Citations

AbstractBackgroundImpaired olfaction has been associated with neurodegenerative diseases and may be a useful tool to aid in diagnosis or track disease progression. We sought to compare olfaction in participants with the clinical diagnoses of idiopathic REM sleep behavior disorder (RBD), dementia with Lewy bodies (DLB), or Alzheimer’s disease dementia (AD).MethodParticipants from the Mayo Clinic Alzheimer’s Disease Research Center database who had a diagnosis of RBD (n=27, 56% male), DLB (n=51, 84% male), or AD (n=83, 47% male), and were given the Brief Smell Identification Test (B‐SIT) were included in this analysis. The B‐SIT score was calculated as the sum of correct responses (12 possible). Scores were categorized as reflecting anosmia (<6), hyposmia (6‐10), and normal olfactory function (>10). All participants underwent clinical and neuropsychological evaluations.ResultGlobal cognition was lower in AD and DLB than RBD (MMSE 21.5 ±4.9 in AD, 23.3 ±4.3 in DLB, 28.8 ± 1.2 in RBD; p <0.001). The mean B‐SIT total score was significantly lower for DLB (4.5 ± 3.1) compared to RBD (7.5 ± 3.0) or AD (7.0 ± 2.7) (p<0.001) using an ANOVA F‐test. Anosmia was present in 76% of DLB, which compared to 32% in RBD, and 32% in AD (p=0.9). Hyposmia was present in 18% of DLB, 47% of RBD, and 52% of AD (p=ns). Of 22 patients with normal olfaction, X% were DLB, X% RBD, and X% AD (p=X). An item analysis illustrated that participants with DLB were less likely to identify Menthol, Clove, Strawberry, Smoke, and Soap than those with RBD and AD. On serial examination, 12 RBD (60%) patients’ B‐SIT score decreased by at least one point at their most recent visit compared to their baseline visit.ConclusionThese findings suggest that impaired olfactory function is frequent in RBD, DLB and AD, with olfaction being worst in DLB in this cohort. Olfaction was declining in most of those with RBD who underwent serial evaluations. Further analyses are needed to determine if any scents are more sensitive to phenotype, progression, and degree of cognitive impairment. Supported by: NIH grants AG016574, NS100620, AG056639, AG015866

AbstractBackgroundNeocortical tau can be present in over half of patients with Lewy body disease at autopsy, and may influence the timing of when a diagnosis of Dementia with Lewy bodies (DLB) can be made. We examined whether the core features, onset age and latency of probable DLB diagnosis differ by pathological subgroups.MethodAll patients (n=176) met clinical criteria for probable DLB, underwent autopsy and were classified as transitional or diffuse Lewy body disease (TLBD, DLBD). The TLBD and DLBD groups were further subdivided based on the absence of neocortical tangles represented by a low (L) Braak NFT stage of 0‐III or by the presence of neocortical tangles represented by a high (H) Braak NFT stage of IV‐VI. Onset age and latency from cognitive symptom onset to probable DLB were compared for TLBD‐L (n=30), DLBD‐L (n=47), DLBD‐H (n=78) and TLBD‐H (n=21).ResultThe TLBD‐H group met criteria for DLB at an older age (median 76 years) compared to TLBD‐L, DLBD‐L or DLBD‐H groups (median age 70 years, p<0.001). The latency to probable DLB from cognitive onset was shortest for TLBD‐L and DLBD‐L (median < 1 year), which was shorter than DLBD‐H (median 2 years; p=0.016), each of which were shorter than TLBD‐H (median 7.3 years; p<0.001). RBD was more common in men (79% men vs. 40% women, p<0.001) and in patients without neocortical tangles (86% vs. 57%, p<0.001). Patients with RBD met criteria for probable DLB at a younger age than those without a history of RBD (median 69 years vs. 76 years, p<0.001). Men had a shorter latency to probable DLB than women (median 1.0 year vs. 2.8 years, p<0.01), but this was related to RBD status that was more common in men.ConclusionPresence of RBD in both sexes led to a younger onset age and shorter latency to diagnosis of probable DLB. Patients with more widespread tau relative to α‐synuclein pathology (TLBD‐H) were older when probable DLB was diagnosed compared to those with so‐called pure Lewy body disease (TLBD‐L, DLBD‐L) or those with diffuse α‐synuclein and tau pathology (DLBD‐H).

