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.

Keywords: genetic risk score; heart failure; prediction; prognosis; traditional risk factors.

Figure 1

Differential gene expression between 177 failing hearts and 136 healthy donor controls. Volcano plots depicting the extent (x-axis) and significance (y-axis) of differential gene expression between failing and healthy heart samples. Fold change represents failing vs. control hearts.

Figure 2

Distribution of integer risk score for all 998 HF patients. The distribution shows a nearly bell-shaped curve, ranging from 34.82 to 42.23 points with a median value of 38.78.

Figure 3

Prognostic analysis for GRS and composite risk score (A,B). Cox proportional hazards regression model was used for prognosis analysis. (A) Compared with the low-risk group (N = 333), medium (N = 333), and high-risk groups (N = 332) showed increased HF-related mortality risk (HR = 2.78, 95% CI = 1.82–4.24, P = 2 × 10⁻⁶ for medium- vs. low-risk group; HR = 6.54, 95% CI = 4.42–9.71, P = 6 × 10⁻²¹ for high- vs. low-risk group). The statistical significance remains after adjustment for age, gender, hypertension, diabetes, hyperlipidemia, smoking status, and β-blocker use. (B) Composite risk score with medium and high risk showed significantly increased mortality risk of HF (HR = 5.41, 95% CI = 2.72–10.64, P = 1 × 10⁻⁶ for medium vs. low risk; HR = 22.72, 95% CI = 11.90–43.48, P = 5 × 10⁻²¹ for high vs. low risk).

Figure 4

Receiver-operating characteristic curves for HF-related mortality risk. (A,B) Model 1, only age, gender, diabetes, LVEF, log-transformed NT-proBNP, serum creatinine, sodium, potassium, diastolic blood pressure; model 2, only GRS; model 3, composite risk score. AUC, area under the curve.