Citations

AbstractBackgroundThe implications of a positive tau (T) Positron Emission Tomography (PET) with a negative beta amyloid (A) PET are not well understood. We investigated cognitive performance in participants who were positive on tau PET but negative on beta amyloid PET.MethodWe evaluated 98 participants from the Mayo Clinic Study of Aging (MCSA) and the Mayo Clinic Alzheimer’s Disease Research Center (ADRC) who were positive for Flortaucipir (FTP) and negative for PiB PET. Participants were classified as A‐ and T+ based on Standardized Uptake Value Ratios (SUVr) of FTP and PiB PET scans, and were matched 2:1 to amyloid negative and tau negative cognitively unimpaired controls from the MCSA. Cognitive test scores were compared between the combined ADRC/MCSA group and MCSA only group. We also examined clinical diagnoses in our cohort. The most common were Alzheimer’s Disease, mild cognitive impairment (MCI), Dementia with Lewy Bodies (DLB) and Frontotemporal dementia (FTD) amongst others.ResultThere were no significant differences in demographic features or PiB SUVr between groups. There were significant differences in APOE genotype between groups. In the combined ADRC/MCSA group, 25% of A‐T‐ and 13% of A‐T+ were APOE e4 carriers (p = 0.016), and in the MCSA only group, 27% of A‐T‐and 10% of A‐T+ were APOE e4 carriers (p = 0.010). In the combined ADRC/MCSA group, the A‐T+ group demonstrated lower performance than the A‐T‐ group on the Mini Mental Status Exam (MMSE) (p<0.001), Wechsler Memory Scale‐Revised Logical Memory I (p<0.001) and Logical Memory II (p<0.001), Auditory verbal Learning Test Delayed Recall (AVLT) (p = 0.004), Category fluency animals (p = 0.005), Category fluency vegetables (p = 0.021), Trailmaking test A (p<0.001), Trail making test B (p<0.001), Wechsler Adult Intelligence Scale‐IV (WAIS‐IV) (p = 0.002), and Unified Parkinson’s Disease Rating Scale part III (UPDRS III) (p = 0.003). In the MCSA only group, the A‐T+ group had lower scores on Logical Memory II (p = 0.022), AVLT delated recall (p = 0.04), and WAIS‐R block design (p = 0.035) compared to the A‐T‐group.ConclusionPositive tau PET in the absence of beta‐amyloid positivity is associated with lower cognitive performance.

AbstractMayo Clinic Alzheimer’s Disease Research Center (ADRC) has been operational since 1990 supported by the US National Institutes of Health. In addition, in the aging and Alzheimer’s disease (AD) spectrum research, Mayo Clinic has been active in since 1986 with the Mayo Alzheimer’s Disease Patient Registry that evolved to the Mayo Clinic Study of Aging (MCSA) to become a population‐based study of aging and cognitive impairment in 2004.The overall goal of Mayo Clinic ADRC is to provide a structured environment that supports innovative research and enable translation aimed at accelerating the development of effective treatment and prevention for AD and related dementias at all stages of the disease. Our comprehensive ADRC is located in Rochester, Minnesota (MCR), and Jacksonville, Florida (MCF), and shares its operations in all of the Cores between the two geographic sites. ADRC core activities include the Administrative Core, Clinical Core, Data Management and Statistics Core, Neuropathology Core, Neuroimaging Core, Biomarker Core, the Outreach and Education Core (ORE Core), and the Research Education Component. ADRC Cores also interact directly with the MCSA.The theme of the center during the current cycle is “Similarities and Differences Among Neurodegenerative Disorders”. Therefore, in addition to focusing on the recruitment of participants along the AD spectrum, we continue to focus on the recruitment of persons on the frontotemporal lobar degeneration (FTLD) and the Lewy body disorders (LBD) spectra, including the prodromal stages of the neurodegenerative dementias.This focus expands on previous accomplishments of the ADRC across the neurodegenerative disease spectra. The acquisition and development of new biomarkers for the neurodegenerative diseases is a major focus of the Center, and the recruitment of research participants in all three disorders enhance randomized controlled trial studies.The Mayo ADRC interacts with other ADRCs on various collaborative efforts and supports Developmental Projects that utilize ADRC resources with the goal of attracting early‐stage and established investigators into the AD research field.

