Nutritional Metabolomics and the Classification of Dietary Biomarker Candidates: A Critical Review

Workplace food purchases, dietary intake, and gut microbial metabolites in a secondary analysis of the ChooseWell 365 study

BACKGROUND

Dietary choices can affect human health through alterations in gut microbial metabolism, and gut microbial metabolites could serve as biomarkers for disease risk conferred by dietary intake. However, self-reported dietary intake may not reflect true intake.

OBJECTIVES

We identified circulating metabolites, including gut microbiome-related metabolites, associated with adherence to a healthy diet in the ChooseWell 365 study. In this randomized clinical trial, the dietary choices of hospital employees were assessed over 24 mo using not only 24-h dietary recalls but also electronic records of hospital cafeteria purchases.

METHODS

Plasma metabolites were profiled from 470 participants. Two targeted metabolomics methods were developed and implemented to expand detection coverage for metabolites related to gut microbial activity. Linear regression models were used to associate metabolites with Healthy Purchasing Scores (HPSs) derived from cafeteria purchases and Healthy Eating Index-2015 (HEI-15) scores derived from dietary recalls.

RESULTS

Fourteen metabolites were concordantly associated with the HPS and HEI-15 scores in multivariable models adjusted for age, gender, and race, including the gut microbiome-related metabolites indole-3-propionic acid (HPS, β: 0.16, 95% CI: 0.07, 0.26, P = 7.32 × 10-4; HEI-15, β: 0.16, 95% CI: 0.07, 0.25, P = 6.79 × 10-4), hippuric acid (HPS, β: 0.11, 95% CI: 0.02, 0.21, P = 1.97 × 10-2; HEI-15, β: 0.10, 95% CI: 0.01, 0.19, P = 3.14 × 10-2), and indoxyl sulfate (HPS, β = -0.13, 95% CI: -0.23, -0.03, P = 8.21 × 10-3; HEI-15, β: -0.12, 95% CI: -0.22, -0.03, P = 8.50 × 10-3). These gut microbial metabolites were associated with the intake of specific food groups, such as whole fruits. These metabolites were also associated with clinical variables, including blood pressure, diabetes or prediabetes, and body mass index.

CONCLUSIONS

In a secondary analysis of the ChooseWell 365 study, associations between circulating gut microbiome-related metabolites and a healthy diet were confirmed using both objective and subjective measures of consumption. Accurate identification of diet-associated metabolites may help guide dietary or microbiome-based interventions aimed at disease prevention.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

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.

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.

Effects of Zinc Supplementation on Metabolomic Profiles in Tanzanian Infants: A Randomized Trial

BACKGROUND

Provision of zinc supplementation to young children has been associated with reduced infectious morbidity and better growth outcomes. However, the metabolic pathways underlying these outcomes are unclear, and metabolomic data from humans undergoing zinc supplementation, particularly infants, are generally lacking.

OBJECTIVES

This study aimed to examine the effect of zinc supplementation on metabolic profiles in Tanzanian infants aged 6 wk and 6 mo.

METHODS

Blood samples were collected at age 6 wk and 6 mo from 50 Tanzanian infants who were enrolled in a randomized placebo-controlled trial of zinc supplementation (5 mg oral daily). Metabolomic analysis using an ultrahigh-performance liquid chromatography/tandem mass spectroscopy platform was performed to identify potential metabolomic profiles and biomarkers associated with zinc supplementation. Principal component analysis (PCA) was used to summarize metabolomic data from all samples. Two-way repeated measures analysis of variance with compound symmetry covariance structures were used to compare metabolome levels over time between infants in the 2 treatment arms.

RESULTS

In PCA, the samples tended to be more separated by child age (6 wk compared with 6 mo) than by zinc supplementation status. We found that zinc supplementation affected a variety of metabolites associated with amino acid, lipid, nucleotide, and xenobiotic metabolism, including indoleacetate in the tryptophan metabolism pathway; 3-methoxytrosine and 4-hydrxoyphenylphruvate in the tyrosine pathway; eicosanedioate, 2-aminooctanoate, and N-acetyl-2-aminooctanoate in the fatty acid pathway; and N6-succinyladenosine in the purine metabolism pathway. Compared to the relatively small number of metabolites associated with zinc supplements, many infant metabolites changed significantly from age 6 wk to 6 mo.

CONCLUSIONS

Zinc supplementation, despite having overall clinical benefits, appears to induce limited metabolomic changes in blood metabolites in young infants. Future larger studies may be warranted to further examine metabolic pathways associated with zinc supplementation. The parent trial was registered at clinicaltrials.gov as NCT00421668.

