Microbial Metabolomic Fingerprinting in Urine after Regular Dealcoholized Red Wine Consumption in Humans

The regular consumption of dealcoholized red wine (DRW) has demonstrated benefits in cardiovascular risk factors. The analysis of phenolic metabolites formed in the organism, especially those that could come from microbiota metabolism, would help to understand these benefits. The aim of this study was to determine the widest urinary metabolomic fingerprinting of phenolics and microbial-derived phenolic acids (<i>n</i> = 61) after regular intake of DRW in men at high cardiovascular risk by UPLC-MS/MS using a targeted approach. Up to 49 metabolites, including phase II and microbial phenolic metabolites, increased after DRW consumption compared to baseline (<i>P</i> < 0.05). The highest percentage of increase was found for microbial metabolites from anthocyanin degradation such as syringic, <i>p</i>-coumaric, gallic acids and pyrogallol and from flavan-3-ols degradation such as hydroxyphenylvalerolactones and (epi)­catechins. These findings provide the most complete metabolic fingerprinting after wine consumption, amplifying the spectrum of microbial derived metabolites and their potential bioactivity related with health benefits

Ultraviolet and visible Raman analysis of thin a-C films grown by filtered cathodic arc deposition

Amorphous carbon thin films with a wide range of sp2 fraction from 20 to 90% grown by filtered cathodic arc deposition have been examined by ultraviolet (UV) at 325 nm and visible Raman spectroscopy at 457 nm excitation wavelength. The comprehensive study of behaviour of G, D and T band with sp2/sp3 content has been carried out. The upwards shift of the G peak with sp3 content was observed for both excitation wavelengths. It was also found that the I(D)/I(G) ratio decreases with sp3 content for UV and visible excitations, and for high sp3 content I(D)/I(G) tends to zero. The dispersion of the G peak is also investigated in this work as a function of sp2 content

Opciones para la convergencia entre la Alianza del Pacífico y el Mercado Común del Sur (MERCOSUR): la regulación de la inversión extranjera directa

El presente documento analiza los avances de la Alianza del Pacífico (AP) y el MERCOSUR en la regulación internacional de la inversión extranjera. A partir de dicho análisis, se delinean los posibles contenidos de un eventual acuerdo sobre inversión entre ambas agrupaciones. De este modo se espera contribuir con propuestas sustantivas a la agenda en curso de "convergencia en la diversidad".Resumen .-- Introducción .-- I. La Alianza del Pacífico y el MERCOSUR en el contexto de los flujos mundiales y regionales de IED .-- II. La regulación de la IED: breve panorama mundial, en la Alianza del Pacífico y en el MERCOSUR .-- III. Principales cuestionamientos a la actual gobernanza internacional de la inversión .-- IV. Los modelos de la Alianza del Pacífico y el Brasil: un análisis comparativo .-- V. Conclusiones

Additional file 11: Figure S9. of Serum metabolites in non-alcoholic fatty-liver disease development or reversion; a targeted metabolomic approach within the PREDIMED trial

Volcano plot [−log10 (p-value) vs. log2 (fold-change)] for the comparison between the baseline and final states in group 3 (suspected NAFLD reversion cases). Abbreviations: AA, amino acids; SFA, saturated fatty acids; PUFA, polyunsaturated fatty acids; MUFA, monounsaturated fatty acids; NAE, N-acyl ethanolamines; FFAox, free fatty acid oxidised; AC, acyl carnitines; PC, phosphatidylcholine; LPC, lysophosphatidylcholine; PE, phatidylethanolamine; LPE, lysophosphatidylethanolamine; PI, phatidylinositols; LPI, lysophosphatidylinositols; Cer, ceramides; SM, sphingomyelin; ChoE, cholesteryl esters; Chol, cholesterol; TAG, triacylglycerols; DAG, diacylglycerols, BA, bile acids; CMH, monohexosylceramides (DOCX 50 kb

Urinary ¹H Nuclear Magnetic Resonance Metabolomic Fingerprinting Reveals Biomarkers of Pulse Consumption Related to Energy-Metabolism Modulation in a Subcohort from the PREDIMED study

