Longitudinal association of urinary DHPPA and DHBA with cardiometabolic risk markers in Chinese community-dwelling adults

PURPOSE

Epidemiological studies using dietary biomarkers to investigate the association between whole grain intake and cardiometabolic health are lacking. We evaluate the 1-year longitudinal associations between urinary alkylresorcinol (AR) metabolites including 3-(3,5-dihydroxyphenyl)-1-propanoic acid (DHPPA) and 3,5-dihydroxybenozoic acid (DHBA), biomarkers of whole grain wheat and rye intakes, and cardiometabolic risk markers.

METHODS

Total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were repeatedly measured at baseline and after 1-year in 482 Chinese community-dwelling adults. Blood lipids and fasting glucose were assayed by electrochemical luminescence method. We assessed makers of inflammation and insulin response at baseline. Brachial-ankle pulse wave velocity (baPWV) and ankle-brachial index (ABI) were measured after 1-year. We used linear models and linear mixed-effects models to analyze single measurement and repeated measurements, respectively.

RESULTS

Each 1 µg/g creatinine increase in urinary DHPPA concentrations was associated with a -0.72% (P = 0.027) and - 1.26% (P = 0.005) decrease in TC and LDL-C, respectively. Urinary DHPPA was also correlated inversely with interleukin 6 (β = -0.371, P = 0.018) and C-reactive protein (β = -0.487, P = 0.036) at baseline. Higher urinary DHBA levels were associated with lower levels of SBP and DBP, with the percentage differences per 1 µg/g creatinine increment of -0.65% and - 0.74%, respectively (all P < 0.05). No association was found between the urinary AR metabolites and arterial stiffness.

CONCLUSIONS

Our study suggests that high intakes of whole grain wheat and rye, reflected by urinary DHPPA and DHBA, are favorably associated with cardiometabolic risk markers.

Effects of endocrine disrupting chemicals, blood metabolome, and epigenetics on breast cancer risk: A multi-dimensional mendelian randomization study

Current research on the relationship between environmental endocrine disrupting chemicals (EDCs) and breast cancer remains insufficient, with limited evidence and inconsistent conclusions. Mendelian randomization (MR) is a robust method for establishing causality, as it reduces biases from confounding factors and reverse causation. This study uses MR to investigate the effects of three types of EDCs, including bisphenols, parabens, and phthalates, on the risk of overall breast cancer and its subtypes-Luminal A, Luminal B, triple negative, human epidermal growth factor receptor 2-enriched, and estrogen receptor-positive/negative. The study also examines the 1400 blood metabolome as potential mediators and explores EDCs-associated DNA methylation changes as potential factors, with a focus on European populations. Our results shows that n-butyl paraben (n-BuP) is positively associated with Luminal A, mono-methyl phthalate is negatively associated with Luminal B, and mono-iso-butyl phthalate (MiBP) is positively associated with triple negative breast cancer (TNBC). Mediation analysis reveals that blood metabolites, such as caffeic acid sulfate and the caffeine-to-paraxanthine ratio, mediate the effect of n-BuP on Luminal A, while methylsuccinate mediate the effect of MiBP on TNBC. Epigenetic analysis shows associations between EDCs exposure-related DNA methylation changes at specific CpG sites (cg26325335, cg08537847, cg27454300) and different breast cancer risks. These findings not only suggest potential biomarkers for early detection and intervention but also underscore the imperative for further research to rigorously validate these associations.

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.

Adjusting for covariates and assessing modeling fitness in machine learning using MUVR2

Motivation

Machine learning (ML) methods are frequently used in Omics research to examine associations between molecular data and for example exposures and health conditions. ML is also used for feature selection to facilitate biological interpretation. Our previous MUVR algorithm was shown to generate predictions and variable selections at state-of-the-art performance. However, a general framework for assessing modeling fitness is still lacking. In addition, enabling to adjust for covariates is a highly desired, but largely lacking trait in ML. We aimed to address these issues in the new MUVR2 framework.

Results

The MUVR2 algorithm was developed to include the regularized regression framework elastic net in addition to partial least squares and random forest modeling. Compared with other cross-validation strategies, MUVR2 consistently showed state-of-the-art performance, including variable selection, while minimizing overfitting. Testing on simulated and real-world data, we also showed that MUVR2 allows for the adjustment for covariates using elastic net modeling, but not using partial least squares or random forest.

Availability and implementation

Algorithms, data, scripts, and a tutorial are open source under GPL-3 license and available in the MUVR2 R package at https://github.com/MetaboComp/MUVR2.

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.

Metabolism of phenolics in coffee and plant-based foods by canonical pathways: an assessment of the role of fatty acid β-oxidation to generate biologically-active and -inactive intermediates

