Association between Relative Fat Mass and Cardiovascular Disease in Middle-Aged and Older Adults: A Cross-Sectional and Longitudinal Study Based on CHARLS

Huilong Chen¹, Yunchu Liao¹, Yuwei Liu¹, Zhenghui Kong¹, Xinghui Huang², Jiahui Xu², Na Qi², Yuanping Wang², Wenjian Liang²*

¹ The Fifth Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
² Guangdong Second Traditional Chinese Medicine Hospital, Guangzhou 510095, China

Corresponding author: Wenjian Liang, Chief of TCM; E-mail: 499576210@qq.com

Abstract

Background In recent years, an association has been found between relative fat mass (RFM) and cardiovascular disease (CVD). However, nationwide cohort studies on RFM and the risk of CVD in the Chinese population are scant. Objective To analyze the association between RFM and the risk of CVD in middle-aged and elderly Chinese population (≥ 45 years old) using the data of China Health and Retirement Longitudinal Study (CHARLS). Methods This was a cross-sectional and longitudinal study using data available from CHARLS 2011-2018. In the cross-sectional study, 12,867 middle-aged and elderly individuals aged 45 years or older were included. A total of 11,171 middle-aged and elderly individuals who were not diagnosed with CVD in the cross-sectional study in 2011 were included in the longitudinal study and followed up until 2018. Multivariate logistic regression and restricted cubic splines (RCS) were used to analyze the cross-sectional association between RFM and CVD. Kaplan-Meier curves, multivariate Cox proportional hazards regression models, and RCS were used to analyze the longitudinal association between different baseline RFM levels in 2011 and the risk of incident CVD. Subgroup analysis was used to investigate the association between RFM and CVD across subgroups, and sensitivity analysis was used to verify the stability of the model. Results Multivariate logistic regression analysis showed that RFM was a risk factor for CVD (OR=1.03, 95%CI=1.02-1.04, P<0.05). Compared with the Q1 group, the Q2 group (OR=1.26, 95%CI=1.07-1.49), Q3 group (OR=1.78, 95%CI=1.47-2.16), and Q4 group (OR=1.81, 95%CI=1.49-2.19) had a significantly higher risk of CVD (P<0.05). During the follow-up period, a total of 1,655 (14.9%) individuals were diagnosed with CVD for the first time. Multivariate Cox regression analysis showed that RFM was a risk factor for CVD (HR=1.03, 95%CI=1.02-1.04, P<0.05). Compared with Q1, the Q2 group (HR=1.31, 95%CI=1.12-1.52), the Q3 group (HR=1.34, 95%CI=1.12-1.61), and the Q4 group (HR=1.79, 95%CI=1.49-2.14) had a significantly higher risk of new-onset CVD. Subgroup analysis showed that RFM had an interaction with marital status (P=0.022). The results of sensitivity analysis were consistent with the trends of the above results. Conclusion Higher levels of RFM are associated with an increased risk of CVD, suggesting that RFM may be of potential value in CVD prevention and treatment.

Keywords: Cardiovascular disease; Relative fat mass; Middle-aged and elderly people; China health and retirement longitudinal study; Cohort study

Introduction

According to the China Cardiovascular Health and Disease Report 2023 [1], approximately 330 million people in China currently suffer from cardiovascular disease (CVD), and the prevalence continues to rise. CVD has become the leading cause of death among urban and rural residents. Obesity, a global public health concern, represents one of the key risk factors for CVD [2]. By 2022, the global number of adults with obesity had reached nearly 880 million, with the obesity rate among Chinese women increasing from 2.0% in 1990 to 7.8% in 2022, and among men from 0.8% to 8.9% [3].

Although traditional obesity assessment indicators such as BMI are widely used, they have limitations in distinguishing between fat and muscle mass components [4] and cannot accurately reflect individual fat distribution patterns. Furthermore, the correlation between BMI and various diseases remains controversial [5]. Consequently, identifying more precise obesity assessment indicators to better measure CVD risk has become a research priority [6-8].

Visceral and ectopic fat distribution exerts significantly more negative cardiovascular effects than subcutaneous fat [9-10]. While clinical techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) can assess visceral fat accumulation, their high cost and radiation risks limit widespread application. In recent years, researchers have proposed a new obesity assessment index—relative fat mass (RFM)—based on gender, height, and waist circumference [11]. Compared with BMI, RFM more effectively reflects visceral fat accumulation by incorporating waist circumference and accounts for gender differences in body structure and fat distribution, thereby providing more accurate estimates of whole-body fat percentage. This index has been widely applied in clinical and public health settings for metabolic diseases and cerebrovascular conditions [12].

