Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018

Background: Metabolic syndrome has increased to epidemic levels in low- and middle-income countries. The knowledge on metabolic syndrome and its related diseases constitutes a clinical, epidemiological, and economic challenge of great relevance. The frequency of metabolic syndrome may vary between p...

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Autores:
Higuita Gutiérrez, Luis Felipe
Martínez Quiroz, Wilson de Jesús
Cardona Arias, Jaiberth Antonio
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Universidad Cooperativa de Colombia
Repositorio:
Repositorio UCC
Idioma:
OAI Identifier:
oai:repository.ucc.edu.co:20.500.12494/17478
Acceso en línea:
https://doi.org/10.2147/DMSO.S242826
https://hdl.handle.net/20.500.12494/17478
Palabra clave:
Prevalence
Metabolic syndrome
Risk factor
Colombia
Prevalence
Metabolic syndrome
Risk factor
Colombia
Rights
openAccess
License
Atribución – No comercial
id COOPER2_ff66625e6232ef27de94ae8955606f7c
oai_identifier_str oai:repository.ucc.edu.co:20.500.12494/17478
network_acronym_str COOPER2
network_name_str Repositorio UCC
repository_id_str
dc.title.spa.fl_str_mv Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018
title Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018
spellingShingle Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018
Prevalence
Metabolic syndrome
Risk factor
Colombia
Prevalence
Metabolic syndrome
Risk factor
Colombia
title_short Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018
title_full Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018
title_fullStr Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018
title_full_unstemmed Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018
title_sort Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018
dc.creator.fl_str_mv Higuita Gutiérrez, Luis Felipe
Martínez Quiroz, Wilson de Jesús
Cardona Arias, Jaiberth Antonio
dc.contributor.advisor.none.fl_str_mv Comite Evaluador, Dovepress
dc.contributor.author.none.fl_str_mv Higuita Gutiérrez, Luis Felipe
Martínez Quiroz, Wilson de Jesús
Cardona Arias, Jaiberth Antonio
dc.subject.spa.fl_str_mv Prevalence
Metabolic syndrome
Risk factor
Colombia
topic Prevalence
Metabolic syndrome
Risk factor
Colombia
Prevalence
Metabolic syndrome
Risk factor
Colombia
dc.subject.other.spa.fl_str_mv Prevalence
Metabolic syndrome
Risk factor
Colombia
description Background: Metabolic syndrome has increased to epidemic levels in low- and middle-income countries. The knowledge on metabolic syndrome and its related diseases constitutes a clinical, epidemiological, and economic challenge of great relevance. The frequency of metabolic syndrome may vary between populations depending on age, sex, lifestyle, and culture; however, in Colombia, there is only little research, and the available studies focus on small populations that do not allow estimating their prevalence and distribution in different sociodemographic groups. We aimed to estimate the prevalence of metabolic syndrome and its association with sociodemographic characteristics in participants attending public chronic disease control programs in Medellin, Colombia, in the year 2018. Methods: We conducted a cross-sectional study in all patients who participated in a public chronic disease control program. Involved in this study were 68,288 individuals who attended at 10 hospital units and were strategically distributed in the city. The diagnostic criteria of the metabolic syndrome and its components were based on the consensus of the Latin American Diabetes