Karhunen, V., Daghlas, I., Zuber, V. et al. Leveraging human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide signalling. Diabetologia (2021). https://doi.org/10.1007/s00125-021-05564-7
Leveraging human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide signalling
|Author:||Karhunen, Ville1,2,3; Daghlas, Iyas4; Zuber, Verena1,5;|
1Department of Epidemiology and Biostatistics, Imperial College London, London, UK
2Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
3Center for Life Course Health Research, University of Oulu, Oulu, Finland
4Harvard Medical School, Boston, MA, USA
5Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
6Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
7Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
8Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark
9Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK
10Radcliffe Department of Medicine, University of Oxford, Oxford, UK
11Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George’s University Hospitals NHS Foundation Trust, London, UK
12Clinical Pharmacology and Therapeutics Section, Institute for Infection and Immunity, St George’s, University of London, London, UK
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021102051709
|Publish Date:|| 2021-10-20
Aims/hypothesis: The aim of this study was to leverage human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide (GIP) signalling.
Methods: Data were obtained from summary statistics of large-scale genome-wide association studies. We examined whether genetic associations for type 2 diabetes liability in the GIP and GIPR genes co-localised with genetic associations for 11 cardiometabolic outcomes. For those outcomes that showed evidence of co-localisation (posterior probability > 0.8), we performed Mendelian randomisation analyses to estimate the association of genetically proxied GIP signalling with risk of cardiometabolic outcomes, and to test whether this exceeded the estimate observed when considering type 2 diabetes liability variants from other regions of the genome.
Results: Evidence of co-localisation with genetic associations of type 2 diabetes liability at both the GIP and GIPR genes was observed for five outcomes. Mendelian randomisation analyses provided evidence for associations of lower genetically proxied type 2 diabetes liability at the GIP and GIPR genes with lower BMI (estimate in SD units −0.16, 95% CI −0.30, −0.02), C-reactive protein (−0.13, 95% CI −0.19, −0.08) and triacylglycerol levels (−0.17, 95% CI −0.22, −0.12), and higher HDL-cholesterol levels (0.19, 95% CI 0.14, 0.25). For all of these outcomes, the estimates were greater in magnitude than those observed when considering type 2 diabetes liability variants from other regions of the genome.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3142 Public health care science, environmental and occupational health
VK and DG are supported by the British Heart Foundation Centre of Research Excellence (RE/18/4/34215) at Imperial College
London. DG is supported by a National Institute for Health Research Clinical Lectureship at St George’s, University of London (CL-2020-16-001). VK is supported by the Academy of Finland Profi 5 funding for mathematics and AI: data insight for high dimensional dynamics and the Academy of Finland [Project 312123], and European Union’s Horizon 2020 research and innovation programme under grant agreement no. 848158.
|EU Grant Number:||
(848158) EarlyCause - Causative mechanisms & integrative models linking early-life-stress to psycho-cardio-metabolic multi-morbidity
|Academy of Finland Grant Number:||
312123 (Academy of Finland Funding decision)
All data used in this study are publicly available. The scripts for the analysis are available at: https://github.com/vkarhune/GeneticallyProxiedGIP.
© The Author(s) 2021. 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.