Evaluation of anthropometric indices as metabolic syndrome predictors in Ecuadorian Military Personnel
Barra lateral del artículo
Publicado:
Nov 16, 2020
Palabras clave:
riesgo cardiovascular, antropometría, dislipidemia, curva ROC, síndrome metabólico
Contenido principal del artículo
Resumen
Las mediciones antropométricas son técnicas simples y efectivas para la evaluación de la
obesidad central o abdominal. Aunque se conoce que tienen un buen valor predictivo, no existe un consenso
sobre cuáles son mejores en el diagnóstico de Síndrome Metabólico (MetSyn), utilizando los criterios del
Panel de Tratamiento de Adultos III (ATP III). Las medidas antropométricas incluyen la circunferencia de
la cintura (WC), el índice cintura-cadera (WHR), el índice de la altura de la cintura (WHtR) y el índice
de masa corporal (IMC). En este estudio se evaluó la prevalencia de MetSyn y se comparó con índices
antropométricos para determinar el valor de predicción óptimo con sus respectivos puntos de corte para
el diagnóstico de MetSyn entre los miembros del ejército en ESFORSE, Ecuador. El estudio incluye 181
participantes (175 hombres y 6 mujeres), la edad promedio es de 37 ± 6 años, la prevalencia de MetSyn es
del 8%, con CC (p <.001), WHtR (p .009) y WHR (p .020) como variables estadísticamente significativas.
Analizamos el área bajo la curva (AUC) en una curva de Característica Operativa del Receptor (ROC),
con las medidas antropométricas. Por tanto, WC y WHtR representan el AUC más alto (WC: 0.77, IC del
95%: 0.69-0.86; WHtR: 0.70, IC del 95%: 0.59-0.82). Los valores de corte óptimos para predecir MetSyn
son 92 cm en WC, 0.52 en WHtR y 0.93 en WHR. Por lo tanto, los miembros del ejército tienen una baja
prevalencia de MetSyn, con WC como el mejor valor de predicción.
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1.
Muñoz G, Muñoz Gualan AP. Evaluation of anthropometric indices as metabolic syndrome predictors in Ecuadorian Military Personnel. REMCB [Internet]. 16 de noviembre de 2020 [citado 19 de abril de 2024];41(2):141-7. Disponible en: https://remcb-puce.edu.ec/remcb/article/view/872
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Artículos Científicos
Citas
ALAD. 2010. Consenso Latinoamericano de la Asociación Latinoamericana de Diabetes. Asoc Latinoam diabetes. 18(1):25–44.
Alberti K, Eckel R, Grundy S, Zimmet P, Cleeman J, Donato K, Fruchart J, James P, Loria C, Smith S. 2009. 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. Circ - AHA. 120:1640–1645. doi:10.1161/CIRCULATIONAHA.109.192644.
Alberti K, Zimmet P. 1998. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications, Part 1: Diagnosis and Classification of Diabetes Mellitus, Provisional Report of a WHO Consultation. Diabet Med. 15:539–553. doi:10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S.
Balkau B, Charles M. 1999. Comment on the Provisional Report from the WHO Consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med. 16(5):442–3. doi:10.1046/j.1464-5491.1999.00059.x.
Barzin M, Hosseinpanah F, Fekri S, Azizi F. 2011. Predictive Value of body mass index and waist circumference for metabolic syndrome in 6-12 year olds. J Paediatr. 100(5):722–727.
Bener A, Yousafzai M, Darwish S, Al-Hamaq A, Nasralla E, Abdul-Ghani M. 2013. Obesity Index That Better Predict Metabolic Syndrome: Body Mass Index, Waist Circumference, Waist Hip Ratio, or Waist Height Ratio. J Obes. doi:10.1155/2013/269038.
Bhurosy T, Jeewon R. 2014. Overweight and Obesity Epidemic in Developing Countries: A Problem with Diet, Physical Activity, or Socioeconomic Status? Sci World J. doi:10.1155/2014/964236.
Camaggi C, Molina A. 2010. Descriptive Study of Metabolic Syndrome in Adults from the East Area of Santiago. Rev Medica Clínica Condes. 21(5):839–844. doi:10.1016/S0716-8640(10)70605-2.
Concepción L, Aliaga R, Delgado F, Morillas C, Hernández A, Martí-Bonmatí L. 2001. Abdominal fat assessment by magnetic resonance: comparison with biometric profiles and cardiovascular risk markers. Med Clin (Barc). 117(10):366–369. doi:10.1016/S0025-7753(01)72117-3.
Delvarianzadeh M, Abbasian M, Khosravi F, Ebrahimi H, Ebrahimi M, Fazli M. 2017. Appropriate anthropometric indices of obesity and overweight for diagnosis of metabolic syndrome and its relationship with oxidative stress. Diabetes Metab Syndr Clin Res Rev. doi:10.1016/j. dsx.2017.07.014.
