Relative fat mass and cardiovascular risk in Chinese adults: A nationwide prospective cohort study

Relative fat mass and cardiovascular risk in Chinese adults: A nationwide prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relative fat mass and cardiovascular risk in Chinese adults: A nationwide prospective cohort study Fang Wang, Xingliang Li, Long Wang, Jingang Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6407425/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Dec, 2025 Read the published version in Nutrition Journal → Version 1 posted 8 You are reading this latest preprint version Abstract Background: The association between relative fat mass (RFM), a sex-specific adiposity index incorporating waist circumference and height, and incident cardiovascular diseases (CVDs) remains underexplored. This study aimed to evaluate RFM’s predictive value for CVD risk in Chinese adults. Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS), a nationwide prospective cohort, we analyzed 7,027 adults aged ≥45 years without baseline CVD (2011-2020). RFM was calculated as 64 - (20 × height [cm]/waist circumference [cm]) for men and 76 - (20 × height/waist circumference) for women. Incident CVDs were identified via physician-diagnosed events. Multivariable Cox regression assessed RFM-CVD associations. The incremental predictive value of RFM beyond conventional risk factors was evaluated using area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Mediation analyses explored biological pathways. Results: Over a median 9-year follow-up, 24.6% (n=1,405) developed CVDs. Each 1-unit RFM increase conferred a 3% higher CVD risk (adjusted HR=1.03, 95% CI: 1.02-1.05). Participants in the highest RFM quartile (Q4: 40.6-52.4) had 46% greater CVD risk versus Q1 (adjusted HR=1.46, 95% CI: 1.05-2.03; P trend =0.004). A linear dose-response relationship was observed ( P nonlinearity =0.327). RFM improved CVD prediction when added to Framingham risk factors (AUC: 0.640 vs. 0.634; NRI=0.140, IDI=0.004; P <0.001). Arterial stiffness, insulin resistance, and dyslipidemia mediated 15.4%, 8.3%, and 3.8% of the RFM-CVD association, respectively ( P <0.05). Conclusions: Higher RFM independently predicts incident CVDs in Chinese adults, with arterial stiffness being the predominant mediator. RFM enhances conventional risk stratification, supporting its utility in primary CVD prevention. Relative fat mass Incident cardiovascular diseases Prospective cohort study Predictive value Mediation analysis China Health and Retirement Longitudinal Study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cardiovascular diseases (CVDs) continue to be the leading cause of premature mortality globally and the primary contributor to disability-adjusted life years 1 . Epidemiological data indicate a marked increase in CVD-related deaths, rising from 12.4 million in 1990 to 19.8 million in 2022. This trend is expected to continue, particularly affecting middle-aged and elderly populations and imposing significant socioeconomic burdens. In China, age-standardized CVD mortality rates rose by 58.5% between 1990 and 2016. Since 2010, CVD has been the leading cause of death in China, accounting for nearly twice the proportion observed in Western countries 1 – 5 . Obesity is a key modifiable risk factor for cardiovascular disease 6 , 7 . Evidence shows that abdominal adiposity, measured by waist-to-hip ratio, is associated with a higher cardiovascular risk than general obesity assessed by body mass index (BMI), primarily due to metabolic dysregulation from visceral adiposity 8 , 9 . BMI, though widely used for obesity classification, has limitations in capturing adiposity heterogeneity. It does not consider variations in body composition and fat distribution, especially the differential risks of visceral versus subcutaneous adiposity 10 – 12 . Conventional BMI stratification cannot adequately distinguish visceral adiposity, a metabolically active fat depot with cardiovascular implications 10 . Waist circumference (WC) measurement is now considered the gold standard for assessing abdominal obesity due to its strong correlation with visceral fat mass 12 – 14 . Recent advances have introduced relative fat mass (RFM), a sex-specific algorithm using waist circumference and height 15 – 17 . RFM shows better diagnostic accuracy in quantifying total body fat percentage than BMI 18 , 19 . Studies link higher RFM to cardiometabolic morbidity, including venous thromboembolism 20 , severity of coronary artery disease (CAD) 21 , hypertension 22 , and type 2 diabetes 17 . The association between RFM and incident CVDs, particularly the dose-response relationship, is not well characterized. To address this knowledge gap, we used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort, to investigate the association between RFM and CVD incidence in adults aged ≥ 45 years. We assessed the clinical utility of adding RFM to conventional Framingham risk factors for CVD risk stratification and performed causal mediation analyses to explore biological pathways underlying the RFM-CVD association. This study aims to establish RFM as a superior anthropometric index for primary CVD prevention in clinical practice. Methods Study Population and Ethical Oversight This cohort study presents a secondary analysis utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative, ongoing longitudinal study. CHARLS, renowned for its demographic representation of China's population, commenced its inaugural nationwide survey in 2011 (Wave 1), recruiting 17,708 participants from 10,257 households across 150 counties and 450 villages/resident committees. Follow-up surveys were executed biennially in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5), with comprehensive details of CHARLS's sampling methodologies, anthropometric assessments, and blood biomarker protocols documented in prior publications. Our analysis focused on 7,027 adults aged 45 years and older within the CHARLS cohort. The exclusion criteria comprised baseline cardiovascular disease (CVD), incomplete anthropometric or biochemical data, and age below 45 years ( Fig. 1 ). The CHARLS study was conducted in adherence to the ethical principles of the Declaration of Helsinki and obtained approval from the Institutional Review Board of Peking University (IRB00001052-11015). All participants provided written informed consent prior to their involvement. The data utilized in this study is accessible to the public via the CHARLS website: https://charls.pku.edu.cn/. The manuscript adheres to the STROBE guidelines for reporting observational studies in epidemiology. Clinical trial number: not applicable. Calculation of the RFM and other parameters The RFM was calculated using sex-specific equations: Men: RFM = 64 – (20 × height [cm]/waist circumference [cm]) Women: RFM = 76 – (20 × height [cm]/waist circumference [cm]) The estimated pulse wave velocity (ePWV) was derived from age and MBP using the previously described formula: ePWV=9.587–0.402*age+4.5610^(-3)*age^2–2.62110^(-5)*age^2*MBP+3.17610^(-3)*age*MBP-1.83210^(-2)*MBP. Mean blood pressure (MBP) was calculated with the formula: MBP = 0.6 × diastolic blood pressure (DBP) + 0.4 × systolic blood pressure (SBP). In these equations, age is expressed in years, DBP and SBP are measured in millimeters of mercury (mmHg), and the ePWV is calculated in meters per second (m/s). This approach maintains unit consistency and appropriateness for the calculations and final result. Triglyceride-glucose (TyG) index = Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2] Assessment of Cardiovascular Disease Events The primary outcome in our study was the incidence of CVD events. In line with previous research, we used a standardized questionnaire to identify incident CVD events. Participants were asked: "Have you been informed by a healthcare professional that you have been diagnosed with a myocardial infarction, coronary artery disease, angina, congestive heart failure, or any other cardiac conditions?" and "Have you been informed by a healthcare professional that you have been diagnosed with a stroke?" Those who reported a new diagnosis of heart disease or stroke during follow-up were classified as having an incident CVD event. The date of CVD diagnosis was recorded as the time between the last interview and the interview in which the incident CVD was reported. Covariates Our study collected comprehensive data including: (i) demographic characteristics (gender, age, residence, marital status); (ii) anthropometric measurements (systolic blood pressure, diastolic blood pressure, body mass index); (iii) lifestyle factors (smoking and alcohol consumption); (iv) medical history (dyslipidemia, hypertension, diabetes, kidney disease, liver disease, and current treatments); and (v) laboratory test results (glycosylated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), serum creatinine (Scr)). We gathered comprehensive data on sociodemographic and health-related factors through structured questionnaires administered by trained interviewers. Health-related factors included categorized smoking and drinking habits, as well as physician-diagnosed medical histories. Marital status was categorized into married and other statuses. Anthropometric assessments were conducted by trained nurses, with blood pressure values determined by the means of two consecutive measurements. Body mass index was calculated using weight divided by height squared. Covariates for our analysis were selected based on previous literature and available variables, ensuring relevance and evidence-based support to enhance the validity of our findings. Statistical analysis Continuous variables with normal distribution were presented as mean ± standard deviation (SD), while those with skewed distributions were shown as median (interquartile range, IQR). Categorical variables were expressed as frequency and percentage (n, %). The Shapiro-Wilk test was used to assess normality. RFM was analyzed both as a continuous variable and categorically in quartiles, with the first quartile as the reference group. One-way analysis of variance (ANOVA) and chi-square tests were used to evaluate baseline characteristic differences across RFM quartiles for continuous and categorical variables, respectively. The Kaplan-Meier method generated cumulative incidence curves for CVDs, with log-rank tests for comparisons. Multivariate Cox regression models calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CVDs associated with RFM, including unadjusted and adjusted models (Model I-III). Covariates included age, gender, smoking status, alcohol intake, comorbidities (dyslipidemia, hypertension, diabetes, kidney disease, and liver disease), medication history (lipid-lowering, antihypertensive, and hypoglycemic treatments), and metabolic biomarkers (HbA1c, TG, TC, and Scr). Trend tests used linear regression, incorporating each RFM quartile's median value as a continuous variable. A generalized additive model with restricted cubic spline terms delineated