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Record W4415879366 · doi:10.1016/j.jad.2025.120478

Blood metabolites mediate gut microbiota effects on depression: A Mendelian randomization study

2025· article· en· W4415879366 on OpenAlexaboutno aff
Yaowen Zhang, Shasha Zhao, Xueyan Li

Bibliographic record

VenueJournal of Affective Disorders · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
Fundersnot available
KeywordsMendelian randomizationGut floraGut–brain axisDepression (economics)Gut microbiomeMendelian inheritanceMetabolomeMetabolite

Abstract

fetched live from OpenAlex

Accumulating evidence indicates that gut microbiota (GM) influence depression via gut-brain axis signaling, yet the causal relationships and underlying mechanisms are not fully understood. We conducted bidirectional Mendelian randomization (MR) to assess GM-Depression causal effects, with additional analyses examining blood metabolite mediation. We conducted a two-sample Mendelian randomization (MR) analysis using genetic instruments for gut microbiota (GM) from the FINRISK 2002 cohort ( n = 5959). Depression data were obtained from the FinnGen R11 database ( n = 448,069, European ancestry), along with three additional independent datasets: Pan-UK Biobank ( n = 370,457), Jamapsy_Giannakopoulou ( n = 194,548), and PGC-noUKBB ( n = 688,808). A Mendelian randomization meta-analysis was performed combining these datasets. Mediation analysis was conducted using multivariable MR (MVMR) with blood metabolite data from the Canadian Longitudinal Study on Aging (CLSA, n = 8299). Pathway analysis was performed using MetaboAnalyst 5.0. Our bidirectional MR identified Bifidobacteriaceae as a protective factor against depression (IVW OR = 0.93, 95 % CI: 0.89–0.97, P < 0.001), confirmed by meta-analysis (β = −0.05, P = 0.002). The protection was mediated by metabolites (e.g., glycolithocholate sulfate; OR = 0.80, 95 % CI: 0.71–0.90) enriched in BCAA pathways (FDR = 0.013). Conversely, N egativibacillus sp000435195 showed nominal risk effects in IVW (OR = 1.09, 95 % CI: 1.04–1.15, P < 0.001), though meta-analysis did not support this ( P > 0.05; I 2 = 68.5 %). Its potential risk involved metabolites (e.g., sulfated piperine; OR = 1.27, 95 %CI:1.13–1.42) enriched in glycine/serine/threonine pathways (FDR = 0.002). Reverse MR excluded reverse causation (all P > 0.05). Our findings provide genetic evidence for the causal involvement of specific GM in Depression, mediated by distinct metabolic pathways, suggesting potential microbiota-based interventions for Depression treatment. • Bifidobacteriaceae reduce the risk of depression via branched-chain amino acid (BCAA) metabolism. • Negativibacillus raises depression risk by disrupting glycine, serine, and threonine metabolism. • Mendelian randomization minimizes confounding in microbiota-depression research. • Probiotic-dietary interventions may present a new therapeutic intervention for depression

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.043
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.007
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.002
GPT teacher head0.259
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2025
Admission routes1
Has abstractyes

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