Evidence map›Paper›PMID 42601979›Full record

ArticleFrontiers in cardiovascular medicine2026

Association between cardiometabolic index change patterns and incident cardiovascular disease in middle-aged and older adults.

Hongyan Li, Hongliang Tian, Haitao Dong, Hui Gao, Bo Yang, Shijie Kou, Hongmei Tian

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Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hongyan LiDepartment of Cardiovascular Medicine, Affiliated Hospital of Chifeng University, Chifeng, China.
Hongliang TianDepartment of Cardiology, Zibo Central Hospital, Zibo, China.
Haitao DongDepartment of Cardiovascular Medicine, Affiliated Hospital of Chifeng University, Chifeng, China.
Hui GaoGraduate School, Dalian Medical University, Dalian, China.
Bo YangDepartment of Cardiovascular Medicine, Affiliated Hospital of Chifeng University, Chifeng, China.
Shijie KouDepartment of Cardiovascular Medicine, Affiliated Hospital of Chifeng University, Chifeng, China.
Hongmei TianDepartment of Cardiovascular Medicine, Affiliated Hospital of Chifeng University, Chifeng, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: No evidence exists regarding the association of long-term cardiometabolic index (CMI) changes with cardiovascular disease (CVD). Our aim is to investigate the association and dose-response relationship between cumulative and long-term changes of CMI and incident CVD risk. Materials and methods: The study participants were drawn from the China Health and Retirement Longitudinal Survey (CHARLS) and English Longitudinal Study of Ageing (ELSA) cohorts, and Electronic Medical Records (EMR) in a hospital of Inner Mongolia, China. K-means clustering was used to identify CMI change patterns. Cox proportional hazards models were employed to assess the association between cumulative CMI and CMI change patterns and incident CVD risk. Restricted cubic spline (RCS) analysis was used to account for the nonlinear relationship of cumulative CMI. Results: Univariate Cox models revealed a significantly higher CVD incidence risk in the higher CMI trajectory groups. This association remained significant after adjusted. Compared with the lowest CMI group, the hazard ratios (HR) for incident CVD in the highest CMI group were 1.82 (95% CI: 1.47-2.26), 1.92 (95% CI: 1.30-2.85), and 2.00 (95% CI: 1.45-2.76) in the three cohorts, respectively. Cumulative CMI was associated with increased CVD risk, with HR of 1.05 (95% CI: 1.01-1.09), 1.15 (95% CI: 1.07-1.23), and 1.04 (95% CI: 1.01-1.07), respectively, and a nonlinear dose-response relationship was observed. Conclusion: Persistently elevated CMI levels are significantly associated with an increased risk of incident CVD among middle-aged and older adults. Long-term monitoring and management of CMI contribute to reducing CVD incidence and improving prognosis.

Indexed as

cardiometabolic indexcardiovascular diseasechange patterncumulative effecttrajectory

Identifiers

PMID42601979
PMCPMC13472845

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