Citations

Abstract Background Tau PET tracers present distinct binding characteristics that might influence their trajectories and relationship with other biomarkers along the AD continuum. In a head‐to‐head study, we investigated the relationship between the emergence of PET tracers MK6240 and Flortaucipir, and plasma p ‐tau217 abnormalities as a function of Aβ PET deposition. We further assessed the concordance between tau PET and plasma p ‐tau217 positivity and its relationship with cognitive scores. Method We evaluated 352 individuals from the HEAD study (205 cognitively unimpaired and 147 cognitively impaired) with Aβ PET, MK6240 and Flortaucipir tau PET, and plasma p ‐tau217 (ALZpath) and GFAP (Quanterix) measures. Tau PET Braak regions, plasma p ‐tau217 trajectories were modeled as functions of Aβ burden (Centiloid scale) using the Lowess method. Biomarkers were z‐scored anchored on young individuals ( n = 19, < 28 years old) as anchors. Tau PET (Braak 1 region) and plasma p ‐tau217 were considered positive/abnormal when surpassing 2.5 z‐score. Result Among the tested markers, MK6240 was the earliest to show abnormality as a function of Centiloid, occurring at 22 Centiloids, followed by plasma p ‐tau217 at 38 Centiloid and Flortaucipir at Centiloid 56 (Figure 1). Tau PET and plasma p ‐tau217 positivity showed an overall high concordance (∼80% for both tracers; Figure 2). In MK6240 discordant cases, most individuals were MK6240+/p‐tau217‐ (13.8%), while 6.6% were MK6240‐/p‐tau217+ (Figure 2A). For Flortaucipir, 15% of discordant cases were Flortaucipir‐/p‐tau217+, while 4.8% were Flortaucipir+/p‐tau217‐ (Figure 2B). The discordant groups present higher Centiloid than the ptau217‐/Tau‐ group and overall decreased scores in cognitive tests, except for p ‐tau217+/MK6240‐ group (Figure 3). Conclusion MK6240 becomes abnormal at lower levels of Aβ burden compared to plasma p ‐tau217 and Flortaucipir. The relatively high prevalence of discordant tau PET positive or plasma p ‐tau217 positive and suggests that some individuals may show tau PET positivity first, while others may exhibit plasma p ‐tau217 positivity first. The distinct cognitive profiles of this groups suggest potential clinical relevance, warranting further investigation.

Abstract Background Individuals with preclinical AD are difficult to detect using traditional clinical tools. Yet, many individuals classified as cognitively unimpaired (CU) can exhibit a heterogeneous pattern of subtle clinical abnormalities, potentially linked to early Alzheimer's disease (AD). Primary care providers often have access only to clinical tests to study at‐risk community‐dwelling older adults who visit them without cognitive complaints. Since early intervention is crucial in AD, identifying clinical tools to detect preclinical AD is important in areas with limited access to blood or imaging biomarkers. In this study, we evaluate the link between Montreal Cognitive Assessment (MoCA) total scores and AD pathology in CU individuals. Methods We studied 204 cognitively unimpaired (CU) older adults who underwent both 18 F‐FTP and 18 F‐MK6240 scans. These individuals were stratified based on their amyloid PET status, determined by visual reading, into CU A+ ( n = 35) and CU A‐ ( n = 137), as part of the ongoing HEAD study. CU adults had Clinical Dementia Rating (CDR) of 0 and were clinically identified as non‐MCI and non‐demented. Voxel‐wise linear regression models tested the association between MoCA total scores and tau pathology. Results CU A+T+ had significantly lower total MoCA scores than A‐T‐ individuals (Figure 1). Lesser MoCA total score was significantly associated with greater mediobasal temporal tau deposition, using both 18 F‐MK6240 and 18 F‐FTP [Figure 2]. When we separated the population by Aβ status, we found that these results were driven by the CU A+ group (Figure 3A) and were not present in CU A‐ individuals (Figure 3B). Discussion These findings indicate that the MoCA, a simple routine in‐office clinical test, can detect subtle cognitive dysfunction associated with preclinical AD. This suggests that simple cognitive testing can play a role in the early detection of AD and therefore an option for prescreening older adults in non‐specialized clinical settings, the initial interface for most older adults without cognitive complaints. Conclusion MoCA total, the common dementia screening test also plays a valuable role in detecting preclinical AD.

