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      Relationship between Serum 25(OH)D and Depression: Causal Evidence from a Bi-Directional Mendelian Randomization Study

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          Abstract

          The relationship between depression and vitamin D deficiency is complex, with evidence mostly from studies affected by confounding and reverse causality. We examined the causality and direction of the relationship between 25-hydroxyvitamin D (25(OH)D) and depression in bi-directional Mendelian randomization (MR) analyses using information from up to 307,618 white British participants from the UK Biobank and summary results from the SUNLIGHT ( n = 79,366) and Psychiatric Genomics consortia (PGC 113,154 cases and 218,523 controls). In observational analysis, the odds of depression decreased with higher 25(OH)D concentrations (adjusted odds ratio (OR) per 50% increase 0.95, 95%CI 0.94–0.96). In MR inverse variance weighted (IVW) using the UK Biobank, there was no association between genetically determined serum 25(OH)D and depression (OR per 50% higher 0.97, 95%CI 0.90–1.05) with consistent null association across all MR approaches and in data from PGC consortium. In contrast, genetic liability to depression was associated with lower 25(OH)D concentrations (MR IVW −3.26%, −4.94%–−1.55%), with the estimates remaining generally consistent after meta-analysing with the consortia. In conclusion, we found genetic evidence for a causal effect of depression on lower 25(OH)D concentrations, however we could not confirm a beneficial effect of nutritional vitamin D status on depression risk.

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          Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

          Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
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            UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

            Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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              Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

              ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.

                Author and article information

                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                30 December 2020
                January 2021
                : 13
                : 1
                : 109
                Affiliations
                [1 ]Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia; anwarmulugeta.gebremichael@ 123456unisa.edu.au (A.M.); amanda.lumsden@ 123456unisa.edu.au (A.L.)
                [2 ]Department of Pharmacology and Clinical Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa 1000, Ethiopia
                [3 ]South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
                [4 ]Unit of Clinical and Health Sciences, University of South Australia, Adelaide, SA 5000, Australia
                [5 ]Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
                Author notes
                [* ]Correspondence: elina.hypponen@ 123456unisa.edu.au ; Tel.: +61-(08)-83022518
                Author information
                https://orcid.org/0000-0002-8018-3454
                https://orcid.org/0000-0002-0214-6498
                Article
                nutrients-13-00109
                10.3390/nu13010109
                7823924
                33396887
                ec79e654-7da4-4da2-bc59-e964600daf10
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 10 November 2020
                : 25 December 2020
                Categories
                Article

                Nutrition & Dietetics
                25(oh)d,nutritional vitamin d status,depression,observational analysis,mendelian randomization,uk biobank

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