ビッグデータ解析でアルツハイマー病の克服を目指す(Big Data Analysis Powers the Fight Against Alzheimer’s)

アルツハイマー病の進行の説明と重症度の予測に役立つ新たな研究成果 New research helps explain the progression of Alzheimer’s disease and predicts its severity

2022-11-30 パシフィック・ノースウェスト国立研究所(PNNL)

<関連情報>

• https://www.pnnl.gov/news-media/big-data-analysis-powers-fight-against-alzheimers
• https://www.science.org/doi/10.1126/sciadv.abo6764

アルツハイマー病の進行と異質性に関するエピゲノム、トランスクリプトーム、プロテオーム、メタボロームによる統一的な分類法 Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity

Yasser Iturria-Medina,Quadri Adewale,Ahmed F. Khan,Simon Ducharme,Pedro Rosa-Neto,Kieran O’Donnell,Vladislav A. Petyuk,Serge Gauthie,Philip L. De Jage ,John Breitner,David A. Bennett
Science Advances  Published:18 Nov 2022
DOI: 10.1126/sciadv.abo6764

Abstract

Alzheimer’s disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data (N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.