The role of dietary diversity in preventing metabolic‐related outcomes: Findings from a systematic review
Bibliographic record
Abstract
Dietary diversity has been linked to insulin resistance; however, studies are inconsistent on whether dietary diversity protects against metabolic-related outcomes. We aimed to comprehensively assess metabolic-related outcomes of greater diversity across the diet and within major food groups. A systematic search of peer-reviewed literature was done in bibliographic databases (Medline, Scopus, and Web of Science) for longitudinal studies that reported on original research. Data extraction and quality appraisal used predefined criteria; reported findings were synthesized through a narrative approach. Fourteen studies were identified as eligible. Greater dietary diversity across major food groups, and diversity within fruits and/or vegetables, was associated with reduced risk of type 2 diabetes (T2D). Effects varied based on exposure definition and adjustment for known confounders. While diversity of less healthy foods was associated with greater adiposity, diversity of all foods and healthy foods was associated with reduced incidence of depression and cognitive decline. Evidence supports the protective effect of dietary diversity against cognitive decline and T2D. The association between dietary diversity and adiposity may be dependent on the healthiness of foods. Public health efforts to prevent metabolic-related diseases should include an emphasis on a varied diet as a healthy eating strategy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".