Association. The data on age, sex, blood pressure, weight, height, physical activity, medications, lipid profile, and glycemic and glycosylated hemoglobin levels were obtained for clinical records. The prevalence, Pearson’s chi-square test, prevalence ratios (Kato-Katz method), and odds ratios (Woolf method) were estimated with 95% confidence intervals. A multivariate adjustment model was used with a logistic regression model to identify potential confounders using Epidat 4.2 and SPSS® 25.0. Results: The prevalence of the syndrome was 35.4%, with abdominal obesity in 82.3% individuals, hypertension in 48.6%, glucose intolerance in 25.5%, and hypertriglyceridemia in 22%. The prevalence of the syndrome exhibited statistical differences according to the area of residence. It was 15% higher in women; 31% and 59% higher in young and older adults, respectively, than in individuals aged < 25 years; 11% and 13% higher in the illiterate population and population with primary studies than in individuals with higher education; and approximately 200 times higher than those who are sedentary. Conclusion: A high prevalence of the syndrome and its constitutive factors in the study population demonstrated the importance of controlling it and increasing community-based prevention strategies, prioritizing the identified groups that are at the highest risk.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-04-28T19:22:42Z
dc.date.available.none.fl_str_mv 2020-04-28T19:22:42Z
dc.date.issued.none.fl_str_mv 2020-04-15
dc.type.none.fl_str_mv Artículo
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dc.identifier.bibliographicCitation.spa.fl_str_mv Higuita-Gutiérrez, L. F., Martínez Quiroz, W. de J., & Cardona-Arias, J. A. (2020). Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Volume 13, 1161–1169. https://doi.org/10.2147/dmso.s242826
identifier_str_mv 1178-7007
Higuita-Gutiérrez, L. F., Martínez Quiroz, W. de J., & Cardona-Arias, J. A. (2020). Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Volume 13, 1161–1169. https://doi.org/10.2147/dmso.s242826
url https://doi.org/10.2147/DMSO.S242826
https://hdl.handle.net/20.500.12494/17478
dc.relation.isversionof.spa.fl_str_mv https://www.dovepress.com/prevalence-of-metabolic-syndrome-and-its-association-with-sociodemogra-peer-reviewed-article-DMSO
dc.relation.ispartofjournal.spa.fl_str_mv Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy
dc.relation.references.spa.fl_str_mv World Health Organization. Obesity and overweight; 2019. Available from: https://www.who.int/es/news-room/fact-sheets/detail/obesityand-overweight. Accessed April 1, 2020
World Health Organization. Ten facts about obesity; 2017. Available from: https://www.who.int/features/factfiles/obesity/es/. Accessed April 1, 2020.
World Health Organization. Chronic diseases; 2018. Available from: https://www.who.int/topics/chronic_diseases/es/. Accessed April 1, 2020
Misra A, Bhardwaj S. Obesity and the metabolic syndrome in developing countries: focus on South Asians. Nestle Nutr Inst Workshop Ser. 2014;78:133–140. doi:10.1159/000354952
Misra A, Khurana L. Obesity and the metabolic syndrome in developing countries. J Clin Endocrinol Metab. 2008;93:S9–S30. doi:10.1210/jc.2008-1595
Ranasinghe P, Mathangasinghe Y, Jayawardena R, Hills A, Misra A. Prevalence and trends of metabolic syndrome among adults in the Asia-pacific region: a systematic review. BMC Public Health. 2017;17:101. doi:10.1186/s12889-017-4041-1
