Baseline Aβ Research Articles

Tau PET positivity predicts clinically relevant cognitive decline driven by Alzheimer's disease compared to comorbid cases; proof of concept in the ADNI study.

β-amyloid (Aβ) pathology is not always coupled with Alzheimer's disease (AD) relevant cognitive decline. We assessed the accuracy of tau PET to identify Aβ(+) individuals who show prospective disease progression. 396 cognitively unimpaired and impaired individuals with baseline Aβ and tau PET and a follow-up of ≥ 2 years were selected from the Alzheimer's Disease Neuroimaging Initiative dataset. The participants were dichotomously grouped based on either clinical conversion (i.e., change of diagnosis) or cognitive deterioration (fast (FDs) vs. slow decliners (SDs)) using data-driven clustering of the individual annual rates of cognitive decline. To assess cognitive decline in individuals with isolated Aβ(+) or absence of both Aβ and tau (T) pathologies, we investigated the prevalence of non-AD comorbidities and FDG PET hypometabolism patterns suggestive of AD. Baseline tau PET uptake was higher in Aβ(+)FDs than in Aβ(-)FD/SDs and Aβ(+)SDs, independently of baseline cognitive status. Baseline tau PET uptake identified MCI Aβ(+) Converters and Aβ(+)FDs with an area under the curve of 0.85 and 0.87 (composite temporal region of interest) respectively, and was linearly related to the annual rate of cognitive decline in Aβ(+) individuals. The T(+) individuals constituted largely a subgroup of those being Aβ(+) and those clustered as FDs. The most common biomarker profiles in FDs (n = 70) were Aβ(+)T(+) (n = 34, 49%) and Aβ(+)T(-) (n = 19, 27%). Baseline Aβ load was higher in Aβ(+)T(+)FDs (M = 83.03 ± 31.42CL) than in Aβ(+)T(-)FDs (M = 63.67 ± 26.75CL) (p-value = 0.038). Depression diagnosis was more prevalent in Aβ(+)T(-)FDs compared to Aβ(+)T(+)FDs (47% vs. 15%, p-value = 0.021), as were FDG PET hypometabolism pattern not suggestive of AD (86% vs. 50%, p-value = 0.039). Our findings suggest that high tau PET uptake is coupled with both Aβ pathology and accelerated cognitive decline. In cases of isolated Aβ(+), cognitive decline may be associated with changes within the AD spectrum in a multi-morbidity context, i.e., mixed AD.

Association of Basal Forebrain Volume with Amyloid, Tau, and Cognition in Alzheimer's Disease.

Degeneration of cholinergic basal forebrain (BF) neurons characterizes Alzheimer's disease (AD). However, what role the BF plays in the dynamics of AD pathophysiology has not been investigated precisely. To investigate the baseline and longitudinal roles of BF along with core neuropathologies in AD. In this retrospective cohort study, we enrolled 113 subjects (38 amyloid [Aβ]-negative cognitively unimpaired, 6 Aβ-positive cognitively unimpaired, 39 with prodromal AD, and 30 with AD dementia) who performed brain MRI for BF volume and cortical thickness, 18F-florbetaben PET for Aβ, 18F-flortaucipir PET for tau, and detailed cognitive testing longitudinally. We investigated the baseline and longitudinal association of BF volume with Aβ and tau standardized uptake value ratio and cognition. Cross-sectionally, lower BF volume was not independently associated with higher cortical Aβ, but it was associated with tau burden. Tau burden in the orbitofrontal, insular, lateral temporal, inferior temporo-occipital, and anterior cingulate cortices were associated with progressive BF atrophy. Lower BF volume was associated with faster Aβ accumulation, mainly in the prefrontal, anterior temporal, cingulate, and medial occipital cortices. BF volume was associated with progressive decline in language and memory functions regardless of baseline Aβ and tau burden. Tau deposition affected progressive BF atrophy, which in turn accelerated amyloid deposition, leading to a vicious cycle. Also, lower baseline BF volume independently predicted deterioration in cognitive function.

Exploring the potential of fully automated LUMIPULSE G plasma assays for detecting Alzheimer’s disease pathology

