Dietary Inflammatory Potential in relation to General and Abdominal Obesity
Bibliographic record
Abstract
Background/Aims: Limited data are available on the association of Dietary Inflammatory Potential (DIP) with general and abdominal obesity in developing countries. The aim of this study was to examine the association between DIP score with general and abdominal obesity among Iranian adults. Methods. This cross‐sectional study was conducted among adults in Isfahan, Iran. Dietary intakes were assessed by using a validated, self‐administrated, dish‐based, semiquantitative food frequency questionnaire. DIP was calculated based on standard method. Data regarding height, weight, and waist circumference (WC) were collected using a self‐reported questionnaire. Overweight or obesity was defined as body mass index (BMI) ≥25 kg/m2, and abdominal obesity was defined as WC ≥ 80 cm for women and ≥94 cm for men. Results. Mean age of study participants was 36.8 ± 8.08 years. The prevalence of general and abdominal obesity was 46.5% and 52.9%, respectively. We observed that higher DIP scores were significantly associated with a lower odds of general obesity (OR: 0.66; 95% CI: 0.58–0.74). Stratified by sex, this significant association was seen only for women (OR: 0.58; 95% CI: 0.46–0.72). In addition, no significant association was found between DIP scores and abdominal obesity. Conclusions. We found a significant inverse association between consumption of a proinflammatory diet and general obesity. In the gender‐stratified analysis, this was seen in women, but not in men. There was no significant association between the DIP scores and abdominal obesity.
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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) | 0.002 | 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".