PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study - PubMed (original) (raw)

doi: 10.1016/S2213-8587(16)30396-5. Epub 2016 Nov 29.

Daniel I Swerdlow 2, Michael V Holmes 3, Riyaz S Patel 4, Zammy Fairhurst-Hunter 5, Donald M Lyall 6, Fernando Pires Hartwig 7, Bernardo Lessa Horta 7, Elina Hyppönen 8, Christine Power 9, Max Moldovan 10, Erik van Iperen 11, G Kees Hovingh 12, Ilja Demuth 13, Kristina Norman 14, Elisabeth Steinhagen-Thiessen 14, Juri Demuth 15, Lars Bertram 16, Tian Liu 17, Stefan Coassin 18, Johann Willeit 19, Stefan Kiechl 19, Karin Willeit 19, Dan Mason 20, John Wright 20, Richard Morris 21, Goya Wanamethee 22, Peter Whincup 23, Yoav Ben-Shlomo 21, Stela McLachlan 24, Jackie F Price 24, Mika Kivimaki 25, Catherine Welch 25, Adelaida Sanchez-Galvez 25, Pedro Marques-Vidal 26, Andrew Nicolaides 27, Andrie G Panayiotou 28, N Charlotte Onland-Moret 29, Yvonne T van der Schouw 29, Giuseppe Matullo 30, Giovanni Fiorito 30, Simonetta Guarrera 30, Carlotta Sacerdote 31, Nicholas J Wareham 32, Claudia Langenberg 32, Robert Scott 32, Jian'an Luan 32, Martin Bobak 25, Sofia Malyutina 33, Andrzej Pająk 34, Ruzena Kubinova 35, Abdonas Tamosiunas 36, Hynek Pikhart 25, Lise Lotte Nystrup Husemoen 37, Niels Grarup 38, Oluf Pedersen 38, Torben Hansen 38, Allan Linneberg 39, Kenneth Starup Simonsen 37, Jackie Cooper 40, Steve E Humphries 40, Murray Brilliant 41, Terrie Kitchner 41, Hakon Hakonarson 42, David S Carrell 43, Catherine A McCarty 44, H Lester Kirchner 45, Eric B Larson 46, David R Crosslin 46, Mariza de Andrade 47, Dan M Roden 48, Joshua C Denny 49, Cara Carty 50, Stephen Hancock 51, John Attia 51, Elizabeth Holliday 51, Martin O'Donnell 52, Salim Yusuf 52, Michael Chong 52, Guillaume Pare 52, Pim van der Harst 53, M Abdullah Said 54, Ruben N Eppinga 54, Niek Verweij 54, Harold Snieder 55; LifeLines Cohort study group; Tim Christen 56, Dennis O Mook-Kanamori 56, Stefan Gustafsson 57, Lars Lind 57, Erik Ingelsson 58, Raha Pazoki 59, Oscar Franco 59, Albert Hofman 59, Andre Uitterlinden 60, Abbas Dehghan 61, Alexander Teumer 62, Sebastian Baumeister 63, Marcus Dörr 64, Markus M Lerch 65, Uwe Völker 66, Henry Völzke 62, Joey Ward 6, Jill P Pell 6, Daniel J Smith 6, Tom Meade 67, Anke H Maitland-van der Zee 68, Ekaterina V Baranova 69, Robin Young 70, Ian Ford 70, Archie Campbell 71, Sandosh Padmanabhan 72, Michiel L Bots 29, Diederick E Grobbee 29, Philippe Froguel 73, Dorothée Thuillier 74, Beverley Balkau 75, Amélie Bonnefond 73, Bertrand Cariou 76, Melissa Smart 77, Yanchun Bao 77, Meena Kumari 77, Anubha Mahajan 5, Paul M Ridker 78, Daniel I Chasman 78, Alex P Reiner 79, Leslie A Lange 80, Marylyn D Ritchie 81, Folkert W Asselbergs 82, Juan-Pablo Casas 83, Brendan J Keating 84, David Preiss 3, Aroon D Hingorani 85; UCLEB consortium; Naveed Sattar 86

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PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study

Amand F Schmidt et al. Lancet Diabetes Endocrinol. 2017 Feb.

