Preclinical Alzheimer Research Articles

Amyloid β‐dependent tau phosphorylation is triggered by reactive astrocytes in preclinical Alzheimer’s disease

AbstractBackgroundA significant percentage of Aβ‐positive cognitively unimpaired (CU) individuals do not develop detectable downstream tau pathology and, consequently, cognitive decline.Experimental literature suggest that reactive astrocytes are necessary to unleashing Aβ effects in pathological tau phosphorylation.Here we aimed to investigate whether astrocyte reactivity is key to determining the association of Aβ burden with early tau phosphorylation in preclinical Alzheimer’s disease (AD).MethodWe assessed 1,016 CU individuals from two research and one population‐based cohort (TRIAD, Pittsburgh and MYHAT) with Aβ (plasma or PET), plasma p‐tau and GFAP measures. Individuals were classified as positive (Ast+) or negative (Ast‐) for astrocyte reactivity using a cutoff based on plasma GFAP of younger Aβ‐ individuals.Lowess method and linear regressions accounting for age and sex were used to model the trajectories of plasma p‐tau epitopes as a function of Aβ burden. Cohen’s d corrected for age and sex was used to estimate effect sizes between groups.ResultWe observed that plasma p‐tau181 levels increased as a function of Aβ only in CU Ast+ individuals from Pittsburgh (β = ‐0.35,t = 3.10,p = 0.003,Fig.1a,b), MYHAT (β = ‐0.20,t = 2.26,p = 0.026, Fig.1d,e) and TRIAD (β = 0.46,t = 2.92,p = 0.004;Fig.1g,h) cohorts. A significant interaction between Aβ burden and astrocyte reactivity status on plasma p‐tau181 levels was observed in the Pittsburgh (β = ‐0.29,t = 2.30,p = 0.022;Fig.1b), MYHAT (β = ‐0.19,t = 2.07,p = 0.038;Fig.1e) and TRIAD (β = 0.46,t = 2.92,p = 0.004;Fig.1h) cohorts.Cohen’s d analysis revealed that the presence of Aβ+ and Ast+ has a large magnitude of effect on tau phosphorylation (Cohen’s d:Pittsburgh = 0.67; MYHAT = 0.69;TRIAD = 0.98;Fig.1c,f,i), whereas Aβ+ in the absence of Ast+ presented a negligible effect size. Similar results were observed for plasma p‐tau231 and p‐tau217.Voxel‐wise analysis confirmed that Aβ levels in brain regions known to present early Aβ accumulation in AD associated with plasma p‐tau181 only in Ast+ individuals (Fig.2).Tau‐PET deposition occurred as a function of Aβ burden only in CU Ast+ (Fig.3a), affecting 100% and 62% of the extension of the Braak I and II regions, respectively (Fig.3b)ConclusionWe observed biomarker evidence across multiple cohorts that the presence of astrocyte reactivity, measured by plasma GFAP, plays a key role in the association of Aβ with early tau pathology in preclinical AD.Our results might have implications for the biological definition of preclinical AD and selecting individuals for early preventive clinical trials.

Amyloid β‐dependent tau phosphorylation is triggered by reactive astrocytes in preclinical Alzheimer’s disease

AbstractBackgroundA significant percentage of Aß‐positive cognitively unimpaired (CU) individuals do not develop detectable downstream tau pathology and, consequently, cognitive decline.Experimental literature suggest that reactive astrocytes are necessary to unleashing Aß effects in pathological tau phosphorylation.Here we aimed to investigate whether astrocyte reactivity is key to determining the association of Aß burden with early tau phosphorylation in preclinical Alzheimer’s disease (AD).MethodWe assessed 1,016 CU individuals from two research and one population‐based cohort (TRIAD, Pittsburgh and MYHAT) with Aß (plasma or PET), plasma p‐tau and GFAP measures. Individuals were classified as positive (Ast+) or negative (Ast‐) for astrocyte reactivity using a cutoff based on plasma GFAP of younger Aß‐ individuals.Lowess method and linear regressions accounting for age and sex were used to model the trajectories of plasma p‐tau epitopes as a function of Aß burden. Cohen’s d corrected for age and sex was used to estimate effect sizes between groups.ResultWe observed that plasma p‐tau181 levels increased as a function of Aß only in CU Ast+ individuals from Pittsburgh (ß = ‐0.35,t = 3.10,p = 0.003,Fig.1a,b), MYHAT (ß = ‐0.20,t = 2.26,p = 0.026, Fig.1d,e) and TRIAD (ß = 0.46,t = 2.92,p = 0.004;Fig.1g,h) cohorts. A significant interaction between Aß burden and astrocyte reactivity status on plasma p‐tau181 levels was observed in the Pittsburgh (ß = ‐0.29,t = 2.30,p = 0.022;Fig.1b), MYHAT (ß = ‐0.19,t = 2.07,p = 0.038;Fig.1e) and TRIAD (ß = 0.46,t = 2.92,p = 0.004;Fig.1h) cohorts.Cohen’s d analysis revealed that the presence of Aß+ and Ast+ has a large magnitude of effect on tau phosphorylation (Cohen’s d:Pittsburgh = 0.67; MYHAT = 0.69;TRIAD = 0.98;Fig.1c,f,i), whereas Aß+ in the absence of Ast+ presented a negligible effect size. Similar results were observed for plasma p‐tau231 and p‐tau217.Voxel‐wise analysis confirmed that Aß levels in brain regions known to present early Aß accumulation in AD associated with plasma p‐tau181 only in Ast+ individuals (Fig.2).Tau‐PET deposition occurred as a function of Aß burden only in CU Ast+ (Fig.3a), affecting 100% and 62% of the extension of the Braak I and II regions, respectively (Fig.3b)ConclusionWe observed biomarker evidence across multiple cohorts that the presence of astrocyte reactivity, measured by plasma GFAP, plays a key role in the association of Aß with early tau pathology in preclinical AD.Our results might have implications for the biological definition of preclinical AD and selecting individuals for early preventive clinical trials.

