Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial

The Influence of Animal- or Plant-Based Diets on Blood and Urine Trimethylamine-N-Oxide (TMAO) Levels in Humans

PURPOSE OF REVIEW

The aim of the review was to evaluate which diets are associated with higher TMAO levels.

RECENT FINDINGS

Several studies have shown that plasma and urinary levels of trimethylamine N-oxide (TMAO) are a reliable indicator of cardiovascular disease risk. Diet certainly has a strong influence on TMAO levels, but there is still uncertainty about which diet is the most effective in reducing this risk factor. PubMed, Web of Science and Scopus were searched for studies that were published up until July 1, 2021 using specific keywords. In total, 447 studies were evaluated, of which papers on individual foods or supplements, or conducted in children, in vitro or in animal model studies were excluded. Twenty-five studies were included in this review. Three studies showed that caloric restriction and (visceral) weight loss improve TMAO levels. Six out of eight studies revealed beneficial effects of plant-based diets on plasma or urinary TMAO concentrations. Most of the studies demonstrated that a diet high in protein, particularly of animal origin, such as diets rich in fish or red meat, have negative effects on TMAO levels. Most studies that have evaluated the relationship between diet and plasma or urinary concentrations of TMAO seem to indicate that plant-based diets (Mediterranean, vegetarian and vegan) are effective in improving TMAO levels, while animal-based diets appear to have the opposite effect. Further long-term studies are needed to assess whether vegetarian or vegan diets are more effective than the Mediterranean diet in reducing TMAO levels.

OUP accepted manuscript

Context

Most methods for assessing dietary intake have considerable measurement error. Dietary biomarkers are objective tools for dietary assessment. Dietary biomarkers of dietary patterns have not been well described, despite modern dietary guidelines endorsing dietary patterns.

Objective

This systematic review sought to describe the dietary biomarkers commonly used to assess dietary patterns, and the novel biomarkers of dietary patterns identified by exploratory studies.

Data Sources

MEDLINE, Embase, Cochrane Central, PreMEDLINE, and CINAHL databases were searched.

Data Extraction

Data extraction and bias assessment were undertaken in duplicate.

Data Analysis

A qualitative approach was applied, without statistical analysis.

Conclusion

In controlled settings, dietary biomarkers of single nutrients or of individual foods or food groups are commonly used to assess compliance with dietary patterns. However, currently, there are no dietary biomarkers or biomarker profiles that are able to identify the specific dietary pattern that has been consumed by an individual. Future work should seek to validate novel dietary biomarkers and biomarker profiles that are indicative of specific dietary patterns and their characteristics. A dietary biomarker panel consisting of multiple biomarkers is almost certainly necessary to capture the complexity of dietary patterns.

Systematic Review Registration

PROSPERO registration no. CRD42019129839.

A systematic review of metabolomic studies of childhood obesity: State of the evidence for metabolic determinants and consequences

Childhood obesity has become a global epidemic and carries significant long-term consequences to physical and mental health. Metabolomics, the global profiling of small molecules or metabolites, may reveal the mechanisms of development of childhood obesity and clarify links between obesity and metabolic disease. A systematic review of metabolomic studies of childhood obesity was conducted, following Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, searching across Scopus, Ovid, Web of Science and PubMed databases for articles published from January 1, 2005 to July 8, 2020, retrieving 1271 different records and retaining 41 articles for qualitative synthesis. Study quality was assessed using a modified Newcastle-Ottawa Scale. Thirty-three studies were conducted on blood, six on urine, three on umbilical cord blood, and one on saliva. Thirty studies were primarily cross-sectional, five studies were primarily longitudinal, and seven studies examined effects of weight-loss following a life-style intervention. A consistent metabolic profile of childhood obesity was observed including amino acids (particularly branched chain and aromatic), carnitines, lipids, and steroids. Although the use of metabolomics in childhood obesity research is still developing, the identified metabolites have provided additional insight into the pathogenesis of many obesity-related diseases. Further longitudinal research is needed into the role of metabolic profiles and child obesity risk.

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.

Odd Chain Fatty Acids Are Not Robust Biomarkers for Dietary Intake of Fiber

SCOPE

Prior investigation has suggested a positive association between increased colonic propionate production and circulating odd-chain fatty acids (OCFAs; pentadecanoic acid [C15:0], heptadecanoic acid [C17:0]). As the major source of propionate in humans is the microbial fermentation of dietary fiber, OCFAs have been proposed as candidate biomarkers of dietary fiber. The objective of this study is to critically assess the plausibility, robustness, reliability, dose-response, time-response aspects of OCFAs as potential biomarkers of fermentable fibers in two independent studies using a validated analytical method.

METHODS AND RESULTS

OCFAs are first assessed in a fiber supplementation study, where 21 participants received 10 g dietary fiber supplementation for 7 days. OCFAs are then assessed in a highly controlled inpatient setting, which 19 participants consumed a high fiber (45.1 g per day) and a low fiber diet (13.6 g per day) for 4 days. Collectively in both studies, dietary intakes of fiber as fiber supplementations or having consumed a high fiber diet do not increase circulating levels of OCFAs. The dose and temporal relations are not observed.

CONCLUSION

Current study has generated new insight on the utility of OCFAs as fiber biomarkers and highlighted the importance of critical assessment of candidate biomarkers before application.

Metabolomics Meets Nutritional Epidemiology: Harnessing the Potential in Metabolomics Data

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

Dietary nutrition for neurological disease therapy: Current status and future directions

Adequate food intake and relative abundance of dietary nutrients have undisputed effects on the brain function. There is now substantial evidence that dietary nutrition aids in the prevention and remediation of neurologic symptoms in diverse pathological conditions. The newly described influences of dietary factors on the alterations of mitochondrial dysfunction, epigenetic modification and neuroinflammation are important mechanisms that are responsible for the action of nutrients on the brain health. In this review, we discuss the state of evidence supporting that distinct dietary interventions including dietary supplement and dietary restriction have the ability to tackle neurological disorders using Alzheimer's disease, Parkinson's disease, stroke, epilepsy, traumatic brain injury, amyotrophic lateral sclerosis, Huntington's disease and multiple sclerosis as examples. Additionally, it is also highlighting that diverse potential mechanisms such as metabolic control, epigenetic modification, neuroinflammation and gut-brain axis are of utmost importance for nutrient supply to the risk of neurologic condition and therapeutic response. Finally, we also highlight the novel concept that dietary nutrient intervention reshapes metabolism-epigenetics-immunity cycle to remediate brain dysfunction. Targeting metabolism-epigenetics-immunity network will delineate a new blueprint for combating neurological weaknesses.

