Prominent mediatory role of gut microbiome in the effect of lifestyle on host metabolic phenotypes
Bibliographic record
Abstract
Lifestyle factors influence both gut microbiome composition and host metabolism, yet their combined and mediating effects on host phenotypes remain poorly characterized in cardiometabolic populations. In 1,643 participants from the MetaCardis study, we developed a composite lifestyle score (QASD: dietary quality, physical activity, smoking, and diet diversity) that outperformed individual lifestyle variables in explaining microbial gene richness and exhibited a significant impact on the gut microbiome composition. While bidirectional pathways linking the QASD score, host phenotypes, and microbiome composition were assessed, causal inference-based mediation analyses indicated stronger effects when the microbiome was modeled as the mediator variable, particularly in relation to the insulin resistance-associated profile. Microbiome gene richness emerged as a key mediator explaining 27.8% of QASD score’s effect on the insulin resistance marker (HOMA-IR), while no significant mediation was observed on BMI. Extended mediation analyses on microbial species and serum metabolomics deconfounded for drug use and clinical profiles identified 47 mediations where microbial taxa mediated more than 20% of the effect of the QASD score on serum metabolites associated with insulin resistance. Notably, several Faecalibacterium lineages enriched in individuals with high QASD score played a significant mediatory role in increasing the serum biomarkers of microbiome diversity (as cinnamoylglycine or 3-phenylpropionate). Conversely, elevated levels of secondary bile acids in individuals with low QASD scores were strongly mediated by high levels of Clostridium bolteae. These findings highlight distinct and clinically relevant microbiome pathways linking lifestyle behaviors to cardiometabolic risks.One sentence summary:The gut microbiome mediates the impact of diet quality and diversity, physical activity and smoking status – combined in a composite lifestyle score – on cardiometabolic phenotypes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".