Comparison of diet and exercise on cardiometabolic factors in young adults with overweight/obesity: multiomics analysis and gut microbiota prediction, a randomized controlled trial
Bibliographic record
Abstract
Abstract The optimal strategy for improving cardiometabolic factors (CMFs) in young obese individuals through diet and exercise remains unclear, as do the potential mechanisms. We conducted an 8‐week randomized controlled trial to compare the effects of different interventions in youth with overweight/obesity. Gut microbes and serum metabolites were examined to identify regulating mechanisms. A total of 129 undergraduates were randomly assigned to fiber‐rich (FR) diet, rope‐skipping (RS), combined FR–RS and control groups. The results showed that single interventions were as effective as combined interventions in improving weight, waist circumference, body fat, and lipid profile compared with control group. Notably, the FR group further reduced low‐density lipoprotein (LDL‐C) and uric acid (UA) (all p < 0.05). Mediation analysis revealed four gut microbiota–metabolite–host axes in improving CMFs. Additionally, we used machine learning algorithms to further predict individual responses based on baseline gut microbiota composition, with specific microbial genera guiding targeted intervention selection. In conclusion, FR diet and/or RS were effective in improving CMFs, with the FR diet particular effectiveness in reducing LDL‐C and UA levels. These benefits may drive by gut microbiome–metabolite–host interactions. Moreover, the predictability of gut microbiota composition supports making targeted decisions in selecting interventions. Trial Registration: NCT04834687.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".