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Loss-of-Function Mutations in APOC3, Triglycerides, and Coronary Disease
Open accessAbstract
BACKGROUND: Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype. METHODS: We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons. RESULTS: An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G→A and IVS3+1G→T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1×10(-20)), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P=8×10(-10)). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P=4×10(-6)). CONCLUSIONS: Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.).
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“Observational cohorts demonstrate that higher TG and remnant cholesterol predict cardiovascular events independently of LDL-C, while Mendelian randomization analyses confirm a causal relationship between lifelong TRL elevation and ASCVD.”
From Targeting triglycerides for cardiovascular risk reduction (2026) · cites this paper as [60]
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Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In contrary, loss-of-function mutations of the ApoC-III are associated with a lower risk of CVD [170–172].”
From Established and Emerging Lipid-Lowering Drugs for Primary and Secondary Cardiovascular Prevention (2023) · cites this paper as [172]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Apolipoprotein C-III is a central regulator of triglyceride metabolism that limits the clearance of triglyceride-rich lipoproteins through inhibition of lipoprotein lipase and hepatic uptake of triglyceride-rich lipoprotein remnants. 6-8”
From Targeting APOC3 with Olezarsen in Moderate Hypertriglyceridemia (2025) · cites this paper as [8]
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In the citing paper
“For instance, loss-of-function mutations in APOC3, which encodes apolipoprotein C-III, result in significantly lower plasma TG levels and confer a 40–50% lower risk of ischemic cardiovascular events [25].”
From Targeting Triglycerides in Cardiovascular Disease Prevention: Evidence, Mechanisms, and Emerging Therapies (2026) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“loss-of-function variants in APOC3 confer protection against coronary artery disease, independent of LDL cholesterol levels [60, 112]”
From Targeting triglycerides for cardiovascular risk reduction (2026) · cites this paper as [60]
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In the citing paper
“Individuals with loss-of-function mutations in APOC-III exhibit significantly reduced levels of triglycerides and remnant cholesterol, along with a nearly 40% reduction in ASCVD risk [32, 33].”
From The Efficacy and Safety of Plozasiran on Lipid Profile in Dyslipidemic Disorders: A Systematic Review and Meta-Analysis (2026) · cites this paper as [32]
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In the citing paper
“Individuals with genetic loss-of-function mutations in the APOC3 gene exhibit markedly lower TG levels and reduced incidence of CAD, highlighting apoC-III as a compelling therapeutic target for HTG and associated complications [25, 29, 43].”
From Targeting Triglycerides in Cardiovascular Disease Prevention: Evidence, Mechanisms, and Emerging Therapies (2026) · cites this paper as [25]
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“Similarly, with genetic studies supporting the role of several novel therapeutic targets (eg, apo C-III and ANGPTL3) in atherosclerosis, ongoing clinical trials using RNA interference therapy (antisense oligonucleotides and small interfering RNAs) have the potential to further inform our understanding of the triglyceride-based atherosclerotic pathways (120,121).”
From 2021 ACC Expert Consensus Decision Pathway on the Management of ASCVD Risk Reduction in Patients With Persistent Hypertriglyceridemia (2021) · cites this paper as [120]
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In the citing paper
“Two large randomized controlled trials (RCTs) evaluating the efficacy of extended-release niacin vs placebo on background statin therapy in individuals with established ASCVD did not demonstrate a reduction in vascular events”
From The Forgotten Lipids: Triglycerides, Remnant Cholesterol, and Atherosclerotic Cardiovascular Disease Risk (2019) · cites this paper as [19]
Experimental — accuracy not yet measuredIn the citing paper
“ApoCIII is a predictor of CV disease and reduced apoCIII levels may have direct beneficial effects on the vessel wall, such as by reducing its retention of LDL particles (LDL-apoCIII) [48–50].”
In the citing paper
“ApoCIII is a predictor of CV disease and reduced apoCIII levels may have direct beneficial effects on the vessel wall, such as by reducing its retention of LDL particles (LDL-apoCIII) [48–50].”
