Association of metabolism score for visceral fat with new-onset cardiovascular disease in patients with metabolic syndrome: two large prospective cohorts in Europe and Asia

Association of metabolism score for visceral fat with new-onset cardiovascular disease in patients with metabolic syndrome: two large prospective cohorts in Europe and Asia | 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 Association of metabolism score for visceral fat with new-onset cardiovascular disease in patients with metabolic syndrome: two large prospective cohorts in Europe and Asia Zhen Tan, Yijun Liu, Lei Liu, Mao Ye, Xinrui Xue, Shuang Li, Xiaoping Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6850201/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: Metabolic syndrome (MetS) significantly increases the risk of cardiovascular disease (CVD). The metabolism score for visceral fat (METS-VF) is a novel assessment tool with potential to replace visceral adipose tissue measurement. This study aimed to investigate the association between METS-VF and new-onset CVD in participants with MetS. Methods: This study utilized data from two prospective cohorts: UK Biobank (UKB) and the China Health and Retirement Longitudinal Study (CHARLS). METS-VF was calculated based on relevant metabolic parameters. Multivariate Cox regression analysis and restricted cubic spline (RCS) analyses were conducted to assess the relationship between METS-VF and new-onset CVD. The interaction between METS-VF and CVD polygenic risk score (PRS) was examined in UKB to explore the contribution of genetic factors. Receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the diagnostic capability of METS-VF for new-onset CVD. Subgroup analyses were performed to confirm the robustness of the results. Results: A total of 101,292 individuals from the UK Biobank and 1,680 individuals from CHARLS were included. The median follow-up periods were 14.6 years in UKB and 5.0 years in CHARLS. High METS-VF was significantly associated with an increased risk of new-onset CVD in both UKB (HR = 1.63, 95% CI: 1.56-1.71) and CHARLS (HR = 2.114, 95% CI: 1.52-2.94) compared with low METS-VF. Individuals with the highest METS-VF and high genetic risk exhibited the highest risk of new-onset CVD (HR = 2.36, 95% CI: 2.14-2.61). The diagnostic capability of METS-VF for new-onset CVD was superior to other obesity-related indicators and demonstrated consistently stable performance. Conclusions: METS-VF is a valuable indicator for predicting new-onset CVD in individuals with MetS, providing new insights into the prevention and management of CVD in high-risk populations. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In recent decades, the global prevalence of MetS has increased due to an ageing population, inactive lifestyles, and unhealthy dietary habits, emerging as a particularly pressing public health concern [1]. MetS, characterized by the association of visceral fat metabolic score with new-onset cardiovascular disease (CVD) in patients with metabolic abnormalities such as central obesity, hyperglycaemia, dyslipidaemia, and hypertension [2, 3], has seen a remarkable increase in incidence. Epidemiological data indicate that the prevalence of MetS in adults has reached 20–30% in developed countries [4], while in China, the prevalence of metabolic syndrome is 31%, affecting over 450 million individuals [5]. This upward trend, closely related to the increasing prevalence of obesity and sedentary lifestyles, imposes an increasing burden on healthcare systems worldwide [6]. The impact of MetS extends far beyond its immediate symptoms, particularly concerning CVD, which are among the leading causes of morbidity and mortality in individuals with MetS [7]. Compared with the general population, individuals with MetS exhibit a significantly higher risk of CVD, CVD-related death, and all-cause mortality [8]. Additionally, MetS is associated with an increased risk of developing type 2 diabetes and several cancers, including breast, endometrial, prostate, pancreatic, hepatobiliary, and colorectal cancers [9, 10, 11, 12, 13]. Given these significant risks, understanding the underlying mechanisms linking MetS to CVD is crucial for the development of effective preventive and therapeutic strategies. Among the factors contributing to MetS and associated CVD, visceral fat plays a central role [14]. As a key component of central obesity, visceral fat is not merely a passive energy storage site but also an active endocrine organ [15]. It secretes a wide range of adipokines and inflammatory mediators, such as tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and adiponectin [16, 17]. These substances disrupt both metabolic and cardiovascular homeostasis, leading to insulin resistance, dyslipidaemia, and endothelial dysfunction [18, 19, 20]. Therefore, quantifying the metabolic function of visceral fat could provide critical insights into CVD risk stratification in individuals with MetS. The metabolic score for visceral fat (METS-VF), based on the metabolic score for insulin resistance (METS-IR), waist-to-height ratio (WHtR), age, and sex, has been proposed as a novel biomarker [21]. By integrating multiple metabolic parameters related to visceral fat function, including lipid metabolism, glucose metabolism, and adipokine secretion [22], METS-VF offers a more comprehensive and accurate assessment compared to traditional measures of adiposity, such as body mass index (BMI), waist circumference (WC), and visceral adiposity index (VAI) [23]. Previous studies have demonstrated that METS-VF is independently associated with coronary artery calcification, non-alcoholic fatty liver disease, and diabetes [24, 25, 26]. However, the relationship between METS-VF and new-onset CVD in individuals with MetS remains unclear. To identify high-risk individuals with MetS and improve CVD prevention strategies, this study aims to investigate the association between METS-VF and new-onset CVD in two large cohorts of MetS patients. Materials and methods Study design and population The data for our study was analysed based on the UK biobank (UKB) and China Health and Aged Care Tracking Survey (CHARLS). The UKB is a large-scale biomedical database and research resource and has collected an unprecedented amount of biological and medical data on more than 500,000 participants from UK. UKB has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval. Researchers do not require separate ethical clearance and can operate under the RTB approval. Data from the UKB are available to researchers after receiving research approvals. This study was conducted under UKB licence (Application ID:106027). The CHARLS was a national longitudinal survey designed to systematically study issues related to ageing. A total of 17,708 participants were included at baseline between 2011 and 2012. Subsequently, participants were followed up every two to three years. To date, five rounds of the survey have been completed: Wave 1 (2011–2012), Wave 2 (2013–2014), Wave 3 (2015–2016), Wave 4 (2018–2019), and Wave 5 (2020–2021). The survey sample covered 150 county-level units and 450 village-level units nationwide, and the target population was adults aged 45 years and older. All participants signed an informed consent form. We defined MetS based on the presence of two or more of the following conditions: 1. Abnormal glucose metabolism: fasting blood glucose ≥ 6.1 mmol/L or 2-hour blood glucose ≥ 7.8 mmol/L on oral glucose tolerance test (OGTT), or a diagnosis of diabetes mellitus and appropriate treatment. 2. Elevated blood pressure: systolic blood pressure ≥ 140mmHg or diastolic blood pressure ≥ 90mmHg, or diagnosed and treated for hypertension. 3. Dyslipidaemia: Triglycerides (TG) ≥ 1.7mmol/L and/or high-density lipoprotein cholesterol (HDL-C) < 0.9mmol/L for men and < 1.0mmol/L for women. 4. Central obesity: WC ≥ 90 cm for men and ≥ 80 cm for women, or BMI ≥ 30 kg/m 2 . 5. Microalbuminuria: urinary albumin excretion rate ≥ 20ug/min or urinary albumin/creatinine ratio ≥ 30 mg/g. The included population needed to fulfil the above conditions. Exclusion criteria included: (1) participants with a history of angina, myocardial infarction (MI), ischemic stroke (ISS), hemorrhagic stroke, atrial fibrillation (AF), and heart failure (HF) before enrollment; (2) participants with missing data on WC, Weight, height, TG, HDL-C, FBG, and FPG at baseline. (3) participants who were unable or unwilling to provide informed consent. (4) lost to follow-up. The specific screening process is shown in Fig. S1 . Assessment covariates and METS-VF Assessment covariates and METS-VF At recruitment, demographic information, including age, gender, race, medical history, including gender, age, race, education level, body mass index (BMI), WC, height, smoking status (never, former, and current), alcohol consumption status (never, former, and current), frequency of physical activity (Never, < 3 times per day, ≥ 3 times per day), household income, hypertension (SBP ≥ 140 mmHg, or DBP ≥ 90 mmHg, or hospital diagnosis record, or use of blood pressure medication or based on specialist diagnosis, drug reimbursement or self-reported information), the use of aspirin, blood pressure medication and cholesterol lowering medication was collected for each participant using computerised questionnaires. Blood samples were collected after an overnight fasting of at least 8 hours to measure fasting plasma glucose (FPG), TG, HDL-C, LDL-C, and other relevant metabolic parameters. The relevant formulae are as follows [27, 28]: METS-VF = 4.466 + 0.011[(Ln(METS-IR))] 3 + 3.239[(Ln(WHTR)) 3 ] + 0.319(gender) + 0.594(Ln(age)) (male = 1,female = 2) METS-IR = Ln[2×(FPG (mg/dL) + TG(mg/dL)]×BMI (kg/m 2 )/Ln[(HDL-C(mg/dL)] BMI = Weight (kg)/Height 2 (m 2 ) TyG = Ln[TG(mg/dl)×FBG (mg/dl) ] VAI = WC (cm)/(39.68+(1.88×BMI))×(TG (mmol/l)/1.03)×(1.31/HDL-C (mmol/I)) (male) VAI = WC (cm)/(36.58+(1.89×BMI)) × (TG (mmol/l)/0.81)×(1.52/HDL-C (mmol/I) (female) All participants in the UBK and CHARLS cohorts were divided into four groups (Q1, Q2, Q3, Q4) based on the METS-VF levels at the 25th, 50th, and 75th centiles, respectively, with Q1 (lowest quartile) as the reference group. Definitions of outcomes and follow-up The definition of CVD in UKB including Ischaemic heart disease (IHD) (angina and MI), stroke (ISS and hemorrhagic stroke), atrial fibrillation (AF), and heart failure (HF) according to previous reported study [29]. The international statistical classification of diseases (ICD-10) was used to define the classification of diseases. The outcomes of the study were the diagnosis of angina (I20), MI (I21-I23), ISS (I63), hemorrhagic stroke (I60-62), AF (codes I48), and HF (codes I50). Follow-up period was calculated from the date of the first repeat visit through date of diagnosis, or withdrawal from the study (death and loss of follow-up), or end of the most recent follow-up (19 December 2022), whichever came first. The primary outcome in CHARLS was new-onset CVD (Heart disease or stroke) from Wave 1 to Wave 5. The incident of heart disease or stroke was defined based on a self-reported physician’s diagnosis by standardized questionnaire (“Has a doctor ever told you that you had any heart disease [myocardial infarction, coronary heart disease, angina, heart failure, or other heart problems] or stroke?”), following previously reported studies in CHARLS [30]. The composite outcome of heart disease and stroke during follow-up, whichever occurred first. The cut-off for follow-up was diagnosis or the end of Wave 5 (2019–2020). Definition of Polygenic Risk Score Standard PRS for CVD, coronary artery disease (CAD), AF, and ISS available from the UKB has been published. The PRS were calculated as the sum of the effect sizes of individual genetic variants multiplied by the allele dosage and were generated using a Bayesian approach applied to meta-analyse summary statistics from genome-wide association study (GWAS) data [31]. In this study, the PRS of CVD, CAD, AF and ISS were divided into low genetic risk (quintile 1), intermediate genetic risk (quintile 2 to 4) and high genetic risk (quintile 5). PRS analyses of IHD, angina, and MI were performed using CAD-PRS. The remaining PRS analyses were performed using the corresponding standard PRS. Statistical analysis R software (version 4.3.0, Institute for Statistics and Mathematics, Vienna, Austria ) were used for the analysis. Continuous variables were expressed as mean ± standard deviation (SD) or median depending on their distribution. Categorical variables were expressed as frequency and percentage. Baseline characteristics were compared using the Wilcoxon rank sum test or the Chi-square test. Multiple imputation was used to account for missing values of covariates, and the maximum proportion of missing values was 5%, and the average value was 0.52%. The relationship between METS-VF and new-onset CVD was analyzed using three multivariate Cox regression models. In UKB cohort, Model 1 was unadjusted, model 2 was adjusted for gender, age, education level, race, smoking status, alcohol consumption status, and physical activity. Model 3 included all variables from Model 2, and further adjusted for hypertension, diabetes, LDL-C, UA, and HbA1c. In CHARLS cohort, Model 1 was unadjusted, model 2 was adjusted for gender, age, education level, smoking status, and alcohol consumption status. Model 3 included all variables from Model 2, and further adjusted for hypertension, diabetes, LDL-C, UA, and HbA1c. To assess the joint effects of PRS on the association of METS-VF with new-onset CVD, analysis were stratified by genetic risk categories (low genetic risk, intermediate genetic risk, and high genetic risk). Different genetic risk category were divided into four groups (Q1, Q2, Q3, Q4) based on METS-VF using the 25th, 50th, and 75th percentiles as cutoff points individually. PRS analysis based on multivariate Cox proportional hazards regression model (Model 3) was used to analyze the association of METS-VF with new-onset CVD. Non-linear correlations between METS-VF and CVD were revealed using a restricted cubic spline (RCS) curve based on Multivariate Cox regression model (Model 3). Receiver operating characteristic (ROC) curves were used to asses the predictive capability of METS-VF for 3 years, 5-years, and 8-years, compared with other indicators of visceral adipose tissue. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics were used to evaluate the predictive capacity of METS-VF. In the subgroup analysis, the effects of sex, age, smoking status, and alcohol consumption status on the associations between METS-VF and the risk of new-onset CVD were further examined. Two-tailed P < 0.05 was considered to indicate statistical significance. Results Demographic characteristics of the study participants A total of 101,292 individuals from the UKB were included in current study. Among the included individuals, 50,469 (49.83%) were male, and the mean age at baseline was 57.83 ± 7.68 years. 21,231 participants with new-onset cardiovascular disease who were more likely to be older, male, overweight, White, drinker, with hypertension, and have lower levels of education. A total of 1,680 individuals from the CHARLS were included in current study. Of these, 1,235 (73.51%) were male, and the median age at baseline was 59 years, 361 participants with new-onset cardiovascular disease who were more likely to be older, male, overweight, drinker, with hypertension, and high school education or above. The median follow-up period were 14.6 years in UKB and 5.0 years in the CHARLS. Baseline characteristics of the participants across the Q1-Q4 groups in UKB and CHARLS were provided in Supplementary Table S1 and Table S2. Association between METS-VF and new-onset CVD Table 1 , Table S3 and Table S4 showed that the association between METS-VF and the risk of new-onset CVD. In UBK, trend tests indicated a significant increased in new-onset CVD risk with increasing METS-VF ( P < 0.001). The risk of CVD (HR = 1.63, 95% CI: 1.56–1.71), IHD (HR = 1.45, 95% CI: 1.36–1.55), Angina (HR = 1.43, 95% CI: 1.31–1.56), MI (HR = 1.20, 95% CI: 1.08–1.33), stroke (HR = 1.41, 95% CI: 1.25–1.58), ISS (HR = 1.43, 95% CI: 1.26–1.63), hemorrhagic stroke (HR = 1.48, 95% CI: 1.19–1.83), AF (HR = 2.03, 95% CI: 1.89–2.18), and HF (HR = 2.45, 95% CI: 2.22–2.70) was significantly increased in highest quartile compared to lowest quartile. When METS-VF was calculated as a continuous variable, each increased in 1-SD of METS-VF was associated with 1.24 times increased risk of new-onset CVD. In CHARLS, trend tests indicated a significant increased in new-onset CVD risk with increasing METS-VF ( P < 0.001). The risk of CVD (HR = 2.114, 95% CI: 1.52–2.94), heart disease (HR = 2.58, 95% CI: 1.73–3.84), and stroke (HR = 1.63, 95% CI: 1.05–2.53) was significantly increased in highest quartile compared to lowest quartile. While each increased in 1-SD of METS-VF was associated with 1.87 times increased risk of new-onset CVD in CHARLS participants. Table 1 Associations of METS-VF with new-onset CVD in UK Biobank and CHARLS Variable Cases/Total Model 1 HR(95%CI) P value Model 2 HR(95%CI) P value Model 3 HR(95%CI) P value UK biobank Per 1 SD increase 21231/101292 2.24 (2.16–2.33) <0.001 1.89 (1.81–1.96) <0.001 1.65 (1.58–1.71) <0.001 Q1 3709/25323 Ref (1) Ref (1) Ref (1) Q2 4535/25323 1.25 (1.19–1.30) <0.001 1.19 (1.13–1.24) <0.001 1.15 (1.09–1.20) <0.001 Q3 5442/25323 1.53 (1.47–1.60) <0.001 1.36 (1.30–1.42) <0.001 1.27 (1.22–1.33) <0.001 Q4 7545/25323 2.24 (2.16–2.33) <0.001 1.86 (1.79–1.95) <0.001 1.63 (1.56–1.71) <0.001 P for trend < 0.001 < 0.001 < 0.001 CHARLS Per 1 SD increase 361/1680 2.87 (2.02–4.09) <0.001 2.70 (1.91–3.80) <0.001 2.04 (1.35–2.98) <0.001 Q1 65/420 Ref (1) Ref (1) Ref (1) Q2 70/420 1.08 (0.77–1.52) 0.624 1.06 (0.76–1.49) 0.725 1.10 (0.78–1.54) 0.605 Q3 101/420 1.63 (1.19–2.63) 0.002 1.61 (1.17–2.20) 0.003 1.64 (1.19–2.25) 0.002 Q4 125/420 2.11 (1.56–2.85) <0.001 2.07 (1.50–2.87) <0.001 2.11 (1.52–2.94) <0.001 P for trend < 0.001 < 0.001 < 0.001 Per increased in 1-SD of METS-VF was 0.436 and 0.384 for UK Biobank and CHARLS Model 1: Unadjusted Model 2: Adjusted for age, gender, race (UKB only), education level, smoking status, alcohol consumption status, and physical activity (UKB only) Model 3: included all variables from Model 2, and further adjusted for hypertention, diabetes, LDL, Ua and HbA1c Abbreviations: CVD: cardiovascular disease; METS-VF: metabolism score for visceral; HR:Hazard ratios; CI: confidence interval; Ref: reference; LDL-C: low-density lipoprotein cholesterol; Ua: uric acid Multivariable adjusted restricted cubic spline (RCS) analyses revealed a positive linear ( P for non-linear = 0.532) association between METS-VF and new-onset CVD in CHARLS, whereas a non-linear ( P for non-linear < 0.001) association was observed in UKB (Fig. 1 ). Therefore, we identified a inflection points of 7.285 for new-onset CVD, with the risk increasing by 30% per 1-unit increase in MEST-VF up to this inflection point(Table S5). However, after the inflection point, each 1-unit increase in MEST-VF was associated with a 1.45 times increase in risk. We further investigate the various CVD endpoint events as mentioned above using RCS analyses. In UKB, RCS analyses revealed non-linear associations between METS-VF and IHD ( P for non-linear < 0.001), angina ( P for non-linear = 0.006), stoke ( P for non-linear = 0.013), ISS ( P for non-linear = 0.017), hemorrhagic stroke ( P for non-linear = 0.029), AF ( P for non-linear < 0.001), and HF ( P for non-linear < 0.001), and the inflection points were 7.244, 7.244, 7.213, 7.225, 6.928, 7.361, and 7.440, respectively. However, a positive linear association was observed between METS-VF and MI (Fig. S2, Table S5). In CHARLS, RCS analyses revealed a positive linear association between METS-VF and heart disease ( P for non-linearity = 0.629), as well as a non-linear association between METS-VF and stroke (P for non-linearity = 0.005). The inflection point for stroke was 7.068 (Fig. S3, Table S6). In both UKB and CHARLS, the risk of new-onset CVD increased progressively with increasing METS-VF. Joint association of eGDR and PRS with AF, HF and cardiovascular mortality Compared to low genetic risk, high genetic risk was associated with increased risk of CVD (HR = 1.59, 95% CI: 1.52–1.66), IHD (HR = 2.32, 95% CI: 2.18–2.67), angina (HR = 2.40, 95% CI: 2.19–2.62), MI (HR = 3.18, 95% CI: 2.84–3.57), ISS (HR = 2.01, 95% CI: 1.77–2.28), and AF (HR = 3.36, 95% CI: 3.12–3.62), respectively (Table 2 ). Acquired environmental factors and genetic factors jointly contributed to the risk of CVD. Therefore, we further examined the association between METS-VF and new-onset CVD across different genetic risk groups. Figure 2 showed the highest quartile of METS-VF was associated with an increased risk of CVD (HR = 1.25 95% CI: 1.15–1.45), IHD (HR = 1.25 95% CI: 1.15–1.45), angina (HR = 1.30, 95% CI: 1.11–1.52), MI (HR = 1.48, 95% CI: 1.23–1.77), ISS (HR = 1.27, 95% CI: 1.00-1.62), and AF (HR = 1.98, 95% CI: 1.75–2.24) across low, intermediate, and high genetic risk groups, except ISS ( P = 0.051). Especially for AF, Q2-Q4 quartiles of METS-VF was associated with the increased risk of new-onset CVD across all genetic risk groups. In addition, individuals with high METS-VF and high genetic risk exhibited a significantly increased risk of new-onset CVD (HR = 2.36 95% CI: 2.14–2.61), IHD (HR = 2.84, 95% CI: 2.54–3.17), angina (HR = 3.30, 95% CI: 2.49–3.72), MI (HR = 4.43, 95% CI: 3.38–5.82), ISS (HR = 2.33, 95% CI: 1.77–3.07), and AF (HR = 6.72, 95% CI: 5.59–8.72) compared to those with low METS-VF and low genetic risk (Fig. 3 , Table S7). Table 2 The associations between PRS and risk of new on-set CVD in UKB Variables Case, n% Model1 Model2 Model3 