Natural cubic splines for the analysis of Alzheimer’s trials

AbstractBackgroundMixed model repeated measures (MMRM) is the most common analysis approach in clinical trials for Alzheimer’s disease. The model treats time as a categorical variable, which can be problematic when assessments occur off‐schedule, as including off‐schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. Since MMRM entails a mean parameter for each group and visit, the model can be inefficient for longer trials of Preclinical Alzheimer’s. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time.MethodWe use model selection techniques to compare the predictive value of spline models to slope models, MMRM, and models that assume a proportional treatment effect using data from several completed trials. These trials include the ADCS trial of donepezil in MCI, trials of intranasal insulin and a fyn kinase inhibitor in mild Alzheimer’s dementia, and the A4 trial of solanezumab in Preclinical Alzheimer’s. Simulation studies assess the power and Type I error of these models for trials in Preclinical Alzheimer’s, MCI, and mild dementia.ResultThe spline model with ADAS‐Cog version effect attained the best predictive value for the trials in mild dementia. The proportional model was preferred for the trial in MCI, but the proportional effect assumption was violated, and the treatment effect estimate was in the wrong direction. Considering that the spline with two degrees of freedom and version effect provide fits that are similar to MMRM in Figure 1, we concluded that two degrees of freedom appear to be sufficient to capture enough of the general trend to assess the treatment effect. AIC results from the A4 Study will be considered and included after the study is completed in Spring 2023.ConclusionThe spline model is shown to have better predictive value and precision in real datasets and has better power and Type I error control in a variety of simulation scenarios.

Examining a telecognitive assessment‐derived Preclinical Alzheimer’s Cognitive Composite, the tPACC, for cognitive migration over one year in community‐dwelling older adults

AbstractBackgroundADRD clinical trials often target preclinical disease, where hope of averting the pathologic cascade and eventual cognitive decline remains highest. The preclinical Alzheimer’s cognitive composite (PACC5) was developed for in‐person administration to capture subtle cognitive decline. Early cognitive fluctuations may inform multidirectional disease progression, defined as cognitive migration. It is desirable to have a harmonized composite measurement derived from in‐person and remote assessments for detecting cognitive migration.MethodWe examined in‐person data from 417 adults (70.6±8.0y) from the Wake Forest ADRC Clinical Core, who received MRI, clinical evaluation and cognitive testing. The primary outcome was cognitive migration status determined by global CDR (0 and 0.5) increase/decrease between baseline (BL) and follow‐up (mean difference = 13.9 months): CDR‐0 Stable (51% maintained CDR‐0), CDR‐0.5 Stable (27% maintained CDR‐0.5), Reverter+ (13% migrated from CDR‐0.5 to CDR‐0) and Migrant‐ (9% migrated from CDR‐0 to CDR‐0.5). In‐person PACC5 was calculated (RAVLT Delayed Recall, Digit Symbol Coding [DSC], semantic fluency, Craft Story Delayed Verbatim, total MMSE) using BL participants with global CDR = 0 as reference. tPACC used measurements available at both in‐person and remote visits with minor modifications from PACC5: Montreal Cognitive Assessment (MoCA) replaced MMSE and DSC not included. We performed intraclass correlation coefficient (ICC) analysis and generated Bland‐Altman plots for BL tPACC and PACC5. We also evaluated associations between tPACC and neuroimaging.ResultParticipant characteristics and group differences in MRI markers are in Table 1. Bland‐Altman plots show differences and mean of tPACC and PACC5 (dashed line: ±1.96 SD, solid line: mean difference/bias). 92% of values lie within limit of agreement, indicating good agreement (overall ICC = .960, 95%CI = [.952, .967]), between tPACC and PACC5 in each group (Figure 1). At BL, there was a significant absolute agreement between in‐person tPACC and PACC5 in all groups (Figure 1). tPACC showed significant associations (Table 2) with Temporal Meta ROI thickness, hippocampal volume as % head size (ICV) and white matter hyperintensities (logWMH controlled for ICV) overall and in some cognitive migration groups.ConclusionThere is generally good agreement between BL tPACC and PACC5 across cognitive migration groups. BL tPACC may predict BL neuroimaging based on cognitive migration status. Longitudinal tPACC examination is warranted.

The utility of the digital clock drawing test as an accurate early screening tool for those with preclinical Alzheimer’s disease

AbstractBackgroundWith new drug treatments emerging, the need for early Alzheimer’s disease (AD) detection methods that can be done in a primary care setting has become urgent. Significant advances have been made in validating blood‐based AD biomarkers, but the absence of a validated, non‐invasive clinical indicator remains a barrier for determining those who might be optimal candidates for treatment. The digital clock drawing test (dCDT) has been previously shown to be related to AD PET biomarkers. However, it is unknown whether and the extent to which dCDT would predict AD as compared to blood‐based AD biomarkers. The current study used an interpretable machine learning approach to determine whether the dCDT, independently and in combination with blood‐based AD biomarkers (e.g., amyloid‐β 42 (Aβ42); total tau (T‐tau)), was predictive of incident AD.MethodThis study included participants from the Framingham Heart Study Generation 2 cohort (Gen 2; n = 527) who had dCDT measures, plasma Aβ42, and T‐tau. The fast interpretable greedy‐tree sums method was used to identify clinical decision rules for AD prediction. Synthetic minority oversampling technique was employed to handle the class imbalance. The predictive ability of plasma Aβ42, T‐tau and dCDT measures was evaluated by both area under the curve (AUC) and standardized partial AUC. The permutation feature importance method was used to show the importance of each measure.ResultInterpretable clinical decision rules were generated as multivariate biomarker signatures for AD prediction (Figure 1). The dCDT (AUC 0.859) was better at incident AD prediction than AD blood‐based biomarkers (AUC 0.839). The combination of dCDT, Aβ42, and T‐tau best predicted incident AD (AUC 0.884) (Figure 2). The combination of dCDT and blood‐based biomarkers also has higher partial AUC than individual ones (Table 1). The total score and information processing scores were the most important contributing dCDT measures.ConclusionThese results highlight the efficiency of detecting early AD through the integration of digital measures as a clinical indicator combined with AD specific biomarkers. The interpretable decision rules have the potential to provide automated results that can be used in practical clinical settings, although external validation is warranted.