Ansarimoghaddam A, Adineh H, Zareban I, Iranpour S, HosseinZadeh A, Kh F. Prevalence of metabolic syndrome in Middle-East countries: meta-analysis of cross-sectional studies. Diabetes Metab Syndr. 2018;12 (2):195–201. doi:10.1016/j.dsx.2017.11.004
Lemus-Lemus F, Díaz Quijano DM, Rincón-Rodríguez CJ, HuertasMoreno ML. Advances in understanding Colombia’s nutrition transition. Rev Gerenc Polit Salud. 2012;11(23):121–133
Mendoza-Romero D, Urbina A, Cristancho-Montenegro A, Rombaldi A. Impact of smoking and physical inactivity on self-rated health in women in Colombia. Prev Med Rep. 2019;16:100976. doi:10.1016/j.pmedr.2019.100976
DANE. National Population and Housing Census; 2018. Available from: https://www.dane.gov.co/index.php/estadisticas-por-tema/demo grafia-y-poblacion/censo-nacional-de-poblacion-y-vivenda-2018/ cuantos-somos. Accessed April 1, 2020
Suarez-Ortegón M, Aguilar-de Plata C. Prevalence of metabolic syndrome in children aged 5–9 years from southwest Colombia: a cross-sectional study. World J Pediatr. 2016;12(4):477–483. doi:10.1007/s12519-016-0008-z
Rodríguez-Miranda C, Jojoa-Ríos J, Orozco-Acosta L, NietoCárdenas O. Metabolic syndrome in public service drivers in Armenia, Colombia. Rev salud pública. 2017;19(4):499–505. doi:10.15446/rsap.v19n4.69758
Sánchez F, Jaramillo N, Vanegas A, et al. Prevalence and behaviour of risk factors in metabolic syndrome according to different age intervals, in a female cohort of the area of influence of the Clínica de las Américas in Medellín, Colombia. Rev Colomb Cardiol. 2008;15(3):102–110
Medellin How are we doing. Medellin quality of life report; 2018. Available from: https://www.medellincomovamos.org/download/doc umento-informe-de-calidad-de-vida-de-medellin-2018/. Accessed April 1, 2020
Rosas Guzmán J, González Chávez A, Aschner P, Bastarrachea R. Latin American Consensus of the Latin American Diabetes Association (LADA) Epidemiology, Diagnosis, Control, Prevention and Treatment of Metabolic Syndrome in Adults. LADA. 2010;XVIII (1):25–44
Davila EP, Quintero MA, Orrego ML, et al. Prevalence and risk factors for metabolic syndrome in Medellin and surrounding municipalities, Colombia, 2008–2010. Prev Med. 2013;56(1):30–34. doi:10.1016/j.ypmed.2012.10.027
Serrano N, Villa-Roel C, Gamboa-Delgado EM, Barrera JG, Quintero-Lesmes DC. Early evaluation of the metabolic syndrome in Bucaramanga, Colombia. Transl Pediatr. 2019;8(5):363–370. doi:10.21037/tp.2019.04.04
Martínez-Torres J, Correa-Bautista J, González-Ruíz K, et al. A Cross-sectional study of the prevalence of metabolic syndrome and associated factors in colombian collegiate students: the FUPRECOL-adults study. Int J Environ Res Public Health. 2017;14 (3):233. doi:10.3390/ijerph14030233
González-Zapata LI, Deossa GC, Monsalve-Álvarez J, Díaz-García J, Babio N, Salas-Salvadó J. Metabolic syndrome in healthcare personnel of the university of Antioquia-Colombia; LATINMETS study. Nutr Hosp. 2013;28(2):522–531. doi:10.3305/nh.2013.28.2.6315
Bradshaw PT, Monda KL, Stevens J. Metabolic syndrome in healthy obese, overweight, and normal weight individuals: the Atherosclerosis Risk in Communities Study. Obesity (Silver Spring). 2013;21(1):203–209. doi:10.1002/oby.20248
Engin A. The definition and prevalence of obesity and metabolic syndrome. Adv Exp Med Biol. 2017;960:1–17. doi:10.1007/978- 3-319-48382-5_1
Molina D, Muñoz D. Metabolic syndrome in women. Rev Colomb Cardiol. 2018;25(S1):21–29. doi:10.1016/j.rccar.2017.12.006
World Health Organization. Global health risks. Mortality and burden of disease attributable to selected major risks. Geneva; 2009. Available from: https://apps.who.int/iris/handle/10665/44203. Accessed April 1, 2020.
