The relationship between frailty and major adverse cardiovascular and cerebrovascular events in Chinese older adults: the mediating effect of lipid accumulation products

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However, the potential mechanisms need to be further explored. This study aimed to investigate the mediating effect of lipid accumulation products (LAP) in the relationship between frailty and MACCE. Methods This study recruited 7901 participants aged 45 and above from wave 2011 and 2018 of the China Longitudinal Study of Health and Retirement (CHARLS). Logistic regression models were employed to examine the relationship between frailty and MACCE and the mediating effects of LAP, using the bootstrap method to confirm path effects. Results Frailty group presented the highest risk of MACCE (OR 1.07, 95% CI 1.03–1.10). Frailty directly impacted MACCE (β = 0.045, P = 0.007). Frailty had a significant effect on LAP (β = 12.21, P < 0.01), while LAP had a significant impact on MACCE (β = 11.14, p = 0.014). The mediation effect of LAP accounted for 1.7% of the total effect regarding the frailty with MACCE. Conclusion LAP mediate the relationship between frailty and MACCE. Our findings suggest that instructing frailty patients to have a reasonable diet and exercise to control LAP at a low level may be an effective measure to reduce MACCE. frailty lipid accumulation products major adverse cardiovascular and cerebrovascular events older adults Figures Figure 1 Figure 2 Figure 3 Introduction As a result of the ageing of the population and changes in national lifestyles, the morbidity and mortality of cardiovascular and cerebrovascular diseases among Chinese residents continue to rise 1 . According to the Chinese Center for Cardiovascular Disease, the number of people suffering from cardiovascular and cerebrovascular diseases in China is estimated to be about 330 million. In 2020, the number of deaths from cardiovascular and cerebrovascular diseases in China was 4.58 million, an increase of 48.2% from 2005 2 . Cardiovascular and cerebrovascular diseases bring heavy economic burden to residents and society, and have become a serious public health problem. Therefore, it is necessary to explore the risk factors of cardiovascular and cerebrovascular diseases in depth. Frailty, a state of vulnerability caused by a decline in multiple system functional reserves, is a common geriatric syndrome that encompasses physical, social, and cognitive dimensions 3 , 4 . Frailty increases the risk of adverse health and is a predictor of a wide range of age-related chronic diseases 5 . The frailty index (FI) includes the impact of physical, psychological, and social factors on the human body, reflecting the accumulation of physiological defects in multiple systems 6 . Studies have shown that progression of frailty increases the risk of cardiovascular disease (CVD), while recovery from frailty decreases the risk of CVD 7 . Frailty is also a risk factor for stroke and is significantly associated with adverse outcomes after mechanical thrombectomy (MT) 8 , 9 . Exploring the interrelationship between frailty and cardiovascular and cerebrovascular diseases could be valuable in guiding the improvement of patient prognosis. Obesity, especially central obesity, is an important risk factor for cardiovascular disease 10 . The prevalence of overweight and obesity among Chinese adults is estimated to be 33.3% and 14.1%, respectively, and obesity and overweight are expected to affect 800 million by 2030 11 . Body mass index (BMI) is a simple indicator of obesity, but it does not accurately reflect body fat distribution 12 , 13 . Lipid accumulation products (LAP) combine waist circumference and triglycerides to reflect the extent of body fat accumulation and visceral adiposity 14 . Higher LAP levels have been shown to be significantly associated with all-cause and CVD mortality, and maintaining a low LAP status may reduce the risk of death 10 . Cumulative LAP is positively associated with the risk of stroke, particularly ischemic stroke 15 . The American Heart Association (AHA) has identified Life’ simple (LS7) as a heart-health intervention, which includes seven metrics such as weight management, physical activity, and lipids. Optimizing LS7 not only reduces the incidence of CVD, but also reduces the risk of frailty in old age 16 . It remains unknown whether there was a moderating effect of LAP on the association of FI with major adverse cardiovascular and cerebrovascular events (MACCE). The objectives of this study were: 1) to explore the association between FI and MACCE in Chinese middle-aged and elderly people; 2) to explore whether LAP is a mediator of the above relationship. Clarifying the role of LAP in the association between FI and MACCE will provide new perspectives for more effective and precise prevention of cardiovascular and cerebrovascular diseases. Methods Study Design and Participants This study adopted a prospective design and utilized CHARLS data from 2011 and 2018. The CHARLS database, which focuses specifically on the health status of middle-aged and elderly adults in China, conducted the first national baseline survey in 2011 and completed four follow-up waves in 2013, 2015, 2018, and 2020. The present study used the 2011 data as the baseline and conducted the follow-up in 2018. Of these, 8,542 were excluded because they did not meet the following criteria: 1) Lack of available data on LAP, waist circumference, and triglycerides (n = 3911); 2) Age < 45 or history of stroke and heart diseases (n = 814); 3) Lack of follow-up data on cardiovascular and cerebrovascular diseases or those who were lost to follow-up (n = 3817); 4) Frailty index related indicators are not available (n = 1262). Finally, 7901 patients were enrolled in the study. (Fig.1) Data collection Frailty assessment Frailty was assessed by the FI, which was calculated by accumulating multiple age-related health deficits. In this study, we selected 28 projects for FI construction, involving disease (excluding heart disease and stroke), disability, physical function, depression, and cognition. Items 1-27 were dichotomized into 1 (defective) and 0 (non-defective). The cognitive score is a continuous variable ranging from 0 to 1. A higher cognitive score indicates a lower cognitive performance. For each participant, FI was calculated by dividing the sum of current health deficits by 28. Therefore, FI is a continuous variable from 0 to 1, with a higher FI indicating a higher level of frailty. With reference to previous studies 17 , FI was categorized into three categories: robust (FI ≤ 0.10), pre-frailty (0.10 <FI < 0.25), and frailty (FI ≥ 0.25). Calculation of the LAP LAP was calculated using fasting triglycerides and waist circumference as follows 10 : Male LAP= [WC (cm) -65] × [TG (mmol/L)] and Female LAP= [WC (cm) -58] × [TG (mmol/L)] Follow-up and assessment of outcomes The endpoint of this study was new-onset MACCE in the 2018 follow-up population. In CHARLS, based on self-reported physician diagnosed heart disease (myocardial infarction, coronary heart disease, angina, congestive heart failure and other heart diseases) and stroke (cerebral infarction and cerebral hemorrhage). When participants were asked if they had been diagnosed with heart disease or stroke by their physicians, those who reported a diagnosis of heart disease or stroke were considered to have had MACCE, and the 2011 questionnaire was reviewed to exclude participants with a history of MACCE, and finally to specify participants with newly MACCE. Covariates Covariates in the baseline survey were included: age, gender, marital status, educational level, smoking status, drinking status, location, average sleep time at night. Marital status is divided into: married and non-married (“never-married/separated/widowed”), education level is divided into: junior high school and below, high school, college and above. Smoking status was classified as never smokers and smokers (including still smoking now and those who have smoked but have now quit smoking), and drinking status was classified as never drinking, less than once a month, and more than once a month. Statistical analysis Continuous variables with normal distribution were expressed by means and standard deviations (SD), while categorical variables were expressed by percentages. One-way ANOVA and chi-square tests were employed to compare the baseline characteristics. Binary logistic regression was used to test the association between FI and MACCE, and covariates including gender, age, education level, marital status, location and sleep time were considered throughout the analysis. In the mediation analysis, we used the mediation model proposed by Baron and Kenny to examine the mediating role of the baseline LAP in the relationship between the baseline FI and MACCE: 1) the relationship between the FI and the LAP was analyzed; 2) the relationship between FI and MACCE was analyzed; 3) the relationship between FI, LAP, and MACCE was analyzed with LAP as a mediating variable. Finally, we evaluated total, indirect, and direct effects with the nonparametric bootstrap method 1000 times. Forest plots were used to demonstrate the results of stratified analysis. R 4.4.1 software was used to analyze the data, and P < 0.05 was considered statistically significant (two-sided). Results General Characteristics of Participants Descriptive statistics for participants are shown in Table 1. A total of 7901 participants, with a mean age of 58.7 years were recruited into the study, including 3550 males and 4351 females. 90.1% of the participants had a junior high school education or below, and only 1.2% had a college level of education or higher. 93.4% of the participants lived in rural areas. At the follow-up in 2018, a total of 214 participants experienced MACCE, accounting for 2.7%. The mean LAP index at baseline was 45.9 in the MACCE group and 37.2 in the non-MACCE group. Frailty was 4.7% in the MACCE group and 1.5% in the non-MACCE group. Table 1 Characteristics of participants in 2011 and MACCE in 2018 total(n = 7901) non-MACCE (7687) MACCE (214) P age (mean (SD)) 58.7(8.8) 58.6(8.8) 61.0(8.2) <0.001 gender (%) Female 4351(55.1) 4220(54.9) 131(61.2) 0.078 Male 3550(44.9) 3467(45.1) 83(38.8) education (%) 0.112 Junior high school or below 7119(90.1) 6929(90.1) 190(88.8) High school 685(8.7) 661(8.6) 24(11.2) Advanced professional school and above 97(1.2) 97(1.3) 0 marital (%) 0.495 Married 6720(85.1) 6542(85.1) 178(83.2) Non-married 1181(14.9) 1145(14.9) 36(16.8) location (%) 0.234 City 525(6.6) 506(6.6) 19(8.9) Village 7374(93.4) 7179(93.4) 195(91.1) smoking (%) 0.467 Yes 2975(37.7) 2900(37.7) 75(35.0) No 4925(62.3) 4786(62.3) 139(65.0) drinking (%) 0.483 Drink but less than once a month 634(8.0) 618(8.0) 16(7.5) Drink more than once a month 1951(24.7) 1905(24.8) 46(21.5) None of these 5315(67.3) 5163(67.2) 152(71.0) sleep time (mean (SD)) 6.4(1.9) 6.4(1.9) 6.3(1.8) 0.284 LAP(mean༈SD)༉ 37.4(45.0) 37.2(45.0) 45.9(45.7) 0.006 frailty index (%) 0.10 and<0.25 7283(92.2) 7085(92.2) 198(92.5) ≥0.25 125(1.6) 115(1.5) 10(4.7) LAP_Q(%) <0.001 Q1 1976(25.0) 1946(25.3) 30(14.0) Q2 1975(25.0) 1939(25.2) 36(16.8) Q3 1975(25.0) 1909(24.8) 66(30.8) Q4 1975(25.0) 1893(24.6) 82(38.3) Abbreviations: LAP, lipid accumulation products; MACCE, major adverse cardiovascular and cerebrovascular events Relationship between FI and MACCE Table 2 shows the results of multifactor logistic regression analyzes of FI and new-onset MACCE. Model 1 adjusted only for the LAP, model 2 adjusted for LAP, age, and gender, and model 3 further adjusted for education, location, marital status, smoking, drinking and sleep time based on model 2. Including the FI as a categorical variable suggested that the frailty group (FI ≥ 0.25) was more likely to have MACCE with reference to the robust group (FI ≤ 0.10), whereas the pre-frailty group did not show a statistical difference. The risk for MACCE increased with increasing degree of frailty ( P for trend < 0.001). Model 3 showed that the frailty group had the highest risk of MACCE as compared with the robust group (OR 1.07, 95% CI 1.03–1.10). Analyzed as continuous variables, the FI and MACCE were consistently and significantly associated (OR 1.22, 95% CI 1.12–1.33). Table 2 Associations of frailty with MACCE Model 1 Model 2 Model 3 OR (95% CIs) P -value OR (95% CIs) P -value OR (95% CIs) P -value frailty index(mean༈SD)༉ 1.23(1.13–1.34) < 0.001 1.21(1.11–1.32) < 0.001 1.22(1.12–1.33) 0.10 and<0.25 1.02(0.99–1.02) 0.051 1.01(0.99–1.03) 0.059 1.01(0.99–1.03) 0.059 ≥0.25 1.07(1.04–1.10) < 0.001 1.06(1.03–1.10) < 0.001 1.07(1.03–1.10) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Notes: Model 1, adjusted for LAP; Model 2, adjusted for LAP, age and gender; Model 3, adjusted for variables in model 2 plus education, location, marital, smoking, drinking, and sleep. Abbreviations: FI, frailty index; LAP, lipid accumulation products. OR Odds Ratio, 95% CI 95% Confidence Interval Mediating effects analysis Figure 2 demonstrate the results of the mediation analysis of LAP between FI and MACCE after controlling for covariates. In the total effects regression, FI was a significant predictor of MACCE (β = 0.045, P <0.001). Moreover, FI showed a significant effect on LAP (β = 12.21, P <0.001), whereas LAP exerted a significant effect on MACCE (β = 11.14 P = 0.014). The Bootstrap analysis further showed that the total effect of FI on MACCE was 0.194 ( P < 0.001, 95% CI 0.075–0.31). The profiled-mediated effect via LAP was 0.003 ( P = 0.016, 95% CI 0.0003–0.01). The results suggest although the mediating effect of LAP between FI and MACCE was weak, it still explained 1.7% of the total effect. (Table 3 ) Table 3 Bootstrap tests for mediation models paths bootstrap test bootstrap standard error P LLCI ULCI Indirect effect 0.003 0.024 0.000269 0.01 Direct effect 0.191 <0.001 0.07283 0.31 Total effect 0.194 <0.001 0.077365 0.32 Abbreviations: LLCI, lower limit of credible interval; ULCI, upper limit of credible interval. Stratified analyzes To find out whether the effect of FI on the risk of MACCE differed between subgroups, this study performed stratified analyzes by characteristics. The effect of FI on MACCE was more pronounced among women (OR 1.27, 95% CI 1.13–1.43), FI had a more significant effect on MACCE among participants in the highest quartile of LAP (OR1.43, 95% CI 1.16–1.77). (Fig. 3 ) Discussion This was the first study to explore the mediating role of LAP between the frailty and MACCE using data from the CHARLS. There was a significant association between frailty and LAP and MACCE, with LAP partially mediating the relationship between FI and MACCE. As the world’s population ages, frailty is becoming a common geriatric syndrome. Several studies have shown that frailty is strongly associated with the risk of CVD 8 , 18 – 20 . Boreskie 21 et al included 985 female participants aged ≥ 55 years and found that frailty/pre-frailty was a risk factor for CVD. He 7 et al explored the association between frailty and CVD using three prospective cohorts and found that changes in frailty status significantly affected the risk of CVD, with progression of robust to frailty/ pre-frailty increasing the risk of CVD and vice versa. The prevalence of frailty/ pre-frailty among patients with stroke is 66.8% 20 . A meta-analysis that included 245,773 patients with acute ischemic stroke (AIS) treated with endovascular thrombectomy (EVT) showed that frailty patients had a higher mortality rate(OR 1.036, 95%CI 1.008–1.065) and poorer functional outcomes ༈OR 1.189, 95%CI 1.043–1.357༉ compared with patients without frailty 8 . It was also showed that at least a quarter of stroke survivors were frailty. Frailty has been shown to be associated with reduced NIHSS improvement after thrombolysis and is associated with