BackgroundLUMIPULSE G-automated immunoassays represent a widely used method for the quantification of Alzheimer’s disease (AD) biomarkers in the cerebrospinal fluid (CSF). Less invasive blood-based markers confer a promising tool for AD diagnosis at prodromal stages (mild cognitive impairment (MCI)). Highly sensitive assays for the quantification of amyloid-beta (Aβ) and phosphorylated Tau-181 (p-Tau181) in the blood are showing promising results. In this study, we evaluated the clinical performance of the recently available fully automated LUMIPULSE plasma marker assays for detecting brain AD pathology and for predicting progression from MCI to AD dementia stage.MethodsA retrospective exploratory cohort of 138 individuals (22 neurological controls [NC], 72 MCI, and 44 AD dementia patients) was included. Data regarding baseline CSF concentrations of Aβ42, Aβ40, t-Tau, and p-Tau181 was available and used to establish the presence of AD brain pathology. Baseline Aβ42, Aβ40, and p-Tau181 concentrations were determined in stored plasma samples using high-throughput fully automated LUMIPULSE assays. Progression from MCI to AD dementia was evaluated during follow-up (mean 6.4 ± 2.5 years). Moreover, a prospective validation cohort of 72 individuals with memory complaints underwent AD biomarker quantification, closely mirroring typical clinical practice. This cohort aimed to confirm the study’s main findings.ResultsIn the exploratory cohort, correlations between CSF and plasma were moderate for p-Tau181 (ρ = 0.61, p < 0.001) and weak for Aβ42/Aβ40 ratio (ρ = 0.39, p < 0.001). Plasma p-Tau181 and p-Tau181/Aβ42 concentrations were significantly increased while Aβ42/Aβ40 was significantly decreased (p < 0.001) in patients with AD dementia and prodromal AD, as well as in individuals with CSF abnormal amyloid concentrations (A +). Plasma p-Tau181 showed a robust performance in differentiating patients clinically diagnosed as AD (AUC = 0.89; 95% CI 0.83–0.94); A + vs. A − (AUC = 0.84, 95% CI 0.77–0.91) and also in predicting conversion to AD dementia in MCI patients (AUC = 0.89, 95% CI 0.81–0.96). When tested in the validation cohort, plasma p-Tau181 displayed 83.3% of the overall percentage of agreement according to amyloid status.ConclusionsOur results show that the measurement of p-Tau181 in plasma has great potential as a non-invasive prognostic screening tool for implementation in a clinical setting.

Association between brain amyloid deposition and longitudinal changes of white matter hyperintensities

BackgroundGrowing evidence suggests that not only cerebrovascular disease but also Alzheimer’s disease (AD) pathological process itself cause cerebral white matter degeneration, resulting in white matter hyperintensities (WMHs). Some preclinical evidence also indicates that white matter degeneration may precede or affect the development of AD pathology. This study aimed to clarify the direction of influence between in vivo AD pathologies, particularly beta-amyloid (Aβ) and tau deposition, and WMHs through longitudinal approach.MethodsTotal 282 older adults including cognitively normal and cognitively impaired individuals were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aβ deposition, [18F] AV-1451 PET for measuring tau deposition, and MRI scans with fluid-attenuated inversion recovery image for measuring WMH volume. The relationships between Aβ or tau deposition and WMH volume were examined using multiple linear regression analysis. In this analysis, baseline Aβ or tau were used as independent variables, and change of WMH volume over 2 years was used as dependent variable to examine the effect of AD pathology on increase of WMH volume. Additionally, we set baseline WMH volume as independent variable and longitudinal change of Aβ or tau deposition for 2 years as dependent variables to investigate whether WMH volume could precede AD pathologies.ResultsBaseline Aβ deposition, but not tau deposition, had significant positive association with longitudinal change of WMH volume over 2 years. Baseline WMH volume was not related with any of longitudinal change of Aβ or tau deposition for 2 years. We also found a significant interaction effect between baseline Aβ deposition and sex on longitudinal change of WMH volume. Subsequent subgroup analyses showed that high baseline Aβ deposition was associated with increase of WMH volume over 2 years in female, but not in male.ConclusionsOur findings suggest that Aβ deposition accelerates cerebral WMHs, particularly in female, whereas white matter degeneration appears not influence on longitudinal Aβ increase. The results also did not support any direction of influence between tau deposition and WMHs.

Interaction between Aβ and tau on reversion and conversion in mild cognitive impairment patients: After 2-year follow-up

BackgroundThe role of amyloid-β (Aβ) and tau in reversion and conversion in patients with mild cognitive impairment (MCI) remains unclear. This study aimed to investigate the influence of cerebrospinal fluid (CSF) Aβ and tau on reversion and conversion and the temporal sequence of their pathogenicity in MCI patients. Methods179 MCI patients were recruited from the Alzheimer's Disease Neuroimaging Initiative database and classified into two groups based on cognitive changes after follow-up: reversal group (MCI to cognitively normal) and conversion group (MCI to Alzheimer's disease). CSF biomarkers and cognitive function were measured at baseline and 2-year follow-up. Partial correlation was used to analyze the association between CSF biomarkers and cognitive function, and multivariable logistic regression to identify independent risk factors for cognitive changes at baseline and 2-year follow-up. Receiver operating characteristic (ROC) curves were utilized to evaluate the predictive ability of these risk factors for cognitive changes. ResultsThe differences in cognitive function and CSF biomarkers between the two groups remained consistent with baseline after 2-year follow-up. After controlling for confounding variables, there was still a correlation between CSF biomarkers and cognitive function at baseline and 2-year follow-up. Multivariable regression analysis found that at baseline, only Aβ level was independently associated with cognitive changes, while Aβ and tau were both predictive factors after 2-year follow-up. ROC curve analysis revealed that the combination of Aβ and tau [area under the curve (AUC) 0.91, sensitivity 84%, specificity 86%] in predicting cognitive changes after 2-year follow-up had better efficacy than baseline Aβ alone (AUC 0.81). ConclusionAβ may precede Tau in causing cognitive changes, and the interaction between the two mediates cognitive changes in patients. This study provides new clinical evidence to support the view that Aβ pathology precedes tau pathology, which together contribute to the changes in cognitive function.

