Preclinical Alzheimer Cognitive Composite Research Articles

Longitudinal Trajectories of the Cognitive Function Index in the A4 Study

BackgroundThe Anti-Amyloid in Asymptomatic Alzheimer’s Disease (A4) Study failed to show a treatment benefit with solanezumab, but the longitudinal consequences of elevated amyloid were observed in study participants with objective decline on the Preclinical Alzheimer Cognitive Composite (PACC) and subjective decline on the combined Cognitive Function Index (participant + study partner CFI), during the trial period.ObjectivesWe sought to expand on previous findings by comparing longitudinal patterns of participant and study partner CFI separately and their associations with the PACC stratified by baseline amyloid tertile over the course of the A4 Study.DesignCognitively unimpaired older adult participants and their study partners were independently administered the CFI at screen prior to amyloid PET disclosure and then at 3 subsequent visits (week 48, week 168, week 240) of the study. PACC collected at visits concurrent with CFI administration were also examined longitudinally.SettingThe A4 Study was conducted at 67 sites in Australia, Canada, Japan, and the United States.Participants1,147 participants with elevated amyloid based on florbetapir PET were enrolled in the A4 Study and included in these analyses. 583 were on placebo and 564 were treated with solanezumab.MeasurementsThe PACC was used to assess objective cognitive performance and the CFI was used to assess change in everyday cognitive functioning by the participant and their study partner independently. Amyloid level was characterized by Centiloid tertiles (<46.1 CL, 46.1 to 77.2 CL, >77.2 CL). Participants were aware of their elevated amyloid status, but not their CL tertile, or specific level of amyloid. Longitudinal correlations between participant and study partner CFI and PACC were examined at all visits where assessments were available. The impact of baseline amyloid tertile on CFI and PACC associations was also examined.ResultsBoth participant and study partner CFI increased over the duration of the study indicating worsening cognitive functioning. Results did not differ by treatment group. The association between higher CFI and worse PACC for both for participant and study partner became progressively stronger over the course of the study. PACC had a significantly higher correlation with study partner CFI than with participant CFI by week 168. The stronger correlations between study partner CFI and PACC were driven by those in the highest amyloid tertile.ConclusionBoth participant and study partner report captured subtle changes in everyday cognitive functioning for participants with biomarker confirmed and disclosed preclinical AD. Moreover, study partner report was most highly aligned with cognitive decline, particularly among those with the highest amyloid load.

Associations Between Self and Study Partner Report of Cognitive Decline With Regional Tau in a Multicohort Study.

Self-reported cognitive decline is an early behavioral manifestation of Alzheimer disease (AD) at the preclinical stage, often believed to precede concerns reported by a study partner. Previous work shows cross-sectional associations with β-amyloid (Aβ) status and self-reported and study partner-reported cognitive decline, but less is known about their associations with tau deposition, particularly among those with preclinical AD. This cross-sectional study included participants from the Anti-Amyloid Treatment in Asymptomatic AD/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration studies (N = 444) and the Harvard Aging Brain Study and affiliated studies (N = 231), which resulted in a cognitively unimpaired (CU) sample of individuals with both nonelevated (Aβ-) and elevated Aβ (Aβ+). All participants and study partners completed the Cognitive Function Index (CFI). Two regional tau composites were derived by averaging flortaucipir PET uptake in the medial temporal lobe (MTL) and neocortex (NEO). Global Aβ PET was measured in Centiloids (CLs) with Aβ+ >26 CL. We conducted multiple linear regression analyses to test associations between tau PET and CFI, covarying for amyloid, age, sex, education, and cohort. We also controlled for objective cognitive performance, measured using the Preclinical Alzheimer Cognitive Composite (PACC). Across 675 CU participants (age = 72.3 ± 6.6 years, female = 59%, Aβ+ = 60%), greater tau was associated with greater self-CFI (MTL: β = 0.28 [0.12, 0.44], p < 0.001, and NEO: β = 0.26 [0.09, 0.42], p = 0.002) and study partner CFI (MTL: β = 0.28 [0.14, 0.41], p < 0.001, and NEO: β = 0.31 [0.17, 0.44], p < 0.001). Significant associations between both CFI measures and MTL/NEO tau PET were driven by Aβ+. Continuous Aβ showed an independent effect on CFI in addition to MTL and NEO tau for both self-CFI and study partner CFI. Self-CFI (β = 0.01 [0.001, 0.02], p = 0.03), study partner CFI (β = 0.01 [0.003, 0.02], p = 0.01), and the PACC (β = -0.02 [-0.03, -0.01], p < 0.001) were independently associated with MTL tau, but for NEO tau, PACC (β = -0.02 [-0.03, -0.01], p < 0.001) and study partner report (β = 0.01 [0.004, 0.02], p = 0.002) were associated, but not self-CFI (β = 0.01 [-0.001, 0.02], p = 0.10). Both self-report and study partner report showed associations with tau in addition to Aβ. Additionally, self-report and study partner report were associated with tau above and beyond performance on a neuropsychological composite. Stratification analyses by Aβ status indicate that associations between self-reported and study partner-reported cognitive concerns with regional tau are driven by those at the preclinical stage of AD, suggesting that both are useful to collect on the early AD continuum.