However, nationwide studies on the association between RFM and CVD in Chinese populations remain limited [13]. Therefore, this study utilized the China Health and Retirement Longitudinal Study (CHARLS) database to investigate the relationship between RFM and CVD among middle-aged and elderly Chinese individuals, aiming to provide new perspectives and evidence for health management in this population.

Methods

1.1 Study Population

This study was based on the CHARLS database, a national population cohort study that includes basic demographic information and health status data [14]. The national baseline survey (Wave 1) was conducted from June 2011 to March 2012, enrolling 17,705 participants using multistage probability sampling from 150 counties/districts and 450 villages/communities across China. Follow-up surveys have been conducted every two years (Wave 2 in 2013, Wave 3 in 2015, Wave 4 in 2018, and Wave 5 in 2020). The CHARLS study was approved by the Ethics Committee of Peking University (approval number: IRB00001052-11015), and all participants provided informed consent.

This study used CHARLS data from 2011-2018 for both cross-sectional and longitudinal analyses. The cross-sectional study included 12,867 middle-aged and elderly individuals based on the following criteria: (1) age ≥ 45 years; (2) participation in physical examinations with complete data for RFM calculation; and (3) reported CVD diagnosis status in 2011. For the longitudinal study, 11,171 participants from the 2011 cross-sectional study who had not been diagnosed with CVD were followed up until 2018.

1.2.1 CVD Assessment

Heart disease diagnosis was determined based on participants' responses to the question: "Has a doctor diagnosed you with a heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems?" Stroke diagnosis was based on responses to: "Has a doctor diagnosed you with stroke?" CVD was defined as self-reported heart disease or stroke. If participants reported heart disease or stroke in a previous wave, they were asked to reaffirm in subsequent waves. Inconsistencies were corrected when participants denied previous self-reported diagnoses. Prior research has demonstrated high consistency between self-reported disease diagnoses and medical records in CHARLS [15].

1.2.2 RFM

RFM was calculated using a validated formula that incorporates gender, age, waist circumference, and height to reflect visceral fat distribution [11]. Height and waist circumference were measured in meters. The formulas were:
Males: RFM = 64 - (20 × height / waist circumference)
Females: RFM = 64 - (20 × height / waist circumference) + 12

1.2.3 Covariates

Collinearity analysis was performed first, using a variance inflation factor threshold of <5 to determine covariates for inclusion. These comprised sociodemographic characteristics (age, education level, residence, marital status, smoking and drinking status) and health conditions (diabetes, hypertension, dyslipidemia). Smoking was defined as current or former smoking; drinking as current or former alcohol consumption. Hypertension was defined as systolic blood pressure ≥ 140 mmHg (1 mmHg = 0.133 kPa) or diastolic blood pressure ≥ 90 mmHg, or self-reported hypertension diagnosis, or use of antihypertensive medication. Diabetes was defined as fasting glucose ≥ 7.0 mmol/L, random glucose ≥ 11.1 mmol/L, HbA1c ≥ 6.5%, or self-reported diabetes diagnosis, or use of glucose-lowering medication. Dyslipidemia was defined as total cholesterol ≥ 240 mg/dL, triglycerides ≥ 150 mg/dL, LDL-C ≥ 160 mg/dL, HDL-C < 40 mg/dL, or self-reported dyslipidemia diagnosis, or lipid-lowering therapy.

2.1 Basic Characteristics of Study Participants

A total of 12,867 respondents were included in the cross-sectional study (Figure 1 [FIGURE:1]). The sample comprised 6,117 males (47.54%) and 6,750 females (52.46%), with a mean age of 59.5 ± 9.6 years. Mean BMI was 23.5 ± 3.9 kg/m², body weight 58.8 ± 11.7 kg, waist circumference 85.3 ± 10.2 cm, and RFM 32.7 ± 8.6. Participants were grouped by CVD diagnosis status: 1,696 (13.18%) in the CVD group and 11,171 (86.82%) in the non-CVD group. No significant differences were observed between groups in education level or smoking status (P > 0.05). However, significant differences were found in age, RFM, BMI, body weight, waist circumference, gender, marital status, residence, drinking status, diabetes, hypertension, dyslipidemia, heart disease, and stroke (P < 0.05) (Table 1 [TABLE:1]).

