Leveraging human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide signalling,Diabetologia

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Leveraging human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide signalling
Diabetologia ( IF 8.2 ) Pub Date : 2021-09-09 , DOI: 10.1007/s00125-021-05564-7
Ville Karhunen _{1,

2,

3} , Iyas Daghlas ₄ , Verena Zuber _{1,

5} , Marijana Vujkovic _{6,

7} , Anette K Olsen ₈ , Lotte Bjerre Knudsen ₈ , William G Haynes _{9,

10} , Joanna M M Howson ₉ , Dipender Gill _{1,

9,

11,

12}

Affiliation

Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland.
Center for Life Course Health Research, University of Oulu, Oulu, Finland.
Harvard Medical School, Boston, MA, USA.
Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK.
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark.
Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK.
Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George's University Hospitals NHS Foundation Trust, London, UK.
Clinical Pharmacology and Therapeutics Section, Institute for Infection and Immunity, St George's, University of London, London, UK.

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.

Conclusions/interpretation

This study provides genetic evidence to support a beneficial role of sustained GIP signalling on cardiometabolic health greater than that expected from improved glycaemic control alone. Further clinical investigation is warranted.

Data availability

All data used in this study are publicly available. The scripts for the analysis are available at: https://github.com/vkarhune/GeneticallyProxiedGIP.

Graphical abstract

中文翻译：

利用人类遗传数据研究葡萄糖依赖性促胰岛素多肽信号传导的心脏代谢作用

目标/假设

本研究的目的是利用人类遗传数据来研究葡萄糖依赖性促胰岛素多肽 (GIP) 信号传导的心脏代谢作用。

方法

数据来自大规模全基因组关联研究的汇总统计数据。我们检查了GIP和GIPR基因中 2 型糖尿病责任的遗传关联是否与 11 种心脏代谢结果的遗传关联共同定位。对于那些显示出共定位证据（后验概率>0.8）的结果，我们进行了孟德尔随机化分析，以估计遗传代理的 GIP 信号传导与心脏代谢结果风险的关联，并测试这是否超过考虑 2 型时观察到的估计值来自基因组其他区域的糖尿病易感性变异。

结果

在五个结果中观察到GIP和GIPR基因与 2 型糖尿病责任的遗传关联共定位的证据。孟德尔随机化分析提供了GIP和GIPR基因的较低遗传代理 2 型糖尿病责任与较低 BMI 相关的证据（以 SD 单位估计 -0.16，95% CI -0.30，-0.02），C 反应蛋白（-0.13， 95% CI -0.19, -0.08) 和甘油三酯水平 (-0.17, 95% CI -0.22, -0.12)，以及更高的 HDL-胆固醇水平 (0.19, 95% CI 0.14, 0.25)。对于所有这些结果，估计值大于考虑来自基因组其他区域的 2 型糖尿病易感性变异时观察到的值。