A primer to nutritional metabolomics by NMR spectroscopy and chemometrics

Quantitative NMR analysis of sugars in natural sweeteners: Profiling in honey, jaggery, date syrup, and coconut sugar

Due to the high demand for natural sweeteners and their perceived health benefits, it is crucial to use analytical techniques for accurately profiling natural sweeteners. The present study describes a simple and fast approach for the analysis of sweeteners using 1D - 1H NMR spectroscopy. This method is based on the direct detection of protons in sugar molecules with an internal standard, without the need for complex derivatization steps. The presented approach offers a faster and more convenient way of quantifying mono-saccharides mainly glucose and fructose and di-saccharides like sucrose in various selected sweeteners. These includes honey, jaggery, coconut sugar, and date syrup. The direct 1D - 1H NMR method with an internal standard yields accurate and precise quantification results with good reproducibility and minute analysis times. This information is of increasing importance to both consumers and the food industry, as it provides a reliable and accurate method for characterizing and verifying natural sweeteners. Overall, 1D - 1H NMR spectroscopy can be a valuable tool for the rapid and easy analysis of sugar content in food products, and it may have potential applications in the food industry.

Bayesian semiparametric inference in longitudinal metabolomics data

The article is motivated by an application to the EarlyBird cohort study aiming to explore how anthropometrics and clinical and metabolic processes are associated with obesity and glucose control during childhood. There is interest in inferring the relationship between dynamically changing and high-dimensional metabolites and a longitudinal response. Important aspects of the analysis include the selection of the important set of metabolites and the accommodation of missing data in both response and covariate values. With this motivation, we propose a flexible but parsimonious Bayesian semiparametric joint model for the outcome and the covariate generating processes, making novel use of nonparametric mean processes, latent factor models, and different classes of continuous shrinkage priors. The proposed approach efficiently addresses daunting dimensionality challenges, simplifies imputation tasks, and automates the selection of important predictors. Implementation via an efficient Markov chain Monte Carlo algorithm appropriately accounts for uncertainty in various aspects of the analysis. Simulation experiments illustrate the efficacy of the proposed methodology. The application to the EarlyBird cohort study illustrates its practical utility in enabling statistical integration of different molecular processes involved in glucose production and metabolism. From this study, we were able to show that glucose levels from 5 to 16 years of age are associated with different circulating levels of metabolites in the blood serum and can be fitted over time for a wide range of shapes of trajectories. The metabolites contributing the most to explaining glucose trajectories tend to be involved in different central energy metabolomic pathways. The methodology provides a tool to generate new hypotheses related to obesity and glucose control during childhood and adolescence.

The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications

Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimensional (1D) NMR spectrum could limit the accuracy of quantitative analysis for metabolomics applications. Two-dimensional (2D) NMR has been applied to solve the 1D NMR overlap problem, but the data processing is still challenging. In this study, we built an automatic approach to process the 2D NMR data for quantitative applications using machine learning approaches. Partial least square discriminant analysis (PLS-DA), artificial neural network classification (ANN-DA), gradient boosted trees classification (XGBoost-DA), and artificial deep learning neural network classification (ANNDL-DA) were applied in combination with an automatic peak selection approach. Standard mixtures, sea anemone extracts, and mouse fecal samples were tested to demonstrate the approach. Our results showed that ANN-DA and ANNDL-DA have high accuracy in selecting 2D NMR peaks (around 90 %), which have a high potential application in 2D NMR-based metabolomics quantitively study, while PLS-DA and XGBoost-DA showed limitations in either data variation or overfitting. Our study built an automatic approach to applying 2D NMR data to routine quantitative analysis in metabolomics.

Impact of a legumes diet on the human gut microbiome articulated with fecal and plasma metabolomes: A pilot study

BACKGROUND & AIMS

Legumes intake is known to be associated with several health benefits the origins of which is still a matter of debate. This paper addresses a pilot small cohort to probe for metabolic aspects of the interplay between legumes intake, human metabolism and gut microbiota.

METHODS

Untargeted nuclear magnetic resonance (NMR) metabolomics of blood plasma and fecal extracts was carried out, in tandem with qPCR analysis of feces, to assess the impact of an 8-week pilot legumes diet intervention on the fecal and plasma metabolomes and gut microbiota of 19 subjects.

RESULTS

While the high inter-individual variability hindered the detection of statistically significant changes in the gut microbiome, increased fecal glucose and decreased threonine levels were noted. Correlation analysis between the microbiome and fecal metabolome lead to putative hypotheses regarding the metabolic activities of prevalent bacteria groups (Clostridium leptum subgroup, Roseburia spp., and Faecalibacterium prausnitzii). These included elevated fecal glucose as a preferential energy source, the involvement of valerate/isovalerate and reduced protein degradation in gut microbiota. Plasma metabolomics advanced mannose and betaine as potential markers of legume intake and unveiled a decrease in formate and ketone bodies, the latter suggesting improved energy utilization through legume carbohydrates. Amino acid metabolism was also apparently affected, as suggested by lowered urea, histidine and threonine levels.

CONCLUSIONS

Despite the high inter-individual gut microbiome variability characterizing the small cohort addressed, combination of microbiological measurements and untargeted metabolomics unveiled several metabolic effects putatively related to legumes intake. If confirmed in larger cohorts, our findings will support the inclusion of legumes in diets and contribute valuable new insight into the origins of associated health benefits.

Metabolomic and Lipidomic Tools for Tracing Fish Escapes from Aquaculture Facilities

During adverse atmospheric events, enormous damage can occur at marine aquaculture facilities, as was the case during Storm Gloria in the southeastern Spanish Mediterranean in January 2020, with massive fish escapes. Fishes that escape were caught by professional fishermen. The objective of this study was to identify biomarkers in fish that enable differentiation among wild fish, escaped farm-raised fish, and farm-raised fish kept in aquaculture facilities until their slaughter. We focused on gilthead sea bream (Sparus aurata). We used nuclear magnetic resonance to search for possible biomarkers. We found that wild gilthead sea bream showed higher levels of taurine and trimethylamine-N-oxide (TMAO) in their muscle and higher levels of ω-3 fatty acids, whereas farm-escaped and farmed gilthead sea bream raised until slaughter exhibit higher levels of ω-6 fatty acids. From choline, carnitine, creatinine, betaine, or lecithin, trimethylamine (TMA) is synthesized in the intestine by the action of bacterial microflora. In the liver, TMA is oxidized to TMAO and transported to muscle cells. The identified biomarkers will improve the traceability of gilthead sea bream by distinguishing wild specimens from those raised in aquaculture.

Human blood plasma biomarkers of diet and weight loss among centrally obese subjects in a New Nordic Diet intervention

Scope

The New Nordic Diet (NND) has been shown to promote weight loss and lower blood pressure amongst obese people. This study investigates blood plasma metabolite and lipoprotein biomarkers differentiating subjects who followed Average Danish Diet (ADD) or NND. The study also evaluates how the individual response to the diet is reflected in the metabolic differences between NND subjects who lost or maintained their pre-intervention weight.

Methods

Centrally obese Danes (BMI >25) followed NND (90 subjects) or ADD (56 subjects) for 6 months. Fasting blood plasma samples, collected at three time-points during the intervention, were screened for metabolites and lipoproteins (LPs) using proton nuclear magnetic resonance spectroscopy. In total, 154 metabolites and 65 lipoproteins were analysed.

Results

The NND showed a relatively small but significant effect on the plasma metabolome and lipoprotein profiles, with explained variations ranging from 0.6% for lipoproteins to 4.8% for metabolites. A total of 38 metabolites and 11 lipoproteins were found to be affected by the NND. The primary biomarkers differentiating the two diets were found to be HDL-1 cholesterol, apolipoprotein A1, phospholipids, and ketone bodies (3-hydroxybutyric acid, acetone, and acetoacetic acid). The increased levels of ketone bodies detected in the NND group inversely associated with the decrease in diastolic blood pressure of the NND subjects. The study also showed that body weight loss among the NND subjects was weakly associated with plasma levels of citrate.

Conclusion

The main plasma metabolites associated with NND were acetate, methanol and 3-hydroxybutyrate. The metabolic changes associated with the NND-driven weight loss are mostly pronounced in energy and lipid metabolism.

Metabolomic NMR analysis and organoleptic perceptions of pomegranate wines: Influence of cultivar and yeast on the product characteristics

Pomegranate (Punica granatum L.) fruits are a historical agricultural product of the Mediterranean basin that became increasingly popular in the latest years for being rich in antioxidants and other micronutrients, and are extensively commercialized as fruits, juice, jams and, in some Eastern countries, as a fermented alcoholic beverage. In this work, four different pomegranate wines specifically designed using combinations of two cultivars (Jolly Red and Smith) and two yeast starters with markedly different characteristics (Saccharomyces cerevisiae Clos and Saccharomyces cerevisiae ex-bayanus EC1118) were analyzed. The chemical characterization of the wines together with the originating unfermented juices was performed by ¹H NMR spectroscopy metabolomic analysis. The full spectra were used for unsupervised and supervised statistical multivariate analysis (MVA), namely Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and sparse PCA (SPCA). The MVA of the wines showed a clear discrimination between the cultivars, and a smaller, yet significant, discrimination between the yeasts used. In particular, a higher content of citrate and gallate was observed for the Smith cv. and, on the contrary, a statistically significant higher content of fructose, malate, glycerol, 2,3 butanediol, trigonelline, aromatic amino acids and 4-hydrophenylacetate was observed in Jolly Red pomegranate wines samples. Significant interaction among the pomegranate cultivar and the fermenting yeast was also observed. Sensorial analysis was performed by a panel of testing experts. MVA of tasting data showed that the cultivar significantly affected the organoleptic parameters considered, while the yeast had a minor impact. Correlation analysis between NMR-detected metabolites and organoleptic descriptors identified several potential sensorially-active molecules as those significantly impacting the characteristics of the pomegranate wines.

Urinary and plasma metabolome of farm mink (Neovison vison) after an intervention with raw or cooked poultry offal: a 1H NMR investigation

The introduction of high amounts of cooked poultry offal in mink feed has been associated with health problems in growing mink. Cooking mink feed is a convenient way of reducing microbiological activity, but it may have a negative effect on raw material quality and animal welfare. This study investigates growth and health of mink fed raw or cooked poultry offal and describes urinary and blood plasma metabolic changes related to the feeding. A total of 65 male mink were divided in three feeding groups, two fed cooked offal and one group fed raw offal, and the plasma and urine samples were collected at 3 time points during the growth. Both bio-fluids and feed samples were measured by ¹H NMR spectroscopy and resulted metabolomics data were analysed using univariate and multivariate statistical methods that revealed dominating effect of the mink growth stages and to a less extent the feeding regime. Metabolome differences in relation to low body mass index (BMI) and kidney lesions were observed in plasma. Disease and decrease in BMI was associated with high creatinine and dimethylglycine content in plasma. These molecules were also particularly indicative of the cooked feeds. Moreover, low urinary taurine levels were also associated with disease and low BMI. Individual mink appeared to show negative effects of the cooked feed diet, including impaired growth and gross pathological lesions involving the kidneys. This may be related to the absorption of essential metabolites such as amino acids and fats, necessary for mink growth, that are negatively impacted by the cooking process.

Healthy and Chronic Kidney Disease (CKD) Dogs Have Differences in Serum Metabolomics and Renal Diet May Have Slowed Disease Progression

Chronic kidney disease (CKD) is highly prevalent in dogs, and metabolomics investigation has been recently introduced for a better understanding of the role of diet in CKD. This study aimed to compare the serum metabolomic profile of healthy dogs (CG) and dogs with CKD (CKD-T0 and CKD-T6) to evaluate whether the diet would affect metabolites. Six dogs (5 females; 1 male; 7.47 ± 2.31 years old) with CKD stage 3 or 4 (IRIS) were included. CG consisted of 10 healthy female dogs (5.89 ± 2.57 years old) fed a maintenance diet. Serum metabolites were analyzed by ¹H nuclear magnetic resonance (¹H NMR) spectra. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed to assess differences in metabolomic profiles between groups and before (CKD-T0) and after renal diet (CKD-T6). Data analysis was performed on SIMCA-P software. Dogs with CKD showed an altered metabolic profile with increased urea, creatinine, creatine, citrate, and lipids. Lactate, branched-chain amino acids (BCAAs), and glutamine were decreased in the CKD group. However, after 6 months of diet, the metabolite profiles of CKD-T0 and CKD-T6 were similar. Metabolomics profile may be useful to evaluate and recognize metabolic dysfunction and progression of CKD, and the diet may have helped maintain and retard the progression of CKD.

Influence of Age, Sex, and Diet on the Human Fecal Metabolome Investigated by 1H NMR Spectroscopy

The human fecal metabolome is increasingly studied to explore the impact of diet and lifestyle on health and the gut microbiome. However, systematic differences and confounding factors related to age, sex, and diet remain largely unknown. In this study, absolute concentrations of fecal metabolites from 205 healthy Danes (105 males and 100 females, 49 ± 31 years old) were quantified using ¹H NMR spectroscopy and the newly developed SigMa software. The largest systemic variation was found to be highly related to age. Fecal concentrations of short-chain fatty acids (SCFA) were higher in the 18 years old group, while amino acids (AA) were higher in the elderly. Sex-related metabolic differences were weak but significant and mainly related to changes in SCFA. The concentrations of butyric, valeric, propionic, and isovaleric acids were found to be higher in males compared to females. Sex differences were associated with a stronger, possibly masking, effect from differential intake of macronutrients. Dietary fat intake decreased levels of SCFA and AA of both sexes, while carbohydrate intake showed weak correlations with valeric and isovaleric acids in females. This study highlights some possible demographic confounders linked to diet, disease, lifestyle, and microbiota that have to be taken into account when analyzing fecal metabolome data.

The plasma metabolome of Atlantic salmon as studied by 1H NMR spectroscopy using standard operating procedures: effect of aquaculture location and growth stage

INTRODUCTION

Metabolomics applications to the aquaculture research are increasing steadily. The use of standardized proton nuclear magnetic resonance (¹H NMR) spectroscopy can provide the aquaculture industry with an unbiased, reproducible, and high-throughput screening tool, which can help to diagnose nutritional and disease-related metabolic disorders in farmed fish.

OBJECTIVE

Standard operating procedures developed for analysing (human) plasma by ¹H NMR were applied to fingerprint the metabolome in plasma samples collected from Atlantic salmon. The aim was to explore the metabolome of salmon plasma in relation to growth stage and sampling site.

METHODS

A total of 72 salmon were collected from three aquaculture sites in Norway (Lat. 65, 67, and 70 °N) and over two sampling events (December 2017 and November 2018). Plasma drawn from each salmon was measured by ¹H NMR and metabolites were quantified using the SigMa software. The NMR data was analysed by principal component analysis (PCA) and ANOVA-simultaneous component analysis (ASCA).

RESULTS

Important metabolic differences were evidenced, with adult salmon having a much higher content of very low-density lipoproteins and cholesterol in their plasma, while smolts displayed significantly higher levels of propylene glycol. Overall, 24% of the metabolite variation was due to the growth stage, whereas 12% of the metabolite variation was related to the aquaculture site and practice (p < 0.001).

CONCLUSION

This study provides a baseline investigation of the plasma metabolome of the Atlantic salmon and demonstrates how ¹H NMR metabolomics can be used in future investigations for comparing aquaculture practices and their influence on the fish metabolome.

First Insight into Nutraceutical Properties of Local Salento Cichorium intybus Varieties: NMR-Based Metabolomic Approach

BACKGROUND

Plants of genus Cichorium are known for their therapeutic and nutraceutical properties determined by a wealth of phytochemical substances contained in the whole plant. The aim of this paper was to characterize the metabolic profiles of local Salento chicory (Cichorium intybus L.) varieties ("Bianca", "Galatina", "Leccese", and "Otranto") in order to describe their metabolites composition together with possible bioactivity and health beneficial properties.

METHODS

The investigation was performed by 1H-NMR spectroscopy and Multivariate Analysis (MVA), by which the metabolic profiles of the samples were easily obtained and compared.

RESULTS

The supervised Partial Least Squares Discriminant Analysis (PLS-DA) analysis showed as "Bianca" and "Galatina" samples grouped together separated by "Leccese" and "Otranto" varieties. A different content of free amino acids and organic acids was observed among the varieties. In particular a high content of cichoric and monocaffeoyl tartaric acid was observed for the "Leccese" variety. The presence of secondary metabolites adds significant interest in the investigation of Cichorium inthybus, as this vegetable may benefit human health when incorporated into the diet.

CONCLUSIONS

The 1H-NMR (Nuclear Magnetic Resonance Spectroscopy) based characterization of Salento chicory varieties allowed us to determine the potential usefulness and nutraceutical properties of the product, also providing a method to guarantee its authenticity on a molecular scale.

Biochemical, compositional, and spectral analyses of İsot (Urfa pepper) seed oil and evaluation of its functional characteristics

In this study, the physicochemical, functional, and antimicrobial properties of pepper seed oil (PSO) were determined. PSO was subjected to differential scanning calorimeter (DSC), fatty acid composition, carotenoid, capsaicin, and tocopherol analyses. LC-ESI-MS/MS and NMR were used to characterize and quantify phytochemicals. Resveratrol, luteolin, and 4-hydroxycinnamic acid were the principal phenolics in PSO. A high concentration of unsaturated fatty acids (85.3%), especially linoleic acid (73.7%) is present in PSO. Capsaicin, dihydrocapsaicin, α-tocopherol, δ-tocopherol, zeaxanthin, and capsanthin were determined in PSO at concentrations of 762.92, 725.73, 62.40, 643.23, 29.51, 16.83 ppm, respectively. PSO displayed inhibitory activity against α-glucosidase rather than α-amylase. The antimicrobial activity of PSO was tested against Escherichia coli, Staphylococcus aureus subsp. aureus, Aspergillus brasiliensis and Candida albicans. The antimicrobial potential of PSO was expressed as minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC) and inhibition zone (IZ) diameter. Polyunsaturated fatty acid, capsaicin, carotenoid, tocopherol, resveratrol contents; the antioxidant, α-glucosidase inhibitory and antimicrobial activities of PSO indicated its nutritional value and health promoting nature for the well-being of humans.

Coffee consumption revealed sex differences in host endogenous metabolism and gut microbiota in healthy adults

Coffee is rich in antioxidant and has been shown to confer various health benefits. Here, we investigated the effect of single-dose coffee consumption in healthy human subjects. About 30 healthy volunteers were recruited and given a serving of sugar free black coffee. Urine and fecal samples were collected and analyzed. Significant changes in urinary metabolites relating to coffee, gut microbial and host energy metabolisms were observed post-coffee consumption. Clear sex differences were also observed in the urinary metabolic profiles pre- and post-coffee consumption. Sex differences in richness and composition of gut microbiota were observed, however, the effect of single-dose coffee consumption on host gut microbiota were unremarkable. These findings indicated that single-dose coffee consumption affects sex-specific host metabolic responses that relates to gut-microbe and energy metabolism. This study demonstrated the utility of systems biology tools to unravel complexity of host-diet biology and gut microbial responses. PRACTICAL APPLICATIONS: This study demonstrated that integrated systems biology approach enabled efficient extractions of host biochemical and microbial information that allows food industry to ascertain the impact of diet and longitudinal assessment of potential functional food in humans.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Human urine 1H NMR metabolomics reveals alterations of protein and carbohydrate metabolism when comparing habitual Average Danish diet vs. healthy New Nordic diet

OBJECTIVES

The aim of this study was to investigate the alteration of the human urine metabolome by means of diet and to compare the metabolic effects of the nutritionally healthy New Nordic Diet (NND) with an Average Danish Diet (ADD). The NND was designed a decade ago by scientists and chefs, based on local and sustainable foods, including fish, shellfish, vegetables, roots, fruit, and berries. The NND has been proven to lower blood pressure, reduce glycemia, and lead to weight loss.

METHODS

The human urine metabolome was measured by untargeted proton nuclear magnetic resonance spectroscopy in samples from 142 centrally obese Danes (20-66 years old), randomized to consume the ADD or the NND. The resulting metabolomics data was processed and analyzed using advanced multivariate data analysis methods to reveal effects related to the design factors, including diet, season, sex, and changes in body weight.

RESULTS

Exploration of the nuclear magnetic resonance profiles revealed unique metabolite markers reflecting changes in protein and carbohydrate metabolism between the two diets. Glycine betaine, glucose, trimethylamine N-oxide and creatinine were increased in urine of the individuals following the NND compared with the ADD population, whereas relative concentrations of tartrate, dimethyl sulfone, and propylene glycol were decreased. Propylene glycol had a strong association with the homeostatic model assessment for insulin resistance in the NND group. The food intake biomarkers found in this study confirm the importance of these as tools for nutritional research.

CONCLUSIONS

Findings from this study provided new insights into the effects of a healthy diet on glycemia, reduction of inflammation, and weight loss among obese individuals, and alteration of the gut microbiota metabolism.

NMR-Based Plant Metabolomics in Nutraceutical Research: An Overview

Few topics are able to channel the interest of researchers, the public, and industries, like nutraceuticals. The ever-increasing demand of new compounds or new sources of known active compounds, along with the need of a better knowledge about their effectiveness, mode of action, safety, etc., led to a significant effort towards the development of analytical approaches able to answer the many questions related to this topic. Therefore, the application of cutting edges approaches to this area has been observed. Among these approaches, metabolomics is a key player. Herewith, the applications of NMR-based metabolomics to nutraceutical research are discussed: after a brief overview of the analytical workflow, the use of NMR-based metabolomics to the search for new compounds or new sources of known nutraceuticals are reviewed. Then, possible applications for quality control and nutraceutical optimization are suggested. Finally, the use of NMR-based metabolomics to study the impact of nutraceuticals on human metabolism is discussed.

Improved workflow for mass spectrometry-based metabolomics analysis of the heart

MS-based metabolomics methods are powerful techniques to map the complex and interconnected metabolic pathways of the heart; however, normalization of metabolite abundance to sample input in heart tissues remains a technical challenge. Herein, we describe an improved GC-MS-based metabolomics workflow that uses insoluble protein-derived glutamate for the normalization of metabolites within each sample and includes normalization to protein-derived amino acids to reduce biological variation and detect small metabolic changes. Moreover, glycogen is measured within the metabolomics workflow. We applied this workflow to study heart metabolism by first comparing two different methods of heart removal: the Langendorff heart method (reverse aortic perfusion) and in situ freezing of mouse heart with a modified tissue freeze-clamp approach. We then used the in situ freezing method to study the effects of acute β-adrenergic receptor stimulation (through isoproterenol (ISO) treatment) on heart metabolism. Using our workflow and within minutes, ISO reduced the levels of metabolites involved in glycogen metabolism, glycolysis, and the Krebs cycle, but the levels of pentose phosphate pathway metabolites and of many free amino acids remained unchanged. This observation was coupled to a 6-fold increase in phosphorylated adenosine nucleotide abundance. These results support the notion that ISO acutely accelerates oxidative metabolism of glucose to meet the ATP demand required to support increased heart rate and cardiac output. In summary, our MS-based metabolomics workflow enables improved quantification of cardiac metabolites and may also be compatible with other methods such as LC or capillary electrophoresis.

Revealing the 1H NMR metabolome of mirasol chili peppers (Capsicum annuum) infected by Candidatus Phytoplasma trifolii

The infection of Capsicum annuum cv. mirasol by Candidatus Phytoplasma trifolii (16SrVI) causes devastating crop losses in northern Mexico. This study addresses the metabolomics profiling of mirasol chili peppers (Capsicum annuum cv. mirasol) infected by Candidatus Phytoplasma trifolii. For this study, 25 diseased fruits and 25 healthy fruits were used. Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) revealed dramatic changes in the content of 42 metabolites which were identified in diseased and healthy mirasol chili peppers. The endogenous levels of fructose, glucose and formic acid were substantially decreased in the diseased chili peppers. In the same group of samples, high concentrations of alanine, asparagine, fumaric acid, sucrose and threonine were observed. The content of Choline didńt present a significant difference. This evidence supports the fact that Candidatus Phytoplasma trifolii infection reduces de CO₂ fixation into carbohydrates, decreases invertase activity, and inhibits glycolysis in the diseased plant tissues. The levels of ascorbic acid, capsaicin and dihydrocapsaicinin in diseased fruits were dramatically decreased, suggesting that Candidatus Phytoplasma trifolii can reduce the pungency and the nutraceutical value of mirasol chili peppers.

Chemometrics Approaches in Forced Degradation Studies of Pharmaceutical Drugs

Chemometrics is the chemistry field responsible for planning and extracting the maximum of information of experiments from chemical data using mathematical tools (linear algebra, statistics, and so on). Active pharmaceutical ingredients (APIs) can form impurities when exposed to excipients or environmental variables such as light, high temperatures, acidic or basic conditions, humidity, and oxidative environment. By considering that these impurities can affect the safety and efficacy of the drug product, it is necessary to know how these impurities are yielded and to establish the pathway of their formation. In this context, forced degradation studies of pharmaceutical drugs have been used for the characterization of physicochemical stability of APIs. These studies are also essential in the validation of analytical methodologies, in order to prove the selectivity of methods for the API and its impurities and to create strategies to avoid the formation of degradation products. This review aims to demonstrate how forced degradation studies have been actually performed and the applications of chemometric tools in related studies. Some papers are going to be discussed to exemplify the chemometric applications in forced degradation studies.

Metabolomic analysis of serum may refine 21-gene expression assay risk recurrence stratification

Despite recent refinements to the 21-gene g score, allowing a better identification of patients who may derive no benefit from the addition of adjuvant chemotherapy to that of endocrine therapy, patients with early breast cancer still stand to be over-treated in the setting of clinical and/or genomic uncertainty or discordance. Here we describe and demonstrate a potential approach of further refining the OncotypeDX risk score by metabolomic analysis of serum. In a clinical dataset (N = 87), the risk of recurrence was further sub-stratified by metabolomic signature, with an effective splitting of each Oncotype risk classification. A total of seven recurrences were recorded, with metabolomic analysis accurately predicting six of these. Contrastingly, the genomic risk score of the seven recurrences ranged across all three Oncotype classifications (one recurrence occurred in the "low"-risk group, three in the "intermediate" group and three in the "high"-risk group).

Data analysis of MS-based clinical lipidomics studies with crossover design: A tutorial mini-review of statistical methods

Clinical lipidomics using mass spectrometry (MS) is important to support discovery of biomarkers for diagnosis and understanding the pathophysiology of diseases. Frequently, lipidomics data from clinical studies have large variations among individuals because the human metabolome/lipidome is strongly influenced by genotype, daily activity, diet and gut flora. This inter-personal variability makes data analysis more complex and normally requires a large cohort for robust statistical analysis. Crossover designed experiments treat each subject as his or her own control, thereby reducing the between-subject variability, such that the effects of exposure/treatment are more likely to be identified when using a relatively small number of subjects. This design repeatedly samples an individual when crossing over from one treatment/exposure to another during the course of the study. The acquired datasets have a distinct data structure resulting from repeated longitudinal measurements. A variety of statistical methods are used in published crossover studies, but many appear to ignore the data structure inherent in the experimental design. An appropriate data analysis approach is critical to discovering robust clinical biomarkers. Hereby, we summarize the statistical methodologies suitable for clinical lipidomics studies using crossover design. To help understand and apply these methods to practical cases, we focused on the general concepts of statistical models in the context of analysis of metabolomics data without spending too much effort on mathematical details. Importantly, we aim to evaluate these methods and provide suggestions for data analysis and biomarker discovery. We applied the discussed methods on a MS-based lipidomics dataset from a double-blind random crossover designed clinical dietary intervention study. The strength and potential pitfalls of each method are briefly discussed and a suggestion for analytic workflow proposed.

Impact of phytosterols on liver and distal colon metabolome in experimental murine colitis model: an explorative study

Phytosterols are known to reduce plasma cholesterol levels and thereby reduce cardiovascular risk. Studies conducted on human and animal models have demonstrated that these compounds have also anti-inflammatory effects. Recently, an experimental colitis model (dextran sulphate sodium-induced) has shown that pre-treatment with phytosterols decreases infiltration of inflammatory cells and accelerates mucosal healing. This study aims to understand the mechanism underlying the colitis by analysing the end-products of the metabolism in distal colon and liver excised from the same mice used in the previous work. In particular, an unsupervised gas chromatography-mass spectrometry (GC-MS) and NMR based metabolomics approach was employed to identify the metabolic pathways perturbed by the dextran sodium sulphate (DSS) insult (i.e. Krebs cycle, carbohydrate, amino acids, and nucleotide metabolism). Interestingly, phytosterols were able to restore the homeostatic equilibrium of the hepatic and colonic metabolome.

Ultra-Clean Pure Shift 1H-NMR applied to metabolomics profiling

Even though Pure Shift NMR methods have conveniently been used in the assessment of crowded spectra, they are not commonly applied to the analysis of metabolomics data. This paper exploits the recently published SAPPHIRE-PSYCHE methodology in the context of plant metabolome. We compare single pulse, PSYCHE, and SAPPHIRE-PSYCHE spectra obtained from aqueous extracts of Physalis peruviana fruits. STOCSY analysis with simplified SAPPHIRE-PSYCHE spectra of six types of Cape gooseberry was carried out and the results attained compared with classical STOCSY data. PLS coefficients analysis combined with 1D-STOCSY was performed in an effort to simplify biomarker identification. Several of the most compromised proton NMR signals associated with critical constituents of the plant mixture, such as amino acids, organic acids, and sugars, were more cleanly depicted and their inter and intra correlation better reveled by the Pure Shift methods. The simplified data allowed the identification of glutamic acid, a metabolite not observed in previous studies of Cape gooseberry due to heavy overlap of its NMR signals. Overall, the results attained indicated that Ultra-Clean Pure Shift spectra increase the performance of metabolomics data analysis such as STOCSY and multivariate coefficients analysis, and therefore represent a feasible and convenient additional tool available to metabolomics.

1H NMR-based fingerprinting of eleven Mexican Capsicum annuum cultivars

Approximately 90% of the chili peppers consumed in the world are harvested in Mexico. The present article describes the untargeted ¹H NMR-based metabolomic profiling of 11 cultivars of Capsicum annuum species which are routinely consumed worldwide. The metabolomic fingerprinting detected via ¹H NMR contained 44 metabolites including sugars, amino acids, organic acids, polyphenolic acids and alcohols which were identified by comparison with the literature data, with Chenomx database and by 2D NMR. Statistical approaches based on principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify the Capsicum annuum cultivars according to their metabolite profile. LDA revealed metabolomic differences and similarities among Capsicum annuum cultivars, whereas hierarchical cluster analysis (HCA) significantly separated the cultivars according to the phylogenetic trees obtained. Substantial endogenous levels of free amino acids and carbohydrates were detected in all the studied cultivars but interestingly, Capsicum annuum cv. mirasol and C. annuum cv. chilaca contained almost three-fold more endogenous levels of vitamin C than the other cultivars. Considering that this antioxidant was found in crude aqueous extracts, its abundance could be directly proportional to its bioavailability for human nutrition. The results suggest that ¹H NMR is an effective method to determine differences among cultivars of the Capsicum annuum species.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

NMR-based metabolomics reveals that plant-derived smoke stimulates root growth via affecting carbohydrate and energy metabolism in maize

INTRODUCTION

It is well known that plant-derived smoke stimulates seed germination and seedling growth in many plants. Although a number of transcriptomics and proteomics studies have been carried out to understand the mode of action of smoke, less is known about the biochemical alterations associated with smoke exposure in plants.

OBJECTIVES

The aims of this study were (1) to determine the metabolic alterations in maize roots pre-treated with various concentrations of smoke solution, and (2) to identify the smoke-responsive metabolic pathways during early root growth period.

METHODS

Maize seeds were pre-treated with different concentrations of smoke solutions for 24 h and then grown for 10 days. 600-MHz ¹H NMR spectroscopy was performed on the aqueous root extracts of maize seedlings. The metabolite data obtained from the NMR spectra were analyzed by several statistical and functional methods, including one-way ANOVA, PCA, PLS-DA and pathway analysis.

RESULTS

Our study identified a total of 29 metabolites belonging to various chemical groups. Concentrations of 20 out of these 29 metabolites displayed significant (p < 0.05) changes after at least one smoke pre-treatment compared to the control. Moreover, functional analyses revealed that smoke pre-treatments markedly affected the carbohydrate- and energy-related metabolic pathways, such as galactose metabolism, glycolysis, glyoxylate metabolism, tricarboxylic acid cycle, and starch/sucrose metabolism.

CONCLUSIONS

To our knowledge, this is the first study that investigates smoke-induced biochemical alterations in early root growth period using NMR spectroscopy. Our findings clearly indicate that smoke either directly or indirectly influences many metabolic processes in maize roots.

NMR detection in biofluid extracts at sub-μM concentrations via para-H2 induced hyperpolarization

NMR spectroscopy is one of the most powerful techniques to simultaneously obtain qualitative and quantitative information in chemical analysis. Despite its versatility, the applications of NMR in the study of biofluids are often limited by the insensitivity of the technique, further aggravated by the poor signal dispersion in the (1)H spectra. Recent advances in para-H2 induced hyperpolarization have proven to address both these limitations for specific classes of compounds. Herein, this approach is for the first time applied for quantitative determination in biofluid extracts. We demonstrate that a combination of solid phase extraction, para-hydrogen induced hyperpolarization and selective NMR detection quickly reveals a doping substance, nikethamide, at sub-μM concentrations in urine. We suggest that this method can be further optimized for the detection of different analytes in various biofluids, anticipating a wider application of hyperpolarized NMR in metabolomics and pharmacokinetics studies in the near future.

Investigation of Variations in the Human Urine Metabolome amongst European Populations: An Exploratory Search for Biomarkers of People at Risk-of-Poverty

SCOPE

According to Eurostat 2016, approximately 119 million European citizens live at-risk-of-poverty (ROP). This subpopulation is highly diverse by ethnicity, age, and culture in the different EU states, but they all have in common a low income that could represent an increased risk of nutrient deficiencies due to poor nutritional habits. This study aims to investigate the human urine metabolome in the search of common biomarkers representing dietary deficiencies amongst European populations at ROP.

METHODS AND RESULTS

2732 urine samples were collected from 1391 subjects across five different European countries, including the United Kingdom, Finland, Italy, Lithuania, and Serbia, and analyzed using ¹ H-NMR spectroscopy. The resulting urine metabolome data were explored according to study design factors including economic status, country, and gender.

CONCLUSION

Partitioning of the effects derived from the study design factors using ANOVA-simultaneous component analysis (ASCA) revealed that country and gender effects were responsible for most of the systematic variation. The effect of economic status was, as expected, much weaker than country and gender, but more pronounced in Lithuania than in other countries. Citrate and hippurate were among the most powerful ROP biomarkers. The possible relationship between these markers and nutritional deficiencies amongst the ROP population is discussed.

1H-NMR metabolomics analysis of nutritional components from two kinds of freshwater fish brain extracts

There are a number of bioactive compounds in freshwater fish brains, and their functional roles have not been clearly elucidated. NMR-based metabolic profiling could enable rapid characterization of the nutritional composition a fish's brain. Here, two kinds of freshwater fish brains were investigated, crucian carp and yellow catfish. A ¹H-NMR based metabolomic approach was used to illustrate the nutritional components of these two kinds of brain. At first, the microwave method was utilized to cease the activity of the enzymes in the brain, and the chemicals were extracted for NMR analysis. These two kinds of brain had significant differences in metabolic patterns, and the chemical compositions of the yellow catfish brain were similar to those of rodent and human brains. Furthermore, most of the different metabolites were significantly higher in the yellow catfish, except for acetamide. This study could provide comprehensive information regarding the utilization of fish heads during processing of fish and dietary nutrition guidance.

There are a number of bioactive compounds in freshwater fish brains, and their functional roles have not been clearly elucidated. NMR-based metabolic profiling could provide a rapid characterization of a fish brain's nutritional composition.

Baseline levels of metabolites in different tissues of mussel Mytilus galloprovincialis (Bivalvia: Mytilidae)

The Mediterranean mussel Mytilus galloprovincialis (Lamarck 1819) is a popular shellfish commonly included in human diet and is routinely used as bioindicator in environmental monitoring programmes worldwide. Recently, metabolomics has emerged as a powerful tool both in food research and ecotoxicology for monitoring mussels' freshness and assessing the effects of environmental changes. However, there is still a paucity of data on complete metabolic baseline of mussel tissues. To mitigate this knowledge gap, similarities and differences in metabolite profile of digestive gland (DG), gills (G), and posterior adductor muscle (PAM) of aquaculture-farmed M. galloprovincialis were herein investigated by a proton nuclear magnetic resonance (¹H NMR)-based metabolomic approach and discussed considering their physiological role. A total of 44 metabolites were identified in mussel tissues and grouped in amino acids, energy metabolites, osmolytes, neurotransmitters, nucleotides, alkaloids, and miscellaneous metabolites. A PCA showed that mussel tissues clustered separately from each other, suggesting a clear differentiation in their metabolic profiles. A Venn diagram revealed that mussel DG, G and PAM shared 27 (61.36%) common metabolites, though with different concentrations. Osmolytes were found to dominate the metabolome of all tissues. The DG exhibited higher level of glutathione and carbohydrates. The G showed greater level of osmolytes and the exclusive presence of neurotransmitters, namely acetylcholine and serotonin. In PAM higher levels of energetics-related metabolites were found. Overall, findings from this study are helpful for a better understanding of mussel tissue-specific physiological functions as well as for future NMR-based metabolomic investigations of marine mussel health and safety.

Metabolomics and Lipidomics of Ischemic Stroke

Ischemic stroke is a sudden loss of brain function due to the reduction of blood flow. Brain tissues cease to function with subsequent activation of the ischemic cascade. Metabolomics and lipidomics are modern disciplines that characterize the metabolites and lipid components of a biological system, respectively. Because the pathogenesis of ischemic stroke is heterogeneous and multifactorial, it is crucial to establish comprehensive metabolomic and lipidomic approaches to elucidate these alterations in this disease. Fortunately, metabolomic and lipidomic studies have the distinct advantages of identifying tissue/mechanism-specific biomarkers, predicting treatment and clinical outcome, and improving our understanding of the pathophysiologic basis of disease states. Therefore, recent applications of these analytical approaches in the early diagnosis of ischemic stroke were discussed. In addition, the emerging roles of metabolomics and lipidomics on ischemic stroke were summarized, in order to gain new insights into the mechanisms underlying ischemic stroke and in the search for novel metabolite biomarkers and their related pathways.

Qualitative and Quantitative NMR Approaches in Blood Serum Lipidomics

Nuclear magnetic resonance (NMR) spectroscopy in combination with chemometrics can be applied in the analysis of complex biological samples in many ways. For example, we can analyze lipids, elucidate their structures, determine their nutritional values, and determine their distribution in blood serum. As lipids are not soluble in water, they are transported in blood as lipid-rich self-assembled particles, divided into different density assemblies from high- to very-low-density lipoproteins (HDL to VLDL), or by combining with serum proteins, such as albumins (human serum albumins (HSA)). Therefore, serum lipids can be analyzed as they are using only a 1:1 (v/v) dilution with a buffer or deuterated water prior to analysis by applying ¹H NMR or ¹H NMR edited-by-diffusion techniques. Alternatively, lipids can be extracted from the serum using liquid partition equilibrium and then analyzed using liquid-state NMR techniques. Our chapter describes protocols that are used for extraction of blood serum lipids and their quantitative ¹H NMR (¹H qNMR) analysis in lipid extracts as well as ¹H NMR edited by diffusion for direct blood serum lipid analysis.

Metabolite patterns predicting sex and age in participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study

Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles.

In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18–80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one- or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms.

Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy.

The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.

