Novel strategies for improving dietary exposure assessment: Multiple-data fusion is a more accurate measure than the traditional single-biomarker approach

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

INTRODUCTION

Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates.

OBJECTIVE

This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health.

MATERIALS AND METHODS

A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24 h dietary recall (24 h-DR).

RESULTS

Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population.

CONCLUSION

A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.

Serum Metabolomic Markers of Protein-Rich Foods and Incident CKD: Results From the Atherosclerosis Risk in Communities Study

Rationale & Objective

While urine excretion of nitrogen estimates the total protein intake, biomarkers of specific dietary protein sources have been sparsely studied. Using untargeted metabolomics, this study aimed to identify serum metabolomic markers of 6 protein-rich foods and to examine whether dietary protein-related metabolites are associated with incident chronic kidney disease (CKD).

Study Design

Prospective cohort study.

Setting & Participants

A total of 3,726 participants from the Atherosclerosis Risk in Communities study without CKD at baseline.

Exposures

Dietary intake of 6 protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, and poultry), serum metabolites.

Outcomes

Incident CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m2 with ≥25% estimated glomerular filtration rate decline relative to visit 1, hospitalization or death related to CKD, or end-stage kidney disease).

Analytical Approach

Multivariable linear regression models estimated cross-sectional associations between protein-rich foods and serum metabolites. C statistics assessed the ability of the metabolites to improve the discrimination of highest versus lower 3 quartiles of intake of protein-rich foods beyond covariates (demographics, clinical factors, health behaviors, and the intake of nonprotein food groups). Cox regression models identified prospective associations between protein-related metabolites and incident CKD.

Results

Thirty significant associations were identified between protein-rich foods and serum metabolites (fish, n = 8; nuts, n = 5; legumes, n = 0; red and processed meat, n = 5; eggs, n = 3; and poultry, n = 9). Metabolites collectively and significantly improved the discrimination of high intake of protein-rich foods compared with covariates alone (difference in C statistics = 0.033, 0.051, 0.003, 0.024, and 0.025 for fish, nuts, red and processed meat, eggs, and poultry-related metabolites, respectively; P < 1.00 × 10-16 for all). Dietary intake of fish was positively associated with 1-docosahexaenoylglycerophosphocholine (22:6n3), which was inversely associated with incident CKD (HR, 0.82; 95% CI, 0.75-0.89; P = 7.81 × 10-6).

Limitations

Residual confounding and sample-storage duration.

Conclusions

We identified candidate biomarkers of fish, nuts, red and processed meat, eggs, and poultry. A fish-related metabolite, 1-docosahexaenoylglycerophosphocholine (22:6n3), was associated with a lower risk of CKD.

The effects of whole grain cereals on tryptophan metabolism and intestinal barrier function: underlying factors of health impact

This review aims to investigate the relationship between the health impact of whole grains mediated via the interaction with intestinal microbiota and intestinal barrier function with special interest on tryptophan metabolism, focusing on the role of the intestinal microbiota and their impact on barrier function. Consuming various types of whole grains can lead to the growth of different microbiota species, which in turn leads to the production of diverse metabolites, including those derived from tryptophan metabolism, although the impact of whole grains on intestinal microbiota composition results remains inconclusive and vary among different studies. Whole grains can exert an influence on tryptophan metabolism through interactions with the intestinal microbiota, and the presence of fibre in whole grains plays a notable role in establishing this connection. The impact of whole grains on intestinal barrier function is closely related to their effects on the composition and activity of intestinal microbiota, and SCFA and tryptophan metabolites serve as potential links connecting whole grains, intestinal microbiota and the intestinal barrier function. Tryptophan metabolites affect various aspects of the intestinal barrier, such as immune balance, mucus and microbial barrier, tight junction complexes and the differentiation and proliferation of epithelial cells. Despite the encouraging discoveries in this area of research, the evidence regarding the effects of whole grain consumption on intestine-related activity remains limited. Hence, we can conclude that we are just starting to understand the actual complexity of the intestinal factors mediating in part the health impacts of whole grain cereals.

In vivo-in vitro correlations (IVIVC) for the assessment of pyrethroid bioavailability in honey

Bioaccessibility/bioavailability is an important factor in assessing the potential human health risk via oral exposure. However, methods for accurately predicting the bioaccessibility/bioavailability of pesticide residues are still limited, preventing accurate measurements of actual exposure to pesticide residues. In this study, pyrethroid bioavailability in honey were analysed using a mouse bioassay and bioaccessibility via in vitro methods with Tenax extraction. The results demonstrated that the combined liver plus kidney data served as an appropriate biomarker to estimate the relative bioavailability. Notably, significant in vivo-in vitro correlations (IVIVC) were observed between bioavailability and bioaccessibility (R2 = 0.7898-0.9793). Estimation of the bioavailability of honey from different nectar plants using derived IVIVC confirmed that different contents and physicochemical properties might affect its bioavailability. The findings provide insight into assessing human exposure to pesticides based on bioavailability and can decrease the uncertainty about the assessment of the risk of dietary exposure to pesticides.

