Background Delineating the natural history of metabolic syndrome (MetS) is prerequisite to prevention. This study aimed to build Markov models to simulate each component’s progress and to test the effect of different initial states on the development of MetS. Methods MetS was defined with revised AHA/NHLBI criteria. Each reversible multistate Markov chain consisted of 8 states (no component, five isolated component states, 2-component state, and MetS state). Yearly transition probabilities were calculated from a five-year population-based follow up studywhich enrolled 2,247 individuals with mean aged 32.4 years at study entry. Results In men, high BP or a 2-component state was most likely to initiate the progress of MetS. In women, abdominal obesity or low HDL were the most likely initiators. Metabolic components were likely to occur together. The development of MetS was an increasing monotonic function of time. MetS was estimated to develop within 15 years in 12.7% of young men with no component, and 2 components developed in 16.3%. MetS was estimated to develop in 10.6% of women with at the age of 47, and 2 components developed in 14.3%. MetS was estimated to develop in 24.6% of men and 27.6% of women with abdominal obesity, a rate higher than in individuals initiating with no component. Conclusions This modeling study allows estimation of the natural history of MetS. Men tended to develop this syndrome sooner than women did, i.e., before their fifth decade of life. Individuals with 1 or 2 components showed increased development of MetS.
References
[1]
Wilson PW, D’Agostino RB, Parise H, Sullivan L, Meigs JB (2005) Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation 112: 3066–3072.
[2]
Prabhakaran D, Anand SS (2004) The metabolic syndrome: an emerging risk state for cardiovascular disease. Vasc Med 9: 55–68.
[3]
Santos AC, Severo M, Barros H (2010) Incidence and risk factors for the metabolic syndrome in an urban South European population. Prev Med 50: 99–105.
[4]
Wilsgaard T, Jacobsen BK (2007) Lifestyle factors and incident metabolic syndrome. The Tromso Study 1979–2001. Diabetes Res Clin Pract 78: 217–224.
[5]
Kim BJ, Kim BS, Kang JH (2012) Alcohol consumption and incidence of metabolic syndrome in korean men. A 3-year follow-up study. Circ J 76: 2363–2371.
[6]
Han TS, Williams K, Sattar N, Hunt KJ, Lean ME, et al. (2002) Analysis of obesity and hyperinsulinemia in the development of metabolic syndrome: San Antonio Heart Study. Obes Res 10: 923–931.
[7]
Haring R, Rosvall M, Volker U, Volzke H, Kroemer H, et al. (2012) A network-based approach to visualize prevalence and progression of metabolic syndrome components. PLoS One 7: e39461.
[8]
Silverstein MD, Albert DA, Hadler NM, Ropes MW (1988) Prognosis in SLE: comparison of Markov model to life table analysis. J Clin Epidemiol 41: 623–633.
[9]
Andersen PK, Keiding N (2002) Multi-state models for event history analysis. Stat Methods Med Res 11: 91–115.
[10]
Beck JR (1988) Markov models of natural history. J Clin Epidemiol 41: 619–621.
[11]
Sonnenberg FA, Beck JR (1993) Markov models in medical decision making: a practical guide. Med Decis Making 13: 322–338.
[12]
Craig BA, Fryback DG, Klein R, Klein BE (1999) A Bayesian approach to modelling the natural history of a chronic condition from observations with intervention. Stat Med 18: 1355–1371.
[13]
Silverstein MD, Loftus EV, Sandborn WJ, Tremaine WJ, Feagan BG, et al. (1999) Clinical course and costs of care for Crohn’s disease: Markov model analysis of a population-based cohort. Gastroenterology 117: 49–57.
[14]
Hwang LC, Bai CH, Chen CJ (2006) Prevalence of obesity and metabolic syndrome in Taiwan. J Formos Med Assoc 105: 626–635.
[15]
Yang T, Chu CH, Bai CH, You SL, Chou YC, et al. (2012) Uric acid level as a risk marker for metabolic syndrome: a Chinese cohort study. Atherosclerosis 220: 525–531.
[16]
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, et al. (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120: 1640–1645.
[17]
Macchia A, Levantesi G, Borrelli G, Franzosi MG, Maggioni AP, et al. (2006) A clinically practicable diagnostic score for metabolic syndrome improves its predictivity of diabetes mellitus: the Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico (GISSI)-Prevenzione scoring. Am Heart J 151: 754.e7–754.e17.
[18]
Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, et al. (2010) The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol 56: 1113–1132.
[19]
Tenenbaum A, Fisman EZ (2011) “The metabolic syndrome… is dead”: these reports are an exaggeration. Cardiovasc Diabetol 10: 11.
[20]
Despres JP, Lemieux I (2006) Abdominal obesity and metabolic syndrome. Nature 444: 881–887.
Cameron AJ, Boyko EJ, Sicree RA, Zimmet PZ, Soderberg S, et al. (2008) Central obesity as a precursor to the metabolic syndrome in the AusDiab study and Mauritius. Obesity (Silver Spring) 16: 2707–2716.
[23]
Regitz-Zagrosek V, Lehmkuhl E, Mahmoodzadeh S (2007) Gender aspects of the role of the metabolic syndrome as a risk factor for cardiovascular disease. Gend Med 4 Suppl B: S162–177.
[24]
Spiegel BM, Targownik L, Dulai GS, Karsan HA, Gralnek IM (2003) Endoscopic screening for esophageal varices in cirrhosis: Is it ever cost effective? Hepatology 37: 366–377.
[25]
Sonnenberg A, Naugler WE (2010) Models of influence in chronic liver disease. Liver Int 30: 718–724.
[26]
Veenstra DL, Spackman DE, Di Bisceglie A, Kowdley KV, Gish RG (2008) Evaluating anti-viral drug selection and treatment duration in HBeAg-negative chronic hepatitis B: a cost-effectiveness analysis. Aliment Pharmacol Ther 27: 1240–1252.
[27]
Sweeting MJ, Farewell VT, De Angelis D (2010) Multi-state Markov models for disease progression in the presence of informative examination times: an application to hepatitis C. Stat Med. 29: 1161–1174.
[28]
Denton BT, Kurt M, Shah ND, Bryant SC, Smith SA (2009) Optimizing the start time of statin therapy for patients with diabetes. Med Decis Making 29: 351–367.
[29]
Grassi A, Gaetano L, Pacini G, Kautzky-Willer A, Tura A (2010) A Markov chain probability model of glucose tolerance in post gestational diabetes follow up study. Stud Health Technol Inform 160: 1155–1159.
[30]
Rigopoulou EI, Dalekos GN (2010) Can mathematical models be useful in clinical hepatology? Liver Int 30: 637–638.
