Lower Diet Quality Associated with Subclinical Gastrointestinal Inflammation in Healthy United States Adults.

BACKGROUND: Higher 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. OBJECTIVES: The purpose of this project was to examine associations between reported dietary intake and markers of GI inflammation in a healthy adult human cohort. METHODS: In 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. RESULTS: Recent 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). CONCLUSIONS: Higher diet quality may be protective against GI inflammation even in healthy adults. This trial was registered at clinicaltrials.gov as NCT02367287.

TaxaHFE: a machine learning approach to collapse microbiome datasets using taxonomic structure.

Motivation: Biologists increasingly turn to machine learning models not just to predict, but to explain. Feature reduction is a common approach to improve both the performance and interpretability of models. However, some biological datasets, such as microbiome data, are inherently organized in a taxonomy, but these hierarchical relationships are not leveraged during feature reduction. We sought to design a feature engineering algorithm to exploit relationships in hierarchically organized biological data. Results: We designed an algorithm, called TaxaHFE, to collapse information-poor features into their higher taxonomic levels. We applied TaxaHFE to six previously published datasets and found, on average, a 90% reduction in the number of features (SD = 5.1%) compared to using the most complete taxonomy. Using machine learning to compare the most resolved taxonomic level (i.e. species) against TaxaHFE-preprocessed features, models based on TaxaHFE features achieved an average increase of 3.47% in receiver operator curve area under the curve. Compared to other hierarchical feature engineering implementations, TaxaHFE introduces the novel ability to consider both categorical and continuous response variables to inform the feature set collapse. Importantly, we find TaxaHFE's ability to reduce hierarchically organized features to a more information-rich subset increases the interpretability of models. Availability and implementation: TaxaHFE is available as a Docker image and as R code at https://github.com/aoliver44/taxaHFE.

Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment.

Photo-based dietary assessment is becoming more feasible as artificial intelligence methods improve. However, advancement of these methods for dietary assessment in research settings has been hindered by the lack of an appropriate dataset against which to benchmark algorithm performance. We conducted the Surveying Nutrient Assessment with Photographs of Meals (SNAPMe) study (ClinicalTrials ID: NCT05008653) to pair meal photographs with traditional food records. Participants were recruited nationally, and 110 enrollment meetings were completed 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-h Dietary Assessment Tool (ASA24®) version 2020. Participants included photos before and after eating non-packaged and multi-serving packaged meals, as well as photos of the front and ingredient labels for single-serving packaged foods. The SNAPMe Database (DB) contains 3311 unique food photos linked with 275 ASA24 food records from 95 participants who photographed all foods consumed and recorded food records in parallel for up to 3 study days each. The use of the SNAPMe DB to evaluate ingredient prediction demonstrated that the publicly available algorithms FB Inverse Cooking and Im2Recipe performed poorly, especially for single-ingredient foods and beverages. Correlations between nutrient estimates common to the Bitesnap and ASA24 dietary assessment tools indicated a range in predictive capacity across nutrients (cholesterol, adjusted R2 = 0.85, p < 0.0001; food folate, adjusted R2 = 0.21, p < 0.05). SNAPMe DB is a publicly available benchmark for photo-based dietary assessment in nutrition research. Its demonstrated utility suggested areas of needed improvement, especially the prediction of single-ingredient foods and beverages.

No Associations between Dairy Intake and Markers of Gastrointestinal Inflammation in Healthy Adult Cohort.

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.

Association of Estimated Daily Lactose Consumption, Lactase Persistence Genotype (rs4988235), and Gut Microbiota in Healthy Adults in the United States.

