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Empathy plays a fundamental role in building relationships. To foster close relationships and lasting engagements between humans and robots, empathy models can provide direct clues into how it can be done. In this study, we focus on capturing in a quantitative way indicators of early empathy realization between a human and a robot using a process that encompasses affective attachment, trust, expectations and reflecting on the other’s perspective within a set of collaborative strategies. We hypothesize that an active collaboration strategy is conducive to a more meaningful and purposeful engagement of realizing empathy between a human and a robot compared to a passive one. With a deliberate design, the interaction with the robot was presented as a maze game where a human and a robot must collaborate in order to reach the goal using two strategies: one maintaining control individually taking turns (passive strategy) and the other one where both must agree on their next move based on reflection and argumentation. Quantitative and qualitative analysis of the pilot study confirmed that a general sense of closeness with the robot was perceived when applying the active strategy. Regarding the specific indicators of empathy realization: (1) affective attachment, affective emulation was equally present throughout the experiment in both conditions, and thus, no conclusion could be reached; (2) trust, quantitative analysis partially supported the hypothesis that an active collaborative strategy will promote teamwork attitudes, where the human is open to the robot’s suggestions and to act as a teammate; and (3) regulating expectation, quantitative analysis confirmed that a collaborative strategy promoted a discovery process that regulates the subject’s expectation toward the robot. Overall, we can conclude that an active collaborative strategy impacts favorably the process of realizing empathy compared to a passive one. The results are compelling to move the design of this experiment forward into more comprehensive studies, ultimately leading to a path where we can clearly study engagements that reduce abandonment and disillusionment with the process of realizing empathy as the core design for active collaborative strategies.

Graphical abstract, Aging is a major risk factor for metabolic impairment that may lead to age-related diseases such as cardiovascular disease. Different mechanisms that may explain the interplay between aging and lipoproteins, and between aging and low-molecular-weight metabolites (LMWMs), in the metabolic dysregulation associated with age-related diseases have been described separately. Here, we statistically evaluated the possible mediation effects of LMWMs on the relationships between chronological age and lipoprotein concentrations in healthy men ranging from 19 to 75 years of age. Relative and absolute concentrations of LMWMs and lipoproteins, respectively, were assessed by nuclear magnetic resonance (NMR) spectroscopy. Multivariate linear regression and mediation analysis were conducted to explore the associations between age, lipoproteins and LMWMs. The statistical significance of the identified mediation effects was evaluated using the bootstrapping technique, and the identified mediation effects were validated on a publicly available dataset. Chronological age was statistically associated with five lipoprotein classes and subclasses. The mediation analysis showed that serine mediated 24.1% (95% CI: 22.9 – 24.7) of the effect of age on LDL-P, and glutamate mediated 17.9% (95% CI: 17.6 – 18.5) of the effect of age on large LDL-P. In the publicly available data, glutamate mediated the relationship between age and an NMR-derived surrogate of cholesterol. Our results suggest that the age-related increase in LDL particles may be mediated by a decrease in the nonessential amino acid glutamate. Future studies may contribute to a better understanding of the potential biological role of glutamate and LDL particles in aging mechanisms and age-related diseases.

Environmental noise affects the health and wellbeing of millions of people in Europe. Some of the health effects of noise exposure include new cases of ischemic heart disease, chronic annoyance, sleep disturbance and premature death. Our home soundscape, which involves both positive (i.e. restorative sounds) and negative sounds (i.e. noise), changed drastically during the first COVID-19 lockdown. Sounds associated with daily activity became almost non-existent, especially during the strictest lockdown weeks. In this context, the Sons al Balco project is aimed at studying the effect that the first lockdown due to the COVID-19 pandemic has caused on the perception of both positive and negative sounds in Catalonia. The hypothesis is that the decrease in outdoor noise, accompanied by a rise of a more positive soundscape would have a positive impact on the degree of annoyance in the population, increasing wellbeing. We performed a socio-acoustic survey targeting all the citizens of Catalonia. The survey included socio-demographic questions as well as questions about 1) the quality of the residential soundscape and 2) the individual positive and negative sound perception before and during the lockdown for a range of sound sources. Furthermore, the survey allowed the participants to upload videos of their residential soundscapes and to characterize several types of sounds present during the recordings. More than 350 participants answered the questionnaire. As a result, we depicted the first soundscape of Catalonia during the first COVID-19 lockdown. Whereas 16.4% of the respondents found their sound environment “very good” before the lockdown, this proportion reached 63.4% during the lockdown. In addition, a 76.4% of respondents did not use the adjectives "strong", "noisy", "disturbing", and "sharp" to describe the sounds in their videos, which suggests the presence of positive soundscapes. Future work will allow us to perform a comparison between both objective (sound and video) and subjective (questionnaires) measures, giving us the opportunity to compare aspects never previously studied.