Feasibility, satisfaction, and test‐retest reliability of repeated remote memory assessments with older adults

AbstractBackgroundFrequent, remote cognitive assessments may improve sensitivity to subtle cognitive decline associated with preclinical Alzheimer’s disease (AD). To gain insight on how use of this technology can be optimized, the objective of this study is to determine if feasibility, satisfaction, or test‐retest reliability (TRR) vary depending on task frequency schedules or demographic characteristics.MethodParticipants were recruited from a longitudinal aging cohort to complete three memory tasks via smartphone or tablet using the neotiv application: Object‐In‐Room Recall (ORR), Mnemonic Discrimination for Objects and Scenes (MDT‐OS), and Complex Scene Recognition (CSR). Participants were randomly assigned to complete 24 remote sessions over one year either: (1) every other week or (2) for multiple days within one week every other month. Each session lasted approximately 10 minutes, including one memory task followed by a delayed retrieval session 90 minutes later. Chi‐square tests were used to assess differences in feasibility, acceptability for participants who completed at least 3 sessions, and satisfaction based on task schedule, gender, age, or education. ICC was used to determine TRR of visit 2 to 3 (“V2‐V3”) and an average of visits 1+2 and 3+4 (“V1+2‐V3+4”).Result115 participants (mean age = 68.4, 66% women) enrolled; 49 completed all remote sessions to date (Table 1). Willingness to enroll differed by education (X2 = 4.2, p = .04), such that participants with at least a bachelor’s degree were more likely to enroll. Withdrawal from the study occurred more often for those scheduled to the every other week condition (X2 = 6.2, p = .01) than the every other month condition. Measures of acceptability and satisfaction did not differ by task schedule or demographics. TRR from V2‐V3 and V1+2‐V3+4 were adequate, (CSR: ICC = 0.56 and 0.64, ORR: ICC = 0.58 and 0.66, MDT‐OS: ICC = 0.38 and 0.52).ConclusionParticipants with a bachelor’s degree or higher were more likely to enroll. Reported satisfaction was similar for both task frequency schedules; however, drop‐outs occurred more frequently in those assigned to complete tasks every other week, which could suggest that it is a less optimal task schedule. TRR was moderate for all three tasks. Altogether, results suggest that repeated mobile cognitive testing can be utilized to track cognitive performance remotely.

Poor objective sleep quality is associated with worse cognition cross‐sectionally and longitudinally in cognitively unimpaired adults

AbstractBackgroundSleep disturbances are prevalent in Alzheimer’s disease (AD), with sleep quality being impaired already in the preclinical stages. Specifically, reduced sleep efficiency and sleep fragmentation have been cross‐sectionally associated with poor cognition in healthy older adults. However, there is limited knowledge of how objectively measured sleep features relate to cognitive change. We aimed to investigate the relationships of actigraphy‐measured sleep with both cross‐sectional and longitudinal cognition in a cohort of cognitively unimpaired (CU) adults at increased risk of AD.MethodThis study included 123 CU adults from the ALFASleep project (mean age 63.9 years, 60.2% females, 50.4% APOE‐ε4 carriers) without sleep medication use (Table 1). Correlates of sleep quality were measured using actigraphy (Actiwatch2®, Philips Respironics) for two weeks. We used separate multivariate linear regressions to assess the association between actigraphy‐derived sleep parameters: total sleep time (TST), sleep efficiency, sleep latency, wake after sleep onset (WASO), and cognitive composite outcomes: Preclinical Alzheimer’s Cognitive Composite (PACC), and executive, memory and attention composites. We corrected all models for age, sex, APOE‐ε4, status, education and the time difference between cognitive and sleep assessments. Furthermore, we performed analyses on cognitive change from a previous visit (performed on average 3 years before) additionally adjusting by the time difference between two cognitive assessments.ResultsIn cross‐sectional analyses, shorter TST was associated with lower PACC (p = 0.049) and executive composite (p = 0.039) scores. Also, lower sleep efficiency and higher WASO (indicating more nocturnal awakenings) were associated with lower performance on executive tasks (p = 0.015; p = 0.026) (Table 2). In longitudinal analyses, shorter TST was associated with poorer longitudinal performance in PACC and executive composite scores (p = 0.020).ConclusionsObjectively measured indicators of poorer sleep quality are associated with poorer cognitive outcomes, both cross‐sectionally and longitudinally, in middle‐aged CU adults. Importantly, the executive function seems to be the most vulnerable to sleep impairment. Further research to unravel the underlying mechanisms using multi‐modal neuroimaging and CSF biomarkers of AD pathology is undergoing.

Examining the overlap between patterns of tau deposition and cortical neurite microstructure in preclinical Alzheimer’s disease