BACKGROUND: Lactase persistence (LP) is a heritable trait in which lactose can be digested throughout adulthood. Lactase nonpersistent (LNP) individuals who consume lactose may experience microbial adaptations in response to undigested lactose. OBJECTIVES: The objective of the study was to estimate lactose from foods reported in the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) and determine the interaction between lactose consumption, LP genotype, and gut microbiome in an observational cross-sectional study of healthy adults in the United States (US). METHODS: Average daily lactose consumption was estimated for 279 healthy US adults, genotyped for the lactase gene -13910G>A polymorphism (rs4988235) by matching ASA24-reported foods to foods in the Nutrition Coordinating Center Food and Nutrient Database. Analysis of covariance was used to identify whether the A genotype (LP) influenced lactose and total dairy consumption, with total energy intake and weight as covariates. The 16S rRNA V4/V5 region, amplified from bacterial DNA extracted from each frozen stool sample, was sequenced using Illumina MiSeq (300 bp paired-end) and analyzed using Quantitative Insights Into Microbial Ecology (QIIME)2 (version 2019.10). Differential abundances of bacterial taxa were analyzed using DESeq2 likelihood ratio tests. RESULTS: Across a diverse set of ethnicities, LP subjects consumed more lactose than LNP subjects. Lactobacillaceae abundance was highest in LNP subjects who consumed more than 12.46 g/d (upper tercile). Within Caucasians and Hispanics, family Lachnospiraceae was significantly enriched in the gut microbiota of LNP individuals consuming the upper tercile of lactose across both sexes. CONCLUSIONS: Elevated lactose consumption in individuals with the LNP genotype is associated with increased abundance of family Lactobacillaceae and Lachnospriaceae, taxa that contain multiple genera capable of utilizing lactose. This trial was registered on clinicaltrials.gov as NCT02367287.

Creation of a milk oligosaccharide database, MilkOligoDB, reveals common structural motifs and extensive diversity across mammals.

The carbohydrate fraction of most mammalian milks contains a variety of oligosaccharides that encompass a range of structures and monosaccharide compositions. Human milk oligosaccharides have received considerable attention due to their biological roles in neonatal gut microbiota, immunomodulation, and brain development. However, a major challenge in understanding the biology of milk oligosaccharides across other mammals is that reports span more than 5 decades of publications with varying data reporting methods. In the present study, publications on milk oligosaccharide profiles were identified and harmonized into a standardized format to create a comprehensive, machine-readable database of milk oligosaccharides across mammalian species. The resulting database, MilkOligoDB, includes 3193 entries for 783 unique oligosaccharide structures from the milk of 77 different species harvested from 113 publications. Cross-species and cross-publication comparisons of milk oligosaccharide profiles reveal common structural motifs within mammalian orders. Of the species studied, only chimpanzees, bonobos, and Asian elephants share the specific combination of fucosylation, sialylation, and core structures that are characteristic of human milk oligosaccharides. However, agriculturally important species do produce diverse oligosaccharides that may be valuable for human supplementation. Overall, MilkOligoDB facilitates cross-species and cross-publication comparisons of milk oligosaccharide profiles and the generation of new data-driven hypotheses for future research.

Associations among Milk Microbiota, Milk Fatty Acids, Milk Glycans, and Inflammation from Lactating Holstein Cows.

Milk oligosaccharides (MOs) can be prebiotic and antiadhesive, while fatty acids (MFAs) can be antimicrobial. Both have been associated with milk microbes or mammary gland inflammation in humans. Relationships between these milk components and milk microbes or inflammation have not been determined for cows and could help elucidate a novel approach for the dairy industry to promote desired milk microbial composition for improvement of milk quality and reduction of milk waste. We aimed to determine relationships among milk microbiota, MFAs, MOs, lactose, and somatic cell counts (SCC) from Holstein cows, using our previously published data. Raw milk samples were collected at three time points, ranging from early to late lactation. Data were analyzed using linear mixed-effects modeling and repeated-measures correlation. Unsaturated MFA and short-chain MFA had mostly negative relationships with potentially pathogenic genera, including Corynebacterium, Pseudomonas, and an unknown Enterobacteriaceae genus but numerous positive relationships with symbionts Bifidobacterium and Bacteroides. Conversely, many MOs were positively correlated with potentially pathogenic genera (e.g., Corynebacterium, Enterococcus, and Pseudomonas), and numerous MOs were negatively correlated with the symbiont Bifidobacterium. The neutral, nonfucosylated MO composed of eight hexoses had a positive relationship with SCC, while lactose had a negative relationship with SCC. One interpretation of these trends might be that in milk, MFAs disrupt primarily pathogenic bacterial cells, causing a relative increase in abundance of beneficial microbial taxa, while MOs respond to and act on pathogenic taxa primarily through antiadhesive methods. Further research is needed to confirm the potential mechanisms driving these correlations. IMPORTANCE Bovine milk can harbor microbes that cause mastitis, milk spoilage, and foodborne illness. Fatty acids found in milk can be antimicrobial and milk oligosaccharides can have antiadhesive, prebiotic, and immune-modulatory effects. Relationships among milk microbes, fatty acids, oligosaccharides, and inflammation have been reported for humans. To our knowledge, associations among the milk microbial composition, fatty acids, oligosaccharides, and lactose have not been reported for healthy lactating cows. Identifying these potential relationships in bovine milk will inform future efforts to characterize direct and indirect interactions of the milk components with the milk microbiota. Since many milk components are associated with herd management practices, determining if these milk components impact milk microbes may provide valuable information for dairy cow management and breeding practices aimed at minimizing harmful and spoilage-causing microbes in raw milk.

