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      Vitamin D and human health: evidence from Mendelian randomization studies

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization : The STROBE-MR Statement

            Mendelian randomization (MR) studies use genetic variation associated with modifiable exposures to assess their possible causal relationship with outcomes and aim to reduce potential bias from confounding and reverse causation.
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              Vitamin D Supplements and Prevention of Cancer and Cardiovascular Disease

              It is unclear whether supplementation with vitamin D reduces the risk of cancer or cardiovascular disease, and data from randomized trials are limited.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                European Journal of Epidemiology
                Eur J Epidemiol
                Springer Science and Business Media LLC
                0393-2990
                1573-7284
                January 12 2024
                Article
                10.1007/s10654-023-01075-4
                fc398e7f-f92f-4536-8670-c4edf1bd5201
                © 2024

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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