Association between serum creatine kinase levels and the risk of all-cause mortality among centenarians: A prospective cohort study in China

Association between serum creatine kinase levels and the risk of all-cause mortality among centenarians: A prospective cohort study in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between serum creatine kinase levels and the risk of all-cause mortality among centenarians: A prospective cohort study in China Xinyan Gong, Qingtao Zhang, Yue Niu, Xiang Yu, Yuwei Ji, Baolong Wang, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6000926/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 Muscle mass (MM) was important because of its strong correlation with all-cause mortality. However, its assessment was complex, nonportable and expensive. Serum creatine kinase (CK) was proposed to counteract this problem. However, its correlation with all-cause mortality was unknown. Objectives The aim of this study was to investigate the associations between serum CK levels and all-cause mortality risk in Chinese centenarians. Methods A prospective cohort study of centenarians was conducted to classify serum CK levels according to all-cause mortality via restricted cubic spline (RCS) analysis, Cox regression analysis and Kaplan‒Meier analysis. Results We included 949 participants with a median follow-up of 29.4 months (interquartile range (IQR) 14.5, 51.7). A total of 92.9% of the subjects died. The RCS analysis revealed an inverse J-shaped relationship between the serum CK level and the risk of death. The mortality rate of centenarians with serum CK levels ranging from 8–66 U/L (Q1–Q2) was 34.3% higher than that of those with serum CK levels ranging from 66–192 U/L (Q3–Q4) (multivariable analysis hazard ratio (HR), 1.343; 95% confidence interval (CI), 1.173–1.538; P < 0.001). Kaplan–Meier analyses revealed that centenarians with lower serum CK levels had significantly shorter median survival time (Q1‒Q2 versus Q3‒Q4: 26 months versus 36 months, log-rank P < 0.001). Conclusions Serum CK levels were negatively associated with centenarian mortality, indicating that they may predict mortality risk in centenarians. CK could be utilized as an independent predictor of mortality in centenarians. Clinical Trial Registry number and website where it was obtained. Not applicable. Registry and registry number for systematic reviews or meta-analyses. Not applicable. centenarians all-cause mortality creatine kinase longevity muscle mass Figures Figure 1 Figure 2 Figure 3 Statement of Significance In this study, we not only found that in the normal range, higher serum CK levels were associated with lower mortality in centenarians but also that this trend was nonlinear. Introduction Adequate nutritional screening and assessment were essential to improve the nutritional status and survival of elderly individuals. Owing to the high prevalence of low muscle mass (MM) among elderly individuals and its correlation with poor outcomes ( 1 – 5 ), MM was recommended as an important component for assessing survival in elderly individuals ( 6 ). However, the assessment of MM with bioimpedance techniques or muscle scanners was complex, nonportable and expensive. Therefore, the use of MM was limited in health care ( 7 – 9 ). Serum creatine kinase (CK) measurement was a cost-effective, noninvasive and simple alternative technique for estimating MM ( 10 – 12 ). CK was a crucial enzyme in the body that was directly related to muscle contraction ( 13 ). Its main mechanism was the reversible catalytic generation of phosphocreatine and adenosine diphosphate (ADP) from creatine and adenosine triphosphate (ATP) ( 14 ). Although CK has received much attention in the assessment of MM in elderly individuals, its direct relationship with mortality in elderly individuals might be neglected. Mortality in elderly individuals was often influenced by various factors, such as age ( 15 ), chronic diseases ( 16 – 18 ), and lifestyle ( 19 – 21 ), rather than CK. Moreover, previous studies often focused on the clinical significance of serum CK levels above the normal range. There were very few studies on the relationship between mortality and CK levels within the normal range among centenarians. Therefore, the aim of this study was to evaluate the relationship between CK and all-cause mortality in centenarians. Methods Study Design and Patients This prospective study of the China Hainan Centenarian Cohort Study (CHCCS) included 1002 centenarians residing in the community from July 2014 to December 2016. In every case, the date of death was verified by the Hainan Provincial Civil Affairs Bureau. The Hainan Provincial Civil Affairs Bureau, which is responsible for monthly pensions for individuals aged 80 and above, verifies at least monthly that the centenarians were alive. The study was authorized by Hainan Hospital's Ethics Committee at Chinese PLA General Hospital (No. 301HNLL-2016-01), and the research adhered to the Declaration of Helsinki and its later amendments. Data collection Skilled medical professionals and nursing staff at the Hainan Hospital of the PLA General Hospital gathered fundamental information regarding age, sex, ethnic background, weight, height, educational attainment, marital status, smoking and alcohol use records, hypertension, diabetes mellitus (DM), coronary heart disease (CHD), and a range of serological measures, including standard blood counts and biochemical tests. Body mass index (BMI) was calculated as weight divided by height squared. Blood samples were collected from participants by an experienced nurse and sent to the laboratory department of the Hainan Hospital of the Chinese PLA General Hospital. The quantification of CK levels was performed by dynamically measuring the absorbance due to the formation of nicotinamide adenine dinucleotide phosphate at 340/660 nm, which was proportional to the activity of CK in a human serum sample. The CK method is a modification of the International Federation of Clinical Chemistry method for measuring the catalytic concentrations of enzymes on a Beckman Coulter analyzer. The normal range was between 2 and 200 U/L. Statistical analysis Prior to performing the statistical evaluations, the data were tested for normality and uniformity. Data that were normally distributed were characterized by the mean plus or minus the standard deviation. The variances among the groups were examined via either an independent samples t test or ANOVA. Data distributed unevenly were characterized as medians and interquartile ranges [M (QL, QU)], and Mann‒Whitney or Kruskal‒Wallis tests were used to examine variances among groups. The categorical variables are presented as numbers and percentages (%) and were examined via the χ2 test. RCS analyses focused on examining the correlations between overall mortality rates and CK levels as a continuous factor in both unmodified and modified models. The CK levels were grouped by IQR as follows: Q1: 8 ≤ CK < 48 U/L; Q2: 48 ≤ CK < 66 U/L; Q3: 66 ≤ CK < 92 U/L; and Q4: 92 ≤ CK ≤ 192 U/L. Prior to performing Cox regression, a proportional hazards assumption test was performed. Both unadjusted and adjusted Cox regressions were conducted: the initial regression assessed the hazard ratio (HR) and 95% confidence intervals (CIs) for mortality, while the latter was modified for confounding variables. The complex model was adjusted for age, sex, ethnicity, marital status, BMI, educational background, smoking habits, alcohol consumption, DM, hypertension, and CHD. Survival rate graphs generated via Kaplan‒Meier and log-rank analyses were generated to analyze the time-to-death distribution. Subgroup analyses were used to examine the potential effects of age, sex, BMI, ethnicity, marital status, education, smoking, drinking, hypertension, DM, and CHD on the relationship between CK and all-cause mortality. P values less than 0.05 were considered statistically significant. The data were processed via R language software, version 4.3.3. Results