GENOME-BASED METHODS TO REDUCE CARDIOVASCULAR RISK

Abstract

This description provides methods for reducing cardiovascular risk by administering a PCSK9 inhibitor to patients who have a genetic profile related to response to PCSK9 inhibitor therapy.

Description

GENOME-BASED METHODS TO REDUCE CARDIOVASCULAR RISK Field This description pertains to the field of therapeutic treatments for diseases and disorders associated with elevated lipid and lipoprotein levels. More specifically, it describes methods for enhancing the efficacy of proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitor therapy in high-risk cardiovascular patients by identifying those likely to respond to PCSK9 inhibitors. Background Despite modern therapy including rapid coronary revascularization, dual antiplatelet therapy, and intensive statin treatment, major adverse cardiovascular events (MACE) occur frequently in patients who have previously experienced MACE. Registry data indicate cardiovascular mortality as high as 13% at 5 years, with the vast majority occurring after initial hospital discharge. Patients with recent MACE are at very high risk of recurrent MACE in the short term. Cardiovascular death, recurrent myocardial infarction, or stroke occurs within 1 year in approximately 10% of patients who have previously experienced MACE. PCSK9 is a serine protease involved in regulating low-density lipoprotein receptor (LDLR) levels. In vitro experiments have shown that adding PCSK9 to HepG2 cells reduces cell surface LDLR levels. Mouse experiments have shown that increased PCSK9 levels decrease LDLR levels in the liver, while PCSK9-inactivated mice have higher LDLR levels in the liver. Furthermore, several human PCSK9 mutations have been identified that result in either increased or decreased plasma LDL levels. PCSK9 has been shown to interact directly with LDLR, undergo endocytosis with LDLR, and exhibit co-immunofluorescence with LDLR via the endosomal pathway.No degradation of LDLR by PCSK9 has been observed, and the mechanism by which extracellular LDLR protein levels decrease is uncertain. The establishment of a link between PCSK9 and cholesterol metabolism was quickly followed by the discovery that selected mutations in the PCSK9 gene caused autosomal dominant hypercholesterolemia, suggesting that the mutations confer a gain of function by increasing normal PCSK9 activity. ML / t / ZUZZ / UIII or I Conversely, loss-of-function mutations of PCSK9 and inhibition of PCSK9 function have been shown to significantly reduce LDL levels and the frequency of MACE. PCSK9 inhibition reduces the risk of major adverse cardiovascular events (MACE) in both primary and secondary intervention settings, but not all patients respond equally well to PCSK9 inhibition therapy. The etiology of cardiovascular disease is complex and can be influenced by genetics, the environment, and a variety of additional risk factors, including dyslipidemia, age, sex, hypertension, diabetes, obesity, and smoking. Genome-wide association studies (GWAS) have identified genetic variants widely associated with coronary artery disease, but genomic data need to be leveraged to identify patients who may specifically benefit from PCSK9 inhibition therapy, with the goal of preventing or reducing the likelihood of MACE. Compendium The present description provides methods for treating a patient at risk of MACE, comprising: determining the patient's polygenic coronary artery disease risk score (CAD-PRS), where CAD-PRS comprises a weighted sum of a plurality of genetic variants associated with coronary artery disease; identifying a patient who is at increased risk of MACE if the patient has a CAD-PRS greater than a CAD-PRS threshold determined from a reference population; and if the patient is identified as being at increased risk of MACE, administering a PCSK9 inhibitor to the patient. The present description also provides methods for lowering serum LDL levels in a patient at high risk of MACE, comprising: determining the patient's CAD-PRS, where CAD-PRS comprises a weighted sum of a plurality of genetic variants related to coronary artery disease; identifying a patient as at high risk of MACE if the patient has a CAD-PRS above a CAD-PRS threshold determined from a reference population; and if the patient is identified as being at high risk of MACE, administering a PCSK9 inhibitor to the subject in an amount effective to lower the patient's serum LDL level. The present description also provides methods for lowering serum LDL levels in a patient at high risk of MACE, comprising: determining the patient's CAD-PRS, where CAD-PRS comprises a weighted sum of a plurality of genetic variants related to coronary artery disease; IVIA / t / ZUZZ / UIII or I identify a patient as at high risk of MACE if the patient has a CADPRS above a CAD-PRS threshold determined from a reference population; and when the patient is identified as at high risk of MACE, administer a PCSK9 inhibitor to the subject in an amount effective to lower the patient's serum LDL level. The present description also provides methods for screening a candidate subject for inclusion in a clinical trial for the treatment of a cardiovascular condition. The method comprises: determining the candidate subject's CAD-PRS, where the CAD-PRS comprises a weighted sum of a plurality of genetic variants associated with coronary artery disease; and when the candidate subject has a CAD-PRS greater than a CAD-PRS threshold determined from a reference population, then the candidate subject is included in the clinical trial; or when the candidate subject has a CAD-PRS less than a CAD-PRS threshold determined from a reference population, then the candidate subject is excluded from the clinical trial. These and other objects and features of the present description will be better understood and appreciated from the following detailed description of this modality, selected for illustrative purposes and shown in the attached drawings. Brief description of the Figures FIG. 1 shows a table listing the demographic and reference characteristics of the patients in the pharmacogenomic analysis, with a comparison of the high and low risk genetic groups and the possibility of generalizing the ODYSSEY OUTCOMES. Figure 2 shows the incidence of MACE and secondary endpoints in the placebo group in the lower genetic risk group (polygenic risk score [PRS] < 90th percentile) and the high genetic risk group (PRS > 90th percentile). The overall incidence of MACE (a composite of death from coronary heart disease, nonfatal myocardial infarction, fatal or nonfatal ischemic stroke, or unstable angina requiring hospitalization) and key secondary endpoints is shown in patients of all ancestries, stratified by genetic risk. The numbers at the bottom of each panel are the number of patients in each group, and the number within each bar is the percentage with MACE in each group.Hazard ratios and p-values ​​were calculated from a Cox proportional hazards model, adjusted for ancestry, baseline LDL-C, Lp(a), age, sex, family history of premature coronary artery disease, and the following medical characteristics prior to the index ACS: myocardial infarction; percutaneous coronary intervention; coronary revascularization surgery; e. IVIA / t / ZUZZ / UIII or I congestive heart failure. Figure 3 shows the incidence of major adverse cardiovascular events (MACE) in the placebo arm in the lower genetic risk group (PRS < 90th percentile) and the high genetic risk group (PRS > 90th percentile), stratified by risk factors at baseline. The overall incidence of MACE in patients of all ancestries is shown, stratified by genetic risk of baseline LDL-C (<100 mg / dL or > 100 mg / dL) (Panel A); Framingham recurrent risk score (median or > median) (Panel B); and reference Lp(a) (<50 mg / dL or > 50 mg / dL) (Panel C). The numbers at the bottom of each panel represent the number of patients in each group, and the number within each bar represents the percentage with MACE in each group.Hazard ratios and p-values ​​were calculated from a Cox proportional hazards model, adjusted for ancestry, baseline LDL-C, Lp(a), age, sex, family history of premature coronary artery disease, and the following medical characteristics prior to the index ACS: myocardial infarction; percutaneous coronary intervention; coronary revascularization surgery; and congestive heart failure. Figure 4 shows the cumulative incidence of MACE in the lower genetic risk group (Panel A; PRS < 90th percentile) and the high genetic risk group (Panel B; PRS > 90th percentile). The cumulative incidence of MACE in patients of all ancestry, stratified by genetic risk, is shown. Hazard ratios and p-values ​​were calculated from a Cox proportional hazards model, adjusted for ancestry, baseline LDL-C, Lp(a), age, sex, family history of premature coronary artery disease, and the following medical characteristics prior to the index ACS: myocardial infarction; percutaneous coronary intervention; coronary artery bypass graft surgery; and congestive heart failure. In addition to the stratified genetic risk analyses, a Cox model was performed that included the treatment arm, genetic risk (high / low), the treatment-genetic risk interaction, and the aforementioned covariates.The p-value for the treatment-genetic risk interaction was 0.040. FIG. 5 shows a table listing the primary and secondary assessment criteria in the low and high risk genetic risk groups. Figure 6 shows the incidence of MACE stratified by genetic risk and baseline LDL cholesterol levels. The percentage with an event (overall incidence) is shown for patients of all ancestry, stratified by genetic risk and / or baseline LDL-C. Panel A stratifies by genetic risk (high genetic risk is defined as PRS > 90th percentile; lowest genetic risk is defined as PRS < 90th percentile). Panel B stratifies by baseline LDL-C (LDL-C > 100 mg / dL and LDL-C < 100 mg / dL). Panel C IVIA / t / ZUZZ / UIII or I stratifies by both genetic risk and reference LDL-C. The numbers at the bottom of each panel represent the number of patients in each group, and the number within each bar represents the percentage with MACE in each group. Hazard ratios and p-values ​​were calculated from a Cox proportional hazards model, adjusted for ancestry, baseline LDL-C, Lp(a), age, sex, family history of premature coronary artery disease, and the following medical characteristics prior to index ACS: myocardial infarction; percutaneous coronary intervention; coronary artery bypass graft surgery; and congestive heart failure. Figure 7 shows the cumulative incidence of MACE stratified by genetic risk and baseline LDL cholesterol levels. The cumulative incidence is shown in patients of all ancestries with high genetic risk (PRS > 90th percentile; Panels A and B) and lower genetic risk (PRS < 90th percentile; Panels C and D), further stratified by baseline LDL-C. Patients with LDL-C <100 mg / dL and high genetic risk are shown in Panel A, and those with LDL-C > 100 mg / dL and high genetic risk are shown in Panel B. Similarly, patients with LDL-C <100 mg / dL and lower genetic risk are shown in Panel C, and those with LDL-C > 100 mg / dL and lower genetic risk are shown in Panel D.Hazard ratios and p-values ​​were calculated from a Cox proportional hazards model, adjusted for ancestry, baseline Lp(a), age, sex, family history of premature coronary heart disease, and the following pre-ACS medical characteristics: myocardial infarction; percutaneous coronary intervention; coronary artery bypass graft surgery; and congestive heart failure. The interaction between treatment and genetic risk by baseline LDL-C value was p > 0.05. FIG. 8 shows a table listing additional demographic and reference characteristics of patients in the pharmacogenomic analysis. Figure 9 shows the results of candidate SNP (27–57), pruning and thresholding (P&T), and LDPred in the UK Biobank (UKB) trial dataset. Results are shown for the combined endpoint of myocardial infarction, angina pectoris, or ischemic stroke. Panel A shows the area under the curve (AUC) and odds ratio per standard deviation (SD) for each candidate SNP list or algorithm fitting parameter set. Panel B shows the number of markers used to generate the genetic risk score for each fitting parameter. Figure 10 shows the results of candidate SNP (27-57), pruning and thresholding (P&T), and LDPred on the DiscovEHR test dataset. The results for the combined endpoint of myocardial infarction, angina pectoris, or ischemic stroke are shown in Panel A. Panel A shows the AUC and odds ratio per SD for each candidate SNP list or algorithm fitting parameter set. Panel B shows the number of markers used to generate the genetic risk score for each fitting parameter. Figure 11 shows the LDPred results (p = 0.001) on UKB and DiscovEHR test datasets. Results are shown for the combined endpoint of myocardial infarction, angina pectoris, or ischemic stroke. Panel A shows the proportion of UKB participants with myocardial infarction, angina, or ischemic stroke, divided into 2.5% percentiles of the genetic risk score. Panel B shows this proportion in the DiscovEHR participants. FIG. 12 shows a table listing the incidence of MACE by ancestral group. Figure 13 shows stratified treatment decile plots for MACE, including the endpoints of death from coronary heart disease, nonfatal myocardial infarction, fatal or nonfatal ischemic stroke, or unstable angina requiring hospitalization. Panel A shows the proportion with one event per genetic risk score decile in the alirocumab arm, while panel B shows the risk per decile in the placebo arm. The mean PRS Z-score for each decile is shown to the right of the decile. The gray dashed line represents the total proportion of events per arm. Figure 14 shows treatment-stratified decile plots for the secondary endpoint: any cardiovascular event. This endpoint includes any death from cardiovascular causes, nonfatal myocardial infarction or unstable angina requiring hospitalization, an ischemia-induced coronary revascularization procedure, or nonfatal ischemic stroke. Panel A shows the proportion with one event per genetic risk score decile in the alirocumab arm, while panel B shows the risk per decile in the placebo arm. The mean PRS Z-score for each decile is shown to the right of the decile. The gray dashed line represents the total proportion of events per arm. Figure 15 shows treatment-stratified decile plots for the secondary endpoint: any coronary heart disease event.This endpoint includes death from coronary heart disease, nonfatal myocardial infarction, unstable angina requiring hospitalization, and ischemia-induced coronary revascularization. Panel A shows the proportion with one event per decile of genetic risk score in the alirocumab arm, while panel B shows the risk per decile in the placebo arm. The mean PRS Z-score for each decile is shown to the right of the decile. The gray dashed line represents the total proportion of events per arm. Figure 16 shows the stratified decile plots of treatment for the secondary endpoint of death from any cause, nonfatal myocardial infarction, or ischemic stroke. Panel A shows the proportion with one event per decile of genetic risk score in the alirocumab arm, while panel B shows the risk per decile in the placebo arm. The mean PRS Z-score for each decile is shown to the right of the decile. The gray dashed line represents the total proportion of events per arm. Figure 17 shows treatment-stratified decile plots for the second endpoint of a generalized coronary heart disease event. This endpoint includes death from coronary heart disease and nonfatal myocardial infarction. Panel A shows the proportion with one event per genetic risk score decile in the alirocumab arm, while panel B shows the risk per decile in the placebo arm. Figure 18 shows treatment-stratified decile plots for the secondary endpoint of ischemia-induced coronary revascularization procedures. Panel A shows the proportion with one event per genetic risk score decile in the alirocumab arm, while panel B shows the risk per decile in the placebo arm. The mean PRS Z-score for each decile is shown to the right of the decile. The gray dashed line represents the total proportion of events per arm. Figure 19 shows the incidence of MACE in the placebo arm in the lower genetic risk group (PRS < 90th percentile) and the high genetic risk group (PRS > 90th percentile), stratified by very high risk (VHR) groups. The overall incidence of MACE in patients of all ancestries, stratified by VHR categories, is shown. The VHR categories follow the definitions described in doi: 10.1161 / CIRCULATIONAHA.119.042551. VHR* (multiple prior events of generalized ASCVD) includes patients with >1 prior ischemic event before the ACS index qualification event, including ischemic stroke, myocardial infarction, or peripheral artery disease.VHR* (previous generalized ASCVD event + multiple high-risk conditions) includes patients with 1 generalized ASCVD event (the qualifying event of the ACS index) and > 2 high-risk conditions (diabetes mellitus, current smoking, age > 65 years, history of hypertension, baseline eGFR > 15 - < 60). mL / t / ZUZZ / UIII or 1 mL-min1-1.73 m - 2, congestive heart failure, revascularization prior to the ACS index, or LDL-C > 100 mg / dL with statin and ezetimibe use). VHR* is the combination of both categories, and no VHR includes patients without any of these risk factors. The numbers at the bottom of each panel are the number of patients in each group, and the number within each bar is the percentage with MACE in each group. Hazard ratios and p-values ​​were calculated from a Cox proportional hazards model, adjusted for ancestry. Because the composite VHR* risk groups comprise multiple risk factors, covariate adjustment for additional risk factors was not included in this model. Figure 20 shows UKB: median Lp(a) nmol / L, excluding / including the LPA gene region. Results are shown for median Lp(a) by percentile, excluding and including the LPA gene region (+ / - 1 MB) in the score. Panel A shows the genome-wide PRS excluding the LPA gene region (+ / - 1 MB); and Panel B shows the genome-wide score. Figure 21 shows ODYSSEY: median Lp(a) mg / dL (Q1-Q3), excluding / including the LPA gene region. Results are shown for median Lp(a) by percentile, excluding and including the LPA gene region (+ / - 1 MB) in the score. Panel A shows the genome-wide PRS excluding the LPA gene region (+ / - 1 MB); and Panel B shows the genome-wide score. Figure 22 shows UKB: composite endpoint of myocardial infarction, angina, or ischemic stroke, excluding / including the LPA gene region. Results are shown for the combined endpoint of myocardial infarction, angina, or ischemic stroke, excluding and including the LPA gene region (+ / - 1 MB) in the score. Panel A shows the genome-wide PRS excluding the LPA gene region (+ / - 1 MB); and Panel B shows the genome-wide score. Figure 23 shows the incidence of MACE in the placebo arm of ODYSSEY, excluding / including the LPA gene region. Results shown for MACE (a composite endpoint including death from coronary heart disease, nonfatal myocardial infarction, fatal or nonfatal ischemic stroke, or unstable angina requiring hospitalization) excluding and including the LPA gene region (+ / - 1 MB) in the score. Panel A shows the genome-wide PRS excluding the LPA gene region (+ / - 1 MB); and Panel B shows the genome-wide score. Figure 24 shows the cumulative incidence of MACE in the lower genetic risk group (PRS < 90th percentile; Panel A) and the high genetic risk group (PRS > 90th percentile; Panel A). IVIA / t / ZUZZ / UIII or reference I. Panel A stratifies by genetic risk (high genetic risk is PRS > 90th percentile; lowest genetic risk is PRS < 90th percentile). Panel B stratifies by Lp(a) at the study reference (Lp(a) > 50 mg / dL and Lp(a) < 50 mg / dL). Panel C stratifies by both genetic risk and Lp(a) at the reference. The numbers at the bottom of each panel are the number of patients in each group, and the number within each bar is the percentage with MACE in each group. Hazard ratios and p-values ​​were calculated from a Cox proportional hazards model, adjusted for ancestry, baseline LDL-C, Lp(a), age, sex, family history of premature coronary artery disease, and the following medical characteristics before the index ACS: myocardial infarction; percutaneous coronary intervention; coronary revascularization surgery; and congestive heart failure. Figure 28 shows MACE stratified by genetic risk and reference Lp(a) levels, taking into account the VHR category. Panel A is stratified by genetic risk, where genetic risk is defined as PRS > 90th percentile and low genetic risk as PRS < 90th percentile. Panel B is stratified by reference Lp(a) levels (Lp(a) > 50 mg / dL and Lp(a) < 50 mg / dL). Panel C is stratified by genetic risk and reference Lp(a) levels. The numbers at the bottom of each panel represent the number of patients in each group, and the number within each bar represents the percentage of patients with MACE in each group.Hazard ratios and p-values ​​were calculated from a Cox proportional hazards model, adjusted for ancestry, baseline LDL-C, Lp(a), age, sex, family history of premature coronary artery disease, and the following medical characteristics prior to the index ACS: myocardial infarction; percutaneous coronary intervention; coronary revascularization surgery; and congestive heart failure. FIG. 29 shows a table listing the genetic decile risk, summarized through the PRS generation algorithms. Figure 30 shows treatment-stratified decile plots for MACE for the LDPred, 27-SNP, and 57-SNP models. This endpoint includes death from coronary heart disease, nonfatal myocardial infarction, fatal or nonfatal ischemic stroke, or unstable angina requiring hospitalization. Panel A shows the results for LDPred (p = 0.001), panel B shows the results for the 27-SNP model, and panel C shows the results for the 57-SNP model. The top row shows the percentage with one event per genetic risk score decile in the alirocumab arm, while the bottom row shows the risk per decile in the placebo arm. Description of the lul modalities Genetic factors can play a significant role in the risk of developing a disease and potentially influence how individuals respond to drug treatment. Risk profiles (RPs) combine information from a large number of genetic variants, derived from disease association studies, to create a single, quantitative composite measure for each individual that reflects their risk of genetically determined disease. An individual with a greater number of risk alleles for a given disease will have a higher RRP than an individual with fewer alleles. Risk can be assessed at various thresholds, such as percentiles or standard deviations from the population distribution.The present description refers in general to the unexpected finding that stratifying subjects by CAD-PRS is useful in identifying subjects who may benefit from treatment with a PCSK9 inhibitor, regardless of traditional clinical criteria such as LDL cholesterol levels. Throughout this specification and the claims, various terms are used with respect to aspects of the description herein. Unless otherwise stated, these terms should be given their common meaning in the art. Other terms specifically defined should be interpreted in a manner consistent with the definitions provided herein. Unless expressly stated otherwise, it is not intended in any way that the steps of any method or aspect set forth herein must be carried out in a specific order. Therefore, where a claim for a method does not specifically state in the claims or descriptions that the steps are limited to a specific order, it is not intended that any order should be inferred. This applies to any possible implicit interpretation, including the logic regarding the arrangement of the steps or the operational flow, the plain meaning derived from the grammatical organization or punctuation, or the number or type of aspects described in the specification. According to their use in the present, the singular forms un, una, el and la include plural referents, unless the context clearly indicates otherwise. As used in this document, the term "around" means that the listed numerical value is approximate and small variations would not significantly affect the practice of the modalities described. When a numerical value is used, unless the context indicates otherwise, the term "around" means that the numerical value may vary by ±10% and remain within the range of the modalities described. ML / t / ZUZZ / UIII or I As used herein, the terms subject and patient are interchangeable. A subject may include any animal, including mammals. Mammals include, but are not limited to, farm animals (such as horses, cows, and pigs), companion animals (such as dogs and cats), laboratory animals (such as mice, rats, and rabbits), and non-human primates. In some modalities, the subject is a human being. As used in this document, major adverse cardiovascular events or MACE refers to one or more of: death from coronary heart disease (CHD death), coronary artery disease (CAD), nonfatal myocardial infarction (MI), unstable angina requiring hospitalization, fatal or nonfatal ischemic stroke, ischemia-induced coronary revascularization, arrhythmias, cardiovascular death, heart valve disease, cardiomyopathy, or congestive heart failure. As used herein, a patient at risk for MACE or a patient at risk refers to a patient with hypercholesterolemia and / or elevated levels of at least one atherogenic lipoprotein. In some modalities, a patient at risk for MACE has hypercholesterolemia and / or elevated levels of at least one atherogenic lipoprotein. In some modalities, a patient at risk for MACE is a patient who has previously had MACE. The term ischemia-driven coronary revascularization refers to percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). For the clinical studies described herein, coronary revascularization procedures performed solely for restenosis at the site of a previous PCI were excluded from this definition. In some modalities, ischemia-driven coronary revascularization must be driven by one of the following: a) acute ischemia, b) new or progressive symptoms (angina or equivalent), or c) new or progressive functional test abnormalities (e.g., stress testing or imaging). As used in this document, death from coronary artery disease, death from coronary heart disease, and death due to coronary artery disease are used interchangeably to refer to the subset of cardiovascular deaths for which there is a clear relationship to underlying coronary artery disease, including death secondary to acute myocardial infarction (MI), sudden death, heart failure, complication of a coronary revascularization procedure performed for symptoms, progression of coronary artery disease or new myocardial ischemia where the cause of death is clearly related to the procedure, unobserved and unexpected death, and others IVIA / t / ZUZZ / UIII or I deaths that definitely cannot be attributed to a non-vascular cause. As used in this document, the terms cardiovascular event or CV event refer to any nonfatal coronary heart disease event, any cardiovascular death, and any nonfatal ischemic stroke. Example CV events include, but are not limited to, myocardial infarction, stroke, unstable angina requiring hospitalization, heart failure requiring hospitalization, and an ischemia-driven coronary revascularization procedure. As used in this document, the terms cardiovascular death, CV death, and cardiovascular mortality are used interchangeably to refer to death resulting from acute myocardial infarction, sudden cardiac death, death from heart failure, death from stroke, and death from other cardiovascular causes. In some modalities, CV death is death from CHD. In other modalities, CV death is selected from the group consisting of heart failure or cardiogenic shock, stroke, ischemic cardiovascular causes, or a cardiovascular cause other than ischemia. As used in this document, the term nonfatal cardiovascular event refers to any CV event that does not result in death. In some modalities, nonfatal CV events may occur consecutively, where an initial CV event (e.g., the first) is followed by a subsequent event (e.g., the second, third, or fourth). As used herein, non-cardiovascular death and non-cardiovascular death are used interchangeably to refer to any death that is not considered a cardiovascular death. Examples of non-cardiovascular death include, but are not limited to, pulmonary infection, pulmonary malignancy, gastrointestinal / hepatobiliary / pancreatic infection, gastrointestinal / hepatobiliary / pancreatic malignancy, hemorrhage, a neurological process other than stroke / hemorrhage, suicide, a non-cardiovascular procedure or surgery, accident or trauma, renal infection, renal malignancy other than a non-cardiovascular infection, and other cardiovascular malignancy. As used in this document, nonfatal myocardial infarction is defined and subclassified according to the ACC / AHA / ESC Universal Definition of Myocardial Infarction (see, Thygesen et al., J. Amer. Coll. CardioL, 2012, 60, 1581-98). As used in this document, coronary artery bypass grafting (CABG) refers to a procedure in which autologous arteries or veins are used as IVIA / t / ZUZZ / UIII or I grafts to divert coronary arteries that are partially or completely obstructed by atherosclerotic plaques (see, Alexander and Smith, New Eng. J. Med, 2016, 374, 1954-64). As used in this document, the terms unstable angina requiring hospitalization and hospitalization for unstable angina are used interchangeably to refer to: admission to the hospital or emergency department with symptoms of myocardial ischemia with a rapid heart rate in the previous 48 hours and / or chest discomfort at rest ≥20 min, further requiring both of the following: a) new or presumed new ischemic ECG changes, defined by ST depression > 0.5 mm in 2 contiguous leads; T wave inversion >1 mm in 2 contiguous leads with prominent R wave or R / S > 1; ST elevation in > 2 contiguous leads > 0.2 mV in V2 or V3 in men, > 0.15 mV in V2 or V3 in women, or > 0.1 mV in other leads; or LBBB; yb) definitive contemporary evidence of coronary obstruction requiring coronary revascularization procedure or at least one epicardial stenosis 1 70%.For the clinical trials described in this document, coronary revascularization procedures or stenoses due solely to restenosis at the previous PCI site were excluded. As used in this document, ischemic stroke refers to: 1) an acute episode of focal cerebral, spinal, or retinal dysfunction caused by an infarct, defined by at least one of the following: a) pathological, imaging, or other objective evidence of focal cerebral, spinal, or retinal ischemic injury in a defined vascular distribution; b) symptoms of acute cerebral, spinal, or retinal ischemic injury that persists for 24 hours or until death, with other etiologies excluded; 2) hemorrhagic infarct, but without stroke, caused by intracerebral or subarachnoid hemorrhage; or 3) strokes not otherwise subclassified. As used in this document, high-intensity statin therapy and high-dose atorvastatin / rosuvastatin are used interchangeably to refer to the administration of 40-80 mg of atorvastatin daily or 20-40 mg of rosuvastatin daily. As used herein, maximally tolerated statin therapy or maximum tolerated dose of statin therapy are used interchangeably to refer to a therapeutic regimen comprising the administration of a daily dose of a statin that is the highest dose of statin that can be administered to a particular patient without causing adverse side effects. Maximally tolerated statin therapy includes, but is not limited to, high-intensity statin therapy. ML / t / ZUZZ / UIII or I As used in this document, a patient is considered statin-intolerant or statin-intolerant if the patient has a history of experiencing one or more adverse reactions that began or worsened while on a daily statin therapy regimen and ceased when statin therapy was discontinued. In some modalities, adverse reactions are musculoskeletal in nature, such as musculoskeletal pain, discomfort, weakness, or cramping (e.g., myalgia, myopathy, rhabdomyolysis, etc.). These adverse reactions are often exacerbated after exercise or exertion. Statin-related adverse reactions also include hepatic, gastrointestinal, and psychiatric symptoms that correlate with statin administration.In some modalities, a patient is considered statin-intolerant or suffering from statin intolerance if, for example, any of the following apply to the patient: (1) has a history of skeletal muscle-related symptoms associated with at least two different and separate daily statin therapy regimens; (2) experiences one or more statin-related adverse reactions at the lowest approved daily doses of one or more statins; (3) is unable to tolerate a cumulative weekly statin dose of seven times the lowest approved tablet size; (4) is able to tolerate low-dose statin therapy but develops symptoms when the dose is increased (e.g., to achieve a target LDL-C level); or (5) statins are contraindicated for the patient. As used in this document, not adequately controlled, with reference to hypercholesterolemia, means that the patient's serum low-density lipoprotein cholesterol (LDL-C) concentration, total cholesterol concentration and / or triglyceride concentration are not reduced to a medically acceptable recognized level (taking into account the patient's relative risk of coronary heart disease) after at least 4 weeks on a therapeutic regimen comprising a stable daily dose of a statin.For example, a patient with hypercholesterolemia that is not adequately controlled by a statin includes a patient or patients with a serum LDL-C concentration greater than or equal to about 70 mg / dL, greater than or equal to about 80 mg / dL, greater than or equal to about 90 mg / dL, greater than or equal to about 100 mg / dL, greater than or equal to about 110 mg / dL, greater than or equal to about 120 mg / dL, greater than or equal to about 130 mg / dL, greater than or equal to about 140 mg / dL, or higher (depending on the patient's underlying risk of heart disease) after the patient has been on a stable daily statin regimen for at least 4 weeks. As used herein, the expression not adequately controlled, IVIA / t / ZUZZ / UIII or I with reference to atherogenic lipoproteins means that the patient's serum low-density lipoprotein cholesterol (LDL-C) concentration, low-density lipoprotein cholesterol concentration and / or apolipoprotein B concentration are not reduced to a medically acceptable recognized level (taking into account the patient's relative risk of coronary heart disease) after at least 4 weeks on a therapeutic regimen comprising a stable daily dose of a statin.For example, a patient with elevated levels of atherogenic lipoproteins that are not adequately controlled with a statin includes a patient or patients with a serum LDL-C concentration greater than or equal to about 70 mg / dL, greater than or equal to about 80 mg / dL, greater than or equal to about 90 mg / dL, greater than or equal to about 100 mg / dL, greater than or equal to about 110 mg / dL, greater than or equal to about 120 mg / dL, greater than or equal to about 130 mg / dL, greater than or equal to about 140 mg / dL, or higher (depending on the patient's underlying risk of heart disease); a non-high-density lipoprotein cholesterol concentration greater than or equal to about 100 mg / dL; or an apolipoprotein B concentration greater than or equal to approximately 80 mg / dL after the patient has been on a stable daily statin regimen for at least 4 weeks. This description generally refers to methods and formulations for treating a patient at increased risk of major adverse cardiovascular events (MACE). In some modalities, a patient at increased risk of MACE who is treatable by the methods described herein has hypercholesterolemia (e.g., a serum LDL-C concentration greater than or equal to 70 mg / dL, or a serum lipoprotein(a) (LPA or LP(a)) level of at least approximately 30 mg / dL). In some modalities, a patient at increased risk of MACE who is treatable by the methods described herein has received or is currently receiving a high dose of a statin. This description also refers generally to methods and formulations for treating a patient at increased risk of major adverse cardiovascular events (MACE) who has elevated levels of atherogenic lipoproteins. In some modalities, the patient at increased risk of MACE who is treatable by the methods described herein has hypercholesterolemia (e.g., a serum LDL-C concentration greater than or equal to 70 mg / dL, or a serum lipoprotein(a) (LPA or LP(a)) level of at least approximately 30 mg / dL). In some modalities, a patient at increased risk of MACE who is treatable by the methods described herein has received or is currently receiving a high dose of a statin. This description refers in general to methods and compositions for IVIA / t / ZUZZ / UIII or I to reduce serum LDL and lipoprotein(a) levels in a patient at increased risk of MACE. In some modalities, a patient at increased risk of MACE who is treatable by the methods described herein has hypercholesterolemia (e.g., a serum LDL-C concentration greater than or equal to 70 mg / dL, or a serum lipoprotein(a) (LPA or LP(a)) level of at least approximately 50 mg / dL). In some modalities, a patient at increased risk of MACE who is treatable by the methods described herein has received or is currently receiving a high dose of a statin. This description also includes methods for treating a patient at increased risk of major adverse cardiovascular events (MACE) with hypercholesterolemia and elevated levels of atherogenic lipoproteins that are not adequately controlled by the maximum tolerated dose of statin therapy. In some modalities, the maximum tolerated dose of statin therapy includes the daily administration of statins such as cerivastatin, pitavastatin, fluvastatin, lovastatin, and pravastatin. Not limited by any particular theory, it is believed that the CAD-PRS calculated according to the methods presented herein allows for the identification of patients at risk of major adverse cardiovascular events (MACE) who are more likely to respond to PCSK9 inhibitor therapy. Furthermore, surprisingly and unexpectedly, CAD-PRS also predicts patient response to PCSK9 inhibitor therapy in patients who do not have elevated levels of lipoprotein(a) (LPA or LP(a)) or LDL-C. In some modalities, a patient at increased risk of MACE treatable by the methods described herein has had a MACE within the past 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, or 18 months. Patients at high cardiovascular risk treatable by the methods described herein include those hospitalized for a MACE. In some modalities, the patient at highest risk of MACE can be selected based on a CAD-PRS, where the CAD-PRS comprises a weighted sum of a plurality of genetic variants associated with coronary artery disease and is calculated using at least around 2, at least around 3, at least around 4, at least around 5, at least around 10, at least around 20, at least around 30, at least around 40, at least around 50, at least around 60, at least approximately 70, at least around 80, at least around 100, at least around 120, at least around 150, at least around 200, at least around 250, at least around 300, at least ML / t / ZUZZ / UIII or I around 400, at least around 500 or at least around 1000 genetic variants, and if the patient has CAD-PRS above a threshold score, administer a PCSK9 inhibitor to the subject in an amount effective to reduce the level of LDL and lipoprotein(a) in serum. Risk assessments that use a large number of genetic variants offer the advantage of greater predictive power. In some cases, one or more of the genetic variants is a single nucleotide polymorphism (SNP). In some cases, one or more of the genetic variants is an insertion. In some cases, one or more of the genetic variants is a deletion. In some cases, one or more of the genetic variants is a structural variant. In some cases, one or more of the genetic variants is a copy number variation. In some modalities, the description includes a large number of alleles in the risk assessment, for example, at least around 500,000 genetic variants, at least around 1,000,000 genetic variants, at least around 2,000,000 genetic variants, at least around 3,000,000 genetic variants, at least around 4,000,000 genetic variants, at least around 5,000,000 genetic variants, or at least around 6,000,000 genetic variants, or at least around 6,500,000 genetic variants, or at least around 7,000,000 genetic variants, or at least around 8,000,000 genetic variants, or at least around 9,000,000 genetic variants, or at least less about 10,000,000 genetic variants from one or more genetic variant databases, such as, for example, the genetic variant database described in Nikpay et al., Nat Genet, 2015, 47, 1121-1130 (the database) and available online at “cardiogramplusc4d.org / media / cardiogramplusc4d-consortium / datadownloads / cad.additive.Oct2015.pub.zip.” In some modalities, risk assessment may involve evaluating all genetic variants listed in the database. In some modalities, the present description provides a method for determining a CAD-PRS in a subject; the method comprises identifying whether at least approximately 2 genetic variants, at least approximately 5 genetic variants, at least approximately 10 genetic variants, at least approximately 15 genetic variants, at least approximately 20 genetic variants, at least approximately 30 genetic variants, at least approximately 40 genetic variants, at least approximately 50 genetic variants, at least approximately 60 genetic variants, at least approximately 70 genetic variants, at least approximately 100 genetic variants, at least approximately 200 genetic variants, at least approximately 500 genetic variants, at least approximately 1000 genetic variants, or at least approximately 1000 genetic variants, are present. IVIA / t / ZUZZ / UIII or I less approximately 2,000 genetic variants, at least approximately 5,000 genetic variants, at least approximately 10,000 genetic variants, at least approximately 20,000 genetic variants, at least approximately 50,000 genetic variants, at least approximately 75,000 genetic variants, at least approximately 100,000 genetic variants, at least approximately 500,000 genetic variants, at least approximately 1,000,000 variant genes, at least approximately 2,000,000 genetic variants, at least approximately 3,000,000 genetic variants, at least approximately 4,000,000 genetic variants, at least approximately 5,000,000 genetic variants, or at least approximately 6,000,000 genetic variants from the database are present in a biological sample of the subject; where the presence of a risk allele increases CAD-PRS, and where the presence of an alternative allele decreases CAD-PRS. In some modalities, the description provides a method for determining the risk of MACE in an individual, which includes identifying whether the genetic variants in the database are present in a biological sample from the individual and calculating a CAD-PRS for the individual based on the identified genetic variants, where CAD-PRS is calculated by summing the weighted risk score associated with each identified genetic variant. The number of identified genetic variants may be at least approximately 2, 5, 10, 15, 20, 30, 40, 50, 95, or 100.at least around 200 genetic variants, at least around 500 genetic variants, at least around 1000 genetic variants, at least around 2000 genetic variants, at least around 5000 genetic variants, at least around 10,000 genetic variants, at least around 20,000 genetic variants, at least around 50,000 genetic variants, at least around 75,000 genetic variants, at least around 100,000 genetic variants, at least around 500,000 genetic variants, at least around 1,000,000 genetic variants, at least around 2,000,000 genetic variants, at least around 3,000,000 genetic variants, at least around 4,000,000 genetic variants, to less around 5,000,000 genetic variants, or at least around 6,000,000 genetic variants, or at least around 6,500,000 genetic variants, or at least around 7,000,000 genetic variants,or at least around 8,000,000 genetic variants, or at least around 9,000,000 genetic variants, or at least, IVIA / t / ZUZZ / UIII or I around 10,000,000 genetic variants. In some modalities, the description provides a method for determining a subject's risk of suffering from MACE, comprising identifying whether the genetic variants in the database are present in a biological sample from the subject, wherein the identification comprises measuring the presence of at least approximately 50 genetic variants, at least approximately 95 genetic variants, at least approximately 100 genetic variants, at least approximately 200 genetic variants, at least approximately 500 genetic variants, at least approximately 1,000 genetic variants, at least approximately 2,000 genetic variants, at least approximately 5,000 genetic variants, at least approximately 10,000 genetic variants, at least approximately 20,000 genetic variants, at least approximately 50,000 genetic variants, at least approximately 75,000 genetic variants, at least approximately 100,000 genetic variants, at least approximately 500,000 genetic variants,at least around 1,000,000 genetic variants, at least around 2,000,000 genetic variants, at least around 3,000,000 genetic variants, at least around 4,000,000 genetic variants, at least around 5,000,000 genetic variants, or at least around 6,000,000 genetic variants, or at least around 6,500,000 genetic variants, or at least around 7,000,000 genetic variants, or at least around 8,000,000 genetic variants, or at least around 9,000,000 genetic variants, or at least around 10,000,000 genetic variants. In some modalities, the description provides a method for determining a subject's risk of suffering from MACE that comprises selecting at least around 50 genetic variants, at least around 95 genetic variants, at least around 100 genetic variants, at least around 200 genetic variants, at least around 500 genetic variants, at least around 1000 genetic variants, at least around 2000 genetic variants, at least around 5000 genetic variants, at least around 10,000 genetic variants, at least around 20,000 genetic variants, at least around 50,000 genetic variants, at least around 75,000 genetic variants, at least around 100,000 genetic variants, at least around 500,000 genetic variants, at least around 1,000,000 variants genetic, at least around 2,000,000 genetic variants, at least around 3,000,000 genetic variants,at least around 4,000,000 genetic variants, at least around 5,000,000 genetic variants, or at least around 6,000,000 genetic variants, or at least around 6,500,000 genetic variants, or at least around 7,000,000 genetic variants, or at least, ML / E / ZuZZ / u / llúl around 8,000,000 genetic variants, or at least around 9,000,000 genetic variants, or at least around 10,000,000 genetic variants from the database; identify whether the genetic variants are present in a biological sample from the subject; and calculate the PRS based on the presence of the genetic variants. In some modalities, the description provides a method for determining a subject's risk of MACE. This method involves identifying whether the genetic variants in the database are present in a biological sample from the subject, calculating a CAD-PRS for the subject based on the identified genetic variants, and assigning the subject to a risk group according to the CAD-PRS. The CAD-PRS can be divided into quintiles, for example, upper quintile, middle quintile, and lower quintile, where the upper quintile of polygenic scores corresponds to the highest genetic risk group and the lower quintile corresponds to the lowest genetic risk group. The number of identified variants can be at least approximately 50 genetic variants, at least approximately 95 genetic variants, at least approximately 100 genetic variants, at least approximately 200 genetic variants, or at least approximately 500 genetic variants.at least around 1,000 genetic variants, at least around 2,000 genetic variants, at least around 5,000 genetic variants, at least around 10,000 genetic variants, at least around 20,000 genetic variants, at least around 50,000 genetic variants, at least around 75,000 genetic variants, at least around 100,000 genetic variants, at least around 500,000 genetic variants, at least around 1,000,000 genetic variants, at least around 2,000,000 genetic variants, at least around 3,000,000 genetic variants, at least around 4,000,000 genetic variants, at least around 5,000,000 genetic variants, or at least around 6,000,000 genetic variants, or at least around 6,500,000 genetic variants, or at least around 7,000,000 genetic variants, or at least around 8,000,000 genetic variants, or at least around 9,000,000 genetic variants, or at least around 10,000,000 genetic variants. In some modalities, the description provides a method for selecting subjects or candidates at risk of developing MACE that comprises identifying whether at least around 50 genetic variants, at least around 95 genetic variants, at least around 100 genetic variants, at least around 200 genetic variants, at least around 500 genetic variants, at least around 1000 genetic variants, at least around 2000 genetic variants, at least around 5000 genetic variants, at least around 10,000 genetic variants, or at least around 10,000 genetic variants, to ML / E / ZuZz / u llúl less than around 20,000 genetic variants, at least around 50,000 genetic variants, at least around 75,000 genetic variants, at least around 100,000 genetic variants, at least around 500,000 genetic variants, at least around 1,000,000 genetic variants, at least around 2,000,000 genetic variants, at least around 3,000,000 genetic variants, at least around 4,000,000 genetic variants, at least around 5,000,000 genetic variants, or at least around 6,000,000 genetic variants, or at least around 6,500,000 variants genetic, or at least around 7,000,000 genetic variants, or at least around 8,000,000 genetic variants, or at least around 9,000,000 genetic variants, or at least around 10,000,000 genetic variants from the database are present in a biological sample from each subject or candidate;Calculate a polygenic CAD-PRS risk score for each subject or candidate based on the identified genetic variants; and select subjects or candidates with a desired risk group. For all MACE risk assessments, incorporating a large number of genetic variants offers the advantage of greater predictive power. The description further provides previously established risk assessments that incorporate, for example, at least 500,000, at least 1,000,000, at least 2,000,000, at least 3,000,000, at least 4,000,000, at least 5,000,000, or at least 6,000,000 genetic variants, or at least 6,500,000 genetic variants, or at least 7,000,000 genetic variants, or at least 8,000,000 genetic variants, or at least 9,000,000 genetic variants, or at least 10,000,000 genetic variants from the database. In some modalities, the description provides a method for selecting a population of subjects or candidates at high risk of developing MACE, which includes identifying whether at least 50 genetic variants, at least 95 genetic variants, at least 100 genetic variants, at least 200 genetic variants, at least 500 genetic variants, at least 1,000 genetic variants, at least 2,000 genetic variants, at least 5,000 genetic variants, at least 10,000 genetic variants, at least 20,000 genetic variants, at least 50,000 genetic variants, at least 75,000 genetic variants, at least 100,000 genetic variants, at least 500,000 genetic variants, at least 1,000,000 genetic variants, at least 2,000,000 genetic variants, at least 3,000,000 genetic variants, at least 4,000,000 genetic variants, at least 5,000,000 genetic variants, or at least 6,000,000 genetic variants, or at least 6,500,000 genetic variants, or at least 7,000,000 genetic variants, or at least 8,000,000 genetic variants, or at least 9,000,000 genetic variants, or at least lul, 10,000,000 genetic variants from the database are present in a biological sample from each subject or candidate; calculate a CAD-PRS for each subject or candidate based on the identified genetic variants; and select the subjects or candidates in the high-risk group. In some modalities, the number of identified genetic variants is at least 20. In some modalities, the number of identified genetic variants is at least 30. In some modalities, the number of identified genetic variants is at least 40. In some modalities, the number of identified genetic variants is at least 50. In some modalities, the number of identified genetic variants is at least 70. In some modalities, the number of identified genetic variants is at least 100. In some modalities, the number of identified genetic variants is at least 500. In some modalities, the number of identified genetic variants is at least 1000. In some modalities, the number of identified genetic variants is at least 2000.In some modalities, the number of identified genetic variants is at least 5,000. In some modalities, the number of identified genetic variants is at least 10,000. In some modalities, the number of identified genetic variants is at least 20,000. In some modalities, the number of identified genetic variants is at least 50,000. In some modalities, the number of identified genetic variants is at least 75,000. In some modalities, the number of identified genetic variants is at least 100,000. In some modalities, the number of identified genetic variants is at least 500,000. In some modalities, the number of identified genetic variants is at least 1,000,000.In some modalities, the number of identified genetic variants is at least 2,000,000. In some modalities, the number of identified genetic variants is at least 3,000,000. In some modalities, the number of identified genetic variants is at least 4,000,000. In some modalities, the number of identified genetic variants is at least 5,000,000. In some modalities, the number of identified genetic variants is at least 6,000,000. In some modalities, the number of identified genetic variants is at least 6,500,000. In some modalities, the number of genetic variants... The number of identified genetic variants is at least 7,000,000. In some modalities, the number of identified genetic variants is at least 8,000,000. In some modalities, the number of identified genetic variants is at least 9,000,000. In some modalities, the number of identified genetic variants is at least 10,000,000. In some description modalities, risk assessments comprise the highest weighted CAD-PRS scores, which include, but are not limited to, the 50%, 55%, 60%, 70%, 80%, 90%, or 95% highest CAD-PRS scores in a patient population. In some modalities, the identified genetic variants comprise the highest risk genetic variants or genetic variants with a weighted risk score in the top 10%, top 20%, top 30%, top 40% or top 50% in the database. In some modalities, the identified genetic variants comprise those genetic variants associated with MACE in the top 10%, top 20%, top 30%, top 40%, or top 50% of the p-value range in the database. In some modalities, each of the identified genetic variants comprises those genetic variants associated with MACE with a p-value no greater than approximately 10.1, approximately 10.2, approximately 10.3, approximately 10.4, approximately 10.5, approximately 10.6, approximately 10.7, 10.8, approximately 10.9, approximately 10.00, approximately 10.11, approximately 10.12, approximately 10.13, approximately 10.14, or approximately 10.15 in the database. In some modalities, the identified genetic variants comprise the genetic variants that have an association with a MACE with a p-value of less than 5 x 10'8 in the database. In some modalities, the identified genetic variants comprise genetic variants that are associated with MACE in high-risk patients compared to the rest of the reference population with odds ratios (OR) around 1.0 or more, around 1.5 or more, around 1.75 or more, around 2.0 or more, or around 2.25 or more for the highest up to 50% of the distribution; or around 1.5 or more, around 1.75 or more, around 2.0 or more, around 2.25 or more, around 2.5 or more, or around 2.75 or more. In some modalities, the odds ratio (OR) can vary from around 1.0 to around 1.5, from around 1.5 to around 2.0, from around 2.0 to around 2.5, from around 2.5 to around 3.0, from around 3.0 to around 3.5, from around 3.5 to around 4.0, from around 4.0 to around 4.5, from around 4.5 to around 5.0, from around MLχ ζυζζ. ΅ΐ Ί lúl .0 around 5.5, from around 5.5 to around 6.0, from around 6.0 to around 6.5, or from around 6.5 to around 7.0. In some modalities, high-risk patients comprise patients who have CAD-PRS scores in the top decile, quintile, or tertile in a reference population. In some modalities, the identified genetic variants comprise those with the highest variant yield in the reference population. In some modalities, variant yield is calculated with respect to coronary artery disease risk based on statistical significance, strength of association, and / or a probability distribution. In some modalities, genetic variant scores are calculated using PRS calculation methodologies, such as the LDPred method (or variations and / or versions thereof), which is a Bayesian approach to calculating a posterior mean effect for all variants based on a previous effect size (from the previous GWAS) and subsequent shrinkage based on linkage disequilibrium. LDPred creates a PRS using genome-wide variation with weights derived from a set of summary GWAS statistics. See Vilhjálmsson et al., Am. J. Hum. Genet., 2015, 97, 576-92. In some modalities, alternative approaches can be used to calculate genetic variant scores, including SBayesR (Lloyd-Jones, LR, internet at “biorxiv.org / content / biorxiv / early / 2019 / 01 / 17 / 522961.full.pdf’), pruning and thresholding (P&T) (Purcell, Nature, 2009, 460, 748-752), and COJO (Yang et al., Nat. Genet., 2012, 44, 369-375).SBayesR is a Bayesian approach similar to LDPred but allows for greater flexibility in post-effects pruning. Pruning and thresholding require specifying a minimum p-value threshold (the p-value associated with the variant in the source data file) and an r² threshold (a measure of disease risk) between variants. P&T identifies the variant with the smallest p-value in each region and then groups all other variants in the region with an r² value greater than the specified r² under that variant. In PRS, the index variant represents all variants in the group (only the index variant is included in the PRS; all other variants are excluded). COJO, or conditional and joint association analysis, is conceptually similar to P&T but incorporates additional variants in a given disease risk block into the score if they demonstrate an independent contribution to disease risk after conditioning on the index variant. In some modalities, the performance of the genetic variant is calculated using the LDPred method, where the p-value is from approximately 0.0001 to approximately 0.5. In some modalities, the performance of the genetic variant is calculated using the LDPred method, where the p-value is approximately 0.5. In some modalities, the The genetic variant performance is calculated using the LDPred method, where the p-value is around 0.1. In some modalities, the genetic variant performance is calculated using the LDPred method, where the p-value is around 0.05. In some modalities, the genetic variant performance is calculated using the LDPred method, where the p-value is around 0.01. In some modalities, the genetic variant performance is calculated using the LDPred method, where the p-value is around 0.005. In some modalities, the genetic variant performance is calculated using the LDpred method, where the p-value is around 0.001. In some modalities, the genetic variant performance is calculated using the LDPred method, where the p-value is around 0.0005.In some modalities, the performance of the genetic variant is calculated using the LDPred method, where the p-value is around 0.0001. In some forms, the method also includes an initial step to obtain a biological sample from the subject. As used in this document, a biological specimen may contain whole cells and / or live cells and / or cell debris. The biological specimen may contain (or be derived from) a body fluid. This description covers modalities in which the body fluid is selected from amniotic fluid, aqueous humor, vitreous humor, bile, blood serum, breast milk, cerebrospinal fluid, cerumen (earwax), chyle, chyme, endolymph, perilymph, exudates, feces, female ejaculate, gastric acid, gastric juice, lymph, mucus (including nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheumatism, saliva, sebum (skin oil), semen, sputum, synovial fluid, sweat, tears, urine, vaginal secretions, vomit, and mixtures of one or more of these. Biological specimens include cell cultures, body fluids, and cell cultures of body fluids.Body fluids can be obtained from a mammalian organism, for example, by puncture or other collection or sampling procedures. In some modalities, the method is used to select a population of subjects or candidates for clinical trials, for example, a clinical trial to determine whether a particular treatment or treatment plan is effective against major adverse cardiovascular events (MACE) or recurrent MACE. In some modalities, the selected candidates or subjects are divided into subgroups based on the genetic variants identified for each subject or candidate, and the method is used to determine whether a particular treatment or treatment plan is effective against a particular genetic variant or group of genetic variants. In other words, the method can be employed to determine the susceptibility of a population of subjects to a particular treatment or treatment plan, where the population of ML / E / ZuZZ / u / llúl subjects are selected based on the genetic variants identified in the subjects. In some modalities, the method is used to select a population of subjects or candidates for clinical trials, for example, a clinical trial to determine whether a particular treatment or treatment plan is effective against major adverse cardiovascular events (MACE) or recurrent MACE. In some modalities, the desired risk group is a population comprising high-risk subjects or candidates. In some modalities, the selected population of subjects or candidates responds; that is, the subjects or candidates respond to the treatment or treatment plan. In some modalities, subjects are selected solely based on their CAD-PRS score. For example, if a patient or candidate has a CAD-PRS score above a predetermined threshold, the patient is selected to begin treatment, or a candidate is enrolled in the clinical trial. In some modalities, the threshold for initiating treatment or enrolling in a clinical trial is determined relatively. For example, in some modalities, the threshold CAD-PRS score is 50% higher than a reference population. In some modalities, the threshold CAD-PRS score is 40% higher than a reference population. In some modalities, the threshold CAD-PRS score is 30% higher than a reference population. In some modalities, the threshold CAD-PRS score is 25% higher than a reference population.In some modalities, the CAD-PRS threshold score is 20% higher within a reference population. In some modalities, the CAD-PRS threshold score is 15% higher within a reference population. In some modalities, the CAD-PRS threshold score is 10% higher (decile) within a reference population. In some modalities, the CAD-PRS threshold score is 5% higher within a reference population. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 100 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 200 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 500 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 1000 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 3000 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 5000 patients. In some modalities, the population The reference population for determining the relative CAD-PRS score is at least approximately 7,500 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 10,000 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 12,000 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 15,000 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 20,000 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 30,000 patients.In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 50,000 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 70,000 patients. In some modalities, the reference population for determining the relative CAD-PRS score is at least approximately 100,000 patients. In some modalities, the reference population is enriched to include members of an ancestry group. In some modalities, the ancestry group is self-indicating. In some modalities, the ancestry group is derived from a principal component ancestry analysis. In some modalities, the ancestry group is European. In some modalities, the ancestry group is African. In some modalities, the ancestry group is mixed American. In some modalities, the ancestry group is East Asian. In some modalities, the ancestry group is South Asian. In some modalities, the ancestry group is any mixture of two or more of the following populations: European, African, mixed American, East Asian, and South Asian. In some modalities, the method also includes determining a composite risk score comprising DKA-PRS and the low-density lipoprotein (LDL) level in a biological sample obtained from the patient. For example, if a patient or candidate subject has DKA-PRS and LDL levels in a biological sample obtained from the patient or a test subject above a predetermined threshold, the patient is selected to initiate treatment or a candidate subject is included in the clinical trial. In some modalities, the biological sample comprises blood serum. In some modalities, the threshold serum LDL level is at least approximately 100 mg / dL. In some modalities, the threshold level of serum LDL is at least around 120 mg / dL. In some modalities, the threshold level of serum LDL is at least around 140 mg / dL. In some modalities, the threshold level of serum LDL is at least around 160 mg / dL. In some modalities, the threshold level of serum LDL is at least around 180 mg / dL. In some modalities, the threshold level of serum LDL is at least around 200 mg / dL. In some modalities, the method also includes determining a composite risk score comprising DKA-PRS and the lipoprotein(a) (LPA or LP(a)) level in a biological sample from the patient. For example, if a patient or candidate subject has DKA-PRS and LPA levels in a biological sample obtained from the patient or test subject above a predetermined threshold, the patient is selected to initiate treatment or a candidate subject is included in the clinical trial. In some modalities, the biological sample comprises blood serum. In some modalities, the threshold level of serum LPA is at least approximately 30 mg / dL. In some modalities, the threshold level of serum LPA is at least approximately 40 mg / dL. In some modalities, the threshold level of serum LPA is at least approximately 50 mg / dL. In some modalities, the threshold level of serum LPA is at least approximately 120 mg / dL.In some modalities, the threshold level of serum LPA is at least around 60 mg / dL. In some modalities, the threshold level of serum LPA is at least around 70 mg / dL. In some modalities, the threshold level of serum LPA is at least around 80 mg / dL. In some modalities, the threshold level of serum LPA is at least around 100 mg / dL. In some modalities, the threshold level of serum LPA is at least around 120 mg / dL. In some modalities, the threshold level of serum LPA is at least around 140 mg / dL. In some modalities, the method also involves determining a composite risk score comprising DKA-PRS, LPA level, and LDL-C level in a biological sample obtained from the patient. For example, if a patient or a candidate subject has DKA-PRS, LDL, and LPA levels in a biological sample obtained from the patient or a test subject above a predetermined threshold, the patient is selected to begin treatment, or a candidate subject is included in the clinical trial. In some modalities, the method also includes determining a composite risk score comprising CAD-PRS and the LDL level in a biological sample obtained from the patient. In some modalities, the method also includes determining a composite risk score comprising CAD-PRS, the LDL level, and the LDL level in a biological sample obtained from the patient. In some modalities, the method additionally includes determining a composite risk score comprising CAD-PRS and the Framingham Recurrent Risk Score (FHS) (see D'Agostino et al., Am. Heart J., 2000, 139, 272-281) in a biological sample obtained from the patient. In some modalities, the method additionally includes determining a composite risk score comprising CAD-PRS and the very high risk (VHR) groups (Roe et al., Circulation, 2019, 140, 1578-1589) in a biological sample obtained from the patient.Therefore, in some modalities, the composite risk score may comprise the CADPRS and any one or more of the LPA level, LDL level, Framingham Recurrent Risk Score (FHS), and VHR groups in a biological sample obtained from the patient. In some modalities, the biological sample comprises blood. In some modalities, the method also includes initiating treatment in the subject. This treatment may include statins, ezetimibe, beta-blockers, angiotensin-converting enzyme inhibitors, aspirin, anticoagulants, antiplatelet agents, angiotensin II receptor blockers, angiotensin receptor blockers, calcium channel blockers, cholesterol-lowering drugs, vasodilators, antidiuretics, renin-angiotensin system agents, lipid-modifying drugs, anti-inflammatory agents, nitrates, antiarrhythmic drugs, steroidal or non-steroidal anti-inflammatory drugs, DNA methyltransferase inhibitors, and / or histone deacetylase inhibitors. DNA methyltransferase inhibitors may be any DNA methyltransferase known to the art, for example, 5-aza-2'-deoxycytidine or 5-azacytidine.Histone deacetylase inhibitors can be any histone deacetylase inhibitor known to the art, for example, varinostat, romidepsin, panobinostat, belinostat, or entinostat. Statins can be any statin known to the art, for example, atorvastatin, fluvastatin, lovastatin, pravastatin, rosuvastatin, and simvastatin. Lipid-modifying drugs can be any lipid-modifying compound known to the art, for example, a PCSK9 inhibitor, an antisense oligonucleotide targeting apolipoprotein C-11, and an antisense oligonucleotide for reducing lipoprotein(a). Initiating treatment may involve designing a treatment plan based on the patient's risk group, which corresponds to their calculated CAD-PRS score. In some modalities, the CAD-PRS score predicts treatment efficacy or the patient's response to a therapeutic regimen. In some modalities, the composite risk score (CAD-PRS combined with LDL levels, LPA levels, or both) is predictive of treatment efficacy or the patient's response to a therapeutic regimen. Therefore, treatment can be determined or adjusted according to the CAD-PRS score. ML / E / ZuZZ / u llúl In some modalities, initiating treatment involves modifying the dose or regimen of a treatment already being received by a patient at risk of MACE or hypercholesterolemia (for example, statin therapy that does not adequately control hypercholesterolemia) based on the patient's calculated DKA-PRS. In some modalities, initiating treatment involves switching from one therapeutic agent to another based on the calculated DKA-PRS for the patient's risk of MACE or hypercholesterolemia, for example, if the patient is statin intolerant.In some modalities, initiating treatment involves starting a regimen of a therapeutic agent in addition to a therapeutic agent a patient is already receiving; for example, starting a PCSK9 inhibitor regimen in a patient at risk of major adverse cardiovascular events (MACE) or hypercholesterolemia who is receiving statin therapy, such as high-intensity statin therapy or maximally tolerated statin therapy. In some modalities, initiating treatment involves starting a therapeutic regimen in a previously untreated patient at risk of MACE or hypercholesterolemia. In some modalities, the therapeutic agent is a human PCSK9 inhibitor. In some modalities, CAD-PRS predicts treatment efficacy or a patient's response to PCSK9 inhibition therapy. In some modalities, the composite risk score (CAD-PRS combined with LDL levels, LPA levels, or both) is predictive of a patient's response to PCSK9 inhibitor therapy. Therefore, PCSK9 inhibitor therapy can be determined or adjusted according to the patient's calculated CAD-PRS score. The term proprotein convertase subtilisin-kexin type 9 or PCSK9, as used herein, refers to human PCSK9 having the nucleic acid sequence shown in SEQ ID NO:1: GTCCGATGGGGCTCTGGTGGCGTGATCTGCGCGCCCCAGGCGTCAAGGC ACCCACACCCTAGAAGGTTTCCGCAGCGACGTCGAGGCGCTCATGGTTGCAGGCGGGC GCCGCCGTTCAGTTCAGGGTCTGAGCCTGGAGGAGTGAGCCAGGCAGTGAGACTGGCT CGGGCGGGCCGGGACGCGTCGTTGCAGCAGCGGCTCCCAGCTCCCAGCCAGGATTCC GCGCGCCCCTTCACGCGCCCTGCTCCTGAACTTCAGCTCCTGCACAGTCCTCCCCACC GCAAGGCTCAAGGCGCCGCCGGCGTGGACCGCCAGGCCTCTAGGTCTCCTCGCCA GGACAGCAACCTCTCCCCTGGCCCTCATGGGCACCGTCAGCTCCAGGCGGTCCTGGTG GCCGCTGCCACTGCTGCTGCTGCTGCTGCTCCTGGGTCCCGCGGGCGCCCGTGC GCAGGAGGACGAGGACGGCGACTACGAGGAGCTGGTGCTAGCCTTGCGTTCCGGA GGACGGCCTGGCCGAAGCACCCGAGCACGGAACCACAGCCACCTTCCACCGCTGCGC CAAGGATCCGTGGAGGTTGCCTGGCACCTACGTGGTGGTGCTGAAGGAGGAGACCCAC IVIA / t / ZUZZ / UIII or I CTCTCGCAGTCAGAGCGCACTGCCCGCCGCCTGCAGGCCCAGGCTGCCCGCCGGGGA TACCTCACCAAGATCCTGCATGTCTTCCATGGCCTTCTTCCTGGCTTCCTGGTGAAGATG AGTGGCGACCTGCTGGAGCTGGCCTTGAAGTTGCCCCATGTCGACTACATCGAGGAGG ACTCCTCTGTCTTTGCCCAGAGCATCCCGTGGAACCTGGAGCGGATTACCCCTCCACGG TACCGGGCGGATGAATACCAGCCCCCCGACGGAGGCAGCCTGGTGGAGGTGTATCTCC TAGACACCAGCATACAGAGTGACCACCGGGAAATCGAGGGCAGGGTCATGGTCACCGA CTTCGAGAATGTGCCCGAGGAGGACGGGACCCGCTTCCACAGACAGGCCAGCAAGTGT GACAGTCATGGCACCCACCTGGCAGGGGTGGTCAGCGGCCGGGATGCCGGCGTGGCC AAGGGTGCCAGCATGCGCAGCCTGCGCGTGCTCAACTGCCAAGGGAAGGGCACGGTTA GCGGCACCCTCATAGGCCTGGAGTTTATTCGGAAAAGCCAGCTGGTCCAGCCTGTGGG GCCACTGGTGGTGCTGCTGCCCCTGGCGGGTGGGTACAGCCGCGTCCTCAACGCCGC CTGCCAGCGGCTGGCGAGGGCTGGGGTCGTGCTGGTCACCGCTGCCGGCAACTTCCG GGACGATGCCTGCCTCTACTCCCCAGCCTCAGCTCCCGAGGTCATCACAGTTGGGGCC ACCAATGCCCAAGACCAGCCGGTGACCCTGGGGACTTTGGGGACCAACTTTGGCCGCT GTGTGGACCTCTTTGCCCCAGGGGAGGACATCATTGGTGCCTCCAGCGACTGCAGCAC CTGCTTTGTGTCACAGAGTGGGACATCACAGGCTGCTGCCCACGTGGCTGGCATTGCAGCCATGATGCTGTCTGCCGAGCCGGAGCTCACCCTGGCCGAGTTGAGGCAGAGACTGA TCCACTTCTCTGCCAAAGATGTCATCAATGAGGCCTGGTTCCCTGAGGACCAGCGGGTA CTGACCCCCAACCTGGTGGCCGCCCTGCCCCCCAGCACCCATGGGGCAGGTTGGCAG CTGTTTTGCAGGACTGTATGGTCAGCACACTCGGGGCCTACACGGATGGCCACAGCCG TCGCCCGCTGCGCCCCAGATGAGGAGCTGCTGAGCTGCTCCAGTTTCTCCAGGAGTGG GAAGCGGCGGGGCGAGCGCATGGAGGCCCAAGGGGGCAAGCTGGTCTGCCGGGCCC ACAACGCTTTTGGGGGTGAGGGTGTCTACGCCATTGCCAGGTGCTGCCTGCTACCCCA GGCCAACTGCAGCGTCCACACAGCTCCACCAGCTGAGGCCAGCATGGGGACCCGTGTC CACTGCCACCAACAGGGCCACGTCCTCACAGGCTGCAGCTCCCACTGGGAGGTGGAGG ACCTTGGCACCCACAAGCCGCCTGTGCTGAGGCCACGAGGTCAGCCCAACCAGTGCGT GGGCCACAGGGAGGCCAGCATCCACGCTTCCTGCTGCCATGCCCCAGGTCTGGAATGC AAAGTCAAGGAGCATGGAATCCCGGCCCCTCAGGAGCAGGTGACCGTGGCCTGCGAG GAGGGCTGGACCCTGACTGGCTGCAGTGCCCTCCCTGGGACCTCCCACGTCCTGGGG GCCTACGCCGTAGACAACACGTGTGTAGTCAGGAGCCGGGACGTCAGCACTACAGGCA GCACCAGCGAAGGGGCCGTGACAGCCGTTGCCATCTGCTGCCGGAGCCGGCACCTGG CGCAGGCCTCCCAGGAGCTCCAGTGACAGCCCCATCCCAGGATGGGTGTCTGGGGAG GGTCAAGGGCTGGGGCTGAGCTTTAAAATGGTTCCGACTTGTCCCTCTCTCAGCCCTCCATGGCCTGGCACGAGGGGATGGGGATGCTTCCGCCTTTCCGGGGCTGCTGGCCTGGC CCTTGAGTGGGGCAGCCTCCTTGCCTGGAACTCACTCACTCTGGGTGCCTCCTCCCCA IVIA / t / ZUZZ / UIII or I GGTGGAGGTGCCAGGAAGCTCCCTCCCTCACTGTGGGGCATTTCACCATTCAAACAGGT CGAGCTGTGCTCGGGTGCTGCCAGCTGCTCCCAATGTGCCGATGTCCGTGGGCAGAAT GACTTTTATTGAGCTCTTGTTCCGTGCCAGGCATTCAATCCTCAGGTCTCCACCAAGGAG GCAGGATTCTTCCCATGGATAGGGGAGGGGGCGGTAGGGGCTGCAGGGACAAACATC GTTGGGGGGTGAGTGTGAAAGGTGCTGATGGCCCTCATCTCCAGCTAACTGTGGAGAA GCCCCTGGGGGCTCCCTGATTAATGGAGGCTTAGCTTTCTGGATGGCATCTAGCCAGAG GCTGGAGACAGGTGCGCCCCTGGTGGTCACAGGCTGTGCCTTGGTTTCCTGAGCCACC TTTACTCTGCTCTATGCCAGGCTGTGCTAGCAACACCCAAAGGTGGCCTGCGGGGAGC CATCACCTAGGACTGACTCGGCAGTGTGCAGTGGTGCATGCACTGTCTCAGCCAACCC GCTCCACTACCCGGCAGGGTACACATTCGCACCCCTACTTCACAGAGGAAGAAACCTGG AACCAGAGGGGGCGTGCCTGCCAAGCTCACACAGCAGGAACTGAGCCAGAAACGCAGA TTGGGCTGGCTCTGAAGCCAAGCCTCTTCTTACTTCACCCGGCTGGGCTCCTCATTTTTA CGGGTAACAGTGAGGCTGGGAAGGGGAACACAGACCAGGAAGCTCGGTGAGTGATGG CAGAACGATGCCTGCAGGCATGGAACTTTTTCCGTTATCACCCAGGCCTGATTCACTGG CCTGGCGGAGATGCTTCTAAGGCATGGTCGGGGGAGAGGGCCAACAACTGTCCCTCCT TGAGCACCAGCCCCACCCAAGCAAGCAGACATTTATCTTTTGGGTCTGTCCTCTCTGTTGCCTTTTTACAGCCAACTTTTCTAGACCTGTTTTGCTTTTGTAACTTGAAGATATTTATTG GGTTTTGTAGCATTTTTTAATGGTGACTTTTAAAATAAACAAACAAACGTTGTCC TAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA; and the amino acid sequence of SEQ ID NO: 2: MGTVSSRRSWWPLPLLLLLLLLLGPAGARAQEDEDGDYEELVLALRSEEDGL AEAPEHGTTATFHRCAKDPWRLPGTYVVVLKEETHLSQSERTARRLQAARRGYLTKILH VFHGLLPGFLVKMSGDLLELALKLPHVDYIEEDSSVQFAQSPRYPPDW GSLVEVYLLDTSIQSDHREIEGRVMVTDFENVPEEDGTRFHRQCDSHGTHLAGVVSGR DAGVAKGASSMRSLRVLNCQGKGTVSGTLIGTLIGLEFIRKSQLVQPVGPLVVLLPLAGGYSRVLNA ACQRLARAGVVLVTAAGNFRDACLISPEQGVDQTLAGGGTLAGTLAGGYSRVLNA LFAPGEDIIGASSDCSTCFVSQSGTSQAAAHVAGIAAMMLSAEPLTLAELRQRLIHFSAKDVI NEAWFPEDQRVLTPNLVAALPPSTHGAGWQLFCRTVWSAHSGPTRMATAVARCAPDEELL SCSSFSRSGKRRGERMEAQGGKLVCRAHNAFGGEGLPVACHVACH SMGTRVHCHQQGHVLTGCSSHWEVEDLGTHKPPVLRPRGQPNQCVGHREASIHASCCHA PGLECKVKEHGIPAPQEQVTVACEEGWTLTGC, or a biologically active fragment thereof. As used in this document, the term inhibitor means that a given compound is capable of inhibiting the activity of the respective protein or other substance in the cell, at least to a certain extent. This can be achieved through an interaction. Protein expression inhibition can be achieved through direct inhibition of the compound with the given protein or substance (direct inhibition) or through interaction of the compound with other proteins or substances inside or outside the cell, leading to at least partial inhibition of the protein or substance's activity (indirect inhibition). Inhibition of protein activity can also be achieved by suppressing the expression of a target protein. Techniques for inhibiting protein expression include, but are not limited to, antisense inhibition, siRNA-mediated inhibition, miRNA-mediated inhibition, ribozyme-mediated inhibition, DNA-directed RNA interference (DdRNAi), RNA-directed DNA methylation, transcription activator-like effector nuclease (TALEN)-mediated inhibition, zinc finger nuclease-mediated inhibition, aptamer-mediated inhibition, and CRISPR-mediated inhibition. Antisense inhibition refers to a reduction in the levels of target nucleic acid in the presence of a complementary antisense compound with respect to a target nucleic acid compared to the levels of target nucleic acid in the absence of the antisense compound. In some forms, the PCSK9 inhibitor is a small molecule. Numerous small-molecule PCSK9 inhibitors are described, for example, in U.S. Patent No. 210,131,637. In some forms, the PCSK9 inhibitor is a siRNA. An example siRNA includes, but is not limited to, inclisiran (see Ray et al., Circulation, 2018, 138, 1304-1316). In some formulations, the PCSK9 inhibitor is an anti-PCSK9 antibody or an antigen-binding portion thereof. The term antibody, as used herein, is intended to refer to immunoglobulin molecules comprising four polypeptide chains, two heavy chains (H) and two light chains (L) interconnected by disulfide bonds, as well as multimers thereof (e.g., IgM). Each heavy chain comprises a heavy chain variable region (abbreviated herein as HCVR or VH) and a heavy chain constant region. The heavy chain constant region comprises three domains, Ch1, Ch2, and Ch3. Each light chain comprises a light chain variable region (abbreviated herein as LCVR or VL) and a light chain constant region. The light chain constant region comprises one domain (Cl1).The VH and VL regions can be further subdivided into regions of hypervariability, called complementarity determinant regions (CDRs), interspersed with more conserved regions called framework regions (FRs). Each VH and VL is composed of three CDRs and four FRs, arranged from the amino terminus. ML / E / ZuZZ / u llúl to the carboxy terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. In different modalities, the FRs of the anti-PCSK9 antibody (or its antigen-binding portion) can be identical to human germline sequences, or they can be naturally or artificially modified. It is possible to define a consensus amino acid sequence based on a comparative analysis of two or more CDRs. The term antibody, as used herein, also includes antigen-binding fragments of whole antibody molecules. The expressions antigen-binding part of an antibody, antigen-binding fragment of an antibody, and similar terms, as used herein, include any naturally occurring, enzymatically produced, synthetic, or genetically altered glycoprotein or polypeptide that specifically binds to an antigen to form a complex. Antigen-binding fragments of an antibody may be derived, for example, from whole antibody molecules by any suitable routine technique such as proteolytic digestion or recombinant genetic modification techniques involving the manipulation and expression of DNA encoding variable and optionally constant antibody domains.This DNA is available, for example, through commercial sources, DNA banks (including, for example, antibody-phage banks), or it can be synthesized. DNA can be sequenced and manipulated chemically or using molecular biology techniques, for example, to order one or more variable and / or constant domains into a suitable configuration, or to introduce codons, create cysteine ​​residues, modify, add, or delete amino acids, etc. Anti-PCSK9 antibodies include, but are not limited to, evolocumab, alirocumab, and bococizumab. Additional anti-PCSK9 antibodies are described, for example, in U.S. patents Nos. 210,259,885, 10,023,654, 9,266,961, 9,561,155, 9,550,837, 9,540,449, 9,029,515, 8,951,523, 8,859,741, 8,530,414, 8,829,165, 8,802,827, 8,710,192, 8,344,114, and 8,188,233. Additional anti-PCSK9 antibodies include antibodies comprising the VH, VLy / or CDR of evolocumab, alirocumab, or bococizumab. In the context of the methods, additional therapeutically active components, for example, any of the agents listed above or derivatives thereof, may be administered immediately before, at the same time as, or shortly after the administration of a PCSK9 inhibitor; (for the purposes of this description, such administration regimens are considered to be the administration of a PCSK9 inhibitor in combination with an additional therapeutically active component). The methods presented herein include pharmaceutical compositions and methods of use thereof in which a PCSK9 inhibitor is co-formulated with one or more of the therapeutically active components. ML / E / ZuZZ / u llúl additional assets as described elsewhere in this document. All patent documents, websites, or other publications, registration numbers, and the like mentioned above or hereafter are incorporated by reference in their entirety for all purposes, in the same manner as if each individual element were specifically and individually incorporated by reference. When different versions of a sequence are associated with a registration number at different times, the intended reference is to the version associated with the registration number on the effective filing date of this application. The effective filing date means the earlier of the actual filing date and the filing date of any priority application referencing the registration number, if any.Furthermore, if different versions of a publication, website, or similar material are published at different times, the reference shall be to the version most recently published as of the effective filing date of the application, unless otherwise stated. Any feature, stage, element, modality, or aspect of this description may be used in combination with any other feature, stage, element, modality, or aspect, unless specifically stated otherwise. Although this description has been provided in detail for illustrative purposes and as an example for clarity and understanding, it will be evident that certain changes and modifications may be made within the scope of the appended claims. The following examples are provided to describe the embodiments in greater detail. These are intended to illustrate, not limit, the claimed embodiments. The following examples provide those skilled in the art with a disclosure and description of how the compounds, compositions, articles, devices, and / or methods described herein are made and evaluated, and are intended to be merely illustrative and not to limit the scope of any of the claims. Every effort has been made to ensure accuracy with respect to values ​​(such as, for example, quantities, temperatures, etc.), but the possibility of certain errors and deviations should be assumed. Unless otherwise stated, parts are parts by weight, temperature is expressed in °C or is at ambient temperature, and pressure is atmospheric pressure or close thereto. Examples Example 1: Clinical trial with ODYSSEY OUTCOMES The ODYSSEY OUTCOMES trial was a randomized, double-blind comparison of alirocumab or placebo in 18,924 patients with a recent hospitalization (1 to 12 months prior) for ACS (myocardial infarction or unstable angina). Patients who qualified Patients with LDL cholesterol > 70 mg / dL, apolipoprotein B > 80 mg / dL, or non-HDL cholesterol > 100 mg / dL despite high-intensity or maximum-tolerance statin therapy were included. Patients were randomized 1:1 to alirocumab or the corresponding placebo every two weeks. The primary endpoint, MACE, was a composite of death from coronary heart disease, non-fatal myocardial infarction, ischemic stroke, or unstable angina requiring hospitalization. The median follow-up was 2.8 years. A major adverse cardiovascular event (MACE) occurred in 1052 patients (11.1%) in the placebo group and 903 patients (9.5%) in the alirocumab group (hazard ratio (HR), 0.85; 95% confidence interval (CI), 0.78 to 0.93; p-value <0.001). Genetic data generation DNA samples were obtained from 12,118 trial participants who provided written informed consent to participate in the pharmacogenomic study. The samples were genotyped using Illumina Global Screening Array (GSA), v1.0. Additional genetic data were entered using Minimac3 software. Reference populations for entry were obtained from the 1000-genome phase 3 version 5 data. Of the 12,118 samples, 11,953 (98%) met the quality control procedures for genetic data. The genetic variants and summary statistics used to develop the PRS were obtained from a genome-wide meta-analysis of coronary artery disease in 60,801 cases and 123,504 controls. These variants (up to n = 6,579,025) and their corresponding disease association effect sizes (odds ratios) were used to develop the genome-wide PRS using the pruning and thresholding (P&T) approach and the LDPred algorithm. For comparison with previous publications of CAD PRS in response to statins, models with 27 and 57 variants were also evaluated. PRSs were calculated for each patient by taking the product of the patient's number of risk alleles and the respective variant weights (log odds ratio or LDPred-adjusted odds ratio) for each variant and summing all variants.These scores were tested and validated using two large, independent databases: DiscovEHR (n = 84,243) and UK Biobank (n = 446,208). Patients in the ODYSSEY OUTCOMES trial were assigned to one of five ancestral groups (African, American mixed, East Asian, European, or South Asian). Ancestral population classification was based on the similarity between each patient's genotype and publicly available genetic data from the International HapMap project. Population structure was assessed using [the following method / method / etc.]. Principal component analysis was performed using Plink software. Subsequent risk score calculations were stratified by ancestry. Within each ancestral group, PRS scores were standardized to a mean of zero and a standard deviation of 1, and the datasets were combined to allow cross-ancestral comparisons. High genetic risk was defined as patients within the upper decile of the PRS distribution (>90th percentile PRS). Those below the upper decile were defined as having lower genetic risk (<90th percentile PRS). This threshold was selected in a post-hoc analysis that evaluated high genetic risk thresholds ranging from 50% to 90%, in 10% increments. PRS was also assessed as a continuous measure. Genetic data processing Genotyping methodology. Illumina Global Screening Array (GSA), v1.0 (GSA-24v1-0_A1) was used to generate microarray genotypes for genome-wide association studies (GWAS). This array contains approximately 660,000 markers, with an average marker spacing of 4.2 kb. Quality control of Illumina microarray genotyping data. Individual samples with a reliability rate <90% and genetic variants with a reliability rate <90% or Hardy-Weinberg equilibrium p-value <1 x 10⁶ were excluded from the analysis. In paired samples with an Eli >0.25, the sample with the lowest reliability rate was excluded. Samples were also excluded if gender discrepancy was detected between the sex inferred from the X chromosome and the sex indicated in the clinical database. Principal component analysis (PCA). Population structure was assessed using PCA within plink version 1.9. Two sets of analyses were performed: 1) ancestral group assignment; and 2) generation of ancestry-specific PCs. Ancestral population assignment was based on the similarity between each patient's genotypes and publicly available genetic data from the International HapMap project. PCA was performed on a combined dataset of ODYSSEY CVOT and HapMap samples. The probability of each sample belonging to one of the five HapMap ancestral superpopulations / groups (African (AFR); American Mixed (AMR); East Asian (EAS); European (EUR); or South Asian (SAS)) was calculated and used to classify the sample. PCA was performed on the overall PGx population and within the ancestry group to generate ancestry-specific PCs.The 4-12 main PCs (according to ancestry) were used as covariates in the analyses. Imputation. Genotype entry was performed using Minimac3. Reference populations for entry were obtained from 1000 phase 3 version 5 genomes. The variants ML / E / ZuZZ / uI llúl after quality control were restricted to those with an INFO score > 0.3. Similar thresholds were applied with respect to absence and HWE. For entered variants, allele dosages were used to calculate PRS. Generation of polygenic risk scores Datasets. The primary data source for the polygenic risk score was a CAD risk GWAS comprising 9.4 million variants from a meta-analysis of 60,801 CAD cases and 123,504 controls. A set of PRS algorithm fitting parameters was evaluated based on its performance in two datasets, UK Biobank (UKB) and DiscovEHR. A composite cardiovascular endpoint of myocardial infarction, unstable angina, and ischemic stroke (as defined by ICD-10 codes I2G, I22*, I23*, 124.1, I25.2, I2.0, I63.0) was used, along with self-report codes 20002* (UKB), to define case and control status. Selection of the PRS algorithm. Three approaches to generating polygenic risk scores were tested: candidate SNP models from previous work on PRS in statins, comprising 27 and 57 variants; pruning and thresholding (P&T); and LDPred. P&T identifies the variant with the smallest p-value in each region and then groups under that variant all other variants in the region with an r² value greater than the specified r². In PRS, the index variant represents all variants in the group (only the index variant is included in the PRS; all other variants are excluded). LDPred is a Bayesian approach to PRS development that calculates a mean posterior effect (adjusted effect size) for all variants based on prior information and LDP from a reference panel.Heuristically, the effect sizes generated from LDPred differ from P&T in that LDPred jointly models the effect size and variance of each marker, incorporating the linkage disequilibrium (LD) structure when reducing effect sizes. The adjustment or reduction of variant weights is based not only on the magnitude of variant association with disease but also on linkage disequilibrium (LD) between variants. For both P&T and LDPred approaches, data from 1000 phase 3 version 5 genomes were used for the LD reference panel. PRS Calculation. Using the LDPred or P&T approach, a set of variants was generated and their respective weights were calculated. In the case of P&T, the weights were the logarithm of the likelihood ratios of the meta-analysis source data. In the case of LDPred, the variant weights are the adjusted logarithm of the likelihood ratio (posterior mean). After generating the weights, the process for calculating and normalizing scores is identical. For a set of i = 1, ..., M variants in j = 1, ..., A / ML / t / ZUZZ / UIII or I For patients, the PRS for patient j is calculated by: PRS¡j= , where B, is the logarithm of the odds ratio for variant i and x¡i is the number of risk alleles that the patient carries in variant i (for entered variants, the dosage of the allele for variant i). Scores were standardized to ~ N(0,1) by subtracting the mean PRS and dividing by the standard deviation of PRS within each ancestry group. Testing and validation of PRS algorithms. For each set of LDPred or P&T fitting parameters, a PRS was calculated and a logistic regression was performed with the composite endpoint as the dependent variable and PRS, age, sex, genotyping matrix (UK Biobank only), and ancestry covariates as independent variables. The odds ratio (OR) per standard deviation (SD) of PRS and the area under the curve (AUC) were reported for each model. Twenty-eight P&T models (p-values ​​ranging from 5 x 10⁻¹ to 5 x 10⁸, and r² values ​​of 0.2, 0.4, 0.6, and 0.8) and eight LDPred models (p = 3 x 10⁻¹, 1 x 10⁻¹, 3 x 10⁻², 1 x 10⁻², 3 x 10⁻³, 1 x 10⁻³, 3 x 10⁻⁴, and 3 x 10⁻⁴) were evaluated. In the UKB and DiscovEHR datasets, LDPred with p = 0.001 showed the best performance and was used in the primary analysis; the results are shown in FIGS. 9-11. Selection of a threshold to define high risk. Previous publications on the risk of coronary artery disease (CAD) by polygenic risk scores (PRS) have varied in the threshold used to define high risk by PRS, with most thresholds ranging from the upper tertile to the upper quintile. High genetic risk was defined as patients within the upper decile of the polygenic risk score distribution. This threshold was selected in a post hoc analysis that evaluated high genetic risk thresholds ranging from the upper 10% to the upper 50%, in 10% increments. In the placebo group, the risk of an event in the upper 10% by PRS was consistently higher than the overall event rate, and the effect was also consistent across all ancestral groups (Fig. 12). While no trend in risk was observed in the placebo group (specifically, 70th–90th percentiles) for the primary endpoint (Fig. 13).13), these deciles had a higher-than-average risk on several secondary endpoints, including any coronary artery disease (FIG. 15), the endpoint that most closely aligns with the CAD criteria used in the source dataset. However, although a trend could be discerned on some of the secondary endpoints, the only decile with a consistent difference in treatment benefit was the top 10%. Further analyses indicated that the difference in risk for the primary variable in the top decile differed from all other deciles. Thirty-six genetic risk scores were calculated using ODISSEY OUTCOMES. (ML / E / ZuZZ / u) (a range of PRS methods and thresholds described above). With each score, Cox models were run within each treatment arm to assess the risk of the primary endpoint in each decile versus all other deciles. Second, within each decile, the percentage with an event was calculated for each arm, as well as the hazard ratio for the treatment difference (risk in alirocumab versus placebo). These results are summarized in FIG. 29 and present the median HR (or percentage with an event) by decile, as well as the results for the first (Q1) and third (Q3) quartiles. Columns 2 and 3 show the results for genetic risk (risk in that decile versus all others), and columns 4 to 6 focus on the risk differences by treatment within the decile. In these summary estimates, only the highest decile showed an elevated risk in the placebo group and a treatment benefit greater than the overall study estimate. The genetic hazard ratio associated with the risk in the placebo group increased from the highest decile, from 1.03 to 1.24. In contrast, the difference was modest in the alirocumab group (1.07 versus 1.09). The results for the percentage with an event were similar; in the placebo arm, the percentage in the highest decile was >2% higher than any other decile, while the risk in the highest decile in the alirocumab arm was indistinguishable from the other deciles. Because of the risk differences between the placebo and alirocumab arms in the highest decile, the median hazard ratio for the treatment difference was 0.70, compared to estimates of 0.80 to 0.89 in all other deciles. In the top decile, it is worth noting that across the 36 genetic risk scoring algorithms, the greatest difference in treatment was observed in the LDPred-derived genetic score (rho = 0.001). This method was selected a priori from testing on two independent datasets because it demonstrated the best ability to discriminate between DKA cases and controls. Across a variety of genetic risk scoring algorithms, with scores comprising fewer than 100 markers to more than 6 million genetic markers, the effect in the top decile for the placebo group was generally different from that in the other deciles. The median observed treatment benefit (HR = 0.70) in the top genetic decile was of greater magnitude than that observed in patients with elevated LDL-C at baseline in the overall study (HR = 0.76).These PRS findings align with a previous analysis of genetic risk scores in statin therapy (n = 10,456), where the benefit of statin use did not follow a clear linear trend. While there may be some variability in the magnitude of the treatment effect, the overall compilation of results suggests that patients in the high genetic risk group (top 10%) receive the greatest benefit from treatment. Statistical analysis Baseline disease characteristics and medical history were analyzed to assess the distribution of cardiovascular risk factors by genetic risk status, high (> percentile threshold) versus low (< percentile threshold). Continuous baseline characteristics were compared using a t-test, and binary or categorical characteristics were tested using a chi-square test or Fisher's exact test. Baseline lipids were regressed for age, sex, and ancestry covariates; model residuals were transformed using an inverse rank normal transformation (RINT) before comparing genetic risk groups. The change in lipids at 4 months was analyzed similarly, using RINT residuals from a linear regression model adjusting for baseline covariates, age, sex, and ancestry. In this analysis, MACE and all secondary endpoints followed the definitions of the ODYSSEY OUTCOMES trial and an intention-to-treat approach. The primary analysis was time to first occurrence of any component of the composite primary endpoint. The assessment of the relationship between PRS and MACE or other efficacy endpoints was performed using two different analytical approaches. First, the risk of MACE and the secondary endpoint in placebo-treated patients was assessed using Cox proportional hazards models. PRS was modeled as a continuous and binary covariate (above / below the threshold). Second, treatment efficacy was also assessed using Cox models, stratified by genetically defined high- and low-risk groups.To determine whether the benefit of alirocumab treatment differed among the genetically defined risk groups, an unstratified Cox model with one treatment interaction term per genetic risk was also performed. Inverse variance weighted meta-analyses were also used to combine the PRS risk estimates for placebo and alirocumab. Unless otherwise noted, all inferential analyses were performed with adjustment for clinical and reference covariates, including ancestry, age, sex, reference LDL-C, Lp(a), family history of premature coronary heart disease, and the following pre-ACS index medical characteristics that were strongly prognostic for the study endpoints and unbalanced among the genetically defined risk groups: myocardial infarction; percutaneous coronary intervention; coronary revascularization surgery; and other conditions. IVIA / t / ZUZZ / UIII or I congestive heart failure. Stratified analyses were also performed using risk factors, including Lp(a) (>50 mg / dL vs. <50 mg / dL), LDL-C (>100 mg / dL vs. <100 mg / dL), Framingham Recurrent Risk Score (FHS), and very high risk (VHR) groups. The FHS and VHR risk algorithms are described in this document. With the exception of the VHR analysis, all these analyses included the covariates mentioned above (apart from the stratification factor, if applicable). As this was an exploratory analysis, p-values ​​<0.05 for the covariate-adjusted Cox models were considered significant. Framingham Recurrent Coronary Artery Disease Risk Score. Scores are based on regression coefficients for up to 4 years risk prediction, according to age, log-transformed total cholesterol to HDL ratio, diabetes status, systolic blood pressure (women), and smoking (women). Scores were calculated for all ODYSSEY patients and analyzed as a continuous measure and stratified by mean score (> median vs < median). The second risk factor analysis categorized patients into very high risk (VHR) categories (as described in Roe et al., Circulation, 2019, 140, 1578–1589). VHRs were classified using two sets of criteria. The first criterion (multiple prior ASCSD events) identified patients with at least one prior ischemic event, including ischemic stroke, myocardial infarction, or peripheral artery disease.The second set of criteria (previous generalized ASCVD event + multiple high-risk conditions) identified patients with 1 generalized ASCVD event (the ACS index rating event) and at least 2 high-risk conditions, including diabetes mellitus, current smoking, age >65 years, history of hypertension, baseline eGFR >15 - <60 mL-min1-1.73 m-2, congestive heart failure, revascularization before the ACS index, or LDL-C >100 mg / dL with both statin and ezetimibe use. Example 2: Results Identification of patients at higher risk of cardiovascular events using polygenic risk scores. PRSs for CAD were first tested and validated for their association with CAD prevalence in two large, independent databases with a combined total of >530,000 individuals (DiscovEHR, n = 84,243; UK Biobank, n = 446,208). From