A Diagnostic Window Without a Diverse Cohort: Dual Diversity Risks in Emerging CTE Biomarker Research

Definitive diagnosis of chronic traumatic encephalopathy (CTE) requires postmortem neuropathology, but in vivo tau positron emission tomography (PET) is beginning to change what can be studied in living people. Tau-PET studies in former American football players with the Alzheimer’s disease tracers flortaucipir and [18F]MK-6240 have reported group-level differences from controls, and the first application of [18F]OXD-2314, a tracer designed for non-Alzheimer’s tau, to suspected CTE reported elevated signal at the gray–white matter junction in three retired collision sport athletes. These preliminary findings, available only as a conference abstract, are an early proof of mechanism, not a validated diagnostic signal. CTE research is not starting from zero, but dedicated artificial intelligence (AI) models and reporting conventions are emerging now against an uncertain reference standard: traumatic encephalopathy syndrome criteria had a positive predictive value of about 24% for neuropathologically confirmed CTE in one brain bank study. I apply my Dual Diversity framework, which treats population sampling bias and neurobiological heterogeneity suppression as compounding threats to validity, and propose a prospective design framework integrating population diversity, exposure heterogeneity, biological heterogeneity, and reference-standard uncertainty. It specifies minimum reporting fields, diagnostic-contrast cohorts, and evaluation requirements for heterogeneity-aware models. The proposals are conceptual and unvalidated.