Baseline characteristics and follow-up Fifty-three centenarians were excluded because their basic information was incomplete or their CK levels were above the normal range, and a total of 949 centenarians, with 81.9% females, were included in this study (Table 1 and Supplementary Table 1 ). The average age at baseline was 102 years (IQR: 101–104). Approximately 73.8% of the participants had hypertension, while the prevalence rates of DM (9.3%) and CHD (4.2%) were very low. Over a median follow-up of 29.4 months (IQR: 14.5–51.7), the mortality rate was 92.9% (882/949). Table 1 Characteristics of the study population based on creatine kinase quartiles at baseline Variable Overall Q1 [8, 48) Q2 [48, 66) Q3 [66, 92) Q4 [92, 192] P value N 949 224 249 236 240 Age, years 102.00(101.00–104.00) 102.00(101.00–104.00) 102.00(101.00–104.00) 102.00(101.00–104.00) 102.00(101.00–104.00) 0.147 Female,% 777 (81.87) 191 (85.27) 207 (83.13) 195 (82.63) 184 (76.67) 0.090 Follow-up time, months 29.40(14.50,51.70) 23.20(11.35,36.83) 28.00(12.80,50.30) 35.80(15.07,55.73) 37.40(19.05,61.73) < 0.001 Death,% 882 (92.94) 218 (97.32) 228 (91.57) 221 (93.64) 215 (89.58) 0.009 Ethnicity 0.624 Han,% 843 (88.83) 204 (91.07) 221 (88.76) 206 (87.29) 212 (88.33) Other,% 106 (11.17) 20 (8.93) 28 (11.24) 30 (12.71) 28 (11.67) Marital status 0.400 Married,% 99 (10.43) 30 (13.39) 22 (8.84) 23 (9.75) 24 (10.00) Separation/Divorce/Widowhood,% 850 (89.57) 194 (86.61) 227 (91.16) 213 (90.25) 216 (90.00) Education 0.481 No education, % 865 (91.15) 202 (90.18) 235 (94.38) 213 (90.25) 215 (89.58) Elementary school,% 64 (6.74) 18 (8.04) 11 (4.42) 17 (7.20) 18 (7.50) Junior high school and above,% 20 (2.11) 4 (1.79) 3 (1.20) 6 (2.55) 7 (2.92) Smoking habits 0.998 Never,% 845 (89.04) 202 (90.18) 221 (88.76) 210 (88.99) 212 (88.33) Past,% 70 (7.38) 15 (6.70) 19 (7.63) 17 (7.20) 19 (7.92) Now,% 34 (3.58) 7 (2.12) 9 (3.61) 9 (3.81) 9 (3.75) Alcohol consumption 0.293 Never,% 782 (82.40) 182 (81.25) 206 (82.73) 202 (85.59) 192 (80.00) Past,% 73 (7.69) 24 (10.71) 17 (6.83) 12 (5.08) 20 (8.33) Now,% 94 (9.91) 18 (8.04) 26 (10.44) 22 (9.3)2 28 (11.67) Hypertension,% 700(73.76) 159 (71.98) 179 (71.89) 183 (77.54) 179 (74.58) 0.366 Diabetes,% 88 (9.27) 18 (8.04) 20 (8.03) 19 (8.05) 31 (12.92) 0.167 Coronary heart disease,% 40 (4.21) 8 (3.57) 9 (3.61) 12 (5.08) 11 (4.58) 0.807 Body mass index, kg/m 2 18.00(16.04, 19.98) 17.86(16.04, 19.10) 17.65(15.72, 20.05) 18.42(16.43, 20.23) 18.40(16.54, 20.23) 0.008 Creatine kinase,U/L 66.00(48.00,92.00) 38.00(32.00,43.00) 57.00(52.00,61.00) 78.00(71.00,84.00) 116.00(103.00,133.25) < 0.001 RCS analyses of serum CK The RCS analysis revealed that before and after accounting for interfering variables, the serum CK level had an inverse J-shaped relationship with mortality risk. An increase in the serum CK level was correlated with a lower risk of death among centenarians (univariate analysis, P < 0.001, P nonlinear < 0.001, Fig. 1 ; multivariate analysis, P < 0.001, P nonlinear = 0.001, Fig. 2 ). Cox analyses and Kaplan‒Meier curves of the serum CK concentration Before Cox analyses, tests for the proportional hazards assumption were conducted in the RCS models. The P-proportional values of CK surpassed the 0.05 threshold in both the unadjusted and adjusted analyses, indicating that the prerequisites for Cox survival analyses were met. For every 10 U/L increase in the serum CK concentration, the risk of death was reduced by 5.7% (95% CI: 3.7%-7.7%) and 5.8% (95% CI: 4.7%-7.8%) according to the univariate and multivariate models, respectively (Table 2 ). The univariate analysis revealed greater mortality risks for Q1, Q2, and Q3 than for Q4, with Q1 having the greatest risk (HR, 1.779; 95% CI, 1.471–2.152; P < 0.001). In the multivariate analysis, the risk of death was greater for Q1, Q2, and Q3 than for Q4, with the highest risk observed for Q1 (HR, 1.811; 95% CI 1.491–2.199; P < 0.001). When two groups were created on the basis of the median value (Q1‒Q2 vs. Q3‒Q4), CK levels were significantly correlated with mortality according to univariate analysis (HR, 1.351; 95% CI, 1.183‒1.542; P < 0.001) and multivariate analysis (HR, 1.343; 95% CI, 1.173‒1.548; P < 0.001). This indicated that for centenarians with a CK level below 66 U/L (Q1–Q2), the overall mortality rate increased by 34.3%. Table 2 Cox regression analysis of the association between CK and all-cause mortality. Terms Count Univariate analysis Multivariate -adjusted analysis CK 949 HR P HR P Continuous, per 10 U/L 0.943 (0.923–0.963) < 0.001 0.942 (0.922–0.963) < 0.001 Grouped into quartiles Q1 [8, 48) 224 1.779 (1.471–2.152) < 0.001 1.811 (1.491–2.199) < 0.001 Q2 [48, 66) 249 1.242 (1.031–1.497) 0.023 1.274 (1.054–1.541) 0.012 Q3 [66, 92) 236 1.163 (0.964–1.403) 0.116 1.230 (1.016–1.488) 0.034 Q4 [92, 192] 240 1 (Reference) 1 (Reference) P for trend < 0.001 < 0.001 Grouped by medium value Q1-Q2 [8,66) 473 1.351 (1.183, 1.542) < 0.001 1.343 (1.173, 1.538) < 0.001 Q3-Q4 [66,192] 476 1 (Reference) 1 (Reference) P for trend < 0.001 < 0.001 Multivariate analysis adjusted for age, sex, ethnicity, marital status, education, smoking status, alcohol consumption status, DM, hypertension, CHD, and BMI. Kaplan‒Meier curves and log-rank tests revealed that centenarians in the Q1 group experienced a notably reduced median lifespan compared with those in the Q4 group (23 versus 37 months; log-rank P < 0.001) (Fig. 3 ). In terms of the median CK, centenarians in the low CK group had a significantly shorter median survival time than those in the high CK group did (26 versus 36 months; log-rank, P < 0.001) (Fig. 4). Subgroup analyses via Cox proportional hazards regression models revealed that there were no significant influences of age, sex, BMI, ethnicity, marital status, education, smoking status, alcohol consumption, hypertension, DM, or CHD on the relationship between CK and all-cause mortality (Fig. 5 ). Discussion This study investigated the relationship between serum CK levels and the risk of all-cause mortality among centenarians. In addition to finding that higher serum CK levels were associated with lower mortality in centenarians, our study also revealed that this trend was nonlinear. After adjusting for population characteristics and comorbidities in multivariate-adjusted analyses, the change in the serum CK concentration was still significantly correlated with mortality, suggesting that CK was suitable for predicting overall mortality in centenarians and that it might be a potential independent predictor of death. Sarcopenia is defined as an age-related decline in skeletal MM and function ( 22 ). In elderly individuals, sarcopenia is recognized as a reliable marker of frailty and poor prognosis and is often the result of a combination of physiological changes associated with aging and disease ( 4 , 23 ). In a prospective cohort study in Italy, data were collected from 197 older people aged between 80 and 85 years living in the Sirente geographic area. The main outcome indicator was all-cause mortality during the 7-year follow-up period. The results of the present study revealed that patients with sarcopenia had a greater risk of all-cause mortality than nonsarcopenic patients did (HR: 2.32, 95% CI: 1.01–5.43). It was therefore concluded that sarcopenia was associated with mortality, independent of age and other clinical and functional variables ( 24 ). Low muscle mass and/or sarcopenia were still found to be associated with increased mortality in a study of patients ≥ 65 years of age who were at a trauma center from 2009–2010 ( 25 ). Muscle tissue in the arms and legs, called appendicular lean mass (ALM), makes up 75% of the skeletal muscle in the body and is required for walking activities and body functions. In a cohort study that included 487 65-year-olds, it was concluded that higher ALM was associated with a lower risk of death ( 26 ). Low MM, assessed by calf circumference (CC), arm circumference (AC), arm muscle circumference (AMC), and corrected arm