the nonlinear RFM-incident CVDs risk association. The smoothing curve informed a linear regression model, and the restricted cubic spline regression used four knots covering 100% of RFM values. Subgroup analyses with stratified Cox regression models assessed RFM effects across populations, using the likelihood ratio test for interactions. Participants were stratified by age (＜60 years and ≥60 years), gender, and comorbid conditions (dyslipidemia, kidney disease, hypertension, or diabetes). The biomarkers' incremental predictive value was evaluated via area under curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). Mediation analysis explored arterial stiffness (ePWV), insulin resistance (TyG), and dyslipidemia (low-density lipoprotein cholesterol, LDL-C) as mediators of the RFM-incident CVDs link, using the ‘mediation’ package and adjusting for gender, age, smoking, alcohol consumption, and histories of dyslipidemia, hypertension, and diabetes. All analyses used R (The R Foundation) and Free Statistics software version 2.1, with a two-sided P value <0.05 indicating significance. Results Figure 1 presents the flowchart of the study population screening process. We analyzed 7,027 participants with a median age of 58.0 years, of whom 47.7% were male and 24.4% had CVDs, as detailed in Table 1 . Participants were stratified into quartiles by RFM. CVD incidence rose from 17.6% in Q1 to 30.8% in Q4 ( P < 0.001). Age distribution varied ( P < 0.001), with Q3 participants being younger. Gender distribution differed markedly ( P < 0.001), with Q1 having only males and Q4 no males. BMI and blood pressure showed significant differences ( P < 0.001), with higher values in upper RFM quartiles. Lifestyle factors like smoking and alcohol consumption, comorbidities (hypertension, diabetes, dyslipidemia), and metabolic biomarkers also varied significantly across quartiles ( P < 0.001), generally increasing with RFM, as shown in Table 1 . Table 1 Baseline characteristics of participants classified by quartiles of the RFM Variables Overall Quartiles of the RFM Q1 (15.3–25.4) Q2 (25.4–33.7) Q3 (33.7–40.6) Q4 (40.6–52.4) P-value Number of participants 7027 1757 1756 1757 1757 Incidence of CVDs, n (%) 1716 (24.4) 309 (17.6) 446 (25.4) 420 (23.9) 541 (30.8) < 0.001 Age, years 58.0 (52.0, 65.0) 59.0 (53.0, 65.0) 59.0 (52.0, 65.0) 56.0 (49.0, 62.0) 59.0 (53.0, 67.0) < 0.001 Male, n (%) 3351 (47.7) 1757 (100) 1561 (88.9) 33 (1.9) 0 (0) < 0.001 Married, n (%) 6236 (88.7) 1588 (90.4) 1625 (92.5) 1540 (87.6) 1483 (84.4) < 0.001 Residence (rural), n (%) 4662 (66.3) 1278 (72.7) 1096 (62.4) 1169 (66.5) 1119 (63.7) < 0.001 BMI (kg/m 2 ) 23.2 (21.1, 25.7) 20.9 (19.7, 22.2) 24.4 (22.6, 26.5) 22.3 (20.7, 23.9) 26.2 (24.2, 28.3) < 0.001 Blood pressure, mmHg Systolic 129.1 ± 21.0 125.5 ± 19.4 131.8 ± 20.2 124.8 ± 20.6 134.4 ± 22.2 < 0.001 Diastolic 75.3 ± 12.1 73.7 ± 12.2 77.3 ± 12.3 72.9 ± 11.5 77.2 ± 11.8 < 0.001 Current smoking, n (%) 2763 (39.3) 1386 (78.9) 1140 (64.9) 113 (6.4) 124 (7.1) < 0.001 Current alcohol consumption, n (%) 2821 (40.1) 1168 (66.5) 1101 (62.7) 287 (16.3) 265 (15.1) < 0.001 History of comorbidities Hypertension, n (%) 1605 (22.8) 236 (13.4) 484 (27.6) 292 (16.6) 593 (33.8) < 0.001 Diabetes, n (%) 369 (5.3) 39 (2.2) 111 (6.3) 80 (4.6) 139 (7.9) < 0.001 Dyslipidemia, n (%) 550 (7.8) 59 (3.4) 172 (9.8) 125 (7.1) 194 (11) < 0.001 Kidney disease, n (%) 345 (4.9) 94 (5.4) 94 (5.4) 84 (4.8) 73 (4.2) 0.299 History of medication Antihypertensive treatment, n (%) 1160 (16.5) 152 (8.7) 367 (20.9) 187 (10.6) 454 (25.8) < 0.001 Hypoglycemic treatment, n (%) 230 (3.3) 24 (1.4) 74 (4.2) 50 (2.8) 82 (4.7) < 0.001 Lipid-lowering treatment, n (%) 277 (3.9) 28 (1.6) 97 (5.5) 49 (2.8) 103 (5.9) < 0.001 Metabolic biomarkers Total cholesterol, mg/dL 193.7 ± 37.7 184.4 ± 36.0 192.2 ± 37.5 195.6 ± 36.2 202.5 ± 39.0 < 0.001 Triglycerides, mg/dL 105.3 (74.3, 153.1) 85.8 (62.8, 122.1) 109.7 (78.8, 162.8) 101.8 (74.3, 144.3) 126.6 (92.0, 181.4) < 0.001 Serum creatinine, mg/dL 0.8 (0.6, 0.9) 0.8 (0.7, 0.9) 0.8 (0.7, 1.0) 0.7 (0.6, 0.8) 0.7 (0.6, 0.8) < 0.001 HbA1c, % 5.3 ± 0.8 5.1 ± 0.7 5.3 ± 0.8 5.2 ± 0.8 5.4 ± 0.9 < 0.001 Data are shown as mean ± SD, median (IQR), or n (%). Abbreviations: BMI, body mass index; CVDs, cardiovascular diseases; RFM, relative fat mass; HbA1c, glycosylated hemoglobin A1c. Kaplan-Meier analysis revealed progressive divergence in cardiovascular event rates across RFM quartiles over nine years (log-rank P < 0.001). The highest-risk quartile (Q4) demonstrated 30.8% cumulative CVD incidence versus 17.6% in Q1 at nine-year follow-up, with consistent risk stratification across interim timepoints (Fig. 2 ). We assessed the link between RFM and incident CVDs using Cox regression models. In the crude model, each 1-unit RFM increase was associated with a 3% higher CVD risk (HR = 1.03, 95% CI: 1.02–1.03). After adjusting for age and gender in Model I, the HR increased to 1.06 (95% CI: 1.05–1.07). Further adjustments in Models II and III slightly reduced the association, but they remained significant (HR = 1.03, 95% CI: 1.02–1.05 in both models). Analyzing RFM categorically, participants in the highest quartile (Q4: 40.6–52.4) faced a significantly higher CVD risk compared to the lowest quartile (Q1: 15.3–25.4) in the fully adjusted Model III (HR = 1.46, 95% CI: 1.05–2.03). A significant linear trend was observed across RFM quartiles ( P for trend = 0.004) (Table 2 ). After adjustment, the HR of incident CVDs rose with RFM, showing a linear relationship ( P for non-linearity = 0.327). Each RFM increase was linked to a proportional CVD risk elevation (Fig. 3 ). These findings indicate that higher RFM levels are independently associated with an increased risk of incident CVDs, with the relationship maintaining significance after comprehensive covariate adjustment. Table 2 Associations of the RFM with the risk of incident CVDs RFM Number of participants Incident CVDs (n, %) Crude model HR (95% CI) Model I HR (95% CI) Model II HR (95% CI) Model III HR (95% CI) Continuous 7027 1716 (24.4) 1.03 (1.02–1.03) 1.06 (1.05–1.07) 1.03 (1.02–1.05) 1.03 (1.02–1.05) Categorical Q1 (15.3–25.4) 1757 309 (17.6) 1(Ref) 1(Ref) 1(Ref) 1(Ref) Q2 (25.4–33.7) 1756 446 (25.4) 1.51 (1.30–1.74) 1.54 (1.33–1.78) 1.30 (1.12–1.52) 1.28 (1.10–1.50) Q3 (33.7–40.6) 1757 420 (23.9) 1.41 (1.22–1.64) 1.79 (1.29–2.47) 1.37 (0.98–1.89) 1.33 (0.97–1.84) Q4 (40.6–52.4) 1757 541 (30.8) 1.90 (1.65–2.18) 2.29 (1.65–3.17) 1.53 (1.10–2.12) 1.46 (1.05–2.03) P for trend <0.001 <0.001 0.001 0.004 Model I adjusts for age and gender; Model II adjusts for model I + smoking status, alcohol intake, history of comorbidities, and history of medication; Model III adjusts for model II + metabolic biomarkers. Abbreviations: CVDs, cardiovascular diseases; CI, confidence interval; RFM, relative fat mass; HR, hazard ratio; Ref, reference. Subgroup analyses revealed differential associations between RFM quartiles and clinical outcomes (Fig. 4 ). Among participants aged < 60 years, a significant positive trend was observed across quartiles (adjusted HR for Q4 vs Q1: 1.84, 95% CI 1.17–2.90; P for trend = 0.014), whereas this association attenuated in those ≥ 60 years ( P for trend = 0.05; P for interaction = 0.084). Men demonstrated a progressive risk elevation with higher RFM ( P for trend = 0.002), while hypertension modified this relationship ( P for interaction = 0.463), with stronger trends in non-hypertensive individuals ( P 0.1). The association remained robust across most subgroups, particularly in participants without comorbidities. The addition of RFM to the Framingham risk model demonstrated statistically significant incremental predictive value for incident CVDs (Table 3 ). The model combining traditional risk factors (age, gender, residence, marital status, smoking/drinking status, hypertension, diabetes, kidney disease, dyslipidemia) with RFM yielded an AUC of 0.640 (95% CI: 0.625–0.655), showing a marginal yet significant improvement over the base model (AUC: 0.634, 95% CI: 0.619–0.649; P = 0.049). Reclassification metrics further supported this enhancement, with a continuous NRI of 0.1402 (95% CI: 0.0859–0.1944; P < 0.001) and an IDI of 0.0041 (95% CI: 0.0025–0.0058; P < 0.001). These results indicate that RFM provides clinically meaningful stratification beyond conventional cardiovascular risk factors. Table 3 Incremental predictive value of RFM for incident CVDs AUC NRI IDI Estimate (95% CI) P value Estimate (95% CI) P value Estimate (95% CI) P value Framingham model 0.634 (0.619, 0.649) Reference Reference Framingham model + RFM 0.640 (0.625, 0.655) 0.049 0.1402 (0.0859, 0.1944) < 0.001 0.0041 (0.0025, 0.0058) < 0.001 Framingham model included: age, gender, residence, marital status, smoking and drinking status, hypertension, diabetes, kidney disease, and dyslipidemia. Abbreviations: AUC, area under curve; CVDs, cardiovascular diseases; CI, confidence interval; RFM, relative fat mass; NRI, net reclassification improvement; IDI, integrated discrimination improvement. Mediation analysis showed that arterial stiffness (ePWV), insulin resistance (TyG), and dyslipidemia (LDL-C) partially mediated the association between RFM and incident CVDs (Fig. 5 ). Arterial stiffness contributed the most to the mediation effect, accounting for 15.44% (95%CI: 9.35%, 24.59%) of the total effect, followed by insulin resistance (8.25%, 95%CI: 0.03%, 17.36%) and dyslipidemia (3.84%, 95%CI: 1.31%, 7.50%). All indirect effects were statistically significant ( P < 0.0001 for ePWV and LDL-C; P = 0.048 for TyG). Discussion Our nationwide prospective cohort study provides critical insights into the role of RFM in CVD risk stratification among Chinese adults. Three key findings emerge: (1) RFM exhibits a linear, dose-dependent association with incident CVDs, independent of traditional risk factors; (2) RFM enhances predictive accuracy beyond Framingham risk parameters; and (3) arterial stiffness mediates 15.4% of the RFM-CVD relationship, surpassing contributions from insulin resistance and dyslipidemia. These results underscore RFM’s clinical relevance, particularly in populations prone to central adiposity, while highlighting mechanistic pathways distinct from conventional obesity metrics. Our study represents the first prospective cohort analysis to systematically evaluate the longitudinal association between RFM and incident CVDs in Chinese adults, while concurrently assessing its predictive utility and mediating pathways. Wang et al. demonstrated in a prospective trial of 26,754 individuals that elevated RFM correlated with metabolic abnormalities and increased risks of coronary artery disease (CAD) and hypertension. Notably, their work identified sex-specific nonlinear associations, with cardiovascular mortality risk escalating significantly when RFM exceeded thresholds of 30 in men and 45 in women 23 . These findings align with longitudinal data from Peng et al. , who established RFM’s predictive capacity for hypertension incidence in Chinese adults, proposing sex-specific cutoffs (24.67 for men, 35.73 for women) to stratify risk 22 . Cross-sectional evidence further supports RFM’s clinical relevance: Zwartkruis et al. identified robust associations between RFM and CAD, heart failure, and atrial fibrillation in 95,003 participants 24 , while Shen et al. reported significant links between RFM and self-reported CVD prevalence in the general Chinese population, advocating its utility for primary screening 25 . The linear dose-response relationship observed in our cohort (adjusted HR 1.46 for Q4 vs. Q1; P trend =0.004) reinforces these earlier observations while extending them to hard clinical endpoints. RFM’s incremental predictive value beyond traditional risk models (ΔAUC 0.006; NRI = 0.140, IDI = 0.004) substantiates emerging evidence that abdominal adiposity indices enhance conventional risk stratification 26 – 30 . Mechanistically, RFM’s superiority over isolated waist circumference measurements likely stems from its mathematical integration of height—a modification that accounts for body proportionality and improves detection of visceral adiposity, particularly in normal-weight individuals with disproportionate fat distribution. This biological plausibility supports RFM’s clinical value in identifying high-risk phenotypes often overlooked by BMI categorization 13 , 18 . By corroborating RFM’s independent association with incident CVD and quantifying its predictive utility within a nationally representative Chinese cohort, our findings strengthen the rationale for incorporating this anthropometric index into routine cardiovascular risk assessment protocols. Abdominal obesity is characterized by adipocyte hyperplasia and hypertrophy, which induce functional alterations in adipose tissue dynamics. These changes include dysregulated release of proinflammatory adipocytokines 31 , excessive reactive oxygen species (ROS) generation 32 , and diminished nitric oxide (NO) bioavailability 33 — molecular perturbations that collectively promote vascular endothelial dysfunction, insulin resistance, and atherogenesis 34 , 35 . The pathophysiological cascade extends to lipid metabolism derangements, with obesity driving elevated circulating levels of very-low-density lipoprotein cholesterol (VLDL-C) and triglycerides (TG), increased formation of atherogenic small dense low-density lipoprotein (sdLDL) particles, and reduced high-density lipoprotein cholesterol (HDL-C) concentrations 6 . These lipid abnormalities synergistically accelerate atherosclerotic plaque formation and progression 6 . Hemodynamically, obesity-induced expansion of intravascular volume and elevation of systemic vascular resistance precipitate hypertension while promoting structural remodeling of arterial walls 36 . Our mediation analysis elucidates novel mechanistic pathways through which RFM influences cardiovascular risk. Arterial stiffness — quantified noninvasively via ePWV 37 – 39 — emerged as the predominant mediator, accounting for 15.4% of RFM's cardiovascular effects. This finding corroborates experimental evidence demonstrating that perivascular adipose tissue secretes paracrine factors (e.g., leptin, resistin) that impair endothelial-dependent vasodilation and promote collagen deposition in the vascular media 6 , 27 , 33 , 36 . The TyG index (reflecting insulin resistance) and LDL-C (representing dyslipidemia) mediated 8.3% and 3.8% of this association, respectively. The comparatively modest contribution of insulin resistance contrasts with observations in diabetic cohorts 40 – 42 , suggesting that RFM-associated cardiovascular risk operates through both canonical metabolic pathways and distinct biomechanical mechanisms related to arterial compliance. This study benefits from a nationally representative sample, standardized measurements, and advanced statistical methods to explore biological pathways. However, several limitations should be noted. First, while self-reported cardiovascular diagnoses carry a risk of inaccuracy, previous checks showed that 84% of these reports matched hospital records in the CHARLS database. Second, we lacked direct body fat measurements (e.g., MRI scans) to compare RFM with precise fat distribution data. Third, residual confounding from unmeasured factors cannot be excluded. Fourth, as an observational study, it cannot prove direct cause-and-effect relationships. Future research should focus on two priorities: 1) defining RFM risk thresholds tailored to Asian populations, where central obesity develops earlier than in Western groups; and 2) testing RFM in real-world clinics to see if it improves patient outcomes. Combining RFM with simple artery health tests (e.g., blood pressure-based stiffness assessments) could create practical risk screening tools for communities with limited medical resources. These steps would help translate our findings into better prevention strategies, particularly in regions battling obesity-related heart disease epidemics. Conclusion Our prospective cohort analysis of 7,027 Chinese adults demonstrates that elevated RFM, a sex-specific abdominal adiposity index, is independently associated with a 46% increased risk of incident CVDs in fully adjusted models (HR = 1.46, 95% CI:1.05–2.03), exhibiting a linear dose-response relationship ( P for trend = 0.004). These findings support RFM’s clinical utility for CVD risk stratification, where it provided incremental predictive value over Framingham risk factors (ΔAUC = 0.006, NRI = 14.0%, IDI = 0.41%; all P < 0.05). Mediation analyses revealed arterial stiffness (15.4% of total effect), insulin resistance (8.3%), and dyslipidemia (3.8%) as key biological pathways linking RFM to CVD pathogenesis. While RFM shows promise as a cost-effective adiposity metric for primary prevention, population-specific validation and investigation into visceral fat dynamics through advanced imaging are warranted to optimize its clinical implementation. Abbreviations CHARLS China Health and Retirement Longitudinal Study ePWV estimated pulse wave velocity RFM relative fat mass Scr serum creatinine TC total cholesterol TG triglycerides TyG triglyceride-glucose WC waist circumference Declarations Acknowledgements We are grateful to all CHARLS participants and staff for their contributions, which were essential to completing this study. We also thank the Free Statistics team for their technical support and provision of critical analytical tools. Special thanks to Dr. Jie Liu from the Chinese PLA General Hospital's Department of Vascular and Endovascular Surgery for statistical expertise, study design advice, and editorial assistance. Author contributions Fang Wang and Jingang Zheng developed the study concept and design, managed data acquisition, performed statistical analysis, interpreted results, and guided the overall study direction. Xingliang Li and Long Wang managed data acquisition and quality control. Fang Wang drafted the manuscript, with Jingang Zheng providing critical revisions. All authors reviewed and approved the final manuscript. Funding This work was funded by grants from the National High Level Hospital Clinical Research Funding & Elite Medical Professionals Project of China-Japan Friendship Hospital (Number: ZRJY2021-QM05), National Natural Science Foundation of China (Number: 81700411), Young Elite Scientists Sponsorship Program by China Association of Chinese Medicine (Number: 2021-QNRC2-A02) and National Key Clinical Specialty Construction Project of China (Number: 2020-QTL-009). Data availability The data utilized in this study is accessible to the public via the CHARLS website: https://charls.pku.edu.cn/. Human ethics and consent to participate declarations The CHARLS study was conducted in adherence to the ethical principles of the Declaration of Helsinki and obtained approval from the Institutional Review Board of Peking University (IRB00001052-11015). All participants provided written informed consent prior to their involvement. Clinical trial number: not applicable. Consent to publish declarations Not applicable. Competing interests The authors declare no competing interests. Disclosures None References Evangelou K, Vasileiou PVS, Papaspyropoulos A, Hazapis O, Petty R, Demaria M, Gorgoulis VG. Cellular senescence and cardiovascular diseases: moving to the heart of the problem. Physiol Rev. 2023;103:609–47. 10.1152/physrev.00007.2022 . Zhao Y, Man Ki K, Catherine Mary S, Jing L. Changes in Cardiovascular Disease Burden in China after Release of the 2011 Chinese Guidelines for Cardiovascular Disease Prevention: A Bayesian Causal Impact Analysis. CVIA. 2023;8. 10.15212/CVIA.2023.0069 . Liu S, Li Y, Zeng X, Wang H, Yin P, Wang L, Liu Y, Liu J, Qi J, Ran S, et al. Burden of Cardiovascular Diseases in China, 1990–2016: Findings From the 2016 Global Burden of Disease Study. JAMA Cardiol. 2019;4:342–52. 10.1001/jamacardio.2019.0295 . 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Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women. Circulation. 2008;117:1658–67. 10.1161/circulationaha.107.739714 . Balkau B, Deanfield JE, Després JP, Bassand JP, Fox KA, Smith SC Jr., Barter P, Tan CE, Van Gaal L, Wittchen HU, et al. International Day for the Evaluation of Abdominal Obesity (IDEA): a study of waist circumference, cardiovascular disease, and diabetes mellitus in 168,000 primary care patients in 63 countries. Circulation. 2007;116:1942–51. 10.1161/circulationaha.106.676379 . Piché ME, Poirier P, Lemieux I, Després JP. Overview of Epidemiology and Contribution of Obesity and Body Fat Distribution to Cardiovascular Disease: An Update. Prog Cardiovasc Dis. 2018;61:103–13. 10.1016/j.pcad.2018.06.004 . Hainer V, Aldhoon-Hainerová I. Obesity paradox does exist. Diabetes Care. 2013;36(Suppl 2):S276–281. 10.2337/dcS13-2023 . Clark AL, Fonarow GC, Horwich TB. Waist circumference, body mass index, and survival in systolic heart failure: the obesity paradox revisited. J Card Fail. 2011;17:374–80. 10.1016/j.cardfail.2011.01.009 . Ross R, Neeland IJ, Yamashita S, Shai I, Seidell J, Magni P, Santos RD, Arsenault B, Cuevas A, Hu FB, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol. 2020;16:177–89. 10.1038/s41574-019-0310-7 . Zhao G, Ford ES, Li C, Tsai J, Dhingra S, Balluz LS. Waist circumference, abdominal obesity, and depression among overweight and obese U.S. adults: National Health and Nutrition Examination Survey 2005–2006. BMC Psychiatry. 2011;11:130. 10.1186/1471-244x-11-130 . Wu X, Song Y, Wu S. The development and evaluation of nine obesity-based indices for gallstones in U.S. adults. Int J Surg. 2025;111:2348–57. 10.1097/js9.0000000000002237 . Wang D, Chen Z, Wu Y, Ren J, Shen D, Hu G, Mao C. Association between two novel anthropometric measures and type 2 diabetes in a Chinese population. Diabetes Obes Metab. 2024;26:3238–47. 10.1111/dom.15651 . Suthahar N, Wang K, Zwartkruis VW, Bakker SJL, Inzucchi SE, Meems LMG, Eijgenraam TR, Ahmadizar F, Sijbrands EG, Gansevoort RT, et al. Associations of relative fat mass, a new index of adiposity, with type-2 diabetes in the general population. Eur J Intern Med. 2023;109:73–8. 