Differential effect of maternal diet supplementation with α-Linolenic adcid or n-3 long-chain polyunsaturated fatty acids on glial cell phosphatidylethanolamine and phosphatidylserine fatty acid profile in neonate rat brains

<p>Abstract</p> <p>Background</p> <p>Dietary long-chain polyunsaturated fatty acids (LC-PUFA) are of crucial importance for the development of neural tissues. The aim of this study was to evaluate the impact of a dietary supplementation in n-3 fatty acids in female rats during gestation and lactation on fatty acid pattern in brain glial cells phosphatidylethanolamine (PE) and phosphatidylserine (PS) in the neonates.</p> <p>Methods</p> <p>Sprague-Dawley rats were fed during the whole gestation and lactation period with a diet containing either docosahexaenoic acid (DHA, 0.55%) and eicosapentaenoic acid (EPA, 0.75% of total fatty acids) or α-linolenic acid (ALA, 2.90%). At two weeks of age, gastric content and brain glial cell PE and PS of rat neonates were analyzed for their fatty acid and dimethylacetal (DMA) profile. Data were analyzed by bivariate and multivariate statistics.</p> <p>Results</p> <p>In the neonates from the group fed with n-3 LC-PUFA, the DHA level in gastric content (+65%, P < 0.0001) and brain glial cell PE (+18%, P = 0.0001) and PS (+15%, P = 0.0009) were significantly increased compared to the ALA group. The filtered correlation analysis (P < 0.05) underlined that levels of dihomo-γ-linolenic acid (DGLA), DHA and n-3 docosapentaenoic acid (DPA) were negatively correlated with arachidonic acid (ARA) and n-6 DPA in PE of brain glial cells. No significant correlation between n-3 and n-6 LC-PUFA were found in the PS dataset. DMA level in PE was negatively correlated with n-6 DPA. DMA were found to occur in brain glial cell PS fraction; in this class DMA level was correlated negatively with DHA and positively with ARA.</p> <p>Conclusion</p> <p>The present study confirms that early supplementation of maternal diet with n-3 fatty acids supplied as LC-PUFA is more efficient in increasing n-3 in brain glial cell PE and PS in the neonate than ALA. Negative correlation between n-6 DPA, a conventional marker of DHA deficiency, and DMA in PE suggests n-6 DPA that potentially be considered as a marker of tissue ethanolamine plasmalogen status. The combination of multivariate and bivariate statistics allowed to underline that the accretion pattern of n-3 LC-PUFA in PE and PS differ.</p

Alpsnmr: an r package for signal processing of fully untargeted nmr-based metabolomics

Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines

Consensus Clustering of temporal profiles for the identification of metabolic markers of pre-diabetes in childhood (EarlyBird 73)

In longitudinal clinical studies, methodologies available for the analysis of multivariate data with multivariate methods are relatively limited. Here, we present Consensus Clustering (CClust) a new computational method based on clustering of time pro les and posterior identi cation of correlation between clusters and predictors. Subjects are rst clustered in groups according to a response variable temporal pro le, using a robust consensus-based strategy. To discover which of the remaining variables are associated with the resulting groups, a non-parametric hypothesis test is performed between groups at every time point, and then the results are aggregated according to the Fisher method. Our approach is tested through its application to the EarlyBird cohort database, which contains temporal variations of clinical, metabolic, and anthropometric pro les in a population of 150 children followed-up annually from age 5 to age 16. Our results show that our consensus-based method is able to overcome the problem of the approach-dependent results produced by current clustering algorithms, producing groups de ned according to Insulin Resistance (IR) and biological age (Tanner Score). Moreover, it provides meaningful biological results con rmed by hypothesis testing with most of the main clinical variables. These results position CClust as a valid alternative for the analysis of multivariate longitudinal data

Improvement of cardiometabolic markers after fish oil intervention in young Mexican adults and the role of PPARα L162V and PPARγ2 P12A

