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Gender-specific predictive ability for the risk of hypertension incidence related to baseline level or trajectories of adiposity indices: a cohort study of functional community

International Journal of Obesity volume 46, pages 1036–1043 (2022)Cite this article

Subjects

Abstract

Background

Early prevention of hypertension is important for global cardiovascular disease morbidity and mortality. This study aims to explore better predictors for hypertension incidence related to baseline level or trajectories of adiposity indices, as well as the gender-specific effect.

Methods

6085 subjects from a functional community cohort in urban Beijing participated in our study. Restricted cubic splines were used to estimate nonlinear associations of body mass index (BMI) and waist-to-height ratio (WHtR) as continuous variable with risk of hypertension. Stepwise logistic regression model was performed to estimate the relative risks (RRs) of adiposity indices and metabolic status, adjusted for covariates. Nomogram models and receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive power of BMI trajectory groups and WHtR trajectory groups on hypertension incidence. Further, all analysis were performed by gender.

Results

The risk of hypertension incidence was related to BMI trajectory groups (persistent overweight: RR = 1.88, 95% CI: 1.48–2.37; persistent obesity: RR = 2.79, 95% CI: 2.18–3.56; persistent the highest: RR = 4.30, 95% CI: 3.20–5.78) and WHtR trajectory groups (persistent medium: RR = 2.69, 95% CI: 2.07–3.50; persistent high: RR = 3.85, 95% CI: 2.92–5.09; increasing to higher: RR = 7.00, 95% CI: 4.96–9.89). In total population, BMI trajectories and WHtR trajectories showed similar ability to predict the risk of hypertension incidence with AUC 0.723 and 0.726, respectively. After stratified by gender, both BMI trajectories and WHtR trajectories showed higher power in female than male (BMI trajectories: 0.762 vs. 0.661; WHtR trajectories: 0.768 vs. 0.661).

Conclusions

BMI and WHtR trajectories have higher predictive power for hypertension incidence compared to baseline data. Females are more vulnerable to obesity than males.

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Fig. 1
Fig. 2: The trajectory groups of BMI and WHtR over time from 2015 to 2019.
Fig. 3: RRs and 95% CIs of hypertension incidence related to BMI trajectory groups and WHtR trajectory groups in total population and stratified by gender.
Fig. 4: The predictive power of adiposity indexes trajectories for risk of hypertension incidence using ROC curve analysis by gender.

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Data availability

Data are available on reasonable request to the corresponding author.

References

  1. Luo D, Cheng Y, Zhang H, Ba M, Chen P, Li H, et al. Association between high blood pressure and long term cardiovascular events in young adults: systematic review and meta-analysis. BMJ. 2020;370:m3222.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Saiz LC, Gorricho J, Garjon J, Celaya MC, Erviti J, Leache L. Blood pressure targets for the treatment of people with hypertension and cardiovascular disease. Cochrane Database Syst Rev. 2020;9:CD010315.

    PubMed  Google Scholar 

  3. Yang CY, Liu JS, Tseng WC, Tsai MT, Lin MH, Kao ZK, et al. Effect of renin-angiotensin-aldosterone system blockade on long-term outcomes in postacute kidney injury patients with hypertension. Crit Care Med. 2020.

  4. Wu X, Duan X, Gu D, Hao J, Tao S, Fan D. Prevalence of hypertension and its trends in Chinese populations. Int J Cardiol. 1995;52:39–44.

    Article  CAS  PubMed  Google Scholar 

  5. Gao Y, Chen G, Tian H, Lin L, Lu J, Weng J, et al. Prevalence of hypertension in China: a cross-sectional study. PLoS ONE. 2013;8:e65938.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ke L, Ho J, Feng J, Mpofu E, Dibley MJ, Li Y, et al. Prevalence, awareness, treatment and control of hypertension in Macau: results from a cross-sectional epidemiological study in Macau, China. Am J Hypertens. 2015;28:159–65.

    Article  CAS  PubMed  Google Scholar 

  7. Ezzati M, Vander Hoorn S, Lawes CM, Leach R, James WP, Lopez AD, et al. Rethinking the “diseases of affluence” paradigm: global patterns of nutritional risks in relation to economic development. PLoS Med. 2005;2:e133.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Heianza Y, Kodama S, Arase Y, Hsieh SD, Yoshizawa S, Tsuji H, et al. Role of body mass index history in predicting risk of the development of hypertension in Japanese individuals: Toranomon Hospital Health Management Center Study 18 (TOPICS 18). Hypertension. 2014;64:247–52.

    Article  CAS  PubMed  Google Scholar 

  9. Wang S, Liu Y, Li F, Jia H, Liu L, Xue F. A novel quantitative body shape score for detecting association between obesity and hypertension in China. BMC Public Health. 2015;15:7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bohn B, Muller MJ, Simic-Schleicher G, Kiess W, Siegfried W, Oelert M, et al. BMI or BIA: is body mass index or body fat mass a better predictor of cardiovascular risk in overweight or obese children and adolescents? A German/Austrian/Swiss Multicenter APV analysis of 3,327 children and adolescents. Obes Facts. 2015;8:156–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Xie D, Bollag WB. Obesity, hypertension and aldosterone: is leptin the link? J Endocrinol. 2016;230:F7–F11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chen Y, Liang X, Zheng S, Wang Y, Lu W. Association of body fat mass and fat distribution with the incidence of hypertension in a population-based Chinese Cohort: a 22-year follow-up. J Am Heart Assoc. 2018;7.

  13. Vasan RS, Larson MG, Leip EP, Kannel WB, Levy D. Assessment of frequency of progression to hypertension in non-hypertensive participants in the Framingham Heart Study: a cohort study. Lancet. 2001;358:1682–6.

    Article  CAS  PubMed  Google Scholar 

  14. Ishikawa-Takata K, Ohta T, Moritaki K, Gotou T, Inoue S. Obesity, weight change and risks for hypertension, diabetes and hypercholesterolemia in Japanese males. Eur J Clin Nutr. 2002;56:601–7.

    Article  CAS  PubMed  Google Scholar 

  15. Juntunen M, Niskanen L, Saarelainen J, Tuppurainen M, Saarikoski S, Honkanen R. Changes in body weight and onset of hypertension in perimenopausal females. J Hum Hypertens. 2003;17:775–9.

    Article  CAS  PubMed  Google Scholar 

  16. Droyvold WB, Midthjell K, Nilsen TI, Holmales J. Change in body mass index and its impact on blood pressure: a prospective population study. Int J Obes (Lond). 2005;29:650–5.

    Article  CAS  Google Scholar 

  17. Williams PT. Increases in weight and body size increase the odds for hypertension during 7 years of follow-up. Obesity (Silver Spring). 2008;16:2541–8.

    Article  Google Scholar 

  18. He J, Whelton PK, Appel LJ, Charleston J, Klag MJ. Long-term effects of weight loss and dietary sodium reduction on incidence of hypertension. Hypertension. 2000;35:544–9.

    Article  CAS  PubMed  Google Scholar 

  19. Stevens VJ, Obarzanek E, Cook NR, Lee IM, Appel LJ, Smith West D, et al. Long-term weight loss and changes in blood pressure: results of the Trials of Hypertension Prevention, phase II. Ann Intern Med. 2001;134:1–11.

    Article  CAS  PubMed  Google Scholar 

  20. Moore LL, Visioni AJ, Qureshi MM, Bradlee ML, Ellison RC, D’Agostino R. Weight loss in overweight adults and the long-term risk of hypertension: the Framingham study. Arch Intern Med. 2005;165:1298–303.

    Article  PubMed  Google Scholar 

  21. Despres JP. Body fat distribution and risk of cardiovascular disease: an update. Circulation. 2012;126:1301–13.

    Article  PubMed  Google Scholar 

  22. Choi JR, Koh SB, Choi E. Waist-to-height ratio index for predicting incidences of hypertension: the ARIRANG study. BMC Public Health. 2018;18:767.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Chua EY, Zalilah MS, Haemamalar K, Norhasmah S, Geeta A. Obesity indices predict hypertension among indigenous adults in Krau Wildlife Reserve, Peninsular Malaysia. J Health Popul Nutr. 2017;36:24.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Maffeis C, Banzato C, Talamini G, Obesity Study Group of the Italian Society of Pediatric E, Diabetology. Waist-to-height ratio, a useful index to identify high metabolic risk in overweight children. J Pediatr. 2008;152:207–13.

    Article  PubMed  Google Scholar 

  25. Wang XH, Lin JN, Liu GZ, Fan HM, Huang YP, Li CJ, et al. Females are at a higher risk of chronic metabolic diseases compared to males with increasing body mass index in China. Front Endocrinol (Lausanne). 2020;11:127.

    Article  Google Scholar 

  26. Hu L, Hu G, Huang X, Zhou W, You C, Li J, et al. Different adiposity indices and their associations with hypertension among Chinese population from Jiangxi province. BMC Cardiovasc Disord. 2020;20:115.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  27. Karelis AD, Faraj M, Bastard JP, St-Pierre DH, Brochu M, Prud’homme D, et al. The metabolically healthy but obese individual presents a favorable inflammation profile. J Clin Endocrinol Metab. 2005;90:4145–50.

    Article  CAS  PubMed  Google Scholar 

  28. Karelis AD. Metabolically healthy but obese individuals. Lancet. 2008;372:1281–3.

    Article  PubMed  Google Scholar 

  29. Marini MA, Succurro E, Frontoni S, Hribal ML, Andreozzi F, Lauro R, et al. Metabolically healthy but obese females have an intermediate cardiovascular risk profile between healthy nonobese females and obese insulin-resistant females. Diabetes Care. 2007;30:2145–7.

