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Published in: BMC Endocrine Disorders 1/2021

01-12-2021 | Obesity | Research article

Dynamic behavior of metabolic syndrome progression: a comprehensive systematic review on recent discoveries

Authors: Pezhman Bagheri, Davood Khalili, Mozhgan Seif, Abbas Rezaianzadeh

Published in: BMC Endocrine Disorders | Issue 1/2021

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Abstract

Background

The assessment of the natural history of metabolic syndrome (MetS) has an important role in clarifying the pathways of this disorder.

Objective

This study purposed to provide a rational statistical view of MetS progression pathway.

Methods

We performed a systematic review in accordance with the PRISMA Statement until September 2019 in the Medline/PubMed, Scopus, Embase, Web of Science and Google Scholar databases. From the 68 found studies, 12 studies were eligible for review finally.

Results

The selected studies were divided in 2 groups with Markovian and non-Markovian approach. With the Markov approach, the most important trigger for the MetS chain was dyslipidemia with overweight/obesity in the under-50 and with hypertension in the over-50 age group, where overweight/obesity was more important in women and hypertension in men. In non-Markov approach, the most common trigger was hypertension. Transition probability (TP) from no component to MetS were higher in all Markovian studies in men than in women. In the Markovians the combination of dyslipidemia with overweight/obesity and in non-Markovians, hyperglycemia with overweight/obesity were the most common combinations. Finally, the most important components, which predict the MetS, were 2-component states and hyperglycemia in Markovian approach and overweight/obesity in non-Markovians.

