Lactase persistence (LP) is a trait in which lactose can be digested throughout adulthood, while lactase non-persistence (LNP) can cause lactose intolerance and influence dairy consumption. One single nucleotide polymorphism (SNP ID: rs4988235) is often used as a predictor for dairy intake, since it is responsible for LP in people in European descent, and can occur in other ethnic groups. The objective of this study was to determine whether rs4988235 genotypes and ethnicity influence reported dairy consumption in the United States (U.S.). A food frequency questionnaire (FFQ) and multiple Automated Self-Administered 24-h recalls (ASA24®) were used to measure habitual and recent intake, respectively, of total dairy, cheese, cow’s milk, plant-based alternative milk, and yogurt in a multi-ethnic U.S. cohort genotyped for rs4988235. Within Caucasian subjects, LP individuals reported consuming more recent total dairy and habitual total cow’s milk intake. For subjects of all ethnicities, LP individuals consumed more cheese (FFQ p = 0.043, ASA24 p = 0.012) and recent total dairy (ASA24 p = 0.005). For both dietary assessments, Caucasians consumed more cheese than all non-Caucasians (FFQ p = 0.036, ASA24 p = 0.002) independent of genotype, as well as more recent intake of yogurt (ASA24 p = 0.042). LP subjects consumed more total cow’s milk than LNP, but only when accounting for whether subjects were Caucasian or not (FFQ p = 0.015). Fluid milk and alternative plant-based milk consumption were not associated with genotypes or ethnicity. Our results show that both LP genotype and ethnicity influence the intake of some dairy products in a multi-ethnic U.S. cohort, but the ability of rs4988235 genotypes to predict intake may depend on ethnic background, the specific dairy product, and whether intake is reported on a habitual or recent basis. Therefore, ethnicity and the dietary assessment method should also be considered when determining the suitability of rs4988235 as a proxy for dairy intake.

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, ...

Background Automated dietary assessment tools such as ASA24® are useful for collecting 24-hour recall data in large-scale studies. Modifications made during manual data cleaning may affect nutrient intakes. Objectives We evaluated the effects of modifications made during manual data cleaning on nutrients intakes of interest: energy, carbohydrate, total fat, protein, and fiber. Methods Differences in mean intake before and after data cleaning modifications for all recalls and average intakes per subject were analyzed by paired t-tests. Chi-squared test was used to determine whether unsupervised recalls had more open-ended text responses that required modification than supervised recalls. We characterized food types of text response modifications. Correlations between predictive energy requirements, measured total energy expenditure (TEE), and mean energy intake from raw and modified data were examined. Results After excluding 11 recalls with invalidating technical errors, 1499 valid recalls completed by 393 subjects were included in this analysis. We found significant differences before and after modifications for energy, carbohydrate, total fat, and protein intakes for all recalls (p < 0.05). Limiting to modified recalls, there were significant differences for all nutrients of interest, including fiber (p < 0.02). There was not a significantly greater proportion of text responses requiring modification for home compared to supervised recalls (p = 0.271). Predicted energy requirements correlated highly with TEE. There was no significant difference in correlation of mean energy intake with TEE for modified compared to raw data. Mean intake for individual subjects was significantly different for energy, protein, and fat intakes following cleaning modifications (p < 0.001). Conclusions Manual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect correlation of energy intake with predictive requirements and measured expenditure. Investigators can consider their research question and nutrients of interest when deciding to make cleaning modifications.

