Hackshaw Kevin V, Yao Siyu, Bao Haona, de Lamo Castellvi Silvia, Aziz Rija, Nuguri Shreya Madhav, Yu Lianbo, Osuna-Diaz Michelle M, Brode W Michael, Sebastian Katherine R, Giusti M Monica, Rodriguez-Saona Luis
Department of Internal Medicine, Division of Rheumatology, Dell Medical School, The University of Texas, 1601 Trinity St., Austin, TX 78712, USA.
Department of Food Science and Technology, The Ohio State University, Columbus, OH 43210, USA.
Biomedicines. 2023 Oct 5;11(10):2704. doi: 10.3390/biomedicines11102704.
Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30-40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM. Blood samples were obtained from LC ( = 50) and FM ( = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.
新型冠状病毒感染的急性后遗症（PASC或长新冠）的特征是在最初的新冠疾病后症状持续存在，这通常会使人衰弱。在高达30%-40%的感染者中可见。长新冠（LC）患者患有自主神经功能障碍、不适、疲劳和疼痛等多种其他症状。纤维肌痛（FM）是一种慢性肌肉骨骼疼痛障碍，常导致功能残疾和严重的生活质量受损。LC和FM有几个共同的临床特征，包括疼痛，这常常使它们难以区分。本研究的目的是开发一种使用便携式傅里叶变换中红外（FT-MIR）光谱技术的代谢指纹图谱方法，以诊断临床相似的LC和FM。从LC（n = 50）和FM（n = 50）患者中采集血样，并储存在传统的血斑蛋白保存卡上。采用半透膜过滤方法提取血样，并使用便携式FT-MIR光谱仪收集光谱数据。通过对光谱数据的去卷积分析，基于统计学显著分析确定了在1565 cm处有一个独特的光谱标记，仅在FM患者中存在。这个红外波段与谷氨酸侧链的存在有关。使用1500至1700 cm的光谱区域创建的OPLS-DA算法能够将光谱准确分类到相应类别（Rcv > 0.96），准确率和特异性均为100%。这种高通量方法能够识别与LC和FM相关的独特代谢特征，从而区分这些病症并用于临床诊断，这对于指导未来的治疗方法至关重要。