AbstractBackgroundThere have been no standard definitions of successful cognitive aging (SA). Our goal was to apply different SA definitions to the 80+ population in the Mayo Clinic Study of Aging (MCSA) on the basis of (i) cross‐sectional vs longitudinal cognition (“Exceptionality” vs “Maintenance”) and (ii) memory vs global cognitive scores and explore the participant characteristics for each SA definition on the basis of baseline imaging markers and mid/late‐life health.MethodWe included 2157 participants from the MCSA, all ≥80 years at baseline and classified them into two SA definitions using memory z‐scores or global z‐scores: “Exceptionality” when high cognitive performance was observed (z‐scores ≥ 1 SD), “Maintenance” when cognitive function was preserved over time (Slope ≥ ‐0.1/year using mixed‐effect models). Our imaging markers were: PET‐based amyloid positivity (A+), MRI‐based neurodegeneration positivity (N+), WMH, FDG‐PET SUVR markers of resilience (anterior cingulate, ACC) and AD‐related areas (posterior cingulate). We also examined APOE4 status and other health characteristics. Group comparison was performed within each definition (e.g. exceptional vs non‐exceptional memory) using ANCOVA and binary logistic regressions with adjustments for age, sex, and education.ResultA lower number of participants met the “Exceptionality” criteria (10%: memory and 4%: global). A greater number met the “Maintenance” criteria (53%: memory and 32%: global). Less than 7% showed both “Exceptional” cognition and “Maintenance” over time. Participants across SA definitions were in general younger, more often women, with a higher education/occupation and less often APOE4 carriers. All SA groups showed higher ACC metabolism and lower N+ compared to the corresponding non‐SA group. More than 50% of the participants in the “Exceptional” groups were A+ (57%: memory, 50%: global). A+ frequency was significantly lower in the “Maintainers” (<42%). Better mid/late‐life health indicators were observed in the SA groups, including lower cardio‐metabolic conditions, higher mid‐life cognitive activities, lower depressive symptomatology, lower hypersomnolence and better self‐perception of health.ConclusionDifferent SA definitions identified mainly non‐overlapping 80+ groups that showed several common features including higher education/occupation, percent of women, better lifestyles, lower N+ frequency, and higher ACC metabolism. Lower A+ frequency was a key distinct feature of “Maintenance”.

AbstractBackgroundThe Mayo Clinic Study of Aging and Alzheimer’s Disease Research Center used GE scanners exclusively for MRI from 2005‐2017, employing consistent imaging parameters throughout. In 2017 the MRI protocol was updated and migrated to Siemens Prisma scanners; 113 participants were scanned with each configuration. The GE protocol included a 2D FLAIR. The Siemens protocol includes 3D FLAIR and T2‐weighted images. Both vendor protocols included a 3D T1. Experience with multi‐center trials suggests such changes may introduce systematic variations.MethodsGE scanning was carried out on 3T scanners with 12 channel phased array head coils. Siemens Prisma scanning was carried out with 64 channel head/neck coils. Imaging sequences and parameters are given in Table 1. We used the MCALT template and priors to perform standard six‐class segmentation on T1‐weighted (T1w) images. SPM12 unified segmentation supports the simultaneous use of multiple input contrasts; segmentation of multi‐contrast inputs was done using eight tissue classes including deep grey and white matter hyperintensity (WMH) priors. For a given MRI study (either GE or Siemens) all images were registered and resampled to the study’s T1w image. Two‐channel (T1+FLAIR) segmentation was performed on GE scans. Two and three‐channel segmentations were performed on Siemens scans. Comparisons included total intracranial volume, total tissue volume and total grey matter volumes, as well as hippocampal volumes and grey matter volume in a temporal lobe meta‐ROI.ResultsFour participants were excluded for poor image quality: one poor GE T1w quality, one poor Siemens T1, T2 and FLAIR quality, and two poor Siemens T2 quality. Comparisons were made among 109 participants aged 34–97 (Table 2). By visual assessment, adding T2‐weighted images always improved intracranial volume correctness. Using the combination of single‐channel GE and three‐channel Siemens generally improved cross‐vendor agreement on grey matter volumes with the exceptions of hippocampal and intracranial volume estimates.ConclusionRequiring multiple input channels increases the likelihood that the result of a study being excluded. Combining T1w, FLAIR and T2w images allows the delineation of WMH and deep grey matter probability outputs but may introduce differences in GM volumes.

