Baseline Aβ Research Articles

The combined effect of β ‐amyloid and white matter hyperintensities on present and future executive function performance in cognitively normal older adults

AbstractBackgroundβ‐amyloid (Aβ) and White matter hyperintensities (WMH) are key drivers of cognitive decline in healthy older adults and both are considered biomarkers of preclinical dementia. Deficit in executive function (EF) linked to WMHs and Aβ burden, have been shown to predict the development of dementia. The present study aimed to explore the interactions of regional and global Aβ with WMH on baseline and longitudinal performance in EF in cognitively normal (CN) older adults.MethodGlobal and regional Standard Uptake Value ratios (SUVr) from Aβ‐PET, WMH volumes from MRI FLAIR images, and composite scores of EF at baseline and after approximately 2 years were analyzed across a sample of 714 clinically‐normal (Mini‐Mental State Examination (MMSE) ≥ 26), aged participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI2). We used multiple regression models including the interaction tem and main effects for Aβ and WMH to investigate the independent and combined associations of these pathologies with EF. Age, ApoE, education, baseline EF score, and time to follow up EF testing were entered as covariates in the regression models.ResultThe moderation regression analysis showed that additive effects on Aβ burden and WMH in relation to baseline EF with p value for the interaction term between the two pathologies 0.07, and significant interaction effects of global, frontal and cingulate Aβ burden, and WMH in relation to longitudinal EF performance with p values of 0.037, 0.03, and 0.032 respectively. The main effect from regression analysis demonstrated that baseline EF performance was predicted by the combination of Aβ burden and WMH, while Aβ burden was the only predictor for longitudinal EF performance.ConclusionThese data demonstrated that baseline Aβ and WMHs have both independent effects on EF scores at baseline and synergistic effects on longitudinal EF performance. The biological relationships and interplay between Ab and WMH responsible for the synergistic effects seen on longitudinal EF performance warrants further study.

Longitudinal basal forebrain atrophy in association with amyloid‐β positivity and dementia severity

AbstractBackgroundDysfunction of the cholinergic basal forebrain (BF) system and deposition of amyloid‐β (Aβ) are early pathological features in Alzheimer’s disease (AD). Degeneration of the BF in association with Aβ burden and cognitive decline has not been well studied, particularly in AD prior to dementia. We assessed the longitudinal trajectory patterns of the global BF volume, and the relationship to Aβ levels and clinical disease severity in older adults. The hippocampal volume (HV) trajectories were also assessed for reference.MethodVolumetric measures of the BF and HV were quantified from the MRIs of 521 participants (age = 72.6±5.9, 52.8% female, CDR range 0‐1) from the Australian Imaging, Biomarker and Lifestyle (AIBL) study, who had Aβ‐PET imaging and longitudinal structural MRI (follow‐up over 4.3±2.6 years). Aβ load was quantified using CapAIBL and Aβ+ was defined for centiloid values >20. Participants were classified at baseline based on their being cognitively unimpaired (CU, CDR = 0) or symptomatic (CDR = 0.5‐1) and Aβ status, which were CU Aβ‐, CU Aβ+ (preclinical AD), symptomatic Aβ‐ and symptomatic Aβ+ (prodromal AD and AD) (Table 1). BF and HV volumes were standardized using the mean and standard deviation of the relevant measures in the CU Aβ‐ group at baseline. Linear mixed effect models were used to compare the changes over time (slope) of BF and HV in different clinical groups. The magnitude of the difference in slopes between groups was assessed using Cohen’s d (summarised in Table 2). ResultIn CU Aβ‐, age‐related atrophy was greater in the BF compared to the HV (Cohen’s d = 0.811). Compared to the CU Aβ‐ group, the rate of atrophy was significantly faster for BF and HV in the preclinical AD group, and this decline was even greater in the prodromal AD and AD group (Figure 1 and Table 2). The symptomatic Aβ‐ group showed greater rate of BF atrophy than the CU Aβ‐ group, while there was no difference in the rate of HV atrophy.ConclusionThese findings provided prospective evidence confirming early vulnerability of the BF in aging as well as differentiated BF degeneration that was associated with baseline Aβ positivity and dementia severity.

Association of APOE-ε4, Osteoarthritis, β-Amyloid, and Tau Accumulation in Primary Motor and Somatosensory Regions in Alzheimer Disease.

One of the most prevalent chronic diseases, osteoarthritis (OA), may work in conjunction with APOE-ε4 to accelerate Alzheimer disease (AD) alterations, particularly in the primary motor (precentral) and somatosensory (postcentral) cortices. To understand the reasoning behind this, we investigated how OA and APOE-ε4 influence the accumulation of β-amyloid (Aβ) and tau accumulation in primary motor and somatosensory regions in Aβ-positive (Aβ+) older individuals. We selected Aβ+ Alzheimer Disease Neuroimaging Initiative participants, defined by baseline 18F-florbetapir (FBP) Aβ PET standardized uptake value ratio (SUVR) of AD summary cortical regions, who had longitudinal Aβ PET, the records of OA medical history, and APOE-ε4 genotyping. We examined how OA and APOE-ε4 relate to baseline and longitudinal Aβ accumulation and tau deposition measured at follow-up in precentral and postcentral cortical areas and how they modulate Aβ-associated future higher tau levels, adjusting for age, sex, and diagnosis and using multiple comparison corrections. A total of 374 individuals (mean age 75 years, 49.2% female, 62.8% APOE-ε4 carriers) who underwent longitudinal FBP PET with a median follow-up of 3.3 years (interquartile range [IQR] 3.4, range 1.6-9.4) were analyzed, and 96 people had 18F-flortaucipir (FTP) tau PET measured at a median of 5.4 (IQR 1.9, range 4.0-9.3) years postbaseline FBP PET. Neither OA nor APOE-ε4 was related to baseline FBP SUVR in precentral and postcentral regions. At follow-up, OA rather than APOE-ε4 was associated with faster Aβ accumulation in postcentral region (β = 0.005, 95% CI 0.001-0.008) over time. In addition, OA but not the APOE-ε4 allele was strongly linked to higher follow-up FTP tau levels in precentral (β = 0.098, 95% CI 0.034-0.162) and postcentral (β = 0.105, 95% CI 0.040-0.169) cortices. OA and APOE-ε4 were also interactively associated with higher follow-up FTP tau deposition in precentral (β = 0.128, 95% CI 0.030-0.226) and postcentral (β = 0.124, 95% CI 0.027-0.223) regions. This study suggests that OA was associated with faster Aβ accumulation and higher Aβ-dependent future tau deposition in primary motor and somatosensory regions, providing novel insights into how OA increases the risk of AD.