In this study, we aim to study the effect that the lockdown due to the COVID-19 pandemic has caused on the perception of noise in Catalonia. The hypothesis is that the annoyance coming from outdoor noise, minimized by the lockdown, could be associated with better perception of the soundscape by people. The project we have designed allows to validate this hypothesis in two different ways. On the one hand, by means of subjective questionnaires conducted to people living in pre-defined diverse acoustic areas (urban, suburban and rural environments), and on the other hand, by the use of objective measurements of the noise levels , and the study of the soundscape in these areas, by means of short pieces of video collected by citizens. For this purpose, we have designed an on line test to be conducted by any citizen aiming to contribute to this wide study for all the territory of Catalonia, both from rural areas and from cities. A communication campaign was conducted to reach a significant participation. During the lockdown, more than 350 questionnaires and videos were collected, and a first map of the soundscape of the confinement in Catalonia was depicted.

Altres ajuts: GLD13/00168 Altres ajuts: GLD14/293 Altres ajuts: PERIS/SLT002-16-00101 Poor immunological recovery in treated HIV-infected patients is associated with greater morbidity and mortality. To date, predictive biomarkers of this incomplete immune reconstitution have not been established. We aimed to identify a baseline metabolomic signature associated with a poor immunological recovery after antiretroviral therapy (ART) to envisage the underlying mechanistic pathways that influence the treatment response. This was a multicentre, prospective cohort study in ART-naive and a pre-ART low nadir (

Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we test the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of co-varying features derived from Principal Component Analysis (PCA), the Iterative Signature Algorithm (ISA) and Averaged Correlation Profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. Based on the number and quality of the matches we concluded that both ISA and ACP can robustly identify about a dozen metabolites, half of which were shared, while PCA did not produce any signatures with robust matches.

PCSK9 is a pivotal molecule in the regulation of lipid metabolism. Previous studies have suggested that PCSK9 expression and its function in LDL receptor regulation could be altered in the context of diabetes. The aim was to assess PCSK9 plasma levels in patients with type 2 diabetes (T2DM) and other related metabolic disorders as well as its relation to the metabolomic profile generated by nuclear magnetic resonance (NMR) and glucose homeostasis.There were recruited a total of 457 patients suffering from T2DM and other metabolic disorders (metabolic syndrome (MetS), obesity and atherogenic dyslipidaemia (AD) and other disorders). Anamnesis, anthropometry and physical examinations were conducted, and vascular and abdominal adiposity imaging were carried out. Biochemical studies were performed to determine PCSK9 plasma levels 6 weeks after lipid lowering drug wash-out in treated patients. A complete metabolomic lipid profile was also generated by NMR. The rs505151 and rs11591147 genetic variants of PCSK9 gene were identified in patients.The results showed that PCSK9 levels are increased in patients with T2DM and MetS (14% and 13%; p0.005, respectively). Circulating PCSK9 levels were correlated with an atherogenic lipid profile and with insulin resistance parameters. PCSK9 levels were also positively associated with AD, as defined by lipoprotein particle number and size. The rs11591147 genetic variant resulted in lower levels of circulating PCSK9 and LDL cholesterol (LDL-C).PCSK9 plasma levels are increased in T2DM and MetS patients and are associated with LDL-C and other parameters of AD and glucose metabolism.

A metabolome-wide genome-wide association study (mGWAS) aims to discover the effects of genetic variants on metabolome phenotypes. Most mGWASes use as phenotypes concentrations of limited sets of metabolites that can be identified and quantified from spectral information. In contrast, in an untargeted mGWAS both identification and quantification are forgone and, instead, all measured metabolome features are tested for association with genetic variants. While the untargeted approach does not discard data that may have eluded identification, the interpretation of associated features remains a challenge. To address this issue, we developed metabomatching to identify the metabolites underlying significant associations observed in untargeted mGWASes on proton NMR metabolome data. Metabomatching capitalizes on genetic spiking, the concept that because metabolome features associated with a genetic variant tend to correspond to the peaks of the NMR spectrum of the underlying metabolite, genetic association can allow for identification. Applied to the untargeted mGWASes in the SHIP and CoLaus cohorts and using 180 reference NMR spectra of the urine metabolome database, metabomatching successfully identified the underlying metabolite in 14 of 19, and 8 of 9 associations, respectively. The accuracy and efficiency of our method make it a strong contender for facilitating or complementing metabolomics analyses in large cohorts, where the availability of genetic, or other data, enables our approach, but targeted quantification is limited., Author summary Metabolome-wide genome-wide association studies aim to discover how genetic variation affects metabolome traits. Such studies typically follow an acquire-identify-associate procedure: metabolome data are acquired experimentally, metabolites are identified in the experimental data and their concentrations quantified, and the metabolite concentrations are tested for association with genetic variants. The untargeted approach follows instead an acquire-associate-identify procedure: the experimental data are binned into metabolome features, and the features tested directly for genetic association. When the metabolome is measured by proton NMR spectroscopy, genetically associated features tend to correspond to peaks in the NMR spectrum of the underlying metabolites. This inherent property of the untargeted approach acts as a genetic spiking which informs on the identities of involved metabolites. Metabomatching is a method that uses genetic spiking information to identify the metabolite candidates, listed in a spectral database, most likely to underlie observed feature associations. Here, we present the method and its software, and evaluate its performance.

New approaches are increasingly being used for studying and evaluating coronary heart disease (CHD), especially since the irruption of metabolomics. The classical approach is to use enzymatically-measured standard lipids and these are still the main markers for assessing risk of CHD. Since metabolomics relies on advanced analytical technologies, such as MS and NMR, using them to estimate standard lipids would be of great interest because there is no need for additional biochemical measures. The present study evaluates partial least squares and N-way partial least squares regression models to predict standard lipid concentrations by using serum and plasma sample sets from various clinical centres. Information provided by editing NMR techniques and 2D diffusion NMR was incorporated in these models using four different data structures. Firstly, the models were calibrated and validated with three of the four sample sets (n = 591) involved. Then the best estimation models were selected and applied to the left-out sample set. This evaluation of a new sample set gave correlation coefficients of predicted versus biochemical variables above 0.86 and %rRMSE lower than 18 %. These values are similar to those found by other studies although, in our case, the results are more general because we used a higher number of samples (n = 785) from different sample sets, different clinical centres and different blood matrices (serum and plasma). Finally, we compared the performance of NMR predicted lipids and enzymatically measured lipids in a clinical case study.

Determination of lipoprotein particle size and number using advanced lipoprotein tests (ALTs) is of particular importance to improve cardiovascular risk prediction. Here we present the Liposcale test, a novel ALT based on 2D diffusion-ordered 1H NMR spectroscopy. Our method uses diffusion coefficients to provide a direct measure of the mean particle sizes and numbers. Using 177 plasma samples from healthy individuals and the concentration of ApoB and ApoA from isolated lipoprotein fractions, our test showed a stronger correlation between the NMR-derived lipoprotein particle numbers and apolipoprotein concentrations than the LipoProfile ® test commercialized by Liposcience. We also converted LDL particle numbers to ApoB equivalents (milligrams per deciliter) and our test yielded similar values of LDL-ApoB to the LipoProfile ® test (absolute mean bias of 8.5 and 7.4 mg/dl, respectively). In addition, our HDL particle number values were more concordant with the calibrated values determined recently using ion mobility. Finally, principal component analysis distinguished type 2 diabetic patients with and without atherogenic dyslipidemia (AD) on a second cohort of 307 subjects characterized using the Liposcale test (area under the curve = 0.88) and showed concordant relationships between variables explaining AD. Altogether, our method provides reproducible and reliable characterization of lipoprotein particles and it is applicable to pathological states such as AD. Copyright © 2015 by the American Society for Biochemistry and Molecular Biology, Inc.

