全疾患段階にわたるライム病診断を改善するための多重単層イムノアッセイ

Lyme disease is the most common vector-borne infection in North America and Europe, yet current two-tier serologic testing shows poor sensitivity during early infection when antibiotic treatment is most effective. We developed and validated a single-tier multiplex immunoassay that combines ten Borrelia burgdorferi antigens with machine-learning classification to detect antibody responses across all disease stages. A total of 364 cases were obtained, which included well characterized prospective blood samples obtained during early infection. In a cohort of samples from the Johns Hopkins University SLICE studies, the classifier identified all 30 early Lyme disease cases and their 1-3-month post-treatment follow-up samples. To assess generalizability, an independent EU laboratory synthesized the antigens de novo and a distinct 259-person cohort was evaluated on newly built assays. The classifier achieved an AUC of 0.98 in both cohorts. This single-test platform delivers quantitative serologic classification using standard clinical laboratory instrumentation, addressing critical gaps in Lyme disease diagnosis.