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Rhee JW, Pillai R, He T, Bosworth A, Chen S, Atencio L, Oganesyan A, Peng K, Guzman T, Lukas K, Sigala B, Iukuridze A, Lindenfeld L, Jamal F, Natarajan P, Goldsmith S, Krishnan A, Rosenzweig M, Wong FL, Forman SJ, Armenian S. Clonal Hematopoiesis and Cardiovascular Disease in Patients With Multiple Myeloma Undergoing Hematopoietic Cell Transplant. JAMA Cardiol 2024; 9:16-24. [PMID: 37938837 PMCID: PMC10633387 DOI: 10.1001/jamacardio.2023.4105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/18/2023] [Indexed: 11/10/2023]
Abstract
Importance There is a paucity of information on the association between clonal hematopoiesis of indeterminate potential (CHIP) and cardiovascular disease (CVD) in patients with cancer, including those with multiple myeloma (MM) undergoing hematopoietic cell transplant (HCT), a population at high risk of developing CVD after HCT. Objective To examine the association between CHIP and CVD in patients with MM and to describe modifiers of CVD risk among those with CHIP. Design, Setting, and Participants This was a retrospective cohort study of patients with MM who underwent HCT between 2010 and 2016 at City of Hope Comprehensive Cancer Center in Duarte, California, and had pre-HCT mobilized peripheral blood stem cell (PBSC) products cryopreserved and accessible for CHIP analyses. The study team performed targeted panel DNA sequencing to detect the presence of CHIP (variant allele frequency 2% or more). Main Outcomes and Measures The primary end point was the 5-year cumulative incidence and risk for developing de novo CVD (heart failure, coronary artery disease, or stroke) after HCT. Results Of 1036 consecutive patients with MM (580 male [56%]; median age, 60.0 years) who underwent a first autologous HCT, 201 patients had at least 1 CHIP variant (19.4%) and 35 patients had 2 or more variants (3.4%). The 5-year incidence of CVD was significantly higher in patients with CHIP (21.1% vs 8.4%; P < .001) compared with those without CHIP; the 5-year incidence among those with 2 or more variants was 25.6%. In the multivariable model, CHIP was associated with increased risk of CVD (hazard ratio [HR], 2.72; 95% CI, 1.70-4.39), as well as of individual outcomes of interest, including heart failure (HR, 4.02; 95% CI, 2.32-6.98), coronary artery disease (HR, 2.22; 95% CI, 1.06-4.63), and stroke (HR, 3.02; 95% CI, 1.07-8.52). Patients who had both CHIP and preexisting hypertension or dyslipidemia were at nearly 7-fold and 4-fold increased risk of CVD, respectively (reference: no CHIP, no hypertension, or dyslipidemia). Conclusion and Relevance CHIP was significantly and independently associated with risk of CVD in patients with MM undergoing HCT and may serve as a novel biologically plausible biomarker for CVD in this cohort. Patients with MM and both CHIP and cardiovascular risk factors had an exceptionally high risk of CVD. Additional studies are warranted to determine if cardiovascular preventive measures can reduce CHIP-associated CVD risk.
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Affiliation(s)
- June-Wha Rhee
- Department of Medicine, City of Hope Comprehensive Cancer Center, Duarte, California
| | - Raju Pillai
- Department of Pathology, City of Hope Comprehensive Cancer Center, Duarte, California
| | - Tianhui He
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Alysia Bosworth
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Sitong Chen
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Liezl Atencio
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Artem Oganesyan
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Kelly Peng
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Tati Guzman
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Kara Lukas
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Brianna Sigala
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Aleksi Iukuridze
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Lanie Lindenfeld
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Faizi Jamal
- Department of Medicine, City of Hope Comprehensive Cancer Center, Duarte, California
| | - Pradeep Natarajan
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Scott Goldsmith
- Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California
| | - Amrita Krishnan
- Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California
| | - Michael Rosenzweig
- Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California
| | - F. Lennie Wong
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
| | - Stephen J. Forman
- Department of Hematology & Hematopoietic Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California
| | - Saro Armenian
- Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte California
- Department of Pediatrics, City of Hope Comprehensive Cancer Center, Duarte, California
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Patel R, Peesay T, Krishnan V, Wilcox J, Wilsbacher L, Khan SS. Prioritizing the primary prevention of heart failure: Measuring, modifying and monitoring risk. Prog Cardiovasc Dis 2024; 82:2-14. [PMID: 38272339 PMCID: PMC10947831 DOI: 10.1016/j.pcad.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 01/07/2024] [Indexed: 01/27/2024]
