Citations

AbstractBackgroundIdentification of cognitively unimpaired (CU) individuals who may progress to mild cognitive impairment (MCI), is a pressing issue in the Alzheimer’s disease (AD) field, since therapeutic interventions may be more effective in the absence of cognitive impairment and neurodegeneration. CU individuals positive for amyloid and tau PET are very likely in the AD pathway. In out‐patient cognitive screening, we use rapid and simple tests such as The Montreal Cognitive Assessment (MoCA) ‐ a composite of executive, visuospatial, naming, attention, language, abstraction, delayed recall, and orientation performances. We hypothesize that sub‐categorizing the CU group based on the presence of amyloid (A) and tau (T) pathologies, as measured by PET, can reveal subtle cognitive deficits, even in a clinical test with low sensitivity such as MoCA.MethodWe included 88 CU [defined as CDR global score of 0] older adults with available head‐to‐head MK6240 tau‐PET and Flortaucipir tracers from the ongoing HEAD multi‐site observational study. They also underwent Amyloid PET imaging using PiB or NAV4694. MoCA testing was done around the imaging time. To increase similarity with clinical practice, Amyloid and Tau PET positivity was defined using visual reads. Based on tracer uptake status, we categorized the individuals into A‐T‐, A‐T+, A+T‐ and A‐T‐ groups. Unpaired t‐test and one‐way ANOVA were used to test for significant differences between groups.Result2‐tailed p‐values were significant between the A‐T‐ group and the A+T+ group for MoCA total score [p: 0.0025 and p: 0.0025]. When we stratified MoCA in the different cognitive domains, we found that the results were driven by MoCA (memory) Delayed Recall score [p: 0.0007 and p: 0.0162]. A‐T+ vs A+T+ was also significant [p: 0.0099] for MoCA Delayed Recall. Both tau tracers (MK6240 and Flortaucipir) showed similar performance for determining T+ and identifying cognitive decline.ConclusionMoCA, a simple and commonly administered in‐office neuropsychological test, can detect subtle cognitive dysfunction in CU older adults who are Aβ and tau positive (A+/T+). These CU A+/T+ individuals are likely on the path to developing AD dementia.

AbstractBackgroundLate‐Onset Alzheimer’s Disease (LOAD) is characterized by genetic heterogeneity and there is no single model explaining the genetic mode of inheritance. To date, more than 70 genetic loci associated with AD have been identified but they explain only a small proportion of AD heritability. Structural variants (SVs) may explain some of the missing AD heritability, and specifically, their segregation in AD families has yet to be investigated.MethodWe analyzed WGS data from 197 NHW families (926 subjects, 58.5% affected) and 214 CH families (1,340 subjects, 59.17% affected). Manta, Absinthe, and MELT were used for large insertions/deletions calling from short‐read WGS, combined with Sniffles2 calls from 4 ONT‐sequenced genomes and an external SV call set from HGSVC on 32 PacBio‐sequenced genomes from the 1000 Genomes Project. Genotyping produced a unified project‐level VCF. We identified 45,251 insertions and 76,566 deletions genome‐wide. Variants were tested for segregation and pathogenicity using Annot‐SV, cadd‐SV, and Variant Effect Predictor. Segregation required SV presence in all affected family members and only in unaffected members five years younger than average disease onset.ResultWe identified 453 insertions and 598 deletions segregating in 78.68% and 87.31% of NHW families, respectively. In CH families, 432 insertions and 460 deletions were segregating in 75.23% and 72.90% of the families, respectively. Genes overlapping with the SVs exhibited high expression levels in brain tissues. Notably, around 93% of insertions and 76% of deletions segregating in NHW and CH families were less than 1 kilobase pair (1kbp) in length. A total of 79 insertions and 96 deletions were found to be segregating in both NHW and CH families. Interestingly, a segregating insertion was observed in CH families overlapping within the CACNA2D3 gene, which was previously reported in a CH GWAS for clinical AD. A deletion segregating in NHW overlapped with the PSEN1, and another in a CH family overlapped with the PTK2B gene.ConclusionOur findings suggested that there are several SVs associated with familial AD across CH and NHW families. Prioritizing the SVs based on their effects on gene function and expression will be helpful in understanding their contributions in AD.