Objectives To examine and compare reported food and nutrient intake in men and women who differ in cognitive restraint (CR) and disinhibition (D) scores. We hypothesize that average intake of sodium, total sugar, saturated fat, and Healthy Eating Index (HEI) scores of diet quality will differ between low and high D groups. Methods 330 adults from a cross-sectional study who completed 3 dietary recalls and the three-factor eating questionnaire (TFEQ) were included in this analysis. Participants were classified into 4 groups based on TFEQ scores for CR and D: high CR + high D (CRD, n = 46); high CR + low D (CR, n = 104); low CR + high D (D, n = 42); and low CR + low D (LL, n = 138). The Automated Self-Administered 24-hour (ASA24) tool was used to obtain dietary recalls on 2 weekdays and 1 weekend day. Nutrient intakes were averaged for the 3 recalls, and HEI scores were calculated using the HEI-2015 scoring standards. Differences in average calories, sodium, total sugar, saturated fat, and HEI scores between groups were analyzed using analysis of covariance with age, sex, and BMI as covariates. Results There were differences in average sodium and saturated fat intake, with CR group reporting lower sodium intake (p = 0.041) and lower saturated fat (p = 0.007) intake compared to the LL group only. There were no differences in calorie or total sugar intake between groups. Interestingly, added sugar intake based on HEI-2015 scoring showed group differences, with CR reporting lower added sugar intake than LL group (p = 0.042). HEI subscore for refined grain intake was also higher in LL group compared to CR (p = 0.002) and CRD (p = 0.023). Total HEI score was lower in LL (59.5 ± 1.0) compared to CR (65.1 ± 1.15, p = 0.002) and CRD (65.6 ± 1.75, p = 0.017). Conclusions High cognitive restraint was associated with a more “healthful” diet with lower sodium and saturated fat intakes compared to groups with low restraint. While there were no associations between total sugar intake and cognitive restraint, reduced consumption of added sugar and refined grains were reported by the cognitively restrained participants, regardless of disinhibition status. Contrary to our hypothesis, high cognitive restraint was the predominant behavior associated with diet quality, not disinhibition. Funding Sources Funding was provided through the USDA.

The molecular complexity of the carbohydrates consumed by humans has been deceptively oversimplified due to a lack of analytical methods that possess the throughput, sensitivity, and resolution required to provide quantitative structural information. However, such information is becoming an integral part of understanding how specific glycan structures impact health through their interaction with the gut microbiome and host physiology. This work presents a detailed catalogue of the glycans present in complementary foods commonly consumed by toddlers during weaning and foods commonly consumed by American adults. The monosaccharide compositions of over 800 foods from diverse food groups including Fruits, Vegetables, Grain Products, Beans, Peas, Other Legumes, Nuts, Seeds; Sugars, Sweets and Beverages; Animal Products, and more were obtained and used to construct the “Davis Food Glycopedia” (DFG), an open-access database that provides quantitative structural information on the carbohydrates in food. While many foods within the same group possessed similar compositions, hierarchical clustering analysis revealed similarities between different groups as well. Such a Glycopedia can be used to formulate diets rich in specific monosaccharide residues to provide a more targeted modulation of the gut microbiome, thereby opening the door for a new class of prophylactic or therapeutic diets.

Dairy products are a good source of essential nutrients and past reviews have shown associations of dairy consumption with decreased systemic inflammation. Links between dairy intake and gastrointestinal (GI) inflammation are under-investigated. Therefore, we examined associations between reported dairy intake and markers of GI inflammation in healthy adults in a cross-sectional observational study, hypothesizing a negative association with yogurt intake, suggesting a protective effect, and no associations with total dairy, fluid milk, and cheese intake. Participants completed 24-h dietary recalls and a food frequency questionnaire (FFQ) to assess recent and habitual intake, respectively. Those who also provided a stool sample (n = 295), and plasma sample (n = 348) were included in analysis. Inflammation markers from stool, including calprotectin, neopterin, and myeloperoxidase, were measured along with LPS-binding protein (LBP) from plasma. Regression models tested associations between dairy intake variables and inflammation markers with covariates: age, sex, and body mass index (BMI). As yogurt is episodically consumed, we examined differences in inflammation levels between consumers (>0 cup equivalents/day reported in recalls) and non-consumers. We found no significant associations between dairy intake and markers of GI inflammation. In this cohort of healthy adults, dairy intake was not associated with GI inflammation.