Healthy for My Baby Research Protocol- a Randomized Controlled Trial Assessing a Preconception Intervention to Improve the Lifestyle of Overweight Women and Their Partners

Background: Preconception lifestyle interventions appear promising to reduce pregnancy complications, prevent adult cardiometabolic diseases, and prevent childhood obesity. These interventions have almost exclusively been studied in populations of obese infertile women. The development of preconception lifestyle interventions targeting a broader population of overweight and obese women without a history infertility and their partners is needed.

Methods: This study is a multicenter open label parallel group randomized controlled trial. Sixty-eight non-infertile women with overweight or obesity in the preconception period and their partners will be recruited from the Sherbrooke and Quebec City regions. The couples will be randomized in a 1:1 ratio to receive the Healthy for my Baby intervention or standard care in the preconception period and pregnancy. Women and their partners will be invited to take part in this lifestyle intervention which includes motivational interviews and daily self-monitoring of lifestyle goals through a mobile phone application. The primary endpoint of this study is the diet quality of women during the preconception period, which will be evaluated using the C-HEI 2007 score at baseline, 2, 4- and 6-months following study enrolment. Women's dietary quality will also be evaluated through the measure of urinary biomarkers of habitual dietary intake at baseline and 2 months in preconception, and 24–26 weeks in pregnancy. Additional indicators of women's lifestyle as well as anthropometric measures will be documented in preconception and pregnancy. For the pregnancy period, the main secondary endpoint is the pattern of gestational weight gain. Pregnancy and neonatal complications will also be evaluated. For partners, diet quality, other lifestyle habits, and anthropometric measures will be documented in the preconception and pregnancy periods.

Discussion: This study will evaluate the effectiveness of a low-cost intervention designed to improve diet and other lifestyle characteristics of women in the preconception period who are overweight or obese. If the Healthy for my Baby intervention is efficacious regarding dietary measures, larger trials will be needed to evaluate the impact of this intervention on the rates of pregnancy complications, childhood obesity, and adult cardiometabolic disease.

Clinical Trial Registration:clinicaltrials.gov (NCT04242069).

Association of Postoperative Clinical Outcomes With Sarcopenia, Frailty, and Nutritional Status in Older Patients With Colorectal Cancer: Protocol for a Prospective Cohort Study

Background

Older patients account for a significant proportion of patients undergoing colorectal cancer surgery and are vulnerable to a number of preoperative risk factors that are not often present in younger patients. Further, three preoperative risk factors that are more prevalent in older adults include frailty, sarcopenia, and malnutrition. Although each of these has been studied in isolation, there is little information on the interplay between them in older surgical patients. A particular area of increasing interest is the use of urine metabolomics for the objective evaluation of dietary profiles and malnutrition.

Objective

Herein, we describe the design, cohort, and standard operating procedures of a planned prospective study of older surgical patients undergoing colorectal cancer resection across multiple institutions in the United Kingdom. The objectives are to determine the association between clinical outcomes and frailty, nutritional status, and sarcopenia.

Methods

The procedures will include serial frailty evaluations (Clinical Frailty Scale and Groningen Frailty Indicator), functional assessments (hand grip strength and 4-meter walk test), muscle mass evaluations via computerized tomography morphometric analysis, and the evaluation of nutritional status via the analysis of urinary dietary biomarkers. The primary feasibility outcome is the estimation of the incidence rate of postoperative complications, and the primary clinical outcome is the association between the presence of postoperative complications and frailty, sarcopenia, and nutritional status. The secondary outcome measures are the length of hospital stay, 30-day hospital readmission rate, and mortality rate at days 30 and 90.

Results

Our study was approved by the National Health Service Research Ethics Committee (reference number: 19/WA/0190) via the Integrated Research Application System (project ID: 231694) prior to subject recruitment. Cardiff University is acting as the study sponsor. Our study is financially supported through an external, peer-reviewed grant from the British Geriatrics Society and internal funding resources from Cardiff University. The results will be disseminated through peer-review publications, social media, and conference proceedings.

Conclusions

As frailty, sarcopenia, and malnutrition are all areas of common derangement in the older surgical population, prospectively studying these risk factors in concert will allow for the analysis of their interplay as well as the development of predictive models for those at risk of commonly tracked surgical complications and outcomes.

International Registered Report Identifier (IRRID)

PRR1-10.2196/16846

An umbrella review of the evidence associating diet and cancer risk at 11 anatomical sites

There is evidence that diet and nutrition are modifiable risk factors for several cancers, but associations may be flawed due to inherent biases. Nutritional epidemiology studies have largely relied on a single assessment of diet using food frequency questionnaires. We conduct an umbrella review of meta-analyses of observational studies to evaluate the strength and validity of the evidence for the association between food/nutrient intake and risk of developing or dying from 11 primary cancers. It is estimated that only few single food/nutrient and cancer associations are supported by strong or highly suggestive meta-analytic evidence, and future similar research is unlikely to change this evidence. Alcohol consumption is positively associated with risk of postmenopausal breast, colorectal, esophageal, head & neck and liver cancer. Consumption of dairy products, milk, calcium and wholegrains are inversely associated with colorectal cancer risk. Coffee consumption is inversely associated with risk of liver cancer and skin basal cell carcinoma.

Prebiotic inulin as a treatment of obesity related nonalcoholic fatty liver disease through gut microbiota: a critical review

The microbial-derived products, including short chain fatty acids, lipopolysaccharide and secondary bile acids, have been shown to participate in the regulation of hepatic lipid metabolism. Previous studies have demonstrated that prebiotics, such as oligosaccharide and inulin, have abilities to change the concentration of microbial-derived products through modulating the microbial community structure, thus controlling body weight and alleviating hepatic fat accumulation. However, recent evidence indicates that there are individual differences in host response upon inulin treatment due to the differences in host microbial composition before dietary intervention. Probably it is because of the multiple relationships among bacterial species (e.g., competition and mutualism), which play key roles in the degradation of inulin and the regulation of microbial structure. Thereby, analyzing the composition and function of initial gut microbiota is essential for improving the efficacy of prebiotics supplementation. Furthermore, considering that different structures of polysaccharides can be used by different microorganisms, the chemical structure of processed inulin should be tested before using prebiotic inulin to treat obesity related nonalcoholic fatty liver disease.

Nutrition and Frailty: Opportunities for Prevention and Treatment

Frailty is a syndrome of growing importance given the global ageing population. While frailty is a multifactorial process, poor nutritional status is considered a key contributor to its pathophysiology. As nutrition is a modifiable risk factor for frailty, strategies to prevent and treat frailty should consider dietary change. Observational evidence linking nutrition with frailty appears most robust for dietary quality: for example, dietary patterns such as the Mediterranean diet appear to be protective. In addition, research on specific foods, such as a higher consumption of fruit and vegetables and lower consumption of ultra-processed foods are consistent, with healthier profiles linked to lower frailty risk. Few dietary intervention studies have been conducted to date, although a growing number of trials that combine supplementation with exercise training suggest a multi-domain approach may be more effective. This review is based on an interdisciplinary workshop, held in November 2020, and synthesises current understanding of dietary influences on frailty, focusing on opportunities for prevention and treatment. Longer term prospective studies and well-designed trials are needed to determine the causal effects of nutrition on frailty risk and progression and how dietary change can be used to prevent and/or treat frailty in the future.