ω-Phenyl-alkenoic acids are abundant in coffee, fruits, and vegetables. Along with ω-phenyl-alkanoic acids, they are produced from numerous dietary (poly)phenols and aromatic amino acids in vivo. This review addresses how phenyl-ring substitution and flux modulates their gut microbiota and endogenous β-oxidation. 3',5'-Dihydroxy-derivatives (from alkyl-resorcinols, flavanols, proanthocyanidins), and 4'-hydroxy-phenolic acids (from tyrosine, p-coumaric acid, naringenin) are β-oxidation substrates yielding benzoic acids. In contrast, 3',4',5'-tri-substituted-derivatives, 3',4'-dihydroxy-derivatives and 3'-methoxy-4'-hydroxy-derivatives (from coffee, tea, cereals, many fruits and vegetables) are poor β-oxidation substrates with metabolism diverted via gut microbiota dehydroxylation, phenylvalerolactone formation and phase-2 conjugation, possibly a strategy to conserve limited pools of coenzyme A. 4'-Methoxy-derivatives (citrus fruits) or 3',4'-dimethoxy-derivatives (coffee) are susceptible to hepatic "reverse" hydrogenation suggesting incompatibility with enoyl-CoA-hydratase. Gut microbiota-produced 3'-hydroxy-4'-methoxy-derivatives (citrus fruits) and 3'-hydroxy-derivatives (numerous (poly)phenols) are excreted as the phenyl-hydracrylic acid β-oxidation intermediate suggesting incompatibility with hydroxy-acyl-CoA dehydrogenase, albeit with considerable inter-individual variation. Further investigation is required to explain inter-individual variation, factors determining the amino acid to which C6-C3 and C6-C1 metabolites are conjugated, the precise role(s) of l-carnitine, whether glycine might be limiting, and whether phenolic acid-modulation of β-oxidation explains how phenolic acids affect key metabolic conditions, such as fatty liver, carbohydrate metabolism and insulin resistance.

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

Recent advances in metabolomics allow for more objective assessment of contemporary food exposures, which have been proposed as an alternative or complement to self-reporting of food intake. However, the quality of evidence supporting the utility of dietary biomarkers as valid measures of habitual intake of foods or complex dietary patterns in diverse populations has not been systematically evaluated. We reviewed nutritional metabolomics studies reporting metabolites associated with specific foods or food groups; evaluated the interstudy repeatability of dietary biomarker candidates; and reported study design, metabolomic approach, analytical technique(s), and type of biofluid analyzed. A comprehensive literature search of 5 databases (PubMed, EMBASE, Web of Science, BIOSIS, and CINAHL) was conducted from inception through December 2020. This review included 244 studies, 169 (69%) of which were interventional studies (9 of these were replicated in free-living participants) and 151 (62%) of which measured the metabolomic profile of serum and/or plasma. Food-based metabolites identified in ≥1 study and/or biofluid were associated with 11 food-specific categories or dietary patterns: 1) fruits; 2) vegetables; 3) high-fiber foods (grain-rich); 4) meats; 5) seafood; 6) pulses, legumes, and nuts; 7) alcohol; 8) caffeinated beverages, teas, and cocoas; 9) dairy and soya; 10) sweet and sugary foods; and 11) complex dietary patterns and other foods. We conclude that 69 metabolites represent good candidate biomarkers of food intake. Quantitative measurement of these metabolites will advance our understanding of the relation between diet and chronic disease risk and support evidence-based dietary guidelines for global health.

Effects of Bilberry and Oat intake on lipids, inflammation and exercise capacity after Acute Myocardial Infarction (BIOAMI): study protocol for a randomized, double-blind, placebo-controlled trial

Background

Bilberries from Sweden, rich in polyphenols, have shown cholesterol-lowering effects in small studies, and the cholesterol-lowering properties of oats, with abundant beta-glucans and potentially bioactive phytochemicals, are well established. Both may provide cardiometabolic benefits following acute myocardial infarction (AMI), but large studies of adequate statistical power and appropriate duration are needed to confirm clinically relevant treatment effects. No previous study has evaluated the potential additive or synergistic effects of bilberry combined with oats on cardiometabolic risk factors. Our primary objective is to assess cardioprotective effects of diet supplementation with dried bilberry or with bioprocessed oat bran, with a secondary explorative objective of assessing their combination, compared with a neutral isocaloric reference supplement, initiated within 5 days following percutaneous coronary intervention (PCI) for AMI.

Methods

The effects of Bilberry and Oat intake on lipids, inflammation and exercise capacity after Acute Myocardial Infarction (BIOAMI) trial is a double-blind, randomized, placebo-controlled clinical trial. A total of 900 patients will be randomized post-PCI to one of four dietary intervention arms. After randomization, subjects will receive beverages with bilberry powder (active), beverages with high-fiber bioprocessed oat bran (active), beverages with bilberry and oats combined (active), or reference beverages containing no active bilberry or active oats, for consumption twice daily during a 3-month intervention. The primary endpoint is the difference in LDL cholesterol change between the intervention groups after 3 months. The major secondary endpoint is exercise capacity at 3 months. Other secondary endpoints include plasma concentrations of biochemical markers of inflammation, metabolomics, and gut microbiota composition after 3 months.

Discussion

Controlling hyperlipidemia and inflammation is critical to preventing new cardiovascular events, but novel pharmacological treatments for these conditions are expensive and associated with negative side effects. If bilberry and/or oat, in addition to standard medical therapy, can lower LDL cholesterol and inflammation more than standard therapy alone, this could be a cost-effective and safe dietary strategy for secondary prevention after AMI.

Trial registration

ClinicalTrials.gov NCT03620266. Registered on August 8, 2018.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05287-5.

