Jack, Clifford R.

Hormone therapy improves sleep in menopausal women and recent data suggest that transdermal 17β-estradiol may reduce the accumulation of cortical amyloid-β. However, how menopausal hormone therapies modify the associations of amyloid-β accumulation with sleep quality is not known. In this study, associations of sleep quality with cortical amyloid-β deposition and cognitive function were assessed in a subset of women who had participated in the Kronos early estrogen prevention study. It was a randomized, placebo-controlled trial in which recently menopausal women (age, 42–58; 5–36 months past menopause) were randomized to (1) oral conjugated equine estrogen (n = 19); (2) transdermal 17β-estradiol (tE2, n = 21); (3) placebo pills and patch (n = 32) for 4 years. Global sleep quality score was calculated using Pittsburgh sleep quality index, cortical amyloid-β deposition was measured with Pittsburgh compound-B positron emission tomography standard uptake value ratio and cognitive function was assessed in four cognitive domains 3 years after completion of trial treatments. Lower global sleep quality score (i.e., better sleep quality) correlated with lower cortical Pittsburgh compound-B standard uptake value ratio only in the tE2 group (r = 0.45, P = 0.047). Better global sleep quality also correlated with higher visual attention and executive function scores in the tE2 group (r = −0.54, P = 0.02) and in the oral conjugated equine estrogen group (r = −0.65, P = 0.005). Menopausal hormone therapies may influence the effects of sleep on cognitive function, specifically, visual attention and executive function. There also appears to be a complex relationship between sleep, menopausal hormone therapies, cortical amyloid-β accumulation and cognitive function, and tE2 formulation may modify the relationship between sleep and amyloid-β accumulation.

AbstractBackgroundMany biomarkers have been identified which are relevant to studies of Alzheimer’s disease (AD), especially pertaining to the evolution of amyloid plaque, tau tangle pathology and loss of brain tissue. There remains, however, the need for additional biomarkers that reflect pathologic processes affecting neuronal function during pre‐clinical and prodromal stages, to help accelerate drug development efforts.MethodA retrospective investigation of longitudinal changes in concentrations of five analytes (CgA, NPTX2, VGF, SCG2, FABP3) was performed in CSF of ADNI subjects. Rates of longitudinal change in these candidate proteins were compared between: a) Cognitively normal subjects (CN; n = 76) versus subjects with mild cognitive impairment (MCI; n = 111) at baseline; b) Subjects categorized as p‐Tau181/Aβ1‐42 ratio positive versus negative at baseline; and c) ‘Progressors’, i.e., subjects who progressed from CN to MCI or MCI to Dementia within a predefined period versus subjects categorized as ‘non‐progressors’. Following a pre‐specified analysis plan involving mixed‐effects linear models adjusting for relevant covariates, the association between changes in each analyte’s concentration and subjects’ clinical progression was quantified.ResultNPTX2, a protein involved in synaptic function, showed the strongest association with baseline clinical diagnosis of MCI, and a positive p‐Tau181/Aβ1‐42 ratio – the biomarker profile indicative of AD. Differences in the rates of decline in NPTX2 concentration between subjects classified as CN and MCI as well as between p‐Tau181/Aβ1‐42 ratio positive and negative subjects were highly significant (p=0.008 and p<0.0001 resp.), suggesting a complex interaction between the rate of decline in subjects at various stages along the disease continuum. Of the five analytes, only the rates of change in NPTX2 concentrations differed between progressors and non‐progressors (mean difference: 0.08 ± 0.02 ng/mL/year); p = 0.0004), further validating the association of changes in NPTX2 concentration and clinical prognosis. Additional exploratory analyses indicated the presence of a correlation between NPTX2 rates of change and declining cognition measured by MMSE (coef. = 0.3, p = 0.02), and Adas‐Cog 13 (coef. = ‐0.3, p = 0.01).ConclusionThese results suggest that NPTX2 concentration may serve as a prognostic biomarker of accelerated cognitive decline in at least a subset of individuals with AD.

