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AbstractBackgroundIn this pilot study, the diagnostic agreement for sleep biomarker‐based neurodegenerative disorder (NDD) risk probabilities was evaluated in patients with Alzheimer’s disease dementia (AD), Lewy body disease (LBD), mild cognitive impairment (MCI), and controls (CG) with a Mini‐Mental State Exam scores ≥28.MethodsSleep biomarkers recorded with the Sleep Profiler (Advanced Brain Monitoring, Inc.) were used as inputs to a 4‐class machine learning algorithm trained to assign NDD risk probabilities to AD=27, LBD=19, isolated REM sleep behavior disorder=15, and CG=58. Input variables included age, time‐REM, non‐REM hypertonia, autonomic‐activation index, spindle‐duration, atypical‐N3, time‐supine, sleep‐efficiency, relative‐theta, and theta/alpha. Distributions of NDD risk were then assigned to AD=37, LBD=19, MCI=71, and CG=106 records.Records with CG output probabilities ≥70% were labeled Probably‐Normal, and from 45‐70% Likely‐Normal with indications of a NDD added when the LBD, AD or prodromal synucleinopathy (pSYN) risks exceeded approximately 25%. Similar thresholds were used to assign Likely‐ or Probably‐ LBD, AD, pSYN, or Mixed based on the group probabilities. Likely‐Normal with indications consistent with the diagnosis trended toward agreement.ResultsThe proportions of each group with classifications consistent with the diagnosis or trended toward agreement were CG=86%, MCI=75%, LBD=79%, and AD=87%.In the CG group, NDD risk assignments were Probably‐Normal=68% and Likely‐Normal with NDD indications=18%, Likely‐NDD=10%, and Probable‐NDD=4%.The MCI group was assigned Probable‐NDD=38% (AD=26%, Mixed=10%, LBD/pSYN=2%), Likely‐NDD=17% (AD=7%, Mixed=6%, LBD=3%, pSYN=1%), Likely‐Normal with indications=20% (AD=9%, pSYN=7%, Mixed=4%) and Probably‐Normal=25%The LBD group’s assigned risks consistent with the diagnoses included Probable‐NDD=74% and Likely‐Normal with pSYN indications=6% but included inconsistencies from Probable‐AD=5%, Likely‐Normal with AD indications=5%, and Probably‐Normal=11%.AD group’s consistent risk classifications included Probable‐NDD=60%, Likely‐NDD=11%, Likely‐Normal with AD/Mixed indications=13% and countered by inconsistencies from Probable‐LBD/pSYN=8%, Likely‐Normal with pSYN indications=3%, and Probably‐Normal=5%.ConclusionsConsistencies between the diagnoses and assigned NDD risks agreements exceeded 75% in all four groups. Studies are underway to confirm the test‐retest reliability and/or evaluate NDD risk probabilities in other patient populations.Support: NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, R34AG056639.

AbstractBackgroundGrowth Differentiation Factor 15 (GDF15) is a TGF‐beta superfamily protein upregulated during immune‐mediated stress responses. Peripheral GDF15 is linked to adverse aging across organ systems including the brain. Immune dysregulation is implicated in frontotemporal dementia (FTD) pathogenesis. We examined how plasma GDF15 levels associated with longitudinal cognitive and neurofilament light chain (NfL) trajectories in adults with FTD and controls. Furthermore, we tested the utility of GDF15 as a prognostic biomarker against common inflammatory proteins (CRP and IL6).Method231 adults with FTD (24.35% sporadic FTD syndromes; 19.19% MAPT; 19.56% GRN; 22.14% C9orf72 pathogenic carriers) and 40 normal controls from the ALLFTD Consortium completed longitudinal neuropsychological testing and a blood draw with plasma analyzed on SomaScan V4.0 (baseline only: GDF15, CRP, and IL6) and Simoa (longitudinal: NfL). CRP and IL6 were selected as control markers to test the specificity of GDF15. Linear mixed effects models separately examined longitudinal cognition (global, executive functioning, memory, and language z‐scores) and plasma NfL levels as a function of baseline plasma GDF15, time, and their interaction, controlling for baseline age, sex, and education. Parallel models were conducted for CRP and IL6.ResultIn the whole cohort, higher baseline plasma GDF15 levels associated with steeper global cognitive decline (b = ‐3.91, p = 0.0092) and NfL accumulation (b = 3.88, p = 0.00088) over time. When examining cognitive domains, we found GDF15 only associated with worse executive functioning trajectories (b = ‐3.19, p = 0.0021). In contrast, baseline levels of CRP and IL6 did not display statistically significant relationships with cognitive or NfL trajectories. Prognostic effects of GDF15 remained statistically significant when restricting analyses to those with FTD pathogenic variants (b = ‐2.98, p = 0.0054 [global cognition], b = 2.69, p = 0.0098 [NfL]).ConclusionAdults with FTD and higher baseline peripheral GDF15 levels exhibit accelerated cognitive decline and neurodegeneration. Our results support GDF15 as a potential prognosticator of adverse clinical outcomes in FTD and highlight the relevance of GDF15‐specific pathways in FTD progression. Plasma GDF15 may provide utility over commonly examined inflammatory proteins and further work to explore its clinical value is indicated.