Abstract Background Alzheimer Disease (AD) pathology evolves over decades, and understanding this progression is critical to the understanding of the disease and timing therapeutic interventions. Since individuals with Autosomal Dominant AD (ADAD) develop symptoms around the same age as their parent, it is possible to predict symptom onset and stage individuals by their estimated years to symptom onset (EYO). This approach does not generalize to other forms of AD, thus there is a pressing need for the timecourse of ADAD to be defined in broadly relevant terms. The objective of this project is to validate the Sampled Iterative Local Approximation (SILA) algorithm in a cohort with a known disease timecourse. SILA generates an estimate of time from amyloid positivity (A time ) based on longitudinal PET data. Method We evaluated A time in a longitudinal ADAD sample ( N = 316) with PET PiB data in three ways. First, we compared predicted age at amyloid positive (A+) to observed age at A+ for individuals who became A+ during enrollment. Next, using linear regression, we compared estimated age at A+ to estimated age at symptom onset (EYO=0). Finally, we used generalized additive models to compare the amount of variance in concurrent cognitive performance explained both A time and EYO. Result We observed a mean average error of 1.15 years between actual age at A+ ( N = 26) and the SILA‐predicted A time . Across all participants, SILA‐estimated age at A+ explained 39% of the variance in estimated age at symptom onset ( β = 0.918, p < 0.0001). Finally, we observed a nonlinear association between cognition and both A time and EYO. A time explained 19% of the variance in the general cognitive composite while EYO explained 43% of the variance. Conclusion SILA produces a valid estimate of time‐from‐amyloid positivity in ADAD. This work allows for disease stage in ADAD to be compared to staging for broad forms of AD, which was not previously possible using EYO. However, this work also illustrates that there is a high degree of heterogeneity in preclinical disease duration that is not explained by amyloid alone.

ABSTRACT Aims To investigate whether the association between the nitrite‐generating capacity of the subgingival microbiome and early cardiometabolic risk biomarkers varies by dietary nitrate intake. Materials and Methods Cross‐sectional data from 668 participants (mean age 31 ± 9 years, 73% women) were analysed. Dietary nitrate intake was calculated from food frequency questionnaires. Subgingival 16S rRNA sequencing (Illumina, MiSeq) and PICRUSt2 estimated microbial genes. The Microbiome‐Induced Nitric Oxide Enrichment Score (MINES) was calculated as a ratio of microbial gene abundances representing enhanced net capacity for NO generation. Adjusted multivariable linear models regressed cardiometabolic risk biomarkers (HbA1c, glucose, insulin, insulin resistance (HOMA‐IR), blood pressure) on nitrate intake and MINES together with a MINES × nitrate intake interaction term. Results Mean nitrate intake was 190 ± 171 mg/day. Significant interactions of MINES and nitrate intake were observed for insulin and HOMA‐IR ( p < 0.05). Among participants with a low MINES, higher nitrate intake was associated with lower HOMA‐IR (1.2 [1.1–1.4] vs. 1.5 [1.3–1.6]; p = 0.002), but levels were similar in those with high MINES ( p = 0.84). Conclusions A biomarker of higher microbial NO‐generating capacity in subgingival plaque is associated with lower insulin and insulin resistance among individuals with lower dietary nitrate intake. Future trials evaluating the cardiometabolic benefits of nitrate‐rich diets should incorporate measures of the entire oral microbiome.

