Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Franzmeier, Nicolai; Koutsouleris, Nikolaos; Benzinger, Tammie L.S.; Goate, Alison; Karch, Celeste M.; Fagan, Anne M.; McDade, Eric; Duering, Marco; Dichgans, Martin; Xiong, Chengjie; Gordon, Brian A.; Lim, Yen Ying; Masters, Colin L.; Rossor, Martin N.; Fox, Nick C.; O’Connor, Antoinette; Chhatwal, Jasmeer P.; Salloway, Stephen; Danek, Adrian; Hassenstab, Jason; Schofield, Peter R.; Morris, John C.; Bateman, Randall J.; Ewers, Michael

doi:10.1002/alz.12032

Cited by 61 publications

(55 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition to specific gene expression changes, the pervasive and global nature of the diseaseassociated changes across multiple cell types, and almost all cells within each class, underlines the global nature of the disease in its latter stages. As such, it is consistent with a disease process that begins in early adulthood (52), and supports the hypothesis that successful treatments for monogenic AD will likely need to be administered decades before the typical age at onset of clinical symptoms (4,5). Heatmaps show log-transformed fold change between APP and PSEN1 and matched controls.…”

supporting

confidence: 74%

Molecular and cellular pathology of monogenic Alzheimer’s disease at single cell resolution

Marinaro

Haneklaus

Zhang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Cell and molecular biology analyses of sporadic Alzheimer's disease brain are confounded by clinical variability, ageing and genetic heterogeneity. Therefore, we used single-nucleus RNA sequencing to characterize cell composition and gene expression in the cerebral cortex in early-onset, monogenic Alzheimer's disease. Constructing a cellular atlas of frontal cortex from 8 monogenic AD individuals and 8 matched controls, provided insights into which neurons degenerate in AD and responses of different cell types to AD at the cellular and systems level. Such responses are a combination of positively adaptive and deleterious changes, including large-scale changes in synaptic transmission and marked metabolic reprogramming in neurons. The nature and scale of the transcriptional changes in AD emphasizes the global impact of the disease across all brain cell types.

show abstract

supporting

confidence: 74%

Molecular and cellular pathology of monogenic Alzheimer’s disease at single cell resolution

Marinaro

Haneklaus

Zhang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Regardless, the key finding is that our approach was able to stratify a clinical trial population into potential responders and non-responders using only baseline/screening data. This supports the notion that computational, data-driven screening can substantially reduce the size (and cost) of a clinical trial, without sacrificing statistical power (see also Franzmeier et al (27)).…”

Section: Discussionsupporting

confidence: 74%

Targeted screening for Alzheimer’s disease clinical trials using data-driven disease progression models

Oxtoby

Shand

Cash

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…Resting-state functional connectivity (FC) noninvasively measures the association of signaling among brain regions and can be used to identify resting-state networks (RSNs) (Franzmeier et al, 2020). The inter-relationships among RSNs is sensitive to neuronal dysfunction and is associated with the degree of cognitive impairment (Frisoni et al, 2010;Frisoni., 2012;Frost and Diamond, 2010).…”

Section: Introductionmentioning

confidence: 99%

Resting-State Functional Connectivity Disruption as a Pathological Biomarker in Autosomal Dominant Alzheimer Disease

et al. 2021

Self Cite

View full text Add to dashboard Cite

Aim: Identify a global resting-state functional connectivity (gFC) signature in mutation carriers (MC) from the Dominantly Inherited Alzheimer Network (DIAN). Assess the gFC with regard to amyloid (A), tau (T), and neurodegeneration (N) biomarkers, and estimated years to symptom onset (EYO). Introduction: Cross-sectional measures were assessed in MC (n = 171) and mutation noncarrier (NC) (n = 70) participants. A functional connectivity (FC) matrix that encompassed multiple resting-state networks was computed for each participant. Methods: A global FC was compiled as a single index indicating FC strength. The gFC signature was modeled as a nonlinear function of EYO. The gFC was linearly associated with other biomarkers used for assessing the AT(N) framework, including cerebrospinal fluid (CSF), positron emission tomography (PET) molecular biomarkers, and structural magnetic resonance imaging. Results: The gFC was reduced in MC compared with NC participants. When MC participants were differentiated by clinical dementia rating (CDR), the gFC was significantly decreased in MC CDR >0 (demented) compared with either MC CDR 0 (cognitively normal) or NC participants. The gFC varied nonlinearly with EYO and initially decreased at EYO = À24 years, followed by a stable period followed by a further decline near EYO = 0 years. Irrespective of EYO, a lower gFC associated with values of amyloid PET, CSF Ab 1-42 , CSF p-tau, CSF t-tau, 18F-fluorodeoxyglucose, and hippocampal volume. Conclusions: The gFC correlated with biomarkers used for defining the AT(N) framework. A biphasic change in the gFC suggested early changes associated with CSF amyloid and later changes associated with hippocampal volume.

show abstract

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Cited by 61 publications

References 52 publications

Molecular and cellular pathology of monogenic Alzheimer’s disease at single cell resolution

Molecular and cellular pathology of monogenic Alzheimer’s disease at single cell resolution

Targeted screening for Alzheimer’s disease clinical trials using data-driven disease progression models

Resting-State Functional Connectivity Disruption as a Pathological Biomarker in Autosomal Dominant Alzheimer Disease

Contact Info

Product

Resources

About