researches on autism spectrum disorder

• Open access
• Published: 10 June 2024

New advances in the diagnosis and treatment of autism spectrum disorders

• Lei Qin 1 ,
• Haijiao Wang 2 ,
• Wenjing Ning 1 ,
• Mengmeng Cui 1 &
• Qian Wang 3  

European Journal of Medical Research volume  29 , Article number:  322 ( 2024 ) Cite this article

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Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders that affect individuals' social interactions, communication skills, and behavioral patterns, with significant individual differences and complex etiology. This article reviews the definition and characteristics of ASD, epidemiological profile, early research and diagnostic history, etiological studies, advances in diagnostic methods, therapeutic approaches and intervention strategies, social and educational integration, and future research directions. The highly heritable nature of ASD, the role of environmental factors, genetic–environmental interactions, and the need for individualized, integrated, and technology-driven treatment strategies are emphasized. Also discussed is the interaction of social policy with ASD research and the outlook for future research and treatment, including the promise of precision medicine and emerging biotechnology applications. The paper points out that despite the remarkable progress that has been made, there are still many challenges to the comprehensive understanding and effective treatment of ASD, and interdisciplinary and cross-cultural research and global collaboration are needed to further deepen the understanding of ASD and improve the quality of life of patients.

Autism spectrum disorders (ASD) are a broad group of neurodevelopmental disorders that affect an individual's social interactions, communication skills, and behavioral patterns [ 1 , 2 ]. The characteristics of ASD vary significantly between individuals, from mild social impairments to severe communication and behavioral problems, a diversity that reflects the use of the term “spectrum” [ 3 ]. Although the exact causes of ASD are not fully understood, research suggests that both genetic and environmental factors play a key role in its development [ 4 ].

Characteristics of ASD

Difficulties in social interaction.

Individuals with ASD often exhibit significant difficulties in social interactions. These difficulties may include difficulty understanding the feelings and intentions of others, maintaining eye contact and facial expressions, and adapting to social norms and expectations. Individuals with ASD may experience challenges in establishing and maintaining friendships, they may not understand the two-way nature of social interactions, or they may feel uncomfortable sharing interests and activities [ 5 ].

Communication disorders

Communication deficits are another core feature of ASD. This may manifest itself in delays in language development, including delays in uttering first words or simple sentences. Some individuals with ASD may not use language to communicate at all. Even among individuals with ASD who have normal language skills, they may have difficulty using language in conversations to communicate thoughts, feelings, or needs. In addition, nonverbal communication, such as the understanding and use of body language and facial expressions, may also be affected [ 6 ].

Repetitive behaviors and interests

Individuals with ASD often display restricted, repetitive patterns of behavior and interests. These may include a strong fixation on specific topics or activities, repetitive body movements (e.g., rocking, clapping), and an overreliance on daily routines. These repetitive behaviors are sometimes seen as a way of self-soothing or as an attempt to control an environment that otherwise feels unpredictable and overwhelming to them [ 7 ].

Sensory sensitivity

Many individuals with ASD have abnormalities in sensory processing and may have very strong or delayed responses to sound, light, touch, taste or odor. For example, some individuals with ASD may find background noises in their everyday environment unusually harsh, or they may not notice pain or other bodily sensations [ 8 ].

Epidemiologic profile of ASD

According to the World Health Organization (WHO), the average prevalence of ASD among children globally is approximately 1% [ 9 ]. However, this figure varies significantly between regions and countries. For example, the Centers for Disease Control and Prevention (CDC) reports that the prevalence of ASD among 8-year-olds in the U.S. is 1 to 54. ASD is significantly more prevalent in males than females, at a ratio of approximately 4:1 [ 10 ]. This gender difference may reflect differences in genetic susceptibility and/or gender bias in the diagnostic process. Early diagnosis is key to improving developmental outcomes for children with ASD. Despite this, many children are not diagnosed by age 3. The CDC reports that most children are first evaluated for ASD by age 4, but diagnosis may occur later. Research suggests that ASD is highly heritable, but multiple genetic variants are associated with disease risk and environmental factors also play a role [ 11 ]. For example, there is an increased risk of ASD in preterm and low birth weight infants. Socioeconomic factors influence ASD diagnosis and treatment access. Families of lower socioeconomic status may face greater challenges, including barriers to accessing early intervention services, etc. ASD is a global public health problem, and its incidence, time to diagnosis, and treatment access are influenced by multiple factors [ 12 ]. Ongoing epidemiologic research and the advancement of a deeper understanding of ASD are critical to the development of effective prevention, diagnosis, and interventions.

Historical background

Early history of research and diagnosis of asd.

The concept of ASD was first clearly defined in the 1940s, when a group of children exhibiting extreme self-isolation and lack of responsiveness to the environment was first described by American psychiatrist Leo Kanner [ 13 ]. Almost simultaneously, Austrian child psychologist Hans Asperger described a similar but higher level of functioning in a condition that came to be known as Asperger’s syndrome [ 14 ]. These two independent studies laid the foundation for the modern understanding of ASD. For the first few decades, ASD was considered extremely rare and was often confused with schizophrenia. Due to a lack of in-depth understanding of ASD, early diagnostic criteria were unclear and treatment was largely limited to behavioral interventions and psychotherapy. Over time, researchers began to pay more attention to the genetic and neurobiological underpinnings of ASD, thus contributing to a more comprehensive understanding of this complex condition. Since the 1990s, the diagnosis of ASD has risen significantly, as diagnostic criteria have continued to be refined and public awareness has increased. This period has also witnessed an increased awareness of the importance of early diagnosis and intervention for ASD, which has led to significant improvements in the prognosis and quality of life for many children and adults with ASD [ 15 ].

Evolution of research paradigms

The research paradigm for ASD has undergone a remarkable evolution since the mid-twentieth century, a process that reflects a deepening of the understanding of ASD as well as advances in scientific research methods [ 16 ]. In the early stages, ASD research focused on behavioral observations and psychoanalysis, when ASD was often mistaken for an emotional disorder due to an indifferent mother. During this period, understanding of ASD was relatively limited and treatments focused primarily on psychotherapy and behavior modification. Into the second half of the twentieth century, with advances in genetics and neuroscience, researchers began to explore the biological basis of ASD. This marked a shift from a psychosocial to a biomedical model, and the focus of research gradually shifted to genetic factors and abnormalities in brain structure and function. Through a large number of family and twin studies, scientists found that ASD has a high genetic predisposition, while neuroimaging studies revealed the specificity of brain development in ASD patients. In the twenty-first century, with the application of bioinformatics and high-throughput gene sequencing technology, the study of ASD has entered a new stage [ 17 ]. Researchers have not only been able to identify specific genetic variants associated with ASD, but have also begun to explore the interaction between environmental factors and genetic susceptibility. In addition, the adoption of interdisciplinary research approaches, such as combining neuroscience, genetics, psychology, and computational modeling, has provided new perspectives for understanding the complexity of ASD.

Recently, the concepts of precision medicine and personalized treatment strategies have been introduced to the study of ASD, aiming to develop customized intervention programs based on each patient’s genetic background and symptom profile. With advances in technology and improved methods of data analysis, future research on ASD is expected to reveal more knowledge about its pathomechanisms and provide more effective support and treatment for patients with ASD.

Etiologic studies

Genetic factors, monogenic genetic cases.

The etiology of ASD is multifactorial, involving a complex interaction of genetic and environmental factors. Although most cases of ASD are thought to be the result of polygenic interactions, there are some cases that are directly associated with variations in a single gene, and these are referred to as monogenic genetic cases. Monogenic genetic cases provide an important window into understanding the genetic basis of ASD, although they represent a relatively small proportion of all ASD cases [ 18 ]. A number of specific genetic syndromes, such as fragile X syndrome, tuberous sclerosis, 15q11-q13 duplication syndrome, and Rett syndrome, have been found to be associated with a higher risk of ASD. These conditions, often caused by mutations or abnormalities in a single gene, can lead to significant differences in brain development and function, thereby increasing the probability of an ASD phenotype. Fragile X syndrome is one of the most common forms of inherited intellectual disability and the single-gene disorder known to be most strongly associated with ASD. It is caused by a repeat expansion on the FMR1 gene [ 19 ]. Tuberous sclerosis (TSC) is an inherited disorder that affects multiple systems and is caused by mutations in the TSC1 or TSC2 genes, and the prevalence of ASD is higher in patients with TSC. 15q11-q13 duplication syndrome (Dupuy 15q syndrome) involves a region of chromosome 15, the duplication of which is associated with an increased risk of ASD [ 20 ]. Rett syndrome, which predominantly affects females, is caused by mutations in the MECP2 gene, and patients often exhibit some of the features of ASD, such as impaired social interactions [ 21 ]. The association of these classical candidate genes with ASD is summarized in Table  1 .

The discovery of these monogenic genetic cases is not only crucial for understanding the genetic mechanisms of ASD, but also potentially valuable for the development of interventional and therapeutic strategies targeting specific genetic variants. However, even in these cases, the expression of the genetic variants showed a degree of heterogeneity, suggesting that the diversity of phenotypic features and clinical manifestations, even in monogenic genetic cases, may be influenced by other genetic and environmental factors. Therefore, an in-depth study of these conditions will not only improve our understanding of the genetic basis of ASD, but also provide clues for the development of more personalized therapeutic strategies.

Multigene interactions

The development of ASD is widely recognized as a result of the interaction of genetic and environmental factors, with polygenic interactions occupying a central position in the genetic background of the disease. Unlike monogenic cases, polygenic interactions involve variants or polymorphisms in multiple genes that together increase the risk of ASD. These genetic variants may contribute a smaller effect in each individual, but when acting together they can significantly increase the probability of ASD development [ 30 ]. Current research suggests that no single gene can explain all cases of ASD. Instead, hundreds of genetic loci have been identified that are associated with an increased risk of ASD. These genes are often involved in key processes such as brain development, neuronal signaling, and intercellular communication, suggesting that ASD involves extensive regulation of brain function and structure. The complexity of multigene interactions means that genetic studies of ASD require large-scale genomic data and sophisticated statistical methods to reveal those genomic variants that increase risk.

Meta-analyses of large-sample genome-wide association studies (GWAS) have identified several consistently replicated ASD risk gene loci, such as those in the chromosomal regions 3p21, 5p14, 7q35, and 20p12. These loci contain genes like CNTN4, CNTNAP2, and NRXN1, which play crucial roles in neurodevelopment and synaptic function, particularly in processes such as synaptic adhesion and neurotransmission. These findings provide a more robust understanding of the genetic architecture of ASD and highlight the importance of integrating genetic findings with functional studies to advance our understanding of the disorder. They also have implications for future research, such as the development of personalized diagnostic and therapeutic strategies based on an individual's genetic profile. Through genome-wide association studies (GWAS) and other genomic approaches, scientists are gradually unraveling the genetic landscape of this complex disease. Understanding the impact of multiple gene interactions on ASD not only helps us understand its genetic basis, but also opens up the possibility of developing personalized treatment and intervention strategies [ 31 ].

Environmental factors

Maternal exposure.

Exposure during pregnancy refers to a mother’s exposure to specific environmental factors or substances during fetal development, which may increase the child's risk of developing ASD in the future. These exposures include certain prescription medications (e.g., anti-seizure medications and opioids), environmental pollutants (e.g., heavy metals and air pollutants), infections (e.g., rubella and influenza viruses), and poor nutrition or deficiencies in specific nutrients (e.g., folic acid). These factors may increase the risk of ASD by affecting fetal brain development and the maturation process of the nervous system. Understanding the effects of exposure during pregnancy can help to take preventive measures to reduce the incidence of ASDs [ 32 ].

Effects of early developmental stages

The early developmental stages of ASD are influenced by a variety of factors that include genetic predisposition, environmental exposures, and early life experiences. During a child's early development, the brain experiences rapid growth and the formation of neural networks. Any disruption during this critical period may interfere with the proper development of brain structure and function, thereby increasing the risk of ASD. For example, very early lack of social interaction, delayed language development or abnormal sensory processing may be early signs of ASD. These developmental abnormalities reflect difficulties in the brain’s nervous system in processing information, making connections and adapting to environmental changes. Early identification and intervention are essential to promote optimal development in children with ASD [ 33 ].

Genetic–environmental interactions

The genetic–environmental interactions are summarized in Fig.  1 . ASD develops as a result of the interaction between genetic and environmental factors, and this interaction reflects the complexity of the combination of genetic background and external environmental factors that influence ASD risk. Specifically, certain genetic susceptibilities may be activated in response to environmental triggers, leading to the development of ASD. For example, genetic variants may make individuals more sensitive to certain environmental exposures (e.g., substance use during pregnancy, environmental pollutants, or maternal nutritional status), which together may increase the risk of ASD by acting on key brain developmental stages [ 34 ]. This complex genetic–environmental interaction underscores the need to understand multifactorial etiological models of ASD and the importance of developing personalized intervention strategies.

Advances in diagnostic methods

Traditional diagnostic methods.

Traditional diagnostic methods for ASD rely heavily on detailed assessments of behavior and developmental history. These assessments are usually conducted by specialized health care providers such as pediatricians, neuropsychologists, or psychiatrists. The diagnostic process encompasses direct observation of the child as well as in-depth interviews with parents or caregivers to gather information about the child's social interactions, communication skills, and behavioral patterns [ 35 ]. Diagnostic tools include, but are not limited to, the Childhood Autism Rating Scale (CARS), the Autism Diagnostic Observation Scale (ADOS), and the Autism Diagnostic Interview-Revised (ADI-R). These tools are designed to identify core symptoms of ASD, such as social communication deficits and repetitive behaviors or interests. In addition, the doctor may perform a series of developmental or cognitive assessments to rule out other conditions that may explain the child’s behavior, such as language disorders or other neurodevelopmental disorders [ 36 ]. While these traditional diagnostic methods are highly effective in recognizing ASD, they rely on subjective assessments and the experience of the professional, and therefore may have some degree of variability. In recent years, with a deeper understanding of ASDs, new diagnostic techniques and methods are being developed and adopted to improve diagnostic accuracy and efficiency.

Latest diagnostic techniques and tools

Genetic testing.

Genetic testing for ASD is a method of identifying risks associated with ASD by analyzing genetic variants in an individual's DNA. This testing looks for specific genetic variants that have been linked by scientific research to the development of ASD. Although the genetic background of ASD is extremely complex, involving multiple genes and the interaction of genes with environmental factors, variants in specific genes have been identified as having a significant impact on ASD risk [ 37 ]. For example, variants in the SHANK3 gene are associated with Phelan–McDermid syndrome, and patients with this syndrome often exhibit ASD features. Variants in the FMR1 gene are responsible for fragile X syndrome, which is the most common single-gene cause of ASD known to be associated with ASD. Mutations in the MECP2 gene have been associated with Rett syndrome, and patients with Rett syndrome often exhibit ASD condition. In addition, variants in the NRXN1 and NLGN3/4 genes have been found to increase the risk of ASD [ 38 ]. Genetic testing can help provide more precise diagnostic information, and in those cases of ASD where the cause is unknown, it may even reveal the underlying genetic cause. This will not only help to understand the genetic mechanisms of ASD, but also provide more targeted intervention and support strategies for patients and families.

Neuroimaging

Neuroimaging techniques in the study of ASD provide a non-invasive way to explore changes in brain structure and function, helping scientists better understand the biological basis of ASD. These techniques include functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and positron emission tomography (PET). Through these neuroimaging techniques, researchers are able to observe structural and functional differences in specific regions and networks of the brain in individuals with ASD [ 39 ]. For example, fMRI can reveal patterns of brain activity when performing specific tasks, helping to understand the impairments in social, language, and cognitive functioning in individuals with ASD. dTI focuses on the microstructure of the brain’s white matter, revealing the connections of bundles of nerve fibers, which can help to study neural connectivity issues in ASD. PET scans, on the other hand, are able to assess the activity of specific chemicals in the brain, providing clues to study the neurochemical basis of ASD [ 40 ]. With these advanced neuroimaging techniques, researchers will not only be able to delve deeper into the neurodevelopmental abnormalities of ASD, but also identify possible novel therapeutic targets that can provide a scientific basis for developing more effective interventions. However, while these techniques provide valuable perspectives in understanding ASD, a complete understanding of the complexity of the brain remains a challenge for future research.

Early screening methods

Recently, the field of early screening for ASD has witnessed the application of a number of innovative techniques designed to improve the accuracy and convenience of screening. One notable new approach is the use of artificial intelligence (AI) and machine learning techniques to analyze children's behavioral videos and biomarkers. By training algorithms to recognize specific behavioral patterns and physiological signals associated with ASD, these technologies can help physicians and researchers identify potential ASD symptoms earlier [ 41 ]. Another area of innovation is eye-tracking technology, which assesses children’s social and cognitive development by analyzing their eye movement patterns when viewing pictures or videos. Studies have shown that the eye movement patterns of children with ASD while viewing social scenes differ from those of typically developing children, providing a non-invasive window for early screening [ 42 ]. The application of these state-of-the-art technologies not only improves the efficiency and accessibility of early screening, but also provides new perspectives for understanding the complexity and individual differences in ASD [ 43 ]. Although these approaches are still in the research and development stage, they demonstrate the great potential of utilizing technological advances to improve the process of ASD screening and diagnosis. With further validation and refinement of these techniques, it is expected that they will make a significant contribution to the early identification and intervention of ASD in the future.

Treatment approaches and intervention strategies

Behavioral and educational interventions, applied behavior analysis (aba).

Applied behavior analysis (ABA) is an intervention approach based on the principles of behavioral psychology that is widely used in the treatment of children with autism spectrum disorders (ASD). ABA works to understand and improve specific behaviors, particularly to enhance social, communication, academic skills, and daily living skills, while reducing maladaptive behaviors. It helps individuals learn new skills and behaviors by systematically applying reinforcement strategies that encourage and reward desired behaviors [ 44 ]. ABA therapy is highly individualized and customized to each child’s specific needs and abilities. Treatment planning begins with a detailed behavioral assessment to identify target behaviors and intervention strategies. Learned behaviors are then reinforced and cemented through one-on-one teaching sessions using positive reinforcement. ABA also emphasizes the importance of data, which is collected and analyzed on an ongoing basis by the therapist to monitor progress and adjust the treatment plan as necessary [ 45 ]. Research has shown that ABA is an effective way to improve social interactions, communication skills, and learning in children with ASD. Through early and consistent intervention, ABA can significantly improve the independence and overall quality of life of children with ASD. Although ABA treatment requires a commitment of time and resources, the long-term benefits it brings to children with ASD and their families are immeasurable.

Social skills training

Social skills training (SST) for children with autism spectrum disorders (ASD) is an intervention designed to improve their ability to interact socially in everyday life. This training focuses on teaching children with ASD the ability to understand social cues, establish effective communication skills, and develop friendships. Through SST, children learn how to recognize and interpret other people's facial expressions, body language, and social etiquette, which are essential for building positive relationships [ 46 ]. Social skills training typically includes a series of structured instructional activities such as role-playing, social stories, interactive group exercises, and peer modeling. These activities are designed to provide practice in real-world social situations in a supportive and interactive manner, helping children with ASD learn and practice new skills in a safe environment [ 47 ]. In addition, SST can include teaching emotion management and conflict resolution skills to help children with ASD better understand and express their emotions and cope with challenges in social interactions. Through regular and consistent practice, children with ASD can improve their self-confidence, increase their social engagement, and ultimately improve their social competence and quality of life. SST has been shown to be significantly effective in enhancing social adjustment and interpersonal interactions in children with ASD [ 48 ].

