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      Using genetics to understand the causal influence of higher BMI on depression

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          Abstract

          Background

          Depression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women.

          Methods

          We performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to ‘uncouple’ the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways.

          Results

          Higher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m 2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m 2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence.

          Conclusions

          Higher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.

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          Most cited references22

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          The role of inflammation in depression: from evolutionary imperative to modern treatment target.

          Crosstalk between inflammatory pathways and neurocircuits in the brain can lead to behavioural responses, such as avoidance and alarm, that are likely to have provided early humans with an evolutionary advantage in their interactions with pathogens and predators. However, in modern times, such interactions between inflammation and the brain appear to drive the development of depression and may contribute to non-responsiveness to current antidepressant therapies. Recent data have elucidated the mechanisms by which the innate and adaptive immune systems interact with neurotransmitters and neurocircuits to influence the risk for depression. Here, we detail our current understanding of these pathways and discuss the therapeutic potential of targeting the immune system to treat depression.
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            USING THE CORRECT STATISTICAL TEST FOR THE EQUALITY OF REGRESSION COEFFICIENTS

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              Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic

              Background MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error’ (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. Methods An adaptation of the I 2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it I G X 2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. Results In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of I G X 2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of I G X 2 close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. Conclusions Care must be taken to assess the NOME assumption via the I G X 2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If I G X 2 is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered.
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                Author and article information

                Journal
                Int J Epidemiol
                Int J Epidemiol
                ije
                International Journal of Epidemiology
                Oxford University Press
                0300-5771
                1464-3685
                June 2019
                13 November 2018
                13 November 2018
                : 48
                : 3 , Special theme: Mendelian randomization
                : 834-848
                Affiliations
                [1 ]Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
                [2 ]Australian Centre for Precision Health, University of South Australia Cancer Research Institute, Adelaide, SA, Australia
                [3 ]Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
                Author notes
                Corresponding author. Australian Centre for Precision Health, University of South Australia Cancer Research Institute, GPO Box 2471, Adelaide, SA 5001, Australia. E-mail: Elina.Hypponen@ 123456unisa.edu.au

                Jessica Tyrrell, Anwar Mulugeta, Timothy M Frayling and Elina Hyppönen authors contributed equally.

                Author information
                http://orcid.org/0000-0002-9256-6065
                Article
                dyy223
                10.1093/ije/dyy223
                6659462
                30423117
                bbaa1d6d-3237-4236-a275-8a8d32579591
                © The Author(s) 2018. Published by Oxford University Press on behalf of the International Epidemiological Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 September 2018
                Page count
                Pages: 15
                Funding
                Funded by: Diabetes Research and Wellness Foundation Fellowship
                Funded by: Australian Research Training Program Scholarship
                Funded by: Medical Research Council 10.13039/501100000265
                Award ID: MR/M005070/1
                Funded by: Wellcome Trust Institutional Strategic Support Award
                Award ID: WT097835MF
                Funded by: European Research Council 10.13039/100010663
                Award ID: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC
                Funded by: Wellcome Trust 10.13039/100004440
                Funded by: Royal Society 10.13039/501100000288
                Award ID: 104150/Z/14/Z
                Funded by: Wellcome Trust and Royal Society
                Award ID: 104150/Z/14/Z
                Funded by: Gillings Family Foundation
                Funded by: Diabetes UK RD Lawrence
                Award ID: 17/0005594
                Funded by: National Institute for Health Research 10.13039/501100000272
                Funded by: NIHR 10.13039/100006662
                Funded by: Biomedical Research Centre
                Funded by: NHS
                Funded by: Foundation Trust and King’s College London
                Funded by: NHS
                Funded by: NIHR 10.13039/100006662
                Funded by: Department of Health and Social Care
                Categories
                Mendelian Randomization

                Public health
                body mass index,depression,mendelian randomization,uk biobank
                Public health
                body mass index, depression, mendelian randomization, uk biobank

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