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      Clinical Utility of Lipoprotein(a) and LPA Genetic Risk Score in Risk Prediction of Incident Atherosclerotic Cardiovascular Disease

      research-article
      , MSc 1 , 2 , , PhD 2 , , BS 2 , 3 , 4 , , MD, MS 2 , 3 , 4 , 5 , , MD, MMSc 2 , 3 , 4 , 5 ,
      JAMA Cardiology
      American Medical Association

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          Key Points

          Question

          Does measurement of lipoprotein(a) and/or LPA genetic risk score (GRS) have clinical utility in risk prediction of incident atherosclerotic cardiovascular disease (ASCVD)?

          Findings

          In this cohort of 283 540 adults recruited by the UK Biobank, both measured lipoprotein(a) and LPA GRS were associated with comparable risk of incident ASCVD events. The LPA GRS did not yield additional prognostic information beyond measured lipoprotein(a), and both measured lipoprotein(a) and LPA GRS yielded modest improvements in the discrimination of ASCVD risk relative to the Pooled Cohort Equations or QRISK3.

          Meaning

          Cardiovascular risk assessment with lipoprotein(a) may be achieved with either direct measurement or an LPA GRS.

          Abstract

          Importance

          Lipoprotein(a) is a highly heritable biomarker independently associated with atherosclerotic cardiovascular disease (ASCVD). It is unclear whether measured lipoprotein(a) or genetic factors associated with lipoprotein(a) can provide comparable or additional prognostic information for primary prevention.

          Objective

          To determine whether a genetic risk score (GRS) comprising 43 variants at the LPA gene, which encodes apolipoprotein(a), has clinical utility in assessing ASCVD risk compared with and in addition to lipoprotein(a) measurement.

          Design, Setting, and Participants

          The UK Biobank is a prospective observational study of approximately 500 000 volunteers aged 40 to 69 years who were recruited from 22 sites across the United Kingdom between 2006 and 2010. Using externally derived weights, an LPA GRS was calculated for 374 099 unrelated individuals with array-derived genotypes and lipoprotein(a) measures. Data were analyzed from April 2020 to March 2020.

          Exposures

          Measured lipoprotein(a) and LPA GRS.

          Main Outcomes and Measures

          We estimated the associations between measured lipoprotein(a) and LPA GRS with the incidence of ASCVD (peripheral arterial disease, coronary artery disease, myocardial infarction, ischemic stroke, and cardiovascular mortality) using Cox proportional hazards models. To determine the utility of using measured lipoprotein(a) and LPA GRS as risk enhancers for ASCVD, we assessed the potential improvement in ASCVD risk discrimination by QRISK3 and Pooled Cohort Equations among individuals with borderline to intermediate risk (n = 113 703 and 144 350, respectively).

          Results

          The mean age of the overall study population was 57.6 years, and 204 355 individuals were female (54.6%). During a median follow-up of 11.1 years (interquartile range, 1.4 years), 15 444 individuals developed an incident ASCVD event (5.1%). The LPA GRS explained approximately 60% of the variation in measured lipoprotein(a) for White/European individuals. Independently, both lipoprotein(a) and LPA GRS were associated with incident, composite ASCVD (hazard ratio per 120 nmol/L increase, 1.26; 95% CI, 1.23-1.28 vs hazard ratio, 1.29; 95% CI, 1.26-1.33; P < .001). The association between LPA GRS and ASCVD was substantially attenuated after adjusting for measured lipoprotein(a). Adding measured lipoprotein(a) or LPA GRS to QRISK3 provided modest improvements to the risk discrimination of incident ASCVD events (area under the receiver operating curve, 0.640; 95% CI, 0.633-0.647 vs 0.642; 95% CI, 0.635-0.649 for both; P = .005 and P = .01, respectively).

          Conclusions and Relevance

          When indicated, cardiovascular risk assessment with lipoprotein(a) at middle-age may include direct measurement or an LPA GRS.

          Abstract

          This study investigates whether a genetic risk score comprising 43 variants at the LPA gene, which encodes apolipoprotein(a), has clinical utility in assessing atherosclerotic cardiovascular disease risk compared with and in addition to lipoprotein(a) measurement.

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          Most cited references47

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          pROC: an open-source package for R and S+ to analyze and compare ROC curves

          Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
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            The UK Biobank resource with deep phenotyping and genomic data

            The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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              2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk

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                Author and article information

                Journal
                JAMA Cardiol
                JAMA Cardiol
                JAMA Cardiol
                JAMA Cardiology
                American Medical Association
                2380-6583
                2380-6591
                6 October 2020
                March 2021
                6 October 2020
                : 6
                : 3
                : 1-9
                Affiliations
                [1 ]Centre for Heart Lung Innovation, The University of British Columbia, Vancouver, British Columbia, Canada
                [2 ]Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard, Cambridge, Massachusetts
                [3 ]Massachusetts General Hospital, Cardiology Division, Harvard Medical School, Boston, Massachusetts
                [4 ]Cardiovascular Research Center, Massachusetts General Hospital, Boston
                [5 ]Massachusetts General Hospital, Department of Medicine, Harvard Medical School, Boston
                Author notes
                Article Information
                Corresponding Author: Pradeep Natarajan, MD, MMSc, Massachusetts General Hospital, 185 Cambridge St, CPZN 3.184, Boston, MA 02114 ( pnatarajan@ 123456mgh.harvard.edu ).
                Accepted for Publication: September 15, 2020.
                Published Online: October 6, 2020. doi:10.1001/jamacardio.2020.5398
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Trinder M et al. JAMA Cardiology.
                Author Contributions: Mr Trinder and Dr Natarajan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Trinder, Aragam, Natarajan.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Trinder, Natarajan.
                Critical revision of the manuscript for important intellectual content: All authors.
                Statistical analysis: Trinder, Uddin, Aragam, Natarajan.
                Obtained funding: Aragam, Natarajan.
                Administrative, technical, or material support: Trinder, Finneran, Aragam, Natarajan.
                Supervision: Aragam, Natarajan.
                Conflict of Interest Disclosures: Dr Natarajan reported grants from Amgen during the conduct of the study and grants from Boston Scientific; grants and personal fees from Apple; personal fees from Novartis and Blackstone Life Sciences; and other support from Vertex outside the submitted work. No other disclosures were reported.
                Funding/Support: This work was supported by UK Biobank application 7089. Dr Natarajan is supported by grants from the National Heart, Lung, and Blood Institute (R01HL142711, R01HL148565, R01HL148050), Fondation Leducq (TNE-18CVD04), and a Hassenfeld Scholar Award from the Massachusetts General Hospital. Amgen Inc (Thousand Oaks, California) funded this study.
                Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Meeting Presentation: This paper was presented at the European Atherosclerosis Congress; October 6, 2020; virtual conference.
                Additional Contributions: We thank the participants and individuals involved in establishing the UK Biobank project and others for establishing the computational resources, tools, and support for this project. The UK Biobank analyses were performed using application 7089.
                Article
                hoi200075
                10.1001/jamacardio.2020.5398
                7539232
                33021622
                9f331f6e-4cd0-4181-abdc-4033b65bf9a5
                Copyright 2020 Trinder M et al. JAMA Cardiology.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 25 August 2020
                : 15 September 2020
                Categories
                Research
                Research
                Original Investigation
                Online First

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