Association of Vitamin D With Insulin Resistance and β-Cell Dysfunction in Subjects at Risk for Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To examine cross-sectional associations of serum vitamin D [25-hydroxyvitamin D, 25(OH)D] concentration with insulin resistance (IR) and beta-cell dysfunction in 712 subjects at risk for type 2 diabetes. RESEARCH DESIGN AND METHODS: Serum 25(OH)D was determined using a chemiluminescence immunoassay. Insulin sensitivity/resistance were measured using the Matsuda insulin sensitivity index for oral glucose tolerance tests (IS(OGTT)) and homeostasis model assessment of insulin resistance HOMA-IR. beta-Cell function was determined using both the insulinogenic index (IGI) divided by HOMA-IR (IGI/IR) and the insulin secretion sensitivity index-2 (ISSI-2). RESULTS Linear regression analyses indicated independent associations of 25(OH)D with IS(OGTT) and HOMA-IR (beta = 0.004, P = 0.0003, and beta = -0.003, P = 0.0072, respectively) and with IGI/IR and ISSI-2 (beta = 0.004, P = 0.0286, and beta = 0.003, P = 0.0011, respectively) after adjusting for sociodemographics, physical activity, supplement use, parathyroid hormone, and BMI. CONCLUSIONS: Vitamin D may play a role in the pathogenesis of type 2 diabetes, as 25(OH)D concentration was independently associated with both insulin sensitivity and beta-cell function among individuals at risk of type 2 diabetes.
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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 | 0.002 |
| 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 | 0.001 | 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".