Citations

AbstractBackgroundTau deposition is a key biological feature of Alzheimer’s disease (AD) that is closely related to cognitive impairment. However, it remains poorly understood why certain individuals may be more susceptible to tau accumulation while others are more resistant. The recent availability of tau positron emission tomography (tau‐PET) provides an opportunity to test the hypothesis that genetic factors are associated with differential tau deposition.MethodsWe performed a genome‐wide association study (GWAS) of tau‐PET on a sample of 754 individuals over age 50 (mean age 72.4 years, 54.6% men, 87.4% cognitively unimpaired) from the population‐based Mayo Clinic Study of Aging. Following genotyping with the Illumina GSA array and standard quality control, 515,206 single nucleotide polymorphisms (SNPs) were tested for association with entorhinal cortex 18F‐flortaucipir tau‐PET burden using linear regression under an additive genetic model and covarying for age and sex.ResultA genome‐wide significant association was identified for rs75546066, an intergenic SNP on chromosome 9 between MED27 (mediator complex subunit 27) and NTNG2 (netrin G2), with the minor (A) allele associated with lower tau burden (p=2.85 x 10‐8, β=‐0.48). Linkage disequilibrium and public expression quantitative trait loci (eQTL) data suggested a functional relationship between rs75546066 and NTNG2, the latter of which is highly expressed in the brain. In addition to this novel finding, three SNPs within MAPT (microtubule‐associated protein tau) displayed nominal associations to tau‐PET burden, including rs3785883 (p=0.04, β=0.07) which was previously associated with higher cerebrospinal fluid tau in an independent cohort. No associations with tau‐PET burden were identified for the APOE (apolipoprotein E) ɛ4 or ɛ2 alleles or for genotyped SNPs previously associated with AD in large case‐control studies.ConclusionTo our knowledge, this study represents the first reported neuroimaging GWAS of tau pathology. Our findings confirm the association of MAPT with tau burden and nominate the rs75546066‐NTNG2 locus as a novel potential protective influence against tau pathology which warrants further functional characterization. Our data also supports the inference that tau accumulation may have a genetic architecture distinct from APOE and other known AD susceptibility genes, which may have implications for improved risk stratification and therapeutic targeting models.

AbstractBackgroundPrevious studies have reported that age modifies the distribution and burden of tau (and, to a lesser extent, amyloid) pathology in sporadic Alzheimer’s disease (AD). Here we present preliminary baseline amyloid and tau PET results from the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS), a multi‐site longitudinal study of sporadic early‐onset AD.Method135 patients meeting clinical criteria for MCI or probable AD and 50 cognitively normal controls (all age<65 at enrollment) were enrolled at 12 US centers between August 2018 and December 2019 (Table 1). 18F‐Florbetaben amyloid‐PET (FBB) was used to assign patients to EOAD (amyloid‐positive) or EOnonAD (amyloid‐negative) subgroups based on visual rating and semi‐quantification. 130 patients and all controls had 18F‐Flortaucipir tau‐PET (FTP). Regional Standardized Uptake Value Ratios (SUVR) for FBB (whole cerebellum reference) and FTP (inferior cerebellar gray reference) were extracted using co‐registered 3T‐MRI.Result98 patients (72.6%) were amyloid PET‐positive (EOAD) and 37 (27.4%) were amyloid PET‐negative (EOnonAD). Compared to EOAD, EOnonAD patients had higher MMSE, MOCA and CDR sum‐of‐boxes (CDR‐SB) and were more frequently male (Table 1). Patients with EOAD showed elevated FBB and FTP SUVR in temporoparietal and frontal cortex compared to CN and EOnonAD (Figures 1‐2). In EOAD, MMSE, MOCA and CDR‐SB were significantly correlated with FTP SUVR (Figure 3), while no significant correlations were found with FBB SUVR. EOnonAD patients showed variable FTP binding ranging from negative to mildly elevated binding in anterior temporal and frontal cortex and underlying white matter. Two EOnonAD cases showed intense FTP binding comparable to typical EOAD cases, despite visually and quantitatively negative FBB scans.ConclusionPatients with clinically mild, sporadic EOAD typically show an extensive distribution and burden of tau pathology in the setting of positive amyloid PET. Global clinical measures correlate with tau but not amyloid PET. Over 25% of patients meeting clinical criteria for early‐onset MCI/probable AD have negative amyloid PET, suggesting alternative etiologies for cognitive decline. These findings will inform future design of drug trials in this important and under‐studied population.

