Modeling the natural history of fatty liver using lifestyle–related risk factors: Effects of body mass index (BMI) on the life–course of fatty liver

Autoři: Mika Aizawa aff001;  Seiichi Inagaki aff002;  Michiko Moriyama aff003;  Kenichiro Asano aff004;  Masayuki Kakehashi aff001
Působiště autorů: Department of Health Informatics, Graduate School of Biomedical & Health Sciences, Hiroshima University, Kasumi, Hiroshima, Japan aff001;  International University of Health and Welfare, Narita, Chiba, Japan aff002;  Department of Chronic Care and Family Nursing, Graduate School of Biomedical & Health Sciences, Hiroshima University, Kasumi, Hiroshima, Japan aff003;  Human Resources Department Health Management Promotion Office, Fujikura Ltd. Kiba, Koto Ward, Tokyo, Japan aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223683



Incident fatty liver increases the risk of nonalcoholic fatty liver disease (NAFLD), which may lead to end-stage liver diseases, and increase the risk of cardiovascular disease and diabetes. For its prevention, modeling the natural history of fatty liver is useful to demonstrate which lifestyle-related risk factors (e.g. body mass index and cholesterol) play the greatest role in the life-course of fatty liver.


Model predictors and their predictive algorithms were determined by prospective regression analyses using 5–year data from approximately 2000 Japanese men aged 20–69 years. The participants underwent health examinations and completed questionnaires on their lifestyle behaviors annually from 2012 to 2016. The life–course of fatty liver was simulated based on this participant data using Monte Carlo simulation methods. Sensitivity analyses were performed. The validity of the model was discussed.


The body mass index (BMI) and low–density/high–density lipoprotein cholesterol (LDL–C/HDL–C) ratio significantly aided in predicting incident fatty liver. When the natural history of fatty liver was simulated using the data of participants aged 30–39 years, the prevalence increased from 20% to 32% at 40–59 years before decreasing to 24% at 70–79 years. When annual updates of BMI and LDL–C/HDL–C ratio decreased/increased by 1%, the peak prevalence of fatty liver (32%) changed by −8.0/10.7% and −1.6/1.4%, respectively.


We modeled the natural history of fatty liver for adult Japanese men. The model includes BMI and LDL‒C/HDL‒C ratio, which played a significant role in predicting the presence of fatty liver. Specifically, annual changes in BMI of individuals more strongly affected the life‒course of fatty liver than those in the LDL–C/HDL–C ratio. Sustainable BMI control for individuals may be the most effective option for preventing fatty liver in a population.

Klíčová slova:

Alcohol consumption – Algorithms – Fatty liver – Cholesterol – Medical risk factors – Monte Carlo method – Regression analysis – Simulation and modeling


1. Younossi Z, Anstee QM, Marietti M, Hardy T, Henry L, Eslam M, et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2018;15(1):11–20. doi: 10.1038/nrgastro.2017.109 28930295

2. Marchesini G, Bugianesi E, Forlani G, Cerrelli F, Lenzi M, Manini R, et al. Nonalcoholic fatty liver, steatohepatitis, and the metabolic syndrome. Hepatology. 2003;37(4):917–23. doi: 10.1053/jhep.2003.50161 12668987

3. Fazel Y, Koenig AB, Sayiner M, Goodman ZD, Younossi ZM. Epidemiology and natural history of non–alcoholic fatty liver disease. Metabolism. 2016;65(8):1017–25. doi: 10.1016/j.metabol.2016.01.012 26997539

4. Kojima S, Watanabe N, Numata M, Ogawa T, Matsuzaki S. Increase in the prevalence of fatty liver in Japan over the past 12 years: analysis of clinical background. J Gastroenterol. 2003;38(10):954–61. doi: 10.1007/s00535-003-1178-8 14614602

5. Pais R, Charlotte F, Fedchuk L, Bedossa P, Lebray P, Poynard T, et al. A systematic review of follow–up biopsies reveals disease progression in patients with non–alcoholic fatty liver. J Hepatol. 2013;59(3):550–6. doi: 10.1016/j.jhep.2013.04.027 23665288

