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      Metabolic phenotyping of malnutrition during the first 1000 days of life

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          Abstract

          Nutritional restrictions during the first 1000 days of life can impair or delay the physical and cognitive development of the individual and have long-term consequences for their health. Metabolic phenotyping (metabolomics/metabonomics) simultaneously measures a diverse range of low molecular weight metabolites in a sample providing a comprehensive assessment of the individual’s biochemical status. There are a growing number of studies applying such approaches to characterize the metabolic derangements induced by various forms of early-life malnutrition. This includes acute and chronic undernutrition and specific micronutrient deficiencies. Collectively, these studies highlight the diverse and dynamic metabolic disruptions resulting from various forms of nutritional deficiencies. Perturbations were observed in many pathways including those involved in energy, amino acid, and bile acid metabolism, the metabolic interactions between the gut microbiota and the host, and changes in metabolites associated with gut health. The information gleaned from such studies provides novel insights into the mechanisms linking malnutrition with developmental impairments and assists in the elucidation of candidate biomarkers to identify individuals at risk of developmental shortfalls. As the metabolic profile represents a snapshot of the biochemical status of an individual at a given time, there is great potential to use this information to tailor interventional strategies specifically to the metabolic needs of the individual.

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          Most cited references81

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          Maternal and child undernutrition and overweight in low-income and middle-income countries

          The Lancet, 382(9890), 427-451
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            Undernutrition as an underlying cause of child deaths associated with diarrhea, pneumonia, malaria, and measles.

            Previous analyses derived the relative risk (RR) of dying as a result of low weight-for-age and calculated the proportion of child deaths worldwide attributable to underweight. The objectives were to examine whether the risk of dying because of underweight varies by cause of death and to estimate the fraction of deaths by cause attributable to underweight. Data were obtained from investigators of 10 cohort studies with both weight-for-age category ( -1 SD) and cause of death information. All 10 studies contributed information on weight-for-age and risk of diarrhea, pneumonia, and all-cause mortality; however, only 6 studies contributed information on deaths because of measles, and only 3 studies contributed information on deaths because of malaria or fever. With use of weighted random effects models, we related the log mortality rate by cause and anthropometric status in each study to derive cause-specific RRs of dying because of undernutrition. Prevalences of each weight-for-age category were obtained from analyses of 310 national nutrition surveys. With use of the RR and prevalence information, we then calculated the fraction of deaths by cause attributable to undernutrition. The RR of mortality because of low weight-for-age was elevated for each cause of death and for all-cause mortality. Overall, 52.5% of all deaths in young children were attributable to undernutrition, varying from 44.8% for deaths because of measles to 60.7% for deaths because of diarrhea. A significant proportion of deaths in young children worldwide is attributable to low weight-for-age, and efforts to reduce malnutrition should be a policy priority.
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              A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.

              The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.
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                Author and article information

                Contributors
                +34 977770958 , Jordi.mayneris@eurecat.org
                +44 20 7594 0728 , j.swann@imperial.ac.uk
                Journal
                Eur J Nutr
                Eur J Nutr
                European Journal of Nutrition
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1436-6207
                1436-6215
                11 April 2018
                11 April 2018
                2019
                : 58
                : 3
                : 909-930
                Affiliations
                [1 ]Unit of Nutrition and Health and Unit of Omics Sciences, Eurecat-Technology Centre of Catalonia, Avinguda Universitat 1, 43204 Reus, Spain
                [2 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Division of Computational and Systems Medicine, Department of Surgery and Cancer, , Imperial College London, ; London, SW7 2AZ UK
                Author information
                http://orcid.org/0000-0003-3788-3815
                http://orcid.org/0000-0002-6485-4529
                Article
                1679
                10.1007/s00394-018-1679-0
                6499750
                29644395
                5025c7c9-8079-4d38-a09e-c2dca75c905c
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 1 March 2018
                : 26 March 2018
                Categories
                Review
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2019

                Nutrition & Dietetics
                metabonomics,metabolomics,metabolism,metabolic phenotyping,profiling,childhood,early-life,undernutrition,malnutrition,preterm,intrauterine growth restriction,iron deficiency,zinc deficiency,environmental enteric dysfunction,nmr spectroscopy,mass spectrometry

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