Cardiovascular-kidney-metabolic syndrome predicted the risks of geriatric syndromes and mortality of major chronic diseases

Cardiovascular-kidney-metabolic syndrome predicted the risks of geriatric syndromes and mortality of major chronic diseases | 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 Article Cardiovascular-kidney-metabolic syndrome predicted the risks of geriatric syndromes and mortality of major chronic diseases Meng Hao, Hui Zhang, Yi Li, Jingyi Wu, Zixin Hu, Shuai Jiang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4987225/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 Background The American Heart Association (AHA) has introduced the cardiovascular-kidney-metabolic (CKM) syndrome as a systemic health disorder affecting nearly all organ systems. However, evidence supporting CKM syndrome health-related effects remains scarce, and its effects beyond its components are not studied. Methods Here, we used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations of CKM syndrome with adverse health outcomes, including functional disability, physical frailty, falls, hospitalization and mortality. Then, we examined the associations between CKM syndrome and all-cause and cause-specific mortalityusing data from the US National Health and Nutrition Examination Survey (NHANES). Results CKM syndrome stages were independently associated with incident geriatric syndromes, including incident disability (OR, 1.2, [95% CI, 1.11-1.29]), frailty (OR, 1.28, [95% CI, 1.15-1.42]), falls (OR, 1.11, [95% CI, 1.06-1.17]), and hospitalization (OR, 1.19, [95% CI, 1.14-1.25]). Moreover, CKM syndrome stages independently predicted all-cause mortality (CHARLS: HR, 1.12 [95% CI, 1.06-1.18]; NHANES: HR, 1.34 [95% CI, 1.29-1.40]), and cause-specific mortality, including CVD (HR, 1.62, [95% CI, 1.50-1.75]), malignant neoplasm (HR, 1.20, [95% CI, 1.10-1.30]), respiratory disease (HR, 1.26, [95% CI, 1.07-1.49]). After adjusting for CKM components, the positive associations of CKM syndrome with geriatric syndrome and mortality were persisted, indicating that CKM syndrome has health implications beyond those of its component disorders. There were dose-effect relationships between CKM stages and risks of geriatric syndromes and mortality. Especially, advanced CKM syndrome (stage 3-4) exhibited higher risks of adverse outcomes along the CKM staging spectrum. Conclusion The findings revealed that CKM syndrome significantly impacted the overall health among middle-aged and older adults, advocating its application from CVD to geriatric community and multidiscipline clinical settings. It suggested the need for CKM syndrome assessment to predict and prevent geriatric syndromes and major chronic conditions, thereby reducing social and healthcare burdens. Moreover, these results provided empirical evidence for the validity and robustness of the CKM staging classification. Health sciences/Diseases/Cardiovascular diseases Health sciences/Diseases/Endocrine system and metabolic diseases Health sciences/Health care/Public health CKM syndrome CKM staging construct disability frailty mortality Key Points Question What’s the cardiovascular-kidney-metabolic syndrome health-related effects on geriatric syndrome and risks of mortality? Findings In this cohort study involving 21920 participants at baseline with 4028 deaths, and 672, 346, 1766, 1834 incident cases of disability, physical frailty, falls and hospitalization during follow-up periods, CKM syndrome was significantly associated with increased risks of these geriatric syndromes, all-cause mortality and cause-specific mortality, including cardiovascular disease (CVD), cancer, respiratory disease, and other causes. Meaning The findings supported the American Heart Association's proposal that CKM syndrome manifested as a systemic health disorder impacting overall health, advocating CKM application beyond CVD to the geriatric community and multidiscipline clinical settings. Introduction The historically high prevalence of obesity and diabetes has become a significant social burden, impacting a transition from affecting primarily adults to now affecting youth from an early age 1,2 . These metabolic risk factors, including obesity and diabetes, are associated with increased risks of cardiovascular disease (CVD) events 3–5 . It’s projected that CVD will impact over 180 million adults (about 60%) in the US in the coming decades, leading to substantial rises in healthcare costs and productivity losses 6 . Based on both social needs and medical rationale, the American Heart Association (AHA) introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome 7 , arising from the connections among obesity, diabetes, chronic kidney disease (CKD), and CVD. To improve CKM health and related outcomes, the CKM staging construct was proposed to rationally categorize individuals at risk for or affected by CVD, aiding in screening and risk assessment. According to AHA, CKM syndrome manifested as a holistic and systemic health disorder that impacted nearly all organ systems and contributed to multiorgan diseases 7 . Despite its recent proposal, evidence supporting CKM syndrome health-related effects remains scarce, and its effects beyond its components are not studied. Emerging evidences suggest significant association between CKM syndrome components and risks of geriatric syndromes and major chronic diseases (e.g., CVD, cancer, respiratory disease). CKM syndrome tended to progressed with age in parallel with the progression of geriatric syndromes 8,9 . Moreover, the components of CKM syndrome, such as abdominal obesity 10,11 , metabolic syndrome 12,13 , diabetes 14,15 , hypertension 16 , higher cardiovascular risk burden 17,18 , and impaired renal function 19–21 increased the risks of geriatric syndromes (e.g., functional disability, frailty). Within the CKM staging construct, CKM risk factors (such as obesity 4 , diabetes 5 , and metabolic syndrome 22 ) were linked to increased risks of CVD. In addition, the CKM syndrome components were also associated non-CVD outcomes. For instance, metabolic syndrome increased risk of cognitive decline 23 , nonalcoholic fatty liver disease 24 , kidney disease 25 , and cancer 26 . This evidence underscored that CKM syndrome might increase the risks of geriatric syndromes and chronic diseases. Therefore, we aimed to provide evidence for broader application of CKM syndrome in multidisciplinary clinical and community settings, particularly within geriatric medicine and community. Here, we used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations of CKM syndrome with incident geriatric syndromes and mortality. These geriatric syndromes included functional disability, physical frailty, falls, and hospitalization, which were considered as serious, intermediate, accidental, and healthcare resource utilization-related outcomes. Then, we examined the association between CKM syndrome and all-cause and cause-specific mortality using data from the US National Health and Nutrition Examination Survey (NHANES). Methods Study populations The CHARLS is an ongoing prospective population-based longitudinal cohort study of middle-aged and older Chinese adults 27 . The baseline survey was conducted between June 2011 and March 2012, and the participants were followed up biennially until 2020. The 2011 wave was taken as baseline, and 9389 subjects (aged 45–85 years) with routine clinical tests were enrolled. CHARLS was approved by the Ethics Review Board of Peking University, which was conducted in accordance with the Declaration of Helsinki and other relevant guidelines and regulations. The US NHANES is a nationally representative cross-sectional survey of civilian, noninstitutionalized persons living in the US. We used data from the NHANES 1998–2018, from a publicly available data dictionary 28 . The demographic information, physical examination results, questionnaire items, and mortality information were collected. The mortality status was linked from the National Death Index. NHANES was approved by the NCHS Ethics Review Board (ERB). All participants provided informed consent. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cohort studies. Biomarkers collection For CHARLS, the baseline (2011 wave) collected fasting blood samples for complete blood count (CBC) tests and blood-based bioassays, in which 16 biomarkers were measured for the participants. For NHANES, we collected physical, hematological and biochemical biomarkers. For CKM stage evaluation, body mass index (BMI), waist circumstance (WC), systolic and diastolic pressure (SBP, DBP) were collected. Total cholesterol (TC), high-density and low-density lipoprotein cholesterol (HDL, LDL), triglyceride (TG), plasma glucose, glycosylated hemoglobin (HbA1c) and creatinine were measured. Fasting blood glucose (100 mg/dL to < 126 mg/dL) or glycated hemoglobin (5.7% to < 6.5%) were used to diagnose individuals with prediabetes. Estimated glomerular filtration rate (GFR) was calculated using the race-free CKD-EPI 2021 creatinine equation. Assessment of CKM syndrome According to the guideline, five stages of CKM syndrome were assessed: stage 0 (no CKM risk factors), stage 1 (excess and/or dysfunctional adiposity), stage 2 (metabolic risk factors and kidney disease), stage 3 (subclinical CVD) and stage 4 (clinical CVD) 29 . In detail, CKM Stage 0 included participants with normal BMI (< 23 kg/m2) and normal WC (< 80 for women and < 90 cm for men among Asians, respectively). Participants meet criteria for the other stages were diagnosed as higher stages. CKM Stage 1 identified individuals with elevated BMI (≥ 23 kg/m2), or elevated WC (≥ 80 and ≥ 90 cm for women and men, respectively), or prediabetes. CKM Stage 2 identified participants with metabolic risk factors or moderate-to-high-risk CKD. metabolic risk factors included elevated fasting serum triglycerides (≥ 135 mg/dL), hypertension, diabetes, or metabolic syndrome. The metabolic syndrome was evaluated through WC, HDL, TG, blood pressure, and prediabetes, in which individuals with more than three abnormalities. CKM Stage 3 was identified based of the presence of very-high-risk KDIGO CKD stages, or a self-reported chronic kidney disease, or a high-predicted 10-year CVD risk (≥ 20%). The 10-year cardiovascular risk was estimated with the AHA Predicting Risk of CVD EVENTs (PREVENT) equations 30 . The calculation details of the PREVENT were described in Supplementary. CKM Stage 5 was identified based on self-reported established cardiovascular disease (coronary heart disease, angina, heart attack, heart failure, and stroke). Assessment of geriatric syndromes In CHARLS, six items of daily self-care ability were collected from each participant based on the Katz index 31 , including dressing, bathing, transferring from a chair to bed, toilet use, feeding and walking. These items were asked with four responses, i.e., “No, I do not have any difficulty”, “I have difficulty but still can do it”, “Yes, I have difficulty and need help” and “I cannot do it”. As reported in previous studies, ADL disability was defined if the participants have difficulty to perform at least one of 6 daily self-care tasks. Frailty was evaluated through the widely used criteria originally proposed by Fried et al. 32 , modified with the information available in the CHARLS 33 . In the definition, frailty contained five components of exhaustion, weakness, low physical activity, weight loss, and slowness. In our study, the five components of frailty were evaluated and defined as follows: (1) Exhaustion: exhaust was defined according to the Chinese version of the Center for Epidemiologic Studies-Depression scale (CES-D) questions (20): “I felt everything I did was an effort during last week” or “I could not get going during last week.”