www.ncbi.nlm.nih.gov/pmc/articles/PMC10491411/#:~:text=Early%20diagnosis%20of%20ASD%20benefits,and%20increased%20independence%20in%20adulthood
• Retrieved on February 15, 2024, from cdc.gov/ncbddd/autism/signs.html
• Retrieved on February 15, 2024, from nidcd.nih.gov/health/autism-spectrum-disorder-communication-problems-children0expressions
• Retrieved on February 15, 2024, from kennedykrieger.org/patient-care/conditions/restrictive-and-repetitive-behavior
• Retrieved on February 15, 2024, from autismspeaks.org/sensory-issues
• Retrieved on February 15, 2024, from www.ncbi.nlm.nih.gov/pmc/articles/PMC10491411/
• Retrieved on February 15, 2024, from www.cdc.gov/ncbddd/autism/screening.html
• Retrieved on February 15, 2024, from behavioral-innovations.com/blog/critical-early-intervention-children-autism-spectrum-disorder/
• Retrieved on February 15, 2024, from autismspeaks.org/autism-school-your-childs-rights
• Retrieved on February 15, 2024, crossrivertherapy.com/autism/social-connections-and-autism
• Retrieved on February 15, 2024, autisticadvocacy.org/inclusion-acceptance/
• Retrieved on February 15, 2024, readingrockets.org/topics/autism-spectrum-disorder/articles/top-autism-organizations-and-web-resources
• Retrieved on February 15, 2024, redballoonlearner.org/news-events/neurodiversity-whats-to-celebrate/
• Retrieved on February 16, 2024, from usnews.com/best-graduate-schools/top-education-schools/special-needs-education-rankings

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Is eye movement in autism tied to facial recognition?

by Freda Kreier, Yale University

Some teenagers with autism use a different set of eye-movement patterns from their non-autistic counterparts while recognizing faces, according to James McPartland, Ph.D., Harris Professor in the Yale Child Study Center (YCSC) and a director of the Center for Brain and Mind Health at Yale School of Medicine; and Jason Griffin, Ph.D., the Hilibrand Postdoctoral Fellow at YCSC. Eye movements are part of the process of telling people apart and could provide information to clinicians about how people with autism process social information differently from non-autistic persons.

Autism is a complex neurodevelopmental condition that tends to present itself very differently across the spectrum that defines it. Characteristics of the condition can include differences in language and other aspects of development, as well as heightened sensory sensitivity. But one near-universal autistic trait is a tendency to interact with people in ways that differ from neurotypical people.

One example of this is that people with autism often spend less time making eye contact with others. While not universal, this trait has become the focus of many research studies that seek to explain how the minds of people with autism work.

In a study published in the Journal of Autism and Developmental Disorders , first author Griffin, along with McPartland and colleagues, used a new analysis of existing eye-tracking data from teenagers with autism to see if they could gain new insights into how teens on the spectrum learn and recognize human faces.

The study found that, while teens in the study recalled upright faces as accurately as their non-autistic peers, they took a different approach with their gaze—focusing intently on just one section of the face. The discovery could one day be used by clinicians as a trait to specify potential autistic sub-groups, says McPartland.

The accuracy of the facial recall was actually an uncommon finding. In many previous studies, Griffin notes, young people with autism have shown reduced accuracy in recognizing faces.

How teens with autism memorize a face

Studying and memorizing faces is one of the main ways that people learn to tell people apart. Human brains adapted to register the details of hundreds of faces—a system that helps us distinguish among strangers, friends, and acquaintances,

One of the ways researchers have tried to study this process is through tracking eye movements . However, most of the studies on eye movements in people with autism have tended to focus on analyzing isolated aspects of these data —things like how often or how long someone looks at another person's mouth or eyes.

Hoping to look at the larger pattern of eye movement, Griffin and the research team re-analyzed data collected by McPartland for his doctoral dissertation two decades ago. The original experiment asked 32 teenagers between ages 12 and 17—15 of whom had autism—to memorize the faces of strangers.

The teens were then tested to see if they could distinguish between new faces and ones they'd previously been shown. A camera attached to a baseball cap worn by the participants allowed the researchers to track eye movement during the experiment.

In the new analysis, Griffin tracked which parts of the face the teenagers looked at for 10 seconds at a time. Two patterns emerged: While memorizing faces, teens both with and without autism focused on a small region of the face. But when shown the same face later, neurotypical teens would start with a focused look before darting their eyes to other parts of the face. Teens with autism, on the other hand, stayed focused on just one spot.

Finding biomarkers to identify sub-groups of autism

Exactly what this finding means is still unclear. One possibility is that teenagers with autism use intense focal gazes as a compensatory strategy to ensure they can recall whose face they are looking at. But on the question of why kids with autism operate differently here, "we just know that they do," says McPartland.

However, the findings could be incorporated into part of a larger collection of biomarkers to help identify sub-categories of autism. Autism comes in many forms—and understanding the tendencies of each group could help people prepare for long-term outcomes, such as how much support, if any, they might require.

McPartland and his colleagues are now planning to deploy this same method on a larger dataset with several hundred people with autism to see whether they can categorize eye movement and face recognition in novel ways.

"For a condition that has been studied intensely for a long time, there's surprisingly little we know with certainty," says McPartland. He hopes that studying these types of differences will help clinicians better understand the condition and how to help their patients.

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In celebration of Autism Acceptance Month, the Office of National Autism Coordination (ONAC) within the National Institute of Mental Health (NIMH) honors the contributions of people on the autism spectrum and the autism community to our nation and the world. We join the United Nations and the U.S. government in recognizing the needs of people on the autism spectrum and their families. Our office acknowledges the need to support, understand, accept, include, and empower people on the autism spectrum. Throughout the month of April, we will be updating this page with the latest news and events.

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Genetic contributions to autism spectrum disorder

1 Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway

2 Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway

3 Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway

M. Niarchou

4 Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA

A. Starnawska

5 The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark

6 Department of Biomedicine, Aarhus University, Denmark

7 Center for Genomics for Personalized Medicine, CGPM, and Center for Integrative Sequencing, iSEQ, Aarhus, Denmark

8 College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE

C. van der Merwe

9 Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, MA, USA

10 Department of Psychiatry, Autism Research Centre, University of Cambridge, UK

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous ( de novo ). More than 100 risk genes have been implicated by rare, often de novo , potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse , each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

Definition of autism

Kanner defined autism in 1943 with detailed case descriptions of children showing social aloofness, communication impairments, and stereotyped behaviors and interests, often accompanied by intellectual disability (ID) (Kanner, 1943 ). A year later, Asperger independently published an article on children presenting marked difficulties in social communication and unusually circumscribed and intense interests, despite advanced intellectual and language skills (Asperger, 1944 ). Three decades later, Wing and Gould united Asperger and Kanner's descriptions and conceptualized a spectrum of autistic conditions (Wing and Gould, 1978 , 1979 ).

The onset of autism is during the first years of life, although symptoms may not be fully apparent or recognized until later (American Psychiatric Association, 2013 ). Autism is a heterogeneous and complex group of conditions with considerable variation in core symptoms, language level, intellectual functioning, and co-occurring psychiatric and medical difficulties. Subtype diagnoses such as childhood autism and Asperger's syndrome were previously used to specify more homogeneous presentations, but were unstable over time within individuals and used unreliably by clinicians (Lord et al., 2020 ). Current editions of the major diagnostic manuals have replaced the subtypes with an overarching autism spectrum disorder diagnosis and instead require specification of key sources of heterogeneity; language level, intellectual functioning, and co-occurring conditions (APA, 2013 ; World Health Organization, 2018 ).

Epidemiology

Prevalence estimates of autism have steadily increased from less than 0.4% in the 1970s to current estimates of 1–2% (Fombonne, 2018 ; Lyall et al., 2017 ). The increase is largely explained by broadening diagnostic criteria to individuals without ID and with milder impairments, and increased awareness and recognition of autistic traits (Lord et al., 2020 ; Taylor et al., 2020 ). There are marked sex and gender differences in autism (Halladay et al., 2015 ; Warrier et al., 2020 ). The male-to-female ratio is approximately 4:1 in clinical and health registry cohorts but closer to 3:1 in general population studies with active case-finding (Loomes, Hull, & Mandy, 2017 ) and 1–2:1 in individuals with moderate-to-severe ID (Fombonne, 1999 ; Yeargin-Allsopp et al., 2003 ). The mechanisms underlying the sex difference are mostly unknown, and hypotheses include a female protective effect (aspects of the female sex conferring resilience to risk factors for autism), prenatal steroid hormone exposure, and social factors such as underdiagnosis and misdiagnosis in women (Ferri, Abel, & Brodkin, 2018 ; Halladay et al., 2015 ).

Co-occurring conditions are the rule rather than the exception, estimated to affect at least 70% of people with autism from childhood (Lai et al., 2019 ; Simonoff et al., 2008 ). Common co-occurring conditions include attention-deficit hyperactivity disorder (ADHD), anxiety, depression, epilepsy, sleep problems, gastrointestinal and immune conditions (Davignon, Qian, Massolo, & Croen, 2018 ; Warrier et al., 2020 ). There is an elevated risk of premature mortality from various causes, including medical comorbidities, accidental injury, and suicide (Hirvikoski et al., 2016 ).

