Tracking the impact of dietary quality scores on metabolic health: Insights from the Azar Cohort on patients with type 2 diabetes mellitus
Why this work is in the frame
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Bibliographic record
Abstract
Background: This study examined the association between changes in diet quality-assessed using the healthy eating index-2015 (HEI-2015) and the dietary inflammatory index (DII)-and lipid profiles and glycemic control in adults with type 2 diabetes. Methods: In this longitudinal study, data were collected from 103 adults with type 2 diabetes at two time points, six years apart (baseline and reassessment). The main predictors were changes in HEI-2015 and DII scores over time. The primary outcome measures were lipid profile components (LDL, HDL, total cholesterol, triglycerides) and glycemic control (FBS). Associations were examined using regression models adjusted for age, sex, body mass index (BMI), and energy intake. Results: No statistically significant associations were observed between HEI-2015 or DII scores and lipid or glycemic outcomes in the overall sample. However, subgroup analyses based on adjusted models revealed reduced odds of LDL elevation among individuals aged>60 (OR: 0.14, 95% CI: 0.02-0.91) and those with BMI≥30 (OR: 0.15, 95% CI: 0.02-0.90) in the highest tertile of DII change. These effects were not observed consistently across other subgroups. Conclusion: While no significant associations were found in the overall cohort, subgroup analyses revealed that individuals over 60 and those with BMI≥30 had reduced odds of LDL elevation with higher DII scores. These findings suggest potential population-specific effects of dietary inflammation on lipid metabolism. Despite limitations such as a small sample size and wide confidence intervals, this study provides valuable exploratory evidence and underscores the need for larger, targeted investigations to confirm whether anti-inflammatory diets can improve metabolic outcomes in high-risk subgroups.
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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 | 0.001 | 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 it