Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

Background Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD. Methods and findings Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703–0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009–0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40–75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to <10%) 10-year CVD risk could help prevent 1 additional CVD event for approximately every 340 individuals screened. Such a targeted strategy could help prevent 7% more CVD events than conventional risk prediction alone. Potential gains afforded by assessment of PRSs on top of conventional risk factors would be about 1.5-fold greater than those provided by assessment of C-reactive protein, a plasma biomarker included in some risk prediction guidelines. Potential limitations of this study include its restriction to European ancestry participants and a lack of health economic evaluation. Conclusions Our results suggest that addition of PRSs to conventional risk factors can modestly enhance prediction of first-onset CVD and could translate into population health benefits if used at scale.

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Use of polygenic risk scores and other molecular markers to enhance cardiovascular risk prediction: prospective cohort study and modelling analysis

10.1101/744565 ◽

2019 ◽

Author(s):

Luanluan Sun ◽

Lisa Pennells ◽

Stephen Kaptoge ◽

Christopher P Nelson ◽

Gad Abraham ◽

...

Keyword(s):

Primary Care ◽

Molecular Markers ◽

Prediction Model ◽

Risk Prediction ◽

Risk Scores ◽

Intermediate Risk ◽

Cvd Risk ◽

Polygenic Risk ◽

History Of ◽

Risk Predictors

AbstractBackgroundThere is debate about the value of adding information on genetic and other molecular markers to conventional cardiovascular disease (CVD) risk predictors.MethodsUsing data on 306,654 individuals without a history of CVD from UK Biobank, we calculated measures of risk-discrimination and reclassification upon addition of polygenic risk scores (PRS) and a panel of 27 clinical biochemistry markers to a conventional risk prediction model (i.e., including age, sex, systolic blood pressure, smoking status, history of diabetes, total cholesterol and HDL cholesterol). We then modelled implications of initiating guideline-recommended statin therapy after the assessment of molecular markers for a UK primary-care setting.FindingsThe C-index was 0.710 (95% CI, 0.703-0.717) for a CVD prediction model containing conventional risk predictors alone. The C-index increased by similar amounts when adding information on PRS or biochemistry markers (0.011 and 0.014, respectively; P<0.001), and it increased still further (0.022; P<0.001) when information on both was combined. Among cases and controls, continuous net reclassification improvements were about 12% and 19%, respectively, when both PRS and biochemistry markers were added. If PRS and biochemistry markers were to be assessed in the entire primary care population aged 40-75, then it could help prevent one additional CVD event for every 893 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5-10%) 10-year CVD risk could help prevent one additional CVD event for every 233 individuals screened. This targeted strategy could help reclassify 16% of the intermediate-risk group to the high-risk (i.e., ≥10%) category, preventing 11% more CVD events than conventional risk prediction.InterpretationAdding information on both PRS and selected biochemistry markers moderately enhanced CVD predictive accuracy and could improve primary prevention of CVD. However, our modelling suggested that targeted assessment of molecular markers among individuals at intermediate-risk would be more efficient than blanket approaches.

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Highly elevated polygenic risk scores are better predictors of myocardial infarction risk early in life than later

Genome Medicine ◽

10.1186/s13073-021-00828-8 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Monica Isgut ◽

Jimeng Sun ◽

Arshed A. Quyyumi ◽

Greg Gibson

Keyword(s):

Risk Factors ◽

Myocardial Infarction ◽

Cardiovascular Risk ◽

Risk Prediction ◽

Area Under The Curve ◽

Later Life ◽

European Ancestry ◽

Risk Scores ◽

Polygenic Risk ◽

Conventional Risk Factors

Abstract Background Several polygenic risk scores (PRS) have been developed for cardiovascular risk prediction, but the additive value of including PRS together with conventional risk factors for risk prediction is questionable. This study assesses the clinical utility of including four PRS generated from 194, 46K, 1.5M, and 6M SNPs, along with conventional risk factors, to predict risk of ischemic heart disease (IHD), myocardial infarction (MI), and first MI event on or before age 50 (early MI). Methods A cross-validated logistic regression (LR) algorithm was trained either on ~ 440K European ancestry individuals from the UK Biobank (UKB), or the full UKB population, including as features different combinations of conventional established-at-birth risk factors (ancestry, sex) and risk factors that are non-fixed over an individual’s lifespan (age, BMI, hypertension, hyperlipidemia, diabetes, smoking, family history), with and without also including PRS. The algorithm was trained separately with IHD, MI, and early MI as prediction labels. Results When LR was trained using risk factors established-at-birth, adding the four PRS significantly improved the area under the curve (AUC) for IHD (0.62 to 0.67) and MI (0.67 to 0.73), as well as for early MI (0.70 to 0.79). When LR was trained using all risk factors, adding the four PRS only resulted in a significantly higher disease prevalence in the 98th and 99th percentiles of both the IHD and MI scores. Conclusions PRS improve cardiovascular risk stratification early in life when knowledge of later-life risk factors is unavailable. However, by middle age, when many risk factors are known, the improvement attributed to PRS is marginal for the general population.

