Morris, John C.

AbstractBackgroundOverlap in the presenting clinical features and test results in patients with rapidly progressive dementia (RPD) confounds the etiologic diagnoses of RPD, contributing to diagnostic delays, missed treatment opportunities, and poorer outcomes. There is a need to improve diagnostic accuracy in patients with RPD, with particular emphasis on identifying patients with potentially treatment‐responsive causes of RPD early in the symptomatic course.MethodPatients with RPD were prospectively enrolled and evaluated in inpatient settings and outpatient subspecialty (memory) clinics at Washington University (Saint Louis, MO; 2016–2019) and Mayo Clinic (Jacksonville, FL; 2020–2021). Etiologic diagnoses were independently assigned by two dementia specialists integrating clinical features and the results of widely accessible diagnostic testing and referencing established diagnostic criteria. Diagnostic disagreements were resolved via blinded review by a third specialist. Clinicopathologic correlation was evaluated using neuropathological and genetic data when available.Result160 patients with RPD were assessed, with average age‐at‐symptom onset 60.0±15.9 years; 50% were female. Diagnostic inter‐rater reliability (91% agreement; Cohen’s κ = 0.88, p<0.001) and clinicopathologic correlation were excellent (100% agreement in 24 patients with neuropathologic or genetic data). Autoimmune encephalitis was the leading cause of RPD (39%), followed by Alzheimer disease and related dementias (29%—i.e., frontotemporal dementia, Lewy body dementia, vascular cognitive impairment), Creutzfeldt‐Jakob disease (15%), and other causes (15%—e.g., neurosarcoid, metabolic disruptions, neoplastic disease). The average age of patients with potentially treatable causes (AE and selected “other” causes; 54.4±18.2) was younger than those with neurodegenerative dementing illnesses (67.3±9.5) or CJD (67.4±7.9; p<0.001). Patients with potentially treatable causes were more likely to present with altered level of consciousness and seizures (p<0.05). CSF pleocytosis (>5 WBC/hpf) was more common in AE patients (p<0.05).ConclusionEtiologic diagnoses can be reliably established in RPD patients using available clinical data. Treatment‐responsive causes of RPD are common warranting consideration, particularly in younger patients with altered consciousness, seizures, and CSF pleocytosis.

AbstractBackgroundDyadic (self and study partner) subjective reports of an individual’s recent cognitive and functional decline are an efficient approach to facilitate identification of older adults with, and at risk for, Mild Cognitive Impairment (MCI). Such methods can be easily adapted for remote administration, but few studies have investigated the ability of remotely‐collected, unsupervised dyadic subjective reports to accurately detect MCI and cognitive decline.MethodsIn 289 older adults enrolled in the Brain health Registry (BHR), with clinically‐confirmed diagnoses of Cognitively‐unimpaired (CU) or MCI, we identified the optimal combination of remote measures to accurately distinguish CU from MCI. We then externally validated our predictive model in 217 participants enrolled in a separate cohort, Validation of Online Methods to Predict Cognitive Decline (eVAL). Predictors included Cogstate Brief Battery One Card Learning (OCL) accuracy; self‐ and study partner‐reported memory concern (SMCs); performance on online adaptations of Everyday Cognition Scale (ECog, self‐ and study partner‐report versions), Geriatric Depression Scale Short Form (GDS); family history of AD, age, gender, and education. We calculated 10‐fold cross validated area under the ROC curves (cvAUC), 95% confidence intervals (CI), and the percentage of participants correctly classified (accuracy) for all models. Likelihood ratio (LR) tests were used to compare model fits.ResultsIn the BHR cohort, models including dyad‐report ECog, family history of AD, GDS, and Cogstate OCL scores distinguished CU from MCI with the highest accuracy (cvAUC = 0.90; 95% CI = 0.83, 0.92; accuracy = 85.5%). Models excluding study partner‐report ECog distinguished diagnostic groups with moderate accuracy (cvAUC = 0.83; 95% CI = 0.78, 0.85; accuracy = 75.9%) When applied to the validation cohort, the optimal training model including dyadic report performed with moderate accuracy ( Table 2 ). In the validation cohort, inclusion of study partner (but not self) report SMC, in addition to dyadic‐report ECog, improved model fit (LR test p<0.01).ConclusionsRemotely‐collected dyadic subjective measures, along with other remote measures, accurately distinguish CU from MCI. Subjective cognitive and functional decline and concerns about memory problems independently contribute to diagnostic discrimination. Results support future utilization of remote dyadic subjective measures to efficiently identify older adults with MCI for prodromal AD clinical trials, and in clinical care settings.

