Short report: Transition to International Classification of Diseases, 10th Revision and the prevalence of autism in a cohort of healthcare systems.

LAY ABSTRACT: Currently, the prevalence of autism spectrum disorder (henceforth "autism") is 1 in 36, an increasing trend from previous estimates. In 2015, the United States adopted a new version (International Classification of Diseases, 10th Revision) of the World Health Organization coding system, a standard for classifying medical conditions. Our goal was to examine how the transition to this new coding system impacted autism diagnoses in 10 healthcare systems. We obtained information from electronic medical records and insurance claims data from July 2014 through December 2016 for each healthcare system. We used member enrollment data for 30 consecutive months to observe changes 15 months before and after adoption of the new coding system. Overall, the rates of autism per 1000 enrolled members was increasing for 0- to 5-year-olds before transition to International Classification of Diseases, 10th Revision and did not substantively change after the new coding was in place. There was variation observed in autism diagnoses before and after transition to International Classification of Diseases, 10th Revision for other age groups. The change to the new coding system did not meaningfully affect autism rates at the participating healthcare systems. The increase observed among 0- to 5-year-olds is likely indicative of an ongoing trend related to increases in screening for autism rather than a shift associated with the new coding.

Sensory and Social Subtypes of Japanese Individuals with Autism Spectrum Disorders.

Studies have proposed that individuals with autism spectrum disorder (ASD) can be divided into several subtypes depending on their sensory features. However, consideration of social communication features is also crucial for configuring ASD subtypes, because social and sensory features are tightly interrelated. In this study, we asked Japanese individuals with ASD to answer the Short Sensory Profile (SSP) and the Social Responsiveness Scale, Second Edition (SRS-2), which measure sensory and social aspects, respectively. Consequent latent profile analysis demonstrated that the participants could be divided into five subgroups: two groups exhibited opposite or inconsistent patterns between the SSP and SRS-2 scores, while the other groups exhibited consistent patterns. Our findings indicate the existence of diverse phenotypes in individuals with ASD.

Abnormal mTOR Activity in Pediatric Autoimmune Neuropsychiatric and MIA-Associated Autism Spectrum Disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by the early onset of communication and behavioral problems. ASD is highly heritable; however, environmental factors also play a considerable role in this disorder. A significant part of both syndromic and idiopathic autism cases could be attributed to disorders caused by mammalian target of rapamycin (mTOR)-dependent translation deregulation. This narrative review analyzes both bioinformatic and experimental evidence that connects mTOR signaling to the maternal autoantibody-related (MAR) autism spectrum and autoimmune neuropsychiatric disorders simultaneously. In addition, we reconstruct a network presenting the interactions between the mTOR signaling and eight MAR ASD genes coding for ASD-specific maternal autoantibody target proteins. The research discussed in this review demonstrates novel perspectives and validates the need for a subtyping of ASD on the grounds of pathogenic mechanisms. The utter necessity of designing ELISA-based test panels to identify all antibodies related to autism-like behavior is also considered.

Contribution of Multiple Inherited Variants to Autism Spectrum Disorder (ASD) in a Family with 3 Affected Siblings.

Autism Spectrum Disorder (ASD) is the most common neurodevelopmental disorder in children and shows high heritability. However, how inherited variants contribute to ASD in multiplex families remains unclear. Using whole-genome sequencing (WGS) in a family with three affected children, we identified multiple inherited DNA variants in ASD-associated genes and pathways (RELN, SHANK2, DLG1, SCN10A, KMT2C and ASH1L). All are shared among the three children, except ASH1L, which is only present in the most severely affected child. The compound heterozygous variants in RELN, and the maternally inherited variant in SHANK2, are considered to be major risk factors for ASD in this family. Both genes are involved in neuron activities, including synaptic functions and the GABAergic neurotransmission system, which are highly associated with ASD pathogenesis. DLG1 is also involved in synapse functions, and KMT2C and ASH1L are involved in chromatin organization. Our data suggest that multiple inherited rare variants, each with a subthreshold and/or variable effect, may converge to certain pathways and contribute quantitatively and additively, or alternatively act via a 2nd-hit or multiple-hits to render pathogenicity of ASD in this family. Additionally, this multiple-hits model further supports the quantitative trait hypothesis of a complex genetic, multifactorial etiology for the development of ASDs.

Accurate assessment of low-function autistic children based on EEG feature fusion.