Medical treatment

While there is no cure for ASD, certain medications can be used to manage specific symptoms associated with ASD, such as behavioral problems, attention deficits, anxiety, and mood swings that are common in individuals with autism. Medication is often used as part of a comprehensive intervention program designed to improve the quality of life and daily functioning of the patient [ 49 ]. Medications commonly used for ASD symptom management include antipsychotics, antidepressants, stimulants, and anxiolytics. For example, two antipsychotics, risperidone and aripiprazole, have been approved by the FDA for the treatment of stereotypic and aggressive behavior in children and adolescents with ASD. In addition, selective serotonin reuptake inhibitors (SSRIs) may be helpful in managing anxiety and depressive symptoms in individuals with ASD.

Importantly, medication needs to be closely monitored by a physician to ensure the effectiveness and safety of the medications, as they may have side effects. We have summarized the research evidence on the efficacy and safety of commonly used medications in ASD, including antipsychotics for treating irritability and aggression, antidepressants for co-occurring anxiety and depression, and other medications such as stimulants and melatonin. While these medications can be helpful in managing specific symptoms, they also carry potential side effects and risks, such as weight gain, metabolic disturbances, and behavioral activation. Therefore, a thorough diagnostic evaluation, individualized treatment planning, close monitoring, and regular follow-up are essential when considering pharmacotherapy for individuals with ASD. The decision to medicate should be based on an individualized assessment that takes into account the patient’s specific needs, the severity of symptoms, and possible side effects. At the same time, pharmacological treatments are often used in combination with non-pharmacological treatments such as behavioral interventions and educational support to achieve optimal therapeutic outcomes [ 50 ].

Biofeedback and neuromodulation

Biofeedback and neuromodulation are innovative approaches that have been explored in recent years in the treatment of ASD, aiming to reduce ASD symptoms by improving brain function. Biofeedback techniques enable individuals to learn how to control physiological processes that are not normally under conscious control, such as heart rate, muscle tension, and brainwave activity. Through real-time feedback, patients can learn how to regulate their physiology, resulting in improved concentration, reduced anxiety, and improved emotional regulation. Neuromodulation, specifically transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), affects neural activity in the brain through external stimulation. tMS utilizes a magnetic field to affect neuronal activity in specific areas of the brain, while tDCS modulates neuronal excitability by applying a weak electrical current. These methods have been studied for improving social communication skills and reducing stereotypical behaviors in people with ASD [ 51 ].

Biofeedback helps individuals develop self-regulation skills by providing real-time feedback on physiological states, while neuromodulation techniques like TMS and tDCS modulate cortical excitability and neural plasticity in aberrant circuits implicated in ASD. Current research suggests potential benefits of these techniques in improving emotional regulation, social functioning, and cognitive performance, but mixed results highlight the need for larger, well-controlled trials to validate efficacy, safety, and optimal protocols. Despite challenges, these techniques show promise as adjunctive therapies in the comprehensive management of ASD, warranting further research to guide their translation into clinical practice. Although biofeedback and neuromodulation show potential in the treatment of ASD, research on these techniques is currently in its infancy. More clinical trials and studies are needed to evaluate their effectiveness, safety, and long-term effects and to determine which patients may benefit from these interventions. Nevertheless, as non-pharmacologic treatments, they offer promising complementary options to the comprehensive treatment of ASD.

Emerging intervention approaches

Technology-assisted interventions.

Technology-assisted interventions have become an important development in the field of ASD treatment in recent years, providing new ways for children with ASD to learn and communicate. These interventions utilize computers, tablets, smartphone apps, and virtual reality technology to design a range of interactive learning tools and games designed to improve social skills, communication, and cognitive functioning in children with ASD [ 52 ]. A key advantage of technology-assisted interventions is their ability to provide highly personalized learning experiences. Software and applications can be adapted to a child's specific needs and interests, ensuring that learning content is both engaging and appropriate to the individual's developmental level. In addition, the feedback provided by technology is often immediate and consistent, helping children with ASD to better understand and process information. The use of virtual reality technology, by simulating social situations, provides a safe and controlled environment for children with ASD to practice social interaction and problem-solving skills, which is often difficult to achieve in traditional educational and therapeutic settings [ 53 ]. Although technology-assisted interventions have demonstrated great potential, research on their long-term effects and optimal implementation is still ongoing. To maximize the benefits of these tools, it is often recommended that technology-assisted interventions be used in conjunction with other therapeutic approaches to provide a comprehensive intervention program.

Diet and nutrition interventions

Dietary and nutritional interventions have received increasing attention in the treatment of ASD, based on the observed potential link between nutritional imbalances and ASD symptoms. This intervention approach aims to improve the behavioral performance and overall health of children with ASD by optimizing their diet. Specific strategies include restricting certain foods that may exacerbate symptoms, such as gluten and lactose, as well as increasing intake of foods rich in essential nutrients to support brain development and function [ 54 ]. Several studies support the potential benefits of specific dietary interventions, such as implementing a gluten-free lactose-free (GFCF) diet, which may help improve behavioral and digestive symptoms in some children with ASD. In addition, supplementation with omega-3 fatty acids, vitamins, and minerals (e.g., magnesium and zinc) have been proposed as potentially beneficial strategies to support neurologic health and alleviate ASD-related symptoms [ 55 ]. However, the effectiveness of dietary and nutritional interventions may vary by individual and more scientific research is needed to gain a deeper understanding of their long-term effects on children with ASD. Before implementing any dietary intervention, it is recommended to consult with a physician or nutritional expert to ensure that the individual needs of the child are met and to avoid malnutrition. In combination, dietary and nutritional interventions can be used as part of a comprehensive treatment plan for ASD, complementing traditional behavioral and educational interventions.

Social and educational integration

Educational integration of children with asd.

Educational integration of children with ASD is an inclusive educational practice that seeks to integrate children with ASD into the mainstream educational system to learn and grow with their typically developing peers. This integration model emphasizes individualized learning plans and adaptive teaching strategies to meet the unique needs of children with ASD while promoting their social inclusion and emotional development. Through educational integration, children with ASD are provided with opportunities to interact with other children, which is essential for them to learn social skills, enhance their communication abilities, and improve their ability to adapt to society. To support the successful integration of children with ASD, schools often provide special education services such as speech and language therapy, occupational therapy, and behavioral interventions, which take place in classroom settings to ensure their academic and social progress. Educational inclusion is not only beneficial for children with ASD, but it also helps to foster a sense of inclusion and diversity among their peers. By learning and playing together, all children learn to respect and understand differences, laying the foundation for a more inclusive society. However, effective integrated education requires close collaboration among teachers, parents and professionals, as well as the availability of appropriate resources and support systems [ 56 ].

Social integration and employment of adults with ASD

The social integration and employment of adults with ASD is a current focus of attention in ASD research and social services. For many adults with ASD, social integration challenges include establishing stable relationships, participating in community activities, and finding and keeping a job. Although adults with ASD may have unique skills and interests in specific areas, social communication deficits and fixed patterns of behavior may make it difficult for them in traditional work settings. In recent years, more and more organizations and businesses have begun to recognize the value of diversity and inclusion and are working to create work environments that are better suited for adults with ASD. This includes providing flexible work arrangements, clear communication guidelines, and individualized support measures such as workplace co-worker support and professional career counseling. In addition, social service programs and non-profit organizations offer training and job readiness programs specifically designed for adults with ASD to help them develop necessary vocational skills and social competencies. Through these efforts, adults with ASD will not only be able to find jobs that meet their interests and abilities, but also find a place for themselves in society, enhancing their independence and life satisfaction. However, the realization of this goal requires sustained social awareness-raising and the construction of an ASD-friendly environment [ 57 ].

Future research directions

Application of precision medicine in asd treatment.

The application of precision medicine in the treatment of ASD represents a paradigm of a personalized treatment strategy that aims to tailor the treatment plan to each patient's genetic information, biomarkers, history of environmental exposure, and lifestyle factors. The philosophy behind this approach is that, although ASD is classified as a spectrum, each patient's etiology, symptoms, and their severity are different, and therefore treatment should be highly individualized [ 58 , 59 ]. By fully sequencing a patient's genome, scientists and physicians can identify specific genetic variants that may affect ASD symptoms, allowing them to develop targeted treatments. For example, if a particular ASD patient's symptoms are linked to an abnormality in a specific metabolic pathway, that pathway could be modulated through dietary adjustments, nutritional supplements, or specific medications with a view to improving symptoms. In addition, precision medicine involves the consideration of environmental factors and personal behavior to ensure that treatment options are not only scientifically effective, but also appropriate to the patient's lifestyle. Although precision medicine is still in its early stages in the field of ASD, it offers great potential for delivering more personalized and effective treatment regimens, which are expected to significantly improve the quality of life of people with ASD [ 60 ].

Prospects for emerging biotechnologies

Emerging biotechnologies in the field of ASD, such as gene editing, stem cell therapies, and biomarker development, are opening up new possibilities for treating and understanding ASD. Gene editing technologies, particularly the CRISPR-Cas9 system, provide researchers with the means to precisely modify genetic variants associated with ASD, promising to reveal how specific genetic variants affect brain development and function, thereby providing clues for the development of targeted therapies [ 61 ]. Stem cell therapies utilize a patient's own induced pluripotent stem cells (iPSCs) to study the pathomechanisms of ASD by mimicking the neurodevelopmental process in vitro, as well as exploring potential cellular alternative treatments. In addition, the discovery of biomarkers facilitates early diagnosis and monitoring of disease progression, making personalized treatment possible [ 62 ]. In addition, induced pluripotent stem cell (iPSC)-derived brain organoids from ASD patients have emerged as a powerful tool for studying the neurodevelopmental abnormalities associated with ASD. These 3D, self-organizing models recapitulate key features of human brain development in vitro, allowing researchers to investigate the cellular and molecular mechanisms underlying ASD pathogenesis. By comparing brain organoids derived from ASD patients with those from healthy controls, researchers can identify alterations in neuronal differentiation, migration, and connectivity that may contribute to the development of ASD. Moreover, patient-derived brain organoids provide a personalized platform for drug screening and testing, enabling the identification of targeted therapies that can be tailored to an individual's genetic background. This approach has the potential to revolutionize the development of precision medicine strategies for ASD, by providing a more accurate and relevant model system for investigating disease mechanisms and testing novel therapeutic interventions. As the field continues to advance, iPSC-derived brain organoids are expected to play an increasingly important role in unraveling the complex etiology of ASD and guiding the development of personalized treatment strategies [ 63 ]. The development of these technologies has not only improved our understanding of the complex etiology of ASD, but also provided more precise and effective treatment options for ASD patients. Although most of these emerging biotechnologies are still in the research phase, they bring hope and anticipation for the future of ASD treatment and management. As research progresses and technology matures, it is expected that these innovative approaches will bring substantial benefits to individuals with ASD and their families.

Interaction between social policy and ASD research

The interaction between social policy and ASD research is key to achieving better social inclusion and quality of life for individuals with ASD and their families. Effective social policies can provide the necessary financial support and legal framework for ASD research, promoting a deeper understanding of ASD and the development of new treatments. For example, policies can promote collaboration in interdisciplinary research, encourage the use of innovative technologies and methods, and support long-term follow-up studies. In addition, social policies play a crucial role in ensuring that ASD research results are translated into practical applications and that education, employment, and social services are provided to individuals with ASD. Through the development of inclusive education policies, employment assistance programs, and the provision of integrated social services, policies can help individuals with ASD realize their potential and better integrate into society. At the same time, advances in ASD research also provide a scientific basis for the development of more targeted and effective social policies, helping policymakers understand the needs of individuals with ASD and develop more precise support measures. Thus, there is a close interplay between social policy and ASD research, which together have contributed to the advancement of the understanding of ASD and coping strategies.

Limitations of the current research

Although significant progress has been made in ASD research, a number of key limitations remain. First, the etiology of ASD is extremely complex, involving genetic and environmental factors and their interactions, making it extremely challenging to identify specific etiologies and develop targeted treatment strategies. Second, the heterogeneity of ASD is reflected in the extreme variability of symptoms among patients, which makes it difficult to develop uniform diagnostic criteria and treatment approaches. In addition, most studies have focused on children, and adult patients with ASD have been relatively understudied, which limits the understanding of the full lifespan of ASD. In terms of research methodology, most current ASD research relies on small, short-term studies, which may affect the broad applicability of results and the assessment of long-term effectiveness. In addition, although advances in technology have provided new tools for ASD diagnosis and intervention, the popularization and application of these technologies still face economic and resource constraints. Finally, ASD research is unequal across the globe, with far more research conducted in resource-rich countries and regions than in resource-limited areas. This imbalance limits a comprehensive understanding of ASD in different cultural and social contexts. Therefore, to overcome these limitations, more interdisciplinary, cross-cultural, and long-term research, as well as global collaborations, are needed to deepen the understanding of ASD and improve the quality of life of individuals with ASD.

Perspectives on future research

The outlook for future prevention and treatment of ASD points in a more individualized, integrated, and technology-driven direction. With a deeper understanding of the genetic and environmental factors of ASD, it is expected that more targeted interventions and therapeutic strategies will be developed that will be based on an individual's specific genetic background and pathologic characteristics. The application of precision medicine is expected to improve treatment outcomes, reduce unwanted side effects, and optimize resource allocation. Meanwhile, technological advances, particularly artificial intelligence, machine learning, and virtual reality, are expected to revolutionize the way ASDs are diagnosed, monitored, and treated. These technologies are capable of delivering customized learning and treatment programs that enhance the acceptability and effectiveness of interventions. In addition, interdisciplinary research will be strengthened, and social policies and public health strategies will focus more on early screening and intervention, as well as increasing public awareness and understanding of ASD. Most importantly, the future of ASD prevention and treatment will place greater emphasis on the needs of patients and families, promote social integration and employment of patients, and improve their quality of life. As society's awareness of diversity and inclusion increases, individuals with ASD will receive more support and respect and enjoy fuller opportunities for social participation.

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Authors and affiliations.

Department of Rehabilitation, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China

Lei Qin, Wenjing Ning & Mengmeng Cui

Department of Intensive Care Medicine, Feicheng People’s Hospital, Taian, Shandong, China

Haijiao Wang

Department of Central Laboratory, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China

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LQ, HW and WN wrote the draft of the manuscript. MC and QW revised this manuscript. All the listed authors have made a substantial, direct, and intellectual contribution to the work, and approved its publication.

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Qin, L., Wang, H., Ning, W. et al. New advances in the diagnosis and treatment of autism spectrum disorders. Eur J Med Res 29 , 322 (2024). https://doi.org/10.1186/s40001-024-01916-2

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Autism Spectrum Disorder Articles

At a glance.

Below is a list of recent scientific articles on autism spectrum disorder (ASD) generated from CDC programs and activities.

Key findings and scientific articles

Key findings.

These key findings provide brief summaries of some of CDC's latest ASD research.

Key Findings: ADDM Network Expands Surveillance to Identify Healthcare Needs and Transition Planning for Youth

Five of CDC's ADDM Network sites (Arkansas, Georgia, Maryland, Utah, and Wisconsin) began monitoring autism spectrum disorder (ASD) in 2018 among 16-year-old adolescents who were initially identified as having characteristics of ASD in 2010. (Published: February 25, 2023)

Key Findings: Study Shows Linking Statewide Data for ASD Prevalence is Effective

Linking statewide health and education data is an effective way for states to have actionable local ASD prevalence estimates when resources are limited. (Published: January 18, 2023)

Key Findings: CDC Releases First Estimates of the Number of Adults Living with Autism Spectrum Disorder in the United States

This study fills a gap in data on adults living with ASD in the United States because there is not an existing surveillance system to collect this information. (Published May 10, 2020)

CDC scientific articles

These articles are either from CDC-funded research or have at least one CDC author. These articles are listed by year of publication, with the most recent first.

• Adolescents With Autism Spectrum Disorder: Diagnostic Patterns, Co-occurring Conditions, and Transition Planning. Hughes MM, Shaw KA, Patrick ME, et al. J Adolesc Health. 2023;73(2):271-278.
• Statewide county-level autism spectrum disorder prevalence estimates—seven U.S. states, 2018. Shaw KA, Williams S, Hughes MM, et al. Ann Epidemiol. 2023;79:39-43.
• The Prevalence and Characteristics of Children With Profound Autism, 15 Sites, United States, 2000-2016. Hughes MM, Shaw KA, DiRienzo M, et al. Public Health Rep. 2023;138(6):971-980.
• Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. Maenner MJ, Warren Z, Williams AR, et al. MMWR Surveill Summ. 2023;72(2):1-14. Published 2023 Mar 24. [ Easy-Read Summary ]
• Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. Shaw KA, Bilder DA, McArthur D, et al. MMWR Surveill Summ. 2023;72(1):1-15. Published 2023 Mar 24. [ Easy-Read Summary ]
• Social vulnerability and prevalence of Autism Spectrum Disorder, Metropolitan Atlanta Developmental Disabilities Surveillance Program (MADDSP). Patrick ME, Hughes MM, Ali A, Shaw KA, Maenner MJ. Ann Epidemiol. 2023;83:47-53.e1.
• Individualized Education Programs and Transition Planning for Adolescents With Autism. Hughes MM, Kirby AV, Davis J, et al. Pediatrics. 2023;152(1):e2022060199. [ Watch Video Abstract ]

" There is no epidemic of autism. It's an epidemic of need." ‎

Two authors provide their commentary on CDC's 2023 Community Report in an article published in ST A T News' First Opinion (March 2023).

Read the full article here.

