Citations

AbstractBackgroundCardiovascular health plays an important role in brain aging; yet its role in development of autosomal dominant forms of dementia and frontotemporal dementia (FTD) has not been fully elucidated. Life’s Simple 7 (LS7) is a metric of cardiovascular health associated with reduced dementia risk and may represent a transdiagnostic heuristic to promote brain health. We examined the extent to which baseline LS7 associated with cognitive and brain aging trajectories in individuals with genetic FTD.Method243 adults carrying autosomal dominant genetic mutations for FTD and 189 non‐carriers completed neuroimaging, neuropsychological testing, and medical interviews. LS7 at baseline was computed following established procedures based on physical activity, diet, body mass index, smoking, hypertension, diabetes, and hypercholesterolemia; higher scores indicate more optimal cardiovascular health. We examined interactions between baseline LS7 and mutation carrier status on cognitive and brain structural outcomes at baseline (via linear regression) and longitudinally (via linear mixed‐effects models). All models adjusted for baseline age, sex, and education, and CDR®+NACC FTLD.ResultMutation carriers and non‐carriers did not significantly differ on baseline LS7 (t = ‐1.42, p = 0.156). At baseline, there were significant interactions between LS7 and carrier status on frontotemporal (FTL) volume and executive functioning (all p‐values<0.046), such that positive associations between LS7 and neurobehavioral outcomes were stronger among mutation carriers versus noncarriers. Longitudinally, there was also a significant interaction between carrier status and baseline LS7 on memory over time (b = 0.04,p = 0.035) and the interaction approached significance for language trajectories (b = 0.03,p = 0.079); again, the association between higher LS7 and slower cognitive decline was stronger among mutation carriers versus noncarriers, even when excluding symptomatic carriers (b = 0.04,p = 0.047). Among mutation carriers only, higher baseline LS7 related to slower memory (b = 0.04,p = 0.006) and language declines over time (b = 0.044,p = 0.004), but did not significantly relate to FTL volume (b = ‐0.01,p = 0.307) or executive functioning trajectories (b = 0.004,p = 0.913)ConclusionBetter cardiovascular health relates to larger baseline frontotemporal volume and slower cognitive decline in individuals with autosomal dominant FTD. Interaction models suggested the degree to which cardiovascular health associates with better neurobehavioral outcomes is stronger in mutation carriers. Optimizing cardiovascular health may be a particularly important, modifiable approach to mitigate the symptoms associated with pathogenic variants of FTD.

AbstractBackgroundFrontotemporal Dementia (FTD) patients are geographically dispersed and often have behavioral and/or motor deficits that limit access to in‐person clinical trials and research. To address these participation barriers, we investigated the feasibility of installing in‐home sensor technologies to remotely assess everyday functioning in FTD.MethodWe aimed to recruit 20 FTD participants with a variety of clinical syndromes, including behavioral variant FTD (bvFTD), primary progressive aphasia (PPA), corticobasal syndrome (CBS), and progressive supranuclear palsy (PSP). We also aimed to enroll a study partner for each participant that lived in the same home to serve as a control. The Collaborative Aging Research Using Technology (CART) platform was installed in participants’ homes to collect continuous data for up to two years, using motion sensors, physiological monitoring devices (e.g., scales, sleep sensors), electronic pillboxes, and wearable actigraphs. Computer use and driving patterns were also tracked. Technology was installed either via a study coordinator visit or remotely through video and written instructions, based on schedule and logistics. Participants were asked to complete a weekly Qualtrics survey about time away from home, visitors, and health changes.ResultOf the 159 UCSF participants available for recruitment through ALLFTD and other FTD projects, 28% (N = 45) met inclusion criteria. Of those 45, ten were ineligible due to advanced disease severity or relocation to an assisted living facility. A third (36%, N = 17) declined due to overwhelmed study partners (N = 3), declined any subproject participation (N = 7), sensitivity to participant’s diagnosis (N = 2), or lack of response (N = 5). Two additional participants were recruited through UCLA’s ALLFTD cohort. The platform has been installed in 19 homes (bvFTD: n = 10; PPA: n = 5; CBS/PSP: n = 4). Two homes discontinued due to death or relocation to assisted living facility. Mean data collection is 360 days (max = 527). Of 103 reported technological issues, 92% were resolved without additional home visits, either by phone or email. Participants completed 65% of weekly health questionnaires.ConclusionsAlthough there were some recruitment and retention challenges, these findings generally provide preliminary support for the feasibility and sustainability of conducting comprehensive in‐home passive data research in FTD.