6. Stefan N, Kantartzis K, Häring HU. Causes and metabolic consequences of Fatty liver. Endocr Rev. 2008;29(7):939–60. doi: 10.1210/er.2008-0009 18723451

7. Jimba S, Nakagami T, Takahashi M, Wakamatsu T, Hirota Y, Iwamoto Y, et al. Prevalence of non–alcoholic fatty liver disease and its association with impaired glucose metabolism in Japanese adults. Diabet Med. 2005;22(9):1141–5. doi: 10.1111/j.1464-5491.2005.01582.x 16108839

8. Kadowaki S, Tamura Y, Someya Y, Takeno K, Kaga H, Sugimoto D, et al. Fatty Liver Has Stronger Association With Insulin Resistance Than Visceral Fat Accumulation in Nonobese Japanese Men. J Endocr Soc. 2019;3(7):1409–16. doi: 10.1210/js.2019-00052 31286107

9. Hamaguchi M, Kojima T, Takeda N, Nagata C, Takeda J, Sarui H, et al. Nonalcoholic fatty liver disease is a novel predictor of cardiovascular disease. World J Gastroenterol. 2007;13(10):1579–84. doi: 10.3748/wjg.v13.i10.1579 17461452

10. Bedogni G, Miglioli L, Masutti F, Castiglione A, Crocè LS, Tiribelli C, et al. Incidence and natural course of fatty liver in the general population: the Dionysos study. Hepatology. 2007;46(5):1387–91. doi: 10.1002/hep.21827 17685472

11. Keating SE, Hackett DA, George J, Johnson NA. Exercise and non–alcoholic fatty liver disease: a systematic review and meta–analysis. J Hepatol. 2012;57(1):157–66. doi: 10.1016/j.jhep.2012.02.023 22414768

12. Miyake T, Kumagi T, Hirooka M, Furukawa S, Koizumi M, Tokumoto Y, et al. Body mass index is the most useful predictive factor for the onset of nonalcoholic fatty liver disease: a community–based retrospective longitudinal cohort study. J Gastroenterol. 2013;48(3):413–22. doi: 10.1007/s00535-012-0650-8 22933183

13. Speliotes EK, Massaro JM, Hoffmann U, Vasan RS, Meigs JB, Sahani DV, et al. Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology. 2010;51(6):1979–87. doi: 10.1002/hep.23593 20336705

14. Tomizawa M, Kawanabe Y, Shinozaki F, Sato S, Motoyoshi Y, Sugiyama T, et al. Triglyceride is strongly associated with nonalcoholic fatty liver disease among markers of hyperlipidemia and diabetes. Biomed Rep. 2014;2(5):633–6. doi: 10.3892/br.2014.309 25054002

15. Sun WH, Song MQ, Jiang CQ, Xin YN, Ma JL, Liu YX, et al. Lifestyle intervention in non–alcoholic fatty liver disease in Chengyang District, Qingdao, China. World J Hepatol. 2012;4(7):224–30. doi: 10.4254/wjh.v4.i7.224 22855698

16. Cazzo E, Jimenez LS, Gestic MA, Utrini MP, Chaim FHM, Chaim FDM, et al. Type 2 Diabetes Mellitus and Simple Glucose Metabolism Parameters may Reliably Predict Nonalcoholic Fatty Liver Disease Features. Obes Surg. 2018;28(1):187–94. doi: 10.1007/s11695-017-2829-9 28741239

17. Jung HS, Chang Y, Kwon MJ, Sung E, Yun KE, Cho YK, et al. Smoking and the Risk of Non–Alcoholic Fatty Liver Disease: A Cohort Study. Am J Gastroenterol. 2019;114(3):453–63. doi: 10.1038/s41395-018-0283-5 30353055

18. Sookoian S, Pirola CJ. Shift work and subclinical atherosclerosis: recommendations for fatty liver disease detection. Atherosclerosis. 2009;207(2):346–7. doi: 10.1016/j.atherosclerosis.2009.06.008 19580972