Visceral Adipose Tissue Phospholipid Signature of Insulin Sensitivity and Obesity

Alterations in visceral adipose tissue (VAT) are closely linked to cardiometabolic abnormalities. The aim of this work is to define a metabolic signature in VAT of insulin resistance (IR) dependent on, and independent of, obesity. An untargeted UPLC-Q-Exactive metabolomic approach was carried out on the VAT of obese insulin-sensitive (IS) and insulin-resistant subjects (N = 11 and N = 25, respectively) and nonobese IS and IR subjects (N = 25 and N = 10, respectively). The VAT metabolome in obesity was defined among other things by changes in the metabolism of lipids, nucleotides, carbohydrates, and amino acids, whereas when combined with high IR, it affected the metabolism of 18 carbon fatty acyl-containing phospholipid species. A multimetabolite model created by glycerophosphatidylinositol (18:0); glycerophosphatidylethanolamine (18:2); glycerophosphatidylserine (18:0); and glycerophosphatidylcholine (18:0/18:1), (18:2/18:2), and (18:2/18:3) exhibited a highly predictive performance to identify the metabotype of “insulin-sensitive obesity” among obese individuals [area under the curve (AUC) 96.7% (91.9–100)] and within the entire study population [AUC 87.6% (79.0–96.2)]. We demonstrated that IR has a unique and shared metabolic signature dependent on, and independent of, obesity. For it to be used in clinical practice, these findings need to be validated in a more accessible sample, such as blood.

Blood polyphenol concentrations and differentiated thyroid carcinoma in women from the European Prospective Investigation into Cancer and Nutrition (EPIC) study

BACKGROUND

Polyphenols are natural compounds with anticarcinogenic properties in cellular and animal models, but epidemiological evidence determining the associations of these compounds with thyroid cancer (TC) is lacking.

OBJECTIVES

The aim of this study was to evaluate the relations between blood concentrations of 36 polyphenols and TC risk in EPIC (the European Prospective Investigation into Cancer and Nutrition).

METHODS

A nested case-control study was conducted on 273 female cases (210 papillary, 45 follicular, and 18 not otherwise specified TC tumors) and 512 strictly matched controls. Blood polyphenol concentrations were analyzed by HPLC coupled to tandem MS after enzymatic hydrolysis.

RESULTS

Using multivariable-adjusted conditional logistic regression models, caffeic acid (ORlog2: 0.55; 95% CI: 0.33, 0.93) and its dehydrogenated metabolite, 3,4-dihydroxyphenylpropionic acid (ORlog2: 0.84; 95% CI: 0.71, 0.99), were inversely associated with differentiated TC risk. Similar results were observed for papillary TC, but not for follicular TC. Ferulic acid was also inversely associated only with papillary TC (ORlog2: 0.68; 95% CI: 0.51, 0.91). However, none of these relations was significant after Bonferroni correction for multiple testing. No association was observed for any of the remaining polyphenols with total differentiated, papillary, or follicular TC.

CONCLUSIONS

Blood polyphenol concentrations were mostly not associated with differentiated TC risk in women, although our study raises the possibility that high blood concentrations of caffeic, 3,4-dihydroxyphenylpropionic, and ferulic acids may be related to a lower papillary TC risk.

The MEDGICarb-Study: Design of a multi-center randomized controlled trial to determine the differential health-promoting effects of low- and high-glycemic index Mediterranean-style eating patterns

Adults with central adiposity and other features of the metabolic syndrome have a markedly elevated risk of developing type 2 diabetes (T2D) and cardiovascular disease (CVD). A Mediterranean-style healthy eating pattern (MED-HEP) and consumption of foods with a lower glycemic index (GI) are potential dietary approaches to curb the T2D and CVD epidemic. However, experimental evidence of the effectiveness of MED-HEP and of the contribution of GI towards improving indices of glucose homeostasis, especially among non-diabetic people, are lacking. Therefore, we developed the MedGI-Carb trial, a multi-center (Italy, Sweden, and United States) intervention in adults with at least two components of the metabolic syndrome (elevated waist circumference + one other component) that aims to improve markers of glucose homeostasis through dietary modification. All participants were randomized to consume an isocaloric high- or low-GI MED-HEP for 12 weeks. We hypothesized that indexes of insulinemia (primary outcome: postprandial insulin and glucose after standardized breakfast and lunch; secondary outcomes: fasting plasma glucose and insulin, HbA_1c, 24-h continuous glucose monitoring) would be improved more with the low-GI versus the high-GI MED-HEP. Additionally, we hypothesized that consumption of a MED-HEP would improve other markers of cardiometabolic health and well-being (fasting blood pressure, fasting lipid profile, sleep quality, satiety, global metabolic alterations in the plasma metabolome, changes in the gut microbiota, subjective health and well-being), with no difference between groups. Collectively, the design of MEDGI-Carb allows several different research questions to be explored. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03410719.

Urinary biomarkers of dietary intake: a review

Dietary intakes are commonly assessed by established methods including food frequency questionnaires, food records, or recalls. These self-report methods have limitations impacting validity and reliability. Dietary biomarkers provide objective verification of self-reported food intakes, and represent a rapidly evolving area. This review aims to summarize the urinary biomarkers of individual foods, food groups, dietary patterns, or nutritional supplements that have been evaluated to date. Six electronic databases were searched. Included studies involved healthy populations, were published from 2000, and compared measured dietary intake with urinary markers. The initial search identified 9985 studies; of these, 616 full texts were retrieved and 109 full texts were included. Of the included studies, 67 foods and food components were studied, and 347 unique urinary biomarkers were identified. The most reliable biomarkers identified were whole grains (alkylresorcinols), soy (isoflavones), and sugar (sucrose and fructose). While numerous novel urinary biomarkers have been identified, further validation studies are warranted to verify the accuracy of self-reported intakes and utility within practice.

