Dietary Inflammatory Index and Cardiometabolic Risk Parameters in Overweight and Sedentary Subjects
Bibliographic record
Abstract
Nutrition has been established as a relevant factor in the development of cardiovascular disease (CVD). We aimed to investigate the relationship between the dietary inflammatory index (DII) and cardiometabolic risk parameters in a cohort of 90 overweight and sedentary adults from Bogotá, Colombia. A 24-h dietary record was used to calculate the DII. Body composition variables, flow-mediated dilation (FMD), pulse wave velocity (PWV), lipid profile, glucose, glycosylated hemoglobin (Hb1Ac), and blood pressure were measured and a cardiometabolic risk score (MetScore) was calculated. A lower DII score (anti-inflammatory diet) was significantly associated with higher high-density lipoprotein-cholesterol (HDL-C) and FMD, and lower Hb1Ac and MetScore (p < 0.05). A lower DII score was inversely correlated with plasma triglyceride levels (r = −0.354, p < 0.05), glucose (r = −0.422, p < 0.05), MetScore (r = −0.228, p < 0.05), and PWV (r = −0.437, p < 0.05), and positively with FMD (r = 0.261, p < 0.05). In contrast, a higher DII score (pro-inflammatory diet) showed a positive relationship with MetScore (r = 0.410, p < 0.05) and a negative relationship with FMD (r = −0.233, p < 0.05). An increased inflammatory potential of diet was inversely associated with an improved cardiometabolic profile, suggesting the importance of promoting anti-inflammatory diets as an effective strategy for preventing CVD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".