Metabolic profiles of male meat eaters, fish eaters, vegetarians, and vegans from the EPIC-Oxford cohort

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.

Associations of Amino Acid and Acylcarnitine Profiles With Incident Hyperuricemia in Middle-Aged and Older Chinese Individuals

OBJECTIVE

Little is known about how metabolic perturbations are linked to hyperuricemia in the general population. Therefore we aimed to examine metabolomics profiles in relation to uric acid change and incident hyperuricemia.

METHODS

This study included 1,621 community-dwelling Chinese participants ages 50-70 years without hyperuricemia at baseline, with a mean duration of follow-up of 6 years. A total of 56 metabolites (22 amino acids and 34 acylcarnitines) at baseline were quantified by gas or liquid chromatography coupled to mass spectrometry. Annual change in uric acid was calculated, and incident hyperuricemia was defined as plasma uric acid >420 μmoles/liter in men and >360 μmoles/liter in women.

RESULTS

The mean ± SD annual change in uric acid was 9.6 ± 12.1 μmoles/liter and the incidence of hyperuricemia was 23.1% (375 of 1,621). After adjustment for conventional risk factors, 9 metabolites (cysteine, glutamine, phenylalanine, threonine, and long-chain acylcarnitines C14:1OH, C18, C18:2, C20, and C20:4) were significantly associated with uric acid change (Bonferroni corrected P < 0.05) and with incident hyperuricemia (relative risks ranged from 1.14 to 1.21 per SD increment of metabolites; P < 0.05). A network analysis showed significant associations between the module containing long-chain acylcarnitines and incident hyperuricemia. Moreover, levels of these 9 metabolites were specifically correlated with intake of foods, including red and processed meat or soy products.

CONCLUSION

Plasma cysteine, glutamine, phenylalanine, threonine, and long-chain acylcarnitines are positively associated with incident hyperuricemia. The levels of these metabolites may be partially driven by intakes of meat and soy products that are associated with hyperuricemia.

A lipid-related metabolomic pattern of diet quality

BACKGROUND

Adherence to a healthy diet has been associated with reduced risk of chronic diseases. Identifying nutritional biomarkers of diet quality may be complementary to traditional questionnaire-based methods and may provide insights concerning disease mechanisms and prevention.

OBJECTIVE

To identify metabolites associated with diet quality assessed via the Alternate Healthy Eating Index (AHEI) and its components.

METHODS

This cross-sectional study used FFQ data and plasma metabolomic profiles, mostly lipid related, from the Nurses' Health Study (NHS, n = 1460) and Health Professionals Follow-up Study (HPFS, n = 1051). Linear regression models assessed associations of the AHEI and its components with individual metabolites. Canonical correspondence analyses (CCAs) investigated overlapping patterns between AHEI components and metabolites. Principal component analysis (PCA) and explanatory factor analysis were used to consolidate correlated metabolites into uncorrelated factors. We used stepwise multivariable regression to create a metabolomic score that is an indicator of diet quality.

RESULTS

The AHEI was associated with 83 metabolites in the NHS and 96 metabolites in the HPFS after false discovery rate adjustment. Sixty-three of these significant metabolites overlapped between the 2 cohorts. CCA identified "healthy" AHEI components (e.g., nuts, whole grains) and metabolites (n = 27 in the NHS and 33 in the HPFS) and "unhealthy" AHEI components (e.g., red meat, trans fat) and metabolites (n = 56 in the NHS and 63 in the HPFS). PCA-derived factors composed of highly saturated triglycerides, plasmalogens, and acylcarnitines were associated with unhealthy AHEI components while factors composed of highly unsaturated triglycerides were linked to healthy AHEI components. The stepwise regression analysis contributed to a metabolomics score as a predictor of diet quality.

CONCLUSION

We identified metabolites associated with healthy and unhealthy eating behaviors. The observed associations were largely similar between men and women, suggesting that metabolomics can be a complementary approach to self-reported diet in studies of diet and chronic disease.

Diet, Physical Activity and Adiposity as Determinants of Circulating Amino Acid Levels in a Multiethnic Asian Population

Profiles of circulating amino acids have been associated with cardiometabolic diseases. We investigated the associations between dietary protein intake, physical activity and adiposity and serum amino acid profiles in an Asian population. We used data from 3009 male and female participants from the Singapore Prospective Study Program cohort. Dietary and physical activity data were obtained from validated questionnaires; anthropometric measurements were collected during a health examination; and fasting concentrations of 16 amino acids were measured using targeted LC-MS. The association between lifestyle factors and amino acid levels was modeled using multiple linear regression with adjustment for other sociodemographic and lifestyle factors and correction for multiple testing. We observed significant associations between seafood intake (β-coefficient 0.132, 95% CI 0.006, 0.257 for a 100% increment), physical activity (β-coefficient −0.096, 95% CI −0.183, −0.008 in the highest versus lowest quartile) and adiposity (BMI β-coefficient 0.062, 95% CI 0.054, 0.070 per kg/m²; waist circumference β-coefficient 0.034, 95% CI 0.031, 0.037 per cm) and branched-chain amino acid levels (expressed per-SD). We also observed significant interactions with sex for the association between meat and seafood and total intakes and BCAA levels (P for interaction 0.007), which were stronger in females than in males. Our findings suggest novel associations between modifiable lifestyle factors and amino acid levels in Asian populations.

A metabolomic study of red and processed meat intake and acylcarnitine concentrations in human urine and blood

BACKGROUND

Acylcarnitines (ACs) play a major role in fatty acid metabolism and are potential markers of metabolic dysfunction with higher blood concentrations reported in obese and diabetic individuals. Diet, and in particular red and processed meat intake, has been shown to influence AC concentrations but data on the effect of meat consumption on AC concentrations is limited.

OBJECTIVES

To investigate the effect of red and processed meat intake on AC concentrations in plasma and urine using a randomized controlled trial with replication in an observational cohort.

METHODS

In the randomized crossover trial, 12 volunteers successively consumed 2 different diets containing either pork or tofu for 3 d each. A panel of 44 ACs including several oxidized ACs was analyzed by LC-MS in plasma and urine samples collected after the 3-d period. ACs that were associated with pork intake were then measured in urine (n = 474) and serum samples (n = 451) from the European Prospective Investigation into Cancer and nutrition (EPIC) study and tested for associations with habitual red and processed meat intake derived from dietary questionnaires.

RESULTS

In urine samples from the intervention study, pork intake was positively associated with concentrations of 18 short- and medium-chain ACs. Eleven of these were also positively associated with habitual red and processed meat intake in the EPIC cross-sectional study. In blood, C18:0 was positively associated with red meat intake in both the intervention study (q = 0.004, Student's t-test) and the cross-sectional study (q = 0.033, linear regression).

CONCLUSIONS

AC concentrations in urine and blood were associated with red meat intake in both a highly controlled intervention study and in subjects of a cross-sectional study. Our data on the role of meat intake on this important pathway of fatty acid and energy metabolism may help understanding the role of red meat consumption in the etiology of some chronic diseases. This trial was registered at Clinicaltrials.gov as NCT03354130.

Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans

The unabated global increase of allergic patients leads to an unmet need for rapid and inexpensive tools for the diagnosis of allergies and for monitoring the outcome of allergen-specific immunotherapy (SIT). In this proof-of-concept study, we investigated the potential of Fourier-Transform Infrared (FTIR) spectroscopy, a high-resolution and cost-efficient biophotonic method with high throughput capacities, to detect characteristic alterations in serum samples of healthy, allergic, and SIT-treated mice and humans. To this end, we used experimental models of ovalbumin (OVA)-induced allergic airway inflammation and allergen-specific tolerance induction in BALB/c mice. Serum collected before and at the end of the experiment was subjected to FTIR spectroscopy. As shown by our study, FTIR spectroscopy, combined with deep learning, can discriminate serum from healthy, allergic, and tolerized mice, which correlated with immunological data. Furthermore, to test the suitability of this biophotonic method for clinical diagnostics, serum samples from human patients were analyzed by FTIR spectroscopy. In line with the results from the mouse models, machine learning-assisted FTIR spectroscopy allowed to discriminate sera obtained from healthy, allergic, and SIT-treated humans, thereby demonstrating its potential for rapid diagnosis of allergy and clinical therapeutic monitoring of allergic patients.