Biomarkers of Whole-Grain and Cereal-Fiber Intake in Human Studies: A Systematic Review of the Available Evidence and Perspectives

High whole-grain consumption is related to better health outcomes. The specific physiological effect of these compounds is still unrevealed, partly because the accurate estimation of the intake of whole grains from dietary assessments is difficult and prone to bias, due to the complexity of the estimation of the intake by the consumer. A biomarker of whole-grain intake and type of whole-grain intake would be useful for quantifying the exposure to whole-grain intake. In this review, we aim to review the evidence on the potential biomarkers for whole-grain intake in the literature. We conducted a systematic search in Medline, Embase, Web of Science, and the Cochrane database. In total, 39 papers met the inclusion criteria following the PRISMA guidelines and were included. The relative validity, responsiveness, and reproducibility of these markers were assessed for short-, medium-, and long-term exposure as important criteria for the potential use of these biomarkers from a clinical and research perspective. We found three major groups of biomarkers: (1) alkylresorcinol, as well as its homologs and metabolites, assessed in plasma, adipose tissue biopsies, erythrocyte membranes, and urine; (2) avenacosides, assessed in urine samples; and (3) benzoxazinoid-derived phenylacetamide sulfates, assessed in blood and urine samples. The reviewed biomarkers may be used for improved assessment of associations between whole-grain intake and health outcomes.

Biomarkers of cereal food intake

Background/objectives

Cereal foods are major contributors to the daily energy, protein, and dietary fiber intake all over the world. The role of cereals in human health is dependent on whether they are consumed as refined or whole grain and on cereal species. To unravel the underlying mechanisms of health effects attributed to specific cereal foods and to provide more precise dietary advice, there is a need for improved dietary assessment of whole-grain intake. Dietary biomarkers of specific cereals, different fractions or cereal-containing foods could offer such a possibility. The aim of this review was to summarize the current status on biomarkers of different cereals, fractions, and specific cereal foods.

Subjects and methods

A literature review was conducted and putative biomarkers of different cereals and pseudo-cereals (wheat, oats, rye, barley, rice, and quinoa) as well as for different grain fractions (whole grain, refined grain, bran) and foods were summarized and discussed.

Results

Several putative biomarkers have been suggested for different cereals, due to their unique presence in these grains. Among the biomarkers, odd-numbered alkylresorcinols are the most well-studied and -evaluated biomarkers and reflect whole-grain wheat and rye intake. Even-numbered alkylresorcinols have been suggested to reflect quinoa intake. Recent studies have also highlighted the potential of avenanthramides and avenacosides as specific biomarkers of oat intake, and a set of biomarkers have been suggested to reflect rice bran intake. However, there are yet no specific biomarkers of refined grains. Most biomarker candidates remain to be evaluated in controlled interventions and free-living populations before applied as biomarkers of intake in food and health studies.

Conclusion

Several putative biomarkers of different cereals have been suggested and should be validated in human studies using recently developed food intake biomarker validation criteria.

Joint Analysis of Metabolite Markers of Fish Intake and Persistent Organic Pollutants in Relation to Type 2 Diabetes Risk in Swedish Adults

BACKGROUND

There is conflicting evidence regarding the association between fish intake and type 2 diabetes (T2D) incidence, possibly owing to measurement errors in self-reported intake and coexposure to persistent organic pollutants (POPs) present in fish.

OBJECTIVE

The aim of this study was to identify plasma metabolites associated with fish intake and to assess their association with T2D risk, independently of POPs, in Swedish adults.

METHODS

In a case-control study nested in the Swedish Västerbotten Intervention Programme, fasting plasma samples from 421 matched T2D case-control pairs of men and women aged 30-60 y at baseline and 10-y follow-up samples from a subset of 149 pairs were analyzed using untargeted metabolomics. Moreover, 16 plasma POPs were analyzed for the 149 pairs who had repeated samples available. Fish-related plasma metabolites were identified using multivariate modelling and partial correlation analysis. Reproducibility of metabolites and metabolite patterns, derived via principal component analysis (PCA), was assessed by intraclass correlation. A unique component of metabolites unrelated to POPs was dissected by integrating metabolites and POPs using 2-way orthogonal partial least squares regression. ORs of T2D were estimated using conditional logistic regression.

RESULTS

We identified 31 metabolites associated with fish intake that had poor to good reproducibility. A PCA-derived metabolite pattern strongly correlated with fish intake (ρ = 0.37, P < 0.001) but showed no association with T2D risk. Integrating fish-related metabolites and POPs led to a unique metabolite component independent of POPs, which tended to be inversely associated with T2D risk (OR: 0.75; 95% CI: 0.54, 1.02, P = 0.07). This component mainly consisted of metabolites reflecting fatty fish intake.

CONCLUSIONS

Our results suggest that fatty fish intake may be beneficial for T2D prevention, after removing the counteractive effects of coexposure to POPs in Swedish adults. Integrating metabolite markers and POP exposures appears a promising approach to advance the understanding of associations between fish intake and T2D incidence.

Discovery of Volatile Biomarkers of Parkinson's Disease from Sebum

Parkinson's disease (PD) is a progressive, neurodegenerative disease that presents with significant motor symptoms, for which there is no diagnostic chemical test. We have serendipitously identified a hyperosmic individual, a "Super Smeller" who can detect PD by odor alone, and our early pilot studies have indicated that the odor was present in the sebum from the skin of PD subjects. Here, we have employed an unbiased approach to investigate the volatile metabolites of sebum samples obtained noninvasively from the upper back of 64 participants in total (21 controls and 43 PD subjects). Our results, validated by an independent cohort (n=31), identified a distinct volatiles-associated signature of PD, including altered levels of perillic aldehyde and eicosane, the smell of which was then described as being highly similar to the scent of PD by our "Super Smeller".

