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在慢性病中最佳多因素干预的预测
Agnieszka Latosinska1, Ioanna K Mina1,2, Thi Minh Nghia Nguyen1
1Mosaiques Diagnostics GmbH, Rotenburger Straße 20, 30659, Hannover, Germany.
Journal of translational medicine
|August 22, 2025
概括
这项研究表明,在中预测尿的变化可以指导慢性病 (CKD) 的个性化干预. 这种方法有助于确定最佳的治疗组合来减缓CKD的进展.
科学领域:
- 肝脏病学
- 计算生物学
- 精准医学
背景情况:
- 慢性病是全球主要的健康负担,需要及早和有针对性的干预措施.
- 尿路分类器CKD273在早期病检测方面表现有前途.
- 这项研究探讨了 in silico 方法在慢性病中预测治疗反应的潜力.
研究的目的:
- 通过使用in silico方法研究药物诱导尿中的分子变化的可行性.
- 根据预测的治疗效果,为患有慢性病的个体患者选择干预措施.
- 评估CKD273在预测重大脏不良事件和模拟干预措施的影响方面的有效性.
主要方法:
- 在935名参与者的回顾性队列中验证了CKD273分类器,用于预测不良事件.
- 基于单个型的各种干预措施 (药物,饮食,运动) 的治疗效果的预测.
- 在多次干预后重新校准了CKD273得分和综合效应.
主要成果:
- 模拟干预显著降低了CKD273的中位数,这表明可以减缓CKD的进展.
- 最有益的干预措施并不总是所有可用的治疗方法的结合.
- 基线CKD273得分较高的患者需要更复杂的干预组合来最大限度地降低得分.
结论:
- 治疗干预对CKD进展的预测是可行的.
- 这种方法可以为个人慢性病患者确定最佳的治疗组合,从而支持精准医学.
- 计划进行前性研究,以验证in silico引导的干预策略及其临床益处.
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