Jack, Clifford R.

AbstractBackgroundThe implications of a positive tau (T) Positron Emission Tomography (PET) with a negative beta amyloid (A) PET are not well understood. We investigated cognitive performance in participants who were positive on tau PET but negative on beta amyloid PET.MethodWe evaluated 98 participants from the Mayo Clinic Study of Aging (MCSA) and the Mayo Clinic Alzheimer’s Disease Research Center (ADRC) who were positive for Flortaucipir (FTP) and negative for PiB PET. Participants were classified as A‐ and T+ based on Standardized Uptake Value Ratios (SUVr) of FTP and PiB PET scans, and were matched 2:1 to amyloid negative and tau negative cognitively unimpaired controls from the MCSA. Cognitive test scores were compared between the combined ADRC/MCSA group and MCSA only group. We also examined clinical diagnoses in our cohort. The most common were Alzheimer’s Disease, mild cognitive impairment (MCI), Dementia with Lewy Bodies (DLB) and Frontotemporal dementia (FTD) amongst others.ResultThere were no significant differences in demographic features or PiB SUVr between groups. There were significant differences in APOE genotype between groups. In the combined ADRC/MCSA group, 25% of A‐T‐ and 13% of A‐T+ were APOE e4 carriers (p = 0.016), and in the MCSA only group, 27% of A‐T‐and 10% of A‐T+ were APOE e4 carriers (p = 0.010). In the combined ADRC/MCSA group, the A‐T+ group demonstrated lower performance than the A‐T‐ group on the Mini Mental Status Exam (MMSE) (p<0.001), Wechsler Memory Scale‐Revised Logical Memory I (p<0.001) and Logical Memory II (p<0.001), Auditory verbal Learning Test Delayed Recall (AVLT) (p = 0.004), Category fluency animals (p = 0.005), Category fluency vegetables (p = 0.021), Trailmaking test A (p<0.001), Trail making test B (p<0.001), Wechsler Adult Intelligence Scale‐IV (WAIS‐IV) (p = 0.002), and Unified Parkinson’s Disease Rating Scale part III (UPDRS III) (p = 0.003). In the MCSA only group, the A‐T+ group had lower scores on Logical Memory II (p = 0.022), AVLT delated recall (p = 0.04), and WAIS‐R block design (p = 0.035) compared to the A‐T‐group.ConclusionPositive tau PET in the absence of beta‐amyloid positivity is associated with lower cognitive performance.

AbstractBackgroundThe balance between production, clearance, and toxicity of Ab peptides is central to AD pathobiology. Though highly variable in terms of age of symptom onset (AAO), hundreds of pathogenic variants in Presenilin‐1 (PSEN1) cause autosomal dominant forms of AD (ADAD). PSEN1 forms the catalytic core of the γ‐secretase complex and thereby directly mediates the production of longer, aggregation‐prone Aβ peptides relative to shorter, non‐aggregating peptides. We hypothesized that the broad AAO and biomarker heterogeneity seen across ADAD would be predictable based on mutation‐specific differences in γ‐secretase function, as measured by a ratio of production of shorter vs. longer Aβ species.MethodsAβ‐37, 38, 40, 42, and 43 production was quantified from 162 unique PSEN1 variants expressed in HEK293 cells engineered to lack endogenous wild‐type PSEN1/2 (Figure 1). Prediction of AAO was carried out in 107 PSEN1 variants (characterized by Liu et al, 2022) and then replicated with a set 55 unique PSEN1 variants represented in the Dominantly Inherited Alzheimer Network (DIAN; n = 190 corresponding variant carriers with detailed cognitive and biomarker data; Figure 2).ResultsMutation‐level variations in Aβ production, including a novel composite representing γ‐secretase function (lower score = less g‐processivity), from the cellular model were highly predictive of actual AAO across the 162 mutants examined (Non‐DIAN variants [N = 107]: r = 0.71, p <2.2e‐16; DIAN variants [N = 55]: r = 0.61, p = 6.10e‐07). Our cell‐based γ‐secretase function composite was strongly associated with cerebral PiB‐PET β‐amyloid burden (B[SE] = ‐0.03[0.01], p = 4.06e‐07), MRI gray matter volume (B[SE] = 44.22[6.4], p = 5.15e‐01), cerebrospinal fluid Aβ42/40 (B[SE] = 6.3e‐04[1.5e‐04], p = 4.46e‐05) and phosphorylated tau‐217 (B[SE] = ‐0.01[0.002], p = 1.98e‐05), Clinical Dementia Rating®‐ Sum of Boxes (B[SE] = ‐0.07[0.02], p = 4.86e‐05), and Mini‐Mental State Exam (B[SE] = 0.11[.03], p = 2.17e‐04).ConclusionsBiochemical variations in γ‐secretase function across PSEN1 pathogenic variants broadly predicted the cross‐sectional clinical, cognitive, and biomarker course of ADAD, including AAO. These findings elucidate the critical link between γ‐secretase function, Aβ production, and severity of AD. The novel approach designed here also represents a tool to account for heterogeneity in ADAD clinical trials and to assess the pathogenicity of PSEN1 variants of unknown significance or with limited family history.