Autism is also associated with positive traits such as attention to detail and pattern recognition (Baron-Cohen & Lombardo, 2017 ; Bury, Hedley, Uljarević, & Gal, 2020 ). Further, there is wide variability in course and adulthood outcomes with regard to independence, social relationships, employment, quality of life, and happiness (Howlin & Magiati, 2017 ; Mason et al., 2020 ; Pickles, McCauley, Pepa, Huerta, & Lord, 2020 ). Rigorous longitudinal studies and causally informative designs are needed to determine the factors affecting developmental trajectories and outcomes.

Environmental factors

Twin studies suggest that 9–36% of the variance in autism predisposition might be explained by environmental factors (Tick, Bolton, Happé, Rutter, & Rijsdijk, 2016 ). There is observational evidence for association with pre- and perinatal factors such as parental age, asphyxia-related birth complications, preterm birth, maternal obesity, gestational diabetes, short inter-pregnancy interval, and valproate use (Lyall et al., 2017 ; Modabbernia, Velthorst, & Reichenberg, 2017 ). Mixed results are reported for pregnancy-related nutritional factors and exposure to heavy metals, air pollution, and pesticides, while there is strong evidence that autism risk is unrelated to vaccination, maternal smoking, or thimerosal exposure (Modabbernia et al., 2017 ). It is challenging to infer causality from observed associations, given that confounding by lifestyle, socioeconomic, or genetic factors contributes to non-causal associations between exposures and autism. Many putative exposures are associated with parental genotype (e.g. obesity, age at birth) (Gratten et al., 2016 ; Taylor et al., 2019a , Yengo et al., 2018 ), and some are associated both with maternal and fetal genotypes (e.g. preterm birth) (Zhang et al., 2017 ). Studies triangulating genetically informative designs are needed to disentangle these relationships (Davies et al., 2019 ; Leppert et al., 2019 ; Thapar & Rutter, 2019 ).

Twin and pedigree studies

In 1944, Kanner noted that parents shared common traits with their autistic children, introducing the ‘broader autism phenotype’ (i.e. sub-threshold autistic traits) and recognizing the importance of genetics (Harris, 2018 ; Kanner, 1944 ). Thirty years later, twin studies revolutionized the field of autism research (Ronald & Hoekstra, 2011 ).

Twin studies were the first to demonstrate the heritability of autism. In 1977, the first twin-heritability estimate was published, based on a study of 10 dizygotic (DZ) and 11 monozygotic (MZ) pairs (Folstein & Rutter, 1977 ). Four out of the 11 MZ pairs (36%) but none of the DZ pairs were concordant for autism. Subsequently, over 30 twin studies have been published, further supporting the high heritability of autism (Ronald & Hoekstra, 2011 ). A meta-analysis of seven primary twin studies reported that the heritability estimates ranged from 64% to 93% (Tick et al., 2016 ). The correlations for MZ twins were at 0.98 [95% confidence interval (CI) 0.96–0.99], while the correlations for DZ twins were at 0.53 (95% CI 0.44–0.60) when the autism prevalence rate was assumed to be 5% (based on the broader autism phenotype) and increased to 0.67 (95% CI 0.61–0.72) when the prevalence was 1% (based on the stricter definition) (Tick et al., 2016 ). Additionally, family studies have found that the relative risk of a child having autism relates to the amount of shared genome with affected relatives ( Fig. 1 ) (Bai et al., 2019 ; Constantino et al., 2013 ; Georgiades et al., 2013 ; Grønborg, Schendel, & Parner, 2013 ; Risch et al., 2014 ; Sandin et al., 2014 ).

Relative risk of autism by degree of relatedness with a person with autism. Relative risk for full and half siblings, and full cousins was provided in Hansen et al. ( 2019 ). Relative risk for half first cousins was estimated based on Xie et al. ( 2019 ). GS, genome shared.

Early twin and pedigree studies demonstrated that the biological relatives of individuals with autism who did not meet the criteria for an autism diagnosis themselves commonly showed elevated autistic traits such as communication and social interaction difficulties (Le Couteur et al., 1996 ), indicating that the heritability is not restricted to the traditional diagnostic boundaries of autism. Twin studies also indicate that although social communication and repetitive behavior trait dimensions each show strong heritability, there is a limited genetic correlation between them (e.g. for a review, see Ronald & Hoekstra, 2011 ). Further, twin studies have found substantial genetic overlap between autistic traits and symptoms of other psychiatric conditions, including language delay (e.g. Dworzynski et al., 2008 ), ID (e.g. Nishiyama et al., 2009 ), ADHD (e.g. Ronald, Edelson, Asherson, & Saudino, 2010 ), and anxiety (e.g. Lundström et al., 2011 ) (for a review, see Ronald & Hoekstra, 2014 ). Moreover, twin and family studies indicate that the sibling recurrence rate of autism is lower in female than male siblings (Palmer et al., 2017 ; Werling & Geschwind, 2015 ), suggesting the female protective effect hypothesis as a potential explanation for the male preponderance in the diagnosis of autism. The hypothesis was supported by results showing that the siblings of autistic females had a higher likelihood of high autistic trait scores and autism than the siblings of autistic males (Ferri et al., 2018 ; Palmer et al., 2017 ; Robinson, Lichtenstein, Anckarsäter, Happé, & Ronald, 2013 ), consistent with females having a higher liability threshold.

Genetic variants differ in the frequency at which they occur in the population (e.g. rare v. common), the type (i.e. SNPs/CNVs/translocations and inversions/indels), and whether they are inherited or de novo . Here, we summarize the findings on genetic risk for autism from linkage and candidate gene studies, common and rare genetic variation studies, epigenomics, and transcriptomics. A glossary of important terms is in Box 1 .

Candidate gene association study: A study that examines the association between a phenotype and a genetic variant chosen a priori based on knowledge of the gene's biology or functional impact.

Complex trait: A trait that does not follow Mendelian inheritance patterns, but is likely the result of multiple factors including a complex mixture of variation within multiple genes.

Copy number variant (CNV): Deletion or duplication of large genomic regions.

de novo mutation: A mutation that is present in the offspring but is either absent in parents or is present only in parental germ cells.

DNA methylation (DNAm): Epigenetic modification of DNA characterized by the addition of a methyl group (-CH 3 ) to the 5 th position of the pyrimidine ring of cytosine base resulting in 5-methylcytosine (5mC).

Epigenetics: The science of heritable changes in gene regulation and expression that do not involve changes to the underlying DNA sequence.

Epigenome-Wide Association Study (EWAS): A study that investigates associations between DNA methylation levels quantified at tens/hundreds of thousands of sites across the human genome, and the trait of interest.

Genome-Wide Association Study (GWAS): A study scanning genome-wide genetic variants for associations with a given trait.

Genetic correlation: An estimate of the proportion of variance shared between two traits due to shared genetics.

Heritability: An estimate of the proportion of variation in a given trait that is due to differences in genetic variation between individuals in a given population.

Heritability on the liability scale : A heritability estimate adjusted for the population prevalence of a given binary trait, typically disorders.

Genetic linkage studies: A statistical method of mapping genes of heritable traits to their chromosomal locations by using chromosomal co-segregation with the phenotype.

Mendelian inheritance: When the inheritance of traits is passed down from parents to children and is controlled by a single gene for which one allele is dominant and the other recessive.

Methylation Quantitative Trait Locus (mQTL): A SNP at which genotype is correlated with the variation of DNA methylation levels at a nearby ( cis- mQTL) or distal ( trans- mQTL) site.

Phenotype: The observable characteristics of an individual.

Polygenic risk score (PRS): An estimate of an individual's genetic liability for a condition calculated based on the cumulative effect of many common genetic variants.

Single nucleotide polymorphism (SNP): A single base pair change that is common (>1%) in the population.

Single nucleotide variant (SNV): A variation in a single nucleotide without any limitation of frequency.

SNP heritability: The proportion of variance in a given phenotype in a population that is attributable to the additive effects of all SNPs tested. Typically, SNPs included have a minor allele frequency >1%.

Linkage and candidate gene studies

Initial linkage studies were conducted to identify chromosomal regions commonly inherited in affected individuals. Susceptibility loci implicated a range of regions, but only two have been replicated (Ramaswami & Geschwind, 2018 ): at chromosome 20p13 (Weiss, Arking, Daly, & Chakravarti, 2009 ) and chromosome 7q35 (Alarcón, Cantor, Liu, Gilliam, & Geschwind, 2002 ). Lack of replication and inconsistent findings were largely due to low statistical power (Kim & Leventhal, 2015 ). Candidate gene association studies identified over 100 positional and/or functional candidate genes for associations with autism (Bacchelli & Maestrini, 2006 ). However, there was no consistent replication for any of these findings (Warrier, Chee, Smith, Chakrabarti, & Baron-Cohen, 2015 ), likely due to limitations in study design (e.g. low statistical power, population diversity, incomplete coverage of variation within the candidate genes, and false positives arising from publication bias) (Ioannidis, 2005 ; Ioannidis, Ntzani, Trikalinos, & Contopoulos-Ioannidis, 2001 ). The advancement of genome-wide association studies (GWAS) and next-generation sequencing techniques has significantly enhanced gene and variant discovery.

