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An interesting and potentially useful approach to biomarker identification was recently reported by Reddy et al. in the journal Cell (1). Using a combinatorial library of synthetic “shape” molecules, they screened for ligands that bind antibodies in the serum of individuals with the target condition but that do not bind antibodies in the serum of individuals without the target condition. When this approach was investigated with 2 mice immunized with myelin oligodendrocyte glycoprotein (MOG)3 (which then developed a syndrome resembling human multiple sclerosis) and 2 mice not immunized with MOG, the investigators identified 3 peptoids that subsequently discriminated perfectly between new sets of mice—7 immunized with MOG and 7 not immunized with MOG. This result was an elegant and convincing proof-of-principle experiment.The authors then used the same protocol to determine if the approach “is capable of identifying potentially useful diagnostic antibody–peptoid pairs for a human disease state.” They compared the antibody responses to 15 000 peptoids of 6 patients with Alzheimer disease (AD) to the antibody responses of 6 age-matched nondemented control individuals and 6 patients with Parkinson disease. The authors report sensitivities ≥93.7%, specificities ≥93.7%, and areas under the ROC curve of 0.99 for the 3 most discriminatory peptoids when those peptoids were tested with serum samples from 16 different patients with AD and 16 nondemented control individuals.The purpose of this Perspective is to highlight the strengths of this study and, by contrast, the weaknesses of the study design used to develop and evaluate the biomarkers for AD. To the authors' credit, some of these weaknesses were acknowledged in their Cell report. Weak study designs are pervasive in the field of biomarker identification, however, and this flaw may be partly responsible for the slow pace of real progress in the development of clinically useful biomarkers. A …

Improving Biomarker Identification with Better Designs and Reporting