Laboratory of Bioelectrochemistry and Spectroscopy, UMR 7140, University of Strasbourg, CNRS, 4 Rue Blaise Pascal, 67000 Strasbourg, France.
Biopathology of Myelin, Neuroprotection and Therapeutic Strategies, INSERM U1119, Federation of Translational Medicine of Strasbourg, Université of Strasbourg. 1, Rue Eugène Boeckel, 67000 Strasbourg, France.
Int J Mol Sci. 2022 Mar 3;23(5):2791. doi: 10.3390/ijms23052791.
Neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) are both autoimmune inflammatory and demyelinating diseases of the central nervous system. NMOSD is a highly disabling disease and rapid introduction of the appropriate treatment at the acute phase is crucial to prevent sequelae. Specific criteria were established in 2015 and provide keys to distinguish NMOSD and MS. One of the most reliable criteria for NMOSD diagnosis is detection in patient's serum of an antibody that attacks the water channel aquaporin-4 (AQP-4). Another target in NMOSD is myelin oligodendrocyte glycoprotein (MOG), delineating a new spectrum of diseases called MOG-associated diseases. Lastly, patients with NMOSD can be negative for both AQP-4 and MOG antibodies. At disease onset, NMOSD symptoms are very similar to MS symptoms from a clinical and radiological perspective. Thus, at first episode, given the urgency of starting the anti-inflammatory treatment, there is an unmet need to differentiate NMOSD subtypes from MS. Here, we used Fourier transform infrared spectroscopy in combination with a machine learning algorithm with the aim of distinguishing the infrared signatures of sera of a first episode of NMOSD from those of a first episode of relapsing-remitting MS, as well as from those of healthy subjects and patients with chronic inflammatory demyelinating polyneuropathy. Our results showed that NMOSD patients were distinguished from MS patients and healthy subjects with a sensitivity of 100% and a specificity of 100%. We also discuss the distinction between the different NMOSD serostatuses. The coupling of infrared spectroscopy of sera to machine learning is a promising cost-effective, rapid and reliable differential diagnosis tool capable of helping to gain valuable time in patients' treatment.
视神经脊髓炎谱系疾病（NMOSD）和多发性硬化症（MS）都是中枢神经系统自身免疫性炎症和脱髓鞘疾病。NMOSD 是一种高度致残性疾病，在急性期迅速引入适当的治疗对于预防后遗症至关重要。2015 年确立了特定标准，为区分 NMOSD 和 MS 提供了关键。NMOSD 诊断最可靠的标准之一是在患者血清中检测到攻击水通道蛋白 4（AQP-4）的抗体。NMOSD 的另一个靶标是髓鞘少突胶质细胞糖蛋白（MOG），定义了一种称为 MOG 相关疾病的新疾病谱。最后，NMOSD 患者可能同时对 AQP-4 和 MOG 抗体呈阴性。在疾病发作时，NMOSD 的症状在临床和影像学方面与 MS 非常相似。因此，在首次发作时，由于需要紧急开始抗炎治疗，因此需要从 MS 中区分 NMOSD 亚型。在这里，我们使用傅里叶变换红外光谱结合机器学习算法，旨在区分 NMOSD 首发发作和复发性缓解型 MS 首发发作患者血清的红外特征，以及健康受试者和慢性炎症性脱髓鞘性多发性神经病患者的血清。我们的结果表明，NMOSD 患者与 MS 患者和健康受试者的敏感性为 100%，特异性为 100%。我们还讨论了不同 NMOSD 血清状态之间的区别。血清的红外光谱与机器学习相结合是一种很有前途的具有成本效益、快速可靠的鉴别诊断工具，能够帮助患者治疗中争取宝贵的时间。