muscle circumference (CAMC), increased the risk of all-cause mortality in a 10-year prospective cohort study enrolling 418 older adults in Brazil ( 27 ). All of these studies suggested that MM was strongly associated with mortality in elderly individuals, with a gradual increase in mortality as MM declined. Serum CK is derived primarily from skeletal muscle ( 28 ). Skeletal muscle mass and function tend to decline with age, and these decreases are due to a reduction in the number and size of type II muscle fibers. Type II muscle fibers produce considerable amounts of cytosolic CK ( 29 ). Thus, decreased skeletal MM is associated with decreased serum CK levels in elderly individuals. We speculated that CK might be associated with sarcopenia or low MM. On the one hand, serum CK levels are positively correlated with a number of parameters that directly reflect muscle mass, including BMI, creatinine, and urinary creatinine (UCR) (UCR is a known marker of muscle mass) ( 30 , 31 ). A cross-sectional study of 1086 hospitalized T2DM patients revealed that CK was inversely correlated with low MM and was positively correlated with the skeletal muscle index (SMI) ( 10 ). Serum CK levels were negatively correlated with sarcopenia in patients with osteoarthritis in a cross-sectional study of osteoarthritis in Japan ( 11 ). A German cross-sectional study revealed that skeletal muscle mass loss was associated with decreased serum CK levels in older adults ( 32 ). These studies were very suggestive of a strong correlation between CK and MM, and as CK levels decreased, so did MM. In a study of serum CK levels and mortality in patients with chronic kidney disease, a low level of serum CK was associated with an increased risk of death in a CKD population ( 10 ). In a prospective cohort study in Massachusetts, elevated CK levels at admission ≥ 1,000 U/L independently predicted hospital mortality in critically ill patients with coronavirus disease 2019 (COVID-19) ( 33 ). In a retrospective study of patients with acute coronary syndrome in the emergency department, CK was shown to be an independent predictor of in-hospital mortality ( 34 ). In a cross-sectional study in Japan, elevated CK was found to be an independent risk factor for 72-hour mortality in patients with randomized plasma glucose levels ≥ 500 mg/dl ( 35 ). In a retrospective study in Japan, elevated CK was also associated with acute kidney injury in hospitalized patients with seasonal influenza virus infection. Abnormal serum CK levels were associated with mortality or poor prognosis ( 36 ). However, the relationship between serum CK levels (within the normal range) and all-cause mortality in centenarians has rarely been studied. Our study revealed that serum CK levels were associated with the mortality risk of centenarians in an inverse J-shaped pattern. As serum CK levels decline, centenarians have a progressively increased risk of death. Therefore, this study provided evidence for the relationship between serum CK levels and all-cause mortality in centenarians. This study also has several shortcomings. First, the study population was derived mainly from the Chinese Han population and the oldest adult population, so our results might not be applicable to populations with a more diverse ethnic composition or younger individuals. Second, we were unable to dynamically monitor changes in CK levels and thus explore the relationship between dynamic CK and all-cause mortality in centenarians. Third, we did not analyze the correlation between cause-of-death-specific mortality and CK due to the lack of detailed records of causes of death, such as cancer and cardiovascular disease. Conclusions In this study, we observed an inverse association between serum CK levels and all-cause mortality in centenarians. Combined with the clinical accessibility of CK, we might consider improving CK levels to be a potential therapeutic strategy for intervention in older adults seeking healthier outcomes. Abbreviations CK, creatine kinase; MM, muscle mass; RCS, restricted cubic spline; IQR, interquartile range; CI, confidence interval; HR, hazard ratio; ADP, adenosine diphosphate; ATP, adenosine triphosphate; CHCCS, China Hainan Centenarian Cohort Study; BMI, body mass index; CDC, Chinese Center for Disease Control and Prevention; PLA, People’s Liberation Army; DM, diabetes mellitus; CHD, coronary heart disease; NADPH, nicotinamide adenine dinucleotide phosphate; IFCC, International Federation of Clinical Chemistry; UC, urinen creatinine; SMI, skeletal muscle index; ICU, intensive care unit; ALM, appendicular lean mass; CC, calf circumference; AC, arm circumference; AMC, arm muscle circumference; CAMC, corrected arm muscle circumference; COVID-19, coronavirus disease 2019. Declarations Author Contribution XMC, YZC and ZF designed the research; XYG, QTZ, and YN conducted the research; HYH, QZ, YJC, CXN, SHF, SSY, SSW and ML provided essential materials; YLZ, YH, XYJ, JZ, QL, DS, HL, ZHZ, and ZYQ analyzed the data or performed the statistical analysis; XYG, XY, YWJ and BLW wrote the paper; YZC and ZF had primary responsibility for the final content; and all the authors read and approved the final manuscript.We thank all patients and volunteers for their participation in the present study and for their kind assistance in collecting the data and samples. Acknowledgments XMC, YZC and ZF designed the research; XYG, QTZ, and YN conducted the research; HYH, QZ, YJC, CXN, SHF, SSY, SSW and ML provided essential materials; YLZ, YH, XYJ, JZ, QL, DS, HL, ZHZ, and ZYQ analyzed the data or performed the statistical analysis; XYG, XY, YWJ and BLW wrote the paper; YZC and ZF had primary responsibility for the final content; and all the authors read and approved the final manuscript. We thank all patients and volunteers for their participation in the present study and for their kind assistance in collecting the data and samples. Data availability The raw data supporting the conclusions of this article will be made available from the corresponding author upon request and without undue reservation. Funding This study was supported by the National Key Research and Development Program of China (No. 2022YFC3602900, 2022YFC3602902, and 2022YFC3602903), the National Natural Science Foundation of China (No. 82270769, No. 32141005), the Specialized Project of Military Logistics Scientific Research and Health Care (No. 21BJZ37), and Sanya Science and Technology innovation special project (No. 2022KJCX02), the specific research fund of the Innovation Platform for Academicians of Hainan Province, the Beijing Natural Science Foundation (No. 7242033), Capital’s Funds for Health Improvement and Research (No. CFH 2024-1-5021), and the Science & Technology Project of Beijing (No. Z221100007422121). Author Disclosure The authors have no conflicts of interest to declare. Declaration of Generative AI and AI-Assisted Technologies in the Writing Process The authors declare that the article has no use of generative AI or AI-assisted technologies in the writing process. 