Age-specific Modifiable Risk Factor Profiles for Cardiovascular Disease and All-cause Mortality: a Nationwide, Population-based, Prospective Cohort Study

Overview

Journal Lancet Reg Health West Pac

Date 2022 Jan 10

PMID 35005664

Authors

Tiange Wang

Zhiyun Zhao

Xuefeng Yu

Tianshu Zeng

Min Xu

Yu Xu

Ruying Hu

Gang Chen

Qing Su

Yiming Mu

Lulu Chen

Xulei Tang

Li Yan

Guijun Qin

Qin Wan

Zhengnan Gao

Guixia Wang

Feixia Shen

Zuojie Luo

Yingfen Qin

Li Chen

Yanan Huo

Qiang Li

Zhen Ye

Yinfei Zhang

Chao Liu

Youmin Wang

Shengli Wu

Tao Yang

Huacong Deng

Jiajun Zhao

Yiping Xu

Mian Li

Yuhong Chen

Shuangyuan Wang

Guang Ning

Yufang Bi

Lixin Shi

Jieli Lu

Weiqing Wang

Affiliations

Soon will be listed here.

Abstract

Background: National investigations on age-specific modifiable risk factor profiles for cardiovascular disease (CVD) and mortality are scarce in China, the country that is experiencing a huge cardiometabolic burden exacerbated by population ageing.

Methods: This is a nationwide prospective cohort study of 193,846 adults in the China Cardiometabolic Disease and Cancer Cohort Study, 2011-2016. Among 139,925 participants free from CVD at baseline, we examined hazard ratios and population-attributable risk percentages (PAR%s) for CVD and all-cause mortality attributable to 12 modifiable socioeconomic, psychosocial, lifestyle, and metabolic risk factors by four age groups (40-<55 years, 55-<65 years, 65-<75 years, and ≥75 years).

Findings: Metabolic risk factors accounted for 52·4%, 47·2%, and 37·8% of the PAR% for CVD events in participants aged 40-<55 years, 55-<65 years, and 65-<75 years, respectively, with hypertension being the largest risk factor. While in participants aged ≥75 years, lifestyle risk factors contributed to 34·0% of the PAR% for CVD, with inappropriate sleep duration being the predominant risk factor. Most deaths were attributed to metabolic risk factors (PAR% 25·3%) and lifestyle risk factors (PAR% 24·6%) in participants aged 40-<55 years, with unhealthy diet and diabetes being the main risk factors. While in participants aged ≥55 years, most deaths were attributed to lifestyle risk factors (PAR% 26·6%-41·0%) and socioeconomic and psychosocial risk factors (PAR% 26·1%-27·7%). In participants aged ≥75 years, lifestyle risk factors accounted for 41·0% of the PAR% for mortality, with inappropriate sleep duration being the leading risk factor.

Interpretation: We identified age-specific modifiable risk profiles for CVD and all-cause mortality in Chinese adults, with remarkable differences between adults aged ≥75 years and their younger counterparts.

Funding: National Natural Science Foundation of China.

Citing Articles

Comparisons of Post-Load Glucose at Different Time Points for Identifying High Risks of MASLD Progression.

Teng L, Luo L, Sun Y, Wang W, Dong Z, Cao X Nutrients. 2025; 17(1.

PMID: 39796589 PMC: 11723153. DOI: 10.3390/nu17010152.

Association between normal weight obesity and comorbidities and events of cardiovascular diseases among adults in South China.

Ma M, Lv D, Wu X, Chen Y, Dai S, Luo Y PLoS One. 2025; 20(1):e0316346.

PMID: 39777912 PMC: 11709311. DOI: 10.1371/journal.pone.0316346.

Adjustment of the ACEF score by the triglyceride glucose index improves the prediction of clinical outcomes in patients undergoing percutaneous coronary intervention.

Feng Q, Jiang M, Peng X, He H, Jia D, Feng J Cardiovasc Diabetol. 2024; 23(1):440.

PMID: 39695601 PMC: 11657121. DOI: 10.1186/s12933-024-02534-2.

The comparison of the prevalence, awareness, treatment and control of hypertension among adults along the three provinces of the Lancang-Mekong River countries-China, Laos and Cambodia.

Wang H, He L, Ma M, Tang M, Lu J, Sikanha L Arch Public Health. 2024; 82(1):224.

PMID: 39593170 PMC: 11590357. DOI: 10.1186/s13690-024-01458-3.

Patient-specific multifactorial mortality risk assessment using classification and regression tree analysis in the context of ambulatory blood pressure monitoring.

Cetin B, Hasbal N, Cevik E, Sahin O, Akyol M, Kalay Z J Nephrol. 2024; 38(1):197-205.

PMID: 39503965 DOI: 10.1007/s40620-024-02128-x.

