Personalised Interventions - A Precision Approach for the Next Generation of Dietary Intervention Studies

Acknowledgments The research of Baukje de Roos is supported by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS). Lorraine Brennan acknowledges The European Research Council ERC (647783). Conflicts of Interest The authors declare no conflict of interest.Peer reviewedPublisher PD

Metabolomics in nutrition research -a powerful window into nutritional metabolism

Metabolomics is the study of small molecules present in biological samples. In recent years it has become evident that such small molecules, called metabolites, play a key role in the development of disease states. Furthermore, metabolomic applications can reveal information about alterations in certain metabolic pathways under different conditions. Data acquisition in metabolomics is usually performed using nuclear magnetic resonance (NMR)-based approaches or mass spectrometry (MS)-based approaches with a more recent trend including the application of multiple platforms in order to maximise the coverage in terms of metabolites measured. The application of metabolomics is rapidly increasing and the present review will highlight applications in nutrition research

Distinct patterns of personalised dietary advice delivered by a metabotype framework similarly improve dietary quality and metabolic health parameters: secondary analysis of a randomised controlled trial

BackgroundIn a 12-week randomised controlled trial, personalised nutrition delivered using a metabotype framework improved dietary intake, metabolic health parameters and the metabolomic profile compared to population-level dietary advice. The objective of the present work was to investigate the patterns of dietary advice delivered during the intervention and the alterations in dietary intake and metabolic and metabolomic profiles to obtain further insights into the effectiveness of the metabotype framework.MethodsForty-nine individuals were randomised into the intervention group and subsequently classified into metabotypes using four biomarkers (triacylglycerol, HDL-C, total cholesterol, glucose). These individuals received personalised dietary advice from decision tree algorithms containing metabotypes and individual characteristics. In a secondary analysis of the data, patterns of dietary advice were identified by clustering individuals according to the dietary messages received and clusters were compared for changes in dietary intake and metabolic health parameters. Correlations between changes in blood clinical chemistry and changes in metabolite levels were investigated.ResultsTwo clusters of individuals with distinct patterns of dietary advice were identified. Cluster 1 had the highest percentage of messages delivered to increase the intake of beans and pulses and milk and dairy products. Cluster 2 had the highest percentage of messages delivered to limit the intake of foods high in added sugar, high-fat foods and alcohol. Following the intervention, both patterns improved dietary quality assessed by the Alternate Mediterranean Diet Score and the Alternative Healthy Eating Index, nutrient intakes, blood pressure, triacylglycerol and LDL-C (p ≤ 0.05). Several correlations were identified between changes in total cholesterol, LDL-C, triacylglycerol, insulin and HOMA-IR and changes in metabolites levels, including mostly lipids (sphingomyelins, lysophosphatidylcholines, glycerophosphocholines and fatty acid carnitines).ConclusionThe findings indicate that the metabotype framework effectively personalises and delivers dietary advice to improve dietary quality and metabolic health.Clinical trial registrationisrctn.com, identifier ISRCTN15305840

Inferring food intake from multiple biomarkers using a latent variable model

Metabolomic based approaches have gained much attention in recent years due to their promising potential to deliver objective tools for assessment of food intake. In particular, multiple biomarkers have emerged for single foods. However, there is a lack of statistical tools available for combining multiple biomarkers to infer food intake. Furthermore, there is a paucity of approaches for estimating the uncertainty around biomarker based prediction of intake. Here, to facilitate inference on the relationship between multiple metabolomic biomarkers and food intake in an intervention study conducted under the A-DIET research programme, a latent variable model, multiMarker, is proposed. The proposed model draws on factor analytic and mixture of experts models, describing intake as a continuous latent variable whose value gives raise to the observed biomarker values. We employ a mixture of Gaussian distributions to flexibly model the latent variable. A Bayesian hierarchical modelling framework provides flexibility to adapt to different biomarker distributions and facilitates prediction of the latent intake along with its associated uncertainty. Simulation studies are conducted to assess the performance of the proposed multiMarker framework, prior to its application to the motivating application of quantifying apple intake

Probabilistic principal component analysis for metabolomic data

Background: Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results: Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions: The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.Irish Research Council for Science, Engineering and TechnologyHealth Research Boar

Mitochondria-derived glutamate at the interplay between branched-chain amino acid and glucose-induced insulin secretion

AbstractIn pancreatic β-cells, glutamate has been proposed to mediate insulin secretion as a glucose-derived factor, although it is also considered for its sole catabolic function. Hence, changes in cellular glutamate levels are a matter of debate. Here, we investigated the effects of glucose and the glutamate precursor glutamine on kinetics of glutamate levels together with insulin secretion in INS-1E β-cells. Preincubation at low (1 mM) glucose resulted in reduced cellular glutamate levels, which were doubled by exposure to glutamine. In glutamine-deprived cells, 5 mM glucose restored glutamate concentrations. Incubation at 15 mM glucose increased cellular glutamate, along with stimulation of insulin secretion, following both glutamine-free and glutamine-rich preincubations. Nuclear magnetic resonance (NMR) spectroscopy of INS-1E cells exposed to 15 mM D-[1-13C]glucose revealed glutamate as the major glucose metabolic product. Branched-chain amino acids, such as leucine, reduced cellular glutamate levels at low and intermediate glucose. This study demonstrates that glucose stimulates glutamate generation, whereas branched-chain amino acids promote competitive glutamate expenditure