A large-scale multi-centre study characterising atrophy heterogeneity in Alzheimer’s disease
Authors: Venkatraghavan Vikram , Archetti Damiano , Bourgeat Pierrick , Jiang Chenyang , ten Kate Mara , van Loenhoud Anna C. , Ossenkoppele Rik , Teunissen Charlotte E. , van de Giessen Elsmarieke , Pijnenburg Yolande A.L. , Frisoni Giovanni B. , Weiss Béla , Vidnyánszky Zoltán , Auer Tibor , Durrleman Stanley , Redolfi Alberto , Laws Simon M. , Maruff Paul ,   Australian Imaging Biomarkers and Lifestyle Study,   Alzheimer’s Disease Neuroimaging Initiative,   E-DADS Consortium, Oxtoby Neil P. , Altmann Andre , Alexander Daniel C. , van der Flier Wiesje M. , Barkhof Frederik , Tijms Betty M. 
Published: August 2024

Background

Previous studies reported on the existence of atrophy-based Alzheimer’s disease (AD) subtypes that associate with distinct clinical symptoms. However, the consistency of AD atrophy subtypes across approaches remains uncertain. This large-scale study aims to assess subtype concordance in individuals using two methods of data-driven subtyping.

Methods

We includedn= 10,011 patients across the clinical spectrum from 10 AD cohorts across Europe, regional volumes using Freesurfer v7.1.1. To characterise atrophy heterogeneity in the AD continuum, we introduced a hybrid two-step approach called Snowphlake (Staging NeurOdegeneration With PHenotype informed progression timeLine of biomarKErs) to identify subtypes and sequence of atrophy-events within each subtype. We compared our results with SuStaIn (Subtype and Stage Inference) which jointly estimates both, and was trained and validated similarly. The training dataset included Aβ+ participants (n= 1,195), and a control group of Aβ-cognitively unimpaired participants (n= 1,692). We validated model staging within each subtype, in a held-out clinical-validation dataset (n= 6,362) comprising patients across the clinical spectrum irrespective of Aβ biomarker status and an independent external dataset (n= 762). Furthermore, we validated the clinical significance of the detected subtypes, in a subset of Aβ+ validation datasets withn= 1,796 in the held-out sample andn= 159 in the external dataset. Lastly, we performed concordance analysis to assess the consistency between the methods.

Results

In the AD dementia (AD-D) training data, Snowphlake identified four subtypes: diffuse cortical atrophy (21.1%,age67.5 ± 9.3), parieto-temporal atrophy (19.8%,age60.9 ± 7.9), frontal atrophy (24.8%,age67.6 ± 8.8) and subcortical atrophy (25.1%, 68.3 ± 8.2). The subtypes assigned in Aβ+ validation datasets were associated with alterations in specific cognitive domains (Cohen’sf:[0.15 - 0.33]), while staging correlated with Mini-Mental State Examination (MMSE) scores (R: [-0.51to- 0.28]) in the validation datasets. SuStaIn also identified four subtypes: typical (55.7%,age66.7 ± 7.8), limbic-predominant (24.2%,age72.2 ± 6.6), hippocampal-sparing (14.6%,age62.8 ± 6.9), and subcortical (0.8%,age68.2 ± 7.6). The subtypes assigned in Aβ+ validation datasets using SuStaIn were also associated with alterations in specific cognitive domains (Cohen’sf: [0.17 - 0.34]), while staging correlated with MMSE scores in the validation datasets (R: [-0.54to- 0.26]). However, we observed low concordance between Snowphlake and SuStaIn, with 39.7% of AD-D patients consistently grouped in concordant subtypes by both the methods.

Conclusion

In this multi-cohort study, both Snowphlake and SuStaIn identified four subtypes that were associated with different symptom profiles and atrophy-severity measures that were associated with global cognition. The low concordance between Snowphlake and SuStaIn suggests that heterogeneity may rather be a spectrum than discretised by subtypes.