21 research outputs found
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 <sup>1</sup>H 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