The link between attention deficit hyperactivity disorder (ADHD) symptoms and obesity-related traits: genetic and prenatal explanations

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Abstract

Attention-deficit/hyperactivity disorder (ADHD) often co-occurs with obesity, however, the potential causality between the traits remains unclear. We examined both genetic and prenatal evidence for causality using Mendelian Randomisation (MR) and polygenic risk scores (PRS). We conducted bi-directional MR on ADHD liability and six obesity-related traits using summary statistics from the largest available meta-analyses of genome-wide association studies. We also examined the shared genetic aetiology between ADHD symptoms (inattention and hyperactivity) and body mass index (BMI) by PRS association analysis using longitudinal data from Northern Finland Birth Cohort 1986 (NFBC1986, n = 2984). Lastly, we examined the impact of the prenatal environment by association analysis of maternal pre-pregnancy BMI and offspring ADHD symptoms, adjusted for PRS of both traits, in NFBC1986 dataset. Through MR analyses, we found evidence for bidirectional causality between ADHD liability and obesity-related traits. PRS association analyses showed evidence for genetic overlap between ADHD symptoms and BMI. We found no evidence for a difference between inattention and hyperactivity symptoms, suggesting that neither symptom subtype is driving the association. We found evidence for association between maternal pre-pregnancy BMI and offspring ADHD symptoms after adjusting for both BMI and ADHD PRS (association p-value = 0.027 for inattention, p = 0.008 for hyperactivity). These results are consistent with the hypothesis that the co-occurrence between ADHD and obesity has both genetic and prenatal environmental origins.

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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, John Gallacher, Naomi Allen … (2017)

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

Jack Bowden, George Davey Smith, Philip C. Haycock … (2017)

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.

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Author and article information

Contributors

Alina Rodriguez:

ORCID: http://orcid.org/0000-0003-1209-8802

a.rodriguez@imperial.ac.uk

Journal

Journal ID (nlm-ta): Transl Psychiatry

Journal ID (iso-abbrev): Transl Psychiatry

Title: Translational Psychiatry

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2158-3188

Publication date (Electronic): 4 September 2021

Publication date PMC-release: 4 September 2021

Publication date Collection: 2021

Volume: 11

Electronic Location Identifier: 455

Affiliations

[1 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Epidemiology and Biostatistics, , Imperial College London, ; London, UK

[2 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, The University of Queensland Diamantina Institute, The University of Queensland, ; Brisbane, Australia

[3 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, MRC Integrative Epidemiology Unit at the University of Bristol, ; Bristol, UK

[4 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, Population Health Sciences, Bristol Medical School, , University of Bristol, ; Bristol, UK

[5 ]GRID grid.10858.34, ISNI 0000 0001 0941 4873, Research Unit of Clinical Neuroscience, , Psychiatry, University of Oulu, ; Oulu, Finland

[6 ]GRID grid.10858.34, ISNI 0000 0001 0941 4873, PEDEGO Research Unit, Child Psychiatry, , University of Oulu, ; Oulu, Finland

[7 ]GRID grid.412326.0, ISNI 0000 0004 4685 4917, Clinic of Child Psychiatry, , Oulu University Hospital, ; Oulu, Finland

[8 ]GRID grid.412326.0, ISNI 0000 0004 4685 4917, Unit of Primary Care, , Oulu University Hospital, ; Oulu, Finland

[9 ]GRID grid.10858.34, ISNI 0000 0001 0941 4873, Center for Life Course Health Research, Faculty of Medicine, , University of Oulu, ; Oulu, Finland

[10 ]GRID grid.7728.a, ISNI 0000 0001 0724 6933, Department of Life Sciences, College of Health and Life Sciences, , Brunel University, ; London, UK

[11 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Mathematics, , Imperial College London, ; London, UK

[12 ]GRID grid.4868.2, ISNI 0000 0001 2171 1133, Centre for Psychiatry and Mental Health, Wolfson Institute of Population Health, , Queen Mary University London, ; London, UK

Author information

Ville Karhunen http://orcid.org/0000-0001-6064-1588

Tom A. Bond http://orcid.org/0000-0002-9298-6860

Marjo-Riitta Järvelin http://orcid.org/0000-0002-2149-0630

Marina Evangelou http://orcid.org/0000-0003-0789-8944

Alina Rodriguez http://orcid.org/0000-0003-1209-8802

Article

Publisher ID: 1584

DOI: 10.1038/s41398-021-01584-4

PMC ID: 8418601

PubMed ID: 34482360

SO-VID: df1fc7a3-ebdd-473a-88b7-6b226f5e5623

License:

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 11 April 2020

Date revision received : 3 August 2021

Date accepted : 25 August 2021

Funding

Funded by: FundRef https://doi.org/10.13039/100010665, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions);

Award ID: 721567