Polyunsaturated fatty acids (PUFA) contained in fish oil (FO) are ligands for peroxisome proliferator-activated receptors (PPAR) that may induce changes in cardiometabolic markers. Variation in PPAR genes may influence the beneficial responses linked to FO supplementation in young adults. The study aimed to analyze the effect of FO supplementation on glucose metabolism, circulating lipids and inflammation according to PPARα L162V and PPARγ2 P12A genotypes in young Mexican adults. 191 young, non-smoking subjects between 18 and 40 years were included in a one-arm study. Participants were supplemented with 2.7 g/day of EPA+DHA, during six weeks. Dietary analysis, body composition measurements and indicators for glucose metabolism, circulating lipids, and markers for inflammation were analyzed before and after intervention. An overall decrease in triglycerides (TG) and an increase in HS-ω3 index were observed in all subjects [-4.1 mg/dL, (SD:±51.7), P=.02 and 2.6%, (SD:±1.2), P\u3c.001 respectively]. Mean fasting insulin and glycated hemoglobin (HbA1c%) were significantly decreased in all subjects [-0.547mlU/L, (SD:±10.29), P=.034 and-0.07%, (SD:±0.3), P\u3c.001 respectively], whereas there was no change in body composition, fasting glucose, adiponectin and inflammatory markers. Subjects carrying the minor alleles of PPARα L162V and PPARγ2 P12A had higher responses in reduction of TG and fasting insulin respectively. Interestingly, doses below 2.7 g/day (1.8 g/day) were sufficient to induce a significant reduction in fasting insulin and HbA1c% from baseline (P=.019 and P\u3c.001). The observed responses in triglycerides and fasting insulin in the Mexican population give further evidence of the importance of FO supplementation in young people as an early step towards the prevention of cardiometabolic disease. Trial registration: ClinicalTrials.gov NCT02296385

Autosuficiència energètica i hídrica de la masia de Mongofra Nou

El projecte s'ha realitzat a la finca de Mongofra Nou, un lloc menorquí situat dintre del parc natural de s'Albufera, a 12km de la capital, Maó. Es tracta d'una finca de 208 Ha. on s'hi desenvolupen diverses activitats generadores d'alts consums energètics (elèctrics, gas i gasoil) hídrics (33% pou i 67% pluja). L'objectiu principal del projecte és arribar a l'autosuficiència energètica mitjançant la substitució d'energies no renovables per energies renovables i no contaminants. De forma paral·lela es proposa una millora de la gestió hídrica, així com un model que doni resposta a l'agroturisme. S'ha realitzat un inventari dels punts de consum energètics i hídrics de la finca i un posterior tractament de dades per determinar les millors solucions possibles per assolir els objectius sense afectar al correcte desenvolupament i confortabilitat de les activitats actuals. A més s'analitza la viabilitat d'oferir un servei d'agroturisme de cara al futur. Els resultats indiquen que el consum energètic de gasoil per abastir la calefacció és el més elevat (77%), seguit de l'elèctric (21%) i finalment el butà (2%). Aquesta seqüència, i tenint en compte l'impacte que generen, permet establir un grau de prioritats en relació amb l' implantació de mesures correctores. Pel que fa al consum d'aigua, el punt crític de consum correspon amb el sector ramader boví (67%). Seguint l'exemple dels llocs més representatius de Menorca en l'àmbit de l'autosuficiència, es conclou que a Mongofra hi ha la possibilitat d'assolir l'autosuficiència energètica i hídrica a través de la instal·lació de plaques solars, fotovoltaiques i tèrmiques, una caldera híbrida de biomassa, una millora en la gestió de l'aigua i l'aplicació de mesures d'eficiència.El proyecto se ha realizado en la finca de Mongofra Nou, un "lloc" menorquín situado dentro del parque natural de S'albufera, a 12km de la capital, Mahón. Se trata de una finca de 208 Ha. donde se desarrollan diversas actividades generadoras de altos consumos energéticos (eléctricos, gas y gasoil) e hídricos (33% pozo y 67% lluvia). El objetivo principal del proyecto es llegar a la autosuficiencia energética mediante la substitución de energías no renovables por otras renovables y no contaminantes. De forma paralela se propone una mejora de la gestión hídrica, así como un modelo que da respuesta al agroturismo. Se ha realizado un inventario de los puntos de consumo energéticos e hídricos de la finca, y un posterior tratamiento de datos para determinar las mejores soluciones posibles para alcanzar los objetivos sin afectar al correcto desarrollo y confortabilidad de les actividades actuales. Además, se analiza la viabilidad de ofrecer un servicio de agroturismo de cara al futuro. Los resultados indican que el consumo energético de gasóleo para abastecer la calefacción es el más elevado (77%), seguido de l'eléctrico (21%) y finalmente el butano (2%). Esta secuencia, y teniendo en cuenta el impacto que generan, permiten establecer un grado de prioridades en relación a la implantación de medidas correctoras. En referencia al consumo de agua, el punto crítico de consumo corresponde con el sector ganadero bobino (67%). Siguiendo el ejemplo de los "llocs" más representativos de Menorca, se concluye que en Mongofra existe la posibilidad de lograr la autosuficiencia energética e hídrica mediante la instalación de placas solares, fotovoltaicas i térmicas, una caldera híbrida de biomasa, una mejora en la gestión del agua y la aplicación de medidas de eficiencia.The project was developed in an estate called Mongofra Nou, which is a traditional farmhouse located in S'Albufera des Grau, a natural park 12 km far from the capital of Menorca, Maó. There are big tracts of land in the property, 208 Ha. where different activities that generate high levels of energy (electricity, gas and gasoil) and water consumption are performed (67% rain water and 33% well). The main aim of the project is to achieve the energy self-sufficiency through the substitution of non-renewable energies for others that are renewable and clean. In the same way, we propose a better water management and also a model capable to maintain the agritourism. An inventory of the energy and water consumption points was carried out. The subsequent data treatment determined which are the best solutions to reach the objective without impacting the correct performance and comfort of the present and future activities. Furthermore, the viability to offer a future service of agritourism is analyzed. The results show that the energy consumption of gasoil, which supplies the heating, is the highest being the 77%, followed by the electric consume, 21% and finally the butane with the 2%. This sequence, bearing in mind the impact they generate, allows establishing a degree priority in relation with the implantation of improvement proposals. Having a regard on water consumes, the critical point is the consume which corresponds to the livestock (67%). Following the examples of the most representative places in Menorca in the self-sufficiency scope, it can be concluded that in Mongofra Nou there exists the chance of reaching it trough the setting up of solar panels, photovoltaic and thermal, a biomass hybrid boiler, a water management improvement and the implementation of efficiency measures

Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics

Aging is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. Pre-frailty is still not well understood but it has been associated with global imbalance in several physiological systems, including inflammation, and in nutrition. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is essential to move toward more personalized care. The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics, in order to identify specific biomarkers, and study their stability over time. The approach was based on the NU-AGE project (clinicaltrials.gov, NCT01754012) that regrouped 1,250 free-living elderly people (65–79 y.o., men and women), free of major diseases, recruited within five European centers. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al. (2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centers were selected for untargeted serum metabolomics at T0 (baseline) and T1 (follow-up). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate models. Metabolomics enabled to discriminate sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility. The best resulting models included four different metabolites for each gender. They showed very good prediction capacity with AUCs of 0.93 (95% CI = 0.87–1) and 0.94 (95% CI = 0.87–1) for men and women, respectively. Additionally, early and/or predictive markers of pre-frailty were identified for both genders and the gender specific models showed also good performance (three metabolites; AUC = 0.82; 95% CI = 0.72–0.93) for men and very good for women (three metabolites; AUC = 0.92; 95% CI = 0.86–0.99). These results open the door, through multivariate strategies, to a possibility of monitoring the disease progression over time at a very early stage

Aplicació i desenvolupament de tècniques quimiomètriques a l'anàlisi agroalimentària

Consultable des del TDXTítol obtingut de la portada digitalitzadaEn aquesta tesi es presenten diferents treballs relacionats amb l'ús i desenvolupament de diferents tècniques quimiomètriques aplicades a dades d'espectrofotometria. En concret, s'avalúen les seves prestacions en la determinació de diferents paràmetres de qualitat en olis d'oliva de diferents orígens i categoríes fent servir dades espectrofotomètriques d'infrarroig mitjà i proper. S'estudia la determinació de l'índex d'acidesa en mostres d'oli d'oliva de diferents origens i categories, fent servir espectrofotometria FTIR-ATR, es comprova com una acurada selecció de les mostres, tenint en compte altres factors externs, permet obtenir equacions de calibració amb una adeqüada capacitat predictiva. El fraccionament de l'interval d'acideses lliures permet l'obtenció d'equacions amb millor capacitat predictiva. De tots el modes espectrals assajats, Standard Normal Variate (SNV) proporciona resultats comparables als obtinguts fent servir dades d'absorbància, però fent servir equacions más simples. A més, SNV es mostra com l'únic pretractament efectiu al quantificar mostres estressades tèrmicament. En segon terme, s'estudia la viabilitat de les xarxes neuronals artificials (ANN) i de la regressió logística (LR) com a eines classificadores de mostres d'oli d'oliva verge de Catalunya que pertanyen a les Denominacions d'Origen Protegides de Siurana i Les Garrigues. Partint de dades NIR s'assagen perceptrons multicapa amb diferents topologies. El perceptró seleccionat permet discriminar mostres de predicció amb un 100% d'encert. La regressió logística necessita d'una etapa prèvia de selecció de variables que s'aplica al conjunt de dades de dimensionalitat reduïda. Un cop seleccionades les variables, l'equació obtinguda té una capacitat predictiva simil·lar a la dels perceptrons. També s'estudia l'efecte de la correcció ortogonal del senyal (OSC) a un conjunt de dades NIR amb l'intenció de realitzar la determinació quantitativa de tres àcids grassos (Oleic, Linoleic i Linolènic) en olis d'oliva verges fent servir PLSR. Es compara la capacitat predictiva de les equacions calculades fent servir dades tractades amb OSC envers d'altres pretractaments més comuns com les derivades (primera i segona) i SNV. Per altra banda, es comprova quin és l'efecte de la seva aplicació en els espectres NIR i en la seva correlació amb la concentració. Els resultats obtinguts no proporcionen diferències apreciables en la capacitat predictiva, únicament una simplificació de les equacions de calibració. Finalment, es proposen estratègies