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論文
- タイトル
- タイトル（英）
- Predicting Alzheimer’s Trajectory: A Multi-PRS Machine Learning Approach for Early Diagnosis and Progression Forecasting
- 参照URL
- https://researchmap.jp/YuichiroMiyaoka/published_papers/44382863
- 著者
- 著者（英）
- Mashiat Mustaq,Naeem Ahmed,Sazan Mahbub,Clara Li,Yuichiro Miyaoka,Julia TCW,Shea Andrews,Md. Shamsuzzoha Bayzid
- 担当区分
- 概要
- 概要（英）
- Abstract Predicting the early onset of dementia due to Alzheimer’s Disease (AD) has major implications for timely clinical management and outcomes. Current diagnostic methods, which are reliant on invasive and costly procedures, underscore the need for scalable and innovative approaches. To date, considerable effort has been dedicated to developing machine learning (ML)-based approaches using different combinations of medical, demographic, cognitive, and clinical data, achieving varying levels of accuracy. However, they often lack the scalability required for large-scale screening and fail to identify underlying risk factors for AD progression. Polygenic risk scores (PRS) have shown promise in predicting disease risk from genetic data. Here, we aim to leverage ML techniques to develop a multi-PRS model that captures both genetic and non-genetic risk factors to diagnose and predict the progression of AD in different stages in older adults. We developed an automatic feature selection pipeline that identifies the relevant traits that predict AD. Leveraging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Religious Orders Study and Memory and Aging Project (ROSMAP), and the IEU OpenG-WAS Project, our study presents the first known end-to-end ML-based multi-PRS model for AD. This approach provides an automatic mechanism for harnessing genetic data for AD diagnosis and prognosis for comprehending the role of various traits in AD development and progression. Our method produced AUROC scores of 77% on ADNI and 72% on ROSMAP for predicting the diagnosis of AD, substantially surpassing the performance of the uni-variate PRS models. Our models also showed promise in predicting transitions between various cognitive stages. Moreover, the features identified by our automated feature selection pipeline are closely aligned with the widely recognized, potentially modifiable risk factors for AD.
- 出版者・発行元
- 出版者・発行元（英）
- Cold Spring Harbor Laboratory
- 誌名
- 誌名（英）
- medRxiv
- 巻
- 号
- 開始ページ
- 終了ページ
- 出版年月
- 2023年11月29日
- 査読の有無
- 招待の有無
- 掲載種別
- ISSN
- DOI URL
- https://doi.org/10.1101/2023.11.28.23299110
- 共同研究・競争的資金等の研究課題
研究者
宮岡 佑一郎
(ミヤオカ ユウイチロウ)