Abstract

Background: Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way offsets their substantial benefits. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2 diabetes and related biomarkers to gauge the likely effects of PCSK9 inhibitors on diabetes risk.

Methods: In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials, case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol, fasting blood glucose, HbA1c, fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, using a standardised analysis plan, meta-analyses, and weighted gene-centric scores.

Findings: Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analyses of four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lower LDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight (1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50). Based on the collected data, we did not identify associations with HbA1c (0·03%, -0·01 to 0·08), fasting insulin (0·00%, -0·06 to 0·07), and BMI (0·11 kg/m2, -0·09 to 0·30).

Interpretation: PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higher fasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diabetes. In trials of PCSK9 inhibitor drugs, investigators should carefully assess these safety outcomes and quantify the risks and benefits of PCSK9 inhibitor treatment, as was previously done for statins.

Funding: British Heart Foundation, and University College London Hospitals NHS Foundation Trust (UCLH) National Institute for Health Research (NIHR) Biomedical Research Centre.

Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.

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Figures

Figure 1

Figure 1

Association of genetic variants in PCSK9 with circulating LDL cholesterol concentration Effect estimates are presented as mean difference in LDL cholesterol (mmol/L) per LDL cholesterol-lowering allele, with 95% CIs. Results are pooled by use of a fixed-effect model. The size of the black dots representing the point estimates is proportional to the inverse of the variance. Note that results from individual participant data are supplemented by repository data from the Global Lipids Genetics Consortium.

Figure 2

Figure 2

Association of genetic variants in PCSK9 with glycaemic and anthropometric biomarkers Effect estimates are presented as mean difference with 95% CIs. Associations were scaled to a 1 mmol/L reduction in LDL cholesterol. SNP-specific results are pooled by use of a fixed-effect model; weighted gene-centric score (GS) models combining all four SNP-specific estimates are presented as fixed-effect and random-effects estimates. The size of the black dots representing the point estimates is proportional to the inverse of the variance. Between-SNP heterogeneity was measured as a two-sided Q-test (χ2) and an _I_2 with one-sided 97·5% CI. Note that results from individual participant data are supplemented by repository data from the Global Lipids Genetics Consortium, the Meta-Analyses of Glucose and Insulin-related traits Consortium, and the Genetic Investigation of Anthropometric Traits consortium.

Figure 3

Figure 3

Association of genetic variants in PCSK9 with risk of type 2 diabetes, individually (A) and as weighted gene-centric score (B) Effect estimates are presented as odds ratios (ORs) for the incidence or prevalence of type 2 diabetes, with 95% CIs. Associations were scaled to a 1 mmol/L reduction in LDL cholesterol. SNP-specific results are pooled by use of a fixed-effect model; weighted gene-centric score (GS) models combining all four SNP-specific estimates are presented as fixed-effect and random-effects estimates. The size of the black dots representing the point estimates is proportional to the inverse of the variance. Between-SNP heterogeneity was measured as a two-sided Q-test (χ2) and an _I_2 with one-sided 97·5% CI. Results from individual participant data are supplemented by repository data from the Diabetes Genetics Replication and Meta-analysis consortium.

Figure 4

Figure 4

Correlation between PCSK9 associations with LDL cholesterol concentration and type 2 diabetes Effect estimates are presented as mean difference in LDL cholesterol concentration (mmol/L) and odds ratios (ORs) for the incidence or prevalence of type 2 diabetes, with 95% CIs. Associations are presented per LDL cholesterol-decreasing allele. The Pearson correlation coefficient, regression line (grey), and its 95% CI (red) were calculated by weighting the SNPs for the inverse of the variance in the type 2 diabetes association. Excluding the SNP with the largest effect on LDL cholesterol (rs11591147) resulted in a correlation coefficient of 0·993 and a p value of 0·437.

Comment in

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