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

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.

A nutritional biomarker score of the Mediterranean diet and incident type 2 diabetes: Integrated analysis of data from the MedLey randomised controlled trial and the EPIC-InterAct case-cohort study

BACKGROUND

Self-reported adherence to the Mediterranean diet has been modestly inversely associated with incidence of type 2 diabetes (T2D) in cohort studies. There is uncertainty about the validity and magnitude of this association due to subjective reporting of diet. The association has not been evaluated using an objectively measured biomarker of the Mediterranean diet.

METHODS AND FINDINGS

We derived a biomarker score based on 5 circulating carotenoids and 24 fatty acids that discriminated between the Mediterranean or habitual diet arms of a parallel design, 6-month partial-feeding randomised controlled trial (RCT) conducted between 2013 and 2014, the MedLey trial (128 participants out of 166 randomised). We applied this biomarker score in an observational study, the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study, to assess the association of the score with T2D incidence over an average of 9.7 years of follow-up since the baseline (1991 to 1998). We included 22,202 participants, of whom 9,453 were T2D cases, with relevant biomarkers from an original case-cohort of 27,779 participants sampled from a cohort of 340,234 people. As a secondary measure of the Mediterranean diet, we used a score estimated from dietary-self report. Within the trial, the biomarker score discriminated well between the 2 arms; the cross-validated C-statistic was 0.88 (95% confidence interval (CI) 0.82 to 0.94). The score was inversely associated with incident T2D in EPIC-InterAct: the hazard ratio (HR) per standard deviation of the score was 0.71 (95% CI: 0.65 to 0.77) following adjustment for sociodemographic, lifestyle and medical factors, and adiposity. In comparison, the HR per standard deviation of the self-reported Mediterranean diet was 0.90 (95% CI: 0.86 to 0.95). Assuming the score was causally associated with T2D, higher adherence to the Mediterranean diet in Western European adults by 10 percentiles of the score was estimated to reduce the incidence of T2D by 11% (95% CI: 7% to 14%). The study limitations included potential measurement error in nutritional biomarkers, unclear specificity of the biomarker score to the Mediterranean diet, and possible residual confounding.

CONCLUSIONS

These findings suggest that objectively assessed adherence to the Mediterranean diet is associated with lower risk of T2D and that even modestly higher adherence may have the potential to reduce the population burden of T2D meaningfully.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12613000602729 https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=363860.

Identifying Plasma and Urinary Biomarkers of Fermented Food Intake and Their Associations with Cardiometabolic Health in a Dutch Observational Cohort

Identification of food intake biomarkers (FIBs) for fermented foods could help improve their dietary assessment and clarify their associations with cardiometabolic health. We aimed to identify novel FIBs for fermented foods in the plasma and urine metabolomes of 246 free-living Dutch adults using nontargeted LC-MS and GC-MS. Furthermore, associations between identified metabolites and several cardiometabolic risk factors were explored. In total, 37 metabolites were identified corresponding to the intakes of coffee, wine, and beer (none were identified for cocoa, bread, cheese, or yoghurt intake). While some of these metabolites appeared to originate from raw food (e.g., niacin and trigonelline for coffee), others overlapped different fermented foods (e.g., 4-hydroxybenzeneacetic acid for both wine and beer). In addition, several fermentation-dependent metabolites were identified (erythritol and citramalate). Associations between these identified metabolites with cardiometabolic parameters were weak and inconclusive. Further evaluation is warranted to confirm their relationships with cardiometabolic disease risk.

Identification of Single and Combined Serum Metabolites Associated with Food Intake

Assessment of dietary intake is challenging. Traditional methods suffer from both random and systematic errors; thus objective measures are important complements in monitoring dietary exposure. The study presented here aims to identify serum metabolites associated with reported food intake and to explore whether combinations of metabolites may improve predictive models. Fasting blood samples and a 4-day weighed food diary were collected from healthy Swedish subjects (n = 119) self-defined as having habitual vegan, vegetarian, vegetarian + fish, or omnivore diets. Serum was analyzed for metabolites by 1H-nuclear magnetic resonance spectroscopy. Associations between single and combined metabolites and 39 foods and food groups were explored. Area under the curve (AUC) was calculated for prediction models. In total, 24 foods or food groups associated with serum metabolites using the criteria of rho > 0.2, p < 0.01 and AUC ≥ 0.7 were identified. For the consumption of soybeans, citrus fruits and marmalade, nuts and almonds, green tea, red meat, poultry, total fish and shellfish, dairy, fermented dairy, cheese, eggs, and beer the final models included two or more metabolites. Our results indicate that a combination of metabolites improve the possibilities to use metabolites to identify several foods included in the current diet. Combined metabolite models should be confirmed in dose−response intervention studies.