Autism spectrum disorder (ASD) is a very serious neurodevelopmental disorder and diagnosis mainly depends on the clinical scale, which has a certain degree of subjectivity. It is necessary to make accurate evaluation by objective indicators. In this study, we enrolled 96 children aged from 3 to 6 years: 48 low-function autistic children (38 males and 10 females; mean±SD age: 4.9±1.1 years) and 48 typically developing (TD) children (38 males and 10 females; mean±SD age: 4.9 ± 1.2 years) to participate in our experiment. We investigated to fuse multi-features (entropy, relative power, coherence and bicoherence) to distinguish low-function autistic children and TD children accurately. Minimum redundancy maximum correlation algorithm was used to choose the features and support vector machine was used for classification. Ten-fold cross validation was used to test the accuracy of the model. Better classification result was obtained. We tried to provide a reliable basis for clinical evaluation and diagnosis for ASD.

Characterization of Autism Spectrum Disorder (ASD) subtypes based on the relationship between motor skills and social communication abilities.

Motor abnormalities are generally observed in autism spectrum disorder (ASD), and motor difficulties are certainly evident during the early years of life and may thus precede social-communication impairments. The main aim of the present study was to examine ASD subtypes based on the relationship between motor skills and social communication abilities. Motor skills and social communication abilities were evaluated through the Movement Assessment Battery for Children-Second Edition, the Autism Diagnostic Observation Schedule-Second Version and the Psychoeducational Profile-Third Edition. In addition, social communication abilities were classified according to the Autism Classification System of Functioning: Social Communication-ACSF:SC criteria. We found that children with ASD presented poorer motor skills than their TD peers, and motor impairments correlated with poorer social communication abilities in children with ASD. In addition, children with lower social and communication functioning showed a more prominent impairment in manual dexterity and fine motor skills than children with better social and communication functioning. In conclusion, we suggest that stratifying children with ASD based on motor and social endophenotypes may be useful to understand the neurobiological mechanisms of ASD and lead to new types of treatment.

Motor and sensory features successfully decode autism spectrum disorder and combine with the original RDoC framework to boost diagnostic classification.

Sensory processing and motor coordination atypicalities are not commonly identified as primary characteristics of autism spectrum disorder (ASD), nor are they well captured in the NIMH's original Research Domain Criteria (RDoC) framework. Here, motor and sensory features performed similarly to RDoC features in support vector classification of 30 ASD youth against 33 typically developing controls. Combining sensory with RDoC features boosted classification performance, achieving a Matthews Correlation Coefficient (MCC) of 0.949 and balanced accuracy (BAcc) of 0.971 (p = 0.00020, calculated against a permuted null distribution). Sensory features alone successfully classified ASD (MCC = 0.565, BAcc = 0.773, p = 0.0222) against a clinically relevant control group of 26 youth with Developmental Coordination Disorder (DCD) and were in fact required to decode against DCD above chance. These findings highlight the importance of sensory and motor features to the ASD phenotype and their relevance to the RDoC framework.

Comparison of 2 Case Definitions for Ascertaining the Prevalence of Autism Spectrum Disorder Among 8-Year-Old Children.

The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year-old children in multiple US communities. From 2000 to 2016, investigators at ADDM Network sites classified ASD from collected text descriptions of behaviors from medical and educational evaluations which were reviewed and coded by ADDM Network clinicians. It took at least 4 years to publish data from a given surveillance year. In 2018, we developed an alternative case definition utilizing ASD diagnoses or classifications made by community professionals. Using data from surveillance years 2014 and 2016, we compared the new and previous ASD case definitions. Compared with the prevalence based on the previous case definition, the prevalence based on the new case definition was similar for 2014 and slightly lower for 2016. Sex and race/ethnicity prevalence ratios were nearly unchanged. Compared with the previous case definition, the new case definition's sensitivity was 86% and its positive predictive value was 89%. The new case definition does not require clinical review and collects about half as much data, yielding more timely reporting. It also more directly measures community identification of ASD, thus allowing for more valid comparisons among communities, and reduces resource requirements while retaining measurement properties similar to those of the previous definition.

The DSM-5 Clinical and Public Health Committee (CPHC): operations, mechanics, controversies and recommendations.

BACKGROUND: For DSM - 5, the American Psychiatric Association Board of Trustees established a robust vetting and review process that included two review committees that did not exist in the development of prior DSMs, the Scientific Review Committee (SRC) and the Clinical and Public Health Committee (CPHC). The CPHC was created as a body that could independently review the clinical and public health merits of various proposals that would fall outside of the strictly defined scientific process. METHODS: This article describes the principles and issues which led to the creation of the CPHC, the composition and vetting of the committee, and the processes developed by the committee - including the use of external reviewers. RESULTS: Outcomes of some of the more involved CPHC deliberations, specifically, decisions concerning elements of diagnoses for major depressive disorder, autism spectrum disorder, catatonia, and substance use disorders, are described. The Committee's extensive reviews and its recommendations regarding Personality Disorders are also discussed. CONCLUSIONS: On the basis of our experiences, the CPHC membership unanimously believes that external review processes to evaluate and respond to Work Group proposals is essential for future DSM efforts. The Committee also recommends that separate SRC and CPHC committees be appointed to assess proposals for scientific merit and for clinical and public health utility and impact.

Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network.

With the increasing prevalence of autism spectrum disorder (ASD), it is important to identify ASD patients for effective treatment and intervention, especially in early childhood. Neuroimaging techniques have been used to characterize the complex biomarkers based on the functional connectivity anomalies in the ASD. However, the diagnosis of ASD still adopts the symptom-based criteria by clinical observation. The existing computational models tend to achieve unreliable diagnostic classification on the large-scale aggregated data sets. In this work, we propose a novel graph-based classification model using the deep belief network (DBN) and the Autism Brain Imaging Data Exchange (ABIDE) database, which is a worldwide multisite functional and structural brain imaging data aggregation. The remarkable connectivity features are selected through a graph extension of K -nearest neighbors and then refined by a restricted path-based depth-first search algorithm. Thanks to the feature reduction, lower computational complexity could contribute to the shortening of the training time. The automatic hyperparameter-tuning technique is introduced to optimize the hyperparameters of the DBN by exploring the potential parameter space. The simulation experiments demonstrate the superior performance of our model, which is 6.4% higher than the best result reported on the ABIDE database. We also propose to use the data augmentation and the oversampling technique to identify further the possible subtypes within the ASD. The interpretability of our model enables the identification of the most remarkable autistic neural correlation patterns from the data-driven outcomes.

A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder.

Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with >80% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.

Brief Report: Classifying Rates of Students with Autism and Intellectual Disability in North Carolina: Roles of Race and Economic Disadvantage.

We examined special education classifications among students aged 3-21 in North Carolina public schools, highlighting autism spectrum disorder (ASD) and intellectual disability (ID). Results revealed variability by county in ASD and ID prevalence, and in county-level ratios of ID vs. ASD classifications. Sociodemographic characteristics predicted proportion of ASD or ID within a county; correlations showed an association between race and ID, but not ASD. County's median household income predicted proportion of students classified as ASD and ID (opposite directions), controlling for number of students and gender. Variability was unlikely related to biological incidence, and more likely related to district/school practices, or differences in resources. Disparities warrant further examination to ensure that North Carolina's youth with disabilities access necessary, appropriate resources.

Early Neurodevelopmental Trajectories for Autism Spectrum Disorder in Children Born Very Preterm.

BACKGROUND: Children born preterm are at high risk for autism spectrum disorder (ASD). However, there is still a lack of appropriate developmental markers. In this study, we aim to examine whether early mental performance trajectory is related to ASD outcome in the preterm population. METHODS: The population-based cohort included 414 very preterm survivors born between 2008 and 2014. After excluding children with severe neurosensory impairment, 319 children with available records of developmental quotients before age 2 years were enrolled. The trajectory of mental performance evaluated by using the Bayley Scales of Infant Development across 6, 12, and 24 months of age was analyzed with group-based trajectory modeling. At 5 years of age, the ASD diagnosis was established by using the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview-Revised. RESULTS: There were 29 children with ASD and 290 children without ASD. The mental performances from age 6 to 24 months could be classified into 3 trajectory patterns: low declining, high declining, and high stable, which corresponded to ASD prevalence at age 5 years of 35%, 9%, and 3%, respectively. ASD odds was 15 times higher in the low-declining group than in the high-stable group (odds ratio 15; 95% confidence interval 3.8-59; P < .001). Through the analysis of multinomial logistic regression, we found that male infants with longer exposure to oxygen therapy whose mothers had lower maternal education levels tended to follow the low-declining trajectory. CONCLUSIONS: The early-life mental trajectory patterns, by using the Bayley Scales of Infant Development, may lead to identification of vulnerable children born preterm for early ASD diagnosis and targeted intervention.

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

[From autism spectrum to autism constellation]. / Del espectro autista a la constelación autista.

