Citations

AbstractBackgroundBlood‐based biomarkers show promise as a noninvasive, inexpensive method for measuring Alzheimer’s disease pathology throughout the lifecourse. However, the predictive and classification accuracy of these biomarkers at different stages of the lifecourse and among diverse, community‐dwelling populations requires further investigation.MethodBetween 2014 and 2015, 329 dementia‐free participants from the Atherosclerosis Risk in Communities Study underwent brain magnetic resonance imaging (MRI) and florbetapir positron emission tomography (PET). Amyloid positivity was defined as a global cortex standardized uptake value ratio greater than 1.2. Stored plasma samples collected in midlife (1993‐95, mean age 58.5 years) and late‐life (2011‐13, mean age 76.2 years) from a subsample of 259 participants were assayed in 2022 using Quanterix SiMoA. The assay quantified amyloid‐β (Aβ)42/40, phosphorylated tau at threonine 181 (p‐Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Unadjusted and covariate‐adjusted logistic regression models estimated the association between plasma biomarkers from midlife and late‐life and amyloid positivity in late‐life. Unadjusted receiver operating characteristic curves documented the classification accuracy of the plasma biomarkers separately and collectively.ResultThe sample (Table 1) included 151 women (58.1%), 105 Black participants (40.4%), and 136 (52.3%) participants with amyloid positivity. In models adjusted for demographics, lifestyle factors, cardiovascular factors, and the presence of APOE ε4 alleles, assays measured in midlife were not associated with amyloid positivity in late‐life (Table 2). However, late‐life measurements of Aβ42/40, p‐Tau181, and GFAP and change per decade from midlife to late‐life in p‐Tau181 and GFAP were associated with greater odds of amyloid positivity. The greatest classification accuracy (Figure 1) was achieved by using all assays measured in midlife and late‐life (AUC = 0.754), although the accuracy when using only late‐life measures was comparable (AUC = 0.737).ConclusionLate‐life plasma biomarkers of Alzheimer's disease neuropathology and astrogliosis were associated with late‐life amyloid positivity, but midlife plasma biomarkers obtained 16 to 20 years earlier did not exhibit a statistically significant association. Additional longitudinal research is needed to determine whether changes in plasma biomarkers measured before an individual is classified as amyloid positive can be used to identify individuals at risk of developing Alzheimer's disease.

AbstractBackgroundWeakened white matter (WM) integrity is highly associated with dementia risk. Still, not everyone with WM changes develops dementia, suggesting the important role modifiable lifestyle factors may have in reducing dementia risk. We investigated how social relationships in mid‐life may modify the association between WM integrity and incident dementia risk within race and sex subgroups.MethodIn 1,679 Atherosclerosis Risk in Community (ARIC) study participants who were dementia‐free in mid‐life, psychosocial health was assessed via self‐reported questionnaires and participants were classified as having strong or poor social relationships in mid‐life (visit 2: 1990‐1992). Through diffusion tensor imaging, WM integrity was evaluated with 3T brain MRI (visit 5: 2011 – 2013); fractional anisotropy (FA) and mean diffusivity (MD) were estimated. Incident dementia cases were identified from visit 5 through December 31, 2019, with ongoing surveillance. Relative contributions of social relationships and WM integrity to incident dementia were evaluated in Cox‐proportional hazard regression models with race and race‐sex as interaction terms, and in stratified models (White Females, Black Females, White Males, Black Males).ResultSignificant three‐way interactions were observed between race, WM integrity, and mid‐life social relationships (p‐interaction<0.01). In Black participants with strong mid‐life social relationships, higher MD (hazard ratio (HR): 0.93, 95% CI: 0.63 – 1.38) and lower FA (HR: 1.25, 95% CI: 0.84 – 1.86) were not associated with the risk of dementia, although in Black participants with poor social relationships, higher MD remained a significant risk factor for dementia. However, across all White participants, higher MD and lower FA were associated with the risk of dementia, regardless of mid‐life social relationships (Table 1; Figure 1). Analysis of three‐way interactions between race‐sex, WM integrity, and mid‐life social relationships suggested that effect modification by mid‐life social relationships was most evident in Black Females, respective to other groups (Table 2; Figure 1).ConclusionThe contribution of WM integrity to dementia risk may be lower in Black participants with strong social relationships in mid‐life, particularly in Black Females. Further understanding of how contextual social factors may influence brain health in diverse populations should be examined in future studies and intervention efforts.