. Exhaustion was present if the participants answered “Most or all of the time” or “Occasionally or a moderate amount of the time” for the either question. (2) Weakness: weakness was measured using the question “having difficulty in Lifting or carrying weights over 10 jin, like a heavy bag of groceries”. (3) Low physical activity: CHARLS defined WALK as walking those participants might do solely for recreation, sport, excise, or leisure at work and at home, walking to travel from place to place, and any other walking. Low physical activity was considered to be present if the participants had no physical activity or WALK at least 10 min at a time during a usual week. (4) Weight loss: the weight loss was defined as the unintentional loss of 5 or more kilograms in the last year or current body mass index (BMI) ≤ 18.5 kg/m2. (5) Slowness: these questions asked the participants to report whether they have difficulty with walking 100 meters or climbing several flights of stairs without resting. Those participants who had difficulty with performing the walking or climbing were categorized as slowness. In our study, robustness was defined as the absence of any component, pre-frailty as the presence of one or two of these components, and frailty as the presence of three or more components. Information of falls, and hospitalization were obtained through questionnaire at the biennial follow-ups. These geriatric syndromes, including functional disability, physical frailty, falls and hospitalization were evaluated at the baseline (2011 wave) and the follow-ups (2013 and 2015 waves). Individuals identified as ADL disability and frailty, or recorded with history of falls and hospitalization were excluded in association analysis. Assessment of adverse outcomes and mortality In CHARLS, records on death were obtained at the biennial follow-up at the 2011, 2013, 2015, 2018 and 2020 waves. Because the exact date of death was not available, the midpoint of the two follow-up visits was defined as the time of death. In US NHANES, dates and causes of death were linked to the National Death Index records to 31 December 2019. We used the 10th revision of the International Statistical Classification of Diseases (ICD-10) to identify causes of death. Mortality outcomes in this study included the following underlying causes of death: including heart disease, Malignant neoplasms, respiratory disease, cerebrovascular disease, Alzheimer’s disease, diabetes, influenza/pneumonia, and nephritis/nephrosis. Statistical analysis First, continuous and categorical data are presented as the means and standard deviations (SDs) or frequencies (%), respectively. Second, we utilized logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of CKM syndrome stages associated with frailty, disability, falls, hospitalization, using two models. Model 1: crude model; Model 2: adjusted for age (continuous), gender, education, race, smoking status, alcohol consumption status, BMI (continuous), cancer. Model 3: covariates in model 2 and CKM components (hypertension, diabetes, heart disease, stroke); Cox proportional hazard models were employed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) in the associations between CKM syndrome stages and mortality, with crude and adjusted models. We also conducted Kaplan‒Meier survival analysis to plot the associations of CKM syndrome stages with all-cause mortality. All results were considered significant at a P value < 0.05 (2-tailed). All analyses were conducted using R statistical software (version 4.2.1; www.r-project.org ). Results Characteristics of the study population Table 1 shows the characteristics of the study population in the CHARLS and NHANES. The baseline demographic characteristics, lifestyle factors, laboratory biomarkers and chronic diseases of the study population are presented in detail. At baseline, 9389 participants (72.1% aged 45–64 years and 27.9% 65–85 years; 46.7% male) in the CHARLS, and 12531 participants (58.3% aged 45–64 years and 41.7% aged 65–85 years; 49.7% male) in the NHANES were analyzed. The mean ages of the participants were 49.4 (9.2) years in the CHARLS and 62.5 (11.1) in the NHANES. In the CHARLS, the overall prevalence of CKM syndrome from stage 0 to 4 were 11.4%, 23.1%, 39.4%, 12.5% and 13.6%, respectively. Correspondingly, in the NHANES, the prevalence of CKM syndrome stages were 4.6%, 17.9%, 46.8%, 13.6% and 17.1%, respectively. Table 1 Characteristic of Study Population in CHARLS and NHANES. Demographic CHARLS (N = 9389) NHANES (N = 12531) Age, year, M (SD) 59.4 (9.2) 62.5 (11.1) 45–64, years 6774 (72.1) 7307 (58.3) 65–85, years 2615 (27.9) 5224 (41.7) Sex, n (%) Male 4381 (46.7) 6231 (49.7) Female 5008 (53.3) 6300 (50.3) Race and ethnicity White 6109 (48.8) Black 2372 (18.9) Hispanic 3060 (24.4) Asian, Other 9389 (100.0) 990 (7.9) Education Less than high school 8474 (90.3) 3731 (29.8) High school or equivalent 789 (8.4) 2887 (23.1) College or above 123 (1.3) 5897 (47.1) WC, cm, M (SD) 84.3 (12.6) 101 (14.8) BMI, kg/m 2 , M (SD) 23.5 (3.8) 29.1 (6.2) SBP, mmHg, M (SD) 131 (22.1) 130.8 (20.4) DBP, mmHg, M (SD) 75.9 (12.2) 69.9 (14.1) Current smoking, n (%) 2851 (77.3) 2276 (18.2) Current drinking, n (%) 2334 (24.9) 8439 (70.4) Glucose, mg/dL, M (SD) 110.6 (37.6) 114.8 (39.5) HbAc1, %, M (SD) 5.3 (0.8) 6 (1.2) TC, mg/dL, M (SD) 193.7 (38.7) 200.1 (43.1) HDL, mg/dL, M (SD) 51.2 (15.3) 54.5 (16.6) LDL, mg/dL, M (SD) 116.6 (35.1) 118.4 (36.8) TG, mg/dL, M (SD) 133.5 (106.2) 139.6 (113.9) Creatine, mg/dL, M (SD) 0.8 (0.2) 0.9 (0.4) Chronic conditions Hypertension, n (%) 2365 (25.2) 6202 (49.5) Diabetes, n (%) 543 (5.8) 2258 (18) Cancer, n (%) 95 (1) 1759 (14) Heart disease, n (%) 1121 (11.9) 1714 (13.7) Stroke, n (%) 629 (6.7) 698 (5.6) CKM stages Stage 0 1066 (11.4) 579 (4.6) Stage 1 2172 (23.1) 2265 (18.1) Stage 2 3702 (39.4) 5839 (46.6) Stage 3 1176 (12.5) 1706 (13.6) Stage 4 1273 (13.6) 2142 (17.1) Association of CKM stages with frailty, disability, falls and hospitalization The CHARLS documented 672, 346, 1766, 1834 incident cases of ADL disability, physical frailty, falls and hospitalization, respectively. After adjusting for covariates (socioeconomic status, lifestyle factors, etc.), elevated CKM syndrome stages were also associated with increased risk of incident disability (OR, 1.2, [95% CI, 1.11–1.29]), frailty (OR, 1.28, [95% CI, 1.15–1.42]), falls (OR, 1.11, [95% CI, 1.06–1.17]), and hospitalization (OR, 1.19, [95% CI, 1.14–1.25]). Then, the CKM components, including hypertension, diabetes, stroke, and heart disease, were adjusted. Positive associations were also found between CKM stages and incident disability (OR, 1.2 [95% CI, 1.07–1.36]), frailty (OR, 1.18 1.2 [95% CI, 1.01–1.38]), falls (OR, 1.09 [95% CI, 1.02–1.17]) and hospitalization (OR, 1.08 [95% CI, 1.01–1.16]). Compared with participants in the CKM syndrome stage 0, elevated CKM stages showed increasing odds ratios along the CKM staging spectrum (P < 0.05). Especially, participants categorized as advanced CKM syndrome (stage 3–4) exhibited higher risks of adverse outcomes, presenting a 38–140% increase in risk (all P < 0.05). These results suggested dose-effect relationships between CKM stages and various geriatric syndromes. Association of CKM stages with mortality The CHARLS documented 1186 deaths over a median follow-up of 9 years, and the NHANES documented 2842 deaths over a median follow-up of 8.4 years. The results of Cox proportional hazard analyses regarding the association of continuous CKM syndrome stages with the risk of all-cause mortality are presented in Table 2 . After adjusting for covariates (Model 2), elevated CKM syndrome stages were associated with all-cause mortality (CHARLS: HR, 1.12 [95% CI, 1.06–1.18]; NHANES: HR, 1.35 [95% CI, 1.29–1.40]). Then, adjusting for CKM components (Model 3), positive associations were also found between CKM stages and all-cause mortality in NHANES (HR, 1.23 [95% CI, 1.15–1.31]), not CHARLS (HR, 1.06 [95% CI, 0.98–1.14]). Compared with participants in the CKM syndrome stage 0, those in the advanced stages experienced significantly greater risks of mortality (e.g., in CHARLS, stage 3: HR, 2.01 [95% CI, 1.44–2.82], stage 4: HR, 2.45 [95% CI, 1.85–3.24]; in NHANES, stage 3: HR, 2.10 [95% CI, 1.49–2.96], stage 4: HR, 2.54 [95% CI, 1.91–3.38]). The dose-effect relationships also presented between CKM stages and all-cause mortality in CHARLS and NHANES (P < 0.001). Table 2 Associations of CKM syndrome with geriatric syndromes. Model 1 Model 2 CKM stages Events/N OR (95% CI) P OR (95% CI) P Disability Continuous 672/3116 1.27 (1.18–1.36) < 0.001 1.2 (1.11–1.29) < 0.001 Stage 0 52/322 Ref Ref Stage 1 104/643 1 (0.7–1.45) 0.992 1.11 (0.74–1.68) 0.620 Stage 2 242/1217 1.29 (0.93–1.81) 0.130 1.36 (0.93–1.99) 0.112 Stage 3 128/412 2.34 (1.64–3.39) < 0.001 1.98 (1.26–3.15) 0.003 Stage 4 146/522 2.02 (1.42–2.89) < 0.001 1.88 (1.23–2.88) 0.003 Frailty Continuous 346/3469 1.3 (1.18–1.44) < 0.001 1.28 (1.15–1.42) < 0.001 Stage 0 31/388 Ref Ref Stage 1 62/855 0.9 (0.58–1.43) 0.647 1.01 (0.61–1.69) 0.974 Stage 2 127/1401 1.15 (0.77–1.76) 0.509 1.1 (0.69–1.79) 0.701 Stage 3 58/403 1.94 (1.23–3.1) 0.005 2.17 (1.23–3.84) 0.007 Stage 4 68/422 2.21 (1.42–3.51) < 0.001 2.4 (1.38–4.2) 0.002 Falls Continuous 1766/7820 1.14 (1.09–1.19) < 0.001 1.11 (1.06–1.17) < 0.001 Stage 0 180/911 Ref Ref Stage 1 372/1837 1.03 (0.85–1.26) 0.762 1.01 (0.8–1.26) 0.958 Stage 2 692/3141 1.15 (0.96–1.38) 0.142 1.14 (0.92–1.41) 0.228 Stage 3 243/939 1.42 (1.14–1.77) 0.002 1.38 (1.05–1.82) 0.022 Stage 4 279/992 1.59 (1.28–1.97) < 0.001 1.58 (1.21–2.07) < 0.001 Hospitalization Continuous 1834/8207 1.25 (1.19–1.31) < 0.001 1.19 (1.14–1.25) < 0.001 Stage 0 173/979 Ref Ref Stage 1 379/1982 1.1 (0.9–1.35) 0.340 1.15 (0.92–1.43) 0.235 Stage 2 703/3315 1.25 (1.05–1.51) 0.016 1.14 (0.92–1.41) 0.235 Stage 3 261/974 1.71 (1.37–2.12) < 0.001 1.44 (1.09–1.89) 0.009 Stage 4 318/957 2.32 (1.88–2.87) < 0.001 2.11 (1.62–2.74) < 0.001 Model1: crude model; Model2: adjusted for age, gender, body mass index, race, educational levels, smoking status, alcohol drinking, and cancer. Table 3 Hazard ratios of all-cause mortality according to CKM. Model 1 Model 2 NHANES Event HR (95% CI) P-value HR (95% CI) P-value CHARLS Continuous 1186/9389 1.35 (1.29–1.42) < 0.001 1.12 (1.06–1.18) < 0.001 Stage 0 121/1066 1.00 (ref) Stage 1 170/2172 0.68 (0.54–0.85) 0.001 0.89 (0.65–1.22) 0.474 Stage 2 324/3702 0.76 (0.62–0.94) 0.012 1.19 (0.92–1.55) 0.187 Stage 3 344/1176 2.89 (2.35–3.56) < 0.001 2.01 (1.44–2.82) < 0.001 Stage 4 227/1273 1.63 (1.31–2.03) < 0.001 2.45 (1.85–3.24) < 0.001 NHANES Continuous 2953/12531 1.93 (1.86-2.00) < 0.001 1.35 (1.29–1.40) < 0.001 Stage 0 68/579 1.00 (ref) 1.00 (ref) Stage 1 236/2259 1.12 (0.85–1.46) 0.428 0.97 (0.71–1.33) 0.850 Stage 2 841/5833 1.5 (1.17–1.92) 0.001 1.23 (0.94–1.61) 0.125 Stage 3 882/1703 8.22 (6.40-10.55) < 0.001 2.10 (1.49–2.96) < 0.001 Stage 4 926/2141 5.98 (4.67–7.66) < 0.001 2.54 (1.91–3.38) < 0.001 Model1: crude model; Model2: adjusted for age, gender, body mass index, race, educational levels, smoking status, alcohol drinking, and cancer. Table 4 Hazard ratios of cause-specific mortality according to continuous CKM stages in NHANES. Model 1 Model 2 NHANES Events HR (95% CI) P-value HR (95% CI) P-value Underlying cause Cardiovascular disease 941 2.50 (2.35–2.66) < 0.001 1.62 (1.50–1.75) < 0.001 Malignant neoplasm 712 1.78 (1.66–1.90) < 0.001 1.19 (1.10–1.29) < 0.001 Respiratory disease 165 1.93 (1.68–2.23) < 0.001 1.26 (1.07–1.49) 0.005 Alzheimer’s disease 117 1.95 (1.65–2.31) < 0.001 1.15 (0.93–1.42) 0.190 Others 1018 2.07 (1.96–2.20) < 0.001 1.42 (1.33–1.52) < 0.001 Model1: crude model; Model2: adjusted for age, gender, body mass index, race, educational levels, smoking status, alcohol drinking, and cancer. In the NHANES, after adjusting for covariates (Model 2), elevated CKM syndrome stages were also associated with increased risk of mortality of cardiovascular disease (HR, 1.62, [95% CI, 1.50–1.75]), malignant neoplasm (HR, 1.19, [95% CI, 1.10–1.29]), respiratory disease (HR, 1.26, [95% CI, 1.07–1.49]), and other causes (HR, 1.42, [95% CI, 1.33–1.52]). Taking the CKM syndrome stage 0 as reference, CKM advanced stages were associated with increased risks of cardiovascular disease, malignant neoplasms and other causes of mortality. Discussion In this cohort study, we included participants from CHARLS and NHANES to investigate the associations between CKM syndrome stages and the risks of adverse outcomes among middle-aged and older adults. Our findings indicated that CKM syndrome stages, particularly the advanced stages, were significantly associated with increased risks of geriatric syndromes, including disability, frailty, falls, and hospitalization. In addition, CKM syndrome stages were also associated with cause-specific mortality, including CVD, malignant neoplasm, respiratory disease, and other causes. After adjusting for CKM components, the positive associations retained, indicating that CKM syndrome had health-related impacts beyond those of its component disorders. These results highlighted CKM syndrome as a systemic health condition contributing to multiorgan dysfunctions, underscoring its relevance in gerontology, geriatrics, major chronic diseases and broader multidisciplinary contexts. Previous studies focused on CKM components consisting of risk factors, rather than the whole construct of CKM syndrome, regarding to geriatric syndromes. Giltay et al. illustrated that CKM components consisting of BMI, hypertension, diabetes, and serum cholesterol, were predictive of functional disability 34 . Similarly, Cui et al. reported that higher cardiovascular risk burdens, as assessed by the Framingham general cardiovascular risk score, increased the risk of disability and accelerated its progression over time 17 . As for frailty, CKM components, including obesity, hypertension, dyslipidemia, diabetes, increased the risk of frailty 35 . Moreover, cardiovascular disorders, such as stroke and coronary artery disease, were