10.1016/j.ejim.2022.12.024 . Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage A cross-sectional study in American adult individuals. Sci Rep. 2018;8:10980. 10.1038/s41598-018-29362-1 . Ghulam A, Gianfagna F, Bonaccio M, Costanzo S, Di Castelnuovo A, De Curtis A, Gialluisi A, Cerletti C, Donati MB, de Gaetano G, et al. Association between BMI, RFM and mortality and potential mediators: Prospective findings from the Moli-sani study. Int J Obes (Lond). 2023;47:697–708. 10.1038/s41366-023-01313-5 . Caiano LM, Costanzo S, Panzera T, Di Castelnuovo A, de Gaetano G, Donati MB, Ageno W, Iacoviello L. Association between body mass index, waist circumference, and relative fat mass with the risk of first unprovoked venous thromboembolism. Nutr Metab Cardiovasc Dis. 2021;31:3122–30. 10.1016/j.numecd.2021.07.018 . Efe S, Karagoz A, Dogan C, Bayram Z, Kalkan S, Altıntas MS, Yuksel Y, Karabag T, Ayca B, Ozdemir N. Relative Fat Mass Index can be solution for obesity paradox in coronary artery disease severity prediction calculated by SYNTAX Score. Postgrad Med J. 2021;97:434–41. 10.1136/postgradmedj-2020-138926 . Yu P, Huang T, Hu S, Yu X. Predictive value of relative fat mass algorithm for incident hypertension: a 6-year prospective study in Chinese population. BMJ Open. 2020;10:e038420. 10.1136/bmjopen-2020-038420 . Wang J, Guan J, Huang L, Li X, Huang B, Feng J, Zhang Y, Zhang J. Sex differences in the associations between relative fat mass and all-cause and cardiovascular mortality: A population-based prospective cohort study. Nutr Metab Cardiovasc Dis. 2024;34:738–54. 10.1016/j.numecd.2023.10.034 . Zwartkruis VW, Suthahar N, Idema DL, Mahmoud B, van Deutekom C, Rutten FH, van der Schouw YT, Rienstra M, de Boer RA. Relative fat mass and prediction of incident atrial fibrillation, heart failure and coronary artery disease in the general population. Int J Obes (Lond). 2023;47:1256–62. 10.1038/s41366-023-01380-8 . Shen W, Cai L, Wang B, Wang Y, Wang N, Lu Y. Associations of Relative Fat Mass, a Novel Adiposity Indicator, with Non-Alcoholic Fatty Liver Disease and Cardiovascular Disease: Data from SPECT-China. Diabetes Metab Syndr Obes. 2023;16:2377–87. 10.2147/dmso.S423272 . Lopes HF, Corrêa-Giannella ML, Consolim-Colombo FM, Egan BM. Visceral adiposity syndrome. Diabetol Metab Syndr. 2016;8:40. 10.1186/s13098-016-0156-2 . Sun K, Kusminski CM, Scherer PE. Adipose tissue remodeling and obesity. J Clin Invest. 2011;121:2094–101. 10.1172/jci45887 . Chen Q, Zhang Z, Luo N, Qi Y. Elevated visceral adiposity index is associated with increased stroke prevalence and earlier age at first stroke onset: Based on a national cross-sectional study. Front Endocrinol (Lausanne). 2022;13:1086936. 10.3389/fendo.2022.1086936 . Suk SH, Sacco RL, Boden-Albala B, Cheun JF, Pittman JG, Elkind MS, Paik MC. Abdominal obesity and risk of ischemic stroke: the Northern Manhattan Stroke Study. Stroke. 2003;34:1586–92. 10.1161/01.Str.0000075294.98582.2f . Ye J, Hu Y, Chen X, Yin Z, Yuan X, Huang L, Li K. Association between the weight-adjusted waist index and stroke: a cross-sectional study. BMC Public Health. 2023;23:1689. 10.1186/s12889-023-16621-8 . Ouchi N, Parker JL, Lugus JJ, Walsh K. Adipokines in inflammation and metabolic disease. Nat Rev Immunol. 2011;11:85–97. 10.1038/nri2921 . Marseglia L, Manti S, D'Angelo G, Nicotera A, Parisi E, Di Rosa G, Gitto E, Arrigo T. Oxidative stress in obesity: a critical component in human diseases. Int J Mol Sci. 2014;16:378–400. 10.3390/ijms16010378 . Virdis A. Endothelial Dysfunction in Obesity: Role of Inflammation. High Blood Press Cardiovasc Prev. 2016;23:83–5. 10.1007/s40292-016-0133-8 . Lau DC, Dhillon B, Yan H, Szmitko PE, Verma S. Adipokines: molecular links between obesity and atheroslcerosis. Am J Physiol Heart Circ Physiol. 2005;288:H2031–2041. 10.1152/ajpheart.01058.2004 . Wei J, Liu X, Xue H, Wang Y, Shi Z. Comparisons of Visceral Adiposity Index, Body Shape Index, Body Mass Index and Waist Circumference and Their Associations with Diabetes Mellitus in Adults. Nutrients. 2019;11. 10.3390/nu11071580 . Koliaki C, Liatis S, Kokkinos A. Obesity and cardiovascular disease: revisiting an old relationship. Metabolism. 2019;92:98–107. 10.1016/j.metabol.2018.10.011 . Huang H, Bu X, Pan H, Yang S, Cheng W, Shubhra QTH, Ma N. Estimated pulse wave velocity is associated with all-cause and cardio-cerebrovascular disease mortality in stroke population: Results from NHANES (2003–2014). Front Cardiovasc Med. 2023;10:1140160. 10.3389/fcvm.2023.1140160 . Liu HR, Li CY, Xia X, Chen SF, Lu XF, Gu DF, Liu FC, Huang JF. Association of Estimated Pulse Wave Velocity and the Dynamic Changes in Estimated Pulse Wave Velocity with All-Cause Mortality among Middle-Aged and Elderly Chinese. Biomed Environ Sci. 2022;35:1001–11. 10.3967/bes2022.129 . Heffernan KS, Jae SY, Loprinzi PD. Association Between Estimated Pulse Wave Velocity and Mortality in U.S. Adults. J Am Coll Cardiol. 2020;75:1862–4. 10.1016/j.jacc.2020.02.035 . Alizargar J, Bai CH, Hsieh NC, Wu SV. Use of the triglyceride-glucose index (TyG) in cardiovascular disease patients. Cardiovasc Diabetol. 2020;19:8. 10.1186/s12933-019-0982-2 . Zhang Q, Xiao S, Jiao X, Shen Y. The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001–2018. Cardiovasc Diabetol. 2023;22:279. 10.1186/s12933-023-02030-z . Lopez-Jaramillo P, Gomez-Arbelaez D, Martinez-Bello D, Abat MEM, Alhabib KF, Avezum Á, Barbarash O, Chifamba J, Diaz ML, Gulec S, et al. Association of the triglyceride glucose index as a measure of insulin resistance with mortality and cardiovascular disease in populations from five continents (PURE study): a prospective cohort study. Lancet Healthy Longev. 2023;4:e23–33. 10.1016/s2666-7568(22)00247-1 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Dec, 2025 Read the published version in Nutrition Journal → Version 1 posted Editorial decision: Revision requested 01 Sep, 2025 Reviews received at journal 23 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 15 Apr, 2025 Editor assigned by journal 14 Apr, 2025 Submission checks completed at journal 10 Apr, 2025 First submitted to journal 08 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6407425","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443079725,"identity":"6f4b326b-b8d7-4425-8bcb-1a009772b957","order_by":0,"name":"Fang Wang","email":"","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Wang","suffix":""},{"id":443079726,"identity":"f0b2f0ee-6564-484a-927d-f119914a2232","order_by":1,"name":"Xingliang Li","email":"","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xingliang","middleName":"","lastName":"Li","suffix":""},{"id":443079728,"identity":"6b1b949d-a7d0-494d-8dd2-25978af01651","order_by":2,"name":"Long Wang","email":"","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Wang","suffix":""},{"id":443079731,"identity":"7f9a3cf1-6d96-4287-8b84-a768efaf3fd1","order_by":3,"name":"Jingang Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYFCChIQDEAYPiLCBMYjXkgZjGODTAmOAVR4mrEW3PeHh4YJfdnny/mcPPi74dT6xn/3sAeaCij84tZideZBweGZfcrHhgXPJxjP7bifO7MlLYJ5xBrctZjcSEg7z9jAnbmzsMZPm7bmduOFAjgEzbxtBLfWJG5t5zH/z9pxL3H/+DVDLPwJaeH4cTpzPxmPGzPPjQOIGCZAtDXi0gPzC23A8cQMPj7E0b0Oy8YwbbwwO8xwzxq3leE7yZ54/1Ynz+88YAhl2sv39OYaPeWrkcGoBxkICA2MbMB4OANlAhmMDkD6ARz0QsAPlgZEgD1IKZNjjVz0KRsEoGAUjEQAAPPBglqXlJ3cAAAAASUVORK5CYII=","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jingang","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2025-04-09 03:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6407425/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6407425/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12937-025-01267-6","type":"published","date":"2025-12-13T15:59:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82118220,"identity":"506fb9ef-2488-42d1-a2d9-628b57d5f4d4","added_by":"auto","created_at":"2025-05-07 03:05:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Flowchart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis flow chart delineates the participant selection process for our study investigating the association between RFM and incident cardiovascular diseases.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6407425/v1/a8832b6fdfcb5b59266b2e3d.png"},{"id":82118221,"identity":"6ffcbf2e-c503-43fa-afe4-83c7a73e9db1","added_by":"auto","created_at":"2025-05-07 03:05:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier plot of cumulative incidence of new-onset CVDs based on RFM quartiles.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis Kaplan-Meier curve illustrates the cumulative hazard of incident cardiovascular diseases across four RFM quartiles (RFM_Q1 to RFM_Q4) from 2011 to 2020. The y-axis represents the cumulative hazard ranging from 0% to 30%, while the x-axis denotes the follow-up time in years. The number at risk for each quartile is indicated below the curve, showing a gradual decrease over time. A highly significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) exists among the quartiles, indicating that RFM levels are strongly associated with the risk of cardiovascular events, with higher RFM quartiles corresponding to greater cumulative hazards. RFM, relative fat mass.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6407425/v1/9a366fbb112d376437671535.png"},{"id":82118222,"identity":"23952f35-3f4d-471c-b00c-8f9c8e0dc19f","added_by":"auto","created_at":"2025-05-07 03:05:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29321,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose-response relationship\u003c/strong\u003e \u003cstrong\u003eof RFM with the risk of cardiovascular diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis figure presents the dose-response relationship between RFM and the adjusted hazard ratio (HR) of incident cardiovascular diseases. The x-axis represents RFM values ranging from 20 to 50, while the y-axis shows the adjusted HR on a logarithmic scale from 0.1 to 5.0. The count of participants at each RFM level is displayed at the top. The P-value for non-linearity is 0.327, suggesting a roughly linear relationship between RFM and the risk of cardiovascular events. As RFM increases, the adjusted HR for incident cardiovascular diseases also rises, indicating a dose-dependent association. The red solid line represents the HR modeled using a restricted cubic spline with 4 knots of RFM. RFM, relative fat mass.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6407425/v1/684e4286cf8729ed454afa68.png"},{"id":82118223,"identity":"28859c1d-f088-4d33-8620-f5cec74dfaaa","added_by":"auto","created_at":"2025-05-07 03:05:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84056,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of subgroup analyses for the effect of RFM on the risk of cardiovascular diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis forest plot displays the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for incident cardiovascular diseases across different subgroups based on age, gender, hypertension, diabetes, dyslipidemia, and kidney disease. Each subgroup is divided into quartiles of RFM (Q1 to Q4), with corresponding event numbers and percentages provided. The reference group (Q1) has an HR of 1. RFM, relative fat mass.