AbstractBackgroundMidlife vascular risk factors are associated with an increased risk of dementia. However, the overall contribution of modifiable vascular risk factors in midlife and late‐life to dementia remains unclear. In this study, we quantified population attributable fractions, which account for risk factor prevalence and strength of relative risks, of incident dementia from vascular risk factors measured in midlife and early late‐life.MethodWe used 33 years of prospective cohort data in the Atherosclerosis Risk in Communities Study. Incident dementia (community surveillance and algorithmic diagnosis with expert clinical adjudication) was ascertained from baseline (1987‐1989) through December 31, 2020. We quantified population attributable fractions of incident dementia by age 80 from the combination of hypertension (systolic blood pressure ≥130, diastolic blood pressure ≥80, or use of medication), diabetes (fasting glucose ≥126, non‐fasting glucose ≥200, self‐reported physician diagnosis of diabetes, or use of medication for diabetes/insulin), and self‐reported current cigarette smoking (at least one risk factor versus none) measured at different ages (45‐54 [n = 7,731], 55‐64 [n = 12,273]; 65‐74 [n = 6,660]). We also computed attributable fractions stratified by sex, apolipoprotein ε4 genotype, and self‐reported race.ResultThe prevalence of having one or more of the vascular risk factors was 61.6% (95% CI: 60.5, 62.7%) at age 45‐54 and increased to 77.5% (95% CI: 76.5, 78.5%) by age 65‐74. Population attributable fractions of dementia increased from 22.7% (95% CI: 15.3, 30.1%) for vascular risk factors measured at age 45‐54 to 45.4% (95% CI: 32.5, 58.3%) for vascular risk factors assessed at age 65‐74 (see Figure). Attributable fractions were larger in females, participants without apolipoprotein ε4 alleles, and Black participants (Figure).ConclusionBetween 23% to 45% of incident dementia cases by age 80 were attributed to a combination of hypertension, diabetes, or current smoking up through age 74. Attributable fractions for the combined impact of vascular risk varied by key demographics. Public health strategies targeting modifiable vascular risk factors could reduce dementia risk and increase healthy lifespan.

AbstractBackgroundThe HEAD study aims to collect a large dataset of multiple tau‐PET tracers to provide robust anchor values for tau‐PET harmonization. Here, we tested the hypothesis that anchoring two tau tracer uptake values using head‐to‐head measurements has the potential to generate an accurate universal tau‐PET scale, named Uniτ(tau).MethodsWe assessed 200 individuals across the aging and AD spectrum (Training:HEAD data freeze 2.0, n=185; Testing:UPitt dataset (Gogola et al.), n=15) with [18F]Flortaucipir and [18F]MK‐6240 tau‐PET. SUVRs were processed to a common 8mm FWHM, with inferior cerebellar gray matter as the reference region (Pascoal et al.). Uniτ explored two anchoring/harmonization methods. First, we examined within‐tracer anchoring by creating anchor values based on the mean SUVR of Youngs (<25 years) and 95th percentile voxels from cognitively impaired individuals. Second, we explored within‐ plus between‐tracer anchoring, employing linear (e.g., piecewise) and non‐linear regressions. To address the inherent problem of discontinuity of piecewise transformations, we implemented two smoothing methods at the inflection point between equations, transforming them into a continuous function.ResultsUniτ scale anchoring within‐tracer resulted in similar estimates for high values, but less accurate in the lower range (Figure 1,2). Anchoring within‐ plus between‐tracer improved estimate consistency, with the piecewise transformation generating the best results. The piecewise smoothing equation yielded estimates comparable to those obtained from the piecewise method without smoothing. This allowed for the use of a single formula. In addition, this leads to more robust results when the goal is to study longitudinal changes in the scale (data not shown). UPitt testing dataset showed similar results to the training set (Figure 3).ConclusionOur preliminary findings suggest that anchoring tau‐PET values both within and between tracers has the potential to harmonize tau‐PET tracers, while preserving their characteristics. Currently, piecewise smoothing is the preferred method for Uniτ, but we are continuously fine‐tuning scale parameters as we acquire more data. The final scale parameters will be determined based on extensive training and testing data from multiple tracers. This cautious methodology holds the promise of delivering reliable, robust, and reproducible results, ensuring safe usage of the scale in clinical trials, and potentially paving the way for future use in clinical practice.

