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Ideal cardiovascular health metrics and life expectancy free of cardiovascular diseases: a prospective cohort study

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Abstract

Objectives

Whether cardiovascular health (CVH) metrics impact longevity with and without cardiovascular diseases (CVDs) has not been well established. This study aimed to investigate the association between CVH metrics and life expectancy in participants free of CVD events. We hypothesized that ideal CVH status was associated with increased life expectancy and assessed the effect of CVH status as a prevention target of longevity in the framework of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

A total of 92,795 participants in the Kailuan study were examined and thereafter followed up until 2020. We considered three transitions (from non-CVD events to incident CVD events, from non-CVD events to mortality, and from CVD events to mortality). The multistate lifetable method was applied to estimate the life expectancy.

Results

During a median follow-up of 13 years, 12,541 (13.51%) deaths occurred. Compared with poor CVH, ideal CVH attenuated the risk of incident CVD events and mortality without CVD events by approximately 58% and 27%, respectively. Women with ideal CVH at age 35 had a 5.00 (3.23–6.77) year longer life expectancy free of CVD events than did women with poor CVH metrics. Among men, ideal CVH was associated with a 6.74 (5.55–7.93) year longer life expectancy free of CVD events.

Conclusion

An ideal CVH status is associated with a lower risk of premature mortality and a longer life expectancy, either in the general population or in CVD patients, which are cost-effective ways for personalized medicine of potential CVD patients. Our findings suggest that the promotion of a higher CVH score or ideal CVH status would result in reduced burdens of CVD events and extended disease-free life expectancy, which offered an accurate prediction for primary care following the concept of PPPM/3PM.

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Data availability

Data are available on reasonable request from the corresponding author.

Abbreviations

AHA :

American Heart Association

AF :

atrial fibrillation

BMI :

body mass index

BP :

blood pressure

CI :

confidence interval

CVD :

cardiovascular disease

CVH :

cardiovascular health

DBP :

diastolic blood pressure

FBG :

fasting blood glucose

HF :

heart failure

HRs :

hazard ratios

IQR :

interquartile range

MI :

myocardial infarction

PPPM/3PM :

predictive, preventive, and personalized medicine

SBP :

systolic blood pressure

SPACE :

Stochastic Population Analysis for Complex Events

TC :

total cholesterol

References

  1. Cardiovascular diseases (CVDs) [https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)]

  2. Golubnitschaja O, Costigliola V. General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive, Preventive and Personalised Medicine. EPMA J. 2012;3(1):14.

    PubMed  PubMed Central  Google Scholar 

  3. Golubnitschaja O, Watson ID, Topic E, Sandberg S, Ferrari M, Costigliola V. Position paper of the EPMA and EFLM: a global vision of the consolidated promotion of an integrative medical approach to advance health care. EPMA J. 2013;4(1):12.

    PubMed  PubMed Central  Google Scholar 

  4. Golubnitschaja O, Baban B, Boniolo G, Wang W, Bubnov R, Kapalla M, et al. Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016. EPMA J. 2016;7(1):23.

    PubMed  PubMed Central  Google Scholar 

  5. Golubnitschaja O, Kinkorova J, Costigliola V. Predictive, preventive and personalised medicine as the hardcore of 'Horizon 2020': EPMA position paper. EPMA J. 2014;5(1):6.

    PubMed  PubMed Central  Google Scholar 

  6. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation. 2010;121(4):586–613.

    PubMed  Google Scholar 

  7. Ding X, Fang W, Yuan X, Seery S, Wu Y, Chen S, et al. Associations between healthy lifestyle trajectories and the incidence of cardiovascular disease with all-cause mortality: a large, prospective, chinese cohort study. Front Cardiovasc Med. 2021;8:790497.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Ommerborn MJ, Blackshear CT, Hickson DA, Griswold ME, Kwatra J, Djoussé L, et al. Ideal cardiovascular health and incident cardiovascular events: the Jackson Heart study. Am J Prev Med. 2016;51(4):502–6.

    PubMed  PubMed Central  Google Scholar 

  9. Zuo Y, Li H, Chen S, Tian X, Mo D, Wu S, et al. Joint association of modifiable lifestyle and metabolic health status with incidence of cardiovascular disease and all-cause mortality: a prospective cohort study. Endocrine. 2022;75(1):82–91.