UK Nutrition Research Partnership (NRP) workshop: Forum on advancing dietary intake assessment

The development of better and more robust measures of dietary intake in free living situations was identified as a priority for advancing nutrition research by the Office of Strategic Coordination for Health Research (OSCHR) Review of Nutrition and Human Health Research in 2017. The UK Nutrition Research Partnership (NRP) sponsored a workshop on Dietary Intake Assessment methodology alongside its series of ‘Hot Topic’ workshops designed to accelerate progress in nutrition research by bringing together people from a range of different disciplines. The workshop on Dietary Intake Assessment methodology took place via Zoom over two half‐days in January 2021 and included 50 scientists from a wide range of disciplines. The problems with current methods of dietary assessment and how emerging technologies might address them were set out in pre‐recorded presentations and explored in panel discussions. Participants then worked in breakout groups to discuss and prioritise the research questions that should be addressed to best further the field and lead to improvements in dietary assessment methodology. Five priority research questions were selected. Participants were asked to brainstorm potential approaches for addressing them and were then asked to focus on one approach and develop it further. At the end of these sessions, participants presented their project ideas to the rest of the workshop and these will be reported back to the Medical Research Council. It is hoped that potential collaborative projects arising from these discussions will be taken forward in response to future funding calls.

Classifying Individuals Into a Dietary Pattern Based on Metabolomic Data

SCOPE

The objectives are to develop a metabolomic-based model capable of classifying individuals into dietary patterns and to investigate the reproducibility of the model.

METHODS AND RESULTS

K-means cluster analysis is employed to derive dietary patterns using metabolomic data. Differences across the dietary patterns are examined using nutrient biomarkers. The model is used to assign individuals to a dietary pattern in an independent cohort, A-DIET Confirm (n = 175) at four time points. The stability of participants to a dietary pattern is assessed. Four dietary patterns are derived: moderately unhealthy, convenience, moderately healthy, and prudent. The moderately unhealthy and convenience patterns has lower adherence to the alternative healthy eating index (AHEI) and the alternative mediterranean diet score (AMDS) compared to the moderately healthy and prudent patterns (AHEI = 24.5 and 22.9 vs 26.7 and 28.4, p < 0.001). The dietary patterns are replicated in A-DIET Confirm, with good reproducibility across four time points. The stability of participants' dietary pattern membership ranged from 25.0% to 61.5%.

CONCLUSION

The multivariate model classifies individuals into dietary patterns based on metabolomic data. In an independent cohort, the model classifies individuals into dietary patterns at multiple time points furthering the potential of such an approach for nutrition research.

The relationship between urinary polyphenol metabolites and dietary polyphenol intakes in young adults

Spot urinary polyphenols have potential as a biomarker of polyphenol-rich food intakes. The aim of this study is to explore the relationship between spot urinary polyphenols and polyphenol intakes from polyphenol-rich food sources. Young adults (18-24 years old) were recruited into a sub-study of an online intervention aimed at improving diet quality. Participants' intake of polyphenols and polyphenol-rich foods was assessed at baseline and 3 months using repeated 24-h recalls. A spot urine sample was collected at each session, with samples analysed for polyphenol metabolites using LC-MS. To assess the strength of the relationship between urinary polyphenols and dietary polyphenols, Spearman correlations were used. Linear mixed models further evaluated the relationship between polyphenol intakes and urinary excretion. Total urinary polyphenols and hippuric acid (HA) demonstrated moderate correlation with total polyphenol intakes (rs = 0·29-0·47). HA and caffeic acid were moderately correlated with polyphenols from tea/coffee (rs = 0·26-0·46). Using linear mixed models, increases in intakes of total polyphenols or polyphenols from tea/coffee or oil resulted in a greater excretion of HA, whereas a negative relationship was observed between soya polyphenols and HA, suggesting that participants with higher intakes of soya polyphenols had a lower excretion of HA. Findings suggest that total urinary polyphenols may be a promising biomarker of total polyphenol intakes foods and drinks and that HA may be a biomarker of total polyphenol intakes and polyphenols from tea/coffee. Caffeic acid warrants further investigation as a potential biomarker of polyphenols from tea/coffee.

Advances in dietary pattern analysis in nutritional epidemiology

Background and Purpose

It used to be a common practice in the field of nutritional epidemiology to analyze separate nutrients, foods, or food groups. However, in reality, nutrients and foods are consumed in combination. The introduction of dietary patterns (DP) and their analysis has revolutionized this field, making it possible to take into account the synergistic effects of foods and to account for the complex interaction among nutrients and foods. Three approaches of DP analysis exist: (1) the hypothesis-based approach (based on prior knowledge regarding the current understanding of dietary components and their health relation), (2) the exploratory approach (solely relying on dietary intake data), and (3) the hybrid approach (a combination of both approaches). During the recent past, complementary approaches for DP analysis have emerged both conceptually and methodologically.

Method

We have summarized the recent developments that include incorporating the Treelet transformation method as a complementary exploratory approach in a narrative review.

Results

Uses, peculiarities, strengths, limitations, and scope of recent developments in DP analysis are outlined. Next, the narrative review gives an overview of the literature that takes into account potential relevant dietary-related factors, specifically the metabolome and the gut microbiome in DP analysis. Then the review deals with the aspect of data processing that is needed prior to DP analysis, particularly when dietary data arise from assessment methods other than the long-established food frequency questionnaire. Lastly, potential opportunities for upcoming DP analysis are summarized in the outlook.

Conclusion

Biological factors like the metabolome and the microbiome are crucial to understand diet-disease relationships. Therefore, the inclusion of these factors in DP analysis might provide deeper insights.

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.

Metabolomic Biomarkers of Healthy Dietary Patterns and Cardiovascular Outcomes

PURPOSE OF REVIEW

Healthy dietary patterns are recommended for prevention of CVD. Recently, metabolomics has been used to identify biomarkers of healthy dietary patterns and elucidate mechanisms underlying diet-disease associations. This review provides an overview of approaches to define healthy dietary patterns, discusses important issues related to using metabolomics to describe healthy dietary patterns, and summarizes studies identifying blood metabolites associated with hypothesis-driven healthy dietary patterns and cardiovascular risk factors and incident CVD.