associated with falls 36 . A consensus was reached to incorporate cardiovascular assessments in falls risk screening and prevention for older adults at risk of falls 37 . In contrast, our study employed the CKM syndrome construct, encompassing both CVD risk factors and CVD events comprehensively. We found that the CKM stages were associated with increased risks of geriatric syndromes with dose-effect relationships. Therefore, the CKM assessment could refine prediction model for risks stratification of geriatric syndromes. It highlighted the need to promote the concept of CKM staging construct and its education to geriatric community, clinician, and older adults to prevent these geriatric syndromes. Optimizing CKM health could help to enhance the geriatric health conditions and reduce social and healthcare burdens. In addition, these results confirmed the validity and robustness of the CKM staging construct, which enabled researchers and clinicians to categorize the individuals along a continuum of increasing risk in practice. We found that CKM syndromes predicted cause-specific mortality, including CVD, cancer, respiratory disease and other cause. Prior studies had reported similar associations through CKM components or risk factors 38 . For instance, Lau et al. reported that a 10-year ASCVD risk score and natriuretic peptide concentrations were associated with increased risk of incident cancer 39 . Meanwhile, Mercedes et al. demonstrated a cardiovascular disease risk score was strongly associated with cancer mortality, such as lung, breast, and colon cancers 40 . Similarly, respiratory diseases shared CKM risk factors, such as hypertension, dyslipidemias 41 , diabetes and metabolic syndrome. In our study, we applied CKM staging construct to explore its associations with both CVD mortality and non-CVD mortality (e.g., cancer, respiratory disease). The CKM stages were linked to CVD mortality with highest risks, especially the advanced stages. It supported the CKM staging construct’s objective to prevent CVD events and mortality from early CKM stages. These associations between CKM syndrome and mortality of major chronic diseases underscored CKM syndrome as a systemic health condition impacting multiorgan systems and overall health. Prediction of the future aging-associated outcomes (e.g., geriatric syndromes, major chronic diseases), and its clinical utility validated the CKM construct and its substantial applications 42 . Screening for each CKM stage could help predict risks of aging-associated, CVD and non-CVD outcomes related to CKM syndrome. These results advocated for the use of CKM assessment in preventing major chronic diseases among middle-aged and older adults. Our study demonstrated the incident geriatric syndromes and mortality followed advanced CKM stages, marked by subclinical and clinical CVD. Therefore, the primary objective was to prevent geriatric syndromes and major age-related chronic diseases among middle-aged and older adults in advanced CKM stages. Enhanced health care, such as intensified preventive therapies and pharmacological medications, was necessary to prevent CKM progression and mitigate adverse outcomes. The prevalence of early stages and advanced stages of CKM syndrome accounted for approximately 60% and 30% in CHARLS, respectively, highlighting its severe burden in the general population. The CKM staging construct emphasized early detection of CKM-related changes to support prevention efforts. For instance, lifestyle modification and weight loss can address excess or dysfunctional adiposity in early CKM stages. Within the CKM construct, it recognized the importance of the relationships between kidney health and cardiovascular health. Impaired kidney function was linked with elevated risks of CVD and geriatric syndromes 43,44 . Risk prediction equations of kidney function could inform clinical management and prevention of CKM syndrome and geriatric syndrome. Moreover, it started to emphasize the social determinants of health in assessments for CKM risk factors 7 . The social needs, including financial strain, education and mental health, played a critical role in CKM health and healthy aging. Therefore, evaluation of kidney health and social risk factors were critical in risk screening, prediction and prevention of geriatric syndromes. Several limitations should be noted in this study. Firstly, subclinical CVDs and CVDs were determined by predicted risks and self-reports, respectively, which may introduce recall bias and affect associations. Secondly, we focused on disability, frailty, falls, hospitalization, and mortality as outcomes; further studies should consider other metrics, such as cognitive decline, physical function declines, hip fracture and other functional metrics. Lastly, while cause-specific mortality was assessed, evidence regarding disease incidence remained limited for CKM syndrome. In conclusion, the findings highlighted CKM syndrome as a systemic health disorder affecting overall health among middle-aged and older adults, advocating for its broader application in multidisciplinary research, diverse clinical and community settings, especially the gerontology research and geriatrics. Application of CKM in geriatrics will enhance geriatric conditions and promote healthy aging to alleviate the social and healthcare burdens. These results provided substantial evidence to support the validity and robustness of the CKM staging construct, and its goal to prevent CVD events and mortality. Declarations Acknowledgments The data used in this research were obtained from the CHARLS and NHANES. We would like to thank the workers, researchers, and participants involved in the cohorts. Funding This work was supported by grants from the National Natural Science Foundation of China (32300533, 82301768, 32100510), the Shanghai Sailing Program (23YF1430500), and Shanghai Municipal Science and Technology Major Project. Conflict of interest The authors declare no competing interests. Data sharing statement The data from CHARLS is available upon application at http://charls.pku.edu.cn/. The data from the NHANES are available at www.cdc.gov/nchs/nhis/index.htm. Author contributions Dr.Wang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Hao, Zhang, Wang and Jin. Acquisition, analysis, or interpretation of data: Hao and Zhang. Drafting of the manuscript: Hao, Wang and Zhang. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Hao, Zhang, Li, Hu. Administrative, technical, or material support: Zhang, Hao, Jiang, Hu and Wang. Supervision: Wang, Lin, Sun. References Collaboration NCDRF. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet . 2024;403:1027-1050. doi: 10.1016/S0140-6736(23)02750-2 Collaborators GBDD. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet . 2023;402:203-234. doi: 10.1016/S0140-6736(23)01301-6 Chen Y, Yu W, Lv J, Sun D, Pei P, Du H, Yang L, Chen Y, Zhang H, Chen J, et al. Early adulthood BMI and cardiovascular disease: a prospective cohort study from the China Kadoorie Biobank. Lancet Public Health . 2024. doi: 10.1016/S2468-2667(24)00043-4 Powell-Wiley TM, Poirier P, Burke LE, Despres JP, Gordon-Larsen P, Lavie CJ, Lear SA, Ndumele CE, Neeland IJ, Sanders P, et al. Obesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation . 2021;143:e984-e1010. doi: 10.1161/CIR.0000000000000973 Song SH. Early-onset type 2 diabetes: high lifetime risk for cardiovascular disease. Lancet Diabetes Endocrinol . 2016;4:87-88. doi: 10.1016/S2213-8587(15)00390-3 Harris E. Heart Disease Prevalence, Medical Costs Will Soar in US by 2050. JAMA . 2024. doi: 10.1001/jama.2024.11026 Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, Coresh J, Mathew RO, Baker-Smith CM, Carnethon MR, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation . 2023;148:1606-1635. doi: 10.1161/CIR.0000000000001184 Aggarwal R, Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011-2020. JAMA . 2024;331:1858-1860. doi: 10.1001/jama.2024.6892 Minhas AMK, Mathew RO, Sperling LS, Nambi V, Virani SS, Navaneethan SD, Shapiro MD, Abramov D. Prevalence of the Cardiovascular-Kidney-Metabolic Syndrome in the United States. J Am Coll Cardiol . 2024;83:1824-1826. doi: 10.1016/j.jacc.2024.03.368 Qian S, Wen Q, Huang T, Chen J, Feng X. Dynapenic abdominal obesity and incident functional disability: Results from a nationwide longitudinal study of middle-aged and older adults in China. Arch Gerontol Geriatr . 2024;123:105434. doi: 10.1016/j.archger.2024.105434 Yuan L, Chang M, Wang J. Abdominal obesity, body mass index and the risk of frailty in community-dwelling older adults: a systematic review and meta-analysis. Age Ageing . 2021;50:1118-1128. doi: 10.1093/ageing/afab039 Jiang X, Xu X, Ding L, Lu J, Zhu H, Zhao K, Zhu S, Xu Q. The association between metabolic syndrome and presence of frailty: a systematic review and meta-analysis. Eur Geriatr Med . 2022;13:1047-1056. doi: 10.1007/s41999-022-00688-4 Botoseneanu A, Ambrosius WT, Beavers DP, de Rekeneire N, Anton S, Church T, Folta SC, Goodpaster BH, King AC, Nicklas BJ, et al. Prevalence of metabolic syndrome and its association with physical capacity, disability, and self-rated health in Lifestyle Interventions and Independence for Elders Study participants. J Am Geriatr Soc . 2015;63:222-232. doi: 10.1111/jgs.13205 Garcia-Esquinas E, Graciani A, Guallar-Castillon P, Lopez-Garcia E, Rodriguez-Manas L, Rodriguez-Artalejo F. Diabetes and risk of frailty and its potential mechanisms: a prospective cohort study of older adults. J Am Med Dir Assoc . 2015;16:748-754. doi: 10.1016/j.jamda.2015.04.008 Wong E, Backholer K, Gearon E, Harding J, Freak-Poli R, Stevenson C, Peeters A. Diabetes and risk of physical disability in adults: a systematic review and meta-analysis. Lancet Diabetes Endocrinol . 2013;1:106-114. doi: 10.1016/S2213-8587(13)70046-9 Sugimoto K, Yamamoto K. Hypertension, the decline of activities of daily living (ADL) and frailty. Hypertens Res . 2022;45:629-634. doi: 10.1038/s41440-021-00846-4 Cui K, Song R, Xu H, Shang Y, Qi X, Buchman AS, Bennett DA, Xu W. Association of Cardiovascular Risk Burden With Risk and Progression of Disability: Mediating Role of Cardiovascular Disease and Cognitive Decline. J Am Heart Assoc . 2020;9:e017346. doi: 10.1161/JAHA.120.017346 Zhang H, Hao M, Hu Z, Li Y, Hu X, Jiang X, Liu Z, Sun X, Wang X. Causal Association of Cardiac Function by Magnetic Resonance Imaging with Frailty Index: A Mendelian Randomization Study. Phenomics . 2022;2:430-437. doi: 10.1007/s43657-022-00072-z Chin HJ, Ahn SY, Ryu J, Kim S, Na KY, Kim KW, Chae DW, Kim CH, Kim KI. Renal function and decline in functional capacity in older adults. Age Ageing . 2014;43:833-838. doi: 10.1093/ageing/afu071 Dalrymple LS, Katz R, Rifkin DE, Siscovick D, Newman AB, Fried LF, Sarnak MJ, Odden MC, Shlipak MG. Kidney function and prevalent and incident frailty. Clin J Am Soc Nephrol . 2013;8:2091-2099. doi: 10.2215/CJN.02870313 Zhang H, Hao M, Li Y, Jiang X, Wang M, Chen J, Wang X, Sun X. Glomerular filtration rate by different measures and albuminuria are associated with risk of frailty: the Rugao Longitudinal Ageing Study. Aging clinical and experimental research . 2022;34:2703-2711. doi: 10.1007/s40520-022-02245-2 Wilson PW, D'Agostino RB, Parise H, Sullivan L, Meigs JB. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation . 2005;112:3066-3072. doi: 10.1161/CIRCULATIONAHA.105.539528 Ng TP, Feng L, Nyunt MS, Feng L, Gao Q, Lim ML, Collinson SL, Chong MS, Lim WS, Lee TS, et al. Metabolic Syndrome and the Risk of Mild Cognitive Impairment and Progression to Dementia: Follow-up of the Singapore Longitudinal Ageing Study Cohort. JAMA Neurol . 2016;73:456-463. doi: 10.1001/jamaneurol.2015.4899 Yki-Jarvinen H. Non-alcoholic fatty liver disease as a cause and a consequence of metabolic syndrome. Lancet Diabetes Endocrinol . 2014;2:901-910. doi: 10.1016/S2213-8587(14)70032-4 Thomas G, Sehgal AR, Kashyap SR, Srinivas TR, Kirwan JP, Navaneethan SD. Metabolic syndrome and kidney disease: a systematic review and meta-analysis. Clin J Am Soc Nephrol . 2011;6:2364-2373. doi: 10.2215/CJN.02180311 Esposito K, Chiodini P, Colao A, Lenzi A, Giugliano D. Metabolic syndrome and risk of cancer: a systematic review and meta-analysis. Diabetes Care . 2012;35:2402-2411. doi: 10.2337/dc12-0336 Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol . 2014;43:61-68. doi: 10.1093/ije/dys203 Nguyen VK, Middleton LYM, Huang L, Zhao N, Verly E, Jr., Kvasnicka J, Sagers L, Patel CJ, Colacino J, Jolliet O. Harmonized US National Health and Nutrition Examination Survey 1988-2018 for high throughput exposome-health discovery. medRxiv : the preprint server for health sciences . 2023. doi: 10.1101/2023.02.06.23284573 Ndumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, Coresh J, Mathew RO, Baker-Smith CM, Carnethon MR, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation . 2023;148:1606-1635. doi: 10.1161/cir.0000000000001184 Khan SS, Matsushita K, Sang Y, Ballew SH, Grams ME, Surapaneni A, Blaha MJ, Carson AP, Chang AR, Ciemins E, et al. Development and Validation of the American Heart Association's PREVENT Equations. Circulation . 2024;149:430-449. doi: 10.1161/circulationaha.123.067626 Qiao Y, Liu S, Li G, Lu Y, Wu Y, Shen Y, Ke C. Longitudinal Follow-Up Studies on the Bidirectional Association between ADL/IADL Disability and Multimorbidity: Results from Two National Sample Cohorts of Middle-Aged and Elderly Adults. Gerontology . 2021;67:563-571. doi: 10.1159/000513930 Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, et al. Frailty in older adults: evidence for a phenotype. The journals of gerontology Series A, Biological sciences and medical sciences . 2001;56:M146-156. doi: 10.1093/gerona/56.3.m146 Wu C, Smit E, Xue QL, Odden MC. Prevalence and Correlates of Frailty Among Community-Dwelling Chinese Older Adults: The China Health and Retirement Longitudinal Study. The journals of gerontology Series A, Biological sciences and medical sciences . 2017;73:102-108. doi: 10.1093/gerona/glx098 Giltay EJ, Zitman FG, Kromhout D. Cardiovascular risk profile and subsequent disability and mental well-being: the Zutphen Elderly Study. Am J Geriatr Psychiatry . 2008;16:874-882. doi: 10.1097/JGP.0b013e3181784122 Graciani A, Garcia-Esquinas E, Lopez-Garcia E, Banegas JR, Rodriguez-Artalejo F. Ideal Cardiovascular Health and Risk of Frailty in Older Adults. Circ Cardiovasc Qual Outcomes . 2016;9:239-245. doi: 10.1161/CIRCOUTCOMES.115.002294 Joosse HJ, van Oirschot BA, Kooijmans SAA, Hoefer IE, van Wijk RAH, Huisman A, van Solinge WW, Haitjema S. In-vitro and in-silico evidence for oxidative stress as drivers for RDW. Sci Rep . 2023;13:9223. doi: 10.1038/s41598-023-36514-5 Denfeld QE, Turrise S, MacLaughlin EJ, Chang PS, Clair WK, Lewis EF, Forman DE, Goodlin SJ, American Heart Association Cardiovascular Disease in Older Populations Committee of the Council on Clinical C, Council on C, et al. Preventing and Managing Falls in Adults With Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circ Cardiovasc Qual Outcomes . 2022;15:e000108. doi: 10.1161/HCQ.0000000000000108 Koene RJ, Prizment AE, Blaes A, Konety SH. Shared Risk Factors in Cardiovascular Disease and Cancer. Circulation . 2016;133:1104-1114. doi: 10.1161/CIRCULATIONAHA.115.020406 Lau ES, Paniagua SM, Liu E, Jovani M, Li SX, Takvorian K, Suthahar N, Cheng S, Splansky GL, Januzzi JL, Jr., et al. Cardiovascular Risk Factors are Associated with Future Cancer. JACC CardioOncol . 2021;3:48-58. doi: 10.1016/j.jaccao.2020.12.003 Sotos-Prieto M, Mattei J, Cook NR, Hu FB, Willett WC, Chiuve SE, Rimm EB, Sesso HD. Association Between a 20-Year Cardiovascular Disease Risk Score Based on Modifiable Lifestyles and Total and Cause-Specific Mortality Among US Men and Women. J Am Heart Assoc . 2018;7:e010052. doi: 10.1161/JAHA.118.010052 Shrikrishna D, Taylor CJ, Stonham C, Gale CP. Exacerbating the burden of cardiovascular disease: how can we address cardiopulmonary risk in individuals with chronic obstructive pulmonary disease? Eur Heart J . 2024;45:247-249. doi: 10.1093/eurheartj/ehad669 Moqri M, Herzog C, Poganik JR, Ying K, Justice JN, Belsky DW, Higgins-Chen AT, Chen BH, Cohen AA, Fuellen G, et al. Validation of biomarkers of aging. Nat Med . 2024;30:360-372. doi: 10.1038/s41591-023-02784-9 Jankowski J, Floege J, Fliser D, Bohm M, Marx N. Cardiovascular Disease in Chronic Kidney Disease: Pathophysiological Insights and Therapeutic Options. Circulation . 2021;143:1157-1172. doi: 10.1161/CIRCULATIONAHA.120.050686 Chowdhury R, Peel NM, Krosch M, Hubbard RE. Frailty and chronic kidney disease: A systematic review. Arch Gerontol Geriatr . 2017;68:135-142. doi: 10.1016/j.archger.2016.10.007 Additional Declarations There is NO Competing Interest. Supplementary Files supplement.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-4987225","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":350182350,"identity":"8d54ccf3-14fd-4b36-a55f-02c408539f0d","order_by":0,"name":"Meng Hao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACAyBmBpJyIM6BB6RoMQZrSSBeCwNDYgOIR5QWc/bDRzcXFNikzw87/BBoi52cbgMBLZY9aWm3Zxik5W68nWYA1JJsbHaAkMMO5Jjd5jE4nLtxdgJIy4HEbQS1nH8D1pJuODv9A5FabkBsSZCXziHWlhvPwH4x3CCdU3AgwYAYv5xPPna74I+NvPzs9M0fPlTYyRHUgtALVmlArHIQkG8gRfUoGAWjYBSMKAAA/7dIEEDu6YgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5543-5373","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Meng","middleName":"","lastName":"Hao","suffix":""},{"id":350182351,"identity":"c63e0623-4d90-4c99-adad-9b8a8a9ac9bc","order_by":1,"name":"Hui Zhang","email":"","orcid":"https://orcid.org/0000-0002-7358-5375","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhang","suffix":""},{"id":350182352,"identity":"e1323f84-894e-4724-8d7c-8f4aa95821fa","order_by":2,"name":"Yi Li","email":"","orcid":"https://orcid.org/0000-0001-6286-9340","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Li","suffix":""},{"id":350182353,"identity":"fca4614d-7387-446b-bf0c-4b944e994e91","order_by":3,"name":"Jingyi Wu","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Wu","suffix":""},{"id":350182354,"identity":"a4e35bf1-3fd5-4f3f-9087-7e30cc7ad44a","order_by":4,"name":"Zixin Hu","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Zixin","middleName":"","lastName":"Hu","suffix":""},{"id":350182355,"identity":"94ed91d1-6433-47e3-ba07-52900d7f697d","order_by":5,"name":"Shuai Jiang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Jiang","suffix":""},{"id":350182356,"identity":"fd60e5b4-e1e9-4632-81c7-c004f2f88882","order_by":6,"name":"Aijun Sun","email":"","orcid":"","institution":"Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases; Institutes of Biomedical Sciences, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Aijun","middleName":"","lastName":"Sun","suffix":""},{"id":350182357,"identity":"df176ee5-42c0-4037-a06e-43db4c42d257","order_by":7,"name":"Li Jin","email":"","orcid":"https://orcid.org/0000-0001-9201-2321","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Jin","suffix":""},{"id":350182358,"identity":"5d98ae26-bbbb-4bc2-b751-4e741c2bdd8b","order_by":8,"name":"Xiaofeng Wang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-08-28 01:00:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4987225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4987225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65815405,"identity":"7cc5e275-8bea-470e-ae9c-20778d6c392c","added_by":"auto","created_at":"2024-10-03 05:49:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":813418,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4987225/v1/05cc365d-fbc4-4719-9b79-9cc67c5809e3.pdf"},{"id":65814593,"identity":"7b16f6a2-07ca-49e7-8e66-701623e252db","added_by":"auto","created_at":"2024-10-03 05:32:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-4987225/v1/9727dd7e3bd0e22a697e35d3.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Cardiovascular-kidney-metabolic syndrome predicted the risks of geriatric syndromes and mortality of major chronic diseases","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u003cstrong\u003eQuestion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhat’s the cardiovascular-kidney-metabolic syndrome health-related effects on geriatric syndrome and risks of mortality?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this cohort study involving 21920 participants at baseline with 4028 deaths, and 672, 346, 1766, 1834 incident cases of disability, physical frailty, falls and hospitalization during follow-up periods, CKM syndrome was significantly associated with increased risks of these geriatric syndromes, all-cause mortality and cause-specific mortality, including cardiovascular disease (CVD), cancer, respiratory disease, and other causes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeaning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings supported the American Heart Association's proposal that CKM syndrome manifested as a systemic health disorder impacting overall health, advocating CKM application beyond CVD to the geriatric community and multidiscipline clinical settings.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe historically high prevalence of obesity and diabetes has become a significant social burden, impacting a transition from affecting primarily adults to now affecting youth from an early age\u003csup\u003e1,2\u003c/sup\u003e. These metabolic risk factors, including obesity and diabetes, are associated with increased risks of cardiovascular disease (CVD) events\u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. It\u0026rsquo;s projected that CVD will impact over 180\u0026nbsp;million adults (about 60%) in the US in the coming decades, leading to substantial rises in healthcare costs and productivity losses\u003csup\u003e6\u003c/sup\u003e. Based on both social needs and medical rationale, the American Heart Association (AHA) introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome\u003csup\u003e7\u003c/sup\u003e, arising from the connections among obesity, diabetes, chronic kidney disease (CKD), and CVD. To improve CKM health and related outcomes, the CKM staging construct was proposed to rationally categorize individuals at risk for or affected by CVD, aiding in screening and risk assessment. According to AHA, CKM syndrome manifested as a holistic and systemic health disorder that impacted nearly all organ systems and contributed to multiorgan diseases\u003csup\u003e7\u003c/sup\u003e. Despite its recent proposal, evidence supporting CKM syndrome health-related effects remains scarce, and its effects beyond its components are not studied.\u003c/p\u003e \u003cp\u003eEmerging evidences suggest significant association between CKM syndrome components and risks of geriatric syndromes and major chronic diseases (e.g., CVD, cancer, respiratory disease). CKM syndrome tended to progressed with age in parallel with the progression of geriatric syndromes\u003csup\u003e8,9\u003c/sup\u003e. Moreover, the components of CKM syndrome, such as abdominal obesity\u003csup\u003e10,11\u003c/sup\u003e, metabolic syndrome\u003csup\u003e12,13\u003c/sup\u003e, diabetes\u003csup\u003e14,15\u003c/sup\u003e, hypertension\u003csup\u003e16\u003c/sup\u003e, higher cardiovascular risk burden\u003csup\u003e17,18\u003c/sup\u003e, and impaired renal function\u003csup\u003e19\u0026ndash;21\u003c/sup\u003e increased the risks of geriatric syndromes (e.g., functional disability, frailty). Within the CKM staging construct, CKM risk factors (such as obesity\u003csup\u003e4\u003c/sup\u003e, diabetes\u003csup\u003e5\u003c/sup\u003e, and metabolic syndrome\u003csup\u003e22\u003c/sup\u003e) were linked to increased risks of CVD. In addition, the CKM syndrome components were also associated non-CVD outcomes. For instance, metabolic syndrome increased risk of cognitive decline\u003csup\u003e23\u003c/sup\u003e, nonalcoholic fatty liver disease\u003csup\u003e24\u003c/sup\u003e, kidney disease\u003csup\u003e25\u003c/sup\u003e, and cancer\u003csup\u003e26\u003c/sup\u003e. This evidence underscored that CKM syndrome might increase the risks of geriatric syndromes and chronic diseases. Therefore, we aimed to provide evidence for broader application of CKM syndrome in multidisciplinary clinical and community settings, particularly within geriatric medicine and community.\u003c/p\u003e \u003cp\u003eHere, we used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations of CKM syndrome with incident geriatric syndromes and mortality. These geriatric syndromes included functional disability, physical frailty, falls, and hospitalization, which were considered as serious, intermediate, accidental, and healthcare resource utilization-related outcomes. Then, we examined the association between CKM syndrome and all-cause and cause-specific mortality using data from the US National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy populations\u003c/h2\u003e\n \u003cp\u003eThe CHARLS is an ongoing prospective population-based longitudinal cohort study of middle-aged and older Chinese adults\u003csup\u003e27\u003c/sup\u003e. The baseline survey was conducted between June 2011 and March 2012, and the participants were followed up biennially until 2020. The 2011 wave was taken as baseline, and 9389 subjects (aged 45\u0026ndash;85 years) with routine clinical tests were enrolled. CHARLS was approved by the Ethics Review Board of Peking University, which was conducted in accordance with the Declaration of Helsinki and other relevant guidelines and regulations. The US NHANES is a nationally representative cross-sectional survey of civilian, noninstitutionalized persons living in the US. We used data from the NHANES 1998\u0026ndash;2018, from a publicly available data dictionary\u003csup\u003e28\u003c/sup\u003e. The demographic information, physical examination results, questionnaire items, and mortality information were collected. The mortality status was linked from the National Death Index. NHANES was approved by the NCHS Ethics Review Board (ERB). All participants provided informed consent. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cohort studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eBiomarkers collection\u003c/h2\u003e\n \u003cp\u003eFor CHARLS, the baseline (2011 wave) collected fasting blood samples for complete blood count (CBC) tests and blood-based bioassays, in which 16 biomarkers were measured for the participants. For NHANES, we collected physical, hematological and biochemical biomarkers. For CKM stage evaluation, body mass index (BMI), waist circumstance (WC), systolic and diastolic pressure (SBP, DBP) were collected. Total cholesterol (TC), high-density and low-density lipoprotein cholesterol (HDL, LDL), triglyceride (TG), plasma glucose, glycosylated hemoglobin (HbA1c) and creatinine were measured. Fasting blood glucose (100 mg/dL to \u0026lt;\u0026thinsp;126 mg/dL) or glycated hemoglobin (5.7% to \u0026lt;\u0026thinsp;6.5%) were used to diagnose individuals with prediabetes. Estimated glomerular filtration rate (GFR) was calculated using the race-free CKD-EPI 2021 creatinine equation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of CKM syndrome\u003c/h2\u003e\n \u003cp\u003eAccording to the guideline, five stages of CKM syndrome were assessed: stage 0 (no CKM risk factors), stage 1 (excess and/or dysfunctional adiposity), stage 2 (metabolic risk factors and kidney disease), stage 3 (subclinical CVD) and stage 4 (clinical CVD)\u003csup\u003e29\u003c/sup\u003e. In detail, CKM Stage 0 included participants with normal BMI (\u0026lt;\u0026thinsp;23 kg/m2) and normal WC (\u0026lt;\u0026thinsp;80 for women and \u0026lt;\u0026thinsp;90 cm for men among Asians, respectively). Participants meet criteria for the other stages were diagnosed as higher stages. CKM Stage 1 identified individuals with elevated BMI (\u0026ge;\u0026thinsp;23 kg/m2), or elevated WC (\u0026ge;\u0026thinsp;80 and \u0026ge;\u0026thinsp;90 cm for women and men, respectively), or prediabetes. CKM Stage 2 identified participants with metabolic risk factors or moderate-to-high-risk CKD. metabolic risk factors included elevated fasting serum triglycerides (\u0026ge;\u0026thinsp;135 mg/dL), hypertension, diabetes, or metabolic syndrome. The metabolic syndrome was evaluated through WC, HDL, TG, blood pressure, and prediabetes, in which individuals with more than three abnormalities. CKM Stage 3 was identified based of the presence of very-high-risk KDIGO CKD stages, or a self-reported chronic kidney disease, or a high-predicted 10-year CVD risk (\u0026ge;\u0026thinsp;20%). The 10-year cardiovascular risk was estimated with the AHA Predicting Risk of CVD EVENTs (PREVENT) equations\u003csup\u003e30\u003c/sup\u003e. The calculation details of the PREVENT were described in Supplementary. CKM Stage 5 was identified based on self-reported established cardiovascular disease (coronary heart disease, angina, heart attack, heart failure, and stroke).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of geriatric syndromes\u003c/h2\u003e\n \u003cp\u003eIn CHARLS, six items of daily self-care ability were collected from each participant based on the Katz index\u003csup\u003e31\u003c/sup\u003e, including dressing, bathing, transferring from a chair to bed, toilet use, feeding and walking. These items were asked with four responses, i.e., \u0026ldquo;No, I do not have any difficulty\u0026rdquo;, \u0026ldquo;I have difficulty but still can do it\u0026rdquo;, \u0026ldquo;Yes, I have difficulty and need help\u0026rdquo; and \u0026ldquo;I cannot do it\u0026rdquo;. As reported in previous studies, ADL disability was defined if the participants have difficulty to perform at least one of 6 daily self-care tasks.\u003c/p\u003e\n \u003cp\u003eFrailty was evaluated through the widely used criteria originally proposed by Fried et al.\u003csup\u003e32\u003c/sup\u003e, modified with the information available in the CHARLS\u003csup\u003e33\u003c/sup\u003e. In the definition, frailty contained five components of exhaustion, weakness, low physical activity, weight loss, and slowness. In our study, the five components of frailty were evaluated and defined as follows: (1) Exhaustion: exhaust was defined according to the Chinese version of the Center for Epidemiologic Studies-Depression scale (CES-D) questions (20): \u0026ldquo;I felt everything I did was an effort during last week\u0026rdquo; or \u0026ldquo;I could not get going during last week.\u0026rdquo;. Exhaustion was present if the participants answered \u0026ldquo;Most or all of the time\u0026rdquo; or \u0026ldquo;Occasionally or a moderate amount of the time\u0026rdquo; for the either question. (2) Weakness: weakness was measured using the question \u0026ldquo;having difficulty in Lifting or carrying weights over 10 jin, like a heavy bag of groceries\u0026rdquo;. (3) Low physical activity: CHARLS defined WALK as walking those participants might do solely for recreation, sport, excise, or leisure at work and at home, walking to travel from place to place, and any other walking. Low physical activity was considered to be present if the participants had no physical activity or WALK at least 10 min at a time during a usual week. (4) Weight loss: the weight loss was defined as the unintentional loss of 5 or more kilograms in the last year or current body mass index (BMI)\u0026thinsp;\u0026le;\u0026thinsp;18.5 kg/m2. (5) Slowness: these questions asked the participants to report whether they have difficulty with walking 100 meters or climbing several flights of stairs without resting. Those participants who had difficulty with performing the walking or climbing were categorized as slowness. In our study, robustness was defined as the absence of any component, pre-frailty as the presence of one or two of these components, and frailty as the presence of three or more components.\u003c/p\u003e\n \u003cp\u003eInformation of falls, and hospitalization were obtained through questionnaire at the biennial follow-ups. These geriatric syndromes, including functional disability, physical frailty, falls and hospitalization were evaluated at the baseline (2011 wave) and the follow-ups (2013 and 2015 waves). Individuals identified as ADL disability and frailty, or recorded with history of falls and hospitalization were excluded in association analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of adverse outcomes and mortality\u003c/h2\u003e\n \u003cp\u003eIn CHARLS, records on death were obtained at the biennial follow-up at the 2011, 2013, 2015, 2018 and 2020 waves. Because the exact date of death was not available, the midpoint of the two follow-up visits was defined as the time of death. In US NHANES, dates and causes of death were linked to the National Death Index records to 31 December 2019. We used the 10th revision of the International Statistical Classification of Diseases (ICD-10) to identify causes of death. Mortality outcomes in this study included the following underlying causes of death: including heart disease, Malignant neoplasms, respiratory disease, cerebrovascular disease, Alzheimer\u0026rsquo;s disease, diabetes, influenza/pneumonia, and nephritis/nephrosis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eFirst, continuous and categorical data are presented as the means and standard deviations (SDs) or frequencies (%), respectively. Second, we utilized logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of CKM syndrome stages associated with frailty, disability, falls, hospitalization, using two models. Model 1: crude model; Model 2: adjusted for age (continuous), gender, education, race, smoking status, alcohol consumption status, BMI (continuous), cancer. Model 3: covariates in model 2 and CKM components (hypertension, diabetes, heart disease, stroke); Cox proportional hazard models were employed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) in the associations between CKM syndrome stages and mortality, with crude and adjusted models. We also conducted Kaplan‒Meier survival analysis to plot the associations of CKM syndrome stages with all-cause mortality. All results were considered significant at a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (2-tailed). All analyses were conducted using R statistical software (version 4.2.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.r-project.org\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the characteristics of the study population in the CHARLS and NHANES. The baseline demographic characteristics, lifestyle factors, laboratory biomarkers and chronic diseases of the study population are presented in detail. At baseline, 9389 participants (72.1% aged 45\u0026ndash;64 years and 27.9% 65\u0026ndash;85 years; 46.7% male) in the CHARLS, and 12531 participants (58.3% aged 45\u0026ndash;64 years and 41.7% aged 65\u0026ndash;85 years; 49.7% male) in the NHANES were analyzed. The mean ages of the participants were 49.4 (9.2) years in the CHARLS and 62.5 (11.1) in the NHANES. In the CHARLS, the overall prevalence of CKM syndrome from stage 0 to 4 were 11.4%, 23.1%, 39.4%, 12.5% and 13.6%, respectively. Correspondingly, in the NHANES, the prevalence of CKM syndrome stages were 4.6%, 17.9%, 46.8%, 13.6% and 17.1%, respectively.\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\u003eCharacteristic of Study Population in CHARLS and NHANES.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHARLS (N\u0026thinsp;=\u0026thinsp;9389)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNHANES (N\u0026thinsp;=\u0026thinsp;12531)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.4 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.5 (11.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;64, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6774 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7307 (58.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;85, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2615 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5224 (41.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4381 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6231 (49.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5008 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6300 (50.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace and ethnicity\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6109 (48.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2372 (18.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3060 (24.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian, Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9389 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e990 (7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8474 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3731 (29.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e789 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2887 (23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5897 (47.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC, cm, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.3 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (14.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.5 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.1 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.8 (20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.9 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.9 (14.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2851 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2276 (18.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2334 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8439 (70.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mg/dL, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.6 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.8 (39.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbAc1, %, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mg/dL, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193.7 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200.1 (43.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mg/dL, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.2 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.5 (16.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mg/dL, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.6 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.4 (36.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mg/dL, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.5 (106.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.6 (113.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine, mg/dL, M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic conditions\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2365 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6202 (49.