    CAS  PubMed  Google Scholar 

  10. Guo L, Zhang S. Association between ideal cardiovascular health metrics and risk of cardiovascular events or mortality: a meta-analysis of prospective studies. Clin Cardiol. 2017;40(12):1339–46.

    PubMed  PubMed Central  Google Scholar 

  11. Ramírez-Vélez R, Saavedra JM, Lobelo F, Celis-Morales CA, Pozo-Cruz BD, García-Hermoso A. Ideal cardiovascular health and incident cardiovascular disease among adults: a systematic review and meta-analysis. Mayo Clin Proc. 2018;93(11):1589–99.

    PubMed  Google Scholar 

  12. Zhou L, Zhao L, Wu Y, Wu Y, Gao X, Li Y, et al. Ideal cardiovascular health metrics and its association with 20-year cardiovascular morbidity and mortality in a Chinese population. J Epidemiol Community Health. 2018;72(8):752–8.

    PubMed  Google Scholar 

  13. Gao B, Wang F, Zhu M, Wang J, Zhou M, Zhang L, et al. Cardiovascular health metrics and all-cause mortality and mortality from major non-communicable chronic diseases among Chinese adult population. Int J Cardiol. 2020;313:123–8.

    PubMed  Google Scholar 

  14. Hadaegh F, Hosseinpour-Niazi S, Deravi N, Hasheminia M, Moslehi N, Toreyhi H, et al. Ideal cardiovascular health status and risk of cardiovascular disease and all-cause mortality: over a decade of follow-up in the Tehran lipid and glucose study. Front Cardiovasc Med. 2022;9:898681.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Dong Y, Hao G, Wang Z, Wang X, Chen Z, Zhang L. Ideal cardiovascular health status and risk of cardiovascular disease or all-cause mortality in Chinese middle-aged population. Angiology. 2019;70(6):523–9.

    PubMed  Google Scholar 

  16. Manuel DG, Perez R, Sanmartin C, Taljaard M, Hennessy D, Wilson K, et al. Measuring burden of unhealthy behaviours using a multivariable predictive approach: life expectancy lost in Canada attributable to smoking, alcohol, physical inactivity, and diet. PLoS Med. 2016;13(8):e1002082.

    PubMed  PubMed Central  Google Scholar 

  17. Pan XF, Li Y, Franco OH, Yuan JM, Pan A, Koh WP. Impact of combined lifestyle factors on all-cause and cause-specific mortality and life expectancy in Chinese: the Singapore Chinese health study. J Gerontol A Biol Sci Med Sci. 2020;75(11):2193–9.

    PubMed  Google Scholar 

  18. Li Y, Pan A, Wang DD, Liu X, Dhana K, Franco OH, et al. Impact of healthy lifestyle factors on life expectancies in the US population. Circulation. 2018;138(4):345–55.

    PubMed  PubMed Central  Google Scholar 

  19. Sun Q, Yu D, Fan J, Yu C, Guo Y, Pei P, et al. Healthy lifestyle and life expectancy at age 30 years in the Chinese population: an observational study. Lancet Public Health. 2022;7(12):e994–e1004.

    PubMed  Google Scholar 

  20. Li Y, Schoufour J, Wang DD, Dhana K, Pan A, Liu X, et al. Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort study. BMJ. 2020;368:l6669.

    PubMed  PubMed Central  Google Scholar 

  21. Leskinen T, Stenholm S, Aalto V, Head J, Kivimäki M, Vahtera J. Physical activity level as a predictor of healthy and chronic disease-free life expectancy between ages 50 and 75. Age Ageing. 2018;47(3):423–9.

    PubMed  Google Scholar 

  22. Sun C, Li K, Xu H, Wang X, Qin P, Wang S, et al. Association of healthy lifestyle score with all-cause mortality and life expectancy: a city-wide prospective cohort study of cancer survivors. BMC Med. 2021;19(1):158.

    PubMed  PubMed Central  Google Scholar 

  23. Dhana K, Franco OH, Ritz EM, Ford CN, Desai P, Krueger KR, et al. Healthy lifestyle and life expectancy with and without Alzheimer's dementia: population based cohort study. BMJ. 2022;377:e068390.

