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Metabolism and Metabolomics

Association of fasting plasma glucose trajectory with lifetime risk of cardiovascular disease

European Journal of Clinical Nutrition volume 77, pages 335–341 (2023)Cite this article

Subjects

Abstract

Background

Few studies have used multiple measurements of fasting plasma glucose (FPG) to examine the impact of long-term FPG trajectory patterns on lifetime risk of cardiovascular disease (CVD). We aimed to identify the long-term patterns in FPG trajectories and to estimate the lifetime risk of CVD according to FPG trajectories.

Methods

Individuals free of CVD at index ages 35 (n = 72,324), 45 (n = 62,049), and 55 (n = 38,113) years were included. FPG concentrations were measured in 2006, 2008, and 2010. The FPG trajectories were identified by latent mixture modeling. The modified Kaplan-Meier method was used to calculate lifetime risk of CVD.

Results

We identified five distinct FPG trajectories and named them according to FPG range and changing pattern over time: low-stable, moderate-stable, moderate-increasing, elevated-decreasing, and elevated-stable. At index age 35 years, we documented 3110 CVD events in men during 371,219 person-years of follow-up and 357 CVD events in women during 107,447 person-years of follow-up. Among all participants, the elevated-stable FPG pattern experienced the highest lifetime risk of CVD (44.8%, 95% CI: 37.8–51.9%), low-stable pattern was lowest (24.3%, 95% CI: 23.3–25.2%). At index age 55 years, although the elevated-stable and elevated-decreasing FPG patterns had similar original FPG concentrations, individuals with elevated-decreasing pattern (30.0%, 95% CI: 23.9–36.1%) had approximately one-third less lifetime risk of CVD than those with elevated-stable pattern (43.6%, 95% CI: 31.8–55.3%).

Conclusions

FPG trajectories were significantly associated with the lifetime risk of CVD. Both decrease in FPG over time and consistently lower FPG over 4 years were associated with lower lifetime risk of CVD.

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Fig. 1: Trajectory of fasting plasma glucose (FPG) for all participants during 2006–2010.
Fig. 2: Cumulative incidence of CVD adjusted for the competing risk of death for all participants according to FPG trajectories at index ages 35, 45, and 55 years.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Collaborators GBDRF. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1923–94. https://doi.org/10.1016/S0140-6736(18)32225-6.

    Article  Google Scholar 

  2. Collaborators GBDCoD. 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;392:1736–88. https://doi.org/10.1016/S0140-6736(18)32203-7.

    Article  Google Scholar 

  3. Berry JD, Dyer A, Cai X, Garside DB, Ning H, Thomas A, et al. Lifetime risks of cardiovascular disease. N. Engl J Med. 2012;366:321–9. https://doi.org/10.1056/NEJMoa1012848.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wang Y, Liu J, Wang W, Wang M, Qi Y, Xie W, et al. Lifetime risk for cardiovascular disease in a Chinese population: the Chinese Multi-Provincial Cohort Study. Eur J Prev Cardiol. 2015;22:380–8. https://doi.org/10.1177/2047487313516563.

    Article  PubMed  Google Scholar 

  5. Park C, Guallar E, Linton JA, Lee DC, Jang Y, Son DK, et al. Fasting glucose level and the risk of incident atherosclerotic cardiovascular diseases. Diabetes Care. 2013;36:1988–93. https://doi.org/10.2337/dc12-1577.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Fox CS, Pencina MJ, Wilson PW, Paynter NP, Vasan RS, D’Agostino RB Sr. Lifetime risk of cardiovascular disease among individuals with and without diabetes stratified by obesity status in the Framingham heart study. Diabetes Care. 2008;31:1582–4. https://doi.org/10.2337/dc08-0025.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lloyd-Jones DM, Leip EP, Larson MG, D’Agostino RB, Beiser A, Wilson PW, et al. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation. 2006;113:791–8. https://doi.org/10.1161/CIRCULATIONAHA.105.548206.

    Article  PubMed  Google Scholar 

  8. Song M, Hu FB, Wu K, Must A, Chan AT, Willett WC, et al. Trajectory of body shape in early and middle life and all cause and cause specific mortality: results from two prospective US cohort studies. BMJ. 2016;353:i2195 https://doi.org/10.1136/bmj.i2195.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Franklin JM, Shrank WH, Pakes J, Sanfelix-Gimeno G, Matlin OS, Brennan TA, et al. Group-based trajectory models: a new approach to classifying and predicting long-term medication adherence. Med Care. 2013;51:789–96. https://doi.org/10.1097/MLR.0b013e3182984c1f.

