Background Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these “supra-modal” traits in the autistic population. Methods Leveraging a combined sample of 3,868 autistic youth drawn from 12 distinct data sources (ages 3–18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a “general response pattern” factor for each supra-modal construct and determine the added value of “modality-specific response pattern” scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO and SEEK (sub)constructs. Results All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses unambiguously supported the validity of a supra-modal HYPER construct (ωH = .800), whereas a coherent supra-modal HYPO construct was not supported (ωH = .611), and supra-modal SEEK models suggested a more limited version of the construct that excluded some sensory modalities (ωH = .799; 4/7 modalities). Within each sensory construct, modality-specific subscales demonstrated substantial added value beyond the supra-modal score. Meta-analytic correlations varied by construct, although sensory features tended to correlate most strongly with other domains of core autism features and co-occurring psychiatric symptoms. Certain subconstructs within the HYPO and SEEK domains were also associated with lower adaptive behavior scores. Limitations: Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to parent-report of observable behaviors and excluded multisensory items that reflect many “real-world” sensory experiences. Conclusion Psychometric issues may limit the degree to which some measures of supra-modal HYPO/SEEK can be interpreted. Depending on the research question at hand, modality-specific response pattern scores may represent a valid alternative method of characterizing sensory reactivity in autism.
In autism spectrum disorder (ASD), medical conditions in infancy could be predictive markers for later ASD diagnosis. In this study, electronic medical records of 579 autistic individuals and 1897 matched controls prior to age 2 were analyzed for potential predictive conditions. Using a novel tool, the relative association of each condition in the autistic group was compared to the control group using logistic regressions across medical records. Generalized convulsive epilepsy, nystagmus, lack of normal physiological development, delayed milestones, and strabismus were more likely in those later diagnosed with ASD while perinatal jaundice was less likely to be associated. Lesser-known conditions, such as strabismus and nystagmus, may point to novel predictive co-occurring condition profiles which could improve screening practices for ASD. Keywords Autism • Early identification • Electronic medical recordsIndividuals with autism spectrum disorders (ASD) experience a significant number of comorbid medical conditions, ranging from psychiatric, gastrointestinal, and sleep conditions to neurological conditions throughout their lifetimes (Alexeeff et al., 2017;Croen et al., 2015;Fombonne et al., 2020). Many of these conditions may coincide with an ASD diagnosis or appear prior to an ASD diagnosis. However, few studies to date have investigated the onset patterns of these comorbid conditions, and little remains known about the specific medical phenotypic profiles in infancy that may serve as predictive markers for later ASD diagnosis.Leveraging a large dataset derived from electronic medical records, the current study aimed to fill this gap in knowledge by retrospectively identifying early-onset conditions that may be predictive of a later ASD diagnosis. Identification of these risk markers could aid targeted screenings, improve diagnosis, and allow for earlier intervention. Using the novel tool, pyPheWAS, we hypothesized that there would be multiple comorbidity profiles in this population that would be associated with a later ASD diagnosis. Methods Patient SampleElectronic health record (EHR) data was pulled from an anonymized database at Vanderbilt University Medical Center for individuals with ASD and age-, sex-, and
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