Dietary patterns, untargeted metabolite profiles and their association with colorectal cancer risk

We investigated data-driven and hypothesis-driven dietary patterns and their association to plasma metabolite profiles and subsequent colorectal cancer (CRC) risk in 680 CRC cases and individually matched controls. Dietary patterns were identified from combined exploratory/confirmatory factor analysis. We assessed association to LC-MS metabolic profiles by random forest regression and to CRC risk by multivariable conditional logistic regression. Principal component analysis was used on metabolite features selected to reflect dietary exposures. Component scores were associated to CRC risk and dietary exposures using partial Spearman correlation. We identified 12 data-driven dietary patterns, of which a breakfast food pattern showed an inverse association with CRC risk (OR per standard deviation increase 0.89, 95% CI 0.80-1.00, p = 0.04). This pattern was also inversely associated with risk of distal colon cancer (0.75, 0.61-0.96, p = 0.01) and was more pronounced in women (0.69, 0.49-0.96, p = 0.03). Associations between meat, fast-food, fruit soup/rice patterns and CRC risk were modified by tumor location in women. Alcohol as well as fruit and vegetables associated with metabolite profiles (Q2 0.22 and 0.26, respectively). One metabolite reflecting alcohol intake associated with increased CRC risk, whereas three metabolites reflecting fiber, wholegrain, and fruit and vegetables associated with decreased CRC risk.

Nutritional metabolomics: Recent developments and future needs

Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.

Lipidomic studies reveal two specific circulating phosphatidylcholines as surrogate biomarkers of the omega-3 index

Optimal dietary intake of omega-3 long-chain polyunsaturated fatty acids (n3-LCPUFAs) is critical to human health across the lifespan. However, omega-3 index (O3I) determination is not routinely assessed due to complicated procedures for n3-LCPUFA analysis from the phospholipid (PL) fraction of erythrocytes. Herein, a high-throughput method for lipidomics based on multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry was applied to identify circulating PLs as surrogate biomarkers of O3I in two randomized placebo-controlled trials. An untargeted lipidomic data workflow using a subgroup analysis of serum extracts from sunflower oil versus high-dose fish oil (FO)-supplemented participants revealed that ingested n3-LCPUFAs were primarily distributed as their phosphatidylcholines (PCs) relative to other PL classes. In both high-dose FO (5.0 g/day) and EPA-only trials (3.0 g/day), PC (16:0_20:5) was the most responsive PL, whereas PC (16:0_22:6) was selective to DHA-only supplementation. We also demonstrated that the sum concentration of both these PCs in fasting serum or plasma samples was positively correlated to the O3I following FO (r = 0.708, P = 1.02 × 10-11, n = 69) and EPA- or DHA-only supplementation (r = 0.768, P = 1.01 × 10-33, n = 167). Overall, DHA was more effective in improving the O3I (ΔO3I = 4.90 ± 1.33%) compared to EPA (ΔO3I = 2.99 ± 1.19%) in young Canadian adults who had a poor nutritional status with an O3I (3.50 ± 0.68%) at baseline. Our method enables the rapid assessment of the O3I by directly measuring two circulating PC species in small volumes of blood, which may facilitate screening applications for population and precision health.

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).

Urinary Metabolite Profiling to Non-Invasively Monitor the Omega-3 Index: An Exploratory Secondary Analysis of a Randomized Clinical Trial in Young Adults

The Omega-3 Index (O3I) reflects eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content in erythrocytes. While the O3I is associated with numerous health outcomes, its widespread use is limited. We investigated whether urinary metabolites could be used to non-invasively monitor the O3I in an exploratory analysis of a previous placebo-controlled, parallel arm randomized clinical trial in males and females (n = 88) who consumed either ~3 g/d olive oil (OO; control), EPA, or DHA for 12 weeks. Fasted blood and first-void urine samples were collected at baseline and following supplementation, and they were analyzed via gas chromatography and multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS), respectively. We tentatively identified S-carboxypropylcysteamine (CPCA) as a novel urinary biomarker reflecting O3I status, which increased following both EPA and DHA (p < 0.001), but not OO supplementation, and was positively correlated to the O3I (R = 0.30, p < 0.001). Additionally, an unknown dianion increased following DHA supplementation, but not EPA or OO. In ROC curve analyses, CPCA outperformed all other urinary metabolites in distinguishing both between OO and EPA or DHA supplementation groups (AUC > 80.0%), whereas the unknown dianion performed best in discriminating OO from DHA alone (AUC = 93.6%). Candidate urinary biomarkers of the O3I were identified that lay the foundation for a non-invasive assessment of omega-3 status.

