The food metabolome: a window over dietary exposure

Foodomics: A sustainable approach for the specific nutrition and diets for human health

Foodomics is an interdisciplinary field that integrates various omics technologies to explore the complex relationship between food and human health in depth. This approach offers valuable insights into the biochemical, molecular, and cellular composition of food by employing advanced omics techniques. Its applications span the food industry and human health, including efforts to combat malnutrition, provide dietary recommendations, and ensure food safety. This paper critically examines the successful applications of foodomics across areas such as food safety, quality, traceability, processing, and bioactivity. It highlights the crucial role of metabolomics, proteomics, and transcriptomics in achieving a comprehensive understanding of food components, their functions, and their interactions with human biology.

Meat products in human nutrition and health - About hazards and risks

Meat processing has a long history and involves a wide and ever-increasing range of chemical and physical processes, resulting in a heterogeneous food category with a wide variability in nutritional value. Despite the known benefits of meat consumption, observational epidemiological studies have shown associations between consumption of red and processed meat - but not white meat - and several non-communicable diseases, with higher relative risks for processed meat compared to unprocessed red meat. This has led global and regional nutrition and health organisations to recommend reducing consumption of unprocessed red meat and avoiding processed meat. A plethora of potentially implicated hazardous compounds present in meat or formed during processing or gastrointestinal digestion have been reported in the literature. However, our mechanistic understanding of the impact of meat consumption on human health is still very incomplete and is complicated by the simultaneous occurrence of multiple hazards and interactions with other food compounds and host factors. This narrative review briefly discusses hazards, risks and their assessment in the context of dietary guidelines. It is argued that more mechanistic studies of the interactive effects of meat products with other foods and food compounds in different dietary contexts are needed to refine and increase the evidence base for dietary guidelines. Importantly, the great diversity in the composition and degree of processing of processed meats should be better understood in terms of their impact on human health in order to develop a more nuanced approach to dietary guidelines for this food category.

FoodAtlas: Automated knowledge extraction of food and chemicals from literature

Automated generation of knowledge graphs that accurately capture published information can help with knowledge organization and access, which have the potential to accelerate discovery and innovation. Here, we present an integrated pipeline to construct a large-scale knowledge graph using large language models in an active learning setting. We apply our pipeline to the association of raw food, ingredients, and chemicals, a domain that lacks such knowledge resources. By using an iterative active learning approach of 4120 manually curated premise-hypothesis pairs as training data for ten consecutive cycles, the entailment model extracted 230,848 food-chemical composition relationships from 155,260 scientific papers, with 106,082 (46.0 %) of them never been reported in any published database. To augment the knowledge incorporated in the knowledge graph, we further incorporated information from 5 external databases and ontology sources. We then applied a link prediction model to identify putative food-chemical relationships that were not part of the constructed knowledge graph. Validation of the 443 hypotheses generated by the link prediction model resulted in 355 new food-chemical relationships, while results show that the model score correlates well (R2 = 0.70) with the probability of a novel finding. This work demonstrates how automated learning from literature at scale can accelerate discovery and support practical applications through reproducible, evidence-based capture of latent interactions of diverse entities, such as food and chemicals.

Metabolomics signatures of sweetened beverages and added sugar are related to anthropometric measures of adiposity in young individuals: results from a cohort study

BACKGROUND

The associations of sweetened beverages (SBs) and added sugar (AS) intake with adiposity are still debated. Metabolomics could provide insights into the mechanisms linking their intake to adiposity.

OBJECTIVES

We aimed to identify metabolomics biomarkers of intake of low- and no-calorie sweetened beverages (LNCSBs), sugar-sweetened beverages (SSBs), and ASs and to investigate their associations with body mass index, body fat percentage, and waist circumference.

METHODS

We analyzed 3 data sets from the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) cohort study, of children who provided 2 urine samples (n = 297), adolescents who provided a single urine sample (n = 339), and young adults who provided a single plasma sample (n = 195). Urine and plasma were analyzed using untargeted metabolomics. Dietary intakes were assessed using 3-d weighed dietary records. The random forest, partial least squares, and least absolute shrinkage and selection operator were jointly used for metabolite selection. We examined associations of intakes with metabolites and anthropometric measures using linear and mixed-effects regression.

RESULTS

In adolescents, LNCSB were positively associated with acesulfame (β: 0.0012; 95% confidence interval [CI]: 0.0006, 0.0019) and saccharin (β: 0.0009; 95% CI: 0.0002, 0.0015). In children, the association was observed with saccharin (β: 0.0016; 95% CI: 0.0005, 0.0027). In urine and plasma, SSBs were positively associated with 1-methylxanthine (β: 0.0005; 95% CI: 0.0003, 0.0008; and β: 0.0010, 95% CI 0.0004, 0.0015, respectively) and 5-acetylamino-6-amino-3-methyluracil (β: 0.0005; 95% CI: 0.0002, 0.0008; and β: 0.0009; 95% CI: 0.0003, 0.0014, respectively). AS was associated with urinary sucrose (β: 0.0095; 95% CI: 0.0069, 0.0121) in adolescents. Some of the food-related metabolomics profiles were also associated with adiposity measures.

CONCLUSIONS

We identified SBs- and AS-related metabolites, which may be important for understanding the interplay between these intakes and adiposity in young individuals.

Identification and Replication of Urine Metabolites Associated With Short-Term and Habitual Intake of Sweet and Fatty Snacks in European Children and Adolescents

BACKGROUND

Intake of sweet and fatty snacks may partly contribute to the occurrence of obesity and other health conditions in childhood. Traditional dietary assessment methods may be limited in accurately assessing the intake of sweet and fatty snacks in children. Metabolite biomarkers may aid the objective assessment of children's food intake and support establishing diet-disease relationships.

OBJECTIVES

The present study aimed to identify biomarkers of sweet and fatty snack intake in 2 independent cohorts of European children.

METHODS

We used data from the IDEFICS/I.Family cohort from baseline (2007/2008) and 2 follow-up examination waves (2009/2010 and 2013/2014). In total, 1788 urine samples from 599 children were analyzed for untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry. Short-term dietary intake was assessed by 24-h dietary recalls, and habitual dietary intake was calculated with the National Cancer Institute method. Data from the Dortmund Nutritional and Anthropometric Longitudinal Designed (DONALD) cohort of 24-h urine samples (n = 567) and 3-d weighted dietary records were used for external replication of results. Multivariate modeling with unbiased variable selection in R algorithms and linear mixed models were used to identify novel biomarkers. Metabolite features significantly associated with dietary intake were then annotated.

RESULTS

In total, 66 metabolites were discovered and found to be statistically significant for chocolate candy; cakes, puddings, and cookies; candy and sweets; ice cream; and crisps. Most of the features (n = 62) could not be annotated. Short-term and habitual chocolate intake were positively associated with theobromine, xanthosine, and cyclo(L-prolyl-L-valyl). These results were replicated in the DONALD cohort. Short-term candy and sweet intake was negatively associated with octenoylcarnitine.

CONCLUSIONS

Of the potential metabolite biomarkers of sweet and fatty snacks in children, 3 biomarkers of chocolate intake, namely theobromine, xanthosine, and cyclo(L-prolyl-L-valyl), are externally replicated. However, these potential biomarkers require further validation in children.