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Diseases","correspondingAuthor":false,"prefix":"","firstName":"Xinyan","middleName":"","lastName":"Gong","suffix":""},{"id":415748289,"identity":"c2f63a57-b5d0-47b4-b8e6-75a22c85144f","order_by":1,"name":"Qingtao Zhang","email":"","orcid":"","institution":"The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, National Clinical Research Center for Kidney Diseases","correspondingAuthor":false,"prefix":"","firstName":"Qingtao","middleName":"","lastName":"Zhang","suffix":""},{"id":415748291,"identity":"d741db1a-b4e4-4567-964c-6e8b263c522f","order_by":2,"name":"Yue Niu","email":"","orcid":"","institution":"The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, National Clinical Research Center for Kidney Diseases","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Niu","suffix":""},{"id":415748293,"identity":"1486c8ca-4fb5-4964-a52e-ee896fed3d16","order_by":3,"name":"Xiang Yu","email":"","orcid":"","institution":"The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, National Clinical Research Center for Kidney Diseases","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Yu","suffix":""},{"id":415748295,"identity":"466f8706-6cc1-4238-8c3e-f84c91e2201f","order_by":4,"name":"Yuwei Ji","email":"","orcid":"","institution":"The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, National Clinical Research Center for Kidney Diseases","correspondingAuthor":false,"prefix":"","firstName":"Yuwei","middleName":"","lastName":"Ji","suffix":""},{"id":415748297,"identity":"8fb28e20-91ff-469a-ac8f-4652befb129d","order_by":5,"name":"Baolong Wang","email":"","orcid":"","institution":"The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, National Clinical Research Center for Kidney 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhang","suffix":""},{"id":415748322,"identity":"7f0517a7-84e0-453b-b69e-1ccd2a010d74","order_by":18,"name":"Qing Luo","email":"","orcid":"","institution":"Hainan Hospital of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Luo","suffix":""},{"id":415748323,"identity":"f62c609a-d6e9-4314-855d-c8f23b57a064","order_by":19,"name":"Ding Sun","email":"","orcid":"","institution":"Hainan Hospital of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ding","middleName":"","lastName":"Sun","suffix":""},{"id":415748324,"identity":"af67e7bf-4eba-486c-993d-6e37d816d69f","order_by":20,"name":"Hao Li","email":"","orcid":"","institution":"Hainan Hospital of Chinese PLA General Hospital, Academician Chen Xiangmei of Hainan Province Kidney Diseases Research Team Innovation Center","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Li","suffix":""},{"id":415748326,"identity":"637852fb-85ee-4b79-aefd-8b846322ed11","order_by":21,"name":"Zehao Zhang","email":"","orcid":"","institution":"Hainan Hospital of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zehao","middleName":"","lastName":"Zhang","suffix":""},{"id":415748327,"identity":"95c31333-4ba9-4cf4-a2e5-059631c8ad8a","order_by":22,"name":"Zeyu Qu","email":"","orcid":"","institution":"Hainan Hospital of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zeyu","middleName":"","lastName":"Qu","suffix":""},{"id":415748328,"identity":"1fc4dc1f-f5f5-4f48-bdc8-8ba47c683bd7","order_by":23,"name":"Xiangmei Chen","email":"","orcid":"","institution":"The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, National Clinical Research Center for Kidney Diseases","correspondingAuthor":false,"prefix":"","firstName":"Xiangmei","middleName":"","lastName":"Chen","suffix":""},{"id":415748330,"identity":"26573ac0-ae41-4151-a4ae-d9428b12ee8d","order_by":24,"name":"Yizhi Chen","email":"","orcid":"","institution":"Hainan Hospital of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yizhi","middleName":"","lastName":"Chen","suffix":""},{"id":415748331,"identity":"095abe00-16ad-4f43-8946-2d36f27dbca2","order_by":25,"name":"Zhe Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3RvwuCQBTA8ScH13Jy65Mg/4UDwak/5oHgpOToEBQENkS0CvVH9CdogpM1NzQUQXPR1NKPIWhTx4b7LG+5L7zHAWjaP0IA9hmMdxanI8X99klHioqpY+W3T6SVBtw6JZvmwl5OinsUQ1dlYRkTz0BOZ1SbGKuSnLQCR2U7f0/iAFht17UJw0B5ZgKeysfunvACCsP6hOPgWphPGK0LcCNSRXMiMDAm5hiYlQgXiFokiL7DRAlMCu4hZb5ovMVOvfNdDD9faZ/z2+PZ78npvD75ev02bfNc0zRNa/AGD7hExa4Ac+cAAAAASUVORK5CYII=","orcid":"","institution":"The First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, National Clinical Research Center for Kidney Diseases","correspondingAuthor":true,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2025-02-10 16:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6000926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6000926/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76663009,"identity":"cc7fd6c7-4b38-4aa4-8769-0fd6cfda50d7","added_by":"auto","created_at":"2025-02-19 12:12:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70056,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1 and Figure 2 \u003c/strong\u003eRestricted cubic spline models for the associations of CK with all-cause mortality risk. The solid red line indicates the HR, and the shaded areas represent the 95% CI. The horizontal coordinates indicate CK levels, the left vertical coordinates indicate the HRs for all-cause mortality, and the right vertical coordinates indicate the percentages of centenarian participants with those CK levels. The relationship between CK and all-cause mortality was nonlinear according to the unadjusted and multivariate analyses (\u003cstrong\u003eFigure 1 and Figure 2\u003c/strong\u003e) and restricted cubic spline models. Univariate analysis (\u003cstrong\u003e1\u003c/strong\u003e). Multivariate analysis adjusted for age, sex, ethnicity, marital status, BMI, education, smoking status, alcohol status, and status for DM, hypertension, and CHD (\u003cstrong\u003e2\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6000926/v1/52d00a6162f0eb57f08482f8.png"},{"id":76664677,"identity":"69b9ec7d-d0c9-4a28-a0c9-6231dbc1a48d","added_by":"auto","created_at":"2025-02-19 12:28:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3 and Figure 4 \u003c/strong\u003eKaplan–Meier survival curves based on CK, grouped into quartiles and medium quartiles. The Kaplan–Meier survival curves revealed significant associations between lower serum CK levels and decreased survival time. The median survival time was significantly shorter for participants in the Q1 group than for those in the Q4 group (23 months versus 37 months, log rank \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) (\u003cstrong\u003e3\u003c/strong\u003e). The median survival time was also shorter for centenarians in the low group (Q1--Q2) than for those in the high group (Q3--Q4) (26 months versus 36 months; log-rank, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) (\u003cstrong\u003e4\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6000926/v1/3380e30882671c570e45c7a9.png"},{"id":76663014,"identity":"ecc42b76-dec5-4670-967e-88c57e08258f","added_by":"auto","created_at":"2025-02-19 12:12:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":147073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003e Subgroup analysis of the associations of CK with all-cause mortality.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6000926/v1/0f651059d2ca787f66c7a3e1.png"},{"id":76665694,"identity":"e92ad0ec-370b-460a-8d30-cef0febdec56","added_by":"auto","created_at":"2025-02-19 12:44:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1255890,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6000926/v1/b9ca6b93-3290-4fee-b329-7580238671e2.pdf"},{"id":76663351,"identity":"fb5c2b91-5246-4e63-bb17-50a1633aa5d5","added_by":"auto","created_at":"2025-02-19 12:20:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":67988,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-6000926/v1/dda4ccecabc3aade0f8f49c9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between serum creatine kinase levels and the risk of all-cause mortality among centenarians: A prospective cohort study in China","fulltext":[{"header":"Statement of Significance","content":"\u003cp\u003eIn this study, we not only found that in the normal range, higher serum CK levels were associated with lower mortality in centenarians but also that this trend was nonlinear.