these analyses, the LDPred algorithm (with a fit parameter p = 0.001) was selected as the optimal PRS generation method, consistent with previous CAD studies (FIGS. 9-11). As a continuous score, the PRS was significantly associated with MACE in DiscovEHR (OR = 1.4 per standard deviation (SD) of PRS, p < 0.001) and UK Biobank (OR = IVIA / t / ZUZZ / UIII or I .5 by PRS DE, p <0.001), (FIG. 9-11). Partitioning analysis was performed and the risk of DKA was compared between deciles. The observed risk of MACE in the highest decile compared with the lower deciles was 1.9 OR and 2.3 OR (p <0.001 for each study) in DiscovEHR and UK Biobank, respectively. Reference characteristics of the study population and genetic risk groups. The reference characteristics of the pharmacogenomic study population were assessed against the overall study population (FIG. 1). Because the pharmacogenomics group is a subset of the total study population, p-values ​​were not calculated for these comparisons. The genetic study had a lower percentage of Asian patients than the overall study, largely due to different pharmacogenomics participation rates by trial enrollment region (FIG. 8). Despite this difference, medical characteristics and lipid profiles at baseline were generally very similar across the study and among patients in the genetic analysis. The demographic and baseline characteristics of the high and low genetic subgroups were also compared to determine if there were any imbalances between the groups. At baseline, patients with high genetic risk (PRS > 90%) had several significant differences compared with patients with lower genetic risk (PRS < 90%). Those with high genetic risk were younger (1.8 years), had a higher incidence of, and were more likely to have a prior history (at the index event) of myocardial infarction, percutaneous coronary intervention, coronary artery bypass grafting, congestive heart failure, and a family history of premature coronary artery disease. Patients with high genetic risk had moderately higher baseline concentrations (around 2–5 mg / dL) of total cholesterol, LDL-C, non-HDL cholesterol, and apolipoprotein B.In particular, patients with high genetic risk had substantially elevated median Lp(a) levels at baseline (49.4 mg / dL) compared with patients with lower genetic risk (19.9 mg / dL; beta = 0.48 standard deviation units, p < 0.001). This finding was replicated in the UK Biobank, including 351,224 European individuals with genetic data and Lp(a) levels, where a CAD PRS > 90% was also associated with higher Lp(a) levels (beta = 0.39 standard deviation units, p < 0.001). It should be noted that, although the association between genetic risk score and baseline Lp(a) in this study was statistically very significant, the proportion of variance in serum Lp(a) levels explained by the PRS is a modest 3.1%. Additional patient characteristics are shown in Figure 8. Assessment of the risk of suffering a MACE by genetic risk groups. ML / E / ZYZZ / uΊ llúl We then examined whether the PRS could identify patients at higher risk of cardiovascular events in the post-ACS ODYSSEY study patient population. PRS deciles were assessed to determine the incidence of MACE and each of the secondary endpoints (FIG. 13-18). In the placebo group, the risk of an event for any of the endpoints was consistently higher than the overall event rate and consistent across all ancestral groups (FIG. 12). In the placebo group, patients at high genetic risk (defined as the top decile of PRS) had approximately 50% higher incidence of MACE (17.0% vs 11.4%, HR = 1.59, p <0.001) and 40% higher incidence of the secondary endpoint of any coronary heart disease event (20.4% vs 14.6%, HR = 1.55, p <0.001) compared with patients at lower genetic risk (PRS <90%) (FIG. 2).All analyses were adjusted for the previously specified covariates. It is worth noting that the lower PRS thresholds, >80th percentile and >70th percentile, also showed statistically significant differences in MACE (p = 0.004 and p = 0.013, respectively) between high and low risk in the placebo arm. In a meta-analysis of the placebo and alirocumab treatment arms, the pooled continuous PRS was p = 0.027; the p-values ​​for the placebo and alirocumab groups were 0.079 and 0.202, respectively. Comparison of genetic risk with traditional cardiovascular disease risk factors. In addition to adjusting for baseline clinical characteristics and the risk factors noted above, the effects of these risk factors (LDL-C, Lp(a), and other traditional risk factors) on PRS in placebo-treated patients were further assessed using stratified risk analyses. A stratified analysis of LDL-C (dichotomized at 100 mg / dL) indicated that PRS is independent of baseline LDL-C levels (FIG. 3A). Patients with high baseline LDL-C (>100 mg / dL) and high PRS had the highest incidence of MACE at 22.7%, 95% CI (17.0–28.4), while patients with lower baseline LDL-C and lower genetic risk had the lowest incidence at 9.9%, 95% CI (9.0–10.8).The use of high reference LDL-C and high PRS identifies patients at even greater risk of MACE than any risk factor alone (FIG. 3A). These analyses were expanded to include a broader set of traditional risk factors (age, systolic blood pressure, smoking, lipid levels, and type 2 diabetes) established in the Framingham Heart Study (FHS) for recurrent coronary heart disease. Continuous PRS was associated with MACE even after adjustment for baseline FHS risk score, p = 0.003 (adjusted for age, sex, ancestry, and FHS score). Dichotomous PRS also showed consistent effects on FHS The PRS was stratified by mean score, demonstrating the independent and additive value of these measures (FIG. 3B). PRS was also assessed in the very high risk (VHR) category and showed consistent effects across all risk groups. High genetic risk was still associated with an increased risk of MACE in the absence of VHR criteria (non-VHR), HR = 1.84 (p = 0.007), as described in FIG. 19. The impact of Lp(a) on the association of PRS with MACE risk was then explored. Lp(a) risk was dichotomized at 50 mg / dL, and a combinatorial subgroup analysis with PRS was performed, again demonstrating the additive value of Lp(a) and PRS (FIG. 3C). Due to the strong association between baseline Lp(a) and PRS, the relationship between PRS and Lp(a) levels was further explored. Variants in and around the LPA gene (+ / - 1 MB) were removed from PRS. This modified PRS was evaluated for its effects on Lp(a) levels and MACE risk in ODYSSEY and UK Biobank. The removal of these variants (+ / - 1 MB) attenuated the association of PRS with Lp(a) levels; the proportion of variability explained in Lp(a) by the modified PRS was approximately zero in both studies (FIG. 20-21). In UKB, the modified PRS still showed a strong association with MACE in the UK Biobank (OR by SD 1.5, p <0.001). In the ODYSSEY MACE risk assessment, the removal of these LPA regions split the risk in the placebo arm from the upper decile between the two upper deciles (FIG. 22-23). ​​Among placebo-treated patients in the upper decile who experienced an event, 35% (36 of 104) shifted from the upper decile, with most shifting to the next higher decile (suggesting that the LPA region is a significant but not sole contributor to risk). Among these shifted patients, approximately 28% (10 of 36) had a baseline serum Lp(a) <50 mg / dL. In a further evaluation of this region, it was observed that the PRS of the LPA region correlated only moderately with reference serum Lp(a) levels, r2= 27.7%.The results in the UK Biobank were similar, with the r² between the LPA PRS region and serum Lp(a) levels (nmol / L) being 29.0%. In summary, serum Lp(a) is not a simple proxy for MACE risk from the LPA genomic region, and the PRS still has a strong association with risk after adjusting or stratifying by baseline Lp(a). The LPA genomic region was most influential on the 27 SNP score, as two of the 27 variants in this score are from the LPA region (rs10455872 and rs3798220). Among placebo-treated patients in the highest decile of the 27-variant risk score, 96 experienced a MACE event. After removing these two variants from the score, 56 of 96 patients (58%) moved out of the highest decile. These vanants and other variants of the LPA locus may play an important role in. Μλχζυζζ. υΐ Ί lúl different PRS scores, from smaller scores, such as the 27 SNP score, to larger genome-wide PRS scores. Assessment of genetic risk and its impact on overall cardiovascular events. We then assessed whether patients with high genetic risk would benefit more from alirocumab treatment. Patients with high genetic risk treated with alirocumab showed greater reductions in both the absolute and relative risk of major adverse cardiovascular events (MACE) compared to patients with lower genetic risk. In the high-risk group, the 3-year Kaplan-Meier cumulative incidence of MACE was 11.4% in the alirocumab group and 17.4% in the placebo group, corresponding to an absolute risk reduction of 6.0%. In the lower-risk group, the rates were 10.0% and 11.5%, respectively (Fig. 4), corresponding to an absolute risk reduction of 1.5%.To prevent the occurrence of a primary endpoint, 17 (95% CI, 11–96) patients at high genetic risk or 64 (95% CI, 34–546) patients at lower genetic risk would need to be treated for 3 years. Patients at high genetic risk also had a greater relative reduction in MACE with alirocumab (HR 0.63; 95% CI, 0.46 to 0.86; p = 0.004) compared with those at lower genetic risk (HR 0.87; 95% CI, 0.78 to 0.98; p = 0.022). This difference was statistically significant (PRS-by-treatment interaction; p = 0.04) (FIG. 4). These analyses also demonstrated that patients at high genetic risk showed greater reductions with alirocumab treatment than patients at lower genetic risk in the predefined primary secondary endpoints that were significantly reduced with alirocumab in the overall study (any cardiovascular event, any coronary heart disease event, event of generalized coronary heart disease, and the composite endpoint of death from any cause, non-fatal myocardial infarction, or non-fatal ischemic stroke) (FIG. 5). The analysis of any cause of death was limited by the small number of events in the high-genetic-risk group (44 events in total). While the overall numbers in the high-genetic-risk group were lower in patients treated with alirocumab (n = 20 of 584; 3.4%) compared to patients treated with placebo (n = 24 of 613; 3.4%).9%), the number of events was too small for inferential analysis. Patients of European ancestry comprised 78% of the analysis population, and this subgroup was the largest ancestry group in the overall analysis. Consequently, a subgroup analysis was performed for patients of European ancestry. ML / E / ZuZZ / u / llul europea. The results for patients of European ancestry with high genetic risk (MACE HR 0.64; 95% CI: 0.45-0.92; p = 0.016) were consistent with the overall analysis that included patients of all ancestry (FIGS. 12 and 24). Independent and additive value of pretreatment PRS and LDL-C levels in predicting the benefit of alirocumab was assessed. The relationship between baseline LDL-C (dichotomized to 100 mg / dL), PRS, and treatment with the risk of MACE was also explored. In the group with high genetic risk and high baseline LDL-C levels, the 3-year Kaplan-Meier cumulative incidence of MACE was 22.7% in the placebo group and was markedly reduced with alirocumab treatment (13.4%) (Fig. 6C), corresponding to a 9.2% risk reduction (95% CI: 1.8%–6.6%). In the group with lower genetic risk and low baseline LDL-C levels, the rates were 9.9% and 9.2%, respectively (FIG. 6C), corresponding to a risk reduction of 0.7%, 95% CI (-0.6% to -2.1%). The hazard ratio for MACE (alirocumab: placebo) was numerically lower in patients with high genetic risk and high LDL-C (>100 mg / dL) (HR 0.55; 95% CI: 0.33 to 0.89; p = 0.00).015) and numerically higher in patients with low LDL-C (<100 mg / dL) and lower PRS (HR 0.94; 95% CI 0.81-1.09; p = 0.424, FIG. 7). It should be noted that this difference was not statistically significant when evaluating the full Cox regression model (p> 0.05). Effects of genetic risk and alirocumab treatment by VHR status. The benefit of treatment according to VHR status was assessed using the VHR criteria described above. In the high genetic risk group without VHR, the absolute risk reduction associated with alirocumab was 7.3%, HR = 0.26 (95% CI: 0.10 to 0.63), p = 0.003. In the high genetic risk VHR* group, the absolute risk reduction was 5.6%, HR = 0.73 (95% CI: 0.52–1.03), p = 0.076. There were only 736 patients in the high genetic risk VHR* group; the p-value tends toward significance with a relative risk reduction of 27% (Fig. 28). These results suggest that patients in the high genetic risk group benefit from alirocumab treatment regardless of VHR classification. Effects of genetic risk and alirocumab treatment on lipid reduction. The degree of lipid reduction in high- and low-genetic-risk patients was then examined after alirocumab treatment. The reduction in LDL-C with alirocumab was similar in both PRS groups: at 4 months, the median reduction was 57.0 mg / dL in high-genetic-risk patients and 58.7 mg / dL in lower-genetic-risk patients (FIG. 25). Due to the strong association between Lp(a) reference levels and the In addition to genetic risk (FIG. 1), the effects of genetic risk on changes in Lp(a) due to alirocumab treatment were also explored. Patients in the high genetic risk group had a mean Lp(a) reduction of 8.2 mg / dL (16.6% reduction from mean baseline Lp(a) at month 4 of the study), compared with a mean reduction of 5.1 mg / dL (25.6% reduction from median baseline Lp(a) in the lower genetic risk group) (FIG. 25). In stratified analyses (Lp(a) dichotomized to 50 mg / dL), patients with both high and low Lp(a) levels had greater event reductions in the high genetic risk subgroup compared to the lower genetic risk subgroup (FIG. 26-27). FIG. 28 shows MACE stratified by genetic risk and baseline Lp(a) taking into account the VHR category.Panel A is stratified by genetic risk, where genetic risk is defined as PRS > 90th percentile and low genetic risk as PRS < 90th percentile. Panel B is stratified by baseline Lp(a) (Lp(a) > 50 mg / dL and Lp(a) < 50 mg / dL). Panel C is stratified by genetic risk and baseline Lp(a). These results suggest that the greater reduction in MACE observed in patients at high genetic risk is not fully explained by baseline Lp(a) or the change in Lp(a) due to alirocumab treatment. Comparison of LDpred with PRS models of 27 and 57 variants. An assessment of the benefit of alirocumab treatment was also performed for the selected LDPred model (p = 0.001) and the candidate PRS 27 and 57 models. These analyses were performed using Cox proportional hazards models and adjusted for the covariates described above. The results by decile for each of the 3 models are shown in FIG. 30. In the 27 SNP model, the HR and p values ​​for the high genetic risk group and the low genetic risk group were HR = 0.68, p = 0.008 and HR = 0.85, p = 0.016, respectively. In the 57 SNP model, the HR for high genetic risk was 0.65, p = 0.010 and for low genetic risk was HR = 0.86, p = 0.010. These results are similar to those of LDPred (FIG. 4), with HR of 0.63 (p = 0.004) and 0.87 (p = 0.022), and in contrast to the results from the UK Biobank and DiscovEHR shown in FIG. 9 and 10.In the UK Biobank, the MACE OR at the 90th percentile was 2.33, in contrast to the 27 SNP model, which had an OR of 1.65. The differences between the ODYSSEY findings and these larger EHR datasets may be due to differences in study size or populations, or to the translation of primary EAC risk to assess the benefit of PCSK9 inhibition treatment for recurrent events in high-risk populations. The similarity in results for the genetically high-risk group in ODYSSEY was not due to a strong correlation between genetic risk scores. ΜΛ / Ε / ΖυΖΖ / υΊ llúl for recurrent coronary heart disease. The PRS also identified a group of genetically high-risk patients who benefited most from treatment. These patients experienced a greater benefit from alirocumab treatment in terms of both absolute and relative reductions in MACE (as well as secondary endpoints) (6.0% absolute risk reduction in MACE compared to 1.5% for the lower genetic risk group, and 37% relative risk reduction in MACE compared to 13% for the lower genetic risk group). Furthermore, this study demonstrates that the PRS can be combined with traditional lipid biomarkers not only to identify individuals at higher risk of MACE, but also to pinpoint the locations where the greatest risk reduction with treatment can be achieved. Patients with high PRS and high LDL-C not only had the highest risk of recurrent MACE (22).7%) despite intensive statin therapy, but they had the greatest absolute and relative risk reduction when alirocumab was added to statin therapy (9.2% absolute risk reduction, with a 45% relative risk reduction). Patients with high PRS and low LDL-C, or lower PRS and high LDL-C, had both intermediate risk and intermediate benefit. Patients with lower PRS and low LDL-C had the lowest risk and also benefited the least from alirocumab treatment. These findings have clear implications for targeting access to such therapies to those at highest risk and most likely to benefit. This study also provides further evidence for a different class of lipid-lowering therapies, specifically the PCSK9 inhibitor alirocumab, when added to patients already receiving intensive statin therapy.These results suggest that the improvement in clinical outcomes for genetically high-risk patients is not mediated by greater reductions in LDL-C after treatment, nor by higher LDL-C levels at baseline, nor for statins nor for alirocumab. Lp(a) has been recognized as a major risk factor for coronary artery disease. Lp(a) levels are primarily genetically determined, and PCSK9 inhibition is currently one of the few therapeutic approaches to reduce Lp(a). Overall, in the ODYSSEY study, treatment with alirocumab reduced Lp(a) levels by 23.4%. This study showed a strong association between high genetic risk and baseline Lp(a) levels (FIG. 1). Several lines of evidence demonstrate that the greater benefit with respect to major adverse cardiovascular events (MACE) observed in genetically high-risk patients is not due to either baseline Lp(a) levels or the degree of Lp(a) reduction with alirocumab alone: ​​1) although PRS is significantly associated with elevated serum Lp(a) levels, the proportion of variation in the ML / E / ZυZZZ / υΊ llúl serum Lp(a) levels explained by PRS is only 3.1%; 2) PRS and high genetic risk remained associated with a higher incidence of MACE and a greater reduction in MACE with alirocumab even after adjustment for baseline Lp(a) and Lp(a) reduction; and 3) stratified analysis in patients with low and high baseline Lp(a) levels showed that high genetic risk was associated with a higher incidence and a greater reduction in events, to a similar degree in Lp(a) subgroups and is therefore not fully explained by Lp(a) levels (FIG. 26-27). Taken together, these results suggest that Lp(a) may be a major contributor, but not the sole driving mechanism, to the PRS outcomes in the present study. The PRS in this study was developed using GWAS data from individuals of European ancestry. This analysis of the ODYSSEY OUTCOMES trial applied this PRS to patients from all ancestry groups combined (FIG. 1). An additional subgroup analysis was also performed in patients of European ancestry (FIG. 24), and the results were consistent with the larger analysis that included patients of all ancestry groups. While sample sizes are small for some ancestry groups, the reduction in MACE following alirocumab treatment was also observed to be directionally consistent in genetically high-risk patients across all ancestry groups tested (FIG. 12). As GWAS data become available in more diverse populations, polygenic risk scores are likely to improve over time for non-European populations as well. In fact, several modifications to the subject matter described, in addition to those described herein, will be evident to those skilled in the art from the foregoing description. It is intended that these modifications be covered by the scope of the appended claims. Each reference mentioned in this application (including, but not limited to, journal articles, U.S. and non-U.S. patents, patent application publications, international patent application publications, gene bank accession numbers, and the like) is incorporated herein by reference in its entirety and for all purposes.

