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2. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study

Author

Schmidt, Amand F., Swerdlow, Daniel I., Holmes, Michael V., Patel, Riyaz S., Fairhurst-Hunter, Zammy, Lyall, Donald M., Hartwig, Fernando Pires, Horta, Bernardo Lessa, Hyppönen, Elina, Power, Christine, Moldovan, Max, van Iperen, Erik, Hovingh, G. Kees, Demuth, Ilja, Norman, Kristina, Steinhagen-Thiessen, Elisabeth, Demuth, Juri, Bertram, Lars, Liu, Tian, Coassin, Stefan, Willeit, Johann, Kiechl, Stefan, Willeit, Karin, Mason, Dan, Wright, John, Morris, Richard, Wanamethee, Goya, Whincup, Peter, Ben-Shlomo, Yoav, McLachlan, Stela, Price, Jackie F., Kivimaki, Mika, Welch, Catherine, Sanchez-Galvez, Adelaida, Marques-Vidal, Pedro, Nicolaides, Andrew, Panayiotou, Andrie G., Onland-Moret, N. Charlotte, van der Schouw, Yvonne T., Matullo, Giuseppe, Fiorito, Giovanni, Guarrera, Simonetta, Sacerdote, Carlotta, Wareham, Nicholas J., Langenberg, Claudia, Scott, Robert, Luan, Jian'an, Bobak, Martin, Malyutina, Sofia, Pająk, Andrzej, Kubinova, Ruzena, Tamosiunas, Abdonas, Pikhart, Hynek, Husemoen, Lise Lotte Nystrup, Garup, Niels, Pedersen, Oluf, Hansen, Torben, Linneberg, Allan, Simonsen, Kenneth Starup, Cooper, Jackie, Humphries, Steve E., Brilliant, Murray, Kitchner, Terrie, Hakonarson, Hakon, Carrell, David S., McCarty, Catherine A., Kirchner, H. Lester, Larson, Eric B., Crosslin, David R., de Andrade, Mariza, Roden, Dan M., Denny, Joshua C., Carty, Cara, Hancock, Stephen, Attia, John, Holliday, Elizabeth, O'Donnell, Martin, Yusuf, Salim, Chong, Michael, Pare, Guillaume, van der Harst, Pim, Said, M. Abdullah, Eppinga, Ruben N., Verweij, Niek, Snieder, Harold, Christen, Tim, Mook-Kanamori, Dennis O., Gustafsson, Stefan, Lind, Lars, Ingelsson, Erik, Pazoki, Raha, Franco, Oscar, Hofman, Albert, Uitterlinden, Andre, Dehghan, Abbas, Teumer, Alexander, Baumeister, Sebastian, Dörr, Marcus, Lerch, Markus M., Völker, Uwe, Völzke, Henry, Ward, Joey, Pell, Jill P., Smith, Daniel J., Meade, Tom, Maitland-van der Zee, Anke H., Baranova, Ekaterina V., Young, Robin, Ford, Ian, Campbell, Archie, Padmanabhan, Sandosh, Bots, Michiel L., Grobbee, Diederick E., Froguel, Philippe, Thuillier, Dorothée, Balkau, Beverley, Bonnefond, Amélie, Cariou, Bertrand, Smart, Melissa, Bao, Yanchun, Kumari, Meena, Mahajan, Anubha, Ridker, Paul M., Chasman, Daniel I., Reiner, Alex P., Lange, Leslie A., Ritchie, Marylyn D., Asselbergs, Folkert, Casas, Juan-Pablo, Keating, Brendan J., Preiss, David, Hingorani, Aroon D., Sattar, Naveed, LifeLines Cohort study group, UCLEB consortium, Epidemiology, and Internal Medicine