    PubMed  PubMed Central  Google Scholar 

  24. Wu Z, Guo Z, Zheng Y, Wang Y, Zhang H, Pan H, et al. IgG N-glycosylation cardiovascular age tracks cardiovascular risk beyond calendar age. Engineering. 2023;

  25. Xu C, Zhang P, Cao Z. Cardiovascular health and healthy longevity in people with and without cardiometabolic disease: a prospective cohort study. EClinicalMedicine. 2022;45:101329.

    PubMed  PubMed Central  Google Scholar 

  26. Wang L, Song L, Li D, Zhou Z, Chen S, Yang Y, et al. Ideal cardiovascular health metric and its change with lifetime risk of cardiovascular diseases: a prospective cohort study. J Am Heart Assoc. 2021;10(22):e022502.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang A, Wu J, Zhou Y, Guo X, Luo Y, Wu S, et al. Measures of adiposity and risk of stroke in China: a result from the Kailuan study. PLoS One. 2013;8(4):e61665.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Wu S, Huang Z, Yang X, Li S, Zhao H, Ruan C, et al. Cardiovascular events in a prehypertensive Chinese population: four-year follow-up study. Int J Cardiol. 2013;167(5):2196–9.

    PubMed  Google Scholar 

  29. Wu S, Li Y, Jin C, Yang P, Li D, Li H, et al. Intra-individual variability of high-sensitivity C-reactive protein in Chinese general population. Int J Cardiol. 2012;157(1):75–9.

    CAS  PubMed  Google Scholar 

  30. Kwon H, Yun JM, Park JH, Cho BL, Han K, Joh HK, et al. Incidence of cardiovascular disease and mortality in underweight individuals. J Cachexia Sarcopenia Muscle. 2021;12(2):331–8.

    PubMed  PubMed Central  Google Scholar 

  31. Kee CC, Sumarni MG, Lim KH, Selvarajah S, Haniff J, Tee GHH, et al. Association of BMI with risk of CVD mortality and all-cause mortality. Public Health Nutr. 2017;20(7):1226–34.

    PubMed  Google Scholar 

  32. Corlin L, Short MI, Vasan RS, Xanthakis V. Association of the Duration of Ideal Cardiovascular Health Through Adulthood With Cardiometabolic Outcomes and Mortality in the Framingham Offspring Study. JAMA Cardiol. 2020;5(5):549–56.

    PubMed  Google Scholar 

  33. Limpens MAM, Asllanaj E, Dommershuijsen LJ, Boersma E, Ikram MA, Kavousi M, et al. Healthy lifestyle in older adults and life expectancy with and without heart failure. Eur J Epidemiol. 2022;37(2):205–14.

    PubMed  PubMed Central  Google Scholar 

  34. Wu S, An S, Li W, Lichtenstein AH, Gao J, Kris-Etherton PM, et al. Association of Trajectory of Cardiovascular Health Score and Incident Cardiovascular Disease. JAMA Netw Open. 2019;2(5):e194758.

    PubMed  PubMed Central  Google Scholar 

  35. Chen C, Lu FC. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci. 2004;17(Suppl):1–36.

    PubMed  Google Scholar 

  36. Yu Y, Dong Z, Li Y, Zhang J, Yin S, Gao X, et al. The cardiovascular and cerebrovascular health in North China From 2006 to 2011: results from the KaiLuan Study. Front Cardiovasc Med. 2021;8:683416.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Wu S, Huang Z, Yang X, Zhou Y, Wang A, Chen L, et al. Prevalence of ideal cardiovascular health and its relationship with the 4-year cardiovascular events in a northern Chinese industrial city. Circ Cardiovasc Qual Outcomes. 2012;5(4):487–93.

    PubMed  Google Scholar 

  38. Wu S, Song Y, Chen S, Zheng M, Ma Y, Cui L, et al. Blood Pressure Classification of 2017 Associated With Cardiovascular Disease and Mortality in Young Chinese Adults. Hypertension. 2020;76(1):251–8.

    CAS  PubMed  Google Scholar 

  39. Cai L, Hayward MD, Saito Y, Lubitz J, Hagedorn A, Crimmins E. Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program. Demogr Res. 2010;22(6):129–58.