AbstractBackgroundEarly changes in white matter (WM) measured using diffusion tensor imaging (DTI) are sensitive markers of WM degeneration and predict future cognitive decline in mild cognitive impairment (MCI). We sought to examine whether changes in WM integrity differed among empirically derived subtypes of incident MCI.MethodParticipants included 66 individuals with incident MCI enrolled in the population‐based Mayo Clinic Study of Aging (MCSA) previously identified via cluster analysis as having one of four MCI subtypes: subtle cognitive impairment (SCI; n = 13, mean age 78; 15% women), amnestic (n = 24; mean age 79; 54% women), dysnomic (n = 8; mean age 79; 38% women), and dysexecutive (n = 21; mean age 82; 33% women). All participants completed 3T MRI at the time of the MCI diagnosis. We compared the MCI subtypes to 100 cognitively unimpaired (CU) participants (mean age 77; 49% women). We used tract‐based spatial statistics to examine voxel‐level differences in fractional anisotropy (FA). We also fit a linear regression model to regional FA data from the corpus callosum (CC), the major interhemispheric WM connection, to quantify the relative differences in WM damage.ResultAll MCI subtypes except the subtle cognitive impairment group had widespread decreased FA. Relative to CU, the amnestic cluster showed decreased FA in the CC (genu), cingulum, fornix and uncinate fasciculus (UF); the dysnomic cluster showed decreased FA in the CC (genu, splenium), fornix, UF, anterior thalamic radiation (ATR), and corticospinal tract (CST); and the dysexecutive group showed decreased FA in the CC (genu, body, splenium), cingulum, UF, ATR, CST, and superior longitudinal fasciculus. The dysexecutive cluster had the most severe bilateral involvement among all MCI subtypes. The regional analysis showed that the amnestic, dysnomic, and dysexecutive groups (but not the subtle cognitive impairment group) had significantly lower FA of the CC compared to CU (p<0.001; Figure 1).ConclusionWe found widespread WM degeneration in the commissural and long association fibers in amnestic, dysnomic, and dysexecutive incident MCI subtypes. The extent of CC damage seen in Figure 1. confirmed the voxel‐level results and suggests that interhemispheric WM disconnection is relevant to the development of MCI.

