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BackgroundHigher diet quality has been associated with lower risk of developing inflammatory bowel disease, but associations between diet and gastrointestinal (GI) inflammation in healthy adults prior to disease onset are understudied.ObjectivesThe purpose of this project was to examine associations between reported dietary intake and markers of GI inflammation in a healthy adult human cohort.MethodsIn a cross-sectional observational trial of 358 healthy adults, participants completed ≤3 unannounced 24-h dietary recalls using the Automated Self-Administered Dietary Assessment Tool and a Block 2014 Food Frequency Questionnaire to assess recent and habitual intake, respectively. Those who provided a stool sample were included in this analysis. Inflammation markers from stool, including calprotectin, neopterin, and myeloperoxidase, were measured by ELISA along with LPS-binding protein from plasma.ResultsRecent and habitual fiber intake was negatively correlated with fecal calprotectin concentrations (n = 295, P = 0.011, 0.009). Habitual soluble fiber intake was also negatively correlated with calprotectin (P = 0.01). Recent and habitual legume and vegetable intake was negatively correlated with calprotectin (P = 0.013, 0.026, 0.01, 0.009). We observed an inverse correlation between recent Healthy Eating Index (HEI) scores and calprotectin concentrations (n = 295, P = 0.026). Dietary Inflammatory Index scores were calculated and positively correlated with neopterin for recent intake (n = 289, P = 0.015). When participants with clinically elevated calprotectin were excluded, recent and habitual fiber, legume, vegetable, and fruit intake were negatively correlated with calprotectin (n = 253, P = 0.00001, 0.0002, 0.045, 0.001, 0.009, 0.001, 0.004, 0.014). Recent total HEI score was inversely correlated with subclinical calprotectin (P = 0.003).ConclusionsHigher diet quality may be protective against GI inflammation even in healthy adults. This trial was registered at clini

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.

BackgroundAutomated 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.ObjectivesWe evaluated the effects of modifications made during manual data cleaning on nutrient intakes of interest: energy, carbohydrate, total fat, protein, and fiber.MethodsDifferences in mean intake before and after data cleaning modifications for all recalls and average intakes per subject were analyzed by paired t-tests. The 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.ResultsAfter 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 with 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 with raw data. Mean intake for individual subjects was significantly different for energy, protein, and fat intakes following cleaning modifications (P < 0.001).ConclusionsManual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect the correlation

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.

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.

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 (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 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.

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 (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 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.

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 f