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Background: 18F-flortaucipir PET received FDA approval to visualize aggregated neurofibrillary tangles (NFTs) in brains of adult patients with cognitive impairment being evaluated for Alzheimer’s disease (AD). However, manufacturer’s guidelines for visual interpretation of 18F-flortaucipir PET differ from how 18F-flortaucipir PET has been measured in research settings using standardized uptake value ratios (SUVRs). How visual interpretation relates to 18F-flortaucipir PET SUVR, cerebrospinal fluid (CSF) biomarkers, or longitudinal clinical assessment is not well understood. Objective: We compare various diagnostic methods in participants enrolled in longitudinal observational studies of aging and memory (n = 189, 23 were cognitively impaired). Methods: Participants had tau PET, Aβ PET, MRI, and clinical and cognitive evaluation within 18 months (n = 189); the majority (n = 144) also underwent lumbar puncture. Two radiologists followed manufacturer’s guidelines for 18F-flortaucipir PET visual interpretation. Results: Visual interpretation had high agreement with SUVR (98.4%)and moderate agreement with CSF p-tau181 (86.1%). Two participants demonstrated 18F-flortaucipir uptake from meningiomas. Visual interpretation could not predict follow-up clinical assessment in 9.52% of cases. Conclusion: Visual interpretation was highly consistent with SUVR (discordant participants had hemorrhagic infarcts or occipital-predominant AD NFT deposition) and moderately consistent with CSF p-tau181 (discordant participants had AD pathophysiology not detectable on tau PET). However, close association between AD NFT deposition and clinical onset in group-level studies does not necessarily hold at the individual level, with discrepancies arising from atypical AD, vascular dementia, or frontotemporal dementia. A better understanding of relationships across imaging, CSF biomarkers, and clinical assessment is needed to provide appropriate diagnoses for these individuals.

Cerebrospinal fluid (CSF) amyloid-β peptide (Aβ)42/Aβ40 and the concentration of tau phosphorylated at site 181 (p-tau181) are well-established biomarkers of Alzheimer’s disease (AD). The present study used mass spectrometry to measure concentrations of nine phosphorylated and five nonphosphorylated tau species and phosphorylation occupancies (percentage phosphorylated/nonphosphorylated) at ten sites. In the present study we show that, in 750 individuals with a median age of 71.2 years, CSF pT217/T217 predicted the presence of brain amyloid by positron emission tomography (PET) slightly better than Aβ42/Aβ40 (P = 0.02). Furthermore, for individuals with positive brain amyloid by PET (n = 263), CSF pT217/T217 was more strongly correlated with the amount of amyloid (Spearman’s ρ = 0.69) than Aβ42/Aβ40 (ρ = −0.42, P n = 55 and n = 90), CSF pT217/T217 and pT205/T205 were better correlated with tau PET measures than CSF p-tau181 concentration. These findings suggest that CSF pT217/T217 and pT205/T205 represent improved CSF biomarkers of amyloid and tau pathology in AD.

Background As people with human immunodeficiency virus (HIV) (PWH) age, it remains unclear whether they are at higher risk for age-related neurodegenerative disorders—for example, Alzheimer disease (AD)—and, if so, how to differentiate HIV-associated neurocognitive impairment from AD. We examined a clinically available blood biomarker test for AD (plasma amyloid-β [Aβ] 42/Aβ40 ratio) in PWH who were cognitively normal (PWH_CN) or cognitively impaired (PWH_CI) and people without HIV (PWoH) who were cognitively normal (PWoH_CN) or had symptomatic AD (PWoH_AD). Methods A total of 66 PWH (age >40 years) (HIV RNA Results The plasma Aβ42/Aβ40 ratio was significantly lower, and the APS higher, in PWoH_AD than in other groups. A lower Aβ42/Aβ40 ratio and higher APS was associated with smaller hippocampal volumes for PWoH_AD. The Aβ42/Aβ40 ratio and APS were not associated with cognition or HIV clinical measures for PWH. Conclusions The plasma Aβ42/Aβ40 ratio can serve as a screening tool for AD and may help differentiate effects of HIV from AD within PWH, but larger studies with older PWH are needed.