Subjects
Blood Glucose, STATIN THERAPY, Endocrinology, Diabetes and Metabolism, HEART-DISEASE, Endocrinology and Diabetes, Heart disease, Internal Medicine, Endocrinology, Medical and Health Sciences, HYPERCHOLESTEROLEMIA, Cohort Studies, Endocrinology & Metabolism, SDG 3 - Good Health and Well-being, Health Sciences, Journal Article, Diabetes Mellitus, Humans, LDL-cholesterol, Genetic Predisposition to Disease, Mendelian randomisation, COMMON, METAANALYSIS, Genetic Association Studies, Randomized Controlled Trials as Topic, ARCHITECTURE, Science & Technology, CHOLESTEROL, Diabetes, Genetic Variation, PATHWAYS, nutritional and metabolic diseases, Cholesterol, LDL, ASSOCIATION, Mendelian Randomization Analysis, UCLEB consortium, PCSK9 inhibition, Diabetes and Metabolism, INSIGHTS, Diabetes Mellitus, Type 2, Case-Control Studies, Endokrinologi och diabetes, LDL cholesterol, Proprotein Convertase 9, Blood Glucose/metabolism, Cholesterol, LDL/blood, Cholesterol, LDL/genetics, Diabetes Mellitus, Type 2/blood, Diabetes Mellitus, Type 2/diagnosis, Diabetes Mellitus, Type 2/genetics, Genetic Predisposition to Disease/genetics, Genetic Variation/genetics, Mendelian Randomization Analysis/methods, Proprotein Convertase 9/genetics, Randomized Controlled Trials as Topic/methods, Life Sciences & Biomedicine, LifeLines Cohort study group
Abstract

Background: Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions\ud in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest\ud hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way offsets their\ud substantial benefits. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2\ud diabetes and related biomarkers to gauge the likely effects of PCSK9 inhibitors on diabetes risk.\ud Methods: In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials,\ud case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol,\ud fasting blood glucose, HbA1c, fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, using\ud a standardised analysis plan, meta-analyses, and weighted gene-centric scores.\ud Findings: Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analyses\ud of four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lower\ud LDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight\ud (1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50).\ud Based on the collected data, we did not identify associations with HbA1c (0·03%, –0·01 to 0·08), fasting insulin (0·00%,\ud –0·06 to 0·07), and BMI (0·11 kg/m², –0·09 to 0·30).\ud Interpretation: PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higher\ud fasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diabetes. In trials of\ud PCSK9 inhibitor drugs, investigators should carefully assess these safety outcomes and quantify the risks and benefits\ud of PCSK9 inhibitor treatment, as was previously done for statins.\ud Funding: British Heart Foundation, and University College London Hospitals NHS Foundation Trust (UCLH) National\ud Institute for Health Research (NIHR) Biomedical Research Centre.

Published
2017

3. Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9.

Author

Schmidt, Amand F., Holmes, Michael V., Preiss, David, Swerdlow, Daniel I., Denaxas, Spiros, Fatemifar, Ghazaleh, Faraway, Rupert, Finan, Chris, Valentine, Dennis, Fairhurst-Hunter, Zammy, Hartwig, Fernando Pires, Horta, Bernardo Lessa, Hypponen, Elina, Power, Christine, Moldovan, Max, van Iperen, Erik, Hovingh, Kees, Demuth, Ilja, Norman, Kristina, and Steinhagen-Thiessen, Elisabeth

Subjects
TYPE 2 diabetes, BLOOD lipids, OBSTRUCTIVE lung diseases, TREATMENT effectiveness, ATRIAL fibrillation, FLUTICASONE
Abstract

Background: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.Methods: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration.Results: The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer's disease - outcomes for which large-scale trial data were unavailable.Conclusions: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate. [ABSTRACT FROM AUTHOR]

Published
2019
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4. Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9