AbstractBackgroundRates of cognitive and biomarker change in Autosomal Dominant Alzheimer disease (ADAD) vary substantially across individuals. Prior cross‐sectional work suggests that the location of the pathogenic variant within PSEN1, specifically whether the underlying variant affects transmembrane (TM) or cytoplasmic (CY) domains in PSEN1, may be a key determinant in these differential rates of progression. Here we use longitudinal data from the Dominantly Inherited Alzheimer Network observational study (DIAN‐Obs) to examine whether variants affecting TM versus CY domains in PSEN1 have differential rates of change in key cognitive and neurodegenerative markers, and whether these differences are relevant to ADAD clinical trials.MethodsUsing longitudinal clinical, cognitive, and MRI data from PSEN1 pathogenic variant carriers [TM group N=76 and CY group N=44; Table 1], we assessed rates of change in Mini‐Mental State Exam (MMSE), Clinical Dementia Rating® Sum of Boxes (CDR®‐SOB), and hippocampal volume (HV) using linear mixed effects models accounting for disease stage (estimated years to symptom onset [EYO]). We further assessed how PSEN1 mutation location (TM versus CY) impacts sample size and detectable effect size in a potential ADAD clinical trial (modeled as a 4‐year trial with annual assessments; 80% power; α = 0.05).ResultsPSEN1 TM and PSEN1 CY groups did not differ on baseline age, EYO, or CDR®. The PSEN1 TM group had significantly greater rates of change on MMSE (B[SE] = ‐0.42[0.1], p=0.002), CDR®‐SOB (B[SE] = 0.23[0.1], p=0.001), and HV atrophy (B[SE] = ‐58.93[14.3], p=0.0006 compared to the PSEN1 CY group (Fig.1). Consistent with these differential rates of change, power analyses indicated the required sample size to detect a 30% treatment effect on MMSE or HV would be reduced by 59.6% for MMSE and 91.0% for HV for a trial population comprised of PSEN1 TM versus CY carriers (Fig.2).ConclusionsIndividuals who had a variant affecting the transmembrane domains of PSEN1 had greater rates of cognitive decline and neurodegeneration compared to those with variants affecting cytoplasmic domains. In addition to having implications for ADAD pathophysiology, these results suggest that incorporating information regarding the location of PSEN1 variants may be beneficial in analyzing and designing stratification approaches for ADAD trials.

AbstractBackgroundRates of cognitive and biomarker change in Autosomal Dominant Alzheimer disease (ADAD) vary substantially across individuals. Prior cross‐sectional work suggests that the location of the pathogenic variant within PSEN1, specifically whether the underlying variant affects transmembrane (TM) or cytoplasmic (CY) domains in PSEN1, may be a key determinant in these differential rates of progression. Here we use longitudinal data from the Dominantly Inherited Alzheimer Network observational study (DIAN‐Obs) to examine whether variants affecting TM versus CY domains in PSEN1 have differential rates of change in key cognitive and neurodegenerative markers, and whether these differences are relevant to ADAD clinical trials.MethodsUsing longitudinal clinical, cognitive, and MRI data from PSEN1 pathogenic variant carriers [TM group N=76 and CY group N=44; Table 1], we assessed rates of change in Mini‐Mental State Exam (MMSE), Clinical Dementia Rating® Sum of Boxes (CDR®‐SOB), and hippocampal volume (HV) using linear mixed effects models accounting for disease stage (estimated years to symptom onset [EYO]). We further assessed how PSEN1 mutation location (TM versus CY) impacts sample size and detectable effect size in a potential ADAD clinical trial (modeled as a 4‐year trial with annual assessments; 80% power; α = 0.05).Results PSEN1 TM and PSEN1 CY groups did not differ on baseline age, EYO, or CDR®. The PSEN1 TM group had significantly greater rates of change on MMSE (B[SE] = ‐0.42[0.1], p=0.002), CDR®‐SOB (B[SE] = 0.23[0.1], p=0.001), and HV atrophy (B[SE] = ‐58.93[14.3], p=0.0006 compared to the PSEN1 CY group (Fig.1). Consistent with these differential rates of change, power analyses indicated the required sample size to detect a 30% treatment effect on MMSE or HV would be reduced by 59.6% for MMSE and 91.0% for HV for a trial population comprised of PSEN1 TM versus CY carriers (Fig.2).ConclusionsIndividuals who had a variant affecting the transmembrane domains of PSEN1 had greater rates of cognitive decline and neurodegeneration compared to those with variants affecting cytoplasmic domains. In addition to having implications for ADAD pathophysiology, these results suggest that incorporating information regarding the location of PSEN1 variants may be beneficial in analyzing and designing stratification approaches for ADAD trials.

AbstractBackgroundTau PET imaging is an established tool for studying Alzheimer Disease but its sensitivity to primary age‐related tauopathy (PART) as well as sex effects in individuals without abnormal levels of amyloid are unknown.MethodsCross‐sectional data were collected from 2014 to 2019 in 144 cognitively normal younger, middle‐aged, and older adults free from amyloid pathology as quantified using PET. Regional values of tau PET binding were measured using flortaucipir. Statistical models examined the main effects of increasing age, sex, race, and subthreshold levels of amyloid. Secondary analyses also examined the relationship between tau PET binding and iron as measured using T2* weighted imaging and calcification as measured using CT.ResultsThere were significant positive associations between tau binding and age in 19 regions, with the largest effects seen in the inferior temporal cortex (t=4.59), caudate (t=9.58), and putamen (t=12.57). Iron as measured using T2* imaging mediated only a modest (11.9%) amount of the association between age and tau binding. Elevated tracer uptake in females was present in 23 regions in frontal, lateral parietal, and temporal regions including, most prominently the rostral middle frontal gyrus (t=7.48), superior temporal gyrus (t=5.12) and the inferior temporal gyrus (t=4.22). There were no significant effects of race or subthreshold levels of amyloid.ConclusionsTau PET is sensitive to primary effects of age in regions consistent with the neuropathological definition of PART although strong age effects in regions of off‐target binding were also present. There was a robust effect of sex, suggesting prior observations of elevated binding in women is not solely a potentiation of Alzheimer pathology but instead represents an ubiquitous phenomenon. Understand how both sex and age impact tracer binding is critical to understanding the utility of PET tracers as well as interpreting tracer values in the context of disease.