    PubMed  PubMed Central  Google Scholar 

  40. Dehbi HM, Royston P, Hackshaw A. Life expectancy difference and life expectancy ratio: two measures of treatment effects in randomised trials with non-proportional hazards. BMJ. 2017;357:j2250.

    PubMed  PubMed Central  Google Scholar 

  41. Aneni EC, Crippa A, Osondu CU, Valero-Elizondo J, Younus A, Nasir K, et al. Estimates of mortality benefit from ideal cardiovascular health metrics: a dose response meta-analysis. J Am Heart Assoc. 2017;6(12)

  42. Han C, Liu F, Yang X, Chen J, Li J, Cao J, et al. Ideal cardiovascular health and incidence of atherosclerotic cardiovascular disease among Chinese adults: the China-PAR project. Sci China Life Sci. 2018;61(5):504–14.

    PubMed  Google Scholar 

  43. Isiozor NM, Kunutsor SK, Voutilainen A, Kurl S, Kauhanen J, Laukkanen JA. Ideal cardiovascular health and risk of acute myocardial infarction among Finnish men. Atherosclerosis. 2019;289:126–31.

    CAS  PubMed  Google Scholar 

  44. Isiozor NM, Kunutsor SK, Voutilainen A, Kurl S, Kauhanen J, Laukkanen JA. American heart association's cardiovascular health metrics and risk of cardiovascular disease mortality among a middle-aged male Scandinavian population. Ann Med. 2019;51(5-6):306–13.

    PubMed  PubMed Central  Google Scholar 

  45. Zhang R, Xie J, Yang R, Li R, Chong M, Zhang X, et al. Association between ideal cardiovascular health score trajectories and arterial stiffness: the Kailuan Study. Hypertens Res. 2020;43(2):140–7.

    CAS  PubMed  Google Scholar 

  46. Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, et al. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA. 2012;307(12):1273–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Colafella KMM, Denton KM. Sex-specific differences in hypertension and associated cardiovascular disease. Nat Rev Nephrol. 2018;14(3):185–201.

    PubMed  Google Scholar 

  48. Kist JM, Smit GWG, Mairuhu ATA, Struijs JN, Vos RC, van Peet PG, et al. Large health disparities in cardiovascular death in men and women, by ethnicity and socioeconomic status in an urban based population cohort. EClinicalMedicine. 2021;40:101120.

    PubMed  PubMed Central  Google Scholar 

  49. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315–81.

    PubMed  PubMed Central  Google Scholar 

  50. Mauvais-Jarvis F, Bairey Merz N, Barnes PJ, Brinton RD, Carrero JJ, DeMeo DL, et al. Sex and gender: modifiers of health, disease, and medicine. Lancet. 2020;396(10250):565–82.

    PubMed  PubMed Central  Google Scholar 

  51. Zaninotto P, Head J, Steptoe A. Behavioural risk factors and healthy life expectancy: evidence from two longitudinal studies of ageing in England and the US. Sci Rep. 2020;10(1):6955.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Li K, Hüsing A, Kaaks R. Lifestyle risk factors and residual life expectancy at age 40: a German cohort study. BMC Med. 2014;12:59.

    PubMed  PubMed Central  Google Scholar 

  53. Miao X, Chen J, Meng W, Wu Q, Wu Z, Ren L, et al. Association Between living risk and healthy life years lost due to multimorbidity: observations from the China Health and Retirement Longitudinal Study. Front Med. 2022;9:831544.

    Google Scholar 

  54. Wang X, Ma H, Li X, Heianza Y, Manson JE, Franco OH, et al. Association of cardiovascular health with life expectancy free of cardiovascular disease, diabetes, cancer, and dementia in UK adults. JAMA Intern Med. 2023;183(4):340–9.

    PubMed  Google Scholar 

  55. Peters SA, Huxley RR, Woodward M. Diabetes as a risk factor for stroke in women compared with men: a systematic review and meta-analysis of 64 cohorts, including 775,385 individuals and 12,539 strokes. Lancet. 2014;383(9933):1973–80.

    PubMed  Google Scholar 

  56. Howard VJ, Madsen TE, Kleindorfer DO, Judd SE, Rhodes JD, Soliman EZ, et al. Sex and race differences in the association of incident ischemic stroke with risk factors. JAMA Neurol. 2019;76(2):179–86.

