Exploring the Association Between Human Blood Metabolites and Autism Spectrum Disorder Risk: A Bidirectional Mendelian Randomization Study
Bibliographic record
Abstract
Background and Aims: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a poorly understood etiology. Recent studies have suggested that metabolic dysregulation might be linked to the development of ASD; however, causal relationships remain unclear. This study aimed to investigate the causal association between these factors using two-sample Mendelian randomization (TSMR). Methods: We conducted a TSMR analysis to assess the relationship between blood metabolites and ASD using summarized GWAS data. The metabolite dataset from the Canadian Longitudinal Study of Aging included 1091 metabolites and 309 ratios from 7824 European individuals. The ASD data from the Psychiatric Genomics Consortium comprised 18,381 ASD cases and 27,969 controls. Blood metabolites were set as exposures with ASD as the outcome. We primarily used the inverse-variance weighted method, supplemented by MR-Egger, weighted median, simple mode, and weighted mode methods. We also conducted sensitivity analyses to confirm robustness. Replication, confounding, and reserve analyses were performed to verify causation. Additionally, metabolic pathway and network pharmacology analyses were conducted to explore potential mechanisms. Results: = 0.0388). Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes analysis highlighted crucial pathways, such as cellular glucuronidation, glucuronosyltransferase activity, and bile secretion, and the significance of the apical part of the cell. Conclusions: Our findings indicate that the dodecenedioate, methionine sulfone, cysteine to alanine ratio and proline to glutamate ratio have an impact on ASD. These results enhance our understanding of the metabolic pathways involved in ASD and could lead to new avenues for intervention and prevention. Further research is needed to explore the mechanisms underlying these associations and confirm these findings in different populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".