Biomarkers Of Food Intake Research Articles

Increasing plant protein in the diet induces changes in the plasma metabolome that may be beneficial for metabolic health. A randomized crossover study in males

Background & aimDietary shifts replacing animal protein (AP) with plant protein (PP) sources have been associated with lowering cardiometabolic risk (CMR), but underlying mechanisms are poorly characterized. This nutritional intervention aims to characterize the metabolic changes induced by diets containing different proportions of AP and PP sources in males at CMR. DesignThis study is a 4-week, crossover, randomized, controlled-feeding trial in which 19 males with CMR followed two diets providing either 36% for the control diet (CON-D) or 64% for the flexitarian diet (FLEX-D) of total protein intake from PP sources. Plasma nontargeted metabolomes (LC-MS method) were measured in the fasted state and after a high-fat challenge meal at the end of each intervention arm. Lipogenesis and protein synthesis fluxes, flow-mediated dilatation (FMD) and gluco-lipidic responses were assessed after the challenge meal. Data were analyzed with mixed models, and univariate and multivariate models for metabolomics data. ResultsIn both arms CMR improved with time, with decreased body weight (-0.9%), insulin resistant (-34%, HOMA-IR, Homeostatic Model Assessment for Insulin Resistance) and low-density lipoproteins (LDL)-cholesterol (-11%). Diet had no effect on FMD or metabolic fluxes, but a trend was observed for a stronger decrease in HOMA-IR and lower postprandial glucose after FLEX-D vs CON-D. The abundance of 21 and 37 metabolites differed between diets at fasted and fed states, respectively, including food intake biomarkers of AP (methylhistidine, eicosapentaenoic acid, hydroxyprolines) and PP sources (trigonelline, N-acetyl-ornithine). In fasted or fed states, indole acrylic acid and indole propionic acid, both products of tryptophan catabolism, were higher after FLEX-D vs CON-D, while the indispensable amino acids-related metabolites alpha-aminoadipic acid, hydroxymethylbutyric acids and propionylcarnitine were lower. In the postprandial state only, the ω-oxidation products dodecanedioic, tetradecanedioic and hexadecanedioic acids were higher after FLEX-D vs CON-D. ConclusionsDespite little changes in risk factors after 4 wk, this study evidenced subtle metabolic adaptations in amino acids and lipid metabolism and gut microbiota activity occurring after higher PP source intake that may be beneficial to CMR. ClinicalTrials.gov study identifierNCT04236518 Clinical Trial RegistryNCT04236518 on ClinicalTrials.gov

Metabolomics-based biomarkers of fermented dairy and red meat intake: a randomized controlled trial in healthy adults.

Dietary assessment is usually performed through imprecise tools, leading to error-prone associations between diet and health-related outcomes. Metabolomics has been applied in recent years to develop biomarkers of food intake (BFIs) and to study metabolites in the diet-microbiome crosstalk. Candidate BFIs exist to detect intake of meat and to a lesser extent dairy, but validation and further development of BFIs are needed. Here, we aim to identify biomarkers that differentiate between intakes of red meat and dairy, to validate previously reported BFIs for these foods, and to explore the effect of protein-matched meals on selected microbial metabolites. We conducted a randomized, controlled, cross-over single-meal study comparing a meal with highly fermented yogurt and cheese, and a meal with beef and pork meatballs. Postprandial urine samples from 17 subjects were collected sequentially after each meal up to 24h and analyzed by untargeted metabolomics through ultra-high-performance-liquid chromatography (UHPLC) coupled via electrospray (ESI) source to a qTOF mass spectrometer. Univariate (repeated measures ANOVA) and multivariate (PLSDA, ML-PLSDA) data analyses were used to select BFIs differentiating the two meals. 3-Indoxyl sulfate, p-cresol sulfate, and several other microbial amino acid catabolites were additionally explored within the urine profiles. Thirty-eight markers of meat and dairy intake were selected and are presented along with their excretion kinetics. Carnosine, taurine, and creatine, as well as hydroxyproline-based dipeptides are confirmed as meat BFIs. For dairy, previously reported metabolites such as acyl-glycines are confirmed, while proline-based dipeptides are reported as novel putative BFIs. Microbial metabolites showed only marginal evidence of differential formation after the two meals. This study allowed us to validate the postprandial kinetics of previously suggested biomarkers of meat and dairy intake and to identify new potential biomarkers. The excretion kinetics are useful to ensure that the collection of urine covers the correct time window in future dietary studies. The BFIs add to the existing body of biomarkers and may further be used in combination to provide a more reliable assessment of meat and dairy intake. Proteolytic microbial metabolites should be further investigated to assess the effect of different protein sources on health.