In the citing paper
“and is a genetically validated target to reduce ASCVD risk.8–10,64–66”
From Clinical review on triglycerides (2020) · cites this paper as [64]
In the citing paper
“ApoCIII has been identified as a potentially important new risk factor. 104-106”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [106]
In the citing paper
“Furthermore, loss of function mutations are associated with low TGs as well as with reduced risk for CVD. 106,107”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [106]
In the citing paper
“Furthermore, loss of function mutations are associated with low TGs as well as with reduced risk for CVD.106,107”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [106]
In the citing paper
“defects in genes controlling TG metabolism, including APOC3, ANGPTL3 and ANGPTL4 that have been linked to reduced TG and are associated with reduced coronary calcification25 or reduced CVD risk.26-30”
From Is Isolated Low High-Density Lipoprotein Cholesterol a Cardiovascular Disease Risk Factor?: New Insights From the Framingham Offspring Study (2016) · cites this paper as [27]
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Citations
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,457
Multi-Ethnic Study of Atherosclerosis: Objectives and Design
Bild, D. E. (2002) · American Journal of Epidemiology · doi:10.1093/aje/kwf113 · Cited by 3,942
The Cardiovascular Health Study: design and rationale.
Fried, L. P., Borhani, N. O., Enright, P., Furberg, C. D., Gardin, J. M., Kronmal, R. A., Kuller, L. H., Manolio, T. A., Mittelmark, M. B., Newman, A., et al. (1991) · Annals of Epidemiology · doi:10.1016/1047-2797(91)90005-w · Cited by 3,652
Robust relationship inference in genome-wide association studies
Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W.-M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq559 · Cited by 3,541
THE ATHEROSCLEROSIS RISK IN COMMUNIT (ARIC) STUDY: DESIGN AND OBJECTIVES
THE ARIC INVESTIGATORS (1989) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a115184 · Cited by 3,498
Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487
Sequence Variations in PCSK9, Low LDL, and Protection against Coronary Heart Disease
Cohen, J. C., Boerwinkle, E., Mosley, T. H., & Hobbs, H. H. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa054013 · Cited by 3,181
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Items 401–500 of 1,001
Müller‐Wieland, D., Leiter, L. A., Cariou, B., Letierce, A., Colhoun, H. M., Prato, S. D., Henry, R. R., Tinahones, F. J., Aurand, L. A., Maroni, J. M., et al. (2017) · Cardiovascular Diabetology · doi:10.1186/s12933-017-0552-4 · Cited by 30
Gonna, H., & Ray, K. K. (2019) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13691 · Cited by 30
Treating Coronary Artery Disease: Beyond Statins, Ezetimibe, and PCSK9 Inhibition
Musunuru, K. (2020) · Annual Review of Medicine · doi:10.1146/annurev-med-080819-044918 · Cited by 32
Rare versus common diseases: a false dichotomy in precision medicine
Chung, B. H., Chau, J. F. T., & Wong, G. K. (2021) · npj Genomic Medicine · doi:10.1038/s41525-021-00176-x · Cited by 31
Taskinen, M., Björnson, E., Matikainen, N., Söderlund, S., Rämö, J. T., Ainola, M., Hakkarainen, A., Sihlbom, C., Thorsell, A., Andersson, L. K., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.160607 · Cited by 30
Using genetically isolated populations to understand the genomic basis of disease
Zeggini, E. (2014) · Genome Medicine · doi:10.1186/s13073-014-0083-5 · Cited by 30
APOC3 induces endothelial dysfunction through TNF-α and JAM-1