AbstractBackgroundOptimizing software to measure Flortaucipir (AV‐1451) tau PET SUVR change‐over‐time is crucial for longitudinal observational studies and potential trials of anti‐tau agents. Ideal measurements should have good repeatability (low noise), and should clearly separate clinical cognitive groups.MethodWe compared 456 software methods varying in their use of: two major packages, each with cross‐sectional and longitudinal variants (FreeSurfer and an in‐house pipeline using SPM12, MCALT, and ANTs); three temporal‐lobe target regions (each with/without adjacent WM); seven reference regions; five PVC methods; and two statistics (mean vs. median). We used linear mixed‐effects models to compare methods using scans from 88 Mayo Clinic participants (46 cognitively impaired, 42 impaired, excluding those with symptoms inconsistent with the AD pathology imaged by Flortaucipir), each with three visits with PET and MRI. From these models, we calculated two criteria for each pipeline: relative measurement error (residual from the linear mixed model), and the t‐statistic for testing differences in group‐wise rates of change (which has a Cohen’s d interpretation of effect size for longitudinal data). We plotted both criteria orthogonally, then ranked methods overall according to the average of their ranks on both individual criteria.ResultMethods using the entorhinal cortex target region performed poorly in both criteria (Figure 1). The top 10 methods by the combined criteria (top‐left in Figure 2) all used the Mayo SPM12‐based pipeline with an eroded supratentorial WM (eSWM) or composite (eSWM + pons + cerebellum) reference region. Among them, 9/10 used the longitudinal variant, 8/10 used 2‐compartment PVC, and 7/10 used median. Altogether, our results favored SUVR methods with a longitudinal design (simultaneously calculating all time‐points), a temporal‐lobe meta‐ROI including adjacent WM, a composite reference region, 2‐class voxel‐based (Meltzer) PVC, and median statistics. Methods using GTM‐Tau PVC (Baker et al., Data in Brief, 2017) had the highest group separation but relatively large measurement error, giving them ranks 197 and 199 out of 456 with the joint (average rank) criteria.ConclusionCompared with popular FreeSurfer‐based methods (ranks 176, 258; Figure 2), our top‐ranked approaches approximately halved error in SUVR measurements while maintaining or improving group separation.

AbstractBackgroundBlack/African Americans (B/AA) are nearly twice as likely as White/European Americans (W/EA) to have Alzheimer’s or other dementias and are more likely to provide care for persons with dementia (PwD). Evidence indicates that B/AA desire having someone to talk with who understands their caregiving challenges, yet are less likely than W/EA to ask for support. Further, community resources supporting caregivers’ ability to maintain health and well‐being developed with input from B/AA are lacking. We aimed to determine how a technology‐assisted caregiver matching service designed with user input could address diverse caregiver needs.MethodsFor this Phase I project, 32 caregivers of PwD (31% B/AA) participated in semi‐structured focus groups and completed surveys to provide input regarding characteristics deemed valuable in someone they would choose to be connected with for emotional support. Through an iterative process as new information emerged, a web‐based prototype and matching algorithm were developed.ResultsAge and sex distribution were similar between B/AA and W/EA (71 and 68 yrs; 90% and 86%, respectively). W/EA were most likely caring for a spouse/significant other (73%) whereas B/AA were caring for spouse/significant other (30%), a parent (30%;18% W/EA), a sibling (10%; 0% W/EA), or other (30%; 9%W/EA). Level of emotional support was rated “poor” (50% B/AA, 18% W/EA); “average” (0% B/AA, 14% W/EA); “good” (40% B/AA, 41% W/EA); and “excellent” (10%, 27% W/EA). Characteristics B/AA deemed “very important” or “absolutely essential” for a supportive match included living in close proximity, same ethnicity, similar lifestyle, and common values. Alternatively, W/EA selected similar age, living in close proximity, same sex, similar lifestyle, common values and interests, having the same caregiving relationship, and having the same diagnosis in their PwD (see chart).ConclusionIn this cohort, B/AA reported poorer levels of emotional support than W/EA. B/AA were equally likely to be supporting a spouse/significant other as a parent or other acquaintance, whereas W/EA provided support primarily for a spouse/significant other. Characteristics B/AA and W/EA consider important in a supportive caregiver match show important differences that should be considered when optimizing caregiving resources. Phase II will explore further in a much larger sample.