ChemSweet: An AI-driven computational platform for next-gen sweetener discovery

Nowadays, the overconsumption of artificial sweeteners and their related adverse health impacts have proposed an urgent need to develop safe and healthy alternatives. Herein, we introduce ChemSweet, an AI-based platform for the rapid discovery of potential sweet molecules (http://chemsweet.ddai.tech) with the consideration of their physicochemical properties, sweetness profile, and health risks at the same time. Machine learning prediction models of four important physicochemical and four toxicity properties were established and integrated with the platform to evaluate the candidate molecules' biosafety and stability during the processing processes. Then, a new sweet taste prediction system was developed which ensures the sweet evaluation of six specific kinds of sweeteners. To facilitate the practical application of ChemSweet, the SuperNatural database was integrated for the rational screening of promising new sweeteners. We successfully identified 294 potential sweeteners that simultaneously meet the multiple anticipated criteria. We believe that ChemSweet will serve as a useful tool for identifying safe and healthy sweeteners while reducing the timeframe and high experimental costs.

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.

Pesticide residues and polyphenols in urine - A combined LC-HRMS screening to reveal intake patterns

Human exposure to pesticides in the general population occurs mainly through food consumption. However, specific dietary habits or food products that contribute to pesticide exposure are often unknown. In this study, we propose a combined screening for polyphenols and pesticide residues by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to assess the diet and the associated pesticide exposure. We measured 587 urine samples from women around the 34th week of pregnancy of a prospective mother-child cohort. A non-targeted screening for flavonoid-like compounds related to fruit and vegetable consumption was performed, prioritizing 164 features and identifying a total of 46 features by spectral library search. Based on a subset of markers, k-means clustering was performed, leading to four clusters with presumably similar dietary habits. The clusters were compared against food questionnaire data collected within the period of sample collection. Suspect screening of more than 500 pesticide residues including metabolites was performed, with a total of 40 residues being reported for 27 different pesticides. The detection of pesticide residues was compared across the different clusters of dietary habits. Indications were found that pyrimethanil metabolites might be associated with the consumption of citrus fruits or derivate products. We demonstrate that the method used has the potential to reveal patterns of pesticide intake from specific food commodities.

Nutrition Education in Psychiatry Residency Programs: A Call to Action

The burden of disease attributable to mental health is expected to rise in the coming decades. Poor nutritional status is considered a modifiable risk factor for general mental health. In fact, nutrition interventions are now accepted as a core strategy in mental healthcare to combat physical health inequalities and life-expectancy gap in people with certain psychiatric disorders. However, most psychiatrists are not familiar with evidence for the potential therapeutic benefits of diet in psychiatric illness, and this may be related to sparse nutrition education for physicians. Thus, there is a need to integrate nutritional management in psychiatric practice, but there is a gap in medical education that would support this practice. Here, we discuss evidence for and challenges in 1) assessing diet quality in psychiatric illness, 2) recommending improvements in diet quality and specific dietary patterns in psychiatric illness, and 3) recommending dietary supplements in psychiatric illness. This discussion serves as a call to develop nutrition curricula within psychiatry residency programs.

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.

Impact of a short-term Mediterranean diet intervention on plasma metabolites: a pilot study

BACKGROUND

Dietary habits significantly influence the risks of type 2 diabetes and cardiovascular disease. Through metabolomics, we've previously measured plasma metabolites to gauge dietary quality, introducing a healthy dietary metabolic signature (HDMS) linked to a decreased risk of future type 2 diabetes and coronary artery disease.

OBJECTIVES

To assess the impact of a 6-day dietary intervention on plasma metabolites and the HDMS.

METHODS

Fifty-nine Swedish participants (71% women, mean age 69 years) underwent a 6-day Mediterranean diet (MD) intervention in Italy's Cilento region. All meals, crafted from local recipes and ingredients, were provided. Metabolite profiling pre- and post-intervention was conducted with a UHPLC-QTOF. Alterations in metabolite levels and the HDMS were examined using paired T-test.

RESULTS

The MD intervention notably enhanced the HDMS across participants (mean increase: 1.3 standard deviations (SD), 95% CI 1.1-1.4, p = 6E-25). Out of 109 metabolites, 66 exhibited significant alterations (fdr adjusted p < 0.05). Among the 10 most significant changes, increases were observed in several diet related metabolites such as pipecolate, hippurate, caffeine, homostachydrine, acylcarnitine C11:0, acetylornithine, beta-carotene and 7-methylguanine. The most significant decreases manifested in piperine and 3-methylhistidine.

CONCLUSIONS

The HDMS, which is linked to a healthy diet and inversely associated with cardiometabolic disease, was significantly improved by the 6-day Mediterranean diet intervention. Notably, metabolite markers previously shown to be indicative of the intake of vegetables, fruits, grains, and legumes increased, while markers previously associated with red meat consumption decreased. These findings highlight the potential of short-term dietary interventions to induce significant changes in plasma metabolite profiles.

Blood metabolomic shift links diet and gut microbiota to multiple health outcomes among Hispanic/Latino immigrants in the U.S

Immigrants from less industrialized countries who are living in the U.S. often bear an elevated risk of multiple disease due to the adoption of a U.S. lifestyle. Blood metabolome holds valuable information on environmental exposure and the pathogenesis of chronic diseases, offering insights into the link between environmental factors and disease burden. Analyzing 634 serum metabolites from 7,114 Hispanics (1,141 U.S.-born, 5,973 foreign-born) in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we identified profound blood metabolic shift during acculturation. Machine learning highlighted the prominent role of non-genetic factors, especially food and gut microbiota, in these changes. Immigration-related metabolites correlated with plant-based foods and beneficial gut bacteria for foreign-born Hispanics, and with meat-based or processed food and unfavorable gut bacteria for U.S.-born Hispanics. Cardiometabolic traits, liver, and kidney function exhibited a link with immigration-related metabolic changes, which were also linked to increased risk of diabetes, severe obesity, chronic kidney disease, and asthma.

Graphical abstract

Highlights

A substantial proportion of identified blood metabolites differ between U.S.-born and foreign-born Hispanics/Latinos in the U.S.Food and gut microbiota are the major modifiable contributors to blood metabolomic difference between U.S.-born and foreign-born Hispanics/Latinos.U.S. nativity related metabolites collectively correlate with a spectrum of clinical traits and chronic diseases.

Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations

Fecal metabolites effectively discriminate inflammatory bowel disease (IBD) and show differential associations with diet. Metabolomics and AI-based models, including explainable AI (XAI), play crucial roles in understanding IBD. Using datasets from the UK Biobank and the Human Microbiome Project Phase II IBD Multi'omics Database (HMP2 IBDMDB), this study uses multiple machine learning (ML) classifiers and Shapley additive explanations (SHAP)-based XAI to prioritize plasma and fecal metabolites and analyze their diet correlations. Key findings include the identification of discriminative metabolites like glycoprotein acetyl and albumin in plasma, as well as nicotinic acid metabolites andurobilin in feces. Fecal metabolites provided a more robust disease predictor model (AUC [95%]: 0.93 [0.87-0.99]) compared to plasma metabolites (AUC [95%]: 0.74 [0.69-0.79]), with stronger and more group-differential diet-metabolite associations in feces. The study validates known metabolite associations and highlights the impact of IBD on the interplay between gut microbial metabolites and diet.

