Key result
Machine learning model predicts 1-year CVD in peritoneal dialysis with ~0.82 AUC.
Why the study?
Cardiovascular disease is the leading cause of death among peritoneal dialysis patients, yet peritoneal dialysis-specific biomarkers and prediction models remain limited.
Does a machine learning prediction model integrating glycated peptide biomarkers and clinical indicators improve the prediction of 1-year cardiovascular disease incidence compared to published cardiovascular risk models in peritoneal dialysis patients?
Cohort (n=438)
Yes
Does a machine learning prediction model integrating glycated peptide biomarkers and clinical indicators improve the prediction of 1-year cardiovascular disease incidence compared to published cardiovascular risk models in peritoneal dialysis patients?
Integrating two novel glycated peptide biomarkers with six routine clinical indicators via machine learning accurately predicts 1-year cardiovascular disease risk in peritoneal dialysis patients and outperforms existing risk models.
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 liquid chromatography-tandem mass spectrometry (LC-MS/MS), with glmnet used to prioritise candidate biomarkers. In Phase II, candidate biomarkers were quantified in a prospective internal derivation cohort (n = 240) and a multicentre external validation cohort (n = 102). The combined model was developed using F-test feature selection and elastic-net logistic regression. Its predictive performance was compared with three published cardiovascular risk models. Patients were stratified using a training-derived Youden cutoff. RESULTS: In the discovery cohort, 257 glycated peptides were differentially abundant between groups. Four candidate biomarkers were prioritised, and two peptides, GEAFTLK(g)ATVLNYLPK and K(g)QHLFVK, were retained in the final model together with six clinical indicators: phosphate, diabetes, total Kt/V, albumin, cardiac troponin T, and glycated albumin. The combined model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.784 in the training set, 0.781 in the internal testing set, and 0.822 in the external validation cohort, respectively. Using the training-derived cutoff of 0.373, higher 1-year CVD incidence was observed in the high-risk group in both validation cohorts. The combined model also achieved higher ROC-AUCs than the published cardiovascular risk models. CONCLUSION: We developed and externally validated a PD-specific CVD prediction model by integrating glycated peptide biomarkers with clinical indicators, which may facilitate earlier risk stratification and targeted prevention.
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Ji et al. (2026) conducted a cohort in Cardiovascular disease in peritoneal dialysis patients (n=438). Combined prediction model integrating two glycated peptides (GEAFTLK(g)ATVLNYLPK and K(g)QHLFVK) and six clinical indicators vs. Three published cardiovascular risk models was evaluated on 1-year cardiovascular disease (CVD) incidence and predictive discrimination (ROC-AUC) (ROC-AUC 0.822 (external validation)). A machine learning model combining two glycated peptides and six clinical indicators predicted 1-year CVD in peritoneal dialysis patients with an external validation ROC-AUC of 0.822.
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