19. Lallukka S, Sädevirta S, Kallio MT, Luukkonen PK, Zhou Y, Hakkarainen A, et al. Predictors of Liver Fat and Stiffness in Non–Alcoholic Fatty Liver Disease (NAFLD)–an 11–Year Prospective Study. Sci Rep. 2017;7(1):14561. doi: 10.1038/s41598-017-14706-0 29109528

20. Zelber–Sagi S, Lotan R, Shlomai A, Webb M, Harrari G, Buch A, et al. Predictors for incidence and remission of NAFLD in the general population during a seven–year prospective follow–up. J Hepatol. 2012;56(5):1145–51. doi: 10.1016/j.jhep.2011.12.011 22245895

21. Babad H, Sanderson C, Naidoo B, White I, Wang D. The development of a simulation model of primary prevention strategies for coronary heart disease. Health Care Manag Sci. 2002;5(4):269–74. 12437274

22. Kypridemos C, Guzman–Castillo M, Hyseni L, Hickey GL, Bandosz P, Buchan I, et al. Estimated reductions in cardiovascular and gastric cancer disease burden through salt policies in England: an IMPACTNCD microsimulation study. BMJ Open. 2017;7(1):e013791. doi: 10.1136/bmjopen-2016-013791 28119387

23. Webber L, Divajeva D, Marsh T, McPherson K, Brown M, Galea G, et al. The future burden of obesity–related diseases in the 53 WHO European–Region countries and the impact of effective interventions: a modelling study. BMJ Open. 2014;4(7):e004787. doi: 10.1136/bmjopen-2014-004787 25063459

24. Kopec JA, Sayre EC, Fines P, Flanagan WM, Nadeau C, Okhmatovskaia A, et al. Effects of Reductions in Body Mass Index on the Future Osteoarthritis Burden in Canada: A Population–Based Microsimulation Study. Arthritis Care Res (Hoboken). 2016;68(8):1098–105.

25. Estes C, Anstee QM, Arias-Loste MT, Bantel H, Bellentani S, Caballeria J, et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016–2030. J Hepatol. 2018;69(4):896–904. doi: 10.1016/j.jhep.2018.05.036 29886156

26. Vreman RA, Goodell AJ, Rodriguez LA, Porco TC, Lustig RH, Kahn JG. Health and economic benefits of reducing sugar intake in the USA, including effects via non–alcoholic fatty liver disease: a microsimulation model. BMJ Open. 2017;7(8):e013543. doi: 10.1136/bmjopen-2016-013543 28775179

27. Hashimoto E, Taniai M, Tokushige K. Characteristics and diagnosis of NAFLD/NASH. J Gastroenterol Hepatol. 2013;28 Suppl 4:64–70.

28. Eguchi Y, Hyogo H, Ono M, Mizuta T, Ono N, Fujimoto K, et al. Prevalence and associated metabolic factors of nonalcoholic fatty liver disease in the general population from 2009 to 2010 in Japan: a multicenter large retrospective study. J Gastroenterol. 2012;47(5):586–95. doi: 10.1007/s00535-012-0533-z 22328022

29. Nomura H, Kashiwagi S, Hayashi J, Kajiyama W, Tani S, Goto M. Prevalence of fatty liver in a general population of Okinawa, Japan. Jpn J Med. 1988;27(2):142–9. 3047469

30. Watanabe S, Hashimoto E, Ikejima K, Uto H, Ono M, Sumida Y, et al. Evidence–based clinical practice guidelines for nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. Hepatol Res. 2015;45(4):363–77. doi: 10.1111/hepr.12511 25832328

31. Kopec JA, Finès P, Manuel DG, Buckeridge DL, Flanagan WM, Oderkirk J, et al. Validation of population–based disease simulation models: a review of concepts and methods. BMC Public Health. 2010;10:710. doi: 10.1186/1471-2458-10-710 21087466

32. Ministry of Health, Labor and Welfare, National Health and Nutrition Survey [Internet]. Japan: Ministry of Health, Labor and Welfare; 2012. Table A. Abridged Life Tables for Japan 2012 (in Japanese); 2013 Jul 25 [cited 2018 May 12]. Available from:–06.pdf

33. Yatsuji S, Hashimoto E, Tobari M, Tokushige K, Shiratori K. Influence of age and gender in Japanese patients with non–alcoholic steatohepatitis. Hepatol Res. 2007;37(12):1034–43. doi: 10.1111/j.1872-034X.2007.00156.x 17610504

34. Hashimoto E, Tokushige K. Prevalence, gender, ethnic variations, and prognosis of NASH. J Gastroenterol. 2011;46 Suppl 1:63–9.

