Development and Cross-Validation of Anthropometric Predictive Equations to Estimate Total Body Fat Percentage in Adult Women in Sri Lanka.
Nirmala RathnayakeGayani AlwisJanaka LenoraSarath LekamwasamPublished in: Journal of obesity (2020)
Attempts have been made to estimate body fat using anthropometry, and most of them are country-specific. This study was designed to develop and cross-validate anthropometric predictive equations to estimate the total body fat percentage (TBFP) of Sri Lankan adult women. A cross-sectional study was conducted in Galle, Sri Lanka, with two groups: Group A (group for equation development) and Group B (cross-validation group) (n = 175 each) of randomly selected healthy adult women aged 30-60 years. TBFP (%) was quantified with total body DXA (TBFPDXA). Height (m), weight (kg), and skinfold thickness (SFT, mm) at six sites and circumferences (cm) at five sites were measured. In the first step, four anthropometric equations were developed based on the data obtained from multiple regression analyses (TBFPDXA = dependent variable and anthropometric measurements and age = independent variables) with Group A. They were developed on the basis of circumferences (TBFP1), SFTs (TBFP2), circumferences and SFTs (TBFP3), and highly significant circumferences and SFTs (r ≥ 0.6) (TBFP4). In the second step, the newly developed equations were cross-validated using Group B. Three equations (TBFP1, TBFP2, and TBFP4) showed the agreement with cross-validation criteria. There were no differences between TBFPDXA and TBFP estimated by these equations (p > 0.05). They showed higher measurement concordance with TBFPDXA; correlation between measured TBFP with DXA and estimated with TBFP1, TBFP2, and TBFP4, respectively, was 0.80 (R 2 = 0.65, SEE = 3.10), 0.83 (R 2 = 0.69, SEE = 2.93), and 0.84 (R 2 = 0.72, SEE = 2.78). Three anthropometric measurements based on predictive equations were developed and cross-validated to satisfactorily estimate the TBFP in adult women.
Keyphrases
- body composition
- polycystic ovary syndrome
- pregnancy outcomes
- cervical cancer screening
- bone mineral density
- physical activity
- insulin resistance
- type diabetes
- computed tomography
- magnetic resonance imaging
- weight loss
- risk factors
- pregnant women
- childhood cancer
- postmenopausal women
- machine learning
- deep learning
- young adults
- body weight