We investigated the relationship between preclinical Alzheimer's disease (AD) biomarkers and adverse driving behaviors in a longitudinal analysis of naturalistic driving data.Naturalistic driving data collected using in-vehicle dataloggers from 137 community-dwelling older adults (65+) were used to model driving behavior over time. Cerebrospinal fluid (CSF) biomarkers were used to identify individuals with preclinical AD. Additionally, hippocampal volume and cognitive biomarkers for AD were investigated in exploratory analyses.CSF biomarkers predicted the longitudinal trajectory of the incidence of adverse driving behavior. Abnormal amyloid beta (AβPreclinical AD is associated with increased adverse driving behavior over time that cannot be explained by cognitive changes. Driving behavior as a functional, neurobehavioral marker may serve as an early detection for decline in preclinical AD. Screening may also help prolong safe driving as older drivers age.

The brain changes of Alzheimer’s disease and other degenerative dementias begin long before cognitive dysfunction develops, and in people with subtle cognitive complaints, clinicians often struggle to predict who will develop dementia. The public increasingly sees benefits to accessing dementia risk evidence (DRE) such as biomarkers, predictive algorithms, and genetic information, particularly as this information moves from research to demonstrated usefulness in guiding diagnosis and clinical management. For example, the knowledge that one has high levels of amyloid in the brain may lead one to seek amyloid reducing medications, plan for disability, or engage in health promoting behaviors to fight cognitive decline. Researchers often hesitate to share DRE data, either because they are insufficiently validated or reliable for use in individuals, or there are concerns about assuring responsible use and ensuring adequate understanding of potential problems when one’s biomarker status is known. Concerns include warning people receiving DRE about situations in which they might be compelled to disclose their risk status potentially leading to discrimination or stigma. The Advisory Group on Risk Evidence Education for Dementia (AGREEDementia) welcomes all concerned with how best to share and use DRE. Supporting understanding in clinicians, stakeholders, and people with or at risk for dementia and clearly delineating risks, benefits, and gaps in knowledge is vital. This brief overview describes elements that made this group effective as a model for other health conditions where there is interest in unfettered collaboration to discuss diagnostic uncertainty and the appropriate use and communication of health-related risk information.

As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia.We analyzed longitudinal data from10,000 cognitively unimpaired older adults. Five-year risk of AD dementia was modeled using survival analysis.A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5-year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aβ)42/Aβ40 (AUC = 0.82).An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults.

Genome-wide association studies (GWAS) of cerebrospinal fluid (CSF) Alzheimer’s Disease (ad) biomarker levels have identified novel genes implicated in disease risk, onset, and progression. However, lumbar punctures have limited availability and may be perceived as invasive. Blood collection is readily available and well accepted, but it is not clear whether plasma biomarkers will be informative for genetic studies. Here we perform genetic analyses on concentrations of plasma amyloid-β peptides Aβ40 (n = 1467) and Aβ42 (n = 1484), Aβ42/40 (n = 1467) total tau (n = 504), tau phosphorylated (p-tau181; n = 1079) and neurofilament light (NfL; n = 2058). GWAS and gene-based analysis was used to identify single variant and genes associated with plasma levels. Finally, polygenic risk score and summary statistics were used to investigate overlapping genetic architecture between plasma biomarkers, CSF biomarkers and ad risk. We found a total of six genome-wide significant signals. APOE was associated with plasma Aβ42, Aβ42/40, tau, p-tau181, and NfL. We proposed ten candidate functional genes based on 12 SNP-biomarker pairs and brain differential gene expression analysis. We found a significant genetic overlap between CSF and plasma biomarkers. We also demonstrate that it is possible to improve the specificity and sensitivity of these biomarkers, when genetic variants regulating protein levels are included in the model. This current study using plasma biomarker levels as quantitative traits can be critical to identification of novel genes that impact ad and more accurate interpretation of plasma biomarker levels.