stroke severity in elderly patients 22 , and frailty increases mortality in stroke survivors nearly 4-fold 20 . The biological mechanisms include imbalances between inflammatory and anti-inflammatory pathways, activation of oxidative stress, metabolic disorders, and vascular endothelial dysfunction 23 , 24 . In addition, there are multiple co-morbidities between frailty and MACCE, such as metabolic syndrome, diabetes, atherosclerosis, and cognitive disorders 25 , 26 . Accumulation of visceral fat has been shown to 27 – 29 be more strongly associated with hypertension, diabetes, cardiovascular and cerebrovascular disease than obesity 27 – 29 . LAP is an indicator based on triglycerides and waist circumference proposed by Prof. Kahn in 2005 14 . LAP has been widely recognized as a good indicator of body fat distribution, especially abdominal fat accumulation 29 . In a study of 50,162 individuals followed for 10 years, a significant increase in all-cause mortality(HR 1.54, 95%CI 1.32–1.80)and CVD mortality༈HR 1.55,95༅CI 1.16–2.09༉was found in the highest quartile of LAP compared to the lowest 10 . In a study involving 4,3089 participants followed for 11 years, the HR for ischemic stroke in the LAP Q4 group was 1.56 (1.36–1.79), whereas no statistical significance was observed for hemorrhagic stroke, which may be related to fewer cases of hemorrhagic stroke 15 . Fat tissue is the largest endocrine and immune organ of the human body, and excessive accumulation of visceral fat leads to overproduction of pro-inflammatory factors (e.g. IL-6, TNF-α) as well as downregulation of anti-inflammatory factors (adiponectin) 30 , 31 , triggering inflammation and oxidative stress, causing metabolic syndromes and vascular endothelial damage, and changes in the arterial system increase the risk of cardiovascular and cerebrovascular diseases 32 , 33 . The present study further explored the mediating role of LAP in the association of frailty and MACCE, and although the mechanisms are not clear, some possible explanations exist. 1) inflammation and metabolic abnormalities are the common pathological basis of both. Increased expression of various inflammatory factors (e.g., IL-6, TNF-α) has been observed in both frailty/elevated LAP populations, causing inflammation and oxidative stress, leading to extensive cell damage, which is a risk factors for a wide range of chronic diseases and death 34 , 35 . Both frailty/ elevated LAP can lead to metabolic disorders (lipid metabolism, insulin resistance) and ultimately lead to vascular endothelial damage and an increased risk of cardiovascular and cerebrovascular diseases 23 , 32 . 2) There are multiple co-morbid foundations. Studies have shown that both are closely associated with a variety of chronic diseases, such as hypertension, diabetes, dyslipidemia, atherosclerosis and nutritional disorders, which are important risk factors for cardiovascular and cerebrovascular diseases 25 , 27 . 3) Reasonable exercise can reduce body fat, improve muscle mass and bone density 36 , and prevent abnormal accumulation of lipid and reduce the incidence of related chronic diseases. The core manifestation of frailty is the decrease of physical activity ability 18 , which increases the risk of abnormal lipid accumulation, leading to an increased risk of cardiovascular and cerebrovascular disease. After stratifying by gender and adjusting for relevant covariates, frailty was associated with a higher risk of MACCE for women, but not for men. Previous study has suggested that visceral adiposity index was a risk factor for women, but not for men 37 . A possible explanation is that estrogen plays an important protective, and as estrogen levels in middle-aged and elderly women decrease, they are more susceptible to metabolic disorders 37 . At the same time, some studies have reached the different conclusions. Tan 11 et al. found that the effect of metabolic disorders on CVD did not show gender differences, this may due to the lower volume of visceral fat in women. Our findings may have some implications for the prevention of cardiovascular and cerebrovascular diseases. First, community-based healthcare institutions should regularly assess the frailty of elderly population. Second, instructing frailty patients to have a reasonable diet and exercise to control LAP at a low level may be an effective measure to reduce cardiovascular and cerebrovascular diseases. This study still has some limitations. First, indicators of frailty and LAP were collected at baseline, which could not clarify the causal relationship between the independent and intermediate variables, and future longitudinal studies are needed to clarify the causal relationship between the two. Second, data on frailty and stroke history obtained through self-report questionnaires may be subject to reporting bias. Third, although the study controlled for several covariates, potential confounding factors may still affect the stability of the conclusions. Fourth, we excluded samples with missing values in exposure, outcome, and mediation variables, which probably underestimated the incidence of MACCE. Individuals who were excluded from missing values, loss to follow-up, or death may be less healthy than those included in the analysis, which may seriously underestimate the association between frailty and MACCE, as well as the mediating role. Conclusion This study used data from the CHARLS 2011 and 2018 waves to explore frailty not only directly affecting MACCE but also indirectly through LAP. Measures to reduce the rate of cardiovascular and cerebrovascular diseases in middle-aged and elderly adults include improving frailty and reducing LAP. There is a need to routinely screen middle-aged and elderly adults for frailty and LAP and encourage healthy diet and sensible exercise, which may reduce the incidence of cardiovascular and cerebrovascular diseases and promote healthy aging. Declarations Acknowledgements This study is grateful to the research team and all participants of China Health and Retirement Longitudinal Study (CHARLS). Author Contributions All authors made a significant contribution to the work reported. Zhoucheng Kang: Conception and design of the study; Writing the original draft. Yongli Ye and Hao Xiao: Analyzed and interpreted the data. Lingling Liu: Funding acquisition, Writing, Review and Editing. All the authors have approved the manuscript to be submitted, and agree to be accountable for all aspects of the work. Funding This study was funded by the Luoyang Science and Technology Program Project Fund (2302022Y). Data Availability The data used in this study are accessible to be downloaded publicly at https://charls.charlsdata.com/ Ethics Approval and Informed Consent The CHARLS study received ethical approval from the Beijing University Biomedical Ethics Review Board (IRB00001052-11015), and all participants provided written informed consent. The study methodology was carried out in accordance with approved guidelines. Conflict of interest The authors declare no conflicts of interest. References Wang H, Zhang H, Zou Z. Changing profiles of cardiovascular disease and risk factors in China: a secondary analysis for the Global Burden of Disease Study 2019. Chin Med J. 2023;136:2431–41. Wang W, Liu Y, Liu J, et al. 