The 1H NMR serum metabolomics response to a two meal challenge: a cross-over dietary intervention study in healthy human volunteers

BACKGROUND

Metabolomics represents a powerful tool for exploring modulation of the human metabolome in response to food intake. However, the choice of multivariate statistical approach is not always evident, especially for complex experimental designs with repeated measurements per individual. Here we have investigated the serum metabolic responses to two breakfast meals: an egg and ham based breakfast and a cereal based breakfast using three different multivariate approaches based on the Projections to Latent Structures framework.

METHODS

In a cross over design, 24 healthy volunteers ate the egg and ham breakfast and cereal breakfast on four occasions each. Postprandial serum samples were subjected to metabolite profiling using ¹H nuclear magnetic resonance spectroscopy and metabolites were identified using 2D nuclear magnetic resonance spectroscopy. Metabolic profiles were analyzed using Orthogonal Projections to Latent Structures with Discriminant Analysis and Effect Projections and ANOVA-decomposed Projections to Latent Structures.

RESULTS

The Orthogonal Projections to Latent Structures with Discriminant Analysis model correctly classified 92 and 90% of the samples from the cereal breakfast and egg and ham breakfast, respectively, but confounded dietary effects with inter-personal variability. Orthogonal Projections to Latent Structures with Effect Projections removed inter-personal variability and performed perfect classification between breakfasts, however at the expense of comparing means of respective breakfasts instead of all samples. ANOVA-decomposed Projections to Latent Structures managed to remove inter-personal variability and predicted 99% of all individual samples correctly. Proline, tyrosine, and N-acetylated amino acids were found in higher concentration after consumption of the cereal breakfast while creatine, methanol, and isoleucine were found in higher concentration after the egg and ham breakfast.

CONCLUSIONS

Our results demonstrate that the choice of statistical method will influence the results and adequate methods need to be employed to manage sample dependency and repeated measurements in cross-over studies. In addition, ¹H nuclear magnetic resonance serum metabolomics could reproducibly characterize postprandial metabolic profiles and identify discriminatory metabolites largely reflecting dietary composition.

TRIAL REGISTRATION

Registered with ClinicalTrials.gov, identifier: NCT02039596 . Date of registration: January 17, 2014.

Recent Advances in the Application of Metabolomics for Nutrition and Health

Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

Variable selection and validation in multivariate modelling

MOTIVATION

Validation of variable selection and predictive performance is crucial in construction of robust multivariate models that generalize well, minimize overfitting and facilitate interpretation of results. Inappropriate variable selection leads instead to selection bias, thereby increasing the risk of model overfitting and false positive discoveries. Although several algorithms exist to identify a minimal set of most informative variables (i.e. the minimal-optimal problem), few can select all variables related to the research question (i.e. the all-relevant problem). Robust algorithms combining identification of both minimal-optimal and all-relevant variables with proper cross-validation are urgently needed.

RESULTS

We developed the MUVR algorithm to improve predictive performance and minimize overfitting and false positives in multivariate analysis. In the MUVR algorithm, minimal variable selection is achieved by performing recursive variable elimination in a repeated double cross-validation (rdCV) procedure. The algorithm supports partial least squares and random forest modelling, and simultaneously identifies minimal-optimal and all-relevant variable sets for regression, classification and multilevel analyses. Using three authentic omics datasets, MUVR yielded parsimonious models with minimal overfitting and improved model performance compared with state-of-the-art rdCV. Moreover, MUVR showed advantages over other variable selection algorithms, i.e. Boruta and VSURF, including simultaneous variable selection and validation scheme and wider applicability.

AVAILABILITY AND IMPLEMENTATION

Algorithms, data, scripts and tutorial are open source and available as an R package ('MUVR') at https://gitlab.com/CarlBrunius/MUVR.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Consumption of whole grain/bran rye instead of refined wheat decrease concentrations of TNF-R2, e-selectin, and endostatin in an exploratory study in men with prostate cancer

BACKGROUND & AIMS

Rye consumption has shown beneficial effects on prostate cancer tumors, as indicated by slower initial tumor growth in animal models and lowering of prostate-specific antigen (PSA) in humans. This study evaluated the effects of whole grain/bran rye consumption on low-grade inflammation and endothelial function biomarkers in men with prostate cancer.

METHODS

Seventeen men with untreated, low-grade prostate cancer consumed 485 g rye whole grain and bran products (RP) per day or refined wheat products with added cellulose (WP) in a randomized crossover design. Fasting blood samples were taken before and after 2, 4, and 6 weeks of treatment.

RESULTS

Concentrations of tumor nuclear factor-receptor 2 (TNF-R2), e-selectin, and endostatin were significantly lower after consumption of the RP diet compared with WP (p < 0.05). Cathepsin S concentration was positively correlated to TNF-R2 and endostatin concentrations across all occasions. Strong correlations were consistently found between intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) and between interleukin-6 (IL-6) and interleukin-1 receptor antagonist (IL-1RA). No effect of intervention was found in 92 inflammation-related protein biomarkers measured in a proximity extension assay.

CONCLUSIONS

RP diet lowered TNF-R2, e-selectin, and endostatin, compared with WP in men with prostate cancer. These effects were accompanied by a reduction in PSA.

