Life-course trajectories of body mass index and subsequent cardiovascular risk among Chinese population
Autoři:
Md. Tauhidul Islam aff001; Jette Möller aff001; Xingwu Zhou aff001; Yajun Liang aff001
Působiště autorů:
Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
aff001; Initiative for Non-Communicable Diseases, Health System and Population Studies Division, icddr,b, Dhaka, Bangladesh
aff002; Department of Medical Sciences, Clinical Physiology, Uppsala University, Uppsala, Sweden
aff003
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0223778
Souhrn
Background
Examining body mass index (BMI) change over life course is crucial for cardiovascular health promotion and prevention. So far, there is very few evidence on the long-term change of BMI from childhood to late life. This study aimed to examine the life-course trajectory patterns of BMI and then to link the trajectory patterns to cardiovascular risk factors in adulthood.
Methods
Based on longitudinal data from the China Health and Nutrition Survey, 5276 participants (aged 6–60) at baseline (in 1989) with up to 7 measurements of BMI during 1989–2009 were selected in this study. Cardiovascular risk factors including high blood pressure, high blood glucose and high blood lipids were assessed in 2411 participants in 2009. Latent growth curve modelling was used to analyse the BMI trajectories, and logistic regression was used to examine the associations between trajectory patterns and cardiovascular risk factors.
Results
Four trajectories patterns of BMI over life course (age 6–80) were identified: Normal-Stable (22.4% of the total participants), Low normal-Normal-Stable (44.1%), Low normal-Normal-Overweight (27.2%), and Overweight-Obese (4.3%). Compared to those with Normal-Stable pattern, those with Low normal-Normal-Stable pattern, Low normal-Normal-Overweight pattern and Overweight-Obese pattern had higher risk of high blood pressure (odds ratio range = 1.6–6.6), high blood glucose (1.7–9.1), dyslipidemia (2.6–5.9) and having at least two of the three cardiovascular risk factors (3.9–30.9).
Conclusions
Having a stable BMI within normal range over life course is associated with the lowest cardiovascular risk, whereas remaining overweight and obese over life course is associated with the highest cardiovascular risk.
Klíčová slova:
Blood pressure – Blood sugar – Body Mass Index – Cardiovascular diseases – Hypertension – Childhood obesity – Medical risk factors – Obesity
Zdroje
1. GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2017;390:1151–1210. doi: 10.1016/S0140-6736(17)32152-9 28919116
2. Fuster V, Kelly BB. Promoting cardiovascular health in the developing world: a critical challenge to achieve global health. Washington: National academies press; 2009.
3. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2012; 380:2197–5223. doi: 10.1016/S0140-6736(12)61689-4 23245608
4. World Health Organization. Global Status Report on Noncommunicable Diseases 2010. Geneva: World Health Organization; 2011.
5. World Health Organization. Global action plan for the prevention and control of noncommunicable diseases 2013–2020. Switzerland: World Health Organization; 2013.
6. Chen WW, Gao RL, Liu LS, Zhu ML, Wang W, Wang YJ, et al. China cardiovascular diseases report 2015: a summary. J Geriatr Cardiol. 2017;14:1–10. doi: 10.11909/j.issn.1671-5411.2017.01.012 28270835
7. Moran A, Gu D, Zhao D, Coxson P, Wang YC, Chen C-S, et al. Future cardiovascular disease in china: markov model and risk factor scenario projections from the coronary heart disease policy model- china. Circ Cardiovasc Qual Outcomes. 2010;3:243–252. doi: 10.1161/CIRCOUTCOMES.109.910711 20442213
8. Smith SC. Multiple risk factors for cardiovascular disease and diabetes mellitus. Am J Med. 2007;120:S3–S11.
9. Yan LL, Daviglus ML, Liu K, Stamler J, Wang R, Pirzada A, et al. Midlife body mass index and hospitalization and mortality in older age. JAMA. 2006;295:190–198 16403931
10. Rajjo T, Almasri J, Al Nofal A, Farah W, Alsawas M, Ahmed AT, et al. The association of weight loss and cardiometabolic outcomes in obese children: systematic review and meta-regression. J Clin Endocrinol Metab. 2016;102:758–762. doi: 10.1210/jc.2016-2575 27603909
11. Zomer E, Gurusamy K, Leach R, Trimmer C, Lobstein T, Morris S, et al. Interventions that cause weight loss and the impact on cardiovascular risk factors: a systematic review and meta-analysis. Obes Rev. 2016;17:1001–1011. doi: 10.1111/obr.12433 27324830
12. Hao G, Wang X, Treiber FA, Harshfield G, Kapuku G, Su S. Body mass index trajectories in childhood is predictive of cardiovascular risk: results from the 23-year longitudinal georgia stress and heart study. Int J Obes. 2018;42:923–925.