Abstract
With the rising incidence of heart failure (HF) and increasing burden of morbidity, mortality, and healthcare expenditures, primary prevention of HF targeting individuals in at-risk HF (Stage A) and pre-HF (Stage B) Stages has become increasingly important with the goal to decrease progression to symptomatic (Stage C) HF. Identification of risk based on traditional risk factors (e.g., cardiovascular health which can be assessed with the American Heart Association's Life's Essential 8 framework), adverse social determinants of health, inherited risk of cardiomyopathies, and identification of risk-enhancing factors, such as patients with viral disease, exposure to cardiotoxic chemotherapy, and history of adverse pregnancy outcomes should be the first step in evaluation for HF risk. Next, use of guideline-endorsed risk prediction tools such as Pooled Cohort Equations to Prevent Heart Failure provide quantification of absolute risk of HF based in traditional risk factors. Risk reduction through counseling on traditional risk factors is a core focus of implementation of prevention and may include the use of novel therapeutics that target specific pathways to reduce risk of HF, such as mineralocorticoid receptor agonists (e.g., fineronone), angiotensin-receptor/neprolysin inhibitors, and sodium glucose co-transporter-2 inhibitors. These interventions may be limited in at-risk populations who experience adverse social determinants and/or individuals who reside in rural areas. Thus, strategies like telemedicine may improve access to preventive care. Gaps in the current knowledge base for risk-based prevention of HF are highlighted to outline future research that may target approaches for risk assessment and risk-based prevention with the use of artificial intelligence, genomics-enhanced strategies, and pragmatic trials to develop a guideline-directed medical therapy approach to reduce risk among individuals with Stage A and Stage B HF.
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Affiliation(s)
- Ruchi Patel
- Department of Medicine, Division of General Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Tejasvi Peesay
- Department of Medicine, Division of General Internal Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Vaishnavi Krishnan
- Department of Medicine, Division of Cardiovascular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jane Wilcox
- Department of Medicine, Division of Cardiovascular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Lisa Wilsbacher
- Department of Medicine, Division of Cardiovascular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Sadiya S Khan
- Department of Medicine, Division of Cardiovascular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
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Upadhya B, Hegde S, Tannu M, Stacey RB, Kalogeropoulos A, Schocken DD. Preventing new-onset heart failure: Intervening at stage A. Am J Prev Cardiol 2023; 16:100609. [PMID: 37876857 PMCID: PMC10590769 DOI: 10.1016/j.ajpc.2023.100609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/24/2023] [Accepted: 09/30/2023] [Indexed: 10/26/2023] Open
Abstract
Heart failure (HF) prevention is an urgent public health need with national and global implications. Stage A HF patients do not show HF symptoms or structural heart disease but are at risk of HF development. There are no unique recommendations on detecting Stage A patients. Patients in Stage A are heterogeneous; many patients have different combinations of risk factors and, therefore, have markedly different absolute risks for HF. Comprehensive strategies to prevent HF at Stage A include intensive blood pressure lowering, adequate glycemic and lipid management, and heart-healthy behaviors (adopting Life's Essential 8). First and foremost, it is imperative to improve public awareness of HF risk factors and implement healthy lifestyle choices very early. In addition, recognize the HF risk-enhancing factors, which are nontraditional cardiovascular (CV) risk factors that identify individuals at high risk for HF (genetic susceptibility for HF, atrial fibrillation, chronic kidney disease, chronic liver disease, chronic inflammatory disease, sleep-disordered breathing, adverse pregnancy outcomes, radiation therapy, a history of cardiotoxic chemotherapy exposure, and COVID-19). Early use of biomarkers, imaging markers, and echocardiography (noninvasive measures of subclinical systolic and diastolic dysfunction) may enhance risk prediction among individuals without established CV disease and prevent chemotherapy-induced cardiomyopathy. Efforts are needed to address social determinants of HF risk for primordial HF prevention.Central illustrationPolicies developed by organizations such as the American Heart Association, American College of Cardiology, and the American Diabetes Association to reduce CV disease events must go beyond secondary prevention and encompass primordial and primary prevention.