Common genetic variation

The SNP-heritability (proportion of variance attributed to the additive effects of common genetic variants) of autism ranges from 65% in multiplex families (Klei et al., 2012 ) to 12% in the latest Psychiatric Genomics Consortium GWAS ( Fig. 2 a ) (Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, 2017 ; Grove et al., 2019 ). Variation is largely attributable to sample heterogeneity and differences in methods used to estimate SNP-heritability.

Variance explained by different classes of genetic variants in autism. ( a ) Donut chart of the variance explained by different classes of variants. The narrow-sense heritability (82.7%, Nordic average, shades of green) has been estimated using familial recurrence data from Bai et al. ( 2019 ). The total common inherited heritability (12%) has been estimated using LDSC-based SNP-heritability (additive) from Grove et al. ( 2019 ) and the total rare inherited heritability (3%) has been obtained from Gaugler et al. ( 2014 ). The currently unexplained additive heritability is thus 67.7% (total narrow-sense heritability minus common and rare inherited heritabilities combined). This leaves a total of 17.3% of the variance to shared and unique environmental estimates (Bai et al., 2019 ). The term environmental refers to non-additive and non-inherited factors that contribute to variation in autism liability. Of this, de novo missense and protein-truncating variants (Satterstrom et al., 2020 ) and variation in non-genic regions (An et al., 2018 ) together explain 2.5% of the variance. Whilst de novo variation can be inherited in some cases (germline mutation in the parent) and thus shared between siblings, it is unlikely that this will be shared by other related individuals, and thus unlikely to be included in the narrow-sense heritability in Bai et al. ( 2019 ). This is likely to be a lower-bound of the estimate as we have not included the variance explained by de novo structural variants and tandem repeats. Additionally, non-additive variation accounts for ~4% of the total variance (Autism Sequencing Consortium et al., 2019 ). Thus, ~11% of the total variance is currently unaccounted for, though this is likely to be an upper bound. ( b ) The variance explained is likely to change in phenotypic subgroups. For instance, the risk ratio for de novo protein-truncating variants in highly constrained genes (pLI > 0.9) is higher in autistic individuals with ID compared to those without ID (point estimates and 95% confidence intervals provided; Kosmicki et al., 2017 ). ( c ) Similarly, the proportion of the additive variance explained by common genetic variants is higher in autistic individuals without ID compared to autistic individuals with ID (Grove et al., 2019 ). Point estimates and 95% confidence intervals provided.

Early GWASs of autism were underpowered, partly due to overestimating potential effect sizes. Grove et al. ( 2019 ) conducted a large GWAS of autism combining data from over 18 000 autistic individuals and 27 000 non-autistic controls and an additional replication sample. They identified five independent GWAS loci ( Fig. 3 ). Another recent study (Matoba et al., 2020 ) identified a further novel locus by meta-analyzing the results from Grove et al. ( 2019 ) with over 6000 case-pseudocontrol pairs from the SPARK cohort by employing a massively parallel reporter assay to identify a potential causal variant (rs7001340) at this locus which regulates DDH2 in the fetal brain. The sample sizes are still relatively small compared to other psychiatric conditions (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2020 ; Howard et al., 2019 ), though ongoing work aims to double the sample size and identify additional loci.

Karyogram showing the 102 genes implicated by rare variant findings at a false discovery rate of 0.1 or less (Satterstrom et al., 2020 ) and the five index SNPs identified in GWAS (Grove et al., 2019 ) of autism.

Using genetic correlations and polygenic score analyses, studies have identified modest shared genetics between autism and different definitions of autistic traits in the general population (Askeland et al., 2020 ; Bralten et al., 2018 ; Robinson et al., 2016 ; Taylor et al., 2019 b ). There is some evidence for developmental effects, with greater shared genetics in childhood compared to adolescence (St Pourcain et al., 2018 ). These methods have also identified modest polygenic associations between autism and other neurodevelopmental and mental conditions such as schizophrenia, ADHD, and major depressive disorder, related traits such as age of walking, language delays, neuroticism, tiredness, and self-harm, as well as risk of exposure to childhood maltreatment and other stressful life events (Brainstorm Consortium et al., 2018 ; Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ; Hannigan et al., 2020 ; Lee et al., 2019 , b ; Leppert et al., 2019 ; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013 ; Warrier & Baron-Cohen, 2019 ). Notably, autism is positively genetically correlated with measures of intelligence and educational attainment (EA) (Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ), an observation supported by polygenic score association (Clarke et al., 2016 ). Polygenic Transmission Disequilibrium Tests have identified an over-transmission of polygenic scores for EA, schizophrenia, and self-harm from parents to autistic children, but an absence of such over-transmission to non-autistic siblings (Warrier & Baron-Cohen, 2019 ; Weiner et al., 2017 ), suggesting that these genetic correlations are not explained by ascertainment biases or population stratification. However, a genetic correlation does not necessarily imply a causal relationship between the two phenotypes and may simply index biological pleiotropy. Causal inference methods such as Mendelian randomization can be used to disentangle such relationships (Davies et al., 2019 ; Pingault et al., 2018 ).

The relatively low SNP-heritability in autism compared to other psychiatric conditions may partly be due to phenotypic heterogeneity. In an attempt to reduce phenotypic heterogeneity, Chaste et al. ( 2015 ) identified 10 phenotypic combinations to subgroup autistic individuals. Family-based association analyses did not identify significant loci, and SNP-heritability for the subgroups was negligent. It is unclear if reducing phenotypic heterogeneity increases genetic homogeneity, and investigating this in larger samples is warranted. Another study identified no robust evidence of genetic correlation between social and non-social (restricted and repetitive behavior patterns) autistic traits (Warrier et al., 2019 ). A few studies have investigated the common variant genetic architecture of social and non-social autistic traits in individuals with autism (Alarcón et al., 2002 ; Cannon et al., 2010 ; Cantor et al., 2018 ; Lowe, Werling, Constantino, Cantor, & Geschwind, 2015 ; Tao et al., 2016 ; Yousaf et al., 2020 ) and in the general population (St Pourcain et al., 2014 ; Warrier et al., 2018 , 2019 ), but replication of the identified loci is needed.

Diagnostic classification is another source of heterogeneity: SNP-heritability of Asperger's syndrome (ICD-10 diagnosis) was twice (0.097 ± 0.001) that of childhood autism and unspecified pervasive developmental disorders (Grove et al., 2019 ) [due to overlap in subtype diagnoses, a hierarchy was used: childhood autism>atypical autism>Asperger's syndrome>unspecified subtypes (Grove et al., 2019 )]. Supporting this, polygenic scores for intelligence and EA had larger loadings in the Asperger's syndrome and childhood autism subgroups compared to other subgroups (Grove et al., 2019 ). Additionally, the SNP-heritability of autism (all subtypes) without co-occurring ID diagnosis (0.09 ± 0.005) was three times that of autism with ID (Grove et al., 2019 ) ( Fig. 2 c ).

Rare genetic variation

Rare genetic variants confer significant risk in the complex etiology of autism. They are typically non-Mendelian, with substantial effect sizes and low population attributable risk. It is estimated that ~10% of autistic individuals have been diagnosed with an identifiable rare genetic syndrome characterized by dysmorphia, metabolic, and/or neurologic features (Carter & Scherer, 2013 ; Tammimies et al., 2015 ). Associated syndromes include the 15q11-q13 duplication of the Prader-Willi/Angelman syndrome, fragile X syndrome, 16p11.2 deletion syndrome, and 22q11 deletion syndrome (Sztainberg & Zoghbi, 2016 ). Prevalence estimates for autism vary widely between genetic syndromes; for example, 11% in 22q11.2 deletion syndrome and 54% in Cohen's syndrome (Richards, Jones, Groves, Moss, & Oliver, 2015 ). Of note, estimating the prevalence of autism in the context of genetic syndromes is complex (Havdahl et al., 2016 ; Richards et al., 2015 ).

The rate of gene discovery in autism is a linear function of increasing sample size (De Rubeis et al., 2014 ). Early studies implicated nine genes in the first 1000 autism cases (Neale et al., 2012 ; Sanders et al., 2012 ), increasing to 27 and 33 associated genes from separate analyses of Simons Simplex Collection and Autism Sequencing Consortium (ASC) samples (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Integrating these samples using the TADA framework implicated a total of 65 autism genes (Sanders et al., 2015 ).

The MSSNG initiative analyzed whole genomes from 5205 individuals ( N cases  = 2636), and identified 61 autism-risk genes, of which 18 were new candidates (Yuen et al., 2017 ). More recently, the largest whole-exome sequencing analysis to date conducted by the ASC ( N  = 35 584, N cases  = 11 986) identified 102 autism-associated genes ( Fig. 3 ), many of which are expressed during brain development with roles in the regulation of gene expression and neuronal communication (Satterstrom et al., 2020 ). Rare CNVs and SNVs associated with autism have pleiotropic effects, thus increasing the risk for other complex disorders such as schizophrenia, ADHD, ID, and epilepsy (Gudmundsson et al., 2019 ; Satterstrom et al., 2019 , 2020 ).

