Introduction: Malnutrition and lack of exercise have led to a steep increase in metabolic disorders worldwide. Even though diseases caused by malnutrition have become common, we still lack an accurate, inexpensive, and easily accessible method to assess a person’s risk of developing metabolic diseases. In this work, I test a novel method called 3D optical body composition that I hypothesized would be relatively accessible, accurate, and inexpensive.
Methods: The Shape Up! Adults Study is recruiting 720 adults for measures that include whole-body DXA and 3D optical scans. Like image types were spatially registered using 105 and 75 fiducial points for DXA and 3D optical scans respectively. Statistical appearance and shape modeling were then performed on each image type. The sex-specific population variances for shape (3D Optical) and bone, fat, and lean appearance (DXA) were captured as Principal Components (PCs) resulting in 8 PC models 4 for females and 4 for males. Stepwise linear regression was used to predict DXA PCs from 3D optical PCs and other anthropometric and demographic measurements. The predicted DXA PC coefficients of each participant were then inverted to create a pseudo-DXA image. Additionally, k-means cluster analysis was performed on the participants’ predicted DXA fat PC coefficients to determine different body phenotypes of males and females and corresponding health risks.
Results: A total of 72 men and 104 women were available at the time of the analysis. To describe 95% of the population variance in men, it required the following number of PC modes: 10 (optical), 32 (fat), 35 (lean), 35 (bone), with women having similar results. The pixel-to-pixel differences in mass between actual and predicted DXA values had no mean bias for all models. The difference in the pixel values had root mean square errors (RSMEs) of 0.015 g of fat, 0.023 g of lean, and 0.012 g of bone for the female data, and 0.013 g, 0.024 g, and 0.018 g for the male data respectively. These RMSE values were less than 5% of the maximum pixel value within the population. Lastly, I found 9 female and 5 male phenotypes of body fat that were related to unique metabolic characteristics and risk factors.
Conclusion: Whole body and regional distributions of fat, lean, and bone can be accurately predicted from 3D optical scans. With this accessible and accurate method, body composition and metabolic risk phenotype can now be defined in individuals. Our hope is that this will increase awareness of metabolic risks and motivate those at high risk to seek medical advice for risk-reducing strategies.