On the complexity of biomarker-driven diagnoses of Alzheimer's disease.

Updated criteria pertaining to the diagnosis of Alzheimer's disease (AD) have sparked debate over the reliance on biomarkers-particularly amyloid-β and phosphorylated tau. While biomarkers promise earlier detection and standardized criteria, the potential extension and interpretation of their use in asymptomatic individuals remains controversial. Many individuals with abnormal biomarker profiles never experience cognitive decline, raising concerns about overdiagnosis, unintended negative psychosocial consequences and the blurring line between risk and definitive diagnosis. We, and others, argue that biomarker positivity should be reframed not as a definitive diagnosis but rather as an indicator of elevated risk, particularly in the absence of cognitive symptoms. Doing so better aligns with current evidence, preserves clarity in diagnosis, and avoids unintended psychosocial consequences. Crucially, the role of cognitive reserve-influenced by education, other life experiences and structural inequities-must be considered, particularly among racially and ethnically diverse populations historically underrepresented in AD research. Biomarker thresholds (as well as neuropsychological tools) derived from predominantly non-Hispanic white cohorts may not generalize across groups, risking misclassification and inequity. As the field moves towards precision medicine and AI-driven risk models, inclusive data and culturally valid frameworks are essential. Ultimately, embracing a risk-based, multifactorial approach respects the complexity of AD and promotes equitable care. This perspective calls for interdisciplinary collaboration to refine diagnostic strategies that are scientifically grounded, socially conscious and responsive to the lived realities of diverse populations. Only then can we responsibly integrate biomarkers into practice without sacrificing nuance.