Association Between β-Amyloid Accumulation and Incident Dementia in Individuals 80 Years or Older Without Dementia.

While the highest prevalence of dementia occurs in individuals older than 80 years, most imaging studies focused on younger populations. The rates of β-amyloid (Aβ) accumulation and the effect of Alzheimer disease (AD) pathology on progression to dementia in this age group remain unexplored. In this study, we examined the relationship between changes in Aβ deposition over time and incident dementia in nondemented individuals followed during a period of 11 years. We examined 94 participants (age 85.9 + 2.8 years) who had up to 5 measurements of Pittsburgh compound-B (PiB)-PET and clinical evaluations from 2009 to 2020. All 94 participants had 2 PiB-PET scans, 76 participants had 3 PiB-PET scans, 18 participants had 4 PiB-PET scans, and 10 participants had 5 PiB-PET scans. The rates of Aβ deposition were compared with 120 nondemented individuals younger than 80 years (69.3 ± 5.4 years) from the Australian Imaging, Biomarker, and Lifestyle (AIBL) study who had 3 or more annual PiB-PET assessments. By 2020, 49% of the participants developed dementia and 63% were deceased. There was a gradual increase in Aβ deposition in all participants whether they were considered Aβ positive or negative at baseline. In a Cox model controlled for age, sex, education level, APOE-4 allele, baseline Mini-Mental State Examination, and mortality, short-term change in Aβ deposition was not significantly associated with incident dementia (HR 2.19 (0.41-11.73). However, baseline Aβ burden, cortical thickness, and white matter lesions volume were the predictors of incident dementia. Aβ accumulation was faster (p = 0.01) in the older cohort (5.6%/year) when compared with AIBL (4.1%/year). In addition, baseline Aβ deposition was a predictor of short-term change (mean time 1.88 years). There was an accelerated Aβ accumulation in cognitively normal individuals older than 80 years. Baseline Aβ deposition was a determinant of incident dementia and short-term change in Aβ deposition suggesting that an active Aβ pathologic process was present when these participants were cognitively normal. Consequently, age may not be a limiting factor for the use of the emergent anti-Aβ therapies.

Spatiotemporal Correlation between Amyloid and Tau Accumulations Underlies Cognitive Changes in Aging.

It is poorly known how Aβ and tau accumulations associate at the spatiotemporal level in the in vivo human brain to impact cognitive changes in older adults prior to AD symptoms onset. In this study, we used a graph theory-based spatiotemporal analysis to characterize the cortical patterns of Aβ and tau deposits and their relationship with cognitive changes in the Harvard Aging Brain Study (HABS) cohort. We found that the temporal accumulations of interlinked Aβ and tau pathology display distinctive spatiotemporal correlations associated with early cognitive decline. Notably, we observed that baseline Aβ deposits-Thal amyloid phase Ⅱ-related to future increase of tau deposits, Braak stages Ⅰ-Ⅳ, both displaying linkage to the decline in multi-domain cognitive scores. We also found unimodal tau-to-tau and cognitive impairment associations in broad areas of Braak stages Ⅰ-Ⅳ. The unimodal Aβ-to-Aβ progressions were not associated with cognitive changes. Our results revealed a multifaceted correlation of the spatiotemporal Aβ and tau associations with cognitive decline over time, in which tau-to-tau and tau-Aβ interactions, and not Aβ independently, might be critical contributors to clinical trajectories toward AD in older adults.

Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

The use of biomarkers for the early detection of Alzheimer’s disease (AD) is crucial for developing potential therapeutic treatments. Positron Emission Tomography (PET) is a well-established tool used to detect β-amyloid (Aβ) plaques in the brain. Previous studies have shown that cross-sectional biomarkers can predict cognitive decline (Schindler et al.,2021). However, it is still unclear whether longitudinal Aβ-PET may have additional value for predicting time to cognitive impairment in AD. The current study aims to evaluate the ability of baseline- versus longitudinal rate of change in-11C-Pittsburgh compound B (PiB) Aβ-PET to predict cognitive decline. A cohort of 153 participants who previously underwent PiB-PET scans and comprehensive clinical assessments were used in this study. Our analyses revealed that baseline Aβ is significantly associated with the rate of change in cognitive composite scores, with cognition declining more rapidly when baseline PiB Aβ levels were higher. In contrast, no signification association was identified between the rate of change in PiB-PET Aβ and cognitive decline. Additionally, the ability of the rate of change in the PiB-PET measures to predict cognitive decline was significantly influenced by APOE ε4 carrier status. These results suggest that a single PiB-PET scan is sufficient to predict cognitive decline and that longitudinal measures of Aβ accumulation do not improve the prediction of cognitive decline once someone is amyloid positive.

MK‐6240 tau‐PET in an Aβ‐enriched sample from the 1946 British birth cohort

AbstractBackgroundWe investigated imaging biomarkers of Aβ and neurodegeneration in relation to tau‐PET Braak stage in a preclinical birth cohort.MethodCognitively normal individuals enrolled in Insight 46, the neuroimaging sub‐study of the MRC National Survey of Health and Development (1946 British birth cohort), were scanned on combined PET/MR with [18F]florbetapir Aβ‐PET at age ∼70 years and again at ∼73 years. A sub‐sample enriched for Aβ (Aβ+; Centiloid&gt; = 13, hole cerebellum reference) is currently being assessed with [18F]MK‐6240 tau‐PET at age ∼76 years. For this interim analysis, tau‐PET images (90‐110 minutes post‐injection) were co‐registered with T1‐weighted MRI. Anatomical areas were parcellated on the T1 to form Braak regions. Standard uptake value ratios (SUVRs) were calculated using an inferior cerebellar grey reference without partial volume correction. Tau‐PET positivity (Tau+) was defined using Gaussian mixture modelling in each region. Participants were assigned to Tau‐, Braak I‐II, III‐IV or V‐VI groups based on the most advanced Tau+ region. Group differences in baseline Aβ (Centiloids), Aβ accumulation (Centiloids/year) and hippocampal atrophy rate (%/year) were investigated with Mann‐Whitney U tests.ResultAnalysis included 80 individuals with tau‐PET data (Table 1), 45% of the sample were Aβ+ by age 73. Three individuals did not conform to the Braak stage hierarchy (orange triangles, Figure 1). No Aβ‐ individuals were Tau+ beyond Braak I. Figure 2 shows baseline Aβ, Aβ accumulation and hippocampal atrophy rates for Braak stage groups for participants who had data at all timepoints (N = 78). Tau+ individuals (in any Braak region) had significantly higher baseline Aβ than Tau‐ individuals. Rate of annual Aβ accumulation was higher for Braak III‐IV and Braak V‐VI compared to Tau‐ individuals. Hippocampal atrophy rate was elevated for Braak V‐VI compared to Tau‐, and Braak III‐IV was borderline significant.ConclusionIn this preliminary analysis, tau‐PET positivity beyond Braak I was restricted to individuals who were Aβ+ three years prior. Participants with elevated tau aged 76 had increased Aβ at age 70. Increased rates of hippocampal atrophy were occurring at least three years prior to tau scanning in individuals with advanced tau pathology. These findings will be updated as more data is acquired.

Astrocyte reactivity potentiates longitudinal tau tangle accumulation in cognitively unimpaired individuals

AbstractBackgroundAβ accumulation precedes tau pathology in cognitively unimpaired (CU) individuals, which is an event closely related to the development of cognitive symptoms. However, Aβ leads to tau pathology in some individuals, but not in others, suggesting the presence of other processes triggering the deleterious effects of Aβ in the early Alzheimer’s disease (AD) stages. It was already demonstrated that glial fibrillary acidic protein (GFAP)‐positive astrocytes can internalize tau and might contribute to its propagation. Here we investigated whether astrocyte reactivity is a key element to determining the longitudinal accumulation and spreading of tau pathology in CU individuals.MethodWe assessed 147 CU individuals from the TRIAD cohort with available baseline Aβ‐ and Tau‐PET, plasma p‐tau181 and GFAP. Individuals were classified as positive (Ast+) or negative (Ast‐) for astrocyte reactivity using a cutoff based on plasma GFAP levels of younger Aβ‐ individuals. Longitudinal analysis included 71 CU individuals with Tau‐PET (mean follow‐up:2.3 years).Voxel‐wise associations between biomarkers were tested using linear regressions accounting for age, sex, and adjusting for multiple comparisons. We measured the annual rate of progression in Tau uptake as the difference between follow‐up and baseline uptakes divided by the time between scans. To assess individuals’ percentage of abnormal regions, we used composite brain regions corresponding to Braak histopathological stages.ResultTau‐PET deposition occurred as a function of Aβ burden only in CU Ast+ and in regions expected to present the earliest tau deposition (Fig.1a). In the longitudinal analysis, we observed that the annual rate of tau‐PET accumulation was higher in the CU Ast+ group (Fig.2a) and predicted by baseline Aβ burden only in CU Ast+ (Fig.2b). Interestingly, while the baseline association was confined to the mesial temporal cortex, the longitudinal tau‐PET accumulation as a function of Aβ/Ast presented initial tau spread over the neocortex in Braak III‐IV regions (Fig.2c).ConclusionWe observed that Tau‐PET accumulation was highest in CU Ast+ individuals, with astrocyte reactivity being necessary for the spreading of tau over the neocortex. Our findings suggest that astrocyte reactivity is an important upstream event to unleash tau accumulation and spread in CU individuals, which might have implications for selecting individuals for early preventive clinical trials.