Predicting Driving Cessation Among Cognitively Normal Older Drivers: The Role of Alzheimer Disease Biomarkers and Clinical Assessments.

With the aging US population and increasing incidence of Alzheimer disease (AD), understanding factors contributing to driving cessation among older adults is crucial for clinicians. Driving is integral for maintaining independence and functional mobility, but the risk factors for driving cessation, particularly in the context of normal aging and preclinical AD, are not well understood. We studied a well-characterized community cohort to examine factors associated with driving cessation. This prospective, longitudinal observation study enrolled participants from the Knight Alzheimer Disease Research Center and The DRIVES Project. Participants were enrolled if they were aged 65 years or older, drove weekly, and were cognitively normal (Clinical Dementia Rating [CDR] = 0) at baseline. Participants underwent annual clinical, neurologic, and neuropsychological assessments, including β-amyloid PET imaging and CSF (Aβ42, total tau [t-Tau], and phosphorylated tau [p-Tau]) collection every 2-3 years. The primary outcome was time from baseline visit to driving cessation, accounting for death as a competing risk. The cumulative incidence function of driving cessation was estimated for each biomarker. The Fine and Gray subdistribution hazard model was used to examine the association between time to driving cessation and biomarkers adjusting for clinical and demographic covariates. Among the 283 participants included in this study, there was a mean follow-up of 5.62 years. Driving cessation (8%) was associated with older age, female sex, progression to symptomatic AD (CDR ≥0.5), and poorer performance on a preclinical Alzheimer cognitive composite (PACC) score. Aβ PET imaging did not independently predict driving cessation, whereas CSF biomarkers, specifically t-Tau/Aβ42 (hazard ratio [HR] 2.82, 95% CI 1.23-6.44, p = 0.014) and p-Tau/Aβ42 (HR 2.91, 95% CI 1.28-6.59, p = 0.012) ratios, were independent predictors in the simple model adjusting for age, education, and sex. However, in the full model, progression to cognitive impairment based on the CDR and PACC score across each model was associated with a higher risk of driving cessation, whereas AD biomarkers were not statistically significant. Female sex, CDR progression, and neuropsychological measures of cognitive functioning obtained in the clinic were strongly associated with future driving cessation. The results emphasize the need for early planning and conversations about driving retirement in the context of cognitive decline and the immense value of clinical measures in determining functional outcomes.

Examining a Preclinical Alzheimer's Cognitive Composite for Telehealth Administration for Reliability Between In-Person and Remote Cognitive Testing with Neuroimaging Biomarkers.

The preclinical Alzheimer's cognitive composite (PACC) was developed for in-person administration to capture subtle cognitive decline. At the outset of the COVID-19 pandemic, cognitive testing was increasingly performed remotely by telephone or video administration. It is desirable to have a harmonized composite measurement derived from both in-person and remote assessments for identifying cognitive changes and to examine its relationship with common neuroimaging biomarkers. We defined a telehealth compatible PACC (tPACC) and examined its relationship with neuroimaging biomarkers related to neurodegeneration, brain function and perfusion, white matter integrity, and amyloid-β. We examined 648 participants' neuroimaging and in-person and remote cognitive testing data from the Wake Forest Alzheimer's Disease Research Center's Clinical Core cohort (observational study) to calculate a modified PACC (PACC5-RAVLT) score and tPACC scores (in-person and remote). We performed Spearman/intraclass correlation coefficient (ICC) analyses for reliability of tPACC scores and linear regression models to evaluate associations between tPACC and neuroimaging. Bland-Altman plots for agreement were constructed across cognitively normal and impaired (mild cognitive impairment and dementia) participants. There was a significant positive relationship between tPACCin-person and PACC5-RAVLT (Overall group: r2 = 0.94, N = 648), and tPACCin-person and tPACCremote (validation subgroup: ICC = 0.82, n = 53). Overall, tPACC showed significant associations with brain thickness/volume, gray matter perfusion, white matter free water, and amyloid-β deposition. There is a good agreement between tPACCand PACC5-RAVLTfor cognitively normal and impaired individuals. The tPACC is associated with common neuroimaging markers of Alzheimer's disease.

Impact of amyloid and tau positivity on longitudinal brain atrophy in cognitively normal individuals.