AbstractBackgroundThere has been a rise in remote cognitive testing for neurodegenerative diseases. The ALLFTD Mobile App is designed to capture clinical features of frontotemporal dementia (FTD) through repeated remote assessment on smartphones. This is a departure from traditional neuropsychological practice in which a single examiner‐led assessment occurs in a controlled setting. When participants complete tasks on their own, those with greater disease severity may be more variable in their performance for a variety of reasons (e.g. greater distractibility, less engagement). Here we study whether intraindividual cognitive variability (IIV) is associated with older age and increasing disease severity.MethodParticipants were 279 (mean age = 54.9) healthy controls and participants with sporadic FTD or from familial FTD kindreds with a range of disease severities based on FTLD Clinical Dementia Rating scale (CDR®+NACC‐FTLD Global) scores: 0 (n = 132), 0.5 (n = 54), and ≥1 (n = 47). Participants completed three cognitive tasks at least two times across twelve days. Measures included an adaptive memory task and gamified versions of Flanker and Go/no‐go tasks. IIV was operationalized as the standard deviation in performance on each task. Regression analyses investigated the relationship of IIV with CDR®+NACC‐FTLD group, and with age in those without symptoms. Education and gender were included as covariates in all analyses.ResultGreater disease severity (CDR®+NACC‐FTLD) was associated with greater variability on Flanker (p<.001) and Go/No‐go (p = .02) tasks; a positive but non‐significant association was seen with memory testing (p = .16). Post‐hoc comparisons found that those with CDR®+NACC‐FTLD of 0.5 had significantly more IIV on the Flanker task (𝛃 = .39, p = .004) than those with CDR®+NACC‐FTLD’s of 0. Moreover, those with a CDR®+NACC‐FTLD ≥1 had significantly greater IIV on the Flanker (𝛃 = .85, p<.001) and Go/No‐go tasks (𝛃 = .51, p = .007) than those with a CDR®+NACC‐FTLD of 0. In those without symptoms (CDR®+NACC‐FTLD = 0), age was associated with increased IIV on the Flanker (𝛃 = .27, p<.001) and Go/no‐go (𝛃 = .23, p<.001) tasks.ConclusionIncreased IIV, particularly on processing speed/executive functioning tasks, was associated with greater age and greater functional impairment, even in the mildest stages. Future investigations exploring predictors of IIV and the prognostic value of IIV are warranted.

AbstractBackgroundFrontotemporal dementia (FTD) is a leading cause of dementia in individuals under age 65. Due to limitations in access to in‐person evaluations, there is an urgent need to examine remote, accessible, and low‐burden assessment techniques for FTD. Unobtrusive monitoring of at‐home computer use in older adults has shown that those with mild cognitive impairment (MCI) have significant declines in computer use over time. To extend these findings, we characterized daily trajectories in smartphone battery percentage, a proxy of smartphone use, and examined relationships with clinical indicators of disease severity in FTD.MethodParticipants included 250 adults enrolled in the ALLFTD Mobile App study (mean age = 53.9 [SD = 14.9]; 59% women; 94% White). To assess clinical disease severity, all participants completed the FTLD Clinical Dementia Rating scale (CDR®+NACC FTLD), a brief neuropsychological battery, the Neuropsychiatric Inventory Questionnaire (NPI‐Q), and brain MRI. The ALLFTD Mobile App was installed onto participants’ smartphones for remote, passive, continuous monitoring of smartphone use (average days of monitoring = 82 [range = 7‐541]). Battery percentage was collected every 15 minutes. Linear mixed effects models examined linear, quadratic, and cubic effects of time of day (i.e., hour; modeled as person‐specific random effects) and their interactions with each measure of disease severity on battery percentage. Models covaried for age.Result57% of participants were clinically normal, 21% had MCI, and 22% had dementia. CDR®+NACC FTLD Global score interacted with hour such that participants with MCI and dementia had flatter battery curves (i.e., less change in battery percentage throughout the day – a proxy of less smartphone use) than clinically normal participants (ps<0.001). Additional models showed that worse memory (hour3 x memory score: 𝛃 = 0.59, p<0.001) and executive functioning (hour3 x executive score: 𝛃 = 0.64, p<0.001), more neuropsychiatric symptoms (hour3 x NPI‐Q: 𝛃 = ‐0.22, p = 0.004), and smaller gray matter volumes (controlling for intracranial volume; hour3 x volume: 𝛃 = 0.49, p<0.001) were also statistically significantly related to less battery usage throughout the day.ConclusionThese findings are proof‐of‐concept that passively acquired smartphone data, specifically battery usage, can provide insights about disease progression in FTD. Results support future research focusing on the development of passively collected digital metrics sensitive to clinical declines in neurodegenerative disease.

