Nutrition for the ageing brain: towards evidence for an optimal diet

As people age they become increasingly susceptible to chronic and extremely debilitating brain diseases. The precise cause of the neuronal degeneration underlying these disorders, and indeed normal brain ageing remains however elusive. Considering the limits of existing preventive methods, there is a desire to develop effective and safe strategies. Growing preclinical and clinical research in healthy individuals or at the early stage of cognitive decline has demonstrated the beneficial impact of nutrition on cognitive functions. The present review is the most recent in a series produced by the Nutrition and Mental Performance Task Force under the auspice of the International Life Sciences Institute Europe (ILSI Europe). The latest scientific advances specific to how dietary nutrients and non-nutrient may affect cognitive ageing are presented. Furthermore, several key points related to mechanisms contributing to brain ageing, pathological conditions affecting brain function, and brain biomarkers are also discussed. Overall, findings are inconsistent and fragmented and more research is warranted to determine the underlying mechanisms and to establish dose-response relationships for optimal brain maintenance in different population subgroups. Such approaches are likely to provide the necessary evidence to develop research portfolios that will inform about new dietary recommendations on how to prevent cognitive decline

Comparative Analysis of Sample Preparation Methods To Handle the Complexity of the Blood Fluid Metabolome: When Less Is More

Blood sample preparation before LC-MS metabolomic fingerprinting is one of the most challenging and error-prone parts of the analytical procedure. Besides proteins, phospholipids contained in blood fluids are known to cause matrix effects and ion suppression phenomena, thus masking biological variation. Nevertheless, the commonly used sample preparation techniques do not consider their removal prior to analysis. Pooled plasma and serum samples were used as biological material, partly as raw samples and partly spiked with distinct concentrations of a metabolite mix (1–5 μg/mL). Prior to LC-ESI-qToF-MS-driven metabolomic analysis, samples were subjected to different preparation methods consisting of three extractions with organic solvents (acetonitrile, methanol, and methanol/ethanol), a membrane-based solvent-free technique, and a hybrid method combining solvent extraction and SPE-mediated removal of phospholipids. The comparative analysis among sample preparation procedures was based on the capacity to detect endogenous compounds in raw samples, differentiate raw versus spiked samples, and reveal real-life metabolomic changes, following a dietary intervention. Method speed, minimum sample handling, compatibility to automation, and applicability to large-scale metabolomic studies were also considered. The combination of solvent deproteinization and the selective removal of phospholipids was revealed to be the most suitable method, in terms of improvement of nonlipid metabolite coverage, extraction reproducibility, quickness, and compatibility with automation, the minimization of matrix effects being among the most probable causes for the good extraction performance associated with the removal of phospholipid species. The main advantage of conventional solvent extraction procedures was the metabolite information coverage for lipid low-molecular-weight species, and extraction with acetonitrile was generally the second choice for sample preparation. Ultrafiltration was the least effective method for plasma and serum preparation; thus, its use without a previous solvent extraction step of the samples should be discarded. According to the presented data, there is no apparent reason to believe that sacrificing information on lipid compounds is too high of a price to pay in order to gain more information on nonlipid LMW metabolites

Peak Aggregation as an Innovative Strategy for Improving the Predictive Power of LC-MS Metabolomic Profiles

Liquid chromatography–mass spectrometry (LC-MS)-based metabolomic datasets consist of different features including (de)­protonated molecules, fragments, adducts, and isotopes that may show high correlation values related to a high level of collinearity. There have been described several sources of these high correlation patterns regarding metabolomic datasets. Among these sources, it should be highlighted the high level of correlation computed between features coming from the same metabolite. It is well-known that soft ionization methods (such as electrospray) produce several mass features from a particular compound (i.e., metabolite spectrum). Typically, the statistical methods used in metabolomics consider spectral peaks as variables. However, it has been reported that a high collinearity between variables might be the responsible for high uncertainty values in the predictors of a regression. In this context, this technical note proposes a new strategy based on the application of the so-called peak aggregation methods (NMF Reduction, PCA Decomposition, Maximum Peak, and Spectrum Mean) to take advantage of the variable collinearity and solve the issue of high variable collinearity. A set of real samples obtained after human nutritional intervention with placebo or polyphenol-rich beverages was used to test this methodology. The results showed that applying any peak aggregation method (especially NMF and PCA) improves the statistical prediction power of class pertinence independently of the nature of the classifier (linear PLS-DA or nonlinear SVM). Overall, the introduction of this new approach resulted in a reduction of the dimensionality of the data and, in addition, in a significant increase in the overall predictive power of the data

Untargeted ¹H NMR-Based Metabolomics Analysis of Urine and Serum Profiles after Consumption of Lentils, Chickpeas, and Beans: An Extended Meal Study To Discover Dietary Biomarkers of Pulses

