Autism spectrum disorders affect one in 59 American children by age eight. With no known quantitative biological features, autism diagnoses are currently based on expert assessments of behavioral symptoms, including impaired social skills and communication, repetitive behaviors and restricted interests.
In a paper published in Annals of Neurology, Matthew P. Anderson, MD, Ph.D., a physician-scientist at Beth Israel Deaconess Medical Center (BIDMC), and colleagues report the presence of cellular features consistent with an immune response targeting specialized brain cells in more than two thirds of autistic brains analyzed postmortem.
These cellular characteristics—not previously observed in autism—lend critical new insight into autism’s origins and could pave the way to improved diagnosis and treatment for people with this disorder.
“While further research is needed, determining the neuropathology of autism is an important first step to understanding both its causes and potential treatment,” said Anderson, who is Chief of Neuropathology in the Department of Pathology at BIDMC and an Associate Professor of Pathology at Harvard Medical School.
“Investigators typically aim potential treatments at specific pathologies in brain diseases, such as the tangles and plaques that characterize Alzheimer’s disease and the Lewy bodies seen in Parkinson’s.
Until now, we have not had a promising target like that in autism.“
Anderson was examining brains donated to Autism BrainNet, a non-profit tissue bank, when he noticed the presence of perivascular lymphocyte cuffs – an accumulation of immune cells surrounding blood vessels in the brain.
He also noted mysterious bubbles or blisters that scientists call blebs accumulating around these cuffed blood vessels.
Anderson and colleagues subsequently found these blebs contained debris from a subset of brain cells called astrocytes.
Not previously linked to autism, perivascular lymphocyte cuffing is a well-known indicator of chronic inflammation in the brain.
Lymphocyte cuffs in the brain are telltale signs of viral infections or autoimmune disorders. But the pattern Anderson observed did not match any previously documented infection or autoimmune disorder of the brain.
In the brains Anderson examined, the cuffs were subtle but distinct. “I’ve seen enough brains to know you shouldn’t see that,” he said.
To find out if the perivascular lymphocyte cuffs in this sample of autistic brains were linked to autism spectrum disorder, Anderson and colleagues compared 25 brains from donors diagnosed with the disorder to 30 brains from neurotypical brain donors.
These neurotypical control cases were selected to approximate the age range and medical histories of the autism group. Present in more than two-thirds of the autistic brains, perivascular lymphocyte cuffing significantly surpassed that in the control cases.
In a second set of experiments, Anderson’s team determined that the perivascular cuffs were made up of killer T-cells, a subset of immune cells responsible for attacking and killing damaged, infected or cancerous cells or normal cells in autoimmune diseases.
With no apparent evidence of viruses known to infect the brain, the presence of these tissue-attacking immune cells throughout the autistic brains suggested one of two scenarios, explained Anderson. Either the T-cells are reacting normally to a pathogen such as a virus, or they are reacting abnormally to normal tissue – the definition of an autoimmune disorder.
“With this new research, we haven’t proved causality, but this is one clue in support of the idea that autism might be an autoimmune disorder, just like multiple sclerosis is thought to be,” said Anderson.
In future research, Anderson and colleagues will work to develop a genetically-engineered animal model of this T-lymphocyte cuffing neuropathology in which to conduct studies to determine mechanism as well as cause and effect.
The team also plans to search for biomarkers – a measurable diagnostic signature in patients’ urine or blood or other tissues – that may be used to identify these newly-documented cellular features in living patients.
In turn, these biomarkers could one day assist clinicians in the diagnosis and long-term care of people with autism.
Autism spectrum disorder (ASD) is a developmental disability defined by diagnostic criteria that include deficits in social communication and social interaction, and the presence of restricted, repetitive patterns of behavior, interests, or activities that can persist throughout life (1).
CDC began tracking the prevalence of ASD and characteristics of children with ASD in the United States in 1998 (2,3).
The first CDC study, which was based on an investigation in Brick Township, New Jersey (2), identified similar characteristics but higher prevalence of ASD compared with other studies of that era.
The second CDC study, which was conducted in metropolitan Atlanta, Georgia (3), identified a lower prevalence of ASD compared with the Brick Township study but similar estimates compared with other prevalence studies of that era.
In 2000, CDC established the Autism and Developmental Disabilities Monitoring (ADDM) Network to collect data that would provide estimates of the prevalence of ASD and other developmental disabilities in the United States (4,5).
Tracking the prevalence of ASD poses unique challenges because of the heterogeneity in symptom presentation, lack of biologic diagnostic markers, and changing diagnostic criteria (5).
Initial signs and symptoms typically are apparent in the early developmental period; however, social deficits and behavioral patterns might not be recognized as symptoms of ASD until a child is unable to meet social, educational, occupational, or other important life stage demands (1).
Features of ASD might overlap with or be difficult to distinguish from those of other psychiatric disorders, as described extensively in DSM-5 (1).
Although standard diagnostic tools have been validated to inform clinicians’ impressions of ASD symptomology, inherent complexity of measurement approaches and variation in clinical impressions and decision-making, combined with policy changes that affect eligibility for health benefits and educational programs, complicates identification of ASD as a behavioral health diagnosis or educational exceptionality.
To reduce the influence of these factors on prevalence estimates, the ADDM Network has consistently tracked ASD by applying a surveillance case definition of ASD and using the same record-review methodology and behaviorally defined case inclusion criteria since 2000 (5).
ADDM estimates of ASD prevalence among children aged 8 years in multiple U.S. communities have increased from approximately one in 150 children during 2000–2002 to one in 68 during 2010–2012, more than doubling during this period (6–11).
The observed increase in ASD prevalence underscores the need for continued surveillance using consistent methods to monitor the changing prevalence of ASD and characteristics of children with ASD in the population.