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e543 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2258 (18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1759 (14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1121 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1714 (13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e629 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e698 (5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKM stages\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1066 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e579 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2172 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2265 (18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3702 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5839 (46.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1176 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1706 (13.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1273 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2142 (17.1)\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of CKM stages with frailty, disability, falls and hospitalization\u003c/h2\u003e \u003cp\u003eThe CHARLS documented 672, 346, 1766, 1834 incident cases of ADL disability, physical frailty, falls and hospitalization, respectively. After adjusting for covariates (socioeconomic status, lifestyle factors, etc.), elevated CKM syndrome stages were also associated with increased risk of incident disability (OR, 1.2, [95% CI, 1.11\u0026ndash;1.29]), frailty (OR, 1.28, [95% CI, 1.15\u0026ndash;1.42]), falls (OR, 1.11, [95% CI, 1.06\u0026ndash;1.17]), and hospitalization (OR, 1.19, [95% CI, 1.14\u0026ndash;1.25]). Then, the CKM components, including hypertension, diabetes, stroke, and heart disease, were adjusted. Positive associations were also found between CKM stages and incident disability (OR, 1.2 [95% CI, 1.07\u0026ndash;1.36]), frailty (OR, 1.18 1.2 [95% CI, 1.01\u0026ndash;1.38]), falls (OR, 1.09 [95% CI, 1.02\u0026ndash;1.17]) and hospitalization (OR, 1.08 [95% CI, 1.01\u0026ndash;1.16]). Compared with participants in the CKM syndrome stage 0, elevated CKM stages showed increasing odds ratios along the CKM staging spectrum (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Especially, participants categorized as advanced CKM syndrome (stage 3\u0026ndash;4) exhibited higher risks of adverse outcomes, presenting a 38\u0026ndash;140% increase in risk (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results suggested dose-effect relationships between CKM stages and various geriatric syndromes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of CKM stages with mortality\u003c/h2\u003e \u003cp\u003eThe CHARLS documented 1186 deaths over a median follow-up of 9 years, and the NHANES documented 2842 deaths over a median follow-up of 8.4 years. The results of Cox proportional hazard analyses regarding the association of continuous CKM syndrome stages with the risk of all-cause mortality are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After adjusting for covariates (Model 2), elevated CKM syndrome stages were associated with all-cause mortality (CHARLS: HR, 1.12 [95% CI, 1.06\u0026ndash;1.18]; NHANES: HR, 1.35 [95% CI, 1.29\u0026ndash;1.40]). Then, adjusting for CKM components (Model 3), positive associations were also found between CKM stages and all-cause mortality in NHANES (HR, 1.23 [95% CI, 1.15\u0026ndash;1.31]), not CHARLS (HR, 1.06 [95% CI, 0.98\u0026ndash;1.14]). Compared with participants in the CKM syndrome stage 0, those in the advanced stages experienced significantly greater risks of mortality (e.g., in CHARLS, stage 3: HR, 2.01 [95% CI, 1.44\u0026ndash;2.82], stage 4: HR, 2.45 [95% CI, 1.85\u0026ndash;3.24]; in NHANES, stage 3: HR, 2.10 [95% CI, 1.49\u0026ndash;2.96], stage 4: HR, 2.54 [95% CI, 1.91\u0026ndash;3.38]). The dose-effect relationships also presented between CKM stages and all-cause mortality in CHARLS and NHANES (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of CKM syndrome with geriatric syndromes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKM stages\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvents/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability\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 \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e672/3116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.18\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2 (1.11\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eStage 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52/322\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104/643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.74\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e242/1217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (0.93\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36 (0.93\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128/412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.34 (1.64\u0026ndash;3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.98 (1.26\u0026ndash;3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146/522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02 (1.42\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88 (1.23\u0026ndash;2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrailty\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346/3469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (1.18\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28 (1.15\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eStage 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31/388\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62/855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.58\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.61\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127/1401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.77\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.69\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58/403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94 (1.23\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.17 (1.23\u0026ndash;3.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68/422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21 (1.42\u0026ndash;3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4 (1.38\u0026ndash;4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFalls\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1766/7820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (1.09\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (1.06\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eStage 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180/911\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e372/1837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.85\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.8\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e692/3141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.96\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (0.92\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243/939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.14\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 (1.05\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e279/992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59 (1.28\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58 (1.21\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eHospitalization\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1834/8207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.19\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 (1.14\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eStage 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173/979\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e379/1982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.9\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15 (0.92\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e703/3315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.05\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (0.92\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e261/974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71 (1.37\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44 (1.09\u0026ndash;1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318/957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32 (1.88\u0026ndash;2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11 (1.62\u0026ndash;2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel1: crude model; Model2: adjusted for age, gender, body mass index, race, educational levels, smoking status, alcohol drinking, and cancer.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratios of all-cause mortality according to CKM.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHANES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHARLS\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 \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1186/9389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.29\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12 (1.06\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eStage 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121/1066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170/2172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.54\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.65\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324/3702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.62\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 (0.92\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e344/1176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89 (2.35\u0026ndash;3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01 (1.44\u0026ndash;2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eStage 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227/1273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63 (1.31\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.45 (1.85\u0026ndash;3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eNHANES\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2953/12531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93 (1.86-2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35 (1.29\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eStage 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68/579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236/2259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.85\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.71\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e841/5833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (1.17\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23 (0.94\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e882/1703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.22 (6.40-10.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.10 (1.49\u0026ndash;2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eStage 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e926/2141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.98 (4.67\u0026ndash;7.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.54 (1.91\u0026ndash;3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel1: crude model; Model2: adjusted for age, gender, body mass index, race, educational levels, smoking status, alcohol drinking, and cancer.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratios of cause-specific mortality according to continuous CKM stages in NHANES.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHANES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying cause\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.50 (2.35\u0026ndash;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.62 (1.50\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eMalignant neoplasm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.78 (1.66\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.19 (1.10\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eRespiratory disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.93 (1.68\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.26 (1.07\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.95 (1.65\u0026ndash;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15 (0.93\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.07 (1.96\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42 (1.33\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel1: crude model; Model2: adjusted for age, gender, body mass index, race, educational levels, smoking status, alcohol drinking, and cancer.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the NHANES, after adjusting for covariates (Model 2), elevated CKM syndrome stages were also associated with increased risk of mortality of cardiovascular disease (HR, 1.62, [95% CI, 1.50\u0026ndash;1.75]), malignant neoplasm (HR, 1.19, [95% CI, 1.10\u0026ndash;1.29]), respiratory disease (HR, 1.26, [95% CI, 1.07\u0026ndash;1.49]), and other causes (HR, 1.42, [95% CI, 1.33\u0026ndash;1.52]). Taking the CKM syndrome stage 0 as reference, CKM advanced stages were associated with increased risks of cardiovascular disease, malignant neoplasms and other causes of mortality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cohort study, we included participants from CHARLS and NHANES to investigate the associations between CKM syndrome stages and the risks of adverse outcomes among middle-aged and older adults. Our findings indicated that CKM syndrome stages, particularly the advanced stages, were significantly associated with increased risks of geriatric syndromes, including disability, frailty, falls, and hospitalization. In addition, CKM syndrome stages were also associated with cause-specific mortality, including CVD, malignant neoplasm, respiratory disease, and other causes. After adjusting for CKM components, the positive associations retained, indicating that CKM syndrome had health-related impacts beyond those of its component disorders. These results highlighted CKM syndrome as a systemic health condition contributing to multiorgan dysfunctions, underscoring its relevance in gerontology, geriatrics, major chronic diseases and broader multidisciplinary contexts.\u003c/p\u003e \u003cp\u003ePrevious studies focused on CKM components consisting of risk factors, rather than the whole construct of CKM syndrome, regarding to geriatric syndromes. Giltay et al. illustrated that CKM components consisting of BMI, hypertension, diabetes, and serum cholesterol, were predictive of functional disability\u003csup\u003e34\u003c/sup\u003e. Similarly, Cui et al. reported that higher cardiovascular risk burdens, as assessed by the Framingham general cardiovascular risk score, increased the risk of disability and accelerated its progression over time\u003csup\u003e17\u003c/sup\u003e. As for frailty, CKM components, including obesity, hypertension, dyslipidemia, diabetes, increased the risk of frailty\u003csup\u003e35\u003c/sup\u003e. Moreover, cardiovascular disorders, such as stroke and coronary artery disease, were associated with falls\u003csup\u003e36\u003c/sup\u003e. A consensus was reached to incorporate cardiovascular assessments in falls risk screening and prevention for older adults at risk of falls\u003csup\u003e37\u003c/sup\u003e. In contrast, our study employed the CKM syndrome construct, encompassing both CVD risk factors and CVD events comprehensively. We found that the CKM stages were associated with increased risks of geriatric syndromes with dose-effect relationships. Therefore, the CKM assessment could refine prediction model for risks stratification of geriatric syndromes. It highlighted the need to promote the concept of CKM staging construct and its education to geriatric community, clinician, and older adults to prevent these geriatric syndromes. Optimizing CKM health could help to enhance the geriatric health conditions and reduce social and healthcare burdens. In addition, these results confirmed the validity and robustness of the CKM staging construct, which enabled researchers and clinicians to categorize the individuals along a continuum of increasing risk in practice.\u003c/p\u003e \u003cp\u003eWe found that CKM syndromes predicted cause-specific mortality, including CVD, cancer, respiratory disease and other cause. Prior studies had reported similar associations through CKM components or risk factors\u003csup\u003e38\u003c/sup\u003e. For instance, Lau et al. reported that a 10-year ASCVD risk score and natriuretic peptide concentrations were associated with increased risk of incident cancer\u003csup\u003e39\u003c/sup\u003e. Meanwhile, Mercedes et al. demonstrated a cardiovascular disease risk score was strongly associated with cancer mortality, such as lung, breast, and colon cancers\u003csup\u003e40\u003c/sup\u003e. Similarly, respiratory diseases shared CKM risk factors, such as hypertension, dyslipidemias\u003csup\u003e41\u003c/sup\u003e, diabetes and metabolic syndrome. In our study, we applied CKM staging construct to explore its associations with both CVD mortality and non-CVD mortality (e.g., cancer, respiratory disease). The CKM stages were linked to CVD mortality with highest risks, especially the advanced stages. It supported the CKM staging construct\u0026rsquo;s objective to prevent CVD events and mortality from early CKM stages. These associations between CKM syndrome and mortality of major chronic diseases underscored CKM syndrome as a systemic health condition impacting multiorgan systems and overall health. Prediction of the future aging-associated outcomes (e.g., geriatric syndromes, major chronic diseases), and its clinical utility validated the CKM construct and its substantial applications\u003csup\u003e42\u003c/sup\u003e. Screening for each CKM stage could help predict risks of aging-associated, CVD and non-CVD outcomes related to CKM syndrome. These results advocated for the use of CKM assessment in preventing major chronic diseases among middle-aged and older adults.\u003c/p\u003e \u003cp\u003eOur study demonstrated the incident geriatric syndromes and mortality followed advanced CKM stages, marked by subclinical and clinical CVD. Therefore, the primary objective was to prevent geriatric syndromes and major age-related chronic diseases among middle-aged and older adults in advanced CKM stages. Enhanced health care, such as intensified preventive therapies and pharmacological medications, was necessary to prevent CKM progression and mitigate adverse outcomes. The prevalence of early stages and advanced stages of CKM syndrome accounted for approximately 60% and 30% in CHARLS, respectively, highlighting its severe burden in the general population. The CKM staging construct emphasized early detection of CKM-related changes to support prevention efforts. For instance, lifestyle modification and weight loss can address excess or dysfunctional adiposity in early CKM stages.\u003c/p\u003e \u003cp\u003eWithin the CKM construct, it recognized the importance of the relationships between kidney health and cardiovascular health. Impaired kidney function was linked with elevated risks of CVD and geriatric syndromes\u003csup\u003e43,44\u003c/sup\u003e. Risk prediction equations of kidney function could inform clinical management and prevention of CKM syndrome and geriatric syndrome. Moreover, it started to emphasize the social determinants of health in assessments for CKM risk factors\u003csup\u003e7\u003c/sup\u003e. The social needs, including financial strain, education and mental health, played a critical role in CKM health and healthy aging. Therefore, evaluation of kidney health and social risk factors were critical in risk screening, prediction and prevention of geriatric syndromes.\u003c/p\u003e \u003cp\u003eSeveral limitations should be noted in this study. Firstly, subclinical CVDs and CVDs were determined by predicted risks and self-reports, respectively, which may introduce recall bias and affect associations. Secondly, we focused on disability, frailty, falls, hospitalization, and mortality as outcomes; further studies should consider other metrics, such as cognitive decline, physical function declines, hip fracture and other functional metrics. Lastly, while cause-specific mortality was assessed, evidence regarding disease incidence remained limited for CKM syndrome.\u003c/p\u003e \u003cp\u003eIn conclusion, the findings highlighted CKM syndrome as a systemic health disorder affecting overall health among middle-aged and older adults, advocating for its broader application in multidisciplinary research, diverse clinical and community settings, especially the gerontology research and geriatrics. Application of CKM in geriatrics will enhance geriatric conditions and promote healthy aging to alleviate the social and healthcare burdens. These results provided substantial evidence to support the validity and robustness of the CKM staging construct, and its goal to prevent CVD events and mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this research were obtained from the CHARLS and NHANES. We would like to thank the workers, researchers, and participants involved in the cohorts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (32300533, 82301768, 32100510), the Shanghai Sailing Program (23YF1430500), and Shanghai Municipal Science and Technology Major Project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from CHARLS is available upon application at http://charls.pku.edu.cn/. The data from the NHANES are available at www.cdc.gov/nchs/nhis/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr.Wang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003eConcept and design: Hao, Zhang, Wang and Jin.\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation of data: Hao and Zhang.\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Hao, Wang and Zhang.\u003c/p\u003e\n\u003cp\u003eCritical revision of the manuscript for important intellectual content: All authors.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Hao, Zhang, Li, Hu.\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: Zhang, Hao, Jiang, Hu and Wang.\u003c/p\u003e\n\u003cp\u003eSupervision: Wang, Lin, Sun.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollaboration NCDRF. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. \u003cem\u003eLancet\u003c/em\u003e. 2024;403:1027-1050. doi: 10.1016/S0140-6736(23)02750-2\u003c/li\u003e\n\u003cli\u003eCollaborators GBDD. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. \u003cem\u003eLancet\u003c/em\u003e. 2023;402:203-234. doi: 10.1016/S0140-6736(23)01301-6\u003c/li\u003e\n\u003cli\u003eChen Y, Yu W, Lv J, Sun D, Pei P, Du H, Yang L, Chen Y, Zhang H, Chen J, et al. Early adulthood BMI and cardiovascular disease: a prospective cohort study from the China Kadoorie Biobank. \u003cem\u003eLancet Public Health\u003c/em\u003e. 2024. doi: 10.1016/S2468-2667(24)00043-4\u003c/li\u003e\n\u003cli\u003ePowell-Wiley TM, Poirier P, Burke LE, Despres JP, Gordon-Larsen P, Lavie CJ, Lear SA, Ndumele CE, Neeland IJ, Sanders P, et al. Obesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e. 2021;143:e984-e1010. doi: 10.1161/CIR.0000000000000973\u003c/li\u003e\n\u003cli\u003eSong SH. Early-onset type 2 diabetes: high lifetime risk for cardiovascular disease. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e. 2016;4:87-88. doi: 10.1016/S2213-8587(15)00390-3\u003c/li\u003e\n\u003cli\u003eHarris E. Heart Disease Prevalence, Medical Costs Will Soar in US by 2050. \u003cem\u003eJAMA\u003c/em\u003e. 2024. doi: 10.1001/jama.2024.11026\u003c/li\u003e\n\u003cli\u003eNdumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, Coresh J, Mathew RO, Baker-Smith CM, Carnethon MR, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e. 2023;148:1606-1635. doi: 10.1161/CIR.0000000000001184\u003c/li\u003e\n\u003cli\u003eAggarwal R, Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011-2020. \u003cem\u003eJAMA\u003c/em\u003e. 2024;331:1858-1860. doi: 10.1001/jama.2024.6892\u003c/li\u003e\n\u003cli\u003eMinhas AMK, Mathew RO, Sperling LS, Nambi V, Virani SS, Navaneethan SD, Shapiro MD, Abramov D. Prevalence of the Cardiovascular-Kidney-Metabolic Syndrome in the United States. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2024;83:1824-1826. doi: 10.1016/j.jacc.2024.03.368\u003c/li\u003e\n\u003cli\u003eQian S, Wen Q, Huang T, Chen J, Feng X. Dynapenic abdominal obesity and incident functional disability: Results from a nationwide longitudinal study of middle-aged and older adults in China. \u003cem\u003eArch Gerontol Geriatr\u003c/em\u003e. 2024;123:105434. doi: 10.1016/j.archger.2024.105434\u003c/li\u003e\n\u003cli\u003eYuan L, Chang M, Wang J. Abdominal obesity, body mass index and the risk of frailty in community-dwelling older adults: a systematic review and meta-analysis. \u003cem\u003eAge Ageing\u003c/em\u003e. 2021;50:1118-1128. doi: 10.1093/ageing/afab039\u003c/li\u003e\n\u003cli\u003eJiang X, Xu X, Ding L, Lu J, Zhu H, Zhao K, Zhu S, Xu Q. The association between metabolic syndrome and presence of frailty: a systematic review and meta-analysis. \u003cem\u003eEur Geriatr Med\u003c/em\u003e. 2022;13:1047-1056. doi: 10.1007/s41999-022-00688-4\u003c/li\u003e\n\u003cli\u003eBotoseneanu A, Ambrosius WT, Beavers DP, de Rekeneire N, Anton S, Church T, Folta SC, Goodpaster BH, King AC, Nicklas BJ, et al. Prevalence of metabolic syndrome and its association with physical capacity, disability, and self-rated health in Lifestyle Interventions and Independence for Elders Study participants. \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e. 2015;63:222-232. doi: 10.1111/jgs.13205\u003c/li\u003e\n\u003cli\u003eGarcia-Esquinas E, Graciani A, Guallar-Castillon P, Lopez-Garcia E, Rodriguez-Manas L, Rodriguez-Artalejo F. Diabetes and risk of frailty and its potential mechanisms: a prospective cohort study of older adults. \u003cem\u003eJ Am Med Dir Assoc\u003c/em\u003e. 2015;16:748-754. doi: 10.1016/j.jamda.2015.04.008\u003c/li\u003e\n\u003cli\u003eWong E, Backholer K, Gearon E, Harding J, Freak-Poli R, Stevenson C, Peeters A. Diabetes and risk of physical disability in adults: a systematic review and meta-analysis. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e. 2013;1:106-114. doi: 10.1016/S2213-8587(13)70046-9\u003c/li\u003e\n\u003cli\u003eSugimoto K, Yamamoto K. Hypertension, the decline of activities of daily living (ADL) and frailty. \u003cem\u003eHypertens Res\u003c/em\u003e. 2022;45:629-634. doi: 10.1038/s41440-021-00846-4\u003c/li\u003e\n\u003cli\u003eCui K, Song R, Xu H, Shang Y, Qi X, Buchman AS, Bennett DA, Xu W. Association of Cardiovascular Risk Burden With Risk and Progression of Disability: Mediating Role of Cardiovascular Disease and Cognitive Decline. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e. 2020;9:e017346. doi: 10.1161/JAHA.120.017346\u003c/li\u003e\n\u003cli\u003eZhang H, Hao M, Hu Z, Li Y, Hu X, Jiang X, Liu Z, Sun X, Wang X. Causal Association of Cardiac Function by Magnetic Resonance Imaging with Frailty Index: A Mendelian Randomization Study. \u003cem\u003ePhenomics\u003c/em\u003e. 2022;2:430-437. doi: 10.1007/s43657-022-00072-z\u003c/li\u003e\n\u003cli\u003eChin HJ, Ahn SY, Ryu J, Kim S, Na KY, Kim KW, Chae DW, Kim CH, Kim KI. Renal function and decline in functional capacity in older adults. \u003cem\u003eAge Ageing\u003c/em\u003e. 2014;43:833-838. doi: 10.1093/ageing/afu071\u003c/li\u003e\n\u003cli\u003eDalrymple LS, Katz R, Rifkin DE, Siscovick D, Newman AB, Fried LF, Sarnak MJ, Odden MC, Shlipak MG. Kidney function and prevalent and incident frailty. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e. 2013;8:2091-2099. doi: 10.2215/CJN.02870313\u003c/li\u003e\n\u003cli\u003eZhang H, Hao M, Li Y, Jiang X, Wang M, Chen J, Wang X, Sun X. Glomerular filtration rate by different measures and albuminuria are associated with risk of frailty: the Rugao Longitudinal Ageing Study. \u003cem\u003eAging clinical and experimental research\u003c/em\u003e. 2022;34:2703-2711. doi: 10.1007/s40520-022-02245-2\u003c/li\u003e\n\u003cli\u003eWilson PW, D\u0026apos;Agostino RB, Parise H, Sullivan L, Meigs JB. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. \u003cem\u003eCirculation\u003c/em\u003e. 2005;112:3066-3072. doi: 10.1161/CIRCULATIONAHA.105.539528\u003c/li\u003e\n\u003cli\u003eNg TP, Feng L, Nyunt MS, Feng L, Gao Q, Lim ML, Collinson SL, Chong MS, Lim WS, Lee TS, et al. Metabolic Syndrome and the Risk of Mild Cognitive Impairment and Progression to Dementia: Follow-up of the Singapore Longitudinal Ageing Study Cohort. \u003cem\u003eJAMA Neurol\u003c/em\u003e. 2016;73:456-463. doi: 10.1001/jamaneurol.2015.4899\u003c/li\u003e\n\u003cli\u003eYki-Jarvinen H. Non-alcoholic fatty liver disease as a cause and a consequence of metabolic syndrome. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e. 2014;2:901-910. doi: 10.1016/S2213-8587(14)70032-4\u003c/li\u003e\n\u003cli\u003eThomas G, Sehgal AR, Kashyap SR, Srinivas TR, Kirwan JP, Navaneethan SD. Metabolic syndrome and kidney disease: a systematic review and meta-analysis. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e. 2011;6:2364-2373. doi: 10.2215/CJN.02180311\u003c/li\u003e\n\u003cli\u003eEsposito K, Chiodini P, Colao A, Lenzi A, Giugliano D. Metabolic syndrome and risk of cancer: a systematic review and meta-analysis. \u003cem\u003eDiabetes Care\u003c/em\u003e. 2012;35:2402-2411. doi: 10.2337/dc12-0336\u003c/li\u003e\n\u003cli\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eInt J Epidemiol\u003c/em\u003e. 2014;43:61-68. doi: 10.1093/ije/dys203\u003c/li\u003e\n\u003cli\u003eNguyen VK, Middleton LYM, Huang L, Zhao N, Verly E, Jr., Kvasnicka J, Sagers L, Patel CJ, Colacino J, Jolliet O. Harmonized US National Health and Nutrition Examination Survey 1988-2018 for high throughput exposome-health discovery. \u003cem\u003emedRxiv : the preprint server for health sciences\u003c/em\u003e. 2023. doi: 10.1101/2023.02.06.23284573\u003c/li\u003e\n\u003cli\u003eNdumele CE, Rangaswami J, Chow SL, Neeland IJ, Tuttle KR, Khan SS, Coresh J, Mathew RO, Baker-Smith CM, Carnethon MR, et al. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e. 2023;148:1606-1635. doi: 10.1161/cir.0000000000001184\u003c/li\u003e\n\u003cli\u003eKhan SS, Matsushita K, Sang Y, Ballew SH, Grams ME, Surapaneni A, Blaha MJ, Carson AP, Chang AR, Ciemins E, et al. Development and Validation of the American Heart Association\u0026apos;s PREVENT Equations. \u003cem\u003eCirculation\u003c/em\u003e. 2024;149:430-449. doi: 10.1161/circulationaha.123.067626\u003c/li\u003e\n\u003cli\u003eQiao Y, Liu S, Li G, Lu Y, Wu Y, Shen Y, Ke C. Longitudinal Follow-Up Studies on the Bidirectional Association between ADL/IADL Disability and Multimorbidity: Results from Two National Sample Cohorts of Middle-Aged and Elderly Adults. \u003cem\u003eGerontology\u003c/em\u003e. 2021;67:563-571. doi: 10.1159/000513930\u003c/li\u003e\n\u003cli\u003eFried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, et al. Frailty in older adults: evidence for a phenotype. \u003cem\u003eThe journals of gerontology Series A, Biological sciences and medical sciences\u003c/em\u003e. 2001;56:M146-156. doi: 10.1093/gerona/56.3.m146\u003c/li\u003e\n\u003cli\u003eWu C, Smit E, Xue QL, Odden MC. Prevalence and Correlates of Frailty Among Community-Dwelling Chinese Older Adults: The China Health and Retirement Longitudinal Study. \u003cem\u003eThe journals of gerontology Series A, Biological sciences and medical sciences\u003c/em\u003e. 2017;73:102-108. doi: 10.1093/gerona/glx098\u003c/li\u003e\n\u003cli\u003eGiltay EJ, Zitman FG, Kromhout D. Cardiovascular risk profile and subsequent disability and mental well-being: the Zutphen Elderly Study. \u003cem\u003eAm J Geriatr Psychiatry\u003c/em\u003e. 2008;16:874-882. doi: 10.1097/JGP.0b013e3181784122\u003c/li\u003e\n\u003cli\u003eGraciani A, Garcia-Esquinas E, Lopez-Garcia E, Banegas JR, Rodriguez-Artalejo F. Ideal Cardiovascular Health and Risk of Frailty in Older Adults. \u003cem\u003eCirc Cardiovasc Qual Outcomes\u003c/em\u003e. 2016;9:239-245. doi: 10.1161/CIRCOUTCOMES.115.002294\u003c/li\u003e\n\u003cli\u003eJoosse HJ, van Oirschot BA, Kooijmans SAA, Hoefer IE, van Wijk RAH, Huisman A, van Solinge WW, Haitjema S. In-vitro and in-silico evidence for oxidative stress as drivers for RDW. \u003cem\u003eSci Rep\u003c/em\u003e. 2023;13:9223. doi: 10.1038/s41598-023-36514-5\u003c/li\u003e\n\u003cli\u003eDenfeld QE, Turrise S, MacLaughlin EJ, Chang PS, Clair WK, Lewis EF, Forman DE, Goodlin SJ, American Heart Association Cardiovascular Disease in Older Populations Committee of the Council on Clinical C, Council on C, et al. Preventing and Managing Falls in Adults With Cardiovascular Disease: A Scientific Statement From the American Heart Association. \u003cem\u003eCirc Cardiovasc Qual Outcomes\u003c/em\u003e. 2022;15:e000108. doi: 10.1161/HCQ.0000000000000108\u003c/li\u003e\n\u003cli\u003eKoene RJ, Prizment AE, Blaes A, Konety SH. Shared Risk Factors in Cardiovascular Disease and Cancer. \u003cem\u003eCirculation\u003c/em\u003e. 2016;133:1104-1114. doi: 10.1161/CIRCULATIONAHA.115.020406\u003c/li\u003e\n\u003cli\u003eLau ES, Paniagua SM, Liu E, Jovani M, Li SX, Takvorian K, Suthahar N, Cheng S, Splansky GL, Januzzi JL, Jr., et al. Cardiovascular Risk Factors are Associated with Future Cancer. \u003cem\u003eJACC CardioOncol\u003c/em\u003e. 2021;3:48-58. doi: 10.1016/j.jaccao.2020.12.003\u003c/li\u003e\n\u003cli\u003eSotos-Prieto M, Mattei J, Cook NR, Hu FB, Willett WC, Chiuve SE, Rimm EB, Sesso HD. Association Between a 20-Year Cardiovascular Disease Risk Score Based on Modifiable Lifestyles and Total and Cause-Specific Mortality Among US Men and Women. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e. 2018;7:e010052. doi: 10.1161/JAHA.118.010052\u003c/li\u003e\n\u003cli\u003eShrikrishna D, Taylor CJ, Stonham C, Gale CP. Exacerbating the burden of cardiovascular disease: how can we address cardiopulmonary risk in individuals with chronic obstructive pulmonary disease? \u003cem\u003eEur Heart J\u003c/em\u003e. 2024;45:247-249. doi: 10.1093/eurheartj/ehad669\u003c/li\u003e\n\u003cli\u003eMoqri M, Herzog C, Poganik JR, Ying K, Justice JN, Belsky DW, Higgins-Chen AT, Chen BH, Cohen AA, Fuellen G, et al. Validation of biomarkers of aging. \u003cem\u003eNat Med\u003c/em\u003e. 2024;30:360-372. doi: 10.1038/s41591-023-02784-9\u003c/li\u003e\n\u003cli\u003eJankowski J, Floege J, Fliser D, Bohm M, Marx N. Cardiovascular Disease in Chronic Kidney Disease: Pathophysiological Insights and Therapeutic Options. \u003cem\u003eCirculation\u003c/em\u003e. 2021;143:1157-1172. doi: 10.1161/CIRCULATIONAHA.120.050686\u003c/li\u003e\n\u003cli\u003eChowdhury R, Peel NM, Krosch M, Hubbard RE. Frailty and chronic kidney disease: A systematic review. \u003cem\u003eArch Gerontol Geriatr\u003c/em\u003e. 2017;68:135-142. doi: 10.1016/j.archger.2016.10.007\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"CKM syndrome, CKM staging construct, disability, frailty, mortality","lastPublishedDoi":"10.21203/rs.3.rs-4987225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4987225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe American Heart Association (AHA) has introduced the cardiovascular-kidney-metabolic (CKM) syndrome as a systemic health disorder affecting nearly all organ systems. However, evidence supporting CKM syndrome health-related effects remains scarce, and its effects beyond its components are not studied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere, we used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the associations of CKM syndrome with adverse health outcomes, including functional disability, physical frailty, falls, hospitalization and mortality. Then, we examined the associations between CKM syndrome and all-cause and cause-specific mortalityusing data from the US National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCKM syndrome stages were independently associated with incident geriatric syndromes, including incident disability (OR, 1.2, [95% CI, 1.11-1.29]), frailty (OR, 1.28, [95% CI, 1.15-1.42]), falls (OR, 1.11, [95% CI, 1.06-1.17]), and hospitalization (OR, 1.19, [95% CI, 1.14-1.25]). Moreover, CKM syndrome stages independently predicted all-cause mortality (CHARLS: HR, 1.12 [95% CI, 1.06-1.18]; NHANES: HR, 1.34 [95% CI, 1.29-1.40]), and cause-specific mortality, including CVD (HR, 1.62, [95% CI, 1.50-1.75]), malignant neoplasm (HR, 1.20, [95% CI, 1.10-1.30]), respiratory disease (HR, 1.26, [95% CI, 1.07-1.49]). After adjusting for CKM components, the positive associations of CKM syndrome with geriatric syndrome and mortality were persisted, indicating that CKM syndrome has health implications beyond those of its component disorders. There were dose-effect relationships between CKM stages and risks of geriatric syndromes and mortality. Especially, advanced CKM syndrome (stage 3-4) exhibited higher risks of adverse outcomes along the CKM staging spectrum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings revealed that CKM syndrome significantly impacted the overall health among middle-aged and older adults, advocating its application from CVD to geriatric community and multidiscipline clinical settings. It suggested the need for CKM syndrome assessment to predict and prevent geriatric syndromes and major chronic conditions, thereby reducing social and healthcare burdens. Moreover, these results provided empirical evidence for the validity and robustness of the CKM staging classification.\u003c/p\u003e","manuscriptTitle":"Cardiovascular-kidney-metabolic syndrome predicted the risks of geriatric syndromes and mortality of major chronic diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-03 05:32:53","doi":"10.21203/rs.3.rs-4987225/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":"17df4a1c-2952-48c8-a9a4-2bf17eddfba5","owner":[],"postedDate":"October 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37177335,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":37177336,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases"},{"id":37177337,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2024-10-03T05:32:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-03 05:32:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4987225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4987225","identity":"rs-4987225","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}