Supplementary Material for: 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 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.848 | 0.268 |
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".