Association between whole grain consumption, tryptophan metabolism, and psychological distress: a secondary analysis of a randomised controlled trial.

This study aimed to investigate whether psychological distress, whole grain consumption, and tryptophan metabolism are associated in participants undergoing weight management intervention. Seventy-nine women and men (mean age 49.7 ± 9.0 years; BMI 34.2 ± 2.5 kg/m2) participated in a 7-week weight-loss period (WL), and in a 24-week weight maintenance intervention period (WM). Whole grain consumption was measured using 4-day food diaries. Psychological distress was assessed with the General Health Questionnaire-12 (GHQ), and participants were divided into three GHQ groups based on the GHQ scores before WL. Tryptophan metabolites were determined from the participants' fasting plasma using liquid chromatography-mass spectrometry. GHQ scores were not associated with the whole grain consumption. A positive association was observed between the whole grain consumption and indole propionic acid (IPA) during the WM (p = 0.033). Serotonin levels were higher after the WL in the lowest GHQ tertile (p = 0.033), while the level at the end of the WM was higher compared to other timepoints in the highest GHQ tertile (p = 0.015 and p = 0.001). This difference between groups was not statistically significant. Furthermore, levels of several tryptophan metabolites changed within the groups during the study. Tryptophan metabolism changed during the study in the whole study group, independently from the level of psychological distress. The association between whole grain consumption and IPA is possibly explained by the effects of dietary fibre on gut microbiota. This broadens the understanding of the pathways behind the health benefits associated with the intake of whole grains.

Impacts of maternal microbiota and microbial metabolites on fetal intestine, brain, and placenta.

BACKGROUND: The maternal microbiota modulates fetal development, but the mechanisms of these earliest host-microbe interactions are unclear. To investigate the developmental impacts of maternal microbial metabolites, we compared full-term fetuses from germ-free and specific pathogen-free mouse dams by gene expression profiling and non-targeted metabolomics. RESULTS: In the fetal intestine, critical genes mediating host-microbe interactions, innate immunity, and epithelial barrier were differentially expressed. Interferon and inflammatory signaling genes were downregulated in the intestines and brains of the fetuses from germ-free dams. The expression of genes related to neural system development and function, translation and RNA metabolism, and regulation of energy metabolism were significantly affected. The gene coding for the insulin-degrading enzyme (Ide) was most significantly downregulated in all tissues. In the placenta, genes coding for prolactin and other essential regulators of pregnancy were downregulated in germ-free dams. These impacts on gene expression were strongly associated with microbially modulated metabolite concentrations in the fetal tissues. Aryl sulfates and other aryl hydrocarbon receptor ligands, the trimethylated compounds TMAO and 5-AVAB, Glu-Trp and other dipeptides, fatty acid derivatives, and the tRNA nucleobase queuine were among the compounds strongly associated with gene expression differences. A sex difference was observed in the fetal responses to maternal microbial status: more genes were differentially regulated in male fetuses than in females. CONCLUSIONS: The maternal microbiota has a major impact on the developing fetus, with male fetuses potentially more susceptible to microbial modulation. The expression of genes important for the immune system, neurophysiology, translation, and energy metabolism are strongly affected by the maternal microbial status already before birth. These impacts are associated with microbially modulated metabolites. We identified several microbial metabolites which have not been previously observed in this context. Many of the potentially important metabolites remain to be identified.

Serum metabolites associated with wholegrain consumption using nontargeted metabolic profiling: a discovery and reproducibility study.