S-methyl cysteine sulfoxide and its potential role in human health: a scoping review

Higher intakes of cruciferous and allium vegetables are associated with a lower risk of cardiometabolic-related outcomes in observational studies. Whilst acknowledging the many healthy compounds within these vegetables, animal studies indicate that some of these beneficial effects may be partially mediated by S-methyl cysteine sulfoxide (SMCSO), a sulfur-rich, non-protein, amino acid found almost exclusively within cruciferous and alliums. This scoping review explores evidence for SMCSO, its potential roles in human health and possible mechanistic action. After systematically searching several databases (EMBASE, MEDLINE, SCOPUS, CINAHL Plus Full Text, Agricultural Science), we identified 21 original research articles meeting our inclusion criteria. These were limited primarily to animal and in vitro models, with 14/21 (67%) indicating favorable anti-hyperglycemic, anti-hypercholesterolemic, and antioxidant properties. Potential mechanisms included increased bile acid and sterol excretion, altered glucose- and cholesterol-related enzymes, and improved hepatic and pancreatic β-cell function. Raising antioxidant defenses may help mitigate the oxidative damage observed in these pathologies. Anticancer and antibacterial effects were also explored, along with one steroidogenic study. SMCSO is frequently overlooked as a potential mediator to the benefits of sulfur-rich vegetables. More research into the health benefits of SMCSO, especially for cardiometabolic and inflammatory-based pathology, is warranted. Human studies are especially needed.

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.

Serum Metabolomic Markers of Dairy Consumption: Results from the Atherosclerosis Risk in Communities Study and the Bogalusa Heart Study

BACKGROUND

Dairy consumption is related to chronic disease risk; however, the measurement of dairy consumption has largely relied upon self-report. Untargeted metabolomics allows for the identification of objective markers of dietary intake.

OBJECTIVES

We aimed to identify associations between dietary dairy intake (total dairy, low-fat dairy, and high-fat dairy) and serum metabolites in 2 independent study populations of United States adults.

METHODS

Dietary intake was assessed with food frequency questionnaires. Multivariable linear regression models were used to estimate cross-sectional associations between dietary intake of dairy and 360 serum metabolites analyzed in 2 subgroups of the Atherosclerosis Risk in Communities study (ARIC; n = 3776). Results from the 2 subgroups were meta-analyzed using fixed effects meta-analysis. Significant meta-analyzed associations in the ARIC study were then tested in the Bogalusa Heart Study (BHS; n = 785).

RESULTS

In the ARIC study and BHS, the mean age was 54 and 48 years, 61% and 29% were Black, and the mean dairy intake was 1.7 and 1.3 servings/day, respectively. Twenty-nine significant associations between dietary intake of dairy and serum metabolites were identified in the ARIC study (total dairy, n = 14; low-fat dairy, n = 10; high-fat dairy, n = 5). Three associations were also significant in BHS: myristate (14:0) was associated with high-fat dairy, and pantothenate was associated with total dairy and low-fat dairy, but 23 of the 27 associations significant in the ARIC study and tested in BHS were not associated with dairy in BHS.

CONCLUSIONS

We identified metabolomic associations with dietary intake of dairy, including 3 associations found in 2 independent cohort studies. These results suggest that myristate (14:0) and pantothenate (vitamin B5) are candidate biomarkers of dairy consumption.

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.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

A Scoping Review of Nutritional Biomarkers Associated with Food Security

Food insecurity affects more than 40 million individuals in the United States and is linked to negative health outcomes due, in part, to poor dietary quality. Despite the emergence of metabolomics as a modality to objectively characterize nutritional biomarkers, it is unclear whether food security is associated with any biomarkers of dietary quality. This scoping review aims to summarize studies that examined associations between nutritional biomarkers and food security, as well as studies that investigated metabolomic differences between people with and without food insecurity. PubMed, Embase, Scopus, and AGRICOLA were searched through August 2022 for studies describing food insecurity and metabolic markers in blood, urine, plasma, hair, or nails. The 78 studies included consisted of targeted assays quantifying lipids, dietary nutrients, heavy metals, and environmental xenobiotics as biochemical features associated with food insecurity. Among those biomarkers which were quantified in at least five studies, none showed a consistent association with food insecurity. Although three biomarkers of dietary quality have been assessed between food-insecure versus food-secure populations, no studies have utilized untargeted metabolomics to characterize patterns of small molecules that distinguish between these two populations. Further studies are needed to characterize the dietary quality profiles of individuals with and without food insecurity.

