Background: Metabolic syndrome (MetS) comprises a spectrum of clinical phenotypes in which dyslipidemia, dysglycemia and hypertension are clustered and where all share a high level of oxidative stress and an increased risk of cardiovascular disease. This study examines the effect of a nutritional supplement combining red yeast rice and olive fruit extract on the lipid profile and on oxidative stress in a population of patients with MetS. Methods: In a double blind placebo controlled randomized trial, 50 persons with MetS, as defined by the ATPIII criteria, received the study product or placebo for 8 weeks. The study product contained 10.82 mg of monacolins and 9,32 mg of hydroxytyrosol per capsule, and is commercialized as Cholesfytol plus. The primary outcome measure was the difference in LDL reduction between intervention and control groups. Furthermore, differences in changes of CH, HDL, ApoA1, ApoB, HbA1c and oxLDL were measured, as well as side-effects, CK elevation, changes in clinical parameters and in cardiovascular risk. Results: In the intervention group, LDL cholesterol was lowered by 24% whereas it increased by 1% in the control group (p < 0.001). Other effects observed were a change in total cholesterol (−17% in the intervention group vs +2% in the control group, p < 0.001), apolipoprotein B (−15% vs +6%, p < 0.001), and TG (−9% vs + 16%, p = 0.02). Oxidized LDL decreased by 20% vs an increase of 5% in the control group (p < 0.001). Systolic and diastolic arterial blood pressure decreased significantly by 10 mmHg (vs 0% in the control group, p = 0.001) and 7 mmHg (vs 0% in the control group, p = 0.05) respectively. One person in the intervention group, who suffered from Segawa's syndrome, dropped out because of severe muscle ache. Conclusions:The combination of active products in this study may be an alternative approach to statins in people who do not need, or cannot or do not want to be treated with chemical statins. Side effects, effects on oxidative stress and on glucose metabolism need to be examined more thoroughly.Trial registration: Clinicaltrials.gov NCT02065180 (February 2014).
Filipendula ulmaria (meadowsweet) is traditionally used for the treatment of inflammatory diseases and as a diuretic and antirheumatic. Extracts of Filipendulae herba are on the market in the European Union as food supplements. Nevertheless, its active constituents remain to be revealed. During this study, the phytochemical composition of Filipendulae Ulmariae Herba was comprehensively characterised for the first time with two complementary generic ultrahigh-performance liquid chromatography-photodiode array-accurate mass mass spectrometry methods. Selective ion fragmentation experiments with a hybrid quadrupole-orbital trap mass spectrometer significantly contributed to compound identification: a total of 119 compounds were tentatively identified, 69 new to F. ulmaria. A rich diversity of phenolic constituents was detected and only a few non-phenolic phytochemicals were observed. Metabolisation and pharmacological studies should be conducted to investigate which of these constituents or metabolites there of contribute to the activity of F. ulmaria after oral intake.
It is vital to pay much attention to the design of extraction methods developed for plant metabolomics, as any non-extracted or converted metabolites will greatly affect the overall quality of the metabolomics study. Method validation is however often omitted in plant metabolome studies, as the well-established methodologies for classical targeted analyses such as recovery optimization cannot be strictly applied. The aim of the present study is to thoroughly evaluate state-of-the-art comprehensive extraction protocols for plant metabolomics with liquid chromatography-photodiode array-accurate mass mass spectrometry (LC-PDA-amMS) by bridging the gap with method validation. Validation of an extraction protocol in untargeted plant metabolomics should ideally be accomplished by validating the protocol for all possible outcomes, i.e. for all secondary metabolites potentially present in the plant. In an effort to approach this ideal validation scenario, two plant matrices were selected based on their wide versatility of phytochemicals: meadowsweet (Filipendula ulmaria) for its polyphenols content, and spicy paprika powder (from the genus Capsicum) for its apolar phytochemicals content (carotenoids, phytosterols, capsaicinoids). These matrices were extracted with comprehensive extraction protocols adapted from literature and analysed with a generic LC-PDA-amMS characterization platform that was previously validated for broad range phytochemical analysis. The performance of the comprehensive sample preparation protocols was assessed based on extraction efficiency, repeatability and intermediate precision and on ionization suppression/enhancement evaluation. The manuscript elaborates on the finding that none of the extraction methods allowed to exhaustively extract the metabolites. Furthermore, it is shown that depending on the extraction conditions enzymatic degradation mechanisms can occur. Investigation of the fractions obtained with the different extraction methods revealed a low resolving power for phytochemicals for all methods. Nevertheless, an overall good repeatability was observed for all extraction methods, which is essential to allow direct comparison between samples. In summary, no single procedure outperforms the others and compromises will have to be made during method selection.
Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.
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