The Relationship between Beverages Consumption and Cognitive Impairment in Middle-Aged and Elderly Chinese Population.

Some evidence shows that beverage consumption has an impact on cognitive performance. This is a follow-up study of dietary habits and cognitive function in the Chinese middle-aged and elderly population. The objective of this study was to explore the relationship between beverage consumption and cognitive impairment. The source and grouping of the participants can be seen in the previous article, "Study of Diet Habits and Cognitive Function in the Chinese Middle-Aged and Elderly Population: The Association between Folic Acid, B Vitamins, Vitamin D, Coenzyme Q10 Supplementation and Cognitive Ability". Among 892 participants, one-third (296) completed both Amyloid beta(Aβ)-PET and plasma biomarkers. The results showed that the consumption of beverages (green tea, coffee, pure milk) was a protective factor for cognitive impairment, daily water consumption <1500 mL (especially <500 mL) was a risk factor for cognitive impairment, and the above correlated with baseline cognitive status. The relationship of green tea, coffee, and pure milk consumption with cognitive impairment was related to gender. We also found that among the participants with Aβ deposition, the consumption of pure milk and green tea was associated with low levels of p-Tau-181. In conclusion, the relationship between beverage consumption and cognitive impairment in Chinese middle-aged and elderly adults may be related to baseline cognitive status, gender, and Aβ deposition.

Does statin use affect amyloid beta deposition and brain metabolism?

There are contradictory findings regarding the effect of statin drugs on amyloid β (Aβ) deposition as one of the main hallmarks of Alzheimer's disease (AD), along with tau pathology. We aimed to longitudinally investigate the therapeutic and preventive role of statin drugs by examining the brain Aβ deposition and metabolism rate in AD, mild cognitive impairment (MCI), and healthy controls (HC). The data of 828 subjects including 178 HC, 492 MCI, and 158 AD individuals were obtained from ADNI. The baseline and longitudinal [18 F] AV45 and 18-fluorodeoxyglucose (FDG) PET standard uptake value ratio (SUVR) measures were investigated among statin users and non-users. Our results showed that there is no significant difference in baseline Aβ deposition and metabolism rate between statin users and non-users among HC, MCI, and AD subjects. While there was no significant effect of statin on metabolism rate, there was a significant difference in Aβ deposition change after 4 years (from baseline) between statin users and non-users within HC subjects (p= 0.011). The change of Aβ deposition at 4 years from baseline was -2.0± 6.3% for statin users and 1.4± 4.7% for non-users. There was no significant association between statin duration use with baseline and longitudinal Aβ deposition and metabolism rate. However, statin dosage was significantly associated with Aβ deposition in 2 years (r= -0.412, p= 0.021) in the HC group. Moreover, our analysis showed a significant correlation between total statin exposure (duration×dosage) and Aβ deposition in 2 years visit (r= -0.198, p= 0.037) in HC subjects. Furthermore, we investigated the longitudinal changes within each group of statin users and non-users separately in linear mixed models. Our findings showed that there are no significant changes in AV45 and FDG SUVR among both groups. The present longitudinal analysis revealed that using statins might be beneficial in slowing down or stabilizing the Aβ deposition due to aging in subjects without cognitive impairment. However, once the clinical symptoms of cognitive impairment appear, statins fail to slow down Aβ deposition. Overall, our findings revealed that statin users might have slower Aβ aggregation than non-users.

Initial levels of β-amyloid and tau deposition have distinct effects on longitudinal tau accumulation in Alzheimer’s disease

BackgroundTo better assist with the design of future clinical trials for Alzheimer’s disease (AD) and aid in our understanding of the disease’s symptomatology, it is essential to clarify what roles β-amyloid (Aβ) plaques and tau tangles play in longitudinal tau accumulation inside and outside the medial temporal lobe (MTL) as well as how age, sex, apolipoprotein E (APOE) ε4 (APOE-ε4), and Klotho-VS heterozygosity (KL-VShet) modulate these relationships.MethodsWe divided the 325 Aβ PET-positive (A+) participants into two groups, A+/T− (N = 143) and A+/T+ (N = 182), based on the threshold (1.25) of the temporal meta-ROI 18F-flortaucipir (FTP) standardized uptake value ratio (SUVR). We then compared the baseline and slopes of A+/T− and A+/T+ individuals’ Aβ plaques and temporal meta-ROI tau tangles with those of A−/T− cognitively unimpaired individuals (N = 162) without neurodegeneration. In addition, we looked into how baseline Aβ and tau may predict longitudinal tau increases and how age, sex, APOE-ε4, and KL-VShet affect these associations.ResultsIn entorhinal, amygdala, and parahippocampal (early tau-deposited regions of temporal meta-ROI), we found that baseline Aβ and tau deposition were positively linked to more rapid tau increases in A+/T− participants. However, in A+/T+ individuals, the longitudinal tau accumulation in fusiform, inferior temporal, and middle temporal cortices (late tau-deposited regions of temporal meta-ROI) was primarily predicted by the level of tau tangles. Furthermore, compared to older participants (age ≥ 65), younger individuals (age < 65) exhibited faster Aβ-dependent but slower tau-related tau accumulations. Additionally, compared to the KL-VShet− group, KL-VShet+ individuals showed a significantly lower rate of tau accumulation associated with baseline entorhinal tau in fusiform and inferior temporal regions.ConclusionThese findings offer novel perspectives to the design of AD clinical trials and aid in understanding the tau accumulation inside and outside MTL in AD. In particular, decreasing Aβ plaques might be adequate for A+/T− persons but may not be sufficient for A+/T+ individuals in preventing tau propagation and subsequent downstream pathological changes associated with tau.