Fermented foods and cardiometabolic health: Definitions, current evidence, and future perspectives

Unhealthy diets contribute to the increasing burden of non-communicable diseases. Annually, over 11 million deaths worldwide are attributed to dietary risk factors, with the vast majority of deaths resulting from cardiometabolic diseases (CMDs) including cardiovascular disease (∼10 million) and type II diabetes (∼339,000). As such, defining diets and dietary patterns that mitigate CMD risk is of great public health importance. Recently, the consumption of fermented foods has emerged as an important dietary strategy for improving cardiometabolic health. Fermented foods have been present in the human diet for over 10,000 years, but knowledge on whether their consumption benefits human health, and the molecular and microbiological mechanisms underpinning their purported health benefits, is relatively nascent. This review provides an overview of the definitions of fermented foods, types and qualities of fermented foods consumed in Europe and globally, possible mechanisms between the consumption of fermented foods and cardiometabolic health, as well as the current state of the epidemiological evidence on fermented food intake and cardiometabolic health. Finally, we outline future perspectives and opportunities for improving the role of fermented foods in human diets.

multiMarker: software for modelling and prediction of continuous food intake using multiple biomarkers measurements

Background

Metabolomic biomarkers offer potential for objective and reliable food intake assessment, and there is growing interest in using biomarkers in place of or with traditional self-reported approaches. Ongoing research suggests that multiple biomarkers are associated with single foods, offering great sensitivity and specificity. However, currently there is a dearth of methods to model the relationship between multiple biomarkers and single food intake measurements.

Results

Here, we introduce multiMarker, a web-based application based on the homonymous R package, that enables one to infer the relationship between food intake and two or more metabolomic biomarkers. Furthermore, multiMarker allows prediction of food intake from biomarker data alone. multiMarker differs from previous approaches by providing distributions of predicted intakes, directly accounting for uncertainty in food intake quantification. Usage of both the R package and the web application is demonstrated using real data concerning three biomarkers for orange intake. Further, example data is pre-loaded in the web application to enable users to examine multiMarker’s functionality.

Conclusion

The proposed software advance the field of Food Intake Biomarkers providing researchers with a novel tool to perform continuous food intake quantification, and to assess its associated uncertainty, from multiple biomarkers. To facilitate widespread use of the framework, multiMarker has been implemented as an R package and a Shiny web application.

Evaluating the Robustness of Biomarkers of Dairy Food Intake in a Free-Living Population Using Single- and Multi-Marker Approaches

Studies examining associations between self-reported dairy intake and health are inconclusive, but biomarkers hold promise for elucidating such relationships by offering objective measures of dietary intake. Previous human intervention studies identified several biomarkers for dairy foods in blood and urine using non-targeted metabolomics. We evaluated the robustness of these biomarkers in a free-living cohort in the Netherlands using both single- and multi-marker approaches. Plasma and urine from 246 participants (54 ± 13 years) who completed a food frequency questionnaire were analyzed using liquid and gas chromatography-mass spectrometry. The targeted metabolite panel included 37 previously-identified candidate biomarkers of milk, cheese, and/or yoghurt consumption. Associations between biomarkers and energy-adjusted dairy food intakes were assessed by a ‘single-marker’ generalized linear model, and stepwise regression was used to select the best ‘multi-marker’ panel. Multi-marker models that also accounted for common covariates better captured the subtle differences for milk (urinary galactose, galactitol; sex, body mass index, age) and cheese (plasma pentadecanoic acid, isoleucine, glutamic acid) over single-marker models. No significant associations were observed for yogurt. Further examination of other facets of validity of these biomarkers may improve estimates of dairy food intake in conjunction with self-reported methods, and help reach a clearer consensus on their health impacts.

Dietary phytoestrogens and biomarkers of their intake in relation to cancer survival and recurrence: a comprehensive systematic review with meta-analysis

CONTEXT

Recent studies have outlined the potential role of dietary factors in patients who have survived cancer.

OBJECTIVE

The aim of this study was to summarize the evidence of the relation between dietary intake of phytoestrogens and their blood biomarkers and, overall, cancer-specific mortality and recurrence in patients with cancer.

DATA SOURCES

A systematic search of PubMed, EMBASE, and Web of Science databases of studies published up to September 2019 was performed. Databases were searched for prospective and retrospective cohort studies reporting on dietary phytoestrogen intake and/or blood biomarkers and the outcomes investigated.

DATA EXTRACTION

Data were extracted from each identified study using a standardized form.

