Cochrane corner: PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease

Schmidt, Amand F.; Pearce, Lucy S; Wilkins, John T.; Casas, Juan P.; Hingorani, Aroon D.

doi:10.1136/heartjnl-2017-312858

Cited by 12 publications

(15 citation statements)

References 3 publications

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“…The first and third quartiles (Q) of the CHD odds ratios (OR) per standard deviation (SD) in LDL-C for HMGCR, NPC1L1, PCSK9 (or HDL-C in the case of CETP) indicated modest variability in the point estimate: (Q1 1.61, Q3 1.78) for HMGCR, (Q1 1.42, Q3 1.77) for PCSK9, (Q1 1.19, Q3 1.68) for NPC1L1, and (Q1 0.87, Q3 0.91) for CETP. Between 95 and 99% of the estimates across all four genes were in the expected direction as inferred from the findings of drugs used in clinical trials to target the corresponding proteins [19][20][21][22][23][24][25] .…”

Section: Resultsmentioning

confidence: 99%

Genetic drug target validation using Mendelian randomisation

et al. 2020

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Mendelian randomisation (MR) analysis is an important tool to elucidate the causal relevance of environmental and biological risk factors for disease. However, causal inference is undermined if genetic variants used to instrument a risk factor also influence alternative disease-pathways (horizontal pleiotropy). Here we report how the 'no horizontal pleiotropy assumption' is strengthened when proteins are the risk factors of interest. Proteins are typically the proximal effectors of biological processes encoded in the genome. Moreover, proteins are the targets of most medicines, so MR studies of drug targets are becoming a fundamental tool in drug development. To enable such studies, we introduce a mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. We illustrate key model decisions and introduce an analytical framework for maximising power and evaluating the robustness of analyses.

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Section: Resultsmentioning

confidence: 99%

Genetic drug target validation using Mendelian randomisation

et al. 2020

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“…Statins and PCSK9 inhibitors are well-established interventions for cardiovascular disease, which reduce low density lipoprotein (LDL)-cholesterol, IHD [29][30][31], stroke [29][30][31] and atrial fibrillation (AF) [32].…”

Section: Illustrative Examplementioning

confidence: 99%

Use of multivariable Mendelian randomization to address biases due to competing risk before recruitment

Schooling

Lopez

Zhao

et al. 2019

Preprint

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BackgroundMendelian randomization (MR) provides unconfounded estimates. MR is open to selection bias particularly when the underlying sample is selected on surviving the genetically instrumented exposure and other conditions that share etiology with the outcome (competing risk before recruitment). Few methods to address this bias exist.MethodsWe use directed acyclic graphs to show this selection bias can be addressed by adjusting for common causes of survival and outcome. We use multivariable MR to obtain a corrected MR estimate, specifically, the effect of statin use on ischemic stroke, because statins affect survival and stroke typically occurs later in life than ischemic heart disease so is open to competing risk.ResultsIn univariable MR the genetically instrumented effect of statin use on ischemic stroke was in a harmful direction in MEGASTROKE and the UK Biobank (odds ratio (OR) 1.33, 95% confidence interval (CI) 0.80 to 2.20). In multivariable MR adjusted for major causes of survival and ischemic stroke, (blood pressure, body mass index and smoking initiation) the effect of statin use on stroke in the UK Biobank was as expected (OR 0.81, 95% CI 0.68 to 0.98) with a Q-statistic indicating absence of genetic pleiotropy or selection bias, but not in MEGASTROKE.ConclusionMR studies concerning late onset chronic conditions with shared etiology based on samples recruited in later life need to be conceptualized within a mechanistic understanding, so as to any identify potential bias due to competing risk before recruitment, and to inform the analysis and interpretation.

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“…1.68) for NPC1L1, and (Q1 0.87, Q3 0.91) for CETP. Between 95-99% of the estimates across all four genes were in the expected direction as inferred from the findings of drugs used in clinical trials to target the corresponding proteins [38][39][40][41][42][43][44] .…”

Section: Instrument Selectionmentioning

confidence: 99%

Genetic drug target validation using Mendelian randomization

Schmidt

Finan

Gordillo-Marañón

et al. 2019

Preprint

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Mendelian randomisation analysis has emerged as an important tool to elucidate the causal relevance of a range of environmental and biological risk factors for human disease. However, inference on cause is undermined if the genetic variants used to instrument a risk factor of interest also associate with other traits that open alternative pathways to the disease (horizontal pleiotropy). We show how the 'no horizontal pleiotropy assumption' in MR analysis is strengthened when proteins are the risk factors of interest. Proteins are the proximal effectors of biological processes encoded in the genome, and are becoming assayable on an -omics scale.Moreover, proteins are the targets of most medicines, so Mendelian randomization (MR) studies of drug targets are becoming a fundamental tool in drug development. To enable such studies we introduce a formal mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. Finally, we illustrate key model decisions and introduce an analytical framework for maximizing power and elucidating the robustness of drug target MR analyses.

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Cochrane corner: PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease

Cited by 12 publications

References 3 publications

Genetic drug target validation using Mendelian randomisation

Genetic drug target validation using Mendelian randomisation

Use of multivariable Mendelian randomization to address biases due to competing risk before recruitment

Genetic drug target validation using Mendelian randomization

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