Clinical Implementation of Combined Monogenic and Polygenic Risk Disclosure for Coronary Artery Disease

Maamari, Dimitri J; Brockman, Deanna; Aragam, Krishna; Pelletier, Renee; Folkerts, Emma; Neben, Cynthia L.; Okumura, Sydney; Hull, Leland E.; Philippakis, Anthony; Natarajan, Pradeep; Ellinor, Patrick T.; Ng, Kenney; Zhou, Alicia Y.; Khera, Amit V.; Fahed, Akl C.

doi:10.1016/j.jacadv.2022.100068

Cited by 25 publications

(19 citation statements)

References 39 publications

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“…Additional challenges related to PRS deployment are due to insufficient clinical infrastructure for generating and interpreting PRS results. Partnering with genetic counselors and studying the impact of PRS results on patients are necessary to integrate PRSs effectively and equitably into a clinical setting [7 ▪▪ ,10 ▪▪ ,43 ▪▪ ,50 ▪ ].…”

Section: Challenges In Clinical Deployment Of Polygenic Risk Scoresmentioning

confidence: 99%

“…Rare variants in relevant protein-coding genes could also be considered given their associations with combined hyperlipidemia and diagnostic biomarkers [2 ▪▪ ,21]. There are ongoing research efforts to develop models accounting for risk across the genetic spectrum – from rare to common variants, coding to noncoding regions – as well as determine to what extent a particular polygenic background can augment the effect of a rare variant [7 ▪▪ ,42 ▪ ,43 ▪▪ ].…”

Section: Utilization Of Polygenic Risk Scores In the Clinic For Combi...mentioning

confidence: 99%

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The clinical utility of polygenic risk scores for combined hyperlipidemia

Dron

2022

Current Opinion in Lipidology

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Purpose of reviewCombined hyperlipidemia is the most common lipid disorder and is strongly polygenic. Given its prevalence and associated risk for atherosclerotic cardiovascular disease, this review describes the potential for utilizing polygenic risk scores for risk prediction and management of combined hyperlipidemia.Recent findingsDifferent diagnostic criteria have led to inconsistent prevalence estimates and missed diagnoses. Given that individuals with combined hyperlipidemia have risk estimates for incident coronary artery disease similar to individuals with familial hypercholesterolemia, early identification and therapeutic management of those affected is crucial. With diagnostic criteria including traits such apolipoprotein B, low-density lipoprotein cholesterol, and triglyceride, polygenic risk scores for these traits strongly associate with combined hyperlipidemia and could be used in combination for clinical risk prediction models and developing specific treatment plans for patients.SummaryPolygenic risk scores are effective tools in risk prediction of combined hyperlipidemia, can provide insight into disease pathophysiology, and may be useful in managing and guiding treatment plans for patients. However, efforts to ensure equitable polygenic risk score performance across different genetic ancestry groups is necessary before clinical implementation in order to prevent the exacerbation of racial disparities in the clinic.

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Section: Challenges In Clinical Deployment Of Polygenic Risk Scoresmentioning

confidence: 99%

Section: Utilization Of Polygenic Risk Scores In the Clinic For Combi...mentioning

confidence: 99%

The clinical utility of polygenic risk scores for combined hyperlipidemia

Dron

2022

Current Opinion in Lipidology

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“…Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations [1]. CAD is an atherosclerotic disease that is associated with major complications, including angina, myocardial infarction, and sudden cardiac death [2][3][4][5]. Due to the high prevalence, morbidity, and mortality of CAD, identification of risk factors is a public health priority [6].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, lifestyle factors, such as diet and exercise, have been found to play an important role in the risk for CAD and other cardiovascular diseases [6,[18][19][20]. These studies have been combined to develop joint risk scores, factoring in both physiological covariates (blood pressure, cholesterol) as well as demographic covariates (age, race, gender) [5,8,9,21]. Despite the strong literature studying the risk factors for CAD, most studies focus upon hypothesis testing or epidemiology focusing upon specific risk factors of interest [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning to identify risk factors for coronary artery disease

Huang

2023

PLoS ONE

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Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the Cover statistic to identify risk factors for CAD. Shapely Additive Explanations (SHAP) explanations were utilized to visualize the relationship between these potential risk factors and CAD. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51%) were female, 2,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of other race. A total of 338 (4.5%) of patients had coronary artery disease. These were fitted into the XGBoost model and an AUROC = 0.89, Sensitivity = 0.85, Specificity = 0.87 were observed (Fig 1). The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were age (Cover = 21.1%), Platelet count (Cover = 5.1%), family history of heart disease (Cover = 4.8%), and Total Cholesterol (Cover = 4.1%). Machine learning models can effectively predict coronary artery disease using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

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“…[7][8][9] As a result of these developments, PRS are now being considered for implementation into clinical medicine. Clinical trials and implementation projects are underway, [10][11][12][13][14] and several commercial laboratories already offer PRS products for clinical use. [15][16][17] Most attention has focused on the potential for PRS to identify high-risk subgroups of patients for whom targeted interventions might improve health outcomes through earlier prevention, detection, or treatment; 4,10,18 the potential for PRS to identify low-risk subgroups for whom certain preventive measures might be appropriately deferred remains more theoretical.…”

Section: Introductionmentioning

confidence: 99%

Benefits and barriers to implementing precision preventive care: results of a national physician survey

Vassy

Kerman

Harris

et al. 2022

Preprint

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Background: Clinical implementation of polygenic risk scores (PRS) for precision prevention depends on the utility and barriers primary care physicians (PCPs) perceive to their use. Methods: An online survey asked PCPs in a national database about the clinical utility of PRS they perceived for categories of medical decision-making and perceived benefits of and barriers to that use. Latent class analysis (LCA) was used to identify subgroups of PCPs based on response patterns. Results: Among 367 respondents (email open rate 10.8%; participation rate 96.3%; completion rate 93.1%), mean (SD) age was 54.9 (12.9) years, 137 (37.3%) were female, and mean (SD) time since medical school graduation was 27.2 (13.3) years. Respondents reported greater perceived utility for more clinical action (e.g., earlier or more intensive screening, preventive medications, or lifestyle modification) for patients with high-risk PRS than for delayed or discontinued prevention actions for low-risk patients (p<0.001). Respondents most often chose out-of-pocket costs (48%), lack of clinical guidelines (24%), and patient insurance discrimination concerns (22%) as extreme barriers to PRS implementation. LCA identified 3 subclasses of respondents. Skeptics (n=83, 22.6%) endorsed less agreement with individual clinical utilities, saw patient anxiety and insurance discrimination as significant barriers, and agreed less often that PRS could help patients make better health decisions. Learners (n=134, 36.5%) and enthusiasts (n=150, 40.9%) expressed similar levels of agreement that PRS had utility for preventive actions and that PRS could be useful for patient decision-making. Compared with enthusiasts, however, learners perceived greater barriers to the clinical use of PRS. Conclusion: PCPs generally endorsed using PRS to guide medical decision-making about preventive care, with a preference for more clinical action over less. Barriers identified suggest interventions to address the needs and concerns of PCPs along the spectrum of acceptance and uptake.

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Clinical Implementation of Combined Monogenic and Polygenic Risk Disclosure for Coronary Artery Disease

Cited by 25 publications

References 39 publications

The clinical utility of polygenic risk scores for combined hyperlipidemia

The clinical utility of polygenic risk scores for combined hyperlipidemia

Use of machine learning to identify risk factors for coronary artery disease

Benefits and barriers to implementing precision preventive care: results of a national physician survey

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