Description and Prediction of the Development of Metabolic Syndrome: A Longitudinal Analysis Using a Markov Model Approach

Lee Ching Hwang, Chyi Huey Bai, San Lin You, Chien An Sun, Chien Jen Chen

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11 Citations (Scopus)

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.

Original languageEnglish
Article numbere67436
JournalPLoS One
Volume8
Issue number6
DOIs
Publication statusPublished - Jun 20 2013
Externally publishedYes

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ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

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