Icosapent ethyl (IPE) is a purified eicosapentaenoic acid-only omega-3 fatty acid that significantly reduced cardiovascular (CV) events in patients receiving statins with established cardiovascular disease (CVD) and those with diabetes and additional risk factors in the pivotal REDUCE-IT trial. Since the publication of REDUCE-IT, there has been global interest in determining IPE eligibility in different patient populations, the proportion of patients who may benefit from IPE, and cost effectiveness of IPE in primary and secondary prevention settings. The aim of this review is to summarize information from eligibility and cost effectiveness studies of IPE to date. A total of sixteen studies were reviewed, involving 2,068,111 patients in the primary or secondary prevention settings worldwide. Up to forty-five percent of patients were eligible for IPE, depending on the selection criteria used (ie, REDUCE-IT criteria, US Food and Drug Administration label, Health Canada label, practice guidelines) and the population studied. Overall, eight cost-effectiveness studies across the United States, Canada, Germany, Israel, and Australia were included in this review and findings indicated that IPE is particularly cost effective in patients with established CVD.

Objective

This study evaluated the success in attaining non-HDL-cholesterol (non-HDL-C) goals in the multinational L-TAP 2 study.

Methods

9955 patients ≥20 years of age with dyslipidemia on stable lipid-lowering therapy were enrolled from nine countries.

Results

Success rates for non-HDL-C goals were 86% in low, 70% in moderate, and 52% in high-risk patients (63% overall). In patients with triglycerides of >200 mg/dL success rates for non-HDL-C goals were 35% vs. 69% in those with ≤200 mg/dL (p < 0.0001). Among patients attaining their LDL-C goal, 18% did not attain their non-HDL-C goal. In those with coronary disease and at least two risk factors, only 34% and 30% attained respectively their non-HDL-C and LDL-C goals. Rates of failure in attaining both LDL-C and non-HDL-C goals were highest in Latin America.

Conclusions

Non-HDL-C goal attainment lagged behind LDL-C goal attainment; this gap was greatest in higher-risk patients.

There is a well-established link between dyslipidemia and cardiovascular events, although this risk is modified by age. Little is known about how treatment of dyslipidemia and low-density lipoprotein (LDL) cholesterol goal attainment differ between older and younger patients. We obtained clinical data from 9,926 dyslipidemic patients across 9 countries in North and Latin America, Europe, and Asia from 2006 through 2007. Multivariate regressions were performed to determine predictors of lipid level goal attainment. The study sample consisted of 5,733 adults <65 and 4,193 adults ≥65 years old. Compared with younger patients, older patients were more likely to have diabetes (32.5% vs 30.0%, p = 0.0014) and hypertension (73.4% vs 57.0%, p <0.0001), to be classified as high risk (68.6% vs 53.2%, p <0.0001), and to be taking a statin (79.1% vs 72.0%, p <0.0001). However, they were less likely to smoke (8.2% vs 17.6%, p <0.0001) or to have metabolic syndrome (29.0% vs 34.4%, p <0.0001). Older patients had lower LDL cholesterol levels (95.1 vs 103.9 mg/dl, p <0.0001) and higher levels of high-density lipoprotein cholesterol (54.2 vs 51.5 mg/dl, p <0.0001). LDL cholesterol goal attainment was 74.7% in older and 71.1% in younger patients (p = 0.036). Older patients were more likely to achieve LDL targets whether low risk (89.8% vs 84.6%, p = 0.002), moderate risk (79.0% vs 71.9%, p = 0.0006), or high risk (70.5% vs 64.4%, p <0.0001). In conclusion, older patients had different risk profiles and better lipid levels compared with their younger counterparts. They were more likely to attain their LDL cholesterol goal, perhaps because of greater statin use, different risk profiles, or survival bias.

Background

Data are limited on genome-wide association studies (GWAS) for incident coronary heart disease (CHD). Moreover, it is not known whether genetic variants identified to date also associate with risk of CHD in a prospective setting.

Methods

We performed a two-stage GWAS analysis of incident myocardial infarction (MI) and CHD in a total of 64,297 individuals (including 3898 MI cases, 5465 CHD cases). SNPs that passed an arbitrary threshold of 5×10-6 in Stage I were taken to Stage II for further discovery. Furthermore, in an analysis of prognosis, we studied whether known SNPs from former GWAS were associated with total mortality in individuals who experienced MI during follow-up.

Results

In Stage I 15 loci passed the threshold of 5×10-6; 8 loci for MI and 8 loci for CHD, for which one locus overlapped and none were reported in previous GWAS meta-analyses. We took 60 SNPs representing these 15 loci to Stage II of discovery. Four SNPs near QKI showed nominally significant association with MI (p-value<8.8×10-3) and three exceeded the genome-wide significance threshold when Stage I and Stage II results were combined (top SNP rs6941513: p = 6.2×10-9). Despite excellent power, the 9p21 locus SNP (rs1333049) was only modestly associated with MI (HR = 1.09, p-value = 0.02) and marginally with CHD (HR = 1.06, p-value = 0.08). Among an inception cohort of those who experienced MI during follow-up, the risk allele of rs1333049 was associated with a decreased risk of subsequent mortality (HR = 0.90, p-value = 3.2×10-3).

Conclusions

QKI represents a novel locus that may serve as a predictor of incident CHD in prospective studies. The association of the 9p21 locus both with increased risk of first myocardial infarction and longer survival after MI highlights the importance of study design in investigating genetic determinants of complex disorders.

Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.

Levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10^-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.

Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 × 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.