Integrative multiomics analysis of infant gut microbiome and serum metabolome reveals key molecular biomarkers of early onset childhood obesity

Evidence supports a complex interplay of gut microbiome and host metabolism as regulators of obesity. The metabolic phenotype and microbial metabolism of host diet may also contribute to greater obesity risk in children early in life. This study aimed to identify features that discriminated overweight/obese from normal weight infants by integrating gut microbiome and serum metabolome profiles. This prospective analysis included 50 South Asian children living in Canada, selected from the SouTh Asian biRth cohorT (START). Serum metabolites were measured by multisegment injection-capillary electrophoresis-mass spectrometry and the relative abundance of bacterial 16S rRNA gene amplicon sequence variant was evaluated at 1 year. Cumulative body mass index (BMI_AUC) and skinfold thickness (SSF_AUC) scores were calculated from birth to 3 years as the total area under the growth curve (AUC). BMI_AUC and/or SSF_AUC >85th percentile was used to define overweight/obesity. Data Integration Analysis for Biomarker discovery using Latent cOmponent (DIABLO) was used to identify discriminant features associated with childhood overweight/obesity. The associations between identified features and anthropometric measures were examined using logistic regression. Circulating metabolites including glutamic acid, acetylcarnitine, carnitine, and threonine were positively, whereas γ-aminobutyric acid (GABA), symmetric dimethylarginine (SDMA), and asymmetric dimethylarginine (ADMA) were negatively associated with childhood overweight/obesity. The abundance of the Pseudobutyrivibrio and Lactobacillus genera were positively, and Clostridium sensu stricto 1 and Akkermansia were negatively associated with childhood overweight/obesity. Integrative analysis revealed that Akkermansia was positively whereas Lactobacillus was inversely correlated with GABA and SDMA, and Pseudobutyrivibrio was inversely correlated with GABA. This study provides insights into metabolic and microbial signatures which may regulate satiety, energy metabolism, inflammatory processes, and/or gut barrier function, and therefore, obesity trajectories in childhood. Understanding the functional capacity of these molecular features and potentially modifiable risk factors such as dietary exposures early in life may offer a novel approach for preventing childhood obesity.

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.

Identification of nutritional biomarkers through highly sensitive and chemoselective metabolomics

The importance of a healthy diet for humans is known for decades. The elucidation of key molecules responsible for the beneficial and adverse dietary effects is slowly developing as the tools are missing. Carbonyl-containing metabolites are a common bioproducts through conversion of diet by the microbiome. In here, we have utilized our recently developed mass spectrometric methodology based on chemoselective conjugation of carbonyl-metabolites. The method has been applied for urine sample analysis from a dietary (poly)phenol intervention study (N = 78 individuals) for the first time. We have identified a series of carbonyl-metabolites of dietary origin and the chemical structure was validated for 30 metabolites. Our sensitive analysis led to the discovery of four unknown dietary markers with high sensitivity and selectivity (AUC > 0.91). Our chemical metabolomics method has been successfully applied for large-scale analysis and provides the basis for targeted metabolomics to identify unknown nutritional and disease-related biomarkers.

Identification of Protein Biomarkers of the Dietary Approaches to Stop Hypertension Diet in Randomized Feeding Studies and Validation in an Observational Study

Background The Dietary Approaches to Stop Hypertension (DASH) diet is recommended for cardiovascular disease prevention. We aimed to identify protein biomarkers of the DASH diet using data from 2 randomized feeding studies and validate them in an observational study, the ARIC (Atherosclerosis Risk in Communities) study. Methods and Results Large-scale proteomic profiling was conducted in serum specimens (SomaLogic) collected at the end of 8-week and 4-week DASH diet interventions in multicenter, randomized controlled feeding studies of the DASH trial (N=215) and the DASH-Sodium trial (N=396), respectively. Multivariable linear regression models were used to compare the relative abundance of 7241 proteins between the DASH and control diet interventions. Estimates from the 2 trials were meta-analyzed using fixed-effects models. We validated significant proteins in the ARIC study (N=10 490) using the DASH diet score. At a false discovery rate <0.05, there were 71 proteins that were different between the DASH diet and control diet in the DASH and DASH-Sodium trials. Nineteen proteins were validated in the ARIC study. The 19 proteins collectively improved the prediction of the DASH diet intervention in the feeding studies (range of difference in C statistics, 0.267-0.313; P<0.001 for both tests) and the DASH diet score in the ARIC study (difference in C statistics, 0.017; P<0.001) beyond participant characteristics. Conclusions We identified 19 proteins robustly associated with the DASH diet in 3 studies, which may serve as biomarkers of the DASH diet. These results suggest potential pathways that are impacted by consumption of the DASH diet. Registration URL: https://www.clinicaltrials.gov; Unique identifiers: NCT03403166, NCT00000608.