HR(95%CI) P-value HR(95%CI) P-value HR(95%CI) P-value CVD, n = 20938 PRS low 3510/19975 (17.57) Ref Ref Ref intermediate 12462/59925 (20.80) 1.20 (1.16–1.25) < 0.001 1.25 (1.20–1.30) < 0.001 1.23 (1.19–1.28) < 0.001 high 4966/19979 (24.86) 1.48 (1.42–1.55) < 0.001 1.63 (1.56–1.70) < 0.001 1.58 (1.52–1.66) < 0.001 P for trend < 0.001 < 0.001 < 0.001 IHD, n = 10869 PRS low 1481/19975 (7.41) Ref Ref Ref intermediate 6321/59925 (10.55) 1.45 (1.37–1.53) < 0.001 1.51 (1.43–1.60) < 0.001 1.49 (1.41–1.57) < 0.001 high 3067/19979 (15.35) 2.16 (2.03–2.30) < 0.001 2.37 (2.23–2.52) < 0.001 2.32 (2.18–2.47) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Angina, n = 5600 PRS low 719/19975 (3.60) Ref Ref Ref intermediate 3257/59925 (5.44) 1.53 (1.41–1.65) < 0.001 1.56 (1.44–1.70) < 0.001 1.55 (1.43–1.68) 0.010 high 1624/19979 (8.13) 2.31 (2.12–2.52) < 0.001 2.44 (2.24–2.67) < 0.001 2.39 (2.19–2.62) < 0.001 P for trend < 0.001 < 0.001 < 0.001 MI, n = 3815 PRS low 401/19975 (2.01) Ref Ref Ref intermediate 2225/59925 (3.71) 1.87 (1.68–2.07) < 0.001 1.16 (1.06–1.28) 0.002 1.89 (1.70–2.10) 0.010 high 1189/19979 (5.95) 3.02 (2.70–3.39) < 0.001 1.34 (1.19–1.50) < 0.001 3.18 (2.84–3.57) < 0.001 P for trend < 0.001 < 0.001 < 0.001 ISS, n = 2556 PRS low 360/19975 (1.80) Ref Ref Ref intermediate 1497/59925 (2.50) 1.39 (1.24–1.56) < 0.001 1.42 (1.27–1.60) < 0.001 1.39 (1.24–1.56) 0.010 high 699/19979 (3.50) 1.95 (1.72–2.21) < 0.001 2.09 (1.84–2.38) < 0.001 2.01 (1.77–2.28) < 0.001 P for trend < 0.001 < 0.001 < 0.001 AF, n = 8861 PRS low 953/19975 (4.77) Ref Ref Ref intermediate 4983/59925 (8.32) 1.78 (1.66–1.90) < 0.001 1.80 (1.68–1.93) < 0.001 1.78 (1.66–1.91) < 0.001 high 2925/19979 (14.64) 3.24 (3.02–3.49) < 0.001 3.40 (3.16–3.66) < 0.001 3.36 (3.12–3.62) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Model 1: Unadjusted Model 2: Adjusted for age, gender, education level, smoking status, and alcohol consumption status Model 3: included all variables from Model 2, and further adjusted for hypertention, diabetes, LDL, Ua and HbA1c Abbreviations: METS-VF: metabolism score for visceral; CVD: cardiovascular disease; HR:Hazard ratios; CI: confidence interval; PRS: polygenic risk score; Ref: reference; IHD: ischemic heart disease; MI: myocardial infarction; ISS: ischaemic stroke; AF: atrial fibrillation Comparison of the predictive capability of various visceral fat indices and METS-VF for new-onset CVD The ROC curve analysis demonstrated that METS-VF exhibited the highest predictive capability for 3-year (AUC = 0.596 in UKB; AUC = 0.642 in CHARLS), 5-year (AUC = 0.601 in UKB; AUC = 0.589 in CHARLS), and 8-year (AUC = 0.602 in UKB; AUC = 0.598 in CHARLS) new-onset CVD, surpassing METS-IR, BMI, TyG, and VAI. The predictive effects of different visceral fat indices on new-onset CVD are illustrated in Fig. 4 . Moreover, compared with TyG, VAI, BMI, and METS-IR, METS-VF significantly improved the NRI and IDI for new-onset CVD at 3-year, 5-year, and 8-year time points in both UKB and CHARLS (Table S8). Subgroup analysis In the subgroup analysis, no significant interaction with new-onset CVD risk was observed in all subgroups in the UKB and CHARLS cohorts, except for smoking status ( P = 0.001) in UKB (Fig. 6). Notably, among individuals who were never smokers or former smokers, those in the highest quartile of METS-VF exhibited a 70% increased risk compared with those in the lowest quartile (HR = 1.70, 95% CI: 1.62–1.78). However, this increase was only 46% in smokers (HR = 1.46, 95% CI: 1.30–1.64). Discussion In this study involving two prospective cohorts, we identified a significant association between METS-VF and an increased risk of new-onset CVD in patients with MetS, even after adjustment for traditional cardiovascular risk factors. Higher METS-VF was associated with a higher risk of new-onset CVD. Moreover, individuals with high METS-VF and high genetic risk exhibited a significantly increased risk of new-onset CVD. This finding suggests that METS-VF may serve as a valuable predictor of CVD in the high-risk MetS population. Previous studies have demonstrated that visceral fat plays a critical role in the development of MetS and CVD [32, 33, 34]. Excessive visceral fat accumulation triggers the release of numerous adipokines and inflammatory mediators [35, 36], which contribute to insulin resistance, dyslipidemia, and endothelial dysfunction, ultimately elevating the risk of CVD [37, 38, 39]. A previous study suggested that METS-VF is an independent risk factor for cardiovascular mortality (HR = 2.62, 95% CI: 1.20–5.71), and for every 0.2-unit increase in METS-VF, cardiovascular mortality increases by 18% (HR = 1.18, 95% CI: 1.06–1.31) [40]. Furthermore, a study from CHARLS demonstrated a dose-response relationship between METS-VF and CVD events (HR = 3.31, 95% CI: 1.28–8.54) as well as all-cause mortality (HR = 4.03, 95% CI: 1.72–9.42) across different glucose tolerance statuses [41]. METS-VF, as a comprehensive indicator reflecting the metabolic status of visceral fat, integrates multiple parameters associated with visceral fat metabolism [42]. A cohort study in China involving 41,576 individuals found that high cumulative METS-VF was associated with an increased risk of CVD (HR = 2.78, 95% CI: 2.49–3.17) and mortality (HR = 4.90, 95% CI: 4.36–5.50), with this association becoming stronger as exposure to high METS-VF was prolonged [43]. These studies suggest that a higher METS-VF signifies a more pronounced metabolic disturbance in visceral fat, likely explaining its association with an elevated risk of new-onset CVD. Our findings are consistent with previous studies highlighting the importance of visceral fat metabolism in the pathogenesis of CVD. Additionally, this study is distinct in its use of METS-VF combined with PRS to comprehensively evaluate the relationship between visceral fat metabolism and new-onset CVD in patients with MetS. The results indicate that METS-VF is associated with the risk of new-onset cardiovascular disease in patients with MetS, while genetic risk also plays a vital role. Notably, the incidence of AF was 5.7 times higher in the group with high METS-VF and high genetic risk compared to the group with low METS-VF and low genetic risk. A previous study found that MetS is a strong risk factor for AF, and consistent treatment of MetS can significantly improve the risk and frequency of atrial fibrillation, associated symptoms, and the success of treatment for maintaining cardiac rhythm [44]. In addition, there was an interaction between smoking and METS-VF on new-onset CVD risk in the UKB cohort. It is plausible that MetS and smoking contribute to CVD risk through shared pathophysiological pathways, such as reduced insulin sensitivity, impaired glycemic control, altered lipid profiles, and endothelial dysfunction [45, 46]. A previous study indicated that genetic predisposition toward tobacco smoking was strongly associated with a higher likelihood of MetS (HR = 1.49, 95% CI: 1.47–1.52) in Chinese individuals [47]. However, our study revealed that the risk of new-onset CVD increased in both non-smokers and smokers as METS-VF rose in the UKB cohort. The magnitude of this increased risk was more pronounced among non-smokers or former smokers. These results highlight the significance of METS-VF and warrant further investigation. Both METS-IR, BMI, TyG, and VAI have been used to evaluate the risk of CVD in patients with MetS [23, 48, 49]. Recently, Wang et al. [50] found that METS-VF was strongly associated with left ventricular hypertrophy in T2DM (OR = 9.79, 95% CI: 6.16–15.76), demonstrating superior predictive performance compared to traditional indices (AUC = 0.68, 95% CI: 0.66–0.70). Cao et al. [51] found that METS-VF was independently associated with the risk of stroke (HR = 2.78, 95% CI: 1.71–4.52) and exhibited the highest AUC for stroke prediction (AUC = 0.687, 95% CI: 0.668–0.706), outperforming BMI, WHtR, VAI, and Cardiometabolic Index (CMI). Our study provides further robust evidence elucidating the association between METS-VF and new-onset CVD risk. METS-VF demonstrated the highest diagnostic capability over the long term compared with METS-IR, BMI, TyG, and VAI. Therefore, identifying METS-VF as an independent predictor of CVD in patients with MetS has substantial clinical implications. Clinicians can utilize METS-VF to identify high-risk individuals among those with MetS, enabling more targeted prevention and treatment strategies. For instance, patients with elevated METS-VF could benefit from aggressive lifestyle modifications, such as dietary control and increased physical activity, along with appropriate pharmacological interventions aimed at improving visceral fat metabolism and reducing new-onset CVD risk. These findings are based on prospective cohorts in two distinct populations using rigorous study designs and long-term follow-up. Furthermore, the findings are consistent across both cohorts, suggesting that our results are generalizable to a wider range of populations. Nevertheless, this study has certain limitations. First, while the sample size reflects the research questions to some extent, it is still relatively limited and may not encompass all possible clinical scenarios and population characteristics, particularly as the CHARLS cohort included a smaller number of participants, which might have introduced some bias into the study results. Additionally, participants in CHARLS experienced fewer CVD event outcomes. Second, no dynamic blood sampling was conducted during the UKB follow-up, and in CHARLS, blood samples were collected only at baseline, wave 3, and wave 5. This limitation restricted our ability to further investigate the relationship between changes in METS-VF and new-onset CVD. Third, although we adjusted for traditional cardiovascular risk factors, unmeasured confounding factors might still influence the association between METS-VF and new-onset CVD. In conclusion, individuals with MetS and elevated METS-VF were at a higher risk of new-onset CVD, while those with both high genetic risk and high METS-VF exhibited the highest risk of new-onset CVD. Our study established that high METS-VF was significantly associated with new-onset CVD in patients with MetS, and METS-VF could serve as a more efficient independent predictor of CVD in this population. Declarations Author details 1 Department of Cardiology, Suining Central Hospital, Suining, Sichuan, China. 2 Department of Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. Acknowledgements The authors express their gratitude to the participants and staff of the UK Biobank for their invaluable contributions to this study. This research has been conducted using the UK Biobank Resource under Application Number 106027. We also thank all members of the CHARLS for their efforts and all participants for their contribution. Authors’ contributions The study design was conceived by Hongqiang Ren, Xiaoping Li, and Yijun Liu. Zhen Tan, Lei Liu and Mao Ye organized the data, conducted the analyses, and wrote and edited the manuscript. Shuang Li and Xinrui Xue contributed to the interpretation of the results, revision, and finalization of the manuscript. All authors have reviewed and approved the final version of the manuscript. Funding The authors have no funding sources to declare. Data Availability The data are available on application through approval and oversight by the UK Biobank (www.ukbiobank.ac.uk/). The datasets used in this investigation are available in online repositories (http://charls.pku.edu.cn//). Ethics approval and consent to participates UKB has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval. 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Diabetol Metab Syndr. 2025 Feb 8; 17(1): 50. doi: 10. 1186/s13098-025-01608-9. PMID: 39920850; PMCID: PMC11806899. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6850201","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480184515,"identity":"be3f3745-8894-4754-ba58-1df13a718cba","order_by":0,"name":"Zhen Tan","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Tan","suffix":""},{"id":480184516,"identity":"beb8b97e-68d2-4db4-933d-84699715bdef","order_by":1,"name":"Yijun Liu","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Liu","suffix":""},{"id":480184517,"identity":"ec693397-cf11-490b-bf21-2a7b078eb2cd","order_by":2,"name":"Lei Liu","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Liu","suffix":""},{"id":480184518,"identity":"35e9286a-edd3-4177-aff2-72fc7aeae33e","order_by":3,"name":"Mao Ye","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mao","middleName":"","lastName":"Ye","suffix":""},{"id":480184519,"identity":"57fcac25-5403-40e4-b3f7-c360b169410b","order_by":4,"name":"Xinrui Xue","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinrui","middleName":"","lastName":"Xue","suffix":""},{"id":480184520,"identity":"68dc7c91-9418-4019-a14e-beaaebe9d3f4","order_by":5,"name":"Shuang Li","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Li","suffix":""},{"id":480184521,"identity":"96cb3621-2297-4cb7-89c4-cfc046228792","order_by":6,"name":"Xiaoping Li","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Li","suffix":""},{"id":480184522,"identity":"9dd87922-7306-41a3-8f97-bf5dcf45d5d0","order_by":7,"name":"Hongqiang Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYDACCQaGAx8qJOTY2NsPEK2F8eCMMzbGfDxnEojWwnyYty0tcZ6EgwFxOsxnN284zHPmcHqbBEMCw4+KbYS1yNw5VnBwTsXh3DbpxgOMPWduE+EuiRyDA2/OALXIHEhgZmwjVgtv2+F0NokEA+K1HAR6P4EULWkFoEA2bAMG8kEi/ZK8+QMwKuXl29sPPvhRQYQWIEBExwGi1KNoGQWjYBSMglGAFQAABOxAm0bdu6QAAAAASUVORK5CYII=","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hongqiang","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2025-06-09 03:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6850201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6850201/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86216179,"identity":"b8ccfff4-5296-47d7-a512-45c9305a4a56","added_by":"auto","created_at":"2025-07-08 06:04:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":528215,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of restricted cubic spline regression. (A) non-linear relationship between the METS-VF with CVD in UK biobank. (B) A positive linear relationship between the METS-VF with CVD in CHARLS. Adjusted for age, gender, race, education level, smoking status, alcohol consumption status, physical activity, hypertention, diabetes, LDL, Ua and HbA1c. Abbreviations: HR: Hazard ratios, CI: Confidence interval, CVD: Cardiovascular disease, METS-VF: Metabolic score for visceral fat\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6850201/v1/1b406b2099465cf730596da2.png"},{"id":86216180,"identity":"fd38a323-a89a-4068-9dcf-def9481c3097","added_by":"auto","created_at":"2025-07-08 06:04:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1437148,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of METS-VF and PRS with CVD (A), IHD (B), angina (C), MI (D), ISS (E) and AF (F). Adjusted for age, gender, race, education level, smoking status, alcohol consumption status, physical activity, hypertention, diabetes, LDL, Ua and HbA1c. Abbreviations: HR: Hazard ratios, CI: Confidence interval, METS-VF: Metabolic score for visceral fat. AF: Atrial fibrillation; LDL-C: Low-density lipoprotein cholesterol; Ua: Uric acid.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6850201/v1/d350a5ad99b1741e5d835e91.png"},{"id":86216181,"identity":"1d5221c4-d822-4812-9c5f-ad36b25d3304","added_by":"auto","created_at":"2025-07-08 06:04:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":960433,"visible":true,"origin":"","legend":"\u003cp\u003eJoint Association of METS-VF and PRS with CVD (A), IHD (B), angina (C), MI (D), ISS (E) and AF (F). Adjusted for age, gender, race, education level, smoking status, alcohol consumption status, physical activity, hypertention, diabetes, LDL, Ua and HbA1c. Abbreviations: HR: Hazard ratios, CI: Confidence interval, METS-VF: Metabolic score for visceral fat, AF: Atrial fibrillation, LDL-C: Low-density lipoprotein cholesterol, Ua: Uric acid.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6850201/v1/9a44f6f0d4ddd8ed7c519acb.png"},{"id":86216185,"identity":"42e66920-19ff-4178-bfab-4ef689c79b50","added_by":"auto","created_at":"2025-07-08 06:04:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1760314,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristics curve analysis for METS-VF to discriminate the 3, 5, and 10 years new-onset CVD. Abbreviations: AUC: Area under curve, CVD: Cardiovascular disease. METS-VF: Metabolic score for visceral fat, METS‐IR: Metabolic score for insulin resistance, BMI: Body mass index (BMI), TyG: Triglyceride-glucose index, VAI: Visceral adiposity index.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6850201/v1/ba2850ce7aefe23e17b446f7.png"},{"id":86216849,"identity":"9b85744e-ff4d-4ad5-b279-8f44407883d8","added_by":"auto","created_at":"2025-07-08 06:12:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":945870,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup and interaction analyses between METS-VF and new on-set CVD across age, gender, education, smoking status and alcohol consumption subgroups. Adjusted for age, gender, race, education, smoking, alcohol consumption, moderated physical activity, hypertention, diabetes, LDL, Ua and HbA1c. Abbreviations: HR: Hazard ratios, CI: Confidence interval, METS-VF: Metabolic score for visceral fat. CVD: Cardiovascular disease. AF: Atrial fibrillation; LDL-C: Low-density lipoprotein cholesterol; Ua: Uric acid.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6850201/v1/2a7fb61fea1ab05b1dd5fed0.png"},{"id":96245888,"identity":"9094994a-b083-42b0-b3cf-b9213859ce8e","added_by":"auto","created_at":"2025-11-19 07:23:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6209964,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6850201/v1/ac3cf3f4-02d7-4820-a687-19b8ffb37d15.pdf"},{"id":86216194,"identity":"0a23f063-8646-4347-8c64-b91c722769d2","added_by":"auto","created_at":"2025-07-08 06:04:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3289677,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6850201/v1/406a5f9bf22878d0b39a4908.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of metabolism score for visceral fat with new-onset cardiovascular disease in patients with metabolic syndrome: two large prospective cohorts in Europe and Asia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent decades, the global prevalence of MetS has increased due to an ageing population, inactive lifestyles, and unhealthy dietary habits, emerging as a particularly pressing public health concern [1]. MetS, characterized by the association of visceral fat metabolic score with new-onset cardiovascular disease (CVD) in patients with metabolic abnormalities such as central obesity, hyperglycaemia, dyslipidaemia, and hypertension [2, 3], has seen a remarkable increase in incidence. Epidemiological data indicate that the prevalence of MetS in adults has reached 20\u0026ndash;30% in developed countries [4], while in China, the prevalence of metabolic syndrome is 31%, affecting over 450\u0026nbsp;million individuals [5]. This upward trend, closely related to the increasing prevalence of obesity and sedentary lifestyles, imposes an increasing burden on healthcare systems worldwide [6].\u003c/p\u003e\u003cp\u003eThe impact of MetS extends far beyond its immediate symptoms, particularly concerning CVD, which are among the leading causes of morbidity and mortality in individuals with MetS [7]. Compared with the general population, individuals with MetS exhibit a significantly higher risk of CVD, CVD-related death, and all-cause mortality [8]. Additionally, MetS is associated with an increased risk of developing type 2 diabetes and several cancers, including breast, endometrial, prostate, pancreatic, hepatobiliary, and colorectal cancers [9, 10, 11, 12, 13]. Given these significant risks, understanding the underlying mechanisms linking MetS to CVD is crucial for the development of effective preventive and therapeutic strategies.\u003c/p\u003e\u003cp\u003eAmong the factors contributing to MetS and associated CVD, visceral fat plays a central role [14]. As a key component of central obesity, visceral fat is not merely a passive energy storage site but also an active endocrine organ [15]. It secretes a wide range of adipokines and inflammatory mediators, such as tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and adiponectin [16, 17]. These substances disrupt both metabolic and cardiovascular homeostasis, leading to insulin resistance, dyslipidaemia, and endothelial dysfunction [18, 19, 20]. Therefore, quantifying the metabolic function of visceral fat could provide critical insights into CVD risk stratification in individuals with MetS.\u003c/p\u003e\u003cp\u003eThe metabolic score for visceral fat (METS-VF), based on the metabolic score for insulin resistance (METS-IR), waist-to-height ratio (WHtR), age, and sex, has been proposed as a novel biomarker [21]. By integrating multiple metabolic parameters related to visceral fat function, including lipid metabolism, glucose metabolism, and adipokine secretion [22], METS-VF offers a more comprehensive and accurate assessment compared to traditional measures of adiposity, such as body mass index (BMI), waist circumference (WC), and visceral adiposity index (VAI) [23]. Previous studies have demonstrated that METS-VF is independently associated with coronary artery calcification, non-alcoholic fatty liver disease, and diabetes [24, 25, 26].