Effect of APOE ε4 genotype on amyloid-β, glucose metabolism, and gray matter volume in cognitively normal individuals and amnestic mild cognitive impairment.

The presence of apolipoprotein E ε4 (APOE ε4) is associated with an increased risk of developing Alzheimer disease (AD). The aim of this study was to assess the effects of APOE ε4 on amyloid-β (Aβ) pathology, glucose metabolism, and gray matter (GM) volume and their longitudinal changes in healthy control (HC) and amnestic mild cognitive impairment (aMCI). We included 50 HCs and 109 aMCI patients from the Alzheimer's Disease Neuroimaging Initiative phase 2/GO based on availability of baseline T1-weighted magnetic resonance imaging, 18 F-florbetapir positron emission tomography (PET), and 18 F-fluorodeoxyglucose (FDG) PET. Of these, 35 HCs and 67 aMCI patients who underwent 24-month scans were included for follow-up study. Voxelwise analysis revealed that APOE ε4 carriers exhibited greater baseline Aβ deposition than APOE ε4 noncarriers in both diagnostic groups. However, there was no significant difference between APOE ε4 noncarriers and APOE ε4 carriers in terms of 18 F-FDG PET standardized uptake value ratio and GM volume. Region of interest-based analysis showed statistically significant greater Aβ deposition in APOE ε4 carriers than APOE ε4 noncarriers only in aMCI patients. Furthermore, APOE ε4 carriers generally exhibited a greater magnitude and spatial extent of longitudinal changes in Aβ deposition than APOE ε4 noncarriers in both diagnostic groups. Our findings suggest a differential effect of APOE ε4 on Aβ pathology, glucose metabolism, and GM volume. Studying APOE ε4-related brain changes with neuroimaging biomarkers in preclinical AD offers an opportunity to further our understanding of the pathophysiology of AD at an early stage.

Longitudinal tau burden partially mediates synergistic influence of vascular risk and amyloid‐beta on cognitive decline in clinically normal older adults

AbstractBackgroundVascular risk is increasingly recognized as an important contributor to late‐life cognitive impairment. Elevated vascular risk and amyloid‐beta (Aβ) burden have been synergistically associated with greater cognitive decline, however the underlying mechanisms remain unclear. We examined the combined effects of baseline vascular risk and Aβ on longitudinal tau burden. We further investigated the extent to which tau accumulation mediates the synergistic effects of increased vascular risk and Aβ on prospective cognitive decline.MethodWe included data from 175 older adults (cognitively unimpaired at baseline) from the Harvard Aging Brain Study (HABS) (Table 1). Baseline Aβ burden was measured with Pittsburgh Compound‐B (PiB) PET. Baseline vascular risk was quantified using the office‐based Framingham Heart Study general cardiovascular disease risk score (FHS‐CVD). Tau burden was measured longitudinally with Flortaucipir‐PET in inferior temporal cortex (ITC), an early site of neocortical tau accumulation and a region of interest based on prior work. Cognition was assessed longitudinally using Preclinical Alzheimer Cognitive Composite (PACC). Using linear mixed effects models, we examined the interactive effects of FHS‐CVD and Aβ on longitudinal ITC tau accumulation, adjusting for age, sex and APOE ε4 status. Next, we examined the FHS‐CVD*Aβ interaction using floodlight/Johnson‐Neyman analysis to determine the level of Aβ at which effects on ITC tau accumulation became significant. Using moderated mediation analysis, we further examined whether tau accumulation mediated the FHS‐CVD*Aβ interactive effects on PACC decline.ResultIndividual ITC‐tau trajectories are shown in Figure 1. We found an interaction between higher baseline FHS‐CVD and elevated Aβ with greater longitudinal ITC‐tau accumulation (Figure 2; Table 2). Floodlight analysis showed the FHS‐CVD*Aβ interaction became significant (p&lt;0.05) at PiB‐DVR&gt;1.22, lower than the conventional threshold for amyloid‐positivity (PiB‐DVR&gt;1.32). Moderated mediation models revealed ITC‐tau accumulation partially mediated (30%) the effect of FHS‐CVD on PACC decline at high (PiB‐DVR=1.85: β=‐0.48, p&lt;0.001) but not low (PiB‐DVR=1.17: β=‐0.16, p=0.09) levels of baseline Aβ (Figure 3).ConclusionVascular risk may interact with Aβ to promote cognitive decline partially through accelerating tau accumulation, even at subthreshold levels of Aβ. Further studies are needed to elucidate other mechanisms that mediate the remaining effects of this clinically important interaction.