13. Pryor LE, Tremblay RE, Boivin M, Touchette E, Dubois L, Genolini C, et al. Developmental trajectories of body mass index in early childhood and their risk factors: An 8- year longitudinal study. Arch Pediatr Adolesc Med. 2011;165:906–912 21969392
14. Barker DJP, Osmond C, Forsén TJ, Kajantie E, Eriksson JG. Trajectories of growth among children who have coronary events as adults. N Engl J Med. 2005;353:1802–1809. doi: 10.1056/NEJMoa044160 16251536
15. Boyer BP, Nelson JA, Holub SC. Childhood body mass index trajectories predicting cardiovascular risk in adolescence. J Adolesc Health. 2015;56:599–605. doi: 10.1016/j.jadohealth.2015.01.006 25746172
16. Lacey RE, Sacker A, Bell S, Kumari M, Worts D, McDonough P, et al. Work-family life courses and BMI trajectories in three british birth cohorts. Int J Obes (Lond). 2017;41:332–339.
17. Vanwagner LB, Khan SS, Ning H, Siddique J, Lewis CE, Carr JJ, et al. Body mass index trajectories in young adulthood predict non‐ alcoholic fatty liver disease in middle age: the CARDIA cohort study. Liver Int. 2018;38:706–714. doi: 10.1111/liv.13603 28963767
18. Tu AW, Mâsse LC, Lear SA, Gotay CC, Richardson CG. Body mass index trajectories from ages 1 to 20: results from two nationally representative canadian longitudinal cohorts. Obesity. 2015;23:1703–1711. doi: 10.1002/oby.21158 26179716
19. Elsenburg LK, Smidt N, Hoek HW, Liefbroer AC. Body mass index trajectories from adolescence to early young adulthood: do adverse life events play a role? Obesity. 2017;25:2142–2148. doi: 10.1002/oby.22022 29071799
20. Tirosh A, Shai I, Afek A, Dubnov-Raz G, Ayalon N, Gordon B, et al. Adolescent BMI Trajectory and Risk of Diabetes versus Coronary Disease. N Engl J Med. 2011;364:1315–1325. doi: 10.1056/NEJMoa1006992 21470009
21. Attard SM, Herring AH, Howard AG, Gordon-Larsen P. Longitudinal trajectories of BMI and cardiovascular disease risk: the national longitudinal study of adolescent health. Obesity. 2013;21:2180–2188. doi: 10.1002/oby.20569 24136924
22. Li L, Hardy R, Kuh D, Power C. Life- course body mass index trajectories and blood pressure in mid life in two british birth cohorts: stronger associations in the later- born generation. Int J Epidemiol. 2015;44:1018–1026. doi: 10.1093/ije/dyv106 26078389
23. Ford ND, Martorell R, Mehta NK, Ramirez-Zea M, Stein AD. Life-course body mass index trajectories are predicted by childhood socioeconomic status but not exposure to improved nutrition during the first 1000 Days after conception in guatemalan adults. J Nutr. 2016;146:2368–2374. doi: 10.3945/jn.116.236075 27655759
24. Buscot M-J, Thomson RJ, Juonala M, Sabin MA, Burgner DP, Lehtimäki T, et al. Distinct child-to-adult body mass index trajectories are associated with different levels of adult cardiometabolic risk. Eur Heart J. 2018;39:2263–2270. doi: 10.1093/eurheartj/ehy161 29635282
25. Zhang B, Zhai FY, Du SF, Popkin BM. The china health and nutrition survey, 1989–2011. Obes Rev. 2014;15:2–7.
26. Yan S, Li J, Li S, et al. The expanding burden of cardiometabolic risk in China: the China Health and Nutrition Survey. Obes Rev. 2012;13, 810–821. doi: 10.1111/j.1467-789X.2012.01016.x 22738663
27. World Health Organization. Physical status: The use and interpretation of anthropometry. Report of a WHO Expert Committee Technical Report Series. WHO: Geneva; 1995.