    PubMed  Google Scholar 

  57. Allen NB, Zhao L, Liu L, Daviglus M, Liu K, Fries J, et al. Favorable cardiovascular health, compression of morbidity, and healthcare costs: forty-year follow-up of the CHA study (Chicago Heart Association Detection Project in Industry). Circulation. 2017;135(18):1693–701.

    PubMed  PubMed Central  Google Scholar 

  58. Thielke SM, Diehr PH, Yee LM, Arnold AM, Quiñones AR, Whitson HE, et al. Sex, race, and age differences in observed years of life, healthy life, and able life among older adults in the cardiovascular health study. J Pers Med. 2015;5(4):440–51.

    PubMed  PubMed Central  Google Scholar 

  59. Monma T, Takeda F, Noguchi H, Takahashi H, Watanabe T, Tamiya N. Exercise or sports in midlife and healthy life expectancy: an ecological study in all prefectures in Japan. BMC Public Health. 2019;19(1):1238.

    PubMed  PubMed Central  Google Scholar 

  60. Monma T, Takeda F, Noguchi H, Tamiya N. Age and sex differences of risk factors of activity limitations in Japanese older adults. Geriatr Gerontol Int. 2016;16(6):670–8.

    PubMed  Google Scholar 

  61. Ahlborg HG, Johnell O, Nilsson BE, Jeppsson S, Rannevik G, Karlsson MK. Bone loss in relation to menopause: a prospective study during 16 years. Bone. 2001;28(3):327–31.

    CAS  PubMed  Google Scholar 

  62. Messier V, Rabasa-Lhoret R, Barbat-Artigas S, Elisha B, Karelis AD, Aubertin-Leheudre M. Menopause and sarcopenia: a potential role for sex hormones. Maturitas. 2011;68(4):331–6.

    CAS  PubMed  Google Scholar 

  63. Rentería E, Jha P, Forman D, Soerjomataram I. The impact of cigarette smoking on life expectancy between 1980 and 2010: a global perspective. Tob Control. 2016;25(5):551–7.

    PubMed  Google Scholar 

  64. Preston SH, Wang H. Sex mortality differences in the United States: the role of cohort smoking patterns. Demography. 2006;43(4):631–46.

    PubMed  Google Scholar 

  65. Luy M, Wegner-Siegmundt C. The impact of smoking on gender differences in life expectancy: more heterogeneous than often stated. Eur J Pub Health. 2015;25(4):706–10.

    Google Scholar 

  66. Wubishet BL, Byles JE, Harris ML, Jagger C. Impact of diabetes on life and healthy life expectancy among older women. J Gerontol A Biol Sci Med Sci. 2021;76(5):914–21.

    PubMed  Google Scholar 

  67. Zhang J, Xu AQ, Ma JX, Shi XM, Guo XL, Engelgau M, et al. Dietary sodium intake: knowledge, attitudes and practices in Shandong Province, China, 2011. PLoS One. 2013;8(3):e58973.

    CAS  PubMed  PubMed Central  Google Scholar 

  68. O'Donnell M, Mente A, Yusuf S. Evidence relating sodium intake to blood pressure and CVD. Curr Cardiol Rep. 2014;16(10):529.

    PubMed  Google Scholar 

  69. Wang W. Cardiovascular health in China: low level vs high diversity. Lancet Reg Health West Pac. 2020;3:100038.

    PubMed  PubMed Central  Google Scholar 

  70. Yan N, Zhou Y, Wang Y, Wang A, Yang X, Russell A, et al. Association of ideal cardiovascular health and brachial-ankle pulse wave velocity: a cross-sectional study in Northern China. J Stroke Cerebrovasc Dis. 2016;25(1):41–8.

    PubMed  Google Scholar 

  71. Wang W, Yan Y, Guo Z, Hou H, Garcia M, Tan X, et al. All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine. EPMA J. 2021;12(4):403–33.

    PubMed  PubMed Central  Google Scholar 

  72. Golubnitschaja O, Topolcan O, Kucera R, Costigliola V. 10th Anniversary of the European Association for Predictive, Preventive and Personalised (3P) Medicine - EPMA World Congress Supplement 2020. EPMA J. 2020;11(Suppl 1):1–133.