A Mediterranean Diet-Based Metabolomic Score and Cognitive Decline in Older Adults: A Case-Control Analysis Nested within the Three-City Cohort Study

SCOPE

Evidence on the Mediterranean diet (MD) and age-related cognitive decline (CD) is still inconclusive partly due to self-reported dietary assessment. The aim of the current study is to develop an MD- metabolomic score (MDMS) and investigate its association with CD in community-dwelling older adults.

METHODS AND RESULTS

This study includes participants from the Three-City Study from the Bordeaux (n = 418) and Dijon (n = 422) cohorts who are free of dementia at baseline. Repeated measures of cognition over 12 years are collected. An MDMS is designed based on serum biomarkers related to MD key food groups and using a targeted metabolomics platform. Associations with CD are investigated through conditional logistic regression (matched on age, sex, and education level) in both sample sets. The MDMS is found to be inversely associated with CD (odds ratio [OR] [95% confidence interval (CI)] = 0.90 [0.80-1.00]; p = 0.048) in the Bordeaux (discovery) cohort. Results are comparable in the Dijon (validation) cohort, with a trend toward significance (OR [95% CI] = 0.91 [0.83-1.01]; p = 0.084).

CONCLUSIONS

A greater adherence to the MD, here assessed by a serum MDMS, is associated with lower odds of CD in older adults.

Intake Biomarkers for Nutrition and Health: Review and Discussion of Methodology Issues

Metabolomics profiles from blood, urine, or other body fluids have the potential to assess intakes of foods and nutrients objectively, thereby strengthening nutritional epidemiology research. Metabolomics platforms may include targeted components that estimate the relative concentrations for individual metabolites in a predetermined set, or global components, typically involving mass spectrometry, that estimate relative concentrations more broadly. While a specific metabolite concentration usually correlates with the intake of a single food or food group, multiple metabolites may be correlated with the intake of certain foods or with specific nutrient intakes, each of which may be expressed in absolute terms or relative to total energy intake. Here, I briefly review the progress over the past 20 years on the development and application intake biomarkers for foods/food groups, nutrients, and dietary patterns, primarily by drawing from several recent reviews. In doing so, I emphasize the criteria and study designs for candidate biomarker identification, biomarker validation, and intake biomarker application. The use of intake biomarkers for diet and chronic disease association studies is still infrequent in nutritional epidemiology research. My comments here will derive primarily from our research group's recent contributions to the Women's Health Initiative cohorts. I will complete the contribution by describing some opportunities to build on the collective 20 years of effort, including opportunities related to the metabolomics profiling of blood and urine specimens from human feeding studies that approximate habitual diets.

Human gut microbiome, diet, and mental disorders

Diet is one of the most important external factor shaping the composition and metabolic activities of the gut microbiome. The gut microbiome plays a crucial role in host health, including immune system development, nutrients metabolism, and the synthesis of bioactive molecules. In addition, the gut microbiome has been described as critical for the development of several mental disorders. Nutritional psychiatry is an emerging field of research that may provide a link between diet, microbial function, and brain health. In this study, we have reviewed the influence of different diet types, such as Western, Mediterranean, vegetarian, and ketogenic, on the gut microbiota composition and function, and their implication in various neuropsychiatric and psychological disorders.

Plasma Metabolomics of Dietary Intake of Protein-Rich Foods and Kidney Disease Progression in Children

OBJECTIVE

Evidence regarding the efficacy of a low-protein diet for patients with CKD is inconsistent and recommending a low-protein diet for pediatric patients is controversial. There is also a lack of objective biomarkers of dietary intake. The purpose of this study was to identify plasma metabolites associated with dietary intake of protein and to assess whether protein-related metabolites are associated with CKD progression.

METHODS

Nontargeted metabolomics was conducted in plasma samples from 484 Chronic Kidney Disease in Children (CKiD) participants. Multivariable linear regression estimated the cross-sectional association between 949 known, nondrug metabolites and dietary intake of total protein, animal protein, plant protein, chicken, dairy, nuts and beans, red and processed meat, fish, and eggs, adjusting for demographic, clinical, and dietary covariates. Cox proportional hazards models assessed the prospective association between protein-related metabolites and CKD progression defined as the initiation of kidney replacement therapy or 50% eGFR reduction, adjusting for demographic and clinical covariates.

RESULTS

One hundred and twenty-seven (26%) children experienced CKD progression during 5 years of follow-up. Sixty metabolites were significantly associated with dietary protein intake. Among the 60 metabolites, 10 metabolites were significantly associated with CKD progression (animal protein: n = 1, dairy: n = 7, red and processed meat: n = 2, nuts and beans: n = 1), including one amino acid, one cofactor and vitamin, 4 lipids, 2 nucleotides, one peptide, and one xenobiotic. 1-(1-enyl-palmitoyl)-2-oleoyl-glycerophosphoethanolamine (GPE, P-16:0/18:1) was positively associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 88% higher risk of CKD progression. 3-ureidopropionate was inversely associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 48% lower risk of CKD progression.

CONCLUSIONS

Untargeted plasma metabolomic profiling revealed metabolites associated with dietary intake of protein and CKD progression in a pediatric population.

Dietary (poly)phenols and cardiometabolic health: from antioxidants to modulators of the gut microbiota

(Poly)phenols are plant secondary metabolites widely abundant in plant foods and beverages comprising a very large number of compounds with diverse structure and biological activities. Accumulating evidence indicates that these compounds exert beneficial effects against cardiometabolic diseases, and this review will provide a summary of current knowledge in this area. Epidemiological and clinical data collectively suggest that intake of flavonoids reduces the risk of cardiovascular disease (CVD), with the evidence being particularly strong for the flavan-3-ol subclass. However, to provide adequate dietary recommendations, a better understanding of their estimated content in foods and intake among the general public is needed. Regarding mechanisms of action, we now know that it is unlikely that (poly)phenols act as direct antioxidants in vivo, as it was hypothesised for decades with the popularity of in vitro antioxidant capacity assays. One of the reasons is that upon ingestion, (poly)phenols are extensively metabolised into a wide array of circulating metabolites with different bioactivities than their precursors. Well-conducted in vitro and in vivo studies and human nutrigenomic analysis have revealed new molecular targets that may be underlying the health benefits of (poly)phenols, such as the nitric oxide pathway. Recently, a bi-directional relationship was established between (poly)phenols and the gut microbiota, suggesting that individual gut microbial metabolising capacity may be a key factor explaining the variability in the cardiometabolic response to (poly)phenols. Future research is needed to elucidate which are the key factors affecting such capacity, and whether it can be modulated, along with the mechanisms of action.