Dietary Intake of Monosaccharides from Foods is Associated with Characteristics of the Gut Microbiota and Gastrointestinal Inflammation in Healthy US Adults.

BACKGROUND: Current assessment of dietary carbohydrates does not adequately reflect the nutritional properties and effects on gut microbial structure and function. Deeper characterization of food carbohydrate composition can serve to strengthen the link between diet and gastrointestinal health outcomes. OBJECTIVES: The present study aims to characterize the monosaccharide composition of diets in a healthy US adult cohort and use these features to assess the relationship between monosaccharide intake, diet quality, characteristics of the gut microbiota, and gastrointestinal inflammation. METHODS: This observational, cross-sectional study enrolled males and females across age (18-33 y, 34-49 y, and 50-65 y) and body mass index (normal, 18.5-24.99 kg/m2; overweight, 25-29.99 kg/m2; and obese, 30-44 kg/m2) categories. Recent dietary intake was assessed by the automated self-administered 24-h dietary recall system, and gut microbiota were assessed with shotgun metagenome sequencing. Dietary recalls were mapped to the Davis Food Glycopedia to estimate monosaccharide intake. Participants with >75% of carbohydrate intake mappable to the glycopedia were included (N = 180). RESULTS: Diversity of monosaccharide intake was positively associated with the total Healthy Eating Index score (Pearson's r = 0.520, P = 1.2 × 10-13) and negatively associated with fecal neopterin (Pearson's r = -0.247, P = 3.0 × 10-3). Comparing high with low intake of specific monosaccharides revealed differentially abundant taxa (Wald test, P < 0.05), which was associated with the functional capacity to break down these monomers (Wilcoxon rank-sum test, P < 0.05). CONCLUSIONS: Monosaccharide intake was associated with diet quality, gut microbial diversity, microbial metabolism, and gastrointestinal inflammation in healthy adults. As specific food sources were rich in particular monosaccharides, it may be possible in the future to tailor diets to fine-tune the gut microbiota and gastrointestinal function. This trial is registered at www. CLINICALTRIALS: gov as NCT02367287.

A Low-Starch and High-Fiber Diet Intervention Impacts the Microbial Community of Raw Bovine Milk.