Eckel R, Cornier M. 2014. Update on the NCEP ATP-III emerging cardiometabolic risk factors. BMC Med. 12(115). doi:10.1186/1741-7015-12-115.
Einhorn D, Reaven G, Cobin R, Ford E, Ganda O, Handelsman Y, Hellman R, Jellinger P, Kendall D, Krauss R, et al. 2003. American College of Endocrinology Position Statement on the Insulin Resistance Syndrome. Endocr Pr. 9(23):237–252.
Gharipour M, Sarrafzadegan N, Sadeghi M, Andalib E, Talaie M, Shafie D, Aghababaie E. 2013. Predictors of Metabolic Syndrome in the Iranian Population: Waist Circumference, Body Mass Index, or Waist to Hip Ratio? Cholesterol. doi:10.1155/2013/198384.
Granfeldt G, Ibarra J, Mosso C, Muñoz S, Sáez K, Zapata D. 2015. Predictive capacity of anthropometric indeces in the detection of metabolic syndrome in Chilean adults. Arch Latinoam Nutr. 65(3).
Hossein M, Delvarianzadeh M, Saadat S. 2016. Prevalence of Metabolic Syndrome Among Iranian Occupational Drivers. Diabetes Metab Syndr. 10(1):46–51. doi:10.1016/j.dsx.2015.09.011.
Jahangiri Y, Hadaegh F, Vatankhah N. 2013. Wrist circumference as a novel predictor of diabetes and prediabetes: results of cross-sectional and 8.8 year follow up studies. J Clin Endocrinol Metab. 98(2). doi:10.1210/jc.2012-2416.
Koning L, Merchant A, Pogue J, Anand S. 2007. Waist circumference and waist to hip ratio as predictors of cardiovascular events: mega-regression analysis of prospective studies. Eur Heart J. 28:850–856. doi:10.1093/eurheartj/ehm026.
Lim S, Shin H, Song J, Kwak S, Kang S, Yoon J, Choi S, Cho S, Park K, Lee H, et al. 2011. Increasing Prevalence of Metabolic Syndrome in Korea: The Korean National Health and Nutrition Examination Survey for 1998 - 2007. Diabetes Care. 34(6):1323–1328. doi:10.2337/dc10-2109.
Liu Y, Tong G, Tong W, Lu L, Qin X. 2011. Can Body Mass Index, Waist Circumference, Waist-Hip Ratio and Waist-height Ratio Predict the Presence of Multiple Metabolic Risk Factors in Chinese Subjects? BMC Public Health. 11(35). doi:10.1186/1471-2458-11-35.
Martínez E, Flores Á, Alonso M, Esparza G, Garzón C. 2007. Metabolic syndrome prevalence in military population that goes to annual medical evaluation. Rev Sanid Mil Mex. 61(6):361–366.
Medina J, Alonso C. 2012. Association between serum uric acid levels and the prevalence of metabolic syndrome in airline pilots. Sanid Mil. 68(4):211–215.
Meng Z, Liu M, Zhang Q, Liu L, Song K, Tan J, Jia Q, Zhang G, Wang R, He Y, et al. 2015. Gender and Age Impacts on the Association Between Thyroid Function and Metabolic Syndrome in Chinese. Medicine (Baltimore). 94(50):1–9. doi:10.1097/MD.0000000000002193.
Mohammed E, Abed Y, Rahmat A, Ali F. 2014. Epidemiology of obesity in developing countries: challenges and prevention. Glob Epidemic Obes. doi:10.7243/2052-5966-2-2.
Monroy D. 2018. Prevalencia del Síndrome Metabólico en Pilotos de la Aviación del Ejército Nacional de Colombia. Universidad Nacional de Colombia.
Mozumdar A, Liguori G. 2011. Persistent Increase of Prevalence of Metabolic Syndrome Among U.S Adults: NHANES III to NHANES 1999-2006. Diabetes Care. 34(1):216–219. doi:10.2337/dc10-0879.
Muñoz A, Muñoz G. 2018. Quantification of cardiovascular disease risk, according to the Framingham score, in military personnel during 2015. Rev Ecuat Med Cienc Biol. 39(1). doi:10.26807/remcb.v39i1.560.
NCEP. 2001. Executive Summary of the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 285(19):2486–2497. doi:10.1001/jama.285.19.2486.
Obeidat A, Ahmad M, Haddad F, Azzeh F. 2015. Evaluation of several anthropometric indices of obesity as predictors of metabolic syndrome in Jordanian adults. Nutr Hosp. 1(32):667–677. doi:10.3305/nh.2015.32.2.9063.