Metabolome biomarkers linking dietary fibre intake with cardiometabolic effects: results from the Danish Diet, Cancer and Health-Next Generations MAX study

Biomarkers associated with dietary fibre intake, as complements to traditional dietary assessment tools, may improve the understanding of its role in human health. Our aim was to discover metabolite biomarkers related to dietary fibre intake and investigate their association with cardiometabolic risk factors. We used data and samples from the Danish Diet Cancer and Health Next Generation (DCH-NG) MAX-study, a one-year observational study with evaluations at baseline, six and 12 months (n = 624, 55% female, mean age: 43 years, 1353 observations). Direct associations between fibre intake and plasma concentrations of 2,6-dihydroxybenzoic acid (2,6-DHBA) and indolepropionic acid were observed at the three time-points. Both metabolites showed an intraclass-correlation coefficient (ICC) > 0.50 and were associated with the self-reported intake of wholegrain cereals, and of fruits and vegetables, respectively. Other metabolites associated with dietary fibre intake were linolenoyl carnitine, 2-aminophenol, 3,4-DHBA, and proline betaine. Based on the metabolites associated with dietary fibre intake we calculated predicted values of fibre intake using a multivariate, machine-learning algorithm. Metabolomics-based predicted fibre, but not self-reported fibre values, showed negative associations with cardiometabolic risk factors (i.e. high sensitivity C-reactive protein, systolic and diastolic blood pressure, all FDR-adjusted p-values <0.05). Furthermore, different correlations with gut microbiota composition were observed. In conclusion, 2,6-DHBA and indolepropionic acid in plasma may better link dietary fibre intake with its metabolic effects than self-reported values. These metabolites may represent a novel class of biomarkers reflecting both dietary exposure and host and/or gut microbiota characteristics providing a read-out that is differentially related to cardiometabolic risk.

Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake

BACKGROUND

Metabolomics has the potential to enhance dietary assessment by revealing objective measures of many aspects of human food intake. Although metabolomics studies indicate that hundreds of metabolites are associated with dietary intake, correlations have been modest (e.g., r < 0.50), and few have been evaluated in controlled feeding studies.

OBJECTIVES

The aim of this study was to evaluate associations between metabolites and weighed food and beverage intake in a controlled feeding study of habitual diet.

METHODS

Healthy postmenopausal females from the Women's Health Initiative (N = 153) were provided with a customized 2-wk controlled diet designed to emulate their usual diet. Metabolites were measured by liquid chromatography tandem mass spectrometry in end-of-study 24-h urine and fasting serum samples (1293 urine metabolites; 1113 serum metabolites). We calculated partial Pearson correlations between these metabolites and intake of 65 food groups, beverages, and supplements during the feeding study. The threshold for significance was Bonferroni-adjusted to account for multiple testing (5.94 × 10-07 for urine metabolites; 6.91 × 10-07 for serum metabolites).

RESULTS

Significant diet-metabolite correlations were identified for 23 distinct foods, beverages, and supplements (171 distinct metabolites). Among foods, strong metabolite correlations (r ≥ 0.60) were evident for citrus (highest r = 0.80), dairy (r = 0.65), and broccoli (r = 0.63). Among beverages and supplements, strong correlations were evident for coffee (r = 0.86), alcohol (r = 0.69), multivitamins (r = 0.69), and vitamin E supplements (r = 0.65). Moderate correlations (r = 0.50-0.60) were also observed for avocado, fish, garlic, grains, onion, poultry, and black tea. Correlations were specific; each metabolite correlated with one food, beverage, or supplement, except for metabolites correlated with juice or multivitamins.

CONCLUSIONS

Metabolite levels had moderate to strong correlations with weighed intake of habitually consumed foods, beverages, and supplements. These findings exceed in magnitude those previously observed in population studies and exemplify the strong potential of metabolomics to contribute to nutrition research. The Women's Health Initiative is registered at clinicaltrials.gov as NCT00000611.

Metabolomic Insights on Potassium Excretion, Blood Pressure, and Glucose Homeostasis: The African-PREDICT Study

BACKGROUND

Low-potassium intake is associated with a higher risk of type 2 diabetes and hypertension. Both conditions occur more frequently in Black populations, who also consume less potassium-rich foods.

OBJECTIVES

Using metabolomics to identify dysregulated metabolic pathways associated with low-potassium excretion may procure more accurate entry points for nutritional prevention and intervention for type 2 diabetes and hypertension.

METHODS

A total of 440 White and 350 Black adults from the African-PREDICT study (aged 20-30 y) were included. Twenty-four-hour blood pressure (BP) was measured. Potassium, sodium, and fasting glucose concentrations were analyzed in 24-h urine and plasma samples. Liquid chromatography-tandem mass spectrometry-based metabolomics included the analyses of amino acids and acylcarnitines in spot urine samples.

RESULTS

Black participants had lower urinary potassium concentrations than Whites (36.6 compared with 51.1 mmol/d; P < 0.001). In White but not Black adults, urinary potassium correlated positively with 2-aminoadipic acid (2-AAA) (r = 0.176), C3-[propionyl]carnitine (r = 0.137), C4-[butyryl]carnitine (r = 0.169) and C5-[isovaleryl]carnitine (r = 0.167) in unadjusted and 2-AAA (r = 0.158) and C4-carnitine (r = 0.160) in adjusted analyses (all P < 0.05 and q < 0.05). Elevated C0-, C3-, and C5-carnitine in turn were positively associated with systolic BP (Black and White groups), diastolic BP (Black group), and glucose (White group) (all P < 0.05).

CONCLUSIONS

Racial differences are an important consideration when investigating nutrient-metabolite relationships and the role thereof in cardiovascular disease. Only in White adults did urinary potassium associate with 2-AAA and short-chain acylcarnitines. These metabolites were positively related to BP and fasting plasma glucose concentrations. In White adults, the metabolomic profiles related to potassium excretion may contribute to BP regulation and glucose homeostasis. This trial was registered at clinicaltrials.gov as NCT03292094.

Metabolomics in Preclinical Drug Safety Assessment: Current Status and Future Trends

Metabolomics is emerging as a powerful systems biology approach for improving preclinical drug safety assessment. This review discusses current applications and future trends of metabolomics in toxicology and drug development. Metabolomics can elucidate adverse outcome pathways by detecting endogenous biochemical alterations underlying toxicity mechanisms. Furthermore, metabolomics enables better characterization of human environmental exposures and their influence on disease pathogenesis. Metabolomics approaches are being increasingly incorporated into toxicology studies and safety pharmacology evaluations to gain mechanistic insights and identify early biomarkers of toxicity. However, realizing the full potential of metabolomics in regulatory decision making requires a robust demonstration of reliability through quality assurance practices, reference materials, and interlaboratory studies. Overall, metabolomics shows great promise in strengthening the mechanistic understanding of toxicity, enhancing routine safety screening, and transforming exposure and risk assessment paradigms. Integration of metabolomics with computational, in vitro, and personalized medicine innovations will shape future applications in predictive toxicology.

The Role of the FODMAP Diet in IBS

The low FODMAP (fermentable oligosaccharide, disaccharide, monosaccharide, and polyol) diet is a beneficial therapeutic approach for patients with irritable bowel syndrome (IBS). However, how the low FODMAP diet works is still not completely understood. These mechanisms encompass not only traditionally known factors such as luminal distension induced by gas and water but also recent evidence on the role of FOMAPs in the modulation of visceral hypersensitivity, increases in intestinal permeability, the induction of microbiota changes, and the production of short-chain fatty acids (SCFAs), as well as metabolomics and alterations in motility. Although most of the supporting evidence is of low quality, recent trials have confirmed its effectiveness, even though the majority of the evidence pertains only to the restriction phase and its effectiveness in relieving abdominal bloating and pain. This review examines potential pathophysiological mechanisms and provides an overview of the existing evidence on the effectiveness of the low FODMAP diet across various IBS subtypes. Key considerations for its use include the challenges and disadvantages associated with its practical implementation, including the need for professional guidance, variations in individual responses, concerns related to microbiota, nutritional deficiencies, the development of constipation, the necessity of excluding an eating disorder before commencing the diet, and the scarcity of long-term data. Despite its recognized efficacy in symptom management, acknowledging these limitations becomes imperative for a nuanced comprehension of the role of a low FODMAP diet in managing IBS. By investigating its potential mechanisms and evidence across IBS subtypes and addressing emerging modulations alongside limitations, this review aims to serve as a valuable resource for healthcare practitioners, researchers, and patients navigating the intricate landscape of IBS.

Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease

BACKGROUND

Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD.

METHODS

The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models.

RESULTS

Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61-0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86-1.19; p = 0.817) over the follow-up period.

CONCLUSIONS

This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease.

TRIAL REGISTRATION

ISRCTN35739639.

Diet-microbiota associations in gastrointestinal research: a systematic review

Interactions between diet and gastrointestinal microbiota influence health status and outcomes. Evaluating these relationships requires accurate quantification of dietary variables relevant to microbial metabolism, however current dietary assessment methods focus on dietary components relevant to human digestion only. The aim of this study was to synthesize research on foods and nutrients that influence human gut microbiota and thereby identify knowledge gaps to inform dietary assessment advancements toward better understanding of diet-microbiota interactions. Thirty-eight systematic reviews and 106 primary studies reported on human diet-microbiota associations. Dietary factors altering colonic microbiota included dietary patterns, macronutrients, micronutrients, bioactive compounds, and food additives. Reported diet-microbiota associations were dominated by routinely analyzed nutrients, which are absorbed from the small intestine but analyzed for correlation to stool microbiota. Dietary derived microbiota-relevant nutrients are more challenging to quantify and underrepresented in included studies. This evidence synthesis highlights advancements needed, including opportunities for expansion of food composition databases to include microbiota-relevant data, particularly for human intervention studies. These advances in dietary assessment methodology will facilitate translation of microbiota-specific nutrition therapy to practice.

Exploring Human Metabolome after Wine Intake-A Review

Wine has a rich history dating back to 2200 BC, originally recognized for its medicinal properties. Today, with the aid of advanced technologies like metabolomics and sophisticated analytical techniques, we have gained remarkable insights into the molecular-level changes induced by wine consumption in the human organism. This review embarks on a comprehensive exploration of the alterations in human metabolome associated with wine consumption. A great number of 51 studies from the last 25 years were reviewed; these studies systematically investigated shifts in metabolic profiles within blood, urine, and feces samples, encompassing both short-term and long-term studies of the consumption of wine and wine derivatives. Significant metabolic alterations were observed in a wide variety of metabolites belonging to different compound classes, such as phenolic compounds, lipids, organic acids, and amino acids, among others. Within these classes, both endogenous metabolites as well as diet-related metabolites that exhibited up-regulation or down-regulation following wine consumption were included. The up-regulation of short-chain fatty acids and the down-regulation of sphingomyelins after wine intake, as well as the up-regulation of gut microbial fermentation metabolites like vanillic and syringic acid are some of the most important findings reported in the reviewed literature. Our results confirm the intact passage of certain wine compounds, such as tartaric acid and other wine acids, to the human organism. In an era where the health effects of wine consumption are of growing interest, this review offers a holistic perspective on the metabolic underpinnings of this centuries-old tradition.

Dietary Assessment and Metabolomic Methodologies in Human Feeding Studies: A Scoping Review

Dietary metabolomics is a relatively objective approach to identifying new biomarkers of dietary intake and for use alongside traditional methods. However, methods used across dietary feeding studies vary, thus making it challenging to compare results. The objective of this study was to synthesize methodological components of controlled human feeding studies designed to quantify the diet-related metabolome in biospecimens, including plasma, serum, and urine after dietary interventions. Six electronic databases were searched. Included studies were as follows: 1) conducted in healthy adults; 2) intervention studies; 3) feeding studies focusing on dietary patterns; and 4) measured the dietary metabolome. From 12,425 texts, 50 met all inclusion criteria. Interventions were primarily crossover (n = 25) and parallel randomized controlled trials (n = 22), with between 8 and 395 participants. Seventeen different dietary patterns were tested, with the most common being the "High versus Low-Glycemic Index/Load" pattern (n = 11) and "Typical Country Intake" (n = 11); with 32 providing all or the majority (90%) of food, 16 providing some food, and 2 providing no food. Metabolites were identified in urine (n = 31) and plasma/serum (n = 30). Metabolites were quantified using liquid chromatography, mass spectroscopy (n = 31) and used untargeted metabolomics (n = 37). There was extensive variability in the methods used in controlled human feeding studies examining the metabolome, including dietary patterns tested, biospecimen sample collection, and metabolomic analysis techniques. To improve the comparability and reproducibility of controlled human feeding studies examining the metabolome, it is important to provide detailed information about the dietary interventions being tested, including information about included or restricted foods, food groups, and meal plans provided. Strategies to control for individual variability, such as a crossover study design, statistical adjustment methods, dietary-controlled run-in periods, or providing standardized meals or test foods throughout the study should also be considered. The protocol for this review has been registered at Open Science Framework (https://doi.org/10.17605/OSF.IO/DAHGS).

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

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.

The Microbiome, Epigenome, and Diet in Adults with Obesity during Behavioral Weight Loss

Obesity has been linked to the gut microbiome, epigenome, and diet, yet these factors have not been studied together during obesity treatment. Our objective was to evaluate associations among gut microbiota (MB), DNA methylation (DNAme), and diet prior to and during a behavioral weight loss intervention. Adults (n = 47, age 40.9 ± 9.7 years, body mass index (BMI) 33.5 ± 4.5 kg/m2, 77% female) with data collected at baseline (BL) and 3 months (3 m) were included. Fecal MB was assessed via 16S sequencing and whole blood DNAme via the Infinium EPIC array. Food group and nutrient intakes and Healthy Eating Index (HEI) scores were calculated from 7-day diet records. Linear models were used to test for the effect of taxa relative abundance on DNAme and diet cross-sectionally at each time point, adjusting for confounders and a false discovery rate of 5%. Mean weight loss was 6.2 ± 3.9% at 3 m. At BL, one MB taxon, Ruminiclostridium, was associated with DNAme of the genes COL20A1 (r = 0.651, p = 0.029), COL18A1 (r = 0.578, p = 0.044), and NT5E (r = 0.365, p = 0.043). At 3 m, there were 14 unique MB:DNAme associations, such as Akkermansia with DNAme of GUSB (r = -0.585, p = 0.003), CRYL1 (r = -0.419, p = 0.007), C9 (r = -0.439, p = 0.019), and GMDS (r = -0.559, p = 0.046). Among taxa associated with DNAme, no significant relationships were seen with dietary intakes of relevant nutrients, food groups, or HEI scores. Our findings indicate that microbes linked to mucin degradation, short-chain fatty acid production, and body weight are associated with DNAme of phenotypically relevant genes. These relationships offer an initial understanding of the possible routes by which alterations in gut MB may influence metabolism during weight loss.

The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC-TOF MS metabolome samples

Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the University of California at Davis has collated GC/MS metabolomics data from over 156,000 samples and 2000 studies into the standardized BinBase database. We believe that the observations from these samples will provide meaningful insight to biologists and that our data treatment and webtool will provide insight to others who seek to standardize disparate metabolomics studies. We here developed an easy-to-use query interface, BinDiscover, to enable intuitive, rapid hypothesis generation for biologists based on these metabolomic samples. BinDiscover creates observation summaries and graphics across a broad range of species, organs, diseases, and compounds. Throughout the components of BinDiscover, we emphasize the use of ontologies to aggregate large groups of samples based on the proximity of their metadata within these ontologies. This adjacency allows for the simultaneous exploration of entire categories such as "rodents", "digestive tract", or "amino acids". The ontologies are particularly relevant for BinDiscover's ontologically grouped differential analysis, which, like other components of BinDiscover, creates clear graphs and summary statistics across compounds and biological metadata. We exemplify BinDiscover's extensive applicability in three showcases across biological domains.

