Eke Chima S, Sakr Fatemah, Jammeh E, Zhao Peng, Ifeachor E
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5523-5526. doi: 10.1109/EMBC44109.2020.9175158.
Early detection of Alzheimer's disease (AD) is of vital importance in the development of disease-modifying therapies. This necessitates the use of early pathological indicators of the disease such as amyloid abnormality to identify individuals at early disease stages where intervention is likely to be most effective. Recent evidence suggests that cerebrospinal fluid (CSF) amyloid β (Aβ) level may indicate AD risk earlier compared to amyloid positron emission tomography (PET). However, the method of collecting CSF is invasive. Blood-based biomarkers indicative of CSF Aβ status may remedy this limitation as blood collection is minimally invasive and inexpensive. In this study, we show that APOE4 genotype and blood markers comprising EOT3, APOC1, CGA, and Aβ robustly predict CSF Aβ with high classification performance (0.84 AUC, 0.82 sensitivity, 0.62 specificity, 0.81 PPV and 0.64 NPV) using machine learning approach. Due to the method employed in the biomarker search, the identified biomarker signature maintained high performance in more than a single machine learning algorithm, indicating potential to generalize well. A minimally invasive and cost-effective solution to detecting amyloid abnormality such as proposed in this study may be used as a first step in a multi-stage diagnostic workup to facilitate enrichment of clinical trials and population-based screening.
阿尔茨海默病（AD）的早期检测对于疾病修饰疗法的发展至关重要。这就需要使用该疾病的早期病理指标，如淀粉样蛋白异常，来识别处于疾病早期阶段的个体，在这些阶段进行干预可能最为有效。最近的证据表明，与淀粉样蛋白正电子发射断层扫描（PET）相比，脑脊液（CSF）淀粉样蛋白β（Aβ）水平可能更早地指示AD风险。然而，采集脑脊液的方法具有侵入性。指示脑脊液Aβ状态的血液生物标志物可能会弥补这一局限性，因为采血的侵入性极小且成本低廉。在本研究中，我们表明，使用机器学习方法，APOE4基因型以及包含EOT3、APOC1、CGA和Aβ的血液标志物能够以较高的分类性能（曲线下面积0.84、灵敏度0.82、特异性0.62、阳性预测值0.81和阴性预测值0.64）可靠地预测脑脊液Aβ。由于在生物标志物搜索中采用的方法，所识别的生物标志物特征在不止一种机器学习算法中都保持了高性能，表明其具有良好的泛化潜力。本研究中提出的一种用于检测淀粉样蛋白异常的微创且经济高效的解决方案，可作为多阶段诊断检查的第一步，以促进临床试验和基于人群的筛查的富集。