    Article  PubMed  Google Scholar 

  10. Wang C, Yuan Y, Zheng M, Pan A, Wang M, Zhao M, et al. Association of age of onset of hypertension with cardiovascular diseases and mortality. J Am Coll Cardiol. 2020;75:2921–30. https://doi.org/10.1016/j.jacc.2020.04.038.

    Article  PubMed  Google Scholar 

  11. Wu Z, Jin C, Vaidya A, Jin W, Huang Z, Wu S, et al. Longitudinal patterns of blood pressure, incident cardiovascular events, and all-cause mortality in normotensive diabetic people. Hypertension. 2016;68:71–77. https://doi.org/10.1161/HYPERTENSIONAHA.116.07381.

    Article  CAS  PubMed  Google Scholar 

  12. Jin C, Chen S, Vaidya A, Wu Y, Wu Z, Hu FB, et al. Longitudinal change in fasting blood glucose and myocardial infarction risk in a population without diabetes. Diabetes Care. 2017;40:1565–72. https://doi.org/10.2337/dc17-0610.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wang L, Cui L, Wang Y, Vaidya A, Chen S, Zhang C, et al. Resting heart rate and the risk of developing impaired fasting glucose and diabetes: the Kailuan prospective study. Int J Epidemiol. 2015;44:689–99. https://doi.org/10.1093/ije/dyv079.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Wang L, Lee Y, Wu Y, Zhang X, Jin C, Huang Z, et al. A prospective study of waist circumference trajectories and incident cardiovascular disease in China: the Kailuan Cohort Study. Am J Clin Nutr. 2021;113:338–47. https://doi.org/10.1093/ajcn/nqaa331.

    Article  PubMed  Google Scholar 

  15. Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas AM, Pajak A. Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation. 1994;90:583–612. https://doi.org/10.1161/01.cir.90.1.583.

    Article  CAS  PubMed  Google Scholar 

  16. Stroke-1989. Recommendations on stroke prevention, diagnosis, and therapy. Report of the WHO Task Force on Stroke and other Cerebrovascular Disorders. Stroke. 1989;20:1407–31. https://doi.org/10.1161/01.str.20.10.1407.

    Article  Google Scholar 

  17. Jones BL, Nagin DS, Roeder KA. SAS procedure based on mixture models for estimating developmental trajectories. Sociol Methods Res. 2001;29:374–93.

    Article  Google Scholar 

  18. Odgers DSNCL. Group-based trajectory modeling in clinical research. Ann. Rev. Clin. Psychol. 2010;6:109–38.

    Article  Google Scholar 

  19. Beiser A, D’Agostino RB,Sr, Seshadri S, Sullivan LM, Wolf PA. Computing estimates of incidence, including lifetime risk: Alzheimer’s disease in the Framingham Study. The Practical Incidence Estimators (PIE) macro. Stat Med. 2000;19:1495–522. https://doi.org/10.1002/(sici)1097-0258(20000615/30)19:11/123.0.co;2-e.

    Article  CAS  PubMed  Google Scholar 

  20. Lawes CM, Parag V, Bennett DA, Suh I, Lam TH, Whitlock G, et al. Blood glucose and risk of cardiovascular disease in the Asia Pacific region. Diabetes Care. 2004;27:2836–42. https://doi.org/10.2337/diacare.27.12.2836.

    Article  CAS  PubMed  Google Scholar 

  21. Preiss D, Welsh P, Murray HM, Shepherd J, Packard C, Macfarlane P, et al. Fasting plasma glucose in non-diabetic participants and the risk for incident cardiovascular events, diabetes, and mortality: results from WOSCOPS 15-year follow-up. Eur Heart J. 2010;31:1230–6. https://doi.org/10.1093/eurheartj/ehq095.

    Article  CAS  PubMed  Google Scholar 

  22. Sinha A, Ning H, Ahmad FS, Bancks MP, Carnethon MR, O’Brien MJ, et al. Association of fasting glucose with lifetime risk of incident heart failure: the Lifetime Risk Pooling Project. Cardiovasc Diabetol. 2021;20:66 https://doi.org/10.1186/s12933-021-01265-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Streiner DL. Breaking up is hard to do: the heartbreak of dichotomizing continuous data. Can J Psychiatry. 2002;47:262–6. https://doi.org/10.1177/070674370204700307.

    Article  PubMed  Google Scholar 

  24. Yuan Z, Yang Y, Wang C, Liu J, Sun X, Liu Y, et al. Trajectories of long-term normal fasting plasma glucose and risk of coronary heart disease: a prospective cohort study. J Am Heart Assoc. 2018;7. https://doi.org/10.1161/JAHA.117.007607.