CNVs can impact one or multiple genes and can occur at common or rare frequencies in a population. All CNVs associated with autism have been rare. Recurrent CNVs are among the most convincing rare inherited risk variations for autism, and have a prevalence of about 3% in affected patients (Bourgeron, 2016 ). In comparison, approximately 4–10% of autistic individuals have de novo deletions or duplications (Bourgeron, 2016 ; Pinto et al., 2010 ; Sebat et al., 2007 ) frequently mapped to established risk loci 1q21.1, 3q29, 7q11.23, 15q11.2-13, and 22q11.2 (Sanders et al., 2015 ). A higher global frequency of de novo CNVs is observed in idiopathic autism cases from simplex families (10%) compared to multiplex families (2%) and controls (1%) (Halladay et al., 2015 ; Itsara et al., 2010 ; Sebat et al., 2007 ). Inherited CNVs can be present in unaffected siblings and parents, suggesting a model of incomplete penetrance dependent on the dosage sensitivity and function of the gene(s) they affect (Vicari et al., 2019 ).

Damaging SNVs include nonsense, frameshift, and splice site mutations (collectively referred to as protein-truncating variants, or PTVs), and missense variants. Rare inherited variants have a smaller average effect size and reduced penetrance compared to de novo pathogenic mutations. Early studies on whole exomes from trios established a key role for de novo germline mutations in autism. Whilst analysis in smaller sample sizes indicated only modest increase in de novo mutation rates in autism cases (Neale et al., 2012 ), the rate rose significantly in excess of expectation as the sample size increased (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Most recently, the ASC observed a 3.5-fold case enrichment of damaging de novo PTVs and a 2.1-fold enrichment for damaging de novo missense variants (Satterstrom et al., 2020 ), concluding that all exome de novo SNVs explain 1.92% of the variance in autism liability (Satterstrom et al., 2020 ) ( Fig. 2 a ).

Comparatively, the ASC discovered a 1.2-fold enrichment of rare inherited damaging PTVs in cases compared to unaffected siblings (Satterstrom et al., 2020 ). Similarly, recent whole-genome analysis found no excess of rare inherited SNVs, and no difference in the overall rate of these variants in affected subjects compared to unaffected siblings (Ruzzo et al., 2019 ).

New advancements

It is estimated that de novo mutations in protein-coding genes contribute to risk in ~30% of simplex autism cases (Yuen et al., 2017 ; Zhou et al., 2019 ). However, recent work has also shown that de novo mutations in non-coding regions of the genome (particularly gene promoters) contribute to autism (An et al., 2018 ; Zhou et al., 2019 ). Adapting machine learning techniques may be key to providing novel neurobiological insights to the genetic influences on autism in the future (An et al., 2018 ; Ruzzo et al., 2019 ; Zhou et al., 2019 ). Additionally, rare tandem repeat expansions in genic regions are more prevalent among autism cases than their unaffected siblings, with a combined contribution of ~2.6% to the risk of autism (Trost et al., 2020 ).

Common and rare variant interplay

The largest component of genetic risk is derived from common variants of additive effect with a smaller contribution from de novo and rare inherited variation ( Fig. 2 a ) (de la Torre-Ubieta, Won, Stein, & Geschwind, 2016 ; Gaugler et al., 2014 ). Notably, KMT2E was implicated in both the latest GWAS (Grove et al., 2019 ) and exome sequencing (Satterstrom et al., 2020 ) analyses. It is hypothesized that common genetic variation in or near the genes associated with autism influences autism risk, although current sample sizes lack the power to detect the convergence of the two (Satterstrom et al., 2020 ).

Whilst higher SNP-heritability is observed in autistic individuals without ID ( Fig. 2 b ), de novo PTVs in constrained genes are enriched in autistic individuals with ID ( Fig. 2 a ). However, the genetic architecture of autism is complex and diverse. For example, common genetic variants also contribute to risk in autistic individuals with ID and in autistic individuals carrying known large-effect de novo variants in constrained genes (Weiner et al., 2017 ). Furthermore, an excess of disruptive de novo variants is also observed in autistic individuals without co-occurring ID compared to non-autistic individuals (Satterstrom et al., 2020 ).

Epigenetics

DNA methylation (DNAm), an epigenetic modification, allows for both genetic and environmental factors to modulate a phenotype (Martin & Fry, 2018 ; Smith et al., 2014 ). DNAm affects gene expression, regulatory elements, chromatin structure, and alters neuronal development, functioning, as well as survival (Kundaje et al., 2015 ; Lou et al., 2014 ; Peters et al., 2015 ; Sharma, Klein, Barboza, Lohdi, & Toth, 2016 ; Yu et al., 2012 ; Zlatanova, Stancheva, & Caiafa, 2004 ). Additionally, putative prenatal environmental risk factors impact the offspring's methylomic landscape (Anderson, Gillespie, Thiele, Ralph, & Ohm, 2018 ; Cardenas et al., 2018 ; Joubert et al., 2016 ), thus providing a plausible molecular mechanism to modulate the neurodevelopmental origins of autism.

Autism Epigenome-Wide Association Study (EWAS) meta-analysis performed in blood from children and adolescents from SEED and SSC cohorts ( N cases  = 796, N controls  = 858) identified seven differentially methylated positions (DMPs) associated ( p  < 10 × 10 −05 ) with autism, five of them also reported to have brain-based autism associations. The associated DMPs annotated to CENPM , FENDRR , SNRNP200 , PGLYRP4 , EZH1 , DIO3 , and CCDC181 genes, with the last site having the largest effect size and the same direction of association with autism across the prefrontal cortex, temporal cortex, and cerebellum (Andrews et al., 2018 ). The study reported moderate enrichment of methylation Quantitative Trait Loci (mQTLs) among the associated findings, suggesting top autism DMPs to be under genetic control (Andrews et al., 2018 ). These findings were further extended by the MINERvA cohort that added 1263 neonatal blood samples to the meta-analysis. The SEED-SSC-MINERvA meta-EWAS identified 45 DMPs, with the top finding showing the consistent direction of association across all three studies annotated to ITLN1 (Hannon et al., 2018 ). The MINERvA sample was also used for EWAS of autism polygenic score, hypothesizing that the polygenic score-associated DNAm variation is less affected by environmental risk factors, which can confound case–control EWAS. Elevated autism polygenic score was associated with two DMPs ( p  < 10 × 10 −06 ), annotated to FAM167A / C8orf12 and RP1L1 . Further Bayesian co-localization of mQTL results with autism GWAS findings provided evidence that several SNPs on chromosome 20 are associated both with autism risk and DNAm changes in sites annotated to KIZ , XRN2 , and NKX2-4 (Hannon et al., 2018 ). The mQTL effect of autism risk SNPs was corroborated by an independent study not only in blood, but also in fetal and adult brain tissues, providing additional evidence that autism risk variants can act through DNAm to mediate the risk of the condition (Hammerschlag, Byrne, Bartels, Wray, & Middeldorp, 2020 ).

Since autism risk variants impact an individual's methylomic landscape, studies that investigate DNAm in the carriers of autism risk variants are of interest to provide insight into their epigenetic profiles. A small blood EWAS performed in 52 cases of autism of heterogeneous etiology, nine carriers of 16p11.2del, seven carriers of pathogenic variants in CHD8 , and matched controls found that DNAm patterns did not clearly distinguish autism of the heterogeneous etiology from controls. However, the homogeneous genetically-defined 16p11.2del and CHD8 +/− subgroups were characterized by unique DNAm signatures enriched in biological pathways related to the regulation of central nervous system development, inhibition of postsynaptic membrane potential, and immune system (Siu et al., 2019 ). This finding highlights the need to combine genomic and epigenomic information for a better understanding of the molecular pathophysiology of autism.

It must be noted that a very careful interpretation of findings from peripheral tissues is warranted. DNAm is tissue-specific and therefore EWAS findings obtained from peripheral tissues may not reflect biological processes in the brain. Using the mQTL analytical approach may reduce this challenge, as mQTLs are consistently detected across tissues, developmental stages, and populations (Smith et al., 2014 ). However, not all mQTLs will be detected across tissues and will not necessarily have the same direction of effect (Smith et al., 2014 ). Therefore, it is recommended that all epigenetic findings from peripheral tissues are subjected to replication analyses in human brain samples, additional experimental approaches, and/or Mendelian randomization to strengthen causal inference and explore molecular mediation by DNAm (Walton, Relton, & Caramaschi, 2019 ).

EWASs performed in post-mortem brains have typically been conducted using very small sample sizes, due to limited access to brain tissue (Ladd-Acosta et al., 2014 ; Nardone et al., 2014 ). One of the largest autism EWAS performed in post-mortem brains (43 cases and 38 controls) identified multiple DMPs ( p  < 5 × 10 −05 ) associated with autism (31 DMPs in the prefrontal cortex, 52 in the temporal cortex, and two in the cerebellum) (Wong et al., 2019 ), and autism-related co-methylation modules to be significantly enriched for synaptic, neuronal, and immune dysfunction genes (Wong et al., 2019 ). Another post-mortem brain EWAS reported DNAm levels at autism-associated sites to resemble the DNAm states of early fetal brain development (Corley et al., 2019 ). This finding suggests an epigenetic delay in the neurodevelopmental trajectory may be a part of the molecular pathophysiology of autism.