Biomarker panels for fruit intake assessment: a metabolomics analysis in the ELSA-Brasil study.

Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates. This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health. A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24h dietary recall (24h-DR). Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population. A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.

Knowledge translation and knowledge mobilization from the FoodBAll project.

This report describes the knowledge mobilization and translation outcomes of the Canadian-funded portion of a large, international project called the Food Biomarker Alliance (FoodBAll), which ran from 2015 to 2019. This remarkably successful project led to a large number of important findings, outputs, and impacts. In particular, FoodBAll unequivocally demonstrated that metabolomics could be used to not only discover biomarkers of food intake (BFIs), but also to measure diet in a more objective manner. FoodBAll also created standards for assessing and validating BFIs, papers and databases describing BFIs, and kits for measuring BFIs and it laid the groundwork for many global studies exploring food composition and precision nutrition.

Towards nutrition with precision: unlocking biomarkers as dietary assessment tools.

Precision nutrition requires precise tools to monitor dietary habits. Yet current dietary assessment instruments are subjective, limiting our understanding of the causal relationships between diet and health. Biomarkers of food intake (BFIs) hold promise to increase the objectivity and accuracy of dietary assessment, enabling adjustment for compliance and misreporting. Here, we update current concepts and provide a comprehensive overview of BFIs measured in urine and blood. We rank BFIs based on a four-level utility scale to guide selection and identify combinations of BFIs that specifically reflect complex food intakes, making them applicable as dietary instruments. We discuss the main challenges in biomarker development and illustrate key solutions for the application of BFIs in human studies, highlighting different strategies for selecting and combining BFIs to support specific study designs. Finally, we present a roadmap for BFI development and implementation to leverage current knowledge and enable precision in nutrition research.

An Untargeted Metabolomics Approach to Identify Food Intake Biomarkers of Green Beans

An Untargeted Metabolomics Approach to Identify Food Intake Biomarkers of Green Beans

Mini-MED: study protocol for a randomized, multi-intervention, semi-controlled feeding trial of a Mediterranean-amplified vs. habitual Western dietary pattern for the evaluation of food-specific compounds and cardiometabolic health

BackgroundDiet is among the most influential lifestyle factors impacting chronic disease risk. Nutrimetabolomics, the application of metabolomics to nutrition research, allows for the detection of food-specific compounds (FSCs) that can be used to connect dietary patterns, such as a Mediterranean-style (MED) diet, to health. This validation study is based upon analyses from a controlled feeding MED intervention, where our team identified FSCs from eight foods that can be detected in biospecimens after consumption and may therefore serve as food intake biomarkers.MethodsIndividuals with overweight/obesity who do not habitually consume a MED dietary pattern will complete a 16-week randomized, multi-intervention, semi-controlled feeding study of isocaloric dietary interventions: (1) MED-amplified dietary pattern, containing 500 kcal/day from eight MED target foods: avocado, basil, cherry, chickpea, oat, red bell pepper, walnut, and a protein source (alternating between salmon or unprocessed, lean beef), and (2) habitual/Western dietary pattern, containing 500 kcal/day from six non-MED target foods: cheesecake, chocolate frozen yogurt, refined grain bread, sour cream, white potato, and unprocessed, lean beef. After a 2-week washout, participants complete four, 4-week intervention periods, with biospecimen sampling and outcome assessments at baseline and at intervention weeks 4, 8, 12, and 16. The primary outcome is change in the relative abundance of FSCs from the eight MED target foods in participant biospecimens from baseline to the end of each intervention period. Secondary outcomes include mean change in cardiometabolic health indicators, inflammatory markers, and adipokines. Exploratory outcomes include change in diversity and community composition of the gut microbiota.DiscussionOur stepwise strategy, beginning with identification of FSCs in whole diets and biospecimens, followed by relating these to health indicators will lead to improved methodology for assessment of dietary patterns and a better understanding of the relationship between food and health. This study will serve as a first step toward validating candidate food intake biomarkers and allow for assessment of relationships with cardiometabolic health. The identification of food intake biomarkers is critical to future research and has implications spanning health promotion and disease prevention for many chronic conditions.Trial registrationRegistered at ClinicalTrials.gov: NCT05500976; Date of registration: August 15, 2022.