Orthogonal Comparison of GC-MS and 1H NMR Spectroscopy for Short Chain Fatty Acid Quantitation

Short chain fatty acids (SCFAs) are important regulators of host physiology and metabolism and may contribute to obesity and associated metabolic diseases. Interest in SCFAs has increased in part due to the recognized importance of how production of SCFAs by the microbiota may signal to the host. Therefore, reliable, reproducible, and affordable methods for SCFA profiling are required for accurate identification and quantitation. In the current study, four different methods for SCFA (acetic acid, propionic acid, and butyric acid) extraction and quantitation were compared using two independent platforms including gas chromatography coupled with mass spectrometry (GC-MS) and 1H nuclear magnetic resonance (NMR) spectroscopy. Sensitivity, recovery, repeatability, matrix effect, and validation using mouse fecal samples were determined across all methods. The GC-MS propyl esterification method exhibited superior sensitivity for acetic acid and butyric acid measurement (LOD < 0.01 μg mL-1, LOQ < 0.1 μg mL-1) and recovery accuracy (99.4%-108.3% recovery rate for 100 μg mL-1 SCFA mixed standard spike in and 97.8%-101.8% recovery rate for 250 μg mL-1 SCFAs mixed standard spike in). NMR methods by either quantitation relative to an internal standard or quantitation using a calibration curve yielded better repeatability and minimal matrix effects compared to GC-MS methods. All methods generated good calibration curve linearity (R2 > 0.99) and comparable measurement of fecal SCFA concentration. Lastly, these methods were used to quantitate fecal SCFAs obtained from conventionally raised (CONV-R) and germ free (GF) mice. Results from global metabolomic analysis of feces generated by 1H NMR and bomb calorimetry were used to further validate these approaches.

The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics

Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.

Metabolomic profiling of doxycycline treatment in chronic obstructive pulmonary disease

Serum metabolic profiling can identify the metabolites responsible for discrimination between doxycycline treated and untreated chronic obstructive pulmonary disease (COPD) and explain the possible effect of doxycycline in improving the disease conditions. ¹H nuclear magnetic resonance (NMR)-based metabolomics was used to obtain serum metabolic profiles of 60 add-on doxycycline treated COPD patients and 40 patients receiving standard therapy. The acquired data were analyzed using multivariate principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). A clear metabolic differentiation was apparent between the pre and post doxycycline treated group. The distinguishing metabolites lactate and fatty acids were significantly down-regulated and formate, citrate, imidazole and l-arginine upregulated. Lactate and folate are further validated biochemically. Metabolic changes, such as decreased lactate level, inhibited arginase activity and lowered fatty acid level observed in COPD patients in response to add-on doxycycline treatment, reflect the anti-inflammatory action of the drug. Doxycycline as a possible therapeutic option for COPD seems promising.

Studying the Human Microbiota

There are a range of methodologies available to study the human microbiota, ranging from traditional approaches such as culturing through to state-of-the-art developments in next generation DNA sequencing technologies. The advent of molecular techniques in particular has opened up tremendous new avenues for research, and has galvanised interest in the study of our microbial inhabitants. Given the dazzling array of available options, however, it is important to understand the inherent advantages and limitations of each technique so that the best approach can be employed to address the particular research objective. In this chapter we cover some of the most widely used current techniques in human microbiota research and highlight the particular strengths and caveats associated with each approach.

Counteracting Age-related Loss of Skeletal Muscle Mass: a clinical and ethnological trial on the role of protein supplementation and training load (CALM Intervention Study): study protocol for a randomized controlled trial

Background

Aging is associated with decreased muscle mass and functional capacity, which in turn decrease quality of life. The number of citizens over the age of 65 years in the Western world will increase by 50 % over the next four decades, and this demographic shift brings forth new challenges at both societal and individual levels. Only a few longitudinal studies have been reported, but whey protein supplementation seems to improve muscle mass and function, and its combination with heavy strength training appears even more effective. However, heavy resistance training may reduce adherence to training, thereby attenuating the overall benefits of training. We hypothesize that light load resistance training is more efficient when both adherence and physical improvement are considered longitudinally. We launched the interdisciplinary project on Counteracting Age-related Loss of Skeletal Muscle Mass (CALM) to investigate the impact of lifestyle changes on physical and functional outcomes as well as everyday practices and habits in a qualitative context.

Methods

We will randomize 205 participants older than 65 years to be given 1 year of two daily nutrient supplements with 10 g of sucrose and 20 g of either collagen protein, carbohydrates, or whey. Further, two groups will perform either heavy progressive resistance training or light load training on top of the whey supplement.

Discussion

The primary outcome of the CALM Intervention Study is the change in thigh cross-sectional area. Moreover, we will evaluate changes in physical performance, muscle fiber type and acute anabolic response to whey protein ingestion, sensory adaptation, gut microbiome, and a range of other measures, combined with questionnaires on life quality and qualitative interviews with selected subjects. The CALM Intervention Study will generate scientific evidence and recommendations to counteract age-related loss of skeletal muscle mass in elderly individuals.

Trial registration

ClinicalTrials.gov NCT02034760. Registered on 10 January 2014.

ClinicalTrials.gov NCT02115698. Registered on 14 April 2014.

Danish regional committee of the Capital Region H-4-2013-070. Registered on 4 July 2013.

Danish Data Protection Agency 2012-58-0004 – BBH-2015-001 I-Suite 03432. Registered on 9 January 2015.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Effect of Cosmos caudatus (Ulam raja) supplementation in patients with type 2 diabetes: Study protocol for a randomized controlled trial

BACKGROUND

Type 2 diabetes mellitus is a major health threat worldwide. Cosmos caudatus is one of the medicinal plants used to treat type 2 diabetes. Therefore, this study aims to determine the effectiveness and safety of C. caudatus in patients with type 2 diabetes. Metabolomic approach will be carried out to compare the metabolite profiles between C. Caudatus treated diabetic patients and diabetic controls.

METHODS AND DESIGN

This is a single-center, randomized, controlled, two-arm parallel design clinical trial that will be carried out in a tertiary hospital in Malaysia. In this study, 100 patients diagnosed with type 2 diabetes will be enrolled. Diabetic patients who meet the eligibility criteria will be randomly allocated to two groups, which are diabetic C. caudatus treated(U) group and diabetic control (C) group. Primary and secondary outcomes will be measured at baseline, 4, 8, and 12 weeks. The serum and urine metabolome of both groups will be examined using proton NMR spectroscopy.

DISCUSSION

The study will be the first randomized controlled trial to assess whether C. caudatus can confer beneficial effect in patients with type 2 diabetes. The results of this trial will provide clinical evidence on the effectiveness and safety of C. caudatus in patients with type 2 diabetes.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02322268.