AbstractBackgroundBoth 11C‐Pittsburgh compound‐B (PiB) and 18F‐Flutemetamol (FMT) have off‐target white matter (WM) signal that increases with age, but WM uptake is stronger with FMT than PiB. Although their WM patterns are comparably associated with age, the difference between them may impact visual and quantitative assessments of cortical amyloid‐β uptake. Our objective was to determine whether PiB PET and FMT PET visual assessments agree with quantitative cortical amyloid‐β evaluations.MethodA cohort of cognitively unimpaired (CU) younger adults with median (range) of 39 (30, 48) years (N = 30), CU older adults with age of 67 (61, 83) years (N = 30) and older adults with AD dementia with age of 67 (54‐84) years (N = 23) underwent MRI, PiB PET and FMT PET. PiB‐PET and FMT PET scans were evaluated visually by two nuclear medicine specialists blinded to participant information. Disagreements were reconciled with a second, joint round of visual reads. Readers applied standard visual assessment criteria using the absence of regional gray matter (GM) and WM contrast as an indication of a positive scan. The global cortical PiB standard uptake value ratio (SUVr) was obtained referencing to cerebellar crus uptake and converted to centiloid values. Participants were classified as PiB positive/negative using a centiloid cut‐off of 22.ResultPiB and FMT visual positivity agreed with quantitative positivity on PiB‐based centiloid values in 69/83 for PiB and 78/83 for FMT. There were 10 disagreements with PiB, 1 disagreement with FMT and 4 disagreements with both. Few disagreements occurred near the centiloid threshold. Interestingly, all disagreements between visual reads (amyloid‐β positive) and centiloid (amyloid‐β negative) in the CU younger were with PiB. FMT disagreements were always in the CU older and AD groups.ConclusionDisagreements between amyloid‐β positivity on visual reads and quantitative measurements (centiloid) may depend on age and tracer. PiB WM uptake is lower than FMT WM uptake, which may lead to reduced contrast between GM and WM (part of the standard criteria for visual reads), especially in younger ages. In contrast, WM uptake increases at older ages, and increased WM signal may bleed more into cortical regions, hiding subtle, near‐threshold cortical uptake, especially with FMT.

AbstractBackgroundHearing loss is associated with cognitive decline and dementia. The ongoing ACHIEVE Study was the first randomized trial to test the effects of hearing treatment on three‐year cognitive change. The ACHIEVE – Brain Health Follow‐Up Study will extend follow‐up to six years post‐randomization to determine the long‐term and potentially nonlinear effects of hearing treatment vs. health education control with delayed treatment on dementia and cognitive decline.MethodsParticipants will be recruited from the 977 participants enrolled in the ACHIEVE Study. After Year 3, hearing intervention participants will continue to receive hearing care and the control group will be offered the hearing intervention. Co‐primary study outcomes will be cognitive decline and a composite of incident mild cognitive impairment (MCI)/probable dementia. Secondary outcomes include domain‐specific cognitive decline, brain structural changes on magnetic resonance imaging (MRI), and other functional outcomes (e.g., physical activity, social isolation). Power analyses for the co‐primary outcomes assumed an annual attrition rate of 10% and an immediate, lagged, or cumulative treatment effect.ResultsWhen comparing multi‐year cognitive decline (Table 1), in the scenario of a constant treatment effect that assumes a strong and immediate treatment effect, the benefit of extended follow‐up is not greater power, but additional information on the long‐term effects of hearing intervention. In a scenario of a lagged treatment effect, a two‐year lag in the treatment effect causes the Year 3 assessment to be severely underpowered (28%) but provides sufficient power (80%) at Year 6 despite attrition. In a scenario of a cumulative treatment effect that increases with time, analyses are similarly underpowered at Year 3 (65%) but not at Year 6 (82%). The same pattern is evident when estimating the power at Year 3 (57%) and Year 6 (80%) to detect incident MCI/probable dementia in a Cox regression model that assumes a protective hazard ratio of 0.70 (Figure 1).ConclusionAcross all non‐constant scenarios of potential treatment effect, six years of follow‐up increases precision and provides valuable information to better delineate long‐term cognitive trajectories. The ACHIEVE ‐ Brain Health Follow‐Up Study will be completed in 2025.