Comparison of Plasma Metabolome Response to Diets Enriched in Soybean and Partially-Hydrogenated Soybean Oil in Moderately Hypercholesterolemic Adults-A Pilot Study

Partially-hydrogenated fat/trans fatty acid intake has been associated with adverse effects on cardiometabolic risk factors. Comparatively unexplored is the effect of unmodified oil relative to partially-hydrogenated fat on the plasma metabolite profile and lipid-related pathways. To address this gap, we conducted secondary analyses using a subset of samples randomly selected from a controlled dietary intervention trial involving moderately hypercholesterolemic individuals. Participants (N = 10, 63 ± 8 y, BMI, 26.2 ± 4.2 kg/m2, LDL-C, 3.9 ± 0.5 mmol/L) were provided with diets enriched in soybean oil (SO) and partially-hydrogenated soybean oil (PHSO). Plasma metabolite concentrations were determined using an untargeted approach and pathway analysis using LIPIDMAPS. Data were assessed using a volcano plot, receiver operating characteristics curve, partial least square-discrimination analysis and Pearson correlations. Among the known metabolites higher in plasma after the PHSO diet than the SO diet, the majority were phospholipids (53%) and di- and triglycerides (DG/TG, 34%). Pathway analysis indicated upregulation of phosphatidylcholine synthesis from DG and phosphatidylethanolamine. We identified seven metabolites (TG_56:9, TG_54:8, TG_54:7, TG_54:6, TG_48:5, DG_36:5 and benproperine) as potential biomarkers for PHSO intake. These data indicate that TG-related metabolites were the most affected lipid species, and glycerophospholipid biosynthesis was the most active pathway in response to PHSO compared to SO intake.

Plasma Metabolites Link Dietary Patterns to Stroke Risk

OBJECTIVE

While dietary intake is linked to stroke risk, surrogate markers that could inform personalized dietary interventions are lacking. We identified metabolites associated with diet patterns and incident stroke in a nested cohort from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study.

METHODS

Levels of 162 metabolites were measured in baseline plasma from stroke cases (n = 1,198) and random controls (n = 904). We examined associations between metabolites and a plant-based diet pattern previously linked to reduced stroke risk in REGARDS. Secondary analyses included 3 additional stroke-associated diet patterns: a Mediterranean, Dietary Approaches to Stop Hypertension (DASH), and Southern diet. Metabolites were tested using Cox proportional hazards models with incident stroke as the outcome. Replication was performed in the Jackson Heart Study (JHS). Inverse odds ratio-weighted mediation was used to determine whether metabolites mediated the association between a plant-based diet and stroke risk.

RESULTS

Metabolites associated with a plant-based diet included the gut metabolite indole-3-propionic acid (β = 0.23, 95% confidence interval [CI] [0.14, 0.33], p = 1.14 × 10-6 ), guanosine (β = -0.13, 95% CI [-0.19, -0.07], p = 6.48 × 10-5 ), gluconic acid (β = -0.11, 95% CI [-0.18, -0.04], p = 2.06 × 10-3 ), and C7 carnitine (β = -0.16, 95% CI [-0.24, -0.09], p = 4.14 × 10-5 ). All of these metabolites were associated with both additional diet patterns and altered stroke risk. Mediation analyses identified guanosine (32.6% mediation, p = 1.51 × 10-3 ), gluconic acid (35.7%, p = 2.28 × 10-3 ), and C7 carnitine (26.2%, p = 1.88 × 10-2 ) as mediators linking a plant-based diet to reduced stroke risk.

INTERPRETATION

A subset of diet-related metabolites are associated with risk of stroke. These metabolites could serve as surrogate markers that inform dietary interventions. ANN NEUROL 2023;93:500-510.

Metabolomics signatures of plant protein intake: effects of amino acids and compounds associated with plant protein on cardiometabolic health

PURPOSE OF REVIEW

An increase in the plant-based characteristics of the diet is now recommended for human and planetary health. There is growing evidence that plant protein (PP) intake has beneficial effects on cardiometabolic risk. However, proteins are not consumed isolated and the protein package (lipid species, fiber, vitamins, phytochemicals, etc) may contribute, besides the protein effects per se, to explain the beneficial effects associated with PP-rich diets.

RECENT FINDINGS

Recent studies have shown the potential of nutrimetabolomics to apprehend the complexity of both the human metabolism and the dietary habits, by providing signatures associated to the consumption of PP-rich diets. Those signatures comprised an important proportion of metabolites that were representative of the protein package, including specific amino acids (branched-chain amino acids and their derivates, glycine, lysine), but also lipid species (lysophosphatidylcholine, phosphatidylcholine, plasmalogens) and polyphenol metabolites (catechin sulfate, conjugated valerolactones and phenolic acids).

SUMMARY

Further studies are needed to go deeper in the identification of all metabolites making part of the specific metabolomic signatures, associated to the large range of protein package constituents and their effects on the endogenous metabolism, rather than to the protein fraction itself. The objective is to determine the bioactive metabolites, as well as the modulated metabolic pathways and the mechanisms responsible for the observed effects on cardiometabolic health.

