Vitamin D Insufficiency is Associated with Greater Obesity‐Related Insulin Resistance
Bibliographic record
Abstract
Insulin resistance (IR) and vitamin D insufficiency [circulating 25‐hydroxyvitamin D (25OHD) ≤50nmol/L] are associated with obesity (BMI≥30kg/m2). We reported that visceral adipose tissue (VAT) is a negative determinant of plasma 25OHD. Given that vitamin D may play a role in insulin sensitivity the goal of this study is to explore the association between a vitamin D insufficiency/obesity phenotype with IR in healthy adults (N=364). Subjects were assessed for socio‐demographics; anthropometrics; body fat distribution; fasting plasma glucose, insulin, and 25OHD; and the homeostasis model assessment (HOMA) of IR. Subjects were grouped as: vitamin D adequate/no obesity; vitamin D adequate/obesity; vitamin D insufficiency/no obesity; and vitamin D insufficiency/obesity. Data were analyzed by linear regression adjusted for age, sex, ethnicity, smoking, physical activity, season, and VAT. Vitamin D insufficiency/obesity was observed in 20% of subjects and HOMA‐IR was 21% higher [B=0.207; p=0.033] in these subjects than those in the vitamin D adequate/no obesity group. No associations were observed between HOMA‐IR and adequate vitamin D/obesity or vitamin D insufficiency/no obesity groups. These findings suggest that vitamin D insufficiency in subjects with obesity places them at greater risk for IR, even after adjusting for body fat distribution. Vitamin D may play a role in obesity‐related IR.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".