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 (DIIT3VS1: b = 4.49; 95% CI 1.89, 7.09), oral glucose tolerance (DIIT3VS1: b = 8.76; 95% CI 1.78, 15.73), HbA1c (DIIT3VS1: b = 0.30; 95% CI 0.17, 0.42), low-density lipoprotein (DIIT3VS1: b = 16.37; 95% CI 11.04, 21.69), triglyceride (DIIT3VS1: b = 21.01; 95% CI 8.61, 33.42) and body fat (DIIT3VS1: b = 2.41; 95% CI 0.56, 4.26) and lower high-density lipoprotein (DIIT3VS1: b = −3.39; 95% CI −5.94, −0.84) and lean body mass (DIIT3VS1: 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 (DIIT3VS1: OR = 18.88; 95% CI 7.02, 50.82). Similar results were observed when DII was used as a continuous variable, (DIIcontinuous: 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".