A large-scale multi-centre study characterising atrophy heterogeneity in Alzheimer's disease
(2025) In NeuroImage 318.- Abstract
Previous studies identified atrophy-based Alzheimer's disease(AD) subtypes linked to distinct clinical symptoms, but their consistency across subtyping approaches remains unclear. This large-scale study evaluates subtype concordance using two data-driven approaches. In this work, we analyzed data from n=10,011 patients across 10 AD cohorts spanning Europe, the US, and Australia, extracting regional volumes using Freesurfer. To characterize atrophy heterogeneity in the AD continuum, we developed a two-step approach, Snowphlake (Staging NeurOdegeneration With PHenotype informed progression timeLine of biomarKErs), to identify subtypes and atrophy-event sequences within each subtype. Results were compared with SuStaIn (Subtype and Stage... (More)
Previous studies identified atrophy-based Alzheimer's disease(AD) subtypes linked to distinct clinical symptoms, but their consistency across subtyping approaches remains unclear. This large-scale study evaluates subtype concordance using two data-driven approaches. In this work, we analyzed data from n=10,011 patients across 10 AD cohorts spanning Europe, the US, and Australia, extracting regional volumes using Freesurfer. To characterize atrophy heterogeneity in the AD continuum, we developed a two-step approach, Snowphlake (Staging NeurOdegeneration With PHenotype informed progression timeLine of biomarKErs), to identify subtypes and atrophy-event sequences within each subtype. Results were compared with SuStaIn (Subtype and Stage Inference), which jointly estimates subtypes and staging, using similar training and validation. Training included Aβ+ participants (n=1,195) and Aβ− cognitively unimpaired controls (n=1,692). We validated model-staging in a held-out clinical dataset (n=6,362) and an independent dataset (n=762), and assessed clinical significance in Aβ+ subsets(n=1,796 held-out; n=159 external). Concordance analysis evaluated consistency between methods. In the AD dementia(AD-D) training data, both Snowphlake and SuStaIn identified four subtypes. In the validation datasets, staging with both methods correlated with Mini-Mental State Examination(MMSE) scores. The Snowphlake subtypes assigned in Aβ+ validation datasets were associated with alterations in specific cognitive domains(Cohen's f: [0.15−0.33]). Similarly, the SuStaIn subtypes were also associated specific cognitive domains(Cohen's f:[0.17−0.34]). However, we observed low concordance between Snowphlake and SuStaIn, with 39.7% of AD-D patients grouped in concordant subtypes by both methods. In conclusion, Snowphlake and SuStaIn identified four atrophy-based subtypes that linked to distinct symptom profiles. While this highlights that the neuro-anatomically defined subtypes also meaningfully associate with different cognitive impairments at a group level, the low concordance between methods suggests that future research is needed to better understand the biological and methodological factors contributing to the observed variability.
(Less)
- author
- author collaboration
- organization
- publishing date
- 2025-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Alzheimer's disease, Data-driven, Heterogeneity, MRI, Subtypes
- in
- NeuroImage
- volume
- 318
- article number
- 121381
- publisher
- Elsevier
- external identifiers
-
- scopus:105011514007
- pmid:40695406
- ISSN
- 1053-8119
- DOI
- 10.1016/j.neuroimage.2025.121381
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 The Author(s)
- id
- d1bb65dd-15ac-47ae-9374-882e7c8e4060
- date added to LUP
- 2025-11-24 12:46:19
- date last changed
- 2025-12-08 14:05:14
@article{d1bb65dd-15ac-47ae-9374-882e7c8e4060,
abstract = {{<p>Previous studies identified atrophy-based Alzheimer's disease(AD) subtypes linked to distinct clinical symptoms, but their consistency across subtyping approaches remains unclear. This large-scale study evaluates subtype concordance using two data-driven approaches. In this work, we analyzed data from n=10,011 patients across 10 AD cohorts spanning Europe, the US, and Australia, extracting regional volumes using Freesurfer. To characterize atrophy heterogeneity in the AD continuum, we developed a two-step approach, Snowphlake (Staging NeurOdegeneration With PHenotype informed progression timeLine of biomarKErs), to identify subtypes and atrophy-event sequences within each subtype. Results were compared with SuStaIn (Subtype and Stage Inference), which jointly estimates subtypes and staging, using similar training and validation. Training included Aβ+ participants (n=1,195) and Aβ− cognitively unimpaired controls (n=1,692). We validated model-staging in a held-out clinical dataset (n=6,362) and an independent dataset (n=762), and assessed clinical significance in Aβ+ subsets(n=1,796 held-out; n=159 external). Concordance analysis evaluated consistency between methods. In the AD dementia(AD-D) training data, both Snowphlake and SuStaIn identified four subtypes. In the validation datasets, staging with both methods correlated with Mini-Mental State Examination(MMSE) scores. The Snowphlake subtypes assigned in Aβ+ validation datasets were associated with alterations in specific cognitive domains(Cohen's f: [0.15−0.33]). Similarly, the SuStaIn subtypes were also associated specific cognitive domains(Cohen's f:[0.17−0.34]). However, we observed low concordance between Snowphlake and SuStaIn, with 39.7% of AD-D patients grouped in concordant subtypes by both methods. In conclusion, Snowphlake and SuStaIn identified four atrophy-based subtypes that linked to distinct symptom profiles. While this highlights that the neuro-anatomically defined subtypes also meaningfully associate with different cognitive impairments at a group level, the low concordance between methods suggests that future research is needed to better understand the biological and methodological factors contributing to the observed variability.</p>}},
author = {{Venkatraghavan, Vikram and Archetti, Damiano and Bourgeat, Pierrick and Jiang, Chenyang and ten Kate, Mara and van Loenhoud, Anna C. and Ossenkoppele, Rik and Teunissen, Charlotte E. and van de Giessen, Elsmarieke and Pijnenburg, Yolande A.L. and Frisoni, Giovanni B. and Weiss, Béla and Vidnyánszky, Zoltán and Auer, Tibor and Durrleman, Stanley and Redolfi, Alberto and Laws, Simon M. and Maruff, Paul and Oxtoby, Neil P. and Altmann, Andre and Alexander, Daniel C. and van der Flier, Wiesje M. and Barkhof, Frederik and Tijms, Betty M.}},
issn = {{1053-8119}},
keywords = {{Alzheimer's disease; Data-driven; Heterogeneity; MRI; Subtypes}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{NeuroImage}},
title = {{A large-scale multi-centre study characterising atrophy heterogeneity in Alzheimer's disease}},
url = {{http://dx.doi.org/10.1016/j.neuroimage.2025.121381}},
doi = {{10.1016/j.neuroimage.2025.121381}},
volume = {{318}},
year = {{2025}},
}