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Affiliation(s)
- Bharathi Upadhya
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | | | - Manasi Tannu
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - R. Brandon Stacey
- Section on Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Andreas Kalogeropoulos
- Division of Cardiology, Department of Medicine, Stony Brook University School of Medicine, Long Island, NY, USA
| | - Douglas D. Schocken
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
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Patel AP, Dron JS, Wang M, Pirruccello JP, Ng K, Natarajan P, Lebo M, Ellinor PT, Aragam KG, Khera AV. Association of Pathogenic DNA Variants Predisposing to Cardiomyopathy With Cardiovascular Disease Outcomes and All-Cause Mortality. JAMA Cardiol 2022; 7:723-732. [PMID: 35544052 PMCID: PMC9096692 DOI: 10.1001/jamacardio.2022.0901] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Importance Pathogenic variants associated with inherited cardiomyopathy are recognized as important and clinically actionable when identified, leading some clinicians to recommend population-wide genomic screening. Objective To determine the prevalence and clinical importance of pathogenic variants associated with inherited cardiomyopathy within the context of contemporary clinical care. Design, Setting, and Participants This was a genetic association study of participants in Atherosclerosis in Risk Communities (ARIC), recruited from 1987 to 1989, with median follow-up of 27 years, and the UK Biobank, recruited from 2006 to 2010, with median follow-up of 10 years. ARIC participants were recruited from 4 sites across the US. UK Biobank participants were recruited from 22 sites across the UK. Participants in the US were of African and European ancestry; those in the UK were of African, East Asian, South Asian, and European ancestry. Statistical analyses were performed between August 1, 2021, and February 9, 2022. Exposures Rare genetic variants predisposing to inherited cardiomyopathy. Main Outcomes and Measures Pathogenicity of observed DNA sequence variants in sequenced exomes of 13 genes (ACTC1, FLNC, GLA, LMNA, MYBPC3, MYH7, MYL2, MYL3, PRKAG2, TNNI3, TNNT2, TPM1, and TTN) associated with inherited cardiomyopathies were classified by a blinded clinical geneticist per American College of Medical Genetics recommendations. Incidence of all-cause mortality, heart failure, and atrial fibrillation were determined. Cardiac magnetic resonance imaging, echocardiography, and electrocardiogram measures were assessed in a subset of participants. Results A total of 9667 ARIC participants (mean [SD] age, 54.0 [5.7] years; 4232 women [43.8%]; 2658 African [27.5%] and 7009 European [72.5%] ancestry) and 49 744 UK Biobank participants (mean [SD] age, 57.1 [8.0] years; 27 142 women [54.5%]; 1006 African [2.0%], 173 East Asian [0.3%], 939 South Asian [1.9%], and 46 449 European [93.4%] European ancestry) were included in the study. Of those, 59 participants (0.61%) in ARIC and 364 participants (0.73%) in UK Biobank harbored an actionable pathogenic or likely pathogenic variant associated with dilated or hypertrophic cardiomyopathy. Carriers of these variants were not reliably identifiable by imaging. However, the presence of these variants was associated with increased risk of heart failure (hazard ratio [HR], 1.7; 95% CI, 1.1-2.8), atrial fibrillation (HR, 2.9; 95% CI, 1.9-4.5), and all-cause mortality (HR, 1.5; 95% CI, 1.1-2.2) in ARIC. Similar risk patterns were observed in the UK Biobank. Conclusions and Relevance Results of this genetic association study suggest that approximately 0.7% of study participants harbored a pathogenic variant associated with inherited cardiomyopathy. These variant carriers would be challenging to identify within clinical practice without genetic testing but are at increased risk for cardiovascular disease and all-cause mortality.