\u003c/p\u003e\u003cp\u003eHowever, the relationship between METS-VF and new-onset CVD in individuals with MetS remains unclear. To identify high-risk individuals with MetS and improve CVD prevention strategies, this study aims to investigate the association between METS-VF and new-onset CVD in two large cohorts of MetS patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eThe data for our study was analysed based on the UK biobank (UKB) and China Health and Aged Care Tracking Survey (CHARLS). The UKB is a large-scale biomedical database and research resource and has collected an unprecedented amount of biological and medical data on more than 500,000 participants from UK. UKB has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval. Researchers do not require separate ethical clearance and can operate under the RTB approval. Data from the UKB are available to researchers after receiving research approvals. This study was conducted under UKB licence (Application ID:106027). The CHARLS was a national longitudinal survey designed to systematically study issues related to ageing. A total of 17,708 participants were included at baseline between 2011 and 2012. Subsequently, participants were followed up every two to three years. To date, five rounds of the survey have been completed: Wave 1 (2011\u0026ndash;2012), Wave 2 (2013\u0026ndash;2014), Wave 3 (2015\u0026ndash;2016), Wave 4 (2018\u0026ndash;2019), and Wave 5 (2020\u0026ndash;2021). The survey sample covered 150 county-level units and 450 village-level units nationwide, and the target population was adults aged 45 years and older. All participants signed an informed consent form.\u003c/p\u003e\u003cp\u003eWe defined MetS based on the presence of two or more of the following conditions: 1. Abnormal glucose metabolism: fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;6.1 mmol/L or 2-hour blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.8 mmol/L on oral glucose tolerance test (OGTT), or a diagnosis of diabetes mellitus and appropriate treatment. 2. Elevated blood pressure: systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90mmHg, or diagnosed and treated for hypertension. 3. Dyslipidaemia: Triglycerides (TG)\u0026thinsp;\u0026ge;\u0026thinsp;1.7mmol/L and/or high-density lipoprotein cholesterol (HDL-C)\u0026thinsp;\u0026lt;\u0026thinsp;0.9mmol/L for men and \u0026lt;\u0026thinsp;1.0mmol/L for women. 4. Central obesity: WC\u0026thinsp;\u0026ge;\u0026thinsp;90 cm for men and \u0026ge;\u0026thinsp;80 cm for women, or BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e. 5. Microalbuminuria: urinary albumin excretion rate\u0026thinsp;\u0026ge;\u0026thinsp;20ug/min or urinary albumin/creatinine ratio\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/g. The included population needed to fulfil the above conditions.\u003c/p\u003e\u003cp\u003eExclusion criteria included: (1) participants with a history of angina, myocardial infarction (MI), ischemic stroke (ISS), hemorrhagic stroke, atrial fibrillation (AF), and heart failure (HF) before enrollment; (2) participants with missing data on WC, Weight, height, TG, HDL-C, FBG, and FPG at baseline. (3) participants who were unable or unwilling to provide informed consent. (4) lost to follow-up. The specific screening process is shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssessment covariates and METS-VF\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eAssessment covariates and METS-VF\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cp\u003eAt recruitment, demographic information, including age, gender, race, medical history, including gender, age, race, education level, body mass index (BMI), WC, height, smoking status (never, former, and current), alcohol consumption status (never, former, and current), frequency of physical activity (Never, \u0026lt; 3 times per day, \u0026ge; 3 times per day), household income, hypertension (SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, or DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or hospital diagnosis record, or use of blood pressure medication or based on specialist diagnosis, drug reimbursement or self-reported information), the use of aspirin, blood pressure medication and cholesterol lowering medication was collected for each participant using computerised questionnaires. Blood samples were collected after an overnight fasting of at least 8 hours to measure fasting plasma glucose (FPG), TG, HDL-C, LDL-C, and other relevant metabolic parameters.\u003c/p\u003e\u003cp\u003eThe relevant formulae are as follows [27, 28]:\u003c/p\u003e\u003cp\u003eMETS-VF\u0026thinsp;=\u0026thinsp;4.466\u0026thinsp;+\u0026thinsp;0.011[(Ln(METS-IR))]\u003csup\u003e3\u003c/sup\u003e + 3.239[(Ln(WHTR))\u003csup\u003e3\u003c/sup\u003e]\u0026thinsp;+\u0026thinsp;0.319(gender)\u0026thinsp;+\u0026thinsp;0.594(Ln(age)) (male\u0026thinsp;=\u0026thinsp;1,female\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\u003cp\u003eMETS-IR\u0026thinsp;=\u0026thinsp;Ln[2\u0026times;(FPG (mg/dL)\u0026thinsp;+\u0026thinsp;TG(mg/dL)]\u0026times;BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)/Ln[(HDL-C(mg/dL)]\u003c/p\u003e\u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;Weight (kg)/Height\u003csup\u003e2\u003c/sup\u003e (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003cp\u003eTyG\u0026thinsp;=\u0026thinsp;Ln[TG(mg/dl)\u0026times;FBG (mg/dl) ]\u003c/p\u003e\u003cp\u003eVAI\u0026thinsp;=\u0026thinsp;WC (cm)/(39.68+(1.88\u0026times;BMI))\u0026times;(TG (mmol/l)/1.03)\u0026times;(1.31/HDL-C (mmol/I)) (male)\u003c/p\u003e\u003cp\u003eVAI\u0026thinsp;=\u0026thinsp;WC (cm)/(36.58+(1.89\u0026times;BMI)) \u0026times; (TG (mmol/l)/0.81)\u0026times;(1.52/HDL-C (mmol/I)\u003c/p\u003e\u003cp\u003e(female)\u003c/p\u003e\u003cp\u003eAll participants in the UBK and CHARLS cohorts were divided into four groups (Q1, Q2, Q3, Q4) based on the METS-VF levels at the 25th, 50th, and 75th centiles, respectively, with Q1 (lowest quartile) as the reference group.\u003c/p\u003e\n\u003ch3\u003eDefinitions of outcomes and follow-up\u003c/h3\u003e\n\u003cp\u003eThe definition of CVD in UKB including Ischaemic heart disease (IHD) (angina and MI), stroke (ISS and hemorrhagic stroke), atrial fibrillation (AF), and heart failure (HF) according to previous reported study [29]. The international statistical classification of diseases (ICD-10) was used to define the classification of diseases. The outcomes of the study were the diagnosis of angina (I20), MI (I21-I23), ISS (I63), hemorrhagic stroke (I60-62), AF (codes I48), and HF (codes I50). Follow-up period was calculated from the date of the first repeat visit through date of diagnosis, or withdrawal from the study (death and loss of follow-up), or end of the most recent follow-up (19 December 2022), whichever came first.\u003c/p\u003e\u003cp\u003eThe primary outcome in CHARLS was new-onset CVD (Heart disease or stroke) from Wave 1 to Wave 5. The incident of heart disease or stroke was defined based on a self-reported physician\u0026rsquo;s diagnosis by standardized questionnaire (\u0026ldquo;Has a doctor ever told you that you had any heart disease [myocardial infarction, coronary heart disease, angina, heart failure, or other heart problems] or stroke?\u0026rdquo;), following previously reported studies in CHARLS [30]. The composite outcome of heart disease and stroke during follow-up, whichever occurred first. The cut-off for follow-up was diagnosis or the end of Wave 5 (2019\u0026ndash;2020).\u003c/p\u003e\n\u003ch3\u003eDefinition of Polygenic Risk Score\u003c/h3\u003e\n\u003cp\u003eStandard PRS for CVD, coronary artery disease (CAD), AF, and ISS available from the UKB has been published. The PRS were calculated as the sum of the effect sizes of individual genetic variants multiplied by the allele dosage and were generated using a Bayesian approach applied to meta-analyse summary statistics from genome-wide association study (GWAS) data [31]. In this study, the PRS of CVD, CAD, AF and ISS were divided into low genetic risk (quintile 1), intermediate genetic risk (quintile 2 to 4) and high genetic risk (quintile 5). PRS analyses of IHD, angina, and MI were performed using CAD-PRS. The remaining PRS analyses were performed using the corresponding standard PRS.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eR software (version 4.3.0, Institute for Statistics and Mathematics, Vienna, Austria ) were used for the analysis. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median depending on their distribution. Categorical variables were expressed as frequency and percentage. Baseline characteristics were compared using the Wilcoxon rank sum test or the Chi-square test. Multiple imputation was used to account for missing values of covariates, and the maximum proportion of missing values was 5%, and the average value was 0.52%.\u003c/p\u003e\u003cp\u003eThe relationship between METS-VF and new-onset CVD was analyzed using three multivariate Cox regression models. In UKB cohort, Model 1 was unadjusted, model 2 was adjusted for gender, age, education level, race, smoking status, alcohol consumption status, and physical activity. Model 3 included all variables from Model 2, and further adjusted for hypertension, diabetes, LDL-C, UA, and HbA1c. In CHARLS cohort, Model 1 was unadjusted, model 2 was adjusted for gender, age, education level, smoking status, and alcohol consumption status. Model 3 included all variables from Model 2, and further adjusted for hypertension, diabetes, LDL-C, UA, and HbA1c.\u003c/p\u003e\u003cp\u003eTo assess the joint effects of PRS on the association of METS-VF with new-onset CVD, analysis were stratified by genetic risk categories (low genetic risk, intermediate genetic risk, and high genetic risk). Different genetic risk category were divided into four groups (Q1, Q2, Q3, Q4) based on METS-VF using the 25th, 50th, and 75th percentiles as cutoff points individually. PRS analysis based on multivariate Cox proportional hazards regression model (Model 3) was used to analyze the association of METS-VF with new-onset CVD.\u003c/p\u003e\u003cp\u003eNon-linear correlations between METS-VF and CVD were revealed using a restricted cubic spline (RCS) curve based on Multivariate Cox regression model (Model 3). Receiver operating characteristic (ROC) curves were used to asses the predictive capability of METS-VF for 3 years, 5-years, and 8-years, compared with other indicators of visceral adipose tissue. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics were used to evaluate the predictive capacity of METS-VF.\u003c/p\u003e\u003cp\u003eIn the subgroup analysis, the effects of sex, age, smoking status, and alcohol consumption status on the associations between METS-VF and the risk of new-onset CVD were further examined. Two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eDemographic characteristics of the study participants\u003c/h2\u003e\u003cp\u003eA total of 101,292 individuals from the UKB were included in current study. Among the included individuals, 50,469 (49.83%) were male, and the mean age at baseline was 57.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.68 years. 