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BMC Public Health
Published in: BMC Public Health 1/2018

Open Access 01-12-2018 | Research article

Prediction of the development of metabolic syndrome by the Markov model based on a longitudinal study in Dalian City

Authors: Xiao Tang, Qigui Liu

Published in: BMC Public Health | Issue 1/2018

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Abstract

Background

Metabolic syndrome (MetS) increases the incidence of cardiovascular disease and diabetes mellitus. It is essential to study the natural progression of MetS in the interest of prevention. Information on the dynamic changes in MetS in developing countries is limited. This study aimed to simulate the progression of each component of MetS and explore the potential role of these components in early prevention and intervention.

Methods

This study involved 5881 individuals, aged 20 to 60 at study entry, who underwent at least two consecutive years of health check-ups in the seven-year study period at our institution’s health check-up center. Participants were divided into four groups by age (a 20- to- 40-year-old group and a 40- to 60-year-old group) and gender. A Markov model containing 7 stages (no components, isolated hypertension, isolated obesity, isolated hyperglycemia, isolated dyslipidemia, a 2-component state, and the MetS state) was constructed for each group.

Results

In women and young men (20- to 40-year-old men), dyslipidemia and obesity were the two most probable states for individuals who were transitioning from no components to one of the other six states. Among those who had no components and were 30 years old at study entry, MetS was estimated to develop within 10 years in 11.42% of men and 3.04% of women. Among those who had no components and were 50 years old at study entry, MetS was estimated to develop within 10 years in 25.04% of men and 7.09% of women. The estimated prevalence of MetS over the next 10 years was higher in individuals starting with the obesity component than in individuals starting with any other isolated component. In a comparison of interventions targeting single components, simulations showed that the obesity intervention produced the largest relative reduction in the prevalence of MetS.

