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1. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Author

Suzuki, Ken, Hatzikotoulas, Konstantinos, Southam, Lorraine, Taylor, Henry J., Yin, Xianyong, Lorenz, Kim M., Mandla, Ravi, Huerta-Chagoya, Alicia, Melloni, Giorgio E. M., Kanoni, Stavroula, Rayner, Nigel W., Bocher, Ozvan, Arruda, Ana Luiza, Sonehara, Kyuto, Namba, Shinichi, Lee, Simon S. K., Preuss, Michael H., Petty, Lauren E., Schroeder, Philip, Vanderwerff, Brett, Kals, Mart, Bragg, Fiona, Lin, Kuang, Guo, Xiuqing, Zhang, Weihua, Yao, Jie, Kim, Young Jin, Graff, Mariaelisa, Takeuchi, Fumihiko, Nano, Jana, Lamri, Amel, Nakatochi, Masahiro, Moon, Sanghoon, Scott, Robert A., Cook, James P., Lee, Jung-Jin, Pan, Ian, Taliun, Daniel, Parra, Esteban J., Chai, Jin-Fang, Bielak, Lawrence F., Tabara, Yasuharu, Hai, Yang, Thorleifsson, Gudmar, Grarup, Niels, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloé, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Kwak, Soo-Heon, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Nongmaithem, Suraj S., Noordam, Raymond, Lim, Victor J. Y., Tam, Claudia H. T., Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M., Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R., Chen, Guanjie, Brody, Jennifer A., Kabagambe, Edmond, An, Ping, Xiang, Anny H., Choi, Hyeok Sun, Cade, Brian E., Tan, Jingyi, Broadaway, K. Alaine, Williamson, Alice, Kamali, Zoha, Cui, Jinrui, Thangam, Manonanthini, Adair, Linda S., Adeyemo, Adebowale, Aguilar-Salinas, Carlos A., Ahluwalia, Tarunveer S., Anand, Sonia S., Bertoni, Alain, Bork-Jensen, Jette, Brandslund, Ivan, Buchanan, Thomas A., Burant, Charles F., Butterworth, Adam S., Canouil, Mickaël, Chan, Juliana C. N., Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, Cushman, Mary, Danesh, John, Das, Swapan K., de Silva, H. Janaka, Dedoussis, George, Dimitrov, Latchezar, Doumatey, Ayo P., Du, Shufa, Duan, Qing, Eckardt, Kai-Uwe, Emery, Leslie S., Evans, Daniel S., Evans, Michele K., Fischer, Krista, Floyd, James S., Ford, Ian, Franco, Oscar H., Frayling, Timothy M., Freedman, Barry I., Genter, Pauline, Gerstein, Hertzel C., Giedraitis, Vilmantas, González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Gordon-Larsen, Penny, Gross, Myron, Guare, Lindsay A., Hackinger, Sophie, Hakaste, Liisa, Han, Sohee, Hattersley, Andrew T., Herder, Christian, Horikoshi, Momoko, Howard, Annie-Green, Hsueh, Willa, Huang, Mengna, Huang, Wei, Hung, Yi-Jen, Hwang, Mi Yeong, Hwu, Chii-Min, Ichihara, Sahoko, Ikram, Mohammad Arfan, Ingelsson, Martin, Islam, Md. Tariqul, Isono, Masato, Jang, Hye-Mi, Jasmine, Farzana, Jiang, Guozhi, Jonas, Jost B., Jørgensen, Torben, Kamanu, Frederick K., Kandeel, Fouad R., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kaur, Varinderpal, Kawaguchi, Takahisa, Keaton, Jacob M., Kho, Abel N., Khor, Chiea-Chuen, Kibriya, Muhammad G., Kim, Duk-Hwan, Kronenberg, Florian, Kuusisto, Johanna, Läll, Kristi, Lange, Leslie A., Lee, Kyung Min, Lee, Myung-Shik, Lee, Nanette R., Leong, Aaron, Li, Liming, Li, Yun, Li-Gao, Ruifang, Ligthart, Symen, Lindgren, Cecilia M., Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Locke, Adam E., Louie, Tin, Luan, Jian’an, Luk, Andrea O., Luo, Xi, Lv, Jun, Lynch, Julie