AbstractBackgroundThe HEAD study focuses on collecting an extensive dataset from various tau‐PET tracers, aiming to establish robust anchor values, which are essential for harmonizing tau‐PET measurements. Here, we aim to showcase the capability of converting 3D tau‐PET images into a common scale using the Universal Tau‐PET Scale, Uniτ (tau), and to use these 3D images to subsequently obtain ROIs as needed.MethodsWe assessed 185 individuals across the aging and AD spectrum from the HEAD study, with [18F]Flortaucipir and [18F]MK‐6240 tau‐PET tracers. Tau‐PET SUVR images were standardized to a common 8mm FWHM, using the inferior cerebellar gray matter as the reference region. We generated Uniτ Tau‐PET 3D images using a single formula based on Meta‐temporal parameters in two steps: first, within‐tracer anchoring based on young (<25 years) and cognitively impaired individuals; second, between‐tracer anchoring using a piecewise transformation from [18F]Flortaucipir to [18F]MK‐6240, with smoothing at the piecewise inflection point. Subsequently, we extracted mean Uniτ values from these 3D images for key ROIs, including meta‐temporal, mesial, temporo‐parietal, frontal, and Braak stages III‐VI. Finally, we correlated Uniτ values across ROIs between the two tracers to evaluate the accuracy of estimates from the voxel‐wise transformation.ResultsThe original SUVR images present large differences between the two tau‐PET tracers (Figure 1). However, upon applying the Uniτ piecewise transformation with smoothing to all brain voxels, we were able to reasonably harmonize these images to the Uniτ scale, substantially reducing visual variability. Notably, the mean Uniτ values for the ROIs extracted from these harmonized 3D tau‐PET brain images demonstrated a high level of association between the two tracers (Figure 2). Furthermore, estimates generated from ROIs or extracted from our 3D parametric Uniτ model yielded identical estimates (Figure 3).ConclusionThe strong associations between tracers after directly harmonizing 3D images to Uniτ scale using the piecewise transformation with smoothing, underscore the effectiveness of the proposed method. This approach provides a reliable and standardized way to compare tau‐PET data across different tracers. Our results indicate the feasibility of harmonizing 3D tau‐PET images without relying on pre‐established ROIs, overcoming the limitation of restricting the analysis to only few brain regions.

AbstractBackgroundIn vivo studies using the tau PET tracers have shown high performance for the diagnosis of Alzheimer’s disease dementia and patterns of tracer uptake that resemble those observed in post‐mortem studies. However, tau tracers present distinct patterns of binding that might influence their performance in detecting AD pathology. In a head‐to‐head study, we investigated the performance of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of AD.MethodWe assessed 132 individuals from the HEAD study (58 CU Aß‐, 15 CU Aß+, 14 MCI Aß‐, 32 MCI Aß+ and 13 AD dementia) with Aß‐PET, [18F]MK6240 and [18F]Flortaucipir. Voxel‐wise receiver operating characteristic curves (ROC) of the two tau tracers were used to contrast groups provided the area under the curve (AUC) for disease diagnosis or biomarkers positivity.ResultThe brain maps showed numerically higher and more spread AUC for [18F]Flortaucipir to discriminate CU Aß‐ from CU Aß+ (Fig.1A). The difference between tracers’ AUC was greater in Braaks IV and V (Fig.2A), both regions that are not expected to have tau accumulation in CU Aß+ individuals, reflecting a potential off‐target binding for [18F]Flortaucipir. To differentiate CU Aß‐ from MCI Aß+ individuals, [18F]MK6240 presented a numerically higher AUC than [18F]Flortaucipir in Braak I and II and similar AUC in other Braak regions (Fig.1B, Fig. 2B). We observed a high performance of [18F]MK6240 and [18F]Flortaucipir in differentiating AD dementia from CU Aß‐ individuals. However, [18F]MK6240 exhibits a higher AUC than [18F]Flortaucipir in all Braak regions, especially Braak V‐VI (Fig.1C, Fig.2C). Finally, [18F]MK6240 presented higher AUC in all Braak regions to discriminate MCI Aß‐ from MCI Aß+ individuals (Fig.1D, Fig.2D).ConclusionOur results indicate that [18F]MK6240 and [18F]Flortaucipir present high accuracy to discriminate AD from CU Aß‐ individuals. However, [18F]MK6240 presents higher AUC to discriminate AD and MCI Aß+ from CU Aß‐ individuals than [18F]Flortaucipir. Together, our head‐to‐head study sheds light on the distinct patterns of binding for Tau‐PET tracers.