Conclusions

Among the components of the MetS, dyslipidemia and hypertension seems to be the main developer components in natural history of the MetS. Also, in this chain, the most likely combination over time that determines the future status of people seems to be the combination of dyslipidemia with obesity or hyperglycemia. However, more research is needed.
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Literature
1.
go back to reference Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; world heart federation; international atherosclerosis society; and International Association for the Study of obesity. Circulation. 2009;120(16):1640–5.PubMedCrossRef Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; world heart federation; international atherosclerosis society; and International Association for the Study of obesity. Circulation. 2009;120(16):1640–5.PubMedCrossRef
2.
go back to reference Tran BT, Jeong BY, Oh J-K. 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(1):71.PubMedPubMedCentralCrossRef Tran BT, Jeong BY, Oh J-K. 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(1):71.PubMedPubMedCentralCrossRef
3.
go back to reference Slagter SN, Waateringe RP, APV B, Klauw MM, BHR W, Vliet-Ostaptchouk JV. BMI and age differences in metabolic syndrome: the Dutch Lifelines Cohort Study. Sex. 2017;6(4):278. Slagter SN, Waateringe RP, APV B, Klauw MM, BHR W, Vliet-Ostaptchouk JV. BMI and age differences in metabolic syndrome: the Dutch Lifelines Cohort Study. Sex. 2017;6(4):278.
4.
go back to reference Schwarz PEH, Reimann M, Li J, Bergmann A, Licinio J, Wong ML, et al. The metabolic syndrome - a global challenge for prevention. Horm Metab Res. 2007;39(11):777–80.PubMedCrossRef Schwarz PEH, Reimann M, Li J, Bergmann A, Licinio J, Wong ML, et al. The metabolic syndrome - a global challenge for prevention. Horm Metab Res. 2007;39(11):777–80.PubMedCrossRef
5.
go back to reference Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. Jama. 2002;288(21):2709–16.PubMedCrossRef Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. Jama. 2002;288(21):2709–16.PubMedCrossRef
6.
go back to reference Esposito K, Chiodini P, Capuano A, Bellastella G, Maiorino MI, Giugliano D. Metabolic syndrome and endometrial cancer: a meta-analysis. Endocrine. 2014;45(1):28–36.PubMedCrossRef Esposito K, Chiodini P, Capuano A, Bellastella G, Maiorino MI, Giugliano D. Metabolic syndrome and endometrial cancer: a meta-analysis. Endocrine. 2014;45(1):28–36.PubMedCrossRef
7.
go back to reference Sung KC, Ryu S, Reaven GM. Relationship between obesity and several cardiovascular disease risk factors in apparently healthy Korean individuals: comparison of body mass index and waist circumference. Metab Clin Exp. 2007;56(3):297–303.PubMedCrossRef Sung KC, Ryu S, Reaven GM. Relationship between obesity and several cardiovascular disease risk factors in apparently healthy Korean individuals: comparison of body mass index and waist circumference. Metab Clin Exp. 2007;56(3):297–303.PubMedCrossRef
8.
go back to reference Li NY, Yu J, Zhang XW, Wang SX, Chang P, Ding Q, et al. Features of left ventricular hypertrophy in patients with metabolic syndrome with or without comparable blood pressure: a meta-analysis. Endocrine. 2013;43(3):548–63.PubMedCrossRef Li NY, Yu J, Zhang XW, Wang SX, Chang P, Ding Q, et al. Features of left ventricular hypertrophy in patients with metabolic syndrome with or without comparable blood pressure: a meta-analysis. Endocrine. 2013;43(3):548–63.PubMedCrossRef
9.
go back to reference Ford ES, Li C. Defining the metabolic syndrome in children and adolescents: will the real definition please stand up? J Pediatr. 2008;152(2):160–4.PubMedCrossRef Ford ES, Li C. Defining the metabolic syndrome in children and adolescents: will the real definition please stand up? J Pediatr. 2008;152(2):160–4.PubMedCrossRef
10.