• Toileting Resistance Among Preschool-Age Children With and Without Autism Spectrum Disorder. Wiggins LD, Nadler C, Hepburn S, Rosenberg S, Reynolds A, Zubler J. J Dev Behav Pediatr. 2022;43(4):216-223.
• Defining in Detail and Evaluating Reliability of DSM-5 Criteria for Autism Spectrum Disorder (ASD) Among Children Rice CE, Carpenter LA, Morrier MJ, et al. J Autism Dev Disord. 2022;52(12):5308-5320. [published correction appears in J Autism Dev Disord. 2022 Jan 29;:].
• Reasons for participation in a child development study: Are cases with developmental diagnoses different from controls? Bradley CB, Tapia AL, DiGuiseppi CG, et al. Paediatr Perinat Epidemiol. 2022;36(3):435-445.
• Features that best define the heterogeneity and homogeneity of autism in preschool-age children: A multisite case–control analysis replicated across two independent samples. Wiggins LD, Tian LH, Rubenstein E, et al. Autism Res. 2022;15(3):539-550.
• Progress and Disparities in Early Identification of Autism Spectrum Disorder: Autism and Developmental Disabilities Monitoring Network, 2002–2016. Shaw KA, McArthur D, Hughes MM, et al. J Am Acad Child Adolesc Psychiatry. 2022;61(7):905-914.
• Peri-Pregnancy Cannabis Use and Autism Spectrum Disorder in the Offspring: Findings from the Study to Explore Early Development. DiGuiseppi C, Crume T, Van Dyke J, et al. J Autism Dev Disord. 2022;52(11):5064-5071.
• Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses. Grosse SD, Nichols P, Nyarko K, Maenner M, Danielson ML, Shea L. J Autism Dev Disord. 2022;52(9):4150-4163.
• Early identification of autism spectrum disorder among children aged 4 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018. Shaw KA, Maenner MJ, Bakian AV, et al. MMWR Surveill Summ. 2021;70(10):1-14. Published 2021 Dec 3.
• Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2018. Maenner MJ, Shaw KA, Bakian AV, et al. MMWR Surveill Summ. 2021;70(11):1-16. Published 2021 Dec 3.
• Comparison of 2 Case Definitions for Ascertaining the Prevalence of Autism Spectrum Disorder Among 8-Year-Old Children. Maenner MJ, Graves SJ, Peacock G, Honein MA, Boyle CA, Dietz PM. Am J Epidemiol. 2021;190(10):2198-2207.
• Healthcare Costs of Pediatric Autism Spectrum Disorder in the United States, 2003–2015. Zuvekas SH, Grosse SD, Lavelle TA, Maenner MJ, Dietz P, Ji X. J Autism Dev Disord. 2021;51(8):2950-2958.
• Association between pica and gastrointestinal symptoms in preschoolers with and without autism spectrum disorder: Study to Explore Early Development. Fields VL, Soke GN, Reynolds A, et al. Disabil Health J. 2021;14(3):101052.
• Health Status and Health Care Use Among Adolescents Identified With and Without Autism in Early Childhood—Four US Sites, 2018–2020. Powell PS, Pazol K, Wiggins LD, et al. MMWR Morb Mortal Wkly Rep. 2021;70(17):605-611. Published 2021 Apr 30.
• Evaluation of sex differences in preschool children with and without autism spectrum disorder enrolled in the study to explore early development. Wiggins LD, Rubenstein E, Windham G, et al. Res Dev Disabil. 2021;112:103897.
• A Distinct Three-Factor Structure of Restricted and Repetitive Behaviors in an Epidemiologically Sound Sample of Preschool-Age Children with Autism Spectrum Disorder. Hiruma L, Pretzel RE, Tapia AL, et al. J Autism Dev Disord. 2021;51(10):3456-3468.
• Spending on Young Children With Autism Spectrum Disorder in Employer-Sponsored Plans, 2011–2017 Grosse SD, Ji X, Nichols P, Zuvekas SH, Rice CE, Yeargin-Allsopp M. Psychiatr Serv. 2021;72(1):16-22. [published correction appears in Psychiatr Serv. 2021 Jan 1;72(1):97].
• A Preliminary Epidemiology Study of Social (Pragmatic) Communication Disorder Relative to Autism Spectrum Disorder and Developmental Disability Without Social Communication Deficits. Ellis Weismer S, Rubenstein E, Wiggins L, Durkin MS. J Autism Dev Disord. 2021;51(8):2686-2696.
• CE: From the CDC: Understanding Autism Spectrum Disorder. Christensen D, Zubler J. Am J Nurs. 2020;120(10):30-37.
• Early Identification of Autism Spectrum Disorder Among Children Aaged 4 Years—Early Autism and Developmental Disability Monitoring Network, Six Sites, United States, 2016. Shaw KA, Maenner MJ, Baio J, et al. MMWR Surveill Summ. 2020;69(3):1-11. Published 2020 Mar 27.
• Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016. Maenner MJ, Shaw KA, Baio J, et al. MMWR Surveill Summ. 2020;69(4):1-12. Published 2020 Mar 27. [published correction appears in MMWR Morb Mortal Wkly Rep. 2020 Apr 24;69(16):503].
• Disparities in Documented Diagnoses of Autism Spectrum Disorder Based on Demographic, Individual, and Service Factors. Wiggins LD, Durkin M, Esler A, et al. Autism Res. 2020;13(3):464-473.

SEED Research

Researchers working on CDC's Study to Explore Early Development (SEED) have published many studies reporting on important findings related to ASD.

For more information on the methods and descriptions of the SEED study sample, SEED publications, and the evaluation of clinical and laboratory methods using SEED data, click the link below.

Featured Article | Summer 2023

Cdc seed study explores prenatal ultrasound use and risk of autism spectrum disorder.

Prenatal ultrasound use and risk of autism spectrum disorder: Findings from the case-control Study to Explore Early Development (SEED). Christensen D, Pazol K, Overwyk KJ, et al. Paediatr Perinat Epidemiol. 2023;37(6):527-535.

Study findings‎

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

All articles

Search CDC Stacks for articles that have been published by CDC authors within the National Center on Birth Defects and Developmental Disabilities from 1990 to present.

Feature articles and an Easy-Read Summary

Easy-Read Summary

Additional resources

Autism Spectrum Disorder (ASD)

Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication and behavioral challenges. CDC is committed to continuing to provide essential data on ASD and develop resources that help identify children with ASD as early as possible.

For Everyone

Health care providers, public health.

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Research Studies

Currently Recruiting or Active Research Studies

Please download the document below for our current recruiting studies organized by age range. 

 Study Title

Study description, spark (simons powering autism research) study.

Available in English and Spanish.

If you or your child has a professional diagnosis of autism, Stanford University invites you to learn more about SPARK, a new online research study sponsored by the Simons Foundation Autism Research Initiative. The mission of SPARK is clear: speed up research and advance understanding of autism by creating the nation’s largest autism study. Joining SPARK is simple – register online and provide a DNA sample via a saliva collection kit in the comfort of your own home. Together, we can help spark a better future for all individuals and families affected by autism.

Register  by contacting us at [email protected] or online at www.sparkforautism.org/stanford .

SPARK está trabajando para fomentar la investigación y mejorar nuestra comprensión del autismo. Stanford y más de 30 de las principales escuelas de medicina y centros de investigación del autismo del país forman parte de este esfuerzo.

• Participar en SPARK es gratis y se puede hacer completamente desde casa.
• Muchas de las encuestas de SPARK aportan informes personalizados.
• Los participantes serán notificados en caso de haber otras oportunidades de investigación.
• Los individuos con autismo podrán recibir códigos de regalo de Amazon por un valor de hasta 50 dólares (uno por familia) después de la recepción de sus muestras de saliva.

Para inscribirse en SPARK:  https://sparkforautism.org/Stanford/ES

La inscripción suele llevar unos 20 minutos y puede empezar y parar si lo necesita. Una vez que se registre y complete unos cuestionarios en línea, le enviaremos un kit para recolectar saliva a su domicilio. Para obtener más información, envíe un correo electrónico a [email protected]

Language Treatment Trial for Children with Autism

Researchers at Stanford University are currently recruiting children with autism spectrum disorder to identify MRI-based markers of response to treatment with Pivotal Response Treatment (PRT) targeting language abilities. Children with autism spectrum disorder between the ages of 2 and 4 years 11 months are invited to participate. This study involves up to a 5 month time commitment. The participant must be willing to complete cognitive and behavioral assessments (such as IQ and language testing) and be able to either sleep (young children) or lie still in the scanner during an MRI. After a successful MRI, the participant will be randomized into the PRT trial or DTG (Delayed Treatment Group). PRT will consist of 16 weekly, 60-90 minute sessions of parent training in PRT over a 16 week time period. DTG will consist of your child’s treatments as usual in the community and measurements and questionnaires will need to be filled out on three study visits over the course of the 16 weeks. After completion of the DTG, the participant will be offered PRT parent training sessions similar to the PRT group. There is no cost to participate in the study. If you would like to participate or if you have any questions please call (650) 736-1235 or email:  [email protected]  to discuss the study in more detail. 

2 and 4 years,11 months

Targeting the Neurobiology of Restricted and Repetitive Behaviors in Children with Autism Using N-acetylcysteine Randomized Control Trial

We are recruiting children autism to participate in a study examining the treatment effects of an over-the-counter dietary supplement on the brain.   

Eligibility:  Children with autism spectrum disorder who -

·    are aged between 3 and 12 years old

·    exhibit restricted and repetitive behaviors

·    will drink N-acetyl cysteine dissolved in water

·    will undergo brain scanning (asleep or awake) with magnetic resonance  imaging (MRI)

·    will undergo brain scanning with electroencephalography (EEG)

The study will take place over 3 to 6 visits (some remotely over Zoom) and the approximate time required is about 10 to 12 hours. Individuals that are able to complete both of the MRI/EEG sessions will be compensated $50.

You can find more information about our NAC studies at   https://redcap.link/NACforAutism .

If you have any questions  please call 650-736-1235 or email:  [email protected] .

3 to 12 years

Autism Center of Excellence Sleep Study

Dear Parents,

We are excited to tell you about a new research study for children. We are looking to partner with parents who have children that are between the ages of 4 and 17 years old,  with and without  an Autism Spectrum Disorder (ASD) diagnosis.

What is involved?

• In-person cognitive and behavioral assessments
• Day-time Electroencephalogram (EEG)
• In-home, 2 night sleep monitoring session
• Collection of saliva to measure cortisol and melatonin levels
• Wearing a watch device that tracks sleep and daily activity

What will I receive if I participate?

• Research sleep report and behavioral testing summary upon request
• $50 for each in-person visit to Stanford and $100 for the 2 night in-home sleep assessment

Treatment extension study:

• If your child has ASD, sleep difficulties, and ages 8-17, they may also qualify for sleep medication trials

Interested in participating or want to learn more?  Click Here!

If you would like to reach out to our team directly with any questions, please contact our team below!

Email:  [email protected]

650-498-7215

4 to 17 years

Pregnenolone Randomized Controlled Trial

Neurosteroid Pregnenolone Treatment for Irritability in Adolescents with Autism

Medication treatments for core symptoms of autism spectrum disorder (ASD) continue to be unmet medical needs. The only medications approved by the U.S. Food and Drug Administration (FDA) for the treatment of individuals with ASD are effective in treating irritability and associated aggressive behaviors, but these medications can also cause severe long-term side effects such as diabetes and involuntary motor movements. Therefore, effective medications with more tolerable side effect profiles are highly desirable. This profile is consistent with pregnenolone (PREG). PREG belongs to a new class of hormones known as neurosteroids, which have been shown to be effective in treating various psychiatric conditions including bipolar depression and schizophrenia. As compared to currently FDA-approved medications, our preliminary data suggested that PREG may represent a potentially effective and well-tolerated agent for treating irritability in individuals with ASD. In addition, our experience suggests that PREG might be helpful in improving selected core symptoms such as social deficits and sensory abnormalities of ASD. This study provides the opportunity to further explore the usefulness of PREG in the treatment of irritability and some core symptoms of ASD. We are performing a 12-week randomized double-blind controlled pilot trial to examine the effectiveness of orally administered PREG in reducing irritability and associated behaviors in adolescents with ASD. In this study, we also aim to examine the usefulness of biomarkers (blood levels of neurosteroids, eyetracking and brain wave recording) in predicting treatment response and assessing biologic changes with PREG treatment.

Link to study in Stanford's Clinical Trials Directory

14 to 25 years

Trial of Center-Based vs. In-Home Pivotal Response Treatment (PRT) in Autism (PRT-HvC)

Do you have a child (2-5 years old) with autism and want an intensive center-based or in-home intervention?

Stanford University researchers are recruiting children with autism and their parents to participate in a study examining the effectiveness of a center-based vs. in-home Pivotal Response Treatment (PRT) program in targeting social communication abilities in young children with autism.

Participants must:

• Be diagnosed with Autism Spectrum Disorder
• Be between the ages of 2 years and 5 years 11 months
• Be able to attend 3-hour research treatment sessions 4 days per week and participate in parent training

Based on behavioral screening assessments, children who are eligible will be randomly assigned to either center-based intervention, in-home intervention, or treatment as usual. Those assigned to the treatment-as-usual group will receive treatment after the 16–week period is completed.

Call 650-736-1235 or email [email protected] to learn more.

https://clinicaltrials.gov/ct2/show/NCT04899544 

2 to 5 years

Improving Access to Pivotal Response Treatment (PRT) via Telehealth Parent Training

There is an urgent need for improved access to effective autism treatments. With advances in technology, distance learning models have particular promise for families who cannot access evidence-based parent training locally or may be on long wait-lists for behavioral treatments. Pivotal Response Treatment (PRT) is an established treatment for autism spectrum disorder (ASD); however, a telehealth PRT model has not yet been evaluated in a controlled trial. This study will examine the effects of training parents in PRT via secure video conferencing and investigate 1) whether parents can learn via telehealth to deliver PRT in the home setting (PRT-T) and 2) whether their children will show greater improvement in functional communication skills compared to children in a waitlist control group. Participants will include 40 children age 2 to 5 years with ASD and significant language delay. Eligible children will be randomly assigned to either PRT-T or waiting list. Weekly 60-minute parent training sessions will be delivered for 12 weeks via secure video conferencing software by a PRT-trained study therapist. Link:  https://clinicaltrials.gov/ct2/show/NCT04042337

Note: Participants must live at least 200 miles away from Stanford University (i.e., this study is geared towards out-of-state families or families living at a distance)

A Center Based Randomized Controlled Trial of Pivotal Response Treatment for Preschoolers With Autism

Researchers at Stanford University are currently recruiting children with autism and their parents to participate in a study examining the effectiveness of a center-based Pivotal Response Treatment (PRT) program in targeting social communication abilities in young children with autism. We are currently recruiting children diagnosed with ASD and social communication deficits, aged 2:0 to 3:11 years. Children who are eligible based on behavioral screening assessments will be randomly assigned to either an immediate treatment (PRT) group or a delayed treatment group (DTG). If randomized into the PRT group, the 12-week treatment will consist of a combination of one weekly 60-minute individual parent training session and 12 weekly hours (approximately 3 hours per day for 4 days per week) with your child in a center-based group preschool environment at Stanford University. If randomized into the delayed treatment group, the children will wait 12 weeks to receive the PRT treatment and continue any treatment they are receiving as usual in the community. The cost of clinic-based services varies based on individual family health insurance plans.

For more information, please call (650) 736-1235 or email  [email protected]  to discuss the study in more detail. 

2 and 3 years,11 months

Natural History Study of Individuals with Autism and Germline Heterozygous PTEN Mutations

The goal of this study is to gain a better understanding of PTEN mutation syndromes to identify early markers and ultimately effective interventions for autism spectrum disorder. Individuals 18 months or older are eligible to participate if they have been diagnosed with PTEN hamartoma tumor syndrome. The study involves five visits over a two year period. Three of the visits occur on-site at a study location. The other two visits occur as phone calls. The on-site visits include a blood draw, physical/neurological exams and behavioral testing.

Study Webpage    

18 months and older

Active Studies, not Recruiting

An open-label pilot study of esomeprazole in children with autism.

Researchers at Stanford University are currently examining the effectiveness of esomeprazole in improving social communication deficits in children with Autism Spectrum Disorder (ASD). Esomeprazole is currently FDA-approved for children ages 1 and up for gastroesophageal reflux disease (GERD) and has been identified as a potential treatment for improving social communication in children with ASD. Children with ASD ages 2 through 6 years are invited to participate. The child must be willing to take esomeprazole orally for at least 8 weeks, complete diagnostic and behavioral assessments, and be free of serious medical problems. There is also an optional research blood draw. The study will require visits to Stanford University and the parent/caregiver will be required to complete questionnaires for each visit.

For more information, please go to  https://is.gd/ASDstudy ,  call (650) 736-1235, or email  [email protected] .

2 to 6 years

Vasopressin Treatment Trial for Children with Autism

The purpose of this clinical trial is to investigate the effectiveness of vasopressin nasal spray for treating symptoms associated with autism. Vasopressin is a hormone that is produced naturally within the body and has been implicated in regulating social behaviors. It has been proposed that administration of the hormone may also help improve social functioning in individuals with autism.

Link to study at clinicaltrials.gov

6 to 17 years

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Autism spectrum disorder

Affiliations.

• 1 Departments of Psychiatry and School of Education, University of California, Los Angeles, Los Angeles, CA, USA. [email protected].
• 2 Department of Health Sciences, University of Leicester, Leicester, UK.
• 3 Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
• 4 Autistica, London, UK.
• 5 Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France.
• 6 Autism Speaks, New York, NY, USA.
• 7 Centre for Brain & Cognitive Development, University of London, London, UK.
• 8 The Sackler Institute for Developmental Psychobiology, New York, NY, USA.
• 9 The Center for Autism and the Developing Brain, White Plains, NY, USA.
• 10 Department of Psychiatry, Langley Porter Psychiatric Institute and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
• 11 Department of Pediatrics and Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA.
• 12 Department of Psychiatry, Columbia University, New York, NY, USA.
• PMID: 31949163
• PMCID: PMC8900942
• DOI: 10.1038/s41572-019-0138-4

Autism spectrum disorder is a construct used to describe individuals with a specific combination of impairments in social communication and repetitive behaviours, highly restricted interests and/or sensory behaviours beginning early in life. The worldwide prevalence of autism is just under 1%, but estimates are higher in high-income countries. Although gross brain pathology is not characteristic of autism, subtle anatomical and functional differences have been observed in post-mortem, neuroimaging and electrophysiological studies. Initially, it was hoped that accurate measurement of behavioural phenotypes would lead to specific genetic subtypes, but genetic findings have mainly applied to heterogeneous groups that are not specific to autism. Psychosocial interventions in children can improve specific behaviours, such as joint attention, language and social engagement, that may affect further development and could reduce symptom severity. However, further research is necessary to identify the long-term needs of people with autism, and treatments and the mechanisms behind them that could result in improved independence and quality of life over time. Families are often the major source of support for people with autism throughout much of life and need to be considered, along with the perspectives of autistic individuals, in both research and practice.

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Fig. 1 |. Theories and findings regarding…

Fig. 1 |. Theories and findings regarding autism mechanisms, outcomes and heterogeneity.

Original descriptions of…

Fig. 2 |. Environmental risk factors for…

Fig. 2 |. Environmental risk factors for autism.

Data from studies aiming to identify risk…

Fig. 3 |. Encoded proteins associated with…

Fig. 3 |. Encoded proteins associated with autism risk.

Simplified schematic of the major cellular…

Fig. 4 |. Longitudinal trajectories of total…

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Fig. 5 |. Co-occurring disorders.

Primary and…

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Fig. 6 |. Major parental milestones in…

Fig. 6 |. Major parental milestones in advocating and supporting their child with autism.