AbstractBackgroundAfrican Americans (AA) represent 12% of the United States population ≥65 years‐old, but 19% of persons with dementia. Inequalities in social determinants of health may influence access to healthcare resources contributing to increased dementia risk. Modifiable risk factors, including vascular risk factors and depression, may contribute up to 40% of dementia risk and are overrepresented in AA. Accordingly, risk factor modification in AA may improve disparities and decrease dementia risk.MethodDementia risk factors were evaluated in community‐dwelling AA (n = 261) and non‐Hispanic White (NHW; n = 193) participants who completed ≥2 research visits in studies of memory and aging at the Mayo Clinic Florida Alzheimer Disease Research Center. The association between modifiable risk factors and cognitive impairment (global Clinical Dementia Rating® [CDR] ≥0.5), and rates of decline in impaired participants (measured using the CDR Sum‐of‐Boxes; CDR‐sb) were compared between AA and NHW, while controlling for demographics, APOEe4 status, and Area Deprivation Index.ResultModifiable dementia risk factors were common in AA and NHW participants. The ADI was higher in AA denoting greater socioeconomic and environmental deprivation [7.8 (2.3) vs. 4.0 (2.1); p<0.001]. History of stroke/transient ischemic attack (OR, CI95% = 2.52, 1.08‐5.83) was associated with an increased odds of dementia in AA participants only, while depression was associated with an increased odds of dementia in both AA (3.10, 1.52‐6.30) and NHW (3.46, 1.14‐10.48) participants. Moreover, depression (β, CI95% = 1.55, 0.5‐2.6), p = 0.004) was associated with a faster rate of decline (CDR‐sb) in cognitively impaired AA participants. AA participants with depression were disproportionately less likely to report use of antidepressant medications than NHW counterparts (40/45, 89% vs. 21/44, 48%); p<0.001).ConclusionModifiable risk factors associated with dementia were common in AA and NHW participants. Depression was associated with dementia in both AA and NHW but uniquely associated with an accelerated rate of cognitive decline in AA participants. Disparities in use of antidepressant medications may contribute to these differences. Optimizing depression treatment in AA communities may improve cognitive trajectories and dementia risk in this population.

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aβ)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aβ‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aβ‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aβ‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aβ‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aβ‐PET data across 4 tracers in CLs, there were expected differences in Aβ burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.