RECENT FINDINGS

We identified 17 studies which reported on blood metabolomic signatures of 5 healthy dietary patterns (Healthy Eating Index, Alternative Healthy Eating Index, the Dietary Approaches to Stop Hypertension diet, Mediterranean diet, vegetarian diet). Four of these studies evaluated associations between diet-related metabolites and cardiovascular outcomes. Many metabolites replicated across different healthy dietary patterns, which suggest that they may represent biomarkers of generally healthy diets. Unsaturated lipids positively associated with healthy dietary patterns were inversely associated with incident CVD, suggesting that they may be a pathway through which diet is associated with a lower risk of CVD. Although many metabolites replicated across cross-sectional studies, few metabolites identified as candidate biomarkers of healthy diets in feeding studies replicated in observational studies. Additionally, limited evidence exists on the ability of diet-related metabolites to predict cardiovascular outcomes. Replication of candidate biomarkers of dietary patterns in different study designs and more studies evaluating the associations between diet-related metabolites and cardiovascular outcomes are needed.

The effects of sustained fitness improvement on the gut microbiome: A longitudinal, repeated measures case-study approach

The athlete gut microbiome differs from that of non-athletes in its composition and metabolic function. Short-term fitness improvement in sedentary adults does not replicate the microbiome characteristics of athletes. The objective of this study was to investigate whether sustained fitness improvement leads to pronounced alterations in the gut microbiome. This was achieved using a repeated-measures, case-study approach that examined the gut microbiome of two initially unfit volunteers undertaking progressive exercise training over a 6-month period. Samples were collected every two weeks, and microbiome, metabolome, diet, body composition, and cardiorespiratory fitness data were recorded. Training culminated in both participants completing their respective goals (a marathon or Olympic-distance triathlon) with improved body composition and fitness parameters. Increases in gut microbiota α-diversity occurred with sustained training and fluctuations occurred in response to training events (eg, injury, illness, and training peaks). Participants' BMI reduced during the study and was significantly associated with increased urinary measurements of N-methyl nicotinate and hippurate, and decreased phenylacetylglutamine. These results suggest that sustained fitness improvements support alterations to gut microbiota and physiologically-relevant metabolites. This study provides longitudinal analysis of the gut microbiome response to real-world events during progressive fitness training, including intercurrent illness and injury.

Fasting plasma metabolomic profiles are altered by three days of standardized diet and restricted physical activity

Objective

Few studies have examined the effects of participants' diet and activity prior to sample collection on metabolomics profiles, and results have been conflicting. We compared the effects of overnight fasting with or without 3 days of standardized diet and restricted physical activity on the human blood metabolome, and examined the effects of these protocols on our ability to detect differences in metabolomics profiles in adolescent girls with obesity and polycystic ovary syndrome (PCOS) vs. sex and BMI-matched controls.

Methods

This was a cross-sectional study of 16 adolescent girls with obesity and PCOS and 5 sex and BMI-matched controls. Fasting plasma metabolomic profiles were measured twice in each participant: once without preceding restriction of physical activity or control of macronutrient content ("typical fasting visit"), and again after 12 h of monitored inpatient fasting with 3 days of standardized diet and avoidance of vigorous exercise ("controlled fasting visit"). Moderated paired t-tests with FDR correction for multiple testing and multilevel sparse partial least-squares discriminant analysis (sPLS-DA) were used to examine differences between the 2 visits and to compare the PCOS and control groups with the 2 visits combined and again after stratifying by visit.

Results

Twenty-three known metabolites were significantly different between the controlled fasting and typical fasting visits. Hypoxanthine and glycochenodeoxycholic acid had the largest increases in relative abundance at the controlled fasting visit compared to the typical fasting visit, while oleoyl-glycerol and oleamide had the largest increases in relative abundance at the typical fasting visit compared to the controlled fasting visit. sPLS-DA showed excellent discrimination between the 2 visits; however, when the samples from the 2 visits were combined, differences between the PCOS and control groups could not be detected. After stratifying by visit, discrimination of PCOS status was improved.

Conclusions

There were differences in fasting metabolomic profiles following typical fasting vs monitored fasting with preceding restriction of physical activity and control of macronutrient content, and combining samples from the two visits obscured differences by PCOS status. In studies performing metabolomics analysis, careful attention should be paid to acute diet and activity history. Depending on the sample size of the study and the expected effect size of the outcomes of interest, control of diet and physical activity beyond typical outpatient fasting may not be required.

NMR Spectroscopic Windows on the Systemic Effects of SARS-CoV-2 Infection on Plasma Lipoproteins and Metabolites in Relation to Circulating Cytokines

To investigate the systemic metabolic effects of SARS-CoV-2 infection, we analyzed 1H NMR spectroscopic data on human blood plasma and co-modeled with multiple plasma cytokines and chemokines (measured in parallel). Thus, 600 MHz 1H solvent-suppressed single-pulse, spin-echo, and 2D J-resolved spectra were collected on plasma recorded from SARS-CoV-2 rRT-PCR-positive patients (n = 15, with multiple sampling timepoints) and age-matched healthy controls (n = 34, confirmed rRT-PCR negative), together with patients with COVID-19/influenza-like clinical symptoms who tested SARS-CoV-2 negative (n = 35). We compared the single-pulse NMR spectral data with in vitro diagnostic research (IVDr) information on quantitative lipoprotein profiles (112 parameters) extracted from the raw 1D NMR data. All NMR methods gave highly significant discrimination of SARS-CoV-2 positive patients from controls and SARS-CoV-2 negative patients with individual NMR methods, giving different diagnostic information windows on disease-induced phenoconversion. Longitudinal trajectory analysis in selected patients indicated that metabolic recovery was incomplete in individuals without detectable virus in the recovery phase. We observed four plasma cytokine clusters that expressed complex differential statistical relationships with multiple lipoproteins and metabolites. These included the following: cluster 1, comprising MIP-1β, SDF-1α, IL-22, and IL-1α, which correlated with multiple increased LDL and VLDL subfractions; cluster 2, including IL-10 and IL-17A, which was only weakly linked to the lipoprotein profile; cluster 3, which included IL-8 and MCP-1 and were inversely correlated with multiple lipoproteins. IL-18, IL-6, and IFN-γ together with IP-10 and RANTES exhibited strong positive correlations with LDL1-4 subfractions and negative correlations with multiple HDL subfractions. Collectively, these data show a distinct pattern indicative of a multilevel cellular immune response to SARS CoV-2 infection interacting with the plasma lipoproteome giving a strong and characteristic immunometabolic phenotype of the disease. We observed that some patients in the respiratory recovery phase and testing virus-free were still metabolically highly abnormal, which indicates a new role for these technologies in assessing full systemic recovery.

Urine Metabolites Associated with the Dietary Approaches to Stop Hypertension (DASH) Diet: Results from the DASH-Sodium Trial

SCOPE

Serum metabolomic markers of the Dietary Approaches to Stop Hypertension (DASH) diet are previously reported. In an independent study, the similarity of urine metabolomic markers are investigated.

