Physical <scp>activity‐induced</scp> alterations of the gut microbiota are <scp>BMI</scp> dependent
Bibliographic record
Abstract
Abstract Physical inactivity is one of the leading causes of chronic metabolic disease including obesity. Increasing physical activity (PA) has been shown to improve cardiometabolic and musculoskeletal health and to be associated with a distinct gut microbiota composition in trained athletes. However, the impact of PA on the gut microbiota is inconclusive for individuals performing PA in their day‐to‐day life. This study examined the role of PA and hand‐grip strength on gut microbiome composition in middle‐aged adults (40–65 years, n = 350) with normal (18.5–24.9 kg/m 2 ) and overweight (25–29.9 kg/m 2 ) body mass index (BMI). PA was recorded using the International Physical Activity Questionnaire, and hand‐grip strength was measured using a dynamometer. Serum samples were assessed for lipidomics while DNA was extracted from fecal samples for microbiome analysis. Overweight participants showed a higher concentration of triacylglycerols, and lower concentrations of cholesteryl esters, sphingomyelin, and lyso‐phosphotidylcholine lipids ( p < .05) compared with those with normal BMI. Additionally, overweight participants had a lower abundance of the Oscillibacter genus ( p < .05). The impact of PA duration on the gut microbiome was BMI dependent. In normal but not overweight participants, high PA duration showed greater relative abundance of commensal taxa such as Actinobacteria and Proteobacteria phyla, as well as Collinsella and Prevotella genera ( p < .05). Furthermore, in males with normal BMI, a stronger grip strength was associated with a higher relative abundance of Faecalibacterium and F. prausnitzii ( p < .05) compared with lower grip strength. Taken together, data suggest that BMI plays a significant role in modeling PA‐induced changes in gut microbiota.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".