赫尔辛基大学Andrea Ganna小组报道了结合遗传数据可以改善靶试验模拟，并为随机对照试验设计中多基因评分的使用提供信息。2025年6月18日出版的《自然—遗传学》发表了这项成果。

使用FinnGen (n = 425,483)，研究团队模拟了他们的主要心脏代谢随机对照试验，并显示了试验组之间多基因评分（PGS）差异的减少如何跟踪研究设计的改进。仿真研究表明，单靠PGS不能完全适应未测量的混杂。相反，孟德尔随机化分析可以以主题来检测可能的混杂因素。最后，试验模拟提供了一个平台，以评估和完善基因富集策略的PGS实施。通过比较普通人群和模拟试验队列中PGS与试验结果的关联，研究组强调需要在试验相关人群中验证预后强化方法。这些结果突出了结合遗传信息来优化临床试验设计的潜力。

据介绍，随机对照试验（RCTs）仍然是评估医疗干预措施的黄金标准，但伦理、实际和财务限制往往需要依赖观察数据和试验模拟。本研究探讨如何整合基因数据，以提高模拟和传统的试验设计。

附：英文原文

Title: Incorporating genetic data improves target trial emulations and informs the use of polygenic scores in randomized controlled trial design

Author: German, Jakob, Yang, Zhiyu, Urbut, Sarah, Vartiainen, Pekka, Natarajan, Pradeep, Pattorno, Elisabetta, Kutalik, Zoltan, Philippakis, Anthony, Ganna, Andrea

Issue&Volume: 2025-06-18

Abstract: Randomized controlled trials (RCTs) remain the gold standard for evaluating medical interventions, yet ethical, practical and financial constraints often necessitate reliance on observational data and trial emulations. This study explores how integrating genetic data can enhance both emulated and traditional trial designs. Using FinnGen (n=425,483), we emulated four major cardiometabolic RCTs and showed how reduced differences in polygenic scores (PGS) between trial arms track improvement in study design. Simulation studies reveal that PGS alone cannot fully adjust for unmeasured confounding. Instead, Mendelian randomization analyses can be used to detect likely confounders. Finally, trial emulations provide a platform to assess and refine PGS implementation for genetic enrichment strategies. By comparing associations of PGS with trial outcomes in the general population and emulated trial cohorts, we highlight the need to validate prognostic enrichment approaches in trial-relevant populations. These results highlight the growing potential of incorporating genetic information to optimize clinical trial design.

DOI: 10.1038/s41588-025-02229-8

Source: https://www.nature.com/articles/s41588-025-02229-8