METHODS AND RESULTS

In the DASH-Sodium trial, participants are randomly assigned to the DASH diet or control diet, and received three sodium interventions (high, intermediate, low) within each randomized diet group in random order for 30 days each. Urine samples are collected at the end of each intervention period and analyzed for 938 metabolites. Two comparisons are conducted: 1) DASH-high sodium (n = 199) versus control-high sodium (n = 193), and 2) DASH-low sodium (n = 196) versus control-high sodium. Significant metabolites identified using multivariable linear regression are compared and the top 10 influential metabolites identified using partial least-squares discriminant analysis to the results from the DASH trial. Nine out of 10 predictive metabolites of the DASH-high sodium and DASH-low sodium diets are identical. Most candidate biomarkers from the DASH trial replicated. N-methylproline, chiro-inositol, stachydrine, and theobromine replicated as influential metabolites of DASH diets.

CONCLUSIONS

Candidate biomarkers of the DASH diet identified in serum replicated in urine. Replicated influential metabolites are likely to be objective biomarkers of the DASH diet.

Characterization of dietary patterns and assessment of their relationships with metabolomic profiles: A community-based study

BACKGROUND & AIMS

Determining dietary patterns in China is challenging due to lack of external validation and objective measurements. We aimed to characterize dietary patterns in a community-based population and to validate these patterns using external validation cohort and metabolomic profiles.

DESIGN

We studied 5145 participants, aged 18-80 years, from two districts of Hangzhou, China. We used one district as the discovery cohort (N = 2521) and the other as the external validation cohort (N = 2624). We identified dietary patterns using a k-means clustering. Associations between dietary patterns and metabolic conditions were analyzed using adjusted logistic models. We assessed relationships between metabolomic profile and dietary patterns in 214 participants with metabolomics data.

RESULTS

We identified three dietary patterns: the traditional (rice-based), the mixed (rich in dairy products, eggs, nuts, etc.), and the high-alcohol diets. Relative to the traditional diet, the mixed (OR_adj = 1.7, CI 1.3-2.4) and the high-alcohol diets (OR_adj = 1.9, CI 1.3-2.7) were associated with type 2 diabetes and hypertension, respectively. Similar results were confirmed in the external validation cohort. In addition, we also identified 18 and 22 metabolites that could distinguish the mixed (error rate = 12%; AUC = 96%) and traditional diets (error rate = 19%; AUC = 88%) from the high-alcohol diet.

CONCLUSIONS

Despite the complexity of Chinese diet, identifying dietary patterns helps distinguish groups of individuals with high risk of metabolic diseases, which can also be validated by external population and metabolomic profiles.

Postprandial Dried Blood Spot-Based Nutritional Metabolomic Analysis Discriminates a High-Fat, High-Protein Meat-Based Diet from a High Carbohydrate Vegan Diet: A Randomized Controlled Crossover Trial

BACKGROUND

Due to the challenges associated with accurate monitoring of dietary intake in humans, nutritional metabolomics (including food intake biomarkers) analysis as a complementary tool to traditional dietary assessment methods has been explored. Food intake biomarker assessment using postprandial dried blood spot (DBS) collection can be a convenient and accurate means of monitoring dietary intake vs 24-hour urine collection.

OBJECTIVE

The objective of this study was to use nutritional metabolomics analysis to differentiate a high-fat, high-protein meat (HFPM) diet from a high-carbohydrate vegan (HCV) diet in postprandial DBS and 24-hour urine.

DESIGN

This was a randomized controlled crossover feeding trial.

PARTICIPANTS/SETTING

Participants were healthy young adult volunteers (n = 8) in California. The study was completed in August 2019.

INTERVENTION

The standardized isocaloric diet interventions included an HFPM and an HCV diet. Participants attended 2 intervention days, separated by a 2-week washout.

MAIN OUTCOME MEASURES

During each intervention day, a finger-prick blood sample was collected in the fasting state, 3 hours post breakfast, and 3 hours post lunch. Participants also collected their urine for 24 hours. DBS and urine samples were analyzed by ultra-performance liquid chromatography mass spectrometry to identify potential food intake biomarkers.

STATISTICAL ANALYSES PERFORMED

Principal component analysis for discriminatory analysis and univariate analysis using paired t tests were performed.

RESULTS

Principal component analysis found no discrimination of baseline DBS samples. In both the postprandial DBS and 24-hour urine, post-HFPM consumption had higher (P < 0.05) levels of acylcarnitines, creatine, and cis-trans hydroxyproline, and the HCV diet was associated with elevated sorbitol (P < 0.05). The HFPM diet had higher concentrations of triacylglycerols with fewer than 54 total carbons in DBS, and 24-hour urine had higher nucleoside mono- and di-phosphates (P < 0.05).

CONCLUSIONS

Nutritional metabolomics profiles of postprandial DBS and 24-hour urine collections were capable of differentiating the HFPM and HCV diets. The potential use of postprandial DBS-based metabolomic analysis deserves further investigation for dietary intake monitoring.

Investigating the Role of Diet and Exercise in Gut Microbe-Host Cometabolism

We investigated the individual and combined effects of diet and physical exercise on metabolism and the gut microbiome to establish how these lifestyle factors influence host-microbiome cometabolism. Urinary and fecal samples were collected from athletes and less active controls. Individuals were further classified according to an objective dietary assessment score of adherence to healthy dietary habits according to WHO guidelines, calculated from their proton nuclear magnetic resonance (1H-NMR) urinary profiles. Subsequent models were generated comparing extremes of dietary habits, exercise, and the combined effect of both. Differences in metabolic phenotypes and gut microbiome profiles between the two groups were assessed. Each of the models pertaining to diet healthiness, physical exercise, or a combination of both displayed a metabolic and functional microbial signature, with a significant proportion of the metabolites identified as discriminating between the various pairwise comparisons resulting from gut microbe-host cometabolism. Microbial diversity was associated with a combination of high adherence to healthy dietary habits and exercise and was correlated with a distinct array of microbially derived metabolites, including markers of proteolytic activity. Improved control of dietary confounders, through the use of an objective dietary assessment score, has uncovered further insights into the complex, multifactorial relationship between diet, exercise, the gut microbiome, and metabolism. Furthermore, the observation of higher proteolytic activity associated with higher microbial diversity indicates that increased microbial diversity may confer deleterious as well as beneficial effects on the host.IMPORTANCE Improved control of dietary confounders, through the use of an objective dietary assessment score, has uncovered further insights into the complex, multifactorial relationship between diet, exercise, the gut microbiome, and metabolism. Each of the models pertaining to diet healthiness, physical exercise, or a combination of both, displayed a distinct metabolic and functional microbial signature. A significant proportion of the metabolites identified as discriminating between the various pairwise comparisons result from gut microbe-host cometabolism, and the identified interactions have expanded current knowledge in this area. Furthermore, although increased microbial diversity has previously been linked with health, our observation of higher microbial diversity being associated with increased proteolytic activity indicates that it may confer deleterious as well as beneficial effects on the host.