A., Lyssenko, Valeriya, Maeda, Shiro, Mamakou, Vasiliki, Mansuri, Sohail Rafik, Matsuda, Koichi, Meitinger, Thomas, Melander, Olle, Metspalu, Andres, Mo, Huan, Morris, Andrew D., Moura, Filipe A., Nadler, Jerry L., Nalls, Michael A., Nayak, Uma, Ntalla, Ioanna, Okada, Yukinori, Orozco, Lorena, Patel, Sanjay R., Patil, Snehal, Pei, Pei, Pereira, Mark A., Peters, Annette, Pirie, Fraser J., Polikowsky, Hannah G., Porneala, Bianca, Prasad, Gauri, Rasmussen-Torvik, Laura J., Reiner, Alexander P., Roden, Michael, Rohde, Rebecca, Roll, Katheryn, Sabanayagam, Charumathi, Sandow, Kevin, Sankareswaran, Alagu, Sattar, Naveed, Schönherr, Sebastian, Shahriar, Mohammad, Shen, Botong, Shi, Jinxiu, Shin, Dong Mun, Shojima, Nobuhiro, Smith, Jennifer A., So, Wing Yee, Stančáková, Alena, Steinthorsdottir, Valgerdur, Stilp, Adrienne M., Strauch, Konstantin, Taylor, Kent D., Thorand, Barbara, Thorsteinsdottir, Unnur, Tomlinson, Brian, Tran, Tam C., Tsai, Fuu-Jen, Tuomilehto, Jaakko, Tusie-Luna, Teresa, Udler, Miriam S., Valladares-Salgado, Adan, van Dam, Rob M., van Klinken, Jan B., Varma, Rohit, Wacher-Rodarte, Niels, Wheeler, Eleanor, Wickremasinghe, Ananda R., van Dijk, Ko Willems, Witte, Daniel R., Yajnik, Chittaranjan S., Yamamoto, Ken, Yamamoto, Kenichi, Yoon, Kyungheon, Yu, Canqing, Yuan, Jian-Min, Yusuf, Salim, Zawistowski, Matthew, Zhang, Liang, Zheng, Wei, Raffel, Leslie J., Igase, Michiya, Ipp, Eli, Redline, Susan, Cho, Yoon Shin, Lind, Lars, Province, Michael A., Fornage, Myriam, Hanis, Craig L., Ingelsson, Erik, Zonderman, Alan B., Psaty, Bruce M., Wang, Ya-Xing, Rotimi, Charles N., Becker, Diane M., Matsuda, Fumihiko, Liu, Yongmei, Yokota, Mitsuhiro, Kardia, Sharon L. R., Peyser, Patricia A., Pankow, James S., Engert, James C., Bonnefond, Amélie, Froguel, Philippe, Wilson, James G., Sheu, Wayne H. H., Wu, Jer-Yuarn, Hayes, M. Geoffrey, Ma, Ronald C. W., Wong, Tien-Yin, Mook-Kanamori, Dennis O., Tuomi, Tiinamaija, Chandak, Giriraj R., Collins, Francis S., Bharadwaj, Dwaipayan, Paré, Guillaume, Sale, Michèle M., Ahsan, Habibul, Motala, Ayesha A., Shu, Xiao-Ou, Park, Kyong-Soo, Jukema, J. Wouter, Cruz, Miguel, Chen, Yii-Der Ida, Rich, Stephen S., McKean-Cowdin, Roberta, Grallert, Harald, Cheng, Ching-Yu, Ghanbari, Mohsen, Tai, E-Shyong, Dupuis, Josee, Kato, Norihiro, Laakso, Markku, Köttgen, Anna, Koh, Woon-Puay, Bowden, Donald W., Palmer, Colin N. A., Kooner, Jaspal S., Kooperberg, Charles, Liu, Simin, North, Kari E., Saleheen, Danish, Hansen, Torben, Pedersen, Oluf, Wareham, Nicholas J., Lee, Juyoung, Kim, Bong-Jo, Millwood, Iona Y., Walters, Robin G., Stefansson, Kari, Ahlqvist, Emma, Goodarzi, Mark O., Mohlke, Karen L., Langenberg, Claudia, Haiman, Christopher A., Loos, Ruth J. F., Florez, Jose C., Rader, Daniel J., Ritchie, Marylyn D., Zöllner, Sebastian, Mägi, Reedik, Marston, Nicholas A., Ruff, Christian T., van Heel, David A., Finer, Sarah, Denny, Joshua C., Yamauchi, Toshimasa, Kadowaki, Takashi, Chambers, John C., Ng, Maggie C. Y., Sim, Xueling, Below, Jennifer E., Tsao, Philip S., Chang, Kyong-Mi, McCarthy, Mark I., Meigs, James B., Mahajan, Anubha, Spracklen, Cassandra N., Mercader, Josep M., Boehnke, Michael, Rotter, Jerome I., Vujkovic, Marijana, Voight, Benjamin F., Morris, Andrew P., and Zeggini, Eleftheria