Rajpput R, Rajput M, Bairwa M, Singh J, Saini O, Shankar V. 2014. Waist height ratio: A universal screening tool for prediction of metabolic syndrome in urban and rural population of Haryana. Indian J Endocrinol Metab. 18(3).
Rivas D, Miguel P, Llorente Y, Marrero G. 2015. Clinical and Epidemiological Behavior of the Metabolic Syndrome In Adults. Rev Cuba Med Gen Integr. 31(2):259–269.
Rodríguez M, Cabrera A, Aguirre A, Domínguez S, Brito B, Almeida D, Borges C, Del Castillo J, Carrillo L, González A, et al. 2010. The Waist to Height Ratio as an Index of Cardiovascular Risk and Diabetes. Med Clin (Barc). 134(9):386–391. doi:10.1016/j.medcli.2009.09.047.
Shabazian H, Pipelzadeh M. 2015. Efficiency of Anthropometric Indices in Predicting Metabolic Syndrome among Adult Population of Ahvaz, Iran. Diabetes, Obes Metab Disord. 1(3).
Stone N, Bilek S, Rosenbaum S. 2005. Recent National Cholesterol Education Program Adult Treatment Panel III update: adjustments and options. Am J Cardiol. 96(4):53–59. doi:10.1016/j.
Alberti K, Eckel R, Grundy S, Zimmet P, Cleeman J, Donato K, Fruchart J, James P, Loria C, Smith S. 2009. 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. Circ - AHA. 120:1640–1645. doi:10.1161/CIRCULATIONAHA.109.192644.
Alberti K, Zimmet P. 1998. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications, Part 1: Diagnosis and Classification of Diabetes Mellitus, Provisional Report of a WHO Consultation. Diabet Med. 15:539–553. doi:10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S.
Balkau B, Charles M. 1999. Comment on the Provisional Report from the WHO Consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med. 16(5):442–3. doi:10.1046/j.1464-5491.1999.00059.x.
Barzin M, Hosseinpanah F, Fekri S, Azizi F. 2011. Predictive Value of body mass index and waist circumference for metabolic syndrome in 6-12 year olds. J Paediatr. 100(5):722–727.
Bener A, Yousafzai M, Darwish S, Al-Hamaq A, Nasralla E, Abdul-Ghani M. 2013. Obesity Index That Better Predict Metabolic Syndrome: Body Mass Index, Waist Circumference, Waist Hip Ratio, or Waist Height Ratio. J Obes. doi:10.1155/2013/269038.
Bhurosy T, Jeewon R. 2014. Overweight and Obesity Epidemic in Developing Countries: A Problem with Diet, Physical Activity, or Socioeconomic Status? Sci World J. doi:10.1155/2014/964236.
Camaggi C, Molina A. 2010. Descriptive Study of Metabolic Syndrome in Adults from the East Area of Santiago. Rev Medica Clínica Condes. 21(5):839–844. doi:10.1016/S0716-8640(10)70605-2.
Concepción L, Aliaga R, Delgado F, Morillas C, Hernández A, Martí-Bonmatí L. 2001. Abdominal fat assessment by magnetic resonance: comparison with biometric profiles and cardiovascular risk markers. Med Clin (Barc). 117(10):366–369. doi:10.1016/S0025-7753(01)72117-3.
Delvarianzadeh M, Abbasian M, Khosravi F, Ebrahimi H, Ebrahimi M, Fazli M. 2017. Appropriate anthropometric indices of obesity and overweight for diagnosis of metabolic syndrome and its relationship with oxidative stress. Diabetes Metab Syndr Clin Res Rev. doi:10.1016/j. dsx.2017.07.014.
Eckel R, Cornier M. 2014. Update on the NCEP ATP-III emerging cardiometabolic risk factors. BMC Med. 12(115). doi:10.1186/1741-7015-12-115.
Einhorn D, Reaven G, Cobin R, Ford E, Ganda O, Handelsman Y, Hellman R, Jellinger P, Kendall D, Krauss R, et al. 2003. American College of Endocrinology Position Statement on the Insulin Resistance Syndrome. Endocr Pr. 9(23):237–252.
Gharipour M, Sarrafzadegan N, Sadeghi M, Andalib E, Talaie M, Shafie D, Aghababaie E. 2013. Predictors of Metabolic Syndrome in the Iranian Population: Waist Circumference, Body Mass Index, or Waist to Hip Ratio? Cholesterol. doi:10.1155/2013/198384.
Granfeldt G, Ibarra J, Mosso C, Muñoz S, Sáez K, Zapata D. 2015. Predictive capacity of anthropometric indeces in the detection of metabolic syndrome in Chilean adults. Arch Latinoam Nutr. 65(3).