AbstractBackgroundKnowledge of the temporal evolution among plasma Alzheimer’s biomarkers and PET will inform disease pathophysiology and appropriate implementation of plasma biomarkers (Therneau, NeuroImage 2021; Betthauser, Brain 2022). This study investigates the relative timing of progression of plasma p‐tau181, p‐tau217, GFAP, and amyloid PET on individual and population levels.MethodWe included 957 participants from the Mayo Clinic Study of Aging with clinical diagnoses of cognitively unimpaired (n = 732), MCI (n = 205), and Alzheimer’s clinical syndrome dementia (n = 20). An accelerated failure time (AFT) model was fit with amyloid (PiB) PET, plasma p‐tau217 (Meso Scale Discovery, Lilly), plasma p‐tau181, and GFAP (Simoa, Quanterix HD‐X) as quad‐variate endpoints, and sex, APOE genotype status, and education as covariates. The model output included a per subject adjustment or time‐shift for each biomarker indicating how much earlier or later it was estimated to progress relative to the average participant, the correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing. Secondly, we estimated progression at a given age for selected cutpoints.ResultTemporal trajectories for each biomarker are shown in Figure 1. Individual‐level adjustments between plasma p‐tau217 and amyloid PET were highly correlated, R = 0.69(0.64‐0.74); how early or late an individual progressed on plasma p‐tau217 explained 48% of the variation in relative timing of amyloid PET progression. Associations of plasma p‐tau181 and GFAP with amyloid PET individual adjustments were moderate and low, respectively (Figure 2). Using the chosen cutpoints, the estimated order of biomarker progression was GFAP, amyloid PET, plasma p‐tau181, and plasma p‐tau217 (Figure 3).ConclusionOn the individual level, the strong association of the relative timing of progression of plasma p‐tau217 and amyloid PET supports that these processes are mechanistically related. The relatively lower associations of the timing of p‐tau181 and GFAP with amyloid PET likely reflect a lower biological association and greater measurement noise for p‐tau181. The individual adjustments, the primary output of this model, are independent of cutpoints. On the population level, relative timing of biomarker change is highly dependent on the cutpoints applied. This true for any analysis that employs cutpoints, regardless of the approach.

AbstractBackgroundThe pathophysiological mechanisms driving frontotemporal lobar degeneration (FTLD) disease progression and corresponding biomarkers that capture these changes are not fully understood. We leveraged high‐throughput proteomics to identify networks of co‐expressed CSF proteins and candidate biomarkers for prediction of disease progression in adults with autosomal dominant FTLD mutations.Method113 adults carrying autosomal dominant FTLD mutations (C9orf72/GRN/MAPT) and 38 noncarrier controls from the ALLFTD consortium completed baseline lumbar puncture and longitudinal neuropsychological testing. CSF was assayed for SOMAmer proteomics on SOMAscan® v3.0 (4,140 proteins). Network analysis identified weighted protein co‐expression modules, which were annotated via gene ontology and cell‐type enrichment analysis. Disease age estimates that capture proximity to symptom onset were derived from a Bayesian disease progression model incorporating genotype‐specific clinical (cognition, CDR®+NACC FTLD) and biomarker (brain volume, plasma NfL) information. At baseline, local‐weighted regression examined module z‐scores (based on mean/SD of module levels [first principal component] in noncarriers) in relation to disease age. Longitudinal linear mixed‐effects models examined global cognitive trajectories in relation to baseline module z‐scores, as well as individual proteins within prognostic modules.ResultNetwork analysis identified 32 CSF protein co‐expression modules (37 to 223 proteins/module). 16 modules showed mutation‐related deviations from noncarriers. Deviations in ion transport, neurodevelopmental, metabolic, and transcriptional modules occurred several decades before estimated symptom onset (disease age: ‐30 to ‐20 years) and persisted across disease age. Deviations in neuroinflammatory and ubiquitination/autophagy modules occurred ∼10 years before estimated symptom onset. Sharp deviations in RNA binding, protein phosphorylation, neurogenesis, synaptic function, and lysosomal modules occurred near symptom onset (disease age: ‐1 to 4 years). Baseline synaptic and neurogenesis modules were the strongest prognosticators of cognitive decline (module x time: bs>.10, ps<.05). Within these modules, individual proteins with stronger intramodular connectivity exhibited stronger associations with cognitive decline (e.g., synaptic/transmembrane module: r = .40, p = 2.85e‐6; e.g., EPHA5, LRRTM2).ConclusionLarge‐scale analysis of the CSF proteome reveals a biologically‐diverse landscape of protein networks associated with presymptomatic and symptomatic stages of autosomal dominant FTLD. Synaptic and neuron‐enriched protein networks exhibited prodromal deviations that forecasted incipient cognitive decline. ‘Hub’ proteins derived from these affected networks warrant further validation as candidate biomarkers for early FTLD disease progression.

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aß)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aß‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aß‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aß‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aß‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aß‐PET data across 4 tracers in CLs, there were expected differences in Aß burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.