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Towards clinical utility of polygenic risk scores

Human Molecular Genetics ◽

10.1093/hmg/ddz187 ◽

2019 ◽

Vol 28 (R2) ◽

pp. R133-R142 ◽

Cited By ~ 56

Author(s):

Samuel A Lambert ◽

Gad Abraham ◽

Michael Inouye

Keyword(s):

Risk Factors ◽

Family History ◽

Risk Prediction ◽

Clinical Utility ◽

Disease Risk ◽

Risk Scores ◽

Polygenic Risk ◽

Common Diseases ◽

History Of Disease ◽

History Of

Abstract Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.

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Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients

Scientific Reports ◽

10.1038/s41598-019-56400-3 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Li-Na Liao ◽

Tsai-Chung Li ◽

Chia-Ing Li ◽

Chiu-Shong Liu ◽

Wen-Yuan Lin ◽

...

Keyword(s):

Risk Factors ◽

Type 2 Diabetes ◽

Prediction Model ◽

Risk Prediction ◽

Genetic Risk Score ◽

Clinical Risk Factors ◽

Clinical Risk ◽

Conventional Risk Factors ◽

Diabetes Patients

AbstractWe evaluated whether genetic information could offer improvement on risk prediction of diabetic nephropathy (DN) while adding susceptibility variants into a risk prediction model with conventional risk factors in Han Chinese type 2 diabetes patients. A total of 995 (including 246 DN cases) and 519 (including 179 DN cases) type 2 diabetes patients were included in derivation and validation sets, respectively. A genetic risk score (GRS) was constructed with DN susceptibility variants based on findings of our previous genome-wide association study. In derivation set, areas under the receiver operating characteristics (AUROC) curve (95% CI) for model with clinical risk factors only, model with GRS only, and model with clinical risk factors and GRS were 0.75 (0.72–0.78), 0.64 (0.60–0.68), and 0.78 (0.75–0.81), respectively. In external validation sample, AUROC for model combining conventional risk factors and GRS was 0.70 (0.65–0.74). Additionally, the net reclassification improvement was 9.98% (P = 0.001) when the GRS was added to the prediction model of a set of clinical risk factors. This prediction model enabled us to confirm the importance of GRS combined with clinical factors in predicting the risk of DN and enhanced identification of high-risk individuals for appropriate management of DN for intervention.

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A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

Advances in Preventive Medicine ◽

10.1155/2019/8392348 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Xiaona Jia ◽

Mirza Mansoor Baig ◽

Farhaan Mirza ◽

Hamid GholamHosseini

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Heart Rate ◽

Prediction Model ◽

Risk Prediction ◽

Risk Model ◽

Clinical Laboratory ◽

Risk Prediction Model ◽

Validation Dataset ◽

Cvd Risk

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

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Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases

Stroke ◽

10.1161/strokeaha.120.032619 ◽

2021 ◽

Author(s):

Gad Abraham ◽

Loes Rutten-Jacobs ◽

Michael Inouye

Keyword(s):

Risk Prediction ◽

Risk Scores ◽

Future Research ◽

Cvd Risk ◽

Polygenic Risk ◽

Monogenic Disorders ◽

Current State ◽

Common Genetic Variants ◽

Artery Disease

Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

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Polygenic risk score validation using Korean genomes of 265 early-onset acute myocardial infarction patients and 636 healthy controls

PLoS ONE ◽

10.1371/journal.pone.0246538 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246538

Author(s):

Youngjune Bhak ◽

Yeonsu Jeon ◽

Sungwon Jeon ◽

Changhan Yoon ◽

Min Kim ◽

...

Keyword(s):

Risk Factors ◽

Risk Prediction ◽

Risk Score ◽

Early Onset ◽

Area Under The Curve ◽

Polygenic Risk Score ◽

Polygenic Risk ◽

Repeat Revascularization ◽

Artery Disease ◽

Conventional Risk Factors

Background The polygenic risk score (PRS) developed for coronary artery disease (CAD) is known to be effective for classifying patients with CAD and predicting subsequent events. However, the PRS was developed mainly based on the analysis of Caucasian genomes and has not been validated for East Asians. We aimed to evaluate the PRS in the genomes of Korean early-onset AMI patients (n = 265, age ≤50 years) following PCI and controls (n = 636) to examine whether the PRS improves risk prediction beyond conventional risk factors. Results The odds ratio of the PRS was 1.83 (95% confidence interval [CI]: 1.69–1.99) for early-onset AMI patients compared with the controls. For the classification of patients, the area under the curve (AUC) for the combined model with the six conventional risk factors (diabetes mellitus, family history of CAD, hypertension, body mass index, hypercholesterolemia, and current smoking) and PRS was 0.92 (95% CI: 0.90–0.94) while that for the six conventional risk factors was 0.91 (95% CI: 0.85–0.93). Although the AUC for PRS alone was 0.65 (95% CI: 0.61–0.69), adding the PRS to the six conventional risk factors significantly improved the accuracy of the prediction model (P = 0.015). Patients with the upper 50% of PRS showed a higher frequency of repeat revascularization (hazard ratio = 2.19, 95% CI: 1.47–3.26) than the others. Conclusions The PRS using 265 early-onset AMI genomes showed improvement in the identification of patients in the Korean population and showed potential for genomic screening in early life to complement conventional risk prediction.

