Association between the dietary index for gut microbiota and diabetes: the mediating role of phenotypic age and body mass index
Bibliographic record
Abstract
Objectives: The global prevalence of diabetes is continuously rising, and the gut microbiota is closely associated with it. The Dietary Index for Gut Microbiota (DI-GM) assesses the impact of diet on the microbiota, but its association with diabetes risk remains unclear. This study aims to investigate the association between DI-GM and the risk of diabetes and analyze the mediating roles of phenotypic age and body mass index (BMI). Methods: Utilizing data from the National Health and nutrition examination survey (NHANES) 1999-2018, we included 17,444 adults aged 20 years and older. DI-GM (score range: 0-13) was calculated based on dietary recall. Diabetes was diagnosed based on laboratory results and self-reported information. Multivariable logistic regression was used to analyze the association between DI-GM and diabetes, adjusting for relevant covariates. Mediation analysis evaluated the roles of phenotypic age and BMI. Results: < 0.001). Compared to the group with DI-GM scores of 0-3, those with scores of 5 (OR = 0.76, 95% CI = 0.67-0.86) and ≥ 6 (OR = 0.77, 95% CI = 0.68-0.88) had significantly reduced diabetes risk. Phenotypic age and BMI accounted for 41.02 and 25.57% of the association between DI-GM and diabetes, respectively. Conclusion: Higher DI-GM scores are associated with a lower risk of diabetes, partially mediated through reduced phenotypic age and BMI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".