AbstractBackgroundUnderstanding the relationship between AβPET and AβCSF biomarkers will define their potential utility in Aβ treatment. Few longitudinal or neuropathological comparisons have been reported. We assessed the relationship of AβPET and AβCSF biomarkers in a large community cohort.MethodParticipants 50+ years with coincident AβPET and AβCSF biomarkers (pTau181/Aβ42, n=505, and Aβ42/40, n=54) were included. Of these participants, a subgroup was identified that had neuropathology data (n=47) of which many had longitudinal AβPET and CSF testing. The relationships of AβPET and CSF biomarkers were assessed cross‐sectionally in all participants and longitudinally in those with autopsy data. Analyses were performed using AβPET Meta‐ROI and AβPET early‐Aβ‐deposition ROIs (ROIs with early Aβ deposition: fusiform, angular, inferior temporal, middle temporal, middle occipital, and calcarine).ResultIn biomarker tests performed at the same time, more participants were AβPET+ and CSF‐ than AβPET‐ and CSF+ using Meta‐ROI AβPET. This discordance occurred more frequently when using early‐Aβ‐deposition regions (Figure 1). Of 47 participants with autopsy data, 35 had Thal stage ≥1. The sensitivity for earliest detection of cortical Aβ neuropathology by AβPET was 89% (31/35) and by CSF was 66%, (23/35). Eight of these 35 had positive AβPET findings and negative coincident CSF tests at the first time tested. Of these, 5/8 were Meta‐ROI and early AβPET ROI positive. The other 3/8 were only positive on early‐Aβ AβPET ROIs. Four of the 8 with negative CSF and positive AβPET had subsequent testing and 2/4 became positive on CSF tests after 1.5 years while 2/4 continued to be negative on subsequent testing.ConclusionAβPET is more sensitive for detection of early cortical Aβ deposition than CSF biomarkers in cross‐sectional or autopsy data. While the assessment of AβPET by standard Meta‐ROI AβPET methods showed cortical Aβ deposition occurring when not indicated by CSF biomarkers, AβPET ROIs for early Aβ deposition detected additional participants that were AβPET positive and AβCSF negative. These findings have implications for detecting early Aβ neuropathology among CU participants in Aβ prevention trials when robust detection is needed. Further work is needed to compare Aβ plasma biomarkers and AβPET using these methods.

AbstractBackgroundOur Alzheimer Disease Metabolomics Consortium (ADMC), part of the Accelerating Medicines Partnership for AD (AMP‐AD) and in partnership with AD Neuroimaging Initiative (ADNI), applied state‐of‐the‐art metabolomics and lipidomics technologies combined with genomic and imaging data to map metabolic failures across the trajectory of the disease. Our studies confirmed that peripheral metabolic changes influenced by the exposome inform about cognitive changes, brain imaging changes, and ATN markers for disease confirming that peripheral and central changes are connected, in part through the metabolome.MethodsTo map the biochemical changes in AD, we used various targeted and untargeted metabolic platforms to profile ∼800 postmortem brain tissue, and ∼ 5000 blood samples.ResultsRecently, we built a comprehensive reference map of extensive AD‐related metabolic changes in brain, spanning multiple AD‐related traits, including neuropathological b‐amyloid and tau tangle burden, as well as late‐life cognitive performance. Using this resource, we extracted novel metabolic including bioenergetic pathways, cholesterol metabolism, neuroinflammation, broad impairment of osmoregulation, an imbalance between excitatory/inhibitory neurotransmitter ratios and identification of tau load as a potential driver of metabolic dysfunction in the AD brain, with minimal contributions from b‐amyloid load. As AD and progressive supranuclear palsy (PSP) share the pathological feature of tauopathy and metabolic alterations, we compared their metabolomic profiles to identify shared biological pathways that could be targeted for therapeutic interventions. Our findings indicate that both diseases display oxidative stress, mitochondrial dysfunction, and tau‐induced polyamine stress response.ConclusionOverall, through our studies, (1) We identified biochemical processes altered in AD, with findings supported across both metabolomic and proteomic data, indicating multimodal deregulation. (2) Our research pinpointed widespread AD‐related biochemical changes across various brain regions with differing levels of neuropathology. While there are many overlapping changes across the brain regions, each region also has its distinct metabolic alterations. (3) We identified biochemical processes disrupted by AD, with parallel findings in other neurodegenerative diseases, hinting at broader implications in neurodegenerative research. Currently, we are working on mapping widespread connections of the brain metabolome with various determinants of AD namely genome, gut microbiome, exposome, and linking with peripheral metabolic alterations in AD.