AbstractBackgroundPrevious research has suggested that, compared to males, females are at greater risk for and have greater pathology burden in late onset. However, sex differences in early onset AD (EOAD) have not yet been studied.MethodWe included 167 participants [28 cognitively normal (CN, 68% females), 98 early onset AD (EOAD, 55% females), and 41 cognitively impaired amyloid‐negative (EOnonAD, 31% females] subjects from the Longitudinal Early‐Onset AD Study (LEADS) with available Flortaucipir PET, Florbetaben PET, and MRI data. Multiple linear regression (MLR) models including age and MMSE as covariates were used in the pooled sample to examine the effects of sex on hippocampal and white matter hyperintensity volume, mean cortical thickness, mean tau distribution by Braak regions and mean cortical amyloid SUVR. We also ran voxelwise MLR with sex as the predictor and cortical thickness, amyloid SUVR normalized to whole cerebellum, tau SUVR normalized to cerebellar crus, respectively, as the outcome measures while controlling for age, MMSE, and total intracranial volume (MRI only). Results are displayed at a cluster‐level FWE correction of p<0.05.ResultThere were no significant demographic differences between males and females in any diagnostic group. Across the pooled sample females showed significantly greater atrophy of the hippocampus (p=0.0001), greater tau SUVR in Braak regions 3&4 (p=0.05) and 5&6 (p=0.04) and trend for greater global amyloid uptake (p=0.074) (Table 2). The analyses in imaging space confirmed these findings and showed that the effects are driven by the EOAD group. Females showed greater amyloid deposition globally and greater tau deposition in the frontal, inferior parietal and temporal lobes (Figure 3).ConclusionFemale sex is associated with greater pathology burden in EOAD. Longitudinal studies will be needed to establish whether such difference translates in faster rates of progression in women relative to men.

AbstractBackgroundApproximately 5% of the 5.6 million (∼280,000) Americans with Alzheimer’s disease (AD) develop symptoms at age 65 or younger and are classified as having early‐onset AD (EOAD). Although EOAD and late‐onset AD (LOAD) share the same pathologic substrate, there are notable differences in their clinical and biological phenotypes. The Longitudinal Early‐onset AD study (LEADS) is a multi‐site, observational clinical and biomarker study of EOAD. In the present study we utilized the currently available LEADS imaging data to explore the spatial extent and magnitude of neurodegeneration EOADMethod95 amyloid positive patients with EOAD, 37 amyloid negative cognitively impaired patients (EOnonAD) , and 55 cognitively unimpaired controls (CNs) were included in the analysis. The magnitude of cortical thinning in the EOAD and EOnonAD groups was estimated using whole‐brain GLMs compared to the CN group. In addition, w‐scores were derived for each EOAD patient for these ROIs: average cortical thickness, hippocampal volume, AD‐signature cortical thickness (typically used in LOAD), and precuneus cortical thickness controlling for age, sex, and acquisition site. The utility of each ROI to differentiate EOAD patients from CNs was assessed by calculating the percentage of patients falling below a W‐threshold of ‐1.5.ResultIn EOAD, a distributed pattern of cortical thinning was observed in lateral temporal, parietal, and frontal cortex (p<.01), and was most prominent in the precuneus (Cohen’s d’ = 1.5). In the EOnonAD group, cortical thinning was observed but was highly variable across individuals. The w‐score analysis in the EOAD group yielded the following results: Mean Cortical thickness: Mean = ‐3.34, SD = 2.97, Classification = 72%.; Hippocampal volume: Mean = ‐2.96, SD = 2.39, Classification = 74%; AD Signature: Mean = ‐4.63, SD = 3.07, Classification = 80%; Precuneus: Mean = ‐5.98, SD = 4.1, Classification = 90%.ConclusionOur results are consistent with previous findings demonstrating cortical thinning in lateral parietal, precuneus, and lateral temporal lobes in EOAD with relatively less involvement of the medial temporal lobes. Our results also suggest that the precuneus may be especially vulnerable to neurodegeneration in patients with EOAD and may serve as a useful biomarker for patient classification.

