The Association between Biomarker Profiles, Etiology of Chronic Kidney Disease, and Mortality
Bibliographic record
Abstract
BACKGROUND: Prognosis in chronic kidney disease (CKD) for adverse outcomes differs substantially based on the etiology of CKD. We examined whether the biomarker profile differed based on CKD etiology and whether they were associated with mortality. METHODS: Prospective observational study of 1,157 patients, 663 with diabetic kidney disease (DKD), 273 with glomerulonephritis (GN), and 221 with cystic/interstitial disease (polycystic kidney disease, pyelonephritis or chronic tubulointerstitial nephritis [PCK/TIN]) were identified in the Canadian Study of Prediction of Dialysis, Death and Interim Cardiovascular events over Time cohort. The outcome of interest was mortality before commencing dialysis. The biomarker profile consisted of N-terminal pro-brain natriuretic peptide (NT-proBNP), troponin I (TnI), asymmetric dimethylarginine (ADMA), interleukin (IL)-6, high sensitivity C-reactive protein, fibroblast growth factor-23 (FGF23), transforming growth factor-beta, 25-hydroxylvitamin D, and cystatin C (CysC). RESULTS: The mean estimated glomerular filtration rate was 27 mL/min/1.73 m2 and median follow-up time was 44 months. Mortality before dialysis commencement was the greatest in DKD (20%), followed by PCK/TIN (13%), and was least in those GN (8%). The majority of deaths were cardiovascular in nature, 17, 9, and 5.5% for DKD, PCK/TIN, GN, respectively. Those with DKD had higher hazard for mortality, unadjusted (hazard ratio [HR] 2.7, 95% CI 1.7-4.3) and adjusted (HR 1.7, 95% CI 1.1-2.8). The biomarker profiles associated with mortality differed significantly by CKD etiology as follows: DKD was associated with CysC (HR 1.3, 95% CI 1.0-1.6), ADMA (HR 1.3, 95% CI 1.1-1.6), and NT-proBNP (HR 1.7, 95% CI 1.4-2.1), GN was associated with FGF23 (HR 1.8, 95% CI 1.1-2.8), TnI (HR 3.6, 95% CI 1.3-9.5), and transforming growth factor-beta (HR 0.6, 95% CI 0.4-0.9) and PCK/TIN was associated with ADMA (HR 1.5, 95% CI 1.3-1.8) and IL-6 (HR 2.1, 95% CI 1.5-3.1). CONCLUSIONS: Biomarkers profiles differ according to the etiology of CKD and are associated with mortality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".