Association between Dietary Inflammatory Index (DII) and risk of prediabetes: a case-control study
Bibliographic record
Abstract
The possible relationship between diet-related inflammation and the risk of prediabetes requires further investigation, especially in non-Western populations. We examined the ability of the dietary inflammatory index (DII) to predict the risk of prediabetes in a case-control study conducted at specialized centers in Esfahan, Iran. A total of 214 incident cases of prediabetes were selected with the nonrandom sampling procedure, and the 200 controls randomly selected from the same clinics were frequency-matched on age (±5 years) and sex. DII scores were computed based on dietary intake assessed using a validated and reproducible 168-item food-frequency questionnaire. Linear and logistic regression models were used to estimate multivariable beta estimates and odds ratios (ORs). Subjects in tertile 3 versus tertile 1 (T3VS1) of DII had significantly higher fasting plasma glucose (DII T3VS1 : b = 4.49; 95% CI 1.89, 7.09), oral glucose tolerance (DII T3VS1 : b = 8.76; 95% CI 1.78, 15.73), HbA1c (DII T3VS1 : b = 0.30; 95% CI 0.17, 0.42), low-density lipoprotein (DII T3VS1 : b = 16.37; 95% CI 11.04, 21.69), triglyceride (DII T3VS1 : b = 21.01; 95% CI 8.61, 33.42) and body fat (DII T3VS1 : b = 2.41; 95% CI 0.56, 4.26) and lower high-density lipoprotein (DII T3VS1 : b = −3.39; 95% CI −5.94, −0.84) and lean body mass (DII T3VS1 : b = −3.11; 95% CI −4.83, −1.39). After multivariate adjustment, subjects in the most pro-inflammatory DII group had 19 times higher odds of developing prediabetes compared with subjects in tertile 1 (DII T3VS1 : OR = 18.88; 95% CI 7.02, 50.82). Similar results were observed when DII was used as a continuous variable, (DII continuous : OR = 3.62; 95% CI 2.50, 5.22). Subjects who consumed a more pro-inflammatory diet were at increased risk of prediabetes compared with those who consumed a more anti-inflammatory diet.
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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) | 0.001 | 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".