Nutritional impact on Immunological maturation during Childhood in relation to the Environment (NICE): a prospective birth cohort in northern Sweden

INTRODUCTION

Prenatal and neonatal environmental factors, such as nutrition, microbes and toxicants, may affect health throughout life. Many diseases, such as allergy and impaired child development, may be programmed already in utero or during early infancy. Birth cohorts are important tools to study associations between early life exposure and disease risk. Here, we describe the study protocol of the prospective birth cohort, 'Nutritional impact on Immunological maturation during Childhood in relation to the Environment' (NICE). The primary aim of the NICE cohort is to clarify the effect of key environmental exposures-diet, microbes and environmental toxicants-during pregnancy and early childhood, on the maturation of the infant's immune system, including initiation of sensitisation and allergy as well as some secondary outcomes: infant growth, obesity, neurological development and oral health.

METHODS AND ANALYSIS

The NICE cohort will recruit about 650 families during mid-pregnancy. The principal inclusion criterion will be planned birth at the Sunderby Hospital in the north of Sweden, during 2015-2018. Questionnaires data and biological samples will be collected at 10 time-points, from pregnancy until the children reach 4 years of age. Samples will be collected primarily from mothers and children, and from fathers. Biological samples include blood, urine, placenta, breast milk, meconium, faeces, saliva and hair. Information regarding allergic heredity, diet, socioeconomic status, lifestyle including smoking, siblings, pet ownership, etc will be collected using questionnaires. Sensitisation to common allergens will be assessed by skin prick testing and allergic disease will be diagnosed by a paediatrician at 1 and 4 years of age. At 4 years of age, the children will also be examined regarding growth, neurobehavioural and neurophysiological status and oral health.

ETHICS AND DISSEMINATION

The NICE cohort has been approved by the Regional Ethical Review Board in Umeå, Sweden (2013/18-31M). Results will be disseminated through peer-reviewed journals and communicated on scientific conferences.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Plasma metabolites associated with healthy Nordic dietary indexes and risk of type 2 diabetes-a nested case-control study in a Swedish population

Background

Epidemiologic evidence on the association of a healthy Nordic diet and future type 2 diabetes (T2D) is limited. Exploring metabolites as biomarkers of healthy Nordic dietary patterns may facilitate investigation of associations between such patterns and T2D.

Objectives

We aimed to identify metabolites related to a priori-defined healthy Nordic dietary indexes, the Baltic Sea Diet Score (BSDS) and Healthy Nordic Food Index (HNFI), and evaluate associations with the T2D risk in a case-control study nested in a Swedish population-based prospective cohort.

Design

Plasma samples from 421 case-control pairs at baseline and samples from a subset of 151 healthy controls at a 10-y follow-up were analyzed with the use of untargeted liquid chromatography-mass spectrometry metabolomics. Index-related metabolites were identified through the use of random forest modelling followed by partial correlation analysis adjustment for lifestyle confounders. Metabolite patterns were derived via principal component analysis (PCA). ORs of T2D were estimated via conditional logistic regression. Reproducibility of metabolites was assessed by intraclass correlation (ICC) in healthy controls. Associations were also assessed for 10 metabolites previously identified as linking a healthy Nordic diet with T2D.

Results

In total, 31 metabolites were associated with BSDS and/or HNFI (-0.19 ≤ r ≤ 0.21, 0.10 ≤ ICC ≤ 0.59). Two PCs were determined from index-related metabolites: PC1 strongly correlated to the indexes (r = 0.27 for BSDS, r = 0.25 for HNFI, ICC = 0.45) but showed no association with T2D risk. PC2 was weakly associated with the indexes, but more strongly with foods not part of the indexes, e.g., pizza, sausages, and hamburgers. PC2 was also significantly associated with T2D risk. Predefined metabolites were confirmed to be reflective of consumption of whole grains, fish, or vegetables, but not related to T2D risk.

Conclusions

Our study did not support an association between healthy Nordic dietary indexes and T2D. However, foods such as hamburger, sausage, and pizza not covered by the indexes appeared to be more important for T2D risk in the current population.

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.

Impact in Plasma Metabolome as Effect of Lifestyle Intervention for Weight-Loss Reveals Metabolic Benefits in Metabolically Healthy Obese Women

Little is known regarding metabolic benefits of weight loss (WL) on the metabolically healthy obese (MHO) patients. We aimed to examine the impact of a lifestyle weight loss (LWL) treatment on the plasma metabolomic profile in MHO individuals. Plasma samples from 57 MHO women allocated to an intensive LWL treatment group (TG, hypocaloric Mediterranean diet and regular physical activity, n = 30) or to a control group (CG, general recommendations of a healthy diet and physical activity, n = 27) were analyzed using an untargeted ¹H NMR metabolomics approach at baseline, after 3 months (intervention), and 12 months (follow-up). The impact of the LWL intervention on plasma metabolome was statistically significant at 3 months but not at follow-up and included higher levels of formate and phosphocreatine and lower levels of LDL/VLDL (signals) and trimethylamine in the TG. These metabolites were also correlated with WL. Higher myo-inositol, methylguanidine, and 3-hydroxybutyrate, and lower proline, were also found in the TG; higher levels of hippurate and asparagine, and lower levels of 2-hydroxybutyrate and creatine, were associated with WL. The current findings suggest that an intensive LWL treatment, and the consequent WL, leads to an improved plasma metabolic profile in MHO women through its impact on energy, amino acid, lipoprotein, and microbial metabolism.

Plasma metabolites associated with type 2 diabetes in a Swedish population: a case-control study nested in a prospective cohort

AIMS/HYPOTHESIS

The aims of the present work were to identify plasma metabolites that predict future type 2 diabetes, to investigate the changes in identified metabolites among individuals who later did or did not develop type 2 diabetes over time, and to assess the extent to which inclusion of predictive metabolites could improve risk prediction.