Longitudinal tau burden partially mediates synergistic influence of vascular risk and amyloid‐beta on cognitive decline in clinically normal older adults

AbstractBackgroundVascular risk is increasingly recognized as an important contributor to late‐life cognitive impairment. Elevated vascular risk and amyloid‐beta (Aβ) burden have been synergistically associated with greater cognitive decline, however the underlying mechanisms remain unclear. We examined the combined effects of baseline vascular risk and Aβ on longitudinal tau burden. We further investigated the extent to which tau accumulation mediates the synergistic effects of increased vascular risk and Aβ on prospective cognitive decline.MethodWe included data from 175 older adults (cognitively unimpaired at baseline) from the Harvard Aging Brain Study (HABS) (Table 1). Baseline Aβ burden was measured with Pittsburgh Compound‐B (PiB) PET. Baseline vascular risk was quantified using the office‐based Framingham Heart Study general cardiovascular disease risk score (FHS‐CVD). Tau burden was measured longitudinally with Flortaucipir‐PET in inferior temporal cortex (ITC), an early site of neocortical tau accumulation and a region of interest based on prior work. Cognition was assessed longitudinally using Preclinical Alzheimer Cognitive Composite (PACC). Using linear mixed effects models, we examined the interactive effects of FHS‐CVD and Aβ on longitudinal ITC tau accumulation, adjusting for age, sex and APOE ε4 status. Next, we examined the FHS‐CVD*Aβ interaction using floodlight/Johnson‐Neyman analysis to determine the level of Aβ at which effects on ITC tau accumulation became significant. Using moderated mediation analysis, we further examined whether tau accumulation mediated the FHS‐CVD*Aβ interactive effects on PACC decline.ResultIndividual ITC‐tau trajectories are shown in Figure 1. We found an interaction between higher baseline FHS‐CVD and elevated Aβ with greater longitudinal ITC‐tau accumulation (Figure 2; Table 2). Floodlight analysis showed the FHS‐CVD*Aβ interaction became significant (p&lt;0.05) at PiB‐DVR&gt;1.22, lower than the conventional threshold for amyloid‐positivity (PiB‐DVR&gt;1.32). Moderated mediation models revealed ITC‐tau accumulation partially mediated (30%) the effect of FHS‐CVD on PACC decline at high (PiB‐DVR=1.85: β=‐0.48, p&lt;0.001) but not low (PiB‐DVR=1.17: β=‐0.16, p=0.09) levels of baseline Aβ (Figure 3).ConclusionVascular risk may interact with Aβ to promote cognitive decline partially through accelerating tau accumulation, even at subthreshold levels of Aβ. Further studies are needed to elucidate other mechanisms that mediate the remaining effects of this clinically important interaction.

Baseline β‐amyloid and tau predict longitudinal tau accumulation at different stages of Alzheimer’s disease

AbstractBackgroundPrevious literatures suggest that β‐amyloid (Aβ) plaques, initial tau levels, age and Klotho‐VS heterozygosity (KL‐VShet) may be related to longitudinal tau accumulation in Alzheimer’s disease (AD), but it is still not fully understood how they predict tau replication in medial temporal cortex and propagations from the medial temporal lobe to inferior/middle temporal cortices at different stages of AD.MethodWe classified 325 Aβ PET positive (A+) ADNI participants into 143 A+/T‐and 182 A+/T+ according to the threshold (≥1.25) of Temporal‐metaROI 18F‐flortaucipir (FTP) standardized uptake value ratio (SUVR). Besides, 162 A‐/T‐ cognitive unimpaired participants without evidence of neurodegeneration were included as the reference group (Ref). We compared baseline and slopes of Aβ Centiloids and FTP SUVRs in 6 regions within Temporal‐metaROI with the Ref, and also investigated the prediction of longitudinal FTP SUVR changes by baseline Aβ Centiloids and FTP SUVRs in A+/T‐ and A+/T+ groups. Furthermore, we determined how age (Age&lt;65 and Age≥65 years) and KL‐VShet haplotype (KL‐VShet+ and KL‐VShet‐) affect these associations.ResultA+/T‐ showed significantly or marginally higher tau and faster tau increases in amygdala, entorhinal and parahippocampus than the Ref (Figure 1). Both baseline Aβ and entorhinal tau predicted faster rates of FTP SUVR increases in amygdala, entorhinal and parahippocampus in A+/T‐. While entorhinal tau predicted faster FTP SUVR increases in all the cortical regions within Temporal‐metaROI in A+/T+, baseline Aβ predicted faster FTP SUVR increases in fusiform, inferior and middle temporal regions (Figure 2). Additionally, younger ages showed faster baseline Aβ‐related whereas older ages had more tau‐related longitudinal FTP SUVR increases in fusiform and inferior temporal regions (Figure 3). KL‐VShet+ individuals exhibited attenuated entorhinal tau‐related FTP SUVR increases in fusiform and inferior temporal regions than the KL‐VShet‐ individuals (Figure 4).ConclusionAβ plaques and initial tau tangles may play different roles in tau replication and propagation in A+/T‐ and A+/T+ individuals, and may interact differently with age at prediction longitudinal tau accumulation. KL‐VShet may attenuated tau accumulation via modulating entorhinal tau‐related tau propagation. These findings may provide novel insights into understanding how tau tangles replicate and propagate in Temporal‐metaROI region at different stages of AD.