Association between brain amyloid deposition and longitudinal changes of white matter hyperintensities

AbstractBackgroundWhite matter injury had been mostly considered a result of chronic ischemia caused by cerebral small vessel disease. However, recent studies reported that white matter degeneration could also be an important pathophysiological feature in Alzheimer’s disease (AD). This study aimed to identify the relationship between AD biomarkers, particularly amyloid‐beta (Aβ) and tau deposition, with white matter hyperintensities (WMHs) representing cerebral white matter injury.MethodA total 456 older adults aging from 55 to 90 years including cognitively normal and cognitively impaired individuals were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aβ deposition, [18F] AV‐1451 PET for measuring tau deposition, and MRI scans with fluid‐attenuated inversion recovery image for measuring WMH volume. For PET scans, standard uptake value ratio (SUVR) was used for the analyses; combined regions of hemispheric white matter, inferior cerebellum and pons were used as the reference region when obtaining SUVRs. The relationships between Aβ or tau deposition and WMH volume were examined using multiple regression analysis. In this analysis, baseline Aβ or tau were used as independent variables, and baseline as well as change of WMH volume over 2 years were used as dependent variables.ResultBaseline Aβ deposition levels had significant positive association with longitudinal change of WMH volume over 2 years (β = 0.238, p = 0.009), but not with WMH volume at baseline. Baseline tau was not related with any of WMH volume at baseline and longitudinal change of WMH volume over 2 years. We also found significant interaction effect between baseline Aβ deposition level and sex on longitudinal change of WMH volume. Subsequent subgroup analyses showed that high baseline Aβ deposition was associated with increase of WMH volume over 2 years in female (β = 0.427, p = 0.001), but not in male.ConclusionOur findings suggest that Aβ deposition, but not tau, accelerates cerebral white matter injury, particularly in female.

Baseline Amyloid and Tau Deposition Predict Distinct Patterns of Longitudinal White Matter Degeneration: A Diffusion MRI Investigation

AbstractBackgroundNeuritic plaques with fibrillar beta‐amyloid (Aβ) peptides and tau‐protein neurofibrillary tangles are hallmark features of Alzheimer’s disease (AD) pathology. While concomitant differences in white matter (WM) integrity were found in association with Aβ deposition or tau pathology, a unique effect of each pathology on WM integrity remains unknown. In this study, we examined the unique effects of AD pathologies on WM integrity both cross‐sectionally and longitudinally.MethodA total of 334 participants (188 cognitively normal (CN), 114 mild cognitive impairment, and 32 AD) was selected from the ADNI database, based on the availability of two timepoints of diffusion MRI (dMRI) data, and baseline amyloid and tau PET data. The dMRI data were processed using FSL’s TBSS pipeline to quantify the degree of fractional anisotropy (FA) and annualized FA change maps were created to measure voxel‐wise WM change over time. Baseline global amyloid SUVR values were converted to a centiloid scale. Baseline tau SUVRs were evaluated by Braak staging (I‐entorhinal, II‐hippocampal, III/IV‐limbic, V/VI‐neocortex). The associations between AD pathologies and cross‐sectional and longitudinal WM integrity were assessed by FSL’s randomise and cluster‐level correction, controlling for age and sex.ResultCross‐sectionally, baseline tau burden in Braak stages I, III/IV, and V/VI related to lower WM integrity, independent of Aβ, while there was no association between baseline Aβ and WM integrity, independent of tau. Longitudinally, limbic and neocortical tau at baseline uniquely predicted WM integrity loss across widespread regions, including left superior longitudinal fasciculus and the body and splenium of corpus callosum. Moreover, baseline Aβ burden significantly related to WM integrity loss across widespread regions, only when entorhinal and hippocampal tau was controlled for. Similar, but weaker, patterns of the associations between baseline AD pathology and longitudinal WM integrity loss were also found only within CN group.ConclusionThese findings suggest distinctive associations between AD pathology and WM degeneration such that Aß exerts widespread deleterious effects on WM integrity primarily early in AD pathophysiology, whereas, once spreading to the neocortex, tau burden drives WM neurodegeneration. The distinctive downstream pathways associated with each AD pathology may underlie differential cognitive profiles across the disease stages.

Effect of white matter hyperintensities and amyloid on neurodegenerative change and cognitive trajectory in cognitively unimpaired older adults

AbstractBackgroundOverwhelming evidence of association between amyloidosis and neurodegeneration, particularly in the hippocampus, has been reported. However, the recognition of cerebrovascular disease (CVD), the most common comorbidity in elderly patients with dementia2, as a contributor to neurodegeneration process has remained modest. The objective of this study is to investigate the contributions of amyloidosis, as measured by amyloid PET, and CVD, as reflected by white matter hyperintensity (WMH), on neurodegenerative change and how their combination impacts cognitive trajectory in cognitively unimpaired (CU) individuals.Method139 CU participants from the University of California, Davis Alzheimer’s Disease Research Center diversity cohort received at least two cognitive assessments and underwent a PET exam at baseline, and baseline and follow‐up MRI imaging (Table 1). Z‐score measures of DVR19 and SUVR20 values were used as measure of baseline amyloid beta (Aβ) burden. Baseline WMH volume was log‐transformed and regressed against intracranial volume (TCV). Four markers of neurodegeneration were used, including hippocampal volume (regressed against TCV), mean cortical GM thickness and two cognitive‐related signature region of interest (sROI), including episodic memory and executive function. We used structural equation modeling (SEM) analyses (Figure 1) to investigate associations between cognitive performance trajectories (∼6 years), including episodic memory and executive function, and baseline Aβ, baseline WMH and change in the four neurodegeneration markers.ResultSEM analysis revealed significant effects of increased baseline Aβ and greater baseline WMH on accelerated decrease in neurodegeneration marker measures in all models (Figure 2). Increased baseline amyloid and WMH, but not neurodegenerative change measures, were also associated with accelerated decline in episodic memory performance (pvalues&lt;0.05, Figure 2, panels A‐C‐E). Finally, increased baseline Aβ and accelerated decreased in cortical GM and sROI thickness were found to be associated with accelerated decline in executive function (pvalues&lt;0.05, Figure 2, panels B‐D‐F).ConclusionOur findings suggest that both CVD and amyloidosis simultaneously impact neurodegenerative change and that these pathologies are associated with cognitive trajectories, but the dynamics of these trajectory are different for executive and memory, suggesting different early path of vulnerability for these domains and emphasizing the need to thoroughly understand the interaction between amyloidosis, CVD, and neurodegeneration.