Conclusion

Markov models are suitable for describing and predicting the dynamic development of MetS. The occurrence of MetS most frequently began with dyslipidemia or obesity. Obesity played a predominant role in the development of MetS. Early obesity intervention was extremely important for MetS prevention.
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Literature
1.
Trogdon JG, Finkelstein EA, Nwaise IA, Tangka FK, Orenstein D. The economic burden of chronic cardiovascular disease for major insurers. Health PromotPract. 2007;8:234–42.
2.
Xu T, Dainelli L, Yu K, Ma L, Silva Zolezzi L, Detzel P, et al. The short-term health and economic burden of gestational diabetes mellitus in China: a modelling study. BMJ Open. 2017;7:e018893.CrossRefPubMedPubMedCentral
3.
Poh S, Mohamed Abdul RB, Lamoureux EL, Wong TY, Sabanayagam C. Metabolic syndrome and eye diseases. Diabetes Res ClinPract. 2016;113:86–100.CrossRef
4.
Mikolasevic I, Milic S, Orlic L, Poropat G, Jakopcic I, Franjic N, et al. Metabolic syndrome and acute pancreatitis. Eur J Intern Med. 2016;32:79–83.CrossRefPubMed
5.
6.
Gholami A, Zamani F, Hosseini B, Sharafkhani R, Maadi M, Moosavi Jahromi Z, et al. Metabolic syndrome is associated with health-related quality of life in suspected patients with nonalcoholic steatohepatitis. Med Princ Pract. 2018;27:166–72.CrossRefPubMedPubMedCentral
7.
Moraitis AG, Block T, Nguyen D, Belanoff JK. The role of glucocorticoid receptors in metabolic syndrome and psychiatric illness. J Steroid Biochem Mol Biol. 2017;165:114–20.CrossRefPubMed
8.
Trief PM, Cibula D, Delahanty LM, Weinstock RS. Depression, stress, and weight loss in individuals with metabolic syndrome in SHINE, a DPP translation study. Obesity (Silver Spring). 2014;22:2532–8.
9.
O’Neill S, Drisclll L O, Wong RJ. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev. 2015;16:1–12.CrossRefPubMed
10.
Ford ES, Li C, Zhao G. Prevalence and correlates of metabolic syndrome based on a harmonious definition among adults in the U.S. J Diabetes. 2010;2:180–93.CrossRefPubMed
11.
Lao XQ, Ma WJ, Sobko T, Zhang YH, Xu YJ, Xu XJ, et al. Dramatic escalation in metabolic syndrome and cardiovascular risk in a Chinese population experiencing rapid economic development. BMC Public Health. 2014;14:983.CrossRefPubMedPubMedCentral
12.
Tran BT, Bo YJ, Jin-Kyoung O. The prevalence trend of metabolic syndrome and its components and risk factors in Korean adults: results from the Korean national health and nutrition examination survey 2008–2013. BMC Public Health. 2017;17:71.CrossRefPubMedPubMedCentral
13.
Yang T, Chu CH, Bai CH, You SL, Chou YC, Chou WY, et al. Uric acid level as a risk marker for metabolic syndrome: a Chinese cohort study. Atherosclerosis. 2012;220:525–31.CrossRefPubMed
14.
Welty FK, Alfaddagh A, Elajami TK. Targeting inflammation in metabolic syndrome. Transl Res. 2016;167:257–80.CrossRefPubMed
15.
Hashimoto Y, Tanaka M, Kimura T, Kitagawa N, Hamaguchi M, Asano M, et al. Hemoglobin concentration and incident metabolic syndrome: a population-based large-scale cohort study. Endocrine. 2015;50:390–6.CrossRefPubMed
16.
Blackford K, Jancey J, Lee AH, James AP, Waddell T, Howat P. Home-based lifestyle intervention for rural adults improves metabolic syndrome parameters and cardiovascular risk factors: a randomised controlled trial. Prev Med. 2016;89:15–22.CrossRefPubMed
17.
Amiri P, Jalali-Farahani S, Masihay Akbar H, Cheraghi L, Khalili D, Momenan A. The effects of a community-based lifestyle intervention on metabolic syndrome and its components in adolescents: findings of a decade follow-up. Metab Syndr Relat Disord. 2018; https://​doi.​org/​10.​1089/​met.​2017.​0055.
18.
Sakulsupsiri A, Sakthong P, Winit-Watjana W. Cost-effectiveness analysis of the self-management program for Thai patients with metabolic syndrome. Value Health Reg Issues. 2016;9:28–35.CrossRefPubMed
19.
Haring R, Rosvall M, Völker U, Völzke H, Kroemer H, Nauck M, et al. A network-based approach to visualize prevalence and progression of metabolic syndrome components. PLoS One. 2012;7:e39461.CrossRefPubMedPubMedCentral
20.
Hwang LC, Bai CH, You SL, Sun CA, Chen CJ. Description and prediction of the development of metabolic syndrome: a longitudinal analysis using a markov model approach. PLoS One. 2013;8:e67436.CrossRefPubMedPubMedCentral
21.
Chen X, Chen Q, Chen L, Zhang P, Xiao J, Wang S. Description and prediction of the development of metabolic syndrome in Dongying City: a longitudinal analysis using the Markov model. BMC Public Health. 2014;14:1033.CrossRefPubMedPubMedCentral
22.
Ford ES, Giles WH, Mokdad AH. Increasing prevalence of the metabolic syndrome among U.S. adults. Diabetes Care. 2004;27:2444–9.CrossRefPubMed
23.
Hwang LC, Bai CH, Chen CJ. Prevalence of obesity and metabolic syndrome in Taiwan. J Formos Med Assoc. 2006;105:626–35.CrossRefPubMed
24.
25.
Oh EG, Hyun SS, Kim SH, Bang SY, Chu SH, Jeon JY, et al. A randomized controlled trial of therapeutic lifestyle modification in rural women with metabolic syndrome: a pilot study. Metab. 2008;57:255–61.CrossRef
26.
Wang Q, Chair SY, Wong EM. The effects of a lifestyle intervention program on physical outcomes, depression, and quality of life in adults with metabolic syndrome: a randomized clinical trial. Int J Cardiol. 2017;230:461–7.CrossRefPubMed
27.
Andersen PK, Keiding N. Multi-state models for event history analysis. Stat Methods Med Res. 2002;11:91–115.CrossRefPubMed
28.
Veenstra DL, Spackman DE, Di Bisceglie A, Kowdley KV, Gish RG. Evaluating anti-viral drug selection and treatment duration in HBeAg-negative chronic hepatitis B: a cost-effectiveness analysis. Aliment PharmacolTher. 2008;27:1240–52.CrossRef
29.
Wilsgaard T, Jacobsen BK. Lifestyle factors and incident metabolic syndrome. The Tromso Study 1979–2001. Diabetes Res ClinPract. 2007;78:217–24.CrossRef
30.
Palaniappan L, Carnethon MR, Wang Y, Hanley AJ, Fortmann SP, Haffner SM, et al. Predictors of the incident metabolic syndrome in adults: the insulin resistance atherosclerosis study. Diabetes Care. 2004;27:788–93.CrossRefPubMed
31.
Han TS, Williams K, Sattar N, Hunt KJ, Lean ME, Haffner SM. Analysis of obesity and hyperinsulinemia in the development of metabolic syndrome: San Antonio heart study. Obes Res. 2002;10:923–31.CrossRefPubMed
32.
Chiodo L, Casula M, Catapano AL, Baragetti A, Garlaschelli K, Grigore L, et al. Characterization of metabolic syndrome in PLIC cohort. Nutr, Metab Cardiovasc Dis. 2017;27:e14.CrossRef
33.
Chedraui P, Escobar GS, Perez-Lopez FR, Palla G, Montt-Guevara M, Cecchi E, et al. Angiogenesis, inflammation and endothelial function in postmenopausal women screened for the metabolic syndrome. Maturitas. 2014;77:370–4.CrossRefPubMed
34.
Okada S, Suzuki A, Watanabe H, Watanabe T, Aizawa Y. Reversal rat of clustering of cardiovascular disease risk factors of metabolic syndrome in the general population: the Niigata preventive medicine study. J Obes. 2010;2010:1–5.CrossRef
35.
Wiria AE, Linda J, Wammes LJ, Hamid F, Dekkers OM, Prasetyani MA, May L, et al. Relationship between carotid intima media thickness and helminth infections on Flores Island, Indonesia. PLoS One. 2013;8:e54855.CrossRefPubMedPubMedCentral
36.
Chimonas T, Karagiannis A, Athyros VG, Achimastos A, Elisaf M, Panagiotakos DB. Blood pressure levels constitute the most important determinant of the metabolic syndrome in a Mediterranean population: a discrimination analysis. MetabSyndrRelatDisord. 2010;8:523–9.
37.
Cameron AJ, Boyko EJ, Zimmet PZ, Söderberg S, KGMM A, et al. Central obesity as a precursor to the metabolic syndrome in the AusDiab study and Mauritius. Obes. 2008;16:2707–16.CrossRef
38.
Razavi M, Fournier S, Shepard DS, Ritter G, Strickler GK, Stason WB. Effects of lifestyle modification programs on cardiac risk factors. PLoS One. 2014;9:e114772.CrossRefPubMedPubMedCentral
39.
Nidal FE. Predischarge education improves adherence to a healthy lifestyle among Jordanian patients with acute coronary syndrome. Nurs Health Sci. 2013;15:279.
Metadata
Title
Prediction of the development of metabolic syndrome by the Markov model based on a longitudinal study in Dalian City
Authors
Xiao Tang
Qigui Liu
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2018
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-018-5599-y

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