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.

Genetic susceptibility, plant-based dietary patterns, and risk of cardiovascular disease

BACKGROUND

Plant-based dietary patterns may be related to better cardiovascular profiles. Whether a healthy plant-based dietary index is predictive of future cardiovascular disease (CVD) across people with different genetic susceptibility remains uncertain.

OBJECTIVE

We investigated associations of adherence to healthy plant-based diets with the incidence of CVD considering the genetic susceptibility.

METHODS

This prospective cohort study included a total of 156,148 adults initially free of CVD and cancer. We calculated a healthful plant-based diet index (healthful-PDI) in which healthy plant foods received positive scores, and less healthy plant foods and animal foods received reverse scores. Genetic risk scores (GRSs) for myocardial infarction (MI) and stroke were calculated to assess interactions between healthful-PDI and GRSs.

RESULTS

During 5 y of follow-up, we observed 1812 incident cases of CVD. Higher healthful-PDI was associated with a lower CVD risk [HR per 10-unit increment: 0.87 (95% CI: 0.81, 0.93) after adjusting for demographic, lifestyle, and other dietary factors (model 1); HR 0.90 (0.84, 0.97) after further adjusting for obesity and metabolic factors (model 2)]. The risk of CVD was gradually decreased in association with higher adherence to healthful-PDI, regardless of genetic susceptibility. The inverse associations of healthful-PDI with CVD were consistently observed in people with low GRS-MI [HR 0.85 (95% CI: 0.76, 0.94) in model 1; HR 0.88 (0.79, 0.97) in model 2] and those with high GRS-MI [HR 0.91 (0.82, 0.99) in model 1; HR 0.94 (0.86, 1.04) in model 2], without significant interactions (Pinteraction = 0.59 in model 1; Pinteraction = 0.66 in model 2). Similarly, higher healthful-PDI was related to a lower risk of CVD, regardless of low/high GRS-stroke.

CONCLUSION

Adherence to healthy plant-based diets may be associated with a decreased incidence of CVD in the entire population, suggesting that plant-based dietary patterns may modify the risk of CVD, regardless of genetic susceptibility.

Mediterranean diet intervention in overweight and obese subjects lowers plasma cholesterol and causes changes in the gut microbiome and metabolome independently of energy intake

OBJECTIVES

This study aimed to explore the effects of an isocaloric Mediterranean diet (MD) intervention on metabolic health, gut microbiome and systemic metabolome in subjects with lifestyle risk factors for metabolic disease.

DESIGN

Eighty-two healthy overweight and obese subjects with a habitually low intake of fruit and vegetables and a sedentary lifestyle participated in a parallel 8-week randomised controlled trial. Forty-three participants consumed an MD tailored to their habitual energy intakes (MedD), and 39 maintained their regular diets (ConD). Dietary adherence, metabolic parameters, gut microbiome and systemic metabolome were monitored over the study period.

RESULTS

Increased MD adherence in the MedD group successfully reprogrammed subjects' intake of fibre and animal proteins. Compliance was confirmed by lowered levels of carnitine in plasma and urine. Significant reductions in plasma cholesterol (primary outcome) and faecal bile acids occurred in the MedD compared with the ConD group. Shotgun metagenomics showed gut microbiome changes that reflected individual MD adherence and increase in gene richness in participants who reduced systemic inflammation over the intervention. The MD intervention led to increased levels of the fibre-degrading Faecalibacterium prausnitzii and of genes for microbial carbohydrate degradation linked to butyrate metabolism. The dietary changes in the MedD group led to increased urinary urolithins, faecal bile acid degradation and insulin sensitivity that co-varied with specific microbial taxa.

CONCLUSION

Switching subjects to an MD while maintaining their energy intake reduced their blood cholesterol and caused multiple changes in their microbiome and metabolome that are relevant in future strategies for the improvement of metabolic health.

MG-HCr, the Methylglyoxal-Derived Hydroimidazolone of Creatine, a Biomarker for the Dietary Intake of Animal Source Food

In the course of the Maillard reaction in vivo or in food, creatine reacts with the 1,2-dicarbonyl compound methylglyoxal to N-(4-methyl-5-oxo-1-imidazolin-2-yl)sarcosine (MG-HCr). We studied whether the urinary excretion of MG-HCr is affected by its intake with meat or by the intake of creatine and subsequent in vivo formation of MG-HCr. Therefore, 24 h urine of 30 subjects with different dietary habits was analyzed with HPLC-MS/MS. The daily MG-HCr excretion via urine varied between omnivores (0.39-9.67 μmol/day, n = 24), vegetarians (0.18-0.97 μmol/day, n = 19), and vegans (0.10-0.27 μmol/day, n = 8). An intervention study with 18 subjects demonstrated the bioavailability of MG-HCr (ca. 54%) from 200 g of heated meat and its quick excretion with urine. A creatine intervention of 0.44 g did not increase MG-HCr excretion. Thus, the differences in MG-HCr excretion between different diets are mainly caused by the dietary uptake of MG-HCr. We additionally found MG-HCr in milk and egg products, where it is formed during heat treatment. This partly explains differences in MG-HCr excretion of vegetarians and vegans. Hence, MG-HCr in urine is a short-term marker for the intake of heat-processed animal source food.

Nutritional Status and the Influence of the Vegan Diet on the Gut Microbiota and Human Health

The human gut microbiota is considered a well-known complex ecosystem composed of distinct microbial populations, playing a significant role in most aspects of human health and wellness. Several factors such as infant transitions, dietary habits, age, consumption of probiotics and prebiotics, use of antibiotics, intestinal comorbidities, and even metabolic diseases may continously alter microbiota diversity and function. The study of vegan diet-microbiota interactions is a rapidly evolving field, since plenty of research has been focused on the potential effects of plant-based dietary patterns on the human gut microbiota. It has been reported that well-planned vegan diets and their associated components affect both the bacterial composition and metabolic pathways of gut microbiota. Certain benefits associated with medical disorders but also limitations (including nutritional deficiencies) have been documented. Although the vegan diet may be inadequate in calorific value, it is rich in dietary fiber, polyphenols, and antioxidant vitamins. The aim of the present study was to provide an update of the existing knowledge on nutritional status of vegan diets and the influence of their food components on the human gut microbiota and health.

Urine Metabolite Profiles and Nutrient Intake Based on 4-Day Weighed Food Diary in Habitual Vegans, Vegetarians, and Omnivores

BACKGROUND

Increasing interest in diets excluding meat and other products of animal origin emphasizes the importance of objective and reliable methods to measure dietary exposure, to evaluate associations and causation between diet and health, and to quantify nutrient intakes in different diets.

OBJECTIVES

This study aimed to investigate if NMR analysis of urine samples can serve as an objective method to discriminate vegan, vegetarian with or without fish, and omnivore diets. A secondary aim was to assess the influence of dietary nutrient intake on the metabolomics results.

METHODS

Healthy individuals (43 men and 75 women, age 19-57 y) complying with habitual vegan (n = 42), vegetarian (n = 25), vegetarian + fish (n = 13), or omnivore (n = 38) diets were enrolled. Data were collected on clinical phenotype and lifestyle including a 4-d weighed food diary. Urine was analyzed for metabolites by NMR spectroscopy and data normalized using probabilistic quotient normalization and Pareto-scaled before multivariate analysis. Before orthogonal projections to latent structures with discriminant analysis, participants were assigned as meat consumers or nonmeat consumers (vegans and vegetarians), vegans or nonvegans (omnivores, vegetarian, and vegetarian + fish).

RESULTS

The main results showed that it was possible to discriminate meat and nonmeat consumers (91% correctly classified), but discrimination between vegans and nonvegans was less rigorous (75% correctly classified). Secondary outcomes showed that reported intake of protein was higher in omnivores, and saturated fat lower and fiber higher in vegans, compared with the other groups. Discriminating metabolites were mainly related to differences in protein intake.

CONCLUSIONS

NMR urine metabolomics appears suitable to objectively identify and predict habitual intake of meat in healthy individuals, but results should be interpreted with caution because not only food groups but also specific foods contribute to the patterns.This trial was registered at clinicaltrials.gov as NCT02039609.

Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: A prospective study of 3,057 matched case-control sets from EPIC

Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR1SD ) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR1SD  = 0.77, 95% confidence interval 0.66-0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR1SD  = 0.72, 0.57-0.90), or lysophosphatidylcholines (OR1SD  = 0.81, 0.69-0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR1SD  = 0.77, 0.61-0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer.

Which is the urine sample material of choice for metabolomics-driven biomarker studies?

Urine-based metabolomics-driven strategies for the discovery of biomarkers are increasingly developed and applied in analytical chemistry. But valid, data-based recommendations for a urine sample material of choice are lacking. We investigated first and second morning urine (MU), which are the most commonly used urine specimens. Potential major factors biasing metabolomics biomarker results in these sample materials were studied. First, 35 1st and 2nd MU samples were collected from healthy, young men after an overnight fast. Subsequently, two subgroups were built, one having fast food at lunch and dinner (n = 17), the other vegetarian meals (n = 18). Again 1st and 2nd MU were collected. Non-targeted liquid chromatography-mass spectrometry was applied for analyses. More than half of the >5400 urinary ion features showed a significant difference between 1st and 2nd MU. Just two fast food meals on previous day significantly affected around 30% of all metabolites in 1st and 2nd MU. In contrast, the effects of two vegetarian meals in 2nd MU were only minor. Additionally, we describe 47 metabolites in urine, possible hits in biomarker studies, which are susceptible to the diet the day before sample collection. They should be handled with caution until validation in diet-controlled studies. Based on our results we think the second MU, ideally collected after standardized vegetarian meals and drinking only water on the previous day, is most suitable for valid analysis of biomarkers in urine.

Dietary Patterns Are Associated with Serum Metabolite Patterns and Their Association Is Influenced by Gut Bacteria among Older German Adults

BACKGROUND

Although dietary intakes and dietary intake patterns (DPs) have been associated with single metabolites, it is unclear whether DPs are also reflected in specific metabolite patterns (MPs). Moreover, the influence of groups of gut bacteria on the relationship between DPs and MPs is underexplored.

OBJECTIVES

We aimed to investigate the association of DPs and serum MPs and also the modifying effect of the gut bacteria compositional patterns (BCPs).

METHODS

This is a cross-sectional investigation among 225 individuals (median age: 63 y; 53% women) from the European Prospective Investigation into Cancer and Nutrition study. Dietary intakes were assessed by three 24-h dietary recalls, gut bacteria composition was quantified by 16S rRNA gene sequencing, and the serum metabolome was profiled by an untargeted approach. We identified DPs and BCPs by the treelet transform analysis. We modeled associations between DPs and 8 previously published MPs and the modifying effect of BCPs by fitting generalized linear models using DataSHIELD R.

RESULTS

We identified 5 DPs and 7 BCPs. The "bread, margarine, and processed meat" and "fruiting vegetables and vegetable oils" DPs were positively associated with the "amino acids" (β = 0.35; 95% CI: 0.02, 0.69; P = 0.03) and "fatty acids" MPs (β = 0.45; 95% CI: 0.16, 0.74; P = 0.01), respectively. The "tea and miscellaneous" was inversely associated with the "amino acids" (β = -0.28; 95% CI: -0.52, -0.05; P = 0.02) and "amino acid derivatives" MPs (β = -0.21; 95% CI: -0.39, -0.02; P = 0.03). One BCP negatively modified the association between the "bread, margarine, and processed meat" DP and the "amino acids" MP (P-interaction = 0.01).

CONCLUSIONS

In older German adults, DPs are reflected in MPs, and the gut bacteria attenuate 1 DP-MP association. These MPs should be explored as biomarkers of these jointly consumed foods while taking into account a potentially modifying role of the gut bacteria.

Biomarkers of meat and seafood intake: an extensive literature review

Meat, including fish and shellfish, represents a valuable constituent of most balanced diets. Consumption of different types of meat and fish has been associated with both beneficial and adverse health effects. While white meats and fish are generally associated with positive health outcomes, red and especially processed meats have been associated with colorectal cancer and other diseases. The contribution of these foods to the development or prevention of chronic diseases is still not fully elucidated. One of the main problems is the difficulty in properly evaluating meat intake, as the existing self-reporting tools for dietary assessment may be imprecise and therefore affected by systematic and random errors. Dietary biomarkers measured in biological fluids have been proposed as possible objective measurements of the actual intake of specific foods and as a support for classical assessment methods. Good biomarkers for meat intake should reflect total dietary intake of meat, independent of source or processing and should be able to differentiate meat consumption from that of other protein-rich foods; alternatively, meat intake biomarkers should be specific to each of the different meat sources (e.g., red vs. white; fish, bird, or mammal) and/or cooking methods. In this paper, we present a systematic investigation of the scientific literature while providing a comprehensive overview of the possible biomarker(s) for the intake of different types of meat, including fish and shellfish, and processed and heated meats according to published guidelines for biomarker reviews (BFIrev). The most promising biomarkers are further validated for their usefulness for dietary assessment by published validation criteria.

Animal and Plant Protein Sources and Cardiometabolic Health

The sources or types of protein in the diet have long been overlooked regarding their link to cardiometabolic health. The picture is complicated by the fact that animal and plant proteins are consumed along with other nutrients and substances which make up the "protein package" so plant and animal protein come with clear nutrient clusters. This review aimed at deciphering the relation between plant and animal protein and cardiometabolic health by examining different nutritional levels (such as amino acids, protein type, protein foods, protein patterns, and associated overall dietary and nutrient patterns) and varying levels of scientific evidence [basic science, randomized controlled trials (RCTs), observational data]. Plant protein in Western countries is a robust marker of nutrient adequacy of the diet, whereas the contribution of animal protein is highly heterogeneous. Yet recent data from large cohorts have confirmed that total and animal proteins are associated with the risk of cardiovascular disease and diabetes, even when fully adjusting for lifestyle and dietary or nutritional factors. Here again, there is marked variability depending on the type of animal protein. Protein from processed red meat and total red meat on the one hand, and from legumes, nuts, and seeds on the other, are often reported at the extremes of the risk range. RCTs using purified proteins have contributed little to the topic to date, inasmuch as the findings cannot readily be extrapolated to current or near-future diets, but RCTs studying whole protein foods have shown a beneficial effect of pulses. Despite the fact that many of the benefits of plant protein reported in observational or interventional studies may stem from the protein package that they convey and the nutrients that they displace, there are also important indications that protein per se may affect cardiometabolic health via the many amino acids that are present in typically contrasting levels in plant compared with animal proteins.

Untargeted metabolomics reveals alterations in metabolites of lipid metabolism and immune pathways in the serum of rats after long-term oral administration of Amalaki rasayana

Amalaki rasayana, a traditional preparation, is widely used by Ayurvedic physicians for the treatment of inflammatory conditions, cardiovascular diseases, and cancer. Metabolic alterations induced by Amalaki rasayana intervention are unknown. We investigated the modulations in serum metabolomic profiles in Wistar rats following long-term oral administration of Amalaki rasayana. Global metabolic profiling was performed of the serum of rats administered with either Amalaki rasayana (AR) or ghee + honey (GH) for 18 months and control animals which were left untreated. Amalaki rasayana components were confirmed from AR extract using HR-LCMS analysis. Significant reductions in prostaglandin J2, 11-dehydrothromboxane B2, and higher levels of reduced glutathione and glycitein metabolites were observed in the serum of AR administered rats compared to the control groups. Eleven different metabolites classified as phospholipids, glycerophospholipids, glucoside derivatives, organic acids, and glycosphingolipid were exclusively observed in the AR administered rats. Pathway analysis suggests that altered metabolites in AR administered rats are those associated with different biochemical pathways of arachidonic acid metabolism, fatty acid metabolism, leukotriene metabolism, G-protein mediated events, phospholipid metabolism, and the immune system. Targeted metabolomics confirmed the presence of gallic acid, ellagic acid, and arachidonic acid components in the AR extract. The known activities of these components can be correlated with the altered metabolic profile following long-term AR administration. AR also activates IGF1R-Akt-Foxo3 signaling axis in heart tissues of rats administered with AR. Our study identifies AR components that induce alterations in lipid metabolism and immune pathways in animals which consume AR for an extended period.

