Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention.

AIMS: In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort. METHODS AND RESULTS: Using the proximity extension assay, 368 proteins were measured in a nested case-control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001). CONCLUSION: In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention

Twelve Variants Polygenic Score for Low-Density Lipoprotein Cholesterol Distribution in a Large Cohort of Patients With Clinically Diagnosed Familial Hypercholesterolemia With or Without Causative Mutations

: Background A significant proportion of individuals clinically diagnosed with familial hypercholesterolemia (FH), but without any disease-causing mutation, are likely to have polygenic hypercholesterolemia. We evaluated the distribution of a polygenic risk score, consisting of 12 low-density lipoprotein cholesterol (LDL-C)-raising variants (polygenic LDL-C risk score), in subjects with a clinical diagnosis of FH. Methods and Results Within the Lipid Transport Disorders Italian Genetic Network (LIPIGEN) study, 875 patients who were FH-mutation positive (women, 54.75%; mean age, 42.47±15.00 years) and 644 patients who were FH-mutation negative (women, 54.21%; mean age, 49.73±13.54 years) were evaluated. Patients who were FH-mutation negative had lower mean levels of pretreatment LDL-C than patients who were FH-mutation positive (217.14±55.49 versus 270.52±68.59 mg/dL, P<0.0001). The mean value (±SD) of the polygenic LDL-C risk score was 1.00 (±0.18) in patients who were FH-mutation negative and 0.94 (±0.20) in patients who were FH-mutation positive (P<0.0001). In the receiver operating characteristic analysis, the area under the curve for recognizing subjects characterized by polygenic hypercholesterolemia was 0.59 (95% CI, 0.56-0.62), with sensitivity and specificity being 78% and 36%, respectively, at 0.905 as a cutoff value. Higher mean polygenic LDL-C risk score levels were observed among patients who were FH-mutation negative having pretreatment LDL-C levels in the range of 150 to 350 mg/dL (150-249 mg/dL: 1.01 versus 0.91, P<0.0001; 250-349 mg/dL: 1.02 versus 0.95, P=0.0001). A positive correlation between polygenic LDL-C risk score and pretreatment LDL-C levels was observed among patients with FH independently of the presence of causative mutations. Conclusions This analysis confirms the role of polymorphisms in modulating LDL-C levels, even in patients with genetically confirmed FH. More data are needed to support the use of the polygenic score in routine clinical practice

Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention.

AIMS: In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort. METHODS AND RESULTS: Using the proximity extension assay, 368 proteins were measured in a nested case-control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001). CONCLUSION: In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention

Genetically determined hypercholesterolaemia results into premature leucocyte telomere length shortening and reduced haematopoietic precursors

Leucocyte telomere length (LTL) shortening is a marker of cellular senescence and associates with increased risk of cardiovascular disease (CVD). A number of cardiovascular risk factors affect LTL, but the correlation between elevated LDL cholesterol (LDL-C) and shorter LTL is debated: in small cohorts including subjects with a clinical diagnosis of familial hypercholesterolaemia (FH). We assessed the relationship between LDL-C and LTL in subjects with genetic familial hypercholesterolaemia (HeFH) compared to those with clinically diagnosed, but not genetically confirmed FH (CD-FH), and normocholesterolaemic subjects

Prevalence Of familial hypercholeSTerolaemia (FH) in Italian Patients with coronary artERy disease: The POSTER study

