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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".