Área bajo curva ROC de Porcentaje de grasa corporal como estimativo de Síndrome metabólico en adultos de Barranquilla, Colombia
DOI:
https://doi.org/10.14306/renhyd.21.4.398Palabras clave:
Síndrome Metabólico, Tejido Adiposo, Curva ROC, Tamizaje Masivo.Resumen
Introducción: El objetivo del presente trabajo fue determinar la relación entre grasa corporal y Síndrome Metabólico (SM), utilizando curvas ROC, en adultos de una localidad del caribe colombiano.Material y Métodos: Estudio transversal con 552 adultos de 20 a 64 años, con información completa de perfil lipídico, glicemia y mediciones antropométricas: peso, talla, presión arterial, perímetro de cintura y pliegues cutáneos. Se calculó porcentaje de grasa corporal mediante ecuaciones de Siri, Brozeck y Lean y se determinó la presencia de SM mediante 4 consensos: AHA, ATP III, IDF y Armonizado. Para comparar los promedios de grasa corporal según estos se usó T de Student y/o U de Mann Whitney. Se utilizó análisis de curvas ROC para determinar puntos de corte de la grasa corporal para determinar el SM.
Resultados: Las medias de grasa corporal fueron mayores en sujetos con SM independientemente del método utilizado (p<0,05). Las áreas bajo curva ROC oscilaron entre 63% y 76,9%, con sensibilidades entre 50% y 85%, y especificidades entre 51% y 78%. Mediante Lean-cintura y el consenso de AHA se obtuvo el valor más alto del área bajo la curva (0,77; punto de corte: 37,1; sensibilidad: 60,8; especificidad: 78,8%) y utilizando Siri y el consenso Armonizado se obtuvo el valor más bajo (0,63; punto de corte: 28,5; sensibilidad: 80%; especificidad: 42,5%).
Conclusiones: El análisis de curvas ROC permite identificar la relación entre grasa corporal y síndrome metabólico, y podría emplearse como tamizaje, teniendo en cuenta que los valores de sensibilidad y especificidad dependen de las mediciones antropométricas y las ecuaciones empleadas.
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(1) Beltrán-Sánchez H, Harhay MO, Harhay MM, McElligott S. Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999-2010. J Am Coll Cardiol. 2013;62(8):697-703.
(2) de Carvalho Vidigal F, Bressan J, Babio N, Salas-Salvadó J. Prevalence of metabolic syndrome in Brazilian adults: a systematic review. BMC Public Health. 2013;13:1198.
(3) Aguilar M, Bhuket T, Torres S, Liu B, Wong RJ. Prevalence of the metabolic syndrome in the United States, 2003-2012. JAMA. 2015;313(19):1973-4.
(4) Samson SL, Garber AJ. Metabolic syndrome. Endocrinol Metab Clin North Am. 2014;43(1):1-23.
(5) Pearl RL, Wadden TA, Hopkins CM, Shaw JA, Hayes MR, Bakizada ZM, et al. Association between weight bias internalization and metabolic syndrome among treatment-seeking individuals with obesity. Obesity (Silver Spring). 2017;25(2):317-22.
(6) Lee S-R, Cha M-J, Kang D-Y, Oh K-C, Shin D-H, Lee H-Y. Increased prevalence of metabolic syndrome among hypertensive population: ten years’ trend of the Korean National Health and Nutrition Examination Survey. Int J Cardiol. 2013;166(3):633-9.
(7) Lakka H-M, 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.
(8) National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). 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) final report. Circulation. 2002;106(25):3143-421.
(9) Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735-52.
(10) Alberti KGMM, Zimmet P, Shaw J. Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med. 2006;23(5):469-80.
(11) Alberti KGMM, 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.
(12) Tai ES, Lau TN, Ho SC, Fok AC, Tan CE. Body fat distribution and cardiovascular risk in normal weight women. Associations with insulin resistance, lipids and plasma leptin. Int J Obes Relat Metab Disord. 2000;24(6):751-7.
(13) Bastien M, Poirier P, Lemieux I, Després J-P. Overview of epidemiology and contribution of obesity to cardiovascular disease. Prog Cardiovasc Dis. 2014;56(4):369-81.
(14) Lavie CJ, McAuley PA, Church TS, Milani RV, Blair SN. Obesity and cardiovascular diseases: implications regarding fitness, fatness, and severity in the obesity paradox. J Am Coll Cardiol. 2014;63(14):1345-54.
