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TRUVACE RECORD VERSION
record: TRV-2026-0245
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-17T22:07:01.766009Z
status: published
lens: g_space
sector: health
headline: Predictive value of the uric acid to high-density cholesterol ratio (UHR) combined with intact parathyroid hormone for protein-energy wasting after incident hemodialysis: a multicenter study
dek: Protein-energy wasting (PEW) is common in incident hemodialysis patients and linked to poor outcomes. The uric acid/HDL-cholesterol ratio (UHR) and intact parathyroid hormone (iPTH) relate to metabolic, inflammatory, and nutritional disturbances, but their value for predicting PEW in incident hemodialysis is unclear. This retrospective multicenter study included 863 incident hemodialysis patients. PEW was defined according to the International Society of Renal Nutrition and Metabolism criteria. UHR and iPTH were…
gain_title: XGBoost model integrating UHR and iPTH predicted protein-energy wasting in incident hemodialysis patients with AUC 0.801, enabling individualized risk assessment.
problem_title: (none)
trace_subject: (none)
gain_reading: XGBoost model integrating UHR and iPTH predicted protein-energy wasting in incident hemodialysis patients with AUC 0.801, enabling individualized risk assessment.
gain_evidence: XGBoost achieved the best performance (AUC = 0.801) | may assist individualized risk assessment in clinical practice
problem_reading: (none)
problem_evidence: (none)
quick_read: A retrospective multicenter study of 863 incident hemodialysis patients examined whether the uric acid to HDL-cholesterol ratio combined with intact parathyroid hormone could predict protein-energy wasting, which was present in 59.2% of the cohort. Researchers applied ROC analysis, logistic regression, and ten machine learning models with SHAP interpretability, trajectory and mediation analyses.

By the July 2026 publication date, the XGBoost model showed the best discrimination with AUC 0.801 and supported a web-based Shiny tool for individualized risk assessment. The findings suggest metabolic markers can stratify nutritional risk in dialysis initiation, but the retrospective design and observed nonlinear associations leave prospective validation and clinical utility uncertain.
limitation: Retrospective design limits causal inference and generalizability beyond the 863 incident hemodialysis patients studied, with nonlinear and mediated relationships requiring further validation.
tag: Evidence-backed gain
key_points: Retrospective multicenter study of 863 incident hemodialysis patients evaluated UHR and iPTH for PEW prediction. | PEW was identified in 59.2% of patients using International Society of Renal Nutrition and Metabolism criteria. | Ten machine learning models were tested with ROC analysis, multivariable logistic regression, restricted cubic spline, mediation, trajectory analysis, and SHAP interpretability. | A web-based Shiny tool was developed for individualized PEW risk assessment.
rundown: The study analyzed 863 incident hemodialysis patients across multiple centers, defining PEW by International Society of Renal Nutrition and Metabolism criteria and finding 59.2% prevalence. UHR and iPTH were assessed alongside eGFR using ROC analysis, multivariable logistic regression, and ten machine learning models.

Trajectory analysis found both rapidly increasing and decreasing UHR patterns with higher mean levels linked to lowest PEW risk, while mediation analysis indicated iPTH accounted for approximately 10-13% of the UHR-PEW association. Authors reported inverted U-shaped and S-shaped nonlinear relationships and released a Shiny web tool for individualized assessment.
sources:
- peer_reviewed | Renal Failure | https://doi.org/10.1080/0886022x.2026.2700069 | 2026-07-17
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