Evidence map›Paper›PMID 42225325›Full record

ArticleDiabetes, obesity & metabolism2026

Proteomic Profiling Captures Residual Cardiovascular Risk Beyond the PREVENT Model in Individuals With Cardiovascular-Kidney-Metabolic Syndrome Stages 2-3.

Xuefei Han, Jiacheng Ding, Jingqian Li, Yiyin Gao, Yunqian Li

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Article in Diabetes, obesity & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xuefei HanDepartment of Neurosurgery, The First Hospital of Jilin University, Changchun, Jilin, China.
Jiacheng DingJilin University, Changchun, China.ORCID 0009-0008-9242-1115
Jingqian LiJilin University, Changchun, China.
Yiyin GaoJilin University, Changchun, China.
Yunqian LiDepartment of Neurosurgery, The First Hospital of Jilin University, Changchun, Jilin, China.

Funding

Natural Science Foundation of Jilin Province YDZJ202401398ZYTS
6 · The paper itself

Abstract

backgroundCardiovascular-kidney-metabolic (CKM) syndrome reflects complex pathobiological interactions among metabolic disorders, kidney injury, and cardiovascular disease (CVD). Stages 2 and 3 represent critical phases of disease progression characterised by high pathological heterogeneity. This study aimed to develop a CVD protein risk score (PRS) for this population and evaluate its incremental predictive value over the PREVENT model.

methodsThis study included 24 017 participants with CKM Stages 2-3 from the UK Biobank. Using 2923 plasma proteins measured via the Olink platform, a PRS was developed in a training set (n = 19 218) using the LASSO method. In the validation set (n = 4799), the incremental predictive performance of this score over the PREVENT model was assessed using Harrell's C-statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

resultsA risk score comprising 63 proteins was constructed, primarily reflecting inflammation, kidney injury and matrix remodelling. Key proteins included growth differentiation factor 15 (GDF15), hepatitis A virus cellular receptor 1 (HAVCR1), matrix metallopeptidase 12 (MMP12) and NT-proBNP. In the validation set, after adjusting for PREVENT risk factors, individuals in the high PRS group had a 2.56-fold higher risk of CVD compared to those in the low score group (HR: 2.56, 95% CI: 1.96-3.37). Integrating the score into the PREVENT model improved the C-statistic by 0.034 (0.672-0.706) and achieved a 10-year NRI of 15.8% (95% CI: 9.5%-20.9%) and an IDI of 2.2% (95% CI: 1.3%-3.3%).

conclusionCombining the PREVENT model with the PRS developed in this study enhances the prediction of future CVD events in the CKM Stages 2-3 population. This approach facilitates the capture of residual risk and supports precision risk stratification and management for this high-risk group.

Indexed as

Cardio-Renal SyndromeCardiovascular DiseasesMetabolic SyndromeProteomicsAgedBiomarkersDisease ProgressionFemaleHeart Disease Risk FactorsHumansMaleMiddle AgedRisk AssessmentRisk FactorsUK BiobankUnited KingdomBiomarkerscardiovascular diseasecohort studydatabase researchdiabetic nephropathy

Identifiers

PMID42225325
PMCPMC13341408

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.