Tao, Y., Xiong, Y.-S., Wang, H., Chu, S.-P., Zhong, R., Wang, J., Wang, G., Ren, X., & Yu, J. (2016) · Lipids in Health and Disease · doi:10.1186/s12944-016-0326-0 · Cited by 32
Understanding Hypertriglyceridemia: Integrating Genetic Insights
Alves, M., Laranjeira, F., & Correia‐da‐Silva, G. (2024) · Genes · doi:10.3390/genes15020190 · Cited by 35
Drugs for dyslipidaemia: the legacy effect of the Scandinavian Simvastatin Survival Study (4S)
Strandberg, T. E., Kovanen, P. T., Lloyd‐Jones, D. M., Raal, F. J., Santos, R. D., & Watts, G. F. (2024) · The Lancet · doi:10.1016/s0140-6736(24)02089-0 · Cited by 29
Cariou, B., Leiter, L. A., Müller‐Wieland, D., Bigot, G., Colhoun, H. M., Prato, S. D., Henry, R. R., Tinahones, F. J., Letierce, A., Aurand, L. A., et al. (2017) · Diabetes & Metabolism · doi:10.1016/j.diabet.2017.01.004 · Cited by 28
High density lipoprotein and its apolipoprotein-defined subspecies and risk of dementia
Koch, M., DeKosky, S. T., Goodman, M. O., Sun, J., Furtado, J. D., Fitzpatrick, A. L., Mackey, R. H., Cai, T., López, O. L., Kuller, L. H., et al. (2019) · Journal of Lipid Research · doi:10.1194/jlr.p119000473 · Cited by 30
Goldberg, R. B. (2022) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2022.02.011 · Cited by 31
The Future of Cardiovascular Therapeutics
MacRae, C. A., Roden, D. M., & Loscalzo, J. (2016) · Circulation · doi:10.1161/circulationaha.116.023555 · Cited by 29
Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits
Gilly, A., Süveges, D., Kuchenbaecker, K. B., Pollard, M., Southam, L., Hatzikotoulas, K., Farmaki, A., Bjørnland, T., Waples, R. K., Appel, E. V. R., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07070-8 · Cited by 32
The Remnants of Residual Risk∗
Joshi, P. H., Martin, S. S., & Blumenthal, R. S. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.03.543 · Cited by 29
Dron, J. S., Patel, A. P., Zhang, Y., Jurgens, S. J., Maamari, D. J., Wang, M., Boerwinkle, E., Morrison, A. C., de Vries, P. S., Fornage, M., et al. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2022.5271 · Cited by 30
Metabolic Dysfunction-Associated Steatotic Liver Disease: Pathogenetic Links to Cardiovascular Risk
Ionescu, V., Gheorghe, G., Bacalbaşa, N., & Diaconu, C. C. (2025) · Biomolecules · doi:10.3390/biom15020163 · Cited by 33
Wang, Y.-X., Wang, A., Ye, Y.-N., Gao, Z., Tang, X., Yan, L., Wan, Q., Wang, W., Luo, Z., Qin, G., et al. (2019) · Cardiovascular Diabetology · doi:10.1186/s12933-019-0863-8 · Cited by 26
Hu, S., Cui, G., Huang, J., Jiang, J.-G., & Wang, D. W. (2016) · Scientific Reports · doi:10.1038/srep32700 · Cited by 29
Complications of Diabetes Mellitus
Brownlee, M., Aiello, L. P., Cooper, M. E., Vinik, A. I., Plutzky, J., & Boulton, A. J. M. (2016) · Elsevier eBooks · doi:10.1016/b978-0-323-29738-7.00033-2 · Cited by 29
Feingold, K. R. (2015) · Europe PMC (PubMed Central) · Cited by 28
Aroner, S. A., Furtado, J. D., Sacks, F. M., Tsai, M. Y., Mukamal, K. J., McClelland, R. L., & Jensen, M. K. (2019) · Diabetologia · doi:10.1007/s00125-019-4847-8 · Cited by 27
Hyperalphalipoproteinemia and Beyond: The Role of HDL in Cardiovascular Diseases
Giammanco, A., Noto, D., Barbagallo, C. M., Nardi, E., Caldarella, R., Ciaccio, M., Averna, M. R., & Cefalu, A. B. (2021) · Life · doi:10.3390/life11060581 · Cited by 27
RNA-based therapy in the management of lipid disorders: a review
Blom, D. J., Marais, A. D., Moodley, R., van der Merwe, N., van Tonder, A., & Raal, F. J. (2022) · Lipids in Health and Disease · doi:10.1186/s12944-022-01649-3 · Cited by 29
Implication of Lipids in Calcified Aortic Valve Pathogenesis: Why Did Statins Fail?