DATA ANALYSIS

Twenty-eight articles on breast, lung, prostate, and colorectal cancer, and glioma were included for systematic review. Given the availability of studies, a quantitative meta-analysis was performed solely for breast cancer outcomes. A significant inverse association among higher dietary isoflavone intake, higher serum/plasma enterolactone concentrations, and overall mortality and cancer recurrence was found. Among other cancer types, 2 studies reported that higher serum enterolactone and higher intake of lignans were associated with cancer-specific survival for colorectal cancer and glioma, respectively.

CONCLUSIONS

Dietary phytoestrogens may play a role in survival from breast cancer ; evidence regarding other cancers is too limited to draw any conclusions.

Derivation and Validation of a Total Fruit and Vegetable Intake Prediction Model to Identify Targets for Biomarker Discovery Using the UK National Diet and Nutrition Survey

BACKGROUND

Dietary assessments in research and clinical settings are largely reliant on self-reported questionnaires. It is acknowledged that these are subject to measurement error and biases and that objective approaches would be beneficial. Dietary biomarkers have been purported as a complementary approach to improve the accuracy of dietary assessments. Tentative biomarkers have been identified for many individual fruits and vegetables (FVs), but an objective total FV intake assessment tool has not been established.

OBJECTIVES

To derive and validate a prediction model of total FV intake (TFVpred) to inform future biomarker studies.

METHODS

Data from the National Diet and Nutrition Survey (NDNS) were used for this analysis. A modeling group (MG) consisting of participants aged >11 years from the NDNS years 5-6 was created (n = 1746). Intake data for 96 FVs were analyzed by stepwise regression to derive a model that satisfied 3 selection criteria: SEE ≤80, R2 >0.7, and ≤10 predictors. The TFVpred model was validated using comparative data from a validation group (VG) created from the NDNS years 7-8 (n = 1865). Pearson's correlation coefficients were assessed between observed and predicted values in the MG and VG. Bland-Altman plots were used to assess agreement between TFVpred estimates and total FV intake.

RESULTS

A TFVpred model, comprised of tomatoes, apples, carrots, bananas, pears, strawberries, and onions, satisfied the selection criteria (R2 = 0.761; SEE = 78.81). Observed and predicted total FV intake values were positively correlated in the MG (r = 0.872; P < 0.001; R2 = 0.761) and the VG (r = 0.838; P < 0.001; R2 = 0.702). In the MG and VG, 95.0% and 94.9%, respectively, of TFVpred model residuals were within the limits of agreement.

CONCLUSIONS

Intakes of a concise FV list can be used to predict total FV intakes in a UK population. The individual FVs included in the TFVpred model present targets for biomarker discovery aimed at objectively assessing total FV intake.

The Potential of Multi-Biomarker Panels in Nutrition Research: Total Fruit Intake as an Example

Dietary and food intake biomarkers offer the potential of improving the accuracy of dietary assessment. An extensive range of putative intake biomarkers of commonly consumed foods have been identified to date. As the field of food intake biomarkers progresses toward solving the complexities of dietary habits, combining biomarkers associated with single foods or food groups may be required. The objective of this work was to examine the ability of a multi-biomarker panel to classify individuals into categories of fruit intake. Biomarker data was measured using ¹H NMR spectroscopy in two studies: (1) An intervention study where varying amounts of fruit was consumed and (2) the National Adult Nutrition Survey (NANS). Using data from an intervention study a biomarker panel (Proline betaine, Hippurate, and Xylose) was constructed from three urinary biomarker concentrations. Biomarker cut-off values for three categories of fruit intake were developed. The biomarker sum cut-offs were ≤ 4.766, 4.766–5.976, >5.976 μM/mOsm/kg for <100, 101–160, and >160 g fruit intake. The ability of the biomarker sum to classify individuals into categories of fruit intake was examined in the cross-sectional study (NANS) (N = 565). Examination of results in the cross-sectional study revealed excellent agreement with self-reported intake: a similar number of participants were ranked into each category of fruit intake. The work illustrates the potential of multi-biomarker panels and paves the way forward for further development in the field. The use of such panels may be key to distinguishing foods and adding specificity to the predictions of food intake.

Risk Assessment of Veterinary Drug Residues in Meat Products

With the improvement of the global food safety regulatory system, there is an increasing importance for food safety risk assessment. Veterinary drugs are widely used in poultry and livestock products. The abuse of veterinary drugs seriously threatens human health. This article explains the necessity of risk assessment for veterinary drug residues in meat products, describes the principles and functions of risk assessment, then summarizes the risk assessment process of veterinary drug residues, and then outlines the qualitative and quantitative risk assessment methods used in this field. We propose the establishment of a new meat product safety supervision model with a view to improve the current meat product safety supervision system.