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Supplementary Files supplementary1.docx Cite Share Download PDF Status: Published Journal Publication published 12 Feb, 2025 Read the published version in Archives of Public Health → Version 1 posted Editorial decision: Revision requested 30 Nov, 2024 Reviews received at journal 30 Nov, 2024 Reviews received at journal 29 Nov, 2024 Reviewers agreed at journal 26 Nov, 2024 Reviewers agreed at journal 26 Nov, 2024 Reviewers invited by journal 21 Nov, 2024 Editor assigned by journal 30 Oct, 2024 Submission checks completed at journal 30 Oct, 2024 First submitted to journal 26 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5337981","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372667171,"identity":"9b3871bc-956a-4c1e-9ff7-a0279126e6e6","order_by":0,"name":"Zhoucheng Kang","email":"","orcid":"","institution":"No.989 Hospital of Joint Logistic Support Force of PLA","correspondingAuthor":false,"prefix":"","firstName":"Zhoucheng","middleName":"","lastName":"Kang","suffix":""},{"id":372667172,"identity":"9c3a40fa-3bdc-4f9e-a82a-67d6ff63ded2","order_by":1,"name":"Yongli Ye","email":"","orcid":"","institution":"No.989 Hospital of Joint Logistic Support Force of PLA","correspondingAuthor":false,"prefix":"","firstName":"Yongli","middleName":"","lastName":"Ye","suffix":""},{"id":372667173,"identity":"3a07a10a-752d-470b-8aff-61db8dade33e","order_by":2,"name":"Hao Xiao","email":"","orcid":"","institution":"No.989 Hospital of Joint Logistic Support Force of PLA","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Xiao","suffix":""},{"id":372667174,"identity":"b318ab01-17c3-46c8-a163-6423a5e67fe3","order_by":3,"name":"Lingling Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIie3RMQrCMBSA4SeB1OG1wUUqQnuFSKE69DDp4qQguLgLmTxAi5fQG1Sy9gYuhV4gYzeNuImQuDnkJ2M+eHkB8Pn+sIjAqBO8QEpI0+jBgVAChOvDOokCWd7qkwsxZ1K1KkuwzdSYupAAcxJKUsp4oxUgpGza2AbDvA8lNWR7UbsVLOqzsJJlFkp8kwpB8Lud5PNQxq/BOoXUkcyqlmcUW3AldG+WLBIaSG6WHNvfwpi6mq98YHokvdZDkbK5hXwW/3bd5/P5fN97AguZPXyNRuToAAAAAElFTkSuQmCC","orcid":"","institution":"No.980 Hospital of Joint Logistic Support Force of PLA","correspondingAuthor":true,"prefix":"","firstName":"Lingling","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-10-26 14:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5337981/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5337981/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13690-025-01520-8","type":"published","date":"2025-02-12T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70295963,"identity":"ee54d53e-6005-4d7f-9e2a-29637d4c0a5d","added_by":"auto","created_at":"2024-12-02 00:31:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132084,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study participants.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5337981/v1/e26f5e08612a83779c5fcb95.jpg"},{"id":70296696,"identity":"9e908ddd-a1b8-410a-8537-e5d03b6ca43c","added_by":"auto","created_at":"2024-12-02 00:39:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91217,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between frailty index, LAP, and MACCE. MACCE, major adverse cardiovascular and cerebrovascular events; LAP, lipid accumulation products. ACME, average causal mediation; ADE, average direct effects.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5337981/v1/1a630e492410559e5bf50693.jpg"},{"id":70295961,"identity":"3acbf57b-0144-4f72-872d-3cd2a2827ed0","added_by":"auto","created_at":"2024-12-02 00:31:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":210445,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of stratified analysis of the association of FI with the risk of MACCE. FI, frailty index; MACCE, major adverse cardiovascular and cerebrovascular events; LAP, lipid accumulation products.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5337981/v1/152ff3e9d6446371b34b6a1f.jpg"},{"id":76487667,"identity":"33a96e2e-b7b7-4c2e-b32d-f267f74bf760","added_by":"auto","created_at":"2025-02-17 16:10:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1116127,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5337981/v1/7ebdc6b9-d395-413f-b1c5-581edbc5639f.pdf"},{"id":70295960,"identity":"91c548e7-c4de-4717-89bd-ab3d35e4b18a","added_by":"auto","created_at":"2024-12-02 00:31:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19437,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5337981/v1/d560a01757d0a4a0459235ed.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship between frailty and major adverse cardiovascular and cerebrovascular events in Chinese older adults: the mediating effect of lipid accumulation products","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs a result of the ageing of the population and changes in national lifestyles, the morbidity and mortality of cardiovascular and cerebrovascular diseases among Chinese residents continue to rise\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. According to the Chinese Center for Cardiovascular Disease, the number of people suffering from cardiovascular and cerebrovascular diseases in China is estimated to be about 330\u0026nbsp;million. In 2020, the number of deaths from cardiovascular and cerebrovascular diseases in China was 4.58\u0026nbsp;million, an increase of 48.2% from 2005\u003csup\u003e2\u003c/sup\u003e. Cardiovascular and cerebrovascular diseases bring heavy economic burden to residents and society, and have become a serious public health problem. Therefore, it is necessary to explore the risk factors of cardiovascular and cerebrovascular diseases in depth.\u003c/p\u003e \u003cp\u003eFrailty, a state of vulnerability caused by a decline in multiple system functional reserves, is a common geriatric syndrome that encompasses physical, social, and cognitive dimensions \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Frailty increases the risk of adverse health and is a predictor of a wide range of age-related chronic diseases \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The frailty index (FI) includes the impact of physical, psychological, and social factors on the human body, reflecting the accumulation of physiological defects in multiple systems \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Studies have shown that progression of frailty increases the risk of cardiovascular disease (CVD), while recovery from frailty decreases the risk of CVD\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Frailty is also a risk factor for stroke and is significantly associated with adverse outcomes after mechanical thrombectomy (MT) \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Exploring the interrelationship between frailty and cardiovascular and cerebrovascular diseases could be valuable in guiding the improvement of patient prognosis.\u003c/p\u003e \u003cp\u003eObesity, especially central obesity, is an important risk factor for cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The prevalence of overweight and obesity among Chinese adults is estimated to be 33.3% and 14.1%, respectively, and obesity and overweight are expected to affect 800\u0026nbsp;million by 2030\u003csup\u003e11\u003c/sup\u003e. Body mass index (BMI) is a simple indicator of obesity, but it does not accurately reflect body fat distribution\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Lipid accumulation products (LAP) combine waist circumference and triglycerides to reflect the extent of body fat accumulation and visceral adiposity\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Higher LAP levels have been shown to be significantly associated with all-cause and CVD mortality, and maintaining a low LAP status may reduce the risk of death\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Cumulative LAP is positively associated with the risk of stroke, particularly ischemic stroke\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe American Heart Association (AHA) has identified Life\u0026rsquo; simple (LS7) as a heart-health intervention, which includes seven metrics such as weight management, physical activity, and lipids. Optimizing LS7 not only reduces the incidence of CVD, but also reduces the risk of frailty in old age\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. It remains unknown whether there was a moderating effect of LAP on the association of FI with major adverse cardiovascular and cerebrovascular events (MACCE). The objectives of this study were: 1) to explore the association between FI and MACCE in Chinese middle-aged and elderly people; 2) to explore whether LAP is a mediator of the