Changes in dairy product consumption and subsequent type 2 diabetes among individuals with prediabetes: Tehran Lipid and Glucose Study
Bibliographic record
Abstract
BACKGROUND: People with prediabetes can postpone or even reverse progression to type 2 diabetes (T2D) by making dietary changes. This study aimed to examine the association of changes in consumption of total and specific types of dairy products with the subsequent risk of incident T2D among individuals with prediabetes. METHOD: This cohort study included 639 individuals (50% female, mean age 47.3 years) of the Tehran Lipid and Glucose Study (TLGS) who had prediabetes at baseline. We assessed 3-year changes in the consumption of dairy products using a food frequency questionnaire. Using multivariable logistic regression, odds ratios (OR) and 95% confidence intervals (CI) were calculated for the association of changes in intake of total and subtypes of dairy products during a 3-year interval with the risk of incident T2D in the subsequent 3 years. RESULTS: After almost 9 years of follow-up, the incidence of T2D was 25.2%. Compared with individuals whose intake remained relatively stable over 3 years, those who decreased consumption of total dairy (> 0.5 servings/day) had a higher T2D risk (OR = 1.56; 95% CI: 1.02 to 2.41). Increasing low-fat dairy consumption by 0.50 serving/d was associated with a lower risk of T2D (OR = 0.56; 95% CI: 0.35 to 0.90) compared with stable consumption. Those who increased consumption of low-fat milk (OR = 0.59; 95% CI: 0.37 to 0.92) and low-fat yogurt (OR = 0.55; 95% CI: 0.33 to 0.93) had a lower risk of T2D than those who were relatively stable in their consumption. Replacing low-fat milk and yogurt with regular cheese was associated with 66 and 47% higher risk of T2D, respectively. CONCLUSION: In individuals with prediabetes, increasing consumption of low-fat dairy, low-fat milk, and low-fat yogurt had reduced risk of subsequent T2D. These data suggest a role of low-fat dairy products in the prevention of T2D among prediabetes patients.
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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) | 0.000 | 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".