Examining the association between serum galactose-deficient IgA1 and primary IgA nephropathy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: IgA nephropathy (IgAN) is a common primary glomerular disease. The O-glycosylation status of IgA1 plays a crucial role in disease pathophysiology. The level of poorly-O-galactosylated IgA1, or galactose-deficient IgA1 (Gd-IgA1), has also been identified as a potential biomarker in IgAN. We sought to examine the value of serum Gd-IgA1 as a biomarker in IgAN, by investigating its association with clinical, laboratory, and histopathological features of IgAN. METHODS: The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations and was registered in PROSPERO (CRD42021287423). The literature search was conducted in PubMed, Web of Science, Cochrane, and Scopus, and the selected articles were evaluated for eligibility based on predefined criteria. The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale. Statistical analysis was performed to calculate effect sizes and assess heterogeneity among the studies. RESULTS: This review analyzed 29 out of 1,986 studies, conducted between 2005 and 2022, with participants from multiple countries. Gd-IgA1 levels were not associated with age and gender, while associations with hypertension, hematuria, and proteinuria were inconsistent. In the meta-analyses, a correlation between serum Gd-IgA1 and estimated glomerular filtration rate was identified, however, the relationships between Gd-IgA1 levels and chronic kidney disease (CKD) stage and progression to kidney failure were inconsistent. CONCLUSIONS: Serum Gd-IgA1 levels were not associated with validated prognostic risk factors, but were negatively correlated with kidney function. Further research in larger studies using standardized assays are needed to establish the value of Gd-IgA1 as a prognostic risk factor in IgAN.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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, 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".