above relationship. Clarifying the role of LAP in the association between FI and MACCE will provide new perspectives for more effective and precise prevention of cardiovascular and cerebrovascular diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Participants\u003c/p\u003e\n\u003cp\u003eThis study adopted a prospective design and utilized CHARLS data from 2011 and 2018. The CHARLS database, which focuses specifically on the health status of middle-aged and elderly adults in China, conducted the first national baseline survey in 2011 and completed four follow-up waves in 2013, 2015, 2018, and 2020. The present study used the 2011 data as the baseline and conducted the follow-up in 2018. Of these, 8,542 were excluded because they did not meet the following criteria: 1) Lack of available data on LAP, waist circumference, and triglycerides (n = 3911); 2) Age \u0026lt; 45 or history of stroke and heart diseases (n = 814); 3) Lack of follow-up data on cardiovascular and cerebrovascular diseases or those who were lost to follow-up (n = 3817); 4) Frailty index related indicators are not available (n = 1262). Finally, 7901 patients were enrolled in the study. (Fig.1)\u003c/p\u003e\n\u003cp\u003eData collection\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFrailty assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrailty was assessed by the FI, which was calculated by accumulating multiple age-related health deficits. In this study, we selected 28 projects for FI construction, involving disease (excluding heart disease and stroke), disability, physical function, depression, and cognition. Items 1-27 were dichotomized into 1 (defective) and 0 (non-defective). The cognitive score is a continuous variable ranging from 0 to 1. A higher cognitive score indicates a lower cognitive performance. For each participant, FI was calculated by dividing the sum of current health deficits by 28. Therefore, FI is a continuous variable from 0 to 1, with a higher FI indicating a higher level of frailty. With reference to previous studies\u003csup\u003e17\u003c/sup\u003e, FI was categorized into three categories: robust (FI \u0026le; 0.10), pre-frailty (0.10 \u0026lt;FI \u0026lt; 0.25), and frailty (FI \u0026ge; 0.25).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCalculation of the LAP\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLAP was calculated using fasting triglycerides and waist circumference as follows\u003csup\u003e10\u003c/sup\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMale LAP= [WC (cm) -65] \u0026times; [TG (mmol/L)] and\u003c/p\u003e\n\u003cp\u003eFemale LAP= [WC (cm) -58] \u0026times; [TG (mmol/L)]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFollow-up and assessment of outcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe endpoint of this study was new-onset MACCE in the 2018 follow-up population. In CHARLS, based on self-reported physician diagnosed heart disease (myocardial infarction, coronary heart disease, angina, congestive heart failure and other heart diseases) and stroke (cerebral infarction and cerebral hemorrhage). When participants were asked if they had been diagnosed with heart disease or stroke by their physicians, those who reported a diagnosis of heart disease or stroke were considered to have had MACCE, and the 2011 questionnaire was reviewed to exclude participants with a history of MACCE, and finally to specify participants with newly MACCE.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCovariates\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCovariates in the baseline survey were included: age, gender, marital status, educational level, smoking status, drinking status, location, average sleep time at night. Marital status is divided into: married and non-married (\u0026ldquo;never-married/separated/widowed\u0026rdquo;), education level is divided into: junior high school and below, high school, college and above. Smoking status was classified as never smokers and smokers (including still smoking now and those who have smoked but have now quit smoking), and drinking status was classified as never drinking, less than once a month, and more than once a month.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eContinuous variables with normal distribution were expressed by means and standard deviations (SD), while categorical variables were expressed by percentages. One-way ANOVA and chi-square tests were employed to compare the baseline characteristics. Binary logistic regression was used to test the association between FI and MACCE, and covariates including gender, age, education level, marital status, location and sleep time were considered throughout the analysis. In the mediation analysis, we used the mediation model proposed by Baron and Kenny to examine the mediating role of the baseline LAP in the relationship between the baseline FI and MACCE: 1) the relationship between the FI and the LAP was analyzed; 2) the relationship between FI and MACCE was analyzed; 3) the relationship between FI, LAP, and MACCE was analyzed with LAP as a mediating variable. Finally, we evaluated total, indirect, and direct effects with the nonparametric bootstrap method 1000 times. Forest plots were used to demonstrate the results of stratified analysis. R 4.4.1 software was used to analyze the data, and \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 was considered statistically significant (two-sided).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eGeneral Characteristics of Participants\u003c/p\u003e \u003cp\u003eDescriptive statistics for participants are shown in Table\u0026nbsp;1. A total of 7901 participants, with a mean age of 58.7 years were recruited into the study, including 3550 males and 4351 females. 90.1% of the participants had a junior high school education or below, and only 1.2% had a college level of education or higher. 93.4% of the participants lived in rural areas. At the follow-up in 2018, a total of 214 participants experienced MACCE, accounting for 2.7%. The mean LAP index at baseline was 45.9 in the MACCE group and 37.2 in the non-MACCE group. Frailty was 4.7% in the MACCE group and 1.5% in the non-MACCE group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;1 Characteristics of participants in 2011 and MACCE in 2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etotal(n\u0026thinsp;=\u0026thinsp;7901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-MACCE (7687)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMACCE (214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.7(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.6(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.0(8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender (%)\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\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\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\u003e4351(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4220(54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3550(44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3467(45.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\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 \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7119(90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6929(90.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190(88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e685(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e661(8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced professional school and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarital (%)\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 \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\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\u003e6720(85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6542(85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178(83.