Clinical correlates of diagnostic certainty in children and youths with Autistic Disorder.

BACKGROUND: Clinicians diagnosing autism rely on diagnostic criteria and instruments in combination with an implicit knowledge based on clinical expertise of the specific signs and presentations associated with the condition. This implicit knowledge influences how diagnostic criteria are interpreted, but it cannot be directly observed. Instead, insight into clinicians' understanding of autism can be gained by investigating their diagnostic certainty. Modest correlations between the certainty of an autism diagnosis and symptom load have been previously reported. Here, we investigated the associations of diagnostic certainty with specific items of the ADOS as well as other clinical features including head circumference. METHODS: Phenotypic data from the Simons Simplex Collection was used to investigate clinical correlates of diagnostic certainty in individuals diagnosed with Autistic Disorder (n = 1511, age 4 to 18 years). Participants were stratified by the ADOS module used to evaluate them. We investigated how diagnostic certainty was associated with total ADOS scores, age, and ADOS module. We calculated the odds-ratios of being diagnosed with the highest possible certainty given the presence or absence of different signs during the ADOS evaluation. Associations between diagnostic certainty and other cognitive and clinical variables were also assessed. RESULTS: In each ADOS module, some items showed a larger association with diagnostic certainty than others. Head circumference was significantly higher for individuals with the highest certainty rating across all three ADOS modules. In turn, head circumference was positively correlated with some of the ADOS items that were associated with diagnostic certainty, and was negatively correlated with verbal/nonverbal IQ ratio among those assessed with ADOS module 2. LIMITATIONS: The investigated cohort was heterogeneous, e.g. in terms of age, IQ, language level, and total ADOS score, which could impede the identification of associations that only exist in a subgroup of the population. The variability of the certainty ratings in the sample was low, limiting the power to identify potential associations with other variables. Additionally, the scoring of diagnostic certainty may vary between clinicians. CONCLUSION: Some ADOS items may better capture the signs that are most associated with clinicians' implicit knowledge of Autistic Disorder. If replicated in future studies, new diagnostic instruments with differentiated weighting of signs may be needed to better reflect this, possibly resulting in better specificity in standardized assessments.

Using developmental regression to reorganize the clinical importance of autistic atypicalities.

Early regression (ER) is often reported in autistic children with a prototypical phenotype and has been proposed as a possible pathognomonic sign present in most autistic children. Despite the uncertainties attached to its definition and report, using ER to anchor the autism phenotype could help identify the signs that best contribute to an autism diagnosis. We extracted retrospective data from 1547 autistic children between the ages of 6 and 18 years from the Simons Simplex collection. Logistic regression identified the atypicalities associated with a history of ER. Stepwise variable selection using logistic regression analysis followed by a bootstrap procedure of 1000 iterations identified the cluster of atypicalities best associated with ER. Linear and logistic regressions measured the association between combinations of atypicalities within the identified cluster and adaptative behaviors, diagnostic areas of severity, and other categories. Seven atypicalities significantly increased the likelihood of having experienced ER (OR = 1.73-2.13). Four ("hand leading-ever", "pronominal reversal-ever", "never shakes head at age 4-5" and "stereotypic use of objects or interest in parts of objects-ever"), when grouped together, best characterized the phenotype of verbal autistic children with ER. This clustering of signs was associated with certain persistent language difficulties, higher summary scores on a diagnostic scale for autism, and greater odds of receiving an "autistic disorder" diagnosis instead of another pervasive developmental disorder (PDD) diagnosis. These results raise questions about using language as a clinical specifier, defining cross-sectional signs independent of their relationship with an early developmental trajectory, and relying on polythetic criteria or equivalent weighted autistic atypicalities.

Bayonet-shaped language development in autism with regression: a retrospective study.

BACKGROUND: Language delay is one of the major referral criteria for an autism evaluation. Once an autism spectrum diagnosis is established, the language prognosis is among the main parental concerns. Early language regression (ELR) is observed by 10-50% of parents but its relevance to late language level and socio-communicative ability is uncertain. This study aimed to establish the predictive value of ELR on the progression of language development and socio-communicative outcomes to guide clinicians in addressing parents' concerns at the time of diagnosis. METHODS: We used socio-communicative, language, and cognitive data of 2,047 autism spectrum participants from the Simons Simplex Collection, aged 4-18 years (mean = 9 years; SD = 3.6). Cox proportional hazard and logistic regression models were used to evaluate the effect of ELR on language milestones and the probability of using complex and flexible language, as defined by the choice of ADOS module at enrollment. Linear models were then used to evaluate the relationship of ELR and non-verbal IQ with socio-communicative and language levels. RESULTS: ELR is associated with earlier language milestones but delayed attainment of fluent, complex, and flexible language. However, this language outcome can be expected for almost all autistic children without intellectual disability at 18 years of age. It is mostly influenced by non-verbal IQ, not ELR. The language and socio-communicative level of participants with flexible language, as measured by the Vineland and ADOS socio-communicative subscales, was not affected by ELR. LIMITATIONS: This study is based on a relatively coarse measure of ultimate language level and relies on retrospective reporting of early language milestones and ELR. It does not prospectively document the age at which language catches up, the relationship between ELR and other behavioral areas of regression, nor the effects of intervention. CONCLUSIONS: For autistic individuals with ELR and a normal level of non-verbal intelligence, language development follows a "bayonet shape" trajectory: early first words followed by regression, a plateau with limited progress, and then language catch up.

Effect Sizes of Deletions and Duplications on Autism Risk Across the Genome.

OBJECTIVE: Deleterious copy number variants (CNVs) are identified in up to 20% of individuals with autism. However, levels of autism risk conferred by most rare CNVs remain unknown. The authors recently developed statistical models to estimate the effect size on IQ of all CNVs, including undocumented ones. In this study, the authors extended this model to autism susceptibility. METHODS: The authors identified CNVs in two autism populations (Simons Simplex Collection and MSSNG) and two unselected populations (IMAGEN and Saguenay Youth Study). Statistical models were used to test nine quantitative variables associated with genes encompassed in CNVs to explain their effects on IQ, autism susceptibility, and behavioral domains. RESULTS: The "probability of being loss-of-function intolerant" (pLI) best explains the effect of CNVs on IQ and autism risk. Deleting 1 point of pLI decreases IQ by 2.6 points in autism and unselected populations. The effect of duplications on IQ is threefold smaller. Autism susceptibility increases when deleting or duplicating any point of pLI. This is true for individuals with high or low IQ and after removing de novo and known recurrent neuropsychiatric CNVs. When CNV effects on IQ are accounted for, autism susceptibility remains mostly unchanged for duplications but decreases for deletions. Model estimates for autism risk overlap with previously published observations. Deletions and duplications differentially affect social communication, behavior, and phonological memory, whereas both equally affect motor skills. CONCLUSIONS: Autism risk conferred by duplications is less influenced by IQ compared with deletions. The model applied in this study, trained on CNVs encompassing >4,500 genes, suggests highly polygenic properties of gene dosage with respect to autism risk and IQ loss. These models will help to interpret CNVs identified in the clinic.