Neurogenomics Division, Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA.
Biomedical Ethics Program, Mayo Clinic, Scottsdale, AZ 85259, USA.
Cells. 2024 Jan 23;13(3):207. doi: 10.3390/cells13030207.
Alzheimer's disease (AD), due to its multifactorial nature and complex etiology, poses challenges for research, diagnosis, and treatment, and impacts millions worldwide. To address the need for minimally invasive, repeatable measures that aid in AD diagnosis and progression monitoring, studies leveraging RNAs associated with extracellular vesicles (EVs) in human biofluids have revealed AD-associated changes. However, the validation of AD biomarkers has suffered from the collection of samples from differing points in the disease time course or a lack of confirmed AD diagnoses. Here, we integrate clinical diagnosis and postmortem pathology data to form more accurate experimental groups and use small RNA sequencing to show that EVs from plasma can serve as a potential source of RNAs that reflect disease-related changes. Importantly, we demonstrated that these changes are identifiable in the EVs of preclinical patients, years before symptom manifestation, and that machine learning models based on differentially expressed RNAs can help predict disease conversion or progression. This research offers critical insight into early disease biomarkers and underscores the significance of accounting for disease progression and pathology in human AD studies.
阿尔茨海默病（AD）由于其多因素性质和复杂的病因，给研究、诊断和治疗带来了挑战，影响了全球数百万人。为了解决对微创、可重复的措施的需求，这些措施有助于 AD 的诊断和进展监测，利用与人类生物体液中外泌体（EVs）相关的 RNA 的研究揭示了与 AD 相关的变化。然而，AD 生物标志物的验证受到了从疾病时间过程中的不同点收集样本的限制，或者缺乏经过证实的 AD 诊断。在这里，我们整合了临床诊断和死后病理数据，以形成更准确的实验组，并使用小 RNA 测序表明，来自血浆的 EVs 可以作为反映疾病相关变化的 RNA 的潜在来源。重要的是，我们证明了这些变化在临床前患者的 EVs 中是可识别的，在症状出现前数年就可以识别，并且基于差异表达 RNA 的机器学习模型可以帮助预测疾病转化或进展。这项研究为早期疾病生物标志物提供了重要的见解，并强调了在人类 AD 研究中考虑疾病进展和病理的重要性。