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• Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis. Fang Y, Wu J, Wang Q, Qiu S, Bozoki A, Liu M. Fang Y, et al. Pattern Recognit. 2025 Jan;157:110912. doi: 10.1016/j.patcog.2024.110912. Epub 2024 Aug 22. Pattern Recognit. 2025. PMID: 39246820
• Longitudinal trends and correlation between autism spectrum disorder prevalence and sperm quality parameters (2000-2024): a comprehensive statistical analysis. Al-Salihy AAS. Al-Salihy AAS. Front Reprod Health. 2024 Aug 22;6:1438049. doi: 10.3389/frph.2024.1438049. eCollection 2024. Front Reprod Health. 2024. PMID: 39239154 Free PMC article.
• Uncovering convergence and divergence between autism and schizophrenia using genomic tools and patients' neurons. Romanovsky E, Choudhary A, Peles D, Abu-Akel A, Stern S. Romanovsky E, et al. Mol Psychiatry. 2024 Sep 5. doi: 10.1038/s41380-024-02740-0. Online ahead of print. Mol Psychiatry. 2024. PMID: 39237719
• Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network. Bahrami F, Taghizadeh M, Shayegh F. Bahrami F, et al. J Med Signals Sens. 2024 Aug 6;14:24. doi: 10.4103/jmss.jmss_15_24. eCollection 2024. J Med Signals Sens. 2024. PMID: 39234588 Free PMC article.
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This paper establishes that autism is a stable diagnosis (as a spectrum) beginning at least by 2 years of age. The paper also establishes parent interview and clinician observation as predictive of autism at 9 years of age. Finally, it is the first paper that shows that the specific DSM-IV-TR diagnoses is unstable across childhood but that the instability is almost all shifting across categories not outside the spectrum.

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This paper uses active case-finding to provide representative estimates of the prevalence of autism and demonstrated that rates of autism in men and women are equivalent in adults with moderate-to-profound intellectual disability.

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How parents and caregivers can evaluate the research on MERT and other potential autism treatments

by Corinne Purtill, Los Angeles Times

As diagnoses of autism spectrum disorder have increased in the last two decades, so have the number of experimental and off-label therapies seeking to address the condition.

For parents navigating the complex and often contradictory landscape of autism interventions—while also balancing medical appointments, educational specialists and countless other family needs—evaluating these treatments can be daunting.

Experts in autism research talked to The Times about what parents and patients should watch for when evaluating a potential new treatment—for autism or any other condition.

Take testimonials with a grain of salt

Firsthand accounts of a therapy's life-changing effects can be powerfully compelling. But such stories alone can't indicate how effective a treatment will be for anyone else, autism experts said.

"Be wary of therapies that are sold to you with testimonials. If you go to a clinic website and they have dozens of quotes from parents saying 'This changed my child's life in XYZ ways,' that isn't the same as evidence," said Zoe Gross of the Autistic Self Advocacy Network, a nonprofit group run by and for autistic adults.

"If the main way something's advertised is through testimonials, it may be because there isn't research, or what research was done showed it wasn't effective."

Without accompanying data, there is no way to know whether any patient's experience with a treatment is typical or an outlier. A therapy could have only a 1% success rate, Gross said, and still yield dozens of positive outcomes once thousands of people have tried it.

Former patient stories can be a starting point for an exploration of whether a therapy is right for someone, doctors said, but the exploration shouldn't end there.

"There's an old saying in medicine," said Dr. Andrew Leuchter, director of UCLA's TMS Clinical and Research Service. "The plural of anecdote is not data."

Look for—and at—the research

"Right now, it's really sexy to call yourself 'evidence-based,'" said Dr. David Celiberti, executive director of the nonprofit Assn. for Science in Autism Treatment. "For a consumer, that's amazing. You hear 'evidence-based' and of course, you're going to be drawn to it. But people are using that term very loosely."

In the case of magnetic e-resonance therapy, or MERT, its developer Wave Neuroscience features on its website a library of research. Similar links feature on the sites of many licensee clinics.

Most of the publications related to autism cited by MERT clinics—and, at times, by Wave—are either limited in scope or only tangentially related to the therapy, a half-dozen experts said, including some whose work is cited.

One of them, for example, is a brief 2016 article from the Austin Journal of Autism and Related Disabilities titled "The Potential of Magnetic Resonant Therapy in Children with Autism Spectrum Disorder."

Its authors and advisors said they were surprised to learn the paper was being used to advertise the treatment. The paper contains no data or original research and concludes only that MERT could be studied further as an autism therapy without risk of serious harm.

"This isn't an evidence-based paper. It's an opinion piece about the possibilities of this technology," said Dr. John Crawford, a neurologist at Children's Hospital of Orange County and a co-author of the paper. "It's not that impactful from a scientific perspective."

Who else has verified these findings?

Many MERT clinics feature a 2014 electronic poster presentation that examines data from the charts of 141 children who received transcranial magnetic stimulation , the therapy on which MERT is based, for autism.

Until March, Wave featured the poster on its website and highlighted that 59.1% of 44 participants who completed 12 months of treatment improved their scores on the Childhood Autism Rating Scale, an assessment tool used to gauge symptom severity.

A closer look at the report shows that after five days of treatment, 38 patients were dropped from the analysis because their symptoms either showed no improvement or worsened. One had a seizure during treatment.

The authors excluded dozens more patients for various reasons. Of the remaining 44 patients, 26 saw improvement while getting the treatment. That was 59.1% of those remaining, as the poster said, but only 18.4% of the total study population.

The write-up also notes that many of those 26 children were receiving other therapies at the same time that may have been responsible for some or all of the improvements.

Posters are typically prepared as a way to highlight findings at professional conferences and "cannot be interpreted as having undergone rigorous peer review," said USC neurosurgeon Dr. Charles Liu, a co-author on the poster who is not affiliated with Wave or any MERT clinics.

"The main point of the abstract is and remains that more rigorous studies must [be] done."

If research shows changes, how do you know the therapy caused it?

Wave and licensees also highlight a 2022 paper by a technician at a licensee clinic in Australia who is also a doctoral candidate at Australia's University of the Sunshine Coast.

It looks at data from 28 patients at two MERT clinics in Australia whose brains showed "significant improvement" in their individual alpha frequency waves after treatment.

Although some previous research has found correlations between atypical alpha wave frequency and autism diagnoses, six scientists told The Times that there isn't yet enough evidence to understand how changes in alpha waves affect autistic traits, or any scientific consensus on whether "improvement" in this pattern of brain activity has any meaningful effect on autistic behaviors.

The report is a retrospective chart review, which examines existing data from patients' medical records and is often used to identify interesting outcomes worthy of further study.

By design, it does not include a control group, which is what allows researchers to identify whether any changes they see are related to the variable they are studying. Its authors noted in the paper that findings are preliminary and require further study.

"Because this was not a controlled trial or study, [the cause of the changes] could have been anything including placebo effect, any additional therapies the children were receiving, etc.," said Lindsay Oberman, director of the Neurostimulation Research Program at the National Institute of Mental Health.

Medical research follows a hierarchy of evidence. At the bottom are anecdotes and observations: valid points of information that alone aren't enough to draw broad conclusions from.

Above that are observational studies that collect and analyze preexisting data in a systematic way. And at the top are randomized controlled trials, which are designed to eliminate as much bias as possible from the experiment and ensure that the thing being studied is responsible for any changes observed.

"Families need to know that there is this gold standard for studies—to make sure that something works to help people with autism, it needs to have what's called a randomized controlled trial ," said Alycia Halladay, chief science officer at the Autism Science Foundation.

2024 Los Angeles Times. Distributed by Tribune Content Agency, LLC.

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• Systematic Review
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• Published: 15 June 2022

Genetics of autism spectrum disorder: an umbrella review of systematic reviews and meta-analyses

• Shuang Qiu 1 ,
• Yingjia Qiu 2 ,
• Yan Li 3 &
• Xianling Cong   ORCID: orcid.org/0000-0002-5790-4188 1  

Translational Psychiatry volume  12 , Article number:  249 ( 2022 ) Cite this article

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• Autism spectrum disorders

Autism spectrum disorder (ASD) is a class of neurodevelopmental conditions with a large epidemiological and societal impact worldwide. To date, numerous studies have investigated the associations between genetic variants and ASD risk. To provide a robust synthesis of published evidence of candidate gene studies for ASD, we performed an umbrella review (UR) of meta-analyses of genetic studies for ASD (PROSPERO registration number: CRD42021221868). We systematically searched eight English and Chinese databases from inception to March 31, 2022. Reviewing of eligibility, data extraction, and quality assessment were performed by two authors. In total, 28 of 5062 retrieved articles were analyzed, which investigated a combined 41 single nucleotide polymorphisms (SNPs) of nine candidate genes. Overall, 12 significant SNPs of CNTNAP2 , MTHFR , OXTR , SLC25A12 , and VDR were identified, of which associations with suggestive evidence included the C677T polymorphism of MTHFR (under allelic, dominant, and heterozygote models) and the rs731236 polymorphism of VDR (under allelic and homozygote models). Associations with weak evidence included the rs2710102 polymorphism of CNTNAP2 (under allelic, homozygote, and recessive models), the rs7794745 polymorphism of CNTNAP2 (under dominant and heterozygote models), the C677T polymorphism of MTHFR (under homozygote model), and the rs731236 polymorphism of VDR (under dominant and recessive models). Our UR summarizes research evidence on the genetics of ASD and provides a broad and detailed overview of risk genes for ASD. The rs2710102 and rs7794745 polymorphisms of CNTNAP2 , C677T polymorphism of MTHFR , and rs731236 polymorphism of VDR may confer ASD risks. This study will provide clinicians and healthcare decision-makers with evidence-based information about the most salient candidate genes relevant to ASD and recommendations for future treatment, prevention, and research.

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Introduction.

Autism spectrum disorder (ASD) is a group of neurodevelopmental conditions characterized by early-onset dysfunctions in communication, impairments in social interaction, and repetitive and stereotyped behaviors and interests [ 1 ]. Patients develop ASD-related symptoms when they are 12−18 months of age, and diagnosis is generally made at the age of 2 years [ 2 ]. In 2010, 52 million people had been diagnosed with ASD worldwide, which was equivalent to a population prevalence of 7.6 per 1000 or 1 in 132 persons [ 3 ]. ASD is the leading cause of disability in children under 5 years, and people with ASD may require high levels of support, which is costly and thus leads to substantial economic, emotional, and physical burdens on affected families [ 3 ].

Due to the lack of clinical and epidemiological evidence for an ASD cure, researchers have focused on better understanding ASD and advancing risk prediction and prevention [ 3 ]. The causes of ASD are complex and multifactorial, with several associated genes and environmental risk factors [ 4 ]. A previous umbrella review (UR) of environmental risk factors for ASD showed that several maternal factors, including advanced age (≥35 years), chronic hypertension, preeclampsia, gestational hypertension, and being overweight before or during pregnancy, were significantly associated with ASD risk, without any signs of bias [ 5 , 6 ]. Accumulating twin- and family based studies further indicate that genetic factors play critical roles in ASD, such that the concordance rate among monozygotic twins is higher (60–90%) than that among dizygotic twins (0–30%) [ 7 , 8 ]. The heritability of ASD has been estimated to be 50%, indicating that genetic factors are the main contributors to the etiology of ASD [ 8 ].

To date, numerous studies investigating the association between genetic variants and ASD risk have been published [ 9 , 10 , 11 ]. Most of these studies focused on identifying single nucleotide polymorphisms (SNPs) of candidate genes associated with ASD risk. However, these SNP studies had small sample sizes and, therefore, low statistical power to demonstrate statistically significant effects of low-risk susceptibility genes, leading to inconsistent conclusions. Although meta-analyses have been conducted to resolve this problem, single SNPs or genes have usually been investigated.

An UR collects and evaluates multiple systematic reviews and meta-analyses conducted on a specific research topic, provides a robust synthesis of published evidence, and considers the importance of effects found over time [ 12 ]. In addition, the results of UR studies may increase the predictive power with more precise estimates [ 13 ]. Thus, we aimed to perform an UR study of all the systematic reviews and meta-analyses that have been published, assessing candidate genes associated with ASD risk. This study will provide clinicians and healthcare decision-makers with evidence-based information about candidate genes of ASD and recommendations for future prevention and research in less time than would otherwise be required to locate and examine all relevant research individually.

Literature search strategy and eligibility criteria

We systematically searched the PubMed, EMBASE, PsycINFO, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Sinomed, and Wanfang databases from inception to March 31, 2022. The databases were searched using the following strategy: (autis* [All Fields] OR autism* [All Fields] OR autistic* [All Fields] OR ASD [All Fields] OR autism spectrum disorder* [All Fields] OR PDD-NOS [All Fields] OR PDDNOS [All Fields] OR unspecified PDD [All Fields] OR PDD [All Fields] OR pervasive developmental disorder* [All Fields] OR pervasive developmental disorder not otherwise specified [All Fields] OR Asperger* [All Fields] OR Asperger* syndrome [All Fields]) AND (gene* [All Fields] OR genom* [All Fields]) AND (systematic review [All Fields] OR meta-analysis [All Fields]). Authors S. Qiu and Y. Qiu independently conducted literature searches for potential articles included in this review. The references of the relevant articles were manually searched to identify and incorporate eligible studies.

We included meta-analyses of family based and case-control studies that examined associations between ASD and potential risk genes. We only included meta-analyses that reported either effect estimates of individual study or the data necessary to calculate these estimates. We excluded meta-analyses if (1) risk genes were used for screening, diagnostic, or prognostic purposes; (2) a study examined ASD as a risk factor for other medical conditions; (3) a study included fewer than three original studies investigating the association between risk genes and ASD; and (4) a study with missing information after the corresponding author, whom we contacted through email, failed to provide the required information. All articles retrieved were first organized in the reference manager software (Endnote 9, Clarivate Analytics, New York, NY, USA), and duplicates were deleted. S. Qiu and Y. Qiu chose eligible articles by screening the titles, abstracts, and full article texts independently. Disagreements were resolved through a discussion with a third investigator (Y. Li) until a consensus was reached.

Data extraction and quality assessment

From each eligible meta-analysis, we extracted the first author, publication year, genetic risk factors examined, number of studies, number of ASD cases and participants, study-specific relative risk estimates (odds ratio [ OR ]) with the corresponding 95% confidence interval ( CI ), sample size of cases and controls, genotype and allele counts, and individual study designs (case-control, family based or mixed [case-control and family based]). We used the ‘assessment of multiple systematic reviews’ tool, consisting of 11 items, to assess the methodological quality of the meta-analyses [ 14 ]. Data extraction and quality assessment were independently conducted by S. Qiu and Y. Qiu. Disagreements were resolved via a discussion with a third investigator (Y. Li) until a consensus was reached.

Data analysis

In agreement with previous URs, we performed a statistical analysis using a series of tests that were previously developed and reproduced [ 13 , 15 , 16 ]. If more than one meta-analysis on the same research question was eligible, the most recent meta-analysis was retained for the main analysis. For each eligible meta-analysis, we calculated the summary-effect size with 95% CI [ 17 ]. We also calculated the 95% prediction interval ( PI ) to explain the between-study heterogeneity and to assess the uncertainty of a new study [ 18 , 19 ]. Heterogeneity between studies was assessed using the Chi-squared test based Q-statistic and quantified using the I 2 -statistic [ 20 , 21 ]. If there was no substantial statistical heterogeneity ( P  > 0.10, I 2  ≤ 50%), data were pooled using a fixed-effect model; otherwise, heterogeneity was evaluated using a random-effect model [ 22 ]. The Hardy–Weinberg equilibrium (HWE) of meta-analyses in the control group was analyzed using Chi-squared tests. Additionally, small-study effects were evaluated using Egger’s regression asymmetry test. P -values < 0.10 were considered to indicate the presence of small-study effects [ 23 , 24 ]. The Chi-squared test was used to assess the presence of excess significance, which evaluated whether the observed number of studies with significant results ( P  < 0.05) was greater than the expected number [ 22 , 25 ]. All statistical analyses were performed using RStudio 3.6.2. Statistical significance was set at P  < 0.05, except where otherwise specified.

Determining the credibility of evidence

In line with previous URs, we categorized the strength of the evidence of risk genes for ASD into five levels: convincing (class I), highly suggestive (class II), suggestive (class III), weak (class IV), and not significant [ 5 , 26 , 27 , 28 ]. Criteria for the level of evidence included the number of ASD cases, P -values by random effects model, small-study effects, excess significance bias, heterogeneity ( I² ), and 95% CI .

This review was prospectively registered with PROSPERO (registration number: CRD42021221868).

Description of eligible meta-analyses

A total of 5062 articles were identified through an initial search. After removing duplicates, the titles and abstracts of 3182 articles were screened for eligibility. Of the remaining 66 articles that were reviewed in full, 28 eligible articles were selected for data extraction (Fig. 1 ).

Flow chart of literature identification and selection.

The characteristics of the selected studies are presented in Table 1 . Of the 28 included reviews, eight were on methylenetetrahydrofolate reductase ( MTHFR ) [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]; four each on solute carrier family 6 member 4 ( SLC6A4 ) [ 37 , 38 , 39 , 40 ] and contactin associated protein 2 ( CNTNAP2 ) [ 41 , 42 , 43 , 44 ]; three each on oxytocin receptor ( OXTR ) [ 45 , 46 , 47 ] and reelin ( RELN ) [ 48 , 49 , 50 ]; two each on gamma-aminobutyric acid type A receptor subunit beta3 ( GABRB3 ) [ 51 , 52 ], solute carrier family 25 member 12 ( SLC25A12 ) [ 53 , 54 ], and vitamin D receptor ( VDR ) [ 55 , 56 ]; and one on catechol-o-methyltransferase ( COMT ) [ 39 ] (one meta-analysis was on both COMT and SLC6A4 ). These studies were published from 2008 to 2021 and considered the associations between 41 SNPs in nine candidate genes and ASD risk. For quality assessment, 22 articles that scored 5−8 were rated as ‘moderate quality’, and six that scored < 5 were rated as ‘low quality’. Seventeen studies (60.7%) performed the HWE check (Table 1 ). With respect to the study design, 14 (64.3%) studies synthesized case-control studies, two (7.1%) included family based studies, and eight (28.6%) used both case-control and family based studies (Table 1 ).

Summary-effect sizes and significant findings

The results of the associations between the 41 SNPs and ASD risks reported in the meta-analyses are presented in Table 2 under five different genetic models: allelic model (mutant allele vs. wild-type allele), dominant model (mutant homozygote + heterozygote vs. wild-type homozygote), heterozygote model (heterozygote vs. wild-type homozygote), homozygote model (mutant homozygote vs. wild-type homozygote), and recessive model (mutant homozygote vs. wild-type homozygote + heterozygote).

Only one meta-analysis on the rs2710102 polymorphism of CNTNAP2 showed that the polymorphism was associated with ASD susceptibility in allelic, homozygote, and recessive models [ 44 ]. This meta-analysis also found that the rs7794745 polymorphism of CNTNAP2 was associated with an increased risk of ASD in dominant and heterozygote models [ 44 ].

All four meta-analyses reported no significant association between the A1298C polymorphism of MTHFR and ASD risk. All eight meta-analyses on the C677T polymorphism of MTHFR showed that the polymorphism was associated with ASD susceptibility in allelic and heterozygote models [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Seven meta-analyses found that the C677T polymorphism was associated with an increased risk of ASD in dominant [ 29 , 31 , 32 , 33 , 34 , 35 , 36 ] and homozygote [ 29 , 30 , 31 , 33 , 34 , 35 , 36 ] models. Five meta-analyses found that the C677T polymorphism was associated with an increased risk of ASD in the recessive model [ 29 , 30 , 31 , 33 , 34 ].

