Background: The correlation between age at first diagnosis and symptom severity in autism spectrum disorder (ASD) remains inconsistent in existing research, and the age-specific mechanisms through which developmental level and physical development mediate this relationship remain to be elucidated. Methods: A total of 608 children with ASD aged 24∼71 months at first diagnosis were recruited and stratified into a low-age group (24∼47 months) and a high-age group (48∼71 months). The Gesell Developmental Schedule (GDS) was utilized to assess developmental level, the Childhood Autism Rating Scale (CARS) to evaluate symptom severity, and height-for-age Z-score (HAZ), weight-for-age Z-score (WAZ), and body mass index-for-age Z-score (BAZ) were calculated to assess physical development. Integrated machine learning was applied to construct classification models for distinguishing severe from non-severe ASD and to identify age-specific predictive features, and structural equation modeling (SEM) was used to analyze the mediation effects and path mechanisms. Results: Machine learning revealed language as the core predictive feature for the low-age group; whereas in the high-age group, the predictive importance of personal-social, fine motor, gross motor, HAZ, and WAZ increased significantly, with a relative decline in the predictive importance of language. SEM showed a masking effect in the total sample: age at first diagnosis had a positive total indirect effect on symptom severity via developmental level and physical development, but a negative direct effect that offset the total indirect effect, resulting in a non-significant total effect. The mediating role of developmental level was significant, and the effect of developmental level on symptom severity was more pronounced in the low-age group. Conclusion: This study identifies age-specific predictive features—language as the core predictive feature in the low-age group, whereas in the high-age group, the importance of personal-social, motor, and physical development increases while that of language declines. It also reveals a masking effect of age at first diagnosis on ASD symptom severity: a negative direct effect offset by a positive indirect effect via developmental level and physical development, resulting in a non-significant total effect. These findings provide a preliminary reference for age-stratified intervention in children with ASD.

Background: Symptom severity in children with autism spectrum disorder (ASD) correlates with developmental level. Vitamin D is crucial to developmental level. Studies show inconsistent findings on serum 25(OH)D and ASD symptom severity. This study explores their association and the mediating role of developmental level. Methods: A total of 699 children aged 24–71 months were enrolled. Serum 25(OH)D levels were measured and children were categorized into normal vitamin D (NVD) and vitamin D insufficiency (VDI) groups. Symptom severity and developmental level were assessed using the Childhood Autism Rating Scale (CARS) and the Gesell Developmental Schedule (GDS). Structural equation modeling (SEM) was used to analyze mediating effects, and machine learning algorithms were employed to assess the predictive importance of developmental dimensions. Results: In the total sample, SEM revealed a masking effect: the indirect effect of serum 25(OH)D levels on CARS scores via developmental level was negative, whereas the direct effect was positive, offsetting each other and resulting in a non-significant total effect. The mediating effect was attenuated in the VDI group. Machine learning analysis further revealed that language dominates in the NVD group, whereas in the VDI group, the dominance of language is diminished, the predictive importance of personal-social also declines, and fine motor and gross motor show enhanced predictive importance. Conclusion: Vitamin D insufficiency may weaken the predictive importance of language and personal-social while enhancing motor domains. Sufficient vitamin D was associated with better developmental levels and lower symptom severity, highlighting the potential research relevance of nutritional considerations in studies of ASD symptom severity.

Objective: Diabetes-associated cognitive dysfunction (DACD) is now acknowledged as a key complication of diabetes, whereas the precise pathological mechanisms remain unclear. Sericin, a naturally water-soluble protein complex extracted from silkworm cocoons, possesses glycemic-regulating and neuroprotective properties. This study aimed to investigate whether sericin could attenuate DACD via the PI3K/AKT pathway. Methods: A high-fat diet/streptozotocin (HFD/STZ)-induced diabetic Sprague-Dawley rat model was established. The PI3K inhibitor LY294002 was used to verify pathway involvement. Effects of HFD/STZ induction and sericin treatment were evaluated by analyzing cognitive function, lipid profiles, and hippocampal apoptosis and inflammation markers via Morris water maze test, ELISA assays, Western blot, HE staining, immunohistochemistry, immunofluorescence, and transmission electron microscopy. Results: Diabetic rats exhibited hyperglycemia, hyperlipidemia, and cognitive impairment, along with decreased p-PI3K, p-AKT, and Bcl-2 levels, and elevated levels of caspase-3, Bax, IL-1β, and IL-18 in the hippocampus, while NF-κB showed an increasing trend. Sericin treatment significantly ameliorated metabolic disorders, attenuated cognitive deficits, and reduced hippocampal apoptosis and neuroinflammation. Notably, the neuroprotective effects of sericin were partially reversed by LY294002, supporting the involvement of the PI3K/AKT pathway. Conclusion: Sericin intervention attenuates DACD by inhibiting neuronal apoptosis and inflammation with involvement of the PI3K/AKT pathway. Thus, sericin might be a potential neuroprotective intervention for DACD.