Abstract Background Even among cognitively unimpaired (CU) older adults, specific patterns of neuropsychological performance impart differential risk for progression to Alzheimer's disease and related dementias (ADRD). Early identification in the prodromal phase may inform early intervention efforts. We used a data‐driven approach to classify neuropsychological phenotypes within CU older adults from the Atherosclerosis Risk in Communities (ARIC) study and tested for differences in ADRD and cerebrovascular outcomes. Method Participants included 4737 CU (via a previously published algorithm plus consensus diagnosis protocol) older adults (mean age=75.1 years, 60.8% female, 23.1% Black) with neuropsychological data from Visit 5 (2011‐2013) of the ARIC study. Latent profile analysis was leveraged to identify unique cognitive phenotypes using 10 demographically adjusted (age, sex/gender, education, race‐center, WRAT Reading) neuropsychological z‐scores. Cognitive phenotypes were then compared on demographics, depressive symptoms, and vascular risk factors (VRFs). In a subset with available neuroimaging and biofluid data ( n range=1102‐1678), white matter hyperintensity (WMH) and bilateral hippocampal (HV) volumes normalized for intracranial volume and plasma biomarkers (i.e., Aβ42/40, ptau181, NfL, and GFAP) were examined by phenotype. Phenotype comparisons adjusted for demographic variables, VRFs, and an indicator of kidney function (i.e., eGFR‐creatinine) for plasma biomarkers. We applied Tukey adjustment for pairwise comparisons. Result A 6‐class solution of cognitive phenotypes emerged as the best fitting model: Average Overall (28%), Low Overall/Very Low Memory (7%), Average Overall/Super Memory (11%), Subtle Low Overall/Low Executive (22%), Subtle Low Executive/High Memory (13%), and Average Overall/Low Memory (19%; Figure 1). Phenotypes differed on education, race, and word reading, despite prior adjustments for these variables when creating the neuropsychological z‐scores (Table 1). Additionally, phenotypes differed on depressive symptoms, VRFs, WMH, HV, Aβ42/40, and ptau181 (all p s < .05). The Subtle Low Overall/Low Executive group had the highest WMH volumes, the Subtle Low Executive/High Memory group had the lowest Aβ42/40, and the Low Overall/Very Low Memory group had the lowest HV and highest levels of plasma ptau181 (Table 2). Conclusion Cognitive heterogeneity is observed even among CU older adults, with lower executive performance associated with cerebrovascular changes and lower memory associated with certain AD‐related changes, possibly indicating risk for cognitive progression through distinct pathways.

Abstract Background Agitation is a clinically significant symptom contributing to behavioral and psychological symptoms of dementia (BPSD) but is poorly understood across the different syndromes related to Frontotemporal Lobar Degeneration (FTLD). This study investigates sex differences in agitation across FTLD‐related syndromes and its relationship to various neuropsychiatric symptoms (NPS). Method We analyzed data from 1,654 participants (916 males, 738 females; average ages 65.8 and 65.9, respectively) from the National Alzheimer's Coordinating Center (NACC) and ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) study with a FTLD‐related syndrome: behavioral variant FTD (bvFTD), Primary Progressive Aphasia (non‐fluent variant (nfvPPA), semantic variant (svPPA)), Corticobasal syndrome (CBS) and Progressive Supranuclear Palsy (PSP). Participants’ symptoms were assessed using the Neuropsychiatric Inventory (NPI). Prevalence ratios and odds ratios were computed to assess the likelihood of NPS comorbidities when agitation was present in males and females. Principal Component Analysis (PCA) was performed to identify NPS associated with agitation and how they varied by sex. Result Males were more likely to experience anxiety (bvFTD: p <0.001, CBS: p <0.01, PSP: p <0.0001), apathy (nfvPPA: p <0.01, PSP: p <0.01), depression (bvFTD: p <0.0001, PSP<0.01), disinhibition (nfvPPA: p <0.0001), PSP: p <0.001), and motor symptoms (bvFTD: p <0.01, CBS: p <0.01) when agitation was present. In contrast, females had a higher likelihood of experiencing disinhibition in svPPA ( p <0.001). These findings suggest that agitation is associated with a wider range of NPS in males than in females. Agitation and NPS are especially prominent in males with PSP. Conclusion This study reveals significant sex differences in NPS in FTLD‐related syndromes when agitation is present. Males and especially PSP, are more likely to experience a broader range of NPS in association with agitation. These findings underscore the need for further investigation into the underlying mechanisms driving these sex differences, particularly focusing on the neurobiological impact of agitation, the recognition of symptoms in the presence of greater behavioral disturbances, and potential variations from informant reports. Understanding these factors will provide valuable insights into the role of agitation in the presentation of NPS across FTLD‐related syndromes. Ultimately, addressing these gaps will enhance our ability to effectively treat and manage agitation in both male and female patients with FTLD‐related syndromes.