28. Zhou BF. 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. Biomed Environ Sci. 2002;15:83–96. 12046553
29. Ji CY, China WGoOi. Report on childhood obesity in China (1)—body mass index reference for screening overweight and obesity in Chinese school-age children. Biomed Environ Sci. 2005;18:390–400. 16544521
30. Liang Y, Liu R, Du S, Qiu C. Trends in incidence of hypertension in Chinese adults, 1991–2009: the china health and nutrition survey. Int J Cardiol. 2004;175:96–101.
31. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 Report. JAMA. 2003;289:2560–2572 12748199
32. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes care. 2012;35 Suppl 1:S64–S71.
33. Grundy SM, Becker D, Clark L et al. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106:3143–3421. 12485966
34. Liang Y, Welmer AK, Wang R, Song A, Fratiglioni L, Qiu C. Trends in incidence of disability in activities of daily living in chinese older adults: 1993–2006. J Am Geriatr Soc. 2017;65:306–312. doi: 10.1111/jgs.14468 27682324
35. Benoît L, Patrick G, Amanda T, Natasha C, Heather A. Latent class growth modelling: a tutorial. Tutor Quant Methods Psychol. 2009;5:11–24.
36. Wang M, Yi Y, Roebothan B, Colbourne J, Maddalena V, Wang PP, et al. Body mass index trajectories among middle- aged and elderly canadians and associated health outcomes. J Environ Public Health. 2016;2016:7014857. doi: 10.1155/2016/7014857 26925112
37. Wang M, Yi Y, Roebothan B, Colbourne J, Maddalena V, Sun G, et al. Trajectories of body mass index among canadian seniors and associated mortality risk. BMC Public Health. 2017;17:929. doi: 10.1186/s12889-017-4917-0 29202810
38. Zheng H, Tumin D, Qian Z. Obesity and Mortality Risk: New findings from body mass index trajectories. Am J Epidemiol. 2013;178:1591–1599. doi: 10.1093/aje/kwt179 24013201
39. Sinaiko AR, Donahue RP, Jacobs DR, Prineas RJ. Relation of weight and rate of increase in weight during childhood and adolescence to body size, blood pressure, fasting insulin, and lipids in young adults: The minneapolis children’s blood pressure study. Circulation. 1999;99:1471–1476. doi: 10.1161/01.cir.99.11.1471 10086972
40. Everhart JE, Pettitt DJ, Bennett PH, Knowler WC. Duration of obesity increases the incidence of NIDDM. Diabetes. 1992;41:235–240. doi: 10.2337/diab.41.2.235 1733815
41. Lloyd LJ, Langley-Evans SC, Mcmullen S. Childhood obesity and risk of the adult metabolic syndrome: a systematic review. Int J Obes (Lond). 2012;36:1–11.
42. Juonala M, Magnussen CG, Berenson GS, Venn A, Burns TL, Sabin MA, et al. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. 2011;365:1876–1885. doi: 10.1056/NEJMoa1010112 22087679
43. Dean E, Lomi C, Bruno S, Awad H, O’Donoghue G. Addressing the common pathway underlying hypertension and diabetes in people who are obese by maximizing health: the ultimate knowledge translation gap. Int J Hypertens. 2011;2011:835805. doi: 10.4061/2011/835805 21423684
Článek vyšel v časopise
PLOS One
2019 Číslo 10
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Correction: Low dose naltrexone: Effects on medication in rheumatoid and seropositive arthritis. A nationwide register-based controlled quasi-experimental before-after study
- Combining CDK4/6 inhibitors ribociclib and palbociclib with cytotoxic agents does not enhance cytotoxicity
- Experimentally validated simulation of coronary stents considering different dogboning ratios and asymmetric stent positioning
- Risk factors associated with IgA vasculitis with nephritis (Henoch–Schönlein purpura nephritis) progressing to unfavorable outcomes: A meta-analysis
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy