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      Early overnutrition in male mice negates metabolic benefits of a diet high in monounsaturated and omega-3 fats

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          Abstract

          Overconsumption of saturated fats promotes obesity and type 2 diabetes. Excess weight gain in early life may be particularly detrimental by promoting earlier diabetes onset and potentially by adversely affecting normal development. In the present study we investigated the effects of dietary fat composition on early overnutrition-induced body weight and glucose regulation in Swiss Webster mice, which show susceptibility to high-fat diet-induced diabetes. We compared glucose homeostasis between a high-fat lard-based (HFL) diet, high in saturated fats, and a high-fat olive oil/fish oil-based (HFO) diet, high in monounsaturated and omega-3 fats. We hypothesized that the healthier fat profile of the latter diet would improve early overnutrition-induced glucose dysregulation. However, early overnutrition HFO pups gained more weight and adiposity and had higher diabetes incidence compared to HFL. In contrast, control pups had less weight gain, adiposity, and lower diabetes incidence. Plasma metabolomics revealed reductions in various phosphatidylcholine species in early overnutrition HFO mice as well as with diabetes. These findings suggest that early overnutrition may negate any beneficial effects of a high-fat diet that favours monounsaturated and omega-3 fats over saturated fats. Thus, quantity, quality, and timing of fat intake throughout life should be considered with respect to metabolic health outcomes.

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          Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

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            Tracking of childhood overweight into adulthood: a systematic review of the literature

            Obesity Reviews, 9(5), 474-488
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              ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data.

              Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.
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                Author and article information

                Contributors
                tim.kieffer@ubc.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                7 July 2021
                7 July 2021
                2021
                : 11
                : 14032
                Affiliations
                [1 ]GRID grid.17091.3e, ISNI 0000 0001 2288 9830, Department of Cellular and Physiological Sciences, , University of British Columbia, ; Vancouver, BC Canada
                [2 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Physiology, , University of Toronto, ; Toronto, Canada
                [3 ]GRID grid.417184.f, ISNI 0000 0001 0661 1177, Department of Advanced Diagnostics, , Toronto General Hospital Research Institute, University Health Network, ; Toronto, Canada
                [4 ]GRID grid.17091.3e, ISNI 0000 0001 2288 9830, Department of Surgery, , University of British Columbia, ; Vancouver, BC Canada
                [5 ]GRID grid.17091.3e, ISNI 0000 0001 2288 9830, School of Biomedical Engineering, , University of British Columbia, ; Vancouver, BC Canada
                Article
                93409
                10.1038/s41598-021-93409-z
                8263808
                34234216
                2a7408bb-8667-45e4-bf79-77b19df221c6
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 December 2020
                : 21 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: 247020
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                physiology,biomarkers,endocrinology
                Uncategorized
                physiology, biomarkers, endocrinology

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