Multivariate paired data analysis: multilevel PLSDA versus OPLSDA

Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects

Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q2 and Discriminant Q2 (DQ2) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q2 and Discriminant Q2 (DQ2). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ2 and Q2 (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies

Diagnostic properties of metabolic perturbations in rheumatoid arthritis

Introduction: The aim of this study was to assess the feasibility of diagnosing early rheumatoid arthritis (RA) by measuring selected metabolic biomarkers. Methods: We compared the metabolic profile of patients with RA with that of healthy controls and patients with psoriatic arthritis (PsoA). The metabolites were measured using two different chromatography-mass spectrometry platforms, thereby giving a broad overview of serum metabolites. The metabolic profiles of patient and control groups were compared using multivariate statistical analysis. The findings were validated in a follow-up study of RA patients and healthy volunteers. Results: RA patients were diagnosed with a sensitivity of 93% and a specificity of 70% in a validation study using detection of 52 metabolites. Patients with RA or PsoA could be distinguished with a sensitivity of 90% and a specificity of 94%. Glyceric acid, D-ribofuranose and hypoxanthine were increased in RA patients, whereas histidine, threonic acid, methionine, cholesterol, asparagine and threonine were all decreased compared with healthy controls. Conclusions: Metabolite profiling (metabolomics) is a potentially useful technique for diagnosing RA. The predictive value was without regard to the presence of antibodies against cyclic citrullinated peptides

Prognostic value of metabolic response in breast cancer patients receiving neoadjuvant chemotherapy

<p>Abstract</p> <p>Background</p> <p>Today's clinical diagnostic tools are insufficient for giving accurate prognosis to breast cancer patients. The aim of our study was to examine the tumor metabolic changes in patients with locally advanced breast cancer caused by neoadjuvant chemotherapy (NAC), relating these changes to clinical treatment response and long-term survival.</p> <p>Methods</p> <p>Patients (n = 89) participating in a randomized open-label multicenter study were allocated to receive either NAC as epirubicin or paclitaxel monotherapy. Biopsies were excised pre- and post-treatment, and analyzed by high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). The metabolite profiles were examined by paired and unpaired multivariate methods and findings of important metabolites were confirmed by spectral integration of the metabolite peaks.</p> <p>Results</p> <p>All patients had a significant metabolic response to NAC, and pre- and post-treatment spectra could be discriminated with 87.9%/68.9% classification accuracy by paired/unpaired partial least squares discriminant analysis (PLS-DA) (<it>p </it>< 0.001). Similar metabolic responses were observed for the two chemotherapeutic agents. The metabolic responses were related to patient outcome. Non-survivors (< 5 years) had increased tumor levels of lactate (<it>p </it>= 0.004) after treatment, while survivors (≥ 5 years) experienced a decrease in the levels of glycine (<it>p </it>= 0.047) and choline-containing compounds (<it>p </it>≤ 0.013) and an increase in glucose (<it>p </it>= 0.002) levels. The metabolic responses were not related to clinical treatment response.</p> <p>Conclusions</p> <p>The differences in tumor metabolic response to NAC were associated with breast cancer survival, but not to clinical response. Monitoring metabolic responses to NAC by HR MAS MRS may provide information about tumor biology related to individual prognosis.</p

Population-based nutrikinetic modeling of polyphenol exposure

The beneficial health effects of fruits and vegetables have been attributed to their polyphenol content. These compounds undergo many bioconversions in the body. Modeling polyphenol exposure of humans upon intake is a prerequisite for understanding the modulating effect of the food matrix and the colonic microbiome. This modeling is not a trivial task and requires a careful integration of measuring techniques, modeling methods and experimental design. Moreover, both at the population level as well as the individual level polyphenol exposure has to be quantified and assessed. We developed a strategy to quantify polyphenol exposure based on the concept of nutrikinetics in combination with population-based modeling. The key idea of the strategy is to derive nutrikinetic model parameters that summarize all information of the polyphenol exposure at both individual and population level. This is illustrated by a placebo-controlled crossover study in which an extract of wine/grapes and black tea solids was administered to twenty subjects. We show that urinary and plasma nutrikinetic time-response curves can be used for phenotyping the gut microbial bioconversion capacity of individuals. Each individual harbours an intrinsic microbiota composition converting similar polyphenols from both test products in the same manner and stable over time. We demonstrate that this is a novel approach for associating the production of two gut-mediated γ-valerolactones to specific gut phylotypes. The large inter-individual variation in nutrikinetics and γ-valerolactones production indicated that gut microbial metabolism is an essential factor in polyphenol exposure and related potential health benefits

A lipidomic analysis approach to evaluate the response to cholesterol-lowering food intake

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