Metabolic syndrome prevalence in military personnel and correlation with anthropometric values in Ecuador
Keywords:
metabolic syndrome, anthropometric indices, military instructors, optimal cut, Ecuador.Abstract
Introducción: El exceso de grasa en el organismo puede ser un problema multifactorial y predispone a la presencia de enfermedades crónicas no transmisibles, entre las que se encuentran las cardiovasculares.
Objetivo: Establecer el corte óptimo de los índices antropométricos para predecir el síndrome metabólico en personal militar que se atiende en la atención primaria de salud.
Métodos: Estudio analítico, no experimental, llevado a cabo en personal militar de la Universidad de las Fuerzas Armadas, durante el año 2020. La muestra está representada por 203 participantes, los métodos de colección fueron los registros médicos y antropométricos, tomando en consideración variables como talla y peso, circunferencia de cintura y cadera, índice de masa corporal, pruebas de laboratorio, entre otros. Todos los datos fueron analizados usando criterios de clasificación internacional.
Resultado: La prevalencia de síndrome metabólico (MetSyn), según los diferentes criterios es: MetSyn ALAD: 4,08 % (SD: 0,52), MetSyn ATP III: 7,65 % (SD: 0,52), MetSyn HARM: 5,4 % (SD: 0,52) y finalmente, MetSyn OMS: 7,65 % (SD: 0,52). Además, los índices antropométricos predictivos son el WC y WHtR en todos los criterios estudiados, y según MetSyn ATP III, el corte óptimo del WC es de 91 cm y del WHtR es de 0,53.
Conclusiones: Los puntos de corte óptimos para los índices antropométricos que predicen el síndrome metabólico en el personal militar son WC y WHtR, con un punto de corte óptimo inferior a los criterios establecidos por ALAD para el diagnóstico de MetSyn.
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References
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24. Payab M, Hasani Ranjbar S, Merati Y, Esteghamati A, Qorbani M, Hematabadi M, et al. The Prevalence of Metabolic Syndrome and Different Obesity Phenotype in Iranian Male Military Personnel. Am J Mens Health. 2017; 11(2):404-13. DOI: 10.1177/1557988316683120
25. Liu Cheng W, Chang Wei C, Chiao C, Chen Ko H, Wu Yen W, Hwang J. Hyperuricemia Is Associated with a Higher Prevalenceof Metabolic Syndrome in Military Individuals. Mil Med. 2018; 183(11-12):391-5. DOI: 10.1093/milmed/usy097
26. Filho Romário T, D'Oliveira A. The Prevalence of Metabolic Syndrome Among Soldiers of the Military Police of Bahia State, Brazil. Am J Mens Heal. 2014; 8(4):310-5. DOI: 10.1177/1557988313510928
27. Márquez Sandoval F, Macedo Ojeda G, Viramontes Horner D, Fernández Ballart D, Salas Salvado J, Vizmanos B. The prevalence of metabolic syndrome in Latin America:a systematic review. Public Health Nutr. 2011; 14(10):1702-13. DOI: 10.1017/S1368980010003320
28. Pérez Galarza J, Baldeón L, Franco O, Muka T, Drexhage H, Voortman T, Freire W. Prevalence of overweight and metabolic syndrome, and associated sociodemographic factors among adult Ecuadorian populations: the ENSANUT-ECU study. J Endocrinol Invest. 2021; 44(1):63-74. DOI: 10.1007/s40618-020-01267-9
29. Shabazian H, Latifi S, Pipelzadeh M. Efficiency of Anthropometric Indices in Predicting Metabolic Syndrome among Adult Population of Ahvaz, Iran. Diab Obes Metab Disor O A. 2016 [access: 15/09/2022]; 1(3):3. Available at: https://www.kenkyugroup.org/images/articles/24b652ba8f87e5bf78665-aea7d8814ed.pdf
30. Yang Y, Park H, Won K, Chang H, Park G, Kim Y, Ann S, Park E, Kim S, Lee S. Relationship between the optimal cut-off values of anthropometric indices for predicting metabolic syndrome and carotid intima-medial thickness in a Korean population. Medicine (Baltimore). 2019; 98(42):17620. DOI: 10.1097/MD.0000000000017620
31. Aguirre P, Coca A, Aguirre M, Celis G. Waist-to-height ratio and sedentary lifestyle as predictors of metabolic syndrome in children in Ecuador. Hipertens y riesgo Vasc. 2018; 35(3):102-9. DOI: 10.1016/j.hipert.2017.09.002
