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.

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.

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.

Longitudinal Transcriptomic, Proteomic, and Metabolomic Analysis of Citrus limon Response to Graft Inoculation by Candidatus Liberibacter asiaticus.

Presymptomatic detection of citrus trees infected with Candidatus Liberibacter asiaticus (CLas), the bacterial pathogen associated with Huanglongbing (HLB; citrus greening disease), is critical to controlling the spread of the disease. To test whether infected citrus trees produce systemic signals that may be used for indirect disease detection, lemon (Citrus limon) plants were graft-inoculated with either CLas-infected or control (CLas-) budwood, and leaf samples were longitudinally collected over 46 weeks and analyzed for plant changes associated with CLas infection. RNA, protein, and metabolite samples extracted from leaves were analyzed using RNA-Seq, mass spectrometry, and 1H NMR spectroscopy, respectively. Significant differences in specific transcripts, proteins, and metabolites were observed between CLas-infected and control plants as early as 2 weeks post graft (wpg). The most dramatic differences between the transcriptome and proteome of CLas-infected and control plants were observed at 10 wpg, including coordinated increases in transcripts and proteins of citrus orthologs of known plant defense genes. This integrated approach to quantifying plant molecular changes in leaves of CLas-infected plants supports the development of diagnostic technology for presymptomatic or early disease detection as part of efforts to control the spread of HLB into uninfected citrus groves.

Longitudinal Transcriptomic, Proteomic, and Metabolomic Analyses of Citrus sinensis (L.) Osbeck Graft-Inoculated with "Candidatus Liberibacter asiaticus".

"Candidatus Liberibacter asiaticus" (CLas) is the bacterium associated with the citrus disease Huanglongbing (HLB). Current CLas detection methods are unreliable during presymptomatic infection, and understanding CLas pathogenicity to help develop new detection techniques is challenging because CLas has yet to be isolated in pure culture. To understand how CLas affects citrus metabolism and whether infected plants produce systemic signals that can be used to develop improved detection techniques, leaves from Washington Navel orange (Citrus sinensis (L.) Osbeck) plants were graft-inoculated with CLas and longitudinally studied using transcriptomics (RNA sequencing), proteomics (liquid chromatography-tandem mass spectrometry), and metabolomics (proton nuclear magnetic resonance). Photosynthesis gene expression and protein levels were lower in infected plants compared to controls during late infection, and lower levels of photosynthesis proteins were identified as early as 8 weeks post-grafting. These changes coordinated with higher sugar concentrations, which have been shown to accumulate during HLB. Cell wall modification and degradation gene expression and proteins were higher in infected plants during late infection. Changes in gene expression and proteins related to plant defense were observed in infected plants as early as 8 weeks post-grafting. These results reveal coordinated changes in greenhouse navel leaves during CLas infection at the transcript, protein, and metabolite levels, which can inform of biomarkers of early infection.

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.

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.

Association of Lactase Persistence Genotypes (rs4988235) and Ethnicity with Dairy Intake in a Healthy U.S. Population.

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.

Tolerability and safety of the intake of bovine milk oligosaccharides extracted from cheese whey in healthy human adults.

Mechanistic research suggests a unique evolutionary relationship between complex milk oligosaccharides and cognate bifidobacteria enriched in breast-fed infants. Bovine milk oligosaccharides (BMO) were recently identified as structurally and functionally similar to human milk oligosaccharides. The present single-blind three-way crossover study is the first to determine the safety and tolerability of BMO consumption by healthy human participants (n 12) and its effects on faecal microbiota and microbial metabolism. Participants consumed each supplement (placebo-control; low- and high-BMO doses) for eleven consecutive days, followed by a 2-week washout period prior to initiating the next supplement arm. Low and high BMO doses were consumed as 25 and 35 % of each individual's daily fibre intake, respectively. Safety and tolerability were measured using standardised questionnaires on gut and stomach discomfort and stool consistency. Faecal extracts were profiled for bacterial populations by next-generation sequencing (NGS) and bifidobacteria presence was confirmed using quantitative PCR. Urine was analysed for changes in microbial metabolism using nuclear magnetic resonance spectroscopy (1H-NMR). Consumption of both the low and high BMO doses was well tolerated and did not change stool consistency from baseline. Multivariate analysis of the NGS results demonstrated no change in faecal microbiota phyla among the placebo-control and BMO supplement groups. In conclusion, BMO supplementation was well tolerated in healthy adults and has the potential to shift faecal microbiota toward beneficial strains as part of a synbiotic treatment with probiotic cultures that selectively metabolise oligosaccharides.