AbstractBackgroundEthnoculturally diverse individuals from underrepresented populations (URPs), including but not limited to Black/African American and Hispanic/Latinx adults, are disproportionally affected by Alzheimer’s disease (AD) compared to non‐Hispanic/Latinx white (NLW) adults. However, URPs have historically been under‐included in AD studies. The objective of this study is to describe efforts, and the results of those efforts, to increase enrollment and engagement of ethnoculturally diverse individuals from URPs in the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3).MethodsEfforts undertaken by ADNI to increase enrollment and engagement of URPs included (1) launching of a Diversity Taskforce (DVTF) lead by two health disparities experts in July 2020, charged with overseeing ADNI’s mission in this space. Specific DVTF activities included: (2) the establishment, funding, and instrumental support (e.g., training, consultation, resources) of diversity at 13 ADNI3 sites; (3) changing ADNI protocol to facilitate research participation of URPs (e.g., optional lumbar puncture and sharing of amyloid PET results); (4) hiring a marketing agency to launch culturally‐informed digital/social media campaigns for the 13 DVTF Sites, with local websites; (5) development of a database to capture performance metrics of inclusion efforts at the 13 DVTF Sites; and (6) formation of a six‐member External Advisory Board, comprised of leaders in health disparities research, to guide DVTF efforts.ResultURP enrollment at these 13 DVTF sites improved from 1.1 URPs per month previously to >4 URPs per month, a 264% increase. Across all 59 ADNI sites, overall enrollment of ethnoculturally diverse individuals from URPs went from 89 (16.8%) to 126 (21.1%), representing a 25% increase in enrollment between May 2020 and September 2021. Moreover, since February 1st, 2021, ADNI enrolled 43 new participants of which 33 (77%) were from URPs.ConclusionIt is possible to improve the enrollment and retention of URPs in AD research using a multi‐pronged community engaged approach that also provides human capital, infrastructure, and financial resources. The success of the DVTF has led to the formation of a new Engagement Core in ADNI4, tasked with implementing a scaled‐up culturally‐informed, community‐engaged research approach to URP inclusion and retention across all 59 ADNI sites.

AbstractBackgroundMixed model repeated measures (MMRM) is the most common analysis approach in clinical trials for Alzheimer’s disease. The model treats time as a categorical variable, which can be problematic when assessments occur off‐schedule, as including off‐schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. Since MMRM entails a mean parameter for each group and visit, the model can be inefficient for longer trials of Preclinical Alzheimer’s. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time.MethodWe use model selection techniques to compare the predictive value of spline models to slope models, MMRM, and models that assume a proportional treatment effect using data from several completed trials. These trials include the ADCS trial of donepezil in MCI, trials of intranasal insulin and a fyn kinase inhibitor in mild Alzheimer’s dementia, and the A4 trial of solanezumab in Preclinical Alzheimer’s. Simulation studies assess the power and Type I error of these models for trials in Preclinical Alzheimer’s, MCI, and mild dementia.ResultThe spline model with ADAS‐Cog version effect attained the best predictive value for the trials in mild dementia. The proportional model was preferred for the trial in MCI, but the proportional effect assumption was violated, and the treatment effect estimate was in the wrong direction. Considering that the spline with two degrees of freedom and version effect provide fits that are similar to MMRM in Figure 1, we concluded that two degrees of freedom appear to be sufficient to capture enough of the general trend to assess the treatment effect. AIC results from the A4 Study will be considered and included after the study is completed in Spring 2023.ConclusionThe spline model is shown to have better predictive value and precision in real datasets and has better power and Type I error control in a variety of simulation scenarios.

AbstractBackgroundQuantified accelerated aging, or “age gap,” defined as the difference between a person’s estimated age using a biological measurement, such as a brain image, and their true age, has strong associations with multiple neurodegenerative etiologies. As an alternative to imaging‐based measurements, in this study we developed an age prediction model based on gait patterns and used this model to quantify accelerated aging as an indicator of cognitive impairment and predictor of future cognitive decline.MethodA database of 16,917 pressure sensitive walkway exams from 5,307 participants in the Mayo Clinic Study of Aging was used to develop an age estimation model that included two main components. First, a novel dimensionality reduction and feature selection algorithm was used to extract gait features. Second, a regularized linear model was trained using these features to produce a sparse, clinically interpretable estimator. This model was used to calculate a gait age gap metric, which was tested for association with measures of cognitive impairment and future cognitive decline.ResultThe distributions of gait age gap in age‐matched participants defined as having no cognitive impairment (CU, n = 573), mild cognitive impairment (MCI, n = 520), or a clinical diagnosis of dementia due to Alzheimer’s (AD, n = 53) are shown in Fig 1a. A significant shift in the mean is observed between all three groups. Fig 1b. shows the distributions of baseline gait age gap values amongst individuals categorized by cognitive decline: stable CU (n = 644), MCI converters (n = 89), stable MCI (n = 78), and significant impairment converters (SI, n = 35). The baseline gait age gap in stable CU individuals was found to be significantly lower than in the already impaired or converter groups.ConclusionIn this study, we developed a model for estimating accelerated aging from gait variables in the form of a gait age gap metric. This metric was shown to be strongly associated with AD‐driven cognitive impairment as well as predictive of future cognitive decline. These results highlight the potential clinical utility of quantitative gait analysis in behavioral neurologic assessment of cognitive impairment in AD.

