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Record W4416133596 · doi:10.3390/pathogens14111137

Gut Microbiotas, Plasma Metabolites, and Autism Spectrum Disorder: A Bidirectional Mendelian Randomization Analysis

2025· article· en· W4416133596 on OpenAlexaboutno aff
Fu Zhang, Yunfei Gao, Caiyan An, Zhiqiang Zhang, Xin Zhong, Liusuyan Tian, Xiuyan Yang, Junjing Zhang, Qingyuan Zhang, Dilong Wang, Ningning Li

Bibliographic record

VenuePathogens · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMendelian randomizationAutism spectrum disorderGut floraMetabolomeGenome-wide association studyAutismGenetic associationLinkage disequilibriumMicrobiomeMetabolite

Abstract

fetched live from OpenAlex

Background: Previous studies have indicated that the gut microbiome and plasma metabolites play key roles in autism spectrum disorder (ASD), but their causal relationships remain unclear. Linkage disequilibrium score regression (LDSC) and Mendelian randomization (MR) are powerful tools for assessing genetic causality. This study uses LDSC and MR to investigate the genetic links between the gut microbiome and ASD and explore the mediating role of plasma metabolites. Methods: To explore the genetic relationships between the gut microbiome, plasma metabolites, and ASD, we obtained summary statistics from large-scale genome-wide association studies (GWAS). Gut microbiome data came from a MiBioGen consortium meta-analysis (N = 18,340), ASD data from the Danish Psychiatric Central Research Register (DPCRR) (N = 18,382), and plasma metabolite data from the Canadian Longitudinal Study of Aging (CLSA) (N = 8299). We applied LDSC and bidirectional MR to analyze the genetic associations between the gut microbiome and ASD and plasma metabolites and ASD. Mediation MR was used to assess the mediating role of plasma metabolites in the gut microbiome-ASD relationship. Results: LDSC analysis revealed significant genetic correlations between the gut microbiota Lachnospiraceae NK4A136 group and Sellimonas with ASD. Moreover, bidirectional MR demonstrated causal effects of five gut microbial genera on ASD risk, as indicated by inverse variance weighted (IVW) methods. Similarly, we identified 49 plasma metabolites that exhibited genetic correlations with ASD, and 58 metabolites had causal effects on ASD in MR analysis. Mediation analysis revealed that specific bacteria, Ruminiclostridium5, reduce the occurrence of ASD through metabolites Delta-CEHC and Docosadioate (C22-DC). Furthermore, Ruminococcaceae UCG005 and Sutterella modulate ASD by inhibiting Serotonin and N-acetyl-L-glutamine, respectively. Conclusions: This study provides evidence of a causal relationship between the gut microbiome and ASD, with plasma metabolites acting as a potential mediator. Our findings offer new insights into the causal mechanisms linking the gut microbiome and ASD and provide a theoretical foundation for microbiome-based therapeutic strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.231
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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