PURPOSE: To identify fasting serum metabolites associated with WG intake in a free-living population adjusted for potential confounders. METHODS: We selected fasting serum samples at baseline from a subset (n = 364) of the prospective population-based Kuopio Ischaemic Heart Disease Risk Factor Study (KIHD) cohort. The samples were analyzed using nontargeted metabolomics with liquid chromatography coupled with mass spectrometry (LC-MS). Association with WG intake was investigated using both random forest followed by linear regression adjusted for age, BMI, smoking, physical activity, energy and alcohol consumption, and partial Spearman correlation adjusted for the same covariates. Features selected by any of these models were shortlisted for annotation. We then checked if we could replicate the findings in an independent subset from the same cohort (n = 200). RESULTS: Direct associations were observed between WG intake and pipecolic acid betaine, tetradecanedioic acid, four glucuronidated alkylresorcinols (ARs), and an unknown metabolite both in discovery and replication cohorts. The associations remained significant (FDR<0.05) even after adjustment for the confounders in both cohorts. Sinapyl alcohol was positively correlated with WG intake in both cohorts after adjustment for the confounders but not in linear models in the replication cohort. Some microbial metabolites, such as indolepropionic acid, were positively correlated with WG intake in the discovery cohort, but the correlations were not replicated in the replication cohort. CONCLUSIONS: The identified associations between WG intake and the seven metabolites after adjusting for confounders in both discovery and replication cohorts suggest the potential of these metabolites as robust biomarkers of WG consumption.

Maternal microbiota-derived metabolic profile in fetal murine intestine, brain and placenta.

BACKGROUND: The maternal microbiota affects the development of the offspring by microbial metabolites translocating to the fetus. To reveal the spectrum of these molecular mediators of the earliest host-microbe interactions, we compared placenta, fetal intestine and brain from germ-free (GF) and specific pathogen free (SPF) mouse dams by non-targeted metabolic profiling. RESULTS: One hundred one annotated metabolites and altogether 3680 molecular features were present in significantly different amounts in the placenta and/or fetal organs of GF and SPF mice. More than half of these were more abundant in the SPF organs, suggesting their microbial origin or a metabolic response of the host to the presence of microbes. The clearest separation was observed in the placenta, but most of the molecular features showed significantly different levels also in the fetal intestine and/or brain. Metabolites that were detected in lower amounts in the GF fetal organs included 5-aminovaleric acid betaine, trimethylamine N-oxide, catechol-O-sulphate, hippuric and pipecolic acid. Derivatives of the amino acid tryptophan, such as kynurenine, 3-indolepropionic acid and hydroxyindoleacetic acid, were also less abundant in the absence of microbiota. Ninety-nine molecular features were detected only in the SPF mice. We also observed several molecular features which were more abundant in the GF mice, possibly representing precursors of microbial metabolites or indicators of a metabolic response to the absence of microbiota. CONCLUSIONS: The maternal microbiota has a profound impact on the fetal metabolome. Our observations suggest the existence of a multitude of yet unidentified microbially modified metabolites which pass through the placenta into the fetus and potentially influence fetal development.

FADS1 rs174550 genotype and high linoleic acid diet modify plasma PUFA phospholipids in a dietary intervention study.

INTRODUCTION: Fatty acid desaturase 1 (FADS1) gene encodes for delta-5 desaturase enzyme which is needed in conversion of linoleic acid (LA) to arachidonic acid (AA). Recent studies have shown that response to dietary PUFAs differs between the genotypes in circulating fatty acids. However, interactions between the FADS1 genotype and dietary LA on overall metabolism have not been studied. OBJECTIVES: We aimed to examine the interactions of FADS1 rs174550 genotypes (TT and CC) and high-LA diet to identify plasma metabolites that respond differentially to dietary LA according to the FADS1 genotype. METHODS: A total of 59 men (TT n = 26, CC n = 33) consumed a sunflower oil supplemented diet for 4 weeks. Daily dose of 30, 40, or 50 ml was calculated based on body mass index. It resulted in 17-28 g of LA on top of the usual daily intake. Fasting plasma samples at the beginning and at the end of the intervention were analyzed with LC-MS/MS non-targeted metabolomics method. RESULTS: At the baseline, the carriers of FADS1 rs174550-TT genotype had higher abundance of long-chain PUFA phospholipids compared to the FADS1 rs174550-CC one. In response to the high-LA diet, LA phospholipids and long-chain acylcarnitines increased and lysophospholipids decreased in fasting plasma similarly in both genotypes. LysoPE (20:4), LysoPC (20:4), and PC (16:0_20:4) decreased and cortisol increased in the carriers of rs174550-CC genotype; however, these genotype-diet interactions were not significant after correction for multiple testing. CONCLUSION: Our findings show that both FADS1 rs174550 genotype and high-LA diet modify plasma phospholipid composition. TRIAL REGISTRATION: The study was registered to ClinicalTrials: NCT02543216, September 7, 2015 (retrospectively registered).

A non-targeted LC-MS metabolic profiling of pregnancy: longitudinal evidence from healthy and pre-eclamptic pregnancies.

INTRODUCTION: Maternal metabolism changes substantially during pregnancy. However, few studies have used metabolomics technologies to characterize changes across gestation. OBJECTIVES AND METHODS: We applied liquid chromatography-mass spectrometry (LC-MS) based non-targeted metabolomics to determine whether the metabolic profile of serum differs throughout the pregnancy between pre-eclamptic and healthy women in the FINNPEC (Finnish Genetics of Preeclampsia Consortium) Study. Serum samples were available from early and late pregnancy. RESULTS: Progression of pregnancy had large-scale effects to the serum metabolite profile. Altogether 50 identified metabolites increased and 49 metabolites decreased when samples of early pregnancy were compared to samples of late pregnancy. The metabolic signatures of pregnancy were largely shared in pre-eclamptic and healthy women, only urea, monoacylglyceride 18:1 and glycerophosphocholine were identified to be increased in the pre-eclamptic women when compared to healthy controls. CONCLUSIONS: Our study highlights the need of large-scale longitudinal metabolomic studies in non-complicated pregnancies before more detailed understanding of metabolism in adverse outcomes could be provided. Our findings are one of the first steps for a broader metabolic understanding of the physiological changes caused by pregnancy per se.

Serum aromatic and branched-chain amino acids associated with NASH demonstrate divergent associations with serum lipids.

BACKGROUND & AIMS: Non-alcoholic fatty liver disease (NAFLD) has been associated with multiple metabolic abnormalities. By applying a non-targeted metabolomics approach, we aimed at investigating whether serum metabolite profile that associates with NAFLD would differ in its association with NAFLD-related metabolic risk factors. METHODS & RESULTS: A total of 233 subjects (mean ± SD: 48.3 ± 9.3 years old; BMI: 43.1 ± 5.4 kg/m2 ; 64 male) undergoing bariatric surgery were studied. Of these participants, 164 with liver histology could be classified as normal liver (n = 79), simple steatosis (SS, n = 40) or non-alcoholic steatohepatitis (NASH, n = 45). Among the identified fasting serum metabolites with higher levels in those with NASH when compared to those with normal phenotype were the aromatic amino acids (AAAs: tryptophan, tyrosine and phenylalanine), the branched-chain amino acids (BCAAs: leucine and isoleucine), a phosphatidylcholine (PC(16:0/16:1)) and uridine (all FDRp < 0.05). Only tryptophan was significantly higher in those with NASH compared to those with SS (FDRp < 0.05). Only the AAAs tryptophan and tyrosine correlated positively with serum total and LDL cholesterol (FDRp < 0.1), and accordingly, with liver LDLR at mRNA expression level. In addition, tryptophan was the single AA associated with liver DNA methylation of CpG sites known to be differentially methylated in those with NASH. CONCLUSIONS: We found that serum levels of the NASH-related AAAs and BCAAs demonstrate divergent associations with serum lipids. The specific correlation of tryptophan with LDL-c may result from the molecular events affecting LDLR mRNA expression and NASH-associated methylation of genes in the liver.

Putative metabolites involved in the beneficial effects of wholegrain cereal: Nontargeted metabolite profiling approach.

BACKGROUND AND AIMS: Wholegrain cereals have been implicated in the reduction of lifestyle-related chronic diseases risk including cardiovascular diseases and type 2 diabetes. Molecular mechanisms responsible for the beneficial health effects are not entirely understood. The aims of this study were 1) to identify new potential plasma biomarker candidate metabolites of wholegrain cereal foods intake and 2) to examine whether some putative metabolites associated with wholegrain foods intake may play a role in the improvement of cardiometabolic risk factors. METHODS AND RESULTS: Analysis have been conducted in 54 individuals with metabolic syndrome of both genders, age 40-65 years, randomly assigned to 2 dietary interventions lasting 12-week: 1) wholegrain enriched diet (n = 28), and 2) refined-wheat cereals diet (control diet) (n = 26). Nontargeted metabolite profiling analysis was performed on fasting plasma samples collected at baseline and at the end of the experimental diets. Our data show that, at the end of the intervention, a higher intake of wholegrain (tertile 3) was significantly associated with a marked increase in several lipid compounds, as PC (20:4/16:1), LPC (20:4), LPC (22:6), LPC (18:3), LPC (22:5), and a phenolic compound (P < .05 for all). In the wholegrain group, higher concentrations of these metabolites (tertile 3 vs tertile 1 of each metabolite) were significantly associated with lower postprandial insulin and triglyceride responses (P < .05) by 29% and 37%, respectively. CONCLUSION: These observations suggest a possible role of lipid and polyphenol metabolites in the postprandial metabolic benefits of wholegrains in subjects at high risk of cardiovascular disease. In addition, they provide insight into the role of these metabolites as potential candidate biomarkers of wholegrain foods. The study was registered on ClinicalTrials.gov (identifier: NCT00945854).

Changes in the metabolic profile of human male postmortem frontal cortex and cerebrospinal fluid samples associated with heavy alcohol use.

Heavy alcohol use is one of the top causes of disease and death in the world. The brain is a key organ affected by heavy alcohol use. Here, our aim was to measure changes caused by heavy alcohol use in the human brain metabolic profile. We analyzed human postmortem frontal cortex and cerebrospinal fluid (CSF) samples from males with a history of heavy alcohol use (n = 74) and controls (n = 74) of the Tampere Sudden Death Series cohort. We used a nontargeted liquid chromatography mass spectrometry-based metabolomics method. We observed differences between the study groups in the metabolite levels of both frontal cortex and CSF samples, for example, in amino acids and derivatives, and acylcarnitines. There were more significant alterations in the metabolites of frontal cortex than in CSF. In the frontal cortex, significant alterations were seen in the levels of neurotransmitters (e.g., decreased levels of GABA and acetylcholine), acylcarnitines (e.g., increased levels of acylcarnitine 4:0), and in some metabolites associated with alcohol metabolizing enzymes (e.g., increased levels of 2-piperidone). Some of these changes were also significant in the CSF samples (e.g., elevated 2-piperidone levels). Overall, these results show the metabolites associated with neurotransmitters, energy metabolism and alcohol metabolism, were altered in human postmortem frontal cortex and CSF samples of persons with a history of heavy alcohol use.

Quantifying the human diet in the crosstalk between nutrition and health by multi-targeted metabolomics of food and microbiota-derived metabolites.

BACKGROUND: Metabolomics is a powerful tool for investigating the association between nutrition and health status. Although urine is commonly employed for studying the metabolism and transformation of food components, the use of blood samples could be preferable to gain new insights into the bioavailability of diet-derived compounds and their involvement in health. However, the chemical complexity of blood samples hinders the analysis of this biological fluid considerably, which makes the development of novel and comprehensive analytical methods mandatory. METHODS: In this work, we optimized a multi-targeted metabolomics platform for the quantitative and simultaneous analysis of 450 food-derived metabolites by ultra-high performance liquid chromatography coupled to tandem mass spectrometry. To handle the chemical complexity of blood samples, three complementary extraction methods were assayed and compared in terms of recovery, sensitivity, precision and matrix effects with the aim of maximizing metabolomics coverage: protein precipitation, reversed solid-phase extraction, and hybrid protein precipitation with solid-phase extraction-mediated phospholipid removal. RESULTS: After careful optimization of the extraction conditions, protein precipitation enabled the most efficient and high-throughput extraction of the food metabolome in plasma, although solid-phase extraction-based protocols provided complementary performance for the analysis of specific polyphenol classes. The developed method yielded accurate recovery rates with negligible matrix effects, and good linearity, as well as high sensitivity and precision for most of the analyzed metabolites. CONCLUSIONS: The multi-targeted metabolomics platform optimized in this work enables the simultaneous detection and quantitation of 450 dietary metabolites in short-run times using small volumes of biological sample, which facilitates its application to epidemiological studies.

A MYB Triad Controls Primary and Phenylpropanoid Metabolites for Pollen Coat Patterning.

The pollen wall is a complex, durable structure essential for plant reproduction. A substantial portion of phenylpropanoids (e.g. flavonols) produced by pollen grain tapetal cells are deposited in the pollen wall. Transcriptional regulation of pollen wall formation has been studied extensively, and a specific regulatory mechanism for Arabidopsis (Arabidopsis thaliana) pollen flavonol biosynthesis has been postulated. Here, metabolome and transcriptome analyses of anthers from mutant and overexpression genotypes revealed that Arabidopsis MYB99, a putative ortholog of the petunia (Petunia hybrida) floral scent regulator ODORANT1 (ODO1), controls the exclusive production of tapetum diglycosylated flavonols and hydroxycinnamic acid amides. We discovered that MYB99 acts in a regulatory triad with MYB21 and MYB24, orthologs of emission of benzenoids I and II, which together with ODO1 coregulate petunia scent biosynthesis genes. Furthermore, promoter-activation assays showed that MYB99 directs precursor supply from the Calvin cycle and oxidative pentose-phosphate pathway in primary metabolism to phenylpropanoid biosynthesis by controlling TRANSKETOLASE2 expression. We provide a model depicting the relationship between the Arabidopsis MYB triad and structural genes from primary and phenylpropanoid metabolism and compare this mechanism with petunia scent control. The discovery of orthologous protein triads producing related secondary metabolites suggests that analogous regulatory modules exist in other plants and act to regulate various branches of the intricate phenylpropanoid pathway.

Metabolome of canine and human saliva: a non-targeted metabolomics study.

INTRODUCTION: Saliva metabolites are suggested to reflect the health status of an individual in humans. The same could be true with the dog (Canis lupus familiaris), an important animal model of human disease, but its saliva metabolome is unknown. As a non-invasive sample, canine saliva could offer a new alternative material for research to reveal molecular mechanisms of different (patho)physiological stages, and for veterinary medicine to monitor dogs' health trajectories. OBJECTIVES: To investigate and characterize the metabolite composition of dog and human saliva in a non-targeted manner. METHODS: Stimulated saliva was collected from 13 privately-owned dogs and from 14 human individuals. We used a non-targeted ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-qTOF-MS) method to measure metabolite profiles from saliva samples. RESULTS: We identified and classified a total of 211 endogenous and exogenous salivary metabolites. The compounds included amino acids, amino acid derivatives, biogenic amines, nucleic acid subunits, lipids, organic acids, small peptides as well as other metabolites, like metabolic waste molecules and other chemicals. Our results reveal a distinct metabolite profile of dog and human saliva as 25 lipid compounds were identified only in canine saliva and eight dipeptides only in human saliva. In addition, we observed large variation in ion abundance within and between the identified saliva metabolites in dog and human. CONCLUSION: The results suggest that non-targeted metabolomics approach utilizing UHPLC-qTOF-MS can detect a wide range of small compounds in dog and human saliva with partially overlapping metabolite composition. The identified metabolites indicate that canine saliva is potentially a versatile material for the discovery of biomarkers for dog welfare. However, this profile is not complete, and dog saliva needs to be investigated in the future with other analytical platforms to characterize the whole canine saliva metabolome. Furthermore, the detailed comparison of human and dog saliva composition needs to be conducted with harmonized study design.

Factors affecting intake, metabolism and health benefits of phenolic acids: do we understand individual variability?

INTRODUCTION: Phenolic acids are important phenolic compounds widespread in foods, contributing to nutritional and organoleptic properties. FACTORS AFFCETING INDIVIDUAL VARIABILITY: The bioavailability of these compounds depends on their free or conjugated presence in food matrices, which is also affected by food processing. Phenolic acids undergo metabolism by the host and residing intestinal microbiota, which causes conjugations and structural modifications of the compounds. Human responses, metabolite profiles and health responses of phenolics, show considerable individual variation, which is affected by absorption, metabolism and genetic variations of subjects. OPINION: A better understanding of the gut-host interplay and microbiome biochemistry is becoming highly relevant in understanding the impact of diet and its constituents. It is common to study metabolism and health benefits separately, with some exceptions; however, it should be preferred that health responders and non-responders are studied in combination with explanatory metabolite profiles and gene variants. This approach could turn interindividual variation from a problem in human research to an asset for research on personalized nutrition.

Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.

BACKGROUND: LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to aberrations such as missing values. No matter the reason for the missing values in the data, coherent and complete data matrix is always a pre-requisite for accurate and reliable statistical analysis. Therefore, there is a need for proper imputation strategies that account for the missingness and reduce the bias in the statistical analysis. RESULTS: Here we present our results after evaluating nine imputation methods in four different percentages of missing values of different origin. The performance of each imputation method was analyzed by Normalized Root Mean Squared Error (NRMSE). We demonstrated that random forest (RF) had the lowest NRMSE in the estimation of missing values for Missing at Random (MAR) and Missing Completely at Random (MCAR). In case of absent values due to Missing Not at Random (MNAR), the left truncated data was best imputed with minimum value imputation. We also tested the different imputation methods for datasets containing missing data of various origin, and RF was the most accurate method in all cases. The results were obtained by repeating the evaluation process 100 times with the use of metabolomics datasets where the missing values were introduced to represent absent data of different origin. CONCLUSION: Type and rate of missingness affects the performance and suitability of imputation methods. RF-based imputation method performs best in most of the tested scenarios, including combinations of different types and rates of missingness. Therefore, we recommend using random forest-based imputation for imputing missing metabolomics data, and especially in situations where the types of missingness are not known in advance.

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

BACKGROUND: There is conflicting evidence regarding the association between fish intake and type 2 diabetes (T2D) incidence, possibly owing to measurement errors in self-reported intake and coexposure to persistent organic pollutants (POPs) present in fish. OBJECTIVE: The aim of this study was to identify plasma metabolites associated with fish intake and to assess their association with T2D risk, independently of POPs, in Swedish adults. METHODS: In a case-control study nested in the Swedish Västerbotten Intervention Programme, fasting plasma samples from 421 matched T2D case-control pairs of men and women aged 30-60 y at baseline and 10-y follow-up samples from a subset of 149 pairs were analyzed using untargeted metabolomics. Moreover, 16 plasma POPs were analyzed for the 149 pairs who had repeated samples available. Fish-related plasma metabolites were identified using multivariate modelling and partial correlation analysis. Reproducibility of metabolites and metabolite patterns, derived via principal component analysis (PCA), was assessed by intraclass correlation. A unique component of metabolites unrelated to POPs was dissected by integrating metabolites and POPs using 2-way orthogonal partial least squares regression. ORs of T2D were estimated using conditional logistic regression. RESULTS: We identified 31 metabolites associated with fish intake that had poor to good reproducibility. A PCA-derived metabolite pattern strongly correlated with fish intake (ρ = 0.37, P < 0.001) but showed no association with T2D risk. Integrating fish-related metabolites and POPs led to a unique metabolite component independent of POPs, which tended to be inversely associated with T2D risk (OR: 0.75; 95% CI: 0.54, 1.02, P = 0.07). This component mainly consisted of metabolites reflecting fatty fish intake. CONCLUSIONS: Our results suggest that fatty fish intake may be beneficial for T2D prevention, after removing the counteractive effects of coexposure to POPs in Swedish adults. Integrating metabolite markers and POP exposures appears a promising approach to advance the understanding of associations between fish intake and T2D incidence.