AbstractBackgroundSAAs represent a promising biomarker of Lewy Body disease (LBD), with high sensitivity (87.3%, 95%CI: 0.755‐0.947) and specificity (97.2%, 95%CI: 0.922‐0.994) for detection of patients in the Parkinson’s Progression Markers Initiative repository with positive DaT SPECT; and reasonable sensitivity (78.7%, 95%CI: 0.66‐0.88) and specificity (89.5%, 95%CI: 0.78‐0.96 referencing clinical diagnoses in participants included within national research repositories. However, test performance in heterogeneous clinical cohorts is unknown.MethodPatients with typical (n=122) and rapid progressive dementia (n=76) consented to banking of CSF at the time of clinically‐indicated diagnostic lumbar puncture. Clinical data were deidentified and uploaded to a secure research database and clinical (etiologic) diagnoses established by two neurologists following independent review of available records. SAA (SYNTap™) were run on stored CSF by Amprion Diagnostics (San Diego, CA).ResultMedian participant age was 69.1 years (18.4‐86.1); 56.3% were female. CSF was sampled median 1.4 years (0‐10.3) after symptom onset. Pathological alpha‐synuclein was detected in 28/122 patients with typically progressive dementia (including 6/18 patients with presumed LBD; sensitivity 33%, specificity 79%) and 11/76 patients with rapid progressive dementia (including 3/9 patients with presumed LBD; sensitivity 33%, specificity 88%). “Positive” SAA was associated with tremor (OR=3.2, 95%CI: 1.3‐7.8), visual hallucinations (OR=6.14, 95%CI 1.6‐23.6), and REM behavior disorder (OR=3.7, 95%CI: 1.4‐9.5) in patients with typical but not rapid progressive dementia. Brain autopsies were completed in 17 patients: Lewy bodies were detected in 2/3 patients with positive SAA and no patients with negative SAA.Conclusion“Positive” SAA demonstrated reasonable specificity but low sensitivity for the diagnosis of LBD. Additional studies in heterogeneous cohorts with neuropathologic data are needed to optimize clinical applications of SAA.

AbstractBackgroundAccumulating evidence indicates that biological sex may influence clinical manifestation within the spectrum of frontotemporal lobar degeneration (FTLD), implying differences in cognitive reserve. Nonetheless, investigations into the impact of biological sex during the preclinical and minimally symptomatic stages of FTLD are lacking.MethodWe included 275 mutation carriers (158 females; 127 with C9orf72, 68 with GRN, and 80 with MAPT mutations) and 161 non‐carrier familial controls (97 females) from the ALLFTD Consortium (Staffaroni et al., Nat Medicine 2022). Participants underwent magnetic resonance imaging (MRI; 348 baseline, 338 longitudinal) and neuropsychological evaluations (394 baseline, 507 longitudinal). Behavioral symptoms were characterized with the Revised Self‐Monitoring Scale (RSMS) and the Neuropsychiatric Inventory (NPI severity scores). MRI‐derived regional volume estimates (RVE) were computed. Cognitive measures and RVE were normalized against sex‐matched controls. Cognitive composites (language, executive function, and visuospatial function) were calculated by averaging raw scores for each domain. Clinical characteristics and RVE comparisons were made between male and female participants. We adopted the residuals approach to explore behavioral and cognitive reserve by fitting a linear regression model for executive z‐scores as the response value and age, education, and RVE as explanatory variables.ResultIn mutation carriers, sex was not a significant differentiator in age, education level, disease severity, or mutation frequency. Most mutation carriers (188, 68%) were asymptomatic or mildly symptomatic at baseline. However, female mutation carriers exhibited significantly lower visuospatial performance at baseline (Cohen's d = ‐0.34, 95% CI[‐0.58, ‐0.09], p=.001). This difference remained significant among asymptomatic GRN mutation carriers (Cohen’s d = ‐0.73, 95% CI[‐1.22, ‐0.23], p=0.003) but not in other mutations. Following the residuals approach, female mutation carriers showed higher executive performance than males for the same amount of both frontotemporal and global atrophy as quantified by RVE (Cohen's d=0.45, 95 % CI[0.22, 0.67], p<.001). The observed higher executive reserve in women was particularly pronounced in C9orf72 carriers (Cohen's d=0.77, 95% CI[0.35, 1.20], p<.001) but not significant in GRN (p=0.48) and MAPT carriers (p=0.07). No differences in behavioral reserve reached statistical significance.ConclusionFemale sex might modulate early cognitive performance and confer higher executive reserve in FTLD mutation carriers.

AbstractBackgroundThere is increasing need for noninvasive biomarkers of Alzheimer’s Disease (AD) neuropathologic change for early detection and intervention through risk‐factor modification and disease‐modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discriminate between observed biological age from chronological age for healthy aging. Deviation of true age from predicted age has been associated with heart failure, hypertension, and coronary heart disease. However, the association between EKG‐based age and underlying neuropathology has not been studied.MethodWe used a cohort of 336 participants from the Mayo Clinic Study of Aging, a population‐based prospective study of residents in Olmsted County, Minnesota, with antemortem EKG and post‐mortem brain autopsy (Table 1 & 2). Age was predicted from EKG using a validated convolutional neural network algorithm. We used ordinal logistic regression to identify associations between lower neuropathologic scores of AD (Thal phase, neuritic plaque score, Braak stage, and overall AD neuropathologic change) and the Age‐Gap, defined as EKG‐predicted age minus chronological age. Results were controlled for true age, sex, and APOE e4 carrier status. Neuropathologic scales were grouped into none, low, intermediate, and high categories. Observations were weighted by the inverse of years between last EKG and time of death to account for the time difference between acquisition of EKG and autopsy.ResultWe found that an increasing Age‐Gap (i.e. older predicted age) was inversely associated with lower overall AD neuropathologic change (OR 0.93 [0.89, 0.97], p = 0.002). Investigation of the components of AD neuropathologic change showed that an increasing Age‐Gap was also inversely associated with lower amyloid metrics such as Thal phase (OR 0.85 [0.80, 0.89], p < 0.001) and neuritic plaque scores (OR 0.91 [0.87, 0.96], p < 0.001), but not tau metrics like Braak staging (OR 1.05 [1.00, 1.10], p = 0.05).ConclusionIn this work, we show that older predicted age on EKG, a noninvasive biomarker associated with vascular and biological aging, is associated with higher amyloid burden and neuropathologic change of AD.

AbstractBackgroundThe immune system is substantially involved in the development and progression of age‐related cognitive decline and Alzheimer’s disease (AD).MethodAs genetic and environmental factors interactively impact these conditions, we investigated how risk factors such as APOE genotype, age, and sex influence immune activation markers and AD biomarkers in cerebrospinal fluid (CSF) in elderly individuals enrolled in the Mayo Clinic Study of Aging cohort. Among cognitively unimpaired individuals aged over 65 at the baseline visit (N=298), we measured 365 CSF immune activation markers using the proximity extension assay.ResultWe found that age, sex, and diabetes status are associated with altered CSF levels of immune activation markers independently of other factors. For CSF AD biomarkers, we observed significant positive correlations between age and total tau, phosphorylated tau‐181 (p‐tau181), neurofilament light (NfL), and YKL40. APOE4 was also associated with lower Aβ42 and higher SNAP25 in CSF. We further examined whether baseline visit variables can predict cognitive decline, represented by the conversion from CDR=0 to CDR>0. We found that age, Aβ42, NfL, and REG4 were independently correlated with CDR conversion risk. When the cohort was dichotomized by their median values, older participants with lower Aβ42, higher NfL, and higher REG4 at baseline developed cognitive impairment during the follow up with a c‐index of 0.762 while age alone had a c‐index of 0.699.ConclusionTogether, our results suggest that assessing CSF immune activation markers and AD biomarkers can improve the prediction of cognitive impairment risk in the elderly.