go back to reference Athyros VG, Ganotakis ES, Elisaf M, Mikhailidis DP. The prevalence of the metabolic syndrome using the National Cholesterol Educational Program and international diabetes federation definitions. Curr Med Res Opin. 2005;21(8):1157–9.PubMedCrossRef Athyros VG, Ganotakis ES, Elisaf M, Mikhailidis DP. The prevalence of the metabolic syndrome using the National Cholesterol Educational Program and international diabetes federation definitions. Curr Med Res Opin. 2005;21(8):1157–9.PubMedCrossRef
11.
go back to reference Carnethon MR, Loria CM, Hill JO, Sidney S, Savage PJ, Liu K. Risk factors for the metabolic syndrome: the coronary artery risk development in young adults (CARDIA) study, 1985-2001. Diabetes Care. 2004;27(11):2707–15.PubMedCrossRef Carnethon MR, Loria CM, Hill JO, Sidney S, Savage PJ, Liu K. Risk factors for the metabolic syndrome: the coronary artery risk development in young adults (CARDIA) study, 1985-2001. Diabetes Care. 2004;27(11):2707–15.PubMedCrossRef
12.
go back to reference Zuo H, Shi Z, Hu X, Wu M, Guo Z, Hussain A. Prevalence of metabolic syndrome and factors associated with its components in Chinese adults. Metab Clin Exp. 2009;58:1102–8.PubMedCrossRef Zuo H, Shi Z, Hu X, Wu M, Guo Z, Hussain A. Prevalence of metabolic syndrome and factors associated with its components in Chinese adults. Metab Clin Exp. 2009;58:1102–8.PubMedCrossRef
13.
go back to reference Santos AC, Severo M, Barros H. Incidence and risk factors for the metabolic syndrome in an urban south European population. Prev Med. 2010;50(3):99–105.PubMedCrossRef Santos AC, Severo M, Barros H. Incidence and risk factors for the metabolic syndrome in an urban south European population. Prev Med. 2010;50(3):99–105.PubMedCrossRef
14.
go back to reference Liese AD, Mayer-Davis EJ, Haffner SM. Development of the multiple metabolic syndrome: an epidemiologic perspective. Epidemiol Rev. 1998;20(2):157–72.PubMedCrossRef Liese AD, Mayer-Davis EJ, Haffner SM. Development of the multiple metabolic syndrome: an epidemiologic perspective. Epidemiol Rev. 1998;20(2):157–72.PubMedCrossRef
16.
go back to reference 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.PubMedPubMedCentralCrossRef 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.PubMedPubMedCentralCrossRef
17.
go back to reference Scuteri A, Morrell CH, Najjar SS, Muller D, Andres R, Ferrucci L, et al. Longitudinal paths to the metabolic syndrome: can the incidence of the metabolic syndrome be predicted? The Baltimore longitudinal study of aging. J Gerontol A Biol Sci Med Sci. 2009;64(5):590–8.PubMedCrossRef Scuteri A, Morrell CH, Najjar SS, Muller D, Andres R, Ferrucci L, et al. Longitudinal paths to the metabolic syndrome: can the incidence of the metabolic syndrome be predicted? The Baltimore longitudinal study of aging. J Gerontol A Biol Sci Med Sci. 2009;64(5):590–8.PubMedCrossRef
18.
go back to reference Cheung BM, Wat NM, Tam S, Thomas GN, Leung GM, Cheng CH, et al. Components of the metabolic syndrome predictive of its development: a 6-year longitudinal study in Hong Kong Chinese. Clin Endocrinol. 2008;68(5):730–7.CrossRef Cheung BM, Wat NM, Tam S, Thomas GN, Leung GM, Cheng CH, et al. Components of the metabolic syndrome predictive of its development: a 6-year longitudinal study in Hong Kong Chinese. Clin Endocrinol. 2008;68(5):730–7.CrossRef
19.
go back to reference Jia X, Chen Q, Wu P, Liu M, Chen X, Xiao J, et al. Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model. Diabetology & metabolic syndrome. 2018;10:24.CrossRef Jia X, Chen Q, Wu P, Liu M, Chen X, Xiao J, et al. Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model. Diabetology & metabolic syndrome. 2018;10:24.CrossRef
20.
go back to reference Tao LX, Wang W, Zhu HP, Huo D, Zhou T, Pan L, et al. Risk profiles for metabolic syndrome and its transition patterns for the elderly in Beijing, 1992-2009. Endocrine. 2014;47(1):161–8.PubMedCrossRef Tao LX, Wang W, Zhu HP, Huo D, Zhou T, Pan L, et al. Risk profiles for metabolic syndrome and its transition patterns for the elderly in Beijing, 1992-2009. Endocrine. 2014;47(1):161–8.PubMedCrossRef
21.