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6407425/v1/774c4cb67cf2d904130b51ce.png"},{"id":82118224,"identity":"740a17e9-5ad8-47b2-be71-ee119fedee3e","added_by":"auto","created_at":"2025-05-07 03:05:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation analysis on associations between RFM with incident CVDs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanels A, B, and C of this figure present mediation analyses exploring the pathways through which RFM influences incident cardiovascular diseases. In Panel A, arterial stiffness (ePWV) mediates the effect of RFM on CVDs, with a total effect of 0.0027 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) and an indirect effect of 0.0004 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001). The proportion mediated by arterial stiffness is 15.44% (95% CI: 9.35%, 24.59%; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001). Panel B shows that insulin resistance (TyG) partially mediates the relationship, with a total effect of 0.0028 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001), an indirect effect of 0.0002 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001), and a proportion mediated of 8.25% (95% CI: 0.03%, 17.36%; \u003cem\u003eP\u003c/em\u003e=0.048). Panel C indicates that dyslipidemia (LDL-C) also plays a mediating role, with a total effect of 0.0029 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001), an indirect effect of 0.0001 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001), and a proportion mediated of 3.84% (95% CI: 1.31%, 7.50%; \u003cem\u003eP\u003c/em\u003e=0.002). TyG, triglyceride-glucose; RFM, relative fat mass; ePWV, estimated pulse wave velocity; LDL-C, low-density lipoprotein cholesterol.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6407425/v1/332d759dc91203f67b58e4bd.png"},{"id":98244111,"identity":"aaac5a20-8f73-43be-988b-80bd73eec020","added_by":"auto","created_at":"2025-12-15 16:13:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312526,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6407425/v1/3580536b-e92e-4995-abf2-900f19ee4a21.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relative fat mass and cardiovascular risk in Chinese adults: A nationwide prospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) continue to be the leading cause of premature mortality globally and the primary contributor to disability-adjusted life years\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Epidemiological data indicate a marked increase in CVD-related deaths, rising from 12.4\u0026nbsp;million in 1990 to 19.8\u0026nbsp;million in 2022. This trend is expected to continue, particularly affecting middle-aged and elderly populations and imposing significant socioeconomic burdens. In China, age-standardized CVD mortality rates rose by 58.5% between 1990 and 2016. Since 2010, CVD has been the leading cause of death in China, accounting for nearly twice the proportion observed in Western countries\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eObesity is a key modifiable risk factor for cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Evidence shows that abdominal adiposity, measured by waist-to-hip ratio, is associated with a higher cardiovascular risk than general obesity assessed by body mass index (BMI), primarily due to metabolic dysregulation from visceral adiposity\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. BMI, though widely used for obesity classification, has limitations in capturing adiposity heterogeneity. It does not consider variations in body composition and fat distribution, especially the differential risks of visceral versus subcutaneous adiposity\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Conventional BMI stratification cannot adequately distinguish visceral adiposity, a metabolically active fat depot with cardiovascular implications\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Waist circumference (WC) measurement is now considered the gold standard for assessing abdominal obesity due to its strong correlation with visceral fat mass\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Recent advances have introduced relative fat mass (RFM), a sex-specific algorithm using waist circumference and height\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. RFM shows better diagnostic accuracy in quantifying total body fat percentage than BMI\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Studies link higher RFM to cardiometabolic morbidity, including venous thromboembolism\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, severity of coronary artery disease (CAD)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, hypertension\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe association between RFM and incident CVDs, particularly the dose-response relationship, is not well characterized. To address this knowledge gap, we used data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative prospective cohort, to investigate the association between RFM and CVD incidence in adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years. We assessed the clinical utility of adding RFM to conventional Framingham risk factors for CVD risk stratification and performed causal mediation analyses to explore biological pathways underlying the RFM-CVD association. This study aims to establish RFM as a superior anthropometric index for primary CVD prevention in clinical practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population and Ethical Oversight\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cohort study presents a secondary analysis utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative, ongoing longitudinal study. CHARLS, renowned for its demographic representation of China\u0026apos;s population, commenced its inaugural nationwide survey in 2011 (Wave 1), recruiting 17,708 participants from 10,257 households across 150 counties and 450 villages/resident committees. Follow-up surveys were executed biennially in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5), with comprehensive details of CHARLS\u0026apos;s sampling methodologies, anthropometric assessments, and blood biomarker protocols documented in prior publications.\u003c/p\u003e\n\u003cp\u003eOur analysis focused on 7,027 adults aged 45 years and older within the CHARLS cohort. The exclusion criteria comprised baseline cardiovascular disease (CVD), incomplete anthropometric or biochemical data, and age below 45 years (\u003cstrong\u003eFig. 1\u003c/strong\u003e). The CHARLS study was conducted in adherence to the ethical principles of the Declaration of Helsinki and obtained approval from the Institutional Review Board of Peking University (IRB00001052-11015). All participants provided written informed consent prior to their involvement.\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study is accessible to the public via the CHARLS website: https://charls.pku.edu.cn/. The manuscript adheres to the STROBE guidelines for reporting observational studies in epidemiology. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalculation of the RFM and other parameters\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eThe RFM was calculated using sex-specific equations:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eMen:\u003c/em\u003e RFM = 64 \u0026ndash; (20 \u0026times; height [cm]/waist circumference [cm])\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWomen:\u003c/em\u003e RFM = 76 \u0026ndash; (20 \u0026times; height [cm]/waist circumference [cm])\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eThe estimated pulse wave velocity (ePWV) was derived from age and MBP using the previously described formula:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eePWV=9.587\u0026ndash;0.402*age+4.5610^(-3)*age^2\u0026ndash;2.62110^(-5)*age^2*MBP+3.17610^(-3)*age*MBP-1.83210^(-2)*MBP.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMean blood pressure (MBP) was calculated with the formula: MBP = 0.6 \u0026times; diastolic blood pressure (DBP) + 0.4 \u0026times; systolic blood pressure (SBP). In these equations, age is expressed in years, DBP and SBP are measured in millimeters of mercury (mmHg), and the ePWV is calculated in meters per second (m/s). This approach maintains unit consistency and appropriateness for the calculations and final result.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003eTriglyceride-glucose (TyG) index = Ln [fasting triglyceride (mg/dL) \u0026times; fasting glucose (mg/dL)/2]\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Cardiovascular Disease Events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome in our study was the incidence of CVD events. In line with previous research, we used a standardized questionnaire to identify incident CVD events. Participants were asked: \u0026quot;Have you been informed by a healthcare professional that you have been diagnosed with a myocardial infarction, coronary artery disease, angina, congestive heart failure, or any other cardiac conditions?\u0026quot; and \u0026quot;Have you been informed by a healthcare professional that you have been diagnosed with a stroke?\u0026quot; Those who reported a new diagnosis of heart disease or stroke during follow-up were classified as having an incident CVD event. The date of CVD diagnosis was recorded as the time between the last interview and the interview in which the incident CVD was reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study collected comprehensive data including: (i) demographic characteristics (gender, age, residence, marital status); (ii) anthropometric measurements (systolic blood pressure, diastolic blood pressure, body mass index); (iii) lifestyle factors (smoking and alcohol consumption); (iv) medical history (dyslipidemia, hypertension, diabetes, kidney disease, liver disease, and current treatments); and (v) laboratory test results (glycosylated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), serum creatinine (Scr)).\u003c/p\u003e\n\u003cp\u003eWe gathered comprehensive data on sociodemographic and health-related factors through structured questionnaires administered by trained interviewers. Health-related factors included categorized smoking and drinking habits, as well as physician-diagnosed medical histories. Marital status was categorized into married and other statuses. Anthropometric assessments were conducted by trained nurses, with blood pressure values determined by the means of two consecutive measurements. Body mass index was calculated using weight divided by height squared.