AbstractBackgroundAutopsy studies in Lewy Body Disease (LBD) indicate that cardiac sympathetic denervation precedes Lewy body pathology and neuronal loss in the brain. Myocardial 123I‐metaiodobenzylguanidine (MIBG) scintigraphy noninvasively assesses postganglionic cardiac sympathetic denervation in LBD and is considered an important biomarker in the international diagnostic criteria of Dementia with Lewy Bodies and Parkinson’s Disease (PD). Despite the internationally recognized importance of MIBG scintigraphy in LBD, its use in neurodegenerative disorders is not FDA approved for this indication and is rarely used in the US for neurological research. We sought to add MIBG scintigraphy to our multimodal imaging research in prodromal LBD. We launched pilot MIBG scintigraphy study in isolated REM Sleep Behavior Disorder (iRBD) individuals, who are typically regarded as representing evolving LBD.MethodWe conducted MIBG scintigraphy and 123I‐FP‐CIT SPECT in seven individuals with iRBD including two RBD with mild cognitive impairment (MCI), one with PD, and one healthy control (mean age of 71.5±8.3). Heart‐to‐mediastinum (H/M) ratio of the MIBG uptake on delayed image (3‐4 hours after Adreview® injection) was calculated with MIM. Cutoff value of 1.68 was used for determining abnormal H/M ratio (Yoshita et al, 2006). Nigrostriatal dopamine transporter uptake was evaluated with DaTQUANT 2.0 software.ResultFour iRBD participants including two RBD with MCI had abnormal MIBG scintigraphy (mean age 73.5±8.6, delayed H/M ratio 1.25±0.29)) and three iRBD participants had a normal scan (mean age 67.8±4.5, delayed H/M ratio 2.57±0.25). Delayed H/M ratio was 1.00 for the PD participant and 2.60 for healthy control participant. Among the four iRBD participants with abnormal MIBG scintigraphy, one participant had 123I‐FP‐CIT SPECT with DaTQUANT putamen z‐score <‐2.0 and three participants had z‐score in the range of ‐1.5 and ‐1.0. All iRBD participants with normal MIBG scintigraphy had normal 123I‐FP‐CIT SPECT with DaTQUANT putamen z‐score >‐1.0.ConclusionMIBG scintigraphy was abnormal in half of this small cohort. Our preliminary data suggests its utility in detecting early signs of evolving LBD. Increasing the number of the MIBG scintigraphy in iRBD participants and controls are planned to determine if MIBG scintigraphy can facilitate early detection of progression for use in clinical trials targeting LBD.

AbstractBackgroundCorticobasal syndrome (CBS) is a progressive neurodegenerative syndrome that can result from several underlying neuropathologies, including Alzheimer’s disease (AD). Little is known about the utility of blood plasma measures to predict PET biomarker confirmed AD in CBS.MethodEighteen CBS patients (8 PET Aβ+; 10 PET Aβ‐) and 8 cognitively unimpaired Aβ‐ individuals (CU) were recruited by the Neurodegenerative Research group from the Department of Neurology, Mayo Clinic, Rochester, MN, underwent Aβ (Pittsburgh Compound‐B) and tau (18F‐flortaucipir) PET and provided a blood sample. Blood plasma analysis was performed to assess p‐tau 181, total tau, Aβ 42/40, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) using the Quanterix Simoa platform. Plasma concentrations were compared between Aβ‐, and Aβ+ CBS and controls using Kruskal‐Wallis corrected for multiple comparisons using Dunn’s test, with effect size measured with area under the receiver operating characteristic curve (AUROC) analysis. Spearman correlations were calculated to assess relationships between plasma concentrations and Aβ and tau PET uptake.ResultThe CBS Aβ+ group showed reduced Aβ42/40 ratio, with elevated p‐tau 181, GFAP and NfL plasma concentrations compared to CU, while the CBS Aβ‐ group only showed elevated NfL concentration compared to CU (Figure 1). Both p‐tau 181 (AUROC=0.95) and GFAP (AUROC=0.91) plasma concentrations were able to differentiate CBS Aβ‐ from CBS Aβ+. Plasma p‐tau 181 and GFAP concentrations showed a positive correlation to both Aβ and tau PET uptake, and the Aβ42/40 ratio showed a negative correlation to Aβ PET uptake (Figure 2). No correlations were observed between NfL and Aβ and tau PET.ConclusionThis study supports the use of plasma p‐tau 181 and GFAP to detect AD in CBS, while NfL shows potential as a nonspecific disease biomarker of CBS regardless of underlying pathology. Plasma concentrations of these biomarkers may potentially be incorporated into clinical trials targeting CBS patients with underlying AD pathology.

