Citations

Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 ( NBEAL ), 10q23.1 ( TSPAN14/FAM231A ), and 10q24.33 ( SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 ( NOS3 ) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

Abstract Introduction Sleepiness has been associated with functional and cognitive decline, and may present with excessive daytime sleepiness (EDS) and/or increased sleep duration. We investigated whether sleepiness and changes in sleep patterns are associated with FDG-PET levels in wake-promoting regions. Methods From the Mayo Clinic Study of Aging cohort, we identified 373 cognitively-unimpaired middle-aged and older adults (mean +/- s.d. 66.1 +/- 13.2 yo) who underwent FDG-PET. EDS was defined as ESS score >=10. Changes in sleep patterns (sleeping more, less, or no change) were assessed using question #16 of the Beck Depression Inventory-2. We used probabilistic maps to create regions of interest (ROIs): the locus coeruleus (LC), posterior lateral hypothalamus (PLH), and the basal forebrain divided in 1) medial septum/diagonal band of Broca (MS/DB) and 2) nucleus basalis of Meynert (nbM). FDG-PET levels were referenced to the pons (SUVR). In this cross-sectional analysis, we fit linear models to assess the association between EDS and changes in sleeping patterns with FDG SUVR in in each ROI, while controlling for age, sex, education, BMI, witnessed apneas, and cardiovascular risk factors. Results 10.5% had EDS, 15% reported sleeping more and 21% reported sleeping less than usual. 30.7% of participants with EDS reported sleeping more, 25.6% less, and 43.5% the same. EDS was associated with an elevation in FDG-PET SUVR in the MS/DB region (.035 [95% CI .008; .063], p=.012), while sleeping more was associated with a decrease in FDG-PET SUVR in the same region (-.027 [95%CI -.052; -.002], p=.036). Sleeping less was associated with an increase in FDG-PET SUVR in the PLH (.021 [95% CI .005; .03], p=.019). No associations were found in other ROIs. Conclusion Our results suggest that sleepiness and changes in sleep patterns in cognitively-unimpaired middle-aged and older adults were associated with measurable metabolic changes in areas of the brain involved in sleep and wakefulness. Further research should clarify whether these findings could represent different phenotypes of sleepiness with potential diagnostic and prognostic implications. Support NIA/NIH

Abstract We report a group of patients presenting with a progressive dementia syndrome characterized by predominant dysfunction in core executive functions, relatively young age of onset and positive biomarkers for Alzheimer’s pathophysiology. Atypical frontal, dysexecutive/behavioural variants and early-onset variants of Alzheimer’s disease have been previously reported, but no diagnostic criteria exist for a progressive dysexecutive syndrome. In this retrospective review, we report on 55 participants diagnosed with a clinically defined progressive dysexecutive syndrome with 18F-fluorodeoxyglucose-positron emission tomography and Alzheimer’s disease biomarkers available. Sixty-two per cent of participants were female with a mean of 15.2 years of education. The mean age of reported symptom onset was 53.8 years while the mean age at diagnosis was 57.2 years. Participants and informants commonly referred to initial cognitive symptoms as ‘memory problems’ but upon further inquiry described problems with core executive functions of working memory, cognitive flexibility and cognitive inhibitory control. Multi-domain cognitive impairment was evident in neuropsychological testing with executive dysfunction most consistently affected. The frontal and parietal regions which overlap with working memory networks consistently demonstrated hypometabolism on positron emission tomography. Genetic testing for autosomal dominant genes was negative in all eight participants tested and at least one APOE ε4 allele was present in 14/26 participants tested. EEG was abnormal in 14/17 cases with 13 described as diffuse slowing. Furthermore, CSF or neuroimaging biomarkers were consistent with Alzheimer’s disease pathophysiology, although CSF p-tau was normal in 24% of cases. Fifteen of the executive predominate participants enrolled in research neuroimaging protocols and were compared to amnestic (n = 110), visual (n = 18) and language (n = 7) predominate clinical phenotypes of Alzheimer’s disease. This revealed a consistent pattern of hypometabolism in parieto-frontal brain regions supporting executive functions with relative sparing of the medial temporal lobe (versus amnestic phenotype), occipital (versus visual phenotype) and left temporal (versus language phenotype). We propose that this progressive dysexecutive syndrome should be recognized as a distinct clinical phenotype disambiguated from behavioural presentations and not linked specifically to the frontal lobe or a particular anatomic substrate without further study. This clinical presentation can be due to Alzheimer’s disease but is likely not specific for any single aetiology. Diagnostic criteria are proposed to facilitate additional research into this understudied clinical presentation.

