Cluster Analysis of Short Sensory Profile Data Reveals Sensory-based Subgroups in Autism Spectrum Disorder
Bibliographic record
Abstract
Abstract Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairment in social interactions and communication. Additional features include restricted, repetitive patterns of behaviors, and differences in sensory processing. The clinical presentation of patients with ASD is heterogeneous, likely reflecting multiple underlying etiologies. Heterogeneity in presentation and treatment response are barriers to development of precise therapeutic approaches. Therefore, identification of clinically meaningful subgroups within ASD is critical to develop targeted interventions. We hypothesized that sensory features can be used to identify clinically recognizable subgroups with shared underlying etiologies. Methods: Subjects included 378 individuals with a clinical diagnosis of ASD who contributed Short Sensory Profile (SSP) data assessing the frequency of sensory behaviors and whole genome sequencing results to the Autism Speaks’ MSSNG database. To determine if the SSP could be used to subgroup individuals with ASD, we performed cluster analysis on responses to all 38 questions, followed by an independent cluster analysis using only a subset of questions selected specifically to assay hyper- and hypo-sensitivity to sensory stimulation. Cross-validation of the resulting clusters determined the final subgroups. To test for shared underlying etiologies, we correlated variant frequency across subgroups for each of 24,896 genes. Variant frequency included any variation in each gene regardless of the type of variant. To be significantly associated with a subgroup, a gene variant frequency had to be greater than four standard deviations (SD) from the mean frequency for all subgroups and 3 SD different from each subgroup.Results: We identified seven distinct sensory-based ASD subgroups. Subgroup 1, characterized by atypical scores in all sensory areas, was not associated with any genes. Subgroups 2, 4 and 6 were significantly associated with four to six genes each. Subgroups 3, 5 and 7 were enriched for 126, 12 and 50 genes, respectively. Limitations: This study was performed using retrospective data that did not include other phenotypic data such as age, comorbidities, or measures of disease severity. All those likely contribute to the variability of the identified subgroupsConclusions: These results support the use of sensory features to identify ASD subgroups with shared genetic mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".