AbstractBackgroundIncreased Amyloid‐beta (Aβ) is associated with the neuropathological features of Alzheimer’s disease (AD), and it has been suggested to amplify tau spreading in the brain that leads to neurodegeneration. However, little is known about how the pattern of tau spreading overlaps with that of cortical microstructural neurodegeneration in the preclinical stages of the disease. Here, we utilized molecular imaging and neurite orientation dispersion and density imaging (NODDI) to identify the spreading pattern of tau pathology with the disease progression in cross‐sectional data and interaction with the degradation of cortical neurite microstructures.MethodsThe study consisted of normal controls (CN) and patients with mild cognitive impairments (MCI) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Table 1). All participants had structural and multi‐shell diffusion magnetic resonance imaging (MRI), and positron emission tomography (PET) scans using 18F‐florbetapir (AV45) for and 18F‐flortaucipir (AV1451) for tau. The preprocessed PET images were used to generate the standardized uptake value ratio (SUVR) values in each hemisphere consisting of 180 cerebral cortical brain regions defined by the Glasser atlas. The preprocessed dMRI data were used to compute cortical NODDI coefficients (neurite density (intracellular volume fraction; ICVF), orientation dispersion index (ODI), and isotropic volume fraction (FISO)). The tau and NODDI microstructural values are then normalized compared to the Aβ− CN group by adjusting the values for age and sex. The tau spreading order was defined based on the frequencies of the normalized tau values that exceeded the given threshold and compared the patterns with the neurite microstructures.ResultsWe found a sharp increase of tau deposition in patients with Aβ+ MCI, and the medial temporal regions were identified as the earliest region to become tau positive and continue to spread to inferior temporal and inferior parietal regions. Most importantly, we found significant patterns of cortical ODI degradation with the tau spreading in the right entorhinal cortex (p = 0.004) and the right hippocampus (p = 0.02).ConclusionThe findings of this cross‐sectional study provide that regional cortical neurite imaging could provide higher sensitivity to changes in the pathological process of tau spreading in tau epicenters in AD.

Associations of Neighborhood Disadvantage, Cardiometabolic Risk, and Cognition in a Community‐Dwelling Cohort

AbstractBackgroundSocial determinants of health (SDoH) are important environmental factors in cardiometabolic risk and cognitive aging. Area Deprivation Index (ADI) is a measure of neighborhood‐level socioeconomic disadvantage and has been studied in normal cognition (NC), but relatively less is known about impact in those with mild cognitive impairment (MCI).MethodThis study explores, in participants with NC and MCI enrolled in the Wake Forest ADRC Clinical Core, baseline measures of metabolic risk (Oral Glucose Tolerance Testing of blood glucose 120‐minute post‐challenge; OGTT‐120), cognition (Preclinical Alzheimer’s Cognitive Composite; PACC), demographics (age, education, diagnosis, race, sex), and participant national ADI rank (using baseline home address). Consensus diagnosis of NC and MCI was adjudicated by a panel of experts.ResultDemographics of community‐dwelling adults enrolled as a convenience sample in the Wake Forest ADRC with metabolic and cognitive measures by cognitive status is listed in Table 1. MCI participants, compared with CN, were older, more likely male and white, with lower education and PACC, and higher ADI (higher = more disadvantaged). Significant differences in ADI were observed when participants were stratified by race (Figure 1); post‐hoc tests indicate significantly higher ADI in Black compared to white participants, and to some degree, by higher ADI among Black participants compared with Asian participants. OGTT‐120 in participants with NC (n = 265) and MCI (n = 156) was not significantly associated with ADI national rank (Figure 2A); when controlling for age, sex, education, and diagnosis, there was still no association between OGTT‐120 and ADI. Higher ADI was significantly associated with lower PACC scores in individuals with NC (p = 0.044), but not MCI (Figure 2B); this association remained significant (p = 0.004) independent of age, sex, education, and diagnosis (all covariates p<0.001).ConclusionAmong community‐dwelling older adults, ADI national rank differs by participant race. OGTT‐120 does not correlate with ADI in participants with NC or MCI. PACC scores correlate negatively with ADI national rank in participants with NC, but not MCI; this relationship remained when controlling for covariates. Further analyses are necessary to examine the association between cardiometabolic risk and ADI.

Effects of meditation training and non‐native language training on cognition in older‐adults: A secondary analysis of the three‐arm Age‐Well randomized controlled trial

AbstractBackgroundNon‐pharmacological interventions are a potential strategy to maintain or promote cognitive functioning in older adults. We investigated the effects of 18‐months meditation or non‐native language training versus no intervention on cognition in older adults.MethodAge‐Well was an observer‐blind, randomised, controlled clinical trial with three parallel arms. Cognitively healthy adults aged ≥ 65 years and were recruited in France. Participants were randomised (1:1:1) to a meditation training, non‐native language (English) training, or a no intervention group for 18 months. Cognition (a pre‐specified secondary outcome) was assessed pre‐ and post‐intervention via the Preclinical Alzheimer’s Cognitive Composite 5 (PACC5), and composites assessing episodic memory, executive function, and attention. Linear mixed models, adjusted for age, sex, and education, evaluated cognitive changes between arms. Baseline neocortical amyloid deposition was added as a fixed effect in a sensitivity analysis. Additional sensitivity analyses assessed intervention effects for participants who attended at least 20% of intervention classes or were classified as ‘responders’.Results137 participants were randomly assigned to meditation training (n = 45), non‐native language training (n = 46), or no intervention (n = 46). Two participants were excluded for not meeting eligibility criteria, and one died during follow‐up (Figure 1). A positive effect of non‐native language training was observed on episodic memory relative to the no intervention group (0·32 [95% CI: 0·04‐0·61], p = 0·025), and on PACC5 scores compared to meditation training (0·33 [95% CI: 0·01‐0·66], p = 0·045; Figure 2). In sensitivity analyses, neither the inclusion of amyloid as an additional covariate nor the exclusion of participants who attended <20% of intervention classes affected results. Following exclusion of ‘non‐responders’, evidence for beneficial effects (relative to the no intervention group) on episodic memory became significant for meditation training (0·33 [0·01, 0·66]), and was weakened for non‐native language training (0·30 [‐0·02, 0·62]). No other between‐group differences emerged.ConclusionAmong cognitively healthy older adults, there was limited evidence for positive effects of 18‐months meditation training on cognition. However, in addition to having real‐life utility, non‐native language learning in late life may have beneficial effects on cognitive domains affected early in Alzheimer’s disease. Studies investigating whether these effects are long‐lasting are warranted.