Pajuelo J, Sánchez J. Metabolic syndrome in adults in Peru. An Fac Med. 2007;68(1):38–46. doi:10.15381/anales.v68i1.1237
Mejía C, Quiñones D, Cruzalegui C, Arriola I, Pérez L, Gomero R. Age as a risk factor for the occurrence of metabolic syndrome in mining workers at high altitudes. Argentine J Clin Endocrinol Metab. 2016;53(1):29–35. doi:10.1016/j.raem.2016.05.002
Mostafavi F, Ghofranipour F, Feizi A, Pirzadeh A. Improving physical activity and metabolic syndrome indicators in women: a transtheoretical model-based intervention. Int J Prev Med. 2015;6:28. doi:10.4103/2008-7802.154382
Messing S, Rütten A, Abu-Omar K, et al. How can physical activity be promoted among children and teenagers? A systematic review of reviews across settings. Front Public Health. 2019;7:55. doi:10.3389/ fpubh.2019.00055
Cradock A, Barrett J, Kenney E, et al. Using cost-effectiveness analysis to prioritize policy and programmatic approaches to physical activity promotion and obesity prevention in childhood. Prev Med. 2017;95:S17–S27. doi:10.1016/j.ypmed.2016.10.017
Fernández A, Hernández S, Guadalupe M. Social determinants in health: their relationship with metabolic syndrome. Enf Neurol (Mex). 2013;12(3):122–127.
Kim D, Yoon S, Gong Y, et al. The economic burden of cancers attributable to metabolic syndrome in Korea. J Prev Med Public Health. 2015;48(4):180–187. doi:10.3961/jpmph.15.022
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spelling Comite Evaluador, DovepressHiguita Gutiérrez, Luis FelipeMartínez Quiroz, Wilson de JesúsCardona Arias, Jaiberth Antonio132020-04-28T19:22:42Z2020-04-28T19:22:42Z2020-04-151178-7007https://doi.org/10.2147/DMSO.S242826https://hdl.handle.net/20.500.12494/17478Higuita-Gutiérrez, L. F., Martínez Quiroz, W. de J., & Cardona-Arias, J. A. (2020). Prevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Volume 13, 1161–1169. https://doi.org/10.2147/dmso.s242826Background: Metabolic syndrome has increased to epidemic levels in low- and middle-income countries. The knowledge on metabolic syndrome and its related diseases constitutes a clinical, epidemiological, and economic challenge of great relevance. The frequency of metabolic syndrome may vary between populations depending on age, sex, lifestyle, and culture; however, in Colombia, there is only little research, and the available studies focus on small populations that do not allow estimating their prevalence and distribution in different sociodemographic groups. We aimed to estimate the prevalence of metabolic syndrome and its association with sociodemographic characteristics in participants attending public chronic disease control programs in Medellin, Colombia, in the year 2018. Methods: We conducted a cross-sectional study in all patients who participated in a public chronic disease control program. Involved in this study were 68,288 individuals who attended at 10 hospital units and were strategically distributed in the city. The diagnostic criteria of the metabolic syndrome and its components were based on the consensus of the Latin American Diabetes Association. The data on age, sex, blood pressure, weight, height, physical activity, medications, lipid profile, and glycemic and glycosylated hemoglobin levels were obtained for clinical records. The prevalence, Pearson’s chi-square test, prevalence ratios (Kato-Katz method), and odds ratios (Woolf method) were estimated with 95% confidence intervals. A multivariate adjustment model was used with a logistic regression model to identify potential confounders using Epidat 4.2 and SPSS® 25.0. Results: The prevalence of the syndrome was 35.4%, with abdominal obesity in 82.3% individuals, hypertension in 48.6%, glucose intolerance in 25.5%, and hypertriglyceridemia in 22%. The prevalence of the syndrome exhibited statistical differences according to