Graff‐Radford, Jonathan

Abstract Background Monoclonal antibodies targeting amyloid‐β (Aβ), including aducanumab, have been tested to treat Alzheimer's disease (AD). However, limited data exist on neuropathology following treatment and the effects of amyloid‐related imaging abnormalities (ARIA). We report on such data from five aducanumab‐treated study participants with AD of whom two experienced ARIA. Methods Aducanumab‐treated study participants who came to autopsy ( n = 5) were matched to AD controls ( n = 12) based on the presence/type of autosomal dominant AD mutation, APOE genotype, age at cognitive symptom onset, and sex. We assessed cognitive measures, 18 F‐florbetapir PET centiloid, ARIA risk factors, and AD neuropathologic change. Using multiplex immunofluorescence, brain regions affected along Thal Aβ phases were stained for Aβ isoforms and phosphorylated tau, as well as for ARIA‐associated markers, including Perls’ Prussian blue to detect ferric iron, fibrinogen‐α for blood brain barrier integrity, membrane attack complex (C5b‐C9) for complement activation, and activated microglia (IBA1, CD68). Results Study participants included four males and one female, all carrying at least one APOE ε4 allele. Two participants carried a PSEN1 NM_000021.4:c.817G>A , p.Glu273Lys mutation. Cumulative aducanumab dosage ranged between 5–241 mg/kg, with death occurring 5–41 months after the last infusion. Amyloid burden on PET declined in all participants (range 15‐100 centiloids). Two participants experienced ARIA. Compared to AD controls, Aβ aa1‐8 and Aβ 42 were selectively reduced in cortical layer I of aducanumab‐treated study participants ( p <0.05) but not in the total cortex, as shown for the middle frontal cortex in Figure 1. No differences were observed in phosphorylated tau burden. Prussian blue‐stained hemosiderin was present in both treated and untreated AD cases. However, in participants with ARIA, hemosiderin accumulated in superficial cortical layers near CAA‐laden meningeal and penetrating vessels, which also exhibited extensive complement and microglial activation (Figure 2). Conclusions Aβ clearance and ARIA‐related findings were localized predominantly in superficial cortical layers, suggesting that aducanumab biodistribution is more pronounced in these regions rather than deeper cortical layers.

Abstract Background Aging and dementia are generally associated with lower metabolism in gray matter on FDG‐PET and have been widely studied. However, white matter (WM) metabolic changes remain understudied. We investigated the metabolic changes in WM associated with aging and dementia and their relationships with established Alzheimer's Disease (AD) biomarkers and cognitive outcomes. Method We analyzed data from 3,169 participants (72 ± 9 years; 53% male; 65% cognitively unimpaired) enrolled in the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). All participants underwent baseline MRI, amyloid‐PET, and FDG‐PET scans, as well as neuropsychological evaluations, with at least one clinical follow‐up. Voxel‐wise multiple regression analysis identified WM metabolic changes associated with global cognition in cognitively unimpaired MCSA participants ( n = 1,754). Univariate analyses and structural equation models were applied to elucidate the driving factors underlying the WM metabolic changes in MCSA data. Multiple regression and linear mixed‐effects models evaluated the explanatory utility of WM metabolism for baseline global cognition and cognitive decline in both cohorts. In a subset of MCSA participants, we evaluated neurite density index from diffusion MRI as a surrogate of WM health and glial fibrillary acidic protein (GFAP) as a surrogate of neuroinflammation to understand underlying mechanisms. Result We identified an atypical WM signature (AWM‐Signature) in the anterior and superior corona radiata, where glucose metabolism increased as cognitive performance worsened (Figure 1). Hypermetabolism in AWM‐Signature was related to increased age, amyloidosis, vascular disease, and neuroinflammation, as well as decreasing WM neurite density in the same regions (Figure 2). Hypermetabolism in AWM‐Signature regions predicted faster future cognitive decline, even after accounting for demographics, amyloid, and vascular disease metrics. The results observed in the AWM‐Signature regions were consistently in the opposite direction of the associations seen with gray matter and typical WM regions. Conclusion We found unexpected metabolism increases in corona radiata, a hub that carries significant portion of the brain's neural traffic, with aging and disease which likely indicates a compensatory response. These changes may be useful cognitive resilience measures and require further investigation to understand their role in maintaining cognitive function.