The Association between Alcohol Consumption and Serum Metabolites and the Modifying Effect of Smoking

Alcohol consumption is an important lifestyle factor that is associated with several health conditions and a behavioral link with smoking is well established. Metabolic alterations after alcohol consumption have yet to be comprehensively investigated. We studied the association of alcohol consumption with metabolite patterns (MPs) among 2433 individuals from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study, and a potential modification by smoking. Alcohol consumption was self-reported through dietary questionnaires and serum metabolites were measured by a targeted approach. The metabolites were summarized as MPs using the treelet transform analysis (TT). We fitted linear models with alcohol consumption continuously and in five categories. We stratified the continuously modelled alcohol consumption by smoking status. All models were adjusted for potential confounders. Among men, alcohol consumption was positively associated with six MPs and negatively associated with one MP. In women, alcohol consumption was inversely associated with one MP. Heavy consumers differed from other consumers with respect to the "Long and short chain acylcarnitines" MP. Our findings suggest that long and short chain acylcarnitines might play an important role in the adverse effects of heavy alcohol consumption on chronic diseases. The relations seem to depend on gender and smoking status.

Prospective analysis of circulating metabolites and breast cancer in EPIC

BACKGROUND

Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk.

METHODS

A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression.

RESULTS

Among women not using hormones at baseline (n = 2248), and after control for multiple tests, concentrations of arginine (odds ratio [OR] per SD = 0.79, 95% confidence interval [CI] = 0.70-0.90), asparagine (OR = 0.83 (0.74-0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76-0.90)), aa C36:3 (OR = 0.84 (0.77-0.93)), ae C34:2 (OR = 0.85 (0.78-0.94)), ae C36:2 (OR = 0.85 (0.78-0.88)), and ae C38:2 (OR = 0.84 (0.76-0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11-1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06-1.24)) and PC ae C36:3 (OR = 0.88 (0.82-0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity.

CONCLUSIONS

These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies.

Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.

Serum metabolite profiles of habitual diet: evaluation by 1H-nuclear magnetic resonance analysis

BACKGROUND

Objective and reliable methods to measure dietary exposure and prove associations and causation between diet and health are desirable.

OBJECTIVE

The aim of this study was to investigate if 1H-nuclear magnetic resonance (1H-NMR) analysis of serum samples may be used as an objective method to discriminate vegan, vegetarian, and omnivore diets. Specifically, the aim was to identify a metabolite pattern that separated meat-eaters from non-meat-eaters and vegans from nonvegans.

METHODS

Healthy volunteers (45 men and 75 women) complying with habitual vegan (n = 43), vegetarian (n = 24 + vegetarians adding fish n = 13), or omnivore (n = 40) diets were enrolled in the study. Data were collected on clinical phenotype, body composition, lifestyle including a food-frequency questionnaire (FFQ), and a 4-d weighed food diary. Serum samples were analyzed by routine clinical test and for metabolites by 1H-NMR spectroscopy. NMR data were nonnormalized, UV-scaled, and analyzed with multivariate data analysis [principal component analysis, orthogonal projections to latent structures (OPLS) and OPLS with discriminant analysis]. In the multivariate analysis volunteers were assigned as meat-eaters (omnivores), non-meat-eaters (vegans and vegetarians), vegans, or nonvegans (lacto-ovo-vegetarians, vegetarians adding fish, and omnivores). Metabolites were identified by line-fitting of 1D 1H-NMR spectra and the use of statistical total correlation spectroscopy.

RESULTS

Although many metabolites differ in concentration between men and women as well as by age, body mass index, and body composition, it was possible to correctly classify 97.5% of the meat-eaters compared with non-meat-eaters and 92.5% of the vegans compared with nonvegans. The branched-chain amino acids, creatine, lysine, 2-aminobutyrate, glutamine, glycine, trimethylamine, and 1 unidentified metabolite were among the most important metabolites in the discriminating patterns in relation to intake of both meat and other animal products.

CONCLUSIONS

1H-NMR serum metabolomics appears to be a possible objective tool to identify and predict habitual intake of meat and other animal products in healthy subjects. These results should be confirmed in larger cohort studies or intervention trials. This trial was registered at clinicaltrials.gov as NCT02039609.

Identifying Metabolomic Profiles of Insulinemic Dietary Patterns

The food-based empirical dietary index for hyperinsulinemia (EDIH) score assesses the insulinemic potential of diet. This cross-sectional study evaluated associations between EDIH scores from food frequency questionnaires with c-peptide concentrations and with 448 metabolites, from fasting plasma samples, in multivariable linear regression analyses. Metabolites were measured with liquid chromatography tandem mass spectroscopy. Using a robust two-stage study design, discovery of metabolite associations was conducted among 1109 Women's Health Initiative (WHI) Hormone Therapy (HT) trial participants and results replicated in an independent dataset of 810 WHI Observational Study (OS) participants. In both discovery and replication datasets, statistical significance was based on the false-discovery rate adjusted P < 0.05. In the multivariable-adjusted analyses, EDIH was significantly associated with c-peptide concentrations among 919 women (HT & OS) with c-peptide data. On average, c-peptide concentrations were 18% higher (95% CI, 6%, 32%; P-trend < 0.0001) in EDIH quintile 5 compared to quintile 1. Twenty-six metabolites were significantly associated with EDIH in the discovery dataset, and 19 of these were replicated in the validation dataset. Nine metabolites were found to decrease in abundance with increasing EDIH scores and included: C14:0 CE, C16:1 CE, C18:1 CE, C18:3 CE, C20:3 CE, C20:5 CE, C36:1 PS plasmalogen, trigonelline, and eicosapentanoate, whereas the 10 metabolites observed to increase with increasing EDIH scores were: C18:2 SM, C36:3 DAG, C36:4 DAG-A, C51:3 TAG, C52:3 TAG, C52:4, TAG, C54:3 TAG, C54:4 TAG, C54:6 TAG, and C10:2 carnitine. Cholesteryl esters, phospholipids, acylglycerols, and acylcarnitines may constitute circulating metabolites that are associated with insulinemic dietary patterns.

The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

The phenolic interactome and gut microbiota: opportunities and challenges in developing applications for schizophrenia and autism

Schizophrenia and autism spectrum disorder have long been associated with elevated levels of various small phenolic molecules (SPMs). In turn, the gut microbiota (GMB) has been implicated in the kinetics of many of these analytes. Unfortunately, research into the possible relevance of GMB-mediated SPMs to neuropsychiatry continues to be limited by heterogeneous study design, numerous sources of variance and technical challenges. Some SPMs have multiple structural isomers and most have conjugates. Without specialized approaches, SPMs can be incorrectly assigned or inaccurately quantified. In addition, SPM levels can be affected by dietary polyphenol or protein consumption and by various medications and diseases. Nonetheless, heterotypical excretion of various SPMs in association with schizophrenia or autism continues to be reported in independent samples. Recent studies in human cerebrospinal fluid demonstrate the presence of many SPMs A large number of these are bioactive in experimental models. Whether such mechanisms are relevant to the human brain in health or disease is not known. Systematic metabolomic and microbiome studies of well-characterized populations, an appreciation of multiple confounds, and implementation of standardized approaches across platforms and sites are needed to delineate the potential utility of the phenolic interactome in neuropsychiatry.

Plasma Acylcarnitines and Risk of Type 2 Diabetes in a Mediterranean Population at High Cardiovascular Risk

CONTEXT

The potential associations between acylcarnitine profiles and incidence of type 2 diabetes (T2D) and whether acylcarnitines can be used to improve diabetes prediction remain unclear.

OBJECTIVE

To evaluate the associations between baseline and 1-year changes in acylcarnitines and their diabetes predictive ability beyond traditional risk factors.

DESIGN, SETTING, AND PARTICIPANTS

We designed a case-cohort study within the PREDIMED Study including all incident cases of T2D (n = 251) and 694 randomly selected participants at baseline (follow-up, 3.8 years). Plasma acylcarnitines were measured using a targeted approach by liquid chromatography-tandem mass spectrometry. We tested the associations between baseline and 1-year changes in individual acylcarnitines and T2D risk using weighted Cox regression models. We used elastic net regressions to select acylcarnitines for T2D prediction and compute a weighted score using a cross-validation approach.