\u003c/p\u003e\n\u003cp\u003eCovariates for our analysis were selected based on previous literature and available variables, ensuring relevance and evidence-based support to enhance the validity of our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables with normal distribution were presented as mean \u0026plusmn; standard deviation (SD), while those with skewed distributions were shown as median (interquartile range, IQR). Categorical variables were expressed as frequency and percentage (n, %). The Shapiro-Wilk test was used to assess normality. RFM was analyzed both as a continuous variable and categorically in quartiles, with the first quartile as the reference group. One-way analysis of variance (ANOVA) and chi-square tests were used to evaluate baseline characteristic differences across RFM quartiles for continuous and categorical variables, respectively.\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier method generated cumulative incidence curves for CVDs, with log-rank tests for comparisons. Multivariate Cox regression models calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CVDs associated with RFM, including unadjusted and adjusted models (Model I-III). Covariates included age, gender, smoking status, alcohol intake, comorbidities (dyslipidemia, hypertension, diabetes, kidney disease, and liver disease), medication history (lipid-lowering, antihypertensive, and hypoglycemic treatments), and metabolic biomarkers (HbA1c, TG, TC, and Scr). Trend tests used linear regression, incorporating each RFM quartile\u0026apos;s median value as a continuous variable. A generalized additive model with restricted cubic spline terms delineated the nonlinear RFM-incident CVDs risk association. The smoothing curve informed a linear regression model, and the restricted cubic spline regression used four knots covering 100% of RFM values. Subgroup analyses with stratified Cox regression models assessed RFM effects across populations, using the likelihood ratio test for interactions. Participants were stratified by age (＜60 years and \u0026ge;60 years), gender, and comorbid conditions (dyslipidemia, kidney disease, hypertension, or diabetes).\u003c/p\u003e\n\u003cp\u003eThe biomarkers\u0026apos; incremental predictive value was evaluated via area under curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). Mediation analysis explored arterial stiffness (ePWV), insulin resistance (TyG), and dyslipidemia (low-density lipoprotein cholesterol, LDL-C) as mediators of the RFM-incident CVDs link, using the \u0026lsquo;mediation\u0026rsquo; package and adjusting for gender, age, smoking, alcohol consumption, and histories of dyslipidemia, hypertension, and diabetes.\u003c/p\u003e\n\u003cp\u003eAll analyses used R (The R Foundation) and Free Statistics software version 2.1, with a two-sided \u003cem\u003eP\u003c/em\u003e value \u0026lt;0.05 indicating significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the flowchart of the study population screening process. We analyzed 7,027 participants with a median age of 58.0 years, of whom 47.7% were male and 24.4% had CVDs, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants were stratified into quartiles by RFM. CVD incidence rose from 17.6% in Q1 to 30.8% in Q4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age distribution varied (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Q3 participants being younger. Gender distribution differed markedly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with Q1 having only males and Q4 no males. BMI and blood pressure showed significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher values in upper RFM quartiles. Lifestyle factors like smoking and alcohol consumption, comorbidities (hypertension, diabetes, dyslipidemia), and metabolic biomarkers also varied significantly across quartiles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), generally increasing with RFM, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants classified by quartiles of the RFM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eQuartiles of the RFM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1 (15.3\u0026ndash;25.4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2 (25.4\u0026ndash;33.7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3 (33.7\u0026ndash;40.6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4 (40.6\u0026ndash;52.4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence of CVDs, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1716 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e309 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e446 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e420 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e541 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.0 (52.0, 65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.0 (53.0, 65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.0 (52.0, 65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.0 (49.0, 62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.0 (53.0, 67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3351 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1757 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1561 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6236 (88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1588 (90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1625 (92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1540 (87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1483 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (rural), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4662 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1278 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1096 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1169 (66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1119 (63.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.2 (21.1, 25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.9 (19.7, 22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.4 (22.6, 26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.3 (20.7, 23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.2 (24.2, 28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129.1\u0026thinsp;\u0026plusmn;\u0026thinsp;21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2763 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1386 (78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1140 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent alcohol consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2821 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1168 (66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1101 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e265 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1605 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e484 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e292 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e593 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e369 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e550 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e194 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e345 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of medication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive treatment, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1160 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e367 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e454 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoglycemic treatment, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering treatment, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic biomarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193.7\u0026thinsp;\u0026plusmn;\u0026thinsp;37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184.4\u0026thinsp;\u0026plusmn;\u0026thinsp;36.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192.2\u0026thinsp;\u0026plusmn;\u0026thinsp;37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e195.6\u0026thinsp;\u0026plusmn;\u0026thinsp;36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e202.5\u0026thinsp;\u0026plusmn;\u0026thinsp;39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.3 (74.3, 153.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.8 (62.8, 122.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.7 (78.8, 162.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101.8 (74.3, 144.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126.6 (92.0, 181.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.6, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.7, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.7, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7 (0.6, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7 (0.6, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median (IQR), or \u003cem\u003en\u003c/em\u003e (%). Abbreviations: BMI, body mass index; CVDs, cardiovascular diseases; RFM, relative fat mass; HbA1c, glycosylated hemoglobin A1c.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKaplan-Meier analysis revealed progressive divergence in cardiovascular event rates across RFM quartiles over nine years (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest-risk quartile (Q4) demonstrated 30.8% cumulative CVD incidence versus 17.6% in Q1 at nine-year follow-up, with consistent risk stratification across interim timepoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We assessed the link between RFM and incident CVDs using Cox regression models. In the crude model, each 1-unit RFM increase was associated with a 3% higher CVD risk (HR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.02\u0026ndash;1.03). After adjusting for age and gender in Model I, the HR increased to 1.06 (95% CI: 1.05\u0026ndash;1.07). Further adjustments in Models II and III slightly reduced the association, but they remained significant (HR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.02\u0026ndash;1.05 in both models). Analyzing RFM categorically, participants in the highest quartile (Q4: 40.6\u0026ndash;52.4) faced a significantly higher CVD risk compared to the lowest quartile (Q1: 15.3\u0026ndash;25.4) in the fully adjusted Model III (HR\u0026thinsp;=\u0026thinsp;1.46, 95% CI: 1.05\u0026ndash;2.03). A significant linear trend was observed across RFM quartiles (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.004) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjustment, the HR of incident CVDs rose with RFM, showing a linear relationship (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.327). Each RFM increase was linked to a proportional CVD risk elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings indicate that higher RFM levels are independently associated with an increased risk of incident CVDs, with the relationship maintaining significance after comprehensive covariate adjustment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of the RFM with the risk of incident CVDs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncident CVDs (n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1716 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (1.05\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03 (1.02\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategorical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (15.3\u0026ndash;25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e309 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (25.4\u0026ndash;33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e446 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51 (1.30\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54 (1.33\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30 (1.12\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28 (1.10\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (33.7\u0026ndash;40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e420 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41 (1.22\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79 (1.29\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37 (0.98\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.33 (0.97\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (40.6\u0026ndash;52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e541 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90 (1.65\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.29 (1.65\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53 (1.10\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.46 (1.05\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel I adjusts for age and gender; Model II adjusts for model I\u0026thinsp;+\u0026thinsp;smoking status, alcohol intake, history of comorbidities, and history of medication; Model III adjusts for model II\u0026thinsp;+\u0026thinsp;metabolic biomarkers. Abbreviations: CVDs, cardiovascular diseases; CI, confidence interval; RFM, relative fat mass; HR, hazard ratio; Ref, reference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubgroup analyses revealed differential associations between RFM quartiles and clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, a significant positive trend was observed across quartiles (adjusted HR for Q4 vs Q1: 1.84, 95% CI 1.17\u0026ndash;2.90; \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.014), whereas this association attenuated in those\u0026thinsp;\u0026ge;\u0026thinsp;60 years (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.05; \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.084). Men demonstrated a progressive risk elevation with higher RFM (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.002), while hypertension modified this relationship (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.463), with stronger trends in non-hypertensive individuals (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant interaction was detected for sex, diabetes, dyslipidemia, or kidney disease (all \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.1). The association remained robust across most subgroups, particularly in participants without comorbidities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe addition of RFM to the Framingham risk model demonstrated statistically significant incremental predictive value for incident CVDs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The model combining traditional risk factors (age, gender, residence, marital status, smoking/drinking status, hypertension, diabetes, kidney disease, dyslipidemia) with RFM yielded an AUC of 0.640 (95% CI: 0.625\u0026ndash;0.655), showing a marginal yet significant improvement over the base model (AUC: 0.634, 95% CI: 0.619\u0026ndash;0.649; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). Reclassification metrics further supported this enhancement, with a continuous NRI of 0.1402 (95% CI: 0.0859\u0026ndash;0.1944; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and an IDI of 0.0041 (95% CI: 0.0025\u0026ndash;0.0058; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results indicate that RFM provides clinically meaningful stratification beyond conventional cardiovascular risk factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncremental predictive value of RFM for incident CVDs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFramingham model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.634 (0.619, 0.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFramingham model\u0026thinsp;+\u0026thinsp;RFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.640 (0.625, 0.655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1402 (0.0859, 0.1944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0041 (0.0025, 0.0058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFramingham model included: age, gender, residence, marital status, smoking and drinking status, hypertension, diabetes, kidney disease, and dyslipidemia. Abbreviations: AUC, area under curve; CVDs, cardiovascular diseases; CI, confidence interval; RFM, relative fat mass; NRI, net reclassification improvement; IDI, integrated discrimination improvement.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMediation analysis showed that arterial stiffness (ePWV), insulin resistance (TyG), and dyslipidemia (LDL-C) partially mediated the association between RFM and incident CVDs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Arterial stiffness contributed the most to the mediation effect, accounting for 15.44% (95%CI: 9.35%, 24.59%) of the total effect, followed by insulin resistance (8.25%, 95%CI: 0.03%, 17.36%) and dyslipidemia (3.84%, 95%CI: 1.31%, 7.50%). All indirect effects were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for ePWV and LDL-C; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048 for TyG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur nationwide prospective cohort study provides critical insights into the role of RFM in CVD risk stratification among Chinese adults. Three key findings emerge: (1) RFM exhibits a linear, dose-dependent association with incident CVDs, independent of traditional risk factors; (2) RFM enhances predictive accuracy beyond Framingham risk parameters; and (3) arterial stiffness mediates 15.4% of the RFM-CVD relationship, surpassing contributions from insulin resistance and dyslipidemia. These results underscore RFM\u0026rsquo;s clinical relevance, particularly in populations prone to central adiposity, while highlighting mechanistic pathways distinct from conventional obesity metrics.\u003c/p\u003e \u003cp\u003eOur study represents the first prospective cohort analysis to systematically evaluate the longitudinal association between RFM and incident CVDs in Chinese adults, while concurrently assessing its predictive utility and mediating pathways. Wang \u003cem\u003eet al.\u003c/em\u003e demonstrated in a prospective trial of 26,754 individuals that elevated RFM correlated with metabolic abnormalities and increased risks of coronary artery disease (CAD) and hypertension. Notably, their work identified sex-specific nonlinear associations, with cardiovascular mortality risk escalating significantly when RFM exceeded thresholds of 30 in men and 45 in women\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. These findings align with longitudinal data from Peng \u003cem\u003eet al.\u003c/em\u003e, who established RFM\u0026rsquo;s predictive capacity for hypertension incidence in Chinese adults, proposing sex-specific cutoffs (24.67 for men, 35.73 for women) to stratify risk\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Cross-sectional evidence further supports RFM\u0026rsquo;s clinical relevance: Zwartkruis \u003cem\u003eet al.\u003c/em\u003e identified robust associations between RFM and CAD, heart failure, and atrial fibrillation in 95,003 participants\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, while Shen \u003cem\u003eet al.\u003c/em\u003e reported significant links between RFM and self-reported CVD prevalence in the general Chinese population, advocating its utility for primary screening\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe linear dose-response relationship observed in our cohort (adjusted HR 1.46 for Q4 vs. Q1; \u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e=0.004) reinforces these earlier observations while extending them to hard clinical endpoints. RFM\u0026rsquo;s incremental predictive value beyond traditional risk models (ΔAUC 0.006; NRI\u0026thinsp;=\u0026thinsp;0.140, IDI\u0026thinsp;=\u0026thinsp;0.004) substantiates emerging evidence that abdominal adiposity indices enhance conventional risk stratification\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Mechanistically, RFM\u0026rsquo;s superiority over isolated waist circumference measurements likely stems from its mathematical integration of height\u0026mdash;a modification that accounts for body proportionality and improves detection of visceral adiposity, particularly in normal-weight individuals with disproportionate fat distribution. This biological plausibility supports RFM\u0026rsquo;s clinical value in identifying high-risk phenotypes often overlooked by BMI categorization\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. By corroborating RFM\u0026rsquo;s independent association with incident CVD and quantifying its predictive utility within a nationally representative Chinese cohort, our findings strengthen the rationale for incorporating this anthropometric index into routine cardiovascular risk assessment protocols.\u003c/p\u003e \u003cp\u003eAbdominal obesity is characterized by adipocyte hyperplasia and hypertrophy, which induce functional alterations in adipose tissue dynamics. These changes include dysregulated release of proinflammatory adipocytokines\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, excessive reactive oxygen species (ROS) generation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and diminished nitric oxide (NO) bioavailability\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e \u0026mdash; molecular perturbations that collectively promote vascular endothelial dysfunction, insulin resistance, and atherogenesis\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The pathophysiological cascade extends to lipid metabolism derangements, with obesity driving elevated circulating levels of very-low-density lipoprotein cholesterol (VLDL-C) and triglycerides (TG), increased formation of atherogenic small dense low-density lipoprotein (sdLDL) particles, and reduced high-density lipoprotein cholesterol (HDL-C) concentrations\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These lipid abnormalities synergistically accelerate atherosclerotic plaque formation and progression\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Hemodynamically, obesity-induced expansion of intravascular volume and elevation of systemic vascular resistance precipitate hypertension while promoting structural remodeling of arterial walls\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Our mediation analysis elucidates novel mechanistic pathways through which RFM influences cardiovascular risk. Arterial stiffness \u0026mdash; quantified noninvasively via ePWV\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e \u0026mdash; emerged as the predominant mediator, accounting for 15.4% of RFM's cardiovascular effects. This finding corroborates experimental evidence demonstrating that perivascular adipose tissue secretes paracrine factors (e.g., leptin, resistin) that impair endothelial-dependent vasodilation and promote collagen deposition in the vascular media\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The TyG index (reflecting insulin resistance) and LDL-C (representing dyslipidemia) mediated 8.3% and 3.8% of this association, respectively. The comparatively modest contribution of insulin resistance contrasts with observations in diabetic cohorts\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, suggesting that RFM-associated cardiovascular risk operates through both canonical metabolic pathways and distinct biomechanical mechanisms related to arterial compliance.