Voxel-wise Longitudinal Analysis of Weight Gain from Different Dietary Fats using Image Registration-Based 'Imiomics' Analysis

There is an emerging global epidemic of obesity and related complications, such as type 2diabetes (T2D). Alterations in body composition (adipose tissue, muscle volume and fatcontents) are known to be associated with an increased metabolic risk. Understanding of theunderlying mechanisms is key for development of novel intervention strategies. One study investigating the effect on body composition by different diets is Lipogain1. In this study, it was found that a small weight gain induced by polyunsaturated fats (PUFA, n=19) or saturated fats (SFA, n=20) had very different effects on body fat, liver fat and lean tissue mass respectively. The SFA group gained more liver fat and fat mass in general, while the PUFA group gained more muscle mass. These results were determined by magnetic resonance imaging. The goal of this project was to visualize the results from Lipogain1 by utilizing the noveltechnique Imiomics. Imiomics is a method for statistical analysis of whole-body medical images. By utilizing image registration, all images are transformed to a common reference space. This enables point-wise comparisons between all images included in the analysis. In this project, mean images of the alterations in fat content and local volume change of the two groups were created. These were used to visualize the alterations in body composition from the study. Additionally, statistical tests were used to visualize statistically significant differences between the groups. Differences between the groups could be seen in the mean images. Mainly a higher fat content increase was seen in SFA in comparison to PUFA. There was also a larger volume expansion in fat tissue in SFA than in PUFA, while PUFA instead had a larger volume expansion in muscles. An unexpected result was also found; the liver had expanded in PUFA but not in SFA. Unfortunately, few significant differences could be visualized between the groups when the statistical test was performed. The conclusion was that this method is promising for visualization of these kinds of studies, especially due to the potential of finding new, unexpected results. However, a somewhat larger cohort and possibly larger alterations in body composition might be needed to be able to visualize and quantify statistically significant differences between the groups on a voxel-wise level.