RESULTS

An acylcarnitine profile, especially including short- and long-chain acylcarnitines, was significantly associated with a higher risk of T2D independent of traditional risk factors. The relative risks of T2D per SD increment of the predictive model scores were 4.03 (95% CI, 3.00 to 5.42; P < 0.001) for the conventional model and 4.85 (95% CI, 3.65 to 6.45; P < 0.001) for the model including acylcarnitines, with a hazard ratio of 1.33 (95% CI, 1.08 to 1.63; P < 0.001) attributed to the acylcarnitines. Including the acylcarnitines into the model did not significantly improve the area under the receiver operator characteristic curve (0.86 to 0.88, P = 0.61). A 1-year increase in C4OH-carnitine was associated with higher risk of T2D [per SD increment, 1.44 (1.03 to 2.01)].

CONCLUSIONS

An acylcarnitine profile, mainly including short- and long-chain acylcarnitines, was significantly associated with higher T2D risk in participants at high cardiovascular risk. The inclusion of acylcarnitines into the model did not significantly improve the T2D prediction C-statistics beyond traditional risk factors, including fasting glucose.

Metabolomic markers of healthy dietary patterns in US postmenopausal women

BACKGROUND

Healthy diet patterns are associated with lower risk of cancer and other chronic diseases. Metabolomics has the potential to expand dietary biomarker development to include dietary patterns, which may provide a complement or alternative to self-reported diet.

OBJECTIVE

This study examined the correlation of serum untargeted metabolomic markers with 4 diet pattern scores-the alternate Mediterranean diet score (aMED), alternate Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and the Healthy Eating Index (HEI)-2015-and used multivariate methods to identify discriminatory metabolites for each pattern.

METHODS

Among 1367 US postmenopausal women with serum metabolomic data in the Cancer Prevention Study-II Nutrition Cohort, we conducted partial correlation analysis, adjusted for demographic and lifestyle variables, to examine cross-sectional correlations between serum metabolomic markers and healthy diet pattern scores. In a randomly selected "training" set (50%), we conducted orthogonal partial least-squares discriminant analysis to identify metabolites that discriminated the top from bottom diet score quintiles. Combinations of metabolites with a variable importance in projection (VIP) score ≥2.5 were tested for predictability in the "testing" set based on the use of receiver operating characteristic curves.

RESULTS

Out of 1186 metabolites, 32 unique metabolites were considered discriminatory based on a VIP score ≥2.5 in the training dataset with some overlap across scores (aMED = 16; AHEI = 17; DASH = 13; HEI = 12). Spearman partial correlation analyses, applying a cut-point (|r| ≥ 0.15) and Bonferroni correction (P < 1.05 × 10-5), identified similar key metabolites. The top 5 metabolites for each pattern mostly distinguished high compared with low scores; 4 of the 5 (fish-derived) metabolites were the same for aMED and AHEI, 2 of which were identified for HEI; 4 DASH metabolites were unique.

CONCLUSIONS

Metabolomic methods that used a split-sample approach identified potential biomarkers for 4 healthy diet patterns. Similar metabolites across scores reflect fish consumption in healthy dietary patterns. These findings should be replicated in independent populations.

Serum metabolites associated with dietary protein intake: results from the Modification of Diet in Renal Disease (MDRD) randomized clinical trial

BACKGROUND

Accurate assessment of dietary intake is essential, but self-report of dietary intake is prone to measurement error and bias. Discovering metabolic consequences of diets with lower compared with higher protein intake could elucidate new, objective biomarkers of protein intake.

OBJECTIVES

The goal of this study was to identify serum metabolites associated with dietary protein intake.

METHODS

Metabolites were measured with the use of untargeted, reverse-phase ultra-performance liquid chromatography-tandem mass spectrometry quantification in serum specimens collected at the 12-mo follow-up visit in the Modification of Diet in Renal Disease (MDRD) Study from 482 participants in study A (glomerular filtration rate: 25-55 mL · min-1 · 1.73 m-2) and 192 participants in study B (glomerular filtration rate: 13-24 mL · min-1 · 1.73 m-2). We used multivariable linear regression to test for differences in log-transformed metabolites (outcome) according to randomly assigned dietary protein intervention groups (exposure). Statistical significance was assessed at the Bonferroni-corrected threshold: 0.05/1193 = 4.2 × 10-5.

RESULTS

In study A, 130 metabolites (83 known from 28 distinct pathways, including 7 amino acid pathways; 47 unknown) were significantly different between participants randomly assigned to the low-protein diet compared with the moderate-protein diet. In study B, 32 metabolites (22 known from 8 distinct pathways, including 4 amino acid pathways; 10 unknown) were significantly different between participants randomly assigned to the very-low-protein diet compared with the low-protein diet. A total of 11 known metabolites were significantly associated with protein intake in the same direction in both studies A and B: 3-methylhistidine, N-acetyl-3-methylhistidine, xanthurenate, isovalerylcarnitine, creatine, kynurenate, 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPE (P-16:0/20:4), 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4), 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4), sulfate, and γ-glutamylalanine.

CONCLUSIONS

Among patients with chronic kidney disease, an untargeted serum metabolomics platform identified multiple pathways and metabolites associated with dietary protein intake. Further research is necessary to characterize unknown compounds and to examine these metabolites in association with dietary protein intake among individuals without kidney disease.This trial was registered at clinicaltrials.gov as NCT03202914.

Role of metabolomics in identification of biomarkers related to food intake

Dietary assessment methods including FFQ and food diaries are associated with many measurement errors including energy under-reporting and incorrect estimation of portion sizes. Such errors can lead to inconsistent results especially when investigating the relationship between food intake and disease causation. To improve the classification of a person's dietary intake and therefore clarify proposed links between diet and disease, reliable and accurate dietary assessment methods are essential. Dietary biomarkers have emerged as a complementary approach to the traditional methods, and in recent years, metabolomics has developed as a key technology for the identification of new dietary biomarkers. The objective of this review is to give an overview of the approaches used for the identification of biomarkers and potential use of the biomarkers. Over the years, a number of strategies have emerged for the discovery of dietary biomarkers including acute and medium term interventions and cross-sectional/cohort study approaches. Examples of the different approaches will be presented. Concomitant with the focus on single biomarkers of specific foods, there is an interest in the development of biomarker signatures for the identification of dietary patterns. In the present review, we present an overview of the techniques used in food intake biomarker discover, including the experimental approaches used and challenges faced in the field. While significant progress has been achieved in the field of dietary biomarkers in recent years, a number of challenges remain. Addressing these challenges will be key to ensure success in implementing use of dietary biomarkers.

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

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

Biomarkers of Individual Foods, and Separation of Diets Using Untargeted LC-MS-based Plasma Metabolomics in a Randomized Controlled Trial

SCOPE

Self-reported dietary intake does not represent an objective unbiased assessment. The effect of the new Nordic diet (NND) versus average Danish diet (ADD) on plasma metabolic profiles is investigated to identify biomarkers of compliance and metabolic effects.

METHODS AND RESULTS

In a 26-week controlled dietary intervention study, 146 subjects followed either NND, a predominantly organic diet high in fruit, vegetables, whole grains, and fish, or ADD, a diet higher in imported and processed foods. Fasting plasma samples are analyzed with untargeted ultra-performance liquid chromatography-quadruple time-of-flight. It is demonstrated that supervised machine learning with feature selection can separate NND and ADD samples with an average test set performance of up to 0.88 area under the curve. The NND plasma metabolome is characterized by diet-related metabolites, such as pipecolic acid betaine (whole grain), trimethylamine oxide, and prolyl hydroxyproline (both fish intake), while theobromine (chocolate) and proline betaine (citrus) were associated with ADD. Amino acid (i.e., indolelactic acid and hydroxy-3-methylbutyrate) and fat metabolism (butyryl carnitine) characterize ADD whereas NND is associated with higher concentrations of polyunsaturated phosphatidylcholines.

CONCLUSIONS

The plasma metabolite profiles are predictive of dietary patterns and reflected good compliance while indicating effects of potential health benefit, including changes in fat metabolism and glucose utilization.

