Physicians' Academy for Cardiovascular Education

SNP analysis suggests that lipid-lowering drugs in development likely will not induce dysglycaemia

Literature - Tragante V et al., Hum Genet. 2016

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, et al.,
Hum Genet. 2016 Mar 5. [Epub ahead of print]


The downside of the well-established CV benefit of statin treatment to lower LDL-c levels is that their use have been linked to increased risk of type 2 diabetes (T2DM)  [1,2]. Recent evidence suggests that this may be mediated by an on-target effect of inhibition of 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR) [3].
In light of the development of other LDL-c lowering agents, it is important to know whether the T2DM effects of statins are specific to HMGCR inhibition or a general characteristic of LDL-c modification. More specifically, it is important to characterise any glycaemia-modifying properties of drugs in development that target the protein products of for instance the PCSK9, APOB and APOB genes.
This analysis benefits from data of genome wide association studies (GWAS) that have become publicly available. By integrating several GWAS datasets (see below at references), this study aimed to clarify the relationship between LDL-c, CAD and dysglycaemia. It investigated whether the LDL-mediated risk of CAD and of dysglycaemia are intertwined or independent.

Main results

  • Based on GLGC-data, 197 independent single nucleotide polymorphisms (SNPs) at 172 distinct loci (2966 SNPs in total) were significantly associated with LDL-c levels.
  • 22 of the 25 loci of LDL-associated SNPs that also significantly associated with CAD (84/2966), showed the same direction of the effect.
  • Although 61 out of 2966 LDL-c SNPs nominally significantly associated with T2DM, only part (6/15) of the loci showed a positive association with T2DM.
  • 58 SNPs (19 of 172 loci) were nominally associated with fasting glucose (FG), but no consistency of direction of effect was seen (9/19 loci showed a positive association).
  • Of 84 SNPs that associated with both LDL-c and CAD, 17 associated with FG, 13 with T2DM risk, and 6 with both (all associated with higher LDL-c and CAD risk, and lower FG and T2DM risk). 8 of 17 SNPs that associated with TG, CAD and FG were located at the HMGCR locus.
A ‘glycaemic burden composite’ (GBC) was developed, encompassing data on four glycaemic traits (FG, fasting insulin and pro-insulin, HbA1c) and T2DM risk. 306 LDL-c SNPs significantly associated with GBC. Parallel analysis strategies evaluated loci that altered LDL-c levels and CAD risk, but did not yield dysglycaemic effects.
  • Concerning loci that encode LDL-c lowering drug targets, SNPs in HMGCR were found to associate with the GBC, while SNPs in PCSK9, APOB and LPA did not.
  • Protein products of 7 loci that alter LDL-c and CAD risk, but do not affect GBC, were recognised as pharmacodynamics targets, namely PCSK9, APOB, CETP, PLG, NPC1L1, LPA and ALDH2.
  • Loci that associated with LDL, CAD and GBC included HMGCR and SLC22A3, for which drugs exist that target them pharmacodynamically (SLC22A3 is now recognised as the target of metformin)


By exploiting the public availability of data of several large genetic consortia, potential therapeutic targets for CAD prevention were examined, focussing on whether or not they may yield dysglycaemic effects. This integrative use of multiple GWAs datasets allows answering critical questions on disease aetiology and on intended and unintended consequences of modification of biomarkers.
Associations of LDL-c SNPs with CAD risk were clear, while LDL-associated SNPs did not yield a consistent picture of the relationship with fasting glucose or T2DM risk. Thus, LDL-c increases CAD risk and this relationship appears independent of any association of LDL-c with diabetes. The analyses suggest that SNPs at loci that encode targets of novel therapies, namely PCSK9, APOB and LPA, likely will not have an effect on glycaemic status.
Find this article online at Human Genetics


1. Preiss D, Seshasai SR, Welsh P, et al (2011) Risk of incident diabetes with intensive-dose compared with moderate-dose statin therapy: a metaanalysis. JAMA 305:2556–2564. doi:10.1001/jama.2011.860
2. Sattar N, Preiss D, Murray HM, et al (2010) Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet 375:735–742. doi:10.1016/S0140-6736(09)61965-6
3. Swerdlow DI, Preiss D, Kuchenbaecker KB, et al (2015) HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials. Lancet 385:351–361. doi:10.1016/S0140-6736(14)61183-1

Used datasets:

Summary-level data of the following datasets were used: (1) LDL-C from the Global Lipids Genetics Consortium (GLGC); (2) glycaemic traits from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), (3) T2D from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM)
consortium, and (4) CAD from the Coronary ARtery DIsease Genome-wide Replication And Meta Analysis (CARDIoGRAM) plus The Coronary Artery Disease (C4D) Genetics, collectively known as CARDIoGRAMplusC4D consortium.

Share this page with your colleagues and friends: