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      Effects of potential risk factors on the development of cardiometabolic multimorbidity and mortality among the elders in China

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          Abstract

          Objectives

          To examine the impact of demographic, socioeconomic, and behavioral factors on the development of cardiometabolic multimorbidity and mortality in Chinese elders.

          Methods

          Data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2002–2018 was used in the study. Cardiometabolic multimorbidity was defined as the presence of two or more cardiometabolic disorders, such as hypertension, diabetes, cardiovascular disease (CVD), heart disease, or stroke. Cox regression model and multi-state Markov model were developed to evaluate the association of the study factors with the progression of cardiometabolic conditions and mortality. The outcomes included three states (first cardiometabolic disease, cardiometabolic multimorbidity, and all-cause mortality) and five possible transitions among the three states.

          Results

          Of the 13,933 eligible individuals, 7,917 (56.8%) were female, and 9,540 (68.50%) were over 80 years old. 2,766 (19.9%) participants had their first cardiometabolic disease, 975 (7.0%) participants suffered from cardiometabolic multimorbidity, and 9,365 (67.2%) participants died. The progression to cardiometabolic multimorbidity was positively associated with being female (HR = 1.42; 95%CI, 1.10 − 1.85), living in the city (HR = 1.41; 95%CI, 1.04 − 1.93), overweight (HR = 1.43; 95%CI, 1.08 − 1.90), and obesity (HR = 1.75; 95% CI, 1.03 − 2.98). A higher risk for the first cardiometabolic disease was associated with being female (HR = 1.26; 95% CI, 1.15 − 1.39), higher socioeconomic status (SES, HR = 1.17; 95%CI, 1.07 − 1.28), lack of regular physical activity (HR = 1.13; 95%CI, 1.04 − 1.23), smoking (HR = 1.20; 95%CI, 1.08 − 1.33), ≤ 5 h sleep time (HR = 1.15; 95%CI, 1.02 − 1.30), overweight (HR = 1.48; 95% CI, 1.32 − 1.66), and obesity (HR = 1.34; 95%CI, 1.06 − 1.69). It also should be noted that not in marriage, lower SES and unhealthy behavioral patterns were risk factors for mortality.

          Conclusion

          This study emphasized the importance of lifestyle and SES in tackling the development of cardiometabolic conditions among Chinese elders and provided a reference for policy-makers to develop a tailored stage-specific intervention strategy.

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          Most cited references52

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          Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

          Summary Background How long one lives, how many years of life are spent in good and poor health, and how the population’s state of health and leading causes of disability change over time all have implications for policy, planning, and provision of services. We comparatively assessed the patterns and trends of healthy life expectancy (HALE), which quantifies the number of years of life expected to be lived in good health, and the complementary measure of disability-adjusted life-years (DALYs), a composite measure of disease burden capturing both premature mortality and prevalence and severity of ill health, for 359 diseases and injuries for 195 countries and territories over the past 28 years. Methods We used data for age-specific mortality rates, years of life lost (YLLs) due to premature mortality, and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to calculate HALE and DALYs from 1990 to 2017. We calculated HALE using age-specific mortality rates and YLDs per capita for each location, age, sex, and year. We calculated DALYs for 359 causes as the sum of YLLs and YLDs. We assessed how observed HALE and DALYs differed by country and sex from expected trends based on Socio-demographic Index (SDI). We also analysed HALE by decomposing years of life gained into years spent in good health and in poor health, between 1990 and 2017, and extra years lived by females compared with males. Findings Globally, from 1990 to 2017, life expectancy at birth increased by 7·4 years (95% uncertainty interval 7·1–7·8), from 65·6 years (65·3–65·8) in 1990 to 73·0 years (72·7–73·3) in 2017. The increase in years of life varied from 5·1 years (5·0–5·3) in high SDI countries to 12·0 years (11·3–12·8) in low SDI countries. Of the additional years of life expected at birth, 26·3% (20·1–33·1) were expected to be spent in poor health in high SDI countries compared with 11·7% (8·8–15·1) in low-middle SDI countries. HALE at birth increased by 6·3 years (5·9–6·7), from 57·0 years (54·6–59·1) in 1990 to 63·3 years (60·5–65·7) in 2017. The increase varied from 3·8 years (3·4–4·1) in high SDI countries to 10·5 years (9·8–11·2) in low SDI countries. Even larger variations in HALE than these were observed between countries, ranging from 1·0 year (0·4–1·7) in Saint Vincent and the Grenadines (62·4 years [59·9–64·7] in 1990 to 63·5 years [60·9–65·8] in 2017) to 23·7 years (21·9–25·6) in Eritrea (30·7 years [28·9–32·2] in 1990 to 54·4 years [51·5–57·1] in 2017). In most countries, the increase in HALE was smaller than the increase in overall life expectancy, indicating more years lived in poor health. In 180 of 195 countries and territories, females were expected to live longer than males in 2017, with extra years lived varying from 1·4 years (0·6–2·3) in Algeria to 11·9 years (10·9–12·9) in Ukraine. Of the extra years gained, the proportion spent in poor health varied largely across countries, with less than 20% of additional years spent in poor health in Bosnia and Herzegovina, Burundi, and Slovakia, whereas in Bahrain all the extra years were spent in poor health. In 2017, the highest estimate of HALE at birth was in Singapore for both females (75·8 years [72·4–78·7]) and males (72·6 years [69·8–75·0]) and the lowest estimates were in Central African Republic (47·0 years [43·7–50·2] for females and 42·8 years [40·1–45·6] for males). Globally, in 2017, the five leading causes of DALYs were neonatal disorders, ischaemic heart disease, stroke, lower respiratory infections, and chronic obstructive pulmonary disease. Between 1990 and 2017, age-standardised DALY rates decreased by 41·3% (38·8–43·5) for communicable diseases and by 49·8% (47·9–51·6) for neonatal disorders. For non-communicable diseases, global DALYs increased by 40·1% (36·8–43·0), although age-standardised DALY rates decreased by 18·1% (16·0–20·2). Interpretation With increasing life expectancy in most countries, the question of whether the additional years of life gained are spent in good health or poor health has been increasingly relevant because of the potential policy implications, such as health-care provisions and extending retirement ages. In some locations, a large proportion of those additional years are spent in poor health. Large inequalities in HALE and disease burden exist across countries in different SDI quintiles and between sexes. The burden of disabling conditions has serious implications for health system planning and health-related expenditures. Despite the progress made in reducing the burden of communicable diseases and neonatal disorders in low SDI countries, the speed of this progress could be increased by scaling up proven interventions. The global trends among non-communicable diseases indicate that more effort is needed to maximise HALE, such as risk prevention and attention to upstream determinants of health. Funding Bill & Melinda Gates Foundation.
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            2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease

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              Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults--study on optimal cut-off points of body mass index and waist circumference in Chinese adults.

              For prevention of obesity in Chinese population, it is necessary to define the optimal range of healthy weight and the appropriate cut-off points of BMI and waist circumference for Chinese adults. The Working Group on Obesity in China under the support of International Life Sciences Institute Focal point in China organized a meta-analysis on the relation between BMI, waist circumference and risk factors of related chronic diseases (e.g., high diabetes, diabetes mellitus, and lipoprotein disorders). 13 population studies in all met the criteria for enrollment, with data of 239,972 adults (20-70 year) surveyed in the 1990s. Data on waist circumference was available for 111,411 persons and data on serum lipids and glucose were available for more than 80,000. The study populations located in 21 provinces, municipalities and autonomous regions in mainland China as well as in Taiwan. Each enrolled study provided data according to a common protocol and uniform format. The Center for data management in Department of Epidemiology, Fu Wai Hospital was responsible for statistical analysis. The prevalence of hypertension, diabetes, dyslipidemia and clustering of risk factors all increased with increasing levels of BMI or waist circumference. BMI at 24 with best sensitivity and specificity for identification of the risk factors, was recommended as the cut-off point for overweight, BMI at 28 which may identify the risk factors with specificity around 90% was recommended as the cut-off point for obesity. Waist circumference beyond 85 cm for men and beyond 80 cm for women were recommended as the cut-off points for central obesity. Analysis of population attributable risk percent illustrated that reducing BMI to normal range ( or = 28) with drugs could prevent 15%-17% clustering of risk factors. The waist circumference controlled under 85 cm for men and under 80 cm for women, could prevent 47%-58% clustering of risk factors. According to these, a classification of overweight and obesity for Chinese adults is recommended.
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                Author and article information

                Contributors
                Journal
                Front Cardiovasc Med
                Front Cardiovasc Med
                Front. Cardiovasc. Med.
                Frontiers in Cardiovascular Medicine
                Frontiers Media S.A.
                2297-055X
                09 September 2022
                2022
                : 9
                : 966217
                Affiliations
                [1] 1Department of Epidemiology and Health Statistics, School of Public Health, Xi’an Jiaotong University Health Science Center , Xi’an, China
                [2] 2Department of Pediatrics, The Second Affiliated Hospital of Xi’an Jiaotong University , Xi’an, China
                [3] 3Shaanxi Key Laboratory of Ischemic Cardiovascular Disease, Shaanxi Key Laboratory of Brain Disorders, Institute of Basic and Translational Medicine, Xi’an Medical University , Xi’an, China
                [4] 4Biostatistics Unit, Department of Clinical Sciences, Liverpool School of Tropical Medicine , Liverpool, United Kingdom
                Author notes

                Edited by: Xiaoqiang Tang, Sichuan University, China

                Reviewed by: Changle Li, Inner Mongolia Medical University, China; Ke Wan, Sichuan University, China; Xiaoxu Xie, Fujian Medical University, China

                *Correspondence: Leilei Pei, pll_paper@ 123456126.com

                This article was submitted to Cardiovascular Metabolism, a section of the journal Frontiers in Cardiovascular Medicine

                Article
                10.3389/fcvm.2022.966217
                9502033
                36158847
                bcab4e2b-0e99-47fa-a490-89e03afc4654
                Copyright © 2022 Zhang, Duan, Rong, Dang, Yan, Zhao, Chen, Zhou, Chen, Wang and Pei.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 June 2022
                : 16 August 2022
                Page count
                Figures: 2, Tables: 3, Equations: 0, References: 53, Pages: 13, Words: 7984
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 72174167
                Funded by: Natural Science Foundation of Shaanxi Province, doi 10.13039/501100007128;
                Categories
                Cardiovascular Medicine
                Original Research

                multi-state markov model,cardiometabolic disease,multimorbidity,economic status,behavior lifestyle

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