Fibroblast Growth Factor 23 Predicts Mortality and End-Stage Renal Disease in a Canadian Asian Population with Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a leading cause of morbidity and mortality. Biomarkers that predict end-stage renal disease (ESRD) and/or mortality could usher in new therapeutics to halt this onslaught. While fibroblast growth factor (FGF)23 can predict both ESRD and mortality, it has not been studied in North American CKD patients of Asian ethnicity. METHOD: This is a prospective investigation about the role of FGF23 in 998 Canadian patients of Asian descent with CKD defined by an estimated glomerular filtration rate (eGFR) <60 mL/min/m2 and followed up for 3 years. RESULTS: The mean age of patients was 68.9 years and 68.3% were males. The mean (range) eGFR, and median FGF23 were 40.2 (11.0-59.0) mL/min and 154.1 (7.0-7,823.0) RU/mL, respectively. Over the 3 years, higher values of FGF23 levels at baseline were associated with higher risk of ESRD (hazard ratio [HR] for log[Fgf23] = 2.16 [95% CI 1.20-3.89]). Despite the short follow-up, 42 patients died due to cardiovascular diseases (38.8%), cancer (14.9%), and infections (12.7%). Log-FGF23 levels were independently associated with death, HR 1.94, 95% CI 1.24-3.03. Mortality risk increased in FGF23 subgroups from <100 to >400 RU/mL. In a time-changing covariate analysis, serial log-FGF23 levels over the 3 years predicted mortality with a HR of 2.66 (95% CI 1. 79-3.95). CONCLUSION: In a Canadian Asian population with CKD, FGF23 levels obtained at 6-monthly intervals for 3 years predicted ESRD and mortality suggesting that it is also a risk marker in Asians.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".