Abstract Background Chronic disease conditions lead to brain changes elevating dementia risk. We hypothesized that tissue microstructural changes caused by type 2 diabetes mellitus (T2DM) and hypertension could be effectively captured using an advanced diffusion MRI technique, Neurite Orientation Dispersion and Density Imaging (NODDI). Our goal was to evaluate the usefulness of composite NODDI scores, customized for increased hemoglobin (Hb) A1c and systolic blood pressure as predictors of cognition and compare their performance to other measures typically used to assess dementia risk. Method We collected multi‐shell diffusion MR on 1024 Mayo Clinic Study of Aging participants. NODDI measures (neurite density index (NDI), orientation dispersion index (ODI), and free water fraction (FWF)) in gray matter (GM) and white matter (WM) regions were estimated. To identify brain regions susceptible to both conditions, a multiple linear regression model was fitted for each NODDI metric, using hemoglobin (Hb) A1c (in N = 305 participants) or systolic blood pressure ( N = 906) as predictor, with age, sex, and body mass index (BMI) as covariates. Event‐based modeling was performed with the identified NODDI metrics to investigate the chronological pattern along the course of chronic condition development from this cross‐sectional dataset. Composite metrics were created as a weighted sum of susceptible NODDI metrics. We evaluated the impact of both conditions on cognition using gradient boosting models incorporating composite metrics, additional brain measurements, and demographics. Result Increased HbA1c impacts both GM and WM NODDI relatively evenly, whereas increased systolic blood pressure related NODDI changes were mainly seen in WM (data not shown). With increased HbA1c, the earliest changes were observed in GM ODI, followed by changes in the pons and cortical FWF (Figure 1A). With increased systolic blood pressure, the earliest changes occurred in association fibers and middle cerebellar peduncle, followed by FWF change in lobar and projection WM fibers (Figure 1B). Average feature importance from 100 gradient boosting models for cognition prediction suggests that diabetes‐induced GM changes play a moderately important role in cognitive decline (Figure 2). Conclusion Chronic medical conditions, especially T2DM, result in brain changes that are captured by NODDI and contribute moderately to cognitive performance.

Abstract Background Clinically available plasma p ‐tau217 assays may be utilized in the determination of eligibility for amyloid‐lowering therapies. Our institution recently introduced a plasma p ‐tau217 assay in clinical practice using a two‐cutpoint approach that optimizes the accuracy while minimizing false positive risk and categorizing ≤20% of results as intermediate. In this study, we prospectively evaluate the performance of a plasma p ‐tau217 assay for detection of amyloid pathology using these predefined cutpoints. Method Patients recruited were being considered for lecanemab therapy at a tertiary care subspecialty clinic. Plasma p ‐tau217 was measured using the clinically available Fujirebio Lumipulse p ‐tau217 assay at Mayo Clinic, and results (pg/mL) were classified as either negative (≤0.185), intermediate (0.186–0.324) or positive (≥0.325). Amyloid pathology was determined by amyloid‐PET and/or CSF p ‐tau181/Aβ42. Result Ninety‐three patients were recruited between November 2023 and December 2024. Of these, 49 underwent amyloid‐PET, 4 had CSF p ‐tau181/Aβ42 measurements, and 40 had both PET and CSF results (with 3/40 discordant cases), for use in the assessment of amyloid pathology. The three discordant cases were considered amyloid positive for purposes of plasma p ‐tau217 comparison. Overall amyloid positivity was 90%. Plasma p ‐tau217 results were positive in 83% (77/93), negative in 6% (6/93), and intermediate in 11% (10/93) of patients. An elevated p ‐tau217 predicted the presence of amyloid positivity with a diagnostic accuracy of 96% (95% CI 90% − 99%). In the intermediate range, 80% of patients were positive for amyloid pathology. In patients with a definitive p ‐tau217 result (positive or negative), the positive and negative predictive values were 97% and 83%. There were three patients with discordant results: one PET+/p‐tau217‐, one PET‐/p‐tau217+, and one CSF‐/p‐tau217+. Clinical presentation of these discordant cases was examined, and relevant findings will be discussed. Conclusion The Lumipulse plasma p ‐tau217 assay exhibited acceptable diagnostic accuracy for the determination of amyloid pathology when compared to either amyloid‐PET or CSF p ‐tau181/Aβ42. This prospective study confirms the utility a two‐cutpoint approach for interpreting plasma p ‐tau217 for the assessment of amyloid pathology eligibility for amyloid‐lowering therapies in a heterogenous clinical cohort.

