Comment on "A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk"

A 27-protein signature has been proposed to predict cardiovascular disease, but its applicability in clinical decision-making remains unclear

LDL-C Concentrations and the 12-SNP LDL-C Score for Polygenic Hypercholesterolaemia in Self-Reported South Asian, Black and Caribbean Participants of the UK Biobank

Background: Monogenic familial hypercholesterolaemia (FH) is an autosomal dominant disorder characterised by elevated low-density lipoprotein cholesterol (LDL-C) concentrations due to monogenic mutations in LDLR, APOB, PCSK9, and APOE. Some mutation-negative patients have a polygenic cause for elevated LDL-C due to a burden of common LDL-C-raising alleles, as demonstrated in people of White British (WB) ancestry using a 12-single nucleotide polymorphism (SNP) score. This score has yet to be evaluated in people of South Asian (SA), and Black and Caribbean (BC) ethnicities. Objectives: 1) Compare the LDL-C and 12-SNP score distributions across the three major ethnic groups in the United Kingdom: WB, SA, and BC individuals; 2) compare the association of the 12-SNP score with LDL-C in these groups; 3) evaluate ethnicity-specific and WB 12-SNP score decile cut-off values, applied to SA and BC ethnicities, in predicting LDL-C concentrations and hypercholesterolaemia (LDL-C>4.9 mmol/L). Methods: The United Kingdom Biobank cohort was used to analyse the LDL-C (adjusted for statin use) and 12-SNP score distributions in self-reported WB (n = 353,166), SA (n = 7,016), and BC (n = 7,082) participants. To evaluate WB and ethnicity-specific 12-SNP score deciles, the total dataset was split 50:50 into a training and testing dataset. Regression analyses (logistic and linear) were used to analyse hypercholesterolaemia (LDL-C>4.9 mmol/L) and LDL-C. Findings: The mean (±SD) measured LDL-C differed significantly between the ethnic groups and was highest in WB [3.73 (±0.85) mmol/L], followed by SA [3.57 (±0.86) mmol/L, p < 2.2 × 10−16], and BC [3.42 (±0.90) mmol/L] participants (p < 2.2 × 10−16). There were significant differences in the mean (±SD) 12-SNP score between WB [0.90 (±0.23)] and BC [0.72 (±0.25), p < 2.2 × 10−16], and WB and SA participants [0.86 (±0.19), p < 2.2 × 10−16]. In all three ethnic groups the 12-SNP score was associated with measured LDL-C [R2 (95% CI): WB = 0.067 (0.065–0.069), BC = 0.080 (0.063–0.097), SA = 0.027 (0.016–0.038)]. The odds ratio and the area under the curve for hypercholesterolaemia were not statistically different when applying ethnicity-specific or WB deciles in all ethnic groups. Interpretation: We provide information on the differences in LDL-C and the 12-SNP score distributions in self-reported WB, SA, and BC individuals of the United Kingdom Biobank. We report the association between the 12-SNP score and LDL-C in these ethnic groups. We evaluate the performance of ethnicity-specific and WB 12-SNP score deciles in predicting LDL-C and hypercholesterolaemia

Could NICE guidance on the choice of blood pressure lowering drugs be simplified?

Reecha Sofat and colleagues argue that prescribing advice needs updating in the light of recent evidence that all classes of blood pressure lowering drugs are broadly equivalen

Prioritising genetic findings for drug target identification and validation

The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets are often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation

Lipid lowering and Alzheimer disease risk: A mendelian randomization study.

OBJECTIVE: To examine whether genetic variation affecting the expression or function of lipid-lowering drug targets is associated with Alzheimer disease (AD) risk, to evaluate the potential impact of long-term exposure to corresponding therapeutics. METHODS: We conducted Mendelian randomization analyses using variants in genes that encode the protein targets of several approved lipid-lowering drug classes: HMGCR (encoding the target for statins), PCSK9 (encoding the target for PCSK9 inhibitors, eg, evolocumab and alirocumab), NPC1L1 (encoding the target for ezetimibe), and APOB (encoding the target of mipomersen). Variants were weighted by associations with low-density lipoprotein cholesterol (LDL-C) using data from lipid genetics consortia (n up to 295,826). We meta-analyzed Mendelian randomization estimates for regional variants weighted by LDL-C on AD risk from 2 large samples (total n = 24,718 cases, 56,685 controls). RESULTS: Models for HMGCR, APOB, and NPC1L1 did not suggest that the use of related lipid-lowering drug classes would affect AD risk. In contrast, genetically instrumented exposure to PCSK9 inhibitors was predicted to increase AD risk in both of the AD samples (combined odds ratio per standard deviation lower LDL-C inducible by the drug target = 1.45, 95% confidence interval = 1.23-1.69). This risk increase was opposite to, although more modest than, the degree of protection from coronary artery disease predicted by these same methods for PCSK9 inhibition. INTERPRETATION: We did not identify genetic support for the repurposing of statins, ezetimibe, or mipomersen for AD prevention. Notwithstanding caveats to this genetic evidence, pharmacovigilance for AD risk among users of PCSK9 inhibitors may be warranted. ANN NEUROL 2020;87:30-39