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Affiliation(s)
- Aniruddh P Patel
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston.,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston.,Cardiovascular Disease Initiative, Broad Institute of MIT, Harvard, Cambridge, Massachusetts.,Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Jacqueline S Dron
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston.,Cardiovascular Disease Initiative, Broad Institute of MIT, Harvard, Cambridge, Massachusetts
| | - Minxian Wang
- Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - James P Pirruccello
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston.,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston.,Cardiovascular Disease Initiative, Broad Institute of MIT, Harvard, Cambridge, Massachusetts.,Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, Massachusetts
| | - Pradeep Natarajan
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston.,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston.,Cardiovascular Disease Initiative, Broad Institute of MIT, Harvard, Cambridge, Massachusetts.,Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Matthew Lebo
- Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Boston, Massachusetts.,Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts.,Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Patrick T Ellinor
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston.,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston.,Cardiovascular Disease Initiative, Broad Institute of MIT, Harvard, Cambridge, Massachusetts.,Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Krishna G Aragam
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston.,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston.,Cardiovascular Disease Initiative, Broad Institute of MIT, Harvard, Cambridge, Massachusetts.,Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Amit V Khera
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston.,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston.,Cardiovascular Disease Initiative, Broad Institute of MIT, Harvard, Cambridge, Massachusetts.,Department of Medicine, Harvard Medical School, Boston, Massachusetts.,Verve Therapeutics, Cambridge, Massachusetts
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5
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Hammond MM, Everitt IK, Khan SS. New strategies and therapies for the prevention of heart failure in high-risk patients. Clin Cardiol 2022; 45 Suppl 1:S13-S25. [PMID: 35789013 PMCID: PMC9254668 DOI: 10.1002/clc.23839] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/16/2022] [Accepted: 04/19/2022] [Indexed: 11/05/2022] Open
Abstract
Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates as well as hospitalizations and readmissions have increased in the past decade. Increases have been relatively higher among young and middle-aged adults (<65 years). Therefore, identification of individuals HF at-risk (Stage A) or with pre-HF (Stage B) before the onset of overt clinical signs and symptoms (Stage C) is urgently needed. Multivariate risk models (e.g., Pooled Cohort Equations to Prevent Heart Failure [PCP-HF]) have been externally validated in diverse populations and endorsed by the 2022 HF Guidelines to apply a risk-based framework for the prevention of HF. However, traditional risk factors included in the PCP-HF model only account for half of an individual's lifetime risk of HF; novel risk factors (e.g., adverse pregnancy outcomes, impaired lung health, COVID-19) are emerging as important risk-enhancing factors that need to be accounted for in personalized approaches to prevention. In addition to determining the role of novel risk-enhancing factors, integration of social determinants of health (SDoH) in identifying and addressing HF risk is needed to transform the current clinical paradigm for the prevention of HF. Comprehensive strategies to prevent the progression of HF must incorporate pharmacotherapies (e.g., sodium glucose co-transporter-2 inhibitors that have also been termed the "statins" of HF prevention), intensive blood pressure lowering, and heart-healthy behaviors. Future directions include investigation of novel prediction models leveraging machine learning, integration of risk-enhancing factors and SDoH, and equitable approaches to interventions for risk-based prevention of HF.
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Affiliation(s)
- Michael M. Hammond
- Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Ian K. Everitt
- Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Sadiya S. Khan
- Department of MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Preventive MedicineNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
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Khan SS, Barda N, Greenland P, Dagan N, Lloyd-Jones DM, Balicer R, Rasmussen-Torvik LJ. Validation of Heart Failure-Specific Risk Equations in 1.3 Million Israeli Adults and Usefulness of Combining Ambulatory and Hospitalization Data from a Large Integrated Health Care Organization. Am J Cardiol 2022; 168:105-109. [PMID: 35031113 PMCID: PMC8930701 DOI: 10.1016/j.amjcard.2021.12.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/02/2021] [Accepted: 12/06/2021] [Indexed: 12/23/2022]
Abstract
Heart failure (HF) prevalence is increasing worldwide and is associated with significant morbidity and mortality. Guidelines emphasize prevention in those at-risk, but HF-specific risk prediction equations developed in United States population-based cohorts lack external validation in large, real-world datasets outside of the United States. The purpose of this study was to assess the model performance of the pooled cohort equations to prevent HF (PCP-HF) within a contemporary electronic health record for 5- and 10-year risk. Using a retrospective cohort study design of Israeli residents between 2008 and 2018 with continuous membership until end of follow-up, HF, or death, we quantified 5- and 10-year estimated risks of HF using the PCP-HF equations, which integrate demographics (age, gender, and race) and risk factors (body mass index, systolic blood pressure, glucose, medication use for hypertension or diabetes, and smoking status). Of 1,394,411 patients included, 56% were women with mean age of 49.6 (SD 13.2) years. Incident HF occurred in 1.2% and 4.5% of participants over 5 and 10 years of follow-up. The PCP-HF model had excellent discrimination for 5- and 10-year predictions of incident HF (C Statistic 0.82 [0.82 to 0.82] and 0.84 [0.84 to 0.84]), respectively. In conclusion, HF-specific risk equations (PCP-HF) accurately predict the risk of incident HF in ambulatory and hospitalized patients using routinely available clinical data.
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Affiliation(s)
- Sadiya S Khan
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois; Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
| | - Noam Barda
- Clalit Research Institute, Clalit Health Services, Clalit Research Institute, Tel Aviv, Israel; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Philip Greenland
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Noa Dagan
- Clalit Research Institute, Clalit Health Services, Clalit Research Institute, Tel Aviv, Israel; Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Ran Balicer
- Clalit Research Institute, Clalit Health Services, Clalit Research Institute, Tel Aviv, Israel; School of Public Health, Ben-Gurion University, Beer-Sheba, Israel
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
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