UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Predicting Cognitive Decline in Nondemented Elders Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration

Prosser, Lloyd; MacDougall, Amy; Sudre, Carole H; Manning, Emily N; Malone, Ian B; Walsh, Phoebe; Goodkin, Olivia; ... Barnes, Josephine; + view all (2023) Predicting Cognitive Decline in Nondemented Elders Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration. Neurology , 100 (8) e834-e845. 10.1212/WNL.0000000000201572. Green open access

[thumbnail of e834.full.pdf]
Preview
Text
e834.full.pdf

Download (702kB) | Preview

Abstract

BACKGROUND AND OBJECTIVES: Dementia is a growing socio-economic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1-42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds (CMB), whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI), and those with MCI who later converted to an Alzheimer's disease (AD) diagnosis (MCItoAD). METHODS: Standardised baseline biomarker data from ADNI2/Go, and longitudinal diagnostic data (including ADNI3), were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up timepoints available. Models were fit for biomarkers univariately, and together in a multivariable model. Hazard Ratios (HR) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups. RESULTS: For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = .002; fully-adjusted model: HR 1.98, p = .003), and lower hippocampal volume (individual: HR 0.54, p = .001; fully-adjusted: HR 0.40, p < .001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < .001; fully-adjusted model: HR 0.55, p < .001) and whole-brain volume (individual: HR 0.31, p < .001; fully-adjusted: HR 0.48, p = .02), increased CSF ptau (individual: HR 1.88, p < .001; fully-adjusted: HR 1.61, p < .001), and lower CSF amyloid (individual: HR 0.37, p < .001, fully-adjusted: HR 0.62, p = .008) were most strongly associated with conversion to AD individually and independently. DISCUSSION: Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, while WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathological pathway, such as vascular cognitive impairment.

Type: Article
Title: Predicting Cognitive Decline in Nondemented Elders Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1212/WNL.0000000000201572
Publisher version: https://doi.org/10.1212/WNL.0000000000201572
Language: English
Additional information: This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Institute of Cognitive Neuroscience
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine > MRC Unit for Lifelong Hlth and Ageing
URI: https://discovery.ucl.ac.uk/id/eprint/10159641
Downloads since deposit
16Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item