Kojima Naoki, Koido Masaru, He Yunye, Shimmori Yuka, Hachiya Tsuyoshi, Debette Stéphanie, Kamatani Yoichiro
Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Japan (N.K., M.K., Y.H., Y.S., T.H., Y.K.).
Bordeaux Population Health Research Center, University of Bordeaux, Inserm, France (S.D.).
Stroke. 2025 Jun;56(6):1483-1491. doi: 10.1161/STROKEAHA.124.047786. Epub 2025 Mar 26.
Recently, various polygenic risk score (PRS)-based methods were developed to improve stroke prediction. However, current PRSs (including cross-ancestry PRS) poorly predict recurrent stroke. Here, we aimed to determine whether the best PRS for Japanese individuals can also predict stroke recurrence in this population by extensively comparing the methods and maximizing the predictive performance for stroke onset.
We used data from the disease-oriented BBJ1 (BioBank Japan first cohort; recruited between 2003 and 2007, n=179 938) to derive and optimize the PRSs using a 10-fold cross-validation. We integrated the optimized PRSs for multiple traits, such as vascular risk factors and stroke subtypes to generate a single PRS using the meta-scoring approach (metaGRS). We used an independent BBJ2 (BBJ second cohort; recruited between 2012 and 2017, n=41 929) as a test sample to evaluate the association of the metaGRS with stroke and recurrent stroke. In addition, we analyzed its association stratified by risk factors. We administered 3 distinct tests to consider the potential index event bias.
We analyzed recurrent stroke cases (n=174) and nonrecurrent stroke controls (n=1153) among subjects within the BBJ2. After adjusting for known risk factors, metaGRS was associated with stroke recurrence (adjusted odds ratio per SD, 1.18 [95% CI, 1.00-1.39]; =0.044), although no significant correlation was observed with the published PRSs. The outcomes derived from these examinations did not provide any significant indication of the influence of index event bias. The high metaGRS group without a history of hypertension had a higher risk of stroke recurrence than that of the low metaGRS group (adjusted odds ratio, 2.24 [95% CI, 1.07-4.66]; =0.032). There was no association at all in the hypertension group (adjusted odds ratio, 1.21 [95% CI, 0.69-2.13]; =0.50).
The metaGRS developed in a Japanese cohort predicted stroke recurrence in an independent cohort of patients. In particular, it predicted an increased risk of recurrence among stroke patients without hypertension. These findings provide clues for additional genetic risk stratification and help in developing personalized strategies for stroke recurrence prevention.
最近，人们开发了各种基于多基因风险评分（PRS）的方法来改善中风预测。然而，目前的PRS（包括跨血统PRS）对复发性中风的预测效果不佳。在此，我们旨在通过广泛比较各种方法并最大化中风发病的预测性能，来确定针对日本个体的最佳PRS是否也能预测该人群中的中风复发情况。
我们使用了以疾病为导向的BBJ1（日本生物银行第一队列；2003年至2007年招募，n = 179938）的数据，通过10倍交叉验证来推导和优化PRS。我们整合了针对多种性状（如血管危险因素和中风亚型）的优化PRS，使用元评分方法（metaGRS）生成单个PRS。我们使用独立的BBJ2（BBJ第二队列；2012年至2017年招募，n = 41929）作为测试样本，以评估metaGRS与中风和复发性中风的关联。此外，我们按危险因素进行分层分析其关联。我们进行了3种不同的测试以考虑潜在的索引事件偏差。
我们分析了BBJ2中受试者的复发性中风病例（n = 174）和非复发性中风对照（n = 1153）。在调整已知危险因素后，metaGRS与中风复发相关（每标准差调整后的优势比，1.18 [95% CI，1.00 - 1.39]；P = 0.044），尽管与已发表的PRS未观察到显著相关性。这些检查得出的结果未提供任何关于索引事件偏差影响的显著迹象。无高血压病史的高metaGRS组中风复发风险高于低metaGRS组（调整后的优势比，2.24 [95% CI，1.07 - 4.66]；P = 0.