Nsaibia, M. J., Devendran, A. S., Goubaa, E., Bouitbir, J., Capoulade, R., & Bouchareb, R. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11123331 · Cited by 24
Spagnuolo, C. M., & Hegele, R. A. (2023) · Expert Opinion on Pharmacotherapy · doi:10.1080/14656566.2023.2206015 · Cited by 27
Martinelli, N., Baroni, M., Castagna, A., Lunghi, B., Stefanoni, F., Tosi, F., Croce, J., Udali, S., Woodhams, B. J., Girelli, D., et al. (2019) · Thrombosis and Haemostasis · doi:10.1055/s-0038-1676817 · Cited by 27
Relationship between triglyceride levels and ischaemic stroke in elderly hypertensive patients
Huang, Y., Huang, J., Liu, L., Chen, C., Yu, Y., Tang, S., Zhang, B., & Feng, Y. (2019) · Postgraduate Medical Journal · doi:10.1136/postgradmedj-2019-136961 · Cited by 28
Ronsein, G. E., Reyes‐Soffer, G., He, Y., Oda, M. N., Ginsberg, H. N., & Heinecke, J. W. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.054528 · Cited by 27
Pleiotropic effects of fenofibrate therapy on rats with hypertriglycemia
Sun, B., Xie, Y., Jiang, J., Wang, Y., Xu, X., Zhao, C., & Huang, F. (2015) · Lipids in Health and Disease · doi:10.1186/s12944-015-0032-3 · Cited by 26
Drenos, F., Smith, G. D., Ala‐Korpela, M., Kettunen, J., Würtz, P., Soininen, P., Kangas, A. J., Dale, C. E., Lawlor, D. A., Gaunt, T. R., et al. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.115.001302 · Cited by 26
Gilly, A., Ritchie, G. R. S., Southam, L., Farmaki, A., Tsafantakis, E., Dedoussis, G. V., & Zeggini, E. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw088 · Cited by 27
Kanth, V. V. R., Sasikala, M., Sharma, M., Rao, P. N., & Reddy, D. N. (2016) · World Journal of Hepatology · doi:10.4254/wjh.v8.i20.827 · Cited by 26
Apolipoprotein CIII is a new player in diabetes
Juntti‐Berggren, L., & Berggren, P. (2016) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000372 · Cited by 27
Genetics of Dyslipidemia and Ischemic Heart Disease
Sharma, K., & Baliga, R. R. (2017) · Current Cardiology Reports · doi:10.1007/s11886-017-0855-9 · Cited by 26
Tada, H., Nomura, A., Yoshimura, K., Itoh, H., Komuro, I., Yamagishi, M., Takamura, M., & Kawashiri, M. (2020) · Circulation Journal · doi:10.1253/circj.cj-19-0981 · Cited by 26
Drug development advances in human genetics‐based targets
Zhang, X., Yu, W., Li, Y., Wang, A., Cao, H., & Fu, Y. (2024) · MedComm · doi:10.1002/mco2.481 · Cited by 26
Zhang, W., Lu, T., SLADEK, R. M., Li, Y., Najafabadi, H. S., & Dupuis, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae295 · Cited by 28
A 3-day-old neonate with severe hypertriglyceridemia from novel mutations of the GPIHBP1 gene
Buonuomo, P. S., Bartuli, A., Rabacchi, C., Bertolini, S., & Calandra, S. (2014) · Journal of clinical lipidology · doi:10.1016/j.jacl.2014.10.003 · Cited by 26
Jiang, Y., Chen, S., McGuire, D., Chen, F., Liu, M., Iacono, W. G., Hewitt, J. K., Hokanson, J. E., Krauter, K. S., Laakso, M., et al. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007452 · Cited by 28
Sahebkar, A., Simental‐Mendía, L. E., Katsiki, N., Reiner, Ž., Banach, M., Pirro, M., & Atkin, S. L. (2018) · BMJ Open · doi:10.1136/bmjopen-2018-021508 · Cited by 25
Residual cardiovascular risk among people with diabetes