In addition to serving as a basis for ASD prevalence estimates, ADDM data have been used to describe characteristics of children with ASD in the population, to study how these characteristics vary with ASD prevalence estimates over time and among communities, and to monitor progress toward Healthy People 2020 objectives (12).
ADDM ASD prevalence estimates consistently estimated a ratio of approximately 4.5 male:1 female with ASD during 2006–2012 (9–11).
Other characteristics that have remained relatively constant over time in the population of children identified with ASD by ADDM include the median age of earliest known ASD diagnosis, which remained close to 53 months during 2000–2012 (range: 50 months [2012] to 56 months [2002]), and the proportion of children receiving a comprehensive developmental evaluation by age 3 years, which remained close to 43% during 2006–2012 (range: 43% [2006 and 2012] to 46% [2008]).
ASD prevalence by race/ethnicity has been more varied over time among ADDM Network communities (9–11).
Although ASD prevalence estimates have historically been greater among white children compared with black or Hispanic children (13), ADDM-reported white:black and white:Hispanic prevalence ratios have declined over time because of larger increases in ASD prevalence among black children and, to an even greater extent, among Hispanic children, as compared with the magnitude of increase in ASD prevalence among white children (9).
Previous reports from the ADDM Network estimated ASD prevalence among white children to exceed that among black children by approximately 30% in 2002, 2006, and 2010, and by approximately 20% in 2008 and 2012.
Estimated prevalence among white children exceeded that among Hispanic children by nearly 70% in 2002 and 2006, and by approximately 50% in 2008, 2010, and 2012. ASD prevalence estimates from the ADDM Network also have varied by socioeconomic status (SES).
A consistent pattern observed in ADDM data has been higher identified ASD prevalence among residents of neighborhoods with higher socioeconomic status (SES).
Although ASD prevalence has increased over time at all levels of SES, the absolute difference in prevalence between high, middle, and lower SES did not change from 2002 to 2010 (14,15). In the context of declining white:black and white:Hispanic prevalence ratios amidst consistent SES patterns, a complex three-way interaction among time, SES, and race/ethnicity has been proposed (16).
Finally, ADDM Network data have shown a shift toward children with ASD with higher intellectual ability (9–11), as the proportion of children with ASD whose intelligence quotient (IQ) scores fell within the range of intellectual disability (ID) (i.e., IQ <70) has decreased gradually over time.
During 2000–2002, approximately half of children with ASD had IQ scores in the range of ID; during 2006–2008, this proportion was closer to 40%; and during 2010–2012, less than one third of children with ASD had IQ ≤70 (9–11). This trend was more pronounced for females as compared with males (9).
The proportion of males with ASD and ID declined from approximately 40% during 2000–2008 (9) to 30% during 2010–2012 (10,11). The proportion of females with ASD and ID declined from approximately 60% during 2000–2002, to 45% during 2006–2008, and to 35% during 2010–2012 (9–11).
All previously reported ASD prevalence estimates from the ADDM Network were based on a surveillance case definition aligned with DSM-IV-TR diagnostic criteria for autistic disorder; pervasive developmental disorder–not otherwise specified (PDD-NOS, including atypical autism); or Asperger disorder.
In the American Psychiatric Association’s 2013 publication of DSM-5, substantial changes were made to the taxonomy and diagnostic criteria for autism (1,17).
Taxonomy changed from Pervasive Developmental Disorders, which included multiple diagnostic subtypes, to autism spectrum disorder, which no longer comprises distinct subtypes but represents one singular diagnostic category defined by level of support needed by the individual. Diagnostic criteria were refined by collapsing the DSM-IV-TR social and communication domains into a single, combined domain for DSM-5. Persons diagnosed with ASD under DSM-5 must meet all three criteria under the social communication/interaction domain (i.e., deficits in social-emotional reciprocity; deficits in nonverbal communicative behaviors; and deficits in developing, understanding, and maintaining relationships) and at least two of the four criteria under the restrictive/repetitive behavior domain (i.e., repetitive speech or motor movements, insistence on sameness, restricted interests, or unusual response to sensory input).
Although the DSM-IV-TR criteria proved useful in identifying ASD in some children, clinical agreement and diagnostic specificity in some subtypes (e.g., PDD-NOS) was poor, offering empirical support to the notion of two, rather than three, diagnostic domains.
The DSM-5 introduced a framework to address these concerns (18), while maintaining that any person with an established DSM-IV-TR diagnosis of autistic disorder, Asperger disorder, or PDD-NOS would automatically qualify for a DSM-5 diagnosis of autism spectrum disorder.
Previous studies suggest that DSM-5 criteria for ASD might exclude certain children who would have qualified for a DSM-IV-TR diagnosis but had not yet received one, particularly those who are very young and those without ID (19–23). These findings suggest that ASD prevalence estimates will likely be lower under DSM-5 than they have been under DSM-IV-TR diagnostic criteria.
This report provides the latest available ASD prevalence estimates from the ADDM Network based on both DSM-IV-TR and DSM-5 criteria and asserts the need for future monitoring of ASD prevalence trends and efforts to improve early identification of ASD.
The intended audiences for these findings include pediatric health care providers, school psychologists, educators, researchers, policymakers, and program administrators working to understand and address the needs of persons with ASD and their families. These data can be used to help plan services, guide research into risk factors and effective interventions, and inform policies that promote improved outcomes in health and education settings.
More information: Marcello M. DiStasio et al, T‐lymphocytes and Cytotoxic Astrocyte Blebs Correlate Across Autism Brains, Annals of Neurology (2019). DOI: 10.1002/ana.25610
Journal information: Annals of Neurology
Provided by Beth Israel Deaconess Medical Center