Challenges Associated With the Design and Deployment of Food Intake Urine Biomarker Technology for Assessment of Habitual Diet in Free-Living Individuals and Populations-A Perspective

Improvement of diet at the population level is a cornerstone of national and international strategies for reducing chronic disease burden. A critical challenge in generating robust data on habitual dietary intake is accurate exposure assessment. Self-reporting instruments (e.g., food frequency questionnaires, dietary recall) are subject to reporting bias and serving size perceptions, while weighed dietary assessments are unfeasible in large-scale studies. However, secondary metabolites derived from individual foods/food groups and present in urine provide an opportunity to develop potential biomarkers of food intake (BFIs). Habitual dietary intake assessment in population surveys using biomarkers presents several challenges, including the need to develop affordable biofluid collection methods, acceptable to participants that allow collection of informative samples. Monitoring diet comprehensively using biomarkers requires analytical methods to quantify the structurally diverse mixture of target biomarkers, at a range of concentrations within urine. The present article provides a perspective on the challenges associated with the development of urine biomarker technology for monitoring diet exposure in free-living individuals with a view to its future deployment in "real world" situations. An observational study (n = 95), as part of a national survey on eating habits, provided an opportunity to explore biomarker measurement in a free-living population. In a second food intervention study (n = 15), individuals consumed a wide range of foods as a series of menus designed specifically to achieve exposure reflecting a diversity of foods commonly consumed in the UK, emulating normal eating patterns. First Morning Void urines were shown to be suitable samples for biomarker measurement. Triple quadrupole mass spectrometry, coupled with liquid chromatography, was used to assess simultaneously the behavior of a panel of 54 potential BFIs. This panel of chemically diverse biomarkers, reporting intake of a wide range of commonly-consumed foods, can be extended successfully as new biomarker leads are discovered. Towards validation, we demonstrate excellent discrimination of eating patterns and quantitative relationships between biomarker concentrations in urine and the intake of several foods. In conclusion, we believe that the integration of information from BFI technology and dietary self-reporting tools will expedite research on the complex interactions between dietary choices and health.

Urinary Biomarkers for Orange Juice Consumption

SCOPE

As orange juice belongs to one of the most consumed juices worldwide, a human study is performed to identify urinary biomarkers for the consumption of orange juice in order to differentiate between low, medium, and high intake.

METHODS AND RESULTS

The 32 study participants abstained from citrus fruits, juices and products thereof, except for one portion of orange juice, for eight days. Throughout the study, spot urine samples are collected and quantitatively analyzed by high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) regarding their content of several potential biomarkers for orange juice intake after enzymatic treatment with β-glucuronidase. Proline betaine is determined as a long-term biomarker: based on its urinary excretion, orange juice consumption is traceable for at least 72 h after intake. Naringenin and hesperetin are identified as qualitative short-term biomarkers. Synephrine sulfate also showed a fast increase and decrease in a semi-quantitative approach. In the case of phloretin, no correlation between orange juice consumption and the urinary concentration is observed.

CONCLUSION

Proline betaine is the most promising biomarker for orange juice consumption and allows to differentiate between low, medium, and high intake. Hesperetin and naringenin (as well as synephrine) are applicable as supporting biomarkers, whereas phloretin does not represent a reliable biomarker for orange juice consumption.

Blood Metabolomic Profiling Confirms and Identifies Biomarkers of Food Intake

Metabolomics can be a tool to identify dietary biomarkers. However, reported food-metabolite associations have been inconsistent, and there is a need to explore further associations. Our aims were to confirm previously reported food-metabolite associations and to identify novel food-metabolite associations. We conducted a cross-sectional analysis of data from 849 participants (57% men) of the PopGen cohort. Dietary intake was obtained using FFQ and serum metabolites were profiled by an untargeted metabolomics approach. We conducted a systematic literature search to identify previously reported food-metabolite associations and analyzed these associations using linear regression. To identify potential novel food-metabolite associations, datasets were split into training and test datasets and linear regression models were fitted to the training datasets. Significant food-metabolite associations were evaluated in the test datasets. Models were adjusted for covariates. In the literature, we identified 82 food-metabolite associations. Of these, 44 associations were testable in our data and confirmed associations of coffee with 12 metabolites, of fish with five, of chocolate with two, of alcohol with four, and of butter, poultry and wine with one metabolite each. We did not identify novel food-metabolite associations; however, some associations were sex-specific. Potential use of some metabolites as biomarkers should consider sex differences in metabolism.

A Standardized Strategy for Simultaneous Quantification of Urine Metabolites to Validate Development of a Biomarker Panel Allowing Comprehensive Assessment of Dietary Exposure

SCOPE

Metabolites derived from individual foods found in human biofluids after consumption could provide objective measures of dietary intake. For comprehensive dietary assessment, quantification methods would need to manage the structurally diverse mixture of target metabolites present at wide concentration ranges.