Background: A more sustainable dairy cow diet was designed that minimizes use of feed components digestible by monogastric animals by increasing the quantity of forages. Objectives: This study determined if feeding lactating cows the more sustainable, low-starch and high-fiber (LSHF) diet was associated with changes in raw milk microbiota composition and somatic cell count (SCC). Methods: In a crossover design, 76 lactating Holstein cows were assigned to an LSHF diet or a high-starch and low-fiber (HSLF) diet, similar to common dairy cow diets in the United States, for 10 wk then placed on the opposite diet for 10 wk. The LSHF diet contained greater quantities of forages, beet pulp, and corn distillers' grain, but contained less canola meal and no high-moisture corn compared with the HSLF diet. Raw milk samples were collected from each cow 4-5 d before intervention and 5 wk into each diet treatment. Within 4 d, additional milk samples were collected for measurement of SCC using Fossmatic 7. The microbial community was determined by sequencing the 16S rRNA gene V4-V5 region and analyzing sequences with QIIME2. After quality filtering, 53 cows remained. Results: Raw milk microbial communities differed by diet and time. Taxa associated with fiber consumption, such as Lachnospiraceae, Lactobacillus, Bacteroides, and Methanobrevibacter, were enriched with the LSHF diet. Meanwhile, taxa associated with mastitis, such as Pseudomonas, Stenotrophomonas, and Enterobacteriaceae, were enriched with the HSLF diet. Relatedly, an interaction of diet and time was found to impact SCC. Conclusions: In raw milk, consumption of an LSHF diet compared with an HSLF diet was associated with changes in abundance of microbes previously associated with fiber consumption, udder health, and milk spoilage. Further research is needed to determine if an LSHF diet indeed leads to lower rates of mastitis and milk spoilage, which could benefit the dairy industry.

Dietary Fiber to Starch Ratio Affects Bovine Milk Oligosaccharide Profiles.

Background: Bovine milk oligosaccharides (BMOs) have several demonstrated and hypothesized benefits including roles in cognitive development and antipathogenic activities, making them promising ingredients for infant formulas and nutraceutical applications. BMO extraction from bovine milk is challenged by low concentrations relative to nonbioactive simple sugars like lactose. BMO abundances are known to vary with a cow's lactation stage, breed, and parity, but these characteristics are difficult to modify in existing dairy herds. In contrast, diet modification is an accessible target, and is already known to influence milk yield, lipid content, protein levels, and monosaccharide compositions. Objectives: To determine the impact of a low starch high fiber versus a high starch low fiber diet on overall BMO profiles and individual BMO abundances in Holstein dairy cattle. Methods: Milk samples were collected from 59 midlactation Holsteins in a crossover study featuring dietary modification with either a low starch high fiber or high starch low fiber feed. BMO profiles were evaluated by nano-LC quadrupole time-of-flight tandem MS, and differences in BMO abundances between diets were evaluated using linear mixed effects modeling. Results: A total of 19 BMOs were identified across the sample set, including 4 large fucosylated compounds. Seven BMOs were found to have significantly more positive percent changes in yield-adjusted abundance from the pre-experiment baseline period for milk samples collected during feeding with the low starch high fiber diet compared with the high starch low fiber diet. Conclusions: Consuming the low starch high fiber diet promoted greater overall BMO production than the high starch low fiber diet in a population of midlactation Holsteins. Additionally, this study afforded the opportunity to investigate the impact of other factors potentially influencing BMO abundances, furthering understanding of how dairy herd management practices can positively impact milk composition and support the potential use of BMOs as functional ingredients.

Association of Diet and Antimicrobial Resistance in Healthy U.S. Adults.

Antimicrobial resistance (AMR) represents a significant source of morbidity and mortality worldwide, with expectations that AMR-associated consequences will continue to worsen throughout the coming decades. Since resistance to antibiotics is encoded in the microbiome, interventions aimed at altering the taxonomic composition of the gut might allow us to prophylactically engineer microbiomes that harbor fewer antibiotic resistant genes (ARGs). Diet is one method of intervention, and yet little is known about the association between diet and antimicrobial resistance. To address this knowledge gap, we examined diet using the food frequency questionnaire (FFQ; habitual diet) and 24-h dietary recalls (Automated Self-Administered 24-h [ASA24®] tool) coupled with an analysis of the microbiome using shotgun metagenome sequencing in 290 healthy adult participants of the United States Department of Agriculture (USDA) Nutritional Phenotyping Study. We found that aminoglycosides were the most abundant and prevalent mechanism of AMR in these healthy adults and that aminoglycoside-O-phosphotransferases (aph3-dprime) correlated negatively with total calories and soluble fiber intake. Individuals in the lowest quartile of ARGs (low-ARG) consumed significantly more fiber in their diets than medium- and high-ARG individuals, which was concomitant with increased abundances of obligate anaerobes, especially from the family Clostridiaceae, in their gut microbiota. Finally, we applied machine learning to examine 387 dietary, physiological, and lifestyle features for associations with antimicrobial resistance, finding that increased phylogenetic diversity of diet was associated with low-ARG individuals. These data suggest diet may be a potential method for reducing the burden of AMR. IMPORTANCE Antimicrobial resistance (AMR) represents a considerable burden to health care systems, with the public health community largely in consensus that AMR will be a major cause of death worldwide in the coming decades. Humans carry antibiotic resistance in the microbes that live in and on us, collectively known as the human microbiome. Diet is a powerful method for shaping the human gut microbiome and may be a tractable method for lessening antibiotic resistance, and yet little is known about the relationship between diet and AMR. We examined this relationship in healthy individuals who contained various abundances of antibiotic resistance genes and found that individuals who consumed diverse diets that were high in fiber and low in animal protein had fewer antibiotic resistance genes. Dietary interventions may be useful for lessening the burden of antimicrobial resistance and might ultimately motivate dietary guidelines which will consider how nutrition can reduce the impact of infectious disease.

