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中国人群代谢综合征的动态发展及其风险预测:一项使用马尔可夫模型的纵向研究

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

作者信息

Jia Xiaoxian, Chen Qicai, Wu Peipei, Liu Meng, Chen Xiaoxiao, Xiao Juan, Chen Lili, Zhang Pengpeng, Wang Shumei

机构信息

1Department of Epidemiology, School of Public Health, Shandong University, 44 Wenhua West Road, Jinan, 250012 China.

2Department of Prevention and Health Care, Dongying Shengli Oilfield Central Hospital, Dongying, 257000 China.

出版信息

Diabetol Metab Syndr. 2018 Apr 2;10:24. doi: 10.1186/s13098-018-0328-3. eCollection 2018.

DOI:10.1186/s13098-018-0328-3
PMID:29619091
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5880005/
Abstract

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.

摘要

背景

随着代谢综合征(MS)患病率的不断上升,有必要追踪和预测MS的发展。在本研究中,我们建立了一个马尔可夫模型来探索MS的自然史并预测其风险。

方法

对2010年至2015年间至少进行过两次连续健康检查的21777名中国个体进行了研究。MS采用中国糖尿病学会标准进行定义。每个马尔可夫链包含12种代谢异常状态(无组分状态、4种单一组分状态、6种双组分状态和MS状态)。转移概率为连续两年中任意两种状态之间转移的5个概率的平均值。

结果

在18至49岁的个体中,血脂异常或超重/肥胖组分最有可能引发MS的进展。然而,对于50岁以上的个体,MS最可能的起始组分是血脂异常或高血压。最初患有血脂异常的人在50岁之前最有可能发展为血脂异常合并超重/肥胖的状态,但在50岁之后,血脂异常合并高血压的状态最为常见。最初患有超重/肥胖、高血压或高血糖单一状态的受试者(50岁以上最初患有单纯高血糖状态的男性除外)最有可能发展为这些初始状态之一与血脂异常的组合。最初患有单纯高血糖的男性在50岁后倾向于发展为高血压。与最初患有其他异常代谢状态的受试者相比,最初患有单纯高血糖状态或包含高血糖的双组分状态的受试者在10年内发展为MS的可能性更大。

结论

MS的发生在18至49岁的人群中主要始于超重/肥胖或血脂异常。然而,对于50岁以上的人群,MS主要在血脂异常或高血压的情况下起始。当MS在超重/肥胖、高血压或高血糖的情况下起始时,接下来往往会出现血脂异常。最初患有单纯高血糖或包含高血糖的双组分状态的人比其他起始状态的人患MS的风险更高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/ca35d5f7d082/13098_2018_328_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/d0ef4f14cca8/13098_2018_328_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/a0953311f1e9/13098_2018_328_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/57923f44857e/13098_2018_328_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/ca35d5f7d082/13098_2018_328_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/d0ef4f14cca8/13098_2018_328_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/f76865c36abc/13098_2018_328_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/f7da58bc5375/13098_2018_328_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/b2d499a85f5b/13098_2018_328_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/e79c742822de/13098_2018_328_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/b247e1559e40/13098_2018_328_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/a0953311f1e9/13098_2018_328_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/57923f44857e/13098_2018_328_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a6f/5880005/ca35d5f7d082/13098_2018_328_Fig9_HTML.jpg

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