Overall, methylomic studies of autism provide increasing evidence that common genetic risk variants of autism may alter DNAm across tissues, and that the epigenetic dysregulation of neuronal processes can contribute to the development of autism. Stratification of study participants based on their genetic risk variants may provide deeper insight into the role of aberrant epigenetic regulation in subgroups within autism.

Transcriptomics

Transcriptomics of peripheral tissues.

Gene expression plays a key role in determining the functional consequences of genes and identifying genetic networks underlying a disorder. One of the earliest studies on genome-wide transcriptome (Nishimura et al., 2007 ) investigated blood-derived lymphoblastoid cells gene expression from a small set of males with autism ( N  = 15) and controls. Hierarchical clustering on microarray expression data followed by differentially expressed gene (DEG) analysis revealed a set of dysregulated genes in autism compared to controls. This approach was adopted (Luo et al., 2012 ) to investigate DEGs in a cohort of 244 families with autism probands (index autism case in a family) known to carry de novo pathogenic or variants of unknown significance and discordant sibling carriers of non-pathogenic CNVs. From genome-wide microarray transcriptome data, this study identified significant enrichment of outlier genes that are differentially expressed and reside within the proband rare/ de novo CNVs. Pathway enrichment of these outlier genes identified neural-related pathways, including neuropeptide signaling, synaptogenesis, and cell adhesion. Distinct expression changes of these outlier genes were identified in recurrent pathogenic CNVs, i.e. 16p11.2 microdeletions, 16p11.2 microduplications, and 7q11.23 duplications. Recently, multiple independent genome-wide blood-derived transcriptome analysis (Filosi et al., 2020 ; Lombardo et al., 2018 ; Tylee et al., 2017 ) showed the efficiency of detecting dysregulated genes in autism, including aberrant expression patterns of long non-coding RNAs (Sayad, Omrani, Fallah, Taheri, & Ghafouri-Fard, 2019 ).

Transcriptomics of post-mortem brain tissue

Although blood-derived transcriptome can be feasible to study due to easy access to the biological specimen, blood transcriptome results are not necessarily representative of the transcriptional machinery in the brain (GTEx Consortium, 2017 ). Hence, it is extremely hard to establish a causal relationship between blood transcriptional dysregulations and phenotypes in autism. A landmark initiative by Allen Brain Institute to profile human developing brain expression patterns (RNA-seq) from post-mortem tissue enabled neurodevelopmental research to investigate gene expression in the brain (Sunkin et al., 2013 ). Analyzing post-mortem brain tissue, multiple studies identified dysregulation of genes at the level of gene exons impacted by rare/ de novo mutations in autism (Uddin et al., 2014 ; Xiong et al., 2015 ), including high-resolution detection of exon splicing or novel transcript using brain tissue RNA sequencing (RNA-seq). High-resolution RNA-seq enabled autism brain transcriptome analysis on non-coding elements, and independent studies identified an association with long non-coding RNA and enhancer RNA dysregulation (Wang et al., 2015 ; Yao et al., 2015 ; Ziats & Rennert, 2013 ).

Although it is difficult to access post-mortem brain tissue from autistic individuals, studies of whole-genome transcriptome from autism and control brains have revealed significantly disrupted pathways ( Fig. 4 ) related to synaptic connectivity, neurotransmitter, neuron projection and vesicles, and chromatin remodeling pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ). Recently, an integrated genomic study also identified from autism brain tissue a component of upregulated immune processes associated with hypomethylation (Ramaswami et al., 2020 ). These reported pathways are in strong accordance with numerous independent autism studies that integrated genetic data with brain transcriptomes (Courchesne, Gazestani, & Lewis, 2020 ; Uddin et al., 2014 ; Yuen et al., 2017 ). A large-scale analysis of brain transcriptome from individuals with autism identified allele-specific expressions of genes that are often found to be impacted by pathogenic de novo mutations (Lee et al., 2019 a ). The majority of the studies are in consensus that genes that are highly active during prenatal brain development are enriched for clinically relevant mutations in autism (Turner et al., 2017 ; Uddin et al., 2014 ; Yuen et al., 2017 ). Recently, a large number (4635) of expression quantitative trait loci were identified that were enriched in prenatal brain-specific regulatory regions comprised of genes with distinct transcriptome modules that are associated with autism (Walker et al., 2019 ).

Most commonly reported three pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ) associated with autism. ( a ) The synaptic connectivity and neurotransmitter pathway involves genes (yellow rectangular box) within presynaptic and postsynaptic neurons. Neurotransmitter transport through numerous receptors is an essential function of this pathway; ( b ) the chromatin remodeling pathway involves binding of remodeling complexes that initiate the repositioning (move, eject, or restructure) of nucleosomes that potentially can disrupt gene regulation; and ( c ) the neural projection pathway [adapted from Greig, Woodworth, Galazo, Padmanabhan, & Macklis ( 2013 )] involves the projection of neural dendrite into distant regions and the migration of neuronal cells through ventricular (VZ) and subventricular zones (SVZ) into the different cortical layers (I-VI).

Single-cell transcriptomics

Recent advancement of single-cell transcriptomics enables the detection of cell types that are relevant to disorder etiology. A recent case–control study conducted single-cell transcriptomics analysis on 15 autism and 16 control cortical post-mortem brain tissues generating over 100 000 single-cell transcriptomics data (Velmeshev et al., 2019 ). Cell-type analysis revealed dysregulations of a specific group of genes in cortico-cortical projection neurons that correlate with autism severity (Velmeshev et al., 2019 ). Deciphering cell-type identification has future implications, in particular for the implementation of precision medicine. However, single-cell technology is at very early stages of development and computationally it is still very complex to classify cell-type identity.

The emergence of CRISPR/Cas9 genome editing technology can potentially become an effective tool in future therapeutics of genetic conditions associated with autism. Although introducing and reversing DNA mutation is becoming a mature technology within in vitro systems, much work needs to be done for in vivo use of genome editing. Single-cell OMICs is another emerging field that has the potential to decipher developmental (spatio-temporally) brain cell types that are associated with autism. Identifying cell clusters and defining cell identity is a major computational challenge. Artificial intelligence can significantly improve these computational challenges to identify the molecular associations of autism at the single-cell level.

Clinical and therapeutic implications

In some, but not all, best practice clinical guidelines, genetic tests such as fragile X testing, chromosomal microarray, and karyotype testing are part of the standard medical assessment in a diagnostic evaluation of autism to identify potentially etiologically relevant rare genetic variants (Barton et al., 2018 ). The guidelines vary with respect to whether genetic testing is recommended for all people with autism, or based on particular risk factors, such as ID, seizures, or dysmorphic features. The DSM-5 diagnosis of autism includes a specifier for associated genetic conditions (APA, 2013 ). Although genetic test results may not usually have consequences for treatment changes, the results could inform recurrence risk and provide families with access to information about symptoms and prognosis. In the future, gene therapy, CRISPR/Cas9, and genome editing technologies may lead to the gene-specific design of precision medicine for rare syndromic forms of autism (Benger, Kinali, & Mazarakis, 2018 ; Gori et al., 2015 ).

Given that a substantial proportion of the genetic liability to autism is estimated to be explained by the cumulative effect of a large number of common SNPs, polygenic scores have gained traction as potential biomarkers. However, the predictive ability of polygenic scores from the largest autism GWAS to date is too low to be clinically useful. The odds ratio when comparing the top and bottom polygenic score decile groups is only 2.80 (95% CI 2.53–3.10) (Grove et al., 2019 ). Additionally, polygenic scores based on the samples of European ancestry do not translate well in populations with diverse ancestry (Palk, Dalvie, de Vries, Martin, & Stein, 2019 ).

Genetic testing can in the future become useful for informing screening or triaging for diagnostic assessments or identifying who may be more likely to respond to which type of intervention (Wray et al., 2021 ). Genetics may also help identify individuals with autism who are at a high risk of developing co-occurring physical and mental health conditions or likely to benefit from treatments of such conditions. A top research priority for autistic people and their families is addressing co-occurring mental health problems (Autistica, 2016 ), which may sometimes be the primary treatment need as opposed to autism per se . Genomics may also be helpful to repurpose existing treatments and better identify promising treatments. There are active clinical trials to repurpose drugs in autism (Hong & Erickson, 2019 ). Moreover, genetics can be used to identify social and environmental mediating and moderating factors (Pingault et al., 2018 ), which could inform interventions to improve the lives of autistic people.

Notably, there are important ethical challenges related to clinical translation of advances in genetics, including concerns about discriminatory use, eugenics concerning prenatal genetic testing, and challenges in interpretation and feedback (Palk et al., 2019 ). People with autism and their families are key stakeholders in genetic studies of autism and essential to include in discussions of how genetic testing should be used.

Conclusions and future directions

Recent large-scale and internationally collaborative investigations have led to a better understanding of the genetic contributions to autism. This includes identifying the first robustly associated common genetic variants with small individual effects (Grove et al., 2019 ) and over 100 genes implicated by rare, mostly de novo , variants of large effects (Sanders et al., 2015 ; Satterstrom et al., 2020 ). These and other findings show that the genetic architecture of autism is complex, diverse, and context-dependent, highlighting a need to study the interplay between different types of genetic variants, identify genetic and non-genetic factors influencing their penetrance, and better map the genetic variants to phenotypic heterogeneity within autism.

