The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R 2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R 2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24. . Both databases have comparable nutrient completeness, with FNDDS at 100% and NCC at 92-100% completeness, but the databases differ in the number of nutrients reported. The ASA24 output includes 65 nutrients [5] and licensed 2018 NCC Database files include 166 nutrients and food components. Sixty-two nutrients are shared between the ASA24 output and NCC database. The NCC database also outputs nutrients and food components such as lactose, soluble and insoluble fiber, sugar alcohols, and individual amino acids while ASA24 does not [6]. While both ASA24 and NDSR/NCC database have widespread use and contain thousands of foods, there is no unique identifier to match each food on a one-to-one basis. Only a small number of foods in ASA24 have an exact known counterpart in NCC. Manual lookup of foods reported in ASA24 into NDSR based on text descriptions and nutrient profiles is time-consuming but is currently the only method to obtain values of NDSR-exclusive nutrients. This presents a major hurdle for investigating nutrient intake when using ASA24 if the research question requires assessment of a nutrient absent in the underlying database.Our research group is investigating a series of questions that require assessment of a nutrient that is not reported in the ASA24 output: lactose. Most adults worldwide are unable to digest lactose, the primary carbohydrate in milk. Some populations, however, are able to digest lactose into adulthood in a heritable trait known as lactase persistence (LP) [7]. LP genotypes may influence dairy and more specifically, lactose, ...

Objectives Food/residue waste streams may be a significant source of bioactive compounds that benefit human health. Dietary intervention trials demonstrate the health benefits of such residues, but they are resource and time intensive. Bioinformatics meta-analyses can elucidate putative pathways, genes and chemicals that are relevant to human health, hence guiding further experimentation and intervention trials. To this end, we integrated publicly available phytochemical datasets related to general grape marc from different varieties (GM) and Chardonnay grape marc (CM) to investigate their differences and potential implications to human health through a network-based meta-analysis. Methods To characterize the phytochemical profile of grape marc, compositional data was aggregated from publicly available literature. To identify potential health effects based on this chemical information, associations between disease states and the chemical profiles of GM/CM were extracted from the Comparative Toxicogenomics Database (CTD). Disease associative networks were constructed for a) marc products, b) all marc-related phenolics, c) compounds that are differentially abundant in CM. Results The union of available marc composition datasets from 14 articles contained 66 phenolic compounds; 29 of these were associated with at least 1 disease state in the CTD. There were 5 differentially over-abundant compounds in CM versus other grape marcs (red varietals n = 75, white varietals n = 57). These were flavan-3-ols catechin, epicatechin, epigallocatechin, gallocatechin, and proanthocyanidin C1 (P < 0.001); with gallocatechin unique to CM. Studies investigating marc products indicated associations to 15 diseases. CTD evidence from 934 studies associated the phenolic profile of GM to 358 diseases of 34 disease classes. Network-based meta-analysis suggested associations between GM and CM phenolics and several disease targets. This includes confirmatory associations between flavan-3-ols and cardiovascular disease outcomes. Conclusions Chardonnay marc is not widely studied; however, the developed framework of network-based meta-analysis utilizing composition information provides a holistic view of the knowledge space for grape marc, and highlights suggested health effects that can guide future research programs. Funding Sources Sonomaceuticals, LLC.

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