Elevated TyG index is associated with increased risk of vitamin D deficiency among elderly patients with type 2 diabetes
Bibliographic record
Abstract
Vitamin D deficiency (VDD) is associated with increased risk of type 2 diabetes mellitus (T2DM) and insulin resistance (IR). We aimed to investigate the association between the triglyceride-glucose (TyG) index that represents IR and VDD in elderly patients with T2DM. We enrolled 572 elderly participants with T2DM in this study. TyG index was calculated as ln [fasting triglyceride (TG, mg/dL) × fasting blood glucose (mg/dL)/2]. Serum 25-hydroxyvitamin D [25(OH)D] level below 50 nmol/L was defined as VDD. The association between the TyG index and the VDD risk was evaluated by multivariate logistic regression analysis. We observed a significant decreased 25(OH)D level with the increase of the TyG index in elderly diabetic patients, and a negative correlation between the TyG index and 25(OH)D level. The participants in the highest TyG quartile had a 2.40-fold higher risk of VDD than those in the lowest TyG index quartile [OR 2.40; 95% CI 1.47-3.92; P < 0.001]. The association persisted after adjustments for age, sex, smoking, obesity, insulin therapy, hypoglycemic agents' medication, and some biochemical parameters. TyG index may be involved in the pathophysiology of VDD, which could be a predictor for VDD in elderly diabetic patients.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".