Immense collaborative efforts are needed to identify converging and distinct biological mechanisms for autism and subgroups within autism, which can in turn inform treatment (Thapar & Rutter, 2020 ). It is crucial to invest in multidimensional and longitudinal measurements of both core defining traits and associated traits such as language, intellectual, emotional, and behavioral functioning, and to collaboratively establish large omics databases including genomics, epigenomics, transcriptomics, proteomics, and brain connectomics (Searles Quick, Wang, & State, 2020 ). Indeed, large-scale multi-omic investigations are becoming possible in the context of large population-based family cohorts with rich prospective and longitudinal information on environmental exposures and developmental trajectories of different neurodevelopmental traits. Finally, novel methods (Neumeyer, Hemani, & Zeggini, 2020 ) can help investigate causal molecular pathways between genetic variants and autism and autistic traits.

Acknowledgements

We thank the Psychiatric Genomics Consortium, Anders Børglum, and Elise Robinson for their support and advice.

Financial support

Alexandra Havdahl was supported by the South-Eastern Norway Regional Health Authority (#2018059, career grant #2020022) and the Norwegian Research Council (#274611 PI Ted Reichborn-Kjennerud and #288083 PI Espen Røysamb). Maria Niarchou was supported by Autism Speaks (#11680). Anna Starnawska was supported by The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark (R155-2014-1724). Varun Warrier is supported by the Bowring Research Fellowship (St. Catharine's College, Cambridge), the Templeton World Charity Foundation, Inc., the Autism Research Trust, and the Wellcome Trust. Celia van der Merwe is supported by the Simons Foundation NeuroDev study (#599648) and the NIH R01MH111813 grant.

Conflict of interest

2024 Autism Acceptance Month: USC faculty experts list

April is Autism Acceptance Month. The CDC estimates that 1 out of every 36 8-year-olds is affected by autism, a lifelong developmental disorder. As South Carolina’s leader in health sciences, USC has researchers across disciplines who specialize in autism. 

The university has compiled a list of faculty experts to help reporters develop stories about autism spectrum disorder. To interview a faculty member, contact the staff member listed with each expert.

Diagnosing autism

Christian O’Reilly, assistant professor of computer science and engineering, specializes in how different areas of the brain communicate. His research in computational neuroscience, biosignal processing and neuroimaging aims to identify the organizing principles of the brain, notably for the development of biomarkers for the early diagnosis of autism spectrum disorder. He also researches novel ways to study autism and the brain through modeling and artificial intelligence. He is a member of the Artificial Intelligence Institute, the Institute for Mind and Brain and the Carolina Autism and Neurodevelopment Research Center. News contact: Chris Woodley, [email protected] , 803-576-7745

Caitlin Hudac is an associate professor of psychology and director of the Brain Research Across Development (B-RAD) Lab. Her work uses cognitive neuroscience technologies, including eye tracking, EEG and functional MRI, to understand how the brain and body change from birth into adulthood. Hudac is identifying biomarkers such as brain and heart rate signatures that could be critical for generating targeted treatments for those with autism spectrum disorder, intellectual disability and rare genetic conditions. News contact: Bryan Gentry, [email protected] , 803-576-7650

Kimberly Hills, a clinical professor of psychology, specializes in the identification and diagnosis of autism and its coexisting disorders, such as ones involving language, learning, anxiety or attention. She trains graduate students and supervises child and adolescent services, including autism evaluations, at the university’s Psychology Services Center. Hills can also discuss autism as it relates to school and clinical psychology and post-diagnosis recommendations for families. News contact: Bryan Gentry, [email protected] , 803-576-7650

Jessica Bradshaw is an associate professor of psychology and director of the Early Social Development Lab. Her research focuses on methods for early detection of autism in infancy. She studies early development of attention, motor skills and social interaction that predict the emergence of communication skills and symptoms of autism spectrum disorder. News contact: Bryan Gentry, [email protected] , 803-576-7650

Autism intervention

Dan Foster is an assistant professor at the School of Medicine Columbia’s Department of Pharmacology, Physiology and Neuroscience. His research focuses on discovering new drugs to help treat repetitive behaviors seen in individuals on the autism spectrum. Foster’s work studies the brain circuits involved in these behaviors to identify specific targets in the brain through which drugs can modulate these circuits and help suppress repetitive behaviors. News contact: Emily Miles, [email protected] , 803-216-3302

Katie Wolfe is an associate professor of special education and applied behavior analysis in the College of Education. She researches how teachers monitor progress and make instructional decisions to maximize outcomes for students with autism and related disabilities. She can discuss applied behavior analysis (ABA) and evidence-based practices for students with autism, especially those designed to address challenging behaviors and teach language and communication skills.  News contact: Anna Westbury, [email protected] , 803-576-6851

Sarah Edmunds is an assistant professor of psychology. She directs the Community-Oriented Lab for Autism and Behavioral Interventions (COLAB). Her research focuses on interventions for social communication and how we can identify the most effective interventions for each autistic child or teen. She studies emotion regulation and externalizing behavior in autistic toddlers, along with ways of training or supporting community systems to incorporate evidence-based early interventions into their practice with families.  News contact: Bryan Gentry, [email protected] , 803-576-7650

Liz Will is an assistant professor in the Department of Communication Sciences and Disorders at the Arnold School of Public Health. She investigates early atypical development and co-occurring autism in genetic conditions associated with intellectual disability, specifically Down syndrome. She is particularly interested in attention and motor phenotypes and aims to understand how they interact across development to shape outcomes related to cognition, communication and co-occurring conditions, including autism and ADHD. News contact: Erin Bluvas, [email protected] , 843-302-1681

Autism and fragile X

Jane Roberts, professor of psychology, is among a handful of researchers in the world who study autism-fragile X relationships. Fragile X is a single-gene disorder that is the No. 1 known biological cause of autism. Among males, nearly 75 percent of fragile X cases also are diagnosed with autism. She runs the Neurodevelopmental Disorders Lab, and her research focuses on early detection methods among high-risk populations. Roberts can discuss the link between autism and fragile X and her research to understand both. News contact: Bryan Gentry, [email protected] , 803-576-7650

Autism and the language of music

Scott Price is the Carolina Distinguished Professor of piano and piano pedagogy in the School of Music. He is the founder of the Carolina LifeSong Initiative, which provides creative music experiences and piano lessons for students with special needs and is dedicated in fostering best practices in teaching music to students with special needs. His book “Autism and Piano Study: A Basic Teaching Vocabulary” was published in 2023. Price’s work with special needs musicians has been featured by organizations in the United States and internationally. News contact: Marlena Crovatt-Bagwell, [email protected] , 803-777-7962

• The CDC estimates that 1 out of every 36 8-year-olds is affected by autism, a lifelong developmental disorder.
• Boys are four times more likely to be diagnosed with autism spectrum disorder than girls.
• Autism can be diagnosed as early as age 2. The American Academy of Pediatrics recommends screening between 18 and 24 months.
• 38% of children with autism have an intellectual disability.

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• Published: 21 March 2024

Evaluating screening for autism spectrum disorder using cluster randomization

• Sigridur Loa Jonsdottir   ORCID: orcid.org/0000-0001-7688-8294 1 , 2 ,
• Evald Saemundsen 1 , 3 ,
• Elin Astros Thorarinsdottir 4 , 5 &
• Vilhjalmur Rafnsson 6  

Scientific Reports volume  14 , Article number:  6855 ( 2024 ) Cite this article

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• Autism spectrum disorders
• Neurological disorders

We evaluated the rate of autism spectrum disorder (ASD) in a group invited to a screening program compared to the rates in two groups who received usual care. The population eligible for screening was all children in Iceland registered for their 30-month well-child visits at primary healthcare centers (PHCs) from March 1, 2016, to October 31, 2017 ( N  = 7173). The PHCs in the capital area of Reykjavik were the units of cluster randomization. Nine PHCs were selected for intervention (invited group), while eight PHCs received usual care (control group 1). PHCs outside the capital area were without randomization (control group 2). An interdisciplinary team, including a pediatrician contributing with physical and neurological examination, a psychologist evaluating autism symptoms using a diagnostic instrument, and a social worker interviewing the parents, reached a consensus on the clinical diagnosis of ASD according to the ICD-10 diagnostic system. Children in the population were followed up for at least two years and 119 cases were identified. The overall cumulative incidence of ASD was 1.66 (95% confidence interval (CI): 1.37, 1.99). In the invited group the incidence rate was 2.13 (95% CI: 1.60, 2.78); in control group 1, the rate was 1.83 (95% CI: 1.31, 2.50); and in control group 2, the rate was 1.02 (95% CI: 0.66, 1.50). Although the rate of ASD was higher in the invited group than in the control groups, the wide confidence intervals prevented us from concluding definitively that the screening detected ASD more readily than usual care.