AbstractBackgroundWhite matter (WM) changes are widely seen in the elderly due to aging and pathological processes and have significant impact on cognitive aging. It is critical to understand the heterogeneity and drivers (causes) of the longitudinal deterioration of WM microstructural integrity, especially in a population‐based sample, to provide mechanistic insights into cognitive and brain aging.MethodLeveraging longitudinal diffusion tensor imaging (DTI) data from Mayo Clinic Study of Aging, we assessed whether individuals clustered based on distinct longitudinal fractional anisotropy (FA) trajectories. A total of 533 subjects (487 cognitively unimpaired, 62 with mild cognitive impairment and 4 with dementia diagnosis) aged ≥ 60 years and with at least two DTI scans (125 subjects had at least 3 scans) were included. To identify clusters of individuals with common FA profiles over time, we applied an in‐house longitudinal Bayesian mixed‐effects clustering analysis framework to the imaging data. Age at MRI accounted for the longitudinal component of the model, while sex and APOE4 carrier status were used as fixed effects.ResultWe identified four distinct clusters (Figure 1). We found that most WM tracts decline with age across all clusters but brainstem and association fibers were least likely to decline with aging. APOE4 carriership and amyloid deposition did not differ between the clusters. We found a resilient cluster of individuals (34%) (cluster 1) with better baseline WM and systemic vascular health who had a slower rate of cognitive and WM integrity decline over time. These individuals had highest baseline commissural and association fibers. The resilient group was in contrast to an “at risk” cluster of individuals (16%) (cluster 2) who had lower baseline WM integrity and systemic health and declined the fastest in both WM health and cognition over timeConclusionLongitudinal clustering of WM tracts provided insights into the differential aging of WM in the population which may be unrelated to Alzheimer’s disease pathophysiology. Measuring overall WM health and vulnerable tracts in the elderly can be useful as biomarkers and aid in the identification of “at risk” groups for prevention of future cognitive decline.

AbstractBackgroundDisproportionately enlarged subarachnoid‐space hydrocephalus (DESH), characterized by tight high convexity CSF spaces, ventriculomegaly, and enlarged Sylvian fissures, is thought to be an indirect marker of a CSF dynamics disorder. Although this imaging phenotype was originally described in normal pressure hydrocephalus (NPH), it has since been more widely recognized in the aging population with a prevalence of 6‐7% in the Mayo Clinic Study of Aging (MCSA) 1. The clinical significance of DESH with regards to cognitive decline outside of NPH is not yet well‐defined. The goal of this work is to determine if DESH is associated with risk of cognitive impairment.MethodParticipants in the population‐based MCSA who met the following criteria were included: age ≥ 65 years, 3T MRI, cognitively unimpaired at enrollment, and at least one follow‐up visit. A support vector machine based method to detect the DESH imaging features on T1‐weighted MRI was used to calculate “computational DESH” or CDESH scores2 (Figure 1). Cox proportional hazard models were fit with events defined as progression from cognitively unimpaired to mild cognitive impairment or dementia. Time was defined as years from MCSA enrollment to the first visit with cognitive impairment with censoring at last follow‐up. CDESH was modelled as a spline to allow for non‐linear effects. Covariates included age, sex, education, APOE status, cortical thickness, white matter hyperintensity (WMH) volume, and total intracranial volume.ResultParticipant demographics are summarized in Table 1. The model including the above co‐variates showed a significant CDESH effect (p=0.02) with an approximately 20% greater hazard of progression for a CDESH score of +1 versus −1 (HR 1.2, 95% CI 1.0‐1.4) (Figure 2).ConclusionImaging features of disordered CSF dynamics are an independent predictor of subsequent cognitive decline in the MCSA, among other well‐known factors including age, cortical thickness, and APOE status. Therefore, since DESH contributes to cognitive decline and is present in the general population, identifying individuals with DESH features may be important clinically and also used as a selection stratification variable for clinical trials. References: (1) Graff‐Radford et al doi:10.1212/WNL.0000000000008616; (2) N. Gunter et al doi:10.1016/j.nicl.2018.11.015.