Balancing the evolution of longitudinal cognitive assessment with practical considerations like maintaining legacy measures presents a vexing problem for many Alzheimer's Disease Research Centers (ADRCs) in the US. Changes to the Uniform Data Set (UDS) have left many ADRCs with heterogeneous cognitive data that can be challenging to harmonize. Using longitudinal data from the Knight ADRC, we used equipercentile equating to combine cognitive data collected across UDS versions 2 and 3. The combined data was then used to assess whether equated variables could create stable and informative domain-specific and general cognitive composite measures. Exploratory factor analysis (EFA) revealed four underlying clusters of tests that corresponded to episodic memory, semantic memory, working memory, and attention/processing speed. These factors appeared stable across test forms, with equivalent factors observed when EFA was conducted on UDS2 and UDS3 independently, and replicated when UDS2 and UDS3 were harmonized using equated variables. To understand the utility of these domain-specific factors, we also computed a commonly used preclinical Alzheimer cognitive composite (ADCS-PACC), a novel PACC (Knight-PACC), and a global composite consisting of 13 cognitive tasks. Prior work has proposed that these PACCs are sensitive to preclinical changes in AD, while the global composite would reflect general cognitive ability. We compared the relationships between each composite and neuroimaging metrics of amyloid, tau, brain structure, and clinical symptoms, cross-sectionally and longitudinally. We also simulated clinical trials using each composite and compared the sample size estimates needed to power a study expecting to observe a moderate effect size. Overall, global and PACC-based metrics of cognition slightly outperformed domain-specific composites, showing better sensitivity to AD biomarkers, although the working memory composite consistently performed poorly. We also found that data-driven, domain-specific composites can complement already-established general cognitive composites, with the exception of the present working memory composite. Power analyses revealed that the global, Knight-PACC, and attention and processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the ADCS-PACC required two to three times as many participants. Importantly, this work demonstrated the stability of cognitive domains across test versions. Finally, in line with prior work, we also highlight that attention and processing speed may be sensitive metrics of cognitive change in the earliest stages of AD.

Background and ObjectivesTo evaluate whether plasma biomarkers of amyloid (Aβ42/Aβ40), tau (p-tau181 and p-tau231), and neuroaxonal injury (neurofilament light chain [NfL]) detect brain amyloidosis consistently across racial groups.MethodsIndividuals enrolled in studies of memory and aging who self-identified as African American (AA) were matched 1:1 to self-identified non-Hispanic White (NHW) individuals by age,APOEε4 carrier status, and cognitive status. Each participant underwent blood and CSF collection, and amyloid PET was performed in 103 participants (68%). Plasma Aβ42/Aβ40 was measured by a high-performance immunoprecipitation–mass spectrometry assay. Plasma p-tau181, p-tau231, and NfL were measured by Simoa immunoassays. CSF Aβ42/Aβ40 and amyloid PET status were used as primary and secondary reference standards of brain amyloidosis, respectively.ResultsThere were 76 matched pairs of AA and NHW participants (n = 152 total). For both AA and NHW groups, the median age was 68.4 years, 42% wereAPOEε4 carriers, and 91% were cognitively normal. AA were less likely than NHW participants to have brain amyloidosis by CSF Aβ42/Aβ40 (22% vs 43% positive;p= 0.003). The receiver operating characteristic area under the curve of CSF Aβ42/Aβ40 status with the plasma biomarkers was as follows: Aβ42/Aβ40, 0.86 (95% CI 0.79–0.92); p-tau181, 0.76 (0.68–0.84); p-tau231, 0.69 (0.60–0.78); and NfL, 0.64 (0.55–0.73). In models predicting CSF Aβ42/Aβ40 status with plasma Aβ42/Aβ40 that included covariates (age, sex,APOEε4 carrier status, race, and cognitive status), race did not affect the probability of CSF Aβ42/Aβ40 positivity. In similar models based on plasma p-tau181, p-tau231, or NfL, AA participants had a lower probability of CSF Aβ42/Aβ40 positivity (odds ratio 0.31 [95% CI 0.13–0.73], 0.30 [0.13–0.71], and 0.27 [0.12–0.64], respectively). Models of amyloid PET status yielded similar findings.DiscussionModels predicting brain amyloidosis using a high-performance plasma Aβ42/Aβ40 assay may provide an accurate and consistent measure of brain amyloidosis across AA and NHW groups, but models based on plasma p-tau181, p-tau231, and NfL may perform inconsistently and could result in disproportionate misdiagnosis of AA individuals.