go back to reference 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(6):e67436.PubMedPubMedCentralCrossRef 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(6):e67436.PubMedPubMedCentralCrossRef
22.
go back to reference Harati H, Hadaegh F, Momenan AA, Ghanei L, Bozorgmanesh MR, Ghanbarian A, et al. Reduction in incidence of type 2 diabetes by lifestyle intervention in a middle eastern community. Am J Prevent Med. 2010;38(6):628–36 e1.CrossRef Harati H, Hadaegh F, Momenan AA, Ghanei L, Bozorgmanesh MR, Ghanbarian A, et al. Reduction in incidence of type 2 diabetes by lifestyle intervention in a middle eastern community. Am J Prevent Med. 2010;38(6):628–36 e1.CrossRef
24.
go back to reference Tang X, Liu Q. Prediction of the development of metabolic syndrome by the Markov model based on a longitudinal study in Dalian City. BMC Public Health. 2018;18(1):707.PubMedPubMedCentralCrossRef Tang X, Liu Q. Prediction of the development of metabolic syndrome by the Markov model based on a longitudinal study in Dalian City. BMC Public Health. 2018;18(1):707.PubMedPubMedCentralCrossRef
25.
go back to reference Haring R, Rosvall M, Volker U, Volzke H, Kroemer H, Nauck M, et al. A network-based approach to visualize prevalence and progression of metabolic syndrome components. PLoS One. 2012;7(6):e39461.PubMedPubMedCentralCrossRef Haring R, Rosvall M, Volker U, Volzke H, Kroemer H, Nauck M, et al. A network-based approach to visualize prevalence and progression of metabolic syndrome components. PLoS One. 2012;7(6):e39461.PubMedPubMedCentralCrossRef
26.
go back to reference Barcelo MA, Rodriguez-Poncelas A, Saez M, Coll-de-Tuero G. The dynamic behaviour of metabolic syndrome and its components in an eight-year population-based cohort from the Mediterranean. PLoS One. 2017;12(5):e0176665.PubMedPubMedCentralCrossRef Barcelo MA, Rodriguez-Poncelas A, Saez M, Coll-de-Tuero G. The dynamic behaviour of metabolic syndrome and its components in an eight-year population-based cohort from the Mediterranean. PLoS One. 2017;12(5):e0176665.PubMedPubMedCentralCrossRef
27.
go back to reference DeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care. 1991;14(3):173–94.PubMedCrossRef DeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care. 1991;14(3):173–94.PubMedCrossRef
28.
go back to reference Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700.PubMedPubMedCentralCrossRef Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700.PubMedPubMedCentralCrossRef
29.
go back to reference Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.CrossRefPubMed Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.CrossRefPubMed
30.
go back to reference Patel PA, Scott CG, Rodeheffer RJ, Chen HH. The natural history of patients with isolated metabolic syndrome. Mayo Clin Proc. 2016;91(5):623–33.PubMedCrossRef Patel PA, Scott CG, Rodeheffer RJ, Chen HH. The natural history of patients with isolated metabolic syndrome. Mayo Clin Proc. 2016;91(5):623–33.PubMedCrossRef
31.
go back to reference Franco OH, Massaro JM, Civil J, Cobain MR, O'Malley B, D'Agostino RB Sr. Trajectories of entering the metabolic syndrome: the Framingham heart study. Circulation. 2009;120(20):1943–50.PubMedCrossRef Franco OH, Massaro JM, Civil J, Cobain MR, O'Malley B, D'Agostino RB Sr. Trajectories of entering the metabolic syndrome: the Framingham heart study. Circulation. 2009;120(20):1943–50.PubMedCrossRef
32.
go back to reference Morrison JA, Friedman LA, Harlan WR, Harlan LC, Barton BA, Schreiber GB, et al. Development of the metabolic syndrome in black and white adolescent girls: a longitudinal assessment. Pediatrics. 2005;116(5):1178–82.PubMedCrossRef Morrison JA, Friedman LA, Harlan WR, Harlan LC, Barton BA, Schreiber GB, et al. Development of the metabolic syndrome in black and white adolescent girls: a longitudinal assessment. Pediatrics. 2005;116(5):1178–82.PubMedCrossRef
33.
go back to reference Stroock DW. An introduction to Markov processes. Cambridge: Springer; 2005. Stroock DW. An introduction to Markov processes. Cambridge: Springer; 2005.
34.