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Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

10.2337/figshare.13607828 ◽

2021 ◽

Author(s):

Yixuan He ◽

Chirag M Lakhani ◽

Danielle Rasooly ◽

Arjun K Manrai ◽

Ioanna Tzoulaki ◽

...

Keyword(s):

Risk Factors ◽

Risk Prediction ◽

Risk Score ◽

Prediction Models ◽

Disease Risk ◽

Clinical Risk Factors ◽

Risk Scores ◽

Polygenic Risk Score ◽

Polygenic Risk ◽

Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

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Identification of risk factors for daptomycin-associated creatine phosphokinase elevation and development of a risk prediction model for incidence probability

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab568 ◽

2021 ◽

Author(s):

Masaru Samura ◽

Naoki Hirose ◽

Takenori Kurata ◽

Keisuke Takada ◽

Fumio Nagumo ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Risk Prediction ◽

Risk Score ◽

Logistic Regression Analysis ◽

Creatine Phosphokinase ◽

Risk Prediction Model ◽

Risk Scores

Abstract Background In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation. Methods Patients who received daptomycin at our hospital were classified into the normal or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis. Results The normal and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR] 4.45, 95% confidence interval [CI] 1.40–14.47, risk score 4), concomitant antihistamine use (OR 5.66, 95% CI 1.58–20.75, risk score 4), and trough concentration (Cmin) between 20 and <30 µg/mL (OR 14.48, 95% CI 2.90–87.13, risk score 5) and ≥30.0 µg/mL (OR 24.64, 95% CI 3.21–204.53, risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were <10% (low risk), 10%–<25% (moderate risk), and ≥25% (high risk) with the total risk scores of ≤4, 5–6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiving-operating characteristic curve 0.85, 95% CI 0.74–0.95). Conclusions These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.

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Epigenetic age acceleration is associated with cardiometabolic risk factors and clinical cardiovascular disease risk scores in African Americans

Clinical Epigenetics ◽

10.1186/s13148-021-01035-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Farah Ammous ◽

Wei Zhao ◽

Scott M. Ratliff ◽

Thomas H. Mosley ◽

Lawrence F. Bielak ◽

...

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

African Americans ◽

Risk Prediction ◽

Risk Scores ◽

Cvd Risk Factors ◽

Cvd Risk ◽

Clinical Risk ◽

Epigenetic Age ◽

Epigenetic Age Acceleration

Abstract Background Cardiovascular disease (CVD) is the leading cause of mortality among US adults. African Americans have higher burden of CVD morbidity and mortality compared to any other racial group. Identifying biomarkers for clinical risk prediction of CVD offers an opportunity for precision prevention and earlier intervention. Results Using linear mixed models, we investigated the cross-sectional association between four measures of epigenetic age acceleration (intrinsic (IEAA), extrinsic (EEAA), PhenoAge (PhenoAA), and GrimAge (GrimAA)) and ten cardiometabolic markers of hypertension, insulin resistance, and dyslipidemia in 1,100 primarily hypertensive African Americans from sibships in the Genetic Epidemiology Network of Arteriopathy (GENOA). We then assessed the association between epigenetic age acceleration and time to self-reported incident CVD using frailty hazard models and investigated CVD risk prediction improvement compared to models with clinical risk scores (Framingham risk score (FRS) and the atherosclerotic cardiovascular disease (ASCVD) risk equation). After adjusting for sex and chronological age, increased epigenetic age acceleration was associated with higher systolic blood pressure (IEAA), higher pulse pressure (EEAA and GrimAA), higher fasting glucose (PhenoAA and GrimAA), higher fasting insulin (EEAA), lower low density cholesterol (GrimAA), and higher triglycerides (GrimAA). A five-year increase in GrimAA was associated with CVD incidence with a hazard ratio of 1.54 (95% CI 1.22–2.01) and remained significant after adjusting for CVD risk factors. The addition of GrimAA to risk score models improved model fit using likelihood ratio tests (P = 0.013 for FRS and P = 0.008 for ASCVD), but did not improve C statistics (P > 0.05). Net reclassification index (NRI) showed small but significant improvement in reassignment of risk categories with the addition of GrimAA to FRS (NRI: 0.055, 95% CI 0.040–0.071) and the ASCVD equation (NRI: 0.029, 95% CI 0.006–0.064). Conclusions Epigenetic age acceleration measures are associated with traditional CVD risk factors in an African-American cohort with a high prevalence of hypertension. GrimAA was associated with CVD incidence and slightly improved prediction of CVD events over clinical risk scores.

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