Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration.

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.

Presumed small vessel disease, imaging and cognition markers in the Alzheimer's Disease Neuroimaging Initiative.

MRI-derived features of presumed cerebral small vessel disease are frequently found in Alzheimer's disease. Influences of such markers on disease-progression measures are poorly understood. We measured markers of presumed small vessel disease (white matter hyperintensity volumes; cerebral microbleeds) on baseline images of newly enrolled individuals in the Alzheimer's Disease Neuroimaging Initiative cohort (GO and 2) and used linear mixed models to relate these to subsequent atrophy and neuropsychological score change. We also assessed heterogeneity in white matter hyperintensity positioning within biomarker abnormality sequences, driven by the data, using the Subtype and Stage Inference algorithm. This study recruited both sexes and included: controls: [n = 159, mean(SD) age = 74(6) years]; early and late mild cognitive impairment [ns = 265 and 139, respectively, mean(SD) ages =71(7) and 72(8) years, respectively]; Alzheimer's disease [n = 103, mean(SD) age = 75(8)] and significant memory concern [n = 72, mean(SD) age = 72(6) years]. Baseline demographic and vascular risk-factor data, and longitudinal cognitive scores (Mini-Mental State Examination; logical memory; and Trails A and B) were collected. Whole-brain and hippocampal volume change metrics were calculated. White matter hyperintensity volumes were associated with greater whole-brain and hippocampal volume changes independently of cerebral microbleeds (a doubling of baseline white matter hyperintensity was associated with an increase in atrophy rate of 0.3 ml/year for brain and 0.013 ml/year for hippocampus). Cerebral microbleeds were found in 15% of individuals and the presence of a microbleed, as opposed to none, was associated with increases in atrophy rate of 1.4 ml/year for whole brain and 0.021 ml/year for hippocampus. White matter hyperintensities were predictive of greater decline in all neuropsychological scores, while cerebral microbleeds were predictive of decline in logical memory (immediate recall) and Mini-Mental State Examination scores. We identified distinct groups with specific sequences of biomarker abnormality using continuous baseline measures and brain volume change. Four clusters were found; Group 1 showed early Alzheimer's pathology; Group 2 showed early neurodegeneration; Group 3 had early mixed Alzheimer's and cerebrovascular pathology; Group 4 had early neuropsychological score abnormalities. White matter hyperintensity volumes becoming abnormal was a late event for Groups 1 and 4 and an early event for 2 and 3. In summary, white matter hyperintensities and microbleeds were independently associated with progressive neurodegeneration (brain atrophy rates) and cognitive decline (change in neuropsychological scores). Mechanisms involving white matter hyperintensities and progression and microbleeds and progression may be partially separate. Distinct sequences of biomarker progression were found. White matter hyperintensity development was an early event in two sequences.

The age-dependent associations of white matter hyperintensities and neurofilament light in early- and late-stage Alzheimer's disease.

Neurofilament light (NFL) is an emerging marker of axonal degeneration. This study investigated the relationship between white matter hyperintensities (WMHs) and plasma NFL in a large elderly cohort with, and without, cognitive impairment. We used the Alzheimer's Disease Neuroimaging Initiative and included 163 controls, 103 participants with a significant memory concern, 279 with early mild cognitive impairment (EMCI), 152 with late mild cognitive impairment (LMCI), and 130 with Alzheimer's disease, with 3T MRI and plasma NFL data. Multiple linear regression models examined the relationship between WMHs and NFL, with and without age adjustment. We used smoking status, history of hypertension, history of diabetes, and BMI as additional covariates to examine the effect of vascular risk. We found increases of between 20% and 41% in WMH volume per 1SD increase in NFL in significant memory concern, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease groups (p < 0.02). Marked attenuation of the positive associations between WMHs and NFL were seen after age adjustment, suggesting that a significant proportion of the association between NFL and WMHs is age-related. No effect of vascular risk was observed. These results are supportive of a link between WMH and axonal degeneration in early to late disease stages, in an age-dependent, but vascular risk-independent manner.

CSF amyloid is a consistent predictor of white matter hyperintensities across the disease course from aging to Alzheimer's disease.

This study investigated the relationship between white matter hyperintensities (WMH) and cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers. Subjects included 180 controls, 107 individuals with a significant memory concern, 320 individuals with early mild cognitive impairment, 171 individuals with late mild cognitive impairment, and 151 individuals with AD, with 3T MRI and CSF Aß1-42, total tau (t-tau), and phosphorylated tau (p-tau) data. Multiple linear regression models assessed the relationship between WMH and CSF Aß1-42, t-tau, and p-tau. Directionally, a higher WMH burden was associated with lower CSF Aß1-42 within each diagnostic group, with no evidence for a difference in the slope of the association across diagnostic groups (p = 0.4). Pooling all participants, this association was statistically significant after adjustment for t-tau, p-tau, age, diagnostic group, and APOE-ε4 status (p < 0.001). Age was the strongest predictor of WMH (partial R2~16%) compared with CSF Aß1-42 (partial R2~5%). There was no evidence for an association with WMH and either t-tau or p-tau. These data are supportive of a link between amyloid burden and presumed vascular pathology.

Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change.

Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer's (AD) participants. Data were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS' WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer's disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies.

Genetic determinants of white matter hyperintensities and amyloid angiopathy in familial Alzheimer's disease.

Familial Alzheimer's disease (FAD) treatment trials raise interest in the variable occurrence of cerebral amyloid angiopathy (CAA); an emerging important factor in amyloid-modifying therapy. Previous pathological studies reported particularly severe CAA with postcodon 200 PSEN1 mutations and amyloid beta coding domain APP mutations. As CAA may manifest as white matter hyperintensities (WMH) on magnetic resonance imaging, particularly posteriorly, we investigated WMH in 52 symptomatic FAD patients for associations with mutation position. WMH were visually rated in 39 PSEN1 (18 precodon 200); 13 APP mutation carriers and 25 healthy controls. Ten PSEN1 mutation carriers (5 precodon 200) had postmortem examination. Increased WMH were observed in the PSEN1 postcodon 200 group and in the single APP patient with an amyloid beta coding domain (p.Ala692Gly, Flemish) mutation. WMH burden on MRI correlated with severity of CAA and cotton wool plaques in several areas. The precodon 200 group had younger ages at onset, decreased axonal density and/or integrity, and a greater T-lymphocytic response in occipital deep white matter. Mutation site contributes to the phenotypic and pathological heterogeneity witnessed in FAD.