Claims

1. Use of a proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitor for the manufacture of a drug to treat a patient at risk of suffering a major adverse cardiovascular event (MACE), characterized in that such patient is identified as being at increased risk of suffering a MACE by carrying out the steps of: determining a patient's polygenic coronary artery disease risk score (CAD-PRS), wherein the CAD-PRS comprises a weighted sum of a plurality of genetic variants associated with coronary artery disease; identifying a patient who is at increased risk of suffering a MACE if the patient has a CADPRS greater than a threshold CAD-PRS determined from a reference population.

2. Use according to claim 1, characterized in that the CAD-PRS threshold score is 30% higher within a reference population.

3. The use according to claim 1, characterized in that the CAD-PRS threshold score is the upper quintile within a reference population.

4. Use according to claim 1, characterized in that the CAD-PRS threshold score is the highest decile within a reference population.

5. Use in accordance with any of claims 2 to 4, characterized in that the reference population comprises at least 100 patients.

6. Use in accordance with any of claims 2 to 4, characterized in that the reference population comprises at least 1000 patients.

7. Use in accordance with any of claims 2 to 4, characterized in that the reference population comprises at least 5000 patients.

8. Use according to any of claims 2 to 4, characterized in that the reference population comprises at least 10,000 patients.

9. Use in accordance with any of claims 2 to 4, characterized in that the reference population is enriched for members of an ancestry group.

10. The use according to claim 9, characterized in that the reference population is enriched for members of an ancestry group selected from the group consisting of a European ancestry group, an African ancestry group, a mixed American ancestry group, an East Asian ancestry group, or a South Asian ancestry group.

11. Use according to claim 9 or claim 10, characterized in that the ancestry group is self-declared.

12. The use according to claim 9 or claim 10, MLE / E / ZυZZZ / υΊ llúl 54 characterized in that the ancestry group is derived from principal ancestry components.

13. The use according to claim 1, characterized in that the genetic variants are single nucleotide polymorphisms (SNPs), insertions, deletions, structural variants or copy number variations.

14. The use according to claim 1, characterized in that the plurality of genetic variants is determined by calculating the performance of the genetic variant in the reference population and selecting the highest performing genetic variants.

15. The use according to claim 14, characterized in that the genetic variant is calculated with respect to the risk of coronary artery disease based on statistical significance, strength of association and / or a probability distribution.

16. The use according to claim 15, characterized in that CADPRS is calculated using the LDPred method.

17. The use according to claim 16, characterized in that the fraction of causal markers (p) is set at 0.001 and the plurality of genetic variants comprises at least 6,500,000 genetic variants.

18. The use according to claim 15, characterized in that the CADPRS is calculated using the pruning and thresholding method.

19. The use according to claim 18, characterized in that the p-value threshold is 5 x 10 8 and the r2 value is 0.

2.

20. The use according to claim 18, characterized in that the p-value threshold is 5 x 10'2 and the r2 value is 0.

8.

21. The use according to claim 14, characterized in that the plurality of genetic variants comprises at least 20 genetic variants.

22. The use according to claim 14, characterized in that the plurality of genetic variants comprises at least 1000 genetic variants.

23. The use according to claim 14, characterized in that the plurality of genetic variants comprises at least 10,000 genetic variants.

24. The use according to claim 14, characterized in that the plurality of genetic variants comprises at least 100,000 genetic variants.

25. The use according to claim 14, characterized in that the plurality of genetic variants comprises at least 1,000,000 genetic variants.

26. The use according to claim 14, characterized in that the plurality of genetic variants comprises at least 6,500,000 genetic variants.

27. The use according to claim 1, characterized in that the PRS is determined from a biological sample obtained from the patient, wherein the biological sample comprises blood, semen, saliva, urine, feces, hair, teeth, bone, tissue or a cell.

28. The use according to claim 27, characterized in that the biological sample comprises blood.

29. The use according to claim 1, characterized in that it further comprises determining the patient's serum low-density lipoprotein (LDL) level and identifying that the patient is at increased risk of MACE if the patient also has a serum LDL level of at least around 100 mg / dL.