Biomarkers of food intake and their relevance to metabolic syndrome.

Metabolic syndrome (MetS) constitutes a prevalent risk factor associated with non communicable diseases such as cardiovascular disease and type 2 diabetes. A major factor impacting the etiology of MetS is diet. Dietary patterns and several individual food constituents have been related to the risk of developing MetS or have been proposed as adjuvant treatment. However, traditional methods of dietary assessment such as 24 h recalls rely greatly on intensive user-interaction and are subject to bias. Hence, more objective methods are required for unbiased dietary assessment and efficient prevention. While it is accepted that some dietary-derived constituents in blood plasma are indicators for certain dietary patterns, these may be too unstable (such as vitamin C as a marker for fruits/vegetables) or too broad (e.g. polyphenols for plant-based diets) or reflect too short-term intake only to allow for strong associations with prolonged intake of individual food groups. In the present manuscript, commonly employed biomarkers of intake including those related to specific food items (e.g. genistein for soybean or astaxanthin and EPA for fish intake) and novel emerging ones (e.g. stable isotopes for meat intake or microRNA for plant foods) are emphasized and their suitability as biomarker for food intake discussed. Promising alternatives to plasma measures (e.g. ethyl glucuronide in hair for ethanol intake) are also emphasized. As many biomarkers (i.e. secondary plant metabolites) are not limited to dietary assessment but are also capable of regulating e.g. anti-inflammatory and antioxidant pathways, special attention will be given to biomarkers presenting a double function to assess both dietary patterns and MetS risk.

Evaluation of Food Intake Biomarkers for Red Bell Peppers in Human Urine Based on HPLC-MS/MS Analysis.

The validation of dietary biomarkers is essential for the use in objective and quantitative assessment of the human dietary intake. In this study, the urinary excretion of previously identified potential biomarkers after intake of red bell peppers is analyzed. The urine samples obtained after a two-phase dietary intervention study in which 14 volunteers participated are quantitatively analyzed by high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS) after an extensive validation. In the first phase, the volunteers abstain completely from bell peppers and paprika products (control group) and in the second phase, the volunteers consume a defined amount of fresh red bell peppers (case group). After analysis, all potential biomarkers show high dispersions of their concentration, indicating interindividual differences. The glucuronidated apocarotenoid (compound 1), which probably resulted from the main carotenoids of red Capsicum fruits, shows a rapid urinary excretion. The other glucuronidated metabolites (compounds 2-8), described as potential derivatives of capsianosides from Capsicum, show a slightly delayed but longer urinary excretion. A correlation between an intake of red bell pepper and the urinary excretion of recently described potential biomarkers is observed. Due to large interindividual differences, it is reasonable to assume that at least the qualitative detection of the consumption of bell peppers and possibly all Capsicum fruits is feasible.

Fecal Metagenomics to Identify Biomarkers of Food Intake in Healthy Adults: Findings from Randomized, Controlled, Nutrition Trials

BackgroundUndigested components of the human diet affect the composition and function of the microorganisms present in the gastrointestinal tract. Techniques like metagenomic analyses allow researchers to study functional capacity, thus revealing the potential of using metagenomic data for developing objective biomarkers of food intake. ObjectivesAs a continuation of our previous work using 16S and metabolomic datasets, we aimed to utilize a computationally intensive, multivariate, machine-learning approach to identify fecal KEGG (Kyoto encyclopedia of genes and genomes) Orthology (KO) categories as biomarkers that accurately classify food intake. MethodsData were aggregated from 5 controlled feeding studies that studied the individual impact of almonds, avocados, broccoli, walnuts, barley, and oats on the adult gastrointestinal microbiota. Deoxyribonucleic acid from preintervention and postintervention fecal samples underwent shotgun genomic sequencing. After preprocessing, sequences were aligned and functionally annotated with Double Index AlignMent Of Next-generation sequencing Data v2.0.11.149 and MEtaGenome ANalyzer v6.12.2, respectively. After the count normalization, the log of the fold change ratio for resulting KOs between pre- and postintervention of the treatment group against its corresponding control was utilized to conduct differential abundance analysis. Differentially abundant KOs were used to train machine-learning models examining potential biomarkers in both single-food and multi-food models. ResultsWe identified differentially abundant KOs in the almond (n = 54), broccoli (n = 2474), and walnut (n = 732) groups (q < 0.20), which demonstrated classification accuracies of 80%, 87%, and 86% for the almond, broccoli, and walnut groups using a random forest model to classify food intake into each food group’s respective treatment and control arms, respectively. The mixed-food random forest achieved 81% accuracy. ConclusionsOur findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings.