Author

Schmidt, Amand F., Holmes, Michael V., Preiss, David, Swerdlow, Daniel I., Denaxas, Spiros, Fatemifar, Ghazaleh, Faraway, Rupert, Finan, Chris, Valentine, Dennis, Fairhurst-Hunter, Zammy, Hartwig, Fernando Pires, Horta, Bernardo Lessa, Hypponen, Elina, Power, Christine, Moldovan, Max, Van Iperen, Erik, Hovingh, Kees, Demuth, Ilja, Norman, Kristina, Steinhagen-Thiessen, Elisabeth, Demuth, Juri, Bertram, Lars, Lill, Christina M., Coassin, Stefan, Willeit, Johann, Kiechl, Stefan, Willeit, Karin, Mason, Dan, Wright, John, Morris, Richard, Wanamethee, Goya, Whincup, Peter, Ben-Shlomo, Yoav, McLachlan, Stela, Price, Jackie F., Kivimaki, Mika, Welch, Catherine, Sanchez-Galvez, Adelaida, Marques-Vidal, Pedro, Nicolaides, Andrew, Panayiotou, Andrie G., Onland-Moret, N. Charlotte, Van Der Schouw, Yvonne T., Matullo, Giuseppe, Fiorito, Giovanni, Guarrera, Simonetta, Sacerdote, Carlotta, Wareham, Nicholas J., Langenberg, Claudia, Scott, Robert A., Luan, Jian’an, Bobak, Martin, Malyutina, Sofia, Pająk, Andrzej, Kubinova, Ruzena, Tamosiunas, Abdonas, Pikhart, Hynek, Grarup, Niels, Pedersen, Oluf, Hansen, Torben, Linneberg, Allan, Jess, Tine, Cooper, Jackie, Humphries, Steve E., Brilliant, Murray, Kitchner, Terrie, Hakonarson, Hakon, Carrell, David S., McCarty, Catherine A., Lester, Kirchner H., Larson, Eric B., Crosslin, David R., De Andrade, Mariza, Roden, Dan M., Denny, Joshua C., Carty, Cara, Hancock, Stephen, Attia, John, Holliday, Elizabeth, Scott, Rodney, Schofield, Peter, O’Donnell, Martin, Yusuf, Salim, Chong, Michael, Pare, Guillaume, Van Der Harst, Pim, Said, M. Abdullah, Eppinga, Ruben N., Verweij, Niek, Snieder, Harold, Christen, Tim, Mook-Kanamori, D. O., Gustafsson, Stefan, Lind, Lars, Ingelsson, Erik, Pazoki, Raha, Franco, Oscar, Hofman, Albert, Uitterlinden, Andre, Dehghan, Abbas, Teumer, Alexander, Baumeister, Sebastian, Dörr, Marcus, Lerch, Markus M., Völker, Uwe, Völzke, Henry, Ward, Joey, Pell, Jill P., Meade, Tom, Christophersen, Ingrid E., Maitland-Van Der Zee, Anke H., Baranova, Ekaterina V., Young, Robin, Ford, Ian, Campbell, Archie, Padmanabhan, Sandosh, Bots, Michiel L., Grobbee, Diederick E., Froguel, Philippe, Thuillier, Dorothée, Roussel, Ronan, Bonnefond, Amélie, Cariou, Bertrand, Smart, Melissa, Bao, Yanchun, Kumari, Meena, Mahajan, Anubha, Hopewell, Jemma C., Seshadri, Sudha, Dale, Caroline, Costa, Rui Providencia E., Ridker, Paul M., Chasman, Daniel I., Reiner, Alex P., Ritchie, Marylyn D., Lange, Leslie A., Cornish, Alex J., Dobbins, Sara E., Hemminki, Kari, Kinnersley, Ben, Sanson, Marc, Labreche, Karim, Simon, Matthias, Bondy, Melissa, Law, Philip, Speedy, Helen, Allan, James, Li, Ni, Went, Molly, Weinhold, Niels, Morgan, Gareth, Sonneveld, Pieter, Nilsson, Björn, Goldschmidt, Hartmut, Sud, Amit, Engert, Andreas, Hansson, Markus, Hemingway, Harry, Asselbergs, Folkert W., Patel, Riyaz S., Keating, Brendan J., Sattar, Naveed, Houlston, Richard, Casas, Juan P., and Hingorani, Aroon D.

Subjects
Genetic association studies, LDL-cholesterol, Phenome-wide association scan, Mendelian randomisation, Coronary artery disease, 3. Good health, Research Article
Abstract

Background: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9. Methods: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration. Results: The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable. Conclusions: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.

5. Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9

Author

Schmidt, Amand F, Holmes, Michael V, Preiss, David, Swerdlow, Daniel I, Denaxas, Spiros, Fatemifar, Ghazaleh, Faraway, Rupert, Finan, Chris, Valentine, Dennis, Fairhurst-Hunter, Zammy, Hartwig, Fernando Pires, Horta, Bernardo Lessa, Hypponen, Elina, Power, Christine, Moldovan, Max, Van Iperen, Erik, Hovingh, Kees, Demuth, Ilja, Norman, Kristina, Steinhagen-Thiessen, Elisabeth, Demuth, Juri, Bertram, Lars, Lill, Christina M, Coassin, Stefan, Willeit, Johann, Kiechl, Stefan, Willeit, Karin, Mason, Dan, Wright, John, Morris, Richard, Wanamethee, Goya, Whincup, Peter, Ben-Shlomo, Yoav, McLachlan, Stela, Price, Jackie F, Kivimaki, Mika, Welch, Catherine, Sanchez-Galvez, Adelaida, Marques-Vidal, Pedro, Nicolaides, Andrew, Panayiotou, Andrie G, Onland-Moret, N Charlotte, Van Der Schouw, Yvonne T, Matullo, Giuseppe, Fiorito, Giovanni, Guarrera, Simonetta, Sacerdote, Carlotta, Wareham, Nicholas J, Langenberg, Claudia, Scott, Robert A, Luan, Jian'an, Bobak, Martin, Malyutina, Sofia, Pająk, Andrzej, Kubinova, Ruzena, Tamosiunas, Abdonas, Pikhart, Hynek, Grarup, Niels, Pedersen, Oluf, Hansen, Torben, Linneberg, Allan, Jess, Tine, Cooper, Jackie, Humphries, Steve E, Brilliant, Murray, Kitchner, Terrie, Hakonarson, Hakon, Carrell, David S, McCarty, Catherine A, Lester, Kirchner H, Larson, Eric B, Crosslin, David R, De Andrade, Mariza, Roden, Dan M, Denny, Joshua C, Carty, Cara, Hancock, Stephen, Attia, John, Holliday, Elizabeth, Scott, Rodney, Schofield, Peter, O'Donnell, Martin, Yusuf, Salim, Chong, Michael, Pare, Guillaume, Van Der Harst, Pim, Said, M Abdullah, Eppinga, Ruben N, Verweij, Niek, Snieder, Harold, Lifelines Cohort Authors, Christen, Tim, Mook-Kanamori, DO, ICBP Consortium, Gustafsson, Stefan, Lind, Lars, Ingelsson, Erik, Pazoki, Raha, Franco, Oscar, Hofman, Albert, Uitterlinden, Andre, Dehghan, Abbas, Teumer, Alexander, Baumeister, Sebastian, Dörr, Marcus, Lerch, Markus M, Völker, Uwe, Völzke, Henry, Ward, Joey, Pell, Jill P, Meade, Tom, Christophersen, Ingrid E, Maitland-Van Der Zee, Anke H, Baranova, Ekaterina V, Young, Robin, Ford, Ian, Campbell, Archie, Padmanabhan, Sandosh, Bots, Michiel L, Grobbee, Diederick E, Froguel, Philippe, Thuillier, Dorothée, Roussel, Ronan, Bonnefond, Amélie, Cariou, Bertrand, Smart, Melissa, Bao, Yanchun, Kumari, Meena, Mahajan, Anubha, Hopewell, Jemma C, Seshadri, Sudha, METASTROKE Consortium Of The ISGC, Dale, Caroline, Costa, Rui Providencia E, Ridker, Paul M, Chasman, Daniel I, Reiner, Alex P, Ritchie, Marylyn D, Lange, Leslie A, Cornish, Alex J, Dobbins, Sara E, Hemminki, Kari, Kinnersley, Ben, Sanson, Marc, Labreche, Karim, Simon, Matthias, Bondy, Melissa, Law, Philip, Speedy, Helen, Allan, James, Li, Ni, Went, Molly, Weinhold, Niels, Morgan, Gareth, Sonneveld, Pieter, Nilsson, Björn, Goldschmidt, Hartmut, Sud, Amit, Engert, Andreas, Hansson, Markus, Hemingway, Harry, Asselbergs, Folkert W, Patel, Riyaz S, Keating, Brendan J, Sattar, Naveed, Houlston, Richard, Casas, Juan P, and Hingorani, Aroon D

Subjects
Genetic association studies, Serine Proteinase Inhibitors, Anticholesteremic Agents, PCSK9 Inhibitors, Myocardial Infarction, Down-Regulation, Cholesterol, LDL, Polymorphism, Single Nucleotide, Risk Assessment, 3. Good health, Brain Ischemia, Stroke, Treatment Outcome, Risk Factors, LDL-cholesterol, Humans, Phenome-wide association scan, Proprotein Convertase 9, Mendelian randomisation, Biomarkers, Dyslipidemias, Genome-Wide Association Study, Randomized Controlled Trials as Topic
Abstract

BACKGROUND: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9. METHODS: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration. RESULTS: The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer's disease - outcomes for which large-scale trial data were unavailable. CONCLUSIONS: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.

6. Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9

Author

Chris Finan, Yoav Ben-Shlomo, Eric B. Larson, Tine Jess, Richard W Morris, Daniel I. Chasman, Fernando Pires Hartwig, Catherine Welch, Rodney J. Scott, Helen E. Speedy, Andrzej Pajak, Raha Pazoki, André G. Uitterlinden, Torben Hansen, Marc Sanson, Hakon Hakonarson, Claudia Langenberg, Joey Ward, John Wright, Dorothée Thuillier, Ben Kinnersley, Diederick E. Grobbee, Yvonne T. van der Schouw, Pieter Sonneveld, Michiel L. Bots, Harold Snieder, Karim Labreche, Dan M. Roden, Archie Campbell, Melissa C. Smart, Christine Power, Pim van der Harst, Amélie Bonnefond, Ingrid E. Christophersen, Riyaz S. Patel, Uwe Völker, Stephen Hancock, Niels Grarup, Dennis O. Mook-Kanamori, Mariza de Andrade, Caroline Dale, N. Charlotte Onland-Moret, David R. Crosslin, Meena Kumari, Erik Ingelsson, Michael V. Holmes, Spiros Denaxas, Sudha Seshadri, Kees Hovingh, Marcus Dörr, Paul M. Ridker, Stefan Coassin, Albert Hofman, Andrew N. Nicolaides, Oluf Pedersen, Philippe Froguel, Simonetta Guarrera, Murray H. Brilliant, Sara E. Dobbins, Salim Yusuf, Kari Hemminki, Erik P A Van Iperen, Abbas Dehghan, Jill P. Pell, Alexander Teumer, Peter W. Schofield, Aroon D. Hingorani, Dan Mason, Amand F. Schmidt, Rui Bebiano Da Providencia E Costa, James M. Allan, Leslie A. Lange, Niels Weinhold, Stefan Gustafsson, Jackie F. Price, Mika Kivimäki, Hynek Pikhart, Kirchner H. Lester, Lars Lind, Philip J. Law, Cara L. Carty, David Preiss, Richard S. Houlston, Robin Young, Tom W. Meade, Martin O'Donnell, Alexander P. Reiner, Ni Li, Oscar H. Franco, Zammy Fairhurst-Hunter, Ronan Roussel, Tim Christen, Ilja Demuth, David Carrell, Catherine A. McCarty, Juan P. Casas, Johann Willeit, Peter H. Whincup, Stela McLachlan, Adelaida Sanchez-Galvez, Hartmut Goldschmidt, Guillaume Paré, Harry Hemingway, Anubha Mahajan, Elisabeth Steinhagen-Thiessen, Elizabeth G. Holliday, Giuseppe Matullo, Henry Völzke, Ian Ford, Martin Bobak, Pedro Marques-Vidal, Bertrand Cariou, Bernardo L. Horta, Melissa L. Bondy, Goya Wanamethee, Naveed Sattar, Steve E. Humphries, Marylyn D. Ritchie, Kristina Norman, Carlotta Sacerdote, Giovanni Fiorito, Sebastian E. Baumeister, Amit Sud, Dennis Valentine, Andreas Engert, Juri Demuth, Rupert Faraway, Abdonas Tamosiunas, Andrie G. Panayiotou, Terrie Kitchner, Lars Bertram, Sandosh Padmanabhan, Sofia Malyutina, Anke H. Maitland-van der Zee, Alex J. Cornish, Joshua C. Denny, Jian'an Luan, Robert A. Scott, Daniel I. Swerdlow, John Attia, Karin Willeit, Gareth J. Morgan, Michael Chong, Ruben N. Eppinga, Elina Hyppönen, Ekaterina V. Baranova, Jackie A. Cooper, Ghazaleh Fatemifar, Niek Verweij, Max Moldovan, Brendan J. Keating, M. Abdullah Said, Markus M. Lerch, Christina M. Lill, Markus Hansson, Jemma C. Hopewell, Björn Nilsson, Folkert W. Asselbergs, Ruzena Kubinova, Molly Went, Nicholas J. Wareham, Stefan Kiechl, Yanchun Bao, Allan Linneberg, Matthias Simon, Epidemiology and Data Science, Pulmonology, Paediatric Pulmonology, APH - Personalized Medicine, AII - Inflammatory diseases, AII - Cancer immunology, CCA - Cancer biology and immunology, Ear, Nose and Throat, Schmidt, Amand F, Holmes, Michael V, Preiss, David, Swerdlow, Daniel I, Hypponen, Elina, Dehghan, Abbas, Schmidt, Amand F [0000-0003-1327-0424], Apollo - University of Cambridge Repository, Lifelines Cohort, ICBP Consortium, METASTROKE Consortium of the ISGC, PharmacoTherapy, -Epidemiology and -Economics, Cardiovascular Centre (CVC), Life Course Epidemiology (LCE), Schmidt, Amand F. [0000-0003-1327-0424], Epidemiology, and Hematology