Preliminary evaluation of the digital maze test in relation to neuropsychological tests and AD biomarkers

AbstractBackgroundDigital cognitive tools may provide unique opportunities to detect subtle changes in preclinical Alzheimer’s disease (AD). Here, a maze test using a digital pen was evaluated in relation to AD pathological changes measured by amyloid‐β and tau burden.Method172 participants (CN = 161, MCI = 6, dementia = 5) completed the digital maze test, which included multiple “no‐choice” (NC; i.e., no decisions required to complete maze) or “choice” (CH) conditions (i.e., problem‐solving required to complete maze). Maze composite scores were calculated using the total test duration, total number of strokes, and total pen‐off‐page time for the NC and CH conditions separately. Global amyloid‐β PET was quantified with [11C]Pittsburgh‐Compound‐B (PiB) in available participants (n = 171). Entorhinal and inferior temporal tau were quantified with [18F]Flortaucipir (FTP) in available participants (n = 135). Participants completed a battery of traditional neuropsychological tests, with domain factor scores calculated for Executive Functioning (EF), Processing Speed (PS), and Memory (Mem). The associations between maze composite scores, global amyloid‐β, entorhinal tau, and inferior temporal tau were evaluated in separate linear regression models for both the total sample and CN only, correcting for age and education.ResultIn the total sample, CH‐ and NC‐composites were moderately correlated with PS and EF factor scores (r’s = .41‐.46) and less so with Mem factor scores (r’s = 0.2‐0.35). In the total sample, higher PiB was significantly associated with worse performance in NC‐composite (β = 0.543, SE = 0.249, p <.05), but not for CH‐composite (β = 0.467, SE = 0.259, p = .07). Similar results were observed in the CN only sample (NC‐composite: β = 0.468, SE = 0.237, p <.05; CH‐composite: β = 0.469, SE = 0.278, p = .09). PiB was not associated with EF or PS factor scores in the total sample or CN only. Entorhinal and inferior temporal tau were not significantly associated with any maze composites.ConclusionIn a largely cognitively normal sample, performance on the digital maze test was associated with global amyloid‐β burden, particularly in the no‐choice condition, but not with tau. Digitally captured, nuanced performance in EF and PS may be related to early amyloid‐β pathology, but less so with downstream effects of tau.

Spatial Extent as a more sensitive amyloid biomarker for early stages of Alzheimer’s disease

AbstractBackgroundDespite considerable advances in beta amyloid (Aβ)‐PET imaging over the last decade, the standard approach of estimating the average neocortical Aβ burden remains largely unchanged. However, as research and clinical trials increasingly shift earlier in the disease process, measures of how far Aβ has spread throughout the cortex (spatial extent) may prove more sensitive than average neocortical magnitude for detecting and quantifying early Aβ deposits and their association with future tau proliferation and cognitive decline.MethodClinically‐normal individuals (n = 214) were included from the Harvard Aging Brain Study with longitudinal PIB‐PET (2‐4 scans, median = 4.7±2.7 years) and annual cognitive data (median = 5.2±2 years), as well as a subset (n = 181) with flortaucipir (FTP)‐PET. Spatial extent (EXT) was computed as the number of cortical ROIs (n = 62, Desikan atlas) above their ROI‐specific threshold for Aβ positivity. EXT was compared to a traditional mean neocortical DVR using logistic growth modeling. Receiver Operator Characteristic (ROC) curve analyses evaluated EXT’s ability to identify baseline PIB‐ individuals (<1.19DVR/24CL) who progressed to PIB+ at 3‐year follow‐up. Linear Mixed Effects (LME) modeling assessed baseline EXT (or DVR) as a predictor of increasing inferior temporal tau (IT FTP SUVR) and cognitive decline on the Preclinical Alzheimer’s Cognitive Composite (PACC).ResultEXT begins rising below the neocortical DVR threshold (Figure1A), reaching a maximal growth rate of 14 ROIs per 0.1 DVR increase (∼10CL) and plateauing as full cortical EXT is achieved starting at ∼1.5DVR/68CL. A 3‐ROI EXT threshold predicts progression from PIB‐ to PIB+ in 3 years (AUC = .97, SE = .82,SP = .97), outperforming neocortical DVR (AUC = .92, SE = .65,SP = .94, Figure1B). EXT provides a stronger biomarker of Aβ change than DVR (lower coefficient of variation, Figure2) across the Aβ continuum due to its low variance, even after EXT has plateaued. Baseline EXT is also a stronger predictor of increases in IT FTP SUVR (Figure 3A, η2 EXT = .25, η2 DVR = .20) and PACC decline (Figure3B, η2 EXT = .28, η2 DVR = .22).ConclusionBy describing the spread of Aβ throughout the cortex rather than average neocortical Aβ burden, spatial extent provides a more sensitive measure of Aβ at early, preclinical stages of AD that may improve design of AD prevention trials and open new avenues for research into AD pathogenesis.

Hippocampal volume as mediator between physical fitness and cognitive performance in old age – preliminary results

AbstractBackgroundPrevious studies show that physical activity counteracts age‐related memory decline and is positively associated with hippocampal volume. However, only few studies examined if hippocampal volumes mediate the effect of physical activity on cognitive performance. Furthermore, exercise modalities other than aerobic activity are rarely considered in this context. Since sarcopenia (muscle atrophy) is associated with age‐related cognitive decline, muscular capacity seems to be another essential fitness indicator.MethodForty‐one cognitively unimpaired older adults (age: 72.94 ± 7.85yrs; 24 female) underwent a comprehensive neuropsychological test battery assessing, i.a., global cognitive performance using CERAD‐Plus (Consortium to Establish a Registry for Alzheimer’s Disease) and PACC5 (preclinical Alzheimer’s cognitive composite). Their physical fitness was measured in the form of both aerobic (VO2max) and muscular capacity (z‐normalized composite: grip strength, appendicular skeletal muscle mass and walking performance). Anterior (aHCV), posterior and whole hippocampal volumes (wHCV) were extracted from their T1w MRI images (3T MEMPRAGE, 0.8×0.8×0.8mm resolution) using the T1‐ASHS segmentation algorithm (manual correction of 32 hemispheres). Total gray matter volumes (GMV) were extracted using CAT12 in SPM12. Mediation analyses were performed to test the hypothesis that GMV or hippocampal volumes (corrected for intracranial volumes) mediate the positive effect of physical fitness on cognitive performance including age, sex and education years as covariates (bootstrapping approach: 10000 iterations).ResultHigher aerobic capacity (VO2max) was related to better cognitive performance (CERAD, p = .048), as was higher muscular capacity (CERAD, p = .002; PACC5, p = .002). Higher muscular capacity, but not VO2max, further predicted aHCV (p = .021) and wHCV (p = .035). Only wHCV mediated the relationship between muscular capacity and global cognitive performance (PACC5, p = .045).ConclusionOur results provide further evidence that higher aerobic and muscular capacity are associated with better global cognitive performance in older adults. While preliminary, the results suggest that muscular capacity exerts its neuroprotective effects at least partially via greater structural integrity of the hippocampus. The absence of a mediation effect of GMV may support the specific importance of maintained hippocampal structural integrity against cognitive decline. However, our small sample size may limit the statistical power to detect effects.