METHODS

We established a nested case-control study within the Swedish prospective population-based Västerbotten Intervention Programme cohort. Using untargeted liquid chromatography-MS metabolomics, we analysed plasma samples from 503 case-control pairs at baseline (a median time of 7 years prior to diagnosis) and samples from a subset of 187 case-control pairs at 10 years of follow-up. Discriminative metabolites between cases and controls at baseline were optimally selected using a multivariate data analysis pipeline adapted for large-scale metabolomics. Conditional logistic regression was used to assess associations between discriminative metabolites and future type 2 diabetes, adjusting for several known risk factors. Reproducibility of identified metabolites was estimated by intra-class correlation over the 10 year period among the subset of healthy participants; their systematic changes over time in relation to diagnosis among those who developed type 2 diabetes were investigated using mixed models. Risk prediction performance of models made from different predictors was evaluated using area under the receiver operating characteristic curve, discrimination improvement index and net reclassification index.

RESULTS

We identified 46 predictive plasma metabolites of type 2 diabetes. Among novel findings, phosphatidylcholines (PCs) containing odd-chain fatty acids (C19:1 and C17:0) and 2-hydroxyethanesulfonate were associated with the likelihood of developing type 2 diabetes; we also confirmed previously identified predictive biomarkers. Identified metabolites strongly correlated with insulin resistance and/or beta cell dysfunction. Of 46 identified metabolites, 26 showed intermediate to high reproducibility among healthy individuals. Moreover, PCs with odd-chain fatty acids, branched-chain amino acids, 3-methyl-2-oxovaleric acid and glutamate changed over time along with disease progression among diabetes cases. Importantly, we found that a combination of five of the most robustly predictive metabolites significantly improved risk prediction if added to models with an a priori defined set of traditional risk factors, but only a marginal improvement was achieved when using models based on optimally selected traditional risk factors.

CONCLUSIONS/INTERPRETATION

Predictive metabolites may improve understanding of the pathophysiology of type 2 diabetes and reflect disease progression, but they provide limited incremental value in risk prediction beyond optimal use of traditional risk factors.

Urinary Metabolomics Profiles Associated to Bovine Meat Ingestion in Humans

SCOPE

The impact of meat consumption on human health is widely examined in nutritional epidemiological studies, especially due to the connection between the consumption of red and processed meat and the risk of colon cancer. Food questionnaires do not assess the exposure to different methods of meat cooking. This study aimed to identify biomarkers of the acute ingestion of bovine meat cooked with two different processes.

METHODS AND RESULTS

Non-targeted UPLC-MS metabolite profiling was done on urine samples obtained from 24 healthy volunteers before and 8 h after the ingestion of a single meal composed of intrinsically ¹⁵ N labelled bovine meat, either cooked at 55 °C for 5 min or at 90 °C for 30 min. A discriminant analysis extension of independent components analysis was applied to the mass spectral data. After meat ingestion, the urinary excretion of 1-methylhistidine, phenylacetylglutamine, and short- and medium-chained acylcarnitines was observed. ¹⁵ N labelling was detected in these metabolites, thus confirming their origin from ingested meat. However, no difference was observed in urinary metabolomic profiles according to the meat cooking process used.

CONCLUSION

Meat ingestion led to the excretion of several nitrogen-containing compounds, but although a metabolic signature was detected for meat ingestion, the impact of the cooking process was not detectable at the level of urinary metabolic signature in our experimental conditions.

Targeted metabolomics reveals differences in the extended postprandial plasma metabolome of healthy subjects after intake of whole-grain rye porridges versus refined wheat bread

SCOPE

We previously found that whole-grain (WG) rye porridges suppressed appetite and improved glucose metabolism. This study aimed to investigate potential plasma metabolites that may be related to differences in those appetite and glucose responses.

METHODS AND RESULTS

Twenty-one health subjects consumed six isocaloric breakfasts in a randomized cross-over study. Plain WG rye porridges (40 and 55 g), rye porridge enriched with different inulin: gluten proportions (9:3 g; 6:6 g; 3:9 g), and a 55 g refined wheat bread (control) were served as part of complete breakfast, followed by a standardized lunch. NMR metabolomics assessed 36 plasma metabolites and short chain fatty acids were measured by GC-MS from baseline up to 8 h. Pre-lunch plasma essential amino acids reflected protein composition and post-lunch plasma short chain fatty acids varied with fiber content in breakfasts. No correlations were observed between measured metabolites and glucose, insulin, or appetite responses.

CONCLUSIONS

Differences in protein and fiber contents in breakfasts altered postprandial plasma amino acids and short chain fatty acids, respectively, but were unrelated to appetite and glucose responses. Further studies are warrant to identify the underlying mechanisms for the beneficial effects on appetite and second meal glucose responses after rye-based foods.

Microbial and endogenous metabolic conversions of rye phytochemicals

Rye is one of the main cereals produced and consumed in the hemiboreal climate region. Due to its use primarily as wholegrain products, rye provides a rich source of dietary fibre as well as several classes of phytochemicals, bioactive compounds with potentially positive health implications. Here, we review the current knowledge of the metabolic pathways of phytochemical classes abundant in rye, starting from the microbial transformations occurring during the sourdough process and colonic fermentation and continuing with the endogenous metabolism. Additionally, we discuss the detection of specific metabolites by MS in different phases of their journey from the cereal to the target organs and excretion.

