Association of Serum Vitamin D Level and Carotid Atherosclerosis: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
Vitamin D deficiency is associated with an increased risk of subclinical atherosclerosis. To explore the potential link of the serum vitamin D level with carotid atherosclerosis, this meta‐analysis assessed the correlation between vitamin D and carotid intima‐media thickness as well as carotid atherosclerotic plaque. PubMed, Embase, Web of Science, and Cochrane Library databases were searched until the end of March 2017. Clinical studies investigating the relationship between vitamin D and carotid atherosclerosis were included. The outcome data were extracted according to the inclusion criteria and pooled for an effect estimate by a random‐effects model. Of the 506 initially retrieved studies, 11 studies involving a total of 16,434 participants were included in the meta‐analysis. Newcastle‐Ottawa Quality Assessment Scale scores suggested that the included studies were of high quality. The pooled effects estimate showed that the serum vitamin D level was negatively associated with carotid atherosclerosis (odds ratio, 0.95; 95% confidence interval [CI], 0.93–0.96), with substantial heterogeneity among the individual studies (I2 = 54%). Furthermore, a subgroup analysis suggested that hypovitaminosis D was associated with an 0.85‐fold decrease in the odds of having a higher carotid intima‐media thickness (95% CI, 0.76–0.96; P < .05; I2 = 69%). Additionally, the pooled analysis also indicated that the serum vitamin D level was a protective factor against increased carotid plaque (odds ratio, 0.95; 95% CI, 0.93–0.97; P < .05; I2 = 29%). Funnel plots and the Egger regression test showed the absence of a publication bias. In this meta‐analysis, we comprehensively revealed a close link between vitamin D deficiency and carotid atherosclerosis. Patients with hypovitaminosis D might have extra requirements for preventive and therapeutic measures against early atherosclerosis, thus reducing the cardiovascular disease risk in the long term.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".