AbstractBackgroundTau PET provides continuous measurements of tau tangle pathology in the human brain. However, establishing cutoffs is crucial for selecting individuals for treatment in clinical trials or practice. In the absence of postmortem data, PET cutoffs must be established using statistical methods based on what is considered normal tracer uptake. In this study, we tested the impact of various methods to determine tau positivity using two different tau PET tracers in individuals scanned head‐to‐head.MethodsWe studied 147 individuals from Head‐to‐Head Harmonization of Tau Tracers in Alzheimer's Disease (HEAD) study with tau tangle PET scans with [18F]Flortaucipir and [18F]MK‐6240, and amyloid‐ß (Aß) PET. Tau deposition was measured with the standardized uptake value ratio (SUVR) of each agent in the Medial Temporal Lobe (MTL) and the Entorhinal Cortex (EC). To determine Tau positivity three different methods were used: >2.5 standard deviations (SD) than the mean of the young, >1.5 SD mean of the cognitively unimpaired (CU) and >1.5 SD mean of CUAß‐.ResultsDemographic characteristics of the study population are reported in Table 1. Using the cutoff >2.5 SD mean of young, [18F]Flortaucipir was positive in 35 (23.8%) and 69 (46.9%) individuals in the EC and MTL, respectively. [18F]MK‐6240 was positive in 49 (33.3%) and 58 (39.5%) individuals in EC and in MTL. Using >1.5 SD mean of CUAß‐, [18F]Flortaucipir was positive in 38 (25.9%) and 45 (30.6%) individuals in EC and MTL, while [18F]MK‐6240 was positive in 51 (34.7%) and 50 (34.0%) in EC and in MTL (Figure 1).ConclusionsOur findings indicate variations in tau positivity when employing different methods based on either the young or CUAß‐. [18F]Flortaucipir exhibited a higher rate of positive results when the method based on young individuals was applied in the MTL. Conversely, [18F]MK‐6240 showed more consistent and generally higher positivity when other methods were used for cutoff determination and/or in the EC region. Further research with a larger sample size is required to gain a better understanding of the optimal cutoff determination methods for these tracers.

AbstractBackgroundTau‐PET tracers have been used to diagnose and stage Alzheimer’s disease. However, different tau tracers present distinct patterns of binding throughout the brain, challenging the harmonization of their results. We hypothesize that the choice of a reference region can impact the harmonization of the tau‐PET standardized uptake value ratio (SUVR). In this context, we aimed to explore how different cerebellar reference regions impact the association between [18F]Flortaucipir and [18F]MK‐6240 SUVR values.MethodWe studied 185 individuals across the aging and AD spectrum with head‐to‐head Flortaucipir and MK‐6240 tau PET (HEAD Study). SUVRs were processed to a common 8mm FWHM using 15 different reference regions defined in the spatially unbiased atlas template of the cerebellum (SUIT) (Diedrichsen, 2009). Regression models investigated the association between Flortaucipir and MK‐6240 using multiple combinations of reference regions. R2 statistic was used to estimate goodness‐of‐fitting.Result225 combinations of associations Flortaucipir and MK‐6240 SUVR were tested, where the SUVRs are not necessarily quantified using the same reference region. Figure 1 presents the top 25 strongest and 25 weakest associations, ordered based on the coefficient of determination (R²). Flortaucipir and MK‐6240 exhibited the most robust associations when the inferior cerebellar gray matter or Crus I were employed for SUVR determination (R2 = 0.89, Figure 1‐2). Conversely, combinations incorporating the fastigial region consistently yielded weaker associations between the two tracers (R2<0.70, Figure 1‐2).ConclusionInterestingly, we showed that the inferior cerebellum, when used for both tracers, serves as a robust reference region for determining the most similar SUVRs for Flortaucipir and MK‐6240. Notably, these two regions have been commonly utilized in previous studies involving these tau tracers. Our results imply that the inferior cerebellum could be the optimal reference region for research aimed at harmonizing tau PET tracers using statistical scales.

