Recurrent stroke prediction by applying a stroke polygenic risk score in the Japanese population

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 BioBank Japan (BBJ) 1^st cohort (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 BBJ 2^nd cohort (n=41,929) as a test sample to evaluate the association of the metaGRS with stroke and recurrent stroke.

We analyzed recurrent stroke cases (n=174) and non-recurrent stroke controls (n=1,153) among subjects within the BBJ 2^nd cohort. After adjusting for known risk factors, metaGRS was associated with stroke recurrence (adjusted OR per SD 1.18 [95% CI: 1.00-1.39, p=0.044]), although no significant correlation was observed with the published PRSs. We administered three distinct tests to consider the potential index event bias; however, 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 OR 2.24 [95% CI: 1.07-4.66, p=0.032]). However, this association was weak in the hypertension group (adjusted OR 1.21 [95% CI: 0.69-2.13, p=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.