Aerobic Exercise Training Increases Circulating sRAGE in Adults With Type 2 Diabetes: Associations With Sheddase Regulation.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by highly clinical heterogeneity and complex genetic architecture []. The clinical spectrum is vast, ranging from severe impairments in social communication and restrictive/repetitive behaviors (RRB) to complex psychiatric comorbidities [–]. At the genetic level, both rare and common genetic factors contribute to ASD liability. SFARI Gene database have cataloged over a thousand risk genes [], including hundreds of high-confidence (HC) and syndromic genes (SYN) identified by whole-exome (WES) [–] and whole-genome sequencing (WGS) [–] studies, as well as limited genes from common variants identified by GWAS [–]. However, a fundamental challenge remains how this vast genetic landscape translates into the diverse clinical manifestations observed in patients, a gap that significantly limits our ability to develop precision stratification and intervention strategies. To date, research has primarily focused on identifying points of mechanistic convergence to explain how disparate risk genes confer a singular diagnosis of ASD [, ]. This “many-to-one” paradigm has successfully mapped risk genes onto broad, convergent biological pathways []. This convergence is evidenced by static functional annotations indicating that discrete risk gene clusters within core biological pathways such as synaptic signaling and chromatin remodeling [–]. Furthermore, dynamic transcriptomics data from the neurotypical brain has revealed convergent co-expression modules across multiple scales as well. Specifically, bulk transcriptomic data has previously revealed aggregate molecular signatures in synaptic and transcriptional regulation pathways [–], and single-nucleus RNA sequencing (snRNA-seq) has refined these findings to cell-type-specific level [–]. Previous studies have conducted gene enrichment analyses for all identified genes, which, although able to cluster different types of pathways and propose the concept of molecular subtypes, have not formally performed clustering to subgroup ASD genes, nor have they further associated these with phenotypic traits. Genetic observations have long recognized that ASD is not monolithic but genetically partitioned. On one hand, core symptom domains, such as social communication and restrictive behaviors, often display weak genetic correlations and can dissociate within individual patients [, ], underscoring the genetic underpinnings of ASD are partitioned. On the other hand, genomic structural equation modeling (Genomic SEM) has demonstrated that the genetic architecture of ASD can be explicitly decomposed into discrete, independent genetic factors, and each linked to specific biological and clinical profiles []. This reveals that genetic risk is organized into identifiable components, providing a structured genetic foundation for the clinical heterogeneity of ASD. Based on this decomposable architecture of genetic risk, we propose that the hundreds of ASD high confidence and syndromic genes may be accurately captured by the “many-to-few” model.