Deep phenotyping and biomarkers of various dairy fat intakes in an 8-week randomized clinical trial and 2-year swine study

Health effects of dairy fats (DF) are difficult to evaluate, as DF intakes are hard to assess epidemiologically and DF have heterogeneous compositions that influence biological responses. We set out to find biomarkers of DF intake and assess biological response to a summer DF diet (R2), a winter DF diet (R3), and a R3 supplemented with calcium (R4) compared to a plant-fat-based diet (R1) in a randomized clinical trial (n=173) and a 2-year study in mildly metabolically disturbed downsized pigs (n=32). Conventional clinical measures were completed by LC/MS plasma metabolomics/lipidomics. The measured effects were modeled as biological functions to facilitate interpretation. DF intakes in pigs specifically induced a U-shaped metabolic trajectory, reprogramming metabolism to close to its initial status after a one-year turnaround. Twelve lipid species repeatably predicted DF intakes in both pigs and humans (6.6% errors). More broadly, in pigs, quality of DF modulated the time-related biological response (R2: 30 regulated functions, primarily at 6 months; R3: 26 regulated functions, mostly at 6-12 months; R4: 43 regulated functions, mostly at 18 months). Despite this heterogeneity, 9 functions overlapped under all 3 DF diets in both studies, related to a restricted area of amino acids metabolism, cofactors, nucleotides and xenobiotic pathways and the microbiota. In conclusion, over the long-term, DF reprograms metabolism to close to its initial biological status in metabolically-disrupted pigs. Quality of the DF modulates its metabolic influence, although some effects were common to all DF. A resilient signature of DF consumption found in pigs was validated in humans.

Dynamic Metabolic Signatures of Choline and Carnitine across Healthy Pregnancy and in Cord Blood: Association with Maternal Dietary Protein

BACKGROUND

In pregnancy, choline is deemed an essential nutrient and carnitine needs are increased, but amounts remain undefined.

OBJECTIVES

We aimed to measure choline and total dietary protein and dairy protein intake from food and supplements across pregnancy and the response to intake by profiling choline and carnitine metabolites across pregnancy and in cord blood.

METHODS

An exploratory analysis of choline and protein intake from 3-d diet records and measures of 36 serum choline and carnitine metabolites in early (12-17 wk) and late (36-38 wk) pregnancy was conducted in participants from the Be Healthy in Pregnancy study randomized to high dairy protein+walking exercise or usual care. Metabolites were measured in fasted maternal and cord serum using multisegment injection-capillary electrophoresis-mass spectrometry. Mixed ANOVA adjusted for body mass index was performed for comparison of metabolites across pregnancy and between intervention and control.

RESULTS

In 104 participants, the median (Q1, Q3) total choline intake was 347 (263, 427) mg/d in early and 322 (270, 437) mg/d in late pregnancy. Only ∼20% of participants achieved the recommended adequate intake (450 mg/d) and ∼10% consumed supplemental choline (8-200 mg/d). Serum-free choline (μmol/L) was higher in late compared with early pregnancy [12.9 (11.4, 15.1) compared with 9.68 (8.25, 10.61), P < 0.001], but choline downstream metabolites were similar across pregnancy. Serum carnitine [10.3 (9.01, 12.2) compared with 15.9 (14.1, 17.9) μmol/L, P < 0.001] and acetylcarnitine [2.35 (1.92, 2.68) compared with 3.0 (2.56, 3.59), P < 0.001] were significantly lower in late pregnancy. High cord:maternal serum metabolite ratios were found in most measured metabolites.

CONCLUSIONS

Despite inadequate choline intake, serum-free choline was elevated in late pregnancy and enriched in cord blood compared with maternal serum. Serum carnitine declined in late pregnancy despite a high protein diet. The higher cord:maternal concentrations in choline and carnitine metabolites suggest active uptake in late pregnancy, reflecting the importance of these circulating metabolites in fetal development. This trial was registered at clinicaltrials.gov as NCT01689961.

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.

Development of a Multibiomarker Panel of Healthy Eating Index in United States Adults: A Machine Learning Approach

BACKGROUND

Dietary and nutritional biomarkers are objective dietary assessment tools that will enable a more accurate and precise determination of diet-disease relations. However, the lack of established biomarker panels for dietary patterns is concerning, as dietary patterns continue to be the focus of dietary guidelines.

OBJECTIVES

We aimed to develop and validate a panel of objective biomarkers that reflects the Healthy Eating Index (HEI) by applying machine learning approaches to the National Health and Nutrition Examination Survey data.

METHODS

Cross-sectional population-based data (eligible criteria: age ≥20 y, not pregnant, no reported supplement use of dedicated vitamin A, D, E, or fish oils; n = 3481) from the 2003 to 2004 cycle of the NHANES were used to develop 2 multibiomarker panels of the HEI, 1 with (primary panel) and 1 without (secondary panel) plasma FAs. Up to 46 blood-based dietary and nutritional biomarkers (24 FAs, 11 carotenoids, and 11 vitamins) were included for variable selection using the least absolute shrinkage and selection operator controlling for age, sex, ethnicity, and education. The explanatory impact of selected biomarker panels was assessed by comparing the regression models with and without the selected biomarkers. In addition, 5 comparative machine learning models were constructed to validate the biomarker selection.

