Human Blood Lipoprotein Predictions from 1H NMR Spectra: Protocol, Model Performances, and Cage of Covariance.

Lipoprotein subfractions are biomarkers for the early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time-consuming, and there is a need to develop a rapid method for cohort screenings. This study presents partial least-squares regression models developed using 1H nuclear magnetic resonance (NMR) spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. This study explores, for the first time, different regions of the 1H NMR spectrum representing signals of molecules in lipoprotein particles and different lipid species to develop parsimonious, reliable, and optimal prediction models. A total of 65 lipoprotein main and subfractions were predictable with high accuracy, Q2 of >0.6, using an optimal spectral region (1.4-0.6 ppm) containing methylene and methyl signals from lipids. The models were subsequently tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. In addition, an open software tool was developed to predict lipoproteins concentrations in human blood from standardized 1H NMR spectral recordings.

Systematic selection of competing metabolomics methods in a metabolite-sensory relationship study.

INTRODUCTION: The relationship between the chemical composition of food products and their sensory profile is a complex association confronting many challenges. However, new untargeted methodologies are helping correlate metabolites with sensory characteristics in a simpler manner. Nevertheless, in the pilot phase of a project, where only a small set of products are used to explore the relationships, choices have to be made about the most appropriate untargeted metabolomics methodology. OBJECTIVE: To provide a framework for selecting a metabolite-sensory methodology based on: the quality of measurements, the relevance of the detected metabolites in terms of distinguishing between products or in terms of whether they can be related to the sensory attributes of the products. METHODS: In this paper we introduce a systematic approach to explore all these different aspects driving the choice for the most appropriate metabolomics method. RESULTS: As an example we have used a tomato soup project where the choice between two sampling methods (SPME and SBSE) had to be made. The results are not always consistently pointing to the same method as being the best. SPME was able to detect metabolites with a better precision, SBSE seemed to be able to provide a better distinction between the soups. CONCLUSION: The three levels of comparison provide information on how the methods could perform in a follow up study and will help the researcher to make a final selection for the most appropriate method based on their strengths and weaknesses.

Full 1 H and 13 C NMR spectral assignment of conjugated valerolactone metabolites isolated from urine of black tea consumers by means of SPE-prepLC-MS-LC-MS-NMR.

The health benefits of black tea have been linked to polyphenol metabolites that target specific modes of action in the human body. A major bottleneck in unravelling the underlying mechanisms is the preparative isolation of these metabolites, which hampers their structural elucidation and assessment of in vitro bioactivity. A solid phase extraction (SPE)-preparative liquid chromatography (prepLC)-MS-LC-MS-NMR workflow was implemented for preparative isolation of conjugated valerolactone metabolites of catechin-based polyphenols from urine of black tea consumers. First, the urine was cleaned and preconcentrated using an SPE method. Subsequently, the clean urine concentrate was injected on a preparative LC column, and conjugated valerolactones were obtained by MS-guided collection. Reconstituted fractions were further separated on an analytical LC column, and valerolactone fractions were collected in an MS-guided manner. These were reconstituted in methanol-d4 and identified and quantified using 1D and 2D homo- and hetereonuclear NMR experiments (at a field strength of 14.1 T), in combination with mass spectrometry. This resulted in the full spectral 1 H and 13 C NMR assignments of five conjugated valerolactones. These metabolites were collected in quantities of 8-160 µg and purities of 70-91%. The SPE-prepLC-MS-LC-MS-NMR workflow is suitable for isolating metabolites that occur at sub-µM concentrations in a complex biofluid such as urine. The workflow also provides an alternative for cumbersome and expensive de novo synthesis of tea metabolites for testing in bioactivity assays or for use as authentic analytical standards for quantification by mass spectrometry.

Toward Reliable Lipoprotein Particle Predictions from NMR Spectra of Human Blood: An Interlaboratory Ring Test.

