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

Tragante V, Asselbergs FW, Swerdlow DI, Palmer TM, Moore JH, de Bakker PI, Keating BJ, Holmes MV - Hum. Genet. (2016)

Bottom Line: Therapeutic interventions that lower LDL-cholesterol effectively reduce the risk of coronary artery disease (CAD).LDL-C/CAD-associated SNPs showed consistent effect directions (binomial P = 6.85 × 10(-5)).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands.

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.

No MeSH data available.


Related in: MedlinePlus

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)
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Fig5: 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)

Mentions: To exploit all available data, we focused on SNPs in the same four loci (PCSK9, APOB, LPA and HMGCR) and evaluated the physical distribution and associations of these SNPs with the glycemic burden composite (Supplementary Fig. 3). The majority of SNPs in HMGCR associated with the glycemic burden composite, in contrast to the SNPs in PCSK9, APOB or LPA (Fig. 5).Fig. 5


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

Tragante V, Asselbergs FW, Swerdlow DI, Palmer TM, Moore JH, de Bakker PI, Keating BJ, Holmes MV - Hum. Genet. (2016)

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)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835528&req=5

Fig5: 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)
Mentions: To exploit all available data, we focused on SNPs in the same four loci (PCSK9, APOB, LPA and HMGCR) and evaluated the physical distribution and associations of these SNPs with the glycemic burden composite (Supplementary Fig. 3). The majority of SNPs in HMGCR associated with the glycemic burden composite, in contrast to the SNPs in PCSK9, APOB or LPA (Fig. 5).Fig. 5

Bottom Line: Therapeutic interventions that lower LDL-cholesterol effectively reduce the risk of coronary artery disease (CAD).LDL-C/CAD-associated SNPs showed consistent effect directions (binomial P = 6.85 × 10(-5)).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands.

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.

No MeSH data available.


Related in: MedlinePlus