The Sensory Science and Metabolism (SenSMet) laboratory under the leadership of Dr. Paule Joseph conducts translation and clinical inpatient and outpatient studies. Chemosensory perception (taste and smell) is determined by genetic, hormonal and metabolic factors. Both smell and taste information are sent to the feeding and reward system of the brain, playing a significant part in regulating eating behavior. Understanding the driving factors behind food choices is important for the dietary management of obesity and Type 2 diabetes. The SenSMet laboratory had its inception in FY2018. Active collaborations to investigate areas of common interest in nutrition, taste, and obesity have been undertaken. Specifically, two double-blind clinical protocols for which sensory phenotyping measures have been implemented. Genetic and metabolic translational applications in obesity research include our recent investigation of microRNA (miR) signaling pathways in metabolic syndrome and fatty liver disease (MetS-FL) pathogenesis. Based on the previously reported MetS-FL miR trio of hsa-miR-142-3p, hsa-miR-18b, and hsa-miR-890, we examined an epithelial-focused miR network in colorectal cell models. Transfection of CRL-1790 cells with MetS-FL miR mimics led to global changes in cellular miRNome profiles, such as altered miR expression. Colon-specific changes in epithelial barriers, cell junction structure, and a miRNome signaling network are described in this study of a MetS-FL miR trio signature (Joseph et al., 2018). Given that accurate prediction of obesity risk utilizing genetic data remains challenging, we reported a new mathematical method to predict obesity genetic risk through computational biology approaches from collaborative intramural clinical studies. Genetic risk score was computed by adding body mass index (BMI)-increasing alleles. The genetic risk score was significantly correlated with BMI when an optimization algorithm was used that excluded some SNPs. Linear regression and support vector machine models were built to predict obesity risk using gene expression profiles and the genetic risk score. Our computational framework serves as an example for bioinformatic utility with genetic information to predict obesity risk for specific cohorts (Joseph et al., 2018). In a linked cohort of patients, we found discordance between perception and physiology as it relates to eating behavior, stress and adiposity. The purpose of the study was to examine the interrelationships among stress, eating behavior, and adiposity in a cohort of normal- and overweight individuals. Following a cross-sectional and descriptive analysis, significant correlations were found between Disinhibition and Hunger eating behavior subscales and measures of adiposity including BMI and percent body fat. Disinhibition and Hunger correlated positively with perceived stress. Future studies are warranted to better understand the effects of perceived and physiological stress on eating behavior (Joseph et al., 2018). We conducted a State of the Science of Childhood Obesity review to examine literature regarding the determinants of food choice and genetic markers influencing adolescent obesity over the past decade. The results highlighted the research gap within this specific cohort, including merging adolescent participants with younger children. Also, the results demonstrated the importance of several factors, such as genetics/genomics and socioeconomic status on adolescent obesity. In addition, future studies that focus on adolescent obesity are warranted as well as the implication of precision health in obesity treatment and interventions (Campbell, Franks and Joseph, 2018).