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1181(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1145(14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocation (%)\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 \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e525(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e506(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7374(93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7179(93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195(91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoking (%)\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 \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2975(37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2900(37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75(35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4925(62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4786(62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139(65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edrinking (%)\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 \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink but less than once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e634(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e618(8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink more than once a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1905(24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone of these\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5315(67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5163(67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152(71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esleep time (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP(mean༈SD)༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.4(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.2(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.9(45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efrailty index (%)\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e493(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e487(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;0.10 and\u0026lt;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7283(92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7085(92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198(92.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAP_Q(%)\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1976(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1946(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1975(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1939(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1975(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1909(24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1975(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1893(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82(38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: LAP, lipid accumulation products; MACCE, major adverse cardiovascular and cerebrovascular events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eRelationship between FI and MACCE\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of multifactor logistic regression analyzes of FI and new-onset MACCE. Model 1 adjusted only for the LAP, model 2 adjusted for LAP, age, and gender, and model 3 further adjusted for education, location, marital status, smoking, drinking and sleep time based on model 2. Including the FI as a categorical variable suggested that the frailty group (FI\u0026thinsp;\u0026ge;\u0026thinsp;0.25) was more likely to have MACCE with reference to the robust group (FI\u0026thinsp;\u0026le;\u0026thinsp;0.10), whereas the pre-frailty group did not show a statistical difference. The risk for MACCE increased with increasing degree of frailty (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Model 3 showed that the frailty group had the highest risk of MACCE as compared with the robust group (OR 1.07, 95% CI 1.03\u0026ndash;1.10). Analyzed as continuous variables, the FI and MACCE were consistently and significantly associated (OR 1.22, 95% CI 1.12\u0026ndash;1.33).\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of frailty with MACCE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOR (95% CIs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95% CIs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eOR (95% CIs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efrailty index(mean༈SD)༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.23(1.13\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21(1.11\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.22(1.12\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\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\u003eFI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.0(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.0(ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;0.10 and\u0026lt;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.02(0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01(0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.01(0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.07(1.04\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06(1.03\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.07(1.03\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\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\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c15\" namest=\"c1\"\u003e \u003cp\u003eNotes: Model 1, adjusted for LAP; Model 2, adjusted for LAP, age and gender; Model 3, adjusted for variables in model 2 plus education, location, marital, smoking, drinking, and sleep. Abbreviations: FI, frailty index; LAP, lipid accumulation products. \u003cem\u003eOR Odds Ratio, 95% CI 95% Confidence Interval\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMediating effects analysis\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrate the results of the mediation analysis of LAP between FI and MACCE after controlling for covariates. In the total effects regression, FI was a significant predictor of MACCE (β\u0026thinsp;=\u0026thinsp;0.045, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Moreover, FI showed a significant effect on LAP (β\u0026thinsp;=\u0026thinsp;12.21, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), whereas LAP exerted a significant effect on MACCE (β\u0026thinsp;=\u0026thinsp;11.14 P\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Bootstrap analysis further showed that the total effect of FI on MACCE was 0.194 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI 0.075\u0026ndash;0.31). The profiled-mediated effect via LAP was 0.003 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016, 95% CI 0.0003\u0026ndash;0.01). The results suggest although the mediating effect of LAP between FI and MACCE was weak, it still explained 1.7% of the total effect. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBootstrap tests for mediation models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003epaths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003ebootstrap test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebootstrap standard error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.077365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAbbreviations: LLCI, lower limit of credible interval; ULCI, upper limit of credible interval.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003ctd colspan=\"5\"\u003eStratified analyzes\u003c/td\u003e\u003c/p\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTo find out whether the effect of FI on the risk of MACCE differed between subgroups, this study performed stratified analyzes by characteristics. The effect of FI on MACCE was more pronounced among women (OR 1.27, 95% CI 1.13\u0026ndash;1.43), FI had a more significant effect on MACCE among participants in the highest quartile of LAP (OR1.43, 95% CI 1.16\u0026ndash;1.77). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis was the first study to explore the mediating role of LAP between the frailty and MACCE using data from the CHARLS. There was a significant association between frailty and LAP and MACCE, with LAP partially mediating the relationship between FI and MACCE.\u003c/p\u003e \u003cp\u003eAs the world\u0026rsquo;s population ages, frailty is becoming a common geriatric syndrome. Several studies have shown that frailty is strongly associated with the risk of CVD\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Boreskie\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003eet al included 985 female participants aged\u0026thinsp;\u0026ge;\u0026thinsp;55 years and found that frailty/pre-frailty was a risk factor for CVD. He\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e et al explored the association between frailty and CVD using three prospective cohorts and found that changes in frailty status significantly affected the risk of CVD, with progression of robust to frailty/ pre-frailty increasing the risk of CVD and vice versa. The prevalence of frailty/ pre-frailty among patients with stroke is 66.8% \u003csup\u003e20\u003c/sup\u003e. A meta-analysis that included 245,773 patients with acute ischemic stroke (AIS) treated with endovascular thrombectomy (EVT) showed that frailty patients had a higher mortality rate(OR 1.036, 95%CI 1.008\u0026ndash;1.065) and poorer functional outcomes ༈OR 1.189, 95%CI 1.043\u0026ndash;1.357༉ compared with patients without frailty \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It was also showed that at least a quarter of stroke survivors were frailty. Frailty has been shown to be associated with reduced NIHSS improvement after thrombolysis and is associated with stroke severity in elderly patients\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and frailty increases mortality in stroke survivors nearly 4-fold\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The biological mechanisms include imbalances between inflammatory and anti-inflammatory pathways, activation of oxidative stress, metabolic disorders, and vascular endothelial dysfunction \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In addition, there are multiple co-morbidities between frailty and MACCE, such as metabolic syndrome, diabetes, atherosclerosis, and cognitive disorders\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccumulation of visceral fat has been shown to\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e be more strongly associated with hypertension, diabetes, cardiovascular and cerebrovascular disease than obesity \u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. LAP is an indicator based on triglycerides and waist circumference proposed by Prof. Kahn in 2005\u003csup\u003e14\u003c/sup\u003e. LAP has been widely recognized as a good indicator of body fat distribution, especially abdominal fat accumulation\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In a study of 50,162 individuals followed for 10 years, a significant increase in all-cause mortality(HR 1.54, 95%CI 1.32\u0026ndash;1.80)and CVD mortality༈HR 1.55,95༅CI 1.16\u0026ndash;2.09༉was found in the highest quartile of LAP compared to the lowest\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In a study involving 4,3089 participants followed for 11 years, the HR for ischemic stroke in the LAP Q4 group was 1.56 (1.36\u0026ndash;1.79), whereas no statistical significance was observed for hemorrhagic stroke, which may be related to fewer cases of hemorrhagic stroke\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Fat tissue is the largest endocrine and immune organ of the human body, and excessive accumulation of visceral fat leads to overproduction of pro-inflammatory factors (e.g. IL-6, TNF-α) as well as downregulation of anti-inflammatory factors (adiponectin)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, triggering inflammation and oxidative stress, causing metabolic syndromes and vascular endothelial damage, and changes in the arterial system increase the risk of cardiovascular and cerebrovascular diseases\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe present study further explored the mediating role of LAP in the association of frailty and MACCE, and although the mechanisms are not clear, some possible explanations exist. 1) inflammation and metabolic abnormalities are the common pathological basis of both. Increased expression of various inflammatory factors (e.g., IL-6, TNF-α) has been observed in both frailty/elevated LAP populations, causing inflammation and oxidative stress, leading to extensive cell damage, which is a risk factors for a wide range of chronic diseases and death\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Both frailty/ elevated LAP can lead to metabolic disorders (lipid metabolism, insulin resistance) and ultimately lead to vascular endothelial damage and an increased risk of cardiovascular and cerebrovascular diseases\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. 2) There are multiple co-morbid foundations. Studies have shown that both are closely associated with a variety of chronic diseases, such as hypertension, diabetes, dyslipidemia, atherosclerosis and nutritional disorders, which are important risk factors for cardiovascular and cerebrovascular diseases\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. 3) Reasonable exercise can reduce body fat, improve muscle mass and bone density\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, and prevent abnormal accumulation of lipid and reduce the incidence of related chronic diseases. The core manifestation of frailty is the decrease of physical activity ability\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, which increases the risk of abnormal lipid accumulation, leading to an increased risk of cardiovascular and cerebrovascular disease.\u003c/p\u003e \u003cp\u003eAfter stratifying by gender and adjusting for relevant covariates, frailty was associated with a higher risk of MACCE for women, but not for men. Previous study has suggested that visceral adiposity index was a risk factor for women, but not for men\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. A possible explanation is that estrogen plays an important protective, and as estrogen levels in middle-aged and elderly women decrease, they are more susceptible to metabolic disorders\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. At the same time, some studies have reached the different conclusions. Tan\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e et al. found that the effect of metabolic disorders on CVD did not show gender differences, this may due to the lower volume of visceral fat in women.\u003c/p\u003e \u003cp\u003eOur findings may have some implications for the prevention of cardiovascular and cerebrovascular diseases. First, community-based healthcare institutions should regularly assess the frailty of elderly population. Second, instructing frailty patients to have a reasonable diet and exercise to control LAP at a low level may be an effective measure to reduce cardiovascular and cerebrovascular diseases. This study still has some limitations. First, indicators of frailty and LAP were collected at baseline, which could not clarify the causal relationship between the independent and intermediate variables, and future longitudinal studies are needed to clarify the causal relationship between the two. Second, data on frailty and stroke history obtained through self-report questionnaires may be subject to reporting bias. Third, although the study controlled for several covariates, potential confounding factors may still affect the stability of the conclusions. Fourth, we excluded samples with missing values in exposure, outcome, and mediation variables, which probably underestimated the incidence of MACCE. Individuals who were excluded from missing values, loss to follow-up, or death may be less healthy than those included in the analysis, which may seriously underestimate the association between frailty and MACCE, as well as the mediating role.