A LC-MS metabolomics approach to investigate the effect of raw apple intake in the rat plasma metabolome

Fruit and vegetable consumption has been associated with several health benefits; however the mechanisms are largely unknown at the biochemical level. Our research aims to investigate whether plasma metabolome profiling can reflect biological effects after feeding rats with raw apple by using an untargeted UPLC-ESI-TOF-MS based metabolomics approach in both positive and negative mode. Eighty young male rats were randomised into groups receiving daily 0, 5 or 10 g fresh apple slices, respectively, for 13 weeks. During weeks 3-6 some of the animals were receiving 4 mg/ml 1,2-dimethylhydrazine dihydrochloride (DMH) once a week. Plasma samples were taken at the end of the intervention and among all groups, about half the animals were 12 h fasted. An initial ANOVA-simultaneous component analysis with a three-factor or two-factor design was employed in order to isolate potential metabolic variations related to the consumption of fresh apples. Partial least squares-discriminant analysis was then applied in order to select discriminative features between plasma metabolites in control versus apple fed rats and partial least squares modelling to reveal possible dose response. The findings indicate that in laboratory rats apple feeding may alter the microbial amino acid fermentation, lowering toxic metabolites from amino acids metabolism and increasing metabolism into more protective products. It may also delay lipid and amino acid catabolism, gluconeogenesis, affect other features of the transition from the postprandial to the fasting state and affect steroid metabolism by suppressing the plasma level of stress corticosteroids, certain mineralocorticoids and oxidised bile acid metabolites. Several new hypotheses regarding the cause of health effects from apple intake can be generated from this study for further testing in humans. © 2013 Springer Science+Business Media New York

A batch correction method for liquid chromatography–mass spectrometry data that does not depend on quality control samples

The need for reproducible and comparable results is of increasing importance in non-targeted metabolomic studies, especially when differences between experimental groups are small. Liquid chromatography– mass spectrometry spectra are often acquired batch-wise so that necessary calibrations and cleaning of the instrument can take place. However this may introduce further sources of variation, such as differences in the conditions under which the acquisition of individual batches is performed. Quality control (QC) samples are frequently employed as a means of both judging and correcting this variation. Here we show that the use of QC samples can lead to problems. The non-linearity of the response can result in substantial differences between the recorded intensities of the QCs and experimental samples, making the required adjustment difficult to predict. Furthermore, changes in the response profile between one QC interspersion and the next cannot be accounted for and QC based correction can actually exacerbate the problems by introducing artificial differences. ‘‘Background correction’’ methods utilise all experimental samples to estimate the variation over time rather than relying on the QC samples alone. We compare non-QC correction methods with standard QC correction and demonstrate their success in reducing differences between replicate samples and their potential to highlight differences between experimental groups previously hidden by instrumental variation

Characterization of Aegean olive oils by their minor compounds

This study presents combined approach of establishing cultivar difference between Aegean olive oils, obtained from economically important olive oil producing cultivars (cv..Ayvalık and Memecik), based on chemometric evaluation of their content and in particular composition of minor compounds. Evaluation of minor compounds with principal component analysis (PCA) and linear discriminant analysis (LDA) indicated differentiation according to the cultivars. LDA produced a 100 % correct group classification. Moreover, stigmasterol, apparent β-sitosterol and total sterols were found to have the highest discriminating power. Memecik oils were characterized by the highest content of antioxidant compounds (α-tocopherol, phenolic compounds and total phenolic compounds). On the other hand, Ayvalık oil had the highest level of total sterols. The data were analyzed statistically to evaluate the differences according to variety and crop season. The minor compounds of Ayvalık and Memecik oils presented statistically significant differences (p < 0.01) according to variety, except for the hydroxytyrosol and clerosterol content. The amount of α-tocopherol, total phenolic compounds, apparent β-sitosterol and total sterols varied with respect to crop season. A good correlation was observed between the amount of α-tocopherol, total phenolic compounds, apparent β-sitosterol and total sterols and some climatic variables

Analysis of the SYSDIET Healthy Nordic Diet randomized trial based on metabolic profiling reveal beneficial effects on glucose metabolism and blood lipids

Abstract Background &amp; aims: Intake assessment in multicenter trials is challenging, yet important for accurate outcome evaluation. The present study aimed to characterize a multicenter randomized controlled trial with a healthy Nordic diet (HND) compared to a Control diet (CD) by plasma and urine metabolic profiles and to associate them with cardiometabolic markers. Methods: During 18–24 weeks of intervention, 200 participants with metabolic syndrome were advised at six centres to eat either HND (e.g.whole-grain products, berries, rapeseed oil, fish and low-fat dairy) or CD while being weight stable. Of these 166/159 completers delivered blood/urine samples. Metabolic profiles of fasting plasma and 24 h pooled urine were analysed to identify characteristic diet-related patterns. Principal components analysis (PCA) scores (i.e. PC1 and PC2 scores) were used to test their combined effect on blood glucose response (primary endpoint), serum lipoproteins, triglycerides, and inflammatory markers. Results: The profiles distinguished HND and CD with AUC of 0.96 ± 0.03 and 0.93 ± 0.02 for plasma and urine, respectively, with limited heterogeneity between centers, reflecting markers of key foods. Markers of fish, whole grain and polyunsaturated lipids characterized HND, while CD was reflected by lipids containing palmitoleic acid. The PC1 scores of plasma metabolites characterizing the intervention is associated with HDL (β = 0.05; 95% CI: 0.02, 0.08; P = 0.001) and triglycerides (β = −0.06; 95% CI: −0.09, −0.03; P &lt; 0.001). PC2 scores were related with glucose metabolism (2 h Glucose, β = 0.1; 95% CI: 0.05, 0.15; P &lt; 0.001), LDL (β = 0.06; 95% CI: 0.01, 0.1; P = 0.02) and triglycerides (β = 0.11; 95% CI: 0.06, 0.15; P &lt; 0.001). For urine, the scores were related with LDL cholesterol. Conclusions: Plasma and urine metabolite profiles from SYSDIET reflected good compliance with dietary recommendations across the region. The scores of metabolites characterizing the diets associated with outcomes related with cardio-metabolic risk. Our analysis therefore offers a novel way to approach a per protocol analysis with a balanced compliance assessment in larger multicentre dietary trials