Affiliations 

  • 1 Centre for Diabetic Foot Care and Research, Department of Physiotherapy, Manipal College of Health Professions, Manipal Academy of Higher Education, Madhav Nagar, Manipal, Karnataka, India
  • 2 Kasturba Medical College, Department of Medicine, Manipal Academy of Higher Education, Madhav Nagar, Manipal, Karnataka, India
  • 3 Dr. TMA Pai Hospital, Melaka Manipal Medical College, Department of Medicine, Manipal Academy of Higher Education, Madhav Nagar, Manipal, Karnataka, India
J Taibah Univ Med Sci, 2022 Dec;17(6):983-990.
PMID: 36212585 DOI: 10.1016/j.jtumed.2022.05.006

Abstract

OBJECTIVE: Prediabetes is a precursor to type 2 diabetes mellitus and routine screening of prediabetes is crucial. Visceral fat (VF) is associated with prediabetes and insulin resistance. Ethnic and racial differences resulting in different levels of VF in the Indian population necessitates an India-specific study. There is a dearth of literature on the cut-off values of VF measured using a bioelectrical impedance analyzer (BIA) to predict prediabetes in the Indian population. Hence, the main objective of this study was to determine the sex-specific cut-off value of VF on BIA to predict prediabetes in the Indian population.

METHODS: Three hundred individuals aged 18-55 years of both sexes were selected for this cross-sectional study. VF was evaluated as a part of body composition analysis using BIA. The body composition variables for the prediction of prediabetes were examined using backward logistic regression. Optimal cut-off levels of VF to predict prediabetes were identified using receiver operator characteristic curve (ROC) analysis.

RESULTS: VF, total fat, and age were found to be associated with prediabetes (p ≤ 0.05). In females, the cut-off value of VF for predicting prediabetes was identified as 8 with 77.8% sensitivity and 69.3% specificity; in males, it was 11 with 84% sensitivity and 62.9% specificity.

CONCLUSION: This study contributes to the sex-specific cut-off values of VF level on BIA that can be used for predicting prediabetes in the Indian population.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.