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eAdequate nutritional screening and assessment were essential to improve the nutritional status and survival of elderly individuals. Owing to the high prevalence of low muscle mass (MM) among elderly individuals and its correlation with poor outcomes (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), MM was recommended as an important component for assessing survival in elderly individuals (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, the assessment of MM with bioimpedance techniques or muscle scanners was complex, nonportable and expensive. Therefore, the use of MM was limited in health care (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Serum creatine kinase (CK) measurement was a cost-effective, noninvasive and simple alternative technique for estimating MM (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCK was a crucial enzyme in the body that was directly related to muscle contraction (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Its main mechanism was the reversible catalytic generation of phosphocreatine and adenosine diphosphate (ADP) from creatine and adenosine triphosphate (ATP) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Although CK has received much attention in the assessment of MM in elderly individuals, its direct relationship with mortality in elderly individuals might be neglected. Mortality in elderly individuals was often influenced by various factors, such as age (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), chronic diseases (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and lifestyle (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), rather than CK. Moreover, previous studies often focused on the clinical significance of serum CK levels above the normal range. There were very few studies on the relationship between mortality and CK levels within the normal range among centenarians. Therefore, the aim of this study was to evaluate the relationship between CK and all-cause mortality in centenarians.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Patients\u003c/h2\u003e \u003cp\u003eThis prospective study of the China Hainan Centenarian Cohort Study (CHCCS) included 1002 centenarians residing in the community from July 2014 to December 2016. In every case, the date of death was verified by the Hainan Provincial Civil Affairs Bureau. The Hainan Provincial Civil Affairs Bureau, which is responsible for monthly pensions for individuals aged 80 and above, verifies at least monthly that the centenarians were alive. The study was authorized by Hainan Hospital's Ethics Committee at Chinese PLA General Hospital (No. 301HNLL-2016-01), and the research adhered to the Declaration of Helsinki and its later amendments.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eSkilled medical professionals and nursing staff at the Hainan Hospital of the PLA General Hospital gathered fundamental information regarding age, sex, ethnic background, weight, height, educational attainment, marital status, smoking and alcohol use records, hypertension, diabetes mellitus (DM), coronary heart disease (CHD), and a range of serological measures, including standard blood counts and biochemical tests.\u003c/p\u003e \u003cp\u003eBody mass index (BMI) was calculated as weight divided by height squared. Blood samples were collected from participants by an experienced nurse and sent to the laboratory department of the Hainan Hospital of the Chinese PLA General Hospital. The quantification of CK levels was performed by dynamically measuring the absorbance due to the formation of nicotinamide adenine dinucleotide phosphate at 340/660 nm, which was proportional to the activity of CK in a human serum sample. The CK method is a modification of the International Federation of Clinical Chemistry method for measuring the catalytic concentrations of enzymes on a Beckman Coulter analyzer. The normal range was between 2 and 200 U/L.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePrior to performing the statistical evaluations, the data were tested for normality and uniformity. Data that were normally distributed were characterized by the mean plus or minus the standard deviation. The variances among the groups were examined via either an independent samples t test or ANOVA. Data distributed unevenly were characterized as medians and interquartile ranges [M (QL, QU)], and Mann‒Whitney or Kruskal‒Wallis tests were used to examine variances among groups. The categorical variables are presented as numbers and percentages (%) and were examined via the χ2 test.\u003c/p\u003e \u003cp\u003eRCS analyses focused on examining the correlations between overall mortality rates and CK levels as a continuous factor in both unmodified and modified models. The CK levels were grouped by IQR as follows: Q1: 8\u0026thinsp;\u0026le;\u0026thinsp;CK\u0026thinsp;\u0026lt;\u0026thinsp;48 U/L; Q2: 48\u0026thinsp;\u0026le;\u0026thinsp;CK\u0026thinsp;\u0026lt;\u0026thinsp;66 U/L; Q3: 66\u0026thinsp;\u0026le;\u0026thinsp;CK\u0026thinsp;\u0026lt;\u0026thinsp;92 U/L; and Q4: 92\u0026thinsp;\u0026le;\u0026thinsp;CK\u0026thinsp;\u0026le;\u0026thinsp;192 U/L. Prior to performing Cox regression, a proportional hazards assumption test was performed. Both unadjusted and adjusted Cox regressions were conducted: the initial regression assessed the hazard ratio (HR) and 95% confidence intervals (CIs) for mortality, while the latter was modified for confounding variables. The complex model was adjusted for age, sex, ethnicity, marital status, BMI, educational background, smoking habits, alcohol consumption, DM, hypertension, and CHD. Survival rate graphs generated via Kaplan‒Meier and log-rank analyses were generated to analyze the time-to-death distribution. Subgroup analyses were used to examine the potential effects of age, sex, BMI, ethnicity, marital status, education, smoking, drinking, hypertension, DM, and CHD on the relationship between CK and all-cause mortality. P values less than 0.05 were considered statistically significant. The data were processed via R language software, version 4.3.3.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics and follow-up\u003c/h2\u003e \u003cp\u003eFifty-three centenarians were excluded because their basic information was incomplete or their CK levels were above the normal range, and a total of 949 centenarians, with 81.9% females, were included in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). The average age at baseline was 102 years (IQR: 101\u0026ndash;104). Approximately 73.8% of the participants had hypertension, while the prevalence rates of DM (9.3%) and CHD (4.2%) were very low. Over a median follow-up of 29.4 months (IQR: 14.5\u0026ndash;51.7), the mortality rate was 92.9% (882/949).\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\u003eCharacteristics of the study population based on creatine kinase quartiles at baseline\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1 [8, 48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2 [48, 66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3 [66, 92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4 [92, 192]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.00(101.00\u0026ndash;104.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.00(101.00\u0026ndash;104.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.00(101.00\u0026ndash;104.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102.00(101.00\u0026ndash;104.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102.00(101.00\u0026ndash;104.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.147\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\u003e777 (81.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (85.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207 (83.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e195 (82.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e184 (76.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time, months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.40(14.50,51.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.20(11.35,36.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.00(12.80,50.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.80(15.07,55.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.40(19.05,61.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e882 (92.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (97.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228 (91.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e221 (93.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e215 (89.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e843 (88.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (91.