Individuals on the preclinical Alzheimer's continuum, particularly those with both amyloid and tau positivity (A + T +), display a rapid cognitive decline and elevated disease progression risk. However, limited studies exist on brain atrophy trajectories within this continuum over extended periods. This study involved 367 ADNI participants grouped based on combinations of amyloid and tau statuses determined through cerebrospinal fluid tests. Using longitudinal MRI scans, brain atrophy was determined according to the whole brain, lateral ventricle, and hippocampal volumes and cortical thickness in AD-signature regions. Cognitive performance was evaluated with the Preclinical Alzheimer's Cognitive Composite (PACC). A generalized linear mixed-effects model was used to examine group × time interactions for these measures. In addition, progression risks to mild cognitive impairment (MCI) or dementia were compared among the groups using Cox proportional hazards models. A total of 367 participants (48 A + T + , 86 A + T - , 63 A - T + , and 170 A - T - ; mean age 73.8years, mean follow-up 5.1years, and 47.4% men) were included. For the lateral ventricle and PACC score, the A + T - and A + T + groups demonstrated statistically significantly greater volume expansion and cognitive decline over time than the A - T - group (lateral ventricle: β = 0.757 cm3/year [95% confidence interval 0.463 to 1.050], P < .001 for A + T - , and β = 0.889 cm3/year [0.523 to 1.255], P < .001 for A + T + ; PACC: β = - 0.19 /year [- 0.36 to - 0.02], P = .029 for A + T - , and β = - 0.59 /year [- 0.80 to - 0.37], P < .001 for A + T +). Notably, the A + T + group exhibited additional brain atrophy including the whole brain (β = - 2.782 cm3/year [- 4.060 to - 1.504], P < .001), hippocampus (β = - 0.057 cm3/year [- 0.085 to - 0.029], P < .001), and AD-signature regions (β = - 0.02mm/year [- 0.03 to - 0.01], P < .001). Cox proportional hazards models suggested an increased risk of progressing to MCI or dementia in the A + T + group versus the A - T - group (adjusted hazard ratio = 3.35 [1.76 to 6.39]). In cognitively normal individuals, A + T + compounds brain atrophy and cognitive deterioration, amplifying the likelihood of disease progression. Therapeutic interventions targeting A + T + individuals could be pivotal in curbing brain atrophy, cognitive decline, and disease progression.

Small vessel cerebrovascular disease is associated with cognition in prospective Alzheimer’s clinical trial participants

BackgroundSecondary prevention clinical trials for Alzheimer’s disease (AD) target amyloid accumulation in asymptomatic, amyloid-positive individuals, but it is unclear to what extent other pathophysiological processes, such as small vessel cerebrovascular disease, account for participant performance on the primary cognitive outcomes in those trials. White matter hyperintensities are areas of increased signal on T2-weighted magnetic resonance imaging (MRI) that reflect small vessel cerebrovascular disease. They are associated with cognitive functioning in older adults and with clinical presentation and course of AD, particularly when distributed in posterior brain regions. The purpose of this study was to examine to what degree regional WMH volume is associated with performance on the primary cognitive outcome measure in the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) study, a secondary prevention trial.MethodsData from 1791 participants (59.5% women, mean age (SD) 71.6 (4.74)) in the A4 study and the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) companion study at the screening visit were used to quantify WMH volumes on T2-weighted fluid-attenuated inversion recovery (FLAIR) MR images. Cognition was assessed with the preclinical Alzheimer cognitive composite (PACC). We tested the association of total and regional WMH volumes with PACC performance, adjusting for age, education, and amyloid positivity status, with general linear models. We also considered interactions between WMH and amyloid positivity status.ResultsIncreased frontal and parietal lobe WMH volume was associated with poorer performance on the PACC. While amyloid positivity was also associated with lower cognitive test scores, WMH volumes did not interact with amyloid positivity status.ConclusionThese results highlight the potential of small vessel cerebrovascular disease to drive AD-related cognitive profiles. Measures of small vessel cerebrovascular disease should be considered when evaluating outcome in trials, both as potential effect modifiers and as a possible target for intervention or prevention.

A comparison of machine learning based‐composite cognitive test scores to track cognitive decline in early stages of Alzheimer’s disease dementia: ADOPIC study

AbstractBackgroundClinical trials of early Alzheimer’s disease (AD) dementia use cognition as a primary outcome and therefore cognitive measures that accurately reflect disease progression and represent multiple cognitive domains are required. Current cognitive endpoints are computed by averaging standardized change from baseline scores (i.e., preclinical Alzheimer cognitive composite (PACC)). Here we compare the PACC to composite scores for which the combination of multiple performance scores has been optimized using machine learning (ML) algorithms in a large, harmonised data set, namely ADOPIC.MethodsA dataset harmonised across various cognitive scores (Table 1) was used to construct composite scores using ML‐based algorithms: a manifold learning dimension reduction technique (UMAP), principal component analysis (PCA) and Latent variable analysis (LVA). Data from ADNI (n = 1470), AIBL (n = 1105) and OASIS participants (n = 412, Table 2) with ≥3 assessments ≤5 years before clinical progression/last visit were included. Participants were classified clinically as; stable cognitively unimpaired (CU), CU progressing to mild cognitive impairment (MCI) or dementia, stable MCI, MCI progressing to dementia, or AD dementia. For UMAP, all stable participants (including dementia) were used in 4‐fold cross‐validation training sets. However, for testing UMAP and unsupervised models (PCA and LVA) all clinical groups (except for dementia) were used. The validity of ML‐based composites was challenged by modelling mean (SD) change over time in the non‐demented clinical groups, using linear mixed model (LMM) analysis. Signal‐to‐noise ratios (SNRs) were calculated (mean change/SD change) for each composite. The mean change was measured in progressives relative to stable participants of each group.ResultsEach ML‐based cognitive composite showed sensitivity to cognitive decline in the progressor groups (Figure 1 A). For the MCI progressors, PCA and UMAP composites had significantly higher SNRs than PACC (P&lt;0.01; Figure 1B), however, LVA performance was not significantly better than PACC. For the CU progressor group, SNRs for PCA and LVA did not show significant differences with PACC and UMAP performed significantly worse than PACC (P&lt;0.01).ConclusionML‐based cognitive composite score computed using PCA provides a practical solution to track cognitive decline with improved performance in tracking cognitive decline in MCI progressors compared to PACC, while being comparable in CUs.

