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      Causal factors underlying diabetes risk informed by Mendelian randomisation analysis: evidence, opportunities and challenges

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          Abstract

          Diabetes and its complications cause a heavy disease burden globally. Identifying exposures, risk factors and molecular processes causally associated with the development of diabetes can provide important evidence bases for disease prevention and spur novel therapeutic strategies. Mendelian randomisation (MR), an epidemiological approach that uses genetic instruments to infer causal associations between an exposure and an outcome, can be leveraged to complement evidence from observational and clinical studies. This narrative review aims to summarise the evidence on potential causal risk factors for diabetes by integrating published MR studies on type 1 and 2 diabetes, and to reflect on future perspectives of MR studies on diabetes. Despite the genetic influence on type 1 diabetes, few MR studies have been conducted to identify causal exposures or molecular processes leading to increased disease risk. In type 2 diabetes, MR analyses support causal associations of somatic, mental and lifestyle factors with development of the disease. These studies have also identified biomarkers, some of them derived from the gut microbiota, and molecular processes leading to increased disease risk. These studies provide valuable data to better understand disease pathophysiology and explore potential therapeutic targets. Because genetic association studies have mostly been restricted to participants of European descent, multi-ancestry cohorts are needed to examine the role of different types of physical activity, dietary components, metabolites, protein biomarkers and gut microbiome in diabetes development.

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          The online version contains a slide of the figure for download available at 10.1007/s00125-023-05879-7.

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          IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045

          To provide global, regional, and country-level estimates of diabetes prevalence and health expenditures for 2021 and projections for 2045.
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            Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases

            Microbiome-wide association studies on large population cohorts have highlighted associations between the gut microbiome and complex traits, including type 2 diabetes (T2D) and obesity1. However, the causal relationships remain largely unresolved. We leveraged information from 952 normoglycemic individuals for whom genome-wide genotyping, gut metagenomic sequence and fecal short-chain fatty acid (SCFA) levels were available2, then combined this information with genome-wide-association summary statistics for 17 metabolic and anthropometric traits. Using bidirectional Mendelian randomization (MR) analyses to assess causality3, we found that the host-genetic-driven increase in gut production of the SCFA butyrate was associated with improved insulin response after an oral glucose-tolerance test (P = 9.8 × 10-5), whereas abnormalities in the production or absorption of another SCFA, propionate, were causally related to an increased risk of T2D (P = 0.004). These data provide evidence of a causal effect of the gut microbiome on metabolic traits and support the use of MR as a means to elucidate causal relationships from microbiome-wide association findings.
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              Tackling the widespread and critical impact of batch effects in high-throughput data.

              High-throughput technologies are widely used, for example to assay genetic variants, gene and protein expression, and epigenetic modifications. One often overlooked complication with such studies is batch effects, which occur because measurements are affected by laboratory conditions, reagent lots and personnel differences. This becomes a major problem when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. Using both published studies and our own analyses, we argue that batch effects (as well as other technical and biological artefacts) are widespread and critical to address. We review experimental and computational approaches for doing so.
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                Author and article information

                Contributors
                susanna.larsson@ki.se
                Journal
                Diabetologia
                Diabetologia
                Diabetologia
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0012-186X
                1432-0428
                14 February 2023
                14 February 2023
                2023
                : 66
                : 5
                : 800-812
                Affiliations
                [1 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Unit of Cardiovascular and Nutritional Epidemiology, , Institute of Environmental Medicine, Karolinska Institutet, ; Stockholm, Sweden
                [2 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Diabetes Unit and Center for Genomic Medicine, , Massachusetts General Hospital, ; Boston, MA USA
                [3 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, Programs in Metabolism and Medical and Population Genetics, , Eli and Edythe L. Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Medicine, , Harvard Medical School, ; Boston, MA USA
                [5 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, , University of Copenhagen, ; Copenhagen, Denmark
                [6 ]GRID grid.8993.b, ISNI 0000 0004 1936 9457, Unit of Medical Epidemiology, Department of Surgical Sciences, , Uppsala University, ; Uppsala, Sweden
                Article
                5879
                10.1007/s00125-023-05879-7
                10036461
                36786839
                d1e6a8a5-eb12-49c5-accc-2ee573010759
                © The Author(s) 2023

                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
                : 5 October 2022
                : 4 January 2023
                Funding
                Funded by: Karolinska Institute
                Categories
                Review
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2023

                Endocrinology & Diabetes
                causality,diabetes,mendelian randomisation,review,risk factor
                Endocrinology & Diabetes
                causality, diabetes, mendelian randomisation, review, risk factor

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