Effects of 25-Hydroxyvitamin D Levels on Renal Function: A Bidirectional Mendelian Randomization Study
Bibliographic record
Abstract
CONTEXT: Observational studies investigating the role of vitamin D in renal function have yielded inconsistent results. OBJECTIVE: We tested whether 25-hydroxyvitamin D (25[OH]D) serum levels are associated with renal function, and inversely, whether altered renal function causes changes in 25(OH)D, using Mendelian randomization (MR). METHODS: In this two-sample MR study, we used single nucleotide polymorphisms (SNP) associated with 25(OH)D in 443 734 Europeans and evaluated their effects on estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), chronic kidney disease (CKD) risk and progression in genome-wide association studies totaling over 1 million Europeans. To control for pleiotropy, we also used SNPs solely in DHCR7, CYP2R1, and GC, all genes with known roles in vitamin D metabolism. We performed a reverse MR, using SNPs for the above indices of renal function to study causal effects on 25(OH)D levels. RESULTS: We did not find robust evidence supporting effects of 25(OH)D on eGFR, BUN, and CKD or its progression. Our inverse variance weighted MR demonstrated a 0.56 decrease in standardized log-transformed 25(OH)D (95% CI -0.73, -0.41; P = 2.89 × 10-12) per unit increase in log-transformed eGFR. Increased BUN was associated with increased 25(OH)D (β = 0.25, 95% CI 0.15, 0.36; P = 4.12 × 10-6 per unit increase in log-transformed BUN). Finally, genetically predicted CKD conferred a 0.05 increase in standardized log-transformed 25(OH)D level (95% CI 0.04, 0.06; P = 1.06 × 10-13). Other MR methods confirmed the findings of the main analyses. CONCLUSION: Genetically predicted CKD, increased BUN, and decreased eGFR are associated with increased 25(OH)D levels, but we found no causal effect of 25(OH)D on renal function in Europeans.
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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 | 0.030 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".