Leveraging human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide signalling
Karhunen, Ville; Daghlas, Iyas; Zuber, Verena; Vujkovic, Marijana; Olsen, Anette K.; Knudsen, Lotte Bjerre; Haynes, William G.; Howson, Joanna M. M.; Gill, Dipender (2021-09-09)
Karhunen, V., Daghlas, I., Zuber, V. et al. Leveraging human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide signalling. Diabetologia 64, 2773–2778 (2021). https://doi.org/10.1007/s00125-021-05564-7
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https://urn.fi/URN:NBN:fi-fe2021102051709
Tiivistelmä
Abstract
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:
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