Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 May 28:15:1387374.
doi: 10.3389/fendo.2024.1387374. eCollection 2024.

Correlation of cardiometabolic index and sarcopenia with cardiometabolic multimorbidity in middle-aged and older adult: a prospective study

Ling He  1 , Chuyang Lin  2 , Yansong Tu  3 , Yazhi Yang  2 , Ming Lin  4 , Huaijun Tu  1 , Jian Li  1
Affiliations

Correlation of cardiometabolic index and sarcopenia with cardiometabolic multimorbidity in middle-aged and older adult: a prospective study

Ling He et al. Front Endocrinol (Lausanne). .

Abstract

Background: Research has demonstrated that sarcopenia and visceral obesity are significant risk factors for chronic disease in middle-aged and older adults. However, the relationship between sarcopenia, the cardiac metabolic index (CMI), a novel measure of visceral obesity, and cardiometabolic multimorbidity (CMM) remains unclear. In this study, data from the China Longitudinal Study of Health and Retirement (CHARLS) were analyzed to investigate the association between sarcopenia and CMI with CMM in the middle-aged and older adult population.

Methods: The study included 4,959 participants aged 45 and over. Sarcopenia was defined using the criteria of the Asian Sarcopenia Working Group 2019. CMM is defined as having two or more of the following conditions: physician-diagnosed heart disease, diabetes, stroke, and/or hypertension. CMI was calculated using the formula: CMI = (TG/HDL-C) × WHtR. To explore the association between CMI and sarcopenia and CMM, cox proportional risk regression models were used.

Results: The median age of all participants was 57 years, with 47.1% being male. Over the 8-year follow-up, 1,362 individuals developed CMM. The incidence of CMM was 8.7/1,000 person-years in the group without sarcopenia or high CMI, 17.37/1,000 person-years in those with high CMI, 14.22/1,000 person-years in the sarcopenia group, and 22.34/1,000 person-years in the group with both conditions. After adjusting for covariates, the group with both sarcopenia and high CMI had a significantly increased risk of CMM (HR 2.48, 95% CI 1.12-5.51) and heart disease (HR 2.04, 95% CI 1.05-3.98). Among those over 65 years, sarcopenia was discovered to be associated with an increased risk of CMM [HR (95% CI: 4.83 (1.22, 19.06)]. The risk of CMM was further increased to 7.31-fold (95% CI:1.72, 31.15) when combined with high CMI.

Conclusions: The combination of sarcopenia and high CMI is associated with an increased risk of developing CMM. Early identification and intervention of sarcopenia and CMI not only enable the development of targeted therapeutic strategies but also provide potential opportunities to reduce the morbidity and mortality of CMM.

Keywords: CHARLS; cardiac metabolic index; cardiometabolic multimorbidity; sarcopenia; visceral obesity.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart for participants of this current study.
Figure 2
Figure 2
Impact of sarcopenia, high CMI, and sarcopenia combined with high CMI on person-year incidence of CMM from 2011-2018. Bule line: Sarcopenia combined with high CMI; Green line: high CMI only; Yellow line: No sarcopenia or high CMI; orange line: Sarcopenia only.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Jenkins DJA, Willett WC, Yusuf S, Hu FB, Glenn AJ, Liu S, et al. . Association of glycaemic index and glycaemic load with type 2 diabetes, cardiovascular disease, cancer, and all-cause mortality: a meta-analysis of mega cohorts of more than 100 000 participants. Lancet Diabetes Endocrinol. (2024) 12:107–18. doi: 10.1016/S2213-8587(23)00344-3 - DOI - PubMed
    1. Koyama AK, McKeever Bullard K, Xu F, Onufrak S, Jackson SL, Saelee R, et al. . Prevalence of cardiometabolic diseases among racial and ethnic subgroups in adults - behavioral risk factor surveillance system, United States, 2013-2021. MMWR Morbidity mortality weekly Rep. (2024) 73:51–6. doi: 10.15585/mmwr.mm7303a1 - DOI - PMC - PubMed
    1. Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. . 2024 heart disease and stroke statistics: A report of US and global data from the american heart association. Circulation. (2024) 149(8):e347–913. doi: 10.1161/CIR.0000000000001209 - DOI - PubMed
    1. Valabhji J, Barron E, Pratt A, Hafezparast N, Dunbar-Rees R, Turner EB, et al. . Prevalence of multiple long-term conditions (multimorbidity) in England: a whole population study of over 60 million people. J R Soc Med. (2024) 2023:1410768231206033. doi: 10.1177/01410768231206033 - DOI - PMC - PubMed
    1. Gyberg V, De Bacquer D, Kotseva K, De Backer G, Schnell O, Sundvall J, et al. . Screening for dysglycaemia in patients with coronary artery disease as reflected by fasting glucose, oral glucose tolerance test, and HbA1c: a report from EUROASPIRE IV–a survey from the European Society of Cardiology. Eur Heart J. (2015) 36:1171–7. doi: 10.1093/eurheartj/ehv008 - DOI - PubMed