The effect of 25-hydroxyvitamin D on insulin sensitivity in obesity: is it mediated via adiponectin?
Bibliographic record
Abstract
There has been substantial recent interest in using vitamin D to improve insulin sensitivity and preventing/delaying diabetes in those at risk. There is little consensus on the physiological mechanisms and whether the association is direct or indirect through enhanced production of insulin-sensitising chemicals, including adiponectin. We examined cross-sectional associations between serum 25-hydroxyvitamin D (25(OH)D) and insulin sensitivity (Matsuda index), parathyroid hormone (PTH), waist circumference, body mass index (BMI), triglycerides (TG), total and high molecular weight (HMW) adiponectin, HMW : total adiponectin ratio (HMW : total adiponectin), and total cholesterol : HDL cholesterol ratio (TC:HDL cholesterol) in 137 Caucasian adults of mean age 43.3 ± 8.3 years and BMI 38.8 ± 6.9 kg/m(2). Total adiponectin (standardised β = 0.446; p < 0.001), waist circumference (standardised β = -0.216; p < 0.05), BMI (standardised β = -0.212; p < 0.05), and age (standardised β = -0.298; p < 0.001) were independently associated with insulin sensitivity. Serum 25(OH)D (standardised β = 0.114; p = 0.164) was not associated with insulin sensitivity, total or HMW adiponectin, HMW : total adiponectin, or lipids. Our results provide the novel finding that 25(OH)D is not associated with HMW adiponectin or HMW : total adiponectin in nondiabetic, obese adults and support the lack of association between 25(OH)D and lipids noted by others in similar groups of 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".