Molins Claudia R, Ashton Laura V, Wormser Gary P, Hess Ann M, Delorey Mark J, Mahapatra Sebabrata, Schriefer Martin E, Belisle John T
Division of Vector-Borne Diseases, Centers for Disease Control and Prevention.
Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins.
Clin Infect Dis. 2015 Jun 15;60(12):1767-75. doi: 10.1093/cid/civ185. Epub 2015 Mar 11.
Early Lyme disease patients often present to the clinic prior to developing a detectable antibody response to Borrelia burgdorferi, the etiologic agent. Thus, existing 2-tier serology-based assays yield low sensitivities (29%-40%) for early infection. The lack of an accurate laboratory test for early Lyme disease contributes to misconceptions about diagnosis and treatment, and underscores the need for new diagnostic approaches.
Retrospective serum samples from patients with early Lyme disease, other diseases, and healthy controls were analyzed for small molecule metabolites by liquid chromatography-mass spectrometry (LC-MS). A metabolomics data workflow was applied to select a biosignature for classifying early Lyme disease and non-Lyme disease patients. A statistical model of the biosignature was trained using the patients' LC-MS data, and subsequently applied as an experimental diagnostic tool with LC-MS data from additional patient sera. The accuracy of this method was compared with standard 2-tier serology.
Metabolic biosignature development selected 95 molecular features that distinguished early Lyme disease patients from healthy controls. Statistical modeling reduced the biosignature to 44 molecular features, and correctly classified early Lyme disease patients and healthy controls with a sensitivity of 88% (84%-95%), and a specificity of 95% (90%-100%). Importantly, the metabolic biosignature correctly classified 77%-95% of the of serology negative Lyme disease patients.
The data provide proof-of-concept that metabolic profiling for early Lyme disease can achieve significantly greater (P < .0001) diagnostic sensitivity than current 2-tier serology, while retaining high specificity.
早期莱姆病患者常在对病原体伯氏疏螺旋体产生可检测到的抗体反应之前就前往诊所就诊。因此，现有的基于2层血清学的检测方法对早期感染的敏感性较低（29%-40%）。缺乏针对早期莱姆病的准确实验室检测导致了对诊断和治疗的误解，并突出了对新诊断方法的需求。
通过液相色谱-质谱联用（LC-MS）分析来自早期莱姆病患者、其他疾病患者和健康对照的回顾性血清样本中的小分子代谢物。应用代谢组学数据工作流程选择用于区分早期莱姆病患者和非莱姆病患者的生物标志物。使用患者的LC-MS数据训练生物标志物的统计模型，随后将其作为实验诊断工具应用于来自其他患者血清的LC-MS数据。将该方法的准确性与标准的2层血清学进行比较。
代谢生物标志物开发选择了95个分子特征，这些特征可区分早期莱姆病患者和健康对照。统计建模将生物标志物减少到44个分子特征，并正确分类早期莱姆病患者和健康对照，敏感性为88%（84%-95%），特异性为95%（90%-100%）。重要的是，代谢生物标志物正确分类了77%-95%的血清学阴性莱姆病患者。
数据提供了概念验证，即早期莱姆病的代谢谱分析可实现比当前2层血清学显著更高（P < .0001）的诊断敏感性，同时保持高特异性。