Dietary biomarkers-an update on their validity and applicability in epidemiological studies.

The aim of this literature review was to identify and provide a summary update on the validity and applicability of the most promising dietary biomarkers reflecting the intake of important foods in the Western diet for application in epidemiological studies. Many dietary biomarker candidates, reflecting intake of common foods and their specific constituents, have been discovered from intervention and observational studies in humans, but few have been validated. The literature search was targeted for biomarker candidates previously reported to reflect intakes of specific food groups or components that are of major importance in health and disease. Their validity was evaluated according to 8 predefined validation criteria and adapted to epidemiological studies; we summarized the findings and listed the most promising food intake biomarkers based on the evaluation. Biomarker candidates for alcohol, cereals, coffee, dairy, fats and oils, fruits, legumes, meat, seafood, sugar, tea, and vegetables were identified. Top candidates for all categories are specific to certain foods, have defined parent compounds, and their concentrations are unaffected by nonfood determinants. The correlations of candidate dietary biomarkers with habitual food intake were moderate to strong and their reproducibility over time ranged from low to high. For many biomarker candidates, critical information regarding dose response, correlation with habitual food intake, and reproducibility over time is yet unknown. The nutritional epidemiology field will benefit from the development of novel methods to combine single biomarkers to generate biomarker panels in combination with self-reported data. The most promising dietary biomarker candidates that reflect commonly consumed foods and food components for application in epidemiological studies were identified, and research required for their full validation was summarized.

205-LB: Using Metabolomic Biomarkers to Understand Relationships between Avocado Intake and Dysglycemia

This study used metabolomic data from the Multi-Ethnic Study of Atherosclerosis (MESA) to better understand the relationship between avocado intake and dysglycemia. At baseline, 6,224 older adults had data on self-reported avocado intake, as well as fasting glucose and insulin, while a randomly selected subsample (N=3,438) also had plasma untargeted 1H NMR metabolomic features. Subsequently, incident T2D, defined by ADA 2003 criteria, was assessed over an ~18 year period. First, a metabolome-wide association analysis was used to identify metabolomic features associated with avocado consumption. Next, the relationships of these biomarkers, and of avocado consumption, to contemporaneous dysglycemia, and incident T2D, were examined in models that initially adjusted for demographic factors and health behaviors, and subsequently also for BMI. Three highly correlated spectral features (all r≥0.77), tentatively annotated as CH2-lysl, were associated with avocado consumption (P=5.3*10-7). A mean value for CH2 was used as our biomarker, which, when adjusted for BMI, was strongly associated with avocado intake (β= 0.16 ± 0.04, P=4.7*10-5), but not additionally with any of 47 other food groups (P≥.01). Avocado intake showed a modest inverse association with fasting insulin (β= -0.07 ± 0.03, P=.03). When controlling for BMI, this was attenuated (β= -0.02 ± 0.03, P=.37), while strong inverse associations remained between the potential avocado biomarker and fasting glucose (β= -0.22 ± 0.02, P&amp;lt;2.0*10-20), fasting insulin (β= -0.17 ± 0.02, P&amp;lt;2.0*10-20), and T2D incidence (HR: 0.68 [0.63-074], P&amp;lt;2.0*10-20). The highly significant associations between glycemia and avocado-related metabolomic features, which serve as biomarkers of food intake after digestion and absorption, compared to modest relationships between glycemia and avocado consumption, highlights the importance of considering individual differences in metabolism when considering diet-health relationships. Disclosure A. Wood: None. M. O. Goodarzi: Advisory Panel; Nestlé Health Science, Other Relationship; Nestlé Health Science. M. D. Gadgil: None. M. Allison: None. G. Graça: None. M. Y. Mi: None. P. Greenland: Stock/Shareholder; Eli Lilly and Company, Novartis AG. D. M. Herrington: None. J. I. Rotter: None. Funding National Cattleman?s Beef Association; U.S. Department of Agriculture/Agricultural Research Service (58-3092-5-001); National Heart, Lung, and Blood Institute (75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1TR000040, UL1TR001079, UL1TR001420, UL1TR001881, DK063491, R01HL133932, R01HL105756); European Union COMBI-BIO Project (FP7, 305422); National Institute of Diabetes and Digestive and Kidney Diseases (DK063491); National Center for Advancing Translational Sciences (UL1TR001881)

