Gene Polymorphisms as Clinical Tools in Chronic Glomerulopathies: A Prospective Study
Bibliographic record
Abstract
BACKGROUND/AIMS: Studies have proposed various polymorphisms of genes implicated in the physiopathology of chronic kidney disease as risk factors of progression and potential clinical tools. We sought to validate and simultaneously compare their predictive value in a prospective cohort of chronic glomerulopathies receiving recommended antihypertensive and antiproteinuric therapies. METHODS: Using PubMed, we identified 9 polymorphisms previously associated with progression. These were mostly of the renin-angiotensin-aldosterone and inflammation pathways: MCP-1 A2518G, TGF-β1 T869C and C-509T, ACE I/D, AGT M235T, AT1R A1166C, TSC-22 A-396G, eNOS 4b/a and CYP11β2 C-344T. We hypothesized that their determination would identify individuals at higher risk of progression. RESULTS: We recruited 93 predominantly male and Caucasian patients with a mean age of 63 and baseline eGFR of 33 ml/min/1.73 m(2) followed prospectively over a median of 36 months. 61% of patients had diabetic nephropathy, almost all received RAA blockade (90%) and none immunosuppressive therapy. The average blood pressure during follow-up was 140/72 mm Hg, the urinary protein to creatinine ratio 0.15 g/mmol and the rate of renal function decline -3.2 ± 4.1 ml/min/1.73 m(2)/year. Proteinuria and blood pressure strongly predicted progression. However, under recommended therapy, none of the proposed polymorphisms predicted renal function decline. In addition, none showed simple or partial correlations with the severity of proteinuria or blood pressure. Finally, summation variable of risk polymorphisms did not predict progression. CONCLUSION: This study does not validate the use of these 9 polymorphisms as individual clinical tools in patients with chronic glomerulopathies on recommended antihypertensive and antiproteinuric therapies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".