Identifying High-Risk Profiles of Cardiovascular-Kidney-Metabolic Syndrome: A Latent Class Analysis Based on Demographic, Socioeconomics and Lifestyle Factors in China

Abstract

Cardiovascular-Kidney-Metabolic (CKM) syndrome that combines cardiovascular, renal and metabolic disorders is an important public-health challenge for the elderly. Regarding the demography, socioeconomic status, and lifestyle reasons for CKM risk, the issue is less certain, especially, in middle-income countries.Characterization of hidden subcohorts based on demographic, socioeconomic and behavioral characteristics and examination of the burden of CKM will enable focused precision public health interventions.Ten thousand three hundred twenty adults, who were 45 years and older in the 2015 China Health and Retirement Longitudinal Study (CHARLS). Seven covariates were included for the performance of the latent class analysis (LCA): Age, sex, location, education, marital status and the history of smoking and drinking. CKM syndrome was defined through clinical diagnosis or self-report as the co-occurrence of two or more of the following: cardiovascular, kidney, metabolic conditions. Class-specific CKM prevalence investigations then revealed intervention targets.There were four meso-level latent classes identified from exploratory analyses: structurally vulnerable middle-Aged women, behaviorally high-risk older men, transitional working-Age men, and structurally vulnerable older women. CKM's community-level disorganization reach even at 95% is 4, and behaviorally high risk men continue to demonstrate a 4.2% gap to accommodate CKM-free encounters. In contrast, women structurally vulnerable but low on behavioral risks have close to full CKM, suggesting that poverty can overwhelm lifestyle.Averaging in a way where extreme occurrences of the CKM syndrome are not extreme, itself has become grotesquely disequilibrated as sociodemographically disparate cultures have met, interacted, and become entwined, while genuinely effective prevention may need to go beyond lifestyle to address the structural opportunities that are the deepest of the deep causes. Interventions may be required to be further developed by longitudinal approaches combined with biomarker information in terms of risk classification data.