Non-communicable conditions like inflammatory bowel disease (IBD) are increasing in prevalence, especially in the Western world where diets contain little nutrient-dense, fiber-rich food sources. I review the literature in Gastrointestinal Health: from Dietary Data to Human Genetics to the Microbiome to illustrate the need to investigate factors that lead to gut dysfunction. There is evidence that low fiber intake exacerbates gastrointestinal (GI) inflammation and mucus-foraging microbes can compromise the protective mucin matrix lining the gut epithelium. There may be interactions between diet, human genetics, and the microbiome that attenuate or exacerbate GI inflammation, yet these relationships are understudied in healthy human adults where subclinical associations may shed light on prevention strategies. Prior to investigating associations between diet and GI inflammation, we ensured the quality of dietary data. In Chapter 1, I detail methods for cleaning 24h dietary recalls collected during the USDA Western Human Nutrition Research Center Nutritional Phenotyping Study, a cross-sectional study of over 350 adults aged 18-65 years with body mass index (BMI) ranging from 18.5-45.0 kg/m2. Reports are limited on the effect of reviewing and editing open-ended text responses for 24h recalls collected using the National Cancer Institute (NCI) Automated Self-Administered 24h Dietary Assessment Tool® (ASA24). When participants cannot find foods that match their intake, text entries are automatically coded to the Food and Nutrient Database for Dietary Studies (FNDDS), and do not always produce an accurate match. We found that there were changes in energy and macronutrient distributions before and after data cleaning. We did not observe statistically significant differences between correlation of energy intake from raw compared to modified recalls with total energy expenditure (TEE) measured across one week. Changes to nutrient intakes after data cleaning were low in magnitude, but we determined it necessary to ensure accuracy of open-ended text responses because whole foods may be more informative for questions on the microbiome by characterizing nutritional dark matter like polyphenols. Food frequency questionnaires (FFQ) were also collected and reviewed using data cleaning guidelines from NutritionQuest. After cleaning dietary data, we analyzed associations of a food group, dairy, with GI health markers in Chapter 2. We observed no significant correlations between total dairy, fluid milk, and cheese intake with stool inflammation markers calprotectin, myeloperoxidase, and neopterin, or with a proxy for gut permeability, lipopolysaccharide-binding protein (LBP). Although we expected negative correlations between yogurt intake and GI health markers, as a probiotic food, we did not observe significant associations using regression models. This is consistent with reviews of dairy intake and inflammation, primarily systemic, reporting null to beneficial effects. Next, we examined associations of diet, in a broader context, with GI health. We observed in Chapter 3 that recent and habitual fiber intake negatively correlated with fecal calprotectin levels. Whole foods, including legumes and vegetables, were also associated with decreased calprotectin levels. To limit the analysis to a subclinical population, we excluded participants where calprotectin was measured above the clinical threshold (100 µg/g). Recent and habitual fiber, legume, vegetable, and fruit intake were negatively correlated with subclinical calprotectin through regression analysis. Comprehensive estimates of diet quality were also assessed in relation to GI health parameters: the Dietary Inflammatory Index (DII) assigns scores to food components that have exhibited pro- and anti-inflammatory potential in the literature. The Healthy Eating Index (HEI) measures adherence to the United States Dietary Guidelines for Americans based on food group and nutrient scores. With the full cohort, we observed recent total DII scores were positively correlated with neopterin levels, as expected, and recent total HEI scores were negatively correlated with calprotectin levels. When limiting to the subclinical population, recent total HEI scores were also negatively correlated with calprotectin. Finally, we assessed interactions between fiber intake, human genetics, and GI inflammation markers. Secretor status conferred by the alpha-1-2-L-fucosyltransferase 2 (H blood type) (FUT2) gene has been associated with IBD prevalence, particularly the non-secretor genotype is associated with increased risk. We observed that recent, habitual, and soluble fiber intake negatively correlated with calprotectin levels in secretors, but not non-secretors. The microbiome may mediate this relationship; therefore, we analyzed composition by secretor status and compared non-secretors to low and high fiber intake secretors. There was no difference in overall microbiome composition between non-secretors and secretors, but using machine learning models, the phylum Thaumarchaeota and species Blautia faecicola were predictive of the non-secretor genotype. Low fiber intake secretors had higher levels of a mucin-degrading species, Ruminococcus torques, and a species associated with colitis, Collinsella aerofaciens. Deltaproteobacteria, a class of sulfate-reducing bacteria associated with mucin degradation, was association with the non-secretor genotype. Compared to high fiber intake secretors, Blautia wexlerae, a species associated with attenuating inflammation in mouse models, was predictive of the non-secretor genotype. The relationship between fiber intake, secretor status, and GI inflammation in healthy adults is complex, and further investigations of associations in the microbiome may help clarify these interactions. GI inflammation is usually measured only in clinical settings, thus findings from this study can inform interventions to prevent disease development.