多omics整合预测了英国生物库中的17种疾病发生率
Jiawen Du1, Muqing Zhou2, Laura M Raffield2
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
medRxiv : the preprint server for health sciences
|August 13, 2025
概括
添加蛋白质组学和代谢组学数据显著改善了超越传统因素的疾病风险预测. 蛋白质组学对大多数疾病显示出更强大的预测能力,为未来的治疗提供了分子洞察力.
科学领域:
- 发现生物标志物的发现.
- 疾病风险预测疾病风险预测.
- 多领域的整合.
背景情况:
- 传统的临床预测器在捕捉疾病复杂性方面存在局限性.
- 多omics技术为增强风险预测提供了更深入的分子洞察力.
- 综合代谢学和蛋白质学的大规模研究很少.
研究的目的:
- 评估是否添加代谢学和/或蛋白学数据可以改善17种常见疾病的风险预测.
- 为了识别与疾病相关的关键omics特征.
主要方法:
- 利用来自23776名参与者的英国生物库数据.
- 分析了159种基于NMR的代谢物和2923种基于奥林克亲和力的蛋白质.
- 采用考克斯的比例危险模型和哈雷尔的C指数进行预测评估.
主要成果:
- 欧米克斯数据显著改善了所有17种疾病的风险预测 (p < 2E-4).
- 仅使用蛋白质组学模型的表现优于仅使用代谢组学模型的14种疾病.
- 确定了关键生物标志物,包括前列腺癌的KLK3 (PSA) 和白内障的CRYBB2.
结论:
- 整合蛋白质组学和代谢组学可以大大提高疾病风险预测,超越临床因素.
- 蛋白质组学在改善个人风险预测方面显示出显著的临床实用性.
- 这些发现为疾病机制和潜在的治疗点提供了分子洞察力.
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