35. Ministry of Health, Labor and Welfare, National Health and Nutrition Survey [Internet]. Japan: Ministry of Health, Labor and Welfare; 2014. Chapter 2 The results of the physical condition (in Japanese); 2016 Apr 20 [cited 2018 Jun 1]. Available from:–houkoku–05.pdf

36. Manninen V, Tenkanen L, Koskinen P, Huttunen JK, Mänttäri M, Heinonen OP, et al. Joint effects of serum triglyceride and LDL cholesterol and HDL cholesterol concentrations on coronary heart disease risk in the Helsinki Heart Study. Implications for treatment. Circulation. 1992;85(1):37–45. doi: 10.1161/01.cir.85.1.37 1728471

37. Enomoto M, Adachi H, Hirai Y, Fukami A, Satoh A, Otsuka M, et al. LDL–C/HDL–C Ratio Predicts Carotid Intima–Media Thickness Progression Better Than HDL–C or LDL–C Alone. J Lipids. 2011;2011:549137. doi: 10.1155/2011/549137 21773051

38. Ampuero J, Gallego–Durán R, Romero–Gómez M. Association of NAFLD with subclinical atherosclerosis and coronary–artery disease: meta–analysis. Rev Esp Enferm Dig. 2015;107(1):10–6. 25603326

39. Fukuda Y, Hashimoto Y, Hamaguchi M, Fukuda T, Nakamura N, Ohbora A, et al. Triglycerides to high–density lipoprotein cholesterol ratio is an independent predictor of incident fatty liver; a population–based cohort study. Liver Int. 2016;36(5):713–20. doi: 10.1111/liv.12977 26444696

40. Wang K, Shan S, Zheng H, Zhao X, Chen C, Liu C. Non–HDL–cholesterol to HDL–cholesterol ratio is a better predictor of new–onset non–alcoholic fatty liver disease than non–HDL–cholesterol: a cohort study. Lipids Health Dis. 2018;17(1):196. doi: 10.1186/s12944-018-0848-8 30131058

41. Johnson NA, George J. Fitness versus fatness: moving beyond weight loss in nonalcoholic fatty liver disease. Hepatology. 2010;52(1):370–81. doi: 10.1002/hep.23711 20578153

42. Orpana HM, Tremblay MS, Finès P. Trends in weight change among Canadian adults. Health Rep. 2007;18(2):9–16. 17578012

43. Hennessy D, Garner R, Flanagan WM, Wall R, Nadeau C. Development of a population–based microsimulation model of body mass index. Health Rep. 2017;28(6):20–30. 28636070

44. Bajekal M, Scholes S, Love H, Hawkins N, O'Flaherty M, Raine R, et al. Analysing recent socioeconomic trends in coronary heart disease mortality in England, 2000–2007: a population modelling study. PLoS Med. 2012;9(6):e1001237. doi: 10.1371/journal.pmed.1001237 22719232

45. Manuel DG, Tuna M, Hennessy D, Bennett C, Okhmatovskaia A, Finès P, et al. Projections of preventable risks for cardiovascular disease in Canada to 2021: a microsimulation modelling approach. CMAJ Open. 2014;2(2):E94–E101. doi: 10.9778/cmajo.2012-0015 25077135

46. Orenstein PV, Shi L. Microsimulation Modeling of Coronary Heart Disease: Maximizing the Impact of Nonprofit Hospital–Based Interventions. Inquiry. 2016;53.

47. Nagaya T, Tanaka N, Komatsu M, Ichijo T, Sano K, Horiuchi A, et al. Development from simple steatosis to liver cirrhosis and hepatocellular carcinoma: a 27–year follow–up case. Clin J Gastroenterol. 2008;1(3):116–21. doi: 10.1007/s12328-008-0017-0 26193649

Článek vyšel v časopise


2019 Číslo 10