Lipoprotein profiling of human blood by 1H nuclear magnetic resonance (NMR) spectroscopy is a rapid and promising approach to monitor health and disease states in medicine and nutrition. However, lack of standardization of measurement protocols has prevented the use of NMR-based lipoprotein profiling in metastudies. In this study, a standardized NMR measurement protocol was applied in a ring test performed across three different laboratories in Europe on plasma and serum samples from 28 individuals. Data was evaluated in terms of (i) spectral differences, (ii) differences in LPD predictions obtained using an existing prediction model, and (iii) agreement of predictions with cholesterol concentrations in high- and low-density lipoproteins (HDL and LDL) particles measured by standardized clinical assays. ANOVA-simultaneous component analysis (ASCA) of the ring test spectral ensemble that contains methylene and methyl peaks (1.4-0.6 ppm) showed that 97.99% of the variance in the data is related to subject, 1.62% to sample type (serum or plasma), and 0.39% to laboratory. This interlaboratory variation is in fact smaller than the maximum acceptable intralaboratory variation on quality control samples. It is also shown that the reproducibility between laboratories is good enough for the LPD predictions to be exchangeable when the standardized NMR measurement protocol is followed. With the successful implementation of this protocol, which results in reproducible prediction of lipoprotein distributions across laboratories, a step is taken toward bringing NMR more into scope of prognostic and diagnostic biomarkers, reducing the need for less efficient methods such as ultracentrifugation or high-performance liquid chromatography (HPLC).

Rapid and sustained systemic circulation of conjugated gut microbial catabolites after single-dose black tea extract consumption.

Gut microbial catabolites of black tea polyphenols (BTPs) have been proposed to exert beneficial cardiovascular bioactivity. This hypothesis is difficult to verify because the conjugation patterns and pharmacokinetics of these catabolites are largely unknown. The objective of our study was to identify, quantify, and assess the pharmacokinetics of conjugated BTP metabolites in plasma of healthy humans by means of an a priori untargeted LC-MS-based metabolomics approach. In a randomized, open, placebo-controlled, crossover study, 12 healthy men consumed a single bolus of black tea extract (BTE) or a placebo. The relative and, in several cases, absolute concentrations of a wide range of metabolites were determined using U(H)PLC-LTQ-Orbitrap-FTMS. Following BTE consumption, a kinetic response in plasma was observed for 59 BTP metabolites, 11 of these in a quantitative manner. Conjugated and unconjugated catechins appeared in plasma without delay, at 2-4 h, followed by a range of microbial catabolites. Interindividual variation in response was greater for gut microbial catabolites than for directly absorbed BTPs. The rapid and sustained circulation of conjugated catabolites suggests that these compounds may be particularly relevant to proposed health benefits of BTE. Their presence and effects may depend on individual variation in catabolic capacity of the gut microbiota.

A systematic approach to obtain validated partial least square models for predicting lipoprotein subclasses from serum NMR spectra.

A systematic approach is described for building validated PLS models that predict cholesterol and triglyceride concentrations in lipoprotein subclasses in fasting serum from a normolipidemic, healthy population. The PLS models were built on diffusion-edited (1)H NMR spectra and calibrated on HPLC-derived lipoprotein subclasses. The PLS models were validated using an independent test set. In addition to total VLDL, LDL, and HDL lipoproteins, statistically significant PLS models were obtained for 13 subclasses, including 5 VLDLs (particle size 64-31.3 nm), 4 LDLs (particle size 28.6-20.7 nm) and 4 HDLs (particle size 13.5-9.8 nm). The best models were obtained for triglycerides in VLDL (0.82 < Q(2) <0.92) and HDL (0.69 < Q(2) <0.79) subclasses and for cholesterol in HDL subclasses (0.68 < Q(2) <0.96). Larger variations in the model performance were observed for triglycerides in LDL subclasses and cholesterol in VLDL and LDL subclasses. The potential of the NMR-PLS model was assessed by comparing the LPD of 52 subjects before and after a 4-week treatment with dietary supplements that were hypothesized to change blood lipids. The supplements induced significant (p < 0.001) changes on multiple subclasses, all of which clearly exceeded the prediction errors.

Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites.

An automated quantum mechanical total line shape (QMTLS) fitting model was implemented for quantitative nuclear magnetic resonance (NMR)-based profiling of 42 metabolites in ultrafiltrated human serum samples. Each metabolite was described by a set of chemical shifts, J-couplings, and line widths. These parameters were optimized for each metabolite in each sample by iteratively minimizing the difference between the calculated and the experimental spectrum. In total, 92.0 to 98.1 % of the signal intensities in the experimental spectrum could be explained by the calculated spectrum. The model was validated by comparison to signal integration of metabolites with isolated signals and by means of standard additions. Metabolites present at average concentration higher than 50 µM were quantified with average absolute relative error less than 10 % when using different initial parameters for the fitting procedure. Furthermore, the biological applicability of the QMTLS model was demonstrated on 287 samples from an intervention study in 37 human volunteers undergoing an exercise challenge. Our automated QMTLS model was able to cope with the large dynamic range of metabolite concentrations in serum and proved to be suitable for high-throughput analysis.

Metabolic fate of polyphenols in the human superorganism.

Dietary polyphenols are components of many foods such as tea, fruit, and vegetables and are associated with several beneficial health effects although, so far, largely based on epidemiological studies. The intact forms of complex dietary polyphenols have limited bioavailability, with low circulating levels in plasma. A major part of the polyphenols persists in the colon, where the resident microbiota produce metabolites that can undergo further metabolism upon entering systemic circulation. Unraveling the complex metabolic fate of polyphenols in this human superorganism requires joint deployment of in vitro and humanized mouse models and human intervention trials. Within these systems, the variation in diversity and functionality of the colonic microbiota can increasingly be captured by rapidly developing microbiomics and metabolomics technologies. Furthermore, metabolomics is coming to grips with the large biological variation superimposed on relatively subtle effects of dietary interventions. In particular when metabolomics is deployed in conjunction with a longitudinal study design, quantitative nutrikinetic signatures can be obtained. These signatures can be used to define nutritional phenotypes with different kinetic characteristics for the bioconversion capacity for polyphenols. Bottom-up as well as top-down approaches need to be pursued to link gut microbial diversity to functionality in nutritional phenotypes and, ultimately, to bioactivity of polyphenols. This approach will pave the way for personalization of nutrition based on gut microbial functionality of individuals or populations.

Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models.

The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual's metabolic health in silico. A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.67, p < 0.05) and the gold-standard hyperinsulinemic-euglycemic clamp. The model is also shown to quantify liver fat accumulation and ß-cell functionality. Moreover, we show that personalized Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level.

Metabolomics and sensory evaluation of white asparagus ingredients in instant soups unveil important (off-)flavours.

Split-stream processing of asparagus waste stream is a novel approach to produce spray-dried powder and fibre. Asparagus ingredients processed by this method and a commercial asparagus powder were compared by evaluating their flavour profile in a soup formulation. Professional sensory panel and untargeted metabolomics approaches using GC-MS and LC-MS were carried out. Unsupervised and supervised statistical analyses were performed to highlight discriminatory metabolites and correlate these to sensory attributes. The spray-dried powder scored higher on asparagus flavour compared to the commercial powder. The fibre negatively impacted the taste and mouthfeel of the soups. GC-O-MS confirmed the role of dimethyl sulphide, 2-methoxy-3-isopropyl pyrazine and 2-methoxy-3-isobutyl pyrazine in asparagus odour. Seven new volatile compounds are also proposed to contribute to asparagus flavour notes, most of which were more abundant in the spray-dried powder. This research demonstrates the feasibility of upcycling asparagus waste streams into flavour-rich ingredients with good sensorial properties.

Structural elucidation and quantification of phenolic conjugates present in human urine after tea intake.