Hossein M, Delvarianzadeh M, Saadat S. 2016. Prevalence of Metabolic Syndrome Among Iranian Occupational Drivers. Diabetes Metab Syndr. 10(1):46–51. doi:10.1016/j.dsx.2015.09.011.
Jahangiri Y, Hadaegh F, Vatankhah N. 2013. Wrist circumference as a novel predictor of diabetes and prediabetes: results of cross-sectional and 8.8 year follow up studies. J Clin Endocrinol Metab. 98(2). doi:10.1210/jc.2012-2416.
Koning L, Merchant A, Pogue J, Anand S. 2007. Waist circumference and waist to hip ratio as predictors of cardiovascular events: mega-regression analysis of prospective studies. Eur Heart J. 28:850–856. doi:10.1093/eurheartj/ehm026.
Lim S, Shin H, Song J, Kwak S, Kang S, Yoon J, Choi S, Cho S, Park K, Lee H, et al. 2011. Increasing Prevalence of Metabolic Syndrome in Korea: The Korean National Health and Nutrition Examination Survey for 1998 - 2007. Diabetes Care. 34(6):1323–1328. doi:10.2337/dc10-2109.
Liu Y, Tong G, Tong W, Lu L, Qin X. 2011. Can Body Mass Index, Waist Circumference, Waist-Hip Ratio and Waist-height Ratio Predict the Presence of Multiple Metabolic Risk Factors in Chinese Subjects? BMC Public Health. 11(35). doi:10.1186/1471-2458-11-35.
Martínez E, Flores Á, Alonso M, Esparza G, Garzón C. 2007. Metabolic syndrome prevalence in military population that goes to annual medical evaluation. Rev Sanid Mil Mex. 61(6):361–366.
Medina J, Alonso C. 2012. Association between serum uric acid levels and the prevalence of metabolic syndrome in airline pilots. Sanid Mil. 68(4):211–215.
Meng Z, Liu M, Zhang Q, Liu L, Song K, Tan J, Jia Q, Zhang G, Wang R, He Y, et al. 2015. Gender and Age Impacts on the Association Between Thyroid Function and Metabolic Syndrome in Chinese. Medicine (Baltimore). 94(50):1–9. doi:10.1097/MD.0000000000002193.
Mohammed E, Abed Y, Rahmat A, Ali F. 2014. Epidemiology of obesity in developing countries: challenges and prevention. Glob Epidemic Obes. doi:10.7243/2052-5966-2-2.
Monroy D. 2018. Prevalencia del Síndrome Metabólico en Pilotos de la Aviación del Ejército Nacional de Colombia. Universidad Nacional de Colombia.
Mozumdar A, Liguori G. 2011. Persistent Increase of Prevalence of Metabolic Syndrome Among U.S Adults: NHANES III to NHANES 1999-2006. Diabetes Care. 34(1):216–219. doi:10.2337/dc10-0879.
Muñoz A, Muñoz G. 2018. Quantification of cardiovascular disease risk, according to the Framingham score, in military personnel during 2015. Rev Ecuat Med Cienc Biol. 39(1). doi:10.26807/remcb.v39i1.560.
NCEP. 2001. Executive Summary of the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 285(19):2486–2497. doi:10.1001/jama.285.19.2486.
Obeidat A, Ahmad M, Haddad F, Azzeh F. 2015. Evaluation of several anthropometric indices of obesity as predictors of metabolic syndrome in Jordanian adults. Nutr Hosp. 1(32):667–677. doi:10.3305/nh.2015.32.2.9063.
Rajpput R, Rajput M, Bairwa M, Singh J, Saini O, Shankar V. 2014. Waist height ratio: A universal screening tool for prediction of metabolic syndrome in urban and rural population of Haryana. Indian J Endocrinol Metab. 18(3).
Rivas D, Miguel P, Llorente Y, Marrero G. 2015. Clinical and Epidemiological Behavior of the Metabolic Syndrome In Adults. Rev Cuba Med Gen Integr. 31(2):259–269.
Rodríguez M, Cabrera A, Aguirre A, Domínguez S, Brito B, Almeida D, Borges C, Del Castillo J, Carrillo L, González A, et al. 2010. The Waist to Height Ratio as an Index of Cardiovascular Risk and Diabetes. Med Clin (Barc). 134(9):386–391. doi:10.1016/j.medcli.2009.09.047.
Shabazian H, Pipelzadeh M. 2015. Efficiency of Anthropometric Indices in Predicting Metabolic Syndrome among Adult Population of Ahvaz, Iran. Diabetes, Obes Metab Disord. 1(3).
Stone N, Bilek S, Rosenbaum S. 2005. Recent National Cholesterol Education Program Adult Treatment Panel III update: adjustments and options. Am J Cardiol. 96(4):53–59. doi:10.1016/j.