The Gut Microbiome Strongly Mediates the impact of Lifestyle combined variables on Cardiometabolic Phenotypes
Bibliographic record
Abstract
Abstract Individual lifestyle factors moderately impact the gut microbiome and host biology. This study explores whether their combined influence significantly alters the gut microbiome and determines the mediating role of the gut microbiome in the links between lifestyle and phenomes. Analyzing 1,643 individuals from the Metacardis European study, we created a non-exhaustive composite lifestyle score (QASD score) incorporating diet quality and diversity, physical activity and smoking. This score shows higher explanatory power for microbiome composition variation compared to individual lifestyle variables. It positively associates with microbiome gene richness, butyrate-producing bacteria, and serum metabolites like Hippurate linked metabolic health. It inversely associates with Clostridium bolteae and Ruminococcus gnavus, serum branched-chain amino acids and dipeptides observed in chronic diseases. Causal inference analyses found 135 cases where the microbiome mediates >20% of QASD score effects on host metabolome. Microbiome gene richness also emerged as a strong mediator in the QASD score’s impact on markers of host glucose metabolism (27.3% of the effect on HOMA- IR), despite bidirectional associations between the microbiome and clinical phenotypes. This study emphasizes the importance of combining lifestyle factors to understand their collective contribution to the gut microbiota and the mediating effects of the gut microbiome on the impact of lifestyle on host metabolic phenotypes and metabolomic profiles.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".