title: Description and prediction of the development of metabolic syndrome in Dongying City: a longitudinal analysis using the Markov model.
abstract: BACKGROUND
  Metabolic Syndrome (MS) is increasingly becoming a major worldwide clinical and public health issue. Thus it is extremely important to study the history of MS and search for the most likely component contributing to start the cascade of confusions of MS.
  
  
  METHODS
  A longitudinal cohort was involved which included the data of 7510 individuals who had at least two routine health check-ups in a six-year follow-up. Based on the data, a Markov model with each chain containing seven states (no component state, four isolated states, 2-component state, and MS state) was built. Annual transition probability was the mean of five probabilities for the transition between the given states between each pair of consecutive years.
  
  
  RESULTS
  The transition probabilities from the no component state to MS were higher in men than that in women in four age groups. In the young people (men <60 years and women <50 years), the probabilities to the overweight or obesity state and dyslipidemia state were the first two biggest probabilities in transition from no component to the rest six states. However, in the elderly population, the probabilities to hypertension state and 2-component state increased, even surpassed the above two states. The individuals initiating with 2-component states and the isolated hyperglycemia state were more likely to develop MS than the others.
  
  
  CONCLUSIONS
  The Markov model was able to give a better description of the evolutionary history of MS, and to predict the future course based on past evidence. The occurrence of the MS process mostly began with overweight or obesity and dyslipidemia in young people. In the elderly population, many individuals initiating with hypertension or 2 components besides the above two states. Individuals with the isolated hyperglycemia had greater chances to develop MS than other isolated MS' components.
authors.forename: Xiaoxiao
authors.initials: X
authors.lastname: Chen
authors.forename: Qicai
authors.initials: Q
authors.lastname: Chen
authors.forename: Lili
authors.initials: L
authors.lastname: Chen
authors.forename: Pengpeng
authors.initials: P
authors.lastname: Zhang
authors.forename: Juan
authors.initials: J
authors.lastname: Xiao
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Shandong University, Jinan, China. wshm@sdu.edu.cn.
authors.forename: Shumei
authors.initials: S
authors.lastname: Wang
mesh_terms: D000328:Adult; D000368:Aged; D002681:China / Q000453:epidemiology; D050171:Dyslipidemias / Q000453:epidemiology; D005260:Female; D005500:Follow-Up Studies; D005544:Forecasting; D006304:Health Status; D006801:Humans; D006973:Hypertension / Q000453:epidemiology; D008297:Male; D008390:Markov Chains; D024821:Metabolic Syndrome / Q000175:diagnosis / Q000453:epidemiology*; D008954:Models, Biological; D009765:Obesity / Q000175:diagnosis / Q000453:epidemiology*; D050177:Overweight / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D012307:Risk Factors; D055815:Young Adult