AbstractBackgroundWe report the associations between change in renal biomarkers in CKD and non‐ CKD participants and change in structural MRI outcomes over 3 years in the BRain IN Kidney disease (BRINK) study MRI cohort. The imaging outcomes were cortical thickness in Alzheimer’s disease signature regions, white matter hyperintensities (WMH), and fractional anisotropy in the corpus callosum (FA CC), an early marker of cerebrovascular disease.MethodsParticipants were ≥ 45 years with CKD (estimated glomerular filtration rate (eGFR) in mL/min/1.73 m2< 60; non‐ dialysis) or non – CKD (eGFR ≥ 60) in Minneapolis/St. Paul. CKD severity was measured by the eGFR and urine albumin creatinine ratio (UACR‐albuminuria, a measure of microvascular disease). Brain images were obtained on a 1.5 T Phillips Ingenia machine at baseline and 3 years. Linear regression models measured the associations between 3 year change in eGFR and UACR, as predictors of change in MRI measures: AD cortical thickness, WMH, and FA CC over 3 years. Adjusted models included age, gender, AA race, education years, cerebrovascular disease risk factor score (CVD RF), smoker/alcohol use, and pulse pressure as covariates, and TIV for change in WMH models.ResultsAt baseline, mean age of the 97 participants was 67 years, and mean eGFR was 37 for the CKD group vs. 83.2 for non‐ CKD (Table 1). Mean change in eGFR and UACR in the CKD group were ‐3.6 and +212, respectively compared with ‐ 8.5 and +1.7 in the non‐ CKD group. Mean WMH volume increase (+5.8 cc) and mean FA CC decrease (‐0.012) were significant in CKD (p < 0.001) for both (Table 2). In adjusted models both a 1% decrease and a ≥ 3 unit/year decline in eGFR were associated with a decline in FA CC over 3 years in the CKD group (β (SE) =‐0.020 (0.007); p = 0.004) and ‐0.0097 (0.0042); p = 0.025, respectively). Cortical thickness models were not significant. Unexpectedly, change in UACR was not associated with change in any MRI outcomes.ConclusionsDecreased eGFR was associated with decreased FA CC in CKD, suggesting that CKD progression exerts its influence largely through cerebrovascular mechanisms.

AbstractBackgroundThe NIA‐AA Research Framework on Alzheimer’s disease (AD) represents an important advance in the biological characterization of the AD spectrum. While this framework predicts disease progression, definitions of amyloid, tau, and neurodegeneration positivity based on cut‐scores may limit the performance of predictive models.Method252 cognitively normal (CN), 424 amnestic mild cognitive impairment (aMCI) and 128 AD participants were included in this study. aMCI participants at baseline were classified using 2 different methods: 1) Using conventional AT[N] criteria based on proposed cut‐offs in CSF or brain imaging measures: participants were classified on the AD continuum (A+T‐N‐, A+T+N‐, A+T‐N+, A+T+N+) and non‐AD continuum (all other subgroups); and 2) using a machine learning (ML) based method (ensemble linear discriminant model built using CN and AD participants) to classify MCI participants as CN‐like or AD‐like based on continuous CSF biomarkers, regional amyloid PET, FDG‐PET, and volumetric MRI (variables contributing to AT[N] classification; Table 1). Predictive performance of AT[N]‐based classification and ML‐based classification were assessed using clinical outcomes at 0.5, 1, 2, 3, 4, and 5 years of follow‐up.ResultA total of 414 aMCI participants had at least one wave of follow‐up (N of 397, 400, 346, 297, 238, and 93 at 0.5, 1, 2, 3, 4, and 5 years, respectively). The progression rate from aMCI to AD was 5.8% at 6m, 9.5% at 1y, 17.9% at 2y, 19.9% at 3y, 20.2% at 4y, and 15% at 5y of follow‐up. Presence of AD pathology at baseline (AT[N]‐based AD continuum) predicted progression to AD with high sensitivity but modest specificity during follow up (Table 2). ML‐based classification was less sensitive but substantially more specific. ML‐based classification models were significantly more accurate than AT[N]‐based classification at all follow‐up periods (McNemar test, p‐value<0.001 for all).ConclusionA single cut‐point approach used in AT[N] classification lacks accuracy when research questions require high diagnostic or prognostic certainty. A multivariate approach using ML methods can improve accuracy of prediction of disease progression. PPVs were higher at all follow‐up time points for ML‐based classification, indicating that progression to AD occurs at a higher rate in persons identified by ML.