Published
2024
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4. Clonal hematopoiesis, cardiovascular events and treatment benefit in 63,700 individuals from five TIMI randomized trials

Author

Marston, Nicholas A., Pirruccello, James P., Melloni, Giorgio E. M., Kamanu, Frederick, Bonaca, Marc P., Giugliano, Robert P., Scirica, Benjamin M., Wiviott, Stephen D., Bhatt, Deepak L., Steg, Philippe Gabriel, Raz, Itamar, Braunwald, Eugene, Libby, Peter, Ellinor, Patrick T., Bick, Alexander G., Sabatine, Marc S., and Ruff, Christian T.

Abstract

Clonal hematopoiesis of indeterminate potential (CHIP) has been associated with an increased risk of cardiovascular (CV) disease in the general population. Currently, it is unclear whether this association is observed in large clinical trial cohorts with a high burden of existing CV disease or whether CV therapies can mitigate CHIP-associated CV risk. To address these questions, we studied 63,700 patients from five randomized trials that tested established therapies for CV disease, including treatments targeting the proteins PCSK9, SGLT2, P2Y12 and FXa. During a median follow-up of 2.5 years, 7,453 patients had at least one CV event (CV death, myocardial infarction (MI), ischemic stroke or coronary revascularization). The adjusted hazard ratio (aHR) for CV events for CHIP+ patients was 1.07 (95% CI: 0.99–1.16, P= 0.08), with consistent risk estimates across each component of CV risk. Significant heterogeneity in the risk of MI was observed, such that CHIP+ patients had a 30% increased risk of first MI (aHR = 1.31 (1.05–1.64), P= 0.02) but no increased risk of recurrent MI (aHR = 0.94 (0.79–1.13), Pint= 0.008), as compared to CHIP− patients. Moreover, no significant heterogeneity in treatment effect between individuals with and without CHIP was observed for any of the therapies studied in the five trials. These results indicate that in clinical trial populations, CHIP is associated with incident but not recurrent coronary events and that the presence of CHIP does not appear to identify patients who will derive greater benefit from commonly used CV therapies.

Published
2024
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6. Very low-depth whole-genome sequencing in complex trait association studies

Author

Gilly, Arthur, primary, Southam, Lorraine, additional, Suveges, Daniel, additional, Kuchenbaecker, Karoline, additional, Moore, Rachel, additional, Melloni, Giorgio E M, additional, Hatzikotoulas, Konstantinos, additional, Farmaki, Aliki-Eleni, additional, Ritchie, Graham, additional, Schwartzentruber, Jeremy, additional, Danecek, Petr, additional, Kilian, Britt, additional, Pollard, Martin O, additional, Ge, Xiangyu, additional, Tsafantakis, Emmanouil, additional, Dedoussis, George, additional, and Zeggini, Eleftheria, additional

Published
2018
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7. Mutations targeting the coagulation pathway are enriched in brain metastases

Author

Richichi, Cristina, primary, Fornasari, Lorenzo, additional, Melloni, Giorgio E. M., additional, Brescia, Paola, additional, Patanè, Monica, additional, Del Bene, Massimiliano, additional, Mustafa, Dana A. M., additional, Kros, Johan M., additional, Pollo, Bianca, additional, Pruneri, Giancarlo, additional, Sciandivasci, Angela, additional, Munzone, Elisabetta, additional, DiMeco, Francesco, additional, Pelicci, Pier Giuseppe, additional, Riva, Laura, additional, and Pelicci, Giuliana, additional

Published
2017
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9. Very low-depth whole-genome sequencing in complex trait association studies.