The study of nutritional interventions in humans is difficult to assess because the induced metabolic changes are lower than the natural biological variability between subjects. Due to its holistic approach, 1H NMR is one of the preferred technologies for this type of studies, even though it has a very low sensitivity. This work shows how the use of several chemometric algorithms on the measured data compensates for these drawbacks and allows the study of the effects of the nutritional intervention isolating them from the natural variability inherent to human studies. Mild to moderate hypercholesterolemic patients received either placebo or soluble fiber in a low saturated fat diet. Plasma samples were collected at week 0 and week 8. Spectra obtained with NMR equipment were processed with ANOVA simultaneous component analysis (ASCA). The application of clustering techniques revealed different responses based on the patient's basal state, which allowed the identification of responders from non-responders. Results showed a triglyceride level reduction of up to 15% (p = 0.0032), with a higher reduction for those patients with a higher initial lipid profile. Moreover, line-shape fitting techniques applied to the NMR spectra allowed the conclusion that LDL (and VLDL) lipoprotein particles, and more noticeably triglycerides, moved to a profile configuration associated with lower cardiovascular risk. Results shed light on some of the metabolic modifications that husk fiber induces in humans which could not be seen with more conventional data analysis approaches. Our conclusion is that by using the right chemometric techniques it is possible to assess nutritional intervention effects in human NMR human studies despite the low sensitivity and selectivity that the technique offers today.

Background and Aims Dietary fat composition is known to modulate circulating lipid and lipoprotein levels. Although supplementation with long chain omega-3 polyunsaturated fatty acids (LCn-3PUFA) has been shown to reduce plasma triglyceride levels, the effect of the interactions between LCn-3PUFA and the major dietary fats consumed has not been previously investigated. Methods In a randomized controlled parallel design clinical intervention, we examined the effect of diets rich in either saturated fatty acids (SFA) or omega-6 polyunsaturated fatty acids (n-6PUFA) on plasma lipid levels and lipoprotein profiles (lipoprotein size, concentration and distribution in subclasses) in subjects with an adequate omega 3 index. Twenty six healthy subjects went through a four-week pre-supplementation period with LCn-3PUFA and were then randomized to diets rich in either n-6PUFA or SFA both supplemented with LCn-3PUFA. Results The diet rich in n-6PUFA decreased low density lipoprotein (LDL) particle concentration (− 8%, p = 0.013) and LDL cholesterol (LDL-C) level (− 8%, p = 0.021), while the saturated fat rich diet did not affect LDL particle concentration or LDL-C levels significantly. Nevertheless, dietary saturated fatty acids increased LCn-3PUFA in plasma and tissue lipids compared with n-6PUFA, potentially reducing other cardiovascular risk factors such as inflammation and clotting tendency. Conclusion Improvement on the omega 3 index of healthy subjects did not alter the known effects of dietary saturated fats and n-6PUFA on LDL profiles.

Recent studies have shown that pharmacological increases in HDL cholesterol concentrations do not necessarily translate into clinical benefits for patients, raising concerns about its predictive value for cardiovascular events. Here we hypothesize that the size-modulated lipid distribution within HDL particles is compromised in metabolic disorders that have abnormal HDL particle sizes, such as type 2 diabetes mellitus (DM2). By using NMR spectroscopy combined with a biochemical volumetric model we determined the size and spatial lipid distribution of HDL subclasses in a cohort of 26 controls and 29 DM2 patients before and after two drug treatments, one with niacin plus laropiprant and another with fenofibrate as an add-on to simvastatin. We further characterized the HDL surface properties using atomic force microscopy and fluorescent probes to show an abnormal lipid distribution within smaller HDL particles, a subclass particularly enriched in the DM2 patients. The reduction in the size, force cholesterol esters and triglycerides to emerge from the HDL core to the surface, making the outer surface of HDL more hydrophobic. Interestingly, pharmacological interventions had no effect on this undesired configuration, which may explain the lack of clinical benefits in DM2 subjects.

The sizes of certain types of lipoprotein particles have been associated with an increased risk of cardiovascular disease. However, there is currently no gold standard technique for the determination of this parameter. Here, we propose an analytical procedure to measure lipoprotein particles sizes using diffusion-ordered nuclear magnetic resonance spectroscopy (DOSY). The method was tested on six lipoprotein fractions, VLDL, IDL, LDL1, LDL2, HDL2, and HDL3, which were obtained by sequential ultracentrifugation from four patients. We performed a pulsed-field gradient experiment on each fraction to obtain a mean diffusion coefficient, and then determined the apparent hydrodynamic radius using the Stokes–Einstein equation. To validate the hydrodynamic radii obtained, the particle size distribution of these lipoprotein fractions was also measured using transmission electron microscopy (TEM). The standard errors of duplicate measurements of diffusion coefficient ranged from 0.5% to 1.3%, confirming the repeatability of the technique. The coefficient of determination between the hydrodynamic radii and the TEM-derived mean particle size was r2 = 0.96, and the agreement between the two techniques was 85%. Thus, DOSY experiments have proved to be accurate and reliable for estimating lipoprotein particle sizes.