AbstractBackgroundAn analysis of the ethnoracial and educational composition of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, and the relationship between ethnoracial/education and screening, enrollment, dropout, and biomarkers is needed to assess the generalizability of ADNI data to diverse populations, and to inform efforts to increase diversity.MethodData from all 4 ADNI phases were used to determine ethnoracial and educational breakdown and differences of the following ADNI participation metrics: screening, screen fails, enrollment, as well as drop‐out within and between ADNI phases. Multivariable logistic regression was used to analyze the association between ethnoracial and educational group and either amyloid positivity or ApoE e4 status, adjusting for age, gender, and diagnostic group.ResultAcross the 4 ADNI phases (screened: n=3,739; enrolled: n=2,286), 11% of screened and enrolled participants identified as Latinx, non‐Latinx Black, or non‐Latinx‐Asian and 16% of the screened and 15% of the enrolled participants reported having =<12 years of education. In contrast, the 2019 US Census American Community Survey for adults 60+ reports 25% non‐White, and 44 % =< 12 years of education. There were no observed differences between participants from historically underrepresented ethnoracial groups and NLW participants in screen fail and dropout rates. We found that individuals with =<12 years of education failed screening at a significantly higher rate (p=.01) and rolled‐over significantly less to subsequent ADNI phases (p<.001) compared to individuals with an education >12 years. Identifying as non‐Latinx Asian was associated with lower odds of being amyloid positive (OR=0.36; p=.014) and an ApoE4 carrier (OR=0.36; p=.008) compared to NLW. Identifying as Latinx was associated with being less likely to be amyloid positive than NLW participants (OR=0.54; p=.031).ConclusionCompared to the US Census, ADNI underrepresents enrollment of Latinx, non‐Latinx Black, and non‐Latinx Asian participants and those with low educational attainment. Hence, ADNI reflects the on‐going challenges with recruiting and enrolling underrepresented populations into most AD research. Our findings highlight the need for ADNI to increase enrollment and engagement of underrepresented ethnoracial and educational populations. ADNI is actively undertaking steps to implement improved recruitment approaches aimed at increasing ethnoracial and socioeconomical representation in multi‐center cohort studies.

Abstract Frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP) is a complex heterogeneous neurodegenerative disorder for which mechanisms are poorly understood. To explore transcriptional changes underlying FTLD-TDP, we performed RNA-sequencing on 66 genetically unexplained FTLD-TDP patients, 24 FTLD-TDP patients with GRN mutations and 24 control participants. Using principal component analysis, hierarchical clustering, differential expression and coexpression network analyses, we showed that GRN mutation carriers and FTLD-TDP-A patients without a known mutation shared a common transcriptional signature that is independent of GRN loss-of-function. After combining both groups, differential expression as compared to the control group and coexpression analyses revealed alteration of processes related to immune response, synaptic transmission, RNA metabolism, angiogenesis and vesicle-mediated transport. Deconvolution of the data highlighted strong cellular alterations that were similar in FTLD-TDP-A and GRN mutation carriers with NSF as a potentially important player in both groups. We propose several potentially druggable pathways such as the GABAergic, GDNF and sphingolipid pathways. Our findings underline new disease mechanisms and strongly suggest that affected pathways in GRN mutation carriers extend beyond GRN and contribute to genetically unexplained forms of FTLD-TDP-A.

Abstract Introduction We investigated the longitudinal relationship between cortical amyloid deposition, anxiety, and depression and the risk of incident mild cognitive impairment (MCI). Methods We followed 1440 community‐dwelling, cognitively unimpaired individuals aged ≥ 50 years for a median of 5.5 years. Clinical anxiety and depression were assessed using Beck Anxiety and Depression Inventories (BAI, BDI‐II). Cortical amyloid beta (Aβ) was measured by Pittsburgh compound B positron emission tomography (PiB‐PET) and elevated deposition (PiB+) was defined as standardized uptake value ratio ≥ 1.48. We calculated Cox proportional hazards models with age as the time scale, adjusted for sex, education, and medical comorbidity. Results Cortical Aβ deposition (PiB+) independent of anxiety (BAI ≥ 10) or depression (BDI‐II ≥ 13) increased the risk of MCI. There was a significant additive interaction between PiB+ and anxiety (joint effect hazard ratio 6.77; 95% confidence interval 3.58–12.79; P = .031) that is, being PiB+ and having anxiety further amplified the risk of MCI. Discussion Anxiety modified the association between PiB+ and incident MCI.