PLoS ONE (Jan 2020)

Anthropometric indices and cut-off points in the diagnosis of metabolic disorders.

  • Stanisław Głuszek,
  • Elzbieta Ciesla,
  • Martyna Głuszek-Osuch,
  • Dorota Kozieł,
  • Wojciech Kiebzak,
  • Łukasz Wypchło,
  • Edyta Suliga

DOI
https://doi.org/10.1371/journal.pone.0235121
Journal volume & issue
Vol. 15, no. 6
p. e0235121

Abstract

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ObjectiveIdentifying metabolic disorders at the earliest phase of their development allows for an early intervention and the prevention of serious consequences of diseases. However, it is difficult to determine which of the anthropometric indices of obesity is the best tool for diagnosing metabolic disorders. The aims of this study were to evaluate the usefulness of selected anthropometric indices and to determine optimal cut-off points for the identification of single metabolic disorders that are components of metabolic syndrome (MetS).DesignCross-sectional study.ParticipantsWe analyzed the data of 12,328 participants aged 55.7±5.4 years. All participants were of European descent.Primary outcome measureFour MetS components were included: high glucose concentration, high blood triglyceride concentration, low high-density lipoprotein cholesterol concentration, and elevated blood pressure. The following obesity indices were considered: waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), body fat percentage (%BF), Clínica Universidad de Navarra-body adiposity estimator (CUN-BAE), body roundness index (BRI), and a body shape index (ABSI).ResultsThe following indices had the highest discriminatory power for the identification of at least one MetS component: CUN-BAE, BMI, and WC in men (AUC = 0.734, 0.728, and 0.728, respectively) and WHtR, CUN-BAE, and WC in women (AUC = 0.715, 0.714, and 0.712, respectively) (pConclusionsFor the BMI, the optimal cut-off point for the identification of metabolic abnormalities was 27.2 kg/m2 for both sexes. For the WC, the optimal cut-off point was of 94 cm for men and 87 cm for women. Prospective studies are needed to identify those indices in which changes in value predict the occurrence of metabolic disorders best.