Research on autism and mental disorders has been unsuccessful over the past few decades, as can be inferred from the poor results related to advances in other diseases. It is concerning that, after more than a half century of research based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), no biological markers have been found to prove the validity of the DSM mental disorders. Criticisms to DSM have been focused mainly on the categorical conceptualization, false comorbidity and the polythetic nature of diagnostic criteria. The lack of validity of the DSM model requests for a change in research designs, in order to overcome the problems derived from a paradigm that has stopped to be productive. In the field of clinical practice, it is even more pressing a change of mindset in order to incorporate the heterogeneity of endophenotypes that overflows the classification of the DSM, to adopt a dimensional perspective of mental problems and to develop an alternative interpretation for comorbidity. Related to research are suggested designs based on Domain Research Criteria and a multifactorial analysis with very large samples (big data). For clinical practice it is suggested a dimensional approach based on the specificities of each person with autism.

La investigación sobre el autismo, y sobre los trastornos mentales en general, ha sido poco fructífera durante las últimas décadas, como se desprende de los escasos resultados obtenidos en comparación con los avances en otras enfermedades. Preocupa que, tras más de medio siglo de investigación basada en el Diagnostic and Statistical Manual of Mental Disorders (DSM), no se hayan encontrado marcadores biológicos que acrediten la validez de los trastornos mentales que lo configuran. Las críticas al DSM, todas ellas aplicables al autismo, se han centrado principalmente en la conceptualización categórica, en la falsa comorbilidad y en el carácter politético de los criterios diagnósticos. La falta de validez del modelo del DSM insta a un cambio en los diseños de investigación, con el fin de superar el bloqueo derivado de un paradigma que ha dejado de ser productivo. En el terreno de la práctica clínica resulta, incluso más apremiante, un cambio de mentalidad que permita: incorporar la heterogeneidad de endofenotipos que desbordan la clasificación del DSM, adoptar una perspectiva dimensional de los problemas mentales y desarrollar una interpretación alternativa de la comorbilidad Con referencia a la investigación, se proponen diseños basados en criterios de investigación por dominios (Research Domain Criteria) y en análisis multifactoriales con muestras muy grandes (big data). Por lo que respecta a práctica clínica se sugiere un enfoque dimensional basado en las especificidades de cada persona con autismo, lo cual desborda el patrón clínico del espectro.

Exploring Social Subtypes in Autism Spectrum Disorder: A Preliminary Study.

Impairments in social functioning are considered a hallmark diagnostic feature of autism spectrum disorder (ASD). Yet, individuals diagnosed with ASD vary widely with respect to specific presentation, severity, and course across different dimensions of this complex symptom domain. The aim of this investigation was to utilize the Stanford Social Dimensions Scale (SSDS), a newly developed quantitative measure providing parental perspective on their child's social abilities, in order to explore the existence of homogeneous subgroups of ASD individuals who share unique profiles across specific dimensions of the social domain. Parents of 164 individuals with ASD (35 females, 129 males; meanage = 7.54 years, SD = 3.85) completed the SSDS, the Social Responsiveness Scale (SRS-2) and the Child Behavior Checklist (CBCL). Data on children's verbal and nonverbal intellectual functioning (FSIQ) were also collected. The Latent Profile Analysis was used to classify participants according to the pattern of SSDS subscale scores (Social Motivation, Social Affiliation, Expressive Social Communication, Social Recognition, and Unusual Approach). Five profiles were identified. Profiles did not differ in terms of chronological age nor gender distribution but showed distinct patterns of strengths and weaknesses across different social components rather than simply reflecting a severity gradient. Profiles were further differentiated in terms of cognitive ability, as well as ASD and internalizing symptom severity. The implications of current findings and the necessary further steps toward identifying subgroups of individuals with ASD who share particular constellation of strengths and weaknesses across key social domains as a way of informing personalized interventions are discussed. Autism Res 2020. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: People with autism spectrum disorder (ASD) vary greatly in terms of their social abilities and social motivation. However, researchers lack measures that can fully assess different components of social functioning. This paper provides initial evidence for capturing subgroups of individuals with ASD with specific strengths and weakness across different aspects of social functioning. Autism Res 2020, 13: 1335-1342. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.

Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To support an SIB monitoring system for autism spectrum disorder (ASD), we evaluated machine learning methods for detecting and distinguishing diverse SIB types. SIB episodes were captured with body-worn accelerometers from children with ASD and SIB. The highest detection accuracy was found with k-nearest neighbors and support vector machines (up to 99.1% for individuals and 94.6% for grouped participants), and classification efficiency was quite high (offline processing at ~ 0.1 ms/observation). Our results provide an initial step toward creating a continuous and objective smart SIB monitoring system, which could in turn facilitate the future care of a pervasive concern in ASD.