AbstractBackgroundGiven that a proportion of mild cognitive impaired (MCI) individuals are amyloid negative and have cerebrovascular disease (CVD), there is a need for biomarkers that can complement AD biomarkers (amyloid and neurodegeneration) for the prediction of future cognitive decline in MCI. The aim of our study was to determine the utility of diffusion tensor imaging (DTI) as a CVD marker for prediction of future cognitive decline in individuals with MCI in conjunction with amyloidosis and neurodegeneration.MethodWe identified 132 MCI individuals ≥60 years old (55 % were amyloid positive and 59 % were male) from the population based sample of Mayo Clinic Study of Aging (MCSA) who had structural MRI, DTI, amyloid PET, and at least one clinical follow‐up after the imaging at the time of their clinical visit as MCI. All imaging scans were acquired and processed using in house developed protocols and pipelines. We used fractional anisotropy in the genu of corpus callosum (FAGCC), as a CVD marker, education/occupation score as a resilience variable, and global cognitive z‐score as the cognitive outcome. Using mixed effect models for cognitive decline, we evaluated three models to test the prognostic ability of (i) DTI marker alone, (ii) DTI marker and amyloid PET, (iii) DTI marker, amyloid PET, and neurodegeneration (cortical thickness). The final parsimonious models included the significant interactions of potential predictors with time, main effects nested within those interactions, and other significant main effects. Since the adjustment for conventional CVD measures like WMH was important to determine the utility DTI over traditional measures, we further assessed the value of FAGCC in conjunction with white matter hyper intensity (WMH).ResultWe found: i) FAGCC was associated with cognitive decline after adjusting for amyloid PET neurodegeneration (Figure 1 and Table 1) and ii) FAGCC was associated with cognitive decline even after controlling for WMH and WMH was no longer significant after accounting for FAGCC (Table 2).ConclusionDTI has significant utility for prediction of cognitive decline in MCI from population‐based samples and will provide complementary information to AD biomarkers.

AbstractBackgroundRecent studies suggest that lifestyle changes (physical exercise, Mediterranean diet adherence, cognitive stimulation, vascular risk management) may protect against cognitive decline and reduce AD pathophysiology. The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is a 2‐year randomized controlled trial to evaluate whether lifestyle interventions can protect cognitive function in older adults (60‐79 years) who are at increased risk for cognitive impairment and dementia. The POINTER Imaging ancillary study will assess effects of the POINTER lifestyle interventions on neuroimaging biomarkers of AD and cerebrovascular pathophysiology, and whether baseline neuroimaging measures predict cognitive responses to the interventions.MethodThe POINTER Imaging ancillary study aims to enroll 1250 participants from the five U.S. POINTER sites. Ancillary study participants will undergo PET imaging (Baseline, Month 24) to measure Aβ ([18F]Florbetaben) and tau ([18F]MK‐6240) burden, and MRI (Baseline, Months 12 & 24) to measure brain morphometry, white matter (WM) hyperintensities and microstructure, and cerebral blood flow. U.S. POINTER provides standardized cognitive, clinical, genetic (e.g. APOE), and lifestyle activity assessments, and demographics for integration with neuroimaging data.ResultThe study design and methods for the POINTER Imaging ancillary study will be presented. The study will examine whether: (1) neuroimaging biomarkers at baseline (Aβ, tau, hippocampal volume, WM hyperintensities) predict intervention‐related cognitive changes and (2) biomarker outcomes are differentially modified by participation in lifestyle interventions. It will also examine changes in exploratory biomarkers (tensor‐based morphometry, microstructural integrity, microbleeds, cerebral blood flow).ConclusionThe POINTER Imaging study aims to address gaps in our understanding of how a variety of lifestyle practices (diet, exercise, cognitive stimulation and vascular risk management) influence brain health. In particular, the study will determine whether imaging measurements of AD and cerebrovascular pathophysiology are amenable to improvement with the POINTER lifestyle interventions. It also aims to determine pathological profiles of individuals who benefit most from such interventions.