Little is known about the metabolome fingerprint of pulse consumption. The study of robust and accurate biomarkers for pulse dietary assessment has great value for nutritional epidemiology regarding health benefits and their mechanisms. To characterize the fingerprinting of dietary pulses (chickpeas, lentils, and beans), spot urine samples from a subcohort from the PREDIMED study were stratified using a validated food frequency questionnaire. Urine samples of nonpulse consumers (≤4 g/day of pulse intake) and habitual pulse consumers (≥25 g/day of pulse intake) were analyzed using a ¹H nuclear magnetic resonance (NMR) metabolomics approach combined with multi- and univariate data analysis. Pulse consumption showed differences through 16 metabolites coming from (i) choline metabolism, (ii) protein-related compounds, and (iii) energy metabolism (including lower urinary glucose). Stepwise logistic regression analysis was applied to design a combined model of pulse exposure, which resulted in glutamine, dimethylamine, and 3-methylhistidine. This model was evaluated by a receiver operating characteristic curve (AUC > 90% in both training and validation sets). The application of NMR-based metabolomics to reported pulse exposure highlighted new candidates for biomarkers of pulse consumption and the impact on energy metabolism, generating new hypotheses on energy modulation. Further intervention studies will confirm these findings

Additional file 6: Figure S4. of Serum metabolites in non-alcoholic fatty-liver disease development or reversion; a targeted metabolomic approach within the PREDIMED trial

Principal component analysis (PCA) to discriminate between the baseline and final states in group 3 (suspected NAFLD reversion cases). Model diagnostics (A = 6, R2X = 0.628, Q2X = 0.235) (DOCX 42 kb

Additional file 4: Figure S2. of Serum metabolites in non-alcoholic fatty-liver disease development or reversion; a targeted metabolomic approach within the PREDIMED trial

Principal component analysis (PCA) model to discriminate between the baseline and final states in group 1 (participants not meeting the NAFLD criteria at baseline and at the end of follow-up). Model diagnostics (A = 6, R2X = 0.621, Q2X = 0.160) (DOCX 43 kb

Age- and sex-adjusted association analyses between the 5 <i>SCD1</i> inferred haplotypes with frequency >5% and the 8 investigated metabolic traits in the EPIC-Potsdam study.

<p>Haplotypes are composed of variants rs1502593 (C>T), rs522951 (G>C), rs11190480 (A>G), rs3071 (T>G), rs3793767 (T>C), rs10883463 (T>C), rs508384 (C>A) in that order. A indicates common allele, B indicates rare allele.</p>a<p>based on the 615 participants fasting at blood draw.</p>b<p>geometric means and (95% CI);</p>c<p>means and standard error;</p>d<p>inverse and (95% CI);</p>e<p>based on 2077 participants due to missing biomarker data. All the reported significance levels are nominal P values and are not adjusted for multiple comparisons.</p><p><b>P_add</b>: P for trend or P for the additive model; <b>P_dom</b>: P value for the dominant model. <b>P_rec</b>: P value for the recessive model.</p

Age- and sex-adjusted association analyses between the 7 <i>SCD1</i> tag-SNPs and the 8 investigated metabolic traits: the EPIC-Potsdam Study.

<p>Each SNP is coded as 0, 1 and 2 according to the number of minor alleles a participant carries.</p>a<p>based on the 615 participants fasting at blood draw.</p>b<p>geometric means and (95% CI);</p>c<p>means and standard error;</p>d<p>inverse and (95% CI),</p>e<p>based on 2077 participants due to missing biomarker data. All the reported significance levels are nominal P values and are not adjusted for multiple comparisons.</p><p><b>P_add</b>, P for trend or P for the additive model; <b>P_dom</b>, P value for the dominant model. <b>P_rec</b>, P value for the recessive model.</p

Baseline characteristics of the EPIC-Potsdam subcohort and separately for men and women.

<p><b>Subcohort</b>: Mean ± SD, %, or median (25^th percentile; 75^th percentile), all such values. <b>Men and women</b>: mean and 95% confidence interval (CI) or %. Results obtained using analysis of covariance, all variables other than age are adjusted for age.</p>a<p>P value for the difference between men and women.</p>b<p>based on the 615 participants fasting at blood draw.</p>c<p>based on 2077 participants due to missing biomarker data.</p>d<p>Alleles given in brackets (most >less frequent allele).</p