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study used data from the CHARLS 2011 and 2018 waves to explore frailty not only directly affecting MACCE but also indirectly through LAP. Measures to reduce the rate of cardiovascular and cerebrovascular diseases in middle-aged and elderly adults include improving frailty and reducing LAP. There is a need to routinely screen middle-aged and elderly adults for frailty and LAP and encourage healthy diet and sensible exercise, which may reduce the incidence of cardiovascular and cerebrovascular diseases and promote healthy aging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is grateful to the research team and all participants of China Health and Retirement Longitudinal Study (CHARLS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to the work reported. Zhoucheng Kang: Conception and design of the study; Writing the original draft. Yongli Ye and Hao Xiao: Analyzed and interpreted the data. Lingling Liu: Funding acquisition, Writing, Review and Editing. All the authors have approved the manuscript to be submitted, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Luoyang Science and Technology Program Project Fund (2302022Y).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are accessible to be downloaded publicly at https://charls.charlsdata.com/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Informed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS study received ethical approval from the Beijing University Biomedical Ethics Review Board (IRB00001052-11015), and all participants provided written informed consent. The study methodology was carried out in accordance with approved guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang H, Zhang H, Zou Z. Changing profiles of cardiovascular disease and risk factors in China: a secondary analysis for the Global Burden of Disease Study 2019. Chin Med J. 2023;136:2431\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Liu Y, Liu J, et al. Mortality and years of life lost of cardiovascular diseases in China, 2005\u0026ndash;2020: Empirical evidence from national mortality surveillance system. Int J Cardiol. 2021;340:105\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. Frailty: implications for clinical practice and public health. Lancet (London England). 2019;394:1365\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCesari M, Calvani R, Marzetti E. Frailty in Older Persons. Clin Geriatr Med. 2017;33:293\u0026ndash;303.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSi H, Jin Y, Qiao X, Tian X, Liu X, Wang C. Predictive performance of 7 frailty instruments for short-term disability, falls and hospitalization among Chinese community-dwelling older adults: A prospective cohort study. Int J Nurs Stud. 2021;117:103875.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei J, Wang J, Chen J, Yang K, Liu N. Stroke and frailty index: a two-sample Mendelian randomisation study. Aging Clin Exp Res. 2024;36:114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe D, Wang Z, Li J, et al. Changes in frailty and incident cardiovascular disease in three prospective cohorts. 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Front Endocrinol. 2022;13:959860.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aoph","sideBox":"Learn more about [Archives of Public Health](http://archpublichealth.biomedcentral.com/)","snPcode":"13690","submissionUrl":"https://submission.nature.com/new-submission/13690/3","title":"Archives of Public Health","twitterHandle":"@Archpubhealth","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"frailty, lipid accumulation products, major adverse cardiovascular and cerebrovascular events, older adults","lastPublishedDoi":"10.21203/rs.3.rs-5337981/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5337981/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious studies have proven the relationship between frailty and major adverse cardiovascular and cerebrovascular events (MACCE). However, the potential mechanisms need to be further explored. This study aimed to investigate the mediating effect of lipid accumulation products (LAP) in the relationship between frailty and MACCE.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e This study recruited 7901 participants aged 45 and above from wave 2011 and 2018 of the China Longitudinal Study of Health and Retirement (CHARLS). Logistic regression models were employed to examine the relationship between frailty and MACCE and the mediating effects of LAP, using the bootstrap method to confirm path effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrailty group presented the highest risk of MACCE (OR 1.07, 95% CI 1.03\u0026ndash;1.10). Frailty directly impacted MACCE (β\u0026thinsp;=\u0026thinsp;0.045, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Frailty had a significant effect on LAP (β\u0026thinsp;=\u0026thinsp;12.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while LAP had a significant impact on MACCE (β\u0026thinsp;=\u0026thinsp;11.14, p\u0026thinsp;=\u0026thinsp;0.014). The mediation effect of LAP accounted for 1.7% of the total effect regarding the frailty with MACCE.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLAP mediate the relationship between frailty and MACCE. Our findings suggest that instructing frailty patients to have a reasonable diet and exercise to control LAP at a low level may be an effective measure to reduce MACCE.\u003c/p\u003e","manuscriptTitle":"The relationship between frailty and major adverse cardiovascular and cerebrovascular events in Chinese older adults: the mediating effect of lipid accumulation products","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 00:30:59","doi":"10.21203/rs.3.rs-5337981/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-30T22:10:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-30T21:55:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-29T07:09:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47937590423722071482339257780631103811","date":"2024-11-26T16:08:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226524039500462393041453514887320827086","date":"2024-11-26T15:26:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-22T02:01:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-30T10:11:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-30T10:11:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Public Health","date":"2024-10-26T14:24:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aoph","sideBox":"Learn more about [Archives of Public Health](http://archpublichealth.biomedcentral.com/)","snPcode":"13690","submissionUrl":"https://submission.nature.com/new-submission/13690/3","title":"Archives of Public Health","twitterHandle":"@Archpubhealth","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af94d36b-52a3-425e-8b4e-d0153ead6841","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T16:05:49+00:00","versionOfRecord":{"articleIdentity":"rs-5337981","link":"https://doi.org/10.1186/s13690-025-01520-8","journal":{"identity":"archives-of-public-health","isVorOnly":false,"title":"Archives of Public Health"},"publishedOn":"2025-02-12 15:57:25","publishedOnDateReadable":"February 12th, 2025"},"versionCreatedAt":"2024-12-02 00:30:59","video":"","vorDoi":"10.1186/s13690-025-01520-8","vorDoiUrl":"https://doi.org/10.1186/s13690-025-01520-8","workflowStages":[]},"version":"v1","identity":"rs-5337981","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5337981","identity":"rs-5337981","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}