Author

Gilly, Arthur, Southam, Lorraine, Suveges, Daniel, Kuchenbaecker, Karoline, Moore, Rachel, Melloni, Giorgio E M, Hatzikotoulas, Konstantinos, Farmaki, Aliki-Eleni, Ritchie, Graham, Schwartzentruber, Jeremy, Danecek, Petr, Kilian, Britt, Pollard, Martin O, Ge, Xiangyu, Tsafantakis, Emmanouil, Dedoussis, George, and Zeggini, Eleftheria

Subjects
INTERNET servers, GENOTYPES, ALLELES, BIOINFORMATICS, PIPELINES, GENOME-wide association studies, EXOMES, GENETIC correlations
Abstract

Motivation Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking. Results We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design. Availability and implementation The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter , the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2019
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12. The hidden genomic landscape of acute myeloid leukemia: subclonal structure revealed by undetected mutations

Author

Bodini, Margherita, primary, Ronchini, Chiara, additional, Giacò, Luciano, additional, Russo, Anna, additional, Melloni, Giorgio E. M., additional, Luzi, Lucilla, additional, Sardella, Domenico, additional, Volorio, Sara, additional, Hasan, Syed K., additional, Ottone, Tiziana, additional, Lavorgna, Serena, additional, Lo-Coco, Francesco, additional, Candoni, Anna, additional, Fanin, Renato, additional, Toffoletti, Eleonora, additional, Iacobucci, Ilaria, additional, Martinelli, Giovanni, additional, Cignetti, Alessandro, additional, Tarella, Corrado, additional, Bernard, Loris, additional, Pelicci, Pier Giuseppe, additional, and Riva, Laura, additional

Published
2015
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13. DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes.

Author

Melloni, Giorgio E. M., Ogier, Alessandro G. E., de Pretis, Stefano, Mazzarella, Luca, Pelizzola, Mattia, Pelicci, Pier Giuseppe, and Riva, Laura

Subjects
*CANCER genetics, *GENETIC mutation, *GENOMES, *COHORT analysis, *CLINICAL trials, TUMOR genetics
Abstract

A key challenge in the analysis of cancer genomes is the identification of driver genes from the vast number of mutations present in a cohort of patients. DOTS-Finder is a new tool that allows the detection of driver genes through the sequential application of functional and frequentist approaches, and is specifically tailored to the analysis of few tumor samples. We have identified driver genes in the genomic data of 34 tumor types derived from existing exploratory projects such as The Cancer Genome Atlas and from studies investigating the usefulness of genomic information in the clinical settings. DOTS-Finder is available at https://cgsb.genomics.iit.it/wiki/projects/DOTS-Finder/. [ABSTRACT FROM AUTHOR]

Published
2014
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14. The hidden genomic landscape of acute myeloid leukemia: Subclonal structure revealed by undetected mutations

Author

Corrado Tarella, Ilaria Iacobucci, Alessandro Cignetti, Pier Giuseppe Pelicci, Domenico Sardella, Giovanni Martinelli, Giorgio E. M. Melloni, Chiara Ronchini, Lucilla Luzi, Loris Bernard, Anna Di Russo, Anna Candoni, Francesco Lo-Coco, Luciano Giacò, Renato Fanin, Syed Khizer Hasan, Margherita Bodini, Serena Lavorgna, Laura Riva, Eleonora Toffoletti, Tiziana Ottone, Sara Volorio, Bodini, Margherita, Ronchini, Chiara, Giacò, Luciano, Russo, Anna, Melloni, Giorgio E. M., Luzi, Lucilla, Sardella, Domenico, Volorio, Sara, Hasan, Syed K., Ottone, Tiziana, Lavorgna, Serena, Lo-Coco, Francesco, Candoni, Anna, Fanin, Renato, Toffoletti, Eleonora, Iacobucci, Ilaria, Martinelli, Giovanni, Cignetti, Alessandro, Tarella, Corrado, Bernard, Lori, Pelicci, Pier Giuseppe, and Riva, Laura

