acePredicting cardiovascular disease in peritoneal dialysis patients using glycated proteomics and machine learning: A prospective multicentre study
Background: Cardiovascular disease (CVD) is the leading cause of death among peritoneal dialysis (PD) patients, yet PD-specific biomarkers and prediction models remain limited. This study aimed to identify glycated peptide biomarkers associated with CVD in PD patients and develop a prediction model integrating glycated proteomics and clinical indicators using machine learning. Methods: In Phase I, a matched discovery cohort (48 PD patients with CVD and 48 without CVD) was used to identify glycated peptides by li…
A model combining two glycated peptides with six clinical indicators separated high- and low-risk dialysis patients and scored an AUC of 0.822 in an external cohort, outperforming three published risk models.
The model's validation was limited to prediction accuracy in cohorts of 240 and 102 patients, and the text does not demonstrate that using it improves outcomes; the authors only say it may facilitate earlier stratification and prevention.
Evidence
- Peer-reviewedClinical and Translational Medicine2026-11-01
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Truvace Impact Record TRV-2026-2301, v1: “Predicting cardiovascular disease in peritoneal dialysis patients using glycated proteomics and machine learning: A prospective multicentre study.” Truvace, 2026-10-28. /record/TRV-2026-2301 (accessed at citation time). sha256 f21226f168963fe2…
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