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221 (88.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206 (87.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e212 (88.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (11.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (8.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (11.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (12.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (11.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (10.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (13.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (8.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (9.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparation/Divorce/Widowhood,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e850 (89.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (86.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227 (91.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213 (90.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216 (90.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e865 (91.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (90.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e235 (94.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213 (90.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e215 (89.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (6.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (8.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (4.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (7.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school and above,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking habits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e845 (89.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (90.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221 (88.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210 (88.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e212 (88.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (6.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (7.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (7.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (7.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e782 (82.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (81.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206 (82.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202 (85.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e192 (80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (10.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (8.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (8.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (10.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (9.3)2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (11.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e700(73.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (71.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (71.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e183 (77.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e179 (74.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (9.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (8.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (8.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (8.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (12.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.00(16.04, 19.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.86(16.04, 19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.65(15.72, 20.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.42(16.43, 20.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.40(16.54, 20.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine kinase,U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.00(48.00,92.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.00(32.00,43.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.00(52.00,61.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.00(71.00,84.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116.00(103.00,133.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRCS analyses of serum CK\u003c/h2\u003e \u003cp\u003eThe RCS analysis revealed that before and after accounting for interfering variables, the serum CK level had an inverse J-shaped relationship with mortality risk. An increase in the serum CK level was correlated with a lower risk of death among centenarians (univariate analysis, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; multivariate analysis, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P nonlinear\u0026thinsp;=\u0026thinsp;0.001, \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCox analyses and Kaplan‒Meier curves of the serum CK concentration\u003c/h3\u003e\n\u003cp\u003eBefore Cox analyses, tests for the proportional hazards assumption were conducted in the RCS models. The P-proportional values of CK surpassed the 0.05 threshold in both the unadjusted and adjusted analyses, indicating that the prerequisites for Cox survival analyses were met. For every 10 U/L increase in the serum CK concentration, the risk of death was reduced by 5.7% (95% CI: 3.7%-7.7%) and 5.8% (95% CI: 4.7%-7.8%) according to the univariate and multivariate models, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The univariate analysis revealed greater mortality risks for Q1, Q2, and Q3 than for Q4, with Q1 having the greatest risk (HR, 1.779; 95% CI, 1.471\u0026ndash;2.152; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the multivariate analysis, the risk of death was greater for Q1, Q2, and Q3 than for Q4, with the highest risk observed for Q1 (HR, 1.811; 95% CI 1.491\u0026ndash;2.199; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When two groups were created on the basis of the median value (Q1‒Q2 vs. Q3‒Q4), CK levels were significantly correlated with mortality according to univariate analysis (HR, 1.351; 95% CI, 1.183‒1.542; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and multivariate analysis (HR, 1.343; 95% CI, 1.173‒1.548; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicated that for centenarians with a CK level below 66 U/L (Q1\u0026ndash;Q2), the overall mortality rate increased by 34.3%.\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\u003eCox regression analysis of the association between CK and all-cause mortality.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate -adjusted analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous, per 10 U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.943 (0.923\u0026ndash;0.963)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.942 (0.922\u0026ndash;0.963)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eGrouped into quartiles\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\u003eQ1 [8, 48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.779 (1.471\u0026ndash;2.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.811 (1.491\u0026ndash;2.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eQ2 [48, 66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.242 (1.031\u0026ndash;1.497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.274 (1.054\u0026ndash;1.541)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 [66, 92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.163 (0.964\u0026ndash;1.403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.230 (1.016\u0026ndash;1.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 [92, 192]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (Reference)\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\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrouped by medium value\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\u003eQ1-Q2 [8,66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.351 (1.183, 1.542)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.343 (1.173, 1.538)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eQ3-Q4 [66,192]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (Reference)\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\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eMultivariate analysis adjusted for age, sex, ethnicity, marital status, education, smoking status, alcohol consumption status, DM, hypertension, CHD, and BMI.