AbstractIntroductionThe Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to validate biomarkers for Alzheimer's disease (AD) clinical trials. To improve generalizability, ADNI4 aims to enroll 50‐60% of its new participants from underrepresented populations (URPs) using new biofluid and digital technologies. ADNI4 has received funding from the National Institute on Aging beginning September 2022.MethodsADNI4 will recruit URPs using community‐engaged approaches. An online portal will screen 20,000 participants, 4000 of whom (50‐60% URPs) will be tested for plasma biomarkers and APOE. From this, 500 new participants will undergo in‐clinic assessment joining 500 ADNI3 rollover participants. Remaining participants (∼3500) will undergo longitudinal plasma and digital cognitive testing. ADNI4 will add MRI sequences and new PET tracers. Project 1 will optimize biomarkers in AD clinical trials.Results and DiscussionADNI4 will improve generalizability of results, use remote digital and blood screening, and continue providing longitudinal clinical, biomarker, and autopsy data to investigators.

AbstractThe temporal evolutions and relative orderings of Alzheimer disease biomarkers, including CSF amyloid-β42 (Aβ42), Aβ40, total tau (Tau) and phosphorylated tau181 (pTau181), standardized uptake value ratio (SUVR) from the molecular imaging of cerebral fibrillar amyloid-β with PET using the 11C-Pittsburgh Compound-B (PiB), MRI-based hippocampal volume and cortical thickness and cognition have been hypothesized but not yet fully tested with longitudinal data for all major biomarker modalities among cognitively normal individuals across the adult lifespan starting from 18 years. By leveraging a large harmonized database from 8 biomarker studies with longitudinal data from 2609 participants in cognition, 873 in MRI biomarkers, 519 in PET PiB imaging and 475 in CSF biomarkers for a median follow-up of 5–6 years, we estimated the longitudinal trajectories of all major Alzheimer disease biomarkers as functions of baseline age that spanned from 18 to 103 years, located the baseline age window at which the longitudinal rates of change accelerated and further examined possible modifying effects of apolipoprotein E (APOE) genotype. We observed that participants 18–45 years at baseline exhibited learning effects on cognition and unexpected directions of change on CSF and PiB biomarkers. The earliest acceleration of longitudinal change occurred for CSF Aβ42 and Aβ42/Aβ40 ratio (with an increase) and for Tau, and pTau181 (with a decrease) at the next baseline age interval of 45–50 years, followed by an accelerated increase for PiB SUVR at the baseline age of 50–55 years and an accelerated decrease for hippocampal volume at the baseline age of 55–60 years and finally by an accelerated decline for cortical thickness and cognition at the baseline age of 65–70 years. Another acceleration in the rate of change occurred at the baseline age of 65–70 years for Aβ42/Aβ40 ratio, Tau, pTau181, PiB SUVR and hippocampal volume. Accelerated declines in hippocampal volume and cognition continued after 70 years. For participants 18–45 years at baseline, significant increases in Aβ42 and Aβ42/Aβ40 ratio and decreases in PiB SUVR occurred in APOE ɛ4 non-carriers but not carriers. After age 45 years, APOE ɛ4 carriers had greater magnitudes than non-carriers in the rates of change for all CSF biomarkers, PiB SUVR and cognition. Our results characterize the temporal evolutions and relative orderings of Alzheimer disease biomarkers across the adult lifespan and the modification effect of APOE ɛ4. These findings may better inform the design of prevention trials on Alzheimer disease.

AbstractIntroductionTo address the need for remote assessments of cognitive decline and dementia, we developed and administered electronic versions of the Clinical Dementia Rating (CDR®) and the Financial Capacity Instrument‐Short Form (FCI‐SF) (F‐CAP®), called the eCDR and eFCI, respectively.MethodsThe CDR and FCI‐SF were adapted for remote, unsupervised, online use based on item response analysis of the standard instruments. Participants completed the eCDR and eFCI first in clinic, and then at home within 2 weeks.ResultsOf the 243 enrolled participants, 179 (73%) cognitively unimpaired (CU), 50 (21%) with mild cognitive impairment (MCI) or dementia, and 14 (6%) with an unknown diagnosis, 84% and 85% of them successfully completed the eCDR and eFCI, respectively, at home.DiscussionThese results show initial feasibility in developing and administering online instruments to remotely assess and monitor cognitive decline along the CU to MCI/very mild dementia continuum. Validation is an important next step.