Percentage of body fat and fat mass index as a screening tool for metabolic syndrome prediction in Colombian university students

Robinson Ramírez-Vélez, Jorge Enrique Correa-Bautista, Alejandra Sanders-Tordecilla, Mónica Liliana Ojeda-Pardo, Elisa Andrea Cobo-Mejía, Rocío del Pilar Castellanos-Vega, Antonio García-Hermoso, Emilio González-Jiménez, Jacqueline Schmidt-Riovalle, Katherine González-Ruíz

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36 Scopus citations

Abstract

High body fat is related to metabolic syndrome (MetS) in all ethnic groups. Based on the International Diabetes Federation (IDF) definition of MetS, the aim of this study was to explore thresholds of body fat percentage (BF%) and fat mass index (FMI) for the prediction of MetS among Colombian University students. A cross-sectional study was conducted on 1687 volunteers (63.4% women, mean age = 20.6 years). Weight, waist circumference, serum lipids indices, blood pressure, and fasting plasma glucose were measured. Body composition was measured by bioelectrical impedance analysis (BIA) and FMI was calculated. MetS was defined as including more than or equal to three of the metabolic abnormalities according to the IDF definition. Receiver operating curve (ROC) analysis was used to determine optimal cut-off points for BF% and FMI in relation to the area under the curve (AUC), sensitivity, and specificity in both sexes. The overall prevalence of MetS was found to be 7.7%, higher in men than women (11.1% vs. 5.3%; p < 0.001). BF% and FMI were positively correlated to MetS components (p < 0.05). ROC analysis indicated that BF% and FMI can be used with moderate accuracy to identify MetS in university-aged students. BF% and FMI thresholds of 25.55% and 6.97 kg/m2 in men, and 38.95% and 11.86 kg/m2 in women, were found to be indicative of high MetS risk. Based on the IDF criteria, both indexes’ thresholds seem to be good tools to identify university students with unfavorable metabolic profiles.

Translated title of the contributionPorcentaje de grasa corporal e índice de masa grasa como herramienta de detección para la predicción del síndrome metabólico en estudiantes universitarios colombianos
Original languageEnglish (US)
Article number1009
JournalNutrients
Volume9
Issue number9
DOIs
StatePublished - Sep 13 2017

All Science Journal Classification (ASJC) codes

  • Food Science
  • Nutrition and Dietetics

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