Subjects
Myeloid, Immunology, DNA Mutational Analysis, Genomics, Biology, Acute, Genome, Biochemistry, DNA sequencing, DNA Mutational Analysi, Databases, Gene Frequency, medicine, Humans, Allele frequency, Genetics, Leukemia, Nucleic Acid, Genome, Human, Medicine (all), Myeloid leukemia, Computational Biology, High-Throughput Nucleotide Sequencing, Hematology, Cell Biology, medicine.disease, Leukemia, Myeloid, Acute, Databases, Nucleic Acid, Mutation, medicine.anatomical_structure, Genomic, Human genome, Settore MED/15 - Malattie del Sangue, Perspectives, Human
Abstract

The analyses carried out using 2 different bioinformatics pipelines (SomaticSniper and MuTect) on the same set of genomic data from 133 acute myeloid leukemia (AML) patients, sequenced inside the Cancer Genome Atlas project, gave discrepant results. We subsequently tested these 2 variant-calling pipelines on 20 leukemia samples from our series (19 primary AMLs and 1 secondary AML). By validating many of the predicted somatic variants (variant allele frequencies ranging from 100% to 5%), we observed significantly different calling efficiencies. In particular, despite relatively high specificity, sensitivity was poor in both pipelines resulting in a high rate of false negatives. Our findings raise the possibility that landscapes of AML genomes might be more complex than previously reported and characterized by the presence of hundreds of genes mutated at low variant allele frequency, suggesting that the application of genome sequencing to the clinic requires a careful and critical evaluation. We think that improvements in technology and workflow standardization, through the generation of clear experimental and bioinformatics guidelines, are fundamental to translate the use of next-generation sequencing from research to the clinic and to transform genomic information into better diagnosis and outcomes for the patient.

Published
2015

15. Genetic Risk Score to Identify Risk of Venous Thromboembolism in Patients With Cardiometabolic Disease.

Author

Marston NA, Melloni GEM, Gurmu Y, Bonaca MP, Kamanu FK, Roselli C, Lee C, Cavallari I, Giugliano RP, Scirica BM, Bhatt DL, Steg PG, Cohen M, Storey RF, Keech AC, Raz I, Mosenzon O, Braunwald E, Lubitz SA, Ellinor PT, Sabatine MS, and Ruff CT

Subjects
Aged, Female, Humans, Male, Metabolic Syndrome pathology, Middle Aged, Proportional Hazards Models, Proprotein Convertase 9 genetics, Risk Factors, Venous Thromboembolism etiology, Metabolic Syndrome genetics, Venous Thromboembolism diagnosis
Abstract

Background: Venous thromboembolism (VTE) is a major cause of cardiovascular morbidity and mortality and has a known genetic contribution. We tested the performance of a genetic risk score for its ability to predict VTE in 3 cohorts of patients with cardiometabolic disease., Methods: We included patients from the FOURIER (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Patients With Elevated Risk), PEGASUS-TIMI 54 (Prevention of Cardiovascular Events in Patients With Prior Heart Attack Using Ticagrelor Compared to Placebo on a Background of Aspirin), and SAVOR-TIMI 53 (Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus) trials (history of a major atherosclerotic cardiovascular event, myocardial infarction, and diabetes, respectively) who consented for genetic testing and were not on baseline anticoagulation. We calculated a VTE genetic risk score based on 297 single nucleotide polymorphisms with established genome-wide significance. Patients were divided into tertiles of genetic risk. Cox proportional hazards models were used to calculate hazard ratios for VTE across genetic risk groups. The polygenic risk score was compared with available clinical risk factors (age, obesity, smoking, history of heart failure, and diabetes) and common monogenic mutations., Results: A total of 29 663 patients were included in the analysis with a median follow-up of 2.4 years, of whom 174 had a VTE event. There was a significantly increased gradient of risk across VTE genetic risk tertiles ( P -trend <0.0001). After adjustment for clinical risk factors, patients in the intermediate and high genetic risk groups had a 1.88-fold (95% CI, 1.23-2.89; P =0.004) and 2.70-fold (95% CI, 1.81-4.06; P <0.0001) higher risk of VTE compared with patients with low genetic risk. In a continuous model adjusted for clinical risk factors, each standard deviation increase in the genetic risk score was associated with a 47% (95% CI, 29-68) increased risk of VTE ( P <0.0001)., Conclusions: In a broad spectrum of patients with cardiometabolic disease, a polygenic risk score is a strong, independent predictor of VTE after accounting for available clinical risk factors, identifying 1/3 of patients who have a risk of VTE comparable to that seen with established monogenic thrombophilia.

