Dietary pattern scores in relation to pre-diabetes regression to normal glycemia or progression to type 2 diabetes: a 9-year follow-up
Bibliographic record
Abstract
BACKGROUND: We aimed to assess potential associations of habitual dietary pattern scores in relation to the risk of pre-diabetes (Pre-DM) progression to type 2 diabetes mellitus (T2DM) or the chance of returning to normal glycemia. METHODS: This cohort study included 334 Pre-DM individuals (mean age of 49.4 years, and 51.5% men) who participated in the third phase of the Tehran Lipid and Glucose Study (2006-2008) and followed up for a median of 9 years. A validated food frequency questionnaire at baseline assessed usual intakes of the participants. Major dietary patterns were identified using principal component analysis. The DASH score and Mediterranean diet score (MDS) were also calculated. Multinomial logistic regression analysis was used to estimate the odds ratios (95% confidence intervals (CIs)) of developing T2DM and returning to normal glycemia in relation to dietary pattern scores. RESULTS: During the study follow-up, 39.8% progressed to T2DM, and 39.8% returned to normal glycemia. Three following major dietary patterns, including Western-style (with a higher load of red meats, hydrogenated fats, sodium, and total fat intakes), healthy pattern (with a higher load of whole grains, vegetables, and dairy products), and processed-foods pattern (with a higher load of processed-meats, fast-foods, salty snakes, and sweets and candies) were identified. The Western-style dietary pattern increased the risk of progressing to T2DM by 38% (OR = 1.38; 95% CI = 1.00 to 1.89, P = 0.050). Other dietary pattern scores were not related to regression or progression from Pre-DM. CONCLUSION: The Western-style dietary pattern (characterized by higher load of red meats, hydrogenated fats, sodium intake, and high-GI foods) may accelerate the progression of Pre-DM to T2DM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".