Longitudinal changes in Alzheimer's disease (AD) biomarkers, including cerebrospinal fluid (CSF) analytes, amyloid uptakes from positron emission tomography (PET), structural outcomes from magnetic resonance imaging (MRI), and cognition, have not been compared between Blacks and Whites.A total of 179 Blacks and 1180 Whites who were cognitively normal at baseline and had longitudinal data from at least one biomarker modality were analyzed for the annual rates of change.CSF amyloid beta (Aβ)42/Aβ40 declined more slowly (P = .0390), and amyloid (PET) accumulated more slowly (P = .0157), in Blacks than Whites. CSF Aβ42 changed in opposite directions over time between Blacks and Whites (P = .0039). The annual increase in CSF total tau and phosphorylated tau181 for Blacks was about half of that for Whites.Longitudinal racial differences in amyloid biomarkers are observed. It will be important to comprehensively and prospectively examine the effects of apolipoprotein E genotype and sociocultural factors on these differences.

ObjectivesThere is much interest in utilizing clinical data for developing prediction models for Alzheimer’s disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured electronic health record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR.Materials and MethodsWe developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by 2 clinical dementia experts for AD-related clinical phenotypes including medical comorbidities, biomarkers, neurobehavioral test scores, behavioral indicators of cognitive decline, family history, and neuroimaging findings.ResultsDocumentation rates for each phenotype varied in the structured versus unstructured EHR. Interannotator agreement was high (Cohen’s kappa = 0.72–1) and positively correlated with the NLP-based phenotype extraction pipeline’s performance (average F1-score = 0.65–0.99) for each phenotype.DiscussionWe developed an automated NLP-based pipeline to extract informative phenotypes that may improve the performance of eventual machine learning predictive models for AD. In the process, we examined documentation practices for each phenotype relevant to the care of AD patients and identified factors for success.ConclusionSuccess of our NLP-based phenotype extraction pipeline depended on domain-specific knowledge and focus on a specific clinical domain instead of maximizing generalizability.

Background:Estimates of ‘brain-predicted age’ quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored.Methods:We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A−) participants (18–89 years old). In independent samples of 144 CN/A−, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite.Results:All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A−. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance.Conclusions:Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences.Funding:This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer’s Association (SG-20-690363-DIAN).

Although theAPOEε4 allele is the strongest genetic risk factor for sporadic Alzheimer’s disease (AD), the relationship between apolipoprotein (apoE) and AD pathophysiology is not yet fully understood. Relatively little is known about the apoE protein species, including post-translational modifications, that exist in the human periphery and CNS. To better understand these apoE species, we developed a LC-MS/MS assay that simultaneously quantifies both unmodified and O-glycosylated apoE peptides. The study cohort included 47 older individuals (age 75.6 ± 5.7 years [mean ± standard deviation]), including 23 individuals (49%) with cognitive impairment. Paired plasma and cerebrospinal fluid samples underwent analysis. We quantified O-glycosylation of two apoE protein residues – one in the hinge region and one in the C-terminal region – and found that glycosylation occupancy of the hinge region in the plasma was significantly correlated with plasma total apoE levels,APOEgenotype and amyloid status as determined by CSF Aβ42/Aβ40. A model with plasma glycosylation occupancy, plasma total apoE concentration, andAPOEgenotype distinguished amyloid status with an AUROC of 0.89. These results suggest that plasma apoE glycosylation levels could be a marker of brain amyloidosis, and that apoE glycosylation may play a role in the pathophysiology of AD.HighlightsSimultaneous quantification of unmodified and O-glycosylated apoE via LC-MS/MS.Total plasma apoE varies byAPOEgenotype in an isoform, dose-dependent fashion.CNS-derived apoE is more extensively O-glycosylated than peripherally derived apoE.Hinge region glycosylation occupancy of plasma apoE is associated with amyloidosis.

