Hu Dong, Xiao Lei, Li Shiyang, Hu Senlin, Sun Yang, Wang Yan, Wang Dao Wen
Division of Cardiology, Department of Internal Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.
Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Wuhan, China.
Front Cardiovasc Med. 2021 Apr 26;8:634966. doi: 10.3389/fcvm.2021.634966. eCollection 2021.
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). 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 < 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, = 2 × 10) and 6.54 (95%CI = 4.42-9.71, = 6 × 10) -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, = 1 × 10) for medium vs. low risk and HR = 22.72 (95% CI = 11.9-43.48, = 5 × 10) 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). 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. https://www.clinicaltrials.gov/, identifier: NCT03461107.
常见变异可能导致个体心力衰竭（HF）患者预后存在差异，但尚未使用转录组学和全外显子组测序（WES）数据进行系统分析。我们旨在构建一个遗传风险评分（GRS），并评估其单独以及与传统风险因素（TRF）联合作为HF相关死亡风险预测工具的潜力。我们重新分析了177例衰竭心脏和136例健康供体的转录组学数据。使用我们的全外显子组测序以及998例HF患者的随访数据，选择差异表达基因（倍数变化>1.5或<0.68且校正后<0.05）进行预后分析。为构建GRS准备这些基因中具有统计学意义的变异。将传统风险变量与GRS相结合构建复合风险评分。分别使用Kaplan-Meier曲线和受试者工作特征（ROC）分析来评估GRS和复合风险评分对HF预后的影响以及判别能力。我们发现157个上调基因和173个下调基因。在这些基因中，最终鉴定出31个与HF预后相关的变异以构建GRS。与低风险评分个体相比，中风险和高风险评分患者的死亡风险分别高出2.78倍（95%CI = 1.82 - 4.24，P = 2×10⁻⁴）和6.54倍（95%CI = 4.42 - 9.71，P = 6×10⁻⁷）。结合GRS和TRF的复合风险评分预测中风险与低风险患者的死亡风险时HR = 5.41（95%CI = 2.72 - 10.64，P = 1×10⁻⁴），高风险与低风险患者的死亡风险时HR = 22.72（95%CI = 11.9 - 43.48，P = 5×10⁻⁷）。GRS的判别能力出色，C统计量为0.739，与TRF相当（C统计量 = 0.791）。GRS和TRF的联合可显著提高预测能力（C统计量 = 0.853）。31个单核苷酸多态性的GRS能够很好地区分预后较差和较好的HF患者，并为临床医生进行强化治疗提供参考，尤其是与TRF联合使用时。https://www.clinicaltrials.gov/，标识符：NCT03461107。