30. The use according to claim 1, characterized in that it further comprises determining the patient's serum lipoprotein(a) level (LPA or LP(a)) and identifying that the patient is at increased risk of suffering from MACE if the patient further has a serum LPA level of at least around 30 mg / dL.

31. The use according to claim 1, characterized in that it further comprises determining the patient's serum lipoprotein(a) level (LPA or LP(a)) and identifying that the patient is at increased risk of MACE if the patient also has a serum LPA level of at least around 50 mg / dL.

32. The use according to claim 1, characterized in that it further comprises determining the patient's serum LDL level and LPA level, and identifying that the patient is at increased risk of MACE if the patient further has a serum LDL level of at least around 100 mg / dL and a serum LPA level of at least 30 mg / dL.

33. The use according to claim 1, characterized in that it further comprises determining the patient's serum LDL level and LPA level and identifying that the patient is at increased risk of MACE if the patient further has a serum LDL level of at least around 100 mg / dL and a serum LPA level of at least around 50 mg / dL.

34. Use according to claim 1, characterized in that the patient has previously had a MACE.

35. The use according to claim 1, characterized in that the patient has received or is currently receiving a high dose of a statin.

36. The use according to claim 1, characterized in that the PCSK9 inhibitor is alirocumab.

37. The use according to claim 1, characterized in that the PCSK9 inhibitor is evolocumab or bococizumab.

38. The use according to claim 1, characterized in that MACE comprises coronary artery disease (CAD), myocardial infarction (MI), unstable angina, ischemic stroke, ischemia-induced coronary revascularization, arrhythmias, cardiovascular death, heart valve disease, cardiomyopathy, or congestive heart failure.

39. Use of a proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitor for the manufacture of a drug to lower serum LDL levels in a patient at increased risk of major adverse cardiovascular event (MACE), characterized in that such patient is identified as being at increased risk of MACE by performing the steps of: determining a patient's polygenic coronary artery disease risk score (CAD-PRS), wherein the CAD-PRS comprises a weighted sum of a plurality of genetic variants associated with coronary artery disease; identifying that a patient is at increased risk of MACE if the patient has a CAD-PRS greater than a CAD-PRS threshold determined from a reference population.

40. The use according to claim 39, characterized in that the CAD-PRS threshold score is 30% higher within a reference population.

41. The use according to claim 39, characterized in that the CAD-PRS threshold score is the upper quintile within a reference population.

42. The use according to claim 39, characterized in that the CAD-PRS threshold score is the highest decile within a reference population.

43. Use according to any of claims 39 to 42, characterized in that the reference population comprises at least 1000 patients.

44. Use according to any of claims 39 to 42, characterized in that the reference population comprises at least 5000 patients.

45. Use according to any of claims 39 to 42, characterized in that the reference population comprises at least 10,000 patients.

46. ​​Use in accordance with any of claims 39 to 42, characterized in that the reference population is enriched for members of an ancestry group.

47. The use according to claim 46, characterized in that the reference population is enriched for members of a selected ancestry group from the group consisting of a European ancestry group, an African ancestry group, a mixed American ancestry group, an East Asian ancestry group, or a South Asian ancestry group.

48. Use according to claim 46 or claim 47, characterized in that the ancestry group is self-declared.

49. The use according to claim 46 or claim 47, characterized in that the ancestry group is derived from principal ancestry components.

50. The use according to claim 39, characterized in that the genetic variants are single nucleotide polymorphisms (SNPs), insertions, deletions, structural variants or copy number variations.

51. The use according to claim 39, characterized in that the plurality of genetic variants is determined by calculating the performance of the genetic variant in the reference population and selecting the highest performing genetic variants.

52. The use according to claim 51, characterized in that the performance of the genetic variant is calculated with respect to the risk of coronary artery disease based on statistical significance, strength of association and / or a probability distribution.

53. The use according to claim 52, characterized in that CADPRS is calculated using the LDPred method.

54. The use according to claim 53, characterized in that the fraction of causal markers (p) is set at 0.001 and the plurality of genetic variants comprises at least 6,500,000 genetic variants.

55. The use according to claim 52, characterized in that the CADPRS is calculated using the pruning and thresholding method.

56. The use according to claim 55, characterized in that the p-value threshold is 5 x 108 and the r2 value is 0.

2.

57. The use according to claim 55, characterized in that the p-value threshold is 5 x 10'2 and the r2 value is 0.

8.

58. The use according to claim 51, characterized in that the plurality of genetic variants comprises at least 20 genetic variants.

59. The use according to claim 51, characterized in that the plurality of genetic variants comprises at least 1000 genetic variants. ML / E / ZuZZ / u llúl 60. The use according to claim 51, characterized in that the plurality of genetic variants comprises at least 10,000 genetic variants.

61. The use according to claim 51, characterized in that the plurality of genetic variants comprises at least 100,000 genetic variants.

62. The use according to claim 51, characterized in that the plurality of genetic variants comprises at least 1,000,000 genetic variants.

63. The use according to claim 51, characterized in that the plurality of genetic variants comprises at least 6,500,000 genetic variants.

64. The use according to claim 39, characterized in that the PRS is determined from a biological sample obtained from the patient, wherein the biological sample comprises blood, semen, saliva, urine, feces, hair, teeth, bone, tissue or a cell.

65. The use according to claim 64, characterized in that the biological sample comprises blood.

66. The use according to claim 39, characterized in that it further comprises determining the patient's serum low-density lipoprotein (LDL) level and identifying that the patient is at increased risk of MACE if the patient also has a serum LDL level of at least around 100 mg / dL.

67. The use according to claim 39, characterized in that it further comprises determining the patient's serum lipoprotein(a) level (LPA or LP(a)) and identifying that the patient is at increased risk of MACE if the patient further has a serum LPA level of at least around 30 mg / dL.

68. The use according to claim 39, characterized in that it further comprises determining the patient's serum lipoprotein(a) level (LPA or LP(a)) and identifying that the patient is at increased risk of suffering from MACE if the patient further has a serum LPA level of at least around 50 mg / dL.

69. The use according to claim 39, characterized in that it further comprises determining the patient's serum LDL level and LPA level, and identifying that the patient is at increased risk of MACE if the patient further has a serum LDL level of at least around 100 mg / dL and a serum LPA level of at least 30 mg / dL.

70. The use according to claim 39, characterized in that it further comprises determining the LDL level and the LPA level in the serum of the patient, and identifying that the patient is at increased risk of suffering from MACE if the patient further has a serum LDL level of at least around 100 mg / dL and a serum LPA level of at least 50 mg / dL.

71. Use according to claim 39, characterized in that the patient has previously had a MACE.

72. The use according to claim 39, characterized in that the patient has received or is currently receiving a high dose of a statin.

73. The use according to claim 39, characterized in that the PCSK9 inhibitor is alirocumab.

74. The use according to claim 39, characterized in that the PCSK9 inhibitor is evolocumab.

75. The use according to claim 39, characterized in that MACE comprises coronary artery disease (CAD), myocardial infarction (MI), unstable angina, ischemic stroke, ischemia-induced coronary revascularization, arrhythmias, cardiovascular death, heart valve disease, cardiomyopathy, or congestive heart failure.

76. Use of a proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitor for the manufacture of a drug to lower the serum lipoprotein(a) (LPA or LP(a)) level in a patient at increased risk of suffering a major adverse cardiovascular event (MACE), characterized in that such patient is identified as being at increased risk of suffering a MACE by carrying out the steps of: determining a patient's polygenic coronary artery disease risk score (CAD-PRS), wherein the CAD-PRS comprises a weighted sum of a plurality of genetic variants associated with coronary artery disease; identifying that a patient is at increased risk of suffering a MACE if the patient further has a CAD-PRS greater than a threshold CAD-PRS determined from a reference population.

77. The use according to claim 76, characterized in that the CAD-PRS threshold score is 30% higher within a reference population.

78. The use according to claim 76, characterized in that the CAD-PRS threshold score is the upper quintile within a reference population.

79. The use according to claim 76, characterized in that the CAD-PRS threshold score is the highest decile within a reference population.

80. Use according to any of claims 77 to 79, characterized in that the reference population comprises at least 1000 patients.

81. Use according to any of claims 77 to 79, characterized in that the reference population comprises at least 5000 patients.

82. Use according to any of claims 77 to 79, characterized in that the reference population comprises at least 10,000 patients.

83. Use according to any of claims 77 to 79, characterized in that the reference population is enriched for members of an ancestry group.

84. The use according to claim 83, characterized in that the reference population is enriched for members of an ancestry group selected from the group consisting of a European ancestry group, an African ancestry group, a mixed American ancestry group, an East Asian ancestry group, or a South Asian ancestry group.

85. Use according to claim 83 or claim 84, characterized in that the ancestry group is self-declared.

86. The use according to claim 82 or claim 83, characterized in that the ancestry group is derived from principal ancestry components.

87. The use according to claim 76, characterized in that the genetic variants are single nucleotide polymorphisms (SNPs), insertions, deletions, structural variants or copy number variations.

88. The use according to claim 76, characterized in that the plurality of genetic variants is determined by calculating the performance of the genetic variant in the reference population and selecting the highest performing genetic variants.

89. The use according to claim 88, characterized in that the performance of the genetic variant is calculated with respect to the risk of coronary artery disease based on statistical significance, strength of association and / or a probability distribution.

90. The use according to claim 89, characterized in that CADPRS is calculated using the LDPred method.

91. The use according to claim 90, characterized in that the fraction of causal markers (p) is set at 0.001 and the plurality of genetic variants comprises at least 6,500,000 genetic variants.

92. The use according to claim 89, characterized in that the CADPRS is calculated using the pruning and thresholding method. IVIA / t / ZUZZ / UIII or I 93. The use according to claim 92, characterized in that the p-value threshold is 5 x 10 8 and the r2 value is 0.

2.

94. The use according to claim 92, characterized in that the p-value threshold is 5 x 10-2 and the r2 value is 0.

8.

95. The use according to claim 88, characterized in that the plurality of genetic variants comprises at least 70 genetic variants.

96. The use according to claim 88, characterized in that the plurality of genetic variants comprises at least 1000 genetic variants.

97. The use according to claim 88, characterized in that the plurality of genetic variants comprises at least 10,000 genetic variants.

98. The use according to claim 88, characterized in that the plurality of genetic variants comprises at least 100,000 genetic variants.

99. The use according to claim 88, characterized in that the plurality of genetic variants comprises at least 1,000,000 genetic variants.

100. The use according to claim 88, characterized in that the plurality of genetic variants comprises at least 6,500,000 genetic variants.

101. The use according to claim 76, characterized in that the PRS is determined from a biological sample obtained from the patient, wherein the biological sample comprises blood, semen, saliva, urine, feces, hair, teeth, bone, tissue or a cell.

102. The use according to claim 101, characterized in that the biological sample comprises blood.

103. Use according to claim 76, characterized in that the patient has previously had a MACE.

104. The use according to claim 76, characterized in that the patient has received or is currently receiving a high dose of a statin.

105. The use according to claim 76, characterized in that the PCSK9 inhibitor is alirocumab.

106. The use according to claim 76, characterized in that the PCSK9 inhibitor is evolocumab.

107. The use according to claim 76, characterized in that MACE comprises coronary artery disease (CAD), myocardial infarction (MI), unstable angina, ischemic stroke, ischemia-induced coronary revascularization, arrhythmias, cardiovascular death, heart valve disease, cardiomyopathy, or congestive heart failure.

108. The use of any of claims 1 or 39 or 76, characterized in that it further comprises determining a composite risk score comprising the PRS and the LPA level in the patient.

109. The use of any of claims 1 or 39 or 76, characterized in that it further comprises determining a composite risk score comprising the PRS and LDL level in the patient.

110. The use of any of claims 1 or 39 or 76, characterized in that it further comprises determining a composite risk score comprising the PRS, LPA level and LDL level in the patient.

111. A method for screening a candidate subject for the purpose of determining inclusion in a clinical trial for the treatment of a cardiovascular condition, the method being characterized in that it comprises: determining the polygenic coronary artery disease risk score (CAD-PRS) in the subject, wherein the CAD-PRS comprises a weighted sum of a plurality of genetic variants related to coronary artery disease; and when the candidate subject has a CAD-PRS greater than a determined CAD-PRS threshold from a reference population, then including the candidate subject in the clinical trial; or when the candidate subject has a CAD-PRS lower than a determined CAD-PRS threshold from a reference population, then excluding the candidate subject from the clinical trial.

112. The method according to claim 111, characterized in that the CAD-PRS threshold score is 30% higher within a reference population.

113. The method according to claim 111, characterized in that the CAD-PRS threshold score is the upper quintile within a reference population.

114. The method according to claim 111, characterized in that the CAD-PRS threshold score is the highest decile within a reference population.

115. The method according to any one of claims 112 to 114, characterized in that the reference population comprises at least 1000 patients.

116. The method according to any one of claims 112 to 114, characterized in that the reference population comprises at least 5000 patients.

117. The method according to any one of claims 112 to 114, characterized in that the reference population comprises at least 10,000 patients.

118. The method according to any one of claims 112 to 114, characterized in that the reference population is enriched for members of an ancestry group.

119. The method according to claim 118, characterized in that the reference population is enriched for members of an ancestry group selected from the group consisting of a European ancestry group, an African ancestry group, a mixed American ancestry group, an East Asian ancestry group, or a South Asian ancestry group.

120. The method according to claim 118 or claim 119, characterized in that the ancestry group is self-indicated.

121. The method according to claim 118 or claim 119, characterized in that the ancestry group is derived from principal ancestry components.

122. The method according to claim 111, characterized in that the genetic variants are single nucleotide polymorphisms (SNPs), insertions, deletions, structural variants or copy number variations.

123. The method according to claim 111, characterized in that the plurality of genetic variants is determined by calculating the performance of the genetic variant in the reference population and selecting the highest performing genetic variants.

124. The method according to claim 123, characterized in that the genetic variant is calculated with respect to the risk of coronary artery disease based on statistical significance, strength of association and / or a probability distribution.

125. The method according to claim 124, characterized in that CAD-PRS is calculated using the LDPred method.

126. The method according to claim 125, characterized in that the fraction of causal markers (p) is set at 0.001 and the plurality of genetic variants comprises at least 6,500,000 genetic variants.

127. The method according to claim 124, characterized in that the CAD-PRS is calculated using the pruning and thresholding method.

128. The method according to claim 127, characterized in that the p-value threshold is 5 x 10'8 and the r2 value is 0.

2.

129. The method according to claim 127, characterized in that the p-value threshold is 5 x 102 and the r2 value is 0.

8.

130. The method according to claim 123, characterized in that the plurality of genetic variants comprises at least 70 genetic variants.

131. The method according to claim 123, characterized in that the plurality of genetic variants comprises at least 1000 genetic variants.

132. The method according to claim 123, characterized in that the plurality of genetic variants comprises at least 10,000 genetic variants.

133. The method according to claim 123, characterized in that the plurality of genetic variants comprises at least 100,000 genetic variants.

134. The method according to claim 123, characterized in that the plurality of genetic variants comprises at least 1,000,000 genetic variants.

135. The method according to claim 123, characterized in that the plurality of genetic variants comprises at least 6,500,000 genetic variants.

136. The method according to claim 111, characterized in that the PRS is determined from a biological sample obtained from the patient, wherein the biological sample comprises blood, semen, saliva, urine, feces, hair, teeth, bone, tissue or a cell.

137. The method according to claim 136, characterized in that the biological sample comprises blood.

138. The method according to claim 111, characterized in that the patient has previously had a MACE.

139. The method according to claim 111, characterized in that the patient has received or is currently receiving a high dose of a statin.

140. The method according to claim 111, characterized in that MACE comprises coronary artery disease (CAD), myocardial infarction (MI), unstable angina, ischemic stroke, ischemia-induced coronary revascularization, arrhythmias, cardiovascular death, heart valve disease, cardiomyopathy, or congestive heart failure.

141. The method according to claim 111, characterized in that it further comprises determining a composite risk score comprising the PRS and the LPA level in the patient.

142. The method according to claim 111, characterized in that it further comprises determining a composite risk score comprising the PRS and the LDL level in the patient.

143. The method according to claim 111, characterized in that it further comprises determining a composite risk score comprising the PRS, LPA level and LDL level in the patient.