Subjects
Oncology, lcsh:Diseases of the circulatory (Cardiovascular) system, Genetic association studies, Proprotein Convertase 9/genetics, Apolipoprotein B, Anticholesteremic Agents/adverse effects, Myocardial Infarction, Blood lipids, Genome-wide association study, 030204 cardiovascular system & hematology, Coronary artery disease, Gastroenterology, Medical and Health Sciences, Stroke/epidemiology, Brain Ischemia, 0302 clinical medicine, Risk Factors, Dyslipidemias/blood, Medicine, LDL-cholesterol, Cardiac and Cardiovascular Systems, 030212 general & internal medicine, Myocardial infarction, Mendelian randomisation, 1102 Cardiorespiratory Medicine and Haematology, Randomized Controlled Trials as Topic, Kardiologi, biology, Anticholesteremic Agents, PCSK9 Inhibitors, Single Nucleotide, 16. Peace & justice, LDL/blood, 3. Good health, Stroke, Cholesterol, Treatment Outcome, Cholesterol, LDL/blood, ICBP Consortium, Phenome-wide association scan, Proprotein Convertase 9, Cardiology and Cardiovascular Medicine, Research Article, medicine.medical_specialty, Serine Proteinase Inhibitors, Down-Regulation, 610 Medicine & health, Single-nucleotide polymorphism, Placebo, Polymorphism, Single Nucleotide, Risk Assessment, 03 medical and health sciences, Internal medicine, Genetic variation, Myocardial Infarction/epidemiology, Humans, Serine Proteinase Inhibitors/adverse effects, Polymorphism, Dyslipidemias, Genetic association, Lifelines Cohort authors, METASTROKE Consortium of the ISGC, business.industry, PCSK9, Cholesterol, LDL, Odds ratio, medicine.disease, Cardiovascular System & Hematology, lcsh:RC666-701, biology.protein, Brain Ischemia/epidemiology, Clinical Medicine, business, Biomarkers, Biomarkers/blood, Genome-Wide Association Study
Abstract

BackgroundWe characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.MethodsPublished and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentrationResultsThe PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable.ConclusionsGenetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.FundingThis research was funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456, FS/18/23/33512), UCL Hospitals NIHR Biomedical Research Centre, by the Rosetrees and Stoneygate Trusts.Condensed abstractEvidence on the long-term efficacy and safety of therapeutic inhibition of PCSK9 is lacking. To explore potential long-term effects of PCSK9 inhibition, we characterised the phenotypic consequence of LDL-cholesterol lowering variants at the PCSK9 locus. A PCSK9 gene score comprising 4 SNPs recapitulated the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and risk of myocardial infarction, and was associated with an increased risk of type 2 diabetes. No associations with safety outcomes such as cancer, COPD, Alzheimer’s disease or atrial fibrillation were identified. Our findings suggest PCSK9 inhibition may be safe and effective during prolonged use.

Published
2019

7. Prioritising genetic findings for drug target identification and validation.

Author

Hukerikar, Nikita, Hingorani, Aroon D., Asselbergs, Folkert W., Finan, Chris, and Schmidt, Amand F.

Subjects
*GENE ontology, *DRUG target, *NON-alcoholic fatty liver disease, *BLOOD proteins, *DRUG development, *ADIPOSE tissues
Abstract

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 is 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. [Display omitted] • Using genetic data to identify and validate drug targets has been shown to reduce failure rates in clinical drug development. • Widely used techniques include loss-of-function analysis, colocalization, and Mendelian randomisation. • Databases of clinically relevant data can be used to annotate and prioritise targets identified in genetic studies. • Annotation data may include druggability, pathway membership, tissue and cell expression, and mapping of identifiers. • These methods and databases were used to identify 5 potential drug targets for non-alcoholic fatty liver disease. [ABSTRACT FROM AUTHOR]

Published
2024
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