Dash, S., & Leiter, L. A. (2019) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13646 · Cited by 27
Emerging Targets for Cardiovascular Disease Prevention in Diabetes
Stitziel, N. O., Kanter, J. E., & Bornfeldt, K. (2020) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2020.03.011 · Cited by 28
New insights into the pathological development of dyslipidemia in patients with hypothyroidism
Su, X., Chen, X., Peng, H., Song, J., Wang, B., & Wu, X. (2021) · Bosnian Journal of Basic Medical Sciences · doi:10.17305/bjbms.2021.6606 · Cited by 28
Sahebkar, A., Simental‐Mendía, L. E., Mikhailidis, D. P., Pirro, M., Banach, M., Sirtori, C. R., Ruscica, M., & Reiner, Ž. (2018) · Journal of clinical lipidology · doi:10.1016/j.jacl.2018.01.008 · Cited by 25
Volanesorsen for treatment of familial chylomicronemia syndrome
Lazarte, J., & Hegele, R. A. (2021) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2021.1955348 · Cited by 25
Gagnon, É., & Arsenault, B. J. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.117501 · Cited by 23
Moens, S. J. B., van Capelleveen, J. C., & Stroes, E. S. G. (2014) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000130 · Cited by 24
Kypreos, K. E., Bitzur, R., Karavia, E. A., Xepapadaki, E., Panayiotakopoulos, G. D., & Constantinou, C. (2018) · Angiology · doi:10.1177/0003319718779533 · Cited by 26
D’Erasmo, L., Gallo, A., Costanzo, A. D., Bruckert, É., & Arca, M. (2020) · Expert Opinion on Pharmacotherapy · doi:10.1080/14656566.2020.1787380 · Cited by 24
Papadopoulou, E., Nicolescu, A. F., Haug, L. S., Husøy, T., Deleanu, C., Dirven, H. A. A. M., & Lindeman, B. (2022) · Environmental Pollution · doi:10.1016/j.envpol.2022.119664 · Cited by 24
Butler, A. A., Price, C. A., Graham, J. L., Stanhope, K. L., King, S. M., Hung, Y.-H., Sethupathy, P., Wong, S. C., Hamilton, J., Krauss, R. M., et al. (2019) · Journal of Lipid Research · doi:10.1194/jlr.m089508 · Cited by 25
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Poznyak, A. V., Grechko, A. V., Wetzker, R., & Orekhov, A. N. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21062097 · Cited by 27
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Miname, M. H., Rocha, V. Z., & Santos, R. D. (2021) · Current Atherosclerosis Reports · doi:10.1007/s11883-021-00936-1 · Cited by 25
Merging Electronic Health Record Data and Genomics for Cardiovascular Research
Hall, J. L., Ryan, J. J., Bray, B. E., Brown, C. M., Lanfear, D. E., Newby, L. K., Relling, M. V., RISCH, N. J., Roden, D. M., Shaw, S. Y., et al. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/hcg.0000000000000029 · Cited by 24
Apolipoprotein C3 induces inflammasome activation only in its delipidated form
Hsu, C.-C., Shao, B., Kanter, J. E., He, Y., Vaisar, T., Witztum, J. L., Snell‐Bergeon, J. K., McInnes, G., Bruse, S., Gottesman, O., et al. (2023) · Nature Immunology · doi:10.1038/s41590-023-01423-2 · Cited by 25
The Inherited Basis of Coronary Artery Disease
Schunkert, H., Natarajan, P., & Samani, N. J. (2026) · New England Journal of Medicine · doi:10.1056/nejmra2405153 · Cited by 23
ANGPTL3 and ApoC-III inhibitors for treating hypertriglyceridemia in context: horses for courses?