For OXTR , 19 SNPs were summarized. LoParo et al. [ 45 ] found that the mutant allele of rs2268491, wild-type allele of rs237887, and mutant allele of rs7632287 were risk-inducing SNPs of ASD. In addition, Kranz et al. [ 46 ] found that the mutant allele of rs237889 was associated with ASD risk.

Regarding SLC25A12 , both Aoki et al. [ 53 ] and Liu et al. [ 54 ] found that the mutant alleles of rs2056202 and rs2292813 significantly increased ASD risk in family-based and mixed studies. We excluded the results of the associations between rs2292813 and ASD risk based on the case-control design reported by Liu et al. [ 54 ], as the authors included only two case–control studies.

Sun et al. [ 55 ] found that the rs2228570 polymorphism of VDR was associated with an increased ASD risk in homozygote and recessive models, while Yang et al. [ 56 ] did not find significant associations in any genetic model. Both authors [ 55 , 56 ] found that the rs731236 polymorphism of VDR was significantly associated with ASD risk in allelic, homozygote, and recessive models. Sun et al. [ 55 ] found that the rs731236 polymorphism was significantly associated with ASD risk in the dominant model. Both Sun et al. [ 55 ] and Yang et al. [ 56 ] found that the mutant allele of rs7975232 of VDR was significantly associated with a decreased ASD risk (Table 2 ). There were no significant SNPs in COMT , GABRB3 , RELN , and SLC6A4 .

When more than one meta-analysis on the same research question was eligible, the most recent one was retained for the main analysis. After comparing the publication year and sample size of each meta-analysis, 11 meta-analyses were retained for further analysis, of which two each study were on RELN and MTHFR , and one each was on CNTNAP2 , COMT , GABRB3 , OXTR , SLC25A12 , SLC6A4 , and VDR . We extracted the allele and genotype frequencies of each SNP in case and control groups from the original research for further analysis. However, the allele and genotype frequencies of some SNPs in the compared groups could not be extracted from the original research that did not contain the information, and we could not obtain this information from the corresponding authors of the studies. Finally, we analyzed the data of 20 SNPs with allele frequencies in 10 meta-analyses from 117 original studies and 16 SNPs with genotype frequencies in eight meta-analyses from 101 original studies. Associations were measured using five different genetic models (Tables 3 , 4 ).

We found that the rs2710102 polymorphism of CNTNAP2 was associated with a decreased ASD risk in the allelic ( OR  = 0.849, 95% CI  = 0.734–0.981, P  = 0.0263), homozygote ( OR  = 0.668, 95% CI  = 0.470–0.950, P  = 0.0248), and recessive ( OR  = 0.715, 95% CI  = 0.563–0.909, P  = 0.0062) models. In addition, we found that the mutant allele of rs7794745 ( CNTNAP2 ) increased ASD risk based on the dominant ( OR  = 1.300, 95% CI  = 1.109–1.523, P  = 0.0012) and heterozygote ( OR  = 1.275, 95% CI  = 1.081–1.504, P  = 0.0039) models. The C677T polymorphism of MTHFR was associated with an increased ASD risk in the allelic ( OR  = 1.799, 95% CI  = 1.303–2.483, P  = 0.0004), dominant ( OR  = 1.959, 95% CI  = 1.402–2.738, P  < 0.0001), heterozygote ( OR  = 1.767, 95% CI  = 1.343–2.330, P  < 0.0001), and homozygote ( OR  = 1.795, 95% CI  = 1.158–2.782, P  = 0.0089) models. The rs607755 polymorphism of RELN was associated with an increased ASD risk in the allelic ( OR  = 1.316, 95% CI  = 1.029–1.683, P  = 0.0284), dominant ( OR  = 1.520, 95% CI  = 1.061–2.178, P  = 0.0226), heterozygote ( OR  = 1.483, 95% CI  = 1.016–2.165, P  = 0.0411), and homozygote ( OR  = 1.816, 95% CI  = 1.051–3.136, P  = 0.0324) models. The rs731236 polymorphism of VDR was associated with an increased ASD risk in the allelic ( OR  = 1.297, 95% CI  = 1.125–1.494, P  = 0.0003), dominant ( OR  = 1.304, 95% CI  = 1.082–1.571, P  = 0.0053), homozygote ( OR  = 1.741, 95% CI  = 1.258–2.409, P  = 0.0008), and recessive ( OR  = 1.613, 95% CI  = 1.187–2.190, P  = 0.0022) models. In addition, we found that the mutant allele of rs7975232 ( VDR ) decreased ASD risk ( OR  = 0.823, 95% CI  = 0.681–0.993, P  = 0.0425) based on the allelic model. There was no significant association between the other SNPs and ASD risk (all P  > 0.05; Table 4 ).

As for the results of PI , the null value was excluded in only four SNPs of rs2710102 ( CNTNAP2 ) under the allelic, homozygote, and recessive models; rs7794745 ( CNTNAP2 ) under the heterozygote model; rs607755 ( RELN ) and rs731236 ( VDR ) under the allelic and homozygote models (Table 4 ). When evaluating small-study effects using Egger’s regression asymmetry test, evidence for statistically significant small-study effects in the meta-analyses was identified in some SNPs. Supporting evidence included a meta-analysis on A1298C ( MTHFR ) under the allelic, dominant, and heterozygote models; a meta-analysis on C677T ( MTHFR ) under the five genetic models; a meta-analysis on rs20317 ( GABRB3 ) under the dominant and heterozygote models; one each on rs736707 ( RELN ) and rs1544410 ( VDR ) under the recessive and allelic models, respectively; and three meta-analyses on rs607755 ( RELN ), 5-HTTLPR ( SLC6A4 ), and rs7975232 ( VDR ) under the heterozygote model ( P  < 0.10).

Hints of excess-statistical-significance bias were observed in rs2710102 ( CNTNAP2 ) under the allelic, homozygote, and recessive models; rs4680 ( COMT ) under the allelic model; rs20317 ( GABRB3 ) under the heterozygote model; A1298C ( MTHFR ) under allelic, dominant, heterozygote, and recessive models; C677T ( MTHFR ) under homozygote and recessive models; rs736707 ( RELN ) under allelic, dominant, and homozygote models; 5-HTTLPR ( SLC6A4 ) under allelic and recessive models; rs11568820 ( VDR ) under the dominant model; and rs731236 ( VDR ) under the heterozygote model, with statistically significant ( P  < 0.05) excess of positive studies (Table 4 ).

We categorized the strength of the evidence of 20 SNPs for ASD into five levels. According to the criteria for the level of evidence, for rs2710102 ( CNTNAP2 ), the P -value based on the random effects model was significant at P  < 0.05 under allelic, homozygote, and recessive models. Between-study heterogeneity was not significant ( P  > 0.10, I²  < 50.0%), the 95% PI did not exclude the null value, and there was no excess significance bias ( P  > 0.05) under the five genetic models. For rs7794745 ( CNTNAP2 ), the P -value based on the random effects model was significant at P  < 0.05 under dominant and heterozygote models. For C677T ( MTHFR ), there was a total of 2147 ASD cases, which was > 1000, and the P -value based on the random effects model was significant at P  < 10 –3 under allelic, dominant, and heterozygote models. Moreover, it was significant at P  < 0.05 under the homozygote model. Between-study heterogeneity was large ( I²  > 50.0%) under the five genetic models, the 95% PI did not exclude the null value under the five genetic models, and there was no excess significance bias ( P  > 0.05) under allelic, dominant, and heterozygote models. For rs731236 ( VDR ), there was a total of 1088 ASD cases, which was >1000, the P -value based on the random effects model was significant at P  < 10 –3 under allelic and homozygote models, and the P -value was significant at P  < 0.05 under dominant and recessive models. Between-study heterogeneity was not significant ( P  > 0.10, I²  < 50.0%), the 95% PI excluded the null value, and there was no small-study effect ( P  > 0.10) and excess significance bias ( P  > 0.05) under the five genetic models (Table 4 ). Thus, the rs2710102 ( CNTNAP2 ) was graded as weak evidence (class IV) under allelic, homozygote, and recessive models; rs7794745 ( CNTNAP2 ) was graded as weak evidence (class IV) under dominant and heterozygote models; the C677T ( MTHFR ) was graded as suggestive evidence (class III) under allelic, dominant, and heterozygote models; C677T ( MTHFR ) was graded as weak evidence (class IV) under the homozygote model; VDR (rs731236) was graded as suggestive evidence (class III) under allelic and homozygote models; and VDR (rs731236) was graded as weak evidence (class IV) under dominant and recessive models.

This UR summarizes evidence on the genetic basis of ASD. Our study design provides a robust and significant synthesis of published evidence and increases the conclusive power with more precise estimates. Overall, 12 significant SNPs of CNTNAP2 , MTHFR , OXTR , SLC25A12 , and VDR were identified from 41 SNPs of nine candidate genes in 28 meta-analyses. Of those, associations with suggestive evidence (class III) were the C677T polymorphism of MTHFR (under allelic, dominant, and heterozygote models) and rs731236 polymorphism of VDR (under allelic and homozygote models). Associations with weak evidence (class IV) were the rs2710102 polymorphism of CNTNAP2 (under allelic, homozygote, and recessive models), rs7794745 polymorphism of CNTNAP2 (under dominant and heterozygote models), C677T polymorphism of MTHFR (under homozygote model), and rs731236 polymorphism of VDR (under dominant and recessive models).

ASD remains a ‘disease of theories’, as multiple genes and environmental risk factors are probably involved in its pathogenesis. However, to date, the etiology and pathological mechanism of ASD are still unknown [ 57 ]. The genetic architecture of ASD is complex. Moreover, most research in this field has focused on candidate genes, primarily those with a plausible role in the known underlying pathophysiology, including mitochondrial dysfunction, abnormal neurodevelopment, and dysfunction of synapse formation and stability during neurodevelopment [ 58 , 59 ].

CNTNAP2 is a member of neurexin superfamily and is a synaptic protein [ 60 ]. It plays a major role in neural development, crucial for neural circuit assembly [ 61 ]. CNTNAP2 mutations may be linked to the abnormal behavior of ASD by altering synaptic neurotransmission, functional connectivity, and neuronal network activity [ 61 , 62 ]. The rs2710102 and rs7794745 are two common non-coding variants in CNTNAP2 , with four and three meta-analyses reporting the associations with ASD, respectively. The results of the meta-analysis by Uddin et al. were inconsistent with the other authors’ [ 44 ]. We further re-analyzed and categorized the strengths of evidence. Both the rs2710102 and rs7794745 polymorphisms of CNTNAP2 were associated with decreased risk of ASD. The rs2710102 was graded as having a weak association with ASD under allelic, homozygote, and recessive models. The rs7794745 was graded as having a weak association with ASD under dominant and heterozygote models. Therefore, it is likely that the rs2710102 and rs7794745 polymorphisms of CNTNAP2 influence the risk of ASD.

MTHFR is one of the most frequently-researched genes in ASD, with four and eight meta-analyses for A1298C [ 29 , 31 , 32 , 33 ] and C667T [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] polymorphisms, respectively. The A1298C and C667T polymorphisms of MTHFR are associated with reduced enzymatic activity, which affects folate metabolism, and, consequently, fetal brain development [ 29 , 32 , 33 ]. Dysfunction of the brain is indicated in ASD etiology; thus, MTHFR has been the focal point of investigation in this disorder. The meta-analysis by Li et al. was selected because it was the most recent among the examined meta-analyses [ 34 ]. The genotype distributions of the A1298C and C667T polymorphisms of MTHFR in the control group were not found in the HWE, which may be due to selection bias, population stratification, and genotyping errors within the original studies. We found no significant association between the A1298C polymorphism of MTHFR and ASD risk in the five genetic models, which was consistent with the four meta-analyses, indicating that the A1298C polymorphism of MTHFR may not be a risk SNP of ASD. We found that the C667T polymorphism of MTHFR was associated with an increased risk of ASD, graded as having suggestive association under allelic, dominant, and heterozygote models and weak association under the homozygote model. Thus, the C667T polymorphism of MTHFR may confer ASD risk.

OXTR, a neuropeptide gene, is also one of the most frequently-studied genes associated with ASD [ 45 ]. Oxytocin plays an important role in a range of human behaviors, including affiliative behavior to social bonding, and is differentially expressed in the blood of individuals with autism compared to that of non-autistic individuals [ 45 , 63 ]. Three meta-analyses investigated 19 SNPs and ASD risk. Of these, only rs2254298 and rs53576 were analyzed in two meta-analyses [ 45 , 46 ], and the remaining SNPs were unique in one meta-analysis. Three SNPs (rs2268491, rs237887, and rs7632287) were significantly associated with ASD risk [ 45 , 46 ]; however, we failed to determine the credibility of the evidence because of the lack of original data.

RELN encodes a large secreted extracellular matrix protein considered to be involved in neuronal migration, brain structure construction, synapse formation, and stability during neurodevelopment [ 59 ]. Fatemi et al. found decreased levels of reelin mRNA and protein and increased levels of reelin receptors in the brain and plasma of individuals with autism [ 64 ]. Dysfunction of the reelin signaling pathway has been found in ASD, schizophrenia, epilepsy, bipolar disorder, mental retardation, depression, Alzheimer’s disease, and lissencephaly [ 59 , 65 ]. Genetic association studies have been conducted to investigate the associations between SNPs within RELN and ASD with conflicting results. None of the three meta-analyses found significant associations [ 48 , 49 , 50 ]. The meta-analysis by Hernández-García et al. was retained for further analysis of the original studies after comparing publication years and sample sizes of the three meta-analyses [ 50 ]. Hernández-García et al. did not find a significant association between RELN and ASD risk [ 50 ]. In our analysis, because there was no substantial statistical heterogeneity under the five genetic models (all P  > 0.10, I 2  ≤ 50%), a fixed model was applied to pool the effect size. We found that the rs607755 of RELN was associated with ASD risk in allelic, dominant, heterozygote, and homozygote models. This inconsistent result was caused by different pooling methods, indicating that it is necessary to perform an UR to provide a robust synthesis of published evidence and evaluate the importance of genetic factors related to ASD. Our UR results showed that the rs607755 of RELN was not significant when we categorized the strength of the evidence. Thus, it may not be a risk factor for ASD.

SLC25A12 encodes the mitochondrial aspartate/glutamate carrier of the brain, a calcium-binding solute carrier located in the inner mitochondrial membrane that is expressed principally in the heart, brain, and skeletal muscle [ 66 , 67 ]. Rossignol et al. found that individuals with ASD had a significantly higher prevalence of mitochondrial diseases than that of controls, indicating the involvement of mitochondrial dysfunction in ASD [ 58 ]. Thus, an increasing number of genetic studies on ASD have focused on SLC25A12 . However, the results on the association between SNPs of SLC25A12 and ASD risk are inconsistent. Two meta-analyses were performed by Aoki et al. [ 53 ] and Liu et al. [ 54 ], and despite differences in the number of studies between the two meta-analyses, both found a higher risk of ASD in individuals with the mutant allele of rs2056202 or rs2292813. However, we failed to determine the credibility of the evidence because of a lack of original data.

Vitamin D plays a significant role in brain homeostasis, neurodevelopment, and immunological modulation, and its deficiency has been reported in children with ASD [ 68 ]. Hence, changes in the genes involved in the transport or binding of vitamin D may be associated with ASD risk. Notably, vitamin D exerts its effects on genes via the VDR gene, to which changes may be an underlying risk factor for ASD. Sun et al. [ 55 ] and Yang et al. [ 56 ] performed meta-analyses to pool the effect size of inconsistent conclusions from original studies on the associations between SNPs in VDR and ASD risks. We further re-analyzed and categorized the strengths of evidence. The rs731236 polymorphism of VDR was associated with an increased risk of ASD, graded as having a suggestive association under allelic and homozygote models and a weak association under dominant and recessive models without small-study effects, excess significance bias, and large heterogeneity. It is likely that the VDR rs731236 polymorphism influences the risk of ASD.

Our study has some limitations. First, associations between several SNPs and ASD risks under five genetic models or in different populations were not fully assessed in our UR, partly due to insufficient original data. Second, our UR is limited by significant heterogeneity that may be caused by population stratification, study design, and differences in the pattern of linkage disequilibrium structure. Finally, ASD is a complex disorder with different causative factors (multiple genetic and environmental factors). We did not investigate the involvement of environmental factors in ASD. Despite these limitations above, our UR includes its prospective registration with PROSPERO, an extensive search strategy, clear criteria of inclusion and exclusion, duplicated processing by two authors, accurate quality assessment, systematic assessment and critical comparison of meta-analyses, and consistent standards for re-analysis of original data.

In conclusion, our UR summarizes evidence on the genetics of ASD and provides a broad and detailed overview of risk genes for ASD. The rs2710102 and rs7794745 polymorphisms of CNTNAP2 , C677T polymorphism of MTHFR , and rs731236 polymorphism of VDR may confer ASD risk. This study will aid clinicians in decision-making through the use of evidence-based information on the most salient candidate genes relevant to ASD and recommendations for future treatment, prevention, and research.

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Acknowledgements

This study was funded by the Science and Technology Department of Jilin Province (grant number: 20200601010JC).

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Shuang Qiu & Xianling Cong

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Yingjia Qiu

Department of Epidemiology, School of Public Health, Beihua University, Jilin, 132013, Jilin, China

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Study design: S.Q. and X.C. Data collection, analysis, and interpretation: S.Q., Y.Q., and Y.L. Drafting of the manuscript: S.Q. Critical revision of the manuscript: X.C. Approval of the final version for publication: all co-authors.