32. Orces C, Montalvan M, Tettamanti D. Optimal waist circumference cutoff values for predicting metabolic syndrome among older adults in Ecuador. Diabetes Metab Syndr. 2019; 13(2):1015-20. DOI: 10.1016/j.dsx.2019.01.013
2. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004; 363(9403):157-63. DOI: 10.1016/S0140-6736(03)15268-3
3. Bonet M, Varona P. III Encuesta Nacional de Factores de Riesgo y actividades preventivas de enfermedades no transmisibles. Cuba 2010-2011. Editorial de Ciencias Médicas. 2018. [access: 15/09/2022]. Available at: http://www.ecimed.sld.cu/2014/08/07/1897/
4. Hernández Rodríguez J, Duchi Jimbo P. Índice cintura/talla y su utilidad para detectar riesgo cardiovascular y metabólico. Rev Cuba Endocrinol. 2015 [access: 15/09/2022]; 26(1):66-76. Available at: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1561-29532015000100006
5. Coniglio R. Relación entre la obesidad central y los componentes del síndrome metabólico. Acta Bioquímica Clínica Latinoam. 2014 [access: 15/09/2022]; 48(2):191-201. Available at: http://www.scielo.org.ar/scielo.php?script=sci_arttext&pid=S0325-29572014000200004
6. Marques López I. Diámetro abdominal sagital: un indicador de grasa visceral que se debe tener en cuenta en la práctica clínica. Rev Española Nutr Humana y Dietética. 2012; 16(4):121-2. DOI: 10.14306/renhyd.16.4.54
7. WHO. Cardiovascular diseases (CVDs). Fact sheets/ Detail. 2017. [access: 15/09/2022]. Available at: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
8. Hussain M, Oldenburg B, Wang Y, Zoungas S, Tonkin A. Assessment of Cardiovascular Disease Risk in South Asian Populations. Int J Vasc Med. 2013; 2013(786801). DOI: 10.1155/2013/786801
9. Communication S. American Medical Association. Executive Summary of the Third Report (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). J Am Med Assoc. 2001; 285(19):2486-97. DOI: 10.1001/jama.285.19.2486
10. Hosein R, Hamid R, Tavakoli M, Hossein R, Mohammad M. Metabolic Syndrome Prevalence among Armed Forces Personnel (Military Personnel and Police Officers): A Systematic Review and Meta-Analysis. Mil Med. 2019; 184(9-10):417-25. DOI: 10.1093/milmed/usz144
11. Baygi F, Herttua J, Olaf C, Djalalinia S, Ghorabi A, Asayesh H, Qorbani M. Global prevalence of cardiometabolic risk factors in the military population: a systematic review and meta-analysis. BMC Endrocine Disord. 2020; 20(8):1-17. DOI: 10.1186/s12902-020-0489-6
12. Muñoz G, Muñoz Gualán A. Evaluation of anthropometric indices as metabolic syndrome predictors in Ecuadorian Military Personnel. Rev Ecuat Med Cienc Biol. 2020; 41(2):141-7. DOI: 10.26807/remcb.v41i2.241
13. Muñoz A, Muñoz G. Cuantificación del riesgo de enfermedad cardiovascular, según el score Framingham, en el personal militar durante el año 2015. Rev Ecuat Med Cienc Biol. 2018; 39(1):31-7. DOI: 10.26807/remcb.v39i1.560
14. Muñoz Gualán G, Muñoz Gualán A. Prevalence of Dyslipidemia in Military Students and Military Personnel Attending Primary Care in Ecuador and Correlation with Anthropometric Values. Revista Med. 2021; 29(2):11-18. DOI: 10.18359/rmed.5827
15. WHO. Obesity: preventing and managing the global epidemic Report of a WHO Consultation (WHO Technical Report Series 894). Geneva: WHO; 2000. [access: 15/09/2022]. Available at: https://apps.who.int/iris/handle/10665/42330
16. Lanas F, Avezum A, Bautista L, Diaz R, Luna M, Shofiqul I, Salim Y. Risk Factors for Acute Myocardial Infarction in Latin America: The INTERHEART Latin American Study. Circulation. 2007; 115(9):1067-74. DOI: 10.1161/CIRCULATIONAHA.106.633552
17. Tseng C, Chong C, Chan T, Bai C, You S, Chiou H, Su T, Chen C. Optimal anthropometric factor cutoffs for hyperglycemia, hypertension and dyslipidemia for the Taiwanese population. Atherosclerosis. 2010; 210(2):585-9. DOI: 10.1016/j.atherosclerosis.2009.12.015