Preclinical Alzheimer’s disease is associated with distinct gut microbial metagenomic profiles

AbstractBackgroundAlterations in gut microbiome in individuals with Alzheimer’s disease (AD) may contribute to development or exacerbation of AD pathology via the microbiota‐gut‐brain axis. Composition and diversity of gut microbiota have been studied previously in AD dementia; here we investigate the gut microbial genes involved and their association with AD pathology prior to development of dementia.MethodCerebrospinal fluid (CSF; N = 98) and fecal samples (N = 333) were collected from participants enrolled in the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center studies. CSF biomarkers of AD, β‐amyloid (Aβ)42/Aβ40 and phosphorylated‐tau‐181 (phospho‐tau [181P]), were quantified using robust prototype assays from the Roche NeuroToolKit (Roche Diagnostics International Ltd, Rotkreuz, Switzerland). Fecal samples were analyzed by whole genome sequencing. Sequences were filtered, quality controlled, and profiled for taxonomy and functional classification at species level using MetaPhlAn 4.0/HUMAnN 3.0 software. Compositional/functional differential abundance, alpha‐/beta‐diversity, correlation between feature abundance and sample metadata, gene‐set enrichment (GSE), and network analyses were performed using R packages (phyloseq/vegan/DESeq2/NetCoMi/rstatix/clusterProfiler). Analyses compared clinical status and biomarker groups.ResultThe relative abundance of phylum Firmicutes and Bacteroidetes decreased and increased, respectively, in individuals with AD versus cognitively unimpaired (CU) individuals. In CU individuals, functional profiles differed in Aβ‐positive (A+) and phospho‐tau [181P]‐positive (T+) groups versus Aβ‐ and phospho‐tau [181P]‐negative (A−T−) individuals. In the microbial metagenome, 64 genes were significantly increased (p < 0.05) in the A+T+ group versus the A−T− group; 21/64 genes had ≥1 significant correlation with the AD biomarkers (e.g., toxic protein SymE positively correlated with phospho‐tau [181P] and negatively correlated with Aβ42/Aβ40 in CU individuals, suggesting that it may be a contributor to pathophysiological metabolic pathways in AD). Three KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways identified in GSE analyses, including fatty acid metabolism/degradation and cationic antimicrobial peptide resistance, were significantly associated (p < 0.05) with genes that were enriched in the AD population versus CU individuals.ConclusionIndividuals with AD have distinct differential abundance in microbiota composition, in addition to an increase and enrichment of genes associated with AD pathogenesis pathways. These genes were associated with AD biomarkers prior to development of AD dementia.

Examining the influence of changes in amyloid burden on both contemporaneous and subsequent cognitive decline: using a latent change score approach

AbstractBackgroundA critical question for understanding the natural history of preclinical Alzheimer’s disease (AD) is the extent to which changes in pathology affect contemporaneous and/or subsequent (lagged) cognitive manifestations of the disease. The most common approach in the literature involves the extraction and correlation of time‐demarcated slopes from different variables. Our objective was to employ a powerful and flexible structural equation modelling approach, latent change score models (LCSM), to directly address this question while also adjusting for demographics, a medial temporal lobe (MTL) tau burden composite, and an AD‐relevant glucose metabolism composite.Method131 Harvard Aging Brain Study participants (Agemean:73(6), Female:62%) had complete data for eight time‐points of neuropsychological assessment (Preclinical Alzheimer’s Cognitive Composite; PACC) and three timepoints of global PiB‐PET. In a contemporaneous bivariate LCSM (Fig1), we tested the influence of both cross‐sectional PiB‐PET(α) and change in PiB‐PET(β) on changes in PACC in the following year. Within a bivariate lagged LCSM (Fig2), we examined the effect of both cross‐sectional PiB‐PET(α) and change in PiB‐PET(β) on changes in PACC two years later. Regression coefficients were specified to be invariant over time. Both models included assumptions of cross‐sectional PiB(λ) and PiB change(μ) to predict subsequent PiB changes, and the same for PACC. Baseline age, sex, education, APOEe4, an MTL tau (Flortaucipir)‐PET composite (amygdala/entorhinal/parahippocampal), and an FDG‐PET composite (hippocampal/inferior parietal/posterior cingulate) were included as covariates. All data were z‐scored (PACCmean:0.21(0.6), PiBmean:1.16(0.19)).ResultHigher cross‐sectional PiB‐PET was not significantly associated with contemporaneous cognitive decline (α;Table1), but PiB‐PET accumulation was significantly associated with faster cognitive decline in the following year (β;Table1). Within the lagged model, PiB‐PET accumulation, not cross‐sectional, was significantly associated with faster PACC decline two years later (β;Table1). The lagged model was the better fitting model and demonstrated much larger change‐on‐change effect sizes (Table1). The strongest covariate relationships were between age/education and PACCintercept, and APOEe4/tau‐PET burden with the global PiB‐PETintercept and PiB‐PETslope (Table1).ConclusionOur findings suggest that change in PiB‐PET is more strongly associated with subsequent changes in cognition rather than contemporaneous cognitive change. Further, this relationship exists above and beyond the influence of demographics, cross‐sectional MTL tau burden, and glucose metabolism.

