Predicting cardiovascular disease in peritoneal dialysis patients using glycated proteomics and machine learning: A prospective multicentre study
10 affiliations
- 1Department of Nephrology Zhongshan Hospital, Fudan University Shanghai China
- 2Shanghai Medical Center of Kidney Shanghai China
- 3Shanghai Key Laboratory of Kidney and Blood Purification Shanghai China
- 4Shanghai Institute of Kidney and Dialysis Shanghai China
- 5Department of Nephrology Renji Hospital, School of Medicine, Shanghai Jiaotong University Shanghai China
- 6Division of Nephrology Minhang Hospital, Fudan University Shanghai China
- 7Department of Nephrology Shanghai Baoshan District Wusong Central Hospital (Wusong Branch, Zhongshan Hospital Affiliated to Fudan University) Shanghai China
- 8Department of Nephrology Second Hospital, Dalian Medical University Dalian China
- 9Department of Chemistry Liver Cancer Institute, Zhongshan Hospital, Fudan University Shanghai China
- 10Institutes of Biomedical Sciences and NHC Key Laboratory of Glycoconjugates Research, Fudan University Shanghai China
Abstract
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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BibTeX
@article{ji2026predicting,
title = {{Predicting cardiovascular disease in peritoneal dialysis patients using glycated proteomics and machine learning: A prospective multicentre study}},
author = {Ji Ji and Zhaoying Nie and Na Jiang and Ting Xie and Lei Zhang and Xiaoyan Liu and Min Lu and Bo Xiang and Weiwei Wu and Xiaotian Jiang and Lin Zhang and Jun Ji and Jianzhou Zou and Xiaoqiang Ding and Haojie Lu and Xiaofang Yu},
journal = {Clinical and Translational Medicine},
year = {2026},
month = oct,
doi = {10.1002/ctm2.70889},
url = {https://doi.org/10.1002/ctm2.70889},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predicting cardiovascular disease in peritoneal dialysis patients using glycated proteomics and machine learning: A prospective multicentre study
AU - Ji Ji
AU - Zhaoying Nie
AU - Na Jiang
AU - Ting Xie
AU - Lei Zhang
AU - Xiaoyan Liu
AU - Min Lu
AU - Bo Xiang
AU - Weiwei Wu
AU - Xiaotian Jiang
AU - Lin Zhang
AU - Jun Ji
AU - Jianzhou Zou
AU - Xiaoqiang Ding
AU - Haojie Lu
AU - Xiaofang Yu
PY - 2026
DA - 2026/10/27
JO - Clinical and Translational Medicine
DO - 10.1002/ctm2.70889
UR - https://doi.org/10.1002/ctm2.70889
AB - 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.
SN - 2001-1326
ER -