Alzheimer's Disease Research Center Research Articles

Longitudinal normative standards for cognitive tests and composites using harmonized data from two Wisconsin AD-risk-enriched cohorts.

Published norms are typically cross-sectional and often are not sensitive to preclinical cognitive changes due to dementia. We developed and validated demographically adjusted cross-sectional and longitudinal normative standards using harmonized outcomes from two Alzheimer's disease (AD) risk-enriched cohorts. Data from the Wisconsin Registry for Alzheimer's Prevention and the Wisconsin Alzheimer's Disease Research Center were combined. Quantile regression was used to develop unconditional (cross-sectional) and conditional (longitudinal) normative standards for 18 outcomes using data from cognitively unimpaired participants (N=1390; mean follow-up=9.25 years). Validity analyses (N=2456) examined relationships between percentile scores (centiles), consensus-based cognitive statuses, and AD biomarker levels. Unconditional and conditional centiles were lower in those with consensus-based impairment or biomarker positivity. Similarly, quantitative biomarker levels were higher in those whose centiles suggested decline. This study presents normative standards for cognitive measures sensitive to pre-clinical changes. Future directions will investigate potential clinical applications of longitudinal normative standards. Quantile regression was used to construct longitudinal norms for cognitive tests. Poorer percentile scores were related to concurrent diagnosis and Alzheimer's disease biomarkers. A ShinyApp was built to display test scores and norms and flag low performance.

Multi-shell diffusion MRI of the fornix as a biomarker for cognition in Alzheimer's disease

Background and purposeA substantial fraction of those who had Alzheimer's Disease (AD) pathology on autopsy did not have dementia in life. While biomarkers for AD pathology are well-developed, biomarkers specific to cognitive domains affected by early AD are lagging. Diffusion MRI (dMRI) of the fornix is a candidate biomarker for early AD-related cognitive changes but is susceptible to bias due to partial volume averaging (PVA) with cerebrospinal fluid. The purpose of this work is to leverage multi-shell dMRI to correct for PVA and to evaluate PVA-corrected dMRI measures in fornix as a biomarker for cognition in AD. MethodsThirty-three participants in the Cleveland Alzheimer's Disease Research Center (CADRC) (19 with normal cognition (NC), 10 with mild cognitive impairment (MCI), 4 with dementia due to AD) were enrolled in this study. Multi-shell dMRI was acquired, and voxelwise fits were performed with two models: 1) diffusion tensor imaging (DTI) that was corrected for PVA and 2) neurite orientation dispersion and density imaging (NODDI). Values of tissue integrity in fornix were correlated with neuropsychological scores taken from the Uniform Data Set (UDS), including the UDS Global Composite 5 score (UDSGC5). ResultsStatistically significant correlations were found between the UDSGC5 and PVA-corrected measure of mean diffusivity (MDc, r = −0.35, p < 0.05) from DTI and the intracelluar volume fraction (ficvf, r = 0.37, p < 0.04) from NODDI. A sensitivity analysis showed that the relationship to MDc was driven by episodic memory, which is often affected early in AD, and language. ConclusionThis cross-sectional study suggests that multi-shell dMRI of the fornix that has been corrected for PVA is a potential biomarker for early cognitive domain changes in AD. A longitudinal study will be necessary to determine if the imaging measure can predict cognitive decline.

Disease staging of Alzheimer’s disease using a CSF-based biomarker model

Biological staging of individuals with Alzheimer’s disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aβ42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF stages (0–5) demonstrated a correlation with increased abnormalities in other AD-related biomarkers, such as Aβ-PET and tau-PET, and aligned with longitudinal biomarker changes reflective of AD progression. Higher CSF stages at baseline were associated with an elevated hazard ratio of clinical decline. This study highlights a common molecular pathway underlying AD pathophysiology across all patients, suggesting that a single CSF collection can accurately indicate the presence of AD pathologies and characterize the stage of disease progression. The proposed staging model has implications for enhancing diagnostic and prognostic assessments in both clinical practice and the design of clinical trials.

Deriving life-course residential histories in brain bank cohorts: A feasibility study.

