Dietary Factors Associated With Dyslipidemia Traits in Individuals With Impaired Glucose Tolerance
Bibliographic record
Abstract
Background: Impaired glucose tolerance (IGT) is an independent risk factor of cardiovascular diseases. This increased risk can be partly explained by dyslipidemia traits, such as low levels of high-density lipoprotein-cholesterol (HDL-C) or high levels of triglyceride (TG). However, the sex-based association has been rarely reported. The study aimed to investigate the association between dietary factors and dyslipidemia traits in individuals with IGT. Methods: The cross-sectional study included 124 female and 121 male with IGT. Demographic and biochemical parameters including body mass index, serum TG, HDL-C, and insulin resistance index were measured. Dietary intake was assessed using a food frequency questionnaire, and dietary intake was assessed. Results: Male had significantly higher TG and lower HDL-C levels as well as higher carbohydrate intake and significantly higher daily alcohol intake than female. The multiple regression analyses showed that alcohol intake positively correlated to the TG level, although carbohydrate intake negatively correlated to the HDL-C level in male. In female, carbohydrate intake positively correlated to the TG level and alcohol intake positively correlated to the HDL-C level. The carbohydrate intake is a predictor of the HDL-C level in male and a possible predictor of the TG level in female, whereas alcohol intake is a predictor of the TG and HDL-C levels in both male and female, respectively. Conclusions: These findings may facilitate the development of a sex-specific dietary strategy to improve dyslipidemia traits among individuals with IGT. J Endocrinol Metab. 2021;11(1):22-27 doi: https://doi.org/10.14740/jem721
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".