METHODS AND RESULTS

A strategy for selection of candidate dietary exposure biomarkers is developed. An analytical method for 62 food biomarkers is validated by extensive analysis of chromatographic and ionization behavior characteristics using triple quadrupole mass spectrometry. Urine samples from two food intervention studies are used: a controlled, inpatient study (n = 19) and a free-living study where individuals (n = 15) are provided with food as a series of menu plans. As proof-of-principle, it is demonstrated that the biomarker panel could discriminate between menu plans by detecting distinctive changes in the concentration in urine of targeted metabolites. Quantitative relationships between four biomarker concentrations in urine and dietary intake are shown.

CONCLUSION

Design concepts for an analytical strategy are demonstrated, allowing simultaneous quantification of a comprehensive panel of chemically-diverse biomarkers of a wide range of commonly-consumed foods. It is proposed that integration of self-reported dietary recording tools with biomarker approaches will provide more robust assessment of dietary exposure.

Food intake biomarkers for berries and grapes

Grapes and berries are two types of widely consumed fruits characterized by a high content in different phytochemicals. However, their accurate dietary assessment is particularly arduous, because of the already wide recognized bias associated with self-reporting methods, combined with the large range of species and cultivars and the fact that these fruits are popularly consumed not only in fresh and frozen forms but also as processed and derived products, including dried and canned fruits, beverages, jams, and jellies. Reporting precise type and/or quantity of grape and berries in FFQ or diaries can obviously be affected by errors. Recently, biomarkers of food intake (BFIs) rose as a promising tool to provide accurate information indicating consumption of certain food items. Protocols for performing systematic reviews in this field, as well as for assessing the validity of candidate BFIs have been developed within the Food Biomarker Alliance (FoodBAll) Project. This paper aims to evaluate the putative BIFs for blueberries, strawberries, raspberries, blackberries, cranberries, blackcurrant, and grapes. Candidate BFIs for grapes were resveratrol metabolites and tartaric acid. The metabolites considered as putative BFI for berries consumption were mostly anthocyanins derivatives together with several metabolites of ellagitannins and some aroma compounds. However, identification of BFIs for single berry types encountered more difficulties. In the absence of highly specific metabolites reported to date, we suggested some multi-metabolite panels that may be further investigated as putative biomarkers for some berry fruits.

Discovery of Intake Biomarkers of Lentils, Chickpeas, and White Beans by Untargeted LC-MS Metabolomics in Serum and Urine

SCOPE

To identify reliable biomarkers of food intake (BFIs) of pulses.

METHODS AND RESULTS

A randomized crossover postprandial intervention study is conducted on 11 volunteers who consumed lentils, chickpeas, and white beans. Urine and serum samples are collected at distinct postprandial time points up to 48 h, and analyzed by LC-HR-MS untargeted metabolomics. Hypaphorine, trigonelline, several small peptides, and polyphenol-derived metabolites prove to be the most discriminating urinary metabolites. Two arginine-related compounds, dopamine sulfate and epicatechin metabolites, with their microbial derivatives, are identified only after intake of lentils, whereas protocatechuic acid is identified only after consumption of chickpeas. Urinary hydroxyjasmonic and hydroxydihydrojasmonic acids, as well as serum pipecolic acid and methylcysteine, are found after white bean consumption. Most of the metabolites identified in the postprandial study are replicated as discriminants in 24 h urine samples, demonstrating that in this case the use of a single, noninvasive sample is suitable for revealing the consumption of pulses.

CONCLUSIONS

The results of the present untargeted metabolomics work reveals a broad list of metabolites that are candidates for use as biomarkers of pulse intake. Further studies are needed to validate these BFIs and to find the best combinations of them to boost their specificity.

Habitual Nut Exposure, Assessed by Dietary and Multiple Urinary Metabolomic Markers, and Cognitive Decline in Older Adults: The InCHIANTI Study

SCOPE

The association between self-reported dietary intake and urinary metabolomic markers of habitual nut exposure with cognitive decline over a 3-year follow-up in an older Italian population is prospectively evaluated.

METHODS AND RESULTS

A total of 119 older participants are selected, based on self-referred nut intake: the non-nut consumer (n = 72) and the regular consumer (≥2.9 g d^-1 , n = 47). Nut exposure is measured at baseline either with the use of a validated food frequency questionnaire or with an HPLC-Q-ToF-MS metabolomic approach. Three years after, 28 from the nonconsumers and 10 from the consumers experienced cognitive decline. Dietary nut exposure is characterized by urinary metabolites of polyphenols and fatty acids pathways. Nut consumption estimated either by the dietary marker or by the urinary marker model is in both cases associated with less cognitive decline (OR: 0.78, 95% CI: 0.61,0.99; p = 0.043 and OR: 0.995, 95% CI: 0.991,0.999; p = 0.016, respectively) with AUCs 73.2 (95% CI: 62.9, 83.6) and 73.1 (62.5, 83.7), respectively.

