Brown, Sherry‐Ann

Variability in response to antiplatelet therapy can be explained in part by pharmacogenomics, particularly of the CYP450 enzyme encoded by CYP2C19. Loss-of-function and gain-of-function variants help explain these interindividual differences. Individuals may carry multiple variants, with linkage disequilibrium noted among some alleles. In the current pharmacogenomics era, genomic variation in CYP2C19 has led to the definition of pharmacokinetic phenotypes for response to antiplatelet therapy, in particular, clopidogrel. Individuals may be classified as poor, intermediate, extensive, or ultrarapid metabolizers, based on whether they carry wild type or polymorphic CYP2C19 alleles. Variant alleles differentially impact platelet reactivity, concentration of plasma clopidogrel metabolites, and clinical outcomes. Interestingly, response to clopidogrel appears to be modulated by additional factors, such as sociodemographic characteristics, risk factors for ischemic heart disease, and drug-drug interactions. Furthermore, systems medicine studies suggest that a broader approach may be required to adequately assess, predict, preempt, and manage variation in antiplatelet response. Transcriptomics, epigenomics, exposomics, miRNAomics, proteomics, metabolomics, microbiomics, and mathematical, computational, and molecular modeling should be integrated with pharmacogenomics for enhanced prediction and individualized care. In this review of pharmacogenomic variation of CYP450, a systems medicine approach is described for tailoring antiplatelet therapy in clinical practice of precision cardiovascular medicine.

Background— Whether disclosing genetic risk for coronary heart disease (CHD) to individuals influences information seeking and information sharing is not known. We hypothesized that disclosing genetic risk for CHD to individuals influences information seeking and sharing. Methods and Results— The MI-GENES study (Myocardial Infarction Genes) randomized participants (n=203) aged 45 to 65 years who were at intermediate CHD risk based on conventional risk factors and not on statins to receive their conventional risk score alone or also a genetic risk score based on 28 variants. CHD risk was disclosed by a genetic counselor and then discussed with a physician. Surveys assessing information seeking were completed before and after risk disclosure. Information sharing was assessed post-disclosure. Six-month post-disclosure, genetic risk score participants were more likely than conventional risk score participants to visit a website to learn about CHD (odds ratio [OR], 4.88 [confidence interval (CI), 1.55–19.13]; P =0.01), use the internet for information about how genetic factors affect CHD risk (OR, 2.11 [CI, 1.03–4.47]; P =0.04), access their CHD risk via a patient portal (OR, 2.99 [CI, 1.35–7.04]; P =0.01), and discuss their CHD risk with others (OR, 3.13 [CI, 1.41–7.47]; P =0.01), particularly their siblings (OR, 1.92 [CI, 1.06–3.51]; P =0.03), extended family (OR, 3.8 [CI, 1.37–12.38]; P= 0.01), coworkers (OR, 2.42 [CI, 1.09–5.76]; P =0.03), and primary care provider (PCP; OR, 2.00 [CI, 1.08–3.75]; P =0.03). Conclusions— Disclosure of a genetic risk score for CHD increased information seeking and sharing. Clinical Trial Registration— URL: https://clinicaltrials.gov/ . Unique identifier: NCT01936675.

AbstractLittle is known about individuals’ motivation, perception, and treatment beliefs towards the use of genetic information in risk estimates for coronary heart disease (CHD). In this study, participants at intermediate 10‐year risk of CHD were randomized to receive either their estimated conventional risk score (CRS) alone, or a CRS and a genetic risk score (GRS), by a genetic counselor. Surveys on motivation to participate in and perception of genetic testing for CHD were administered at 3 months and treatment beliefs at 6 months following risk disclosure. Survey responses used Likert scales. Linear and logistic regression were used for analysis. Overall, motivation to participate in genomic clinical trials was favorable and did not differ between the CRS and GRS groups (16.95 ± 0.82 vs. 17.58 ± 0.83, p = 0.091), but participants who initially received their GRS indicated a greater desire to find ways to improve health as a reason for participation (OR: 0.53 (95%CI: 0.29, 0.94), p = 0.028). Perception of genetic testing was also favorable in both groups (15.29 ± 0.39 vs. 15.12 ± 0.40, p = 0.835). Participants who initially received their GRS were more inclined to recommend genetic testing to family and friends (9.95 ± 1.88 vs. 10.52 ± 2.17, p = 0.023). In the MI‐GENES study, motivation to participate in and perception of genetic testing among study participants were overall favorable. Genetic risk disclosure was associated with increased motivation to recommend genetic testing to family and friends.