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.

Protective effects of sleep duration and physical activity on cognitive performance are influenced by β‐amyloid and brain volume but not tau burden among cognitively unimpaired older adults

AbstractBackgroundSleep and physical activity have gained traction as modifiable risk factors for Alzheimer’s disease (AD). Sleep duration is linked to amyloid‐β (Aβ) clearance while physical activity is associated with brain volume maintenance. We investigate associations between sleep duration and physical activity and cognition by testing if sleep duration—cognition and physical activity—cognition are explained by Aβ deposition and brain volume, respectively. Additionally, we explore the mediating role of tau burden in these relationships.MethodThis cross‐sectional study obtained data from participants in the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) study, a randomized clinical trial. Cognitively unimpaired participants underwent Aβ and tau PET and brain MRI prior to randomization. Cognitive performance was assessed using the Preclinical Alzheimer Cognitive Composite (PACC). Self‐reported nightly sleep duration and weekly physical activity were the primary predictors. Regional Aβ and tau pathologies and volumes were the proposed variables influencing sleep duration—cognition or physical activity—cognition relationships.ResultAβ PET data were obtained from 4322 participants (1208 with MRI, 59% female, 29% amyloid positive). Sleep duration was associated with an Aβ composite burden (β = ‐0.005,CI:(‐0.01,‐0.001)) and Aβ burden in the anterior cingulate (ACC) (β = ‐0.012,CI:(‐0.017,‐0.006)) and medial orbitofrontal cortices (MOC) (β = ‐0.009,CI:(‐0.014,‐0.005)) but not with brain structure. Composite (β = ‐1.54,CI:(‐1.93,‐1.15)), ACC (β = ‐1.22,CI:(‐1.54,‐0.90)) and MOC (β = ‐1.44,CI:(‐1.86,‐1.02)) Aβ burden were also associated with PACC. Sleep duration—PACC association was explained by Aβ burden in path analyses (Figure 1). Physical activity was not associated with Aβ but was associated with hippocampal (β = 10.57,CI: (1.06,20.08)), parahippocampal (β = 9.3,CI:(1.69,16.91)), entorhinal (β = 14.68,CI:(1.75,27.61), and fusiform gyral (β = 38.38,CI:(5.57, 71.18)) volumes, which were also positively associated with PACC (p&lt;0.02 for hippocampus, entorhinal cortex, and fusiform gyrus). Physical activity—cognition relationship was explained by regional volumes (Figure 2). Tau was not related to sleep duration or physical activity, and it did not moderate relationships between cognition and sleep duration or physical activity.ConclusionSleep duration and physical activity are associated with cognition through independent paths of brain Aβ and brain volume, respectively, but not tau burden. Dementia risk reduction approaches that emphasize the adequate sleep duration and a physically active lifestyle may benefit those with risk for AD.

Voxel‐level of abnormal β‐amyloid accumulation and spatiotemporal distribution patterns in Alzheimer’s Disease

AbstractBackgroundThe spatial patterns of β‐amyloid (Aβ) accumulation are critical for early diagnosis and clinical trials of Alzheimer’s Disease (AD). In this study, we explored a new voxel‐level Aβ index and investigated spatial patterns of voxels with abnormal Aβ deposition in different stages of AD.MethodWe identified 944 ADNI participants with Aβ 18F‐florbetapir positron emission tomography (PET) image. All Aβ PET images were co‐registered with their corresponding magnetic resonance imaging (MRI) scans, intensity‐normalized to the whole cerebellum, and spatial‐normalized to the Montreal Neurological Institute (MNI) space. We defined the threshold of each voxel as the mean+2SD of 18F‐florbetapir PET images of 51 participants who were Aβ PET negative (A‐), CSF p‐Tau181 negative (T‐), hippocampal atrophy negative (N‐), and cognitively unimpaired (CU) at both baseline and follow‐up. The positive voxel number (NoVoxel+) was counted based on the threshold of each voxel. The cross‐sectional and longitudinal correlations between NoVoxel+ and cortical summary Aβ standard uptake value ratio (SUVR) were determined in A‐ and Aβ‐positive (A+) separately. We compared baseline NoVoxel+ among different stages of AD, as well as how it related to longitudinal changes of Aβ SUVR, CSF p‐Tau181, residual hippocampal volume (rHCV), temporal meta‐ROI cortical thickness, and preclinical Alzheimer cognitive composite (PACC) score, and follow‐up tau PET levels, controlling for age, sex and APOE4. Finally, we investigated the spatial patterns of NoVoxel+ in different stages of AD.ResultThe new Aβ index NoVoxel+ was strongly correlated with the cortical summary Aβ SUVR cross‐sectionally and longitudinally regardless of Aβ positivity (Figure 1). Cognitively impaired (MCI and dementia) individuals showed larger (p&lt;0.001) NoVoxel+ than the CU individuals, A+ group had greater NoVoxel+ than the A‐ group (Figure 2). NoVoxel+ was related to faster rates of increases in Aβ SUVR and CSF p‐Tau, decreases in rHCV, temporal meta‐ROI cortical thickness, and PACC. Abnormal Aβ voxels were primarily located in precuneus, medial orbital frontal and anterior/posterior cingulate regions in A‐/CU group (Figure 3).ConclusionThe proposed voxel‐level Aβ deposition NoVoxel+ may be useful to investigate the spatial and temporal patterns of Aβ accumulation in AD, particularly in early amyloidosis stage.

Predicting change in depressive symptoms using longitudinal regional amyloid and cognition in cognitively unimpaired older adults

AbstractBackgroundDepression is a neuropsychiatric symptom of Alzheimer’s disease (AD) and has been linked to greater cortical amyloid burden in preclinical AD. We sought to examine whether amyloid accumulation in brain regions subserving emotional control and changes in cognitive performance may contribute to increased depressive symptomatology over time.Method155 cognitively unimpaired participants who were below the cortical amyloid (&lt;0.86DVR) and clinical depression thresholds (&lt;12 on Geriatric Depression Scale (GDS); mean age = 72.6) at baseline were included from the Harvard Aging Brain Study, with annual GDS and Preclinical Alzheimer Cognitive Composite (PACC) assessment and MRI/PiB‐PET every three years (mean years follow‐up = 7.06). PiB slopes were calculated for FreeSurfer‐defined bilateral regions of interest (ROI) implicated in emotional control: amygdala, medial and lateral orbitofrontal cortices (mOFC, lOFC), superior and middle frontal (MFC) cortices, anterior cingulate (ACC), posterior cingulate (PCC), and isthmus cingulate (IC). Linear mixed‐effects models assessed whether main‐effects of ROI PiB slope and PACC slope predicted longitudinal GDS scores for each ROI, covarying for age, sex, education, and random intercept/slope, adjusted for multiple comparisons. Post‐hoc linear regression models assessed relationships between PiB and PACC slopes.ResultSteeper PiB slopes in the mOFC, MFC, ACC, and IC were associated with increasing GDS scores over time, while a decreasing PACC slope was predictive of increasing GDS scores over time in all regions (Table 1). Post‐hoc analyses indicated that PiB slopes were not significantly correlated with PACC slope (Figure 1).ConclusionIn a cohort of cognitively unimpaired older adults with low cortical amyloid and no/subclinical depressive symptoms at baseline, increasing depressive symptoms over time were significantly associated with both amyloid accumulation in specific regions associated with emotional control (i.e., mOFC, MFC, ACC, and IC) and worsening cognitive performance. Furthermore, changes in cognition were not significantly associated with changes in amyloid accumulation in regions involved in emotional control, suggesting that different factors may be independently influencing trajectories in depression versus cognition over time. These results shed light on the neurobiology of depression in older individuals and underscore the importance of monitoring new and increasing neuropsychiatric symptoms in addition to cognitive symptoms when screening for AD.

Neuroimaging Results and A4 in the Context of the LEARN Study

AbstractBackgroundThe A4 Study was a 240‐week, double‐blind, placebo‐controlled Phase 3 secondary prevention trial in cognitively unimpaired adults (ages 65‐85) with elevated amyloid on PET testing solanezumab, a monoclonal antibody targeting soluble amyloid‐beta. The companion LEARN Study enrolled participants who were otherwise eligible for A4 but did not show elevated amyloid.MethodEntry criteria included CDR‐Global Scale (CDR‐GS) = 0, MMSE≥25, WMS Logical Memory = 6‐18, and elevated amyloid on florbetapir PET (for A4). The primary outcome measure was the Preclinical Alzheimer Cognitive Composite (PACC) with key secondary functional measures: Cognitive Function Index (CFI), Activities of Daily Living Scale (ADL), and CDR‐GS. A subset of participants underwent flortaucipir Tau PET.ResultFor A4, 1169 randomized: 578 to solanezumab and 591 to placebo. Treatment groups were well matched. Solanezumab did not slow cognitive decline on the PACC (mean change (95% CI): placebo ‐1.13 (‐1.45,‐0.81); solanezumab ‐1.43 (‐1.83,‐1.03); p = .260). Secondary clinical outcomes were consistent, numerically favoring placebo. Across both arms, 36.1% progressed to symptomatic AD, defined as CDR‐GS&gt; 0 at two consecutive visits or final visit. Higher baseline amyloid levels were strongly associated with faster cognitive decline and greater risk of progression to symptomatic AD across both groups (p&lt;.001). Amyloid continued to accumulate in both placebo (65.9 centiloid baseline, 19.3 increase) and solanezumab (66.2 centiloid baseline, 11.6 increase) groups. Tau PET showed similar increases in placebo and solanezumab groups in medial temporal lobe and neocortical composites, that were negatively correlated with PACC decline. No serious safety signals for solanezumab were identified, with only one case of ARIA‐E. ARIA‐H rates were similar across groups. LEARN participants (N = 538, 4.2 centiloid baseline on amyloid PET) did not demonstrate cognitive decline.ConclusionThe A4 Study demonstrated the feasibility of conducting a large‐scale trial in preclinical AD, with cognitive and functional measures sensitive to decline. Solanezumab did not slow cognitive decline or clinical progression. Baseline amyloid PET was among the strongest predictors of decline across both placebo and solanezumab. LEARN participants did not demonstrate decline. These results suggest that amyloid plaque removal may be necessary even at this very early stage of AD.

Sex differential effects on the joint contribution of air pollution and biological aging on cognitive performance in individuals at risk of Alzheimer’s disease

AbstractBackgroundAir pollution is associated with cognitive performance, risk for AD and may accelerate biological aging. Biological aging due to telomere shortening has been associated with cognitive impairment. However, few studies have looked at the link between air pollution, telomere length, and cognitive performance among sexes. In this study, we investigated the joint effect of sex, air pollution, and leukocyte telomere length (LTL), on cognitive performance in cognitively unimpaired (CU) individuals at risk of AD.MethodThe study included 1,472 middle‐aged CU adults with increased risk of AD from the ALFA study [Table 1]. Residential exposure to air pollutants including nitrogen dioxide (NO2) and particulate matter (PM2.5, PM10) was estimated by land use regression models. LTL was determined by qPCR from DNA extracted from peripheral blood leukocytes. LTL was residualized against chronological age and sex (rLTL). A cognitive battery was administered to assess verbal memory, psychomotor speed, visual processing and executive function. Further, episodic memory (EM), executive function and Preclinical Alzheimer Cognitive Composite (PACC) composites were calculated by averaging normalized raw scores of the subtests. Models were corrected by chronological age, years of education and APOE‐ε4 status. Sex‐specific effects were investigated by adjusting interaction models.ResultThere was no overall effect of neither rLTL nor air pollution on cognitive performance, but crossover interactions with sex were found. Exposure to higher levels of NO2 and PM2.5 were associated with worse EM only in men [Figure 1]. Three‐way interaction models revealed longer rLTL and lower levels of air pollution were associated with better performance on PACC (for PM2.5, PM10 and NO2) and EM (for PM10 and NO2) among men. In women, these associations were only observed at higher exposure levels of air pollution [Figure 2].ConclusionThe results show that lower levels of air pollution allow for the protective effect of rLTL on cognitive performance only in men for whom air pollution had a stronger negative impact. In women, the results show different mechanisms through which air pollution and rLTL affect cognitive performance. Further analyses will be conducted to gain insight into the observed effects.

Estimating a clinically normal individual’s position along a preclinical Alzheimer’s disease continuum using cognitive and amyloid trajectories

AbstractBackgroundOptimizing longitudinal cognitive and biomarker trajectories can distill multiple observations from one individual into a single metric. Relative to other individuals, this metric can represent an individual’s distance from an anchor‐point based on their rate and non‐linearity of change. We have recently developed a cognitive time (c‐time) based on the cognitive trajectories of clinically normal older adults. We examined the association between c‐time and a previously published ‘time‐to‐Aβ+ threshold’ and how these metrics align with demographics and other biomarkers.MethodWe identified 135 clinically normal older adults from the Harvard Aging Brain Study (Agemean:73years(±5.9); Female:61%) with ≥3 neuropsychological assessments and PiB‐PET, ≥1 Flortaucipir‐PET, ≥2 volumetric MRI, and diagnostic follow up. We defined c‐time using iterative non‐linear least‐squares optimization to define a curvilinear function that described the group‐level Preclinical Alzheimer Cognitive Composite (PACC) trajectory (Fig1D). Each participant’s PACC trajectory was subsequently located on the curve using the same optimization framework (Fig1E). We identified the anchor‐point of cognitive decline (c‐time) using piecewise linear mixed‐effects models. Time‐to‐Aβ+ was calculated using the published sampled iterative local approximation (SILA; Fig1A) algorithm with the anchor‐point indicating Aβ+ threshold (Fig1B). We examined associations between c‐time and time‐to‐Aβ+ using linear regression. Individuals were subsequently placed into groups depending on their position relative to the anchor‐point on each axis, as well as the line‐of‐best‐fit (Fig2). We compared the groups on demographics, and both cross‐sectional and longitudinal indices of medial temporal (MTL) Flortaucipir‐PET (entorhinal, parahippocampal, amygdala) and ICV‐adjusted hippocampal volume.ResultC‐time and time‐to‐Aβ+ were significantly associated (r = 0.42,p&lt;0.001). Only one participant (who progressed to MCI/dementia) was post‐c‐time and remained pre‐time‐to‐Aβ+, supporting the notion that time‐to‐Aβ+ occurs prior to cognitive inflection. Individuals post‐c‐time and post‐time‐to‐Aβ+ (Group 1) were more likely to be APOEε4 carriers, progressors to MCI/dementia, have significantly higher baseline MTL tau and lower hippocampal volume, and faster hippocampal atrophy (Fig3). Group 2 (post‐time‐to‐Aβ+/pre‐c‐time) were more likely APOEε4 carriers. Notably, no age or other effects were apparent between groups.ConclusionOptimizing longitudinal cognitive and biomarker data to estimate a preclinical disease continuum can provide unique, and potentially age‐independent, information about the distance an individual might be from disease‐relevant events.