The exposome is theorized to interact with biological mechanisms to influence risk for Alzheimer's disease but is not well-integrated into existing Alzheimer's Disease Research Center (ADRC) brain bank data collection. We apply public data tracing, an iterative, dual abstraction and validation process rooted in rigorous historic archival methods, to develop life-course residential histories for 1254 ADRC decedents. The median percentage of the life course with an address is 78.1% (IQR 24.9); 56.5% of the sample has an address for at least 75% of their life course. Archivists had 89.7% agreement at the address level. This method matched current residential survey methodology 97.4% on average. This novel method demonstrates feasibility, reproducibility, and rigor for historic data collection. To our knowledge, this is the first study to show that public data tracing methods for brain bank decedent residential history development can be used to better integrate the social exposome with biobank specimens. Public data tracing compares favorably to survey-based residential history. Public data tracing is feasible and reproducible between archivists. Archivists achieved 89.7% agreement at the address level. This method identifies residences for nearly 80% of life-years, on average. This novel method enables brain banks to add social characterizations.

Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits.

There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies, which motivates the use of unsupervised learning. A mixture of regressions is an unsupervised method that can simultaneously identify clusters from multiple biomarkers while learning within-cluster demographic effects. Cerebrospinal fluid (CSF) biomarkers for AD have detection limits, which create additional challenges. We apply a mixture of regressions with a multivariate truncated Gaussian distribution (also called a censored multivariate Gaussian mixture of regressions or a mixture of multivariate tobit regressions) to over 3,000 participants from the Emory Goizueta Alzheimer's Disease Research Center and Emory Healthy Brain Study to examine amyloid-beta peptide 1-42 (Abeta42), total tau protein and phosphorylated tau protein in CSF with known detection limits. We address three gaps in the literature on mixture of regressions with a truncated multivariate Gaussian distribution: software availability; inference; and clustering accuracy. We discovered three clusters that tend to align with an AD group, a normal control profile and non-AD pathology. The CSF profiles differed by race, gender and the genetic marker ApoE4, highlighting the importance of considering demographic factors in unsupervised learning with detection limits. Notably, African American participants in the AD-like group had significantly lower tau burden.

Comparison of cerebrospinal fluid, plasma and neuroimaging biomarker utility in Alzheimer's disease.

Alzheimer's disease biomarkers are crucial to understanding disease pathophysiology, aiding accurate diagnosis and identifying target treatments. Although the number of biomarkers continues to grow, the relative utility and uniqueness of each is poorly understood as prior work has typically calculated serial pairwise relationships on only a handful of markers at a time. The present study assessed the cross-sectional relationships among 27 Alzheimer's disease biomarkers simultaneously and determined their ability to predict meaningful clinical outcomes using machine learning. Data were obtained from 527 community-dwelling volunteers enrolled in studies at the Charles F. and Joanne Knight Alzheimer Disease Research Center at Washington University in St Louis. We used hierarchical clustering to group 27 imaging, CSF and plasma measures of amyloid beta, tau [phosphorylated tau (p-tau), total tau t-tau)], neuronal injury and inflammation drawn from MRI, PET, mass-spectrometry assays and immunoassays. Neuropsychological and genetic measures were also included. Random forest-based feature selection identified the strongest predictors of amyloid PET positivity across the entire cohort. Models also predicted cognitive impairment across the entire cohort and in amyloid PET-positive individuals. Four clusters emerged reflecting: core Alzheimer's disease pathology (amyloid and tau), neurodegeneration, AT8 antibody-associated phosphorylated tau sites and neuronal dysfunction. In the entire cohort, CSF p-tau181/Aβ40lumi and Aβ42/Aβ40lumi and mass spectrometry measurements for CSF pT217/T217, pT111/T111, pT231/T231 were the strongest predictors of amyloid PET status. Given their ability to denote individuals on an Alzheimer's disease pathological trajectory, these same markers (CSF pT217/T217, pT111/T111, p-tau/Aβ40lumi and t-tau/Aβ40lumi) were largely the best predictors of worse cognition in the entire cohort. When restricting analyses to amyloid-positive individuals, the strongest predictors of impaired cognition were tau PET, CSF t-tau/Aβ40lumi, p-tau181/Aβ40lumi, CSF pT217/217 and pT205/T205. Non-specific CSF measures of neuronal dysfunction and inflammation were poor predictors of amyloid PET and cognitive status. The current work utilized machine learning to understand the interrelationship structure and utility of a large number of biomarkers. The results demonstrate that, although the number of biomarkers has rapidly expanded, many are interrelated and few strongly predict clinical outcomes. Examining the entire corpus of available biomarkers simultaneously provides a meaningful framework to understand Alzheimer's disease pathobiological change as well as insight into which biomarkers may be most useful in Alzheimer's disease clinical practice and trials.