Total liver phosphatidylcholine content associates with non-alcoholic steatohepatitis and glycine N-methyltransferase expression.

BACKGROUND & AIMS: Alterations in liver phosphatidylcholine (PC) metabolism have been implicated in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). Although genetic variation in the phosphatidylethanolamine N-methyltransferase (PEMT) enzyme synthesizing PC has been associated with disease, the functional mechanism linking PC metabolism to the pathogenesis of non-alcoholic steatohepatitis (NASH) remains unclear. METHODS: Serum PC levels and liver PC contents were measured using proton nuclear magnetic resonance (NMR) spectroscopy in 169 obese individuals [age 46.6 ± 10 (mean ± SD) years, BMI 43.3 ± 6 kg/m2 , 53 men and 116 women] with histological assessment of NAFLD; 106 of these had a distinct liver phenotype. All subjects were genotyped for PEMT rs7946 and liver mRNA expression of PEMT and glycine N-methyltransferase (GNMT) was analysed. RESULTS: Liver PC content was lower in those with NASH (P = 1.8 x 10-6 ) while serum PC levels did not differ between individuals with NASH and normal liver (P = 0.591). Interestingly, serum and liver PC did not correlate (rs  = -0.047, P = 0.557). Serum PC and serum cholesterol levels correlated strongly (rs  = 0.866, P = 7.1 x 10-49 ), while liver PC content did not correlate with serum cholesterol (rs  = 0.065, P = 0.413). Neither PEMT V175M genotype nor PEMT expression explained the association between liver PC content and NASH. Instead, liver GNMT mRNA expression was decreased in those with NASH (P = 3.8 x 10-4 ) and correlated with liver PC content (rs  = 0.265, P = 0.001). CONCLUSIONS: Decreased liver PC content in individuals with the NASH is independent of PEMT V175M genotype and could be partly linked to decreased GNMT expression.

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

AIMS/HYPOTHESIS: The aims of the present work were to identify plasma metabolites that predict future type 2 diabetes, to investigate the changes in identified metabolites among individuals who later did or did not develop type 2 diabetes over time, and to assess the extent to which inclusion of predictive metabolites could improve risk prediction. METHODS: We established a nested case-control study within the Swedish prospective population-based Västerbotten Intervention Programme cohort. Using untargeted liquid chromatography-MS metabolomics, we analysed plasma samples from 503 case-control pairs at baseline (a median time of 7 years prior to diagnosis) and samples from a subset of 187 case-control pairs at 10 years of follow-up. Discriminative metabolites between cases and controls at baseline were optimally selected using a multivariate data analysis pipeline adapted for large-scale metabolomics. Conditional logistic regression was used to assess associations between discriminative metabolites and future type 2 diabetes, adjusting for several known risk factors. Reproducibility of identified metabolites was estimated by intra-class correlation over the 10 year period among the subset of healthy participants; their systematic changes over time in relation to diagnosis among those who developed type 2 diabetes were investigated using mixed models. Risk prediction performance of models made from different predictors was evaluated using area under the receiver operating characteristic curve, discrimination improvement index and net reclassification index. RESULTS: We identified 46 predictive plasma metabolites of type 2 diabetes. Among novel findings, phosphatidylcholines (PCs) containing odd-chain fatty acids (C19:1 and C17:0) and 2-hydroxyethanesulfonate were associated with the likelihood of developing type 2 diabetes; we also confirmed previously identified predictive biomarkers. Identified metabolites strongly correlated with insulin resistance and/or beta cell dysfunction. Of 46 identified metabolites, 26 showed intermediate to high reproducibility among healthy individuals. Moreover, PCs with odd-chain fatty acids, branched-chain amino acids, 3-methyl-2-oxovaleric acid and glutamate changed over time along with disease progression among diabetes cases. Importantly, we found that a combination of five of the most robustly predictive metabolites significantly improved risk prediction if added to models with an a priori defined set of traditional risk factors, but only a marginal improvement was achieved when using models based on optimally selected traditional risk factors. CONCLUSIONS/INTERPRETATION: Predictive metabolites may improve understanding of the pathophysiology of type 2 diabetes and reflect disease progression, but they provide limited incremental value in risk prediction beyond optimal use of traditional risk factors.