Clinical Manifestations.

Balancing the need to standardize cognitive measures with the need to evolve alongside advances in scientific knowledge is an inherent challenge for longitudinal studies of Alzheimer disease (AD). The Uniform Data Set (UDS) offers a solution for this in cognitive batteries and is implemented across Alzheimer Disease Research Centers (ADRCs). Using equipercentile equating, we combined data spanning two versions of the UDS and derived domain-specific and general cognitive composites. We compared the relationships of these measures with common biomarkers of AD, as well as their statistical power to detect cognitive change in clinical trials. Using longitudinal data from the Knight ADRC, domain-specific cognitive composites were created using an exploratory factor analysis. The resulting four factors clustered episodic memory, semantic memory, working memory, and attention/processing speed tasks (Figure 1). We also computed a commonly-used preclinical Alzheimer cognitive composite (PACC), an alternative Knight-ADRC-PACC, which did not include MMSE, and a global composite. Relationships between each cognitive composite and neuroimaging metrics of amyloid, tau, atrophy, and clinical symptoms, were compared cross-sectionally and longitudinally (Figure 2). We also performed power analyses of all composites to calculate necessary sample sizes to observe a medium-sized effect in a four-year clinical trial. Overall, general metrics of cognition slightly outperformed domain-specific composites, although the working memory composite consistently reflected a poor relationship with AD biomarkers (Figure 2). Power analyses revealed that the global, Knight-ADRC-PACC, and attention/processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the ADCS-PACC, required two to three times as many individuals (Figure 3). We found that data-driven, domain-specific, composites of cognitive performance can complement already-established general cognitive composites, with the possible exception of the present working memory composite. In line with prior work, we highlight that attention and processing speed may be sensitive metrics of cognitive change in AD. The inclusion of the MMSE, such as in the ADCS-PACC, may greatly reduce the sensitivity and statistical power of summary cognitive composites. Our results have important implications for the development of cognitive outcome metrics for future clinical trials.

Sex Differences in the Associations of Obesity with Tau, Amyloid PET, and Cognitive Outcomes in Preclinical Alzheimer's Disease: Cross-Sectional A4 Study.

The association between obesity and Alzheimer's disease (AD) is complex. Recent studies indicated the relationships between obesity and AD may differ by sex, and women may benefit from being overweight in terms of AD risk. We investigated whether sex modifies the associations of obesity with tau positron emission tomography (PET), amyloid PET, and cognition in preclinical AD. We included 387 cognitively-unimpaired amyloid-positive participants (221 women, 166 men, 87.6% non-Hispanic White) with available 18F-flortaucipir PET, 18F-florbetapir PET, and completed the Preclinical Alzheimer Cognitive Composite (PACC) tests from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study. Participants were categorized based on body mass index (BMI: kg/m2): normal-weight (BMI: 18.5-25), overweight (BMI: 25-30), and obese (BMI≥30). Significant sex by BMI category interactions on PACC and its components: Mini-Mental State Examination (MMSE) and Reminding Test-Free+Total Recall (FCSRT96) revealed that overweight and obese women outperformed normal-weight women on FCSRT96, while obese men showed poorer MMSE performance than normal-weight men. These interactions were independent of APOE4. There were no significant interactions of sex by BMI category on tau and amyloid PET. However, sex-stratified analyses observed obesity was associated with less regional tau and mean cortical amyloid in women, not in men. This study found that in preclinical AD, overweight and obesity were associated with better verbal memory in women, whereas obesity was associated with worse global cognition among men. Future studies focusing on the mechanism for this relationship may inform sex-specific interventions for AD prevention.

Association of Alcohol Consumption with Cognition in Older Population: The A4 Study.

Alcohol use disorders have been categorized as a 'strongly modifiable' risk factor for dementia. To investigate the cross-sectional association between alcohol consumption and cognition in older adults and if it is different across sexes or depends on amyloid-β (Aβ) accumulation in the brain. Cognitively unimpaired older adults (N = 4387) with objective and subjective cognitive assessments and amyloid positron emission tomography (PET) imaging were classified into four categories based on their average daily alcohol use. Multivariable linear regression was then used to test the main effects and interactions with sex and Aβ levels. Individuals who reported no alcohol consumption had lower scores on the Preclinical Alzheimer Cognitive Composite (PACC) compared to those consuming one or two drinks/day. In sex-stratified analysis, the association between alcohol consumption and cognition was more prominent in females. Female participants who consumed two drinks/day had better performance on PACC and Cognitive Function Index (CFI) than those who reported no alcohol consumption. In an Aβ-stratified sample, the association between alcohol consumption and cognition was present only in the Aβ- subgroup. The interaction between Aβ status and alcohol consumption on cognition was not significant. Low or moderate consumption of alcohol was associated with better objective cognitive performance and better subjective report of daily functioning in cognitively unimpaired individuals. The association was present only in Aβ- individuals, suggesting that the pathophysiologic mechanism underlying the effect of alcohol on cognition is independent of Aβ pathology. Further investigation is required with larger samples consuming three or more drinks/day.