Food matrix-flavonoid interactions and their effect on bioavailability

Flavonoid compounds exhibit a wide range of health benefits as plant-derived dietary components. Typically, co-consumed with the food matrix,they must be released from the matrix and converted into an absorbable form (bioaccessibility) before reaching the small intestine, where they are eventually absorbed and transferred into the bloodstream (bioavailability) to exert their biological activity. However, a large number of studies have revealed the biological functions of individual flavonoid compounds in different experimental models, ignoring the more complex but common relationships established in the diet. Besides, it has been appreciated that the gut microbiome plays a crucial role in the metabolism of flavonoids and food substrates, thereby having a significant impact on their interactions, but much progress still needs to be made in this area. Therefore, this review intends to comprehensively investigate the interactions between flavonoids and food matrices, including lipids, proteins, carbohydrates and minerals, and their effects on the nutritional properties of food matrices and the bioaccessibility and bioavailability of flavonoid compounds. Furthermore, the health effects of the interaction of flavonoid compounds with the gut microbiome have also been discussed.HIGHLIGHTSFlavonoids are able to bind to nutrients in the food matrix through covalent or non-covalent bonds.Flavonoids affect the digestion and absorption of lipids, proteins, carbohydrates and minerals in the food matrix (bioaccessibility).Lipids, proteins and carbohydrates may favorably affect the bioavailability of flavonoids.Improved intestinal flora may improve flavonoid bioavailability.

Nutritional Metabolomics in Diet-Breast Cancer Relations: Current Research, Challenges, and Future Directions-A Review

Breast cancer is one of the most common types of cancer in women worldwide, and its incidence is increasing. Diet has been identified as a modifiable risk factor for breast cancer, but the complex interplay between diet, metabolism, and cancer development is not fully understood. Nutritional metabolomics is a rapidly evolving field that can provide insights into the metabolic changes associated with dietary factors and their impact on breast cancer risk. The review's objective is to provide a comprehensive overview of the current research on the application of nutritional metabolomics in understanding the relationship between diet and breast cancer. The search strategy involved querying several electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. The search terms included combinations of relevant keywords such as "nutritional metabolomics", "diet", "breast cancer", "metabolites", and "biomarkers". In this review, both in vivo and in vitro studies were included, and we summarize the current state of knowledge on the role of nutritional metabolomics in understanding the diet-breast cancer relationship, including identifying specific metabolites and metabolic pathways associated with breast cancer risk. We also discuss the challenges associated with nutritional metabolomics research, including standardization of analytical methods, interpretation of complex data, and integration of multiple-omics approaches. Finally, we highlight future directions for nutritional metabolomics research in studying diet-breast cancer relations, including investigating the role of gut microbiota and integrating multiple-omics approaches. The application of nutritional metabolomics in the study of diet-breast cancer relations, including 2-amino-4-cyano butanoic acid, piperine, caprate, rosten-3β,17β-diol-monosulfate, and γ-carboxyethyl hydrochroman, among others, holds great promise for advancing our understanding of the role of diet in breast cancer development and identifying personalized dietary recommendations for breast cancer prevention, control, and treatment.

Human blood plasma biomarkers of diet and weight loss among centrally obese subjects in a New Nordic Diet intervention

Scope

The New Nordic Diet (NND) has been shown to promote weight loss and lower blood pressure amongst obese people. This study investigates blood plasma metabolite and lipoprotein biomarkers differentiating subjects who followed Average Danish Diet (ADD) or NND. The study also evaluates how the individual response to the diet is reflected in the metabolic differences between NND subjects who lost or maintained their pre-intervention weight.

Methods

Centrally obese Danes (BMI >25) followed NND (90 subjects) or ADD (56 subjects) for 6 months. Fasting blood plasma samples, collected at three time-points during the intervention, were screened for metabolites and lipoproteins (LPs) using proton nuclear magnetic resonance spectroscopy. In total, 154 metabolites and 65 lipoproteins were analysed.

Results

The NND showed a relatively small but significant effect on the plasma metabolome and lipoprotein profiles, with explained variations ranging from 0.6% for lipoproteins to 4.8% for metabolites. A total of 38 metabolites and 11 lipoproteins were found to be affected by the NND. The primary biomarkers differentiating the two diets were found to be HDL-1 cholesterol, apolipoprotein A1, phospholipids, and ketone bodies (3-hydroxybutyric acid, acetone, and acetoacetic acid). The increased levels of ketone bodies detected in the NND group inversely associated with the decrease in diastolic blood pressure of the NND subjects. The study also showed that body weight loss among the NND subjects was weakly associated with plasma levels of citrate.

Conclusion

The main plasma metabolites associated with NND were acetate, methanol and 3-hydroxybutyrate. The metabolic changes associated with the NND-driven weight loss are mostly pronounced in energy and lipid metabolism.

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.

Nutritional Epidemiology and Dietary Assessment for Patients With Kidney Disease: A Primer

Nutritional epidemiology seeks to understand nutritional determinants of disease in human populations using experimental and observational study designs. Though randomized controlled trials provide the strongest evidence of causality, the expense and difficulty of sustaining adherence to dietary interventions are substantial barriers to investigating dietary determinants of kidney disease. Therefore, nutritional epidemiology commonly employs observational study designs, particularly prospective cohort studies, to investigate long-term associations between dietary exposures and kidney disease. Due to the covarying nature and synergistic effects of dietary components, holistic characterizations of dietary exposures that simultaneously consider patterns of foods and nutrients regularly consumed are generally more relevant to disease etiology than single nutrients or foods. Dietary intakes have traditionally been self-reported and are subject to bias. Statistical methods including energy adjustment and regression calibration can reduce random and systematic measurement errors associated with self-reported diet. Novel approaches that assess diet more objectively are gaining popularity but have not yet fully replaced self-report and require refinement and validation in populations with chronic kidney disease. More accurate and frequent diet assessment in existing and future studies will yield evidence to better personalize dietary recommendations for the prevention and treatment of kidney disease.

Systemic Metabolomics in a Framework of Genetics and Lifestyle in Age-Related Macular Degeneration

Insights into the pathogenesis of age-related macular degeneration (AMD), a leading cause of blindness, point towards a complex interplay of genetic and lifestyle factors triggering various systemic pathways. This study aimed to characterize metabolomic profiles for AMD and to evaluate their position in the trias with genetics and lifestyle. This study included 5923 individuals from five European studies. Blood metabolomics were assessed using a nuclear magnetic resonance platform of 146 metabolites. Associations were studied using regression analyses. A genetic risk score (GRS) was calculated using β-values of 49 AMD variants, a lifestyle risk score (LRS) using smoking and diet data, and a metabolite risk score (MRS) using metabolite values. We identified 61 metabolites associated with early-intermediate AMD, of which 94% were lipid-related, with higher levels of HDL-subparticles and apolipoprotein-A1, and lower levels of VLDL-subparticles, triglycerides, and fatty acids (false discovery rate (FDR) p-value < 1.4 × 10^-2). Late AMD was associated with lower levels of the amino acids histidine, leucine, valine, tyrosine, and phenylalanine, and higher levels of the ketone bodies acetoacetate and 3-hydroxybutyrate (FDR p-value < 1.5 × 10^-3). A favorable lifestyle characterized by a healthy diet was associated with higher levels of amino acids and lower levels of ketone bodies, while an unfavorable lifestyle, including smoking, showed opposite effects (FDR p-value < 2.7 × 10^-2). The MRS mediated 5% of the effect of the GRS and 20% of that of the LRS on late AMD. Our findings show that metabolomic profiles differ between AMD stages and show that blood metabolites mostly reflect lifestyle. The severity-specific profiles spur further interest into the systemic effects related to disease conversion.