Abstract Background Tau‐PET provides critical topographic information on tau deposition in the living brain. Integrating research‐based practices into clinical settings through qualitative methods, such as visual reads, is becoming increasingly important. This study aims to compare the performance of existing visual reading methods for [ 18 F]Flortaucipir (FTP) and [ 18 F]MK‐6240 (MK) and provide preliminary data toward a unified visual reading approach for all tau‐PET tracers using a head‐to‐head dataset. Methods The study design and development plans are illustrated in Figure 1. To evaluate previously published visual reading methods, two blinded raters conducted FTP and MK visual reads on 340 participants from the HEAD study. A unified visual reading method was developed and tested on 101 participants, using a composite of adjusted Braak regions to determine 3 stages of severity while harmonizing between tracers. For external validation, the method will be tested on four additional cohorts, each using one of the following tracers: FTP, MK, PI, or RO. Results Inter‐rater agreement using previous methods (Figure 2A) showed high Cohen's kappa values (0.71‐0.77) for the low and high tau burden categories in both FTP and MK visual reads. However, agreement was substantially lower in the non‐AD‐like category , leading to overall agreement rates of 0.55 for FTP and 0.69 for MK . In contrast, the method developed here improved inter‐rater agreement across all categories (0.87‐0.96) and introduced a moderate tau (0.79‐0.83) burden group (Figure 2B), leading to higher overall agreement rates of 0.81 for FTP and 0.87 for MK (Figure 2C). For inter‐tracer agreement (Figure 3), previous methods resulted in discordant visual reads for FTP and MK in 34.4% of cases for rater 1 and 24.7% for rater 2. The developed method reduced this disagreement to 11.9% (rater 1) and 15.8% (rater 2), demonstrating improved consistency across different tracers. Conclusion This study demonstrates that previously published visual reading methods produced varying classifications depending on the tracer used. The method proposed here improved inter‐rater and inter‐tracer visual read agreements by developing a unified, tracer‐agnostic approach easy to be used. Importantly, this clinician‐friendly method has the potential for widespread adoption, offering a single harmonized visual reading technique for all tau‐PET tracers.

Abstract Background To promote equitable access to neurologic diagnostics, our team developed MASTERMIND—the Mobile Assessment of Speech To Evaluate, Recognize, and Monitor Indicators of Neurologic health and Disease. One subtest, the MASTERMIND Assessment of Naming, includes ten pictures randomly selected from a list of 45 words varying in frequency. This study aimed to 1) evaluate the feasibility of a remotely self‐administered naming test for patients with cognitive, linguistic, and/or communication difficulties and 2) assess the sensitivity and specificity of performance measures (accuracy, error type, and response time) for detecting aphasia and cognitive‐communication difficulties. Method The task was remotely administered to 138 controls, 26 cognitively impaired individuals, 15 with motor speech disorders, 8 with cognitive concerns, and 6 with aphasia. Participants named pictures displayed on‐screen, advancing through the task by clicking “Next.” Responses were processed using natural language processing (NLP). Audio was transcribed with Whisper Large‐v3, and responses were matched to stimuli within a 0.4‐second threshold. Errors were categorized by type (semantic, phonemic, or unclear source) through consensus between two raters. Performance metrics (accuracy, error type, response time) were analyzed using Kruskal‐Wallis and Pearson's Chi‐Square tests, with AUROC analysis evaluating diagnostic performance. Results Differences in accuracy, response time, and error type were observed across groups (Table 1). Controls and cognitively impaired participants performed similarly, with longer response times observed in patients with aphasia and motor speech disorders. Accuracy alone was a poor predictor of all patients versus controls (AUROC = 0.468) but prediction was better in distinguishing aphasia patients from 1) controls (AUROC = 0.698), 2) cognitively impaired participants (AUROC = 0.679), and 3) those with MSDs (AUROC = 0.670). Conclusions The MASTERMIND Assessment of Naming is a feasible, scalable, self‐administered diagnostic tool enhanced by automated NLP. It provides critical insights through response time, accuracy, and error analysis. Limitations include potential bias in error classification and fixed processing thresholds. The study highlights MASTERMIND's potential for patient triaging and screening. Future research will evaluate the sensitivity of combined performance metrics to disease progression, expand sample sizes and diversity, and refine usability based on participant feedback.