Longitudinal trajectories of self‐reported subjective cognitive decline in relation to objective cognitive performance in a population‐based cohort

AbstractBackgroundSubjective Cognitive Decline (SCD) may represent the onset of cognitive decline before impairment on standard cognitive tests occurs (Jessen et al., 2014). Previous cross‐sectional studies were shown to have limited ability to capture objective differences in cognitive performance between groups with and without SCD (Cacciamani et al., 2017). Understanding the interplay between changes in subjective memory ratings and objective cognitive function over time may have utility in identifying people at risk of dementia.Method370 cognitively normal (CN) individuals with complete cognitive data (Table 1 for characteristics) from Insight 46, a longitudinal neuroscience sub‐study of the MRC National Survey for Health and Development (the British 1946 Birth Cohort), underwent cognitive, clinical, and physical assessments, and neuroimaging (combined MRI/18F‐Florbetapir‐PET). SCD was measured using MyCog (Rami et al., 2014) a validated tool, from the SCD‐Questionnaire, where higher scores indicate greater subjective complaints. Linear regression models were used to test whether longitudinal cognitive change as measured using the Preclinical Alzheimer Cognitive Composite (PACC) and a visual short‐term memory binding task were influenced by baseline MyCog and whether there are parallel associations between rates of change in MyCog and longitudinal cognitive change. Covariates were sex, age at baseline visit, childhood cognitive ability, educational attainment, and adult socioeconomic position.ResultBaseline MyCog scores did not predict rates of change in PACC or visual short‐term memory binding outcome measures (Table 2). Rates of MyCog change alone did not predict rate of change in PACC, localisation error, total correct number of trials or proportion of misbinding errors (Table 3). However, we observed an interaction effect with baseline amyloid‐b status, whereby greater rates of subjective memory concerns predicted faster rates of decline in the proportion of misbinding errors in amyloid‐b positive participants only (Table 3; Figure 1).ConclusionThese findings suggest that higher amyloid‐b burden in CN individuals with subjective memory complaints is associated with faster rates of decline in visual working memory. Corroborating previous reports that feature binding tasks can objectify subtle cognitive deficits found in SCD (Koppara et al., 2015) and serve as a promising tool for identifying people at risk of dementia.

Early Detection of Dementia using Risk Classification in MCI: Outcomes of Shanghai Mild Cognitive Impairment Cohort Study.

The purpose of this study is to identify the risk factors and risk classification associated with the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia to facilitate early intervention and the design of clinical trials for AD. The study comprised a prospective cohort study of 400 subjects with MCI who had annual follow-ups for 3 years. During an average follow-up period of 3.5 years, 109 subjects were diagnosed with all cause of dementia, of whom 104 subjects converted to Alzheimer's dementia and 5 subjects converted to other types of dementia. The cumulative conversion rate was 5.5% (95% CI: 3.4, 8.6), 16.3% (95% CI: 12.9, 21.1), and 31.0% (95% CI: 25.4, 36.5) in each of the first 3 follow-up years, respectively. The factors associated with a greater risk of conversion from MCI to AD included smoking status, ApoE4 carrier status, right hippocampal volume (rt. HV), left temporal lobe volume, and scores on the Revised Chinese version of the Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog-C). The risk classification of the ADAS-Cog-C or Preclinical Alzheimer Cognitive Composite (PACC) score combined with the rt. HV showed a conversion difference among the groups at every annual follow-up. A simple risk classification using the rt. HV and neuropsychological test scores, including those from the ADAS-Cog-C and PACC, could be a practicable and efficient approach to indentify individuals at risk of all-cause dementia.

Subjective cognitive decline across ethnoracial groups in the A4 study.

The associations between subjective cognitive decline (SCD),cognition,and amyloid were explored across diverse participants in the A4 study. Five thousand one hundred and fifty-one non-Hispanic White, 262 non-Hispanic Black, 179 Hispanic-White, and 225 Asian participants completed the Preclinical Alzheimer Cognitive Composite (PACC),self- and study partner-reported Cognitive Function Index (CFI). A subsample underwent amyloid positron emission tomography (18 F-florbetapir) (N=4384). We examined self-reported CFI, PACC, amyloid, and study partner-reported CFI by ethnoracial group. The associations between PACC-CFI and amyloid-CFI were moderated by race. The relationships were weaker or non-significant in non-Hispanic Black and Hispanic White groups. Depression and anxiety scores were stronger predictors of CFI in these groups. Despite group differences in the types of study partners, self- and study partner-CFI were congruent across groups. SCD may not uniformly relate to cognition or AD biomarkers in different ethnoracial groups. Nonetheless, self- and study partner-SCD were congruent despite differences in study partner type. Highlights Association between SCD and objective cognition was moderated by ethnoracial group. Association between SCD and amyloid was moderated by ethnoracial group. Depression and anxiety were stronger predictors of SCD in Black and Hispanic groups. Study-partner and self-reported SCD are congruent across groups. Study-partner report was consistent despite difference in study partner types.