go back to reference Andersen PK, Keiding N. Multi-state models for event history analysis. Stat Methods Med Res. 2002;11(2):91–115.PubMedCrossRef Andersen PK, Keiding N. Multi-state models for event history analysis. Stat Methods Med Res. 2002;11(2):91–115.PubMedCrossRef
35.
go back to reference Beck JR, Pauker SG. The Markov process in medical prognosis. Med Decis Mak. 1983;3(4):419–58.CrossRef Beck JR, Pauker SG. The Markov process in medical prognosis. Med Decis Mak. 1983;3(4):419–58.CrossRef
37.
go back to reference Teede HJ, Lombard C, Deeks AA. Obesity, metabolic complications and the menopause: an opportunity for prevention. Climacteric : the journal of the International Menopause Society. 2010;13(3):203–9.CrossRef Teede HJ, Lombard C, Deeks AA. Obesity, metabolic complications and the menopause: an opportunity for prevention. Climacteric : the journal of the International Menopause Society. 2010;13(3):203–9.CrossRef
38.
go back to reference Wu SI, Chou P, Tsai ST. The impact of years since menopause on the development of impaired glucose tolerance. J Clin Epidemiol. 2001;54(2):117–20.PubMedCrossRef Wu SI, Chou P, Tsai ST. The impact of years since menopause on the development of impaired glucose tolerance. J Clin Epidemiol. 2001;54(2):117–20.PubMedCrossRef
39.
go back to reference Tomlinson JW, Finney J, Gay C, Hughes BA, Hughes SV, Stewart PM. Impaired glucose tolerance and insulin resistance are associated with increased adipose 11beta-hydroxysteroid dehydrogenase type 1 expression and elevated hepatic 5alpha-reductase activity. Diabetes. 2008;57(10):2652–60.PubMedPubMedCentralCrossRef Tomlinson JW, Finney J, Gay C, Hughes BA, Hughes SV, Stewart PM. Impaired glucose tolerance and insulin resistance are associated with increased adipose 11beta-hydroxysteroid dehydrogenase type 1 expression and elevated hepatic 5alpha-reductase activity. Diabetes. 2008;57(10):2652–60.PubMedPubMedCentralCrossRef
40.
go back to reference Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis. 2017;11(8):215–25.PubMedPubMedCentralCrossRef Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis. 2017;11(8):215–25.PubMedPubMedCentralCrossRef
42.
go back to reference Boden G, Shulman GI. Free fatty acids in obesity and type 2 diabetes: defining their role in the development of insulin resistance and beta-cell dysfunction. Eur J Clin Investig. 2002;32(Suppl 3):14–23.CrossRef Boden G, Shulman GI. Free fatty acids in obesity and type 2 diabetes: defining their role in the development of insulin resistance and beta-cell dysfunction. Eur J Clin Investig. 2002;32(Suppl 3):14–23.CrossRef
43.
go back to reference Chedraui P, Escobar GS, Pérez-López 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(4):370–4.PubMedCrossRef Chedraui P, Escobar GS, Pérez-López 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(4):370–4.PubMedCrossRef
45.
go back to reference Tenenbaum A, Fisman EZ. “The metabolic syndrome... is dead”: These reports are an exaggeration. Cardiovascular Diabetol. 2011;10(1):11.CrossRef Tenenbaum A, Fisman EZ. “The metabolic syndrome... is dead”: These reports are an exaggeration. Cardiovascular Diabetol. 2011;10(1):11.CrossRef
46.
go back to reference Savage DB, Petersen KF, Shulman GI. Disordered lipid metabolism and the pathogenesis of insulin resistance. Physiol Rev. 2007;87(2):507–20.PubMedCrossRef Savage DB, Petersen KF, Shulman GI. Disordered lipid metabolism and the pathogenesis of insulin resistance. Physiol Rev. 2007;87(2):507–20.PubMedCrossRef
47.
go back to reference Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97.CrossRef Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97.CrossRef
48.
go back to reference Ortega Francisco B, Lavie Carl J, Blair SN. Obesity and cardiovascular disease. Circ Res. 2016;118(11):1752–70.PubMedCrossRef Ortega Francisco B, Lavie Carl J, Blair SN. Obesity and cardiovascular disease. Circ Res. 2016;118(11):1752–70.PubMedCrossRef
49.
50.
go back to reference Halpern A, Mancini MC. Treatment of obesity: an update on anti-obesity medications. Obesity Rev. 2003;4(1):25–42.CrossRef Halpern A, Mancini MC. Treatment of obesity: an update on anti-obesity medications. Obesity Rev. 2003;4(1):25–42.CrossRef
