title: Development of a metabolic biosignature for detection of early Lyme disease.
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.
authors.affiliation: Division of Vector-Borne Diseases, Centers for Disease Control and Prevention.
authors.forename: Claudia R
authors.initials: CR
authors.lastname: Molins
authors.affiliation: Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins.
authors.forename: Laura V
authors.initials: LV
authors.lastname: Ashton
authors.affiliation: Department of Medicine, Division of Infectious Diseases, New York Medical College, Valhalla, New York.
authors.forename: Gary P
authors.initials: GP
authors.lastname: Wormser
authors.affiliation: Department of Statistics, Colorado State University, Fort Collins.
authors.forename: Ann M
authors.initials: AM
authors.lastname: Hess
authors.affiliation: Division of Vector-Borne Diseases, Centers for Disease Control and Prevention.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Delorey
authors.affiliation: Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins.
authors.forename: Sebabrata
authors.initials: S
authors.lastname: Mahapatra
authors.affiliation: Division of Vector-Borne Diseases, Centers for Disease Control and Prevention.
authors.forename: Martin E
authors.initials: ME
authors.lastname: Schriefer
authors.affiliation: Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins.
authors.forename: John T
authors.initials: JT
authors.lastname: Belisle
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000097:blood* / Q000378:metabolism; D025065:Borrelia burgdorferi; D002648:Child; D002853:Chromatography, Liquid; D005260:Female; D006801:Humans; D008193:Lyme Disease / Q000175:diagnosis* / Q000453:epidemiology / Q000378:metabolism*; D008297:Male; D013058:Mass Spectrometry; D055442:Metabolome / Q000502:physiology; D055432:Metabolomics; D008875:Middle Aged; D012372:ROC Curve; D012189:Retrospective Studies; D055815:Young Adult
chemical_list: D015415:Biomarkers
keywords: Borrelia burgdorferi; Lyme disease; diagnostics; infectious diseases; metabolomics