AbstractIntroductionWe examined differences in cortical thickness in empirically derived mild cognitive impairment (MCI) subtypes in the Mayo Clinic Study of Aging.MethodsWe compared cortical thickness of four incident MCI subtypes (n = 192) to 1257 cognitive unimpaired individuals.ResultsThe subtle cognitive impairment cluster had atrophy in the entorhinal and parahippocampal cortex. The amnestic, dysnomic, and dysexecutive clusters also demonstrated entorhinal cortex atrophy as well as thinning in temporal, parietal, and frontal isocortex in somewhat different patterns.DiscussionWe found patterns of atrophy in each of the incident MCI clusters that corresponded to their patterns of cognitive impairment. The identification of MCI subtypes based on cognitive and structural features may allow for more efficient trial and study designs. Given individuals in the subtle cognitive impairment cluster have less structural changes and cognitive decline and may represent the earliest group, this could be a unique group to target with early interventions.

Abstract Alzheimer’s disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer’s disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer’s disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer’s disease (18 with typical amnestic Alzheimer’s disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer’s disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer’s disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer’s disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer’s disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer’s disease.

AbstractObjectiveTo examine the association of baseline elevated brain amyloid and neurodegeneration with changes in activities of daily living in participants without dementia (ND; i.e., cognitively unimpaired and participants with mild cognitive impairment) at baseline in the population‐based Mayo Clinic Study of Aging.MethodsWe included 1747 ND participants with 11C‐PiB PET and MR imaging in the study, with data on activities of daily living (as assessed by the Functional Activities Questionnaire (FAQ) and the Clinical Dementia Rating scale Sum of Boxes for functional domains (CDR‐SOB (functional)), with a median (range) of 4.3 (0.0–12.7) years of follow‐up. Abnormal (elevated; A+) 11C‐PiB‐PET retention ratio was defined as standardized uptake value ratio ≥ 1.48, and abnormal (reduced) AD signature cortical thickness as ≤ 2.68 mm (neurodegeneration; N+). Associations were examined with mixed effects models, adjusting for age, sex, education, apolipoprotein E ε4 allele carrier status, and global cognitive z‐score.ResultsMean age (SD) was 71.4 years (10.1), 46.7% were females, 195 (11.2%) had A+N‐, 442 (25.3%) had A‐N+, and 339 (19.4%) had A+N+ biomarkers. The A+N+ group had the largest annualized change in the FAQ score from baseline (difference in annual change A+N+ vs. A‐N‐; ß (SE): 0.80 (0.07)); associations were substantially attenuated when a time‐varying global cognitive composite was included in the model (A+N+ vs. A‐N‐; ß (SE): 0.31 (0.05)). CDR‐SOB (functional) findings partly agreed with FAQ score findings.InterpretationThe longitudinal increase in functional limitations is greater for individuals with abnormal neuroimaging biomarkers, especially for those with both elevated brain amyloid and neurodegeneration.

AbstractObjectiveTo assess the relationships between MRI volumetry and [18F]flortaucipir PET of typical and atypical clinical phenotypes of Alzheimer’s disease, by genarian (age by decade).MethodsFive‐hundred and sixty‐four participants including those with typical (n = 86) or atypical (n = 80) Alzheimer’s dementia and normal controls (n = 398) underwent apolipoprotein E genotyping, MRI, flortaucipir, and 11C‐PiB; all 166 Alzheimer’s participants were beta‐amyloid positive and all controls were beta‐amyloid negative. Grey matter volume and flortaucipir standard uptake value ratios were calculated for hippocampus, entorhinal cortex, and neocortex. Ratios of hippocampal‐to‐neocortical and entorhinal‐to‐neocortical volume and flortaucipir uptake were also calculated. Linear regression models assessed relationships among regional volume, flortaucipir uptake, and ratios and phenotypes, within three genarians (50–59, 60–69, and 70+). Voxel‐level analyses were also performed.ResultsFor 50–59 greater medial temporal atrophy and flortaucipir uptake was observed in the typical compared with atypical phenotype. The typical phenotype also showed greater frontal neocortex uptake with the voxel‐level analysis. For 60–69 and 70+ there was greater hippocampal volume loss in the typical compared with atypical phenotype while only the 60–69, but not the 70+ group, showed a difference in hippocampal flortaucipir uptake. We also observed a pattern for higher neocortical flortaucipir uptake to correlate with younger age decade for both phenotypes.InterpretationMRI volumetry versus flortaucipir PET relationships differ across Alzheimer’s clinical phenotypes, and also within phenotype across age decades. This suggests that there is potential risk of masked effects by not accounting for genarian in participants with beta‐amyloid and tau‐positive biomarker defined Alzheimer’s disease.