Highly accurate blood test for Alzheimer’s disease is similar or superior to clinical cerebrospinal fluid tests

With the emergence of Alzheimer’s disease (AD) disease-modifying therapies, identifying patients who could benefit from these treatments becomes critical. In this study, we evaluated whether a precise blood test could perform as well as established cerebrospinal fluid (CSF) tests in detecting amyloid-β (Aβ) plaques and tau tangles. Plasma %p-tau217 (ratio of phosporylated-tau217 to non-phosphorylated tau) was analyzed by mass spectrometry in the Swedish BioFINDER-2 cohort (n = 1,422) and the US Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) cohort (n = 337). Matched CSF samples were analyzed with clinically used and FDA-approved automated immunoassays for Aβ42/40 and p-tau181/Aβ42. The primary and secondary outcomes were detection of brain Aβ or tau pathology, respectively, using positron emission tomography (PET) imaging as the reference standard. Main analyses were focused on individuals with cognitive impairment (mild cognitive impairment and mild dementia), which is the target population for available disease-modifying treatments. Plasma %p-tau217 was clinically equivalent to FDA-approved CSF tests in classifying Aβ PET status, with an area under the curve (AUC) for both between 0.95 and 0.97. Plasma %p-tau217 was generally superior to CSF tests in classification of tau-PET with AUCs of 0.95–0.98. In cognitively impaired subcohorts (BioFINDER-2: n = 720; Knight ADRC: n = 50), plasma %p-tau217 had an accuracy, a positive predictive value and a negative predictive value of 89–90% for Aβ PET and 87–88% for tau PET status, which was clinically equivalent to CSF tests, further improving to 95% using a two-cutoffs approach. Blood plasma %p-tau217 demonstrated performance that was clinically equivalent or superior to clinically used FDA-approved CSF tests in the detection of AD pathology. Use of high-performance blood tests in clinical practice can improve access to accurate AD diagnosis and AD-specific treatments.

Association of PM2.5 Exposure and Alzheimer Disease Pathology in Brain Bank Donors-Effect Modification by APOE Genotype.

Fine particulate matter (PM2.5) exposure has been found to be associated with Alzheimer disease (AD) and is hypothesized to cause inflammation and oxidative stress in the brain, contributing to neuropathology. The APOE gene, a major genetic risk factor of AD, has been hypothesized to modify the association between PM2.5 and AD. However, little prior research exists to support these hypotheses. This study investigates the association between traffic-related PM2.5 and AD hallmark pathology, including effect modification by APOE genotype, in an autopsy cohort. A cross-sectional study was conducted using brain tissue donors enrolled in the Emory Goizueta AD Research Center who died before 2020 (n = 224). Donors were assessed for AD pathology including the Braak stage, Consortium to Establish a Registry for AD (CERAD) score, and combined AD neuropathologic change (ABC) score. Traffic-related PM2.5 concentrations were modeled for the metro-Atlanta area during 2002-2019 with a spatial resolution of 200-250 m. One-year, 3-year, and 5-year average PM2.5 concentrations before death were matched to participants' home address. We assessed the association between traffic-related PM2.5 and AD hallmark pathology and effect modification by APOE genotype, using adjusted ordinal logistic regression models. Among the 224 participants, the mean age of death was 76 years, and 57% had at least 1 APOE ε4 copy. Traffic-related PM2.5 was significantly associated with the CERAD score for the 1-year exposure window (odds ratio [OR] 1.92; 95% CI 1.12-3.30) and the 3-year exposure window (OR 1.87; 95% CI 1.01-3.17). PM2.5 was also associated with higher Braak stage and ABC score albeit nonsignificantly. The strongest associations between PM2.5 and neuropathology markers were among those without APOE ε4 alleles (e.g., for the CERAD score and 1-year exposure window, OR 2.31; 95% CI 1.36-3.94), though interaction between PM2.5 and APOE genotype was not statistically significant. Our study found traffic-related PM2.5 exposure was associated with the CERAD score in an autopsy cohort, contributing to epidemiologic evidence that PM2.5 affects β-amyloid deposition in the brain. This association was particularly strong among donors without APOE ε4 alleles. Future studies should further investigate the biological mechanisms behind this association.

Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates.

Although the growth of digital tools for cognitive health assessment, there's a lack of known reference values and clinical implications for these digital methods. This study aims to establish reference values for digital neuropsychological measures obtained through the smartphone-based cognitive assessment application, Defense Automated Neurocognitive Assessment (DANA), and to identify clinical risk factors associated with these measures. The sample included 932 cognitively intact participants from the Framingham Heart Study, who completed at least one DANA task. Participants were stratified into subgroups based on sex and three age groups. Reference values were established for digital cognitive assessments within each age group, divided by sex, at the 2.5th, 25th, 50th, 75th, and 97.5th percentile thresholds. To validate these values, 57 cognitively intact participants from Boston University Alzheimer's Disease Research Center were included. Associations between 19 clinical risk factors and these digital neuropsychological measures were examined by a backward elimination strategy. Age- and sex-specific reference values were generated for three DANA tasks. Participants below 60 had median response times for the Go-No-Go task of 796 ms (men) and 823 ms (women), with age-related increases in both sexes. Validation cohort results mostly aligned with these references. Different tasks showed unique clinical correlations. For instance, response time in the Code Substitution task correlated positively with total cholesterol and diabetes, but negatively with high-density lipoprotein and low-density lipoprotein cholesterol levels, and triglycerides. This study established and validated reference values for digital neuropsychological measures of DANA in cognitively intact white participants, potentially improving their use in future clinical studies and practice.

INviting Veterans InTo Enrollment in Alzheimer's Disease Research Centers (INVITE-ADRC): An NIA and VA-sponsored initiative to increase veteran participation in aging and dementia research.

Older military veterans often present with unique and complex risk factors for Alzheimer's disease (AD) and related dementias. Increasing veteran participation in research studies offers one avenue to advance the field and improve health outcomes. To this end, the National Institute on Aging (NIA) and Department of Veterans Affairs (VA) partnered to build infrastructure, improve collaboration, and intensify targeted recruitment of veterans. This initiative, INviting Veterans InTo Enrollment in Alzheimer's Disease Research Centers (INVITE-ADRC), provided funding for five sites and cross-site organizing structure. Diverse and innovative recruitment strategies were used. Across five sites, 172 veterans entered registries, and 99 were enrolled into ADRC studies. Of the enrolled, 39 were veterans from historically underrepresented racial and ethnic groups. This initiative laid the groundwork to establish sustainable relationships between the VA and ADRCs. The partnership between both federal agencies demonstrates how mutual interests can accelerate progress. In turn, efforts can help our aging veterans.

COVID-19 pandemic's relationship with enrollment at US Alzheimer's Disease Research Centers.

We aimed to characterize the COVID-19 pandemic's relationship with enrollment in US Alzheimer's Disease Research Centers (ADRCs). Using data on 10,105 participants from 30 ADRCs, we conducted interrupted time series analyses to assess the relationship of the pandemic with enrollment and calculate projected dates of enrollment recovery. Participants enrolled during the pandemic (vs pre-pandemic) were more likely to have dementia and be referred by health professionals. The pandemic was associated with a 77% drop in enrollment, with projected trend recovery in March 2024 and 100% recovery in September 2024. COVID was associated with a 91% drop in Black/African American participants, compared to 71% in White participants. Enrollment of both Hispanic and female participants was declining 1.4% and 0.3%/month pre-pandemic. Funders and researchers should account for ongoing COVID-19 impact on ADRD research enrollment. Strategies to speed enrollment recovery are needed, especially for Black/African American and Hispanic groups. Tested COVID pandemic association with enrollment at Alzheimer's Disease Research Centers. During versus pre-pandemic enrollees differed on demographic and clinical variables. Interrupted time series analyses: immediate 77% drop in enrollment related to COVID. Recovery projections: trend recovery in March 2024, 100% recovery in September 2024. Enrollment of African American and Hispanic participants should be prioritized.