The role of circulating FABP5 on metabolic alterations is under active evaluation. On the other hand, FABP5 SNPs (rs454550 and rs79370435) seem to modulate its effect.Our aim was to examine the role of circulating FABP5 levels and its main SNPs in atherogenic dyslipidemia (AD) assessed by 2D-Nuclear Magnetic Resonance (NMR) and related metabolic and inflammation markers. We hypothesized that circulating FABP5 may be a biomarker for metabolic risk.We studied 459 subjects admitted to the metabolism unit because of lipid metabolism disturbances and/or associated disorders. After a 6-week lipid-lowering drug wash-out period, anamnesis and physical examination were performed. Carotid intime-media thickness (cIMT) was measured by ultrasound. FABP5, FABP4, lipids, metabolic proteins, and enzymes were determined by biochemical methods. The lipid profile was assessed by NMR. The rs454550 and rs79370435 FABP5 gene variants were also determined.The FABP5 plasma levels were positively correlated with adiposity, glucose metabolism, and lipolysis parameters and were associated with AD, as assessed by NMR. There was a significant positive correlation between hsCRP and FABP5. The presence of type 2 diabetes, obesity, metabolic syndrome, or AD was associated with higher FABP5 plasma levels (P.005). The FABP5 concentrations, but not those of FABP4, were higher in patients with carotid plaques. FABP5 was a main determinant of plaque presence according to logistic regression analysis. The rare rs454550 allele was hyper-represented in nonobese subjects (P = .011).FABP5 is a biomarker of adiposity-associated metabolic derangements that include AD thus underscoring the concomitant presence of inflammation. FABP5 is associated with increased subclinical atherosclerosis.

Hyperinsulinaemic androgen excess (HIAE) in prepubertal and pubertal girls usually precedes a broader pathological phenotype in adulthood that is associated with anovulatory infertility, metabolic syndrome and type 2 diabetes. The metabolic derangements that determine these long-term health risks remain to be clarified. Here we use NMR and MS-based metabolomics to show that serum levels of methionine sulfoxide in HIAE girls are an indicator of the degree of oxidation of methionine-148 residue in apolipoprotein-A1. Oxidation of apo-A1 in methionine-148, in turn, leads to an impaired maturation of high-density lipoproteins (HDL) that is reflected in a decline of large HDL particles. Notably, such metabolic alterations occur in the absence of impaired glucose tolerance, hyperglycemia and hypertriglyceridemia and were partially restored after 18 months of treatment with a low-dose combination of pioglitazone, metformin and flutamide.

Patients with type 1 diabetes (T1D) present increased risk of cardiovascular disease (CVD). The aim of this study is to improve the assessment of lipoprotein profile in patients with T1D by using a robust developed method 1H nuclear magnetic resonance spectroscopy (1H NMR), for further correlation with clinical factors associated to CVD. Thirty patients with T1D and 30 non-diabetes control (CT) subjects, matched for gender, age, body composition (DXA, BMI, waist/hip ratio), regular physical activity levels and cardiorespiratory capacity (VO2peak), were analyzed. Dietary records and routine lipids were assessed. Serum lipoprotein particle subfractions, particle sizes, and cholesterol and triglycerides subfractions were analyzed by 1H NMR. It was evidenced that subjects with T1D presented lower concentrations of small LDL cholesterol, medium VLDL particles, large VLDL triglycerides, and total triglycerides as compared to CT subjects. Women with T1D presented a positive association with HDL size (p