AbstractBackgroundThe association between [18F]Flortaucipir (FTP) and [18F]MK6240, two commonly used tau‐PET tracers in Alzheimer’s disease (AD), varies due to distinct binding properties and off‐target signal regions. Our study aims to elucidate the biological factors influencing this association and evaluate the applicability of a common equation across different on‐target regions.Method113 individuals from the HEAD dataset (11 young, 58 cognitively unimpaired elderly, and 44 cognitively impaired) underwent [18F]MK6240, [18F]FTP and Aβ‐PET scans. Images were processed using the inferior cerebellar grey (CG) as the reference region, with subjects categorized by Aβ status. Stepwise linear fitting identified off‐target (OFF) regions impacting tracer associations within the whole cortex, Braak and temporal‐meta regions of interest (ROIs). OFF regions included were a composite mask (pallidum, putamen, caudate ‐ PPC) to address collinearity in subcortical nuclei, as well as the region with the greatest partial volume effects (PVE) on the ROI (meninges, choroid plexus, and the hotspot medial to entorhinal cortices (EC)). Akaike information criteria (AIC) analyses tested linear models with various off‐target regions.ResultPPC and PVE regions influenced tracer association in on‐target regions. PPC binding is associated with age. PPC and meninges retention, along with Aβ status interaction, demonstrated the best model fit for the whole cortex (Figure 1). Weighting factors derived from the whole cortex model were applied to Braak regions and temporal meta‐ROI. Braak III‐VI and temporal‐meta ROI showed optimal models including OFF regions and Aβ status interaction (Figure 2). Forcing the whole equation gave optimal results. For Braak I (EC), adding the OFF covariates only was the best model. For Braak II (hippocampus), the best model included OFF regions and the interaction with Aβ status (Figure 2). Forcing the whole cortex equation onto Braak I and Braak II yielded suboptimal fits.ConclusionHarmonizing tau‐PET tracers involves considering significant covariates. Integrating meningeal and age‐related off‐target regions enhances [18F]MK6240 and [18F]FTP association in on‐target regions, further improved by Aβ status inclusion. While the whole cortex model may not suit early ROI, it proves effective for Braak III and beyond. Creating an equation for tracer translation necessitates incorporating meningeal and subnuclei off‐target retention.

AbstractBackgroundTau‐PET tracers allow for in vivo Braak staging of individuals in the Alzheimer’s disease (AD) continuum. The impact of tracers’ characteristics for Braak staging using tau‐PET remains unclear. Therefore, we performed a head‐to‐head comparison of Braak staging using first‐ and second‐generation tau‐PET tracers.MethodWe assessed 51 cognitively unimpaired (CU) and 49 cognitively impaired participants (mean [SD] age 69.4 [7.5] years) with at least two tau‐PET ligands ([18F]MK6240, [18F]AV1451, and/or [18F]RO948) at McGill University, as part of the HEAD study. We calculated standardized uptake value ratios (SUVR) in Braak‐like regions of interest (ROI) for each ligand and investigated their association using Spearman’s correlation. Thresholds defined the presence of tauopathy in each Braak ROI, which was used to assign a Braak stage to each participant. Finally, we assessed the agreement between the Braak staging provided by each tracer, both for the traditional (using separate Braak stages – 0, I, II, III, IV, V, and VI) and simplified (using joint Braak stages – 0, I‐II, III‐IV, and V‐VI) frameworks.ResultIn all Braak ROIs, we found positive correlations between the SUVR values of the three tracers (Figure 1). The strongest correlation was between [18F]MK6240 and [18F]RO948 in Braak II (r=0.94), and the weakest between [18F]AV1451 and [18F]RO948 in VI (r=0.51). Inter‐tracer agreement regarding the latest stage of abnormality was substantial or nearly perfect between pairs of tracers but moderate between the three tracers (Figure 2A). The simplified framework presented greater agreement compared to traditional Braak staging, except between [18F]MK6240 and [18F]RO948. At the ROI level, the lowest agreements were observed for Braak II and I‐II, when [18F]MK6240 and [18F]RO948 were compared to [18F]AV1451 (Figure 2B‐C). [18F]AV1451 had the highest probability of overestimating the Braak stage assigned by [18F]MK6240 and by [18F]RO948, especially at earlier stages (Figure 3).ConclusionThe ligands presented moderate to nearly perfect agreement for in vivo staging of AD severity. Off‐target binding of [18F]AV1451 to the choroid plexus might explain disagreements at early stages. Overall, slight improvements in agreements were achieved with the simplified framework, as observed in histopathological staging.