Serum Metabolites Associated with Healthy Diets in African Americans and European Americans

BACKGROUND

High diet quality is associated with a lower risk of chronic diseases. Metabolomics can be used to identify objective biomarkers of diet quality.

OBJECTIVES

We used metabolomics to identify serum metabolites associated with 4 diet indices and the components within these indices in 2 samples from African Americans and European Americans.

METHODS

We studied cross-sectional associations between known metabolites and Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension Trial (DASH) diet, alternate Mediterranean diet (aMED), and their components using untargeted metabolomics in 2 samples (n1 = 1,806, n2 = 2,056) of the Atherosclerosis Risk in Communities study (aged 45-64 y at baseline). Dietary intakes were assessed using an FFQ. We used multivariable linear regression models to examine associations between diet indices and serum metabolites in each sample, adjusting for participant characteristics. Metabolites significantly associated with diet indices were meta-analyzed across 2 samples. C-statistics were calculated to examine if these candidate biomarkers improved prediction of individuals in the highest compared with lowest quintile of diet scores beyond participant characteristics.

RESULTS

Seventeen unique metabolites (HEI: n = 6; AHEI: n = 5; DASH: n = 14; aMED: n = 2) were significantly associated with higher diet scores after Bonferroni correction in sample 1 and sample 2. Six of 17 significant metabolites [glycerate, N-methylproline, stachydrine, threonate, pyridoxate, 3-(4-hydroxyphenyl)lactate)] were associated with ≥1 dietary pattern. Candidate biomarkers of HEI, AHEI, and DASH distinguished individuals with highest compared with lowest quintile of diet scores beyond participant characteristics in samples 1 and 2 (P value for difference in C-statistics <0.02 for all 3 diet indices). Candidate biomarkers of aMED did not improve C-statistics beyond participant characteristics (P value = 0.930).

CONCLUSIONS

A considerable overlap of metabolites associated with HEI, AHEI, DASH, and aMED reflects the similar food components and similar metabolic pathways involved in the metabolism of healthy diets in African Americans and European Americans.

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.

The Effects of a Mediterranean Diet Intervention on Targeted Plasma Metabolic Biomarkers among US Firefighters: A Pilot Cluster-Randomized Trial

Metabolomics is improving the understanding of the mechanisms of the health effects of diet. Previous research has identified several metabolites associated with the Mediterranean Diet (MedDiet), but knowledge about longitudinal changes in metabolic biomarkers after a MedDiet intervention is scarce. A subsample of 48 firefighters from a cluster-randomized trial at Indianapolis fire stations was randomly selected for the metabolomics study at 12 months of follow up (time point 1), where Group 1 (n = 24) continued for another 6 months in a self-sustained MedDiet intervention, and Group 2 (n = 24), the control group at that time, started with an active MedDiet intervention for 6 months (time point 2). A total of 225 metabolites were assessed at the two time points by using a targeted NMR platform. The MedDiet score improved slightly but changes were non-significant (intervention: 24.2 vs. 26.0 points and control group: 26.1 vs. 26.5 points). The MedDiet intervention led to favorable changes in biomarkers related to lipid metabolism, including lower LDL-C, ApoB/ApoA1 ratio, remnant cholesterol, M-VLDL-CE; and higher HDL-C, and better lipoprotein composition. This MedDiet intervention induces only modest changes in adherence to the MedDiet and consequently in metabolic biomarkers. Further research should confirm these results based on larger study samples in workplace interventions with powerful study designs.

Innovations in Infant Feeding: Future Challenges and Opportunities in Obesity and Cardiometabolic Disease

The field of nutrition in early life, as an effective tool to prevent and treat chronic diseases, has attracted a large amount of interest over recent years. The vital roles of food products and nutrients on the body’s molecular mechanisms have been demonstrated. The knowledge of the mechanisms and the possibility of controlling them via what we eat has opened up the field of precision nutrition, which aims to set dietary strategies in order to improve health with the greatest effectiveness. However, this objective is achieved only if the genetic profile of individuals and their living conditions are also considered. The relevance of this topic is strengthened considering the importance of nutrition during childhood and the impact on the development of obesity. In fact, the prevalence of global childhood obesity has increased substantially from 1990 and has now reached epidemic proportions. The current narrative review presents recent research on precision nutrition and its role on the prevention and treatment of obesity during pediatric years, a novel and promising area of research.

The Relationship between Dietary Polyphenol Intakes and Urinary Polyphenol Concentrations in Adults Prescribed a High Vegetable and Fruit Diet

Urinary polyphenol metabolites are potential biomarkers of dietary polyphenol intake. The current study aims to evaluate associations between total diet, vegetable and fruit polyphenol intakes with urinary polyphenol metabolite concentrations in a sample of adults prescribed a diet rich in vegetables and fruit. Thirty-four participants completed a 10-week pre-post study. Participants were asked to consume Australian recommended daily vegetable and fruit serves and attend measurement sessions at baseline and at weeks 2 and 10. Two 24-h diet recalls were collected at each time-point and polyphenol intakes were calculated using the Phenol-Explorer database. Spot urine samples, collected at each time-point, were analyzed for 15 polyphenol metabolites using liquid chromatography-mass spectroscopy. Spearman’s correlation analyzes assessed the strength of relationships between urinary and dietary polyphenols. Linear mixed models were used to investigate relationships between polyphenol excretion and intake. Total urinary polyphenols were significantly correlated with total polyphenol intakes at week 10 (r_s = 0.47) and fruit polyphenols at week 2 (r_s = 0.38). Hippuric acid was significantly correlated with vegetable polyphenols at baseline (r_s = 0.39). Relationships were identified between individual polyphenol metabolites and vegetable and fruit polyphenols. Linear mixed model analyzes identified that for every 1 mg increase in polyphenol intakes, urinary polyphenol excretion increased by 16.3 nmol/g creatinine. Although the majority of relationships were not sufficiently strong or consistent at different time-points, promising relationships were observed between total urinary polyphenols and total polyphenol intakes, and hippuric acid and vegetable polyphenols.