Background and aims Nuts have been demonstrated to improve several cardiovascular risk factors and the lipid profile in diabetic and pre-diabetic subjects. However, analysis of conventional serum lipid profiles does not completely explain the atherogenic risk associated with pre-diabetes. We therefore investigated whether chronic consumption of pistachio modifies the lipoprotein subclasses to a healthier profile in pre-diabetic subjects. Methods and results Randomized cross-over clinical trial in 54 subjects with pre-diabetes. Subjects consumed a pistachio-supplemented diet (PD, 50% carbohydrates, 33% fat, including 57 g/d of pistachios daily) and a control diet (CD, 55% carbohydrates, 30% fat) for 4 months each, separated by a 2-week wash-out. Diets were isocaloric and matched for protein, fiber and saturated fatty acids. Nuclear magnetic resonance (NMR) was performed to determine changes in plasma lipoprotein subclasses. Small low-density lipoprotein particles (sLDL-P) significantly decreased after pistachio consumption compared to the nut-free diet (P = 0.023). The non-high-density lipoprotein particles (non-HDL-P i.e. VLDL-P plus LDL-P) significantly decreased under the PD compared to CD (P = 0.041). The percentage of sHDL-P increased by 2.23% after the PD compared with a reduction of 0.08% after the CD (P = 0.014). Consequently, the overall size of HDL-P significantly decreased in the PD (P = 0.007). Conclusion Chronic pistachio consumption could modify the lipoprotein particle size and subclass concentrations independently of changes in total plasma lipid profile, which may help to explain the decreased risk of cardiovascular disease and mortality associated with those individuals who frequently consumed nuts. Registration number This study is registered at www.clinicaltrials.gov as NCT01441921 .

One of the main challenges in nuclear magnetic resonance (NMR) metabolomics is to obtain valuable metabolic information from large datasets of raw NMR spectra in a high throughput, automatic, and reproducible way. To date, established software packages used to match and quantify metabolites in NMR spectra remain mostly manually operated, leading to low resolution results and subject to inconsistencies not attributable to the NMR technique itself. Here, we introduce a new software package, called Dolphin, able to automatically quantify a set of target metabolites in multiple sample measurements using an approach based on 1D and 2D NMR techniques to overcome the inherent limitations of 1D (1)H-NMR spectra in metabolomics. Dolphin takes advantage of the 2D J-resolved NMR spectroscopy signal dispersion to avoid inconsistencies in signal position detection, enhancing the reliability and confidence in metabolite matching. Furthermore, in order to improve accuracy in quantification, Dolphin uses 2D NMR spectra to obtain additional information on all neighboring signals surrounding the target metabolite. We have compared the targeted profiling results of Dolphin, recorded from standard biological mixtures, with those of two well established approaches in NMR metabolomics. Overall, Dolphin produced more accurate results with the added advantage of being a fully automated and high throughput processing package.

Untargeted metabolomic analyses of plasma and red blood cells (RBCs) can provide complementary information on biomarkers of food consumption. To assess blood collection differences in biomarkers, fasting blood was drawn from 10 healthy individuals using sodium citrate and lithium heparin as anticoagulants. Plasma and RBCs were separated into aqueous and lipid fractions to be analyzed using 1D and 2D (1)H NMR spectroscopy. Fatty acids were analyzed using gas chromatography-mass spectrometry (GC-MS). Polyphenols were extracted from plasma and RBCs by micro-elution solid-phase extraction and analyzed by ultra performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). (1)H NMR demonstrated higher aqueous metabolites such as glucose in plasma compared to RBCs, while RBCs contained higher ADP-ATP, creatine and acetone than plasma. Lipoproteins and their subclasses were higher in plasma than in RBCs. Percentages of saturated fatty acids (SFA) 16 : 0, 17 : 0, 20 : 0, 24 : 0 and polyunsaturated fatty acids (PUFA) 22 : 6 n-3 (docosahexaenoic acid) and 20 : 4 n-6 (arachidonic acid) were higher in RBCs than in plasma (p0.05), while SFA 14 : 0, monounsaturated fatty acids (MUFA) 14 : 1 n-5, 16 : 1 n-7, 17 : 1 n-7 and 18 : 1 n-9 and PUFA 18 : 3 n-3, 18 : 2 n-6, 18 : 3 n-6 and 20 : 3 n-6 were higher in plasma than in RBCs (p0.05). Polyphenols differed in plasma from those of RBCs. Biomarker concentrations were lower in sodium citrate compared to lithium heparin plasma. In conclusion, metabolomic profiles generated by NMR spectroscopy, GC-MS and UPLC-MS/MS analyses of RBCs versus plasma show complementary information on several specific molecular biomarkers that could be applied in nutritional assessment.