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Qiu, S., Qiu, Y., Li, Y. et al. Genetics of autism spectrum disorder: an umbrella review of systematic reviews and meta-analyses. Transl Psychiatry 12 , 249 (2022). https://doi.org/10.1038/s41398-022-02009-6

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DOI : https://doi.org/10.1038/s41398-022-02009-6

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BMI indicates body mass index; and KPSC, Kaiser Permanente Southern California.

eTable 1. Characteristics of the Cohort at the Time of the Index Pregnancy

eTable 2. Associations Between Labor Epidural Analgesia (LEA) Use at Delivery and Risk of Autism Spectrum Disorders (ASD) in Offspring (Randomly Select One Child per Family)

• Association of Epidural Analgesia During Labor and Delivery With Autism Spectrum Disorder in Offspring JAMA Original Investigation September 28, 2021 This cohort study assesses the association of maternal use of epidural analgesia during labor and delivery with autism spectrum disorder in offspring using a large population-based data set of term singleton children born in British Columbia, Canada, between April 1, 2000, and December 31, 2014. Gillian E. Hanley, PhD; Celeste Bickford, BSc; Angie Ip, MD; Nancy Lanphear, MD; Bruce Lanphear, MD, MPH; Whitney Weikum, PhD; Lonnie Zwaigenbaum, MD, MSc; Tim F. Oberlander, MD
• Association of Labor Epidural Analgesia With Autism Spectrum Disorder in Children JAMA Original Investigation September 28, 2021 This nationwide retrospective cohort study among children in Denmark assesses whether maternal exposure to epidural analgesia during labor is associated with incidence of autism spectrum disorder in offspring. Anders Pretzmann Mikkelsen, MD; Iben Katinka Greiber, MD; Nikolai Madrid Scheller, MD; Øjvind Lidegaard, MD, DMSc
• Methodologic Concerns With Concluding a Link Between Epidural and Autism Spectrum Disorder JAMA Pediatrics Comment & Response May 1, 2021 Allison Lee, MD, MS; Jean Guglielminotti, MD, PhD; Ruth Landau, MD
• Methodologic Concerns With Concluding a Link Between Epidural and Autism Spectrum Disorder JAMA Pediatrics Comment & Response May 1, 2021 Adina R. Kern-Goldberger, MD, MPH; Heather H. Burris, MD, MPH; Lisa D. Levine, MD, MSCE
• Epidural Analgesia and Autism Spectrum Disorder Risk JAMA Pediatrics Editorial July 1, 2021 Gillian E. Hanley, PhD; Angie Ip, MD; Tim F. Oberlander, MD
• Epidural Labor Analgesia and Offspring Risk of Autism Spectrum Disorders JAMA Pediatrics Original Investigation July 1, 2021 This population-based cohort study examines the association between epidural labor analgesia and offspring risk of autism spectrum disorder, adjusting for a large set of potential confounders, in the Canadian province of Manitoba. Elizabeth Wall-Wieler, PhD; Brian T. Bateman, MD, MSc; Ana Hanlon-Dearman, MD; Leslie L. Roos, PhD; Alexander J. Butwick, MBBS, MS
• More on Epidurals and Autism JAMA Pediatrics Editor's Note July 1, 2021 Dimitri A. Christakis, MD, MPH

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As far as I can make out, the authors of this study were attempting to control for a large number of potential confounders (the abstract mentions 15 geographic, socioeconomic and biological factors) but failed to consider the 2 most intuitively obvious: maternal (i.e. epidural decision maker's) diagnosis of ASD and family history on both parental sides. It is very well established that the genetic/heritable component in autism is very strong. This oversight renders the study findings questionable.

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Qiu C , Lin JC , Shi JM, et al. Association Between Epidural Analgesia During Labor and Risk of Autism Spectrum Disorders in Offspring. JAMA Pediatr. 2020;174(12):1168–1175. doi:10.1001/jamapediatrics.2020.3231

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Association Between Epidural Analgesia During Labor and Risk of Autism Spectrum Disorders in Offspring

• 1 Department of Anesthesiology, Kaiser Permanente Baldwin Park Medical Center, Baldwin Park, California
• 2 Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena
• 3 Department of Internal Medicine, Kaiser Permanente Baldwin Park Medical Center, Baldwin Park, California
• 4 Department of Pediatrics, Kaiser Permanente Baldwin Park Medical Center, Baldwin Park, California
• 5 Department of Obstetrics & Gynecology, Kaiser Permanente Baldwin Park Medical Center, Baldwin Park, California
• 6 Department of Anesthesiology, Wake Forest School of Medicine, Winston-Salem, North Carolina
• Editorial Epidural Analgesia and Autism Spectrum Disorder Risk Gillian E. Hanley, PhD; Angie Ip, MD; Tim F. Oberlander, MD JAMA Pediatrics
• Editor's Note More on Epidurals and Autism Dimitri A. Christakis, MD, MPH JAMA Pediatrics
• Original Investigation Association of Epidural Analgesia During Labor and Delivery With Autism Spectrum Disorder in Offspring Gillian E. Hanley, PhD; Celeste Bickford, BSc; Angie Ip, MD; Nancy Lanphear, MD; Bruce Lanphear, MD, MPH; Whitney Weikum, PhD; Lonnie Zwaigenbaum, MD, MSc; Tim F. Oberlander, MD JAMA
• Original Investigation Association of Labor Epidural Analgesia With Autism Spectrum Disorder in Children Anders Pretzmann Mikkelsen, MD; Iben Katinka Greiber, MD; Nikolai Madrid Scheller, MD; Øjvind Lidegaard, MD, DMSc JAMA
• Comment & Response Methodologic Concerns With Concluding a Link Between Epidural and Autism Spectrum Disorder Allison Lee, MD, MS; Jean Guglielminotti, MD, PhD; Ruth Landau, MD JAMA Pediatrics
• Comment & Response Methodologic Concerns With Concluding a Link Between Epidural and Autism Spectrum Disorder Adina R. Kern-Goldberger, MD, MPH; Heather H. Burris, MD, MPH; Lisa D. Levine, MD, MSCE JAMA Pediatrics
• Original Investigation Epidural Labor Analgesia and Offspring Risk of Autism Spectrum Disorders Elizabeth Wall-Wieler, PhD; Brian T. Bateman, MD, MSc; Ana Hanlon-Dearman, MD; Leslie L. Roos, PhD; Alexander J. Butwick, MBBS, MS JAMA Pediatrics

Question   Is there an association between maternal labor epidural analgesia given for vaginal delivery and risk of autism spectrum disorders in children?

Findings   In this multiethnic population-based clinical birth cohort that included 147 895 children, autism spectrum disorders were diagnosed in 1.9% of the children delivered vaginally with epidural analgesia vs 1.3% of the children delivered vaginally without the exposure, a 37% relative increase in risk that was significant after adjusting for potential confounders.

Meaning   This study suggests that exposure to epidural analgesia for vaginal delivery may be associated with increased risk of autism in children; further research is warranted to confirm the study findings and understand the potential mechanisms.

Importance   Although the safety of labor epidural analgesia (LEA) for neonates has been well documented, the long-term health effects of LEA on offspring remain to be investigated.

Objective   To assess the association between maternal LEA exposure and risk of autism spectrum disorders (ASDs) in offspring.

Design, Setting, and Participants   Data for this retrospective longitudinal birth cohort study were derived from electronic medical records from a population-based clinical birth cohort. A total of 147 895 singleton children delivered vaginally between January 1, 2008, and December 31, 2015, in a single integrated health care system were included. Children were followed up from the age of 1 year until the first date of the following occurrences: clinical diagnosis of ASD, last date of health plan enrollment, death, or the study end date of December 31, 2018.

Exposures   Use and duration of LEA.

Main Outcomes and Measures   The main outcome was clinical diagnosis of ASD. Cox proportional hazards regression analysis was used to estimate the hazard ratio (HR) of ASD associated with LEA exposure.

Results   Among the cohort of 147 895 singleton children (74 425 boys [50.3%]; mean [SD] gestational age at delivery, 38.9 [1.5] weeks), 109 719 (74.2%) were exposed to maternal LEA. Fever during labor was observed in 13 055 mothers (11.9%) in the LEA group and 510 of 38 176 mothers (1.3%) in the non-LEA group. Autism spectrum disorders were diagnosed in 2039 children (1.9%) in the LEA group and 485 children (1.3%) in the non-LEA group. After adjusting for potential confounders, including birth year, medical center, maternal age at delivery, parity, race/ethnicity, educational level, household income, history of comorbidity, diabetes during pregnancy, smoking during pregnancy, preeclampsia or eclampsia, prepregnancy body mass index, gestational weight gain, gestational age at delivery, and birth weight, the HR associated with LEA vs non-LEA exposure was 1.37 (95% CI, 1.23-1.53). Relative to the unexposed group, the adjusted HR associated with LEA exposure of less than 4 hours was 1.33 (95% CI, 1.17-1.53), with LEA exposure of 4 to 8 hours was 1.35 (95% CI, 1.20-1.53), and with LEA exposure of more than 8 hours was 1.46 (95% CI, 1.27-1.69). Within the LEA group, there was a significant trend of ASD risk associated with increasing duration of LEA exposure after adjusting for covariates (HR for linear trend, 1.05 [95% CI, 1.01-1.09] per 4 hours). Adding fever to the model did not change the HR estimate associated with LEA exposure (adjusted HR for LEA vs non-LEA, 1.37 [95% CI, 1.22-1.53]).

Conclusions and Relevance   This study suggests that maternal LEA may be associated with increased ASD risk in children. The risk appears to not be directly associated with epidural-related maternal fever.

Labor epidural analgesia (LEA) is the most commonly administered neuraxial anesthesia for labor pain. 1 In the United States, more than 70% of women receive some form of a neuraxial procedure during labor. Although the effectiveness of neuraxial anesthesia for labor pain management and the safety of neuraxial anesthesia for the fetus and newborns during the perinatal period have been well documented, the long-term effects of neuraxial anesthesia on the offspring are largely unknown. 2 - 4 Limited toxicology and animal studies have shown that standard clinical doses of local anesthetics (LAs) can produce neurotoxic effects and alter normal behavioral development in rhesus monkeys. 5 Recent observational studies with humans found that general anesthesia for cesarean deliveries was associated with an approximately 50% increased risk for children to develop autism spectrum disorders (ASDs) compared with vaginal deliveries. 6 , 7 A recent meta-analysis comprising 61 studies with 20 million deliveries concluded that birth by cesarean delivery was significantly associated with the risk of ASD. 8 However, these studies did not address the potential risk associated with the common use of neuraxial anesthesia for routine vaginal delivery. Given that LEA is currently the criterion standard for labor pain management for routine vaginal delivery, it is critical to assess whether maternal LEA exposure has any long-term association with outcomes in offspring.

The purpose of this study was to assess whether LEA exposure for routine vaginal delivery was associated with ASD risk in offspring. Because LEA can induce fever during delivery, we also assessed the role that maternal fever plays in the association between LEA exposure and risk of ASD in offspring. 9 , 10 Autism spectrum disorder is a neurodevelopmental disorder diagnosed relatively early in life that carries various lifelong disabilities. 11 - 13 The increasing prevalence of ASD is not fully explained by improvement in ascertainment. 14 Genetic and environmental factors both in early life and prenatally are thought to play important roles in the development of ASD. 15 , 16 In this study, we controlled for various important potential confounders while assessing the risk of ASD associated with LEA exposure. Data were derived from a large birth cohort from an integrated health care system with standard clinical practices and comprehensive electronic medical records. Part of the cohort has been used in previous studies of the association between maternal diabetes and risk of ASD in offspring. 17 - 19

This retrospective longitudinal cohort study included singleton children born by vaginal delivery at 28 to 44 weeks’ gestation in Kaiser Permanente Southern California (KPSC) hospitals between January 1, 2008, and December 31, 2015. Kaiser Permanente Southern California is an integrated health care delivery system in which the continuum of prenatal, perinatal, and postnatal care for both mother and baby are standardized. All medical care data, including anesthesia records, have been captured in a systemwide integrated electronic medical record (EMR) data system. Per KPSC guidelines, a brief screening checklist (a modified version of the Checklist for Autism in Toddlers 20 ) is administered to all children between the ages of 18 and 24 months to screen for developmental delays, including ASD. A clinical diagnosis of ASD is based on pediatric developmental specialist evaluations. Methods to obtain the demographic characteristics, covariates, and ASD diagnoses in children have been described in previously published studies on the association between maternal diabetes during pregnancy and risk of ASD in children, which broadly represent the Southern California population. 17 - 19 The KPSC Institutional Review Board approved this study and waived individual participant consent; as it was a data-only study, institutional safeguards to maintain risk were well detailed, including deidentification of patient information, the research involved minimal risk to participants, and the waiver would not adversely affect the rights and welfare of participants.

Screening for ASD did not begin in KPSC until after 1 year of age; therefore, children who did not enroll as KPSC health plan members by 1 year of age were not eligible. Follow-up ended on the first date that any one of the following occurred: clinical diagnosis of ASD, last date of continuous KPSC plan membership, death of the child (any cause), or study end date of December 31, 2018.

All maternal and child data were extracted from the KPSC EMR and birth certificate records and were linked by a unique membership identifier used for all patient care delivery. All data were validated for quality through data plots and frequency tables. Potential outliers and data errors were rectified by cross-checking against historical data in the EMR. Validation of the data was established in previous reports. 17 - 19 , 21

The exposure variable was LEA administered during labor and delivery. This information was extracted from LEA procedure notes and pharmacy data stored in the EMR. The duration of LEA exposure was approximated by the duration between the LEA placement time and the delivery time. Fever was defined as a body temperature of 38 °C or higher at any time between hospital admission and time of delivery for the non-LEA group, and epidural-related maternal fever (ERMF) was defined as a body temperature of ≥38 °C or higher after LEA placement and before the time of delivery for the LEA group.

The outcome measure was the presence or absence of ASD during the follow-up period, which was identified by International Classification of Diseases, Ninth Revision codes 299.x or equivalent KPSC codes. These codes included autistic disorders, Asperger syndrome, or pervasive developmental disorder not otherwise specified and excluded childhood disintegrative disorder and Rett syndrome. Codes from at least 2 separate visits were required for an ASD diagnosis; these codes were validated with a positive predictive value of 88% and used in previous publications. 17 - 19

Covariates to control for potential confounders at the time of the epidural event were maternal social demographic characteristics (age at delivery, parity, educational level, self-reported maternal race/ethnicity, and median family household income based on census tract of residence), medical center of delivery, history of comorbidity (≥1 diagnoses of heart, lung, kidney, or liver disease or cancer), maternal obesity (prepregnancy body mass index and gestational weight gain), diabetes (preexisting type 1 or 2 diabetes or gestational diabetes), preeclampsia or eclampsia, and smoking during pregnancy, as well as child characteristics at delivery (gestational age at delivery, birth weight, sex, and presence of any birth defect).

Maternal characteristics, obstetrical outcomes, and neonatal outcomes were compared between the LEA and non-LEA groups by use of χ 2 tests for proportions and t tests for mean values. The cumulative incidence of ASD in each exposure group was estimated by use of the Kaplan-Meier method. Relative risks of ASD were estimated by hazard ratios (HRs) using Cox proportional hazards regression models in which the time variable is child’s age minus 1. To control for potential correlation owing to multiple siblings born to the same mother, robust SEs were used for statistical testing. Data analyses were also repeated by randomly sampling 1 child per family. The proportional hazard assumption was assessed by examining the log (−log) plot of the survival function vs the log of the child’s age and showed a parallel association and, thus, was not violated. Use of LEA was modeled as a binary (yes or no) variable. Duration of LEA exposure was considered as a categorical variable with the following 3 strata: less than 4 hours, 4 to 8 hours, and more than 8 hours. Birth year was included as a covariate to control for possible confounding due to changes in delivery practice and ASD screening during the study period. Medical center of delivery was included as a covariate to control for geographical variation in LEA use and ASD diagnosis. 1 , 19 , 21 , 22 Maternal social demographic characteristics, history of comorbidity, obesity, diabetes, preeclampsia or eclampsia, and smoking during pregnancy as well as gestational age at delivery and birth weight were included as covariates to adjust for potential confounding. The child’s sex was similarly distributed between the LEA and non-LEA groups; although boys have a much higher ASD prevalence than girls, additionally adjusting for the child’s sex in the data analysis did not change the risk estimates associated with LEA use. Results are presented without the adjustment of the child’s sex.

Primary data analyses used inverse probability of treatment weighting (IPTW) to balance all potential confounders between LEA and non-LEA use as well as standard covariate adjustment. In the IPTW analysis, the propensity of receiving LEA was calculated using a logistic regression model of all covariates included in the adjusted analysis. Sensitivity analyses were conducted by excluding children with preterm birth (defined as gestational age of delivery, <37 weeks) and excluding children with any birth defect. We also reported potential confounding due to unmeasured confounders by computing the E-value. 23 Data analysis was conducted using SAS Enterprise Guide, version 7.1 (SAS Institute Inc) and R, version 3.6.0 (64 bit; R Foundation for Statistical Computing). All P values were from 2-sided tests and results were deemed statistically significant at P  < .05. Point estimates and 95% CIs are presented.

Of the 147 895 children (74 425 boys [50.3%]; mean [SD] gestational age at delivery, 38.9 [1.5] weeks) born to 119 973 unique mothers and included in the data analysis, 109 719 (74.2%) were born to women exposed to LEA. Figure 1 depicts the derivation of the study cohort. The LEA and non-LEA groups differed in all covariates (maternal age, race/ethnicity, parity, educational level, household income, diabetes status, comorbidity, smoking during pregnancy, preeclampsia or eclampsia, prepregnancy body mass index, gestational weight gain, birth weight, gestational age at delivery, and presence of any birth defect) except for sex of the child ( Table 1 ). The LEA group had a higher fever rate than the non-LEA group (13 055 [11.9%] vs 510 [1.3%]; P  < .001), where 1227 mothers [1.1%] in the LEA group had fever prior to LEA.

Of children born to mothers in the LEA group, 32 433 (29.6%) were exposed to LEA for less than 4 hours (median, 2 hours [interquartile range, 1-3 hours]), 50 248 (45.8%) were exposed to LEA for 4 to 8 hours (median, 6 hours [interquartile range, 4-7 hours]), and 27 038 (24.6%) were exposed to LEA for more than 8 hours (median, 11 hours [interquartile range, 10-14 hours]). The ERMF rate increased with increasing duration of LEA exposure (811 of 32 433 [2.5%] for less than 4 hours LEA, 4994 of 50 248 [9.9%] for 4 to 8 hours LEA, and 7250 of 27 038 [26.8%] for more than 8 hours LEA).

A total of 2524 children received a diagnosis of ASD during follow-up: 2039 (1.9%) in the LEA group and 485 (1.3%) in non-LEA group. A total of 527 of 32 433 children (1.6%) who had LEA exposure for less than 4 hours, 911 of 50 248 children (1.8%) who had LEA exposure for 4 to 8 hours, and 601 of 27 038 children (2.2%) who had LEA exposure for more than 8 hours received a diagnosis of ASD. Figure 2 depicts the unadjusted cumulative incidence of ASD by LEA exposure groups.

In the bivariable analysis adjusted for birth year, the HR of ASD associated with LEA relative to non-LEA was 1.48 (95% CI, 1.34-1.65) ( Table 2 ). In the IPTW analyses to balance the covariate distribution between LEA exposure and nonexposure, the risk associated with LEA was 1.38 (95% CI, 1.24-1.53). The stabilized IPTW resulted in a well-balanced covariate distribution between the LEA and non-LEA groups with standardized differences of less than 0.1 for all covariates (eTable 1 in the Supplement ). Including the potential confounders as covariates in the model resulted in an HR of 1.37 (95% CI, 1.23-1.53) ( Table 2 ). Thus, the HRs estimated from the IPTW and covariate adjustment are almost identical.

Table 2 also presents the HRs associated with LEA exposure of less than 4 hours, 4 to 8 hours, and more than 8 hours relative to no LEA exposure, and the linear trend associated with duration of LEA within the LEA group by treating LEA duration as a continuous variable. There was no significant nonlinear association between duration of LEA and ASD risk. After adjusting for potential confounders, the HR associated with LEA exposure of less than 4 hours was 1.33 (95% CI, 1.17-1.53), with LEA exposure of 4 to 8 hours was 1.35 (95% CI, 1.20-1.53), and with LEA exposure of more than 8 hours was 1.46 (95% CI, 1.27-1.69) ( Table 2 ). Within the LEA group, the trend of ASD risk associated with an increased duration of LEA exposure was statistically significant (adjusted HR, 1.05 [95% CI, 1.01-1.09] per 4 hours).