Role of astrogliosis in the relationship between cerebrovascular burden and Alzheimer’s disease pathology

AbstractBackgroundCerebrovascular disease represents a common co‐pathology of Alzheimer's disease (AD) contributing to cognitive decline. While both vascular injury and AD pathology were previously linked to reactive astrocytes (astrogliosis), the interrelationship between these biomarkers remains unclear in AD. Here, we investigated potential effects of plasma glial fibrillary acidic protein (GFAP), an assumed marker of astrogliosis, on the relationship between vascular burden and AD pathology.MethodWe enrolled 210 participants across the AD continuum, including 111 cognitively normal amyloid‐negative (CN Aβ‐), 37 CN Aβ+, and 62 MCI/AD Aβ+ (TRIAD). Amyloid‐β pathology was quantified through 18F‐NAV4694‐PET standardized uptake value ratio (SUVR) in the AD‐signature regions, while tau pathology was quantified through 18F‐MK6240‐PET SUVR in regions corresponding to Braak stages. Vascular burden was quantified as the log‐transform of white matter hyperintensity volume by intracranial volume (WMH). GFAP levels were log‐transformed. At baseline, we employed linear regression models to investigate the effects of WMH and GFAP on tau pathology. Longitudinally, we performed linear mixed effect modeling to investigate the effects of baseline WMH and GFAP on tau accumulation over time (613±252 days). All analyses were adjusted for baseline age, sex, APOE‐ε4, and cortical Aβ. Last, we investigated a potential mediating effect of GFAP in the relationship between WMH and tau accumulation over time, using bias‐corrected bootstrapping with 5,000 replications.ResultAt baseline, GFAP (P=0.001), but not WMH, was positively associated with tau in the lower Braak I‐II regions. Conversely, both GFAP and WMH were positively associated with tau in the higher Braak regions (P&lt;0.001), as was their interaction (WMH*GFAP; Braak III‐IV β=0.14±0.06, P=0.014; Braak V‐VI β=0.19±0.06, P=0.001). Longitudinally, baseline Aβ but not WMH or GFAP predicted tau accumulation (P&lt;0.001). Serial mediation analysis revealed a significant indirect effect of WMH→GFAP→cortical Aβ→Tau increases [95%CI 0.03,0.29].ConclusionWe observed an interaction effect between higher WMH volumes and plasma GFAP levels on tau pathology across the AD continuum. Furthermore, we observed an indirect effect of WMH volumes on tau accumulation over time through amyloidosis and plasma GFAP. These results may indicate that reactive astrogliosis, potentially secondary to vascular injury, may be a factor contributing to AD progression.

Subjective vs. Objective Memory Predictors of AD Biomarkers and Future Cognitive, Clinical, and Functional Decline

AbstractBackgroundSubjective and objective memory have been previously linked to Alzheimer’s disease (AD) biomarkers and future clinical and cognitive decline. We the ability of two subjective memory assessments and two objective memory assessments to predict cross‐sectional amyloid, tau, and neurodegenerative (A/T/N) biomarkers, as well as future cognitive, clinical, and functional decline.Methods298 participants from ADNI were included, specifically 128 with normal cognition without memory concerns (CN), 123 with significant memory concerns (SMC; defined as a score of ≥20 on the first 12 items of the Cognitive Change Index (CCI)), and 47 with early mild cognitive impairment (EMCI). Objective memory was defined as immediate and delayed scores of Logical Memory and the Rey AVLT (RAVLT). Subjective memory measures included the memory sub‐scores of the ECog and CCI. Baseline Aβ and tau positivity were defined as positive on either neuroimaging and/or CSF biomarkers using standard cut‐offs. Neurodegeneration positive was defined as a hippocampal volume w‐score of 1.5 standard deviations below all CNs with 3T MRI in ADNI (adjusted for age, sex, and total intracranial volume). Cognitive decline was defined as a negative slope on the MMSE, while functional decline was defined as a longitudinal increase on the CDR‐sum of boxes and/or FAQ. For CN/SMC, conversion from normal cognition to MCI was also assessed. ROC curves were calculated for each objective and subjective measure for predicting conversion, functional decline, MMSE decline, and A/T/N positivity.ResultsFigure 1 shows ROC curves for the best predictor of each outcome. Baseline Aβ positivity, MCI conversion, and MMSE decline were best predicted by RAVLT immediate recall. However, functional decline and N positivity were best predicted by the CCI‐12. Tau positivity was also best predicted by subjective memory, the ECog memory sub‐score.ConclusionObjective memory provided the best prediction of Aβ positivity, MCI conversion, and cognitive decline, while subjective memory was the best predictor of T and N positivity and functional decline. These findings suggest that objective and subjective performance provide complementary information about prognosis in preclinical and very mild symptomatic individuals. Future evaluations of prognostication of these measures in a clinical setting will expand these findings.

Defining the ATN framework using longitudinal biomarker trajectories reveals an emerging amyloid accumulation group

