How reproducible are data-driven subtypes of Alzheimer's disease atrophy?

BackgroundAlzheimer's disease (AD) exhibits substantial clinical and biological heterogeneity, complicating efforts in treatment and intervention development. While new computational methods offer insights into AD subtyping and disease staging, the reproducibility of these subtypes across datasets remains understudied, particularly concerning the robustness of subtype definitions when validated on diverse databases.ObjectiveThis study evaluates the robustness of the AD progression subtypes identified by the Subtype and Stage Inference (SuStaIn) algorithm on a larger and more diverse cohort.MethodsWe extracted T1-weighted MRI data for 5444 subjects from ANMerge, OASIS, and ADNI datasets, forming four independent cohorts. Each cohort was analyzed with SuStaIn under two conditions: one using the full cohort, including cognitively normal controls, and another excluding controls to test subtype robustness.ResultsResults confirm the three primary atrophy subtypes identified in earlier studies: Typical, Cortical, and Subcortical, as well as the emergence of rare and atypical AD variants such as posterior cortical atrophy. Notably, each subtype displayed varying robustness to the inclusion of controls, with certain subtypes, like the Subcortical subtype, more influenced by cohort composition.ConclusionsThis investigation underscores SuStaIn's reliability for defining stable AD subtypes and suggests its utility in clinical stratification for trials and diagnosis. However, our findings also highlight the need for improved dataset ethnic and demographic diversity, particularly in terms of ethnic representation, to enhance generalizability and support broader clinical application.