PSXI-4 - Use of Foodomics Analysis to Biochemically Compare Different Pet Food Ingredient Categories

Abstract: With an increased focus on pet health and longevity, ingredient nutritional quality and functionality is of great interest to the pet food industry. Characterizing the natural bioactive molecules present in different ingredient categories may identify those most likely to support health and/or manage disease. Over the past couple decades, high-throughput ‘omics’ technologies have been used to characterize the DNA, RNA, proteins, and metabolite profiles of dogs and cats. Similar technologies may now be applied to foods, a process referred to as ‘foodomics’. The objective of this study was to use foodomics analysis to characterize and compare various fruit-, vegetable-, tuber-, legume-, and grain-based ingredients. A total of 35 ingredients were subsampled, ground with liquid nitrogen, and sent to Metabolon, Inc. (Durham, NC) for untargeted high-throughput metabolomics analysis using gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. Over 640 named bioactive molecules were identified in the samples. Principal component analysis showed that ingredient classes tended to cluster together and separately from others, with distinct fruit-, vegetable-, and grain-based clusters identified. Legumes clustered most closely to tubers and vegetables. Random forest analysis had a nearly perfect predictive accuracy and was used to identify molecules most influential in separating ingredient classes. This dataset has provided a foundation from which the pet foodomics field may grow and has identified molecule signatures specific to ingredient type. Consequent studies may identify bioactive molecules possessing beneficial properties (e.g., antioxidant, antibacterial) that may be used to target obesity or support digestive health, skin and coat health, and joint health. Such analysis may also identify ingredient-specific biomarkers that may increase food consumption accuracy.