the area of residence. It was 15% higher in women; 31% and 59% higher in young and older adults, respectively, than in individuals aged < 25 years; 11% and 13% higher in the illiterate population and population with primary studies than in individuals with higher education; and approximately 200 times higher than those who are sedentary. Conclusion: A high prevalence of the syndrome and its constitutive factors in the study population demonstrated the importance of controlling it and increasing community-based prevention strategies, prioritizing the identified groups that are at the highest risk.http://scienti.colciencias.gov.co:8081/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001501791https://orcid.org/0000-0003-1361-3124https://orcid.org/0000-0002-7101-929Xhttps://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000011355luis.higuita@campusucc.edu.cop.1161–1169.Universidad Cooperativa de Colombia, Facultad de Ciencias de la Salud, Medicina, Medellín y EnvigadoMedicinaMedellínhttps://www.dovepress.com/prevalence-of-metabolic-syndrome-and-its-association-with-sociodemogra-peer-reviewed-article-DMSODiabetes, Metabolic Syndrome and Obesity: Targets and TherapyWorld Health Organization. Obesity and overweight; 2019. Available from: https://www.who.int/es/news-room/fact-sheets/detail/obesityand-overweight. Accessed April 1, 2020World Health Organization. Ten facts about obesity; 2017. Available from: https://www.who.int/features/factfiles/obesity/es/. Accessed April 1, 2020.World Health Organization. Chronic diseases; 2018. Available from: https://www.who.int/topics/chronic_diseases/es/. Accessed April 1, 2020Misra A, Bhardwaj S. Obesity and the metabolic syndrome in developing countries: focus on South Asians. Nestle Nutr Inst Workshop Ser. 2014;78:133–140. doi:10.1159/000354952Misra A, Khurana L. Obesity and the metabolic syndrome in developing countries. J Clin Endocrinol Metab. 2008;93:S9–S30. doi:10.1210/jc.2008-1595Ranasinghe P, Mathangasinghe Y, Jayawardena R, Hills A, Misra A. Prevalence and trends of metabolic syndrome among adults in the Asia-pacific region: a systematic review. BMC Public Health. 2017;17:101. doi:10.1186/s12889-017-4041-1Ansarimoghaddam A, Adineh H, Zareban I, Iranpour S, HosseinZadeh A, Kh F. Prevalence of metabolic syndrome in Middle-East countries: meta-analysis of cross-sectional studies. Diabetes Metab Syndr. 2018;12 (2):195–201. doi:10.1016/j.dsx.2017.11.004Lemus-Lemus F, Díaz Quijano DM, Rincón-Rodríguez CJ, HuertasMoreno ML. Advances in understanding Colombia’s nutrition transition. Rev Gerenc Polit Salud. 2012;11(23):121–133Mendoza-Romero D, Urbina A, Cristancho-Montenegro A, Rombaldi A. Impact of smoking and physical inactivity on self-rated health in women in Colombia. Prev Med Rep. 2019;16:100976. doi:10.1016/j.pmedr.2019.100976DANE. National Population and Housing Census; 2018. Available from: https://www.dane.gov.co/index.php/estadisticas-por-tema/demo grafia-y-poblacion/censo-nacional-de-poblacion-y-vivenda-2018/ cuantos-somos. Accessed April 1, 2020Suarez-Ortegón M, Aguilar-de Plata C. Prevalence of metabolic syndrome in children aged 5–9 years from southwest Colombia: a cross-sectional study. World J Pediatr. 2016;12(4):477–483. doi:10.1007/s12519-016-0008-zRodríguez-Miranda C, Jojoa-Ríos J, Orozco-Acosta L, NietoCárdenas O. Metabolic syndrome in public service drivers in Armenia, Colombia. Rev salud pública. 2017;19(4):499–505. doi:10.15446/rsap.v19n4.69758Sánchez F, Jaramillo N, Vanegas A, et al. Prevalence and behaviour of risk factors in metabolic syndrome according to different age intervals, in a female cohort of the area of influence of the Clínica de las Américas in Medellín, Colombia. Rev Colomb Cardiol. 