Suh Erica H, Lee Garam, Jung Sang-Hyuk, Wen Zixuan, Bao Jingxuan, Nho Kwangsik, Huang Heng, Davatzikos Christos, Saykin Andrew J, Thompson Paul M, Shen Li, Kim Dokyoon
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
Front Aging Neurosci. 2023 Oct 26;15:1281748. doi: 10.3389/fnagi.2023.1281748. eCollection 2023.
Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors.
Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort ( = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores.
adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature.
Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.
使用多基因风险评分（PRS）将阿尔茨海默病（AD）患者分层为风险亚组，为临床试验和疾病修饰疗法的开发带来了新机遇。然而，AD的异质性本质继续给PRS在临床广泛应用带来重大挑战。PRS在风险预测方面仍无法证明具有足够的准确性，特别是对于轻度认知障碍（MCI）患者，并且无法对导致疾病风险的特定基因或单核苷酸多态性（SNP）进行可行的解读。我们提出了adORS，一种用于AD的新型寡基因风险评分，通过使用优化的相关遗传风险因素列表来更好地预测疾病风险。
利用来自阿尔茨海默病神经影像倡议（ADNI）队列（n = 1545）的全基因组测序数据，我们选择了20个与氟代脱氧葡萄糖正电子发射断层扫描（FDG-PET）和淀粉样蛋白PET（AV45-PET）表现出最强相关性的基因，AV45-PET是公认的可检测AD患者大脑功能变化的神经影像生物标志物。将这一基因子集纳入adORS，以与PRS相比，评估其在认知正常（CN）与AD分类及MCI转化预测中的预测准确性、ADNI队列的风险分层以及评分中所含遗传信息的可解释性。
在CN与AD分类及MCI转化预测方面，adORS的曲线下面积（AUC）评分均优于PRS。该寡基因模型还优化了基于风险的分层，即使在没有载脂蛋白E（APOE）辅助的情况下，因此与PRS相比反映了ADNI队列的真实患病率。解读分析表明，adORS中包含的基因，如活化转录因子6（ATF6）、EF手型结构域蛋白11（EFCAB11）、ING5蛋白、盐诱导激酶3（SIK3）和膜辅蛋白（CD46），在类似的神经退行性疾病中已被观察到和/或得到AD相关文献的支持。
与传统的PRS相比，在临床研究或临床环境中，adORS可能被证明是将患者区分为AD高遗传风险或低遗传风险的更合适选择。此外，解读特定遗传信息的能力使关注点能够从基于给定人群的一般相对风险转移到adORS可为个体提供的信息上，从而使AD个性化治疗成为可能。