AbstractBackgroundHigher blood pressure (BP) is associated with greater white matter hyperintensity (WMH) volume, but research is limited in recently menopausal and postmenopausal women. Postmenopausal women have a greater burden of WMH than men and premenopausal women, but whether hormone therapy (HT) modifies the association between systolic and diastolic BP (SBP, DBP) and WMH is unknown. We investigated the association of SBP and DBP in recently postmenopausal women who participated in an HT trial with WMH volumes measured 14 years later and explored whether HT modified the results.MethodWomen with good cardiovascular (CV) health, who were previously enrolled in a randomized placebo‐controlled HT trial, the Kronos Early Estrogen Prevention Study (KEEPS), participated in the current observational KEEPS continuation (n = 212; median age = 67; range 58‐72). SBP and DBP were measured at KEEPS baseline (median age = 54 years; range 44‐59) and continuation. Antihypertensive users were excluded (baseline, n = 34; continuation, n = 61). Approximately 10 years after the end of KEEPS HT versus placebo 4‐year interventions, automated WMH volumes were measured from 3D‐FLAIR MRI. Associations of KEEPS baseline and continuation SBP and DBP with log WMH volume at KEEPS continuation were tested using linear regression analyses adjusting for covariates in model 1 (age, total intracranial volume, study site) and model 2 (model 1 plus cardiovascular risk factors (CVRF)).ResultHigher KEEPS baseline SBP and DBP were associated with greater WMH volume measured 14 years later, and after adjusting for CVRF (p≤0.01). Similarly, in KEEPS continuation, higher SBP and DBP were associated with greater WMH volume measured concurrently and after adjusting for CVRF (p<0.001). We did not find statistically significant evidence that HT versus placebo modified these associations. Topographically, higher SBP and DBP were associated with greater periventricular WMH in the frontal and parietal lobes after adjusting for age (Figure).ConclusionHigher SBP and DBP in recently menopausal women with good CV health were associated with greater WMH volumes more than a decade later after adjusting for CVRF, independent of randomization to HT. Our results show the importance of blood pressure control in recently menopausal women, that may reduce the white matter injury later in life.

AbstractBackgroundQuantified accelerated aging, or “age gap,” defined as the difference between a person’s estimated age using a biological measurement, such as a brain image, and their true age, has strong associations with multiple neurodegenerative etiologies. As an alternative to imaging‐based measurements, in this study we developed an age prediction model based on gait patterns and used this model to quantify accelerated aging as an indicator of cognitive impairment and predictor of future cognitive decline.MethodA database of 16,917 pressure sensitive walkway exams from 5,307 participants in the Mayo Clinic Study of Aging was used to develop an age estimation model that included two main components. First, a novel dimensionality reduction and feature selection algorithm was used to extract gait features. Second, a regularized linear model was trained using these features to produce a sparse, clinically interpretable estimator. This model was used to calculate a gait age gap metric, which was tested for association with measures of cognitive impairment and future cognitive decline.ResultThe distributions of gait age gap in age‐matched participants defined as having no cognitive impairment (CU, n = 573), mild cognitive impairment (MCI, n = 520), or a clinical diagnosis of dementia due to Alzheimer’s (AD, n = 53) are shown in Fig 1a. A significant shift in the mean is observed between all three groups. Fig 1b. shows the distributions of baseline gait age gap values amongst individuals categorized by cognitive decline: stable CU (n = 644), MCI converters (n = 89), stable MCI (n = 78), and significant impairment converters (SI, n = 35). The baseline gait age gap in stable CU individuals was found to be significantly lower than in the already impaired or converter groups.ConclusionIn this study, we developed a model for estimating accelerated aging from gait variables in the form of a gait age gap metric. This metric was shown to be strongly associated with AD‐driven cognitive impairment as well as predictive of future cognitive decline. These results highlight the potential clinical utility of quantitative gait analysis in behavioral neurologic assessment of cognitive impairment in AD.