2.2 Cross-Sectional Analysis of RFM and CVD Risk

Statistical analysis was performed using R 4.4.1 (R Foundation for Statistical Computing) with packages including "tidyverse," "gtsummary," "ggrcs," "forestmodel," "rms," and "ggplot2." Normally distributed continuous variables (e.g., age) were expressed as mean ± standard deviation and compared between groups using independent samples t-tests. Non-normally distributed variables were expressed as median (P25, P75) and compared using rank-sum tests. Categorical variables (e.g., gender, marital status) were expressed as frequencies (percentages) and compared using chi-square tests. Multivariate logistic regression and restricted cubic spline (RCS) curves were used to analyze the cross-sectional association between RFM and CVD. Kaplan-Meier curves, multivariate Cox proportional hazards regression models, and RCS curves were used to analyze the longitudinal association between baseline RFM levels in 2011 and incident CVD risk. Subgroup analysis explored the moderating effects of sociodemographic characteristics and health status on the relationship between RFM and incident CVD, while sensitivity analysis verified model stability. Statistical significance was set at P < 0.05.

2.2.1 Multivariate Logistic Regression Analysis of RFM and CVD Risk

Using CVD occurrence as the dependent variable (yes = 1, no = 0) and RFM (continuous) and RFM quartiles as independent variables—Q1 (RFM 0.76-25.27, n = 3,217), Q2 (RFM 25.28-32.83, n = 3,217), Q3 (RFM 32.84-40.47, n = 3,216), and Q4 (RFM 40.48-53.97, n = 3,217)—multivariate logistic regression was performed. Model 1 was unadjusted, Model 2 adjusted for age (continuous), marital status (married/cohabiting = 1, unmarried/separated/widowed = 2), education (below primary = 1, primary = 2, secondary = 3, high school and above = 4), and residence (rural = 1, urban = 2). Model 3 additionally adjusted for smoking (never = 1, current = 2), drinking (never = 1, current = 2), hypertension (no = 1, yes = 2), diabetes (no = 1, yes = 2), and dyslipidemia (no = 1, yes = 2). All models showed that elevated RFM was a risk factor for CVD, with increasing CVD risk as RFM levels rose (P < 0.05) (Table 2 [TABLE:2]).

2.2.2 RCS Analysis of RFM and CVD Risk

RCS curves were used to explore the dose-response relationship between RFM and CVD risk. The results indicated no non-linear association (P-overall < 0.001, P-non-linear = 0.655, Figure 2 [FIGURE:2]).

2.3 Longitudinal Analysis of RFM and Incident CVD Risk

2.3.1 Kaplan-Meier Cumulative Incidence Curves

By the end of follow-up, 1,655 (14.9%) participants had been newly diagnosed with CVD, with 294, 430, 368, and 563 cases in Q1, Q2, Q3, and Q4 groups, respectively. Kaplan-Meier analysis showed that cumulative incidence of new-onset CVD increased over follow-up time across all four groups, with statistically significant differences among groups (χ² = 109.165, P < 0.05, Figure 3 [FIGURE:3]).

2.3.2 Multivariate Cox Regression Analysis

Using incident CVD as the dependent variable (yes = 1, no = 0) and RFM (continuous) and quartiles as independent variables—Q1 (RFM 0.76-24.98, n = 2,793), Q2 (RFM 24.99-32.29, n = 2,793), Q3 (RFM 32.30-40.19, n = 2,794), and Q4 (RFM 40.20-53.97, n = 2,791)—multivariate Cox regression was performed. Model 1 was unadjusted, Model 2 adjusted for age, residence, marital status, and education, and Model 3 additionally adjusted for smoking, drinking, hypertension, diabetes, and dyslipidemia. All models showed that elevated RFM was a risk factor for CVD, with increasing risk as RFM levels rose (P < 0.05) (Table 3 [TABLE:3]).

2.3.3 RCS Analysis of RFM and Incident CVD Risk

RCS curves revealed no non-linear association between RFM and incident CVD risk (P-overall < 0.001, P-non-linear = 0.153, Figure 4 [FIGURE:4]).

