Claudia R Molins1, Laura V Ashton2, Gary P Wormser3, Ann M Hess4, Mark J Delorey1, Sebabrata Mahapatra2, Martin E Schriefer1, John T Belisle2. 1. Division of Vector-Borne Diseases, Centers for Disease Control and Prevention. 2. Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins. 3. Department of Medicine, Division of Infectious Diseases, New York Medical College, Valhalla, New York. 4. Department of Statistics, Colorado State University, Fort Collins.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: Early Lyme diseasepatients 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. METHODS: 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 diseasepatients. 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. RESULTS: Metabolic biosignature development selected 95 molecular features that distinguished early Lyme diseasepatients from healthy controls. Statistical modeling reduced the biosignature to 44 molecular features, and correctly classified early Lyme diseasepatients 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 diseasepatients. CONCLUSIONS: 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.
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