Utilising the precision nutrition toolkit in the path towards precision medicine.

The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Joint Microbiota Activity and Dietary Assessment through Urinary Biomarkers by LC-MS/MS.

Accurate dietary assessment in nutritional research is a huge challenge, but essential. Due to the subjective nature of self-reporting methods, the development of analytical methods for food intake and microbiota biomarkers determination is needed. This work presents an ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) method for the quantification and semi quantification of 20 and 201 food intake biomarkers (BFIs), respectively, as well as 7 microbiota biomarkers applied to 208 urine samples from lactating mothers (M) (N = 59). Dietary intake was assessed through a 24 h dietary recall (R24h). BFI analysis identified three distinct clusters among samples: samples from clusters 1 and 3 presented higher concentrations of most biomarkers than those from cluster 2, with dairy products and milk biomarkers being more concentrated in cluster 1, and seeds, garlic and onion in cluster 3. Significant correlations were observed between three BFIs (fruits, meat, and fish) and R24h data (r > 0.2, p-values < 0.01, Spearman correlation). Microbiota activity biomarkers were simultaneously evaluated and the subgroup patterns detected were compared to clusters from dietary assessment. These results evidence the feasibility, usefulness, and complementary nature of the determination of BFIs, R24h, and microbiota activity biomarkers in observational nutrition cohort studies.

Fecal Microbiota Composition as a Metagenomic Biomarker of Dietary Intake.

Gut microbiota encompasses the set of microorganisms that colonize the gastrointestinal tract with mutual relationships that are key for host homeostasis. Increasing evidence supports cross intercommunication between the intestinal microbiome and the eubiosis-dysbiosis binomial, indicating a networking role of gut bacteria as potential metabolic health surrogate markers. The abundance and diversity of the fecal microbial community are already recognized to be associated with several disorders, such as obesity, cardiometabolic events, gastrointestinal alterations, and mental diseases, which suggests that intestinal microbes may be a valuable tool as causal or as consequence biomarkers. In this context, the fecal microbiota could also be used as an adequate and informative proxy of the nutritional composition of the food intake and about the adherence to dietary patterns, such as the Mediterranean or Western diets, by displaying specific fecal microbiome signatures. The aim of this review was to discuss the potential use of gut microbial composition as a putative biomarker of food intake and to screen the sensitivity value of fecal microbiota in the evaluation of dietary interventions as a reliable and precise alternative to subjective questionnaires.

Effects of sub-chronic CRH administration into the hypothalamic paraventricular and central amygdala nuclei in male rats with a focus on food intake biomarkers

Effects of sub-chronic CRH administration into the hypothalamic paraventricular and central amygdala nuclei in male rats with a focus on food intake biomarkers

Metabolomic based approach to identify biomarkers of broccoli intake.