Examining Complex Relationships among Cerebrovascular, β‐Amyloid, Tau and Cognitive Measures in a Cognitively Unimpaired Late‐Middle Aged Sample

AbstractBackgroundRecent research linking vascular risk with promotion of tau pathology in the inferior temporal cortex (ITC) suggests a potential mechanism connecting cerebrovascular risk to AD pathology and cognitive decline among cognitively unimpaired (CU) individuals (Yau et al, 2022).We used MRI White Matter Hyperintensity (WMH), a neuroimaging marker of chronic cerebral small‐vessel disease/cerebrovascular disease, to further elucidate longitudinal relationships between cerebrovascular risk, PET AD pathological burden, and cognition.MethodWe examined baseline CU late middle‐aged adults from the University of Wisconsin’s Alzheimer’s disease research cohorts. β‐Amyloid burden was measured with 11C‐Pittsburgh compound B (“PiB”; DVR derived from a 0‐70 min dynamic PET scan; PET A+ threshold∼1.16 DVR/∼CL 17). Cerebrovascular risk was quantified using WMH volumes (T2‐FLAIR images; SPM Lesion Segmentation Tool). Tau burden was measured with florquinitau PET (MK6240 SUVR from 70‐90 min post‐injection). Tau outcomes included bilateral volume‐weighted averages for ITC, Medial temporal lobe (MTL) and Meta‐temporal (MTC) composites. Biomarker values were z‐transformed relative to CU/PET A‐ values. Random intercept models examined interactive effects of baseline WMH and Aβ levels on longitudinal tau (covarying baseline tau age, sex, APOE ε4 count). We modeled time two ways: years since baseline PiB (TimePiB1; replicating Yau, et al models); and, in parallel models, amyloid duration (years) at tau scans was substituted for years since PiB1 in the A+ subset (TimePiB1/PiB+). We also used LME to examine Aβ, WMH, and time interactions in longitudinal cognition (PACC3 and Digit Symbol Substitution).ResultThe mean(SD) tau baseline age was 67.9(6.8) years (sample details in Table1). The most parsimonious models using TimePiB1 included WMH*Aβ*Time for ITC and MTC and WMH*Aβ for MTL. Patterns showed a synergistic effect of WMH and Aβ on tau accumulation that was attenuated using TimePiB1/PiB+ where the most parsimonious models included Aβ_status*TimePiB1/PiB+ (ITC, MTC) or WMH*Aβ_status*TimePiB1/PiB+ (MTL; Figure1). AICc model fits were substantially better using TimePiB1/PiB+. Both Aβ and WMH contributed to cognitive trajectories (Figure2).ConclusionAlthough cerebrovascular risk’s apparent contribution to tau accumulation varied depending on how time was modeled, results suggest that cerebrovascular risk may complement imaging biomarkers in assessing risk of tau accumulation and cognitive decline in preclinical Alzheimer disease.

Plasma biomarkers rates of change across the preclinical stage of Alzheimer’s disease: a longitudinal study

AbstractBackgroundWhether plasma biomarkers steadily increase during the preclinical stage of Alzheimer’s disease (AD) is unknown. Herein, we aimed to determine the rate‐of‐change of plasma biomarkers throughout preclinical AD. This may be important to determine the optimal time window for treatment.MethodWe included baseline and follow‐up plasma biomarkers measurements (follow‐up: 3.37±0.40 years) of 240 cognitively unimpaired participants of the ALFA+ cohort (mean age: 60.75±4.85 years). Plasma Aβ40, Aβ42, GFAP and NfL were measured with the Simoa N4PE Advantage Kit, and plasma p‐tau231 with a Simoa‐validated in‐house assay.For each participant we calculated the difference between follow‐up and baseline levels, and corrected for sex, time between measurements, and age. We computed z‐scores from the corrected values, and we applied a bootstrapped regression approach to model the rate‐of‐change of each plasma biomarker as a function of baseline age or Aβ PET centiloids (CL).Moreover, we also computed the plasma biomarkers rate‐of‐change in three stages of preclinical AD: (I) CSF/PET Aβ‐negative group, (II) CSF Aβ‐positive/PET Aβ‐negative (CL &lt; 30), and (III) CSF/PET Aβ‐positive.Significance was determined if the 95% confidence interval of the rate‐of‐change did not overlap with zero.ResultSignificant acceleration in the rate‐of‐change of plasma GFAP was observed with ageing (Fig. 1), becoming significant at 60 years. There was also a significant accelerated change of p‐tau231 from 55 to 68 years.Plasma NfL was the only biomarker whose rate‐of‐change accelerated with Aβ accumulation, and that rate became significant at 40CL (Fig. 2). Consistently, we observed a significant acceleration of plasma NfL when there was overt Aβ pathology (CSF/PET Aβ‐positive group; Fig. 3)The rest of plasma biomarker rates‐of‐change did not significantly increase, some were even deaccelerating (plasma Aβ42/40 and p‐tau231), as Aβ accumulated (Fig. 2 and 3).ConclusionPlasma biomarkers rate‐of‐change throughout the preclinical AD continuum differ. Plasma NfL rate‐of‐change accelerates in the later stage of preclinical AD, when overt Aβ pathology is present. The deceleration on the rate‐of‐change of plasma Aβ42/40 and p‐tau231 may explain why their early increase in the preclinical AD continuum tends to plateau in later stages.