A Machine Learning Model to Aid Detection of Familial Hypercholesterolemia

Background: People with monogenic familial hypercholesterolemia (FH) are at an increased risk of premature coronary heart disease and death. With a prevalence of 1:250, FH is relatively common; but currently there is no population screening strategy in place and most carriers are identified late in life, delaying timely and cost-effective interventions. // Objectives: The purpose of this study was to derive an algorithm to identify people with suspected monogenic FH for subsequent confirmatory genomic testing and cascade screening. // Methods: A least absolute shrinkage and selection operator logistic regression model was used to identify predictors that accurately identified people with FH in 139,779 unrelated participants of the UK Biobank. Candidate predictors included information on medical and family history, anthropometric measures, blood biomarkers, and a low-density lipoprotein cholesterol (LDL-C) polygenic score (PGS). Model derivation and evaluation were performed in independent training and testing data. // Results: A total of 488 FH variant carriers were identified using whole-exome sequencing of the low-density lipoprotein receptor, apolipoprotein B, apolipoprotein E, proprotein convertase subtilisin/kexin type 9 genes. A 14-variable algorithm for FH was derived, with an area under the curve of 0.77 (95% CI: 0.71-0.83), where the top 5 most important variables included triglyceride, LDL-C, apolipoprotein A1 concentrations, self-reported statin use, and LDL-C PGS. Excluding the PGS as a candidate feature resulted in a 9-variable model with a comparable area under the curve: 0.76 (95% CI: 0.71-0.82). Both multivariable models (w/wo the PGS) outperformed screening-prioritization based on LDL-C adjusted for statin use. // Conclusions: Detecting individuals with FH can be improved by considering additional predictors. This would reduce the sequencing burden in a 2-stage population screening strategy for FH

Fulfilling the Promise of Personalized Medicine? Systematic Review and Field Synopsis of Pharmacogenetic Studies

BACKGROUND: Studies of the genetic basis of drug response could help clarify mechanisms of drug action/metabolism, and facilitate development of genotype-based predictive tests of efficacy or toxicity (pharmacogenetics). OBJECTIVES: We conducted a systematic review and field synopsis of pharmacogenetic studies to quantify the scope and quality of available evidence in this field in order to inform future research. DATA SOURCES: Original research articles were identified in Medline, reference lists from 24 meta-analyses/systematic reviews/review articles and U.S. Food and Drug Administration website of approved pharmacogenetic tests. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS, AND INTERVENTION CRITERIA: We included any study in which either intended or adverse response to drug therapy was examined in relation to genetic variation in the germline or cancer cells in humans. STUDY APPRAISAL AND SYNTHESIS METHODS: Study characteristics and data reported in abstracts were recorded. We further analysed full text from a random 10% subset of articles spanning the different subclasses of study. RESULTS: From 102,264 Medline hits and 1,641 articles from other sources, we identified 1,668 primary research articles (1987 to 2007, inclusive). A high proportion of remaining articles were reviews/commentaries (ratio of reviews to primary research approximately 25 ratio 1). The majority of studies (81.8%) were set in Europe and North America focussing on cancer, cardiovascular disease and neurology/psychiatry. There was predominantly a candidate gene approach using common alleles, which despite small sample sizes (median 93 [IQR 40-222]) with no trend to an increase over time, generated a high proportion (74.5%) of nominally significant (por=4 studies, only 31 meta-analyses were identified. The majority (69.4%) of end-points were continuous and likely surrogate rather than hard (binary) clinical end-points. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: The high expectation but limited translation of pharmacogenetic research thus far may be explained by the preponderance of reviews over primary research, small sample sizes, a mainly candidate gene approach, surrogate markers, an excess of nominally positive to truly positive associations and paucity of meta-analyses. Recommendations based on these findings should inform future study design to help realise the goal of personalised medicines. SYSTEMATIC REVIEW REGISTRATION NUMBER: Not Registered

Biological mechanisms of aging predict age-related disease co-occurrence in patients

Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age-related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co-occurrence of age-related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non-random associations between age-related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age-related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age-related diseases. Mechanisms of aging hence contribute both together and individually to age-related disease co-occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity

How to assess pharmacogenomic tests for implementation in the NHS in England

AIMS: Pharmacogenomic testing has the potential to target medicines more effectively towards those who will benefit and avoid use in individuals at risk of harm. Health economies are actively considering how pharmacogenomic tests can be integrated into health care systems to improve use of medicines. However, one of the barriers to effective implementation is evaluation of the evidence including clinical usefulness, cost-effectiveness, and operational requirements. We sought to develop a framework that could aid the implementation of pharmacogenomic testing. We take the view from the National Health Service (NHS) in England. METHODS: We used a literature review using EMBASE and Medline databases to identify prospective studies of pharmacogenomic testing, focusing on clinical outcomes and implementation of pharmacogenomics. Using this search, we identified key themes relating to the implementation of pharmacogenomic tests. We used a clinical advisory group with expertise in pharmacology, pharmacogenomics, formulary evaluation, and policy implementation to review data from our literature review and the interpretation of these data. With the clinical advisory group, we prioritized themes and developed a framework to evaluate proposals to implement pharmacogenomics tests. RESULTS: Themes that emerged from review of the literature and subsequent discussion were distilled into a 10-point checklist that is proposed as a tool to aid evidence-based implementation of pharmacogenomic testing into routine clinical care within the NHS. CONCLUSION: Our 10-point checklist outlines a standardized approach that could be used to evaluate proposals to implement pharmacogenomic tests. We propose a national approach, taking the view of the NHS in England. Using this approach could centralize commissioning of appropriate pharmacogenomic tests, reduce inequity and duplication using regional approaches, and provide a robust and evidence-based framework for adoption. Such an approach could also be applied to other health systems