ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimer’s Pathology in a Population-Based Cohort

Theory driven ATN classification and data driven biomarker phenotyping capture partially overlapping but largely distinct information. Modest concordance (ARI=0.119) reflects GFAP's contribution to shared structure, with most alignment arising from GFAP rather than from the three ATN biomarkers alone (ARI=0.03). The primary source of discordance remains the binary versus continuous representation of biomarker variation. Sensitivity analyses showed that k=4 provides finer biomarker resolution than k=3, and that Cluster 4 represents a small but reproducible biomarker defined subgroup. Both approaches predicted cognitive decline with modest effect sizes (R²=1.9-2.4%), consistent with population based studies. Integrating theory driven and data driven frameworks may support a more comprehensive characterization of AD related pathology in population research.