18F-flortaucipir-PET received FDA approval to visualize tauopathy in the brains of adult patients with cognitive impairment being evaluated for Alzheimer disease (AD). However, manufacturer’s guidelines for the visual interpretation of18F-flortaucipir-PET differs greatly from how18F-flortaucipir-PET has been measured in research settings using standardized uptake value ratios (SUVRs). How visual interpretation relates to18F-flortaucipir-PET SUVR, CSF biomarkers, or longitudinal clinical assessment is not well understood. Here we compare these various diagnostic methods in participants enrolled in studies of aging and memory (n=189, of whom 23 were cognitively impaired). Visual interpretation had high agreement with SUVR (98.4%); discordant participants had hemorrhagic infarcts or atypical AD tauopathies. Visual interpretation had moderate agreement with CSF p-tau181 (86.1%). Two participants demonstrated18F-flortaucipir uptake from meningiomas. Visual interpretation could not predict follow-up clinical assessment in 9.52% of cases. We conclude that close association between AD tauopathy and clinical onset in group-level studies does not always hold at the individual level, with discrepancies arising from atypical AD, vascular dementia, or frontotemporal dementia. A better understanding of relationships across imaging, CSF biomarkers, and clinical assessment is needed to provide appropriate diagnoses for these individuals.

The apolipoprotein E ( APOE ) ε4 allele is strongly linked with cerebral β-amyloidosis, but its relationship with tauopathy is less established. We investigated the relationship between APOE ε4 carrier status, regional amyloid-β (Aβ), magnetic resonance imaging (MRI) volumetrics, tau positron emission tomography (PET), APOE messenger RNA (mRNA) expression maps, and cerebrospinal fluid phosphorylated tau (CSF ptau 181 ). Three hundred fifty participants underwent imaging, and 270 had ptau 181 . We used computational models to evaluate the main effect of APOE ε4 carrier status on regional neuroimaging values and then the interaction of ε4 status and global Aβ on regional tau PET and brain volumes as well as CSF ptau 181 . Separately, we also examined the additional interactive influence of sex. We found that, for the same degree of Aβ burden, APOE ε4 carriers showed greater tau PET signal relative to noncarriers in temporal regions, but no interaction was present for MRI volumes or CSF ptau 181 . This potentiation of tau aggregation irrespective of sex occurred in brain regions with high APOE mRNA expression, suggesting local vulnerabilities to tauopathy. There were greater effects of APOE genotype in females, although the interactive sex effects did not strongly mirror mRNA expression. Pathology is not homogeneously expressed throughout the brain but mirrors underlying biological patterns such as gene expression.

Background and ObjectivesTo predict when cognitively normal individuals with brain amyloidosis will develop symptoms of Alzheimer disease (AD).MethodsBrain amyloid burden was measured by amyloid PET with Pittsburgh compound B. The mean cortical standardized uptake value ratio (SUVR) was transformed into a timescale with the use of longitudinal data.ResultsAmyloid accumulation was evaluated in 236 individuals who underwent >1 amyloid PET scan. The average age was 66.5 ± 9.2 years, and 12 individuals (5%) had cognitive impairment at their baseline amyloid PET scan. A tipping point in amyloid accumulation was identified at a low level of amyloid burden (SUVR 1.2), after which nearly all individuals accumulated amyloid at a relatively consistent rate until reaching a high level of amyloid burden (SUVR 3.0). The average time between levels of amyloid burden was used to estimate the age at which an individual reached SUVR 1.2. Longitudinal clinical diagnoses for 180 individuals were aligned by the estimated age at SUVR 1.2. In the 22 individuals who progressed from cognitively normal to a typical AD dementia syndrome, the estimated age at which an individual reached SUVR 1.2 predicted the age at symptom onset (R2 = 0.54, p < 0.0001, root mean square error [RMSE] 4.5 years); the model was more accurate after exclusion of 3 likely misdiagnoses (R2 = 0.84, p < 0.0001, RMSE 2.8 years).ConclusionThe age at symptom onset in sporadic AD is strongly correlated with the age at which an individual reaches a tipping point in amyloid accumulation.