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Introduction

Screening for autism spectrum disorder (ASD) has been frequently applied in many countries and in different settings with the aim of an early detection 1 , 2 , 3 . Both the screening procedure and the screening test, for example the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F) 4 , have been evaluated in several population-based studies 5 , 6 , 7 , 8 , 9 , 10 . Most of the screening studies in the literature deal solely with follow-up of the screen positive children 1 , 2 , 3 , but in the present study we aim to evaluate how effective screening is to detect ASD. Thus, to find out whether ASD is in fact detected earlier in a group offered screening than in a control group receiving usual care, we used randomized screening trials 11 . We are not aware of any existing studies that evaluate screening for ASD using randomization, wherein a group invited to screening is compared to a control group receiving usual care. However, a similarly designed study has been planned to include screening for ASD and a high-quality treatment with long term follow-up of outcomes 12 .

The present study is part of a project on ASD screening at the 30-month well-child visit at primary healthcare centers (PHCs) in the capital area of Reykjavik. The first phase of the project focused on the education of well-child care professionals and the implementation of ASD screening using the M-CHAT-R/F 13 . The second phase evaluated the ability of the M-CHAT-R/F to detect ASD and calculated the test’s sensitivity and specificity 14 . The aim of this population-based study, the third phase of the project, was to evaluate the rate of ASD in the group invited to the screening program in comparison with the rates in two groups who received usual care.

This is a prospective comparison study. The population eligible for screening included all children in Iceland registered for their 30-month well-child visits at PHCs during the period from March 1, 2016, to October 31, 2017, a total of 7173 children, according to the National Registry. The National Registry is the basic register of the Icelandic population. It provides current information about Icelandic citizens and foreign citizens who are or have been domiciled in Iceland. It includes information of name, sex, birthplace, personal identity number, nationality, family relationship, and residential address. Based on the National Registry, information was obtained on the registration of an individual child to a particular PHC in the capital area. Using the National Registry, it was possible to count the children outside the capital area. The setting in the capital area of Reykjavik, Iceland, with its comprehensive health care and population registers, offers an opportunity to define and recruit populations and to assign them to different groups with the same inclusion and exclusion criteria: one which is invited to participate in a screening program for ASD while the other receives usual care. A nationwide database on diagnosed ASD cases at the State Diagnostic and Counseling Center (SDCC) ensured proper follow-up and rigorous establishing of the diagnoses without discrimination between children based on residence or ethnicity 15 .

The rationale for choosing the capital area of Reykjavik for screening, as opposed to other parts of the country, was based on accessibility: all the investigators worked in that area, and the SDCC, a governmental tertiary institution serving children with serious neurodevelopmental disabilities, is located there. Included in the capital area of Reykjavik are 17 PHCs. The plan for the screening project was introduced to the administrators of the Development Center for Primary Healthcare in Iceland and of the Primary Healthcare in the Capital Area, who all expressed their willingness to participate 13 . Description of the screening project was published in our previous studies 13 , 14 and a summary is given later in this section.

Cluster randomization was used with the PHC as the unit of randomization. Altogether nine PHCs were randomly selected to participate in the screening project by an independent person in the presence of the investigators by drawing lots with their names. Following introductory meetings at each of these nine PHCs, their directors all gave their consent to participate in the project. Eight PHCs received usual care and constituted control group 1. A total of 4714 children in the target population were living in the capital area and were registered at the 17 PHCs that were randomized. Of them, 2531 children were assigned to be invited to screening, called the invited group, and 2183 children were assigned to control group 1. PHCs outside the capital area were without randomization; there, 2459 children were registered and were assigned to control group 2.

No child in the target population was excluded from the study as no child had been diagnosed with ASD before the start of the screening trial on March 1, 2016, according to the files of the SDCC. Thus, all children living in Iceland and registered for their 30-month well-child visits at a PHC during the period from March 1, 2016, to October 31, 2017, were included in the study.

For this study, a nationwide database on ASD diagnoses was kept at the SDCC. Using this database, children in the target population were followed-up from March 1, 2016, the beginning of the screening, to identify cases. The closing date for follow-up was October 31, 2019, when the children were between 54 and 79 months of age. The investigators scrutinized the records at the Center for Child Development and Behavior, the Department of Child and Adolescents Psychiatry at the Landspitali University Hospital, the Pediatric Department of Akureyri Hospital, and the Social Insurance Administration, and did not find children diagnosed with ASD, within the age range included in the study, which had been the case in a recent study of older children in Iceland 15 , indicating that there were no missing cases. Thus, only cases of ASD occurring in the SDCC database were included.

The healthcare system in Iceland is state-centered, mostly publicly funded, and with universal coverage. The following sections describe the content of the usual care. PHCs throughout the country offer a broad range of primary care services, including well-child care. After initial home visits by a nurse or a midwife in the first six weeks after birth, young children attend their neighborhood PHC 11 times until they start elementary school. During these visits, they receive developmental surveillance and broadband developmental screening as well as participate in a comprehensive vaccination program with over 95% participation rate 16 , 17 . The developmental screening instruments include the Parents’ Evaluation of Developmental Status 18 which is administered at 12 and 18 months of age, accompanied by the Brigance Early Childhood Screen II 19 at 30 and 48 months of age 17 .

The child’s first contact with the educational system is with the preschool service (nursery school), which children usually enter between ages 1 and 2 years, and where the goal is to monitor and promote the development of the children in close cooperation with the parents 20 . In 2018, 40% of the personnel in the preschools had a university degree in preschool teaching or other comparable education, including degrees or courses in the development and learning psychology of young children. The proportion of children in the population attending preschool at the age of 1 year is 48%, and 95–97% at the age of 2–5 years. At the preschool, approximately 10% of the children receive special support due to disability or social or emotional difficulties 21 . Education is compulsory for children aged 6–16 years and includes accommodation for special educational needs 22 . Social services provide financial support to parents of children with serious developmental disabilities or long-term illnesses, based on a valid medical certificate 23 .

If concerns of developmental disabilities are raised by parents or by professionals in the health care- or educational systems, this can, in collaboration with the parents, lead to preliminary assessment and subsequent referral to the SDCC for diagnosis. The SDCC provides each child with an interdisciplinary assessment, including a physical and a neurological examination by a pediatrician, an evaluation of autism symptoms by a psychologist using at least one standardized diagnostic instrument, and an interview with the parents by a social worker. Cognitive tests are administered at either the secondary or tertiary level of services. The interdisciplinary team reaches a consensus on the clinical diagnoses. If a clinical diagnosis is warranted, it is given based on the ICD-10 diagnostic system 24 .

In the present study, instruments used during the diagnostic process included the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2; Modules 1 and 2) 25 for assessment of autism symptoms, administrated by reliable clinicians; the Icelandic standardization of the Wechsler Preschool and Primary Scale of Intelligence, Revised (WPPSI-RIS) for assessment of cognitive ability 26 ; and the Bayley Scales of Infant and Toddler Development, Third Edition 27 for children who were not able to complete the WPPSI-RIS.

The screening program for ASD was implemented in the invited group ( n  = 2531), of which 1586 children were screened with the M-CHAT-R/F, as reported in previous studies 13 , 14 . The M-CHAT-R/F is a two-stage parent-report screening instrument designed to identify 16–30-month-old children who should receive a more comprehensive assessment 4 , 8 . An Icelandic translation and cultural adaptation of the instrument was used, and sensitivity in the sample of the population of 30-months-old children was 0.62, specificity 0.99, positive predictive value 0.72, and negative predictive value 0.99 14 .

The main outcome of the study is the rate of ASD according to the nationwide database at the SDCC. We also describe sex, origin of parents, age at referral, age at diagnosis, ADOS-2 comparison score, IQ/DQ verbal and performance scores among cases of ASD.

The occurrence of ASD cases during the follow-up from June 15, 2017, when the first child was diagnosed, until October 31, 2019, was measured by cumulative incidence during a 27-and-a-half-month period. The analysis was conducted in accordance with the intention-to-screen principle. For the calculation, the numerator was children diagnosed with ASD in the population and in the corresponding groups, while the denominator was all children registered for their 30-month well-child visits at the PHCs during the period from March 1, 2016, to October 31, 2017, and the number of children in the corresponding groups. Rate ratios were calculated with 95% confidence intervals (CIs). The rates were calculated as cumulative incidence (ASD/100 children), and exact rate ratio estimates, confidence limits, and p -values (two-tailed) were calculated using the Martin and Austin method 28 . The analyses were performed in Epi Info™.

Regarding ethical approval and consent, the study was conducted in accordance with the guidelines of the Declaration of Helsinki, approved by the Icelandic Data Protection Authority, the National Bioethics Committee of Iceland (VSNb2015110029/03.01; License Date: January 12, 2016), the Scientific Committee of the Healthcare of the Capital Area, and the University of Iceland (License Date: November 11, 2015), and the Scientific Committee at the SDCC (License Date: November 25, 2015). Written informed consent was obtained from the parents of all children who underwent screening; however, informed consent was not necessary from parents of the children who did not participate in the screening, or belonged to the control groups, since the study of these children was solely data-based, according to the approvals.

Altogether 119 children of the entire study population were diagnosed with ASD during the period from June 15, 2017, until October 31, 2019. The 2531 children in the invited group were offered screening 14 , and of these 1586 children participated, while 945 did not participate. The children who participated in the screening and screened positive were referred for assessment and in this way 18 children were diagnosed with ASD. Of the children who screened negative 11 were subsequently diagnosed with ASD. Among the children who did not participate in the screening 23 children were diagnosed with ASD. At the start of the screening two children were already suspected to have ASD and were later diagnosed with ASD, and this adds up to 54 children with ASD in the invited group. The children in the control groups received usual care; 40 children in control group 1 were diagnosed with ASD and 25 children in control group 2. Of the 119 children with ASD, 98 were male (82.4%) and 21 were female (17.6%), with a male-to-female ratio of 4.7:1. Eighty children (67.2%) had both parents of Icelandic origin and 16 children (13.5%) had one parent of Icelandic origin. The rest of the children had both parents of non-Icelandic origin ( n  = 23; 19.3%).

Figure  1 shows how the 119 ASD cases were divided into each of the three groups: the invited group, control group 1, and control group 2. Some of the children diagnosed in the invited group, altogether 29 children (18 true-positive and 11 false-negative), participated in the screening project during their 30-month well-child visit 14 . In addition, two children had been excluded from the previous screening study since developmental concerns had already been raised and referrals for diagnostic assessment were being prepared 13 . These children were included in the present study since they met criteria for diagnosis during the above-mentioned period. The rest of the children in the invited group ( n  = 23) did not receive autism-specific screening since their parents did not consent to participate in the screening study, had not attended the 30-month well-child visit at the participating PHCs during the inclusion period, or failed to receive an invitation to participate in the screening 14 .

Children diagnosed with ASD in a group invited to a screening program and in two control groups. The flowchart shows the study population that included all children in Iceland who were registered at primary healthcare centers and were targeted to attend a routine well-child visit at 30 months of age from March 1, 2016, to October 31, 2017. Also, the number of children in each of the study groups, i.e., the group invited to the screening program during the above-mentioned period and the control groups who received usual care. Children in the invited group and control group 1 were registered at PHCs in the capital area of Reykjavik that were a part of the cluster randomization. Children in control group 2 were registered at PHCs that were outside the capital area and were not a part of the randomization. Finally, the number of children in each group who were diagnosed with ASD, according to a nationwide ASD registry, from June 15, 2017, when the first child was diagnosed, and to the end of the follow-up period on October 31, 2019. ASD, autism spectrum disorder. a True-positive = 18, false-negative = 11, did not participate in the screening = 23, identified with concerns before screening = 2.

The overall cumulative incidence of ASD was 1.66 (95% CI: 1.37, 1.99), as shown in Table 1 alongside the cumulative incidence for the invited group and the control groups. The rate was highest in the invited group and lowest in control group 2. The comparison of the rate of ASD in the invited group with the rates in the combined control groups, control group 1, and control group 2, are shown in Table 2 by rate ratio with the corresponding 95% CI. The rate ratio of the invited group versus the combined control groups was 1.52 (95% CI: 1.06, 2.19); the rate ratio of invited group versus control group 1 was 1.16 (95% CI: 0.77, 1.75); and the rate ratio of invited group versus control group 2 was 2.10 (95% CI: 1.31, 3.37).

The clinical characteristics of the ASD cases in the groups are shown in Table 3 . The proportion of males was highest in the invited group, and lowest in control group 1. Age at referral to the SDCC for diagnostic assessment and age at diagnosis were similar in all groups. However, the age at referral was lowest in the invited group, second lowest in control group 1, and highest in control group 2. The differences in age at referral were small, approximately one month or less.

Our study compared the rates of ASD detected in a group invited to a screening program with the rates of ASD detected in two groups that received usual care. The invited group had a higher rate of ASD than the combined control groups, but that was only evident in the comparison with control group 2. The comparison with control group 1 yielded an elevated rate ratio, but with a wide 95% CI, which included one. Cluster randomization of children with PHCs as the unit of randomization enabled us to compare the invited group to control group 1. These groups were considered comparable in terms of cultural and social status and were determined to have equal access to specialized developmental services. The comparison between the invited group and control group 1 was the most important. Based on that comparison, the screening did not have a clear impact on the detection of ASD. In addition, we found that children in the invited group were not referred for diagnostic assessment at a younger age than children in each of the control groups, nor did they receive their ASD diagnosis earlier.

The overall cumulative incidence of ASD for the three groups was 1.66%. This result is higher than in a recent systematic review where the overall median global prevalence of ASD was estimated to be 1% 29 . Studies that have investigated prevalence or cumulative incidence by age found that it ranged from 0.37 to 1.56% among children 4 years of age 30 , 31 , 32 .

We are not aware of other published studies comparing a group invited to screening to an external comparable control group using cluster randomization for the purpose of evaluating whether ASD was detected earlier and at higher rates in an invited group than in a control group. The United States Preventive Services Task Force has not recommended screening for ASD in the general pediatric population because of lack of evidence from studies comparing a screened group with a group receiving usual care indicating that treatment starting at a younger age improves outcomes measured with standardized tests for symptom severity and cognitive functioning 33 . To evaluate screening for ASD, such a study is planned and intends to screen for ASD and administer high-quality treatment with long term follow-up of outcomes 12 .

Amongst the strengths of the present study is that it was population-based: the main comparison (between the invited group and the control group 1) was between groups that share a common cultural and social background as well as equal proximity to the institution responsible for the diagnosis of the cases. The assignment to the groups was based on cluster randomization. The ASD diagnoses were received from a nationwide database of ASD cases, the diagnostic procedure was based on interdisciplinary teamwork, and the use of a gold standard diagnostic instrument.

This study has some limitations. Partly due to privacy protection issues, it was not possible to follow an individual child in each group from the start of the screening to the diagnosis of ASD or to the end of follow-up. However, the age at referral and the age at diagnosis were similar in the study groups, and the cases had similar clinical features. Because of the similarity of age at referral and age at diagnosis, it was considered unnecessary to test different lengths of follow-up. In addition, it was not practical or possible to blind the clinical personnel at the SDCC ascertaining the diagnoses. The rationale for using cluster randomization with PHCs in the capital area as the unit of randomization was mainly the accessibility, and that may have diminished the risk of contamination of the control groups. However, we cannot be sure that the reported gains resulting from the educational course on ASD, held for clinicians serving children in the invited group 13 , did not contaminate the control groups, particularly in the capital area, for reasons such as temporary rotations of staff between different PHCs. The comparability between the invited group and control group 2 was hampered not only by the fact that control group 2 included rural areas and does not have the same access to care as the groups in the capital area, but also because of a difference in educational levels. Several studies have found that the higher the prevalence of ASD, the greater the level of urbanicity 34 , 35 , 36 , 37 , and that a higher educational level of parents is associated with ASD 34 . We did not collect data on parental education. However, a study in Iceland found that university education was twice as common in the capital area as in other parts of the country 38 . The lower rate of ASD in the rural area (control group 2) than in the urban area (control group 1) calls for improved access to developmental services in rural areas. The study base was the entire population of children in Iceland and, with the definition of the inclusions criteria, framed the size of the study. By extending the inclusion period we would have obtained larger groups. However, simultaneously that may have introduced time-trend effects in the detection of ASD.

Similarly designed studies, comparing groups invited to screening to external control groups, are needed to explore whether screening detects ASD earlier than usual care, as the present study may be considered not large enough. The question remains as to whether children with ASD detected through screening will have better long-term outcomes because of early intervention than children detected through the usual care.

The invited group and control group 1 were considered comparable in terms of cultural and social status, access to specialized services, and proximity to the institution responsible for the ASD diagnoses. The children were assigned to groups via cluster randomization, where the unit of randomization was the PHC. The rate of ASD was higher in the invited group than in the control groups; however, interpreting the results is difficult because of the wide confidence intervals. So, one cannot firmly conclude from this study that the screening program detected ASD more readily than did the usual care.

Data availability

The data presented in this study are available from the corresponding author upon request, dependent on permission from the Data protection Authority and the National Bioethics Committee of Iceland.

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Acknowledgements

The authors would like to thank all the families that participated in the screening. We gratefully acknowledge the support from our collaborators at the Development Center for Primary Healthcare in Iceland, the PHCs in the Capital Area, and the SDCC. Thank you as well to Lilja Björk Kristinsdóttir, who provided us with data from the PHCs in the Capital Area on children in the target group who were registered at each of the centers located there, and from the National Registry on children registered outside the capital area.

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Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland

Sigridur Loa Jonsdottir

Faculty of Medicine, University of Iceland, Reykjavík, Iceland

Evald Saemundsen

Department of Psychology, University of Iceland, Reykjavík, Iceland

Elin Astros Thorarinsdottir

Center of Children’s Mental Health, Reykjavík, Iceland

Department of Preventive Medicine, Faculty of Medicine, University of Iceland, Reykjavík, Iceland

Vilhjalmur Rafnsson

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S.L.J., V.R. and E.S. designed the study. S.L.J. and E.A.T. collected the data. All authors analyzed the data and interpreted the results. S.L.J. and V.R. wrote the manuscript. All authors commented on previous versions of the manuscript, participated in its revision, and agreed to the final version.

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Correspondence to Sigridur Loa Jonsdottir .

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Jonsdottir, S.L., Saemundsen, E., Thorarinsdottir, E.A. et al. Evaluating screening for autism spectrum disorder using cluster randomization. Sci Rep 14 , 6855 (2024). https://doi.org/10.1038/s41598-024-57656-0

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Received : 28 June 2023

Accepted : 20 March 2024

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DOI : https://doi.org/10.1038/s41598-024-57656-0

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