21,231 participants with new-onset cardiovascular disease who were more likely to be older, male, overweight, White, drinker, with hypertension, and have lower levels of education. A total of 1,680 individuals from the CHARLS were included in current study. Of these, 1,235 (73.51%) were male, and the median age at baseline was 59 years, 361 participants with new-onset cardiovascular disease who were more likely to be older, male, overweight, drinker, with hypertension, and high school education or above. The median follow-up period were 14.6 years in UKB and 5.0 years in the CHARLS. Baseline characteristics of the participants across the Q1-Q4 groups in UKB and CHARLS were provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S2.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociation between METS-VF and new-onset CVD\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S3 and Table S4 showed that the association between METS-VF and the risk of new-onset CVD. In UBK, trend tests indicated a significant increased in new-onset CVD risk with increasing METS-VF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The risk of CVD (HR\u0026thinsp;=\u0026thinsp;1.63, 95% CI: 1.56\u0026ndash;1.71), IHD (HR\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.36\u0026ndash;1.55), Angina (HR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.31\u0026ndash;1.56), MI (HR\u0026thinsp;=\u0026thinsp;1.20, 95% CI: 1.08\u0026ndash;1.33), stroke (HR\u0026thinsp;=\u0026thinsp;1.41, 95% CI: 1.25\u0026ndash;1.58), ISS (HR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.26\u0026ndash;1.63), hemorrhagic stroke (HR\u0026thinsp;=\u0026thinsp;1.48, 95% CI: 1.19\u0026ndash;1.83), AF (HR\u0026thinsp;=\u0026thinsp;2.03, 95% CI: 1.89\u0026ndash;2.18), and HF (HR\u0026thinsp;=\u0026thinsp;2.45, 95% CI: 2.22\u0026ndash;2.70) was significantly increased in highest quartile compared to lowest quartile. When METS-VF was calculated as a continuous variable, each increased in 1-SD of METS-VF was associated with 1.24 times increased risk of new-onset CVD. In CHARLS, trend tests indicated a significant increased in new-onset CVD risk with increasing METS-VF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The risk of CVD (HR\u0026thinsp;=\u0026thinsp;2.114, 95% CI: 1.52\u0026ndash;2.94), heart disease (HR\u0026thinsp;=\u0026thinsp;2.58, 95% CI: 1.73\u0026ndash;3.84), and stroke (HR\u0026thinsp;=\u0026thinsp;1.63, 95% CI: 1.05\u0026ndash;2.53) was significantly increased in highest quartile compared to lowest quartile. While each increased in 1-SD of METS-VF was associated with 1.87 times increased risk of new-onset CVD in CHARLS participants.\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\u003eAssociations of METS-VF with new-onset CVD in UK Biobank and CHARLS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCases/Total\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHR(95%CI) P value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 2\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eHR(95%CI) P value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 3\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eHR(95%CI) P value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUK biobank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21231/101292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.24 (2.16\u0026ndash;2.33) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.89 (1.81\u0026ndash;1.96) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.65 (1.58\u0026ndash;1.71) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3709/25323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef (1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4535/25323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25 (1.19\u0026ndash;1.30) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.19 (1.13\u0026ndash;1.24) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.15 (1.09\u0026ndash;1.20) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5442/25323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53 (1.47\u0026ndash;1.60) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36 (1.30\u0026ndash;1.42) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.27 (1.22\u0026ndash;1.33) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7545/25323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.24 (2.16\u0026ndash;2.33) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.86 (1.79\u0026ndash;1.95) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.63 (1.56\u0026ndash;1.71) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP for trend\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003e\u003cb\u003eCHARLS\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer 1 SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e361/1680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.87 (2.02\u0026ndash;4.09) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.70 (1.91\u0026ndash;3.80) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.04 (1.35\u0026ndash;2.98) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65/420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef (1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70/420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08 (0.77\u0026ndash;1.52) 0.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06 (0.76\u0026ndash;1.49) 0.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.10 (0.78\u0026ndash;1.54) 0.605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101/420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.63 (1.19\u0026ndash;2.63) 0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.61 (1.17\u0026ndash;2.20) 0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.64 (1.19\u0026ndash;2.25) 0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125/420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.11 (1.56\u0026ndash;2.85) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.07 (1.50\u0026ndash;2.87) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.11 (1.52\u0026ndash;2.94) \u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP for trend\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ePer increased in 1-SD of METS-VF was 0.436 and 0.384 for UK Biobank and CHARLS\u003c/p\u003e\u003cp\u003eModel 1: Unadjusted\u003c/p\u003e\u003cp\u003eModel 2: Adjusted for age, gender, race (UKB only), education level, smoking status, alcohol consumption status, and physical activity (UKB only)\u003c/p\u003e\u003cp\u003eModel 3: included all variables from Model 2, and further adjusted for hypertention, diabetes, LDL, Ua and HbA1c\u003c/p\u003e\u003cp\u003eAbbreviations: CVD: cardiovascular disease; METS-VF: metabolism score for visceral; HR:Hazard ratios; CI: confidence interval; Ref: reference; LDL-C: low-density lipoprotein cholesterol; Ua: uric acid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMultivariable adjusted restricted cubic spline (RCS) analyses revealed a positive linear (\u003cem\u003eP\u003c/em\u003e for non-linear\u0026thinsp;=\u0026thinsp;0.532) association between METS-VF and new-onset CVD in CHARLS, whereas a non-linear (\u003cem\u003eP\u003c/em\u003e for non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.001) association was observed in UKB (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, we identified a inflection points of 7.285 for new-onset CVD, with the risk increasing by 30% per 1-unit increase in MEST-VF up to this inflection point(Table S5). However, after the inflection point, each 1-unit increase in MEST-VF was associated with a 1.45 times increase in risk.\u003c/p\u003e\u003cp\u003eWe further investigate the various CVD endpoint events as mentioned above using RCS analyses. In UKB, RCS analyses revealed non-linear associations between METS-VF and IHD (\u003cem\u003eP\u003c/em\u003e for non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.001), angina (\u003cem\u003eP\u003c/em\u003e for non-linear\u0026thinsp;=\u0026thinsp;0.006), stoke (\u003cem\u003eP\u003c/em\u003e for non-linear\u0026thinsp;=\u0026thinsp;0.013), ISS (\u003cem\u003eP\u003c/em\u003e for non-linear\u0026thinsp;=\u0026thinsp;0.017), hemorrhagic stroke (\u003cem\u003eP\u003c/em\u003e for non-linear\u0026thinsp;=\u0026thinsp;0.029), AF (\u003cem\u003eP\u003c/em\u003e for non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and HF (\u003cem\u003eP\u003c/em\u003e for non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the inflection points were 7.244, 7.244, 7.213, 7.225, 6.928, 7.361, and 7.440, respectively. However, a positive linear association was observed between METS-VF and MI (Fig. S2, Table S5). In CHARLS, RCS analyses revealed a positive linear association between METS-VF and heart disease (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.629), as well as a non-linear association between METS-VF and stroke (P for non-linearity\u0026thinsp;=\u0026thinsp;0.005). The inflection point for stroke was 7.068 (Fig. S3, Table S6). In both UKB and CHARLS, the risk of new-onset CVD increased progressively with increasing METS-VF.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eJoint association of eGDR and PRS with AF, HF and cardiovascular mortality\u003c/h2\u003e\u003cp\u003eCompared to low genetic risk, high genetic risk was associated with increased risk of CVD (HR\u0026thinsp;=\u0026thinsp;1.59, 95% CI: 1.52\u0026ndash;1.66), IHD (HR\u0026thinsp;=\u0026thinsp;2.32, 95% CI: 2.18\u0026ndash;2.67), angina (HR\u0026thinsp;=\u0026thinsp;2.40, 95% CI: 2.19\u0026ndash;2.62), MI (HR\u0026thinsp;=\u0026thinsp;3.18, 95% CI: 2.84\u0026ndash;3.57), ISS (HR\u0026thinsp;=\u0026thinsp;2.01, 95% CI: 1.77\u0026ndash;2.28), and AF (HR\u0026thinsp;=\u0026thinsp;3.36, 95% CI: 3.12\u0026ndash;3.62), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Acquired environmental factors and genetic factors jointly contributed to the risk of CVD. Therefore, we further examined the association between METS-VF and new-onset CVD across different genetic risk groups. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the highest quartile of METS-VF was associated with an increased risk of CVD (HR\u0026thinsp;=\u0026thinsp;1.25 95% CI: 1.15\u0026ndash;1.45), IHD (HR\u0026thinsp;=\u0026thinsp;1.25 95% CI: 1.15\u0026ndash;1.45), angina (HR\u0026thinsp;=\u0026thinsp;1.30, 95% CI: 1.11\u0026ndash;1.52), MI (HR\u0026thinsp;=\u0026thinsp;1.48, 95% CI: 1.23\u0026ndash;1.77), ISS (HR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 1.00-1.62), and AF (HR\u0026thinsp;=\u0026thinsp;1.98, 95% CI: 1.75\u0026ndash;2.24) across low, intermediate, and high genetic risk groups, except ISS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.051). Especially for AF, Q2-Q4 quartiles of METS-VF was associated with the increased risk of new-onset CVD across all genetic risk groups. In addition, individuals with high METS-VF and high genetic risk exhibited a significantly increased risk of new-onset CVD (HR\u0026thinsp;=\u0026thinsp;2.36 95% CI: 2.14\u0026ndash;2.61), IHD (HR\u0026thinsp;=\u0026thinsp;2.84, 95% CI: 2.54\u0026ndash;3.17), angina (HR\u0026thinsp;=\u0026thinsp;3.30, 95% CI: 2.49\u0026ndash;3.72), MI (HR\u0026thinsp;=\u0026thinsp;4.43, 95% CI: 3.38\u0026ndash;5.82), ISS (HR\u0026thinsp;=\u0026thinsp;2.33, 95% CI: 1.77\u0026ndash;3.07), and AF (HR\u0026thinsp;=\u0026thinsp;6.72, 95% CI: 5.59\u0026ndash;8.72) compared to those with low METS-VF and low genetic risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S7).