Pathologic correlates of aging-related tau astrogliopathy: ARTAG is associated with LATE-NC and cerebrovascular pathologies, but not with ADNC

Age-related tau astrogliopathy (ARTAG) is detectable in the brains of over one-third of autopsied persons beyond age 80, but the pathoetiology of ARTAG is poorly understood. Insights can be gained by analyzing risk factors and comorbid pathologies. Here we addressed the question of which prevalent co-pathologies are observed with increased frequency in brains with ARTAG. The study sample was the National Alzheimer's Coordinating Center (NACC) data set, derived from multiple Alzheimer's disease research centers (ADRCs) in the United States. Data from persons with unusual conditions (e.g. frontotemporal dementia) were excluded leaving 504 individual autopsied research participants, clustering from 20 different ADRCs, autopsied since 2020; ARTAG was reported in 222 (44.0%) of included participants. As has been shown previously, ARTAG was increasingly frequent with older age and in males. The presence and severity of other common subtypes of pathology that were previously linked to dementia were analyzed, stratifying for the presence of ARTAG. In logistical regression-based statistical models that included age and sex as covariates, ARTAG was relatively more likely to be found in brains with limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), and in brains with comorbid cerebrovascular pathology (arteriolosclerosis and/or brain infarcts). However, ARTAG was not associated with severe Alzheimer's disease neuropathologic change (ADNC), or primary age-related tauopathy (PART). In a subset analysis of 167 participants with neurocognitive testing data, there was a marginal trend for ARTAG pathology to be associated with cognitive impairment as assessed with MMSE scores (P = 0.07, adjusting for age, sex, interval between final clinic visit and death, and ADNC severity). A limitation of the study was that there were missing data about ARTAG pathologies, with incomplete operationalization of ARTAG according to anatomic region and pathologic subtypes (e.g., thorn-shaped or granular-fuzzy astrocytes). In summary, ARTAG was not associated with ADNC, whereas prior observations about ARTAG occurring with increased frequency in aging, males, and brains with LATE-NC were replicated. It remains to be determined whether the increased frequency of ARTAG in brains with comorbid cerebrovascular pathology is related to local infarctions or neuroinflammatory signaling, or with some other set of correlated factors including blood-brain barrier dysfunction.

Disparate Dementia Risk Factors Are Associated with Cognitive Impairment and Rates of Decline in African Americans.

This study was undertaken to evaluate the frequency of modifiable dementia risk factors and their association with cognitive impairment and rate of decline in diverse participants engaged in studies of memory and aging. Modifiable dementia risk factors and their associations with cognitive impairment and cognitive decline were determined in community-dwelling African American (AA; n = 261) and non-Hispanic White (nHW; n = 193) participants who completed ≥2 visits at the Mayo Clinic Alzheimer Disease Research Center in Jacksonville, Florida. Risk factors and their associations with cognitive impairment (global Clinical Dementia Rating [CDR] ≥ 0.5) and rates of decline (CDR Sum of Boxes) in impaired participants were compared in AA and nHW participants, controlling for demographics, APOE ɛ4 status, and Area Deprivation Index. Hypertension, hypercholesterolemia, obesity, and diabetes were overrepresented in AA participants, but were not associated with cognitive impairment. Depression was associated with increased odds of cognitive impairment in AA (odds ratio [OR] = 4.30, 95% confidence interval [CI] = 2.13-8.67) and nHW participants (OR = 2.79, 95% CI = 1.21-6.44) but uniquely associated with faster decline in AA participants (β = 1.71, 95% CI = 0.69-2.73, p = 0.001). Fewer AA participants reported antidepressant use (9/49, 18%) than nHW counterparts (57/78, 73%, p < 0.001). Vitamin B12 deficiency was also associated with an increased rate of cognitive decline in AA participants (β = 2.65, 95% CI = 0.38-4.91, p = 0.023). Modifiable dementia risk factors are common in AA and nHW participants, representing important risk mitigation targets. Depression was associated with dementia in AA and nHW participants, and with accelerated declines in cognitive function in AA participants. Optimizing depression screening and treatment may improve cognitive trajectories and outcomes in AA participants. ANN NEUROL 2024;95:518-529.

