Association of serum uromodulin with diabetic kidney disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Several studies have investigated the association between the changes of serum uromodulin and diabetic kidney disease (DKD). However, the results are still controversial. Therefore, this meta-analysis was conducted to provide a comprehensive evaluation of the association between serum uromodulin levels and DKD. Methods PubMed, Cochrane library, Web of Science, and Scopus were systemically searched following the PRISMA protocol to identify the studies that reported the relationship between serum uromodulin level and DKD. To investigate the association between uromodulin and DKD, a standardized mean difference (SMD) with a 95% confidence interval (CI) was used. When significant heterogeneity was detected ( I 2 > 50%), sensitivity and subgroup analyses were performed to determine the source of heterogeneity. The quality assessment was determined using the Newcastle-Ottawa scale (NOS), and the publications bias were determined by the funnel plot and Egger’s test. Results In total, 6 studies with 1774 patients were included in the final analysis. The random effect model was used. The pooled results showed that the serum uromodulin levels were significantly decreased in patients with DKD (SMD: -0.31; 95% CI: -0.48 to -0.13) ( I 2 = 45%). Upon applying the sensitivity analysis, it showed (SMD: -0.38; 95% CI: -0.49 to -0.27) ( I 2 = 3%). Subgroup analysis showed that uromodulin level was significantly decreased in DKD regardless of the region of study, in America (SMD: -0.34; 95% CI: -0.51 to -0.17; p < 0.0001), Europe (SMD: -0.54; 95% CI: -1.06 to -0.02; p = 0.04), and Asia (SMD: -0.63; 95% CI: -1.15 to -0.11; p = 0.02), with stronger predictive value in America and Asia than in Europe. Additionally, uromodulin levels were significantly decreased in both type 1 (SMD: -0.34; 95% CI: -0.51 to -0.17; p < 0.0001) and type 2 diabetes (SMD: -0.58; 95% CI: -0.95 to -0.22; p = 0.002). Conclusion This meta-analysis showed a significant association between low levels of serum uromodulin and DKD. So, it could have a predictive role for DKD. However, its performance varied across subgroup analyses restricted by race and clinical settings. Moreover, further studies are required with a focus on the cut-off value for predicting diagnostic accuracy.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".