\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\u003eThe associations between PRS and risk of new on-set CVD in UKB\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eVariables Case, n% \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel1\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel2\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel3\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHR(95%CI) P-value HR(95%CI) P-value HR(95%CI) P-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eCVD, n\u0026thinsp;=\u0026thinsp;20938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003ePRS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3510/19975 (17.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12462/59925 (20.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20 (1.16\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.25 (1.20\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.23 (1.19\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4966/19979 (24.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48 (1.42\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.63 (1.56\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.58 (1.52\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\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;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eIHD, n\u0026thinsp;=\u0026thinsp;10869\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003ePRS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1481/19975 (7.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6321/59925 (10.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.45 (1.37\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.51 (1.43\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.49 (1.41\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3067/19979 (15.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.16 (2.03\u0026ndash;2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.37 (2.23\u0026ndash;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.32 (2.18\u0026ndash;2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\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;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eAngina, n\u0026thinsp;=\u0026thinsp;5600\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003ePRS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e719/19975 (3.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3257/59925 (5.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53 (1.41\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.56 (1.44\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.55 (1.43\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1624/19979 (8.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.31 (2.12\u0026ndash;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.44 (2.24\u0026ndash;2.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.39 (2.19\u0026ndash;2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\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;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eMI, n\u0026thinsp;=\u0026thinsp;3815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003ePRS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e401/19975 (2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2225/59925 (3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.87 (1.68\u0026ndash;2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16 (1.06\u0026ndash;1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.89 (1.70\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1189/19979 (5.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.02 (2.70\u0026ndash;3.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.34 (1.19\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.18 (2.84\u0026ndash;3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\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;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eISS, n\u0026thinsp;=\u0026thinsp;2556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003ePRS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e360/19975 (1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1497/59925 (2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.39 (1.24\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42 (1.27\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.39 (1.24\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e699/19979 (3.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.95 (1.72\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.09 (1.84\u0026ndash;2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.01 (1.77\u0026ndash;2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\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;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eAF, n\u0026thinsp;=\u0026thinsp;8861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003ePRS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e953/19975 (4.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintermediate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4983/59925 (8.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.78 (1.66\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.80 (1.68\u0026ndash;1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.78 (1.66\u0026ndash;1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2925/19979 (14.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.24 (3.02\u0026ndash;3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.40 (3.16\u0026ndash;3.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.36 (3.12\u0026ndash;3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\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;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eModel 1: Unadjusted\u003c/p\u003e\u003cp\u003eModel 2: Adjusted for age, gender, education level, smoking status, and alcohol consumption status\u003c/p\u003e\u003cp\u003eModel 3: included all variables from Model 2, and further adjusted for hypertention, diabetes, LDL, Ua and HbA1c\u003c/p\u003e\u003cp\u003eAbbreviations: METS-VF: metabolism score for visceral; CVD: cardiovascular disease; HR:Hazard ratios; CI: confidence interval; PRS: polygenic risk score; Ref: reference; IHD: ischemic heart disease; MI: myocardial infarction; ISS: ischaemic stroke; AF: atrial fibrillation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eComparison of the predictive capability of various visceral fat indices and METS-VF for new-onset CVD\u003c/h2\u003e\u003cp\u003eThe ROC curve analysis demonstrated that METS-VF exhibited the highest predictive capability for 3-year (AUC\u0026thinsp;=\u0026thinsp;0.596 in UKB; AUC\u0026thinsp;=\u0026thinsp;0.642 in CHARLS), 5-year (AUC\u0026thinsp;=\u0026thinsp;0.601 in UKB; AUC\u0026thinsp;=\u0026thinsp;0.589 in CHARLS), and 8-year (AUC\u0026thinsp;=\u0026thinsp;0.602 in UKB; AUC\u0026thinsp;=\u0026thinsp;0.598 in CHARLS) new-onset CVD, surpassing METS-IR, BMI, TyG, and VAI. The predictive effects of different visceral fat indices on new-onset CVD are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Moreover, compared with TyG, VAI, BMI, and METS-IR, METS-VF significantly improved the NRI and IDI for new-onset CVD at 3-year, 5-year, and 8-year time points in both UKB and CHARLS (Table S8).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\u003cp\u003eIn the subgroup analysis, no significant interaction with new-onset CVD risk was observed in all subgroups in the UKB and CHARLS cohorts, except for smoking status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) in UKB (Fig.\u0026nbsp;6). Notably, among individuals who were never smokers or former smokers, those in the highest quartile of METS-VF exhibited a 70% increased risk compared with those in the lowest quartile (HR\u0026thinsp;=\u0026thinsp;1.70, 95% CI: 1.62\u0026ndash;1.78). However, this increase was only 46% in smokers (HR\u0026thinsp;=\u0026thinsp;1.46, 95% CI: 1.30\u0026ndash;1.64).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study involving two prospective cohorts, we identified a significant association between METS-VF and an increased risk of new-onset CVD in patients with MetS, even after adjustment for traditional cardiovascular risk factors. Higher METS-VF was associated with a higher risk of new-onset CVD. Moreover, individuals with high METS-VF and high genetic risk exhibited a significantly increased risk of new-onset CVD. This finding suggests that METS-VF may serve as a valuable predictor of CVD in the high-risk MetS population.\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated that visceral fat plays a critical role in the development of MetS and CVD [32, 33, 34]. Excessive visceral fat accumulation triggers the release of numerous adipokines and inflammatory mediators [35, 36], which contribute to insulin resistance, dyslipidemia, and endothelial dysfunction, ultimately elevating the risk of CVD [37, 38, 39]. A previous study suggested that METS-VF is an independent risk factor for cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;2.62, 95% CI: 1.20\u0026ndash;5.71), and for every 0.2-unit increase in METS-VF, cardiovascular mortality increases by 18% (HR\u0026thinsp;=\u0026thinsp;1.18, 95% CI: 1.06\u0026ndash;1.31) [40]. Furthermore, a study from CHARLS demonstrated a dose-response relationship between METS-VF and CVD events (HR\u0026thinsp;=\u0026thinsp;3.31, 95% CI: 1.28\u0026ndash;8.54) as well as all-cause mortality (HR\u0026thinsp;=\u0026thinsp;4.03, 95% CI: 1.72\u0026ndash;9.42) across different glucose tolerance statuses [41]. METS-VF, as a comprehensive indicator reflecting the metabolic status of visceral fat, integrates multiple parameters associated with visceral fat metabolism [42]. A cohort study in China involving 41,576 individuals found that high cumulative METS-VF was associated with an increased risk of CVD (HR\u0026thinsp;=\u0026thinsp;2.78, 95% CI: 2.49\u0026ndash;3.17) and mortality (HR\u0026thinsp;=\u0026thinsp;4.90, 95% CI: 4.36\u0026ndash;5.50), with this association becoming stronger as exposure to high METS-VF was prolonged [43]. These studies suggest that a higher METS-VF signifies a more pronounced metabolic disturbance in visceral fat, likely explaining its association with an elevated risk of new-onset CVD. Our findings are consistent with previous studies highlighting the importance of visceral fat metabolism in the pathogenesis of CVD. Additionally, this study is distinct in its use of METS-VF combined with PRS to comprehensively evaluate the relationship between visceral fat metabolism and new-onset CVD in patients with MetS. The results indicate that METS-VF is associated with the risk of new-onset cardiovascular disease in patients with MetS, while genetic risk also plays a vital role. Notably, the incidence of AF was 5.7 times higher in the group with high METS-VF and high genetic risk compared to the group with low METS-VF and low genetic risk. A previous study found that MetS is a strong risk factor for AF, and consistent treatment of MetS can significantly improve the risk and frequency of atrial fibrillation, associated symptoms, and the success of treatment for maintaining cardiac rhythm [44].