RESULTS

The primary multibiomarker panel (8 FAs, 5 carotenoids, and 5 vitamins) significantly improved the explained variability of the HEI (adjusted R² increased from 0.056 to 0.245). The secondary multibiomarker panel (8 vitamins and 10 carotenoids) had lesser predictive capabilities (adjusted R² increased from 0.048 to 0.189).

CONCLUSIONS

Two multibiomarker panels were developed and validated to reflect a healthy dietary pattern consistent with the HEI. Future research should seek to test these multibiomarker panels in randomly assigned trials and identify whether they have broad application in healthy dietary pattern assessment.

Environmental exposure to perchlorate, nitrate and thiocyanate, and thyroid function in Chinese adults: A community-based cross-sectional study

BACKGROUND

Evidence on environmental exposure to perchlorate, nitrate, and thiocyanate, three thyroidal sodium iodine symporter (NIS) inhibitors, and thyroid function in the Chinese population remains limited.

OBJECTIVE

To investigate the associations of environmental exposure to perchlorate, nitrate, and thiocyanate with markers of thyroid function in Chinese adults.

METHODS

A total of 2441 non-pregnant adults (mean age 50.4 years and 39.1% male) with a median urinary iodine of 180.1 μg/L from four communities in Shenzhen were included in this cross-sectional study. Urinary perchlorate, nitrate, thiocyanate, and thyroid profiles, including serum free thyroxine (FT4), total thyroxine (TT4), free triiodothyronine (FT3), total triiodothyronine (TT3), and thyroid stimulating hormone (TSH), were measured. Generalized linear model was applied to investigate the single-analyte associations. Weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) models were used to examine the association between the co-occurrence of three anions and thyroid profile.

RESULTS

The median levels of urinary perchlorate, nitrate, and thiocyanate were 5.8 μg/g, 76.4 mg/g, and 274.1 μg/g, respectively. After adjusting for confounders, higher urinary perchlorate was associated with lower serum FT4, TT4, and TT3, and higher serum FT3 and TSH (all P < 0.05). Comparing extreme tertiles, subjects in the highest nitrate tertile had marginally elevated TT3 (β: 0.02, 95% CI: 0.00-0.04). Each 1-unit increase in log-transformed urinary thiocyanate was associated with a 0.04 (95% CI: 0.02-0.06) pmol/L decrease in serum FT3. The WQS indices were inversely associated with serum FT4, TT4, and FT3 (all P < 0.05). In the BKMR model, the mixture of three anions was inversely associated with serum FT4, TT4, and FT3.

CONCLUSIONS

Our study provides evidence that individual and combined environmental exposure to perchlorate, nitrate, and thiocyanate are associated with significant changes in thyroid function markers in the Chinese population with adequate iodine intake.

Identifying Future Study Designs for Mental Health and Social Wellbeing Associated with Diets of a Cohort Living in Eco-Regions: Findings from the INSUM Expert Workshop

Diets influence our mental health and social wellbeing (MHSW) in multiple ways. A rising community concept, Eco-Regions, has gained interest. The research project "Indicators for assessment of health effects of consumption of sustainable, organic school meals in Ecoregions" (INSUM) aims to develop future-oriented research approaches to measure the potential health effects of more sustainable and healthy diets. This first part of the project focuses on MHSW with the goal to identify suitable study designs and indicators. The methodology is based on a 2-day workshop with an interdisciplinary group of experts. This paper describes commonly applied research methods on the nexus between diet and MHSW as presented by the experts and summarises key points from the discussions. The results show that the dominating tool to investigate MSHW is questionnaires. Questionnaires vary largely depending on the research design, such as participants or distribution channels. Cohort studies addressing families and including in-depth interventional and/or experimental studies may be suitable for an Eco-Region investigation. Those MHSW studies can be conducted and combined with measurements of somatic health effects. We conclude that indicators should be seen as complementary rather than independent. Explorative research designs are required to investigate complex Eco-Regions.

Nontargeted and Targeted Metabolomics Identifies Dietary Exposure Biomarkers for Navy Bean and Rice Bran Consumption in Children and Adults

Dietary exposure biomarkers are needed for advancing knowledge on healthy foods. This study examined biomarkers for navy beans and rice bran in children and adults. Plasma, urine, stool, and study foods from dietary intervention studies were analyzed by metabolomics. A total of 38 children and 49 adults were assessed after consuming navy beans and/or rice bran for 2-, 4-, 6-, or 12 weeks. From the 138-175 metabolites modulated by diet, 11 were targeted for quantification. Trigonelline and pipecolate concentrations increased in children and adult plasma after 4 weeks compared to baseline. Increased xanthurenate (46%) was observed in children plasma after rice bran intake for 4 weeks. Study foods with navy beans had higher S-methylcysteine compared to control and supported the increased urine S-methylcysteine sulfoxide. Nontargeted metabolomics was moderately effective to identify target molecules as candidate biomarkers. Study limitations include interindividual metabolite variations before diet intervention. Validation is warranted using cross-over designs and larger sample sizes.

