A Metabolomic Framework in Predicting Optimal Fitness and Performance

The availability of the human genome and its potential in disease and health applications has spurred the development of novel methodologies in genomics, proteomics, and metabolomics. These technologies represent an untapped promise in optimizing nutrition and diet for achieving optimal health and fitness levels. Defense-based programs stand to benefit highly from such initiatives because of the criticality of personnel fitness and the importance of understanding of individualized metabolism for strategic planning. A key objective of this program is to demonstrate initial analytics to link an individual's metabolic state and fitness level using techniques of genomic and metabolomic profiling. We present a partnership between the Tufts School of Medicine and Silico Insights, a computationally-driven pathway discovery company. We propose to leverage data from existing clinical studies at Tufts on the syndrome lipodystrophy as a sub-optimal fitness state and establish molecular signatures that enhance well-being based on exercise or diet. Dr. Abby Shevitz at Tufts has been leading clinical research in disease management with nutritional and exercise regimens. Silico Insights brings proven expertise in multivariate statistical analysis and an established computational infrastructure. The collaboration will merge expertise in medical, nutritional and multivariate analysis disciplines to meet feasibility objectives of this proposal.