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Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model

BACKGROUND: With the increasing prevalence of metabolic syndrome (MS), there is a need to track and predict the development of MS. In this study, we established a Markov model to explore the natural history and predict the risk of MS. METHODS: A total of 21,777 Chinese individuals who had at least t...

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Autores principales: Jia, Xiaoxian, Chen, Qicai, Wu, Peipei, Liu, Meng, Chen, Xiaoxiao, Xiao, Juan, Chen, Lili, Zhang, Pengpeng, Wang, Shumei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5880005/
https://www.ncbi.nlm.nih.gov/pubmed/29619091
http://dx.doi.org/10.1186/s13098-018-0328-3
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author Jia, Xiaoxian
Chen, Qicai
Wu, Peipei
Liu, Meng
Chen, Xiaoxiao
Xiao, Juan
Chen, Lili
Zhang, Pengpeng
Wang, Shumei
author_facet Jia, Xiaoxian
Chen, Qicai
Wu, Peipei
Liu, Meng
Chen, Xiaoxiao
Xiao, Juan
Chen, Lili
Zhang, Pengpeng
Wang, Shumei
author_sort Jia, Xiaoxian
collection PubMed
description BACKGROUND: With the increasing prevalence of metabolic syndrome (MS), there is a need to track and predict the development of MS. In this study, we established a Markov model to explore the natural history and predict the risk of MS. METHODS: A total of 21,777 Chinese individuals who had at least two consecutive health check-ups between 2010 and 2015 were studied. MS was defined using the Chinese Diabetes Society criteria. Twelve metabolic abnormal states (the no component state, four isolated component states, six 2-component states, and the MS state) were contained in each Markov chain. The transition probability was the mean of five probabilities for the transition between any two states in 2 consecutive years. RESULTS: The dyslipidemia or overweight/obesity components were most likely to initiate the progress of MS in individuals aged 18–49. However, for individuals over 50 years old, the most likely initiating component of MS was dyslipidemia or hypertension. People who initially had dyslipidemia were most likely to develop the combined state of dyslipidemia with overweight/obesity before the age of 50, but after 50 years of age, the state of dyslipidemia merged with hypertension was the most common. Subjects (with the exception of males over 50 years of age who initially had an isolated state of hyperglycemia) who initially had an isolated state of overweight/obesity, hypertension, or hyperglycemia were most likely to develop a combination of one of these initial states with dyslipidemia. Males who initially had isolated hyperglycemia tended to develop hypertension after age 50. There was a greater chance for subjects who initially had an isolated hyperglycemia state or 2-component state that contained hyperglycemia to develop MS within 10 years compared to those who initially had other abnormal metabolic states. CONCLUSIONS: The occurrence of MS primarily began with overweight/obesity or dyslipidemia in people aged 18–49. However, for those over 50 years old, MS primarily initiated under the conditions of dyslipidemia or hypertension. When MS started under the conditions of overweight/obesity, hypertension or hyperglycemia, dyslipidemia tended to occur next. People who initially had isolated hyperglycemia or a 2-component state that contained hyperglycemia had a higher risk of developing MS than those with other initiating states.
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spelling pubmed-58800052018-04-04 Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model Jia, Xiaoxian Chen, Qicai Wu, Peipei Liu, Meng Chen, Xiaoxiao Xiao, Juan Chen, Lili Zhang, Pengpeng Wang, Shumei Diabetol Metab Syndr Research BACKGROUND: With the increasing prevalence of metabolic syndrome (MS), there is a need to track and predict the development of MS. In this study, we established a Markov model to explore the natural history and predict the risk of MS. METHODS: A total of 21,777 Chinese individuals who had at least two consecutive health check-ups between 2010 and 2015 were studied. MS was defined using the Chinese Diabetes Society criteria. Twelve metabolic abnormal states (the no component state, four isolated component states, six 2-component states, and the MS state) were contained in each Markov chain. The transition probability was the mean of five probabilities for the transition between any two states in 2 consecutive years. RESULTS: The dyslipidemia or overweight/obesity components were most likely to initiate the progress of MS in individuals aged 18–49. However, for individuals over 50 years old, the most likely initiating component of MS was dyslipidemia or hypertension. People who initially had dyslipidemia were most likely to develop the combined state of dyslipidemia with overweight/obesity before the age of 50, but after 50 years of age, the state of dyslipidemia merged with hypertension was the most common. Subjects (with the exception of males over 50 years of age who initially had an isolated state of hyperglycemia) who initially had an isolated state of overweight/obesity, hypertension, or hyperglycemia were most likely to develop a combination of one of these initial states with dyslipidemia. Males who initially had isolated hyperglycemia tended to develop hypertension after age 50. There was a greater chance for subjects who initially had an isolated hyperglycemia state or 2-component state that contained hyperglycemia to develop MS within 10 years compared to those who initially had other abnormal metabolic states. CONCLUSIONS: The occurrence of MS primarily began with overweight/obesity or dyslipidemia in people aged 18–49. However, for those over 50 years old, MS primarily initiated under the conditions of dyslipidemia or hypertension. When MS started under the conditions of overweight/obesity, hypertension or hyperglycemia, dyslipidemia tended to occur next. People who initially had isolated hyperglycemia or a 2-component state that contained hyperglycemia had a higher risk of developing MS than those with other initiating states. BioMed Central 2018-04-02 /pmc/articles/PMC5880005/ /pubmed/29619091 http://dx.doi.org/10.1186/s13098-018-0328-3 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Jia, Xiaoxian
Chen, Qicai
Wu, Peipei
Liu, Meng
Chen, Xiaoxiao
Xiao, Juan
Chen, Lili
Zhang, Pengpeng
Wang, Shumei
Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model
title Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model
title_full Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model
title_fullStr Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model
title_full_unstemmed Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model
title_short Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model
title_sort dynamic development of metabolic syndrome and its risk prediction in chinese population: a longitudinal study using markov model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5880005/
https://www.ncbi.nlm.nih.gov/pubmed/29619091
http://dx.doi.org/10.1186/s13098-018-0328-3
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