Vitamin D Deficiency, Interleukin 17, and Vascular Function in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Vitamin D deficiency is associated with increased cardiovascular (CV) disease risk in the general population. We examined the association between vitamin D deficiency and CV risk in rheumatoid arthritis (RA). METHODS: We measured large artery compliance by pulse wave velocity and microvascular function by the reactive hyperemia index in patients with stable RA (n = 87). We quantified CV risk factors, serum 25-hydroxyvitamin D [25(OH)D], and interleukin 17 (IL-17), and RA disease activity by Disease Activity Score of 28 joints. We used linear regression to test associations between serum 25(OH)D and CV risk factors. RESULTS: The mean serum 25(OH)D level in the cohort was 27.1 ± SD 13.6 ng/ml. Fifty-nine patients (68%) were vitamin D-insufficient (25(OH)D < 30 ng/ml; mean 20.2 ± 5.9 ng/ml) and of these, 25 (29%) were vitamin D-deficient (25(OH)D < 20 ng/ml; mean 14.4 ± 3.4 ng/ml). In the whole cohort and the vitamin D-insufficient group, serum 25(OH)D was inversely associated with IL-17 (log IL-17; β = -0.83, p = 0.04; β = -0.63, p = 0.004, respectively) by univariate analysis, which persisted after adjustment for season, and in multivariate analysis after adjustment for confounders (log IL-17; β = -0.74, p = 0.04; β = -0.53, p = 0.02). In vitamin D-deficient patients, serum 25(OH)D was positively associated with microvascular function by univariate and multivariate analysis after adjustment for confounders (β = 2.1, p = 0.04; β = 2.7, p = 0.04). CONCLUSION: Vitamin D deficiency in RA may affect Th17 responses and microvascular function. Maintaining normal serum vitamin D levels may protect against IL-17-mediated inflammation and vascular dysfunction in RA.
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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 | 0.001 | 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 | 0.000 | 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".