S2248 Association Between Celiac Disease and Risk of Kidney Diseases: A Meta-Analysis
Bibliographic record
Abstract
Introduction: Celiac disease (CD), an autoimmune disorder, has been linked to various extraintestinal manifestations, including chronic kidney disease. This meta-analysis investigates the association between celiac disease and the risk of kidney disease. Methods: A systematic literature search was conducted using PubMed, Embase, and Google Scholar to identify relevant studies up to March 31, 2024, using predefined eligibility standards, including observational studies reporting on CD and kidney diseases. The random effect model calculated the pooled prevalence and associated 95% confidence interval (CI). Besides, random-effects models were used to estimate pooled odds ratios (ORs) and 95% confidence intervals (CIs) to report the overall effect size. Statistical significance was set at P < 0.05. Study quality was evaluated using the Newcastle-Ottawa Scale (NCOS), which categorizes bias risk as low, moderate, or high. Publication bias was assessed using Egger's regression and Begg's rank correlation tests. Results: Nine studies encompassing 2,022,686 patients were included in the final analysis. The pooled prevalence of any 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 to 4.59), respectively. Furthermore, CD was associated with a significantly higher risk of developing any kidney disease (OR = 1.98, 95% CI 1.48–2.65) and glomerulonephritis (OR = 2.68, 95% CI 1.54 to 4.66) as well as ESRD (OR = 2.58, 95% CI 1.32 to 5.03). According to NCOS, most studies were of high or moderate quality. According to Egger's regression and Begg's rank correlation tests, all analyses were free of publication bias. Conclusion: Our findings confirm an elevated risk of various kidney diseases, including glomerulonephritis and end-stage renal disease, in CD patients. These findings underscore the importance of monitoring renal function, early diagnosis, and managing CD to mitigate potential kidney damage. Future research should prioritize investigating the mechanisms underlying this association. Additionally, research should explore targeted interventions, including dietary modifications or medications, to prevent or delay the onset of renal complications in CD patients (see Figure 1).Figure 1.: Forest plots depicting the association between CD and kidney diseases. A) Forest plot for any kidney disease in patients with CD B) Forest plot for glomerulonephritis in patients with CD C) Forest plot for end stage renal disease in patients with CD. CD, celiac disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.061 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".