Chan, D. C., & Watts, G. F. (2024) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000920 · Cited by 24
Aroner, S. A., Koch, M., Mukamal, K. J., Furtado, J. D., Stein, J. H., Tattersall, M. C., McClelland, R. L., & Jensen, M. K. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.007824 · Cited by 23
Dai, W., Zhang, Z., & Zhao, S. (2018) · Lipids in Health and Disease · doi:10.1186/s12944-018-0923-1 · Cited by 23
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Variants inANGPTL4and the Risk of Coronary Artery Disease
Morrison, A. R., Fu, Y., & O’Donnell, C. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmc1607380 · Cited by 24
Ballantyne, C. M., & Norata, G. D. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf606 · Cited by 23
Tuettenberg, A., Steinbrink, K., & Schuppan, D. (2016) · Nanomedicine · doi:10.2217/nnm-2016-0208 · Cited by 22
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Abramowitz, Y., Roth, A., Keren, G., Isakov, O., Shomron, N., Laitman, Y., Weissglas‐Volkov, D., Arbel, Y., Banai, S., Finkelstein, A., et al. (2016) · Coronary Artery Disease · doi:10.1097/mca.0000000000000357 · Cited by 20
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Metadata
- Authors
- The TG and HDL Working Group of the Exome Sequencing Project, N. H.
- Corresponding author
- The TG and HDL Working Group of the Exome Sequencing Project, National Heart, Lung, and Blood Institute
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2014-07-03
- Volume
- 371
- Issue
- 1
- Pages
- 22-31
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC4180269
- OpenAlex
- W2170009139
- Semantic Scholar
- b3c2b9d4976782980ff38a64e223209866004dcc
- Cited by
- 1,071
- References
- 66
- Citations by year
- 2026: 52 · 2025: 73 · 2024: 48 · 2023: 56 · 2022: 75 · 2021: 93 · 2020: 110 · 2019: 90 · 2018: 81 · 2017: 113 · 2016: 132 · 2015: 107 · 2014: 41
- FWCI
- 89.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
- Discovery Research Portal (University of Dundee) (submittedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion, other-oa)
- mediaTUM (Technical University of Munich) (submittedVersion)
- Site cant be reached (submittedVersion)
- Funders
- U.S. Department of Agriculture
- Massachusetts General Hospital (R01HL107816)
- University of Wisconsin-Milwaukee
- George Washington University
- University of Washington
- Donovan Family Foundation
- University of Ottawa
- Norges Teknisk-Naturvitenskapelige Universitet
- Queen Mary University of London
- Wellcome Trust
- Deutsches Zentrum für Herz-Kreislaufforschung
- Università degli Studi di Milano
- University of Texas Health Science Center at Houston
- Broad Institute
- Russell and Diana Hawkins Family Foundation
- National Institute for Health and Care Research (NF-SI-0611-10170)
- British Heart Foundation
- University of Leicester
- University of Oxford
- University of Leeds
- Fondation Leducq (T32HL007208)
- Lunds Universitet
- Hjärt-Lungfonden
- King Abdulaziz University
- Universität zu Lübeck
- Umeå Universitet
- Technische Universität München
- Università degli Studi di Verona
- Rheinische Friedrich-Wilhelms-Universität Bonn
- Novo Nordisk Fonden (NNF14OC0009819, NNF13OC0005339, NNF14OC0011049)
- Regione Emilia-Romagna
- National Institutes of Health (1rc2hl103010-01)
- School of Medicine, Stanford University
- University of North Carolina at Chapel Hill
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai
- Canadian Institutes of Health Research
- Medical Research Council
- Barts and The London School of Medicine and Dentistry
- Helmholtz Zentrum München
- National Institute on Aging (P30AG010129, U01AG049505)
- National Heart, Lung, and Blood Institute (HL 102923, RC2HL102925, HL-102924, K08HL114642, RC2HL102926, RC2HL102923, RC2HL-103010, HL 103010, RC2HL102924, R01HL107816, T32HL007208, R01HL109946)
- National Cancer Institute (R01CA082659)
- National Institute of Neurological Disorders and Stroke (R01NS017950)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipid metabolism and disorders
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- apolipoprotein C-III, loss-of-function mutations, plasma triglycerides, coronary artery disease, rare variants, splice site mutation, nonsense mutation, missense mutations, genetic risk, European ancestry, African ancestry
- MeSH terms
- Coronary Disease, Genotype, Heterozygote, Humans, Liver, Mutation, Risk Factors, Triglycerides, Sequence Analysis, DNA, Black People, White People, Apolipoprotein C-III, Exome
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