Association between dietary index for gut microbiota and diarrhea among US adults: a cross-sectional analysis of NHANES 2005–2010
Bibliographic record
Abstract
Objectives Growing attention has been paid to the relationship between the gut microbiota and diarrhea. A recently proposed Dietary Index for Gut Microbiota (DI-GM) reflects the overall dietary quality as it pertains to gut microbiota diversity. However, evidence regarding the association between DI-GM and diarrhea is still lacking. This study aims to investigate the association between DI-GM and the risk of diarrhea. Methods A total of 15,590 adults (≥20 years old) from National Health and Nutrition Examination Survey (NHANES) 2005–2010 were included in this analysis. Diarrhea was defined by self-reported common Bristol Stool Form Scale (BSFS) type 6 or 7, or more than three bowel movements per day. DI-GM comprises 14 food/nutrient components known to be associated with gut microbiota. A higher score indicates a more favorable diet for the gut microbiota. Multivariable logistic regression was used to evaluate the association between DI-GM and diarrhea, with subgroup and sensitivity analyses performed to confirm robustness. Results After adjusting for age, gender, race, physical activity, chronic diseases, and other confounders, each 1-point increase in DI-GM was associated with a 5% reduction in diarrhea risk (adjusted OR = 0.95, 95% CI: 0.91–0.98, p = 0.005). Compared with those who had a DI-GM score of 0–3, individuals with a DI-GM score ≥ 6 demonstrated a significantly lower risk of diarrhea (OR = 0.77, 95% CI: 0.65–0.91, p = 0.002). Subgroup and sensitivity analyses further supported this negative association. Notably, the “beneficial component” was found to have a more pronounced effect on reducing diarrhea risk. Conclusion Based on a large representative population, our findings suggest that a higher DI-GM score is significantly associated with a lower risk of diarrhea, underscoring the importance of overall dietary patterns in maintaining gut function and homeostasis.
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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".