Association between celiac disease and risk of kidney diseases: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Celiac disease (CD), an autoimmune disorder, has been linked to chronic kidney disease but results are inconsistent. This meta-analysis investigates the association between CD and the risk of kidney disease. EVIDENCE ACQUISITION: A systematic literature search was conducted using PubMed, Embase, and Google Scholar up to March 31, 2024. Studies included observational reports on CD and kidney diseases. Random-effects models estimated pooled odds ratios (ORs) and 95% confidence intervals (CIs) to report the overall effect size, with significance set at P<0.05. Study quality was assessed using the Newcastle-Ottawa Scale (NCOS). Publication bias was evaluated using Egger's regression and Begg's rank correlation tests. EVIDENCE SYNTHESIS: Nine studies with 2022,686 patients were included. The pooled prevalence of overall kidney disease, glomerulonephritis, and end-stage renal disease (ESRD) in CD patients was 2.29% (95% CI 1.34-3.49), 1.31% (95% CI 0.52-2.47), and 1.55% (95% CI 0.11-4.59), respectively. CD was significantly associated with a higher risk of overall kidney disease (OR=1.98, 95% CI 1.48-2.65), glomerulonephritis (OR=2.68, 95% CI 1.54-4.66), and ESRD (OR=2.58, 95% CI 1.32-5.03). Most studies were of high or moderate quality according to NCOS, and no publication bias was detected. CONCLUSIONS: CD patients have an elevated risk of various kidney diseases, including glomerulonephritis and ESRD. Monitoring renal function and managing CD are crucial to mitigating potential kidney damage. Future research should investigate the mechanisms underlying this association and explore targeted interventions to prevent or delay renal complications in CD patients.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".