SBIR-STTR Award

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

The objective of Phase II is prospective studies on healthy subjects to develop a predictor describing fitness states. The Phase I demonstration of metabolomic spectra to discriminate different levels of exercise response provides a solid foundation. We will combine the clinical expertise at Tufts University’s Human Nutrition Research Center with Nuvera’s computational research capability. The key objectives of Phase II: 1. Discover molecular markers based on metabolomic analyses. Subjects from 3 sub-groups will be recruited, (i) sedentary, (ii) recreational runners, and (iii) weight-trainers. 2. Develop a molecular predictor and evaluate its predictive characteristics. 3. Implement an interventional exercise study to validate the molecular predictor. 4. Propose potential diagnostic test and steps for commercial development. NMR analyses will be done in urine and/or blood samples. Nuvera’s algorithms will help provide (i) statistically valid sets of molecular predictors, and (ii) combine the best predictor variables into a model of fitness state for validation. Year 2 will select about 10 individuals from the sedentary sub-group for a nutrition/exercise intervention. Using the predictor, individuals likely to be high responders will be predicted and the algorithm refined and validated. The deliverable will design a possible diagnostic tool for performance monitoring and optimization.

Keywords:
Metabolomics, Genomics, Predictive Diagnostics, Molecular Signatures, Fitness, Performance