Kaishima Misato, Ito Junichi, Takahashi Kentaro, Tai Kenji, Kuromitsu Junro, Bun Shogyoku, Ito Daisuke
Eisai-Keio Innovation Laboratory for Dementia (EKID), Deep Human Biology Learning (DHBL), Eisai Co., Ltd., Tokyo, Japan.
Human Biology Integration Foundation, DHBL, Eisai Co., Ltd., Tsukuba, Japan.
Alzheimers Res Ther. 2025 May 22;17(1):112. doi: 10.1186/s13195-025-01754-2.
The use of polygenic risk scores (PRS) for predicting disease risk in Japanese populations, particularly for dementia and related phenotypes, remains markedly unexplored. The aim of this study was to bridge this gap by developing a novel PRS model designed to predict amyloid-β (Aβ) deposition utilizing positron emission tomography (PET) imaging data from a Japanese cohort.
Using the polygenic risk score-continuous shrinkage (PRS-CS) algorithm, we calculated PRS based on significant single nucleotide polymorphisms (SNPs) associated with Alzheimer's disease (AD) in this population. We applied a PRS calculation approach informed by Japanese genome-wide association studies (GWAS) summary statistics into a Japanese dementia cohort from Keio University.
Our findings revealed that a p-value threshold of pT < 0.1 optimally enhanced the predictive capability of the Japanese Aβ PET positivity risk model. Moreover, we demonstrated that distinguishing between the counts of APOE2 and APOE4 alleles in our calculations significantly elevated model performance, achieving an area under the curve (AUC) of 0.759. Remarkably, this predictive accuracy remained robust even when the pT was adjusted to be < 1.0 × 10, maintaining an AUC of 0.735. This study validated the efficacy of the model in identifying individuals with a increased risk of amyloid pathology.
These findings highlight the potential of PRS as a noninvasive tool for early detection and risk stratification of AD, which could lead to enhanced clinical applications and interventions.
在日本人群中，使用多基因风险评分（PRS）预测疾病风险，尤其是痴呆症及相关表型的风险，仍明显未得到充分探索。本研究的目的是通过开发一种新型PRS模型来填补这一空白，该模型旨在利用来自日本队列的正电子发射断层扫描（PET）成像数据预测淀粉样β（Aβ）沉积。
我们使用多基因风险评分-连续收缩（PRS-CS）算法，基于该人群中与阿尔茨海默病（AD）相关的显著单核苷酸多态性（SNP）计算PRS。我们将一种由日本全基因组关联研究（GWAS）汇总统计数据得出的PRS计算方法应用于庆应义塾大学的一个日本痴呆症队列。
我们的研究结果表明，p值阈值pT < 0.1能最佳地增强日本Aβ PET阳性风险模型的预测能力。此外，我们证明在计算中区分APOE2和APOE4等位基因的数量可显著提高模型性能，曲线下面积（AUC）达到0.759。值得注意的是，即使将pT调整为 < 1.0×10，这种预测准确性仍然很强，AUC维持在0.735。本研究验证了该模型在识别淀粉样蛋白病理风险增加个体方面的有效性。
这些发现突出了PRS作为AD早期检测和风险分层的非侵入性工具的潜力，这可能会带来更多的临床应用和干预措施。
