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

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.

Trial registration: ClinicalTrials.gov NCT01475825 NCT02160899.

Fig. 1

Relationship of LDL-C-associated loci with risk of CAD. The majority (22 of 25) of loci showed a consistent direction of effect with risk of CAD. LDL-C effect estimates are per SD; whiskers represent 95 % CI

Fig. 2

Mendelian randomization to investigate the causal relationship of a one standard deviation genetically-instrumented increase in LDL-C with risk of coronary artery disease (CAD), type 2 diabetes (T2D) and levels of fasting glucose. Single nucleotide polymorphisms (SNPs) were initially selected based on their independent association with LDL-C at R ² < 0.8 (n = 197; “All SNPs” stratum). Thereafter, we removed SNPs that associated with T2D risk at P < 0.01 (15 SNPs removed) and P < 0.05 (34 SNPs removed). Findings for the analysis using a stricter R ² threshold (<0.2) are presented in Supplementary Figure 4

Fig. 3

Relationship of LDL-C-associated loci with risk of T2D. Six of the 15 loci showed a positive association with T2D risk. LDL-C effect estimates are per SD; whiskers represent 95 % CI

Fig. 4

Relationship of LDL-C-associated loci with fasting glucose. Nine of 19 loci showed a positive association with fasting glucose. LDL-C effect estimates are per SD; Fasting glucose effect estimates are in mmol/l; whiskers represent 95 % CI

Fig. 5

Circos diagram to show association of SNPs in PCSK9, APOB, LPA, LDLR and HMGCR with glycemic burden composite. The outer ring represents the genomic/chromosomal location. Each SNP is a green, orange or red point in the graph. Green dots in green shaded ring represent SNPs with 1 > P ≥ 0.05; orange circles in orange shaded ring correspond to SNPs within 0.05 > P ≥ 0.001 and; red triangles in red shaded ring represent SNPs with P < 0.001. 61 % of HMGCR SNPs associated with the glycemic burden composite (at P < 0.05) vs. less than 5 % for SNPs in PCSK9, APOB and LPA (color figure online)