A novel polygenic risk score improves prognostic prediction of heart failure with preserved ejection fraction in the Chinese Han population.

AIMS:Mortality risk assessment in patients with heart failure (HF) with preserved ejection fraction (HFpEF) presents a major challenge. We sought to construct a polygenic risk score (PRS) to accurately predict the mortality risk of HFpEF.METHODS AND RESULTS:We first carried out a microarray analysis of 50 HFpEF patients who died and 50 matched controls who survived during 1-year follow-up for candidate gene selection. The HF-PRS was developed using the independent common (MAF > 0.05) genetic variants that showed significant associations with 1-year all-cause death (P < 0.05) in 1442 HFpEF patients. Internal cross-validation and subgroup analyses were performed to evaluate the discrimination ability of the HF-PRS. In 209 genes identified by microarray analysis, 69 independent variants (r < 0.1) were selected to develop the HF-PRS model. This model yielded the best discrimination capability for 1-year all-cause mortality with an area under the curve (AUC) of 0.852 (95% CI 0.827-0.877), which outperformed the clinical risk score consisting of 10 significant traditional risk factors for 1-year all-cause mortality (AUC 0.696, 95% CI 0.658-0.734, P = 4 × 10-11), with net reclassification improvement (NRI) of 0.741 (95% CI 0.605-0.877; P < 0.001) and integrated discrimination improvement (IDI) of 0.181 (95% CI 0.145-0.218; P < 0.001). Individuals in the medium and the highest tertile of the HF-PRS had nearly a five-fold (HR = 5.3, 95% CI 2.4-11.9; P = 5.6 × 10-5) and 30-fold (HR = 29.8, 95% CI 14.0-63.5; P = 1.4 × 10-18) increased risk of mortality compared to those in the lowest tertile, respectively. The discrimination ability of the HF-PRS was excellent in cross validation and throughout the subgroups regardless of comorbidities, gender, and patients with or without a history of heart failure.CONCLUSION:The HF-PRS comprising 69 genetic variants provided an improvement of prognostic power over the contemporary risk scores and NT-proBNP in HFpEF patients.

Cohort Profile: ChinaHEART (Health Evaluation And risk Reduction through nationwide Teamwork) Cohort.

Prognostic Value of Multiple Circulating Biomarkers for 2-Year Death in Acute Heart Failure With Preserved Ejection Fraction.

Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF. Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771-0.895) and validation set (AUC 0.798, 95% CI: 0.719-0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05). Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.

A novel polygenic risk score improves prognostic prediction of heart failure with preserved ejection fraction in the Chinese Han population.

AIMS:Mortality risk assessment in patients with heart failure (HF) with preserved ejection fraction (HFpEF) presents a major challenge. We sought to construct a polygenic risk score (PRS) to accurately predict the mortality risk of HFpEF.METHODS AND RESULTS:We first carried out a microarray analysis of 50 HFpEF patients who died and 50 matched controls who survived during 1-year follow-up for candidate gene selection. The HF-PRS was developed using the independent common (MAF > 0.05) genetic variants that showed significant associations with 1-year all-cause death (P < 0.05) in 1442 HFpEF patients. Internal cross-validation and subgroup analyses were performed to evaluate the discrimination ability of the HF-PRS. In 209 genes identified by microarray analysis, 69 independent variants (r < 0.1) were selected to develop the HF-PRS model. This model yielded the best discrimination capability for 1-year all-cause mortality with an area under the curve (AUC) of 0.852 (95% CI 0.827-0.877), which outperformed the clinical risk score consisting of 10 significant traditional risk factors for 1-year all-cause mortality (AUC 0.696, 95% CI 0.658-0.734, P = 4 × 10-11), with net reclassification improvement (NRI) of 0.741 (95% CI 0.605-0.877; P < 0.001) and integrated discrimination improvement (IDI) of 0.181 (95% CI 0.145-0.218; P < 0.001). Individuals in the medium and the highest tertile of the HF-PRS had nearly a five-fold (HR = 5.3, 95% CI 2.4-11.9; P = 5.6 × 10-5) and 30-fold (HR = 29.8, 95% CI 14.0-63.5; P = 1.4 × 10-18) increased risk of mortality compared to those in the lowest tertile, respectively. The discrimination ability of the HF-PRS was excellent in cross validation and throughout the subgroups regardless of comorbidities, gender, and patients with or without a history of heart failure.CONCLUSION:The HF-PRS comprising 69 genetic variants provided an improvement of prognostic power over the contemporary risk scores and NT-proBNP in HFpEF patients.

Cohort Profile: ChinaHEART (Health Evaluation And risk Reduction through nationwide Teamwork) Cohort.

Prognostic Value of Multiple Circulating Biomarkers for 2-Year Death in Acute Heart Failure With Preserved Ejection Fraction.

Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly recognized as a major global public health burden and lacks effective risk stratification. We aimed to assess a multi-biomarker model in improving risk prediction in HFpEF. Methods: We analyzed 18 biomarkers from the main pathophysiological domains of HF in 380 patients hospitalized for HFpEF from a prospective cohort. The association between these biomarkers and 2-year risk of all-cause death was assessed by Cox proportional hazards model. Support vector machine (SVM), a supervised machine learning method, was used to develop a prediction model of 2-year all-cause and cardiovascular death using a combination of 18 biomarkers and clinical indicators. The improvement of this model was evaluated by c-statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: The median age of patients was 71-years, and 50.5% were female. Multiple biomarkers independently predicted the 2-year risk of death in Cox regression model, including N-terminal pro B-type brain-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-TnT), growth differentiation factor-15 (GDF-15), tumor necrosis factor-α (TNFα), endoglin, and 3 biomarkers of extracellular matrix turnover [tissue inhibitor of metalloproteinases (TIMP)-1, matrix metalloproteinase (MMP)-2, and MMP-9) (FDR < 0.05). The SVM model effectively predicted the 2-year risk of all-cause death in patients with acute HFpEF in training set (AUC 0.834, 95% CI: 0.771-0.895) and validation set (AUC 0.798, 95% CI: 0.719-0.877). The NRI and IDI indicated that the SVM model significantly improved patient classification compared to the reference model in both sets (p < 0.05). Conclusions: Multiple circulating biomarkers coupled with an appropriate machine-learning method could effectively predict the risk of long-term mortality in patients with acute HFpEF. It is a promising strategy for improving risk stratification in HFpEF.