The Development of the Davis Food Glycopedia-A Glycan Encyclopedia of Food.

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.

Tree-Based Analysis of Dietary Diversity Captures Associations Between Fiber Intake and Gut Microbiota Composition in a Healthy US Adult Cohort.

BACKGROUND: Diet patterns are a significant and modifiable contributing factor to the composition of the human gut microbiota. OBJECTIVES: We set out to identify reproducible relationships between diet and gut microbial community composition in a diverse, healthy US adult cohort. METHODS: We collected 2 to 3 automated self-administered 24-hour dietary recalls over 10-14 days, together with a single stool sample, from 343 healthy adults in a cross-sectional phenotyping study. This study examined a multi-ethnic cohort balanced for age (18-65 years), sex, and BMI (18.5-45 kg/m2). Dietary data were edited to a tree format according to published methods. The tree structure was annotated with the average total grams of dry weight, fat, protein, carbohydrate, or fiber from each food item reported. The alpha and beta diversity measurements, calculated using the tree structure, were analyzed relative to the microbial community diversity, determined by a Quantitative Insights Into Microbial Ecology (QIIME) 2 analysis of the bacterial 16S ribosomal RNA V4 region, sequenced from stool samples. K-means clustering was used to form groups of individuals consuming similar diets, and gut microbial communities were compared among groups using differential expression analysis for sequence count data. RESULTS: The alpha diversity of diet dry weight was significantly correlated with the gut microbial community alpha diversity (r = 0.171). The correlation improved when diet was characterized using grams of carbohydrates (r = 0.186) or fiber (r = 0.213). Bifidobacterium was enriched with diets containing higher levels of total carbohydrate from cooked grains. Lachnospira, was enriched with diet patterns containing high consumption of fiber from fruits excluding berries. CONCLUSIONS: The tree structure, annotated with grams of carbohydrate, is a robust analysis method for comparing self-reported diet to the gut microbial community composition. This method identified consumption of fiber from fruit robustly associated with an abundance of pectinolytic bacterial genus, Lachnospira, in the guts of healthy adults. This trial was registered at clinicaltrials.gov as NCT02367287.

Unique Transcriptomic Changes Underlie Hormonal Interactions During Mammary Histomorphogenesis in Female Pigs.

Successful lactation and the risk for developing breast cancer depend on growth and differentiation of the mammary gland (MG) epithelium that is regulated by ovarian steroids (17ß-estradiol [E] and progesterone [P]) and pituitary-derived prolactin (PRL). Given that the MG of pigs share histomorphogenic features present in the normal human breast, we sought to define the transcriptional responses within the MG of pigs following exposure to all combinations of these hormones. Hormone-ablated female pigs were administered combinations of E, medroxyprogesterone 17-acetate (source of P), and either haloperidol (to induce PRL) or 2-bromo-α-ergocryptine. We subsequently monitored phenotypic changes in the MG including mitosis, receptors for E and P (ESR1 and PGR), level of phosphorylated STAT5 (pSTAT5), and the frequency of terminal ductal lobular unit (TDLU) subtypes; these changes were then associated with all transcriptomic changes. Estrogen altered the expression of approximately 20% of all genes that were mostly associated with mitosis, whereas PRL stimulated elements of fatty acid metabolism and an inflammatory response. Several outcomes, including increased pSTAT5, highlighted the ability of E to enhance PRL action. Regression of transcriptomic changes against several MG phenotypes revealed 1669 genes correlated with proliferation, among which 29 were E inducible. Additional gene expression signatures were associated with TDLU formation and the frequency of ESR1 or PGR. These data provide a link between the hormone-regulated genome and phenome of the MG in a species having a complex histoarchitecture like that in the human breast, and highlight an underexplored synergy between the actions of E and PRL during MG development.