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKaplan‒Meier curves and log-rank tests revealed that centenarians in the Q1 group experienced a notably reduced median lifespan compared with those in the Q4 group (23 versus 37 months; log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In terms of the median CK, centenarians in the low CK group had a significantly shorter median survival time than those in the high CK group did (26 versus 36 months; log-rank, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;4). Subgroup analyses via Cox proportional hazards regression models revealed that there were no significant influences of age, sex, BMI, ethnicity, marital status, education, smoking status, alcohol consumption, hypertension, DM, or CHD on the relationship between CK and all-cause mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the relationship between serum CK levels and the risk of all-cause mortality among centenarians. In addition to finding that higher serum CK levels were associated with lower mortality in centenarians, our study also revealed that this trend was nonlinear. After adjusting for population characteristics and comorbidities in multivariate-adjusted analyses, the change in the serum CK concentration was still significantly correlated with mortality, suggesting that CK was suitable for predicting overall mortality in centenarians and that it might be a potential independent predictor of death.\u003c/p\u003e \u003cp\u003eSarcopenia is defined as an age-related decline in skeletal MM and function (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In elderly individuals, sarcopenia is recognized as a reliable marker of frailty and poor prognosis and is often the result of a combination of physiological changes associated with aging and disease (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In a prospective cohort study in Italy, data were collected from 197 older people aged between 80 and 85 years living in the Sirente geographic area. The main outcome indicator was all-cause mortality during the 7-year follow-up period. The results of the present study revealed that patients with sarcopenia had a greater risk of all-cause mortality than nonsarcopenic patients did (HR: 2.32, 95% CI: 1.01\u0026ndash;5.43). It was therefore concluded that sarcopenia was associated with mortality, independent of age and other clinical and functional variables (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Low muscle mass and/or sarcopenia were still found to be associated with increased mortality in a study of patients\u0026thinsp;\u0026ge;\u0026thinsp;65 years of age who were at a trauma center from 2009\u0026ndash;2010 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Muscle tissue in the arms and legs, called appendicular lean mass (ALM), makes up 75% of the skeletal muscle in the body and is required for walking activities and body functions. In a cohort study that included 487 65-year-olds, it was concluded that higher ALM was associated with a lower risk of death (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Low MM, assessed by calf circumference (CC), arm circumference (AC), arm muscle circumference (AMC), and corrected arm muscle circumference (CAMC), increased the risk of all-cause mortality in a 10-year prospective cohort study enrolling 418 older adults in Brazil (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). All of these studies suggested that MM was strongly associated with mortality in elderly individuals, with a gradual increase in mortality as MM declined.\u003c/p\u003e \u003cp\u003eSerum CK is derived primarily from skeletal muscle (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Skeletal muscle mass and function tend to decline with age, and these decreases are due to a reduction in the number and size of type II muscle fibers. Type II muscle fibers produce considerable amounts of cytosolic CK (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Thus, decreased skeletal MM is associated with decreased serum CK levels in elderly individuals. We speculated that CK might be associated with sarcopenia or low MM. On the one hand, serum CK levels are positively correlated with a number of parameters that directly reflect muscle mass, including BMI, creatinine, and urinary creatinine (UCR) (UCR is a known marker of muscle mass) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). A cross-sectional study of 1086 hospitalized T2DM patients revealed that CK was inversely correlated with low MM and was positively correlated with the skeletal muscle index (SMI) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Serum CK levels were negatively correlated with sarcopenia in patients with osteoarthritis in a cross-sectional study of osteoarthritis in Japan (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). A German cross-sectional study revealed that skeletal muscle mass loss was associated with decreased serum CK levels in older adults (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). These studies were very suggestive of a strong correlation between CK and MM, and as CK levels decreased, so did MM.\u003c/p\u003e \u003cp\u003eIn a study of serum CK levels and mortality in patients with chronic kidney disease, a low level of serum CK was associated with an increased risk of death in a CKD population (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In a prospective cohort study in Massachusetts, elevated CK levels at admission\u0026thinsp;\u0026ge;\u0026thinsp;1,000 U/L independently predicted hospital mortality in critically ill patients with coronavirus disease 2019 (COVID-19) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In a retrospective study of patients with acute coronary syndrome in the emergency department, CK was shown to be an independent predictor of in-hospital mortality (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In a cross-sectional study in Japan, elevated CK was found to be an independent risk factor for 72-hour mortality in patients with randomized plasma glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;500 mg/dl (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In a retrospective study in Japan, elevated CK was also associated with acute kidney injury in hospitalized patients with seasonal influenza virus infection. Abnormal serum CK levels were associated with mortality or poor prognosis (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, the relationship between serum CK levels (within the normal range) and all-cause mortality in centenarians has rarely been studied. Our study revealed that serum CK levels were associated with the mortality risk of centenarians in an inverse J-shaped pattern. As serum CK levels decline, centenarians have a progressively increased risk of death. Therefore, this study provided evidence for the relationship between serum CK levels and all-cause mortality in centenarians.\u003c/p\u003e \u003cp\u003eThis study also has several shortcomings. First, the study population was derived mainly from the Chinese Han population and the oldest adult population, so our results might not be applicable to populations with a more diverse ethnic composition or younger individuals. Second, we were unable to dynamically monitor changes in CK levels and thus explore the relationship between dynamic CK and all-cause mortality in centenarians. Third, we did not analyze the correlation between cause-of-death-specific mortality and CK due to the lack of detailed records of causes of death, such as cancer and cardiovascular disease.