Cardiovascular precision medicine - A pharmacogenomic perspective

Precision medicine envisages the integration of an individual's clinical and biological features obtained from laboratory tests, imaging, high-throughput omics and health records, to drive a personalised approach to diagnosis and treatment with a higher chance of success. As only up to half of patients respond to medication prescribed following the current one-size-fits-all treatment strategy, the need for a more personalised approach is evident. One of the routes to transforming healthcare through precision medicine is pharmacogenomics (PGx). Around 95% of the population is estimated to carry one or more actionable pharmacogenetic variants and over 75% of adults over 50 years old are on a prescription with a known PGx association. Whilst there are compelling examples of pharmacogenomic implementation in clinical practice, the case for cardiovascular PGx is still evolving. In this review, we shall summarise the current status of PGx in cardiovascular diseases and look at the key enablers and barriers to PGx implementation in clinical practice.

Establishing plausibility of cardiovascular adverse effects of immunotherapies using Mendelian randomisation

Immune checkpoint inhibitors (ICIs) and Janus kinase inhibitors (JAKis) have raised concerns over serious unexpected cardiovascular adverse events. The widespread pleiotropy in genome-wide association studies offers an opportunity to identify cardiovascular risks from in-development drugs to help inform appropriate trial design and pharmacovigilance strategies. This study uses the Mendelian randomization (MR) approach to study the causal effects of 9 cardiovascular risk factors on ischemic stroke risk both independently and by mediation, followed by an interrogation of the implicated expression quantitative trait loci (eQTLs) to determine if the enriched pathways can explain the adverse stroke events observed with ICI or JAKi treatment. Genetic predisposition to higher systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), waist-to-hip ratio (WHR), low-density lipoprotein cholesterol (LDL), triglycerides (TG), type 2 diabetes (T2DM), and smoking index were associated with higher ischemic stroke risk. The associations of genetically predicted BMI, WHR, and TG on the outcome were attenuated after adjusting for genetically predicted T2DM [BMI: 53.15% mediated, 95% CI 17.21%-89.10%; WHR: 42.92% (4.17%-81.67%); TG: 72.05% (10.63%-133.46%)]. JAKis, programmed cell death protein 1 and programmed death ligand 1 inhibitors were implicated in the pathways enriched by the genes related to the instruments for each of SBP, DBP, WHR, T2DM, and LDL. Overall, MR mediation analyses support the role of T2DM in mediating the effects of BMI, WHR, and TG on ischemic stroke risk and follow-up pathway enrichment analysis highlights the utility of this approach in the early identification of potential harm from drugs.

Survey and Evaluation of Hypertension Machine Learning Research

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

A genomic deep field view of hypertension

Blood pressure is regulated by a complex neurohumoral system including the renin-angiotensin-aldosterone system, natriuretic peptides, endothelial pathways, the sympathetic nervous system, and the immune system. This review charts the evolution of our understanding of the genomic basis of hypertension at increasing resolution over the last 5 decades from monogenic causes to polygenic associations, spanning ∼30 monogenic rare variants and >1500 single nucleotide variants. Unexpected early wins from blood pressure genomics include deepening of our understanding of the complex causation of hypertension; refinement of causal estimates bidirectionally between blood pressure, risk factors, and outcomes through Mendelian randomization; risk stratification using polygenic risk scores; and opportunities for precision medicine and drug repurposing.

Machine learning integration of multimodal data identifies key features of blood pressure regulation

BACKGROUND

Association studies have identified several biomarkers for blood pressure and hypertension, but a thorough understanding of their mutual dependencies is lacking. By integrating two different high-throughput datasets, biochemical and dietary data, we aim to understand the multifactorial contributors of blood pressure (BP).

METHODS

We included 4,863 participants from TwinsUK with concurrent BP, metabolomics, genomics, biochemical measures, and dietary data. We used 5-fold cross-validation with the machine learning XGBoost algorithm to identify features of importance in context of one another in TwinsUK (80% training, 20% test). The features tested in TwinsUK were then probed using the same algorithm in an independent dataset of 2,807 individuals from the Qatari Biobank (QBB).

FINDINGS

Our model explained 39·2% [4·5%, MAE:11·32 mmHg (95%CI, +/- 0·65)] of the variance in systolic BP (SBP) in TwinsUK. Of the top 50 features, the most influential non-demographic variables were dihomo-linolenate, cis-4-decenoyl carnitine, lactate, chloride, urate, and creatinine along with dietary intakes of total, trans and saturated fat. We also highlight the incremental value of each included dimension. Furthermore, we replicated our model in the QBB [SBP variance explained = 45·2% (13·39%)] cohort and 30 of the top 50 features overlapped between cohorts.