To assess the role that maternal fever plays in the association between LEA and ASD, we excluded the 1227 mothers in the LEA group (1.1%) who had fever before LEA and assessed the association between fever after LEA and risk of ASD within the LEA group. Fever after LEA was not associated with ASD risk after adjusting for the same potential confounders included in the primary analysis for LEA (adjusted HR, 1.03 [95% CI, 0.89-1.20]). Thus, the risk of ASD associated with LEA exposure was not mediated by fever. Adding the presence or absence of any fever to the model for the overall cohort did not change the HR estimate associated with LEA exposure (adjusted HR, 1.37 [95% CI, 1.22-1.53]), and fever itself remained not associated with ASD (adjusted HR, 1.05 [95% CI, 0.91-1.21]).

Analyses limited to 1 child per family gave slightly higher HR estimates, but the overall conclusions remained the same as those in the full cohort analyses (eTable 2 in the Supplement ). Analyses excluding children with preterm birth or children with birth defects at delivery for the full cohort also gave slightly higher HR estimates associated with LEA than the primary analyses ( Table 3 ). In a multivariable adjusted model, LEA exposure was associated with an HR of 1.40 (95% CI, 1.25-1.57) after excluding 8805 children born at less than 37 weeks’ gestation and 1.46 (95% CI, 1.29-1.65) after excluding 18 606 children with any birth defects. The E-value for the HR of 1.37 from the full cohort was 2.08, with a lower confidence of 1.76. Thus, a minimum risk ratio of 1.76 would be required for an unmeasured confounder to be associated with both the exposure and the outcome, conditional on the measured covariates, to fully explain the observed association between LEA exposure and risk of ASD.

In this large cohort comprised of multiethnic births, we found that maternal exposure to LEA was associated with a 37% increased risk of ASD in children after adjusting for potential confounders. Longer duration of epidural exposure was associated with greater ASD risk, in which the risk was 33% greater for LEA exposure of less than 4 hours, 35% greater for LEA exposure of 4 to 8 hours, and 46% greater for LEA exposure of more than 8 hours, compared with the unexposed group. The association with LEA exposure remained at approximately 40% after including only 1 child per family, excluding children with preterm birth or excluding children with birth defects. Despite the higher frequency of fever in the LEA group that was associated with epidural duration, the fever itself appeared to not be associated with ASD risk and did not explain the association between LEA exposure and risk of ASD.

Our findings are intriguing and bring a concern for the safety and long-term health of offspring regarding the short-term epidural use for labor pain. The current evidence on LEA safety was primarily established using the perinatal outcomes of mothers and newborns. 2 , 4 A previous animal study has reported that labor anesthesia drugs can alter normal behavioral development in rhesus monkeys. 5 Limited human studies have reported that anesthesia drug exposure for labor and delivery may be associated with ASD risk in children. 3 , 24 , 25 Using a population-based case-control design, Glasson et al 26 found that labor duration was not associated with ASD risk; however, the mothers of children with ASD were more likely exposed to epidural or caudal anesthesia. In a small survey study, Smallwood et al 24 found that labor and delivery medications were significantly associated with elevated ASD risk, which included epidurally administered medications. To our knowledge, our study is the first large longitudinal birth cohort study that has addressed the association between regional anesthesia of LEA and ASD risk in offspring. Our findings of increased ASD risk associated with LEA are consistent with previous reports. Furthermore, we encountered a novel finding that the risk was increased with increasing duration of exposure to LEA.

Potential mechanisms showing an association between LEA and risk of ASD are largely unknown and require further studies. Although LEA can effectively block labor pain and pain-related hormonal release and changes, 27 , 28 we speculate that its commencement may represent the beginning of a novel maternal and fetal physiology, a new homeostasis, and a dynamic biochemical equilibrium, which encompass the principles of physiology, endocrinology, immunology, pharmacology and toxicology, epigenetics, and psychology. Some mechanisms are transient, but others may be persistent and may affect major body systems. 2 , 4 , 25 , 29 Although LEA can prolong labor, 2 , 4 , 29 longer labor has not been demonstrated to be associated with an increased ASD risk. 24 , 25 , 30 In this study, we found that longer duration of LEA use was associated with a higher ASD risk in the fully adjusted model, suggesting that there may be an association between anesthesia exposure and risk of ASD.

In addition, owing to their low molecular weight, all LAs given epidurally can cross the placenta and be redistributed into the maternal and fetal circulation and thereby may subject both the mother and fetus to the risk of toxic effects. 31 - 34 The latter include abnormalities in synaptogenesis, neurogenesis, and neuronal apoptosis. 35 , 36 These neurotoxic effects have been observed in the usual clinical concentration 37 and have been reported to alter normal behavioral development in rhesus monkeys. 5 Furthermore, the fetus has lower levels of serum protein binding sites, lower blood pH, more porous blood-brain barriers, and immature liver function. These factors, coupled with a larger blood supply to the fetal brain, may converge to potentially greater neurotoxic effects. 38 , 39 Our results suggest that there is a need for further study of the neurodevelopmental effects of LAs beyond ASD.

Labor epidural analgesia may also precipitate maternal immune activation, which is a state of immune dysregulation induced by procedural trauma or by LAs. 9 , 40 , 41 It can be associated with an imbalance of proinflammatory and anti-inflammatory cytokines. In animals, cytokines such as interleukin-6 (IL-6) not only can precipitate and sustain a state of maternal immune activation but also induce fetal neuroinflammation and an ASD-like phenotype. 9 , 42 In humans, maternal IL-6 is associated with neuroinflammatory and morphologic changes in the child’s brain detected on MRI scans. 43 , 44 In this study, we found an association between duration of LEA exposure and rate of ERMF, which was consistent with previous reports. 2 , 4 , 29 However, we did not find that ERMF was associated with a risk of ASD. This result suggests that LEA-associated ASD risk may not be directly linked to ERMF and that other mechanisms may be responsible for the observation. However, the possibility of an association between maternal immune activation and ASD may still exist because maternal immune activation, a potential cause of ERMF, has many overlapping causes. 9 , 10 Furthermore, ERMF is neither sensitive nor specific for maternal immune activation or underlying severity of cytokine abnormalities. 45 , 46

This study has some strengths, including the large and multiethnic birth cohort, well-documented exposure, and outcome. An additional strength was that all data featuring continuous perinatal and pediatric care originated from a single integrated health care delivery system, in which standardized care, documentation, and screening and diagnosis of ASD were carried out systemically. We were thus able to control many confounding factors in this study. Furthermore, although this was a retrospective study, all data were captured prospectively, which we believe minimized the risk of systematic recall or ascertainment biases.

Our study has several limitations, and our findings should be interpreted with caution given the wide varieties of LEA practice and cannot be interpreted as a demonstration of a causal link between LEA exposure and subsequent development of ASD. Although the timing of LEA initiation was precisely established and the duration of exposure was reasonably approximated, in this study, the onset of the pathologic processes of ASD is unknown. Potential uncontrolled confounders may explain the association that we observed. These confounders may include factors both antecedent and subsequent to the peripartum period, such as paternal history, genetic predisposition, viral or bacterial infection, and exposure to other environmental toxins. Furthermore, the variations in the selection and total dosage of LAs, the accumulated dose, the additives such as epinephrine and opioids, and the continuous infusion rates, as well as the timing, frequency, and amount of patient-controlled bolus, may be important aspects of LEA exposure but have not been assessed.

The widespread use of LEA during the past few decades has significantly improved perinatal outcomes for mothers and their newborns; however, our findings raise the concern that the short duration of LEA exposure may be associated with long-term neurodevelopmental disorders in offspring. We believe that further research is warranted to confirm our study findings and to investigate the probable mechanistic association between LEA and ASD.

Accepted for Publication: June 17, 2020.

Corresponding Authors: Chunyuan Qiu, MD, MS, Department of Anesthesiology, Kaiser Permanente Baldwin Park Medical Center, 1011 Baldwin Park Blvd, Baldwin Park, CA 91706 ( [email protected] ); Anny H. Xiang, PhD, Department of Research & Evaluation, Kaiser Permanente Southern California, 100 S Los Robles, Pasadena, CA 91101 ( [email protected] ).

Published Online: October 12, 2020. doi:10.1001/jamapediatrics.2020.3231

Author Contributions: Dr Qiu had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Qiu, Shi, Desai, Nguyen, Feldman, Segal, Xiang.

Acquisition, analysis, or interpretation of data: Qiu, Lin, Shi, Chow, Desai, Nguyen, Riewerts, Segal, Xiang.

Drafting of the manuscript: Qiu, Lin, Desai, Nguyen, Xiang.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Lin, Shi, Segal, Xiang.

Obtained funding: Shi, Xiang.

Administrative, technical, or material support: Qiu, Chow, Desai, Nguyen, Feldman, Segal.

Supervision: Qiu, Xiang.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was partially supported by Kaiser Permanente Southern California Clinical Investigator Program and Direct Community Benefit funds.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The opinions expressed are solely the responsibility of the authors and do not necessarily reflect the official views of the Kaiser Permanente Clinical Investigator Program and Community Benefit Funds.

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Home / News / Education & Society / FSU professor awarded nearly $2 million to develop shared reading intervention for preschoolers with autism spectrum disorder

FSU professor awarded nearly $2 million to develop shared reading intervention for preschoolers with autism spectrum disorder

Florida State University Associate Professor of Special Education Veronica Fleury will help enhance early literacy for preschool children with autism spectrum disorder (ASD) through support from the Institute of Education Sciences.

Fleury and her colleagues from Oklahoma State and Vanderbilt universities will develop a project that includes creating a shared reading intervention for preschool children with ASD that is delivered by caregivers in their homes. The work is supported by nearly $2 million in funding from the National Center for Special Education Research.

“The importance of supporting children’s emergent literacy development cannot be underestimated; children who fall behind in oral language and literacy development are less likely to be successful readers, and their achievement lag is likely to persist throughout primary grades and beyond,” Fleury said. “Reading with young children (referred to as shared book reading) provides them with opportunities to develop language and early literacy skills that are foundational for reading.”

Shared book reading interventions have proven to be an effective method in improving language and early literacy among preschool children. While discrepancies may exist in how responsive different children are to the reading programs, Fleury and her colleagues have the goal of creating an adaptive shared reading program for caregivers to be able to tailor to the learners’ individual needs.

“Shared reading is a social activity,” Fleury added. “Many children with autism will have difficulty actively engaging in shared reading activities because of social communication difficulties that are characteristic of autism. This project will allow us to determine what instructional supports are needed, and for whom.”

“Shared reading is a social activity. Many children with autism will have difficulty actively engaging in shared reading activities because of social communication difficulties that are characteristic of autism. This project will allow us to determine what instructional supports are needed, and for whom.” – Professor Veronica Fleury

The adaptive intervention will use two evidence-based shared reading practices in dialogic reading (DR) and print referencing (PR). DR includes the adult asking the child questions to converse about the story and improve their vocabulary and listening comprehension.

PR involves improving print and alphabet knowledge, where the adult has the child focus explicitly on the print of the story and asks questions, makes comments and tracks words.

Children who are non-responsive to the first-stage interventions will have additional second-stage intervention options designed to increase their own responses about the book or initiate comments during reading.

Using three different types of studies, the research team will conduct a sequential multiple assignment randomized trial to further evaluate the adaptive intervention. PR and DR will then be used through a four-week period, and the children’s joint attention during their final three sessions of their initial intervention will be used to determine what their second-stage intervention option will be.

“A home-based literacy program delivered by caregivers allows us to leverage familiar individuals and routines within children’s natural environments,” Fleury said. “Importantly, caregivers may be empowered by learning new strategies to positively interact with their child with ASD within the context of shared reading.

Fleury’s research at FSU focuses on optimizing learning opportunities for individuals with ASD. Her intervention work with young children with ASD is grounded in applied behavior analysis, and she has been awarded several contracts and grants over her tenure.

The FSU College of Education, Health, and Human Sciences was recently ranked as the best public college of education in the state of Florida by the U.S. News & World Report. It was also rated as the No. 6 public college of education nationally.

The special education program earned a top 15 mark by the U.S. News & World Report and is part of the college’s school of teacher education. Students accepted into the program begin to excel working with others who have ASD, learning disabilities, emotional and behavioral disorders and intellectual disabilities.

For more information about the College of Education, Health, and Human Sciences visit https://cehhs.fsu.edu .

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NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

Grabrucker AM, editor. Autism Spectrum Disorders [Internet]. Brisbane (AU): Exon Publications; 2021 Aug 20. doi: 10.36255/exonpublications.autismspectrumdisorders.2021.diagnosis

Autism Spectrum Disorders [Internet].

Chapter 2 autism spectrum disorders: diagnosis and treatment.

Ronan Lordan , Cristiano Storni , and Chiara Alessia De Benedictis .

Affiliations

The diagnostic criteria and treatment approaches of autism spectrum disorders (ASD) have changed greatly over the years. Currently, diagnosis is conducted mainly by observational screening tools that measure a child’s social and cognitive abilities. The two main tools used in the diagnosis of ASD are DSM-5 and M-CHAT, which examine persistent deficits in interaction and social communication, and analyze responses to “yes/no” items that cover different developmental domains to formulate a diagnosis. Treatment depends on severity and comorbidities, which can include behavioral training, pharmacological use, and dietary supplement. Behavior-oriented treatments include a series of programs that aim to re-condition target behaviors, and develop vocational, social, cognitive, and living skills. However, to date, no single or combination treatments have been able to reverse ASD completely. This chapter provides an overview of the current diagnostic and treatment strategies of ASD.

• INTRODUCTION

Autism spectrum disorders (ASD) are complex, highly heritable neurodevelopmental diseases characterized by individuals with a combination of behavioral and cognitive impairments. These include impaired or diminished social communication skills, repetitive behaviors, and restricted sensory processing or interests ( 1 – 3 ). Swiss psychiatrist Eugen Bleuler first coined the term autism in 1908 to describe symptoms associated with severe schizophrenia, hallucinations, and unconscious fantasy in infants. Since then, the classification, diagnosis, and meaning of autism have radically changed ( 4 ). Between the 1940s and 1980s, ASD was described as abnormalities in language development, display of ritualistic and compulsive behaviors, and disturbance in interpersonal relationships. In the 1970s, sensory deficits in infancy were recognized in autistic children and became a defining feature of ASD ( 4 ). In 1980, the 3 rd edition of the American Psychiatric Association’s (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM)-III), listed autism as a subgroup within the diagnostic category of pervasive developmental disorders (PDD) to convey the view that there is a broader spectrum of social communication deficits. The PPD contained four categories: infantile autism, childhood-onset PDD, residual autism, and an atypical form ( 5 ). At this point, it was recognized that the previously described symptomatology resembling schizophrenia was not a component of ASD because of the research conducted by Kolvin Rutter and others in the early 1970s. Consequently, childhood schizophrenia was excluded from DSM-III ( 1 , 4 ). In the 1980s, Wing and Gould placed autistic children on a continuum with other abnormal children and discussed autism in behavioral terms rather than psychosis ( 4 , 6 ).

Asperger’s syndrome, an ASD named after Hans Asperger, who first described its symptomatology in 1944, gained prominence in the ASD literature due to the works of psychiatrist Lorna Wing, who coined the term in 1976 ( 4 ). In 1981, Wing proposed that autism is part of a wider group of conditions that share commonalities, including impairments of communication, imagination, and social interactions. Asperger’s syndrome was eventually included in the DSM-IV in 1994 ( 7 ). In the mid to late 1980s, works by Simon Baron-Cohen, Uta Frith, and Alan Leslie led to the hypothesis that autistic children lacked “theory of mind”, which is the ability to attribute mental states to others and ourselves, an essential component of social interaction ( 8 ). In 1990, autism was first classified as a disability ( 9 ). Moving to the present day, due to the difficulty in defining and distinguishing between the various PDD, DSM-5 and the International Classification of Diseases 11 th revision use ‘ASD’ as a blanket term and distinguish individuals using clinical specifiers and modifiers ( 1 ). Our knowledge of pathology, etiology, and behavior of ASD continues to evolve. Nowadays, ASD is widely recognized as a somewhat common condition that, for many, but not all, requires lifelong support ( 2 ).

The diagnostic features historically associated with ASD are a triad of impaired social interactions, verbal and nonverbal communication deficits, and restricted, repetitive behavior patterns. These core features are observed irrespective of race, ethnicity, culture, or socioeconomic status. However, ASD individuals tend to differ from one another, so one feature may be more prevalent than another ( 2 , 10 ) (see Chapter 1 ). Despite recent advancements, there are currently no reliable biomarkers for ASD ( 11 ). Consequently, today’s clinical diagnosis of ASD is based on assessing behaviors as outlined in APA’s DSM-5 criteria ( 2 , 12 ). Other disorders that may co-occur with ASD. These include psychiatric disorders such as attention deficit hyperactivity disorder (ADHD), which is considered the most common comorbidity in people with ASD (~ 28%) ( 13 ), along with other conditions and diseases including anxiety and phobias, dissociative disorders, depression, bipolar disorder, and episodic mood disorders ( 13 , 14 ). Physiological disorders (e.g., gastrointestinal disorders) and genetic disorders (e.g., fragile X syndrome) may also be prevalent ( 2 , 14 ).

Hallmarks of ASD and gender prevalence

In 2010, the prevalence of autism was estimated to be 1 in 132 individuals (7.6 per 1,000), affecting approximately 52 million people globally ( 15 ). However, estimates can vary due to the diagnostic methodologies used and the definition of ASD adopted in studies. It was approximated in 2016 that 1 in 54 children in the USA was diagnosed with ASD ( 16 ). Generally, there appears to be epidemiological evidence of ASD sexual dimorphism. ASD is more prevalent in males than females in a ratio of 3:1, ranging from 2:1 to 5:1 ( 17 ). However, it has been proposed that females are more likely to be diagnosed with ASD later than males or may never be diagnosed ( 18 ). Biological determinants are now under investigation to resolve whether females have better adaptation/compensatory behaviors or if diagnostic biases play a role ( 19 , 20 ).

Persistent issues with social communication can manifest in various contexts. For example, ASD individuals may persistently fail to hold a normal conversation or have an unorthodox approach to social situations. Some ASD individuals may also present deficits in non-verbal communication behaviors such as difficulty maintaining eye contact or abnormalities in using or understanding body language or gestures. ASD individuals may also have trouble understanding relationships or social interactions that may lead them to have difficulties developing and maintaining relationships ( 2 ). Repetitive or restricted behavioral patterns include movements, speech, play, use of objects, resistance to change, and insistence on sameness. In addition, fixations on certain interests with abnormal focus or intensity or periods of hyperactivity and hypoactivity in response to sensory inputs are also associated with ASD. These symptoms are mostly present in early life but may not fully manifest until social interaction is warranted. These symptoms can be somewhat masked later in life by coping strategies learned ( 2 ).

Early signs and symptoms

Early identification and evaluation of ASD in children has become an important public health objective due to the potential association between early intervention and improved development of children with ASD ( 21 – 23 ). Early presentation of ASD often occurs due to parental concerns spurred by recognizing some of the hallmarks of ASD previously outlined ( 24 ), which has increased due to greater awareness of ASD hallmarks among parents, healthcare specialists, and childcare workers ( 25 ). Some video studies suggest that it is possible to identify symptoms of ASD in children as young as 6–12 months old ( 26 , 27 ). There is increased interest to monitor the emergence of ASD prodromes such as reduced motor control or abnormal social development in the first year of life ( 24 , 28 ). As research has developed, it is now known that the prevalence of ASD is particularly high in preterm infants ( 29 ), indicating a requirement for additional vigilance in the preterm population.

Diagnostic tools

Numerous diagnostic guidelines of varying quality are available ( 30 ). The essential features of ASD diagnosis include observing a child’s relationship and exchange with their parents and with an individual unknown to the child during unstructured and structured assessment activities and a detailed history of the child’s development ( 1 ). ASD diagnosis can occur at any age but most frequently occurs early in childhood. Although there is a lack of a universal screening instruments, public health systems in various countries in Europe such as Spain and Ireland have programs in place to identify young children with ASD (~ 18–30 months) using M-CHAT (Modified Checklist for Autism in Toddlers) and similar tools ( 31 ). The sensitivity of these screening methods has been questioned as they fail to identify most children with ASD before their parents have already reported delayed development ( 32 ). There may also be racial disparities in early diagnosis of Black and Hispanic children versus white children, which has been reported in the United States. It was found that the first evaluation of ASD in Black children is less likely to occur by 36 months of age than in white children with ASD (40% early evaluation of black children versus 45% of white children) ( 33 ).

Inconsistencies aside, several standardized screening tools exist to diagnose ASD at an early age, many of which focus on high-risk individuals, e.g., with a family member previously diagnosed with ASD ( 24 ). These include the Screening Tool for Autism in Toddlers and Young Children (STAT™), a 20 min observation of young children, established in 2000. The longer and widely researched Autism Diagnostic Observation Schedule (ADOS™) is a 45 min observation conducted by a professional or clinician to diagnose ASD from 12 months to adulthood ( 31 ). There also exists screening tools suitable for research, such as the Diagnostic Instrument for Social Communication Disorders (DISCO) and the Autism Diagnostic Interview-Revised (ADI-R) ( 31 ) in the UK. Other screening tools such as the Social Responsiveness Scale (SRS), the Social Communication Questionnaire (SCQ), and the Childhood Autism Rating Scale (CARS) can be used to assess a child’s symptoms of ASD. While many tools to screen and diagnose ASD exist, two of the leading autism diagnostic tools in use today are DSM-5 and M-CHAT (Modified Checklist for Autism in Toddlers).

Since 2013, DSM-5 has been used as a diagnostic tool for ASD worldwide ( 1 , 12 ). According to DSM-5, to be diagnosed with ASD, a child must have persistent deficits in the following three areas of social communication and interaction: (i) social-emotional reciprocity; (ii) developing, understanding, and maintaining relationships; and (iii) nonverbal communication. In addition, at least two of the following four behaviors should be present: (i) inflexible to changes in routine; (ii) restrictive or fixated interests that may be abnormal in focus or intensity; (iii) hypo- or hyperactivity in response to sensory input or abnormal fixation with sensory aspects of the environment; and (iv) repetitive movements, speech, or use of items. Symptoms should be present early in the development (in some cases symptoms may be masked in early stages and become prevalent later) and cause clinically significant impairment of function. Finally, ASD may be suspected if the symptoms cannot be better explained by other causes of intellectual disability or developmental issues.

DSM-5 is unique in that it classifies ASD as a spectrum that now also includes Asperger’s syndrome. DSM-5 also recognizes that early symptom onset can occur or that the manifestations of ASD may not be recognized until later in childhood or even adulthood, even in those who were monitored early in life ( 34 ). Furthermore, under the repetitive and restrictive diagnoses domain, sensation-seeking behavior, and hypo-sensory and hyper-sensory responsiveness are now included in DSM-5 in contrast to earlier iterations ( 1 , 35 ). DSM-5 also allows for dual diagnoses of ASD and other comorbidities such as ADHD (28% of ASD individuals have ADHD) or other co-occurring conditions such as psychiatric disorders (e.g., anxiety, depression, aggression) or genetic disorders (e.g., fragile X syndrome) ( 31 ). As a result, DSM-5 is one of the most reliable diagnostic tools of ASD and is trusted internationally. DSM-5 is also used by the Centers for Disease Control and Prevention in the USA ( 30 ), UK’s National Institute for Health and Care Excellence Guideline ( 36 ), and New Zealand’s Autism Spectrum Disorder Guidelines ( 37 ).

M-CHAT, derived from the less sensitive Checklist for Autism in Toddlers (CHAT), and the less common Communication and Symbolic Behavior Scales (CSBS) ( 38 ) have become mainstream among parents and even professionals due to their low-cost and accessibility ( 39 ). M-CHAT is reliable and has been independently assessed in primary care settings ( 40 , 41 ). M-CHAT is available internationally in several different languages ( 42 , 43 ), and it can now even be accessed electronically via tablet devices ( 44 ). The M-CHAT is intended to screen children aged between 16 and 30 months. It contains 23 ‘yes/no’ items that span several developmental domains and encompasses an interview with parents to clarify parent questionnaires and reduce the possibility of false positives ( 24 ). This checklist relies on the parent’s report of the child’s behaviors and skills rather than the observations of a professional. Since 2009, the M-CHAT-revised with follow-up (M-CHAT-R/F) has been validated and is used widely ( 39 ). The M-CHAT-R/F) now has 20 ‘yes/no’ items, includes a component for a professional such as a clinician to review, and only necessitates a follow-up interview for those who are perceived to be of medium ASD risk ( 39 ).

Whether and to what extent ASD can or should be treated is a controversial topic, especially considering the noticeable heterogeneity within ASD children. Many approaches are available to improve the abilities and skills, and quality of life of individuals with ASD ( 45 – 48 ). These approaches involve families, clinical practitioners, and educators ( 49 ). However, to date, information on positive outcomes of a specific intervention, and the mechanism that leads to these improvements is scant ( 50 ). In this section, we provide an overview of the current interventional approaches to treat individuals with ASD.

Behavioral therapy

Depending on the severity and comorbidities, many treatment approaches are available but only a few of these approaches are considered ‘evidence-based’ with proven benefits ( 51 ).

Educational and behavioral interventions play a central role in addressing communication, social skills, play, daily living competencies, academic skills, and inappropriate behavior ( 52 – 54 ). The varied symptoms and functioning levels of autistic individuals requires individualized treatments ( 55 ). There is consensus on the importance of providing therapy as soon as possible, immediately after diagnosis or even in the case of suspected diagnosis ( 56 – 61 ). The involvement and training of parents ( 62 – 64 ), siblings, and peers are also important ( 65 ).

Applied Behavior Analysis (ABA) is one of the widely used evidence-based approaches ( 66 ). ABA interventions operate under the principle of re-conditioning target behavior. The main principle is breaking down specific skills or activities into small elements, and teaching these in a progressive and systematic manner through reinforcement. It has shown substantial improvements in language, IQ, and academic skills ( 67 , 68 ). Discrete Trial Training (DTT), Early Intensive Behavioral Interventions (EIBI), Pivotal Response Training (PRT), and Verbal Behavioral Intervention (VBI) are different types of ABA intervention. DTT is for preschool (3–5 years old) individuals, and it is conducted in a classroom setting ( 69 ). It breaks down learning outputs and uses trials of 5 parts to simplifying instructions and teach skills. The parts include cue, prompt, response, consequences, and inter-trial intervals to teach a desired response. EIBI are used for early detection in children who are younger than three years old. VBI involves various protocols that target language and speech ( 70 – 72 ). ABA and DDT are criticized for targeting certain behavior but not the inner motivations underlying such behavior, and its moderate effectiveness in adaptive behavior and socialization ( 73 ). Koegel et al. developed a more naturalistic approach to compensate for some of these limitations: the Pivotal Response Treatment (PRT) ( 73 ). While ABA is highly structured and led by a therapist, PRT targets key areas (rather than individual behavior) such as motivation, self-management, and initiative in social interaction through play-based and child-initiated activities. The goal is to produce a positive change in the pivotal behaviors that are supposed to lead to improvements in social, communication, and play skills. Often used to complement ABA or DTT approaches in structured settings, PRT is offered in natural environments.

TEACCH (Treatment and Education of Autistic and related Communication-Handicapped Children) is commonly used in association with ABA for early intervention ( 74 , 75 ). This framework was developed in the 70s by Scholper and colleagues. It targets the development of vocational, social, and living skills and teaches these skills in a structured environment where a sequence of activities is organized predictably, often associated with visual prompts (e.g., individualized visual schedules), to support the establishment of learning routines. TEACCH can be used across different environments.

Developmental models focus on teaching skills essential to a child’s development, such as emotional relationship and regulation, social communication, and various cognitive abilities. These models usually involve clinical observation of a child’s social responses, review of the child’s developmental history and evaluation of the child’s response to treatments, and in some cases, biomedical evaluation (e.g., genetics). Several developmental models are currently available that show positive outcomes: the Denver Model, the Early Start Denver Model (ESDM), the developmental individual difference (DIR), the Relationship developmental intervention (RDI), and the Responsive Teaching (RT).

The Denver model is one of the most studied developmental models developed initially by Rogers et al. ( 76 ). Therapists focus on deficit areas, particularly at the level of imitation, understanding and sharing emotions, theory of mind, and social perception but follow the developmental sequence of normally developed children. Interventions aim at creating a warm environment and positive relationship between children and adults. Teaching mainly occurs in naturalistic settings, involving parents as co-therapists. As the vital role of early intervention is widely acknowledged and the benefits of the Denver Model appreciated, the model has been adapted to toddlers and preschoolers, giving way to the ESDM. Significant improvements in adaptive behavior, language, and IQ were identified in randomized control trials ( 77 ).

DIR was developed by Dr. Greenspan in the 1980s and his focus was on ‘floor time’ and ‘child-led’ play. DIR also focuses on the child’s development. It comprises a series of strategies to enhance relationships and social/emotional communication to support cognitive and emotional development. Instead of identifying deficits, it focuses on meeting the child at his/her developmental levels (e.g., in terms of shared attention and self-regulation, engagement and relating, back and forth interactions and communications, play and symbolic thinking). It also acknowledges the different sensory and motor profiles of the individual by assessing and working on motor planning and sequencing, sensory processing (visual, auditory, proptioceptual), and modulation. Finally, it leverages the children’s strengths by establishing relationships and environments that support such strengths to develop emotional, social, and cognitive capabilities. Growing evidence seems to support this approach ( 74 , 78 ).

RDI focuses on activities that facilitate interactive behavior and positive engagement in social relationships to motivate the child to learn social skills and sustain social relationships ( 74 ). The program is based on the assumption that autistic children lack flexible thinking, and so it helps them develop dynamic intelligence to cope with changes and new information. RDI has six objectives: emotional referencing, social coordination, declarative language, flexible thinking, relational information processing, foresight and hindsight. Evaluations of this approach seem promising, showing reductions in autistic symptoms and increased mainstream placement ( 79 ).

Other developmental approaches under the label of “Skill-based developmental training” are also available. These include PECS (Picture Exchange Communication System) and PBS (Positive Behavior Support). PECS is used in children who are non-verbal as it is an augmentative communication system based on exchanging flashcards with images (replacing or integrating speech). It is based on the ABA principles of prompt, reinforce, reward success/correct, and error. Evidence supporting this approach is accumulating ( 80 ), but more evidence is needed ( 81 ). PBS is a comprehensive intervention that include ABA, normalization/inclusion movements, and person-centered values ( 82 ). The main goal is to help the children become more autonomous and less dependent on family members and therapists. One of the distinguishing features of this approach is the idea that changes must occur in the social system and the surrounding environment in which the individual is in, rather than the individual alone ( 83 ). This more ‘humanistic’ approach to treatment tries to focus on manipulating antecedent triggers to maladaptive behavior rather than showing the adverse effects of such behavior. Two PBS techniques have been developed: one is called the antecedent-based techniques ( 84 ) and involves the use of visual schedules to build activity patterns and offer choice ( 84 ), and the other focuses on understanding the problem behavior and developing educational strategies and reinforcements to improve lifestyle ( 85 ).

Several approaches with unproven benefits are also available. These include sensory integration therapy, auditory integration, music therapy, and animal-based therapy. Sensory integration therapy focuses on the neurophysiological processing of sensory information, which is known to be different in autistic individuals. The goal is not to teach a skill or correct behavior but to allow the child to interact with an environment in an adaptive way, thus developing a coping mechanism to correct the underlying sensory-motor dysfunctions ( 86 ). The treatment involves engagement of full body movements in environments designed to offer tactile, proprioceptive, gravitational, auditory, visual, and vestibular stimulation. Auditory integration therapy is based on sensory abnormalities and language disorders often associated with auditory issues. Treatment involves exposing children to filtered and modulated music (in terms of volume and pitch). It is based on the assumption that continued exposure to modulated sounds can functionally modify the central auditory processing system, thus impacting language and behavior ( 87 ). Animal-based therapy is another intervention that has generated enthusiasm ( 88 ). There are several types of animal-based intervention, involving dogs, horses, and dolphins. For example, dolphin-therapy consists of interacting with dolphins in captivity ( 51 ). It is believed that these animals can help humans communicate better with one another. Horse-riding therapy is another animal-based intervention based on the idea that it involves multiple functioning domains, including social, cognitive, and gross motor ( 89 ). It is also believed that the movements during riding help children self-regulate and demonstrate improvements in distractibility, attention, and social motivation ( 90 ). Horse-riding is also called exercise intervention (along with jogging, martial arts, swimming, or yoga/dance), which can result in improvements in numerous behavioral outcomes, including stereotypical behavior, social-emotional functioning, cognition, and attention ( 91 ). Music therapy is based on the assumption that certain processes in musical improvisation and coordination with other music players may help autistic individuals develop social interaction and communicative skills. Music therapy may help in the emotional and motivational responses of the involved individuals, though conclusive results are still lacking ( 92 ).

Pharmacological and dietary interventions

The most commonly prescribed drugs for individuals with ASD are Abilify (aripiprazole) and Risperdal (risperidone). While the FDA has approved these drugs for use in individuals with ASD, they have not been developed specifically to treat ASD. For example, aripiprazole is an atypical antipsychotic ( 93 ). In addition, comorbidities such as gastrointestinal problems (reflux, chronic constipation, and diarrhea) occur in 46–85% of children with ASD ( 94 , 95 ). Seizures occur in 11–39% of ASD cases ( 96 ). Sleep problems, depression, emotional reactions and behaviors, sinusitis, headaches, mood swings and bipolar disorders are other observed comorbidities ( 97 ). Melatonin could effectively treat sleep disturbance and insomnia by improving sleep onset ( 98 – 101 ). Pediatric insomnia is also treated using antihistamines, alpha-2-agonists, benzodiazepines, and chloral hydrate ( 102 ). In addition, valproic acid has been used to treat mood swings and bipolar disorders and seizures in people with ASD ( 103 ). Another drug for seizures is dimethylglycine ( 104 ).

The effects of chelation therapy with 2,3-dimercaptosuccinic acid (DMSA) or 2,3-dimercaptopropane-1-sulfonate (DMPS) to bind and eliminate heavy harmful metals ( 105 ), intravenous immunoglobulins to regulate immune response ( 106 , 107 ), and hyperbaric oxygen therapy to decrease the inflammation by increasing the oxygen levels ( 108 ) have been equivocal. Gastrointestinal therapy is a diet program that aims to introduce a gluten-free/casein-free diet, considering that peptides derived from gluten and casein may be involved in the origins of autism. No significant beneficial results were reported after this intervention ( 109 ). Diet interventions also include introducing vitamins and minerals to restore metal homeostasis, which is crucial for the normal neurodevelopment and brain function. Vitamins B6, C, magnesium, and Omega-3 fatty acids may be linked with improvements in the behavior of children with ASD ( 110 – 113 ).

Several experimental therapies are currently in development. For example, the use of ampakines in the treatment of ASD is presently investigated. Ampakines act as positive modulators of synaptic AMPA-type glutamate receptors. Pre-clinical studies have shown that the ampakines CX1837 and CX1739 can improve learning, memory, and social behaviors in animal models of ASD ( 114 ). Insulin-like growth factor 1 (IGF-1) is altered in ASD. Besides many other physiological functions, IGF-1 reduces inflammation by modulating cytokine levels and synapse function. IGF-1 was shown to have beneficial effects in Rett syndrome and ASD ( 115 , 116 ). Similarly, intranasal insulin has shown promising effects in children with Phelan McDermid Syndrome (22q13.3 deletion syndrome), a disorder with frequently occurring autistic behaviors ( 117 , 118 ). Insulin and IGF-1 activate insulin receptors. Intranasal insulin thereby modulates the Ras-MAPK pathway. Trofinetide (NNZ-2566), currently in phase 3 for Rett syndrome and phase 2 for Fragile X syndrome, is a modified form of glypromate, a protein fragment resulting from IGF-1 metabolism in the brain ( 119 ). AMO-01 is another RAS-MAPK modulator that has been shown to rescue the neuronal phenotype in multiple knockout mouse models of intellectual disability. This drug is currently in Phase 2 clinical trials ( 120 ). Thus, targeting IGF-1 signaling seems a promising strategy for the future treatment of ASD.

ASD is a lifelong condition that may result from different genetic and environmental factors. ASD phenotypes vary considerably from one person to another, complicating the diagnosis and treatment strategies. Although significant results have been achieved in the ASD diagnosis, there are no consistent ASD biomarkers at the moment. Over the years, the diagnostic tools have increased. Early identification of children with ASD allows selecting a suitable treatment to improve communication, social and living skills, and reduce maladaptive behaviors and comorbidities. Although significant progress has been made, the therapeutic options to treat individuals with ASD remain limited.

Acknowledgment: This research was supported by funding from NIH (NIH.3247) and NHC (Ref. No. VYB89). Ronan Lordan would like to thank Ms. Eimear Conway for her valuable discussions.

Conflict of interest: The authors declare no potential conflicts of interest with respect to research, authorship, and/or publication of this manuscript.

Copyright and permission statement: The authors confirm that the materials included in this chapter do not violate copyright laws. Where relevant, appropriate permissions have been obtained from the original copyright holder(s), and all original sources have been appropriately acknowledged or referenced.

Doi: https://doi ​.org/10.36255 ​/exonpublications ​.autismspectrumdisorders.2021.diagnosis

Licence: This open access article is licenced under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/

• Cite this Page Lordan R, Storni C, De Benedictis CA. Autism Spectrum Disorders: Diagnosis and Treatment. In: Grabrucker AM, editor. Autism Spectrum Disorders [Internet]. Brisbane (AU): Exon Publications; 2021 Aug 20. Chapter 2. doi: 10.36255/exonpublications.autismspectrumdisorders.2021.diagnosis
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