AbstractBackgroundIdentification of cognitively unimpaired (CU) individuals who may progress to mild cognitive impairment (MCI), is a pressing issue in the Alzheimer’s disease (AD) field, since therapeutic interventions may be more effective in the absence of cognitive impairment and neurodegeneration. CU individuals positive for amyloid and tau PET are very likely in the AD pathway. In out‐patient cognitive screening, we use rapid and simple tests such as The Montreal Cognitive Assessment (MoCA) ‐ a composite of executive, visuospatial, naming, attention, language, abstraction, delayed recall, and orientation performances. We hypothesize that sub‐categorizing the CU group based on the presence of amyloid (A) and tau (T) pathologies, as measured by PET, can reveal subtle cognitive deficits, even in a clinical test with low sensitivity such as MoCA.MethodWe included 88 CU [defined as CDR global score of 0] older adults with available head‐to‐head MK6240 tau‐PET and Flortaucipir tracers from the ongoing HEAD multi‐site observational study. They also underwent Amyloid PET imaging using PiB or NAV4694. MoCA testing was done around the imaging time. To increase similarity with clinical practice, Amyloid and Tau PET positivity was defined using visual reads. Based on tracer uptake status, we categorized the individuals into A‐T‐, A‐T+, A+T‐ and A‐T‐ groups. Unpaired t‐test and one‐way ANOVA were used to test for significant differences between groups.Result2‐tailed p‐values were significant between the A‐T‐ group and the A+T+ group for MoCA total score [p: 0.0025 and p: 0.0025]. When we stratified MoCA in the different cognitive domains, we found that the results were driven by MoCA (memory) Delayed Recall score [p: 0.0007 and p: 0.0162]. A‐T+ vs A+T+ was also significant [p: 0.0099] for MoCA Delayed Recall. Both tau tracers (MK6240 and Flortaucipir) showed similar performance for determining T+ and identifying cognitive decline.ConclusionMoCA, a simple and commonly administered in‐office neuropsychological test, can detect subtle cognitive dysfunction in CU older adults who are Aß and tau positive (A+/T+). These CU A+/T+ individuals are likely on the path to developing AD dementia.

AbstractBackgroundRecent studies showed that neuroinflammation plays a key role in triggering specific neuropsychiatric symptoms (NPS), such as irritability and agitation, in individuals with Alzheimer’s disease (AD). While prior studies showed an association between tau pathology and all NPS domains, the extent to which tau influences each specific NPS domain remains unclear. Here, we aim to investigate the association of tau and NPS domains in the AD continuum. We hypothesize that tau plays a comparatively greater effect on the emergence of psychotic symptoms compared to other NPS domains.MethodWe assessed 56 individuals (21 cognitively unimpaired (CU), 23 MCI, and 12 AD dementia) from the HEAD study who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q) and had positron emission tomography (PET) for amyloid‐ß (Aß) ([18F]AZD4694 or [11C]PiB), and tau tangles ([18F]MK6240) at the same visit. We selected individuals with an NPI‐Q total score =1. Tau SUVR values were tailored with a mask from Braak stages I‐VI, using the inferior cerebellar gray matter as reference region. Leave‐one‐out voxel wise and linear regression tested the association between each NPI‐Q domain and biomarkers accounting for age, sex, cognitive status, and study site.ResultCI individuals had significantly higher NPI‐Q score and Braak VI PET SUVR than CU individuals (Table 1). NPI‐Q score was significantly associated with tau‐PET in the periRolandic and supplementary motor cortex (Figures 1A). Linear regression showed that NPI‐Q associates with tau‐PET in the Braak stage VI (Figure 1B). Leave‐one‐out regression analysis revealed that delusions, motor disturbances, and anxiety contributed most to the association between tau‐PET and NPS (Figure 2A, C). These domains presented a higher magnitude of association compared to each other NPI‐Q domain (Figure 2B). Notably, irritability, agitation, and disinhibition exerted a negative effect to the association, emphasizing that tau may not play a role in the development of these symptoms (Figure 2A, D)ConclusionOur study supports previous evidence suggesting that irritability and agitation may not be triggered by tau, but rather by other pathological process such as neuroinflammation. These findings provide additional rationale for the therapeutics aiming to mitigate irritability and agitation in AD patients.