INTERPRETATION

We show that an integrated analysis of omics, biochemical and dietary data improves our understanding of their in-between relationships and expands the range of potential biomarkers for blood pressure. Our results point to potentially key biological pathways to be prioritised for mechanistic studies.

FUNDING

Chronic Disease Research Foundation, Medical Research Council, Wellcome Trust, Qatar Foundation.

Abstract P265: Hypertension, Vascular Dysfunction And Downregulation Of The Renin Angiotensin System Sequelae Of COVID-19

Hypertension, vascular dysfunction and downregulation of the renin angiotensin system as sequelae of COVID-19

The long-term CV consequences of COVID are unknown however the potential for ongoing cardiac and vascular inflammation with RAAS alteration may increase the risk of developing hypertension and CV disease. Non-hypertensive patients hospitalised in April-May 2020 with either confirmed COVID19 (cases) or non-COVID (controls) diagnosis were recruited ≥12 weeks post-discharge. All underwent detailed BP and vascular/immune and RAAS phenotyping. The primary outcome was ABPM 24-hr SBP. Paired t-tests and multivariable regression models used to assess differences. Thirty cases and eighteen controls completed the study. Cases were older (51±7 vs 45±9 years) with lower discharge SBP (121±10 vs 128±15 mmHg; p0.01). ABPM at study visit was higher in the cases compared to controls (24-hour SBP (OR[95%CI]: 8.6[0.9-16.3]; p0.03), day-time SBP (8.6[1.5-17.3]; p0.02), day-time DBP (4.6[0.1-9.1]; p<0.05). Paired analysis of office BP showed a 11 mmHg difference between cases and controls (11.5[3.12];19.8; p=0.008; figure) Cases had lowerRenin and Ang-1-10 levels (-0.4[-0.9-0.1]; p0.08; -0.7[-1.2- -0.1]; p0.02 respectively) and higher TNF-alpha (0.5[0.1-0.9]; p0.01). Confirmed COVID requiring hospitalisation is associated with elevated SBP, reduced renin and Ang-1-10 and elevated TNF-alpha at ≥12 weeks post-discharge.

Abstract P259: Cardiovascular Pharmacotherapy And Covid-19

COVID-19 illness is associated with cardiovascular vulnerabilities at all stages pre-infection, acute phase and subsequent chronic phase. The major cardiometabolic drivers identified to have epidemiological and mechanistic associations with COVID-19 are abnormal adiposity, dysglycaemia, dyslipidaemia, and hypertension. Recent findings demonstrate that cardiovascular risk reduction interventions may have short-term benefits on COVID-19 risk and outcomes. Our aim was to investigate the effect of cardiovascular pharmacotherapy on SARS-CoV-2 infection. Demographic data, cardiovascular disease and other comorbidities, prescription data, discharge outcomes, RT-PCR tests, and investigations were obtained through record linkage for all patients admitted between 01/04/2020 and 31/05/2020 to NHS Greater Glasgow & Clyde hospitals. The relationship between cardiovascular pharmacotherapy (antihypertensives and statins) and SARS-CoV-2 infection was analysed using multivariable logistic regression models. From 218,472 patient records available in Glasgow SafeHaven. 3,610 patients had confirmed COVID-19 based on clinical criteria or a positive RT-PCR test) and 18,050 propensity matched controls were selected. The average age was 62 years, 55% (1,985 of 3,610) females, 43% (1,552 of 3,610) resided in the most deprived areas (SIMD:1). There were 46% (1,660 of 3,610) on antihypertensive therapy and 35% (1,264 of 3,610) were on statin therapy. The risk of SARS-CoV-2 infection was greater in those with cancer, pulmonary or renal disease in the multivariable logistic regression model. Those on antihypertensive or statin therapy had an 11% (O.R.[95% CI]: 0.89 [0.82-0.97], p=0.007) and 9% (0.91 [0.83-0.99], p=0.03) lower risk of SARS-CoV-2 infection, respectively. The protective effect of statins and antihypertensive drugs were apparent only in those on both drug therapies (0.82 [0.74-0.92], p=0.001). Prior treatment with statins and antihypertensive drugs are associated with a lower risk of SARS-CoV-2 infection. The observed beneficial effect of statins and antihypertensives therapy warrants further investigation to address critical gaps in knowledge in prevention, treatment and long term follow up of patients with COVID-19.