de validació de l'algoritme OSC. Es comprova quin és l'efecte dels possibles paràmetres d'ajust i es proposen dues estratègies clàssiques de validació (test set i validació creuada) per a determinar els valors òptims d'aquests paràmetres. Ambdúes estratègies proporcionen pràcticament els mateixos resultats quantitatius quan s'apliquen a conjunts de dades NIR enregistrades de forma diferent (reflectància i transmitància).In this thesis, several works related to the use and development of different chemometrical techniques applied to spectrophotometrical data are presented. In fact, their performance in the determination of different quality parameters in olive oil of different origins and categories, using spectrophotometrical data in the Mid- and Near IR, is evaluated. The free acidity index in olive oil samples of different origins is studied, using FTIR-ATR spectrophotometry. Also, is proved how an accurate selection of the samples, paying attention other external factors, allow to obtain calibration eqüations with improved predictive ability. From all the spectral modes checked, Standard Normal Variate (SNV) gives quantitative results similar to those obtained using absorbance data, but using simpler equations. Added to this, SNV appears as the unique effective pretreatment when thermally stressed samples are quantified. On second place, the properlyness of Artificial Neural Networks (ANN) and Logistic Regression as classification techniques of virgin olive oil samples from Catalonia, which belong to the protected denominations of origin of Siurana and Les Garigues, is studied. Starting up from NIR data, several multilayer perceptrons with different topologies are assayed. The selected percepreon allows to discriminate prediction samples with a 100% of hits. Logistic Regression needs a previous step of variable selection, which is applied to a dimensionality-reduced data set. Once the variables are selected, the predictive ability of the obtained equation has a predictive ability similar to the perceptrons. The effect of Orthogonal Signal Correction (OSC) on a NIR data set is also studied, in order to determine quantitatively three free fatty acids (Oleic, Linoleic and Linolenic) in virgin olive oils by using PLSR. The predictive ability of the equations obtained with OSC data is compared to those obtained using more common pretreatments, such derivatives (first and second) and SNV. By the other hand, the effect of its application on NIR spectra and on their correlation with the concentration is evaluated. The results so obtained, don't show appreciable differences in their predictive ability. Only a simplification of the calibration equations. At the end, validation strategies for the OSC algorithm are proposed. The effect of the possible adjust parameters is studied, and two classical validation methods (Test Set and Cross Validation) are suggested in order to obtain the optimal values for those parameters. Both strategies give nearly the same quantitative results, when they are applied to NIR data sets obtained in different ways (reflectance and transmittance)

Multivariate curve resolution applied to temperature-modulated metal oxide gas sensors

Metal oxide (MOX) gas sensors have been widely used for years. Temperature modulation of gas sensors is as an alternative to increase their sensitivity and selectivity to different gas species. In order to enhance the extraction of useful information from this kind of signals, data processing techniques are needed. In this work, the use of self-modelling curve resolution techniques, in particular multivariate curve resolution-alternating least squares (MCR-ALS), is presented for the analysis of these signals. First, the performance of MCR in a synthetic dataset generated from temperature-modulated gas sensor response models has been evaluated, showing good results both in the resolution of gas mixtures and in the determination of concentration/sensitivity profiles. Secondly, experimental confirmation of previously obtained conclusions is attempted using temperature-modulated MOX sensors together with MCR-ALS for the analysis of carbon monoxide (CO) and methane (CH4) gas mixtures in dry air. Results allow confirming the possibility of using the proposed approach as a quantitative technique for gas mixtures analysis, and also reveal some limitations.GOSPEL NoE FP6-IST 507610 for their support and CyCIT project TEC2004-07853-c02-01.Peer reviewe