Dietary Patterns among Asian Indians Living in the United States Have Distinct Metabolomic Profiles That Are Associated with Cardiometabolic Risk

Background

Recent studies, primarily in non-Hispanic whites, suggest that dietary patterns have distinct metabolomic signatures that may influence disease risk. However, evidence in South Asians, a group with unique dietary patterns and a high prevalence of cardiometabolic risk, is lacking.

Objective

We investigated the metabolomic profiles associated with 2 distinct dietary patterns among a sample of Asian Indians living in the United States. We also examined the cross-sectional associations between metabolomic profiles and cardiometabolic risk markers.

Methods

We used cross-sectional data from 145 Asian Indians, aged 45-79 y, in the Metabolic Syndrome and Atherosclerosis in South Asians Living in America (MASALA) pilot study. Metabolomic profiles were measured from fasting serum samples. Usual diet was assessed by using a validated food-frequency questionnaire. We used principal components analysis to derive dietary and metabolomic patterns. We used adjusted general linear regression models to examine associations between dietary patterns, individual food groups, metabolite patterns, and cardiometabolic risk markers.

Results

We observed 2 major principal components or metabolite clusters, the first comprised primarily of medium- to long-chain acylcarnitines (metabolite pattern 1) and the second characterized by branched-chain amino acids, aromatic amino acids, and short-chain acylcarnitines (metabolite pattern 2). A "Western/nonvegetarian" pattern was significantly and positively associated with metabolite pattern 2 (all participants: β ± SE = 0.180 ± 0.090, P = 0.05; participants without type 2 diabetes: β ± SE = 0.323 ± 0.090, P = 0.0005). In all participants, higher scores on metabolite pattern 2 were adversely associated with measures of glycemia (fasting insulin: β ± SE = 2.91 ± 1.29, P = 0.03; 2-h insulin: β ± SE = 22.1 ± 10.3, P = 0.03; homeostasis model assessment of insulin resistance: β ± SE = 0.94 ± 0.42, P = 0.03), total adiponectin (β ± SE = -1.46 ± 0.47, P = 0.002), lipids (total cholesterol: β ± SE = 7.51 ± 3.45, P = 0.03; triglycerides: β ± SE = 14.4 ± 6.67, P = 0.03), and a radiographic measure of hepatic fat (liver-to-spleen attenuation ratio: β ± SE = -0.83 ± 0.42, P = 0.05).

Conclusions

Our findings suggest that a "Western/nonvegetarian" dietary pattern is associated with a metabolomic profile that is related to an adverse cardiometabolic profile in Asian Indians. Public health efforts to reduce cardiometabolic disease burden in this high-risk group should focus on consuming a healthy plant-based diet.

Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project

Population-based prospective cohort studies are indispensable for modern medical research as they provide important knowledge on the influences of many kinds of genetic and environmental factors on the cause of disease. Although traditional cohort studies are mainly conducted using questionnaires and physical examinations, modern cohort studies incorporate omics and genomic approaches to obtain comprehensive physical information, including genetic information. Here, we report the design and midterm results of multi-omics analysis on population-based prospective cohort studies from the Tohoku Medical Megabank (TMM) Project. We have incorporated genomic and metabolomic studies in the TMM cohort study as both metabolome and genome analyses are suitable for high-throughput analysis of large-scale cohort samples. Moreover, an association study between the metabolome and genome show that metabolites are an important intermediate phenotype connecting genetic and lifestyle factors to physical and pathologic phenotypes. We apply our metabolome and genome analyses to large-scale cohort samples in the following studies.

Untargeted Metabolomics Identifies Novel Potential Biomarkers of Habitual Food Intake in a Cross-Sectional Study of Postmenopausal Women

Background

Recent studies suggest that untargeted metabolomics is a promising tool to identify novel biomarkers of individual foods. However, few large cross-sectional studies with comprehensive data on habitual diet and circulating metabolites have been conducted.

Objective

We aimed to identify potential food biomarkers and evaluate their predictive accuracy.

Methods

We conducted a cross-sectional analysis of consumption of 91 food groups or items, assessed by a 152-item food-frequency questionnaire, in relation to 1186 serum metabolites measured by mass spectrometry-based platforms from 1369 nonsmoking postmenopausal women (mean age = 68.3 y). Diet-metabolite associations were selected by Pearson's partial correlation analysis (P < 4.63 × 10-7, |r| > 0.2). The predictive accuracy of the selected food metabolites was evaluated from the area under the curve (AUC) calculated from receiver operating characteristic analysis conducted among women in the top and bottom quintiles of dietary intake.

Results

We identified 379 diet-metabolite associations. Forty-two food groups or items were correlated with 199 serum metabolites. We replicated 63 metabolites as biomarkers of habitual food intake reported in previous cross-sectional studies. Among those not previously shown to be associated with habitual diet, several are biologically plausible and were reported in acute feeding studies including: banana and dopamine 3-O-sulfate (r = 0.34, AUC = 76%) and dopamine 4-O-sulfate (r = 0.33, AUC = 74%), garlic and alliin (r = 0.24, AUC = 69%), N-acetylalliin (r = 0.27, AUC = 70%), and S-allylcysteine (r = 0.23, AUC = 69). Two unannotated metabolites were the strongest predictors for dark fish (X-02269, r = 0.51, AUC = 94%) and coffee intake (X-21442, r = 0.62, AUC = 98%).

Conclusion

In this comprehensive, cross-sectional analysis of habitual food intake and serum metabolites among postmenopausal women, we identified several potentially novel food biomarkers and replicated others. Our findings contribute to the limited literature on food-based biomarkers and highlight the significant and promising role that large cohort studies with archived blood samples could play in this field. This study was registered at clinicaltrials.gov as NCT03282812.

Precision nutrition for prevention and management of type 2 diabetes

Precision nutrition aims to prevent and manage chronic diseases by tailoring dietary interventions or recommendations to one or a combination of an individual's genetic background, metabolic profile, and environmental exposures. Recent advances in genomics, metabolomics, and gut microbiome technologies have offered opportunities as well as challenges in the use of precision nutrition to prevent and manage type 2 diabetes. Nutrigenomics studies have identified genetic variants that influence intake and metabolism of specific nutrients and predict individuals' variability in response to dietary interventions. Metabolomics has revealed metabolomic fingerprints of food and nutrient consumption and uncovered new metabolic pathways that are potentially modified by diet. Dietary interventions have been successful in altering abundance, composition, and activity of gut microbiota that are relevant for food metabolism and glycaemic control. In addition, mobile apps and wearable devices facilitate real-time assessment of dietary intake and provide feedback which can improve glycaemic control and diabetes management. By integrating these technologies with big data analytics, precision nutrition has the potential to provide personalised nutrition guidance for more effective prevention and management of type 2 diabetes. Despite these technological advances, much research is needed before precision nutrition can be widely used in clinical and public health settings. Currently, the field of precision nutrition faces challenges including a lack of robust and reproducible results, the high cost of omics technologies, and methodological issues in study design as well as high-dimensional data analyses and interpretation. Evidence is needed to support the efficacy, cost-effectiveness, and additional benefits of precision nutrition beyond traditional nutrition intervention approaches. Therefore, we should manage unrealistically high expectations and balance the emerging field of precision nutrition with public health nutrition strategies to improve diet quality and prevent type 2 diabetes and its complications.

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.

Use of Metabolomics in Improving Assessment of Dietary Intake

BACKGROUND

Nutritional metabolomics is rapidly evolving to integrate nutrition with complex metabolomics data to discover new biomarkers of nutritional exposure and status.

CONTENT

The purpose of this review is to provide a broad overview of the measurement techniques, study designs, and statistical approaches used in nutrition metabolomics, as well as to describe the current knowledge from epidemiologic studies identifying metabolite profiles associated with the intake of individual nutrients, foods, and dietary patterns.