\u003c/p\u003e \u003cp\u003eThis study benefits from a nationally representative sample, standardized measurements, and advanced statistical methods to explore biological pathways. However, several limitations should be noted. First, while self-reported cardiovascular diagnoses carry a risk of inaccuracy, previous checks showed that 84% of these reports matched hospital records in the CHARLS database. Second, we lacked direct body fat measurements (e.g., MRI scans) to compare RFM with precise fat distribution data. Third, residual confounding from unmeasured factors cannot be excluded. Fourth, as an observational study, it cannot prove direct cause-and-effect relationships.\u003c/p\u003e \u003cp\u003eFuture research should focus on two priorities: 1) defining RFM risk thresholds tailored to Asian populations, where central obesity develops earlier than in Western groups; and 2) testing RFM in real-world clinics to see if it improves patient outcomes. Combining RFM with simple artery health tests (e.g., blood pressure-based stiffness assessments) could create practical risk screening tools for communities with limited medical resources. These steps would help translate our findings into better prevention strategies, particularly in regions battling obesity-related heart disease epidemics.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur prospective cohort analysis of 7,027 Chinese adults demonstrates that elevated RFM, a sex-specific abdominal adiposity index, is independently associated with a 46% increased risk of incident CVDs in fully adjusted models (HR\u0026thinsp;=\u0026thinsp;1.46, 95% CI:1.05\u0026ndash;2.03), exhibiting a linear dose-response relationship (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.004). These findings support RFM\u0026rsquo;s clinical utility for CVD risk stratification, where it provided incremental predictive value over Framingham risk factors (ΔAUC\u0026thinsp;=\u0026thinsp;0.006, NRI\u0026thinsp;=\u0026thinsp;14.0%, IDI\u0026thinsp;=\u0026thinsp;0.41%; all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Mediation analyses revealed arterial stiffness (15.4% of total effect), insulin resistance (8.3%), and dyslipidemia (3.8%) as key biological pathways linking RFM to CVD pathogenesis. While RFM shows promise as a cost-effective adiposity metric for primary prevention, population-specific validation and investigation into visceral fat dynamics through advanced imaging are warranted to optimize its clinical implementation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e \u003cb\u003eCHARLS\u003c/b\u003e China Health and Retirement Longitudinal Study\u003c/p\u003e \u003cp\u003e \u003cb\u003eePWV\u003c/b\u003e estimated pulse wave velocity\u003c/p\u003e \u003cp\u003e \u003cb\u003eRFM\u003c/b\u003e relative fat mass\u003c/p\u003e \u003cp\u003e \u003cb\u003eScr\u003c/b\u003e serum creatinine\u003c/p\u003e \u003cp\u003e \u003cb\u003eTC\u003c/b\u003e total cholesterol\u003c/p\u003e \u003cp\u003e \u003cb\u003eTG\u003c/b\u003e triglycerides\u003c/p\u003e \u003cp\u003e \u003cb\u003eTyG\u003c/b\u003e triglyceride-glucose\u003c/p\u003e \u003cp\u003e \u003cb\u003eWC\u003c/b\u003e waist circumference\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all CHARLS participants and staff for their contributions, which were essential to completing this study. We also thank the Free Statistics team for their technical support and provision of critical analytical tools. Special thanks to Dr. Jie Liu from the Chinese PLA General Hospital\u0026apos;s Department of Vascular and Endovascular Surgery for statistical expertise, study design advice, and editorial assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFang Wang and Jingang Zheng developed the study concept and design, managed data acquisition, performed statistical analysis, interpreted results, and guided the overall study direction. Xingliang Li and Long Wang managed data acquisition and quality control. Fang Wang drafted the manuscript, with Jingang Zheng providing critical revisions. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by grants from the National High Level Hospital Clinical Research Funding \u0026amp; Elite Medical Professionals Project of China-Japan Friendship Hospital (Number: ZRJY2021-QM05), National Natural Science Foundation of China (Number: 81700411), Young Elite Scientists Sponsorship Program by\u0026nbsp;China Association of Chinese Medicine\u0026nbsp;(Number: 2021-QNRC2-A02) and National Key Clinical Specialty Construction Project of China (Number: 2020-QTL-009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data utilized in this study is accessible to the public via the CHARLS website: https://charls.pku.edu.cn/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS study was conducted in adherence to the ethical principles of the Declaration of Helsinki and obtained approval from the Institutional Review Board of Peking University (IRB00001052-11015). All participants provided written informed consent prior to their involvement. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEvangelou K, Vasileiou PVS, Papaspyropoulos A, Hazapis O, Petty R, Demaria M, Gorgoulis VG. Cellular senescence and cardiovascular diseases: moving to the heart of the problem. 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Lancet Healthy Longev. 2023;4:e23\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s2666-7568(22)00247-1\u003c/span\u003e\u003cspan address=\"10.1016/s2666-7568(22)00247-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrition-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutj","sideBox":"Learn more about [Nutrition Journal](http://nutritionj.biomedcentral.com/)","snPcode":"12937","submissionUrl":"https://submission.nature.com/new-submission/12937/3","title":"Nutrition Journal","twitterHandle":"@NutrJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Relative fat mass, Incident cardiovascular diseases, Prospective cohort study, Predictive value, Mediation analysis, China Health and Retirement Longitudinal Study","lastPublishedDoi":"10.21203/rs.3.rs-6407425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6407425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The association between relative fat mass (RFM), a sex-specific adiposity index incorporating waist circumference and height, and incident cardiovascular diseases (CVDs) remains underexplored. This study aimed to evaluate RFM’s predictive value for CVD risk in Chinese adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Using data from the China Health and Retirement Longitudinal Study (CHARLS), a nationwide prospective cohort, we analyzed 7,027 adults aged ≥45 years without baseline CVD (2011-2020). RFM was calculated as 64 - (20 × height [cm]/waist circumference [cm]) for men and 76 - (20 × height/waist circumference) for women. Incident CVDs were identified via physician-diagnosed events. Multivariable Cox regression assessed RFM-CVD associations. The incremental predictive value of RFM beyond conventional risk factors was evaluated using area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Mediation analyses explored biological pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Over a median 9-year follow-up, 24.6% (n=1,405) developed CVDs. Each 1-unit RFM increase conferred a 3% higher CVD risk (adjusted HR=1.03, 95% CI: 1.02-1.05). Participants in the highest RFM quartile (Q4: 40.6-52.4) had 46% greater CVD risk versus Q1 (adjusted HR=1.46, 95% CI: 1.05-2.03; \u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e=0.004). A linear dose-response relationship was observed (\u003cem\u003eP\u003c/em\u003e\u003csub\u003enonlinearity\u003c/sub\u003e=0.327). RFM improved CVD prediction when added to Framingham risk factors (AUC: 0.640 vs. 0.634; NRI=0.140, IDI=0.004; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Arterial stiffness, insulin resistance, and dyslipidemia mediated 15.4%, 8.3%, and 3.8% of the RFM-CVD association, respectively (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Higher RFM independently predicts incident CVDs in Chinese adults, with arterial stiffness being the predominant mediator. RFM enhances conventional risk stratification, supporting its utility in primary CVD prevention.\u003c/p\u003e","manuscriptTitle":"Relative fat mass and cardiovascular risk in Chinese adults: A nationwide prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:05:51","doi":"10.21203/rs.3.rs-6407425/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-01T11:22:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T05:02:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323301545106594749925359483405283706733","date":"2025-06-20T01:54:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174930178095672267644387507555473661571","date":"2025-06-12T09:26:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-15T06:46:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-14T19:19:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-10T09:57:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrition Journal","date":"2025-04-09T03:00:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutj","sideBox":"Learn more about [Nutrition Journal](http://nutritionj.biomedcentral.com/)","snPcode":"12937","submissionUrl":"https://submission.nature.com/new-submission/12937/3","title":"Nutrition Journal","twitterHandle":"@NutrJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca724714-89d1-4754-83ee-0e3c29696457","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:06:22+00:00","versionOfRecord":{"articleIdentity":"rs-6407425","link":"https://doi.org/10.1186/s12937-025-01267-6","journal":{"identity":"nutrition-journal","isVorOnly":false,"title":"Nutrition Journal"},"publishedOn":"2025-12-13 15:59:42","publishedOnDateReadable":"December 13th, 2025"},"versionCreatedAt":"2025-05-07 03:05:51","video":"","vorDoi":"10.1186/s12937-025-01267-6","vorDoiUrl":"https://doi.org/10.1186/s12937-025-01267-6","workflowStages":[]},"version":"v1","identity":"rs-6407425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6407425","identity":"rs-6407425","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}