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      Shared genetics and bidirectional causal relationships between type 2 diabetes and attention-deficit/hyperactivity disorder

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          Abstract

          Background

          Type 2 diabetes (T2D) is a chronic metabolic disorder with high comorbidity with mental disorders. The genetic links between attention-deficit/hyperactivity disorder (ADHD) and T2D have yet to be elucidated.

          Aims

          We aim to assess shared genetics and potential associations between ADHD and T2D.

          Methods

          We performed genetic correlation, two-sample Mendelian randomisation and polygenic overlap analyses between ADHD and T2D. The genome-wide association study (GWAS) summary results of T2D (80 154 cases and 853 816 controls), ADHD2019 (20 183 cases and 35 191 controls from the 2019 GWAS ADHD dataset) and ADHD2022 (38 691 cases and 275 986 controls from the 2022 GWAS ADHD dataset) were used for the analyses. The T2D dataset was obtained from the DIAGRAM Consortium. The ADHD datasets were obtained from the Psychiatric Genomics Consortium. We compared genome-wide association signals to reveal shared genetic variation between T2D and ADHD using the larger ADHD2022 dataset. Moreover, molecular pathways were constructed based on large-scale literature data to understand the connection between ADHD and T2D.

          Results

          T2D has positive genetic correlations with ADHD2019 (r g=0.33) and ADHD2022 (r g=0.31). Genetic liability to ADHD2019 was associated with an increased risk for T2D (odds ratio (OR): 1.30, p<0.001), while genetic liability to ADHD2022 had a suggestive causal effect on T2D (OR: 1.30, p=0.086). Genetic liability to T2D was associated with a higher risk for ADHD2019 (OR: 1.05, p=0.001) and ADHD2022 (OR: 1.03, p<0.001). The polygenic overlap analysis showed that most causal variants of T2D are shared with ADHD2022. T2D and ADHD2022 have three overlapping loci. Molecular pathway analysis suggests that ADHD and T2D could promote the risk of each other through inflammatory pathways.

          Conclusions

          Our study demonstrates substantial shared genetics and bidirectional causal associations between ADHD and T2D.

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          The MR-Base platform supports systematic causal inference across the human phenome

          Gibran Hemani, Jie Zheng, Benjamin Elsworth … (2019)
          Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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            IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045

            Hong Sun, Pouya Saeedi, Suvi Karuranga … (2022)
            To provide global, regional, and country-level estimates of diabetes prevalence and health expenditures for 2021 and projections for 2045.
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              LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

              Hilary Finucane, Stephan Ripke, Nick Patterson … (2015)
              Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Gen Psychiatr
                Journal ID (iso-abbrev): Gen Psychiatr
                Journal ID (hwp): gpsych
                Journal ID (publisher-id): gpsych
                Title: General Psychiatry
                Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                ISSN (Electronic): 2517-729X
                Publication date Collection: 2023
                Publication date (Electronic): 13 March 2023
                Volume: 36
                Issue: 2
                Electronic Location Identifier: e100996
                Affiliations
                [1 ] departmentSchool of Systems Biology , George Mason University , Fairfax, Virginia, USA
                [2 ] Research Centre for Medical Genetics , Moscow, Russian Federation
                [3 ] departmentInstitute of Neuropsychiatry , The Affiliated Brain Hospital of Nanjing Medical University , Nanjing, Jiangsu, China
                [4 ] departmentDepartment of Psychiatry , The Affiliated Brain Hospital of Nanjing Medical University , Nanjing, Jiangsu, China
                Author notes
                [Correspondence to ] Dr Fuquan Zhang; zfqeee@ 123456126.com
                Author information
                http://orcid.org/0000-0003-3204-8191
                Article
                Publisher ID: gpsych-2022-100996
                DOI: 10.1136/gpsych-2022-100996
                PMC ID: 10016243
                PubMed ID: 36937092
                SO-VID: d767d4a1-2628-40b5-8116-2c8edb3898db
                Copyright © © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
                License:

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                Date received : 06 December 2022
                Date accepted : 22 February 2023
                Categories
                Subject: Original Research
                Subject: 1506
                Subject: 2625
                Custom metadata
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                Keywords: genetics, behavioral
                Data availability:
                Keywords: genetics, behavioral

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