In dietary polyphenol exposure studies, annotation and identification of urinary metabolites present at low (micromolar) concentrations are major obstacles. To determine the biological activity of specific components, it is necessary to have the correct structures and the quantification of the polyphenol-derived conjugates present in the human body. We present a procedure for identification and quantification of metabolites and conjugates excreted in human urine after single bolus intake of black or green tea. A combination of a solid-phase extraction (SPE) preparation step and two high pressure liquid chromatography (HPLC)-based analytical platforms was used, namely, accurate mass fragmentation (HPLC-FTMS(n)) and mass-guided SPE-trapping of selected compounds for nuclear magnetic resonance spectroscopy (NMR) measurements (HPLC-TOFMS-SPE-NMR). HPLC-FTMS(n) analysis led to the annotation of 138 urinary metabolites, including 48 valerolactone and valeric acid conjugates. By combining the results from MS(n) fragmentation with the one-dimensional (1D)-(1)H NMR spectra of HPLC-TOFMS-SPE-trapped compounds, we elucidated the structures of 36 phenolic conjugates, including the glucuronides of 3',4'-di- and 3',4',5'-trihydroxyphenyl-γ-valerolactone, three urolithin glucuronides, and indole-3-acetic acid glucuronide. We also obtained 26 h-quantitative excretion profiles for specific valerolactone conjugates. The combination of the HPLC-FTMS(n) and HPLC-TOFMS-SPE-NMR platforms results in the efficient identification and quantification of less abundant phenolic conjugates down to nanomoles of trapped amounts of metabolite corresponding to micromolar metabolite concentrations in urine.

SPE-NMR metabolite sub-profiling of urine.

NMR-based metabolite profiling of urine is a fast and reproducible method for detection of numerous metabolites with diverse chemical properties. However, signal overlap in the (1)H NMR profiles of human urine may hamper quantification and identification of metabolites. Therefore, a new method has been developed using automated solid-phase extraction (SPE) combined with NMR metabolite profiling. SPE-NMR of urine resulted in three fractions with complementary and reproducible sub-profiles. The sub-profile from the wash fraction (100 % water) contained polar metabolites; that from the first eluted fraction (10 % methanol-90 % water) semi-polar metabolites; and that from the second eluted fraction (100 % methanol) aromatic metabolites. The method was validated by analysis of urine samples collected from a crossover human nutritional intervention trial in which healthy volunteers consumed capsules containing a polyphenol-rich mixture of red wine and grape juice extract (WGM), the same polyphenol mixture dissolved in a soy drink (WGM_Soy), or a placebo (PLA), over a period of five days. Consumption of WGM clearly increased urinary excretion of 4-hydroxyhippuric acid, hippuric acid, 3-hydroxyphenylacetic acid, homovanillic acid, and 3-(3-hydroxyphenyl)-3-hydroxypropionic acid. However, there was no difference between the excreted amounts of these metabolites after consumption of WGM or WGM_Soy, indicating that the soy drink is a suitable carrier for WGM polyphenols. Interestingly, WGM_Soy induced a significant increase in excretion of cis-aconitate compared with WGM and PLA, suggesting a higher demand on the tricarboxylic acid cycle. In conclusion, SPE-NMR metabolite sub-profiling is a reliable and improved method for quantification and identification of metabolites in urine to discover dietary effects and markers of phytochemical exposure.

Systemic Lactate Acts as a Metabolic Buffer in Humans and Prevents Nutrient Overflow in the Postprandial Phase.

On an organismal level, metabolism needs to react in a well-orchestrated manner to metabolic challenges such as nutrient uptake. Key metabolic hubs in human blood are pyruvate and lactate, both of which are constantly interconverted by very fast exchange fluxes. The quantitative contribution of different food sources to these metabolite pools remains unclear. Here, we applied in vivo stable isotope labeling to determine postprandial metabolic fluxes in response to two carbohydrate sources of different complexity. Depending on the ingested carbohydrate source, glucose or wheat flour, the net direction of the lactate dehydrogenase, and the alanine amino transferase fluxes were adjusted in a way to ensure sufficient availability, while, at the same time, preventing an overflow in the respective metabolite pools. The systemic lactate pool acts as a metabolic buffer which is fueled in the early- and depleted in the late-postprandial phase and thus plays a key role for systemic metabolic homeostasis.

Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model.

Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity.

Flavor Profiling Using Comprehensive Mass Spectrometry Analysis of Metabolites in Tomato Soups.

Trained sensory panels are regularly used to rate food products but do not allow for data-driven approaches to steer food product development. This study evaluated the potential of a molecular-based strategy by analyzing 27 tomato soups that were enhanced with yeast-derived flavor products using a sensory panel as well as LC-MS and GC-MS profiling. These data sets were used to build prediction models for 26 different sensory attributes using partial least squares analysis. We found driving separation factors between the tomato soups and metabolites predicting different flavors. Many metabolites were putatively identified as dipeptides and sulfur-containing modified amino acids, which are scientifically described as related to umami or having "garlic-like" and "onion-like" attributes. Proposed identities of high-impact sensory markers (methionyl-proline and asparagine-leucine) were verified using MS/MS. The overall results highlighted the strength of combining sensory data and metabolomics platforms to find new information related to flavor perception in a complex food matrix.

Towards superior plant-based foods using metabolomics.

Metabolomics is proving a useful approach for many of the main future goals in agronomy and food production such as sustainability/crop resilience, food quality, safety, storage, and nutrition. Targeted and/or untargeted small-molecule analysis, coupled to chemometric analysis, has already unveiled a great deal of the complexity of plant-based foods, but there is still 'dark matter' to be discovered. Moreover, state-of-the-art food metabolomics offers insights into the molecular mechanisms underlying sensorial and nutritional characteristics of foods and thus enables higher precision and speed. This review describes recent applications of food metabolomics from fork to farm and focuses on the opportunities these bring to continue food innovation and support the shift to plant-based foods.

Stir bar sorptive extraction of aroma compounds in soy sauce: Revealing the chemical diversity.

Fermented soy sauce is used worldwide to enhance the flavour of many dishes. Many types of soy sauce are on the market, and their differences are mostly related to the country of origin, the production process applied and the ratio of ingredients used. Consequently, several aromas, tastes, colours, and textures are obtained. Nowadays, soy sauce can also be produced without microorganisms making the process shorter and cheaper. However, flavour may be lost. We have carried out a comprehensive metabolomics analysis of volatile compounds using stir bar sorptive extraction (SBSE)-GC-MS to relate differences in volatile content to production history and origin. The results revealed major differences between fermented and non-fermented soy sauces, and a list of volatile compounds is reported as being characteristic of each type. This study was able to relate volatiles to the production process using SBSE-GC-MS and to aroma characteristics using GC-O-MS.

Fractionation platform for target identification using off-line directed two-dimensional chromatography, mass spectrometry and nuclear magnetic resonance.

The unambiguous identification of unknown compounds is of utmost importance in the field of metabolomics. However, current identification workflows often suffer from error-sensitive methodologies, which may lead to incorrect structure annotations of small molecules. Therefore, we have developed a comprehensive identification workflow including two highly complementary techniques, i.e. liquid chromatography (LC) combined with mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR), and used it to identify five taste-related retention time and m/z features in soy sauce. An off-line directed two-dimensional separation was performed in order to purify the features prior to the identification. Fractions collected during the first dimension separation (reversed phase low pH) were evaluated for the presence of remaining impurities next to the features of interest. Based on the separation between the feature and impurities, the most orthogonal second dimension chromatography (hydrophilic interaction chromatography or reversed phase high pH) was selected for further purification. Unknown compounds down to tens of micromolar concentrations were tentatively annotated by MS and structurally confirmed by MS and NMR. The mass (0.4-4.2 µg) and purity of the isolated compounds were sufficient for the acquisition of one and two-dimensional NMR spectra. The use of a directed two-dimensional chromatography allowed for a fractionation that was tailored to each feature and remaining impurities. This makes the fractionation more widely applicable to different sample matrices than one-dimensional or fixed two-dimensional chromatography. Five proline-based 2,5-diketopiperazines were successfully identified in soy sauce. These cyclic dipeptides might contribute to taste by giving a bitter flavour or indirectly enhancing umami flavour.

