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BACKGROUND: Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder (ASD). However, the etiology of ASD is not completely understood. The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD. AIM: To estimate and interpret the causal relationship between ASD and metabolite profile, taking into consideration both genetic and environmental influences. METHODS: A two-sample Mendelian randomization (MR) analysis was conducted using summarized data from large-scale genome-wide association studies (GWAS) including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls. Metabolites in plasma were set as exposures with ASD as the main outcome. The causal relationships were estimated using the inverse variant weight (IVW) algorithm. We also performed leave-one-out sensitivity tests to validate the robustness of the results. Based on the drafted metabolites, enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome, SwissTargetPrediction, STRING, and Metascape. RESULTS: Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD (ß = 0.262, SE = 0.064, PIVW = 4.64 × 10-5). The association was robust, with no significant heterogeneity among instrument variables (PMR Egger = 0.663, PIVW = 0.906) and no evidence of pleiotropy (P = 0.949). Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9) bradykinin and ASD. CONCLUSION: Through the application of MR, this study provides practical insights into the potential causal association between plasma metabolites and ASD. These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical practice in treating ASD.
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OBJECTIVE: To study the mental health state of parents of children with autism. METHODS: The mental health state was evaluated by conducting the Symptom Checklist (SCL-90) on parents of 34 children with autism and of 35 healthy children. RESULTS: The SCL-90 total scores in the fathers (162.5±34.0) and mothers of autistic children (175.1±51.0) were significantly higher than those in healthy children's parents (142.4±42.8 and 152.3±40.6, respectively) (P<0.05). The SCL-90 scores of obsessive-compulsive symptoms, depression, anxiety and paranoia in the fathers of autistic children were significantly higher than those in the fathers of healthy children (P<0.05). The SCL-90 scores of obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, paranoia, psychotic symptoms, hostility and sleep/diet were significantly higher in the mothers of autistic children than those in the mothers of healthy children (P<0.05). The mothers of autistic children presented higher SCL-90 factor scores in interpersonal sensitivity, anxiety and psychotic symptoms than the fathers (P<0.05). CONCLUSIONS: We should pay more attention to the mental health of parents of autistic children.