AbstractBackgroundThe ATN framework is defined by cross‐sectional biomarkers of β‐amyloid (Aβ), tau and neurodegeneration. Given that prevention trials, e.g., AHEAD 3‐45, are focused on individuals who have lower Aβ than established thresholds, we investigated whether defining the ATN framework using longitudinal biomarker trajectories might better identify an at‐risk sample within this boundary. Here, we applied a data‐driven method to re‐define the ATN with longitudinal biomarker data from the Harvard Aging Brain Study (HABS) and we then replicated this longitudinal framework in ADNI.Method157 HABS participants were clinically‐normal at baseline and underwent at least two Pittsburgh Compound‐B [PiB]‐PET, Flortaucipir‐PET, and T1‐weighted MRI scans. To define longitudinal ATN, we applied latent class mixture models (LCMM) to each biomarker (global Aβ DVR, entorhinal tau SUVr, ICV‐adjusted hippocampal volume) separately, adjusting for age, and including random intercept and slopes. Akaike information criteria (AIC) determined the best‐fitting models out of two‐group or three‐group solutions with linear or spline‐link functions. We compared longitudinal ATN profiles on demographics and an optimized estimate of cognitive change (derived from longitudinal Preclinical Alzheimer Cognitive Composite (PACC) data).ResultAβ trajectories (Fig.1a) were best categorized by one stable (A→) and two accumulating subgroups, a predominantly amyloid‐negative at baseline subgroup (A‐↑) and an entirely amyloid positive at baseline subgroup (A+↑). Tau (Fig.1b) and neurodegeneration (Fig.1c) were optimally defined by stable (T→/N→) vs accumulating/atrophying (T↑/N↑) groups, respectively. These groups were replicated in ADNI (Fig.2). The entire A‐↑ subgroup were stable on T and N (A‐↑/T→/N→) and were predominantly A‐/T‐/N‐ at baseline (86%; Table 1). By contrast, 38% of A+↑ individuals changed on T, or T&amp;N. A‐↑/T→/N→ demographically most closely resembled the longitudinally‐stable ATN group (A→/T→/N→), but were older, more likely to carry e4+ and exhibited higher baseline Aβ (Table 2). Although demonstrating Aβ accumulation, A‐↑/T→/N→ did not exhibit greater cognitive decline versus the stable group (A→/T→/N→; Fig. 3).ConclusionOur findings suggest that a longitudinal biomarker run‐in of Aβ‐PET may be useful for the identification of early‐risk groups for prevention trials. Future work will establish whether other features (e.g. genetics, neuroinflammatory markers, functional imaging) can help to distinguish this cohort.

Genetic risk for attention‐deficit/hyperactivity disorder is associated with amyloid‐dependent cognitive decline in older adults

AbstractBackgroundRecent epidemiological studies using population‐based registers showed that patients with Attention‐Deficit/Hyperactivity Disorder (ADHD) are more likely to be diagnosed with mild cognitive impairment (MCI) and Alzheimer’s disease (AD), suggesting a higher risk for cognitive impairment in older age. Here, we aimed to assess whether genetic liability for ADHD, as measured by a validated polygenic risk score (ADHD‐PRS), is associated with cognitive decline in cognitively unimpaired (CU) subjects and whether these effects vary according to baseline amyloid‐β (Aβ) burden.MethodWe studied 212 CU participants (116 women [54.7%], mean [SD] age of 73.1 [5.9]) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Subjects had available baseline Aβ burden (measured by [18F]Florbetapir Aβ‐PET) and genetic information, as well as a minimum of 2 cognitive assessments (Preclinical Alzheimer Cognitive Composite [PACC] and MMSE) in a 6‐year period. Aβ positivity was defined using a previously published cutoff of global [18F]Florbetapir PET SUVR &gt; 1.11. We calculated ADHD‐PRS using data from the latest genome‐wide association study on ADHD, with a p‐value threshold of 1. Statistical analyses were performed using linear mixed‐effects models adjusted for age, sex, APOE ε4 status, and the first seven principal components to adjust for population stratification.ResultADHD‐PRS was normally distributed and was not associated with age, sex, or APOE ε4 status (Figure 1). We observed a significant ADHD‐PRS by time interaction (β=‐.03, 95% CI ‐.05 to ‐.01, p‐value=.001 for PACC, β=‐.04, 95% CI ‐.07 to ‐.009, p‐value=.01 for MMSE) on cognitive performance, indicating that higher ADHD‐PRS is associated with decreased cognitive function over time (Figure 2). Additionally, we observed a significant interaction between ADHD‐PRS, time, and baseline Aβ burden (β=‐.05, 95% CI ‐.10 to ‐.008, p‐value=.02 for PACC, β=‐.08, 95% CI ‐.15 to ‐.01, p‐value=.02 for MMSE), suggesting that higher ADHD‐PRS is associated with longitudinal cognitive decline only in Aβ‐positive individuals (Figure 3).ConclusionOur findings indicate that higher genetic liability for ADHD is associated with cognitive decline in Aβ‐positive older adults without cognitive impairment. These findings support the hypothesis that higher rates of MCI and AD in patients with ADHD may result from decreased resilience to Aβ pathology.

Default-Mode Network Connectivity Changes During the Progression Toward Alzheimer's Dementia: A Longitudinal Functional Magnetic Resonance Imaging Study.

Background/Purpose: Brain function changes with Alzheimer's disease (AD) progression. Evaluating those changes longitudinally is important to understand the complex relationships between brain pathologies and cognition. We aimed (1) to identify longitudinal changes in functional connectivity in patients with mild cognitive impairment (MCI) characterized for amyloid-β (Aβ) status and (2) to relate these functional changes to clinical progression. Methods: Forty-four patients with MCI were followed using serial functional magnetic resonance imaging (fMRI) over 1.2 years (three sessions) and cognitive testing over 3.1 years (five sessions). Intra and inter-network connectivities were computed to assess changes in brain connectivity using a network atlas adapted for late adulthood. Sixteen low-Aβ clinically normal older adults underwent a single fMRI session for group comparisons at baseline. Linear mixed-effects models with random intercept and slope were used to predict changes in connectivity based on Aβ status and progression to dementia. Results: At baseline, intra and inter-network resting-state fMRI connectivities did not differ by baseline clinical diagnosis, Aβ status, or clinical progression to dementia. At the final imaging session, progressive MCI had significantly higher connectivity compared with stable MCI, specifically within the default-mode network (DMN). Longitudinally, progressive MCI had increasing intra-DMN connectivity over time compared with stable MCI, and the rate of changes in connectivity was significantly associated with the rate of cognitive decline. Conclusions: Intra-DMN connectivity increases in MCI patients progressing toward dementia, suggesting aberrant synchronization in the symptomatic stages of AD. Impact statement Changes in functional connectivity occur in the course of Alzheimer's disease. We observed a progressive increase over time in resting-state functional connectivity within the default-mode network in patients with mild cognitive impairment who progressed to dementia. The rate of connectivity increase was significantly associated with the rate of cognitive decline. The observation of increased functional connectivity during the progression to dementia, and not only in the pre-clinical stage, is interpreted as an aberrant synchronization rather than a compensation mechanism.

