The Effect of Changing Serum 25-Hydroxyvitamin D Concentrations on Metabolic Syndrome: A Longitudinal Analysis of Participants of a Preventive Health Program
Bibliographic record
Abstract
Several studies have shown that a poor vitamin D status may increase the risk of developing metabolic syndrome, which leaves the question whether improving one's vitamin D status may reduce the risk for the syndrome. Here we investigate the effect of temporal changes in serum 25-hydroxyvitamin D (25(OH)D) concentrations on metabolic syndrome among Canadians enrolled in a preventive health program that promotes vitamin D supplementation. We accessed and analyzed data of 6682 volunteer participants with repeated observations on serum 25(OH)D concentrations and metabolic syndrome. We applied logistic regression to quantify the independent contribution of baseline serum 25(OH)D and temporal increases in serum 25(OH)D to the development of metabolic syndrome. In the first year in the program, participants, on average, increased their serum 25(OH)D concentrations by 37 nmol/L. We observed a statistical significant inverse relationship of increases in serum 25(OH)D with risk for metabolic syndrome. Relative to those without improvements, those who improved their serum 25(OH)D concentrations with less 25 nmol/L, 25 to 50 nmol/L, 50 to 75 nmol/L, and more 75 nmol/L had respectively 0.76, 0.64, 0.59, 0.56 times the risk for metabolic syndrome at follow up. These estimates were independent of the effect of baseline serum 25(OH)D concentrations on metabolic syndrome. Improvement of vitamin D status may help reduce the public health burden of metabolic syndrome, and potential subsequent health conditions including type 2 diabetes and cardiovascular disease.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".