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

TL;DR

This study assesses the reproducibility of Alzheimer's disease subtypes identified by the SuStaIn algorithm across multiple datasets, confirming known subtypes and highlighting the importance of dataset diversity for clinical application.

Contribution

It demonstrates the robustness of AD subtypes identified by SuStaIn across diverse datasets and conditions, emphasizing the need for more diverse data for generalizability.

Findings

Confirmed three primary atrophy subtypes: Typical, Cortical, Subcortical

Identified rare variants like posterior cortical atrophy (PCA)

Subtype robustness varies with dataset composition

Abstract

Alzheimer's disease (AD) exhibits substantial clinical and biological heterogeneity, complicating efforts in treatment and intervention development. While new computational methods offer insights into AD progression, the reproducibility of these subtypes across datasets remains understudied, particularly concerning the robustness of subtype definitions when validated on diverse databases. This study evaluates the consistency of AD progression subtypes identified by the Subtype and Stage Inference (SuStaIn) algorithm using T1-weighted MRI data across 5,444 subjects from ANMerge, OASIS, and ADNI datasets, forming four independent cohorts. Each cohort was analyzed under two conditions: one using the full cohort, including cognitively normal controls, and another excluding controls to test subtype robustness. Results 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 (PCA). Notably, each subtype displayed varying robustness to the inclusion of controls, with certain subtypes, like Subcortical, more influenced by cohort composition. This 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 diversity, particularly in terms of ethnic representation, to enhance generalizability and support broader clinical application.