INTRODUCTION: Apolipoprotein E (APOE) ε4 allele status is associated with amyloid and tau-related pathological changes related to Alzheimer’s disease (AD). However, it is unknown whether brain network changes are related to Aβ and/or tau-related pathology in cognitively normal APOE ε4 carriers with sub-threshold amyloid (Aβ) accumulation. METHODS: Resting state functional connectivity (rs-fc) measures of network integrity were evaluated in cognitively normal individuals (n=121, mean age 76.6 ± 7.8 years, 15% APOE ε4 carriers, 65% female) with minimal Aβ per cerebrospinal fluid (CSF) or amyloid positron emission tomography (PET). RESULTS: APOE ε4 carriers had increased lateralized connections relative to callosal connections within the default-mode, memory, and salience networks (p = 0.02), with significant weighting on linear regression towards CSF total tau (p = 0.03) and CSF phosphorylated tau at codon 181 (p = 0.03), but not CSF Aβ(42). DISCUSSION: Cognitively normal APOE ε4 carriers with sub-threshold amyloid accumulation may have network reorganization associated with tau.

Background: Obesity is related to quantitative neuroimaging abnormalities including reduced gray matter volumes and impaired white matter microstructural integrity, although the underlying mechanisms are not well understood. Objective: We assessed influence of obesity on neuroinflammation imaging that may mediate brain morphometric changes. Establishing the role of neuroinflammation in obesity will enhance understanding of this modifiable disorder as a risk factor for Alzheimer’s disease (AD) dementia. Methods: We analyzed brain MRIs from 104 cognitively normal participants (CDR = 0) and biomarker negativity for CSF amyloid or tau. We classified body mass index (BMI) as normal (BMI

This article discusses how fluid biomarkers can augment the routine dementia evaluation and improve diagnostic accuracy. The tests that are currently available and the indications for their use are described. Further, tests that are under development and likely to be used in the future are identified.Technical improvements in assay sensitivity and precision have led to the rapid development of blood-based biomarkers for Alzheimer disease (AD) over the past several years. Studies have found that the ratio of amyloid-β (Aβ) peptides (Aβ42/Aβ40) and concentrations of phosphorylated tau isoforms in plasma can identify individuals with AD brain pathology. Blood-based tests may enable much broader use of AD biomarkers in the evaluation of patients with cognitive impairment.Even after a detailed history, examination, routine laboratory testing, and brain imaging, the cause of dementia sometimes remains unclear. CSF and blood-based biomarkers can evaluate for a range of neurologic disorders that are associated with dementia, including AD. Integrating data from fluid biomarker tests and the routine dementia evaluation may improve the accuracy of dementia diagnosis.

OBJECTIVES African Americans are at greater risk for developing Alzheimer's disease (AD) dementia than non-Hispanic whites. In addition to biological considerations (eg, genetic influences and comorbid disorders), social and environmental factors may increase the risk of AD dementia. This paper (1) assesses neuroimaging biomarkers of amyloid (A), tau (T), and neurodegeneration (N) for potential racial differences and (2) considers mediating effects of socioeconomic status (SES) and measures of small vessel and cardiovascular disease on observed race differences. METHODS Imaging measures of AT(N) (amyloid and tau positron emission tomography [PET]) structural magnetic resonance imaging (MRI), and resting state functional connectivity (rs-fc) were collected from African American (n = 131) and white (n = 685) cognitively normal participants age 45 years and older. Measures of small vessel and cardiovascular disease (white matter hyperintensities [WMHs] on MRI, blood pressure, and body mass index [BMI]) and area-based SES were included in mediation analyses. RESULTS Compared to white participants, African American participants had greater neurodegeneration, as measured by decreased cortical volumes (Cohen's f2 = 0.05, p