51.
go back to reference Sarebanhassanabadi M, Jalil Mirhosseini S, Mirzaei M, Namayandeh SM, Soltani MH, Pedarzadeh A, et al. The incidence of metabolic syndrome and the Most powerful components as predictors of metabolic syndrome in Central Iran: a 10-year follow-up in a cohort study. Iran Red Crescent Med J. 2017;19(7):e14934.CrossRef Sarebanhassanabadi M, Jalil Mirhosseini S, Mirzaei M, Namayandeh SM, Soltani MH, Pedarzadeh A, et al. The incidence of metabolic syndrome and the Most powerful components as predictors of metabolic syndrome in Central Iran: a 10-year follow-up in a cohort study. Iran Red Crescent Med J. 2017;19(7):e14934.CrossRef
53.
go back to reference Herningtyas EH, Ng TS. Prevalence and distribution of metabolic syndrome and its components among provinces and ethnic groups in Indonesia. BMC Public Health. 2019;19(1):377.PubMedPubMedCentralCrossRef Herningtyas EH, Ng TS. Prevalence and distribution of metabolic syndrome and its components among provinces and ethnic groups in Indonesia. BMC Public Health. 2019;19(1):377.PubMedPubMedCentralCrossRef
54.
go back to reference Osei-Yeboah J, Owiredu WKBA, Norgbe GK, Yao Lokpo S, Gyamfi J, Alote Allotey E, et al. The Prevalence of Metabolic Syndrome and Its Components among People with Type 2 Diabetes in the Ho Municipality, Ghana: A Cross-Sectional Study. Int J Chronic Dis. 2017;2017:8765804.PubMedPubMedCentral Osei-Yeboah J, Owiredu WKBA, Norgbe GK, Yao Lokpo S, Gyamfi J, Alote Allotey E, et al. The Prevalence of Metabolic Syndrome and Its Components among People with Type 2 Diabetes in the Ho Municipality, Ghana: A Cross-Sectional Study. Int J Chronic Dis. 2017;2017:8765804.PubMedPubMedCentral
55.
go back to reference Marbou W, Kuete V. Prevalence of metabolic syndrome and its components in Bamboutos Division’s adults, West Region of Cameroon. BioMed Res Int. 2019;2019:1–12.CrossRef Marbou W, Kuete V. Prevalence of metabolic syndrome and its components in Bamboutos Division’s adults, West Region of Cameroon. BioMed Res Int. 2019;2019:1–12.CrossRef
56.
go back to reference Erem C, Hacihasanoglu A, Deger O, Kocak M, Topbas M. Prevalence of dyslipidemia and associated risk factors among Turkish adults: Trabzon lipid study. Endocrine. 2008;34(1):36–51.PubMedCrossRef Erem C, Hacihasanoglu A, Deger O, Kocak M, Topbas M. Prevalence of dyslipidemia and associated risk factors among Turkish adults: Trabzon lipid study. Endocrine. 2008;34(1):36–51.PubMedCrossRef
57.
go back to reference Wang S, Xu L, Jonas JB, You QS, Wang YX, Yang H. Prevalence and associated factors of dyslipidemia in the adult Chinese population. PLoS One. 2011;6(3):e17326.PubMedPubMedCentralCrossRef Wang S, Xu L, Jonas JB, You QS, Wang YX, Yang H. Prevalence and associated factors of dyslipidemia in the adult Chinese population. PLoS One. 2011;6(3):e17326.PubMedPubMedCentralCrossRef
58.
go back to reference Khosravi A, Akhavan Tabib A, Golshadi I, Dana Siadat Z, Bahonar A, Zarfeshani S, et al. The relationship between weight and CVD risk factors in a sample population from Central Iran (based on IHHP). ARYA Atheroscler. 2012;8(2):82–9.PubMedPubMedCentral Khosravi A, Akhavan Tabib A, Golshadi I, Dana Siadat Z, Bahonar A, Zarfeshani S, et al. The relationship between weight and CVD risk factors in a sample population from Central Iran (based on IHHP). ARYA Atheroscler. 2012;8(2):82–9.PubMedPubMedCentral
59.
go back to reference Park HS, Yun YS, Park JY, Kim YS, Choi JM. Obesity, abdominal obesity, and clustering of cardiovascular risk factors in South Korea. Asia Pac J Clin Nutr. 2003;12(4):411–8.PubMed Park HS, Yun YS, Park JY, Kim YS, Choi JM. Obesity, abdominal obesity, and clustering of cardiovascular risk factors in South Korea. Asia Pac J Clin Nutr. 2003;12(4):411–8.PubMed
60.
61.