It is well-established that consumption of cruciferous and brassica vegetables has a correlation with reduced rates of many negative health outcomes. There is an increased interest in identifying food intake biomarkers to address limitations related to self-reported dietary assessment. The study aims to identify biomarkers of broccoli intake using metabolomic approaches, examine the dose-response relationship, and predict the intake by multimarker panel. Eighteen volunteers consumed cooked broccoli in A-Diet Discovery study and fasting and postprandial urine samples were collected at 2, 4 and 24 hours. Subsequently the A-Diet Dose-response study was performed where volunteers consumed different portions of broccoli (49, 101 or 153 g) and urine samples were collected at the end of each intervention week. Urine samples were analysed by 1H-NMR and LC-MS. Multivariate data analysis and one-way ANOVA were performed to identify discriminating biomarkers. A panel of putative biomarkers was examined for its ability to predict intake through a multiMarker model. Multivariate analysis revealed discriminatory spectral regions between fasting and fed metabolic profiles. Subsequent time-series plots revealed multiple features increased in concentration following the consumption. Urinary S-methyl cysteine sulfoxide (SMCSO) increased as broccoli intake increased (0.17-0.24 μM per mOSM per kg, p < 0.001). Similarly from LC-MS data genipin, dihydro-β-tubaic acid and sinapic acid increased with increasing portions of intake. A panel of 8 features displayed good ability to predict intake from biomarker data only. In conclusion, urinary SMCSO and several LC-MS features appeared as potentially promising biomarkers of broccoli consumption and demonstrated dose-response relationship. Future work should focus on validating these compounds as food intake biomarkers.

Blood metabolite profiles linking dietary patterns with health-Toward precision nutrition.

Diet is one of the most important exposures that may affect health throughout life span. Investigations on dietary patterns rather than single food components are gaining in popularity because they take the complexity of the whole dietary context into account. Adherence to such dietary patterns can be measured by using metabolomics, which allows measurements of thousands of molecules simultaneously. Derived metabolite signatures of dietary patterns may reflect the consumption of specific groups of foods or their constituents originating from the dietary pattern per se, or the physiological response toward the food-derived metabolites, their interaction with endogenous metabolism, and exogenous factors such as gut microbiota. Here, we review and discuss blood metabolite fingerprints of healthy dietary patterns. The plasma concentration of several food-derived metabolites-such as betaines from whole grains and n-3 polyunsaturated fatty acids and furan fatty acids from fish-seems to consistently reflect the intake of common foods of several healthy dietary patterns. The metabolites reflecting shared features of different healthy food indices form biomarker panels for which specific, targeted assays could be developed. The specificity of such biomarker panels would need to be validated, and proof-of-concept feeding trials are needed to evaluate to what extent the panels may mediate the effects of dietary patterns on disease risk indicators or if they are merely food intake biomarkers. Metabolites mediating health effects may represent novel targets for precision prevention strategies of clinical relevance to be verified in future studies.

Obesity Prevalence and Dietary Factors Among Preschool-Aged Head Start Children in Remote Alaska Native Communities: Baseline Data from the "Got Neqpiaq?" Study.

Background: American Indian and Alaska Native preschool-aged children experience a high prevalence of obesity, yet are under-represented in obesity prevention research. This study examined obesity prevalence and dietary risk factors among Alaska Native preschool-aged children in southwest Alaska. Methods: The study used baseline data from "Got Neqpiaq?" a culturally centered multilevel intervention focused on Yup'ik Alaska Native children, aged 3-5 years, enrolled in Head Start in 12 communities in southwest Alaska (n = 155). The primary outcomes were BMI percentile, overweight, and obesity. Dietary factors of interest were measured using biomarkers: traditional food intake (nitrogen stable isotope ratio biomarker), ultraprocessed food intake (carbon stable isotope ratio biomarker), and vegetable and fruit intake (skin carotenoid status biomarker measured by the Veggie Meter). Cardiometabolic markers (glycated hemoglobin [HbA1c] and blood cholesterol) were also measured. Results: Among the Yup'ik preschool-aged children in the study, the median BMI percentile was 91, and the prevalence of overweight or obesity was 70%. The traditional food intake biomarker was negatively associated with BMI, whereas the ultraprocessed foods and vegetable and fruit biomarkers were not associated with BMI. HbA1c and blood cholesterol were within healthy levels. Conclusions: The burden of overweight and obesity is high among Yup'ik preschool-aged children. Traditional food intake is inversely associated with BMI, which underscores the need for culturally grounded interventions that emphasize traditional values and knowledge to support the traditional food systems in Alaska Native communities in southwest Alaska. Registered with ClinicalTrials.gov #NCT03601299.

Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults

The fecal metabolome is affected by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake. We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake. Data were aggregated from 5 controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in ≥80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score). Using the change in relative concentration of 96 fecal metabolites, 6 single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods with one another, almond intake was differentiated from walnut intake with 91% classification accuracy. Our findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.