Nopal feeding reduces adiposity, intestinal inflammation and shifts the cecal microbiota and metabolism in high-fat fed rats.

Nopal is a cactus plant widely consumed in Mexico that has been used in traditional medicine to aid in the treatment of type-2 diabetes. We previously showed that chronic consumption of dehydrated nopal ameliorated hepatic steatosis in obese (fa/fa) rats; however, description of the effects on other tissues is sparse. The aim of the present study was to investigate the effects of nopal cladode consumption on intestinal physiology, microbial community structure, adipose tissue, and serum biochemistry in diet-induced obese rats. Rats were fed either a normal fat (NF) diet or a HF diet containing 4% of dietary fiber from either nopal or cellulose for 6 weeks. Consumption of nopal counteracted HF-induced adiposity and adipocyte hypertrophy, and induced profound changes in intestinal physiology. Nopal consumption reduced biomarkers of intestinal inflammation (mRNA expression of IL-6) and oxidative stress (ROS), modfied gut microbiota composition, increasing microbial diversity and cecal fermentation (SCFA), and altered the serum metabolome. Interestingly, metabolomic analysis of dehydrated nopal revealed a high choline content, which appeared to generate high levels of serum betaine, that correlated negatively with hepatic triglyceride (TAG) levels. A parallel decrease in some of the taxa associated with the production of trimethylamine, suggest an increase in choline absorption and bioavailability with transformation to betaine. The latter may partially explain the previously observed effect of nopal on the development of hepatic steatosis. In conclusion, this study provides new evidence on the effects of nopal consumption on normal and HF-diet induced changes in the intestine, the liver and systemic metabolism.

Infant Maturity at Birth Reveals Minor Differences in the Maternal Milk Metabolome in the First Month of Lactation.

BACKGROUND: Human milk is the gold standard of nutrition for infants, providing both protective and essential nutrients. Although much is known about milk from mothers giving birth to term infants, less is known about milk from mothers giving birth to premature infants. In addition, little is known about the composition and diversity of small molecules in these milks and how they change over the first month of lactation. OBJECTIVE: The objective was to understand how milk metabolites vary over the first month of lactation in mothers giving birth to term and preterm infants. METHODS: (1)H nuclear magnetic resonance (NMR) metabolomics was used to characterize metabolites that were present in micromolar to molar concentrations in colostrum (day 0-5 postpartum), transition milk (day 14), and mature milk (day 28) from mothers who delivered term (n = 15) and preterm (n = 13) infants. Principal components analysis, linear mixed-effects models (LMMs), and linear models (LMs) were used to explore the relation between infant maturity and the postpartum day of collection of milk samples. RESULTS: By using a standard NMR metabolite library, 69 metabolites were identified in the milks, including 15 sugars, 23 amino acids and derivatives, 11 energy-related metabolites, 10 fatty acid-associated metabolites, 3 nucleotides and derivatives, 2 vitamins, and 5 bacteria-associated metabolites. Many metabolite concentrations followed a similar progression over time in both term and preterm milks, with more biological variation in metabolite concentrations in preterm milk. However, although lacto-N-neotetraose (LMM, P = 4.0 × 10(-5)) and lysine (LM, P = 1.5 × 10(-4)) significantly decreased in concentration in term milk over time, they did not significantly change in preterm milk. CONCLUSION: Overall, the metabolic profile of human milk is dynamic throughout the first month of lactation, with more variability in preterm than in term milk and subtle differences in some metabolite concentrations. This trial was registered at clinicaltrials.gov as NCT01841268.

Metabolite signature of Candidatus Liberibacter asiaticus infection in two citrus varieties.

Huanglongbing (HLB), also known as Citrus Greening Disease, is caused by the bacterium 'Candidatus Liberibacter asiaticus' (CLas) and is a serious threat to the citrus industry. To understand the effect of CLas infection on the citrus metabolome, juice from healthy (n = 18), HLB-asymptomatic (n = 18), and HLB-symptomatic Hamlin (n = 18), as well as from healthy (n = 18) and HLB-symptomatic (n = 18) Valencia sweet oranges (from southern and eastern Florida) were evaluated using (1)H NMR-based metabolomics. Differences in the concentration of several metabolites including phenylalanine, histidine, limonin, and synephrine between control or asymptomatic fruit and symptomatic fruit were observed regardless of the citrus variety or location. There were no clear differences between the metabolite profiles of Hamlin fruits classified by PCR as asymptomatic and control, suggesting that some of the control fruit may have been infected. Taken together, these data indicate that infection due to CLas presents a strong metabolic response that is observed across different cultivars and regions, suggesting the potential for generation of metabolite-based biomarkers of CLas infection.