Published
2021
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16. The Effect of PCSK9 (Proprotein Convertase Subtilisin/Kexin Type 9) Inhibition on the Risk of Venous Thromboembolism.

Author

Marston NA, Gurmu Y, Melloni GEM, Bonaca M, Gencer B, Sever PS, Pedersen TR, Keech AC, Roselli C, Lubitz SA, Ellinor PT, O'Donoghue ML, Giugliano RP, Ruff CT, and Sabatine MS

Subjects
Aged, Antibodies, Monoclonal, Humanized adverse effects, Anticholesteremic Agents adverse effects, Biomarkers blood, Clinical Trials as Topic, Dyslipidemias blood, Dyslipidemias diagnosis, Dyslipidemias epidemiology, Female, Humans, Incidence, Male, Middle Aged, Pulmonary Embolism diagnosis, Pulmonary Embolism epidemiology, Pulmonary Embolism prevention & control, Risk Assessment, Risk Factors, Serine Proteinase Inhibitors adverse effects, Time Factors, Treatment Outcome, Venous Thromboembolism diagnosis, Venous Thromboembolism epidemiology, Venous Thrombosis diagnosis, Venous Thrombosis epidemiology, Antibodies, Monoclonal, Humanized therapeutic use, Anticholesteremic Agents therapeutic use, Cholesterol, LDL blood, Dyslipidemias drug therapy, Lipoprotein(a) blood, PCSK9 Inhibitors, Serine Proteinase Inhibitors therapeutic use, Venous Thromboembolism prevention & control, Venous Thrombosis prevention & control
Abstract

Background: The relationship between cholesterol levels and risk of venous thromboembolism (VTE) is uncertain. We set out to determine the effect of PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibition on the risk of VTE, explore potential mechanisms, and examine the efficacy in subgroups with clinically and genetically defined risk., Methods: We performed a post hoc analysis of the FOURIER trial (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk) testing whether evolocumab reduces the risk of VTE events (deep venous thrombosis or pulmonary embolism). Data from FOURIER and ODYSSEY OUTCOMES (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment with Alirocumab) were then combined in a meta-analysis to assess the class effect of PCSK9 inhibition on the risk of VTE. We also analyzed baseline lipids in FOURIER to investigate potential mechanisms explaining the reduction in VTE with evolocumab. Last, an exploratory genetic analysis was performed in FOURIER to determine whether a VTE polygenic risk score could identify high-risk patients who would derive the greatest VTE reduction from evolocumab., Results: In FOURIER, the hazard ratio (HR) for VTE with evolocumab was 0.71 (95% CI, 0.50-1.00; P =0.05), with no effect in the 1st year (HR, 0.96 [95% CI, 0.57-1.62]) but a 46% reduction (HR, 0.54 [95% CI, 0.33-0.88]; P =0.014) beyond 1 year. A meta-analysis of FOURIER and ODYSSEY OUTCOMES demonstrated a 31% relative risk reduction in VTE with PCSK9 inhibition (HR, 0.69 [95% CI, 0.53-0.90]; P =0.007). There was no relation between baseline low-density lipoprotein cholesterol levels and magnitude of VTE risk reduction. In contrast, in patients with higher baseline lipoprotein(a) (Lp[a]) levels, evolocumab reduced Lp(a) by 33 nmol/L and risk of VTE by 48% (HR, 0.52 [95% CI, 0.30-0.89]; P =0.017), whereas, in patients with lower baseline Lp(a) levels, evolocumab reduced Lp(a) by only 7 nmol/L and had no effect on VTE risk ( P interaction 0.087 for HR; P heterogeneity 0.037 for absolute risk reduction). Modeled as a continuous variable, there was a significant interaction between baseline Lp(a) concentration and magnitude of VTE risk reduction ( P interaction =0.04). A polygenic risk score identified patients who were at >2-fold increased risk for VTE and who derived greater relative ( P interaction =0.04) and absolute VTE reduction ( P heterogeneity =0.009) in comparison with those without high genetic risk., Conclusions: PCSK9 inhibition significantly reduces the risk of VTE. Lp(a) reduction may be an important mediator of this effect, a finding of particular interest given the ongoing development of potent Lp(a) inhibitors.

Published
2020
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