Cardiometabolic index as a novel predictor for CVD incidence in middle-aged and elderly individuals: evidence from CHARLS

The Cardiometabolic Index (CMI) is a novel metabolic-related index. This study aims to explore the relationship between the cumulative average CMI and the incidence of cardiovascular diseases (CVD) in the middle-aged and elderly adults using data from the China Health and Retirement Longitudinal Study (CHARLS). This study utilized longitudinal data from CHARLS from 2011 to 2015. During the follow-up period, the occurrence of stroke or related cardiovascular events was classified as incident CVD. Our analytical strategy involved applying the Cox proportional hazards model to investigate the relationship between CMI and the risk of CVD. We used restricted cubic splines (RCS) to identify potential nonlinear relationships and conducted mediation analysis to explore the role of inflammatory factors in the relationship between the cumulative average CMI and CVD. A total of 5,100 participants were included in our analyses for CVD, with a mean age of 58.64 ± 8.78 years. During an average follow-up of 3.84 ± 0.57 years, a total of 510 participants developed incident CVD, resulting in an incidence rate of 10%. There was a significant association between the cumulative average CMI and the incidence of CVD (HR = 1.109, 95% CI = 1.029–1.196). The transition status of CMI was similarly significantly correlated with the incidence of CVD (Q1 to Q3: HR = 1.666, 95%CI: 1.052–2.6378; Q2 to Q3: HR = 1.945, 95%CI: 1.272–2.973; Q2 to Q4: HR = 1.900, 95%CI: 1.143–3.1578; Q3 to Q4: HR = 2.170, 95%CI: 1.447–3.2534). RCS analysis revealed significant nonlinear association between the cumulative average CMI and the incidence of CVD. (P-overall = 0.002, P-non-linear = 0.014). Mediation analysis indicated that White blood cells mediated 10.30% of the association between CMI and the incidence of CVD. Cumulative average CMI was significantly associated with each of the four comorbidities of CVD: hypertension (HR = 1.144, 95% CI = 1.033–1.268), dyslipidemia (HR = 1.282, 95% CI = 1.150–1.428), diabetes (HR = 1.182, 95% CI = 1.025–1.364), and liver disease (HR = 1.306, 95% CI = 1.048–1.627). Our study demonstrates a significant association between CMI and the incidence of CVD in middle-aged and elderly individuals. CMI is a novel and valuable indicator that can be used to predict the occurrence of cardiovascular diseases.

Authors

Institutions

Publication Details

Journal
BMC Cardiovascular Disorders
Published
2026-09-25
DOI
https://doi.org/10.1186/s12872-026-06646-3
Primary Topic
Diabetes, Cardiovascular Risks, and Lipoproteins
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Cardiometabolic index as a novel predictor for CVD incidence in middle-aged and elderly individuals: evidence from CHARLS

Yue Zhao, Chuanyi Ning, Yinxia Liang, Xia Luo et al.
BMC Cardiovascular Disorders
Diabetes, Cardiovascular Risks, and Lipoproteins
article

Cardiometabolic index as a novel predictor for CVD incidence in middle-aged and elderly individuals: evidence from CHARLS

Yue Zhao, Chuanyi Ning, Yinxia Liang, Xia Luo, Xiannen Pan, Chunyan Li, Hongbin Peng, Yingqiong Huang, Li Jiang, Yuxuan Li
article en

Abstract

The Cardiometabolic Index (CMI) is a novel metabolic-related index. This study aims to explore the relationship between the cumulative average CMI and the incidence of cardiovascular diseases (CVD) in the middle-aged and elderly adults using data from the China Health and Retirement Longitudinal Study (CHARLS). This study utilized longitudinal data from CHARLS from 2011 to 2015. During the follow-up period, the occurrence of stroke or related cardiovascular events was classified as incident CVD. Our analytical strategy involved applying the Cox proportional hazards model to investigate the relationship between CMI and the risk of CVD. We used restricted cubic splines (RCS) to identify potential nonlinear relationships and conducted mediation analysis to explore the role of inflammatory factors in the relationship between the cumulative average CMI and CVD. A total of 5,100 participants were included in our analyses for CVD, with a mean age of 58.64 ± 8.78 years. During an average follow-up of 3.84 ± 0.57 years, a total of 510 participants developed incident CVD, resulting in an incidence rate of 10%. There was a significant association between the cumulative average CMI and the incidence of CVD (HR = 1.109, 95% CI = 1.029–1.196). The transition status of CMI was similarly significantly correlated with the incidence of CVD (Q1 to Q3: HR = 1.666, 95%CI: 1.052–2.6378; Q2 to Q3: HR = 1.945, 95%CI: 1.272–2.973; Q2 to Q4: HR = 1.900, 95%CI: 1.143–3.1578; Q3 to Q4: HR = 2.170, 95%CI: 1.447–3.2534). RCS analysis revealed significant nonlinear association between the cumulative average CMI and the incidence of CVD. (P-overall = 0.002, P-non-linear = 0.014). Mediation analysis indicated that White blood cells mediated 10.30% of the association between CMI and the incidence of CVD. Cumulative average CMI was significantly associated with each of the four comorbidities of CVD: hypertension (HR = 1.144, 95% CI = 1.033–1.268), dyslipidemia (HR = 1.282, 95% CI = 1.150–1.428), diabetes (HR = 1.182, 95% CI = 1.025–1.364), and liver disease (HR = 1.306, 95% CI = 1.048–1.627). Our study demonstrates a significant association between CMI and the incidence of CVD in middle-aged and elderly individuals. CMI is a novel and valuable indicator that can be used to predict the occurrence of cardiovascular diseases.

BMC Cardiovascular Disorders
Guangxi Medical University (CN), First Affiliated Hospital of GuangXi Medical University (CN), The Second Nanning People's Hospital (CN), 303 Hospital of People's Liberation Army (CN)
Good health and well-being
Openalex Percentile: Top 11%
Diabetes, Cardiovascular Risks, and Lipoproteins
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.