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Human Genetics

, Volume 135, Issue 5, pp 453–467 | Cite as

Harnessing publicly available genetic data to prioritize lipid modifying therapeutic targets for prevention of coronary heart disease based on dysglycemic risk

  • Vinicius Tragante
  • Folkert W. AsselbergsEmail author
  • Daniel I. Swerdlow
  • Tom M. Palmer
  • Jason H. Moore
  • Paul I. W. de Bakker
  • Brendan J. Keating
  • Michael V. HolmesEmail author
Original Investigation

Abstract

Therapeutic interventions that lower LDL-cholesterol effectively reduce the risk of coronary artery disease (CAD). However, statins, the most widely prescribed LDL-cholesterol lowering drugs, increase diabetes risk. We used genome-wide association study (GWAS) data in the public domain to investigate the relationship of LDL-C and diabetes and identify loci encoding potential drug targets for LDL-cholesterol modification without causing dysglycemia. We obtained summary-level GWAS data for LDL-C from GLGC, glycemic traits from MAGIC, diabetes from DIAGRAM and CAD from CARDIoGRAMplusC4D consortia. Mendelian randomization analyses identified a one standard deviation (SD) increase in LDL-C caused an increased risk of CAD (odds ratio [OR] 1.63 (95 % confidence interval [CI] 1.55, 1.71), which was not influenced by removing SNPs associated with diabetes. LDL-C/CAD-associated SNPs showed consistent effect directions (binomial P = 6.85 × 10−5). Conversely, a 1-SD increase in LDL-C was causally protective of diabetes (OR 0.86; 95 % CI 0.81, 0.91), however LDL-cholesterol/diabetes-associated SNPs did not show consistent effect directions (binomial P = 0.15). HMGCR, our positive control, associated with LDL-C, CAD and a glycemic composite (derived from GWAS meta-analysis of four glycemic traits and diabetes). In contrast, PCSK9, APOB, LPA, CETP, PLG, NPC1L1 and ALDH2 were identified as “druggable” loci that alter LDL-C and risk of CAD without displaying associations with dysglycemia. In conclusion, LDL-C increases the risk of CAD and the relationship is independent of any association of LDL-C with diabetes. Loci that encode targets of emerging LDL-C lowering drugs do not associate with dysglycemia, and this provides provisional evidence that new LDL-C lowering drugs (such as PCSK9 inhibitors) may not influence risk of diabetes.

Keywords

Coronary Artery Disease Ezetimibe Coronary Artery Disease Risk Mendelian Randomization Glycemic Status 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Compliance with ethical standards

Funding

FWA is supported by a Dekker scholarship-Junior Staff Member 2014T001—Netherlands Heart Foundation. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement n° HEALTH-F2-2013-601456 (CVgenes-at-target).

Conflict of interest

DIS is a consultant to Pfizer.

Supplementary material

439_2016_1647_MOESM1_ESM.docx (1.3 mb)
Supplementary material 1 (DOCX 1523 kb)
439_2016_1647_MOESM2_ESM.docx (202 kb)
Supplementary material 2 (DOCX 202 kb) Data File S1: Summary estimates from GWAS meta-analysis of glycemic traits (fasting glucose, fasting insulin, fasting proinsulin and HbA1c) and type 2 diabetes to yield a glycemic burden composite

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© The Author(s) 2016

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Heart and LungsUniversity Medical Center UtrechtUtrechtThe Netherlands
  2. 2.Institute of Cardiovascular ScienceUniversity College LondonLondonUK
  3. 3.Durrer Center for Cardiogenetic ResearchICIN-Netherlands Heart InstituteUtrechtThe Netherlands
  4. 4.Department of MedicineImperial College LondonLondonUK
  5. 5.Department of Mathematics and StatisticsLancaster UniversityLancasterUK
  6. 6.Department of Biostatistics and Epidemiology, Institute for Biomedical InformaticsUniversity of PennsylvaniaPhiladelphiaUSA
  7. 7.Department of Medical Genetics, Center for Molecular MedicineUniversity Medical Center UtrechtUtrechtThe Netherlands
  8. 8.Department of Epidemiology, Julius Center for Health Sciences and Primary CareUniversity Medical Center UtrechtUtrechtThe Netherlands
  9. 9.Department of Surgery, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  10. 10.Center for Applied GenomicsChildren’s Hospital of PhiladelphiaPhiladelphiaUSA
  11. 11.Center for Clinical Epidemiology and Biostatistics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  12. 12.Clinical Trials Services Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population HealthRichard Doll Building, University of OxfordOxfordUK

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