Background Cerebrospinal fluid tau and neurofilament light (NfL) are two biomarkers of neurodegeneration in Alzheimer's disease. Previous reports have shown that the influence of tau on cognitive decline depends on levels of amyloid burden whereas NfL predicts decline independently of amyloid. Most studies use a global cognitive composite as the primary outcome, and it is unknown if critical cognitive domain scores are similarly sensitive to rates of decline due to neurodegeneration. Objective To examine the unique contribution of amyloid, tau, and NfL to rates of cognitive decline in multiple cognitive composites in a cognitively healthy, middle-aged to older adult cohort. Methods A total of 255 participants (55% female; mean age = 66.2 years, range = 42.5-86.7 years) completed CSF studies and serial cognitive assessments to measure global cognition, episodic memory, and attentional control. Linear mixed effects models were used to examine rates of change on each composite score as a function of baseline biomarker levels. Results Total tau predicted decline in attention regardless of amyloid status, but the relationship to global cognition and episodic memory was dependent on amyloid, replicating prior literature. NfL predicted decline in attention and global cognition, but not memory, and this effect was independent of amyloid status. Conclusions These findings suggest that NfL can be used to monitor cognitive decline in aging and Alzheimer's disease and that an attentional control composite may be a better outcome for tracking general neurodegenerative effects on cognition.

BACKGROUND: Changes in resting state functional connectivity (rs-fc) occur in Alzheimer disease (AD), but few longitudinal rs-fc studies have been performed. Most studies focus on single networks and not a global measure of rs-fc. Although the amyloid tau neurodegeneration (AT(N)) framework is increasingly utilized by the AD community, few studies investigated when global rs-fc signature changes occur within this model. OBJECTIVES: 1) Identify a global rs-fc signature that differentiates cognitively normal (CN) individuals from symptomatic Alzheimer Disease (AD). 2) Assess when longitudinal changes in rs-fc occur relative to conversion to symptomatic AD. 3) Compare rs-fc with amyloid, tau and neurodegeneration biomarkers. METHODS: A global rs-fc signature composed of intra-network connections was longitudinally evaluated in a cohort of cognitively normal participants at baseline (n = 335). Biomarkers, including cerebrospinal fluid (Aβ(42) and tau), structural magnetic resonance imaging, and positron emission tomography were obtained. RESULTS: Global rs-fc signature distinguished CN individuals from individuals who developed symptomatic AD. Changes occurred nearly four years before conversion to symptomatic AD. The global rs-fc signature most strongly correlated with markers of neurodegeneration. CONCLUSIONS: The global rs-fc signature changes near symptomatic onset and is likely a neurodegenerative biomarker. Rs-fc changes could serve as a biomarker for evaluating potential therapies for symptomatic conversion to AD.

Alzheimer’s disease (AD) pathology accumulates for decades before the onset of cognitive decline. Cognitively normal individuals with biomarker evidence of AD brain pathology (i.e. biomarker + or preclinical AD) can be differentiated from individuals without AD brain pathology based on naturalistic driving data, such as hard acceleration or braking and speeding, measured using in-vehicle dataloggers. Older adults are at increased risk of injury and death from motor vehicle crashes and driving cessation is also linked to negative health outcomes. Identifying potentially modifiable risk factors that increase driving risk may prolong safe driving in old age. Sleep apnea is associated with adverse driving behaviors across the age span. In this study, we hypothesized that high-risk driving behaviors would be associated with increased sleep apnea severity and AD pathology. We found that higher sleep apnea severity measured by a home sleep apnea test was associated with a higher incidence of adverse driving behaviors even after controlling for multiple confounders (β = 0.24 ± 0.09, p