SUMMARY

A wide range of technologies, databases, and computational tools are available to integrate nutritional metabolomics with dietary and phenotypic information. Biomarkers identified with the use of high-throughput metabolomics techniques include amino acids, acylcarnitines, carbohydrates, bile acids, purine and pyrimidine metabolites, and lipid classes. The most extensively studied food groups include fruits, vegetables, meat, fish, bread, whole grain cereals, nuts, wine, coffee, tea, cocoa, and chocolate. We identified 16 studies that evaluated metabolite signatures associated with dietary patterns. Dietary patterns examined included vegetarian and lactovegetarian diets, omnivorous diet, Western dietary patterns, prudent dietary patterns, Nordic diet, and Mediterranean diet. Although many metabolite biomarkers of individual foods and dietary patterns have been identified, those biomarkers may not be sensitive or specific to dietary intakes. Some biomarkers represent short-term intakes rather than long-term dietary habits. Nonetheless, nutritional metabolomics holds promise for the development of a robust and unbiased strategy for measuring diet. Still, this technology is intended to be complementary, rather than a replacement, to traditional well-validated dietary assessment methods such as food frequency questionnaires that can measure usual diet, the most relevant exposure in nutritional epidemiologic studies.

Biological determinants of health: Genes, microbes, and metabolism exemplars of nursing science

BACKGROUND

Increasingly, nurse scientists are incorporating "omics" measures (e.g., genomics, transcriptomics, proteomics, and metabolomics) in studies of biologic determinants of health and behavior. The role of omics in nursing science can be conceptualized in several ways: (a) as a portfolio of biological measures (biomarkers) to monitor individual risk, (b) as a set of combined data elements that can generate new knowledge based on large and complex patient data sets, (c) as baseline information that promotes health education and potentially personalized interventions, and (d) as a platform to understand how environmental parameters (e.g., diet) interact with the individual's physiology.

PURPOSE

In this article, we provide exemplars of nursing scientists who use omics to better understand specific health conditions.

METHODS

We highlight various ongoing nursing research investigations incorporating omics technologies to study chronic pain vulnerability, risk for a pain-related condition, cardiometabolic complications associated with pregnancy, and as biomarkers of response to a dietary intervention.

DISCUSSION

Omics technologies add an important dimension to nursing science across many foci of investigation. However, there are also challenges and opportunities for nurse scientists who consider using omics in their research.

CONCLUSION

The integration of omics holds promise for increasing the impact of nursing research and practice on population health outcomes.

Blood Metabolic Signatures of Body Mass Index: A Targeted Metabolomics Study in the EPIC Cohort

Metabolomics is now widely used to characterize metabolic phenotypes associated with lifestyle risk factors such as obesity. The objective of the present study was to explore the associations of body mass index (BMI) with 145 metabolites measured in blood samples in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolites were measured in blood from 392 men from the Oxford (UK) cohort (EPIC-Oxford) and in 327 control subjects who were part of a nested case-control study on hepatobiliary carcinomas (EPIC-Hepatobiliary). Measured metabolites included amino acids, acylcarnitines, hexoses, biogenic amines, phosphatidylcholines, and sphingomyelins. Linear regression models controlled for potential confounders and multiple testing were run to evaluate the associations of metabolite concentrations with BMI. 40 and 45 individual metabolites showed significant differences according to BMI variations, in the EPIC-Oxford and EPIC-Hepatobiliary subcohorts, respectively. Twenty two individual metabolites (kynurenine, one sphingomyelin, glutamate and 19 phosphatidylcholines) were associated with BMI in both subcohorts. The present findings provide additional knowledge on blood metabolic signatures of BMI in European adults, which may help identify mechanisms mediating the relationship of BMI with obesity-related diseases.

Treatment of Swedish Patients with Graves' Hyperthyroidism Is Associated with Changes in Acylcarnitine Levels

BACKGROUND

Hyperthyroidism is associated with alterations in metabolism that are currently only partially understood. The objective of the study was to investigate changes in metabolism associated with reinstatement of euthyroidism in Swedish patients.

METHODS

Eighty metabolites in plasma were profiled from 10 subjects with Graves' disease (GD) at baseline and after 9 and 15 months of treatment to reinstate euthyroidism. Thyroid parameters, thyrotropin (TSH), TSH receptor antibodies, free triiodothyronine, and free thyroxine were followed. Main findings were validated in plasma from 20 subjects with GD at baseline and at three, six, and nine months. The study was conducted at the endocrinology clinic in Malmö, Sweden.

RESULTS

Euthyroidism was reinstated at three months, and thyroid status did not change further during the 15-month follow-up. This was paralleled by altered levels of 9/19 detected acylcarnitines (p < 0.05 after adjustment for multiple testing). Levels of short-chain acylcarnitines were decreased, intermediate-chain acylcarnitines elevated, and long-chain acylcarnitines unaltered.

CONCLUSIONS

GD and treatment of the disease is associated with pronounced acyl chain length-dependent alterations in acylcarnitine levels. These changes may be impacted by ethnicity and or dietary differences.

The relationships between serum fructosamine concentrations and lipid profiles in community-dwelling adults

We examined the epidemiological associations between serum fructosamine and dyslipidemia indices in community-dwelling adults. Clinical characteristics and lipid profiles were analyzed in 1352 community-dwelling adults. The demographic characteristics and laboratory results were grouped according to the quartiles of serum fructosamine concentrations in all eligible individuals. From the bottom to the top quartile of serum fructosamine, there were graded increases in age, total cholesterol (TC), fasting blood glucose (FBG), total protein (TP), triglyceride (TG), total cholesterol/ high density lipoprotein-cholesterol (TC/HDL-C) and atherogenic index of plasma (AIP). Serum fructosamine was positive correlated with age, TC, FBG, TP, TG, TC/HDL-C and AIP in whole individuals. The positive correlations were then observed in both genders between serum fructosamine and TC, FBG, TP, TG. Two dominant factors were identified by principal component analysis. Logistic regression analysis showed that the two factors were associated with increased serum fructosamine with adjustment for gender, age, body mass index (BMI), FBG and TP. The similar results were observed in males, but not in females. Dyslipidemia tends to contribute to increased serum fructosamine concentrations in study population, suggesting that elevated serum fructosamine may herald an increased risk of cardiovascular disease among community-dwelling adults, especially in males.

Pre-diagnostic metabolite concentrations and prostate cancer risk in 1077 cases and 1077 matched controls in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Little is known about how pre-diagnostic metabolites in blood relate to risk of prostate cancer. We aimed to investigate the prospective association between plasma metabolite concentrations and risk of prostate cancer overall, and by time to diagnosis and tumour characteristics, and risk of death from prostate cancer.

METHODS

In a case-control study nested in the European Prospective Investigation into Cancer and Nutrition, pre-diagnostic plasma concentrations of 122 metabolites (including acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose and sphingolipids) were measured using targeted mass spectrometry (AbsoluteIDQ p180 Kit) and compared between 1077 prostate cancer cases and 1077 matched controls. Risk of prostate cancer associated with metabolite concentrations was estimated by multi-variable conditional logistic regression, and multiple testing was accounted for by using a false discovery rate controlling procedure.

RESULTS

Seven metabolite concentrations, i.e. acylcarnitine C18:1, amino acids citrulline and trans-4-hydroxyproline, glycerophospholipids PC aa C28:1, PC ae C30:0 and PC ae C30:2, and sphingolipid SM (OH) C14:1, were associated with prostate cancer (p < 0.05), but none of the associations were statistically significant after controlling for multiple testing. Citrulline was associated with a decreased risk of prostate cancer (odds ratio (OR1SD) = 0.73; 95% confidence interval (CI) 0.62-0.86; p trend = 0.0002) in the first 5 years of follow-up after taking multiple testing into account, but not after longer follow-up; results for other metabolites did not vary by time to diagnosis. After controlling for multiple testing, 12 glycerophospholipids were inversely associated with advanced stage disease, with risk reduction up to 46% per standard deviation increase in concentration (OR1SD = 0.54; 95% CI 0.40-0.72; p trend = 0.00004 for PC aa C40:3). Death from prostate cancer was associated with higher concentrations of acylcarnitine C3, amino acids methionine and trans-4-hydroxyproline, biogenic amine ADMA, hexose and sphingolipid SM (OH) C14:1 and lower concentration of glycerophospholipid PC aa C42:4.

CONCLUSIONS

Several metabolites, i.e. C18:1, citrulline, trans-4-hydroxyproline, three glycerophospholipids and SM (OH) C14:1, might be related to prostate cancer. Analyses by time to diagnosis indicated that citrulline may be a marker of subclinical prostate cancer, while other metabolites might be related to aetiology. Several glycerophospholipids were inversely related to advanced stage disease. More prospective data are needed to confirm these associations.