(15) Laakso M, Kuusisto J, Stančáková A, Kuulasmaa T, Pajukanta P, Lusis AJ, et al. The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases. J Lipid Res. 2017;58(3):481-93.
(16) Madeira FB, Silva AA, Veloso HF, Goldani MZ, Kac G, Cardoso VC, et al. Normal weight obesity is associated with metabolic syndrome and insulin resistance in young adults from a middle-income country. PLoS ONE. 2013;8(3):e60673.
(17) Ho-Pham LT, Lai TQ, Nguyen MTT, Nguyen TV. Relationship between Body Mass Index and Percent Body Fat in Vietnamese: Implications for the Diagnosis of Obesity. PLoS ONE. 2015;10(5):e0127198.
(18) Lavie CJ, De Schutter A, Patel DA, Romero-Corral A, Artham SM, Milani RV. Body composition and survival in stable coronary heart disease: impact of lean mass index and body fat in the «obesity paradox». J Am Coll Cardiol. 2012;60(15):1374-80.
(19) Lechuga EN, Moranth RFV, Olaciregui AEA. Grasa corporal total como posible indicador de síndrome metabólico en adultos. Rev Esp Nutr Hum Diet. 2016;20(3):198-207.
(20) Navarro E, Vargas R. Síndrome metabólico en el suroccidente de Barranquilla (Colombia). Salud Uninorte Barranquilla. 2008;24(1):40-52.
(21) Marfell-Jones M, Stewart A. International Standards for Anthropometric Assessment. Ridder J, ISAK, editores. Wellington, Nueva Zelanda: International Society for the Advancement of Kinanthropometry; 2012.
(22) Hormiguera X. Circunferencia de la cintura y riesgo cardiovascular en población española. Clin Invest Arterioscl. 2007;19(2):90-1.
(23) Durnin JV, Womersley J. Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr. 1974;32(1):77-97.
(24) Siri WE. Body composition from fluid spaces and density: analysis of methods. En: Brožek J, Henschel A, editores. Techniques for measuring body composition: proceedings of a conference, Quartermaster Research and Engineering Center, Natick, Massachusetts, January 22-23, 1959. Washington, D.C.: National Academy of Sciences-National Research Council; 1961. p. 223-44.
(25) Brozek J, Grande F, Anderson JT, Keys A. DENSITOMETRIC ANALYSIS OF BODY COMPOSITION: REVISION OF SOME QUANTITATIVE ASSUMPTIONS. Ann N Y Acad Sci. 1963;110:113-40.
(26) Deurenberg P, Weststrate JA, Seidell JC. Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr. 1991;65(2):105-14.
(27) Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240(4857):1285-93.
(28) Zhang Z, Deng J, He L, Ling W, Su Y, Chen Y. Comparison of various anthropometric and body fat indices in identifying cardiometabolic disturbances in Chinese men and women. PLoS ONE. 2013;8(8):e70893.
(29) Liu P, Ma F, Lou H, Liu Y. The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome. BMC Public Health. 2013;13:629.
(30) Zhu S, Wang Z, Shen W, Heymsfield SB, Heshka S. Percentage body fat ranges associated with metabolic syndrome risk: results based on the third National Health and Nutrition Examination Survey (1988-1994). Am J Clin Nutr. 2003;78(2):228-35.
(31) Yan S, Li Y, Shen X, Yang L, Zhang S, Yi R, et al. [Use body fat determination instead of simple body composition parameters evaluate the risk of metabolic syndrome in Fuzhou adults]. Zhonghua Yi Xue Za Zhi. 2010;90(24):1678-81.
(32) Phillips CM, Tierney AC, Perez-Martinez P, Defoort C, Blaak EE, Gjelstad IMF, et al. Obesity and body fat classification in the metabolic syndrome: impact on cardiometabolic risk metabotype. Obesity (Silver Spring). 2013;21(1):E154-161.
(33) De Schutter A, Lavie CJ, Arce K, Menendez SG, Milani RV. Correlation and discrepancies between obesity by body mass index and body fat in patients with coronary heart disease. J Cardiopulm Rehabil Prev. 2013;33(2):77-83.
(34) Weber DR, Leonard MB, Shults J, Zemel BS. A comparison of fat and lean body mass index to BMI for the identification of metabolic syndrome in children and adolescents. J Clin Endocrinol Metab. 2014;99(9):3208-16.
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