Objectives We examined associations between reported fiber intake and markers of gastrointestinal (GI) inflammation in a healthy human population. Methods Participants in the USDA Nutritional Phenotyping Study completed up to three 24-hour recalls using ASA24 and a Block 2014 Food Frequency Questionnaire (FFQ) to assess recent and habitual intake, respectively. Stool samples were stored on ice immediately after collection and homogenized within 24 hours. Markers of inflammation from stool, including calprotectin, neopterin, and myeloperoxidase (MPO) were measured by ELISA along with LPS-binding protein (LBP) from plasma. Associations were tested using regression models. We compared GI marker levels between participants who consumed low and adequate amounts of fiber according to both dietary assessment tools. Analyses were repeated with the subset of participants who had subclinical levels of calprotectin (< 100 μg/g) and myeloperoxidase (<2000 ng/g). Results There were no significant associations between fiber intake and calprotectin or MPO levels (n = 295). Recent and habitual fiber intake were negatively correlated with neopterin levels (n = 289, P = < 0.001 and P = 0.023, respectively). Fiber groups were determined by lowest quartiles of both dietary instruments and minimum recommendations from the Dietary Guidelines for Americans (low: < 15.9 g/day mean ASA24 & FFQ, n = 35; adequate: >28 g/day, n = 68). Participants with low intake had higher neopterin levels (30.7 ± 9.1 nmol/L) than the adequate intake group (16.5 ± 3.3 nmol/L, P = 0.003). Plasma LBP was higher in participants with low fiber intake (12.8 ± 1.2 μg/mol) than those with adequate intake (9.71 ± 0.62 μg/mL, P = 0.01). When fiber was expressed as g/1000 kcal consumed (low: < 8.6 g/1000 kcal, n = 50; adequate: >14 g/1000 kcal, n = 54), LBP levels were higher in the low intake group (12.5 ± 0.96 μg/mL) than adequate (9.9 ± 0.67 μg/mL, P = 0.03). Habitual fiber intake was negatively correlated with calprotectin in the subset of participants with subclinical levels (P = 0.04). Conclusions Dietary fiber intake was negatively correlated with neopterin and subclinical calprotectin levels. Participants with low intake on both instruments had higher neopterin and LBP than those with adequate intake. Fiber consumption may be protective against GI inflammation in healthy adults. Funding Sources USDA ARS 2032-51,530-026-00D.

Objectives Photo-based dietary assessment methods are becoming more feasible as artificial intelligence methods improve. However, advancement of these methods to the level usable in nutrition studies has been hindered by the lack of a dataset against which to benchmark algorithm performance. Here, we introduce the Surveying Nutrient Assessment with Photographs of Meals (SNAPMe) Study (ClinicalTrials ID: NCT05008653), describe the data, and discuss the utility of the data. Methods The purpose of the SNAPMe Study was to pair meal photographs with traditional food records. The goal was to collect approximately 1000 real-world meal photos from 100 participants consuming at least three meals per day, for a total of three study days. Participants were recruited nationally and completed enrollment meetings via web-based video conferencing. Participants uploaded and annotated their meal photos using a mobile phone app called Bitesnap and completed food records using the Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24®) on the same day. A sizing marker with black and white boxes of known size were included in meal photos to assist with portion estimation. Participants included photos before and after eating non-packaged and multi-serving packaged meals, as well as photos of the front package label and ingredient label for single-serving packaged foods. Results By the end of the study, 90 participants had completed all three days of data collection. Examples showing the utility of SNAPMe data with respect to artificial intelligence will be presented. Conclusions The SNAPMe dataset will be made publicly available and will link meal photos, annotations, write-in notes, and ASA24 food records together. These data will be transformative for the improvement of artificial intelligence algorithms for the adoption of photo-based dietary assessment in nutrition research. Funding Sources This work was supported by the United States Department of Agriculture (USDA)/NSF AI Institute for Next Generation Food Systems (AIFS), USDA award number 2020-67,021-32,855.