| BackgroundMetabolic syndrome (MetS) is a clustering of metabolic risk factors, including obesity, hypertension, dyslipidemia, and insulin resistance. With the development of economy and the changes of people life style, about a quarter of the population suffers from MetS worldwide and the prevalence of MetS increases year by year. MetS can cause numerous harm to the patients, it can raise the risk of cardiovascular disease which is the leading killer in China. Evidence also suggests that MetS is strongly correlated with polycystic ovarian disease, nonalcoholic fatty liver disease, and some cancers. In fact, MS is increasingly becoming a major worldwide clinical and public health issue.Due to the great harm caused by MS and the widespread popularity of MetS, vast studies have been done on it. Previous studies mainly focused on searching for early MetS’biomarkers and risk factors, such as white blood cell count, serum uric acid, gamma-glutamyl transpeptidase, alanine aminotransferase, physical inactivity, overeating, alcohol intake, and smoking habits. Only a few studies focused on the onset process of MetS. MetS has four components, thus there are 16 different states and 256 transitions between states. Therefore, the occurrence of MetS is a very complicated process.Besides, MetS is defined as the existence of at least three of the four components at the same time and the development of MetS is not a quick process. Therefore, stating the history of MS and searching for the most likely component contributing to start the cascade of confusions are preconditions for preventing its development. Related studies were conducted by many scholars, just like network-based approach. However, these studies faced many barriers. The Markov model which is frequently used to represent a random process changing with time is an admitted method to simulate the natural history of chronic diseases. A previous study applied a Markov model approach to predict the development of MetS. However, this study was only with young people and without intermediate process conditions, it was limited. Therefore, we conducted a Markov model with a six-year follow-up health check-up including different genders and age groups to describe the natural history of MetS, and to predict the effect of different initial states on the development of MetS.Objectives1. To describe the natural history of MetS, and to search for the most likely component contributing to start the cascade of confusions of MetS2. To predict the risk of different initial states on the development of MetS.MethodsThe participants for this study were collected during the period from September 2006 to September 2011 in the Health Management Center of Shengli Oilfield Central Hospital in Dongying City and the subjects who had at least two health check-ups in the six-year follow-up were involved in this study. Individuals who had a history of coronary heart diseases, type I diabetes, familial hyperlipidemia, and those who did not provide complete information were excluded from the analysis. All subjects underwent a doctor’s interview, anthropometric and laboratory test.The criteria given by Chinese Medical Association Diabetes Branch (CDS) were adopted to define MetS in our study. General statistical analysis of the data and calculation of transition probabilities for the Markov model were performed by S AS 9.1. A Markov model with each chain containing seven states was built based on the theory and TreeAge pro 2011 software was used to construct the model. Transition probabilities for the model were the mean of five probabilities for the transition between the given states between each pair of consecutive years in every age and gender group which would increase the sample size and guarantee the stability of the results.Results1. The general information of the individuals:7510 individuals were included in this study, there were 5118 men and 2392 women. The prevalence of MetS on the baseline was 11.89%, with 15.28%,18.17%,20.24%,22.80%,26.13% in the follow-up years. MetS prevalence increased with greater time since baseline.2. Calculation for the Markov model transition probability:The transition probabilities from no component state to MetS were higher in men than in women in four age groups and the same trend could be seen from MetS state to MetS. In men under 60 years old and women under 50 years old, 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.3. Risk prediction for the Markov model:Individuals initiating with no component state kept a gradually shrinking probability to maintain its own state and more and more people developed to 2-component state or MetS state with the growth of the age. The transition probabilities for people starting with no component state to maintain health were higher in men than that in women in future 10 years. Individuals beginning with any isolated component state or the 2-component state were more likely to develop MetS than the individuals starting with no component in the prediction of 10 years. Similarly, there was a greater chance for the individuals starting with 2-component state and the isolated hyperglycemia state to develop MetS than the others.Conclusions1. Men are more prone to develop MetS than women of the same age.2. 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.3. Individuals with the isolated hyperglycemia had a greater chance to develop MS than the other isolated MS’components. |
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