Chemical Fingerprints of Emotional Body Odor.

Chemical communication is common among animals. In humans, the chemical basis of social communication has remained a black box, despite psychological and neural research showing distinctive physiological, behavioral, and neural consequences of body odors emitted during emotional states like fear and happiness. We used a multidisciplinary approach to examine whether molecular cues could be associated with an emotional state in the emitter. Our research revealed that the volatile molecules transmitting different emotions to perceivers also have objectively different chemical properties. Chemical analysis of underarm sweat collected from the same donors in fearful, happy, and emotionally neutral states was conducted using untargeted two-dimensional (GC×GC) coupled with time of flight (ToF) MS-based profiling. Based on the multivariate statistical analyses, we find that the pattern of chemical volatiles (N = 1655 peaks) associated with fearful state is clearly different from that associated with (pleasant) neutral state. Happy sweat is also significantly different from the other states, chemically, but shows a bipolar pattern of overlap with fearful as well as neutral state. Candidate chemical classes associated with emotional and neutral sweat have been identified, specifically, linear aldehydes, ketones, esters, and cyclic molecules (5 rings). This research constitutes a first step toward identifying the chemical fingerprints of emotion.

Effects of a whole diet approach on metabolic flexibility, insulin sensitivity and postprandial glucose responses in overweight and obese adults - A randomized controlled trial.

BACKGROUND & AIMS: Metabolic flexibility is the ability to adapt fuel oxidation to fuel availability. Metabolic inflexibility has been associated with obesity, the metabolic syndrome and insulin resistance, and can be improved by exercise or weight loss. Dietary changes can modulate metabolic flexibility; however, the effect of a whole diet approach on metabolic flexibility has never been studied. Therefore, our objective was to assess the effect of a healthy diet (HD), as compared to a typical Western diet (WD), on several fasting and postprandial markers of metabolic flexibility and insulin sensitivity. METHODS: In this parallel randomized trial, overweight or obese men and women (50-70 years; BMI 25-35 kg/m2) consumed a healthy diet (HD; high in fruits and vegetables, pulses, fibers, nuts, fatty fish, and low in high-glycemic carbohydrates; n = 19) or a typical Western diet (WD; n = 21) for six weeks, following a two-week run-in period. The change in respiratory quotient upon insulin stimulation (ΔRQ), and insulin sensitivity, expressed as the M-value, were both determined with a hyperinsulinemic euglycemic clamp. Additionally, other fasting and postprandial markers of metabolic flexibility were assessed during a 5-h high-fat high-glycemic mixed meal challenge. RESULTS: ΔRQ (p = 0.730) and insulin sensitivity (p = 0.802) were not significantly affected by diet. Postprandial RQ did also not show significant differences (p = 0.610), whereas postprandial glucose excursions were significantly higher in the HD group at T30 (p = 0.014) and T45 (p = 0.026) after mixed meal ingestion (p = 0.037). Fasting glucose (p = 0.530) and HbA1c (p = 0.124) remained unchanged, whereas decreases in fasting insulin (p = 0.038) and the HOMA-IR (p = 0.050) were significantly more pronounced with the HD. CONCLUSION: A healthy diet for six weeks, without further life-style changes, did not improve metabolic flexibility and whole-body insulin sensitivity, when compared to a Western-style diet. It remains to be determined whether the short time increase in postprandial glucose is physiologically relevant or detrimental to metabolic health. This trial was registered at clinicaltrials.gov as NCT02519127.