    Article  PubMed  Google Scholar 

  30. Zhang T, Zhang H, Li S, Li Y, Liu Y, Fernandez C, et al. Impact of adiposity on incident hypertension is modified by insulin resistance in adults: longitudinal observation from the Bogalusa Heart Study. Hypertension. 2016;67:56–62.

    Article  CAS  PubMed  Google Scholar 

  31. Saki SA, Nessa A, Salma MU, Begum A. Relationship of HDL-C and triglyceride with hypertension in adult males. Mymalessingh Med J. 2019;28:298–301.

    CAS  Google Scholar 

  32. Colman I, Ataullahjan A. Life course perspectives on the epidemiology of depression. Can J Psychiatry. 2010;55:622–32.

    Article  PubMed  Google Scholar 

  33. Guo B, Shi Z, Zhang W, Zhao H, He K, Hu X, et al. Trajectories of body mass index (BMI) and hypertension risk among middle-aged and elderly Chinese people. J Hum Hypertens. 2021;35:537–45.

    Article  CAS  PubMed  Google Scholar 

  34. Zeng Q, Sun L, Zeng Q. Trajectories of mid-life to elderly adulthood BMI and incident hypertension: the China Health and Nutrition Survey. BMJ Open. 2021;11:e047920.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Xi B, Liang Y, He T, Reilly KH, Hu Y, Wang Q, et al. Secular trends in the prevalence of general and abdominal obesity among Chinese adults, 1993–2009. Obes Rev. 2012;13:287–96.

    Article  CAS  PubMed  Google Scholar 

  36. Mi YJ, Zhang B, Wang HJ, Yan J, Han W, Zhao J, et al. Prevalence and secular trends in obesity among chinese adults, 1991–2011. Am J Prev Med. 2015;49:661–9.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Yan YX, Dong J, Liu YQ, Yang XH, Li M, Shia G, et al. Association of suboptimal health status and cardiovascular risk factors in urban Chinese workers. J Urban Health. 2012;89:329–38.

    Article  PubMed  Google Scholar 

  38. Zhang M, Zhao Y, Sun H, Luo X, Wang C, Li L, et al. Effect of dynamic change in body mass index on the risk of hypertension: results from the Rural Chinese Cohort Study. Int J Cardiol. 2017;238:117–22.

    Article  PubMed  Google Scholar 

  39. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112:2735–52.

    Article  PubMed  Google Scholar 

  40. Andruff H, Carraro Natasha, Thompson Amanda, Gaudreau Patrick, Louvet Benoît. Latent class growth modelling: a tutorial. Tutorials in Quantitative Methods for Psychology. 2009;5:11–24.

    Article  Google Scholar 

  41. Moon S, Chung HS, Yu JM, Ko KJ, Choi DK, Kwon O, et al. The association between obesity and the nocturia in the U.S. population. Int Neurourol J. 2019;23:169–76.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Mertens IL, Van Gaal LF. Overweight, obesity, and blood pressure: the effects of modest weight reduction. Obes Res. 2000;8:270–8.

    Article  CAS  PubMed  Google Scholar 

  43. Fantin F, Giani A, Zoico E, Rossi AP, Mazzali G, Zamboni M. Weight loss and hypertension in obese subjects. Nutrients. 2019;11.

  44. Xu J, Zhang R, Guo R, Wang Y, Dai Y, Xie Y, et al. Trajectories of body mass index and risk of incident hypertension among a normal body mass index population: A prospective cohort study. J Clin Hypertens (Greenwich). 2021;23:1212–20.

    Article  Google Scholar 

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Funding

This work was supported by Grants Nos. 81573214 and 81773511 to investigator Yu-Xiang Yan from the National Natural Science Foundation of China.

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Author notes

  1. These authors contributed equally: Ya-Ke Lu, Jing Dong.

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China

    Ya-Ke Lu, Yue Sun, Li-Kun Hu, Yu-Hong Liu & Yu-Xiang Yan

  2. Physical Examination Center, Xuanwu Hospital, Capital Medical University, Beijing, China

    Jing Dong & Xi Chu

  3. Municipal Key Laboratory of Clinical Epidemiology, Beijing, China

    Yu-Xiang Yan

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  1. Ya-Ke Lu
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Contributions

Ya-Ke Lu, Jing Dong, and Yu-Xiang Yan designed and conducted the study. Ya-Ke Lu cleared the data with the help of Li-Kun Hu, Yue Sun and Yu-Hong Liu. Ya-Ke Lu and Jing Dong analyzed the data and wrote the manuscript with support from Yu-Xiang Yan and Xi Chu. All authors reviewed the manuscript.

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Correspondence to Xi Chu or Yu-Xiang Yan.

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Lu, YK., Dong, J., Sun, Y. et al. Gender-specific predictive ability for the risk of hypertension incidence related to baseline level or trajectories of adiposity indices: a cohort study of functional community. Int J Obes 46, 1036–1043 (2022). https://doi.org/10.1038/s41366-022-01081-8

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