High legume intake has been shown to have beneficial effects on the health of humans. The use of nutritional biomarkers, as a complement to self-reported questionnaires, could assist in evaluating dietary intake and downstream effects on human health. The aim of this study was to investigate potential biomarkers of the consumption of pulses (i.e., white beans, chickpeas, and lentils) by using untargeted NMR-based metabolomics. Meals rich in pulses were consumed by a total of 11 participants in a randomized crossover study and multilevel partial least-squares regression was employed for paired comparisons. Metabolomics analysis indicated that trigonelline, 3-methylhistidine, dimethylglycine, trimethylamine, and lysine were potential, though not highly specific, biomarkers of pulse intake. Furthermore, monitoring of these metabolites for a period of 48 h after intake revealed a range of different excretion patterns among pulses. Following the consumption of pulses, a metabolomic profiling revealed that the concentration ratios of trigonelline, choline, lysine, and histidine were similar to those found in urine. In conclusion, this study identified potential urinary biomarkers of exposure to dietary pulses and provided valuable information about the time-response effect of these putative biomarkers

Additional file 2: Table S2. of Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

Enriched data and their main metabolite identifiers for ORA analysis. (XLSX 9ย�kb

New and Vintage Solutions To Enhance the Plasma Metabolome Coverage by LC-ESI-MS Untargeted Metabolomics: The Not-So-Simple Process of Method Performance Evaluation

Although LC-MS untargeted metabolomics continues to expand into exiting research domains, methodological issues have not been solved yet by the definition of unbiased, standardized and globally accepted analytical protocols. In the present study, the response of the plasma metabolome coverage to specific methodological choices of the sample preparation (two SPE technologies, three sample-to-solvent dilution ratios) and the LC-ESI-MS data acquisition steps of the metabolomics workflow (four RP columns, four elution solvent combinations, two solvent quality grades, postcolumn modification of the mobile phase) was investigated in a pragmatic and decision tree-like performance evaluation strategy. Quality control samples, reference plasma and human plasma from a real nutrimetabolomic study were used for intermethod comparisons. Uni- and multivariate data analysis approaches were independently applied. The highest method performance was obtained by combining the plasma hybrid extraction with the highest solvent proportion during sample preparation, the use of a RP column compatible with 100% aqueous polar phase (Atlantis T3), and the ESI enhancement by using UHPLC-MS purity grade methanol as both organic phase and postcolumn modifier. Results led to the following considerations: submit plasma samples to hybrid extraction for removal of interfering components to minimize the major sample-dependent matrix effects; avoid solvent evaporation following sample extraction if loss in detection and peak shape distortion of early eluting metabolites are not noticed; opt for a RP column for superior retention of highly polar species when analysis fractionation is not feasible; use ultrahigh quality grade solvents and “vintage” analytical tricks such as postcolumn organic enrichment of the mobile phase to enhance ESI efficiency. The final proposed protocol offers an example of how novel and old-fashioned analytical solutions may fruitfully cohabit in untargeted metabolomics protocols

Additional file 4: Table S4. of Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

Number of metabolites with identifiers of the following metabolite databases. Metabolite databases are sorted by the number of identifiers found. *LipidMAPS identifiers were only searched in lipids (n = 67), while the rest of identifiers were considered in all the metabolites of the datasets (n = 147). (DOCX 16 kb

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

Dietary Epicatechin Is Available to Breastfed Infants through Human Breast Milk in the Form of Host and Microbial Metabolites

Polyphenols play an important role in human health. To address their accessibility to a breastfed infant, we planned to evaluate whether breast milk (BM) (colostrum, transitional, and mature) epicatechin metabolites could be related to the dietary habits of mothers. The polyphenol consumption of breastfeeding mothers was estimated using a food frequency questionnaire and 24 h recalls. Solid-phase extraction–ultra performance liquid chromatography–tandem mass spectrometry (SPE–UPLC–MS/MS) was applied for direct epicatechin metabolite analysis. Their bioavailability in BM as a result of dietary ingestion was confirmed in a preliminary experiment with a single dose of dark chocolate. Several host and microbial phase II metabolites of epicatechin were detected in BM among free-living lactating mothers. Interestingly, a modest correlation between dihydroxyvalerolactone sulfate and the intake of cocoa products was observed. Although a very low percentage of dietary polyphenols is excreted in BM, they are definitely in the diet of breastfed infants. Therefore, evaluation of their role in infant health could be further promoted

Metabotypes of response to bariatric surgery independent of the magnitude of weight loss

<div><p>Objective</p><p>Bariatric surgery is considered the most efficient treatment for morbid obesity and its related diseases. However, its role as a metabolic modifier is not well understood. We aimed to determine biosignatures of response to bariatric surgery and elucidate short-term metabolic adaptations.</p><p>Methods</p><p>We used a LC- and FIA-ESI-MS/MS approach to quantify acylcarnitines, (lyso)phosphatidylcholines, sphingomyelins, amino acids, biogenic amines and hexoses in serum samples of subjects with morbid obesity (n = 39) before and 1, 3 and 6 months after bariatric surgery. K-means cluster analysis allowed to distinguish metabotypes of response to bariatric surgery.</p><p>Results</p><p>For the first time, global metabolic changes following bariatric surgery independent of the baseline health status of the subjects have been revealed. We identify two metabolic phenotypes (metabotypes) at the interval 6 months-baseline after surgery, which presented differences in the levels of compounds of urea metabolism, gluconeogenic precursors and (lyso)phospholipid particles. Clinically, metabotypes were different in terms of the degree of improvement in insulin resistance, cholesterol, low-density lipoproteins and uric acid independent of the magnitude of weight loss.</p><p>Conclusions</p><p>This study opens new perspectives and new hypotheses on the metabolic benefits of bariatric surgery and understanding of the biology of obesity and its associated diseases.</p></div