Translation of nutrigenomic research for personalised and precision nutrition for cancer prevention and for cancer survivors

Personalised and precision nutrition uses information on individual characteristics and responses to nutrients, foods and dietary patterns to develop targeted nutritional advice that is more effective in improving the diet and health of each individual. Moving away from the conventional 'one size fits all', such targeted intervention approaches may pave the way to better population health, including lower burden of non-communicable diseases. To date, most personalised and precision nutrition approaches have been focussed on tackling obesity and cardiometabolic diseases with limited efforts directed to cancer prevention and for cancer survivors. Advances in understanding the biological basis of cancer and of the role played by diet in cancer prevention and in survival after cancer diagnosis, mean that it is timely to test and to apply such personalised and precision nutrition approaches in the cancer area. This endeavour can take advantage of the enhanced understanding of interactions between dietary factors, individual genotype and the gut microbiome that impact on risk of, and survival after, cancer diagnosis. Translation of these basic research into public health action should include real-time acquisition of nutrigenomic and related data and use of AI-based data integration methods in systems approaches that can be scaled up using mobile devices.

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.

Enhancing Capacity for Food and Nutrient Intake Assessment in Population Sciences Research

Nutrition influences health throughout the life course. Good nutrition increases the probability of good pregnancy outcomes, proper childhood development, and healthy aging, and it lowers the probability of developing common diet-related chronic diseases, including obesity, cardiovascular disease, cancer, and type 2 diabetes. Despite the importance of diet and health, studying these exposures is among the most challenging in population sciences research. US and global food supplies are complex; eating patterns have shifted such that half of meals are eaten away from home, and there are thousands of food ingredients with myriad combinations. These complexities make dietary assessment and links to health challenging both for population sciences research and for public health policy and practice. Furthermore, most studies evaluating nutrition and health usually rely on self-report instruments prone to random and systematic measurement error. Scientific advances involve developing nutritional biomarkers and then applying these biomarkers as stand-alone nutritional exposures or for calibrating self-reports using specialized statistics.

A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota

A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others. In recent years, continuous glucose monitoring has made it possible to establish predictions on the effect of different dietary foods on PPGRs through machine learning methods, which use algorithms that integrate genetic, biochemical, physiological and gut microbiota variables for identifying associations between them and clinical variables with aim of personalize dietary recommendations. This has allowed to improve the concept of personalized nutrition, since it is now possible to recommend through these predictions specific dietary foods to prevent elevated PPGRs that are highly variable among individuals. Additional components that can enrich the predictive algorithms are findings of nutrigenomics, nutrigenetics and metabolomics. Thus, this review aims to summarize the evidence of the components that integrate personalized nutrition focused on the prevention of PPGRs, and to show the future of personalized nutrition by laying the groundwork for the development of individualized dietary management and its impact on the improvement of metabolic diseases.

Metabolomic biomarkers of the mediterranean diet in pregnant individuals: A prospective study

BACKGROUND AND AIMS

Metabolomic profiling is a systematic approach to identifying biomarkers for dietary patterns. Yet, metabolomic markers for dietary patterns in pregnant individuals have not been investigated. The aim of this study was to identify plasma metabolomic markers and metabolite panels that are associated with the Mediterranean diet in pregnant individuals.

METHODS

This is a prospective study of 186 pregnant individuals who had both dietary intake and metabolomic profiles measured from the Fetal Growth Studies-Singletons cohort. Dietary intakes during the peri-conception/1st trimester and the second trimester were accessed at 8-13 and 16-22 weeks of gestation, respectively. Adherence to the Mediterranean diet was measured by the alternate Mediterranean Diet (aMED) score. Fasting plasma samples were collected at 16-22 weeks and untargeted metabolomics profiling was performed using the mass spectrometry-based platforms. Metabolites individually or jointly associated with aMED scores were identified using linear regression and least absolute shrinkage and selection operator (LASSO) regression models with adjustment for potential confounders, respectively.

RESULTS

Among 459 annotated metabolites, 64 and 41 were individually associated with the aMED scores of the diet during the peri-conception/1st trimester and during the second trimester, respectively. Fourteen metabolites were associated with the Mediterranean diet in both time windows. Most Mediterranean diet-related metabolites were lipids (e.g., acylcarnitine, cholesteryl esters (CEs), linoleic acid, long-chain triglycerides (TGs), and phosphatidylcholines (PCs), amino acids, and sugar alcohols. LASSO regressions also identified a 10 metabolite-panel that were jointly associated with aMED score of the diet during the peri-conception/1st trimester (AUC: 0.74; 95% CI: 0.57, 0.91) and a 3 metabolites-panel in the 2nd trimester (AUC: 0.68; 95% CI: 0.50, 0.86).

CONCLUSION

We identified plasma metabolomic markers for the Mediterranean diet among pregnant individuals. Some of them have also been reported in previous studies among non-pregnant populations, whereas others are novel. The results from our study warrant replication in pregnant individuals by future studies.

CLINICAL TRIAL REGISTRATION NUMBER

This study was registered at ClinicalTrials.gov.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

A Comprehensive Evaluation of Data Quality in Nutrient Databases

Nutrient databases are a critical component of nutrition science and the basis of exciting new research in precision nutrition (PN). To identify the most critical components needed for improvement of nutrient databases, food composition data were analyzed for quality, with completeness being the most important measure, and for FAIRness, how well the data conformed with the data science criteria of findable, accessible, interoperable, and reusable (FAIR). Databases were judged complete if they provided data for all 15 nutrition fact panel (NFP) nutrient measures and all 40 National Academies of Sciences, Engineering, and Medicine (NASEM) essential nutrient measures for each food listed. Using the gold standard the USDA standard reference (SR) Legacy database as surrogate, it was found that SR Legacy data were not complete for either NFP or NASEM nutrient measures. In addition, phytonutrient measures in the 4 USDA Special Interest Databases were incomplete. To evaluate data FAIRness, a set of 175 food and nutrient data sources were collected from worldwide. Many opportunities were identified for improving data FAIRness, including creating persistent URLs, prioritizing usable data storage formats, providing Globally Unique Identifiers for all foods and nutrients, and implementing citation standards. This review demonstrates that despite important contributions from the USDA and others, food and nutrient databases in their current forms do not yet provide truly comprehensive food composition data. We propose that to enhance the quality and usage of food and nutrient composition data for research scientists and those fashioning various PN tools, the field of nutrition science must step out of its historical comfort zone and improve the foundational nutrient databases used in research by incorporating data science principles, the most central being data quality and data FAIRness.

Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults

BACKGROUND

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.

OBJECTIVES

We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake.

METHODS

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).

RESULTS

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.

CONCLUSIONS

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.

Untargeted Fecal Metabolomic Analyses across an Industrialization Gradient Reveal Shared Metabolites and Impact of Industrialization on Fecal Microbiome-Metabolome Interactions

The metabolome is a central determinant of human phenotypes and includes the plethora of small molecules produced by host and microbiome or taken up from exogenous sources. However, studies of the metabolome have so far focused predominantly on urban, industrialized populations. Through an untargeted metabolomic analysis of 90 fecal samples from human individuals from Africa and the Americas-the birthplace and the last continental expansion of our species, respectively-we characterized a shared human fecal metabolome. The majority of detected metabolite features were ubiquitous across populations, despite any geographic, dietary, or behavioral differences. Such shared metabolite features included hyocholic acid and cholesterol. However, any characterization of the shared human fecal metabolome is insufficient without exploring the influence of industrialization. Here, we show chemical differences along an industrialization gradient, where the degree of industrialization correlates with metabolomic changes. We identified differential metabolite features such as amino acid-conjugated bile acids and urobilin as major metabolic correlates of these behavioral shifts. Additionally, coanalyses with over 5,000 publicly available human fecal samples and cooccurrence probability analyses with the gut microbiome highlight connections between the human fecal metabolome and gut microbiome. Our results indicate that industrialization significantly influences the human fecal metabolome, but diverse human lifestyles and behavior still maintain a shared human fecal metabolome. This study represents the first characterization of the shared human fecal metabolome through untargeted analyses of populations along an industrialization gradient. IMPORTANCE As the world becomes increasingly industrialized, understanding the biological consequences of these lifestyle shifts and what it means for past, present, and future human health is critical. Indeed, industrialization is associated with rises in allergic and autoimmune health conditions and reduced microbial diversity. Exploring these health effects on a chemical level requires consideration of human lifestyle diversity, but understanding the significance of any differences also requires knowledge of what molecular components are shared between human groups. Our study reveals the key chemistry of the human gut as defined by varied industrialization-based differences and ubiquitous shared features. Ultimately, these novel findings extend our knowledge of human molecular biology, especially as it is influenced by lifestyle and behavior, and provide steps toward understanding how human biology has changed over our species' history.

Unraveling Pathophysiology of Takotsubo Syndrome: The Emerging Role of the Oxidative Stress's Systemic Status

Takotsubo Syndrome (TTS) is usually triggered by emotional or physical stressors, thus suggesting that an increased sympathetic activity, leading to myocardial perfusion abnormalities and ventricular dysfunction, plays a major pathogenetic role. However, it remains to be elucidated why severe emotional and physical stress might trigger TTS in certain individuals but not others. Clinical research has been focused mainly on mechanisms underlying the activation of the sympathetic nervous system and the occurrence of myocardial ischemia in TTS. However, scientific evidence shows that additional factors might play a pathophysiologic role in the condition's occurrence. In this regard, a significant contribution arrived from metabolomics studies that followed the systemic response to TTS. Specifically, preliminary data clearly show that there is an interplay between inflammation, genetics, and oxidative status which might explain susceptibility to the condition. This review aims to sum up the established pathogenetic factors underlying TTS and to appraise emerging mechanisms, with particular emphasis on oxidative status, which might better explain susceptibility to the condition.

Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations

BACKGROUND

Ion mobility (IM) separation capabilities are now widely available to researchers through several commercial vendors and are now being adopted into many metabolomics workflows. The added peak capacity that ion mobility offers with minimal compromise to other analytical figures-of-merit has provided real benefits to sensitivity and structural selectivity and have allowed more specific metabolite annotations to be assigned in untargeted workflows. One of the greatest promises of contemporary IM-enabled instrumentation is the capability of operating multiple analytical dimensions inline with minimal sample volumes, which has the potential to address many grand challenges currently faced in the omics fields. However, comprehensive operation of multidimensional mass spectrometry comes with its own inherent challenges that, beyond operational complexity, may not be immediately obvious to practitioners of these techniques.

AIM OF REVIEW

In this review, we outline the strengths and considerations for incorporating IM analysis in metabolomics workflows and provide a critical but forward-looking perspective on the contemporary challenges and prospects associated with interpreting IM data into chemical knowledge.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We outline a strategy for unifying IM-derived collision cross section (CCS) measurements obtained from different IM techniques and discuss the emerging field of high resolution ion mobility (HRIM) that is poised to address many of the contemporary challenges associated with ion mobility metabolomics. Whereas the LC step limits the throughput of comprehensive LC-IM-MS, the higher peak capacity of HRIM can allow fast LC gradients or rapid sample cleanup via solid-phase extraction (SPE) to be utilized, significantly improving the sample throughput.

Biochemistry of Antioxidants: Mechanisms and Pharmaceutical Applications

Natural antioxidants from fruits and vegetables, meats, eggs and fish protect cells from the damage caused by free radicals. They are widely used to reduce food loss and waste, minimizing lipid oxidation, as well as for their effects on health through pharmaceutical preparations. In fact, the use of natural antioxidants is among the main efforts made to relieve the pressure on natural resources and to move towards more sustainable food and pharmaceutical systems. Alternative food waste management approaches include the valorization of by-products as a source of phenolic compounds for functional food formulations. In this review, we will deal with the chemistry of antioxidants, including their molecular structures and reaction mechanisms. The biochemical aspects will also be reviewed, including the effects of acidity and temperature on their partitioning in binary and multiphasic systems. The poor bioavailability of antioxidants remains a huge constraint for clinical applications, and we will briefly describe some delivery systems that provide for enhanced pharmacological action of antioxidants via drug targeting and increased bioavailability. The pharmacological activity of antioxidants can be improved by designing nanotechnology-based formulations, and recent nanoformulations include nanoparticles, polymeric micelles, liposomes/proliposomes, phytosomes and solid lipid nanoparticles, all showing promising outcomes in improving the efficiency and bioavailability of antioxidants. Finally, an overview of the pharmacological effects, therapeutic properties and future choice of antioxidants will be incorporated.

Metabolic profiles of ultra-processed food consumption and their role in obesity risk in British children

BACKGROUND & AIMS

Higher consumption of ultra-processed foods (UPF) has been associated with childhood obesity, but underlying mechanisms remain unclear. We investigated plasma nuclear magnetic resonance metabolic profiles of higher UPF consumption and their role in obesity risk in the British ALSPAC cohort.

METHODS

We performed cross-sectional and prospective metabolome wide association analyses of UPF, calculated from food diaries using the NOVA classification. In cross-sectional analysis, we tested the association between UPF consumption and metabolic profile at 7 years (N = 4528), and in the prospective analysis we tested the association between UPF consumption at 13 years and metabolic profile at 17 years (N = 3086). Effects of UPF-associated metabolites at 7 years on subsequent fat mass accumulation were assessed using growth curve models.

RESULTS

At 7 years, UPF was associated with 115 metabolic traits including lower levels of branched-chain and aromatic amino acids and higher levels of citrate, glutamine, and monounsaturated fatty acids, which were also associated with greater fat mass accumulation. Reported intake of nutrients mediated associations with most metabolites, except for citrate.

CONCLUSIONS

UPF consumption among British children is associated with perturbation of multiple metabolic traits, many of which contribute to child obesity risk.