Metabolic Phenotyping of Diet and Dietary Intake

Nutrition provides the building blocks for growth, repair, and maintenance of the body and is key to maintaining health. Exposure to fast foods, mass production of dietary components, and wider importation of goods have challenged the balance between diet and health in recent decades, and both scientists and clinicians struggle to characterize the relationship between this changing dietary landscape and human metabolism with its consequent impact on health. Metabolic phenotyping of foods, using high-density data-generating technologies to profile the biochemical composition of foods, meals, and human samples (pre- and postfood intake), can be used to map the complex interaction between the diet and human metabolism and also to assess food quality and safety. Here, we outline some of the techniques currently used for metabolic phenotyping and describe key applications in the food sciences, ending with a broad outlook at some of the newer technologies in the field with a view to exploring their potential to address some of the critical challenges in nutritional science.

Urinary Biomarkers of Whole Grain Wheat Intake Identified by Non-targeted and Targeted Metabolomics Approaches

Mounting evidence suggests that whole grain (WG) intake plays an important role in chronic disease prevention. However, numerous human studies have failed to produce clear-cut conclusions on this topic. Here, a combination of non-targeted and targeted metabolomics approaches, together with kinetic studies, was used to investigate biomarkers of WG wheat intake and further explore the diet-disease associations. Via these integrated approaches, forty-one compounds were identified as the most discriminating endogenous metabolites after WG versus refined grain (RG) wheat bread consumption. The corresponding biological assessment of these endogenous changes suggests that, in contrast to RG consumption, WG wheat consumption may facilitate antioxidant defense systems and moderate the risk factors of cancer, cardiovascular diseases, and other chronic diseases. A panel of urinary markers consisting of seven alkylresorcinol metabolites and five benzoxazinoid derivatives as specific biomarkers, as well as five phenolic acid derivatives, was also established to cover multiple time points and longer time periods for correctly and objectively monitoring WG wheat intake. Through these findings, we have established a comprehensive biomarker pool to better assess WG wheat consumption, and to monitor the endogenous changes that are linked to health effects of WG wheat consumption.

Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction

INTRODUCTION

Liquid chromatography-mass spectrometry (LC-MS) is a commonly used technique in untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, data generated from multiple batches are affected by measurement errors inherent to alterations in signal intensity, drift in mass accuracy and retention times between samples both within and between batches. These measurement errors reduce repeatability and reproducibility and may thus decrease the power to detect biological responses and obscure interpretation.

OBJECTIVE

Our aim was to develop procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality.

METHODS

Algorithms were developed for: (i) alignment and merging of features that are systematically misaligned between batches, through aggregating feature presence/missingness on batch level and combining similar features orthogonally present between batches; and (ii) within-batch drift correction using a cluster-based approach that allows multiple drift patterns within batch. Furthermore, a heuristic criterion was developed for the feature-wise choice of reference-based or population-based between-batch normalisation.

RESULTS

In authentic data, between-batch alignment resulted in picking 15 % more features and deconvoluting 15 % of features previously erroneously aligned. Within-batch correction provided a decrease in median quality control feature coefficient of variation from 20.5 to 15.1 %. Algorithms are open source and available as an R package ('batchCorr').

CONCLUSIONS

The developed procedures provide unbiased measures of improved data quality, with implications for improved data analysis. Although developed for LC-MS based metabolomics, these methods are generic and can be applied to other data suffering from similar limitations.

Characterization and Discrimination of Ancient Grains: A Metabolomics Approach

Hulled, or ancient, wheats were the earliest domesticated wheats by mankind and the ancestors of current wheats. Their cultivation drastically decreased during the 1960s; however, the increasing demand for a healthy and equilibrated diet led to rediscovering these grains. Our aim was to use a non-targeted metabolomic approach to discriminate and characterize similarities and differences between ancient Triticum varieties. For this purpose, 77 hulled wheat samples from three different varieties were collected: Garfagnana T. turgidum var. dicoccum L. (emmer), ID331 T. monococcum L. (einkorn) and Rouquin T. spelta L. (spelt). The ultra high performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-QTOF) metabolomics approach highlighted a pronounced sample clustering according to the wheat variety, with an excellent predictability (Q²), for all the models built. Fifteen metabolites were tentatively identified based on accurate masses, isotopic pattern, and product ion spectra. Among these, alkylresorcinols (ARs) were found to be significantly higher in spelt and emmer, showing different homologue composition. Furthermore, phosphatidylcholines (PC) and lysophosphatidylcholines (lysoPC) levels were higher in einkorn variety. The results obtained in this study confirmed the importance of ARs as markers to distinguish between Triticum species and revealed their values as cultivar markers, being not affected by the environmental influences.

Bacterial associations reveal spatial population dynamics in Anopheles gambiae mosquitoes

The intolerable burden of malaria has for too long plagued humanity and the prospect of eradicating malaria is an optimistic, but reachable, target in the 21(st) century. However, extensive knowledge is needed about the spatial structure of mosquito populations in order to develop effective interventions against malaria transmission. We hypothesized that the microbiota associated with a mosquito reflects acquisition of bacteria in different environments. By analyzing the whole-body bacterial flora of An. gambiae mosquitoes from Burkina Faso by 16 S amplicon sequencing, we found that the different environments gave each mosquito a specific bacterial profile. In addition, the bacterial profiles provided precise and predicting information on the spatial dynamics of the mosquito population as a whole and showed that the mosquitoes formed clear local populations within a meta-population network. We believe that using microbiotas as proxies for population structures will greatly aid improving the performance of vector interventions around the world.