Abstract Background Diffusion tensor imaging along perivascular spaces index (DTI‐ALPS), which measures diffusivity increases in the perivascular spaces along the medullary veins, is being increasingly utilized as a surrogate marker of glymphatic clearance (Taoka et. al. Jpn J Radio 2017). Lower DTI‐ALPS index means higher measured diffusivity from the perivascular space, and so it is expected to decrease with age. However, correlation with aging and dementia markers have not been systematically evaluated. The goal of this work was to automatically estimate DTI‐ALPS in a large sample and evaluate the variance explained by various demographic and imaging markers. Methods We identified 2739 participants aged 30+ years (mean 69.6, 48% female, 88% CU) in the population‐based Mayo Clinic Study of Aging with diffusion MRI. We estimated DTI‐ALPS through automated placement of regions as published previously (Liu et al. AAIC 2023) and shown in Figure 1. We examined the associations of DTI‐ALPS with demographics (age, sex), vascular risk (cardiovascular and metabolic condition score), clinical data (diagnosis, global cognition) and imaging markers (white matter hyperintensity (WMH), global amyloid load from PIB‐PET, and the temporal meta‐ROI Tau‐PET SUVR) using partial Pearson correlations and ANCOVA with adjustments for age and sex. Results DTI‐ALPS was negatively correlated with age, higher in females, declined with vascular risk, and lower in MCI/dementia groups (Figure 2). Higher WMH and higher amyloid were associated with lower DTI‐ALPS whereas Tau‐PET SUVR was not associated with DTI‐ALPS. Conclusions DTI‐ALPS can be automatically computed in large samples. Vascular risk and WMH had stronger associations with the computed DTI‐ALPS compared to AD biomarkers (amyloid and tau) suggesting that the surrogate was more likely linked to vascular injury.

AbstractBackgroundThere is a strong link between tau and progression of Alzheimer’s disease (AD), necessitating an understanding of tau spreading mechanisms. Prior research, predominantly in typical AD, suggested that tau propagates from epicenters (regions with earliest tau) to functionally connected regions. However, given the constrained spatial heterogeneity of tau in typical AD, validating this connectivity‐based tau spreading model in AD variants with distinct tau deposition patterns is crucial.MethodWe included 269 amyloid‐β‐positive (PET/CSF) individuals with clinically diagnosed atypical AD (113 posterior cortical atrophy, PCA‐AD; 83 logopenic variant primary progressive aphasia, lvPPA‐AD; 33 behavioural variant AD, bvAD; 40 corticobasal syndrome, CBS‐AD) and 68 with typical AD from 12 international cohorts, who underwent tau‐PET (54% [18F]AV1451/[18F]flortaucipir/Tauvid, 27% [18F]MK6240, 19% [18F]PI2620). Using Gaussian mixture modeling including amyloid‐β‐negative controls, cross‐sectional tau‐PET standardized uptake value ratios within Schaefer‐200 atlas regions were transformed to tau positivity probabilities. Tau epicenters were defined as the 5% regions with highest tau positivity probabilities. For each variant, the association between functional connectivity‐based distance (using the 30% strongest positive region‐to‐region connections of a group‐average connectivity matrix from ADNI elderly controls) and tau‐PET covariance (group‐average correlation per region pair) was assessed through linear regression, adjusting for age, sex, site, and Euclidean distance. Regions were categorized based on functional proximity to the epicenter (quartiles 1‐4) and tau positivity probabilities were assessed accordingly.ResultTau positivity probabilities matched clinical variants, with a posterior pattern in PCA‐AD, left‐hemispheric dominant pattern in lvPPA‐AD, widespread pattern in bvAD, sensorimotor cortex involvement in CBS‐AD, and temporo‐parietal predominance in typical AD (Figure 1). In line with this, tau epicenters were highly heterogeneous across variants (Figure 1). In all variants, greater tau‐PET covariance was associated with shorter functional connectivity‐based distance (Figure 2). We observed that regions in closer functional proximity to the epicenter exhibited higher tau positivity probabilities than regions functionally further away (p<0.05, Figure 3).ConclusionThis multi‐center study shows that the brain’s functional architecture serves as a universal predictor of tau spreading in AD. Since tau is a key driver of neurodegeneration and cognitive decline in AD, this finding holds potential for personalized medicine and defining participant‐specific endpoints in clinical trials.