Urinary exosomal miRNA signature of IgA nephropathy: a case–control study
Bibliographic record
Abstract
IgA nephropathy is the most common primary glomerulonephritis worldwide and can progress to end-stage kidney disease (ESKD). The current "gold standard" for diagnosis is kidney biopsy, which is invasive and associated with morbidity. miRNAs are small, non-coding endogenous RNA that may serve as non-invasive biomarkers, and that are found in urinary exosomes. Thus far, there is a paucity of studies of the miRNA profile for the diagnosis of IgA nephropathy. Hence, we aimed to study the urinary exosomal miRNA signature of Indian patients with IgA nephropathy. Fifty biopsy-proven IgA nephropathy patients, 50 healthy controls and 25 patients with ESKD (IgA nephropathy) were recruited over 2 years (2020-2022). Urinary exosomes were isolated from which miRNA was extracted . Analysis of urinary exosomal miRNA was done using the digital multiplexed nCounter® human v3 miRNA Expression Assay which contains 799 unique miRNA barcodes. Candidate miRNAs were identified using Lasso regression and consensus clustering. The mean age of IgA nephropathy patients was 36.32 ± 3.067 years, mean creatinine was 2.26 ± 0.318 mg/dl and mean proteinuria was 2.69 ± 0.64 g/day. Compared to healthy controls, the majority (N = 150) of miRNAs were significantly downregulated. Five candidate miRNAs (hsa.miR.146b.3p, hsa.miR.599, hsa.miR.4532, hsa.miR.664b.5p and hsa.miR.221.5p) were able to differentiate between IgA nephropathy cases and controls (AUC > 0.90); the presence of all 5 was associated with 100% specificity and sensitivity for diagnosing IgA nephropathy cases. This study of Indian patients identified that there was a significant difference in the urinary exosomal miRNA profile between IgA nephropathy cases and healthy controls, suggesting that miRNAs may be valuable in the non-invasive diagnosis of IgA nephropathy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".