AbstractBackgroundBlood plasma metrics provide excellent biomarkers of the presence of Alzheimer’s disease (AD) pathology in population cohorts. However, it is less clear whether plasma metrics correlate with PET measures of AD pathology in cohorts of patients with advanced AD disease, particularly in those with atypical clinical presentations of AD.MethodSeventy‐seven patients (visual variant=43, language variant=32, motor variant=1 and dysexecutive variant=1) with PET biomarker‐confirmed atypical AD were recruited by the Neurodegenerative Research group at Mayo Clinic, Rochester, MN, underwent Aβ (Pittsburgh Compound‐B) and tau (18F‐flortaucipir) PET and provided a blood sample. Blood plasma analysis was performed to assess p‐tau 181 and Aβ42/40 ratio concentrations using the Quanterix Simoa platform. Multivariate linear regression models assessed the relationship between plasma concentrations and both global Aβ, atypical AD summary and regional tau PET measures. For each PET measure, two separate models were fit to assess the influence of each plasma biomarker separately, while the third model included all plasma measures to assess the combined influence of all biomarkers on PET uptake. To investigate differences in plasma concentrations between atypical AD phenotypes, we performed Mann‐Whitney’s test across the visual and language variants of AD.ResultPlasma Aβ42/40 ratio showed positive associations with tau PET uptake, specifically within precuneus, parietal and temporal regions and was not associated with Aβ PET. There was no relationship between plasma p‐tau 181 and Aβ or tau PET uptake. However, the model that included both plasma biomarkers showed that with the addition of p‐tau 181, all significant associations between Aβ42/40 ratio and regional tau uptake were weakened to trends. The Aβ42/40 ratio continued to show no relationship with global Aβ‐PET uptake and plasma p‐tau 181 showed no relationship to PET measures (Figure 1). Lastly, no significant difference in plasma Aβ42/40 and p‐tau 181 levels was noted between the visual and language variants of AD (Figure 2).ConclusionThe lack of associations between plasma concentrations and PET measures of Aβ and tau in this relatively advanced AD cohort may suggest that plasma concentrations have greater utility as diagnostic biomarkers of early AD as compared to advanced AD.

AbstractBackgroundSeveral programs are available for automatically “de‐facing” brain images. These are designed to prevent the potential use of face imagery to re‐identify research participants. Previous works have compared available software and mri_reface is frequently among the top methods. The “Minimally Invasive DeFacing of MRI images” (MiDeFace) program was more recently added to FreeSurfer, but minimal validation of MiDeFace has been published.MethodWe ran mri_reface 0.3.3 and MiDeFace 7.3.2 on two datasets: A) 207 participants with face photographs and 3D T1‐weighted and 3D T2‐weighted‐FLAIR MRI from the Mayo Clinic Study of Aging (all unimpaired, Siemens); and B) 300 participants from ADNI3 with 3D T1‐weighted MRI (50 unimpaired + 50 with clinical AD, from each of 3 vendors). For dataset A, we tested the rate at which Microsoft Azure face recognition matched each participant’s photographs with the correct MRI, comparing unmodified images, those from mri_reface, and those from MiDeFace. For dataset B, we compared measured regional brain volume and cortical thickness from original vs. de‐faced images with SPM12, FreeSurfer 7.4.1, and the FSL‐UK Biobank pipeline. For both datasets, images were visually rated to assess removal of the face and retention of brain tissue.ResultAutomated face recognition match rates were lower with mri_reface (17/207 8.2%) than with MiDeFace (24/207 11.6%), indicating stronger identity protection with mri_reface, but both were greatly reduced vs. unmodified images (203/207 98.1%). Example images with both programs are shown in Figure 1. From visual ratings (Table 1), rates of complete success (modification of the entire face while retaining the entire brain) were much higher with mri_reface, but the great majority of failures from MiDeFace were subtle and occurred in relatively less recognizable face regions. The effects of both programs on downstream imaging measurements (Figure 2) were small and very comparable, but MiDeFace was favored mildly for SPM12 and for FSL, while mri_reface was more favored for FreeSurfer.ConclusionAltogether, mri_reface more reliably removed the complete face, leading to smaller rates of potential re‐identification (8.2% vs 11.6%). Effects on downstream image measurements were small and comparable with both methods.