Risk architecture for altered hippocampal connectivity in normal aging differ between men and women

AbstractBackgroundFunctional MRI (fMRI) studies have associated impaired memory networks with increase in AD pathology. Recently, cross‐sectional analyses found b‐amyloid (Aβ) deposition associated with different patterns of hippocampal connectivity in a memory task based on sex and white matter hyperintensity volume (WMHV). However, cross‐sectional studies don’t provide insight into the driving mechanisms of network change. In this longitudinal study, we identify risk factors based on sex that are associated with memory network connectivity changes.MethodOur study included 54 cognitively normal older adults (N = 34 female, mean age 74.6 years) with MRI scans on average 2.43 (SD 0.82) years apart. T1w, T2‐FLAIR MRI, and PiB PET were collected to quantify cortical volume, WMHV and Aβ deposition, respectively. Task fMRI was collected while participants performed face‐name associative memory tasks. Left and right hippocampus seeds were used to estimate functional connectivity between hippocampus and voxels in the brain. Second‐level analyses were performed to determine regions of interest (ROI) with significant differences in sex by time interaction (p&lt;0.001). The mean beta values were extracted from the significant ROIs for post‐hoc analysis where we performed multivariate linear regression analyses to find correlations with connectivity change (follow‐up minus baseline).ResultThree ROIs presented significant sex by time interactions: left‐right hippocampus, hippocampus‐anterior cingulate cortex (ACC), and hippocampus‐medial frontal gyrus. The change in left‐right hippocampus connectivity increased for women and decreased for men (Fig.1). For the other ROIs, connectivity increased for men and decreased for women (Fig.2). Left‐right hippocampal connectivity change had a significant sex‐WMHV interaction effect (p&lt;0.01), where only women had a positive association between connectivity change and baseline WMHV. Hippocampus‐ACC connectivity change had a significant sex‐Aβ interaction (p&lt;0.01), with greater baseline Aβ associated with increased connectivity change for men and decreased for women.ConclusionWe observed sex‐differences in risk architecture for altered hippocampal connectivity. In the presence of vascular pathology and Aβ, women appear to experience local hippocampal connectivity and decrease hippocampal‐frontal connectivity. Meanwhile, men experienced hippocampal‐frontal connectivity with increased Aβ burden. Characterizing these sex differences in memory network alterations can help guide the development of sex‐specific prevention and treatment strategies.

Development of Clinical Trial Simulation Tools for Alzheimer’s Disease through the Critical Path for Alzheimer’s Disease (CPAD) Consortium

AbstractBackgroundOptimized clinical trial design remains a major challenge in drug development for Alzheimer’s Disease AD). To advance our understanding of the underlying pathophysiology of disease and help optimize clinical trial design, CPAD developed a clinical trial simulation (CTS) tool for the pre‐dementia stages of Alzheimer’s disease, using CDR‐SB as the primary endpoint. The tool is based on a disease progression model integrating baseline hippocampal volume, the effect of patient drop‐out and relevant sources of variability. The CTS was enhanced using data from contemporary clinical trials, adding fluid and imaging biomarkers of amyloid (Aβ) and tau pathology. Also included are dropout models, a placebo effect model, as well as drug‐effect models. Positron Emission Tomography (PET) measures of tau deposition, leveraging ongoing collaborative efforts to develop a standardized quantification scale across different tau tracers, will also be incorporated, to develop the first disease progression models utilizing a harmonized tau‐PET scale.MethodData were harmonized and both linear and non‐linear mixed effects models were developed for multiple endpoints, including demographics, time from and to diagnosis, genetic status (APOE4). Monte Carlo simulations were performed to compare the statistical power by sample size in trials with and without enrichment using relevant covariates. A longitudinal mixed‐effects model was fit with longitudinal tau and neurodegeneration outcomes. Cognition and biomarkers were explored in a multivariate model.ResultAs of October 2022, CPAD’s clinical trial repository contains over 7,000 unique participants that have baseline CDR‐SB measurement, 4,000+ participants with baseline Aβ measures (fluid or PET), and 2,000+ participants with baseline measures of amyloid, tau (e.g., cerebrospinal fluid p‐tau and/or tau‐PET), and neurodegeneration (e.g., MRI or neurofilament light). With these data, we modeled longitudinal trajectories of cognitive decline using baseline levels of amyloid, tau, and neurodegeneration. Future work will add models of longitudinal biomarker trajectories.ConclusionThe disease progression models will serve as the basis for a clinical trial simulation tool to facilitate design of contemporary clinical trials based on a quantitative understanding of the biological progression of key pathological features, amyloid and tau deposition, of AD.