References

Li Y, He Y, Zhai F, Yang X, Hu X, Zhao W . [Comparison of assessment of food intakes by using 3 dietary survey methods]. Zhonghua Yu Fang Yi Xue Za Zhi. 2006; 40(4):273-80. View

Wang C, Bangdiwala S, Rangarajan S, Lear S, Alhabib K, Mohan V . Association of estimated sleep duration and naps with mortality and cardiovascular events: a study of 116 632 people from 21 countries. Eur Heart J. 2018; 40(20):1620-1629. PMC: 6528160. DOI: 10.1093/eurheartj/ehy695. View

Ainsworth B, Haskell W, Leon A, Jacobs Jr D, Montoye H, Sallis J . Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc. 1993; 25(1):71-80. DOI: 10.1249/00005768-199301000-00011. View

Rich M, Chyun D, Skolnick A, Alexander K, Forman D, Kitzman D . Knowledge Gaps in Cardiovascular Care of the Older Adult Population: A Scientific Statement From the American Heart Association, American College of Cardiology, and American Geriatrics Society. J Am Coll Cardiol. 2016; 67(20):2419-2440. PMC: 7733163. DOI: 10.1016/j.jacc.2016.03.004. View

Mahmood S, Levy D, Vasan R, Wang T . The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet. 2013; 383(9921):999-1008. PMC: 4159698. DOI: 10.1016/S0140-6736(13)61752-3. View

Zhou M, Wang H, Zhu J, Chen W, Wang L, Liu S . Cause-specific mortality for 240 causes in China during 1990-2013: a systematic subnational analysis for the Global Burden of Disease Study 2013. Lancet. 2015; 387(10015):251-72. DOI: 10.1016/S0140-6736(15)00551-6. View

Lavie C, Milani R, Ventura H . Obesity and cardiovascular disease: risk factor, paradox, and impact of weight loss. J Am Coll Cardiol. 2009; 53(21):1925-32. DOI: 10.1016/j.jacc.2008.12.068. View

Kroenke K, Spitzer R, Williams J . The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001; 16(9):606-13. PMC: 1495268. DOI: 10.1046/j.1525-1497.2001.016009606.x. View

Lu J, He J, Li M, Tang X, Hu R, Shi L . Predictive Value of Fasting Glucose, Postload Glucose, and Hemoglobin A on Risk of Diabetes and Complications in Chinese Adults. Diabetes Care. 2019; 42(8):1539-1548. DOI: 10.2337/dc18-1390. View

10.

Madhavan M, Gersh B, Alexander K, Granger C, Stone G . Coronary Artery Disease in Patients ≥80 Years of Age. J Am Coll Cardiol. 2018; 71(18):2015-2040. DOI: 10.1016/j.jacc.2017.12.068. View

11.

Strand L, Tsai M, Gunnell D, Janszky I, Wen C, Chang S . Self-reported sleep duration and coronary heart disease mortality: A large cohort study of 400,000 Taiwanese adults. Int J Cardiol. 2016; 207:246-51. DOI: 10.1016/j.ijcard.2016.01.044. View

12.

. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018; 392(10159):1736-1788. PMC: 6227606. DOI: 10.1016/S0140-6736(18)32203-7. View

13.

Bell S, Daskalopoulou M, Rapsomaniki E, George J, Britton A, Bobak M . Association between clinically recorded alcohol consumption and initial presentation of 12 cardiovascular diseases: population based cohort study using linked health records. BMJ. 2017; 356:j909. PMC: 5594422. DOI: 10.1136/bmj.j909. View

14.

Yin J, Jin X, Shan Z, Li S, Huang H, Li P . Relationship of Sleep Duration With All-Cause Mortality and Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies. J Am Heart Assoc. 2017; 6(9). PMC: 5634263. DOI: 10.1161/JAHA.117.005947. View

15.

Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W . Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019; 394(10204):1145-1158. PMC: 6891889. DOI: 10.1016/S0140-6736(19)30427-1. View

16.

Wang T, Lu J, Shi L, Chen G, Xu M, Xu Y . Association of insulin resistance and β-cell dysfunction with incident diabetes among adults in China: a nationwide, population-based, prospective cohort study. Lancet Diabetes Endocrinol. 2019; 8(2):115-124. DOI: 10.1016/S2213-8587(19)30425-5. View

17.

Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P . Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet. 2019; 395(10226):795-808. PMC: 8006904. DOI: 10.1016/S0140-6736(19)32008-2. View

18.

Fuchs F, Whelton P . High Blood Pressure and Cardiovascular Disease. Hypertension. 2019; 75(2):285-292. PMC: 10243231. DOI: 10.1161/HYPERTENSIONAHA.119.14240. View

19.

. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020; 396(10258):1223-1249. PMC: 7566194. DOI: 10.1016/S0140-6736(20)30752-2. View

20.

Sarnak M, Amann K, Bangalore S, Cavalcante J, Charytan D, Craig J . Chronic Kidney Disease and Coronary Artery Disease: JACC State-of-the-Art Review. J Am Coll Cardiol. 2019; 74(14):1823-1838. DOI: 10.1016/j.jacc.2019.08.1017. View