Independent associations of plasma GFAP with amyloid‐β and tau in Alzheimer’s disease

AbstractBackgroundGlial fibrillary acidic protein (GFAP) is a reactive astrogliosis biomarker, shown to increase in individuals with preclinical Alzheimer’s disease (AD). First thought to be a great marker of amyloid‐β (Aβ) pathology, recent post‐mortem research has linked it to tau accumulation only. Here we investigate the independent associations of plasma GFAP with imaging markers of AD along the disease spectrum.Method126 individuals from TRIAD cohort underwent [18F]MK6240 tau‐PET and [18F]AZD4694 Aβ‐PET and plasma GFAP assessment using an in‐house assay. Voxel and region of interest regression models evaluated the relationship between PET tracers and plasma GFAP. Models with [18F]AZD4694‐PET as the outcome were adjusted for [18F]MK6240‐PET voxel‐wise and vice‐versa. All models were corrected for age and sex, and RFT was used to correct for multiple comparisons.ResultPlasma GFAP was associated with [18F]AZD4694 signal throughout the cortex (Figure1A), independently of [18F]MK6240. Additionally, it was positively correlated with [18F]MK6240 in the medial temporal and occipital lobes and the anterior cingulate cortex, independently of [18F]AZD4694 (Figure1B). Voxel‐wise findings were confirmed by region‐of‐interest‐based analyses.ConclusionOur findings indicate that plasma GFAP is independently associated with both Aβ and tau pathologies in AD. Aβ‐PET showed associations throughout the entire cortex, suggesting its effectiveness as a reliable marker of Aβ pathology across the AD spectrum. On the other hand, tau‐PET correlations were observed in specific regions associated with memory and behavioral impairments that exhibit early deposition of tau. Overall, these results demonstrate that GFAP, an astrogliosis marker, is independently associated with Aβ and tau.

Developing Topics.

Alzheimer's disease (AD) is a multifactorial disease including pre-clinical, prodromal and clinical phases. CSF "A/T/N" (Ab42, pTau181, tTau) biomarkers help characterize the biological state of AD. The primary aim was to assess contributions of eight CSF markers for predicting changes in cognition. CSF from 237 participants (CN: 176[74.3%], MCI: 33[13.9%], AD: 28[11.8%]) of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing was used to measure Ab42 (GenII assay), pTau181, and tTau (Elecsys®) along with neurogranin, neurofilament light (NFL), α-synuclein, glial fibrillary acidic protein (GFAP), chitinase-3-like protein 1 (YKL-40), soluble triggering receptor expressed on myeloid cells-2 (sTREM2), s100 calcium-binding protein B (s100B), and interleukin-6 (IL-6) from the NeuroToolKit (NTK) panel of exploratory prototype assays (all Roche Diagnostics International Ltd). NTK biomarker concentrations were measured amongst each of four ATN groups at baseline; 1) A-/T-/N- (reference group), 2) A+/T±/N± (amyloid+), 3) A-/T±/N± (amyloid-), and 4) A+/T+/N+. Change in cognition was assessed using the pre-clinical Alzheimer's cognitive composite (PACC) and the clinical dementia rating sum of boxes (CDR-SB) scores, in all four ATN groups, stratified by low/high NTK biomarker over a minimum of 36 months. The primary outcome was defined as change in cognition in amyloid+ participants modulated by the addition of NTK markers to ATN groups. A+/T+/N+ participants had faster cognitive decline on both PACC and CDR-SB over time compared to all other groups (Figure 1A, PACC; Figure 1B, CDR-SB). Within the amyloid+ group, participants with high sTREM2 (K+) had a faster rate of decline in PACC compared to those with low sTREM2 (K-, p = 0.04; Figure 1C), whilst participants with high α-synuclein had a faster increase in CDR-SB compared with those with low α-synuclein (p = 0.005; Figure 1D). Amongst participants who were A+/T+/N+, those with high YKL40 had a significantly faster rate of decline in PACC (p = 0.017; Figure 1E), and a faster increase in CDR-SB, (p = 0.017; Figure 1F) compared with those who with low YKL40. Biomarkers of microglial activation (sTREM2), synaptic function and synuclein metabolism (α-synuclein), and astrocytic activation (YKL-40) may be appropriate to discern rates of cognitive change within amyloid+ participants. Findings are being validated in a separate population.

Combined Digital Clock Drawing Test and Delayed Word Recall and their relationship with PET Amyloid Biomarker Status in Cognitively Unimpaired Older Adults

AbstractBackgroundDCTclock™ is an AI‐enabled digital cognitive assessment (DCA) that strongly discriminates between cognitively impaired and unimpaired individuals (receiver operating area under‐the‐curve AUC = 0.89). Among cognitively unimpaired individuals, DCTclock predicts greater PET Aβ burden and shows stronger discrimination (Cohen’s d = 0.76) between Aβ± individuals than the Preclinical Alzheimer’s Cognitive Composite (d = 0.30) (Rentz et al., 2021). The Digital Clock and Recall (DCR™) was recently created by adding 3‐word delayed recall to DCTclock to assess verbal memory impairment – an early hallmark of Alzheimer’s disease. We assessed whether recreating the DCR with the 3‐word delayed recall from the Mini‐Mental State Examination (MMSE) and DCTclock would improve classification of Aβ PET biomarker status among cognitively unimpaired older adults.MethodDCTclock, MMSE (including delayed recall), and Aβ PET imaging data were collected from 159 cognitively unimpaired older adults in the Harvard Aging Brain Study (age = 78.6±5.9; 98 females; MMSE = 28.9±1.4). Logistic regression classifiers were trained to classify Aβ biomarker status and were assessed with standard metrics and AUC as compared to age‐ and MMSE‐only models.ResultThe best‐performing model classified biomarker status among cognitively unimpaired individuals with an AUC of 0.76 (sensitivity = 0.65, specificity = 0.73, accuracy = 0.71)‐outperforming age‐only and total MMSE‐only models (AUCs = 0.61). Recursive feature elimination identified spatial reasoning, average speed, maximum speed on Copy Clock, oscillatory motion on Command Clock, and delayed recall score as key features. DCTclock or recreated DCR summary scores alone classified biomarker status with AUCs of 0.72 and 0.74, respectively.ConclusionThe DCR, which combines DCTclock with delayed verbal recall, may offer a means to classify Aβ biomarker status among cognitively unimpaired individuals in as little as 3 minutes. Classification models trained on a subset of DCR features performed better than all other models, including traditional neuropsychological testing. These results reinforce the potential for DCR solutions to streamline cognitive testing in the clinic and accelerate diagnosis for individuals with early‐stage Alzheimer’s and other dementias. Future work will examine the best possible combination of additional tests to further increase classification accuracy.