AbstractBackgroundEasily accessible and self‐administered cognitive assessments that can aide early identification of individuals at risk for Alzheimer’s disease (AD) dementia are critical for timely intervention. Mayo Test Drive (MTD) is a remote, self‐administered, multi‐device compatible, web‐based cognitive assessment (Stricker et al., 2022). We investigated continuous associations between MTD and amyloid and tau PET biomarkers and compared the strength of association to PET biomarkers and in‐person cognitive measures.Method556 adults from the Mayo Clinic Study of Aging participated (mean age = 70.6; mean education = 16.0 years; 50.7% female; 97.5% white). Participants were predominantly cognitively unimpaired (95.3%; 4.7% had a consensus diagnosis of mild cognitive impairment). Participants completed (1) brain amyloid and tau PET scans, (2) in‐person neuropsychological assessment including Mayo Preclinical Alzheimer’s disease Cognitive Composite (Mayo‐PACC) and Global‐z, and (3) remote MTD comprised of the Stricker Learning Span (SLS) and the Symbols test, which are combined into an MTD composite. Multiple regressions adjusted for age, sex, and education queried associations between imaging biomarkers [amyloid meta‐region of interest (ROI), tau meta‐ROI, and tau entorhinal cortex (EC)‐ROI)] and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites.ResultLower performances on MTD composite and Mayo‐PACC were associated with higher amyloid meta‐ROI (p’s<.05; Table 1). MTD composite was also negatively associated with tau meta‐ROI and EC‐ROI (p’s<.05). For memory measures, SLS sum of trials was negatively associated with tau meta‐ROI (p = .01) and EC‐ROI (p<.001) with no associations between AVLT sum of trials and PET (p’s≥.06; Table 2). Among processing speed/executive function measures, worse Trails B performance was associated with higher amyloid meta‐ROI (p<.001). Symbols and Digit Symbol Coding were not associated with PET (p’s≥.29). A multivariable model with MTD explained significantly less variance (p = .04) for amyloid meta‐ROI (adjusted R‐squared = 0.165) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.176); remote and in‐person composite measures were comparable for other associations (p’s>.05).ConclusionMTD is associated with continuous measures of amyloid and tau PET biomarkers of AD, demonstrating criterion validity for MTD. Several associations were comparable to relationships between gold‐standard in‐person neuropsychological tests and imaging biomarkers.

AbstractBackgroundMost studies on the relationship between neuropsychiatric symptoms (NPS) and cognitive decline focus on NPS data collected over a short period of time. To better understand the association between NPS and cognitive decline, including their temporal relationship, it is important to know NPS status across a person’s life.MethodWe utilized the unique Rochester Epidemiology Project medical record linkage system that has captured medical records as far back as 1907, to abstract the psychiatric history of 1810 cognitively unimpaired participants in the population‐based Mayo Clinic Study of Aging (MCSA). Trained nurse abstractors ascertained the presence of exposure to three common NPS (anxiety, depression, and unspecified emotional distress [UED]), both before and after entry into the MCSA. We conducted Cox Proportional Hazards (PH) models using age as the time scale and adjusting for sex, education, and APOE E4 allele for each NPS in predicting mild cognitive impairment (MCI), or dementia. Additional analyses considering medication use (yes/no) and timing of NPS were also run.ResultAnxiety (Hazard Ratio [HR] = 1.47, p<0.001), depression (HR = 1.36, p = 0.001), and UED (HR = 1.22, p = 0.039) were associated with a higher risk of MCI. These associations were present regardless of medication use for anxiety (With medication: HR = 1.51, p<0.001; without medication: HR = 1.41, p = 0.015), and for those taking medications for depression (With medication: HR = 1.47, p<0.001; without medication: 1.09, p = 0.597). Anxiety was associated with MCI regardless of timing of onset with the strongest relationship being for those with onset of anxiety before 65 that persisted into later life (HR = 1.88, p = 0.004). Depression was associated regardless of timing of onset if the depression was present in late life too. The results were similar for the outcome of dementia (Anxiety: HR = 1.62, p<0.001; depression: HR = 1.31, p = 0.040; UED: HR = 1.37, p = 0.014). The associations accounting for medication use and timing of NPS were similar for the outcome of dementia too.ConclusionNPS is associated with progression to MCI and dementia. Both medication use and timing of NPS might play a role in these associations.

