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Universitas Hasanuddin
Research output:Contribution to journalArticlepeer-review

Body mass, total body fat percentage, and visceral fat level predict insulin resistance better than waist circumference and body mass index in healthy young male adults in Indonesia

Kurniawan L.B.

Journal of Clinical Medicine

Q1
Published: 2018Citations: 61

Abstract

The incidence of obesity which leads to insulin resistance (IR) and metabolic disorder is increasing in developing countries, including Indonesia. Male adults have a higher risk of abdominal obesity than females. This is associated with cardiometabolic disorders. Several anthropometric measurements have been proposed to predict IR. The aim of this study was to investigate whether body mass, body mass index (BMI), waist circumference (WC), body fat percentage (BF) or visceral fat level (VF) could become a better predictor of IR in healthy young male adults. A total of 140 healthy young male adults ranging from 18⁻25 years were recruited in the study. Insulin resistance was measured by calculating their Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). Subjects with a HOMA-IR value ≥75th percentile, with cut off 3.75, were defined as IR. Anthropometric measurements including body weight, BMI, and WC were performed, whereas BF and VC were measured using bioelectrical impedance analysis (BIA). IR had a strong correlation with body weight, BMI, WC, BF, and VF. In the area under the curve of body mass, BF and VF were slightly greater than WC and BMI. Anthropometric measurements correlated strongly with IR but body weight, BF, VF had a stronger correlation than WC and BMI in healthy young male adults.

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10.3390/jcm7050096

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MedicineSciences
Bioelectrical impedance analysisSciences
WaistSciences
Insulin resistanceSciences
Body mass indexSciences
AnthropometrySciences
Internal medicineSciences
ObesitySciences
EndocrinologySciences
Body fat percentageSciences
Homeostatic model assessmentSciences
Classification of obesitySciences
Body adiposity indexSciences
Fat massSciences