Del espectro autista a la constelación autista / From autism spectrum to autism constellation

La investigación sobre el autismo, y sobre los trastornos mentales en general, ha sido poco fructífera durante las últimas décadas, como se desprende de los escasos resultados obtenidos en comparación con los avances en otras enfermedades. Preocupa que, tras más de medio siglo de investigación basada en el Diagnostic and Statistical Manual of Mental Disorders (DSM), no se hayan encontrado marcadores biológicos que acrediten la validez de los trastornos mentales que lo configuran. Las críticas al DSM, todas ellas aplicables al autismo, se han centrado principalmente en la conceptualización categórica, en la falsa comorbilidad y en el carácter politético de los criterios diagnósticos. La falta de validez del modelo del DSM insta a un cambio en los diseños de investigación, con el fin de superar el bloqueo derivado de un paradigma que ha dejado de ser productivo. En el terreno de la práctica clínica resulta, incluso más apremiante, un cambio de mentalidad que permita: incorporar la heterogeneidad de endofenotipos que desbordan la clasificación del DSM, adoptar una perspectiva dimensional de los problemas mentales y desarrollar una interpretación alternativa de la comorbilidad Con referencia a la investigación, se proponen diseños basados en criterios de investigación por dominios (Research Domain Criteria) y en análisis multifactoriales con muestras muy grandes (big data). Por lo que respecta a práctica clínica se sugiere un enfoque dimensional basado en las especificidades de cada persona con autismo, lo cual desborda el patrón clínico del espectro.

Research on autism and mental disorders has been unsuccessful over the past few decades, as can be inferred from the poor results related to advances in other diseases. It is concerning that, after more than a half century of research based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), no biological markers have been found to prove the validity of the DSM mental disorders. Criticisms to DSM have been focused mainly on the categorical conceptualization, false comorbidity and the polythetic nature of diagnostic criteria. The lack of validity of the DSM model requests for a change in research designs, in order to overcome the problems derived from a paradigm that has stopped to be productive. In the field of clinical practice, it is even more pressing a change of mindset in order to incorporate the heterogeneity of endophenotypes that overflows the classification of the DSM, to adopt a dimensional perspective of mental problems and to develop an alternative interpretation for comorbidity. Related to research are suggested designs based on Domain Research Criteria and a multifactorial analysis with very large samples (big data). For clinical practice it is suggested a dimensional approach based on the specificities of each person with autism.

A clustering approach for autistic trait classification.

Machine learning (ML) techniques can be utilized by physicians, clinicians, as well as other users, to discover Autism Spectrum Disorder (ASD) symptoms based on historical cases and controls to enhance autism screening efficiency and accuracy. The aim of this study is to improve the performance of detecting ASD traits by reducing data dimensionality and eliminating redundancy in the autism dataset. To achieve this, a new semi-supervised ML framework approach called Clustering-based Autistic Trait Classification (CATC) is proposed that uses a clustering technique and that validates classifiers using classification techniques. The proposed method identifies potential autism cases based on their similarity traits as opposed to a scoring function used by many ASD screening tools. Empirical results on different datasets involving children, adolescents, and adults were verified and compared to other common machine learning classification techniques. The results showed that CATC offers classifiers with higher predictive accuracy, sensitivity, and specificity rates than those of other intelligent classification approaches such as Artificial Neural Network (ANN), Random Forest, Random Trees, and Rule Induction. These classifiers are useful as they are exploited by diagnosticians and other stakeholders involved in ASD screening.

The adult outcome of children referred for autism: typology and prediction from childhood.

BACKGROUND: Autism Spectrum Disorder is highly heterogeneous, no more so than in the complex world of adult life. Being able to summarize that complexity and have some notion of the confidence with which we could predict outcome from childhood would be helpful for clinical practice and planning. METHODS: Latent class profile analysis is applied to data from 123 participants from the Early Diagnosis Study (Lord et al., Archives of General Psychiatry, 2006, 63, 694) to summarize in a typology the multifacetted early adult outcome of children referred for autism around age 2. The form of the classes and their predictability from childhood is described. RESULTS: Defined over 15 measures, the adult outcomes were reduced to four latent classes, accounting for much of the variation in cognitive and functional measures but little in the affective measures. The classes could be well and progressively more accurately predicted from childhood IQ and symptom severity measurement taken at age 2 years to age 9 years. Removing verbal and nonverbal IQ and autism symptom severity measurement from the profile of adult measures did not change the number of the latent classes; however, there was some change in the class composition and they were more difficult to predict. CONCLUSIONS: While an empirical summary of adult outcome is possible, careful consideration needs to be given to the aspects that should be given priority. An outcome typology that gives weight to cognitive outcomes is well predicted from corresponding measures taken in childhood, even after account for prediction bias from fitting a complex model to a small sample. However, subjective well-being and affective aspects of adult outcome were weakly related to functional outcomes and poorly predicted from childhood.