Machine Learning Identifies Stool pH as a Predictor of Bone Mineral Density in Healthy Multiethnic US Adults.

BACKGROUND: A variety of modifiable and nonmodifiable factors such as ethnicity, age, and diet have been shown to influence bone health. Previous studies are usually limited to analyses focused on the association of a few a priori variables or on a specific subset of the population. OBJECTIVE: Dietary, physiological, and lifestyle data were used to identify directly modifiable and nonmodifiable variables predictive of bone mineral content (BMC) and bone mineral density (BMD) in healthy US men and women using machine-learning models. METHODS: Ridge, lasso, elastic net, and random forest models were used to predict whole-body, femoral neck, and spine BMC and BMD in healthy US men and women ages 18-66 y, with a BMI (kg/m2) of 18-44 (n = 313), using nonmodifiable anthropometric, physiological, and demographic variables; directly modifiable lifestyle (physical activity, tobacco use) and dietary (via FFQ) variables; and variables approximating directly modifiable behavior (circulating 25-hydroxycholecalciferol and stool pH). RESULTS: Machine-learning models using nonmodifiable variables explained more variation in BMC and BMD (highest R2 = 0.75) compared with when using only directly modifiable variables (highest R2 = 0.11). Machine-learning models had better performance compared with multivariate linear regression, which had lower predictive value (highest R2 = 0.06) when using directly modifiable variables only. BMI, body fat percentage, height, and menstruation history were predictors of BMC and BMD. For directly modifiable features, betaine, cholesterol, hydroxyproline, menaquinone-4, dihydrophylloquinone, eggs, cheese, cured meat, refined grains, fruit juice, and alcohol consumption were predictors of BMC and BMD. Low stool pH, a proxy for fermentable fiber intake, was also predictive of higher BMC and BMD. CONCLUSIONS: Modifiable factors, such as diet, explained less variation in the data compared with nonmodifiable factors, such as age, sex, and ethnicity, in healthy US men and women. Low stool pH predicted higher BMC and BMD. This trial was registered at www.clinicaltrials.gov as NCT02367287.

Technician-Scored Stool Consistency Spans the Full Range of the Bristol Scale in a Healthy US Population and Differs by Diet and Chronic Stress Load.

BACKGROUND: Prior studies of adults with constipation or diarrhea suggest that dietary intake, physical activity, and stress may affect stool consistency. However, the influence of these factors is unresolved and has not been investigated in healthy adults. OBJECTIVES: We assessed the relations of technician-scored stool consistency in healthy adults with self-reported diet, objectively monitored physical activity, and quantifiable markers of stress. METHODS: Stool consistency was scored by an independent technician using the Bristol Stool Form Scale (BSFS) to analyze samples provided by healthy adults, aged 18-65 y, BMI 18-44 kg/m2, in the USDA Nutritional Phenotyping Study (n = 364). A subset of participants (n = 109) were also asked to rate their sample using the BSFS. Dietary intake was assessed with two to three 24-h recalls completed at home and energy expenditure from physical activity was monitored using an accelerometer in the 7-d period preceding the stool collection. Stress was measured using the Wheaton Chronic Stress Inventory and allostatic load (AL). Statistical and machine learning analyses were conducted to determine which dietary, physiological, lifestyle, and stress factors differed by stool form. RESULTS: Technician-scored BSFS scores were significantly further (P = 0.003) from the central score (mean ± SEM distance: 1.41 ± 0.089) than the self-reported score (1.06 ± 0.086). Hard stool was associated with higher (P = 0.005) intake of saturated fat (13.8 ± 0.40 g/1000 kcal) than was normal stool (12.5 ± 0.30 g/1000 kcal). AL scores were lower for normal stool (2.49 ± 0.15) than for hard (3.07 ± 0.18) (P = 0.009) or soft stool (2.89 ± 0.18) (P = 0.049). Machine learning analyses revealed that various dietary components, physiological characteristics, and stress hormones predicted stool consistency. CONCLUSIONS: Technician-scored stool consistency differed by dietary intake and stress hormones, but not by physical activity, in healthy adults.This trial was registered at clincialtrials.gov as NCT02367287.