AbstractBackgroundEasily accessible and self‐administered cognitive assessments that can aide early identification of individuals at risk for Alzheimer’s disease (AD) dementia are critical for timely intervention. Mayo Test Drive (MTD) is a remote, self‐administered, multi‐device compatible, web‐based cognitive assessment (Stricker et al., 2022). We investigated continuous associations between MTD and amyloid and tau PET biomarkers and compared the strength of association to PET biomarkers and in‐person cognitive measures.Method556 adults from the Mayo Clinic Study of Aging participated (mean age = 70.6; mean education = 16.0 years; 50.7% female; 97.5% white). Participants were predominantly cognitively unimpaired (95.3%; 4.7% had a consensus diagnosis of mild cognitive impairment). Participants completed (1) brain amyloid and tau PET scans, (2) in‐person neuropsychological assessment including Mayo Preclinical Alzheimer’s disease Cognitive Composite (Mayo‐PACC) and Global‐z, and (3) remote MTD comprised of the Stricker Learning Span (SLS) and the Symbols test, which are combined into an MTD composite. Multiple regressions adjusted for age, sex, and education queried associations between imaging biomarkers [amyloid meta‐region of interest (ROI), tau meta‐ROI, and tau entorhinal cortex (EC)‐ROI)] and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites.ResultLower performances on MTD composite and Mayo‐PACC were associated with higher amyloid meta‐ROI (p’s<.05; Table 1). MTD composite was also negatively associated with tau meta‐ROI and EC‐ROI (p’s<.05). For memory measures, SLS sum of trials was negatively associated with tau meta‐ROI (p = .01) and EC‐ROI (p<.001) with no associations between AVLT sum of trials and PET (p’s≥.06; Table 2). Among processing speed/executive function measures, worse Trails B performance was associated with higher amyloid meta‐ROI (p<.001). Symbols and Digit Symbol Coding were not associated with PET (p’s≥.29). A multivariable model with MTD explained significantly less variance (p = .04) for amyloid meta‐ROI (adjusted R‐squared = 0.165) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.176); remote and in‐person composite measures were comparable for other associations (p’s>.05).ConclusionMTD is associated with continuous measures of amyloid and tau PET biomarkers of AD, demonstrating criterion validity for MTD. Several associations were comparable to relationships between gold‐standard in‐person neuropsychological tests and imaging biomarkers.

AbstractBackgroundBrief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer’s disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers.MethodParticipants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer’s disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aβ)‐42 and Aβ‐40 to create an Aβ42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites.ResultLower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p’s≤.02), NfL (p’s≤.003), and p‐tau181 (p’s = .04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p’s≤.01) and NfL (p’s≤.03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p’s≤.02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p’s≤.02), and worse Digit Symbol Coding was associated with lower Aβ42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p = .04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p’s>.05).ConclusionRemote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD’s criterion validity.

