{"doi":"10.1101/2023.02.07.23285572","title":"Heterogeneity in Preclinical Alzheimer’s Disease Trial Cohort Identified by Image-based Data-Driven Disease Progression Modelling","abstract":"Abstract Importance Undetected biological heterogeneity adversely impacts trials in Alzheimer’s disease because rate of cognitive decline — and perhaps response to treatment — differs in subgroups. Recent results show that data-driven approaches can unravel the heterogeneity of Alzheimer’s disease progression. The resulting stratification is yet to be leveraged in clinical trials. Objective Investigate whether image-based data-driven disease progression modelling could identify baseline biological heterogeneity in a clinical trial, and whether these subgroups have prognostic or predictive value. Design Screening data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) Study collected between April 2014 and December 2017, and longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) observational study downloaded in February 2022 were used. Setting The A4 Study is an interventional trial involving 67 sites in the US, Canada, Australia, and Japan. ADNI is a multi-center observational study in North America. Participants Cognitively unimpaired amyloid-positive participants with a 3-Tesla T1-weighted MRI scan. Amyloid positivity was determined using florbetapir PET imaging (in A4) and CSF Aβ(1-42) (in ADNI). Main Outcomes and Measures Regional volumes estimated from MRI scans were used as input to the Subtype and Stage Inference (SuStaIn) algorithm. Outcomes included cognitive test scores and SUVr values from florbetapir and flortaucipir PET. Results We included 1,240 Aβ+ participants (and 407 Aβ− controls) from the A4 Study, and 731 A4-eligible ADNI participants. SuStaIn identified three neurodegeneration subtypes — Typical, Cortical, Subcortical — comprising 523 (42%) individuals. The remainder are designated subtype zero (insufficient atrophy). Baseline PACC scores (A4 primary outcome) were significantly worse in the Cortical subtype (median = -1.27, IQR=[-3.34,0.83]) relative to both subtype zero (median=-0.013, IQR=[-1.85,1.67], P<.0001) and the Subcortical subtype (median=0.03, IQR=[-1.78,1.61], P=.0006). In ADNI, over a four-year period (comparable to A4), greater cognitive decline in the mPACC was observed in both the Typical (−0.23/yr; 95% CI, [-0.41,-0.05]; P=.01) and Cortical (−0.24/yr; [-0.42,-0.06]; P=.009) subtypes, as well as the CDR-SB ( Typical : +0.09/yr, [0.06,0.12], P<.0001; and Cortical : +0.07/yr, [0.04,0.10], P<.0001). Conclusions and Relevance In a large secondary prevention trial, our image-based model detected neurodegenerative heterogeneity predictive of cognitive heterogeneity. We argue that such a model is a valuable tool to be considered in future trial design to control for previously undetected variance. Key Points Question Can MRI-based computational subtypes of preclinical neurodegeneration predict cognitive outcomes? Findings In this cross-sectional analysis of magnetic resonance imaging (MRI) data at screening (pre-randomization) in the preclinical Anti-Amyloid Treatment in Asymptomatic Alzheimer disease (A4) Study, we detected considerable neurodegenerative heterogeneity using data-driven disease progression modelling. The MRI-based computational subtypes identified by Subtype and Stage Inference (SuStaIn) differed in baseline cognitive test scores (A4) and in longitudinal cognitive decline (ADNI), with sufficient heterogeneity to potentially obscure treatment effect in A4 trial outcomes. Meaning Data-driven disease progression modelling of screening MRI scans can predict heterogeneity in cognitive performance/decline and potentially reduce heterogeneity in future clinical trials.","journal":"medRxiv","year":2023,"id":391667,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9428,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1068577,"name":"Paweł Markiewicz","orcid":"0000-0002-3114-0773","position":1,"is_corresponding":false},{"id":320896,"name":"David M. Cash","orcid":"0000-0001-7833-616X","position":2,"is_corresponding":false},{"id":244799,"name":"Daniel C. Alexander","orcid":"0000-0003-2439-350X","position":3,"is_corresponding":false},{"id":51716,"name":"Michael Donohue","orcid":"0000-0001-6026-2238","position":4,"is_corresponding":false},{"id":242544,"name":"Frederik Barkhof","orcid":"0000-0003-3543-3706","position":5,"is_corresponding":false},{"id":318443,"name":"Neil P. Oxtoby","orcid":"0000-0003-0203-3909","position":6,"is_corresponding":false},{"id":265450,"name":"the Alzheimer’s Disease Neuroimaging Initiative","orcid":null,"position":7,"is_corresponding":false},{"id":803954,"name":"Cameron Shand","orcid":"0000-0002-1299-890X","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T01:18:47.408273Z","pmid":"36798314","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}