Serum Vitamin D Level Correlates Significantly With Leptin and Tumor Necrosis Factor-Alpha in Overweight Postmenopausal Women With Hypertension
Bibliographic record
Abstract
Background: Association of serum vitamin D (vitD) with leptin (Lep) and tumor necrosis factor-alpha (TNF-α) is not precisely known in overweight hypertensive (OW-HT) postmenopausal (PMP) women. Hence, the present study was carried out to investigate the body mass index (BMI)-based correlation of serum vitD with Lep and TNF-α in OW-HT PMP women. Methods: Women subjects in their early PMP (n = 346, age: 51 - 60 years) categorized into three groups had main inclusion criteria of specified range of age, BMI and blood pressure (BP). Enzyme-linked immunosorbent assay (ELISA) and other kit methods were employed to investigate the role of various variables in three subject groups (normal weight normotensive (NW-NT, n = 116, BMI (kg/m2): 22 - 24.9), normal weight hypertensive (NW-HT, n = 115, BMI: 22 - 24.9) and OW-HT (n = 115, BMI: 25 - 29.9) PMP women). Results: A significant negative linear correlation of vitD with serum Lep and TNF-α, and a significant positive linear correlation of BMI with Lep and TNF-α in OW-HT PMP women were obtained. Significantly higher levels of serum Lep, TNF-α and interleukin-6 (IL-6) were found in OW-HT PMP women, as compared to NW-HT PMP women. Conclusions: The present study suggests that decreased serum vitD levels correlate with the Lep and TNF-α in OW-HT PMP women. However, further studies may help understand the impact of vitD in cardiovascular events and the influencing factors in OW-HT PMP 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 | 0.000 |
| 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".