Design and Characterisation of a Randomized Food Intervention That Mimics Exposure to a Typical UK Diet to Provide Urine Samples for Identification and Validation of Metabolite Biomarkers of Food Intake

Poor dietary choices are major risk factors for obesity and non-communicable diseases, which places an increasing burden on healthcare systems worldwide. To monitor the effectiveness of healthy eating guidelines and strategies, there is a need for objective measures of dietary intake in community settings. Metabolites derived from specific foods present in urine samples can provide objective biomarkers of food intake (BFIs). Whilst the majority of biomarker discovery/validation studies have investigated potential biomarkers for single foods only, this study considered the whole diet by using menus that delivered a wide range of foods in meals that emulated conventional UK eating patterns. Fifty-one healthy participants (range 19-77 years; 57% female) followed a uniquely designed, randomized controlled dietary intervention, and provided spot urine samples suitable for discovery of BFIs within a real-world context. Free-living participants prepared and consumed all foods and drinks in their own homes and were asked to follow the protocols for meal consumption and home urine sample collection. This study also assessed the robustness, and impact on data quality, of a minimally invasive urine collection protocol. Overall the study design was well-accepted by participants and concluded successfully without any drop outs. Compliance for urine collection, adherence to menu plans, and observance of recommended meal timings, was shown to be very high. Metabolome analysis using mass spectrometry coupled with data mining demonstrated that the study protocol was well-suited for BFI discovery and validation. Novel, putative biomarkers for an extended range of foods were identified including legumes, curry, strongly-heated products, and artificially sweetened, low calorie beverages. In conclusion, aspects of this study design would help to overcome several current challenges in the development of BFI technology. One specific attribute was the examination of BFI generalizability across related food groups and across different preparations and cooking methods of foods. Furthermore, the collection of urine samples at multiple time points helped to determine which spot sample was optimal for identification and validation of BFIs in free-living individuals. A further valuable design feature centered on the comprehensiveness of the menu design which allowed the testing of biomarker specificity within a biobank of urine samples.

The Role of Nutrition in the Prevention and Intervention of Type 2 Diabetes

Type 2 diabetes (T2D) is a rapidly growing epidemic, which leads to increased mortality rates and health care costs. Nutrients (namely, carbohydrates, fat, protein, mineral substances, and vitamin), sensing, and management are central to metabolic homeostasis, therefore presenting a leading factor contributing to T2D. Understanding the comprehensive effects and the underlying mechanisms of nutrition in regulating glucose metabolism and the interactions of diet with genetics, epigenetics, and gut microbiota is helpful for developing new strategies to prevent and treat T2D. In this review, we discuss different mechanistic pathways contributing to T2D and then summarize the current researches concerning associations between different nutrients intake and glucose homeostasis. We also explore the possible relationship between nutrients and genetic background, epigenetics, and metagenomics in terms of the susceptibility and treatment of T2D. For the specificity of individual, precision nutrition depends on the person’s genotype, and microbiota is vital to the prevention and intervention of T2D.

An integrative machine learning approach to discovering multi-level molecular mechanisms of obesity using data from monozygotic twin pairs

We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22-36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index > 3 kg m^-2). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.

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.

Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome

The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease has been difficult due to apparent disconnects between animal and human studies and lack of an integrated multi-omics view of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome, and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases. VIDEO ABSTRACT.

Nutrition and the ageing brain: Moving towards clinical applications

The global increases in life expectancy and population have resulted in a growing ageing population and with it a growing number of people living with age-related neurodegenerative conditions and dementia, shifting focus towards methods of prevention, with lifestyle approaches such as nutrition representing a promising avenue for further development. This overview summarises the main themes discussed during the 3^rd Symposium on "Nutrition for the Ageing Brain: Moving Towards Clinical Applications" held in Madrid in August 2018, enlarged with the current state of knowledge on how nutrition influences healthy ageing and gives recommendations regarding how the critical field of nutrition and neurodegeneration research should move forward into the future. Specific nutrients are discussed as well as the impact of multi-nutrient and whole diet approaches, showing particular promise to combatting the growing burden of age-related cognitive decline. The emergence of new avenues for exploring the role of diet in healthy ageing, such as the impact of the gut microbiome and development of new techniques (imaging measures of brain metabolism, metabolomics, biomarkers) are enabling researchers to approach finding answers to these questions. But the translation of these findings into clinical and public health contexts remains an obstacle due to significant shortcomings in nutrition research or pressure on the scientific community to communicate recommendations to the general public in a convincing and accessible way. Some promising programs exist but further investigation to improve our understanding of the mechanisms by which nutrition can improve brain health across the human lifespan is still required.

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study

BACKGROUND

Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D.

METHODS

We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (T_pred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous T_pred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models.

FINDINGS

A higher T_pred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=-0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher T_pred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the T_pred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher T_pred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2.

INTERPRETATION

Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health.

FUNDING

This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies.

Identifying unknown metabolites using NMR-based metabolic profiling techniques

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

The Mediterranean diet, plasma metabolome, and cardiovascular disease risk

AIMS

To investigate whether metabolic signature composed of multiple plasma metabolites can be used to characterize adherence and metabolic response to the Mediterranean diet and whether such a metabolic signature is associated with cardiovascular disease (CVD) risk.

METHODS AND RESULTS

Our primary study cohort included 1859 participants from the Spanish PREDIMED trial, and validation cohorts included 6868 participants from the US Nurses' Health Studies I and II, and Health Professionals Follow-up Study (NHS/HPFS). Adherence to the Mediterranean diet was assessed using a validated Mediterranean Diet Adherence Screener (MEDAS), and plasma metabolome was profiled by liquid chromatography-tandem mass spectrometry. We observed substantial metabolomic variation with respect to Mediterranean diet adherence, with nearly one-third of the assayed metabolites significantly associated with MEDAS (false discovery rate < 0.05). Using elastic net regularized regressions, we identified a metabolic signature, comprised of 67 metabolites, robustly correlated with Mediterranean diet adherence in both PREDIMED and NHS/HPFS (r = 0.28-0.37 between the signature and MEDAS; P = 3 × 10-35 to 4 × 10-118). In multivariable Cox regressions, the metabolic signature showed a significant inverse association with CVD incidence after adjusting for known risk factors (PREDIMED: hazard ratio [HR] per standard deviation increment in the signature = 0.71, P < 0.001; NHS/HPFS: HR = 0.85, P = 0.001), and the association persisted after further adjustment for MEDAS scores (PREDIMED: HR = 0.73, P = 0.004; NHS/HPFS: HR = 0.85, P = 0.004). Further genome-wide association analysis revealed that the metabolic signature was significantly associated with genetic loci involved in fatty acids and amino acids metabolism. Mendelian randomization analyses showed that the genetically inferred metabolic signature was significantly associated with risk of coronary heart disease (CHD) and stroke (odds ratios per SD increment in the genetically inferred metabolic signature = 0.92 for CHD and 0.91 for stroke; P < 0.001).

CONCLUSIONS

We identified a metabolic signature that robustly reflects adherence and metabolic response to a Mediterranean diet, and predicts future CVD risk independent of traditional risk factors, in Spanish and US cohorts.

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.

Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

PURPOSE OF REVIEW

Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer.

