title: Description and prediction of the development of metabolic syndrome: a longitudinal analysis using a markov model approach.
abstract: 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.
authors.affiliation: Department of Family Medicine, Mackay Memorial Hospital, Taipei, Taiwan. hlc@ms2.mmh.org.tw
authors.forename: Lee-Ching
authors.initials: LC
authors.lastname: Hwang
authors.forename: Chyi-Huey
authors.initials: CH
authors.lastname: Bai
authors.forename: San-Lin
authors.initials: SL
authors.lastname: You
authors.forename: Chien-An
authors.initials: CA
authors.lastname: Sun
authors.forename: Chien-Jen
authors.initials: CJ
authors.lastname: Chen
mesh_terms: D000328:Adult; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008390:Markov Chains; D024821:Metabolic Syndrome / Q000453:epidemiology / Q000209:etiology*; D008875:Middle Aged; D008954:Models, Biological; D015995:Prevalence; D012307:Risk Factors; D055815:Young Adult