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we observed an inverse association between serum CK levels and all-cause mortality in centenarians. Combined with the clinical accessibility of CK, we might consider improving CK levels to be a potential therapeutic strategy for intervention in older adults seeking healthier outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCK, creatine kinase; MM, muscle mass; RCS, restricted cubic spline; IQR, interquartile range; CI, confidence interval; HR, hazard ratio; ADP, adenosine diphosphate; ATP, adenosine triphosphate; CHCCS, China Hainan Centenarian Cohort Study; BMI, body mass index; CDC, Chinese Center for Disease Control and Prevention; PLA, People\u0026rsquo;s Liberation Army; DM, diabetes mellitus; CHD, coronary heart disease; NADPH, nicotinamide adenine dinucleotide phosphate; IFCC, International Federation of Clinical Chemistry; UC, urinen creatinine; SMI, skeletal muscle index; ICU, intensive care unit; ALM, appendicular lean mass; CC, calf circumference; AC, arm circumference; AMC, arm muscle circumference; CAMC, corrected arm muscle circumference; COVID-19, coronavirus disease 2019.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXMC, YZC and ZF designed the research; XYG, QTZ, and YN conducted the research; HYH, QZ, YJC, CXN, SHF, SSY, SSW and ML provided essential materials; YLZ, YH, XYJ, JZ, QL, DS, HL, ZHZ, and ZYQ analyzed the data or performed the statistical analysis; XYG, XY, YWJ and BLW wrote the paper; YZC and ZF had primary responsibility for the final content; and all the authors read and approved the final manuscript.We thank all patients and volunteers for their participation in the present study and for their kind assistance in collecting the data and samples.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXMC, YZC and ZF designed the research; XYG, QTZ, and YN conducted the research; HYH, QZ, YJC, CXN, SHF, SSY, SSW and ML provided essential materials; YLZ, YH, XYJ, JZ, QL, DS, HL, ZHZ, and ZYQ analyzed the data or performed the statistical analysis; XYG, XY, YWJ and BLW wrote the paper; YZC and ZF had primary responsibility for the final content; and all the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eWe thank all patients and volunteers for their participation in the present study and for their kind assistance in collecting the data and samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available from the corresponding author upon request and without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Key Research and Development Program of China (No. 2022YFC3602900, 2022YFC3602902, and 2022YFC3602903), the National Natural Science Foundation of China (No. 82270769, No. 32141005), the Specialized Project of Military Logistics Scientific Research and Health Care (No. 21BJZ37), and Sanya Science and Technology innovation special project (No. 2022KJCX02), the specific research fund of the Innovation Platform for Academicians of Hainan Province, the Beijing Natural Science Foundation (No. 7242033), Capital’s Funds for Health Improvement and Research (No. CFH 2024-1-5021), and the Science \u0026amp; Technology Project of Beijing (No. Z221100007422121).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-Assisted Technologies in the Writing Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the article has no use of generative AI or AI-assisted technologies in the writing process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNeves T, Fett CA, Ferriolli E, Crespilho Souza MG, dos, Reis Filho AD, Martin Lopes MB et al. 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J Investig Med. 2023;71(3):279\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWyss M, Smeitink J, Wevers RA, Wallimann T. Mitochondrial creatine kinase: a key enzyme of aerobic energy metabolism. Biochim Biophys Acta. 1992;1102(2):119\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBessman SP, Geiger PJ. Transport of energy in muscle: the phosphorylcreatine shuttle. Science. 1981;211(4481):448\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaul JD, Kim JK, Levine ME, Thyagarajan B, Weir DR, Crimmins EM. Epigenetic-based age acceleration in a representative sample of older Americans: Associations with aging-related morbidity and mortality. Proc Natl Acad Sci U S A. 2023;120(9):e2215840120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang O, Matsushita K, Coresh J, Sharrett AR, McEvoy JW, Windham BG, et al. Mortality Implications of Prediabetes and Diabetes in Older Adults. 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Predicting 72-h mortality in patients with extremely high random plasma glucose levels: A case\u0026ndash;controlled cross-sectional study. Med (Baltim). 2021;100(4):e24510.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshibuchi K, Fukasawa H, Kaneko M, Yasuda H, Furuya R. Elevation of creatine kinase is associated with acute kidney injury in hospitalized patients infected with seasonal influenza virus. Clin Exp Nephrol. 2021;25(4):394\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"centenarians, all-cause mortality, creatine kinase, longevity, muscle mass","lastPublishedDoi":"10.21203/rs.3.rs-6000926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6000926/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\u003eMuscle mass (MM) was important because of its strong correlation with all-cause mortality. However, its assessment was complex, nonportable and expensive. Serum creatine kinase (CK) was proposed to counteract this problem. However, its correlation with all-cause mortality was unknown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of this study was to investigate the associations between serum CK levels and all-cause mortality risk in Chinese centenarians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA prospective cohort study of centenarians was conducted to classify serum CK levels according to all-cause mortality via restricted cubic spline (RCS) analysis, Cox regression analysis and Kaplan‒Meier analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included 949 participants with a median follow-up of 29.4 months (interquartile range (IQR) 14.5, 51.7). A total of 92.9% of the subjects died. The RCS analysis revealed an inverse J-shaped relationship between the serum CK level and the risk of death. The mortality rate of centenarians with serum CK levels ranging from 8–66 U/L (Q1–Q2) was 34.3% higher than that of those with serum CK levels ranging from 66–192 U/L (Q3–Q4) (multivariable analysis hazard ratio (HR), 1.343; 95% confidence interval (CI), 1.173–1.538; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Kaplan–Meier analyses revealed that centenarians with lower serum CK levels had significantly shorter median survival time (Q1‒Q2 versus Q3‒Q4: 26 months versus 36 months, log-rank \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum CK levels were negatively associated with centenarian mortality, indicating that they may predict mortality risk in centenarians. CK could be utilized as an independent predictor of mortality in centenarians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registry number and website where it was obtained.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistry and registry number for systematic reviews or meta-analyses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Association between serum creatine kinase levels and the risk of all-cause mortality among centenarians: A prospective cohort study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 12:12:26","doi":"10.21203/rs.3.rs-6000926/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":"10d99d00-e7d7-409e-871a-2bc1e0840bd9","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-27T10:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-19 12:12:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6000926","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6000926","identity":"rs-6000926","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}