Characteristics of Americans Choosing Vegetarian and Vegan Diets for Health Reasons

OBJECTIVE

Examine the prevalence, patterns, and associated factors of using a vegetarian or vegan diet for health reasons in the US general population.

DESIGN

Cross-sectional data from the 2012 National Health Interview Survey.

PARTICIPANTS

Nationally representative sample (N = 34,525).

VARIABLES MEASURED

Prevalence of ever use and 12-month use of vegetarian or vegan diet for health reasons, patterns of use, and sociodemographic and health-related factor associated with use.

ANALYSIS

Multiple logistic regression analysis.

RESULTS

Prevalence of ever use and 12-month use was 4.0% (n = 1,367) and 1.9% (n = 648), respectively. Health vegetarians and vegans were more likely aged 30-65 years, female, not Hispanic, from the Western US region, at least high school educated, chronically ill, and physically active. They were less likely to be in a relationship, overweight or obese, or smoking, or to have public or private health insurance. Among health vegetarians and vegans, 6.3% consulted with a practitioner for special diets; 26.1% followed the diet because of a specific health problem, mainly high cholesterol, overweight, hypertension, and diabetes; and 59.4% disclosed the diet to their health care provider.

CONCLUSIONS AND IMPLICATIONS

Less than 2% of participants reported using a vegetarian or vegan diet for health reasons within the past 12 months. Despite potential benefits of plant-based nutrition, more research is warranted on the actual use and its effects and safety.

Meat and Seafood Consumption in Relation to Plasma Metabolic Profiles in a Chinese Population: A Combined Untargeted and Targeted Metabolomics Study

We examined the relationship between different patterns of meat and seafood consumption and plasma metabolic profiles in an Asian population. We selected 270 ethnic Chinese men and women from the Singapore Prospective Study Program based on their dietary habits assessed with a validated food frequency questionnaire. Participants were divided into four subgroups: high meat and high seafood (n = 60), high meat and low seafood (n = 64), low meat and high seafood (n = 60), and low meat and low seafood (n = 86) consumers. Plasma metabolites were measured using both targeted and untargeted mass spectroscopy-based analyses. A total of 42 metabolites differed significantly by dietary group. Higher concentrations of essential amino acids, polyunsaturated fatty acids, and d-glucose were found in high meat and/or seafood consumers as compared with the group with a low consumption of these animal foods. Red meat, poultry, fish, shellfish, soy products, and dairy were each correlated with at least one differential metabolite (r = −0.308 to 0.448). Some observations, such as the correlation between fish and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), confirmed previous studies. Other observations, such as the correlation between shellfish and phosphatidylethanolamine (p36:4), were novel. We also observed significant correlations between plasma metabolites and clinical characteristics, such as CMPF with fasting blood glucose (r = 0.401). These findings demonstrate a significant influence of meat and seafood consumption on metabolic profiles in the Asian population.

A comparison of heart rate variability, n-3 PUFA status and lipid mediator profile in age- and BMI-matched middle-aged vegans and omnivores

Low heart rate variability (HRV) predicts sudden cardiac death. Long-chain (LC) n-3 PUFA (C20-C22) status is positively associated with HRV. This cross-sectional study investigated whether vegans aged 40-70 years (n 23), whose diets are naturally free from EPA (20 : 5n-3) and DHA (22 : 6n-3), have lower HRV compared with omnivores (n 24). Proportions of LC n-3 PUFA in erythrocyte membranes, plasma fatty acids and concentrations of plasma LC n-3 PUFA-derived lipid mediators were significantly lower in vegans. Day-time interbeat intervals (IBI), adjusted for physical activity, age, BMI and sex, were significantly shorter in vegans compared with omnivores (mean difference -67 ms; 95 % CI -130, -3·4, P50 % and high-frequency power) were similarly lower in vegans, with no differences during sleep. In conclusion, vegans have higher 24 h SDNN, but lower day-time HRV and shorter day-time IBI relative to comparable omnivores. Vegans may have reduced availability of precursor markers for pro-resolving lipid mediators; it remains to be determined whether there is a direct link with impaired cardiac function in populations with low-n-3 status.

Metabolomic profiles as reliable biomarkers of dietary composition

Background: Clinical nutrition research often lacks robust markers of compliance, complicating the interpretation of clinical trials and observational studies of free-living subjects.Objective: We aimed to examine metabolomics profiles in response to 3 diets that differed widely in macronutrient composition during a controlled feeding protocol.Design: Twenty-one adults with a high body mass index (in kg/m²; mean ± SD: 34.4 ± 4.9) were given hypocaloric diets to promote weight loss corresponding to 10-15% of initial body weight. They were then studied during weight stability while consuming 3 test diets, each for a 4-wk period according to a crossover design: low fat (60% carbohydrate, 20% fat, 20% protein), low glycemic index (40% carbohydrate, 40% fat, 20% protein), or very-low carbohydrate (10% carbohydrate, 60% fat, 30% protein). Plasma samples were obtained at baseline and at the end of each 4-wk period in the fasting state for metabolomics analysis by using liquid chromatography-tandem mass spectrometry. Statistical analyses included adjustment for multiple comparisons.Results: Of 333 metabolites, we identified 152 whose concentrations differed for ≥1 diet compared with the others, including diacylglycerols and triacylglycerols, branched-chain amino acids, and markers reflecting metabolic status. Analysis of groups of related metabolites, with the use of either principal components or pathways, revealed coordinated metabolic changes affected by dietary composition, including pathways related to amino acid metabolism. We constructed a classifier using the metabolites that differed between diets and were able to correctly identify the test diet from metabolite profiles in 60 of 63 cases (>95% accuracy). Analyses also suggest differential effects by diet on numerous cardiometabolic disease risk factors.Conclusions: Metabolomic profiling may be used to assess compliance during clinical nutrition trials and the validity of dietary assessment in observational studies. In addition, this methodology may help elucidate mechanistic pathways linking diet to chronic disease risk. This trial was registered at clinicaltrials.gov as NCT00315354.

Identifying biomarkers of dietary patterns by using metabolomics

BACKGROUND

Healthy dietary patterns that conform to national dietary guidelines are related to lower chronic disease incidence and longer life span. However, the precise mechanisms involved are unclear. Identifying biomarkers of dietary patterns may provide tools to validate diet quality measurement and determine underlying metabolic pathways influenced by diet quality.

OBJECTIVE

The objective of this study was to examine the correlation of 4 diet quality indexes [the Healthy Eating Index (HEI) 2010, the Alternate Mediterranean Diet Score (aMED), the WHO Healthy Diet Indicator (HDI), and the Baltic Sea Diet (BSD)] with serum metabolites.

DESIGN

We evaluated dietary patterns and metabolites in male Finnish smokers (n = 1336) from 5 nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort. Participants completed a validated food-frequency questionnaire and provided a fasting serum sample before study randomization (1985-1988). Metabolites were measured with the use of mass spectrometry. We analyzed cross-sectional partial correlations of 1316 metabolites with 4 diet quality indexes, adjusting for age, body mass index, smoking, energy intake, education, and physical activity. We pooled estimates across studies with the use of fixed-effects meta-analysis with Bonferroni correction for multiple comparisons, and conducted metabolic pathway analyses.

RESULTS

The HEI-2010, aMED, HDI, and BSD were associated with 23, 46, 23, and 33 metabolites, respectively (17, 21, 11, and 10 metabolites, respectively, were chemically identified; r-range: -0.30 to 0.20; P = 6 × 10-15 to 8 × 10-6). Food-based diet indexes (HEI-2010, aMED, and BSD) were associated with metabolites correlated with most components used to score adherence (e.g., fruit, vegetables, whole grains, fish, and unsaturated fat). HDI correlated with metabolites related to polyunsaturated fat and fiber components, but not other macro- or micronutrients (e.g., percentages of protein and cholesterol). The lysolipid and food and plant xenobiotic pathways were most strongly associated with diet quality.

CONCLUSIONS

Diet quality, measured by healthy diet indexes, is associated with serum metabolites, with the specific metabolite profile of each diet index related to the diet components used to score adherence. This trial was registered at clinicaltrials.gov as NCT00342992.