Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration. (21st February 2023)

• Record Type: Journal Article
• Authors: Prosser, Lloyd; Macdougall, Amy; Sudre, Carole H.; Manning, Emily N.; Malone, Ian B.; Walsh, Phoebe; Goodkin, Olivia; Pemberton, Hugh; Barkhof, Frederik; Biessels, Geert Jan; Cash, David M.; Barnes, Josephine
• Is Part Of: Neurology. Volume 100:Number 8(2023)
• Page Start: e834
• Page End: e845
• Publication Date: 2023-02-21
• Subjects: Neurology -- Periodicals; Neurology -- Periodicals; Neurologie -- Périodiques; 616.8
• DOI: 10.1212/WNL.0000000000201572
• Languages: English
• ISSNs: 0028-3878

Abstract

Abstract : Background and Objectives: Dementia is a growing socioeconomic 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, 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 disease (AD) diagnosis (MCItoAD). Methods: Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (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 time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) 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 = 0.002; fully adjusted model: HR 1.98, p = 0.003) and lower hippocampal volume (individual: HR 0.54, p = 0.001; fully adjusted: HR 0.40, p < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < 0.001; fully adjusted model: HR 0.55, p < 0.001) and whole-brain volume (individual: HR 0.31, p < 0.001; fully adjusted: HR 0.48, p = 0.02), increased CSF ptau (individual: HR 1.88, p < 0.001; fully adjusted: HR 1.61, p < 0.001), and lower CSF amyloid (individual: HR 0.37, p < 0.001; fully adjusted: HR 0.62, p = 0.008) 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, whereas 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 pathologic pathway, such as vascular cognitive impairment.