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.

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.

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.

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.

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.

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.

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.

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.

Postprandial Metabolic Effects of Fiber Mixes Revealed by in vivo Stable Isotope Labeling in Humans.

Food supplementation with a fiber mix of guar gum and chickpea flour represents a promising approach to reduce the risk of type 2 diabetes mellitus (T2DM) by attenuating postprandial glycemia. To investigate the effects on postprandial metabolic fluxes of glucose-derived metabolites in response to this fiber mix, a randomized, cross-over study was designed. Twelve healthy, male subjects consumed three different flatbreads either supplemented with 2% guar gum or 4% guar gum and 15% chickpea flour or without supplementation (control). The flatbreads were enriched with ~2% of 13C-labeled wheat flour. Blood was collected at 16 intervals over a period of 360 min after bread intake and plasma samples were analyzed by GC-MS based metabolite profiling combined with stable isotope-assisted metabolomics. Although metabolite levels of the downstream metabolites of glucose, specifically lactate and alanine, were not altered in response to the fiber mix, supplementation of 4% guar gum was shown to significantly delay and reduce the exogenous formation of these metabolites. Metabolic modeling and computation of appearance rates revealed that the effects induced by the fiber mix were strongest for glucose and attenuated downstream of glucose. Further investigations to explore the potential of fiber mix supplementation to counteract the development of metabolic diseases are warranted.

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.

Weight loss moderately affects the mixed meal challenge response of the plasma metabolome and transcriptome of peripheral blood mononuclear cells in abdominally obese subjects.

INTRODUCTION: The response to dietary challenges has been proposed as a more accurate measure of metabolic health than static measurements performed in the fasted state. This has prompted many groups to explore the potential of dietary challenge tests for assessment of diet and lifestyle induced shifts in metabolic phenotype. OBJECTIVES: We examined whether the response to a mixed-meal challenge could provide a readout for a weight loss (WL)-induced phenotype shift in abdominally obese male subjects. The underlying assumption of a mixed meal challenge is that it triggers all aspects of phenotypic flexibility and provokes a more prolonged insulin response, possibly allowing for better differentiation between individuals. METHODS: Abdominally obese men (n = 29, BMI = 30.3 ± 2.4 kg/m2) received a mixed-meal challenge prior to and after an 8-week WL or no-WL control intervention. Lean subjects (n = 15, BMI = 23.0 ± 2.0 kg/m2) only received the mixed meal challenge at baseline to have a benchmark for WL-induced phenotype shifts. RESULTS: Levels of several plasma metabolites were significantly different between lean and abdominally obese at baseline as well as during postprandial metabolic responses. Genes related to oxidative phosphorylation in peripheral blood mononuclear cells (PBMCs) were expressed at higher levels in abdominally obese subjects as compared to lean subjects at fasting, which was partially reverted after WL. The impact of WL on the postprandial response was modest, both at the metabolic and gene expression level in PBMCs. CONCLUSION: We conclude that mixed-meal challenges are not necessarily superior to measurements in the fasted state to assess metabolic health. Furthermore, the mechanisms accounting for the observed differences between lean and abdominally obese in the fasted state are different from those underlying the dissimilarity observed during the postprandial response.

Quantification of Stable Isotope Traces Close to Natural Enrichment in Human Plasma Metabolites Using Gas Chromatography-Mass Spectrometry.

Currently, changes in metabolic fluxes following consumption of stable isotope-enriched foods are usually limited to the analysis of postprandial kinetics of glucose. Kinetic information on a larger diversity of metabolites is often lacking, mainly due to the marginal percentage of fully isotopically enriched plant material in the administered food product, and hence, an even weaker 13C enrichment in downstream plasma metabolites. Therefore, we developed an analytical workflow to determine weak 13C enrichments of diverse plasma metabolites with conventional gas chromatography-mass spectrometry (GC-MS). The limit of quantification was increased by optimizing (1) the metabolite extraction from plasma, (2) the GC-MS measurement, and (3) most importantly, the computational data processing. We applied our workflow to study the catabolic dynamics of 13C-enriched wheat bread in three human subjects. For that purpose, we collected time-resolved human plasma samples at 16 timepoints after the consumption of 13C-labeled bread and quantified 13C enrichment of 12 metabolites (glucose, lactate, alanine, glycine, serine, citrate, glutamate, glutamine, valine, isoleucine, tyrosine, and threonine). Based on isotopomer specific analysis, we were able to distinguish catabolic profiles of starch and protein hydrolysis. More generally, our study highlights that conventional GC-MS equipment is sufficient to detect isotope traces below 1% if an appropriate data processing is integrated.