go back to reference Heufelder AE, Saad F, Bunck MC, Gooren L. Fifty-two-week treatment with diet and exercise plus transdermal testosterone reverses the metabolic syndrome and improves glycemic control in men with newly diagnosed type 2 diabetes and subnormal plasma testosterone. J Androl. 2009;30(6):726–33.PubMedCrossRef Heufelder AE, Saad F, Bunck MC, Gooren L. Fifty-two-week treatment with diet and exercise plus transdermal testosterone reverses the metabolic syndrome and improves glycemic control in men with newly diagnosed type 2 diabetes and subnormal plasma testosterone. J Androl. 2009;30(6):726–33.PubMedCrossRef
62.
go back to reference Kaukua J, Pekkarinen T, Sane T, Mustajoki P. Sex hormones and sexual function in obese men losing weight. Obes Res. 2003;11(6):689–94.PubMedCrossRef Kaukua J, Pekkarinen T, Sane T, Mustajoki P. Sex hormones and sexual function in obese men losing weight. Obes Res. 2003;11(6):689–94.PubMedCrossRef
63.
go back to reference Corona G, Rastrelli G, Monami M, Saad F, Luconi M, Lucchese M, et al. Body weight loss reverts obesity-associated hypogonadotropic hypogonadism: a systematic review and meta-analysis. Eur J Endocrinol. 2013;168(6):829–43.PubMedCrossRef Corona G, Rastrelli G, Monami M, Saad F, Luconi M, Lucchese M, et al. Body weight loss reverts obesity-associated hypogonadotropic hypogonadism: a systematic review and meta-analysis. Eur J Endocrinol. 2013;168(6):829–43.PubMedCrossRef
64.
go back to reference Wang H, Liu A, Zhou Y, Xiao Y, Yan Y, Zhao T, et al. The correlation between serum free thyroxine and regression of dyslipidemia in adult males: A 4.5-year prospective study. Medicine. 2017;96(39):e8163.PubMedPubMedCentralCrossRef Wang H, Liu A, Zhou Y, Xiao Y, Yan Y, Zhao T, et al. The correlation between serum free thyroxine and regression of dyslipidemia in adult males: A 4.5-year prospective study. Medicine. 2017;96(39):e8163.PubMedPubMedCentralCrossRef
65.
go back to reference Rizos CV, Elisaf MS, Liberopoulos EN. Effects of thyroid dysfunction on lipid profile. Open Cardiovascular Med J. 2011;5:76–84.CrossRef Rizos CV, Elisaf MS, Liberopoulos EN. Effects of thyroid dysfunction on lipid profile. Open Cardiovascular Med J. 2011;5:76–84.CrossRef
66.
go back to reference Phan BAP, Toth PP. Dyslipidemia in women: etiology and management. Int J Women's Health. 2014;6:185–94. Phan BAP, Toth PP. Dyslipidemia in women: etiology and management. Int J Women's Health. 2014;6:185–94.
67.
go back to reference Xiao C, Dash S, Morgantini C, Hegele RA, Lewis GF. Pharmacological targeting of the Atherogenic dyslipidemia complex: the next frontier in CVD prevention beyond lowering LDL cholesterol. Diabetes. 2016;65(7):1767–78.PubMedCrossRef Xiao C, Dash S, Morgantini C, Hegele RA, Lewis GF. Pharmacological targeting of the Atherogenic dyslipidemia complex: the next frontier in CVD prevention beyond lowering LDL cholesterol. Diabetes. 2016;65(7):1767–78.PubMedCrossRef
70.
go back to reference Al-Goblan AS, Al-Alfi MA, Khan MZ. Mechanism linking diabetes mellitus and obesity. Diabetes, metabolic syndrome and obesity : targets and therapy. 2014;7:587–91.CrossRef Al-Goblan AS, Al-Alfi MA, Khan MZ. Mechanism linking diabetes mellitus and obesity. Diabetes, metabolic syndrome and obesity : targets and therapy. 2014;7:587–91.CrossRef
71.
go back to reference Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56(14):1113–32.PubMedCrossRef Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56(14):1113–32.PubMedCrossRef
72.
go back to reference Murphy R, Carroll RW, Krebs JD. Pathogenesis of the metabolic syndrome: insights from monogenic disorders. Mediat Inflamm. 2013;2013:920214.CrossRef Murphy R, Carroll RW, Krebs JD. Pathogenesis of the metabolic syndrome: insights from monogenic disorders. Mediat Inflamm. 2013;2013:920214.CrossRef
Metadata
Title
Dynamic behavior of metabolic syndrome progression: a comprehensive systematic review on recent discoveries
Authors
Pezhman Bagheri
Davood Khalili
Mozhgan Seif
Abbas Rezaianzadeh
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Endocrine Disorders / Issue 1/2021
Electronic ISSN: 1472-6823
DOI
https://doi.org/10.1186/s12902-021-00716-7

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