Lessons From the Coronavirus Disease Experience: Research Participant and Staff Satisfaction With Remote Cognitive Evaluations.

In New York City in 2020 the pandemic shut down in-person research. Icahn School of Medicine's Alzheimer's Disease Research Center transitioned longitudinal evaluations from in-person to telephone to enhance equity of access. We assessed diverse research participants' and clinical research coordinators' (CRC) satisfaction with remote evaluation and examined sociodemographic, cognitive, and behavioral factors that might impact satisfaction. Data collected: 241 participants with Clinical Dementia Rating (CDR) = 0/0.5 (3/2020 to 6/2021). A Telehealth Satisfaction Questionnaire for CRCs and participants was administered at the end of remote evaluations. We compared Telehealth Satisfaction Questionnaire items by CDR and Geriatric Depression Scale. Participants' mean age was 78.4, 61.4% were females, 16.2% were Hispanic, 17.1% Asian, 15.8% were non-Hispanic black, and 72.6% CDR = 0. Participant satisfaction was high [14.1 ± 1.4 (out of 15)] but was lower among those with depression. CRC satisfaction was high [16.9 ± 1.8 (out of 18)] but was lower concerning the ability to explain the test battery and interact with participants with CDR = 0.5. Telephone research assessments provide flexibility in a hybrid model. They offer equitable access to research participation for those who do not use computer technology and may promote the retention of diverse elderly research participants.

A novel neuroimaging signature for ADRD risk stratification in the community.

Early risk stratification for clinical dementia could lead to preventive therapies. We identified and validated a magnetic resonance imaging (MRI) signature for Alzheimer's disease (AD) and related dementias (ARDR). An MRI ADRD signature was derived from cortical thickness maps in Framingham Heart Study (FHS) participants with AD dementia and matched controls. The signature was related to the risk of ADRD and cognitive function in FHS. Results were replicated in the University of California Davis Alzheimer's Disease Research Center (UCD-ADRC) cohort. Participants in the bottom quartile of the signature had more than three times increased risk for ADRD compared to those in the upper three quartiles (P<0.001). Greater thickness in the signature was related to better general cognition (P<0.01) and episodic memory (P=0.01). Results replicated in UCD-ADRC. We identified a robust neuroimaging biomarker for persons at increased risk of ADRD. Other cohorts will further test the validity of this biomarker.

Retention of American Indian and Alaska Native participants in the National Alzheimer's Coordinating Center Uniform Data Set.

The number of American Indian and Alaska Native (AI/AN) elders is expected to double by 2060. Thus it is imperative to retain AI/AN participants in longitudinal research studies to identify novel risk factors and potential targets for intervention for Alzheimer's disease and related dementias in these communities. The National Alzheimer's Coordinating Center houses uniformly collected longitudinal data from the network of National Institute on Aging (NIA)-funded Alzheimer's Disease Research Centers (ADRCs). We used logistic regression to quantify participant retention at 43 ADRCs, comparing self-identified AI/AN participants to non-Hispanic White (NHW) participants, adjusting for potential confounding factors including baseline diagnosis, age, sex, education, and smoking. The odds of AI/AN participant retention at the first follow-up visit were significantly lower than those for NHW participants (adjusted odds ratio [aOR]: 0.599; 95%: 0.46-0.78; p<0.001). These results suggest the need for improved strategies to retain AI/AN participants, perhaps including improved researcher-community relationships and community engagement and education. American Indian and Alaska Native (AI/AN) research participants were retained to the first follow-up appointment at lower rates than non-Hispanic White (NHW) participants. AI/AN participants are retained at lower rates than NHW participants for long-term follow-up. The majority of AI/AN participants were not retained to the second follow-up visit.

Clinical Manifestations.