Effect of glycinebetaine on metabolite profiles of cold-stored strawberry revealed by 1H NMR-based metabolomics

Glycinebetaine (GB) has long been used as a preservative for refrigerated fruits, but the effect of GB on the global metabolites of cold-stored strawberries is still unclear. In this study, the effects of exogenous application of GB on quality-related metabolites of cold-stored strawberries were investigated by nuclear magnetic resonance (NMR)-based metabolomic analysis. The results showed that the application of GB (especially at the concentration of 10 mM) on cold-stored strawberries effectively stabilized the sugars (d-xylose and d-glucose) and amino acids (tyrosine, leucine, and tryptophan) content, and lowered the acid (acetic acid) content as well. Additionally, the GB content in strawberries also increased. This implies that the appropriate concentration of GB is a natural and safe treatment, which could maintain the quality of cold-stored strawberries by regulating levels of quality-related metabolites, and the ingestion of GB-preserved strawberries may serve as a source of methyl-donor supplementation in our daily diet.

Sources of Variation in Food-Related Metabolites during Pregnancy

The extent to which variation in food-related metabolites are attributable to non-dietary factors remains unclear, which may explain inconsistent food-metabolite associations observed in population studies. This study examined the association between non-dietary factors and the serum concentrations of food-related biomarkers and quantified the amount of variability in metabolite concentrations explained by non-dietary factors. Pregnant women (n = 600) from two Canadian birth cohorts completed a validated semi-quantitative food frequency questionnaire, and serum metabolites were measured by multisegment injection-capillary electrophoresis-mass spectrometry. Hierarchical linear modelling and principal component partial R-square (PC-PR2) were used for data analysis. For proline betaine and DHA (mainly exogenous), citrus foods and fish/fish oil intake, respectively, explained the highest proportion of variability relative to non-dietary factors. The unique contribution of dietary factors was similar (15:0, 17:0, hippuric acid, TMAO) or lower (14:0, tryptophan betaine, 3-methylhistidine, carnitine) compared to non-dietary factors (i.e., ethnicity, maternal age, gestational age, pre-pregnancy BMI, physical activity, and smoking) for metabolites that can either be produced endogenously, biotransformed by gut microbiota, and/or derived from multiple food sources. The results emphasize the importance of adjusting for non-dietary factors in future analyses to improve the accuracy and precision of the measures of food intake and their associations with health and disease.

Leveraging Observational Cohorts to Study Diet and Nutrition in Older Adults: Opportunities and Obstacles

By 2060, the number of adults aged ≥65 y is expected to double, and the ≥85 y segment of the population is expected to triple in the United States. US federal nutrition guidance is based on the premise that healthy diets contribute to delaying the onset and progression of many age-related diseases and disability. Yet, little is known about the dietary intakes or nutritional needs across the older adulthood age span. This review aims to identify community-based cohorts that collected information on dietary intake of adults ≥65 y in the United States. Thirty-two cohorts met all inclusion criteria. We summarized information on the cohorts' design, demographics, and diet assessment. We also identified key gaps in the existing databases that, if filled, could enhance their utility to address certain research questions. This review serves as a valuable inventory of cohorts that can be leveraged to answer key questions about the diet and nutritional needs of the oldest old, who represent the fastest growing segment of the population in the United States.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Metabolomics Meets Nutritional Epidemiology: Harnessing the Potential in Metabolomics Data

Traditionally, nutritional epidemiology is the study of the relationship between diet and health and disease in humans at the population level. Commonly, the exposure of interest is food intake. In recent years, nutritional epidemiology has moved from a "black box" approach to a systems approach where genomics, metabolomics and proteomics are providing novel insights into the interplay between diet and health. In this context, metabolomics is emerging as a key tool in nutritional epidemiology. The present review explores the use of metabolomics in nutritional epidemiology. In particular, it examines the role that food-intake biomarkers play in addressing the limitations of self-reported dietary intake data and the potential of using metabolite measurements in assessing the impact of diet on metabolic pathways and physiological processes. However, for full realisation of the potential of metabolomics in nutritional epidemiology, key challenges such as robust biomarker validation and novel methods for new metabolite identification need to be addressed. The synergy between traditional epidemiologic approaches and metabolomics will facilitate the translation of nutritional epidemiologic evidence to effective precision nutrition.