ORCID Profile
0000-0002-1299-890X
Current Organisations
University College London
,
University of Manchester
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Publisher: Cold Spring Harbor Laboratory
Date: 10-02-2023
DOI: 10.1101/2023.02.07.23285572
Abstract: 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. 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. 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. 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. 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). 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. 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%) in iduals. 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). 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. Can MRI-based computational subtypes of preclinical neurodegeneration predict cognitive outcomes? 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. Data-driven disease progression modelling of screening MRI scans can predict heterogeneity in cognitive performance/decline and potentially reduce heterogeneity in future clinical trials.
Publisher: Cold Spring Harbor Laboratory
Date: 10-06-2021
DOI: 10.1101/2021.06.09.447713
Abstract: Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression. Tools that enable this are in high demand from clinical and treatment-development communities. Here we describe the pySuStaIn software package, a Python-based implementation of the Subtype and Stage Inference (SuStaIn) algorithm. SuStaIn unravels the complexity of heterogeneous diseases by inferring multiple disease progression patterns ( subtypes ) and in idual severity ( stages ) from cross-sectional data. The primary aims of pySuStaIn are to enable widespread application and translation of SuStaIn via an accessible Python package that supports simple extension and generalization to novel modelling situations within a single, consistent architecture. Current code version v1 . 0 Permanent link to code/repository used of this code version cl-pond ySuStaIn Legal Code License MIT Code versioning system used git Software code languages, tools, and services used Python Compilation requirements, operating environments & dependencies Linux, Mac, Windows Support email for questions leon.aksman@loni.usc.edu , p.wijeratne@ucl.ac.uk , alexandra.young@kcl.ac.uk
Publisher: Cold Spring Harbor Laboratory
Date: 02-2021
DOI: 10.1101/2021.01.29.21250773
Abstract: Heterogeneity in Alzheimer’s disease progression contributes to the ongoing failure to demonstrate efficacy of putative disease-modifying therapeutics that have been trialled over the past two decades. Any treatment effect present in a subgroup of trial participants (responders) can be diluted by non-responders who ideally should have been screened out of the trial. How to identify (screen-in) the most likely potential responders is an important question that is still without an answer. Here we pilot a computational screening tool that leverages recent advances in data-driven disease progression modelling to improve stratification. This aims to increase the sensitivity to treatment effect by screening out non-responders, which will ultimately reduce the size, duration, and cost of a clinical trial. We demonstrate the concept of such a computational screening tool by retrospectively analysing a completed double-blind clinical trial of donepezil in people with amnestic mild cognitive impairment (clinicaltrials.gov: NCT00000173 ), identifying a data-driven subgroup having more severe cognitive impairment who showed clearer treatment response than observed for the full cohort.
Publisher: Elsevier BV
Date: 12-2021
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Cameron Shand.