A Pilot Study to Predict Risk of IgA Nephropathy Progression Based on miR-204 Expression
Bibliographic record
Abstract
INTRODUCTION: Immunoglobulin (Ig)A nephropathy (IgAN) is the most frequently diagnosed primary glomerulonephritis worldwide. Despite the common diagnostic feature of mesangial IgA-containing immune complex deposition, the clinical course of the disease is extremely variable, with 30% of patients developing end-stage kidney disease within 20 years of diagnosis. Therefore, identifying which patients are likely to progress is paramount. RESULTS: In this pilot study, we found that urinary exosomal miR-204 expression was significantly reduced in IgAN compared with healthy subjects. However, there was no difference in miR-204 expression between IgAN and non-IgAN chronic kidney disease controls. Analysis of miR-204 expression in kidney biopsy cores by next-generation sequencing followed by quantitative polymerase chain reaction validation in independent cohorts demonstrated that expression of miR-204 was significantly lower in IgAN compared with thin-membrane nephropathy but not compared with membranous nephropathy. Patients with IgAN at high risk of future progression had significantly lower expression of miR-204 than those at low risk of progression. Cortical localization indicated that miR-204 was preferentially expressed in the interstitium compared with glomeruli in IgAN nonprogressors and that this distribution was lost in IgAN progressors. Receiver operating characteristic curve analysis between the 2 IgAN cohorts revealed an area under the curve of 0.82. In addition, miR-204 expression correlated with known clinicopathological prognostic risk factors. Importantly, incorporating miR-204 into the International IgAN risk prediction tool improved the diagnostic power of the algorithm to predict risk of progression. CONCLUSION: Additional large-scale studies are now needed to validate the additive value of miR-204 in improving risk prediction in IgAN and more broadly in chronic kidney disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".