Effect of Theobromine Consumption on Serum Lipoprotein Profiles in Apparently Healthy Humans with Low HDL-Cholesterol Concentrations.

Scope: Theobromine is a major active compound in cocoa with allegedly beneficial effect on high-density-lipoprotein-cholesterol (HDL-CH). We have investigated the effect of theobromine (TB) consumption on the concentrations of triglyceride (TG) and cholesterol (CH) in various lipoprotein (LP) subclasses. Methods: In a randomized, double-blind, placebo-controlled, cross-over study, 44 apparently healthy women and men (age: 60 ± 6 years, BMI: 29 ± 3 kg/m2) with low baseline HDL-CH concentrations consumed a drink supplemented with 500 mg/d theobromine for 4 weeks. TG and CH concentrations in 15 LP subclasses were predicted from diffusion-edited 1H NMR spectra of fasting serum. Results: The LP phenotype of the subjects was characterized by low CH concentrations in the large HDL particles and high TG concentrations in large VLDL and chylomicron (CM) particles, which clearly differed from a LP phenotype of subjects with normal HDL-CH. TB only reduced CH concentrations in the LDL particles by 3.64 and 6.79%, but had no effect on TG and CH in any of the HDL, VLDL and CM subclasses. Conclusion: TB was not effective on HDL-CH in subjects with a LP phenotype characterized by low HDL-CH and high TG in VLDL.

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).

Assessment of dietary exposure and effect in humans: The role of NMR.

In human nutritional science progress has always depended strongly on analytical measurements for establishing relationships between diet and health. This field has undergone significant changes as a result of the development of NMR and mass spectrometry methods for large scale detection, identification and quantification of metabolites in body fluids. This has allowed systematic studies of the metabolic fingerprints that biological processes leave behind, and has become the research field of metabolomics. As a metabolic profiling technique, NMR is at its best when its unbiased nature, linearity and reproducibility are exploited in well-controlled nutritional intervention and cross-sectional population screening studies. Although its sensitivity is less good than that of mass spectrometry, NMR has maintained a strong position in metabolomics through implementation of standardisation protocols, hyphenation with mass spectrometry and chromatographic techniques, accurate quantification and spectral deconvolution approaches, and high-throughput automation. Thus, NMR-based metabolomics has contributed uniquely to new insights into dietary exposure, in particular by unravelling the metabolic fates of phytochemicals and the discovery of dietary intake markers. NMR profiling has also contributed to the understanding of the subtle effects of diet on central metabolism and lipoprotein metabolism. In order to hold its ground in nutritional metabolomics, NMR will need to step up its performance in sensitivity and resolution; the most promising routes forward are the analytical use of dynamic nuclear polarisation and developments in microcoil construction and automated fractionation.

The effect of plant sterols and different low doses of omega-3 fatty acids from fish oil on lipoprotein subclasses.

SCOPE: Consumption of a low-fat spread enriched with plant sterols (PS) and different low doses (<2 g/day) of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) from fish oil reduces serum triglycerides (TGs) and low-density lipoprotein-cholesterol (LDL-Chol) and thus beneficially affects two blood lipid risk factors. Yet, their combined effects on TG and Chol in various lipoprotein subclasses have been investigated to a limited extent. METHODS AND RESULTS: In a randomized, double-blind, placebo-controlled, parallel study, we determined TG and Chol in 13 LP subclasses in fasting serum of 282 hypercholesterolemic subjects, who consumed either a placebo spread or one of the four spreads containing PS (2.5 g/day) and EPA+DHA (0.0, 0.9, 1.3, and 1.8 g/day) for 4 weeks. After PS treatment, total LDL-Chol was reduced, which was not further changed by EPA+DHA. No shift in the LDL-Chol particle distribution was observed. The addition of EPA+DHA to PS dose-dependently reduced VLDL-Chol and VLDL-TG mainly in larger particles. Furthermore, the two highest doses of EPA+DHA increased Chol and TG in the larger HDL particles, while these concentrations were decreased in the smallest HDL particles. CONCLUSION: The consumption of a low-fat spread enriched with both PS and EPA+DHA induced shifts in the lipoprotein distribution that may provide additional cardiovascular benefits over PS consumption alone.