Genetic Risk Prediction for Primary and Secondary Prevention of Atherosclerotic Cardiovascular Disease: an Update

Common genetic variants do not predict recurrent events in coronary heart disease patients

Background

It is unclear whether genetic variants identified from single nucleotide polymorphisms (SNPs) strongly associated with coronary heart disease (CHD) in genome-wide association studies (GWAS), or a genetic risk score (GRS) derived from them, can help stratify risk of recurrent events in patients with CHD.

Methods

Study subjects were enrolled at the close-out of the LIPID randomised controlled trial of pravastatin vs placebo. Entry to the trial had required a history of acute coronary syndrome 3–36 months previously, and patients were in the trial for a mean of 36 months. Patients who consented to a blood sample were genotyped with a custom designed array chip with SNPs chosen from known CHD-associated loci identified in previous GWAS. We evaluated outcomes in these patients over the following 10 years.

Results

Over the 10-year follow-up of the cohort of 4932 patients, 1558 deaths, 898 cardiovascular deaths, 727 CHD deaths and 375 cancer deaths occurred. There were no significant associations between individual SNPs and outcomes before or after adjustment for confounding variables and for multiple testing. A previously validated 27 SNP GRS derived from SNPs with the strongest associations with CHD also did not show any independent association with recurrent major cardiovascular events.

Conclusions

Genetic variants based on individual single nucleotide polymorphisms strongly associated with coronary heart disease in genome wide association studies or an abbreviated genetic risk score derived from them did not help risk profiling in this well-characterised cohort with 10-year follow-up. Other approaches will be needed to incorporate genetic profiling into clinically relevant stratification of long-term risk of recurrent events in CHD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02520-0.

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.

Interferon Regulatory Factor 5 (IRF5) Gene Haplotypes Are Associated with Premature Coronary Artery Disease. Association of the IRF5 Polymorphisms with Cardiometabolic Parameters. The Genetics of Atherosclerotic Disease (GEA) Mexican Study

Interferon regulatory factor 5 (IRF5) has an important role in the inflammatory process, a fundamental component of coronary artery disease (CAD). Thus, the objective of this study was to evaluate the association of IRF5 polymorphisms with the development of premature CAD (pCAD) and cardiometabolic parameters. IRF5 polymorphisms (rs1874330, rs3778754, rs3757386, rs3757385, rs3807134, rs3807135, and rs6968563) were determined in 1116 pCAD patients and 1003 controls. Polymorphism distribution was similar in patients and controls; however, the haplotype analysis showed five haplotypes with a different distribution. TGCGTCT (OR (odds ratio) = 1.248, p = 0005) and TCTGCCT (OR = 10.73, p < 0.0001) were associated with a high risk, whereas TCCGTCT (OR = 0.155, p < 0.0001), CGCTTTT (OR = 0.108, p < 0.0001), and TCCGCCT (OR = 0.014, p < 0.0001) were associated with a low risk of pCAD. Associations with aspartate aminotransferase, hypertriglyceridemia, magnesium deficiency, triglycerides/HDL-C index, LDL-C, and adiponectin levels were observed in pCAD patients. In controls, associations with hypoalphalipoproteinemia, non-HDL-C, apolipoprotein B, hyperuricemia, TNF-α, IL-6, IL-15, valvular calcification, and subclinical hypothyroidism were observed. In summary, five haplotypes were associated with pCAD, two with high risk and three with low risk. Some IRF5 polymorphisms were associated with cardiometabolic parameters in pCAD patients and control.