Relationship Between Vitamin D and Cardio-Metabolic Biomarkers Among Saudi Postmenopausal Women
Bibliographic record
Abstract
Vitamin D deficiency is prevalent worldwide, and in Saudi Arabia in particular. There is growing evidence that hypovitaminosis D is involved in the pathogenesis of cardiovascular diseases. We determined concentrations of serum 25 hydroxy 25(OH) vitamin D in relation to several metabolic biomarkers including total cholesterol (TC), low density lipoprotein-cholesterol (LDL-C), high density lipoprotein-cholesterol, triglycerides (TG), atherogenic index (AI), glucose, C-reactive protein (CRP), adiposity, and blood pressure in a cross-sectional analysis in 300 Saudi postmenopausal women. Participants completed a detailed questionnaire and fasting blood samples were collected. Vitamin D deficiency was common, affecting 89% of individuals. Higher serum 25(OH) vitamin D levels were consistently found among subjects with no prevalent cardiovascular risk factors (p>0.05) except for those subjects with serum CRP level ≥3mg/dl, HDL-C <1.04mmol/L, AI≥5, exercising ≥3times/week, and those with 4 or more pregnancies. Hypovitaminosis D was inversely correlated with DBP (r=-0.118, p=0.042), TC (r=-0.165, p=0.004), TG (r=-0.119, p=0.040), LDL-C (r=-0.138, p=0.017), AI (r=-0.125, p=0.031), and veiling type (r=-0.127, p=0.028). No significant impact of hypovitaminosis D on CRP, levels of which were similar among vitamin D sufficient and deficient subjects. However, hypovitaminosis D was significantly related to dyslipidemia and diastolic blood pressure in a group of Saudi postmenopausal women.
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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 | 0.000 | 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) | 0.001 | 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".