    PubMed  PubMed Central  Google Scholar 

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Code is available on reasonable request from the corresponding author.

Disclaimer

The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report.

Funding

This work was supported by the National Key R&D Program of China (2017YFE0118800) and Beijing Talents Project (2020A17).

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Author notes

  1. Qiuyue Tian and Shuohua Chen contributed equally.

Authors and Affiliations

  1. Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, 10 YouanmenXitoutiao, Beijing, 100069, China

    Qiuyue Tian, Jie Zhang, Cancan Li & Youxin Wang

  2. Department of Cardiology, Kailuan General Hospital, North China University of Science and Technology, 57 Xinhua East Road, Tangshan, 063000, China

    Shuohua Chen, Shouling Wu & Yanxiu Wang

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Contributions

Youxin Wang and Yanxiu Wang had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Youxin Wang, Yanxiu Wang, Shuohua Chen, and Qiuyue Tian contributed to the study design. Shuohua Chen, Youxin Wang, and Shouling Wu accessed the data, and Youxin Wang, Shouling Wu, Qiuyue Tian, Yanxiu Wang, and Shuohua Chen verified the data. Qiuyue Tian and Shuohua Chen wrote the manuscript. Youxin Wang reviewed and edited the manuscript. All authors made important contributions to editing and critically revising the manuscript for important intellectual content. All authors have read and approved the final manuscript. Youxin Wang, Yanxiu Wang, Shuohua Chen, and Qiuyue Tian were responsible for the decision to submit the manuscript.

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Correspondence to Yanxiu Wang or Youxin Wang.

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The study was approved by the Ethics Committee of the Kailuan General Hospital (Approval No.: 2006-5).

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Appendix

Appendix

Predictive, preventive, and personalized medicine (PPPM) Innovation Highlights

  • A. Working hypothesis in the framework of PPPM

    We searched PubMed from inception to April 28, 2023, using the following search terms: title/abstract—(cardiovascular health* OR CVH) AND (cardiovascular disease* OR CVD*) AND (life expectancy OR LE), with no date or language restrictions. We hypothesized that ideal CVH status is associated with increased life expectancy across the sub-populations. From the viewpoint of PPPM, effective identification of CVH status can provide early screening of the high-risk population, timely prevention of cardiovascular diseases (CVDs) onset or progression, and personalized intervention to understand whether better CVH status has a positive effect on life expectancy for CVD patients and further prolong life expectancy free from diseases.

  • B. Innovation towards the

    1. 1.

      Predictive approach

      Based on the multistate life table method, we investigated the association of CVH metrics (behaviors and biological factors) with healthy life expectancy (free from CVDs), a useful indicator to predict the potential survival years and further promote healthy aging.

    2. 2.

      Targeted prevention

      CVH metrics have the advantage of being modifiable, common, and acceptable, which is consistent with the most cost-effective and inclusive characteristics of the focus of primordial prevention. Based on each individual’s unique risk profile, lifestyle intervention should be tailored; for example, young men (aged 35–44 years) would gain more survival years contributing from the ideal CVH status than women, suggesting that a greater emphasis on these efforts at younger ages in men may have a larger return.

    3. 3.

      Personalization of medical services

      Accumulating evidence suggests that CVH metrics play an essential role in preventing the onset and progression of diseases, especially in suboptimal health status or the preclinical phase of diseases. Contextually, screening programs are recommended to focus on young men, pre- and postmenopause women, and those with a history of diseases. Primary healthcare providers could identify and modify CVH status to fight delayed intervention, untargeted prevention, and ineffective treatment.

  • C. How does the presented innovation go beyond the state of the art contributing to the paradigm shift from reactive medicine to PPPM?

    Maintaining a higher CVH score or ideal CVH status would result in reduced burdens of CVD events and extended disease-free life expectancy and thus potentially elevate the quality of life. The characteristics of the CVH metrics, based on the lifestyles and biological factors, are simple, modifiable, and financially viable, implying a potential value of CVH metrics for the prevention of CVDs and prolonging healthy life expectancy.

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Tian, Q., Chen, S., Zhang, J. et al. Ideal cardiovascular health metrics and life expectancy free of cardiovascular diseases: a prospective cohort study. EPMA Journal 14, 185–199 (2023). https://doi.org/10.1007/s13167-023-00322-8

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