Vitamin D Analogues and Fracture Risk in Chronic Kidney Disease: A Systematic Review and <scp>Meta‐Analysis</scp> of Randomized Controlled Trials
Bibliographic record
Abstract
ABSTRACT Vitamin D receptor agonists (VDRAs) are commonly prescribed in chronic kidney disease (CKD). However, their protective effects on bone remain controversial. We performed a systematic review and meta‐analysis of randomized controlled trials (RCTs) to evaluate the effect of VDRAs on fracture risk and bone mineral density (BMD) in adult patients with CKD. We searched MEDLINE, EMBASE, CENTRAL, ClinicalTrials.gov , and the WHO's International Clinical Trials Registry Platform databases from inception to June 19, 2021. We included RCTs comparing VDRAs, to placebo or another medication, in adults with CKD requiring or not dialysis. Conference abstracts and trials involving kidney transplant recipients and/or comparing VDRAs to antiresorptive or anabolic bone therapy were excluded. Primary outcome was fracture at any anatomical site. Secondary outcomes were BMD at femoral neck, lumbar spine, and/or total hip. Prespecified subgroup analyses were conducted according to baseline demographics, overall risk of bias, and follow‐up time. From 6868 references retrieved, eight RCTs were eligible: five reported fracture, two reported BMD, and one reported both outcomes. As comparator, one study used no VDRAs, one used nutritional intervention and no medication, and six used placebo. In meta‐analysis, VDRAs were not associated with a significant reduction in total fractures in overall (risk ratio = 0.79, 95% confidence interval 0.38–1.65, I 2 = 0%, six trials, 1507 participants, 27 fractures) or in prespecified subgroup analyses. Three trials reported BMD at different sites and with different BMD measurements; thus, a meta‐analysis could not be performed. Two RCTs were at high risk of bias, notably because of deviations from the intended interventions. As limitation, we have to mention the low total number of fractures included in our meta‐analysis. In conclusion, current evidence from RCTs is insufficient to associate VDRAs with bone protection in CKD. Further large and long‐term studies specifically designed to evaluate the efficacy of VDRAs on bone outcomes are thus required. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".