AbstractBackgroundWhite matter changes and Alzheimer’s disease pathology frequently co‐exist in the population. Recently we empirically derived subtypes of incident mild cognitive impairment (MCI) and found that there was significant heterogeneity not only in cognitive impairment profiles but also gray matter atrophy suggesting that multiple etiologies may contribute to cognitive impairment in these subtypes. The aim of this study was to examine differences in white matter hyperintensity (WMH) volume in these same MCI subtypes and investigate amyloid deposition in a smaller subset.MethodWe analyzed data from 188 participants with incident MCI enrolled in the population‐based Mayo Clinic Study of Aging (MCSA) identified as having one of four MCI subtypes: subtle cognitive impairment (SCI), amnestic, dysnomic, and dysexecutive. All participants completed 3T imaging at the time of the MCI diagnosis. Using WMH volume scaled by TIV and PIB SUVR, we compared each MCI subtype to 344 cognitively impaired (CU) participants (mean age 78; 46% women) and each other.ResultThe amnestic (n = 83; mean age 80; 43% women), dysnomic (n = 30; mean age 84; 47% women) and dysexecutive subtypes (n = 49; mean age 83; 35% women) had significantly higher WMH volumes compared to CU (p < .02) while the SCI subtype (n = 26; mean age 80; 23% women) did not differ from CU (p = .76). Amongst the MCI subtypes, the amnestic (p = .04) and dysexecutive subtypes (p = .01) had significantly higher WMH volumes than the SCI subtype [Figure 1]. In a smaller subset of individuals who also completed PiB‐PET (17/26 SCI; 39/83 amnestic; 10/30 dysnomic, 28/49 dysexecutive), we found evidence for higher amyloid load in all MCI subgroups except the SCI group (p<0.05) in comparison to CU.ConclusionWe found significantly elevated levels of WMH and amyloid burden in amnestic, dysnomic, and dysexecutive subtypes of incident MCI supporting the notion that both vascular changes and amyloid contribute to the development of clinical symptoms. Surprisingly, WMH and amyloid positivity were not high in SCI suggesting that the pathophysiology in this group needs further investigation.

Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.

IMPORTANCE Hospitalizations are associated with cognitive decline in older adults. OBJECTIVE To determine the association between hospitalization characteristics and the trajectory of cognitive function in older adults. DESIGN Population‐based longitudinal study of cognitive aging. SETTING Olmsted Medical Center and Mayo Clinic, the only centers in Olmsted County, Minnesota, with hospitalization capacity. PARTICIPANTS Individuals without dementia at baseline, with consecutive cognitive assessments from 2004 through 2017, and at least one visit after the age of 60. MEASUREMENTS The primary outcome was longitudinal changes in global cognitive z ‐score. Secondary outcomes were changes in four cognitive domains: memory, attention/executive function, language, and visuospatial skills. Hospitalization characteristics analyzed included elective versus nonelective, medical versus surgical, critical care versus no critical care admission, and long versus short duration admissions. RESULTS Of 4,587 participants, 1,622 had 1 and more hospital admission. Before hospitalization, the average slope of the global z ‐score was −0.031 units/year. After hospitalization, the rate of annual global z ‐score accelerated by −0.051 (95% CI = −0.057, −0.045) units, P < .001, resulting in an estimated annual slope after the first hospitalization of −0.082. The accelerated decline was found in all four cognitive domains (memory, visuospatial, language, and executive, all P < .001). The acceleration of the decline in global z ‐score following hospitalization was greater for medical compared to surgical hospitalizations (slope change following hospitalization = −0.064 vs −0.034 for medical vs surgical, P < .001), and nonelective compared to elective admissions (slope change following hospitalization = −0.075 vs −0.037 for nonelective vs elective, P < .001). The acceleration of cognitive decline was not different for hospitalization with intensive care unit admission versus not. CONCLUSIONS Hospitalization of older adults is associated with accelerated decline of global and domain‐specific cognitive domains, with the rate of decline dependent upon type of admission. The clinical impact of this accelerated decline will depend on the individual's baseline cognitive reserve and expected longevity.