18. Bauce G, Zulay M, Moya-Sifontes M. Índice Peso Circunferencia de Cintura como indicador complementario de sobrepeso y obesidad en diferentes grupos de sujetos subjects. Rev Digit Postgrado. 2020; 9(1):e195. DOI: 10.37910/RDP.2020.9.1.e195
19. Huaman J, Alvarez M, Gamboa L, Marino F. Índice cintura-estatura como prueba diagnóstica del Síndrome metabólico en adultos de Trujillo. Rev Med Hered. 2017; 28:13-20. DOI: 10.20453/rmh.v28i1.3068
20. Lizarzaburu Robles JC. Síndrome metabólico: concepto y aplicación práctica. An Fac Med. 2013 [access: 15/09/2022]; 74(4):315-20. Available at: http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S1025-55832013000400009
21. González Chávez A, Gómez Miranda J, Elizondo Argueta S, Rangel Mejía M, Sánchez Zúñiga M. Guía de práctica clínica de síndrome metabólico. Revista ALAD. 2019; 9:179-200. DOI: 10.24875/ALAD.19000381
22. Gutiérrez Solis A, Datta Banik S, Méndez González R. Prevalence of Metabolic Syndrome in Mexico: A Systematic Review and Meta-Analysis. Metab Syndr Relat Disord. 2018; 16(8):395-405. DOI: 10.1089/met.2017.0157
23. Herrera Enriquez K, Narvaez Guerra O. Discordance of metabolic syndrome and abdominal obesity prevalence according to different criteria in Andean highlanders: A community-based study. Diabetes Metab Syndr Clin Res Rev. 2017; 11(1):359-64. DOI: 10.1016/j.dsx.2017.03.016
24. Payab M, Hasani Ranjbar S, Merati Y, Esteghamati A, Qorbani M, Hematabadi M, et al. The Prevalence of Metabolic Syndrome and Different Obesity Phenotype in Iranian Male Military Personnel. Am J Mens Health. 2017; 11(2):404-13. DOI: 10.1177/1557988316683120
25. Liu Cheng W, Chang Wei C, Chiao C, Chen Ko H, Wu Yen W, Hwang J. Hyperuricemia Is Associated with a Higher Prevalenceof Metabolic Syndrome in Military Individuals. Mil Med. 2018; 183(11-12):391-5. DOI: 10.1093/milmed/usy097
26. Filho Romário T, D'Oliveira A. The Prevalence of Metabolic Syndrome Among Soldiers of the Military Police of Bahia State, Brazil. Am J Mens Heal. 2014; 8(4):310-5. DOI: 10.1177/1557988313510928
27. Márquez Sandoval F, Macedo Ojeda G, Viramontes Horner D, Fernández Ballart D, Salas Salvado J, Vizmanos B. The prevalence of metabolic syndrome in Latin America:a systematic review. Public Health Nutr. 2011; 14(10):1702-13. DOI: 10.1017/S1368980010003320
28. Pérez Galarza J, Baldeón L, Franco O, Muka T, Drexhage H, Voortman T, Freire W. Prevalence of overweight and metabolic syndrome, and associated sociodemographic factors among adult Ecuadorian populations: the ENSANUT-ECU study. J Endocrinol Invest. 2021; 44(1):63-74. DOI: 10.1007/s40618-020-01267-9
29. Shabazian H, Latifi S, Pipelzadeh M. Efficiency of Anthropometric Indices in Predicting Metabolic Syndrome among Adult Population of Ahvaz, Iran. Diab Obes Metab Disor O A. 2016 [access: 15/09/2022]; 1(3):3. Available at: https://www.kenkyugroup.org/images/articles/24b652ba8f87e5bf78665-aea7d8814ed.pdf
30. Yang Y, Park H, Won K, Chang H, Park G, Kim Y, Ann S, Park E, Kim S, Lee S. Relationship between the optimal cut-off values of anthropometric indices for predicting metabolic syndrome and carotid intima-medial thickness in a Korean population. Medicine (Baltimore). 2019; 98(42):17620. DOI: 10.1097/MD.0000000000017620
31. Aguirre P, Coca A, Aguirre M, Celis G. Waist-to-height ratio and sedentary lifestyle as predictors of metabolic syndrome in children in Ecuador. Hipertens y riesgo Vasc. 2018; 35(3):102-9. DOI: 10.1016/j.hipert.2017.09.002
32. Orces C, Montalvan M, Tettamanti D. Optimal waist circumference cutoff values for predicting metabolic syndrome among older adults in Ecuador. Diabetes Metab Syndr. 2019; 13(2):1015-20. DOI: 10.1016/j.dsx.2019.01.013
Published
2023-03-31
How to Cite
1.
Muñoz Gualan GG, Muñoz Gualan AP. Metabolic syndrome prevalence in military personnel and correlation with anthropometric values in Ecuador. Rev Cubana Med Milit [Internet]. 2023 Mar. 31 [cited 2025 Jan. 15];52(2):e02302401. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/2401
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