![]() ![]() Obesity has long been associated with increased risks of mortality, cardiovascular diseases, diabetes, and cancer, and is associated with significant health and economic burden. ![]() Obesity is a complex and chronic condition, clinically defined as the accumulation of excess body fat to the extent that it may have adverse effects on health. The results confirmed that Singaporean adults have higher BF% at lower BMI compared to US and Europe white counterparts and that BF% in our population has increased over two decades. There is a large discrepancy between BF% and BMI measured obesity in Singaporean adults. However, using the BF% cut-off (> 25% for men and > 35% for women) resulted in very high prevalence of obesity of 82.0% (80.2% men 83.8% women). The overall population-adjusted prevalence of obesity according to WHO International classification (BMI ≥30 kg/m 2) was 12.9% (14.9% men 11.0% women) and 26.6% (30.7% men 22.8% women) according to the MOH classification (BMI ≥27.5 kg/m 2). The current cohort of Singaporeans when compared to Caucasians in the US and Europe as well as a Singapore cohort from 20 years age have higher BF% when matched for BMI, age, and sex. We derived a prediction model to estimate BF% based on BMI, age and sex. ![]() Prevalence of overweight and obesity were estimated using WHO and Singapore Ministry of Health (MOH) Clinical Practice Guidelines for BMI classification, and BF% cut-off points of 25 and 35% for men and women respectively. Relationship between BMI and BF% were analysed using multiple regression models. Anthropometry and body composition were assessed. This was a population-based study of 542 community-dwelling Singaporeans (21–90 years old, 43.1% men). The secondary aim was to determine the prevalence of overweight and obesity based on BF% threshold and the new risk categories for obesity in Singaporean population. The main aim of this study was to the determine relationship between Body Mass Index (BMI) and percentage body fat (BF%) in Singaporean adults, derive a prediction model to estimate BF%, and to report population BF%. ![]()
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