Association between cerebrospinal fluid clusterin and biomarkers of Alzheimer's disease pathology in mild cognitive impairment: a longitudinal cohort study.

Clusterin, a glycoprotein implicated in Alzheimer's disease (AD), remains unclear. The objective of this study was to analyze the effect of cerebrospinal fluid (CSF) clusterin in relation to AD biomarkers using a longitudinal cohort of non-demented individuals. We gathered a sample comprising 86 individuals under cognition normal (CN) and 134 patients diagnosed with MCI via the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. To investigate the correlation of CSF clusterin with cognitive function and markers of key physiological changes, we employed multiple linear regression and mixed-effect models. We undertook a causal mediation analysis to inspect the mediating influence of CSF clusterin on cognitive abilities. Pathological characteristics associated with baseline Aβ42, Tau, brain volume, exhibited a correlation with initial CSF clusterin in the general population, Specifically, these correlations were especially prominent in the MCI population; CSF Aβ42 (PCN = 0.001; PMCI = 0.007), T-tau (PCN < 0.001; PMCI < 0.001), and Mid temporal (PCN = 0.033; PMCI = 0.005). Baseline CSF clusterin level was predictive of measurable cognitive shifts in the MCI population, as indicated by MMSE (β = 0.202, p = 0.029), MEM (β = 0.186, p = 0.036), RAVLT immediate recall (β = 0.182, p = 0.038), and EF scores (β = 0.221, p = 0.013). In MCI population, the alterations in brain regions (17.87% of the total effect) mediated the effect of clusterin on cognition. It was found that variables such as age, gender, and presence of APOE ε4 carrier status, influenced some of these connections. Our investigation underscored a correlation between CSF clusterin concentrations and pivotal AD indicators, while also highlighting clusterin's potential role as a protective factor for cognitive abilities in MCI patients.

Association of APOE-ε4 and GAP-43-related presynaptic loss with β-amyloid, tau, neurodegeneration, and cognitive decline

Apolipoprotein E-ε4 (APOE-ε4) carriers had elevated cerebrospinal fluid (CSF) presynaptic protein growth-associated protein-43 (GAP-43), but the underlying mechanism is not fully understood. We investigated how the APOE-ε4 genotype affects the baseline and longitudinal changes in CSF GAP-43 and their associations with β-amyloid positron emission tomography (Aβ PET), CSF phosphorylated tau 181 (p-Tau181), neurodegeneration, and cognitive decline. Compared to APOE-ε4 non-carriers, APOE-ε4 carriers had higher baseline levels and faster rates of increases in Aβ PET, CSF p-Tau181, and CSF GAP-43. Both higher baseline levels and faster rates of increase in CSF GAP-43 were associated with greater baseline Aβ PET and CSF p-Tau181, which fully mediated the APOE-ε4 effect on CSF GAP-43 elevations. Independent of Aβ PET and CSF p-Tau181, APOE-ε4 carriage was associated with exacerbated GAP-43-related longitudinal hippocampal atrophy and cognitive decline, especially in Aβ+ participants (GAP-43 × time × APOE-ε4). These findings suggest that the APOE-ε4 effect on GAP-43-related presynaptic dysfunction is mediated by primary Alzheimer’s pathologies and independently correlates to hippocampal atrophy and cognitive decline in the future.

Impact of transcranial direct current stimulation on white matter microstructure integrity in mild cognitive impairment patients according to effect modifiers as risk factors for Alzheimer's disease.

Little research exists on how individual risk factors for Alzheimer's disease (AD) affect the intermediate phenotype after transcranial direct current stimulation (tDCS), despite the importance of precision medicine-based therapeutic approaches. To determine how an application of sequential tDCS (2 mA/day, left dorsolateral prefrontal cortex, 10 sessions) affects changes in white matter (WM) microstructure integrity in 63 mild cognitive impairment (MCI) patients with effect modifiers such as Aβ deposition, APOE ε4 carrier status, BDNF Val66Met polymorphism status, and sex. We examined individual effect modifier-by-tDCS interactions and multiple effect modifiers-by-tDCS interactions for diffusion metrics. We also evaluated the association between baseline Aβ deposition and changes in WM microstructure integrity following tDCS. We found that APOE ε4 carrier status and sex had a significant interaction with tDCS, resulting in increased fractional anisotropy (FA) in the right uncinate fasciculus (UF) after stimulation. Additionally, we observed multiple effect modifiers-by-tDCS interactions on WM integrity of the right UF, leading to a more pronounced increase in FA values in APOE ε4 carriers and females with Val66 homozygotes. Finally, baseline Aβ deposition was positively associated with a difference in FA of the left cingulum in the hippocampal area, which showed a positive association with the changes in the score for delayed memory. Our study shows the differential impact of individual AD risk factors on changes in the early intermediate phenotype after sequential tDCS in MCI patients. This research emphasizes the importance of precision medicine approaches in tDCS for the prodromal stages of AD.