Whether disclosure of genetic risk for coronary heart disease (CHD) influences shared decision-making (SDM) regarding use of statins to reduce CHD risk is unknown. We randomized 207 patients, age 45–65 years, at intermediate CHD risk, and not on statins, to receive the 10-year risk of CHD based on conventional risk factors alone (n=103) or in combination with a genetic risk score (n=104). A genetic counselor disclosed this information followed by a physician visit for SDM regarding statin therapy. A novel decision aid was used in both encounters to disclose the CHD risk estimates and facilitate SDM regarding statin use. Patients reported their decision quality and physician visit satisfaction using validated surveys. There were no statistically significant differences between the two groups in the SDM score, satisfaction with the clinical encounter, perception of the quality of the discussion or of participation in decision-making and physician visit satisfaction scores. Quantitative analyses of a random subset of 80 video-recorded encounters using the OPTION5 scale also showed no significant difference in SDM between the two groups. Disclosure of CHD genetic risk using an electronic health record-linked decision aid did not adversely affect SDM or patients’ satisfaction with the clinical encounter. Trial registration number NCT01936675; Results.

Once universally considered a rapidly fatal condition, cancer has increasingly become a chronic medical condition and comorbidities and adverse effects of cancer therapies have become increasingly significant. One of the leading advancements that has gained traction for the treatment of a variety of malignancies is the class of small molecule tyrosine kinase inhibitors (TKIs). Although, in many aspects revolutionary, TKIs have their own profile of side effects, including cardiovascular side effects, the most common being hypertension (HTN), congestive heart failure, corrected QT (QTc) prolongation, and instances of premature coronary heart disease. Herein, we describe the mechanisms of small TKI‐induced cardiovascular toxicity and related intracellular signaling. In particular, systems‐based approaches to the understanding of small TKI‐induced cardiovascular toxicity are addressed. The pathophysiology of synergic cardiovascular toxicity from TKIs, anthracyclines, and monoclonal antibodies (e.g., trastuzumab, bevacizumab) is illustrated. Finally, recommendations are made for implementing systems medicine in clinical practice, for individualized cardiovascular wellness after cancer therapy.

Background— Whether knowledge of genetic risk for coronary heart disease (CHD) affects health-related outcomes is unknown. We investigated whether incorporating a genetic risk score (GRS) in CHD risk estimates lowers low-density lipoprotein cholesterol (LDL-C) levels. Methods and Results— Participants (n=203, 45–65 years of age, at intermediate risk for CHD, and not on statins) were randomly assigned to receive their 10-year probability of CHD based either on a conventional risk score (CRS) or CRS + GRS ( + GRS). Participants in the + GRS group were stratified as having high or average/low GRS. Risk was disclosed by a genetic counselor followed by shared decision making regarding statin therapy with a physician. We compared the primary end point of LDL-C levels at 6 months and assessed whether any differences were attributable to changes in dietary fat intake, physical activity levels, or statin use. Participants (mean age, 59.4±5 years; 48% men; mean 10-year CHD risk, 8.5±4.1%) were allocated to receive either CRS (n=100) or + GRS (n=103). At the end of the study period, the + GRS group had a lower LDL-C than the CRS group (96.5±32.7 versus 105.9±33.3 mg/dL; P =0.04). Participants with high GRS had lower LDL-C levels (92.3±32.9 mg/dL) than CRS participants ( P =0.02) but not participants with low GRS (100.9±32.2 mg/dL; P =0.18). Statins were initiated more often in the + GRS group than in the CRS group (39% versus 22%, P <0.01). No significant differences in dietary fat intake and physical activity levels were noted. Conclusions— Disclosure of CHD risk estimates that incorporated genetic risk information led to lower LDL-C levels than disclosure of CHD risk based on conventional risk factors alone. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT01936675.