Dataset Information

Prediction of HF-Related Mortality Risk Using Genetic Risk Score Alone and in Combination With Traditional Risk Factors.

ABSTRACT: Background: Common variants may contribute to the variation of prognosis of heart failure (HF) among individual patients, but no systematical analysis was conducted using transcriptomic and whole exome sequencing (WES) data. We aimed to construct a genetic risk score (GRS) and estimate its potential as a predictive tool for HF-related mortality risk alone and in combination with traditional risk factors (TRFs). Methods and Results: We reanalyzed the transcriptomic data of 177 failing hearts and 136 healthy donors. Differentially expressed genes (fold change >1.5 or <0.68 and adjusted P < 0.05) were selected for prognosis analysis using our whole exome sequencing and follow-up data with 998 HF patients. Statistically significant variants in these genes were prepared for GRS construction. Traditional risk variables were in combination with GRS for the construct of the composite risk score. Kaplan-Meier curves and receiver operating characteristic (ROC) analysis were used to assess the effect of GRS and the composite risk score on the prognosis of HF and discriminant power, respectively. We found 157 upregulated and 173 downregulated genes. In these genes, 31 variants that were associated with the prognosis of HF were finally identified to develop GRS. Compared with individuals with low risk score, patients with medium- and high-risk score showed 2.78 (95%CI = 1.82-4.24, P = 2 × 10^-6) and 6.54 (95%CI = 4.42-9.71, P = 6 × 10^-21) -fold mortality risk, respectively. The composite risk score combining GRS and TRF predicted mortality risk with an HR = 5.41 (95% CI = 2.72-10.64, P = 1 × 10^-6) for medium vs. low risk and HR = 22.72 (95% CI = 11.9-43.48, P = 5 × 10^-21) for high vs. low risk. The discriminant power of GRS is excellent with a C statistic of 0.739, which is comparable to that of TRF (C statistic = 0.791). The combination of GRS and TRF could significantly increase the predictive ability (C statistic = 0.853). Conclusions: The 31-SNP GRS could well distinguish those HF patients with poor prognosis from those with better prognosis and provide clinician with reference for the intensive therapy, especially when combined with TRF. Clinical Trial Registration: https://www.clinicaltrials.gov/, identifier: NCT03461107.

SUBMITTER: Hu D

PROVIDER: S-EPMC8107241 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundWe aimed to develop a multivariable statistical model for risk stratification in patients with chronic heart failure with systolic dysfunction, using patient data that are routinely collected and easily obtained at the time of initial presentation.Methods and resultsIn a cohort of 2331 patients enrolled in the HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise TraiNing) study (New York Heart Association class II-IV, left ventricular ejection fraction ≤0.35, randomized to exercise training and usual care versus usual care alone, median follow-up of 2.5 years), we performed risk modeling using Cox proportional hazards models and analyzed the relationship between baseline clinical factors and the primary composite end point of death or all-cause hospitalization and the secondary end point of all-cause death alone. Prognostic relationships for continuous variables were examined using restricted cubic spline functions, and key predictors were identified using a backward variable selection process and bootstrapping methods. For ease of use in clinical practice, point-based risk scores were developed from the risk models. Exercise duration on the baseline cardiopulmonary exercise test was the most important predictor of both the primary end point and all-cause death. Additional important predictors for the primary end point risk model (in descending strength) were Kansas City Cardiomyopathy Questionnaire symptom stability score, higher serum urea nitrogen, and male sex (all P<0.0001). Important additional predictors for the mortality risk model were higher serum urea nitrogen, male sex, and lower body mass index (all P<0.0001).ConclusionsRisk models using simple, readily obtainable clinical characteristics can provide important prognostic information in ambulatory patients with chronic heart failure with systolic dysfunction.Clinical trial registrationURL: http://www.clinicaltrials.gov. Unique identifier: NCT00047437.

Dataset Information

Prediction of HF-Related Mortality Risk Using Genetic Risk Score Alone and in Combination With Traditional Risk Factors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets