Public health semantics of the YY paradox

Abstract

Background: Body Mass Index (BMI) has lost its credibility as an indicator of fatness. 3D scan and body composition details of Yajnik and Yudnik, the authors with similar BMI but very different body fat percentage was labelled as ‘YY paradox’. 3D scanners are not widely available; as such dependence on less specific tools is still high. It was assumed that such paradoxes may frequently occur in anthropometrically derived body composition indices and paucity of such information prompted us to explore the nature and usage of YY paradox. Methods: Body composition of 89 medical students from North India was studied using bioelectric-impedance fat monitor and anthropometric techniques. YY phenomenon were identified and studied in 1) same BMI but different body fat (Classic YY), 2) same BVI but different BMI (yy BVI~BMI), 3) same Skeletal mass/body fat but different body volume (yy SKM/BF ~ BV) and 4) same Lean Body mass/body fat but different body volume (yy LBM/BF ~ BV). Results: The study population comprised young adults aged 18 -26 years. Males comprised 51.7 % of the study group. YY phenomenon was found in 44 individuals with respect to same BMI but different body fat; 47 individuals of same BVI but different BMI. Of all the indices studied, lowest number of YYs were found in yy LBM/BF ~ BV index. 14.6% study subjects had high visceral fat. Odds Ratio (OR) for high visceral fat in all the studied indices among subjects showing yy-phenomenon and those not showing yy-phenomenon revealed an OR of 1.09 (CI 0.3-3.7) for yy LBM/BF ~ BV index. This suggests that high visceral fat (VF) is the same in both groups and implies that there is no difference between the two arms i.e., YY and non-YY group contains approximately similar proportion of subjects with high VF. Conclusion: We found a high frequency of such paradoxes in this population and also demonstrated that these are not normally distributed. It is also suggested that a deeper look in this issue could be used for deriving predictive models for disease linked anthropometric markers.