2008;15(3):102–110Medellin How are we doing. Medellin quality of life report; 2018. Available from: https://www.medellincomovamos.org/download/doc umento-informe-de-calidad-de-vida-de-medellin-2018/. Accessed April 1, 2020Rosas Guzmán J, González Chávez A, Aschner P, Bastarrachea R. Latin American Consensus of the Latin American Diabetes Association (LADA) Epidemiology, Diagnosis, Control, Prevention and Treatment of Metabolic Syndrome in Adults. LADA. 2010;XVIII (1):25–44Davila EP, Quintero MA, Orrego ML, et al. Prevalence and risk factors for metabolic syndrome in Medellin and surrounding municipalities, Colombia, 2008–2010. Prev Med. 2013;56(1):30–34. doi:10.1016/j.ypmed.2012.10.027Serrano N, Villa-Roel C, Gamboa-Delgado EM, Barrera JG, Quintero-Lesmes DC. Early evaluation of the metabolic syndrome in Bucaramanga, Colombia. Transl Pediatr. 2019;8(5):363–370. doi:10.21037/tp.2019.04.04Martínez-Torres J, Correa-Bautista J, González-Ruíz K, et al. A Cross-sectional study of the prevalence of metabolic syndrome and associated factors in colombian collegiate students: the FUPRECOL-adults study. Int J Environ Res Public Health. 2017;14 (3):233. doi:10.3390/ijerph14030233González-Zapata LI, Deossa GC, Monsalve-Álvarez J, Díaz-García J, Babio N, Salas-Salvadó J. Metabolic syndrome in healthcare personnel of the university of Antioquia-Colombia; LATINMETS study. Nutr Hosp. 2013;28(2):522–531. doi:10.3305/nh.2013.28.2.6315Bradshaw PT, Monda KL, Stevens J. Metabolic syndrome in healthy obese, overweight, and normal weight individuals: the Atherosclerosis Risk in Communities Study. Obesity (Silver Spring). 2013;21(1):203–209. doi:10.1002/oby.20248Engin A. The definition and prevalence of obesity and metabolic syndrome. Adv Exp Med Biol. 2017;960:1–17. doi:10.1007/978- 3-319-48382-5_1Molina D, Muñoz D. Metabolic syndrome in women. Rev Colomb Cardiol. 2018;25(S1):21–29. doi:10.1016/j.rccar.2017.12.006World Health Organization. Global health risks. Mortality and burden of disease attributable to selected major risks. Geneva; 2009. Available from: https://apps.who.int/iris/handle/10665/44203. Accessed April 1, 2020.Pajuelo J, Sánchez J. Metabolic syndrome in adults in Peru. An Fac Med. 2007;68(1):38–46. doi:10.15381/anales.v68i1.1237Mejía C, Quiñones D, Cruzalegui C, Arriola I, Pérez L, Gomero R. Age as a risk factor for the occurrence of metabolic syndrome in mining workers at high altitudes. Argentine J Clin Endocrinol Metab. 2016;53(1):29–35. doi:10.1016/j.raem.2016.05.002Mostafavi F, Ghofranipour F, Feizi A, Pirzadeh A. Improving physical activity and metabolic syndrome indicators in women: a transtheoretical model-based intervention. Int J Prev Med. 2015;6:28. doi:10.4103/2008-7802.154382Messing S, Rütten A, Abu-Omar K, et al. How can physical activity be promoted among children and teenagers? A systematic review of reviews across settings. Front Public Health. 2019;7:55. doi:10.3389/ fpubh.2019.00055Cradock A, Barrett J, Kenney E, et al. Using cost-effectiveness analysis to prioritize policy and programmatic approaches to physical activity promotion and obesity prevention in childhood. Prev Med. 2017;95:S17–S27. doi:10.1016/j.ypmed.2016.10.017Fernández A, Hernández S, Guadalupe M. Social determinants in health: their relationship with metabolic syndrome. Enf Neurol (Mex). 2013;12(3):122–127.Kim D, Yoon S, Gong Y, et al. The economic burden of cancers attributable to metabolic syndrome in Korea. J Prev Med Public Health. 2015;48(4):180–187. doi:10.3961/jpmph.15.022PrevalenceMetabolic syndromeRisk factorColombiaPrevalenceMetabolic syndromeRisk factorColombiaPrevalence of Metabolic Syndrome and Its Association with Sociodemographic Characteristics in Participants of a Public Chronic Disease Control Program in Medellin, Colombia, in 2018Artículohttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAtribución – No 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