\u003c/p\u003e\u003cp\u003eIn addition, there was an interaction between smoking and METS-VF on new-onset CVD risk in the UKB cohort. It is plausible that MetS and smoking contribute to CVD risk through shared pathophysiological pathways, such as reduced insulin sensitivity, impaired glycemic control, altered lipid profiles, and endothelial dysfunction [45, 46]. A previous study indicated that genetic predisposition toward tobacco smoking was strongly associated with a higher likelihood of MetS (HR\u0026thinsp;=\u0026thinsp;1.49, 95% CI: 1.47\u0026ndash;1.52) in Chinese individuals [47]. However, our study revealed that the risk of new-onset CVD increased in both non-smokers and smokers as METS-VF rose in the UKB cohort. The magnitude of this increased risk was more pronounced among non-smokers or former smokers. These results highlight the significance of METS-VF and warrant further investigation.\u003c/p\u003e\u003cp\u003eBoth METS-IR, BMI, TyG, and VAI have been used to evaluate the risk of CVD in patients with MetS [23, 48, 49]. Recently, Wang et al. [50] found that METS-VF was strongly associated with left ventricular hypertrophy in T2DM (OR\u0026thinsp;=\u0026thinsp;9.79, 95% CI: 6.16\u0026ndash;15.76), demonstrating superior predictive performance compared to traditional indices (AUC\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.66\u0026ndash;0.70). Cao et al. [51] found that METS-VF was independently associated with the risk of stroke (HR\u0026thinsp;=\u0026thinsp;2.78, 95% CI: 1.71\u0026ndash;4.52) and exhibited the highest AUC for stroke prediction (AUC\u0026thinsp;=\u0026thinsp;0.687, 95% CI: 0.668\u0026ndash;0.706), outperforming BMI, WHtR, VAI, and Cardiometabolic Index (CMI). Our study provides further robust evidence elucidating the association between METS-VF and new-onset CVD risk. METS-VF demonstrated the highest diagnostic capability over the long term compared with METS-IR, BMI, TyG, and VAI. Therefore, identifying METS-VF as an independent predictor of CVD in patients with MetS has substantial clinical implications. Clinicians can utilize METS-VF to identify high-risk individuals among those with MetS, enabling more targeted prevention and treatment strategies. For instance, patients with elevated METS-VF could benefit from aggressive lifestyle modifications, such as dietary control and increased physical activity, along with appropriate pharmacological interventions aimed at improving visceral fat metabolism and reducing new-onset CVD risk.\u003c/p\u003e\u003cp\u003eThese findings are based on prospective cohorts in two distinct populations using rigorous study designs and long-term follow-up. Furthermore, the findings are consistent across both cohorts, suggesting that our results are generalizable to a wider range of populations. Nevertheless, this study has certain limitations. First, while the sample size reflects the research questions to some extent, it is still relatively limited and may not encompass all possible clinical scenarios and population characteristics, particularly as the CHARLS cohort included a smaller number of participants, which might have introduced some bias into the study results. Additionally, participants in CHARLS experienced fewer CVD event outcomes. Second, no dynamic blood sampling was conducted during the UKB follow-up, and in CHARLS, blood samples were collected only at baseline, wave 3, and wave 5. This limitation restricted our ability to further investigate the relationship between changes in METS-VF and new-onset CVD. Third, although we adjusted for traditional cardiovascular risk factors, unmeasured confounding factors might still influence the association between METS-VF and new-onset CVD.\u003c/p\u003e\u003cp\u003eIn conclusion, individuals with MetS and elevated METS-VF were at a higher risk of new-onset CVD, while those with both high genetic risk and high METS-VF exhibited the highest risk of new-onset CVD. Our study established that high METS-VF was significantly associated with new-onset CVD in patients with MetS, and METS-VF could serve as a more efficient independent predictor of CVD in this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Cardiology, Suining Central Hospital, Suining, Sichuan, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Cardiology, Sichuan Provincial People\u0026apos;s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the participants and staff of the UK Biobank for their invaluable contributions to this study. This research has been conducted using the UK Biobank Resource under Application Number 106027. We also thank all members of the CHARLS for their efforts and all participants for their contribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design was conceived by Hongqiang Ren, Xiaoping Li, and Yijun Liu. Zhen Tan, Lei Liu and Mao Ye organized the data, conducted the analyses, and wrote and edited the manuscript. Shuang Li and Xinrui Xue contributed to the interpretation of the results, revision, and finalization of the manuscript. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no funding sources to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available on application through approval and oversight by the UK Biobank (www.ukbiobank.ac.uk/). The datasets used in this investigation are available in online repositories (http://charls.pku.edu.cn//).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUKB has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval. Therefore, researchers do not require separate ethical clearance and can operate under the RTB approval and all participants provided written informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNeeland IJ, Lim S, Tchernof A, Gastaldelli A, Rangaswami J, Ndumele CE, Powell-Wiley TM, Despr\u0026eacute;s JP. Metabolic syndrome. Nat Rev Dis Primers. 2024 Oct 17; 10(1): 77. doi: 10.1038/s41572-024-00563-5. PMID: 39420195.\u003c/li\u003e\n\u003cli\u003eRizzo M, Rizvi AA. New Advances in Metabolic Syndrome. Int J Mol Sci. 2024 Jul 30; 25(15): 8311. doi: 10.3390/ijms25158311. 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Front Endocrinol (Lausanne). 2024 Jul 1; 15: 1416462. doi: 10. 3389/fendo. 2024. 1416462. PMID: 39015177; PMCID: PMC11249535. \u003c/li\u003e\n\u003cli\u003eTorun C, Ankaralı H, Caştur L, Uzunlulu M, Erbakan AN, Akbaş MM, G\u0026uuml;nd\u0026uuml;z N, Doğan MB, Oğuz A. Is Metabolic Score for Visceral Fat (METS-VF) a Better Index Than Other Adiposity Indices for the Prediction of Visceral Adiposity. Diabetes Metab Syndr Obes. 2023 Aug 29; 16: 2605-2615. doi: 10. 2147/DMSO. S421623. PMID: 37663201; PMCID: PMC10474894.\u003c/li\u003e\n\u003cli\u003eWang L, Liu S, Ke J, Cao B, Wang D, Zhao Q, Gong H, Fang Y, Zheng Z, Yu C, Wu N, Ma Y, Yu K, Yang L, Zhao D. Association between metabolic visceral fat score and left ventricular hypertrophy in individuals with type 2 diabetes. Diabetol Metab Syndr. 2025 Mar 6; 17(1): 81. doi: 10. 1186/s13098-025-01648-1. PMID: 4005 0939; PMCID: PMC11884144. \u003c/li\u003e\n\u003cli\u003eCao Y, Wen W, Zhang H, Li W, Huang G, Huang Y. The association between visceral fat metabolic score and stroke: mediation by declining kidney function. Diabetol Metab Syndr. 2025 Feb 8; 17(1): 50. doi: 10. 1186/s13098-025-01608-9. PMID: 39920850; PMCID: PMC11806899.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6850201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6850201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eMetabolic syndrome (MetS) significantly increases the risk of cardiovascular disease (CVD). The metabolism score for visceral fat (METS-VF) is a novel assessment tool with potential to replace visceral adipose tissue measurement. This study aimed to investigate the association between METS-VF and new-onset CVD in participants with MetS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study utilized data from two prospective cohorts: UK Biobank (UKB) and the China Health and Retirement Longitudinal Study (CHARLS). METS-VF was calculated based on relevant metabolic parameters. Multivariate Cox regression analysis and restricted cubic spline (RCS) analyses were conducted to assess the relationship between METS-VF and new-onset CVD. The interaction between METS-VF and CVD polygenic risk score (PRS) was examined in UKB to explore the contribution of genetic factors. Receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the diagnostic capability of METS-VF for new-onset CVD. Subgroup analyses were performed to confirm the robustness of the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 101,292 individuals from the UK Biobank and 1,680 individuals from CHARLS were included. The median follow-up periods were 14.6 years in UKB and 5.0 years in CHARLS. High METS-VF was significantly associated with an increased risk of new-onset CVD in both UKB (HR = 1.63, 95% CI: 1.56-1.71) and CHARLS (HR = 2.114, 95% CI: 1.52-2.94) compared with low METS-VF. Individuals with the highest METS-VF and high genetic risk exhibited the highest risk of new-onset CVD (HR = 2.36, 95% CI: 2.14-2.61). The diagnostic capability of METS-VF for new-onset CVD was superior to other obesity-related indicators and demonstrated consistently stable performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eMETS-VF is a valuable indicator for predicting new-onset CVD in individuals with MetS, providing new insights into the prevention and management of CVD in high-risk populations.\u003c/p\u003e","manuscriptTitle":"Association of metabolism score for visceral fat with new-onset cardiovascular disease in patients with metabolic syndrome: two large prospective cohorts in Europe and Asia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 06:04:41","doi":"10.21203/rs.3.rs-6850201/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"838b7016-d5d6-4e8f-97d1-c6a2e45a073d","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-16T21:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 06:04:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6850201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6850201","identity":"rs-6850201","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}