一个基于血蛋白质学的候选生物标志物面板,可以预测肌缩性侧面硬化症
Ruth Chia1, Ruin Moaddel2, Justin Y Kwan3
1Neuromuscular Diseases Research Section, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Nature medicine
|August 19, 2025
概括
研究人员确定了33种血蛋白作为肌缩性侧面硬化症 (ALS) 的潜在生物标志物. 机器学习模型准确诊断了ALS,揭示了这种神经退行性疾病的疾病过程在症状出现前几年就开始了.
科学领域:
- 神经科学是一个神经科学.
- 蛋白质组学是指蛋白质组学.
- 生物标志物发现发现
背景情况:
- 肌缩侧面硬化症 (ALS) 是一种致命的神经退行性疾病,没有明确的早期诊断生物标志物.
- 早期发现ALS对于及时干预和疾病管理至关重要.
研究的目的:
- 调查等离子体蛋白质组学,以识别肌缩性侧面硬化症 (ALS) 的可靠生物标志物.
- 开发一种机器学习模型,使用血蛋白质配置文件进行准确的ALS诊断.
- 在临床表现之前探索ALS病变发生的分子基础和时间表.
主要方法:
- 在一个横截面研究中使用了Olink Explore 3072平台进行高通量等离子体蛋白质组分析.
- 与患有ALS的患者 (n=183) 和健康对照组 (n=309) 的蛋白质丰度进行比较,在独立队列中进行验证 (n=48 ALS,n=75对照组).
- 应用机器学习算法对ALS诊断的验证蛋白质组签名进行了验证.
主要成果:
- 与对照人群相比,在ALS患者中鉴定出33种不同丰富的血蛋白.
- 开发了一种具有高精度 (AUC=98.3%) 的诊断模型来检测ALS.
- 对前症状样本的分析表明,影响骨肌肉,神经和能量代谢的疾病过程在临床发病前几年发生.
结论:
- 血蛋白质组学为开发可靠的肌缩性侧面硬化症生物标志物提供了一个有前途的途径.
- 基于机器学习的蛋白质组签名可以准确诊断ALS.
- 这项研究为ALS的前期阶段提供了分子洞察力,表明了长期的临床前疾病轨迹.
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