CONCLUSIONS

A high intake of nuts may protect older adults from cognitive decline. Metabolomics provides accurate and complementary information of the nut exposure and reinforces the results obtained using dietary information.

Developing a Food Exposure and Urine Sampling Strategy for Dietary Exposure Biomarker Validation in Free-Living Individuals

SCOPE

Dietary choices modulate the risk of chronic diseases and improving diet is a central component of public health strategies. Food-derived metabolites present in urine could provide objective biomarkers of dietary exposure. To assist biomarker validation, this work aims to develop a food intervention strategy mimicking a typical annual diet over a short period of time and assesses urine sampling protocols potentially suitable for future deployment of biomarker technology in free-living populations.

METHODS AND RESULTS

Six different menu plans comprehensively represent a typical UK annual diet that is split into two dietary experimental periods. Free-living adult participants (n = 15 and n = 36, respectively) are provided with all their food, as a series of menu plans, over a period of three consecutive days. Multiple spot urine samples are collected and stored at home.

CONCLUSION

A successful food exposure strategy is established following a conventional UK eating pattern, which is suitable for biomarker validation in free-living individuals. The urine sampling procedure is acceptable for volunteers and delivered samples suitable for biomarker quantification. The study design provides scope for validation of existing biomarker candidates and potentially for discovery of new biomarker leads, and should help inform the future deployment of biomarker technology for habitual dietary exposure measurement.

Addressing the pitfalls when designing intervention studies to discover and validate biomarkers of habitual dietary intake

INTRODUCTION

Dietary exposure monitoring within populations is reliant on self-reported measures such as Food Frequency Questionnaires and diet diaries. These methods often contain inaccurate information due to participant misreporting, non-compliance and bias. Urinary metabolites derived from individual foods could provide additional objective indicators of dietary exposure. For biomarker approaches to have utility it is essential that they cover a wide-range of commonly consumed foods and the methodology works in a real-world environment.

OBJECTIVES

To test that the methodology works in a real-world environment and to consider the impact of the major sources of likely variance; particularly complex meals, different food formulations, processing and cooking methods, as well as the dynamics of biomarker duration in the body.

METHODS

We designed and tested a dietary exposure biomarker discovery and validation strategy based on a food intervention study involving free-living individuals preparing meals and collecting urine samples at home. Two experimental periods were built around three consecutive day menu plans where all foods and drinks were provided (n = 15 and n = 36).

RESULTS

The experimental design was validated by confirming known consumption biomarkers in urinary samples after the first menu plan. We tested biomarker performance with different food formulations and processing methods involving meat, wholegrain, fruits and vegetables.

CONCLUSION

It was demonstrated that spot urine samples, together with robust dietary biomarkers, despite major sources of variance, could be used successfully for dietary exposure monitoring in large epidemiological studies.

Biomarkers of food intake for nuts and vegetable oils: an extensive literature search

Nuts and vegetable oils are important sources of fat and of a wide variety of micronutrients and phytochemicals. Following their intake, several of their constituents, as well as their derived metabolites, are found in blood circulation and in urine. As a consequence, these could be used to assess the compliance to a dietary intervention or to determine habitual intake of nuts and vegetable oils. However, before these metabolites can be widely used as biomarkers of food intake (BFIs), several characteristics have to be considered, including specificity, dose response, time response, stability, and analytical performance. We have, therefore, conducted an extensive literature search to evaluate current knowledge about potential BFIs of nuts and vegetable oils. Once identified, the strengths and weaknesses of the most promising candidate BFIs have been summarized. Results from selected studies have provided a variety of compounds mainly derived from the fatty fraction of these foods, but also other components and derived metabolites related to their nutritional composition. In particular, α-linolenic acid, urolithins, and 5-hydroxyindole-3-acetic acid seem to be the most plausible candidate BFIs for walnuts, whereas for almonds they could be α-tocopherol and some catechin-derived metabolites. Similarly, several studies have reported a strong association between selenium levels and consumption of Brazil nuts. Intake of vegetable oils has been mainly assessed through the measurement of specific fatty acids in different blood fractions, such as oleic acid for olive oil, α-linolenic acid for flaxseed (linseed) and rapeseed (canola) oils, and linoleic acid for sunflower oil. Additionally, hydroxytyrosol and its metabolites were the most promising distinctive BFIs for (extra) virgin olive oil. However, most of these components lack sufficient specificity to serve as BFIs. Therefore, additional studies are necessary to discover new candidate BFIs, as well as to further evaluate the specificity, sensitivity, dose-response relationships, and reproducibility of these candidate biomarkers and to eventually validate them in other populations. For the discovery of new candidate BFIs, an untargeted metabolomics approach may be the most effective strategy, whereas for increasing the specificity of the evaluation of food consumption, this could be a combination of different metabolites.

Biomarker of food intake for assessing the consumption of dairy and egg products

Dairy and egg products constitute an important part of Western diets as they represent an excellent source of high-quality proteins, vitamins, minerals and fats. Dairy and egg products are highly diverse and their associations with a range of nutritional and health outcomes are therefore heterogeneous. Such associations are also often weak or debated due to the difficulty in establishing correct assessments of dietary intake. Therefore, in order to better characterize associations between the consumption of these foods and health outcomes, it is important to identify reliable biomarkers of their intake. Biomarkers of food intake (BFIs) provide an accurate measure of intake, which is independent of the memory and sincerity of the subjects as well as of their knowledge about the consumed foods. We have, therefore, conducted a systematic search of the scientific literature to evaluate the current status of potential BFIs for dairy products and BFIs for egg products commonly consumed in Europe. Strikingly, only a limited number of compounds have been reported as markers for the intake of these products and none of them have been sufficiently validated. A series of challenges hinders the identification and validation of BFI for dairy and egg products, in particular, the heterogeneous composition of these foods and the lack of specificity of the markers identified so far. Further studies are, therefore, necessary to validate these compounds and to discover new candidate BFIs. Untargeted metabolomic strategies may allow the identification of novel biomarkers, which, when taken separately or in combination, could be used to assess the intake of dairy and egg products.

Biomarkers of food intake for cocoa and liquorice (products): a systematic review

Background

To unravel true links between diet and health, it is important that dietary exposure is accurately measured. Currently, mainly self-reporting methods (e.g. food frequency questionnaires and 24-h recalls) are used to assess food intake in epidemiological studies. However, these traditional instruments are subjective measures and contain well-known biases. Especially, estimating the intake of the group of confectionary products, such as products containing cocoa and liquorice, remains a challenge. The use biomarkers of food intake (BFIs) may provide a more objective measurement. However, an overview of current candidate biomarkers and their validity is missing for both cocoa- and liquorice-containing foods.

Objective

The purpose of the current study was to (1) identify currently described candidate BFIs for cocoa (products) and liquorice, (2) to evaluate the validity of these identified candidate BFIs and (3) to address further validation and/or identification work to be done.

Methods

This systematic review was based on a comprehensive literature search of three databases (PubMed, Scopus and ISI web of Science), to identify candidate BFIs. Via a second search step in the Human Metabolome Database (HMDB), the Food Database (FooDB) and Phenol-Explorer, the specificity of the candidate BFIs was evaluated, followed by an evaluation of the validity of the specific candidate BFIs, via pre-defined criteria.

Results

In total, 37 papers were included for cocoa and 8 papers for liquorice. For cocoa, 164 unique candidate BFIs were obtained, and for liquorice, four were identified in total. Despite the high number of identified BFIs for cocoa, none of the metabolites was specific. Therefore, the validity of these compounds was not further examined. For liquorice intake, 18-glycyrrhetinic acid (18-GA) was found to have the highest assumed validity.

Conclusions

For cocoa, specific BFIs were missing, mainly because the individual BFIs were also found in foods having a similar composition, such as tea (polyphenols) or coffee (caffeine). However, a combination of individual BFIs might lead to discriminating profiles between cocoa (products) and foods with a similar composition. Therefore, studies directly comparing the consumption of cocoa to these similar products are needed, enabling efforts to find a unique profile per product. For liquorice, we identified 18-GA as a promising BFI; however, important information on its validity is missing; thus, more research is necessary. Our findings indicate a need for more studies to determine acceptable BFIs for both cocoa and liquorice.

Untargeted 1H NMR-Based Metabolomics Analysis of Urine and Serum Profiles after Consumption of Lentils, Chickpeas, and Beans: An Extended Meal Study To Discover Dietary Biomarkers of Pulses

High legume intake has been shown to have beneficial effects on the health of humans. The use of nutritional biomarkers, as a complement to self-reported questionnaires, could assist in evaluating dietary intake and downstream effects on human health. The aim of this study was to investigate potential biomarkers of the consumption of pulses (i.e., white beans, chickpeas, and lentils) by using untargeted NMR-based metabolomics. Meals rich in pulses were consumed by a total of 11 participants in a randomized crossover study and multilevel partial least-squares regression was employed for paired comparisons. Metabolomics analysis indicated that trigonelline, 3-methylhistidine, dimethylglycine, trimethylamine, and lysine were potential, though not highly specific, biomarkers of pulse intake. Furthermore, monitoring of these metabolites for a period of 48 h after intake revealed a range of different excretion patterns among pulses. Following the consumption of pulses, a metabolomic profiling revealed that the concentration ratios of trigonelline, choline, lysine, and histidine were similar to those found in urine. In conclusion, this study identified potential urinary biomarkers of exposure to dietary pulses and provided valuable information about the time-response effect of these putative biomarkers.