AbstractBackgroundBrief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer’s disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers.MethodParticipants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer’s disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aβ)‐42 and Aβ‐40 to create an Aβ42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites.ResultLower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p’s≤.02), NfL (p’s≤.003), and p‐tau181 (p’s = .04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p’s≤.01) and NfL (p’s≤.03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p’s≤.02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p’s≤.02), and worse Digit Symbol Coding was associated with lower Aβ42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p = .04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p’s>.05).ConclusionRemote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD’s criterion validity.

AbstractBackgroundThe cardiac and respiratory cycles have been suggested as major drivers of cerebrospinal fluid(CSF) flow. The glymphatic system, one of the mechanisms for the clearance of amyloid‐beta and tau proteins, relies heavily on the movement(flow) of the CSF, and thus, investigating the forces that modulate the CSF flow is essential for understanding the glymphatic flux. Because breathing is trainable through exercise and self‐directed methods, various breathing methods may enhance the CSF flow.MethodReal‐time phase‐contrast MRI(pc‐MRI) was used to measure the CSF flow at the foramen magnum on a 3T. Fast resting‐state fMRI(rsfMRI;TR400ms) was conducted on a compact 3T. Under an IRB‐approved protocol, participants were instructed to breathe slowly and deeply, and subsequently switch to their spontaneous breathing. For rsfMRI data preprocessing, we performed motion correction, skull stripping, spatial smoothing(4mm), temporal filtering(0.01 to 0.1Hz), and linear and quadratic temporal trends removal. fMRI time courses were then normalized to Z‐score at each voxel before being averaged. On the pcMRI, the phase was summed in a CSF region‐of‐interest and plotted vs time. Additionally, we confirmed for possible confounding effects such as head motion or motion‐induced susceptibility changes.ResultrsfMRI findings showed that deep breathing promoted large oscillations in the CSF flow in the ventricles, while spontaneous breathing did not(Figure 1). Additionally, deep breathing resulted in large oscillations in lobar cortical regions. Similar results were observed in the pcMRI, with larger CSF velocity through the foramen magnum during the deep breathing compared to the spontaneous breathing(Figure 2A‐B). The cumulative summation result showed that the volume of CSF flow was increased during deep breathing(Figure 2C).ConclusionThis study provides new evidence that slow and deep breathing promotes CSF flow and global brain BOLD signal, which may be useful in promoting circulation in the brain. The study’s findings also open new avenues for research in the field of neurovascular coupling and mechanisms associated with glymphatic clearance. As hampered CSF flow and/or worsen glymphatic clearance have been shown to associate with pathological aging and dementia, including Alzheimer’s disease and Parkinson’s disease, future research is needed explore the potential of breathing training methods as a non‐pharmacological intervention to support brain health.

AbstractBackgroundEarly diagnosis of Alzheimer’s disease (AD) is crucial for ensuring patients have timely access to essential care. Given the symptoms of early AD are often subtle, it is important that both primary care physicians (PCPs) and neurologists are able to diagnose their patients. Several sources of evidence have indicated that most PCPs and neurologists lack knowledge, competence, and confidence regarding recognition of early forms of AD. To address the clinical practice gaps, a series of CME‐certified programs covering the symptoms, assessment, and diagnosis of early forms of AD were developed.MethodThe data presented here are based on outcomes from four online CME‐certified multimedia programs housed on a single destination page. Each CME‐certified program utilized expert physicians to educate learners on recognizing early forms of AD. Each CME program asked a series of pre‐post questions designed to assess immediate changes in knowledge, competence, or confidence. The questions were grouped into clinically relevant themes. The educational effect for all of the programs was determined by using a paired‐samples t‐test to identify significant differences between pre‐ and post‐assessment responses for each question. Data across all four programs were collected from February 2022 through December 2022. All four programs were promoted to an audience of US‐ and ex‐US physicians.ResultParticipation across the four programs ranged from 543 to 2,510 PCPs and 256 to 1,025 neurologists. PCPs and neurologists demonstrated significant (P<0.05) pre‐vs post education improvements on the following themes: biomarkers in AD, clinical trial outcomes, cognitive assessment scales, diagnosis of AD, diagnosis of mild cognitive impairment (MCI), and symptoms differentiating AD from MCI. Participation in the education also significantly (P<0.001) improved confidence in the identification of early forms of AD among PCPs and neurologists. No substantial differences in knowledge, competence, or confidence were seen for either clinician group based on geographic location of the learner.ConclusionThe results indicated that clinicians who participated in CME‐certified curriculum‐based education in multiple formats were better prepared to recognize early forms of AD. Future education should continue to discuss the use of clinical strategies to recognize early forms of AD.