Background and aims: Familial hypercholesterolaemia (FH) is a powerful risk factor for cardiovascular (CV) events. High levels of low-density lipoprotein cholesterol (LDL-C) since birth are linked to the early onset of atherosclerotic disease. A genetic mutation determining FH is present in about one subject out of 250; FH should be more represented among subjects with a documented diagnosis of coronary artery disease (CAD). The POSTER Study evaluated the prevalence of FH in Italian patients with a recent CAD event. Methods: Eighty-two cardiology centres enrolled patients with a documented CAD event; CV risk profile, drug therapy and biochemical parameters were collected. Dutch Lipid Clinic Network (DLCN) criteria were used to define patients with a potential FH diagnosis (score ≥6); these patients underwent molecular testing for genetic diagnosis of FH. Results: Overall, 5415 patients were enrolled and the main index events were myocardial infarction with ST-elevation, non ST-elevation acute coronary syndrome (ACS), or a recent coronary revascularization (34.8%, 37.2%, and 28% respectively). Mean age was 66 ± 11 years, men were 78%; about 40% were already treated with statins, proportion that increased after the acute event (96.5%). Based on the DLCN score, the prevalence of potential FH was 5.1%, 0.9% of them had a diagnosis of definite FH (score >8). These patients were younger than patients with a score 190 mg/dL. FH was genetically confirmed in 42 subjects (15.9%); genetic diagnosis was defined as not conclusive for FH in 63 patients (23.9%). Finally, in 159 subjects (60.2%) no pathogenic mutations in the tested genes were identified, defining them as negative for monogenic familial hypercholesterolemia. Conclusions: Results underscore a relatively high prevalence of potential FH in patients with a recent CAD event. Therefore, an early identification of these subjects may help improve the management of their high CV risk and, by cascade screening, identify possible FH relatives

Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention

AIMS: In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort. METHODS AND RESULTS: Using the proximity extension assay, 368 proteins were measured in a nested case-control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001). CONCLUSION: In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention

Lipoprotein(a) and family history for cardiovascular disease in paediatric patients: A new frontier in cardiovascular risk stratification. Data from the LIPIGEN paediatric group

Background and aims: Little is known about the role of Lp(a) in the assessment of cardiovascular risk in the paediatric population. Trying to clarify the clinical relevance of Lp(a) in risk stratification, the aim of the study is to evaluate the association between Lp(a) plasma levels in children with familial hypercholesterolaemia (FH) and positive family history for premature cardiovascular disease (pCVD) in first-and second-degree relatives. Methods: 653 Caucasian children and adolescents (334 females and 319 males), aged 2-17 years, with diagnosis of FH from a paediatric cohort included in the LIPIGEN Network, were selected. We compared family history of pCVD, lipid and genetic profile in two groups based on Lp(a) levels below or above 30 mg/dL. To determine the independent predictors of pCVD, a multivariate logistic regression was used, with all clinical characteristics and blood measurements as predictors. Results: Subjects with Lp(a) > 30 mg/dl more frequently reported positive family history of pCVD compared to subjects with Lp(a) 30 mg/dl was an independent predictor of family history of pCVD. Comparing subjects with or without family history of pCVD, we reported significant differences for Lp(a) > 30 mg/dl (46.25% vs 17.65%, p 30 mg/dl where more likely to have a positive family history of pCVD. Lp(a) screening in children and adolescents with FH may enhance risk assessment and help identify those subjects, children and relatives, at increased pCVD risk

Evaluation of the performance of Dutch Lipid Clinic Network score in an Italian FH population: The LIPIGEN study

Background and aims: Familial hypercholesterolemia (FH) is an inherited disorder characterized by high levels of blood cholesterol from birth and premature coronary heart disease. Thus, the identification of FH patients is crucial to prevent or delay the onset of cardiovascular events, and the availability of a tool helping with the diagnosis in the setting of general medicine is essential to improve FH patient identification. Methods: This study evaluated the performance of the Dutch Lipid Clinic Network (DLCN) score in FH patients enrolled in the LIPIGEN study, an Italian integrated network aimed at improving the identification of patients with genetic dyslipidaemias, including FH. Results: The DLCN score was applied on a sample of 1377 adults (mean age 42.9 ± 14.2 years) with genetic diagnosis of FH, resulting in 28.5% of the sample classified as probable FH and 37.9% as classified definite FH. Among these subjects, 43.4% had at least one missing data out of 8, and about 10.0% had 4 missing data or more. When analyzed based on the type of missing data, a higher percentage of subjects with at least 1 missing data in the clinical history or physical examination was classified as possible FH (DLCN score 3–5). We also found that using real or estimated pre-treatment LDL-C levels may significantly modify the DLCN score. Conclusions: Although the DLCN score is a useful tool for physicians in the diagnosis of FH, it may be limited by the complexity to retrieve all the essential information, suggesting a crucial role of the clinical judgement in the identification of FH subjects