2.3.4 Subgroup Analysis

Subgroup analysis showed that elevated RFM remained a risk factor for incident CVD across most subgroups, including age >60 years, age 45-60 years, males, females, married individuals, all education levels, urban residents, normal weight, overweight, smokers, non-smokers, drinkers, non-drinkers, and those with or without diabetes, hypertension, or dyslipidemia (P < 0.05). However, no significant association was found among unmarried individuals, rural residents, or underweight participants (P > 0.05). Interaction analysis revealed significant effect modification by marital status (P < 0.05) but not by age, gender, education, residence, BMI, smoking, drinking, diabetes, hypertension, or dyslipidemia (Figure 5 [FIGURE:5]).

2.3.5 Sensitivity Analysis

Three sensitivity analyses were conducted: (1) examining RFM in relation to CVD subtypes (heart disease and stroke); (2) winsorizing RFM at the 0.5th and 99.5th percentiles to reduce extreme value effects; and (3) excluding participants with incident CVD within the first two years of follow-up to eliminate potential early-onset bias. All sensitivity analyses yielded results consistent with the main findings (Table 4 [TABLE:4]).

Discussion

This large-scale cross-sectional and longitudinal study of Chinese middle-aged and elderly adults found a positive association between RFM and CVD risk, with consistent trends across most subgroups, suggesting that RFM may serve as an effective biomarker for CVD risk stratification. Close monitoring and maintaining lower RFM levels may be important for primary prevention of CVD.

RFM is an obesity index calculated based on gender, height, and waist circumference, offering convenience, simplicity, and high accuracy. Compared with BMI, it more accurately reflects whole-body fat percentage. WANG et al. [16] found in a U.S. prospective cohort of 26,754 participants that elevated RFM increased metabolic abnormalities, cardiovascular risk factors, and CVD incidence. Additionally, a U.S. cross-sectional study demonstrated a significant positive association between RFM and stroke [12]. A survey in East China (SPECT-China) also reported significant associations between RFM and CVD prevalence [13], consistent with our findings.

Several mechanisms may explain the relationship between RFM and CVD. First, elevated RFM is often accompanied by dyslipidemia [17], particularly increased LDL-C levels, which oxidize and deposit in arterial walls, triggering inflammatory responses and plaque formation that lead to atherosclerosis. Second, in obesity, adipose tissue (especially visceral fat) releases excessive free fatty acids and induces chronic low-grade inflammation [18], causing metabolic dysfunctions including insulin resistance [19]. Epicardial adipose tissue produces inflammatory mediators and reactive oxygen species that promote atherosclerosis progression [20]. Additionally, obesity contributes to microvascular complications through multiple pathways [21].

Subgroup and interaction analyses revealed an interaction between marital status and RFM. The positive association between RFM and CVD was particularly pronounced among married individuals, possibly because middle-aged married people often bear dual family and social responsibilities—such as career development, child-rearing, and elder care—that create high psychosocial stress [22], while also facing unhealthy lifestyle factors like unbalanced diets and physical inactivity.

This study has several strengths. First, it utilized the large, nationally representative CHARLS cohort with multistage stratified probability sampling, ensuring data reliability. Second, RFM was calculated from objective anthropometric measurements, while CVD diagnoses were based on validated self-reported questionnaires, providing robust data sources. Third, we conducted stratified analyses across multiple subgroups and adjusted for exposure- and outcome-related variables to obtain more precise estimates of the RFM-CVD association.

Limitations should also be acknowledged. First, CVD diagnoses relied solely on self-reported medical history rather than clinical confirmation, which may involve recall bias. Second, detailed information on CVD subtypes was unavailable. Third, due to the long follow-up period, RFM may have changed over time, and baseline measurements may not reflect participants' true long-term exposure.

In conclusion, higher RFM levels are linearly and positively associated with increased CVD risk among Chinese middle-aged and elderly adults. RFM may serve as an effective biomarker for CVD risk stratification, and maintaining lower RFM levels may be important for primary CVD prevention. These findings provide a theoretical basis for developing risk stratification and intervention strategies for CVD in this population.

Author Contributions

Wenjian Liang conceptualized and designed the study and revised the manuscript. Huilong Chen performed statistical analysis, drafted the manuscript, and contributed to methodological design. Yunchu Liao and Yuwei Liu assisted with data analysis and manuscript writing. Zhenghui Kong, Xinghui Huang, and Jiahui Xu obtained and preprocessed the data. Na Qi and Yuanping Wang designed and created the figures and tables. All authors reviewed and approved the final manuscript.

Conflicts of Interest: None declared.

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Received: 2025-04-02; Revised: 2025-05-20
Edited by: Jia Mengmeng