Effect of Manual Data Cleaning on Nutrient Intakes Using the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24).

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 nutrient 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. 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. 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 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). CONCLUSIONS: Manual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect the 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.

Association of the Lactase Persistence Haplotype Block With Disease Risk in Populations of European Descent.

Among people of European descent, the ability to digest lactose into adulthood arose via strong positive selection of a highly advantageous allele encompassing the lactase gene. Lactose-tolerant and intolerant individuals may have different disease risks due to the shared genetics of their haplotype block. Therefore, the overall objective of the study was to assess the genetic association of the lactase persistence haplotype to disease risk. Using data from the 1000Genomes project, we estimated the size of the lactase persistence haplotype block to be 1.9 Mbp containing up to 9 protein-coding genes and a microRNA. Based on the function of the genes and microRNA, we studied health phenotypes likely to be impacted by the lactase persistence allele: prostate cancer status, cardiovascular disease status, and bone mineral density. We used summary statistics from large genome-wide metanalyses-32,965 bone mineral density, 140,306 prostate cancer and 184,305 coronary artery disease subjects-to evaluate whether the lactase persistence allele was associated with these disease phenotypes. Despite the fact that previous work demonstrated that the lactase persistence haplotype block harbors increased deleterious mutations, these results suggest little effect on the studied disease phenotypes.

Reservoirs of antimicrobial resistance genes in retail raw milk.

BACKGROUND: It has been estimated that at least 3% of the USA population consumes unpasteurized (raw) milk from animal sources, and the demand to legalize raw milk sales continues to increase. However, consumption of raw milk can cause foodborne illness and be a source of bacteria containing transferrable antimicrobial resistance genes (ARGs). To obtain a comprehensive understanding of the microbiome and antibiotic resistome in both raw and processed milk, we systematically analyzed 2034 retail milk samples including unpasteurized milk and pasteurized milk via vat pasteurization, high-temperature-short-time pasteurization, and ultra-pasteurization from the United States using complementary culture-based, 16S rRNA gene, and metagenomic sequencing techniques. RESULTS: Raw milk samples had the highest prevalence of viable bacteria which were measured as all aerobic bacteria, coliform, and Escherichia coli counts, and their microbiota was distinct from other types of milk. 16S rRNA gene sequencing revealed that Pseudomonadaceae dominated raw milk with limited levels of lactic acid bacteria. Among all milk samples, the microbiota remained stable with constant bacterial populations when stored at 4 °C. In contrast, storage at room temperature dramatically enriched the bacterial populations present in raw milk samples and, in parallel, significantly increased the richness and abundance of ARGs. Metagenomic sequencing indicated raw milk possessed dramatically more ARGs than pasteurized milk, and a conjugation assay documented the active transfer of blaCMY-2, one ceftazidime resistance gene present in raw milk-borne E. coli, across bacterial species. The room temperature-enriched resistome differed in raw milk from distinct geographic locations, a difference likely associated with regionally distinct milk microbiota. CONCLUSION: Despite advertised "probiotic" effects, our results indicate that raw milk microbiota has minimal lactic acid bacteria. In addition, retail raw milk serves as a reservoir of ARGs, populations of which are readily amplified by spontaneous fermentation. There is an increased need to understand potential food safety risks from improper transportation and storage of raw milk with regard to ARGs. Video Abstract.

Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes.

BACKGROUND: Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX. RESULTS: Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene. CONCLUSIONS: Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.