  25. Ogata S, Watanabe M, Kokubo Y, Higashiyama A, Nakao YM, Takegami M, et al. Longitudinal trajectories of fasting plasma glucose and risks of cardiovascular diseases in middle age to elderly people within the general Japanese population: the Suita Study. J Am Heart Assoc. 2019;8:e010628 https://doi.org/10.1161/JAHA.118.010628.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Edwards A, Elwyn G, Mulley A. Explaining risks: turning numerical data into meaningful pictures. BMJ. 2002;324:827–30. https://doi.org/10.1136/bmj.324.7341.827.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Edwards A, Elwyn G, Stott N. Communicating risk reductions. Researchers should present results with both relative and absolute risks. BMJ. 1999;318:603.

    CAS  PubMed  Google Scholar 

  28. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140:e596–e646. https://doi.org/10.1161/CIR.0000000000000678.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Timmis A, Townsend N, Gale CP, Torbica A, Lettino M, Petersen SE, et al. European Society of Cardiology: cardiovascular disease statistics 2019. Eur Heart J. 2020;41:12–85. https://doi.org/10.1093/eurheartj/ehz859.

    Article  PubMed  Google Scholar 

  30. Decode Study Group EDEG. Is the current definition for diabetes relevant to mortality risk from all causes and cardiovascular and noncardiovascular diseases? Diabetes Care. 2003;26:688–96. https://doi.org/10.2337/diacare.26.3.688.

    Article  Google Scholar 

  31. Meigs JB, Nathan DM, D’Agostino RB Sr, Wilson PW, Framingham Offspring S. Fasting and postchallenge glycemia and cardiovascular disease risk: the Framingham Offspring Study. Diabetes Care. 2002;25:1845–50. https://doi.org/10.2337/diacare.25.10.1845.

    Article  PubMed  Google Scholar 

  32. Sui X, Jackson AS, Church TS, Lee DC, O’Connor DP, Liu J, et al. Effects of cardiorespiratory fitness on aging: glucose trajectory in a cohort of healthy men. Ann Epidemiol. 2012;22:617–22. https://doi.org/10.1016/j.annepidem.2012.05.005.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Li G, Zhang P, Wang J, An Y, Gong Q, Gregg EW, et al. Cardiovascular mortality, all-cause mortality, and diabetes incidence after lifestyle intervention for people with impaired glucose tolerance in the Da Qing Diabetes Prevention Study: a 23-year follow-up study. Lancet Diabetes Endocrinol. 2014;2:474–80. https://doi.org/10.1016/S2213-8587(14)70057-9.

    Article  PubMed  Google Scholar 

  34. O’Connor EA, Evans CV, Rushkin MC, Redmond N, Lin JS. Behavioral counseling to promote a healthy diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2020;324:2076–94. https://doi.org/10.1001/jama.2020.17108.

    Article  PubMed  Google Scholar 

  35. Salas-Salvado J, Diaz-Lopez A, Ruiz-Canela M, Basora J, Fito M, Corella D, et al. Effect of a lifestyle intervention program with energy-restricted mediterranean diet and exercise on weight loss and cardiovascular risk factors: one-year results of the PREDIMED-plus trial. Diabetes Care. 2019;42:777–88. https://doi.org/10.2337/dc18-0836.

    Article  CAS  PubMed  Google Scholar 

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Author notes

  1. These authors contributed equally: Dankang Li, Lulu Song.

Authors and Affiliations

  1. Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No.13 Hangkong Road, Wuhan, 430030, China

    Dankang Li, Lulu Song, Lulin Wang, Yingping Yang, Youjie Wang & Yaohua Tian

  2. Ministry of Education Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No.13 Hangkong Road, Wuhan, 430030, China

    Dankang Li, Lulu Song, Lulin Wang, Yingping Yang, Youjie Wang & Yaohua Tian

  3. Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, No.57 Xinhua East Road, Tangshan City, 063001, China

    Shuohua Chen & Shouling Wu

  4. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No.38 Xueyuan Road, Beijing, 100191, China

    Yonghua Hu

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Contributions

LS, DL, YT, and SW conceived the study. LS, DL, YT, SW, and YW contributed to the study design. SW and SC prepared and cleaned the data. LS, LW, DL, and YY conducted the data analysis. DL, LS and YT drafted the manuscript. LS, DL, YT, SW, YW, and YH critically revised the manuscript for important intellectual content. All authors approved the final version of the manuscript. YT and SW act as guarantors.

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Correspondence to Shouling Wu or Yaohua Tian.

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Li, D., Song, L., Wang, L. et al. Association of fasting plasma glucose trajectory with lifetime risk of cardiovascular disease. Eur J Clin Nutr 77, 335–341 (2023). https://doi.org/10.1038/s41430-022-01243-x

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