Perturbations in immune pathways are strongly associated with Alzheimer's disease. A positive association between autoimmune diseases and frontotemporal dementia has been reported. We explored whether early onset Alzheimer's disease (EOAD) would associate with autoimmune diseases. We compared 313 patients with EOAD from the Mayo Clinic Florida, Alzheimer's Disease Research Center (ADRC) Registry with matched cognitively normal controls (NC) based on age (+/- 2 years) and gender at 1:2 ratio. NC were derived from Mayo Clinic Biobank Project, Rochester (N = 313) and the Mayo Clinic Study of Aging (N = 313). Data abstraction was performed by trained statisticians using ICD 9&10 codes corresponding to 40 specified autoimmune diseases and/or documentation of specified immunomodulatory treatment. Data was also abstracted via retrospective record review. We further reviewed clinical records from EOAD patients with neuropathologically confirmed Alzheimer's disease patients enrolled in the ADRC (n = 10). Each autoimmune disease type was compared between cases and controls using Fisher's exact test. All tests were two-sided with alpha level set at 0.05 for statistical significance. Continuous variables were summarized as median (range) while categorical variables were reported as frequency (percentage). The prevalence of autoimmune disease in the general population was based off Hayter & Cook, 2012 and Roberts & Erdei, 2020. All data abstraction was done blinded to the clinical diagnosis and baseline demographics. Autoimmune disease history was abstracted from 949 patient records (313 EOAD, 10 pathologically-confirmed EOAD, 626 NCI). The median age was 57 years-old. (EOAD range 28,65 yrs.; NC 30,66 yrs.). Autoimmune thyroiditis (p = 0.0008), ulcerative colitis (p = 0.011, psoriasis (<0.0001), rheumatoid arthritis (p = 0.0005), and Raynaud's syndrome (p = 0.0021) were less prevalent in patients with EOAD compared to NC. Furthermore, patients with EOAD had a lower prevalence of immune mediated hematologic disorders. Patients with EOAD have a lower prevalence of autoimmune diseases than NC. Immune activation identified in early onset AD patients may be unrelated to autoimmunity. Findings should be explored in late onset AD patients.

Clinical Manifestations.

Repetitive head impacts (RHI) occur routinely in sports like American football (AmFootball) and are the most well-established risk factor for chronic traumatic encephalopathy, a neurodegenerative tauopathy. However, RHI is not routinely evaluated in older adults with other suspected neurodegenerative diseases. The prevalence of RHI among older adults with neurodegenerative diseases and the influence on clinical symptoms is unknown. RHI was defined by AmFootball participation and analyzed based on frequency of any and "substantial" exposure (> 4 years). The Boston University Head Impact Exposure Assessment was administered to 195 older adult males at the UCSF Memory and Aging Center. Of these 106 (54%) were clinically normal (CN) participants in the Brain Aging Network for Cognitive Health (BRANCH) study and 89 (46%) were cognitively impaired participants from the Alzheimer's Disease Research Center. Diagnostic groups included AD-related phenotypes ("clinAD"; amnestic-predominant MCI/dementia, logopenic PPA; N = 26) and frontotemporal dementia spectrum conditions ("FTD"; behavioral variant FTD, semantic PPA, nonfluent PPA; N = 46). Our aims were: 1) investigate frequency of prior RHI between diagnostic groups (chi square), 2) compare age of symptom onset between participants with and without prior RHI (ANOVA), and 3) evaluate associations between RHI and cognition within the largest single diagnostic group (bvFTD N = 20; ANCOVA adjusted for age and education). Cohen's d effect sizes are reported. Males with clinAD (27%; χ2 = 8.3, p = .009; d = 0.53) and FTD (21%; χ2 = 5.6, p = .02, d = 0.40) were more likely than CN (7%) to have had substantial prior RHI. Prior RHI was associated with younger age of symptom onset (62.1±7.6 vs. 56.7±7.7 years old; p = .001, d = 0.69). This effect was stronger within clinAD (63.1±7.2 vs. 53.3±7.5; d = 1.3) than FTD (60.1±7.0 vs. 55.9±6.0; d = 0.54). In participants with bvFTD, those with prior RHI scored significantly worse on memory testing (w-scores -2.6±1.1 vs. -1.1±1.2, p = .02, d = 1.4). RHI through AmFootball is more common in AD and FTD than healthy controls. Prior RHI may contribute to variability in age of symptom onset in both AD and FTD. Exposure to RHI may also partly explain why some participants with bvFTD, a classically nonamnestic syndrome, develop memory decline. Prior RHI should be routinely evaluated in neurodegenerative disease workups.

Testing bidirectional relationships between functional brain networks and preclinical Alzheimer disease biomarkers

AbstractBackgroundResting‐state functional connectivity (RSFC) provides estimates of brain networks using fMRI. There are conflicting reports regarding the effects of amyloid‐β (Aβ) and tau on RSFC networks in cognitively normal participants. These inconsistencies might be explained by a recently‐proposed theoretical model that Aβ and tau have an interactive effect on RSFC, marked first by hyper‐connectivity in association with Aβ, then by hypo‐connectivity in association with tau. Although there is preliminary support for this model, independent validations and further characterizations of the effects of Aβ and tau on RSFC are needed.MethodWe analyzed RSFC, Aβ PET, and tau PET data from the Knight Alzheimer Disease Research Center (ADRC). All participants were cognitively normal (Clinical Dementia Rating®: 0). RSFC was collected on two scanners in partly‐overlapping samples (PETMR N: 190, TRIO N: 149). RSFC data were processed with framewise censoring and global signal regression. RSFC networks were defined using 298 regions of interest. Aβ was measured with PET PiB and AV‐45 tracers, which were harmonized using a Centiloid conversion. Tau was measured with a PET AV‐1451 tracer and was summarized in pre‐defined regions. Main effect and interaction relationships between Aβ, tau PET, and RSFC were tested with linear regression models, controlling for age, sex, education, and head motion as covariates, with a false discovery rate correction.ResultWe did not observe a bidirectional interaction effect in any of the hypothesized networks (default mode [DMN], salience [SAL], frontoparietal [FPN]). Aβ was positively associated with RSFC, independent of tau, in the DMN and SAL, while tau was negatively associated with RSFC, independent of Aβ, in the SAL only. Exploratory analyses of FC within and between all networks did not identify clear patterns of interactive biomarker effects, nor consistent patterns of main effect relationships within any of the other networks.ConclusionThese results do not support an interactive model of RSFC in preclinical AD. Inconsistencies with previous interactions may be driven by sample‐specific or methodological differences, or the low reliability of FC and brain‐behavior relationships. Prominent biomarker relationships in the DMN and SAL are consistent with regional overlap with Aβ deposition in early AD.

Testing bidirectional relationships between functional brain networks and preclinical Alzheimer disease biomarkers

AbstractBackgroundResting‐state functional connectivity (RSFC) provides estimates of brain networks using fMRI. There are conflicting reports regarding the effects of amyloid‐ß (Aß) and tau on RSFC networks in cognitively normal participants. These inconsistencies might be explained by a recently‐proposed theoretical model that Aß and tau have an interactive effect on RSFC, marked first by hyper‐connectivity in association with Aß, then by hypo‐connectivity in association with tau. Although there is preliminary support for this model, independent validations and further characterizations of the effects of Aß and tau on RSFC are needed.MethodWe analyzed RSFC, Aß PET, and tau PET data from the Knight Alzheimer Disease Research Center (ADRC). All participants were cognitively normal (Clinical Dementia Rating®: 0). RSFC was collected on two scanners in partly‐overlapping samples (PETMR N: 190, TRIO N: 149). RSFC data were processed with framewise censoring and global signal regression. RSFC networks were defined using 298 regions of interest. Aß was measured with PET PiB and AV‐45 tracers, which were harmonized using a Centiloid conversion. Tau was measured with a PET AV‐1451 tracer and was summarized in pre‐defined regions. Main effect and interaction relationships between Aß, tau PET, and RSFC were tested with linear regression models, controlling for age, sex, education, and head motion as covariates, with a false discovery rate correction.ResultWe did not observe a bidirectional interaction effect in any of the hypothesized networks (default mode [DMN], salience [SAL], frontoparietal [FPN]). Aß was positively associated with RSFC, independent of tau, in the DMN and SAL, while tau was negatively associated with RSFC, independent of Aß, in the SAL only. Exploratory analyses of FC within and between all networks did not identify clear patterns of interactive biomarker effects, nor consistent patterns of main effect relationships within any of the other networks.ConclusionThese results do not support an interactive model of RSFC in preclinical AD. Inconsistencies with previous interactions may be driven by sample‐specific or methodological differences, or the low reliability of FC and brain‐behavior relationships. Prominent biomarker relationships in the DMN and SAL are consistent with regional overlap with Aß deposition in early AD.