Evidence map›Paper›PMID 42045990›Full record

ArticleBMC cardiovascular disorders2026

CHG index and ePWV jointly predict cardiovascular disease risk: findings from the CHARLS cohort.

Dengyong Chen, Yuting Deng, Xiangyang Liu, Lizhe Wang, Yuanyuan Rong

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Article in BMC cardiovascular disorders, 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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5 · Who and what money

Authors and funding

5 authors.

Dengyong Chen *Department of Cardiology, No.971 Hospital of the PLA Navy, Qingdao, Shandong, 266071, China.
Yuting Deng *Department of Cardiology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, Shandong, 266000, China.
Xiangyang LiuQingdao Central Hospital, University of Health and Rehabilitation Sciences (Qingdao Central Hospital), Qingdao, Shandong, 266113, China.
Lizhe WangQingdao Central Hospital, University of Health and Rehabilitation Sciences (Qingdao Central Hospital), Qingdao, Shandong, 266113, China.
Yuanyuan RongDepartment of Cardiology, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, Shandong, 266000, China. qingdaoryy@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiovascular disease (CVD) remains the leading cause of mortality worldwide, posing a significant burden on public health systems. Identifying novel biomarkers to improve risk stratification is crucial for effective primary prevention. The cholesterol, high-density lipoprotein, and glucose (CHG) index and estimated pulse-wave velocity (ePWV) reflect metabolic dysregulation and vascular damage, respectively. However, their combined predictive value for CVD risk remains unexplored. This study aimed to investigate the associations of CHG index and ePWV with incident CVD and evaluate whether their combination improves risk prediction.

methodsThis prospective cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS). Participants aged ≥ 45 years without CVD at baseline were included. CHG index and ePWV were calculated using baseline measurements of lipid profiles, fasting glucose, and blood pressure. CVD incidence was ascertained through follow-up questionnaires. Cox proportional hazards models, restricted cubic splines, and receiver operating characteristic (ROC) curves were employed to assess associations, nonlinear relationships, and predictive performance.

resultsA total of 5,431 participants were enrolled, with 1,428 incident CVD cases during follow-up. Both CHG index (HR = 1.40, 95% CI: 1.25–1.57) and ePWV (HR = 1.11, 95% CI: 1.08–1.13) were independently associated with CVD risk. Participants with both high CHG and high ePWV exhibited the highest risk (HR = 2.12, 95% CI: 1.83–2.44). Restricted cubic splines revealed a linear association for CHG index and a nonlinear association for ePWV with CVD risk. The combination of CHG index and ePWV demonstrated superior predictive performance (AUC = 0.611) compared to CHG index alone (AUC = 0.555) or ePWV alone (AUC = 0.603).

conclusionsThe CHG index and ePWV are independently and synergistically associated with CVD risk in Chinese adults aged ≥ 45 years. Their combination may serve as a novel and effective strategy for enhancing risk stratification in the primary prevention of CVD within this middle-aged and older population. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Blood GlucoseCardiovascular DiseasesCholesterolCholesterol, HDLPulse Wave AnalysisAgedBiomarkersChinaFemaleHeart Disease Risk FactorsHumansIncidenceLongitudinal StudiesMaleMiddle AgedPredictive Value of TestsBiomarkersBlood GlucoseCholesterolCholesterol, HDLCardiovascular diseaseCholesterol, high-density lipoprotein, and glucose indexEstimated pulse-wave velocityPrimary preventionRisk stratification

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PMID42045990
PMCPMC13262413

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