Pirruccello, James P.

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

Genetic Associations and EpidemiologyLipoproteins and Cardiovascular Health

Cited by 3,487

From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus

Musunuru, K., Strong, A., Frank-Kamenetsky, M., Lee, N. E., Ahfeldt, T., Sachs, K. V., Li, X., Li, H., Kuperwasser, N., Ruda, V. M., et al. (2010) · Nature

Genetic Associations and EpidemiologyCholesterol and Lipid Metabolism

Cited by 1,140

Exome Sequencing, ANGPTL3 Mutations, and Familial Combined Hypolipidemia

Musunuru, K., Pirruccello, J. P., Do, R., Peloso, G. M., Guiducci, C., Sougnez, C., Garimella, K. V., Fisher, S., Abreu, J., Barry, A. J., et al. (2010) · New England Journal of Medicine

Lipid metabolism and disordersLipoproteins and Cardiovascular Health

Cited by 754

Genetics of 35 blood and urine biomarkers in the UK Biobank

Sinnott-Armstrong, N., Tanigawa, Y., Amar, D., Mars, N., Benner, C., Aguirre, M., Venkataraman, G. R., Wainberg, M., Ollila, H. M., Kiiskinen, T., et al. (2021) · Nature Genetics

Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and Animals

Cited by 744

Lp(a) (Lipoprotein[a]) Concentrations and Incident Atherosclerotic Cardiovascular Disease: New Insights From a Large National Biobank

Patel, A. P., Wang, M., Pirruccello, J. P., Ellinor, P. T., Ng, K., Kathiresan, S., & Khera, A. V. (2021) · Arteriosclerosis, Thrombosis, and Vascular Biology

Lipoproteins and Cardiovascular HealthAtherosclerosis and Cardiovascular Diseases

Cited by 567

Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy

Pirruccello, J. P., Bick, A., Wang, M., Chaffin, M., Friedman, S., Yao, J., Guo, X., Venkatesh, B. A., Taylor, K. D., Post, W. S., et al. (2020) · Nature Communications

Cardiomyopathy and Myosin StudiesGenetic Associations and Epidemiology

Cited by 267

Deep learning enables genetic analysis of the human thoracic aorta

Pirruccello, J. P., Chaffin, M. D., Chou, E. L., Fleming, S. J., Lin, H., Nekoui, M., Khurshid, S., Friedman, S. F., Bick, A. G., Arduini, A., et al. (2022) · Nature Genetics

Genetic Associations and EpidemiologyAortic Disease and Treatment Approaches

Cited by 210

Dnmt3a -mutated clonal hematopoiesis promotes osteoporosis

Kim, P. G., Niroula, A., Shkolnik, V., McConkey, M., Lin, A. E., Słabicki, M., Kemp, J. P., Bick, A., Gibson, C. J., Griffin, G., et al. (2021) · Journal of Experimental Medicine

Bone Metabolism and DiseasesBone health and osteoporosis research

Cited by 164

Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study

Voight, B. F., Peloso, G. M., Orho-Melander, M., Frikke-Schmidt, R., Barbalic, M., Jensen, M. K., Hindy, G., Hólm, H., Ding, E. L., Johnson, T., et al. (2012) · The Lancet

Genetic Associations and EpidemiologyLipoproteins and Cardiovascular Health

Cited by 148

Machine learning enables new insights into genetic contributions to liver fat accumulation

Haas, M. E., Pirruccello, J. P., Friedman, S. N., Wang, M., Emdin, C. A., Ajmera, V. H., Simon, T. G., Homburger, J. R., Guo, X., Budoff, M., et al. (2021) · Cell Genomics

Liver Disease Diagnosis and TreatmentGenetic Associations and Epidemiology

Cited by 118

Cardiovascular and Kidney Outcomes Across the Glycemic Spectrum

Honigberg, M. C., Zekavat, S. M., Pirruccello, J. P., Natarajan, P., & Vaduganathan, M. (2021) · Journal of the American College of Cardiology

Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Treatment and Management

Cited by 108

Artificial Intelligence in Cardiovascular Care—Part 2: Applications

Jain, S. S., Elias, P., Poterucha, T., Randazzo, M., Lopez Jimenez, F., Khera, R., Perez, M., Ouyang, D., Pirruccello, J., Salerno, M., et al. (2024) · Journal of the American College of Cardiology

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 74

Wearable accelerometer-derived physical activity and incident disease

Khurshid, S., Weng, L.-C., Nauffal, V., Pirruccello, J. P., Venn, R. A., Al-Alusi, M. A., Benjamin, E. J., Ellinor, P. T., & Lubitz, S. A. (2022) · npj Digital Medicine

Physical Activity and HealthContext-Aware Activity Recognition Systems

Cited by 63

How will machine learning inform the clinical care of atrial fibrillation?

Siontis, K. C., Yao, X., Pirruccello, J. P., Philippakis, A. A., & Noseworthy, P. A. (2020) · Circulation Research

Atrial Fibrillation Management and OutcomesMachine Learning in Healthcare

Cited by 61

Rare Genetic Variants in LDLR , APOB , and PCSK9 Are Associated With Aortic Stenosis

Rämö, J. T., Jurgens, S. J., Kany, S., Choi, S. H., Wang, X., Smirnov, A. N., Friedman, S. F., Maddah, M., Khurshid, S., Ellinor, P. T., et al. (2024) · Circulation

Lipoproteins and Cardiovascular HealthCardiac Valve Diseases and Treatments

Cited by 26

Moving Beyond the Model

Jain, S. S., Mortazavi, B. J., Chan You, S., Yao, X., Lam, C. S. P., Elias, P., Poterucha, T. J., Avram, R., Tison, G. H., Pirruccello, J., et al. (2025) · JACC

Clinical practice guidelines implementationHealth and Medical Research Impacts

Cited by 13

“Road Map” to Improving Enrollment in Cardiac Rehabilitation: Identifying Barriers and Evaluating Alternatives

Pirruccello, J. P., Traynor, K., & Aragam, K. G. (2017) · Journal of the American Heart Association

Cardiac Health and Mental HealthMedical Practices and Rehabilitation

Cited by 12

Artificial Intelligence-Enabled ECG Analysis to Predict Incident Heart Failure

Khurshid, S., Friedman, S. F., Kany, S., D’Souza, V., Roshandelpoor, A., Kosyakovsky, L. B., Chitsazan, M., Cunningham, J. W., Singh, P., Lau, E. S., et al. (2026) · Circulation: Heart Failure

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 2