Abstract Background Use of software to “de‐face” research brain images has grown as advances in automated face recognition have made it increasingly possible to re‐identify participants. Imaging studies of Alzheimer's Disease and related diseases have included more participants from historically underrepresented groups, but de‐facing software have primarily been validated using images from mostly non‐Hispanic White participants, and its performance has not been directly compared across ethnoracial groups. Method From The Health and Aging Brain Study: Health Disparities (HABS‐HD) study, we constructed a sample of 305 T1‐weighted MRI from HABS‐HD baseline visits, matched across three ethnoracial groups (non‐Hispanic White (NHW, n = 104), non‐Hispanic African American (AA, n = 104), Mexican American (MA, n = 97)) according to age‐decade, sex, cognitive status (impaired/unimpaired), and MRI scanner model. We ran mri_reface 0.3.5 on each image, and we ran FreeSurfer 7.4.1 on each unmodified image and on each de‐faced output. We then compared regional gray matter (GM) volume and cortical thickness measurements by subtracting each de‐faced image measurement from the corresponding unmodified image measurement. We compared these differences between de‐faced and unmodified images across the three matched groups using GLM analyses adjusted for age, sex, and cognitive status, and Bonferroni corrected. Result When aggregating across all brain regions, differences in cortical thickness measures due to de‐facing were significantly different between the NHW and MA groups, and between AA and MA, but not between NHW and AA (Figure 1). Differences between these group means were <0.005mm. For GM volume, differences were significant between AA and MA (magnitude < 0.2%), but not between NHW and AA or NHW and MA (Figure 2). Examining each region individually (Figure 3), cortical thickness differences were significant for only 4 regions (all with magnitudes <0.03mm), and GM volume differences were significant for only 6 regions (all with magnitudes <2%). Conclusion Effects of mri_reface on FreeSurfer brain measurements were significantly different across ethnoracial groups. Our future work will optimize mri_reface to eliminate these differences. However, the magnitudes of these differences are sufficiently small that they are unlikely to have a practical effect on analyses of data using current versions.

Abstract Background Apolipoprotein E (apoE) facilitates the fibrillization and deposition of amyloid β (Aβ) in Alzheimer's Disease (AD). Supported by robust literature from various groups, in our current study we have investigated the relative amyloidogenic properties of the three major human APOE alleles. Knowledge from such studies could provide insight into the role of ApoE protein in the deposition, evolution and maturation of parenchymal Aβ. Method We used hAbetaSAA (Jax Labs) mice crossed with human APOE targeted replacement mice to create hAbetaSAA mice homozygous for human APOE E2, E3 and E4 . Furthermore, APPsi mice (hAPPswe/indiana: Borchelt) expressing human APOE E3 or E4 were injected intracerebrally with AD brain lysates to examine the role of APOE genotype on Aβ seeding. Neuropathological examination was conducted via immunohistochemistry, immunofluorescence and immunoblotting. Result hAbetaSAA mice expressing human APOE E4 showed higher Aβ deposition relative to APOE E3 and APOE E2 by 5 months of age and progressing through 8 months and 13 months of age. In comparison, APP mice expressing humanized Aβ (Jax Labs) with human APOE E4 did not develop deposits in their lifetime. hAbetaSAA mice with endogenous mouse Apoe had the highest Aβ plaque deposition, with an approximately 60‐fold increase in Aβ burden compared to SAA APP mice expressing APOE E3 , especially in the earlier deposition phase. Removing mouse Apoe resulted in dose‐dependent reduction in Aβ deposition, consistent with previously published literature. Plaque morphology did not show any notable differences between APOE genotypes, and colocalization of apoE with Aβ were relatively equivalent across different APOE genotypes. Further, in the APPsi model seeded with AD‐derived seeds, we found that mice with APOE E4 or APOE E3 developed equal burdens of diffuse parenchymal Aβ, with no detectable changes in plaque morphology. Conclusion We conclude that apoE variants broadly influence the rate of amyloid deposition, but do not modify the morphology of the resulting deposits or colocalization with Aβ. Notably, when seeding with AD patient brain lysate, there was no significant difference in Aβ burden between APPsi mice expressing APOE E3 and E4 . Our study adds insights into the role of apoE isoforms in determining Aβ‐related phenotype.