Mass spectrometry-based analysis of whole-grain phytochemicals.

Whole grains are a rich source of several classes of phytochemicals, such as alkylresorcinols, benzoxazinoids, flavonoids, lignans, and phytosterols. A high intake of whole grains has been linked to a reduced risk of some major noncommunicable diseases, and it has been postulated that a complex mixture of phytochemicals works in synergy to generate beneficial health effects. Mass spectrometry, especially when coupled with liquid chromatography, is a widely used method for the analysis of phytochemicals owing to its high sensitivity and dynamic range. In this review, the current knowledge of the mass spectral properties of the most important classes of phytochemicals found in cereals of common wheat, barley, oats, and rye is discussed.

Non-targeted metabolite profiling highlights the potential of strawberry leaves as a resource for specific bioactive compounds.

BACKGROUND: The non-edible parts of horticultural crops, such as leaves, contain substantial amounts of valuable bioactive compounds which are currently only little exploited. For example, strawberry (Fragaria × ananassa) leaves may be a promising bioresource for diverse health-related applications. However, product standardization sets a real challenge, especially when the leaf material comes from varying cultivars. The first step towards better quality control of berry fruit leaf-based ingredients and supplements is to understand metabolites present and their stability in different plant cultivars, so this study surveyed the distribution of potentially bioactive strawberry leaf metabolites in six different strawberry cultivars. Non-targeted metabolite profiling analysis using LC/qTOF-ESI-MS with data processing via principal component analysis and k-means clustering analysis was utilized to examine differences and commonalities between the leaf metabolite profiles. RESULTS: Quercetin and kaempferol derivatives were the dominant flavonol groups in strawberry leaves. Previously described and novel caffeic and chlorogenic acid derivatives were among the major phenolic acids. In addition, ellagitannins were one of the distinguishing compound classes in strawberry leaves. In general, strawberry leaves also contained high levels of octadecatrienoic acid derivatives, precursors of valuable odour compounds. CONCLUSION: The specific bioactive compounds found in the leaves of different strawberry cultivars offer the potential for the selection of optimized leaf materials for added-value food and non-food applications. © 2016 Society of Chemical Industry.

Non-targeted metabolite profiling reveals changes in oxidative stress, tryptophan and lipid metabolisms in fearful dogs.

BACKGROUND: Anxieties, such as shyness, noise phobia and separation anxiety, are common but poorly understood behavioural problems in domestic dogs, Canis familiaris. Although studies have demonstrated genetic and environmental contributions to anxiety pathogenesis, better understanding of the molecular underpinnings is needed to improve diagnostics, management and treatment plans. As a part of our ongoing canine anxiety genetics efforts, this study aimed to pilot a metabolomics approach in fearful and non-fearful dogs to identify candidate biomarkers for more objective phenotyping purposes and to refer to potential underlying biological problem. METHODS: We collected whole blood samples from 10 fearful and 10 non-fearful Great Danes and performed a liquid chromatography combined with mass spectrometry (LC-MS)-based non-targeted metabolite profiling. RESULTS: Non-targeted metabolomics analysis detected six 932 metabolite entities in four analytical modes [RP and HILIC; ESI(-) and ESI(+)], of which 239 differed statistically between the test groups. We identified changes in 13 metabolites (fold change ranging from 1.28 to 2.85) between fearful and non-fearful dogs, including hypoxanthine, indoxylsulfate and several phospholipids. These molecules are involved in oxidative stress, tryptophan and lipid metabolisms. CONCLUSIONS: We identified significant alterations in the metabolism of fearful dogs, and some of these changes appear relevant to anxiety also in other species. This pilot study demonstrates the feasibility of the non-targeted metabolomics and warrants a larger replication study to confirm the role of the identified biomarkers and pathways in canine anxiety.