Abstract Background The Mayo Clinic Study of Aging (MCSA) is a longitudinal, population‐based study of residents of Olmsted County, Minnesota, USA. MCSA is releasing de‐identified clinical and imaging data on GAAIN.org to benefit the research community. Method We included longitudinal clinical data from all participants of the Mayo Clinic Study of Aging 30‐90 years of age (average 70.0y) through the first 15 years of the study. Clinical data includes age, sex, self‐reported race/ethnicity, APoE4 allele status, cognitive impairment status (normal, MCI, dementia, or other), height, weight, blood pressure, multi‐domain neuropsychological scores, neuroimaging summary measurements, medications, cardiovascular risk factors, social activity, sleep, mood/anxiety, functional measures, and more. For our initial release of imaging data, we selected the first imaging timepoint acquired on 3T GE MRI scanners and amyloid (PiB) PET during the same visit, from all available imaging participants 50‐90 years of age (average 70.7y). Currently, we included 3D T1‐weighted (MPRAGE) MRI (1x1x1.2mm 3 ), T2‐weighted FLAIR MRI (0.85x0.85x3.6mm 3 ), diffusion MRI (2.7mm resolution with 41 directions at b=1000 and 5 b=0), and numeric cortical global SUVR and Centiloid values from PiB PET scans at 40‐60 minutes post‐injection. All images are released in DICOM format and thoroughly de‐identified using CTP while ensuring preservation of necessary fields for analyses. T1‐weighted and T2‐FLAIR MRI were also de‐faced (identifiable face imagery was removed and replaced with an average) using mri_reface version 0.3.3 to further protect participants’ privacy, and all de‐faced images were visually inspected by a trained data scientist for quality assurance. Result The first phase of our public data release includes clinical data from 5925 unique participants from the Mayo Clinic Study of Aging (MCSA), each ranging from 1‐12 visits at roughly 15‐month intervals, and three MRI sequences (T1‐w, T2‐w‐FLAIR, and diffusion) from 1802 unique participants. Demographic summaries are given in Table 1. Conclusion This large, richly characterized, de‐identified clinical and imaging dataset is available to the research community at https://www.gaaindata.org/partner/MCSA , and it is suitable for a wide range of research uses. Additional data will be released over time, including de‐faced amyloid PET images.

AbstractBackgroundBlood‐based biomarkers show promise as a noninvasive, inexpensive method for measuring Alzheimer’s disease pathology throughout the lifecourse. However, the predictive and classification accuracy of these biomarkers at different stages of the lifecourse and among diverse, community‐dwelling populations requires further investigation.MethodBetween 2014 and 2015, 329 dementia‐free participants from the Atherosclerosis Risk in Communities Study underwent brain magnetic resonance imaging (MRI) and florbetapir positron emission tomography (PET). Amyloid positivity was defined as a global cortex standardized uptake value ratio greater than 1.2. Stored plasma samples collected in midlife (1993‐95, mean age 58.5 years) and late‐life (2011‐13, mean age 76.2 years) from a subsample of 259 participants were assayed in 2022 using Quanterix SiMoA. The assay quantified amyloid‐β (Aβ)42/40, phosphorylated tau at threonine 181 (p‐Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Unadjusted and covariate‐adjusted logistic regression models estimated the association between plasma biomarkers from midlife and late‐life and amyloid positivity in late‐life. Unadjusted receiver operating characteristic curves documented the classification accuracy of the plasma biomarkers separately and collectively.ResultThe sample (Table 1) included 151 women (58.1%), 105 Black participants (40.4%), and 136 (52.3%) participants with amyloid positivity. In models adjusted for demographics, lifestyle factors, cardiovascular factors, and the presence of APOE ε4 alleles, assays measured in midlife were not associated with amyloid positivity in late‐life (Table 2). However, late‐life measurements of Aβ42/40, p‐Tau181, and GFAP and change per decade from midlife to late‐life in p‐Tau181 and GFAP were associated with greater odds of amyloid positivity. The greatest classification accuracy (Figure 1) was achieved by using all assays measured in midlife and late‐life (AUC = 0.754), although the accuracy when using only late‐life measures was comparable (AUC = 0.737).ConclusionLate‐life plasma biomarkers of Alzheimer's disease neuropathology and astrogliosis were associated with late‐life amyloid positivity, but midlife plasma biomarkers obtained 16 to 20 years earlier did not exhibit a statistically significant association. Additional longitudinal research is needed to determine whether changes in plasma biomarkers measured before an individual is classified as amyloid positive can be used to identify individuals at risk of developing Alzheimer's disease.