Tau Mediates Synergistic Influence of Vascular Risk and Aβ on Cognitive Decline.

Elevated vascular risk and beta-amyloid (Aβ) burden have been synergistically associated with cognitive decline in preclinical Alzheimer's disease (AD), although the underlying mechanisms remain unclear. We examined whether accelerated longitudinal tau accumulation mediates the vascular risk-Aβ interaction on cognitive decline. We included 175 cognitively unimpaired older adults (age 70.5 ± 8.0 years). Baseline vascular risk was quantified using the office-based Framingham Heart Study general cardiovascular disease risk score (FHS-CVD). Baseline Aβ burden was measured with Pittsburgh Compound-B positron emission tomography (PET). Tau burden was measured longitudinally (3.6 ± 1.5 years) with Flortaucipir PET, focusing on inferior temporal cortex (ITC). Cognition was assessed longitudinally (7.0 ± 2.0 years) using the Preclinical Alzheimer's Cognitive Composite. Linear mixed effects models examined the interactive effects of baseline vascular risk and Aβ on longitudinal ITC tau. Additionally, moderated mediation was used to determine whether tau accumulation mediated the FHS-CVD*Aβ effect on cognitive decline. We observed a significant interaction between elevated baseline FHS-CVD and Aβ on greater ITC tau accumulation (p=0.004), even in individuals with Aβ burden below the conventional threshold for amyloid positivity. Examining individual vascular risk factors, we found elevated systolic blood pressure and body mass index showed independent interactions with Aβ on longitudinal tau (both p < 0.0001). ITC tau accumulation mediated 33% of the interactive association of FHS-CVD and Aβ on cognitive decline. Vascular risks interact with subthreshold levels of Aβ to promote cognitive decline, partially by accelerating early neocortical tau accumulation. Our findings support vascular risk reduction, especially treating hypertension and obesity, to attenuate Aβ-related tau pathology and reduce late-life cognitive decline. ANN NEUROL 2022;92:745-755.

Association of β-Amyloid and Vascular Risk on Longitudinal Patterns of Brain Atrophy.

Vascular risk factors and elevated β-amyloid (Aβ) are commonly observed together among older adults. Here, we examined the interactive vs independent effects of systemic vascular risk and Aβ burden on longitudinal gray matter atrophy and how their co-occurrence may be related to cognitive decline in a cohort of clinically normal adults. A secondary goal was to examine whether vascular risk influences gray matter atrophy independently from markers of white matter injury. Participants were 196 adults (age 73.8 ± 6.1 years) from the Harvard Aging Brain Study. Baseline Aβ burden was quantified with Pittsburgh compound B PET. Baseline vascular risk was measured with the Framingham Heart Study cardiovascular disease risk score. Brain atrophy was quantified longitudinally with structural MRI over a median of 4.50 (±1.26) years. Cognition was assessed yearly with the Preclinical Alzheimer Cognitive Composite over a median of 6.25 (±1.40) years. Linear mixed-effects models examined vascular risk and Aβ burden as interactive vs independent predictors of gray matter atrophy, with adjustment for age, sex, years of education, APOE ε4 status, intracranial volume (when appropriate), and their interactions with time. In subsequent models, we adjusted for markers of white matter injury to determine whether vascular risk accelerated brain atrophy independently from diffusion- and fluid-attenuated inversion recovery (FLAIR)-based markers. Mediation analyses examined whether brain atrophy mediated the interactive association of vascular risk and Aβ burden on cognitive decline. Higher vascular risk and elevated Aβ burden interacted to predict more severe atrophy in frontal and temporal lobes, thalamus, and striatum. Higher Aβ burden, but not vascular risk, was associated with more severe atrophy in parietal and occipital lobes, as well as the hippocampus. Adjusting for diffusion- and FLAIR-based markers of white matter injury had little impact on the above associations. Gray matter atrophy mediated the association between vascular risk and cognitive decline at higher levels of Aβ burden. We observed an interaction between elevated vascular risk and higher Aβ burden with longitudinal brain atrophy, which in turn influenced cognitive decline. These results support vascular risk factor management as a potential intervention to slow neurodegeneration and cognitive decline in preclinical Alzheimer disease.

Associations of β-Amyloid and Vascular Burden With Rates of Neurodegeneration in Cognitively Normal Members of the 1946 British Birth Cohort.

