使用多基因风险得分的途径见解和2型糖尿病的预测建模
Wen-Ling Liao1,2, Jai-Sing Yang3, Ting-Yuan Liu4,5
1Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan.
Scientific reports
|August 7, 2025
概括
我们创建了一个多基因风险评分 (PRS),使用遗传和临床数据来预测2型糖尿病 (T2D) 风险. 该模型准确地识别了高风险的个体,从而实现了针对T2D的个性化预防策略.
科学领域:
- 遗传学 是一个遗传学.
- 流行病学 流行病学
- 精准医学是一门精准的医学.
背景情况:
- 2型糖尿病 (T2D) 是一个重大的全球健康挑战.
- 有效的风险预测模型对于早期干预和预防至关重要.
研究的目的:
- 开发和验证与临床数据集成的多基因风险评分 (PRS) 模型,用于预测T2D风险.
- 为了评估PRS模型的性能和临床实用性,在一个大规模的台湾队列.
主要方法:
- 来自台湾一家主要医疗中心的电子病历分析 (315.424例T2D病例,141.484例对照).
- 使用14个全基因组显著SNP构建一个T2D PRS.
- 整合PRS与临床数据用于预测建模和验证在台湾生物银行.
- 全现象关联研究 (PheWAS) 和途径分析.
主要成果:
- 集成的PRS模型实现了高预测精度 (AUROC 0.842).
- 建立了一个临床风险评分 (0-19).
- PRS与T2D并发症 (例如,糖尿病视网膜病变,高血压) 和生物途径 (例如,IL-15产生,WNT/β-catenin信号传递) 有关.
结论:
- 开发的PRS模型为个性化的T2D风险评估提供了一个强大的工具.
- 遗传和临床数据的整合提高了T2D预测的准确性.
- 研究结果支持在早期T2D预防策略中使用PRS.
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