Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's disease.

IF 7.9 1区医学 Q1 CLINICAL NEUROLOGY

Alzheimer's Research & Therapy Pub Date : 2025-01-03 DOI:10.1186/s13195-024-01651-0

Yi-Long Huang, Tsung-Hsien Tsai, Zhao-Qing Shen, Yun-Hsuan Chan, Chih-Wei Tu, Chien-Yi Tung, Pei-Ning Wang, Ting-Fen Tsai

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引用次数: 0

Abstract

Background: Effective treatment for Alzheimer's disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention.

Methods: Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49). Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models.

Results: We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI.

Conclusions: Blood-based transcriptomic biomarker signatures show great utility in identifying high-risk MCI patients, with mitochondrial processes emerging as a crucial contributor to AD progression.

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从轻度认知障碍到阿尔茨海默病快速进展的转录组学预测因子。

背景：阿尔茨海默病（AD）的有效治疗仍然是一个未满足的需求。因此，识别轻度认知障碍（MCI）患者进展为AD的高风险对于早期干预至关重要。方法：采用纵向研究队列进行基于血液的转录组学分析，比较进展型MCI （P-MCI, n = 28）、稳定期MCI （S-MCI, n = 39）和AD患者（n = 49）。采用统计DESeq2分析和机器学习方法鉴定差异表达基因（DEGs）并建立预测模型。结果：我们发现区分P-MCI和S-MCI的DEGs有显著的性别差异。机器学习模型在区分P-MCI和S-MCI (AUC 0.93)， AD和S-MCI （AUC 0.94）以及AD和P-MCI （AUC 0.92）方面取得了很高的准确性。鉴定出8个基因标记来区分P-MCI和S-MCI。结论：基于血液的转录组生物标志物特征在识别高风险MCI患者中显示出很大的效用，线粒体过程是AD进展的关键因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer's Research & Therapy 医学-神经病学

CiteScore

13.10

自引率

3.30%

发文量

172

审稿时长

>12 weeks

期刊介绍： Alzheimer's Research & Therapy is an international peer-reviewed journal that focuses on translational research into Alzheimer's disease and other neurodegenerative diseases. It publishes open-access basic research, clinical trials, drug discovery and development studies, and epidemiologic studies. The journal also includes reviews, viewpoints, commentaries, debates, and reports. All articles published in Alzheimer's Research & Therapy are included in several reputable databases such as CAS, Current contents, DOAJ, Embase, Journal Citation Reports/Science Edition, MEDLINE, PubMed, PubMed Central, Science Citation Index Expanded (Web of Science) and Scopus.