Risk factors for mortality in patients with kidney failure on hemodialysis identified by proteomic analysis of CRIC and PACE studies
Bibliographic record
Abstract
More than 50% of patients with kidney failure undergoing maintenance hemodialysis die within 5 years, a fate unexplained by traditional risk factors. To identify biological risk factors, we analyze 6287 circulating proteins and mortality in 893 participants undergoing hemodialysis in the Chronic Renal Insufficiency Cohort (CRIC) and Predictors of Arrhythmic and Cardiovascular Risk in End-Stage Renal Disease (PACE) studies. Proteins are measured shortly after (incident period) and one year after (prevalent period) dialysis initiation. In CRIC prevalent period, Sushi von Willebrand factor type A EGF and pentraxin domain-containing protein 1(SVEP1), R-spondin 4, tetranectin and 24 other proteins attain Bonferroni significance (p < 7 × 10-6). At false discovery rate<0.05, 184 proteins are significant in CRIC; 123/184 remain significant after adjustment for covariates including those linked to inflammation. Pathways related to insulin-like growth factor are prominent. In the pooled CRIC + PACE cohort, prevalent time period, AUC(95%CI) for a 3-protein model of 5-year mortality is 0.826 (0.742, 0.896), compared to 0.629 (0.528, 0.722) for a Cohort Clinical model (p < 0.001). Adding the 3 proteins (SVEP1, R-spondin 4 and tetranectin) to the Cohort Clinical model significantly improves the AUC (p < 0.001). These biomarkers should be validated in future studies and their roles as potential disease mediators elucidated. Patients with kidney failure undergoing maintenance hemodialysis have poor long-term survival. Here the authors use affinity-based proteomics to identify circulating risk factors for mortality in patients with kidney failure on hemodialysis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".