Background and ObjectivesThe goals of this work were to quantify the independent and interactive associations of β-amyloid (Aβ) and white matter hyperintensity volume (WMHV), a marker of presumed cerebrovascular disease (CVD), with rates of neurodegeneration and to examine the contributions of APOE ε4 and vascular risk measured at different stages of adulthood in cognitively normal members of the 1946 British Birth Cohort.MethodsParticipants underwent brain MRI and florbetapir-Aβ PET as part of Insight 46, an observational population-based study. Changes in whole-brain, ventricular, and hippocampal volume were directly measured from baseline and repeat volumetric T1 MRI with the boundary shift integral. Linear regression was used to test associations with baseline Aβ deposition, baseline WMHV, APOE ε4, and office-based Framingham Heart Study Cardiovascular Risk Score (FHS-CVS) and systolic blood pressure (BP) at ages 36, 53, and 69 years.ResultsThree hundred forty-six cognitively normal participants (mean [SD] age at baseline scan 70.5 [0.6] years; 48% female) had high-quality T1 MRI data from both time points (mean [SD] scan interval 2.4 [0.2] years). Being Aβ positive at baseline was associated with 0.87–mL/y faster whole-brain atrophy (95% CI 0.03, 1.72), 0.39–mL/y greater ventricular expansion (95% CI 0.16, 0.64), and 0.016–mL/y faster hippocampal atrophy (95% CI 0.004, 0.027), while each 10-mL additional WMHV at baseline was associated with 1.07–mL/y faster whole-brain atrophy (95% CI 0.47, 1.67), 0.31–mL/y greater ventricular expansion (95% CI 0.13, 0.60), and 0.014–mL/y faster hippocampal atrophy (95% CI 0.006, 0.022). These contributions were independent, and there was no evidence that Aβ and WMHV interacted in their effects. There were no independent associations of APOE ε4 with rates of neurodegeneration after adjustment for Aβ status and WMHV, no clear relationships between FHS-CVS or systolic BP and rates of neurodegeneration when assessed across the whole sample, and no evidence that FHS-CVS or systolic BP acted synergistically with Aβ.DiscussionAβ and presumed CVD have distinct and additive effects on rates of neurodegeneration in cognitively normal elderly. These findings have implications for the use of MRI measures as biomarkers of neurodegeneration and emphasize the importance of risk management and early intervention targeting both pathways.

Development of Apathy, Anxiety, and Depression in Cognitively Unimpaired Older Adults: Effects of Alzheimer’s Disease Pathology and Cognitive Decline

BackgroundThe impact of Alzheimer’s disease (AD) pathology and cognitive deficits on longitudinal neuropsychiatric symptoms is unclear, especially in early disease stages. MethodsCognitively unimpaired older adults (N = 356) enrolled in the prospective Swedish BioFINDER study were examined. Neuropsychiatric assessments encompassed the Apathy Evaluation Scale and the Hospital Anxiety and Depression Scale, performed biennially (together with tests of global cognition) for up to 8 years. Biomarkers were measured in cerebrospinal fluid or plasma at baseline. Magnetic resonance imaging quantified white matter lesions. We used linear mixed-effect models to test associations between baseline AD biomarkers (for amyloid-β [Aβ], tau, and neurodegeneration) and white matter lesions with longitudinal neuropsychiatric symptoms (apathy, anxiety, and depressive symptoms). We also tested associations between changes in cognition and changes in neuropsychiatric symptoms. Finally, we tested if change in cognition mediated the effects of different brain pathologies on neuropsychiatric symptoms. ResultsAβ pathology at baseline was associated with increasing levels of apathy (β = −0.284, p = .005) and anxiety (β = −0.060, p = .011) longitudinally. More rapid decline of cognition over time was related to increasing levels of apathy. The effects of baseline Aβ pathology on longitudinal apathy were partly mediated by changes in cognitive performance (proportion mediated 23%). ConclusionsAβ pathology may drive the development of both apathy and anxiety in very early stages of AD, largely independent of cognitive change. The effect of Aβ on apathy is only partially conveyed by worse cognition. Together, these findings highlight certain neuropsychiatric symptoms as early manifestations of AD.

Longitudinal tau accumulation in cognitively normal older adults is associated with baseline age, pathology, and activation

AbstractBackgroundPredictors of longitudinal tau accumulation have not yet been fully characterized, but are critical for understanding progression from normal aging to Alzheimer’s disease (AD). We investigated whether baseline AD pathology, demographic risk factors, and activation during a memory task predicted longitudinal tau deposition in cognitively normal older adults (OA).MethodTau pathology was measured with [18F] Flortaucipir (FTP) at ≥2 time points (44 2TPs; 24 3TPs; 2 4TPs; 2.5±1.1 years follow‐up) in 70 OA (mean age 77 years, 59% female). FTP scans were processed with a longitudinal pipeline using an eroded white matter reference region. Linear mixed models were used to calculate FTP slope in entorhinal cortex (EC), inferior temporal gyrus (IT), and a MetaROI composed of AD‐vulnerable temporal regions. Aβ was measured with global PiB DVR. A subset of 37 OA received baseline fMRI while performing a memory task, and task activation (all images vs. perceptual baseline contrast) was calculated for EC and hippocampus.ResultThere was significant group‐level tau accumulation in EC, IT, and MetaROI across the full sample, and within Aβ+ and Aβ‐ groups (one‐sample t‐tests, all ps&lt;0.001; Figure 1). Older age at baseline was specifically associated with increased FTP slope in EC (Figure 2A). Higher baseline PiB DVR was associated with increased FTP slope in EC, IT, and MetaROI (Figure 2B). ApoE ε4+ was associated with greater FTP slope in IT and MetaROI, but not EC (Figure 2C). Multiple regression models including age, sex, ApoE, and baseline EC FTP and PiB DVR showed a significant interaction between baseline EC FTP and PiB DVR, and an independent effect of ApoE ε4+, in predicting FTP slope in IT and MetaROI (predictor ps&lt;0.01). In the subset with fMRI data, greater task activation in EC and hippocampus was associated with increased EC FTP slope (Figure 3).ConclusionAge was a specific risk factor for tau accumulation in EC, while baseline Aβ and ApoE ε4+ were related to tau accumulation in neocortical regions. Positive associations between activation and tau change within EC support theories of activity‐related tau accumulation. Our findings help elucidate potential underlying causes of tau accumulation in aging.