RECENT FINDINGS

Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

Ultra-Performance Liquid Chromatography-Ion Mobility Separation-Quadruple Time-of-Flight MS (UHPLC-IMS-QTOF MS) Metabolomics for Short-Term Biomarker Discovery of Orange Intake: A Randomized, Controlled Crossover Study

A major problem with dietary assessments is their subjective nature. Untargeted metabolomics and new technologies can shed light on this issue and provide a more complete picture of dietary intake by measuring the profile of metabolites in biological samples. Oranges are one of the most consumed fruits in the world, and therefore one of the most studied for their properties. The aim of this work was the application of untargeted metabolomics approach with the novel combination of ion mobility separation coupled to high resolution mass spectrometry (IMS-HRMS) and study the advantages that this technique can bring to the area of dietary biomarker discovery, with the specific case of biomarkers associated with orange consumption (Citrus reticulata) in plasma samples taken during an acute intervention study (consisting of a randomized, controlled crossover trial in healthy individuals). A total of six markers of acute orange consumption, including betonicines and conjugated flavonoids, were identified with the experimental data and previous literature, demonstrating the advantages of ion mobility in the identification of dietary biomarkers and the benefits that an additional structural descriptor, as the collision cross section value (CCS), can provide in this area.

Nutriome-metabolome relationships provide insights into dietary intake and metabolism

Dietary assessment traditionally relies on self-reported data which are often inaccurate and may result in erroneous diet-disease risk associations. We illustrate how urinary metabolic phenotyping can be used as alternative approach for obtaining information on dietary patterns. We used two multi-pass 24-hr dietary recalls, obtained on two occasions on average three weeks apart, paired with two 24-hr urine collections from 1,848 U.S. individuals; 67 nutrients influenced the urinary metabotype measured with ¹H-NMR spectroscopy characterized by 46 structurally identified metabolites. We investigated the stability of each metabolite over time and showed that the urinary metabolic profile is more stable within individuals than reported dietary patterns. The 46 metabolites accurately predicted healthy and unhealthy dietary patterns in a free-living U.S. cohort and replicated in an independent U.K. cohort. We mapped these metabolites into a host-microbial metabolic network to identify key pathways and functions. These data can be used in future studies to evaluate how this set of diet-derived, stable, measurable bioanalytical markers are associated with disease risk. This knowledge may give new insights into biological pathways that characterize the shift from a healthy to unhealthy metabolic phenotype and hence give entry points for prevention and intervention strategies.

Human postprandial responses to food and potential for precision nutrition

Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866.

RETRACTED ARTICLE: Dietary metabotype modelling predicts individual responses to dietary interventions

Habitual consumption of poor quality diets is linked directly to risk factors for many non-communicable diseases. This has resulted in the vast majority of countries and the World Health Organization developing policies for healthy eating to reduce the prevalence of non-communicable diseases in the population. However, there is mounting evidence of variability in individual metabolic responses to any dietary intervention. We have developed a method for applying a pipeline for understanding interindividual differences in response to diet, based on coupling data from highly controlled dietary studies with deep metabolic phenotyping. In this feasibility study, we create an individual Dietary Metabotype Score (DMS) that embodies interindividual variability in dietary response and captures consequent dynamic changes in concentrations of urinary metabolites. We find an inverse relationship between the DMS and blood glucose concentration. There is also a relationship between the DMS and urinary metabolic energy loss. Furthermore, we use a metabolic entropy approach to visualize individual and collective responses to dietary interventions. Potentially, the DMS offers a method to target and to enhance dietary response at the individual level, thereby reducing the burden of non-communicable diseases at the population level.

Potential of food intake biomarkers in nutrition research

The influence of dietary habits on health/disease is well-established. Accurate dietary assessment is essential to understand metabolic pathways/processes involved in this relationship. In recent years, biomarker discovery has become a major area of interest for improving dietary assessment. Well-established nutrient intake biomarkers exist; however, there is growing interest in identifying and using biomarkers for more accurate and objective measurements of food intake. Metabolomics has emerged as a key tool used for biomarker discovery, employing techniques such as NMR spectroscopy, or MS. To date, a number of putatively identified biomarkers were discovered for foods including meat, cruciferous vegetables and legumes. However, many of the results are associations only and lack the desired validation including dose-response studies. Food intake biomarkers can be employed to classify individuals into consumers/non-consumers of specific foods, or into dietary patterns. Food intake biomarkers can also play a role in correcting self-reported measurement error, thus improving dietary intake estimates. Quantification of food intake was previously performed for citrus (proline betaine), chicken (guanidoacetate) and grape (tartaric acid) intake. However, this area still requires more investigation and expansion to a range of foods. The present review will assess the current literature of identified specific food intake biomarkers, their validation and the variety of biomarker uses. Addressing the utility of biomarkers and highlighting gaps in this area is important to advance the field in the context of nutrition research.

Metabolomics Profiles of Smokers from Two Ethnic Groups with Differing Lung Cancer Risk

African American (AA) smokers are at a higher risk of developing lung cancer compared to whites. The variations in the metabolism of nicotine and tobacco-derived carcinogens in these groups were reported previously with the levels of nicotine metabolites and carcinogen-derived metabolites measured using targeted approaches. While useful, these targeted strategies are not able to detect global metabolic changes for use in predicting the detrimental effects of tobacco use and ultimately lung cancer susceptibility among smokers. To address this limitation, we have performed global untargeted metabolomics profiling in urine of AA and white smokers to characterize the pattern of metabolites, identify differentially regulated pathways, and correlate these profiles with the observed variations in lung cancer risk between these two populations. Urine samples from AA (n = 30) and white (n = 30) smokers were used for metabolomics analysis acquired in both positive and negative electrospray ionization modes. LC-MS data were uploaded onto the cloud-based XCMS online (http://xcmsonline.scripps.edu) platform for retention time correction, alignment, feature detection, annotation, statistical analysis, data visualization, and automated systems biology pathway analysis. The latter identified global differences in the metabolic pathways in the two groups including the metabolism of carbohydrates, amino acids, nucleotides, fatty acids, and nicotine. Significant differences in the nicotine degradation pathway (cotinine glucuronidation) in the two groups were observed and confirmed using a targeted LC-MS/MS approach. These results are consistent with previous studies demonstrating AA smokers with lower glucuronidation capacity compared to whites. Furthermore, the d-glucuronate degradation pathway was found to be significantly different between the two populations, with lower amounts of the putative metabolites detected in AA compared to whites. We hypothesize that the differential regulation of the d-glucuronate degradation pathway is a consequence of the variations in the glucuronidation capacity observed in the two groups. Other pathways including the metabolism of amino acids, nucleic acids, and fatty acids were also identified, however, the biological relevance and implications of these differences across ethnic groups need further investigation. Overall, the applied metabolomics approach revealed global differences in the metabolic networks and endogenous metabolites in AA and whites, which could be used and validated as a new potential panel of biomarkers that could be used to predict lung cancer susceptibility among smokers in population-based studies.