在2型糖尿病患者中,使用多种OMIC数据改善了性别特异性心血管风险预测
Ruijie Xie1,2, Christian Herder3,4,5, Sha Sha1
1Division of Clinical Epidemiology of Early Cancer Detection, German Cancer Research Center, Im Neuenheimer Feld 581, 69120, Heidelberg, Germany.
Cardiovascular diabetology
|December 24, 2025
概括
整合性别特异性蛋白质组学显著改善了2型糖尿病患者的心血管事件预测. 代谢药物进一步增强了男性的风险评估,但多基因风险得分不必用于改善预测.
科学领域:
- 心血管疾病的研究研究.
- 基因组学和精准医学精准医学
- 代谢学和蛋白质学
背景情况:
- 2型糖尿病 (T2D) 具有重大心血管不良事件 (MACE) 的高风险.
- 准确的性别特异性风险预测模型对于管理T2D心血管风险至关重要.
- SCORE2-糖尿病模型是T2D中心血管风险评估的当前标准.
研究的目的:
- 确定是否整合蛋白质组学,代谢组学和心血管疾病多基因风险评分 (CVD-PRS) 可以改善T2D的性别特异性MACE预测.
- 通过使用多omics数据来增强现有的SCORE2-糖尿病模型,以实现更准确的风险分层.
- 评估10年MACE预测的每个奥米克层 (蛋白质组学,代谢组学,CVD-PRS) 的附加值.
主要方法:
- 利用了英国生物库的数据,包括全基因组关联研究 (GWAS),血蛋白质组学 (Olink Explore 3072) 和代谢学 (Nightingale Health NMR).
- 开发了一种使用引导式-LASSO回归的新型性别特异蛋白质算法.
- 在一个T2D子集 (n=990) 中使用Harrell的C指数评估模型性能改进,并提供完整的多omics数据.
主要成果:
- 性别特定的蛋白质组特征显著改善了MACE预测 (C指数从0.766到0.835,P<0.001).
- 结合代谢物进一步提高了模型的性能 (C指数为0.846,P=0.035),主要是在男性中.
- 添加CVD-PRS并没有为多omics模型提供统计学上显著的额外改善 (C指数0.848,P = 0.070).
结论:
- 性别特定的蛋白质组特征大大提高了T2D患者的10年MACE风险预测.
- 当与蛋白质组学相结合时,代谢学可能在男性中提供额外的预测价值,但不是女性.
- 心血管疾病多基因风险评分似乎不必要,以改善风险预测超出多omics整合在这个队列.
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