AbstractBackgroundThe language and visual variants are two common clinical phenotypes of atypical Alzheimer’s disease (AD). There are reports of overlap between these clinical phenotypes, however, the extent and frequency of overlap, and its neuroanatomical underpinnings remain unclear.MethodEighty‐two patients with biomarker‐confirmed AD who presented with either predominant language (n = 34) or visuospatial/perceptual (n = 48) deficits were recruited by the Neurodegenerative Research Group and underwent neurological and neuropsychological testing, MRI and [18F]flortaucipir‐PET. The clinical examination included testing for simultanagnosia, Gerstmann’s syndrome, ideomotor apraxia, visuospatial‐visuoperceptual function, confrontational naming, sentence repetition and letter and animal fluency. Cut‐points for abnormality were defined for each test and used to classify the following groups: language‐pure (no visual‐function deficit), language‐plus (impaired on one visual‐function), language‐mixed (impaired on two visual‐functions), visual‐pure (no language‐function deficit), visual‐plus (impaired on one language‐function) and visual‐mixed (impaired on two language‐functions). Patterns of relative volume loss and flortaucipir uptake were assessed using SPM12.ResultOf the 82 patients, 11 (13%) were language‐pure, 16 (20%) language‐plus, 7 (8%) language‐mixed, 10 (12%) visual‐pure, 17 (21%) visual‐plus and 21 (26%) visual‐mixed, with no difference in disease duration across groups. Visual deficits in the language‐plus group consisted of mild simultanagnosia in 29%, Gerstmann’s syndrome in 24%, and mild visuospatial deficits. In the language‐mixed group 86% had simultanagnosia and 43% had Gerstmann’s syndrome, with visuospatial‐visuoperceptual deficits comparable to the visual‐pure group. Language deficits in the visual‐plus group consisted of mild anomia and reduced letter and animal fluency. The visual‐mixed group reported naming and repetition deficits, and reduced fluency, comparable to the language‐predominant groups. All three language groups showed left temporal volume loss and flortaucipir uptake, with greater involvement of the parieto‐occipital and frontal lobes in language‐plus and language‐mixed groups. All three visual groups showed occipital volume loss and flortaucipir uptake, with greater involvement of the parieto‐temporal and frontal lobes in visual‐plus and visual‐mixed groups.ConclusionOnly 25% of our cohort showed a pure language or visual presentation, highlighting the high frequency of syndromic overlap in atypical AD and the diagnostic challenge of categorical phenotyping. Syndromic overlap was associated with neuroanatomical overlap and more widespread patterns of atrophy and tau deposition.

AbstractBackgroundPosterior cortical atrophy (PCA) and dementia with Lewy bodies (DLB) show overlapping patterns of hypometabolism affecting posterior regions of the brain. Both syndromes show disruptions in functional connectivity, but it is unknown how patterns of disruption within and between networks compare between PCA and DLB.MethodThirty amyloid‐positive PCA, 27 amyloid‐positive DLB (DLB+), 24 amyloid‐negative DLB (DLB‐) patients and 30 cognitively normal amyloid‐negative (CN) individuals were recruited at Mayo Clinic, Rochester, MN, and underwent structural and resting state functional MRI. Spatially preprocessed data was analyzed using the CONN functional connectivity toolbox to explore nine networks: visual, sensorimotor, dorsal‐attention, cerebellar, frontoparietal, memory, language, salience, and default mode networks (DMN). Multivariate linear regression models corrected for multiple comparisons and adjusted for age and sex were fit to analyze within‐network connectivity (Fisher’s R‐to‐Z transformations). Between‐network connectivity was assessed with group level difference maps (two‐sample t‐test) corrected for false discovery rate at p<0.05.ResultMultivariate linear regression models showed reduced within‐network connectivity across all DMN components, sensorimotor, and dorsal‐attention networks, with trends for the visual network in PCA compared to CN. Both DLB‐ and DLB+ showed reduced within‐network connectivity for the cerebellar network, with DLB+ showing reduced connectivity in the posterior‐DMN, memory, and salience networks compared to CN. DLB‐ showed increased connectivity in dorsal‐attention and DMN, while DLB+ showing reduced connectivity in the memory network compared to PCA. Between‐network analysis showed an increase in cerebellar‐to‐sensorimotor connectivity in all three groups, with increase in cerebellar‐to‐dorsal‐attention connectivity in DLB‐ and PCA compared to CN. DLB‐ showed a decrease in posterior‐DMN‐to‐sensorimotor connectivity, while PCA showed reduced connectivity between all DMN components and dorsal‐attention‐to‐sensorimotor connectivity compared to CN. There was an increase in connectivity between all DMN components and a reduction in dorsal‐DMN‐to‐sensorimotor connectivity in both DLB‐ and DLB+ compared to PCA.ConclusionPCA was associated with connectivity disruptions across all DMN components, sensorimotor, dorsal‐attention and visual networks, while both DLB groups showed disruptions in cerebellar networks, with DLB+ showing further disruptions in posterior‐DMN, memory and salience networks. These findings suggest that both PCA and DLB have unique brain network disruptions, with amyloid status influencing connectivity in DLB.