Subtle cognitive difficulties increase risk of neurodegeneration, incident mild cognitive impairment, and dementia in older women.

AbstractBackgroundIdentifying individuals with preclinical Alzheimer’s disease and related disorders (ADRD) is important for prevention and eventual treatment. Here we aimed to integrate overall test performance with episodic memory process scores in a cohort of older women to identify those with subtle cognitive difficulties (SCD; e.g., individuals with the earliest declines in cognitive ability preceding a diagnosis of mild cognitive impairment (MCI) or dementia) and determine subsequent risk of MCI, dementia, and neuroimaging indices of neurodegeneration.MethodParticipants included 2,186 community‐dwelling older women without MCI or dementia (Mage = 73.4±3.8 years) who completed a comprehensive neuropsychological assessment as part of the Women’s Health Initiative Study of Cognitive Aging (WHISCA: 1999‐2000). Women were assessed annually for MCI and dementia until 2021, and 1,024 women underwent structural magnetic resonance imaging (sMRI) of the brain in 2005‐6. Women were classified as cognitively normal (CN), early SCD (e‐SCD; two episodic memory process scores 1 SD below normative means or one process score and one performance score 1 SD below normative means), or late SCD (l‐SCD; two overall performance scores 1 SD below normative means) at WHISCA baseline using validated criteria from the Alzheimer’s Disease Neuroimaging Cohort (Thomas et al., 2018 Journal of Alzheimer’s Disease). We used Cox proportional hazards to examine associations between SCD status and risk of developing MCI/dementia. Linear regressions were used to examine associations between SCD status and MRI indices of AD‐related neurodegeneration, global cerebrovascular damage, and brain volumes of structures that make up the medial temporal lobe. All associations were adjusted for sociodemographic, lifestyle, and clinical characteristics.ResultWomen with e‐SCD and l‐SCD were 1.58 (p<0.001) and 3.01 (p<0.001) times as likely to develop MCI/dementia compared to CN women, respectively (Figure 1). Women with l‐SCD (β = 0.20; p<0.001) had more AD‐related neurodegeneration compared to CN women (Table 1). Women with e‐SCD had marginally significantly greater AD‐related neurodegeneration (β = 0.14; p = 0.073) and had smaller volumes in the entorhinal cortex (β = ‐0.22; p = 0.009) compared to CN women.ConclusionOur study demonstrates the usefulness of the SCD criteria for the early identification of cognitively normal women who are at increased risk of MCI, dementia, and neurodegeneration.

Parental history of memory impairment predicts β‐amyloid burden and positivity in a preclinical population

AbstractBackgroundAlzheimer’s disease (AD) is highly heritable, and previous studies suggest that a maternal family history of AD confers a greater risk for AD than paternal history. Whether the maternal and paternal history of AD dementia impacts AD endophenotypes among cognitively unimpaired individuals is not fully described. We examine the effects of parental history of dementia on brain amyloid levels and both subjective and objective measures of cognition.MethodMeasures of objective and subjective cognition (Preclinical Alzheimer’s Cognitive Composite; Cognitive Function Index), APOE‐e4 genotype, ß‐amyloid (Aß) PET, and self‐reported parental history of memory impairment and AD dementia were obtained from 4,429 cognitively unimpaired individuals screening for the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s (A4) study (mean(SD); age = 71.2(4.7), %male = 40.7, %Non‐Hispanic White = 87.9). Aß was measured with 18F‐florbetapir‐PET using a mean neocortical composite referenced to whole cerebellum (Aß+threshold:1.11 SUVr). Linear regression models assessed the association between maternal or paternal history with cross‐sectional cortical Aß levels and cognition covarying for the participant’s age, sex, and years of education where relevant.ResultAs previously reported, parental history of memory impairment (one or both parents) was significantly associated with elevated neocortical Aß (p<0.001). The association between Aß and history in both parents remained significant when covarying for APOE‐e4 allele count (p = 0.002). In interaction models, parental history in either one or both parents did not modulate the association between APOE‐e4 and Aß. Mean amyloid levels were elevated in individuals with history in both parents (n = 456, p<0.001) and in those with only maternal family history (n = 1776, p<0.001), compared to those with only paternal history (n = 634) or no family history (n = 1563, Figure 1B). Regression models confirmed this finding: ß = 0.03, p<0.001 in individuals with maternal history, ß = 0.01, p = 0.1 in individuals with paternal history. The results remained consistent for participants whose parents received a clinical diagnosis of AD dementia (ßmaternal = 0.02, pmaternal = 0.01; ßpaternal = 0.02, ppaternal = 0.2). No associations with subjective or objective cognition were observed.ConclusionWe confirm that self‐reported family history of dementia is associated with elevated brain amyloid independently of APOE‐e4 status in the A4 Study, with evidence that maternal family history has a stronger impact on Aß burden among asymptomatic older individuals.