Two for the heart: Dutch Pharmacogenetic Working Group prescribing guidance on statins and sulfonylureas to reduce cardiometabolic risk
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Ischemic heart disease (IHD) is the single leading cause of mortality globally. Statins are the mainstay for IHD treatment. However, the specific mechanisms underlying statins’ benefits on IHD have not been clarified. To examine the mechanisms through proteins, we used two-step Mendelian randomization (MR) approach. First, we examined the associations of genetically mimicked statins with 2923 proteins using genome-wide association of proteins from the UK Biobank Pharma Proteomics Project (UKB-PPP) to identify the proteins affected by statins, and replicated the findings using deCODE. Then we examined the associations of selected proteins with IHD risk using CARDIoGRAMplusC4D using MR, and replicated using FinnGen, and using another set of genetic instruments from deCODE. We selected proteins decreased or increased IHD risk and meanwhile increased or lowered by statins. We further examined the role of the selected protein(s) on common IHD comorbidities, including diabetes, chronic kidney disease (CKD), and kidney function (measured by estimated glomerular filtration rate (eGFR)). Nine proteins were affected by statins, including four proteins (PLA2G7, FGFBP1, ANGPTL1, and PTPRZ1) lowered by statins, and five proteins (EFNA4, COL6A3, ASGR1, PRSS8 and PCOLCE) increased by statins. Among these, PLA2G7 was related to higher risk of IHD after controlling for multiple testing. The associations were robust to different analytic methods and replication using another set of genetic instrument from deCODE, and using another GWAS of IHD from FinnGen. Genetically predicted PLA2G7 had null association with diabetes, CKD, and eGFR. We identified 9 proteins affected by statins, including 7 novel proteins which were not reported previously. PLA2G7 is on the pathway underlying statins’ benefits on IHD. The clarification of statins’ mechanisms had close relevance to precision medicine, and provided insights to the development of new treatment strategies.
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Pharmacogenetic testing in the United Kingdom’s National Health Service (NHS) has historically been reactive in nature, undertaken in the context of single gene-drug relationships in specialist settings. Using a discrete choice experiment we aimed to identify healthcare professional preferences for development of a pharmacogenetic testing service in primary care in the NHS. Respondents, representing two professions groups (general practitioners or pharmacists), completed one of two survey versions, asking them to select their preferred pharmacogenetic testing service in the context of a presentation of low mood or joint pain. Responses from 235 individuals were included. All respondents preferred pharmacogenetic testing over no testing, though preference heterogeneity was identified. Both professional groups, but especially GPs, were highly sensitive to service design, with uptake varying depending on the service offered. This study demonstrates uptake of a pharmacogenetic testing service is impacted by service design and highlights key areas which should be prioritised within future initiatives.
Polygenic scores for cardiovascular risk factors improve estimation of clinical outcomes in CCB treatment compared to pharmacogenetic variants alone
Pharmacogenetic variants are associated with clinical outcomes during Calcium Channel Blocker (CCB) treatment, yet whether the effects are modified by genetically predicted clinical risk factors is unknown. We analyzed 32,000 UK Biobank participants treated with dihydropiridine CCBs (mean 5.9 years), including 23 pharmacogenetic variants, and calculated polygenic scores for systolic and diastolic blood pressures, body fat mass, and other patient characteristics. Outcomes included treatment discontinuation and heart failure. Pharmacogenetic variant rs10898815-A (NUMA1) increased discontinuation rates, highest in those with high polygenic scores for fat mass. The RYR3 variant rs877087 T-allele alone modestly increased heart failure risks versus non-carriers (HR:1.13, p = 0.02); in patients with high polygenic scores for fat mass, lean mass, and lipoprotein A, risks were substantially elevated (HR:1.55, p = 4 × 10−5). Incorporating polygenic scores for adiposity and lipoprotein A may improve risk estimates of key clinical outcomes in CCB treatment such as treatment discontinuation and heart failure, compared to pharmacogenetic variants alone.
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