Abstract Background Cerebral amyloid angiopathy (CAA), characterized by the accumulation of amyloid‐beta in the cerebrovasculature, affects blood vessel integrity leading to brain hemorrhages and an accelerated cognitive decline in Alzheimer's Disease (AD) patients. Our previous genome‐wide association study (GWAS) identified a LINC‐PINT splice variant associated with lower CAA levels in individuals without the APOE e4 allele and higher levels of LINC‐PINT expression in the brain of AD donors. In this study, we expand our GWAS to include additional donors with AD and those lacking significant AD neuropathology (non‐AD), with available CAA scores. Method In our prior study we assessed 853 AD donors. We expanded this by addition of genetic data from 550 AD and 502 non‐AD donors from the Mayo Clinic Brain Bank, scored for CAA. We performed QC and imputation (TOPMED) of all datasets. We conducted GWAS in AD only ( N = 1,363), non‐AD only, and all donors ( N = 1,865) by testing imputed variant dosages for association with square root transformed CAA using linear regression, adjusting for relevant covariates. To assess associations in the context of major CAA risk factors, we performed interaction analysis with APOEe4 presence and sex; and pursued stratified analyses. Result Variants at the APOE locus were identified as the most significant in our study. In addition, several other variants approached genome‐wide significance after adjusting for AD neuropathology (Braak stage and Thal phase). The LINC‐PINT splice variant remained associated with lower CAA scores in AD donors without the APOEe4 risk allele . To enhance the robustness of our findings, we are pursuing further expansion of our study cohort to include other available datasets. To explore putative functional consequences of key variants we are collecting peripheral gene expression measures in participants from Mayo Clinic with neuroimaging measures including microhemorrhages. Conclusion We expect this study will provide further insights into the genetic architecture underlying risk for CAA, both in the context of significant AD pathology, and without. Characterization of genetic variants and their functional outcomes may lead to new avenues of research aimed at identifying biomarkers and therapies to treat CAA, a common co‐pathology in those with and without AD.

Abstract Background Individuals with coexisting Alzheimer's disease (AD) and disorders of CSF dynamics such as normal‐pressure hydrocephalus (NPH) have lower CSF Aβ42 levels, which can confound interpretation of AD CSF‐biomarkers. However, little is known about how CSF dynamics influences plasma AD biomarkers. The objective of this study was to determine whether mass spectrometry‐based blood tests for AD are associated with disproportionately enlarged subarachnoid space hydrocephalus (DESH) on MRI. Method 509 Individuals from the Mayo Clinic Study of Aging, with MRI, 11C‐Pittsburgh compound B (Aβ) PET scans, and plasma phosphorylated‐tau protein 217, Aβ40 and Aβ42 were included. We performed an age‐adjusted logistic regression to determine whether abnormal values of p ‐Tau 217 (Abnormal if >1.55 pg/mL), %p‐tau217 defined as the ratio of phosphorylated Tau217 to non‐phosphorylated tau x 100% (Abnormal if >1.55%), Aβ42 (Abnormal if <41.5 pg/mL), and Aβ42/40 ratio (Abnormal if <0.094) were associated with the presence of DESH, as classified by a previously validated automated algorithm. Result Thirty‐two participants were classified as DESH‐positive. The median [IQR] age was 76.8 yrs [70.6, 82.9] for participants with DESH and 72.4 [59.3, 85.5] without DESH (Table 1). An abnormal plasma Aβ42 was associated with DESH (OR, 2.56 [1.19, 5.74], p = 0.018), but having an abnormal Aβ42/40 ratio (OR 2.01 [0.94, 4.56], p = 0.079) was not. There was no association between being DESH‐positive and an abnormal plasma p ‐tau217 (OR 1.30 [0.51, 3.09], p = 0.560) or with abnormal %plasma p ‐tau217 (OR 1.82 [0.74, 4.20], p = 0.17) (Figure 1). Subgroup analysis showed that only those participants who had an abnormal Aβ42 and positive amyloid PET (≥25 Centiloids) showed an association with DESH compared to biomarker‐ and PET‐negative participants (OR 3.10 [1.15, 8.43], p = 0.024 ) . Conclusion Understanding how CSF dynamics affect plasma biomarkers is critical for implementation. The relationship between lower Aβ42 and DESH is expected based on prior CSF studies, which can be addressed by using an amyloid ratio. p ‐tau217 shows robustness against DESH, making it a suitable biomarker in cases of abnormal cerebrospinal fluid dynamics although validation in a more severe CSF dynamics group is warranted.

Abstract Background The objective of this study was to determine the predictive value of amyloid‐PET versus plasma %p‐tau217 (defined as the ratio of phosphorylated to non‐phosphorylated Tau217 × 100), Aβ42/40, and the amyloid probability score (APS2; which combines of Aβ42/40 and %p‐tau217) in tau‐PET transitions (T‐ to T+). Method Participants included were from the Mayo Clinic Study of Aging (MCSA) and had plasma markers measured by mass spectrometry (IP‐MS; C2N), had an amyloid‐PET scan, and had a tau‐PET negative scan (SUVR<1.29, temporal meta‐ROI) at the baseline, along with one or more follow‐up tau‐PET scans. Cox proportional hazard models were used to evaluate the relationship between C2N plasma %P‐Tau217 and APS2 and incident tau‐PET positivity (SUVR>=1.29) adjusting for age, sex, and APOE ε4. An analysis was also performed using amyloid‐PET centiloid as the predictor. Both the plasma markers and amyloid‐PET centiloid were scaled so that a one‐unit change corresponds to the respective interquartile range (IQR) for comparability of hazard ratios (HR). The analyses were repeated in the BioFINDER‐2 cohort for validation. Result 255 MCSA participants (122 female [47.8%] were included with a median age of 71.9 (IQR: 65.8, 78.8) years old, of which 37 progressed to tau‐PET positivity. At baseline, 238 (93.7%) were cognitively unimpaired. Higher baseline %p‐tau217 (HR: 1.52 [95% CI: 1.28, 1.8]), amyloid‐PET centiloid (HR: 1.47 [95% CI: 1.20, 1.79]), and APS2 (HR: 1.62 [95% CI: 1.22, 2.16]) were associated with an increased risk of progressing to tau‐PET positive (Figure 1). Aβ42/40 (HR: 1.06 [95% 0.604–1.867] was not associated with incident tau positivity Among 605 participants who were tau negative at index date in BioFINDER‐2 (316 (52.2%) cognitively unimpaired), with a median age of 70.2 (IQR: 60.5‐76.4), 33 progressed to tau positivity with higher %p‐tau217 (HR: 1.8 [95% CI: 1.5, 2.17]), amyloid‐PET centiloid (HR: 2.3 [95% CI: 1.77, 2.97]), and lower Aβ 42/40 (HR: 0.42 [95% CI: 0.21, 0.85]) at index date all associated with an increased risk of progressing to tau‐PET positive. Conclusion In two independent cohorts , %p‐tau217 provides predictive information about those who will convert from tau negative to tau positive on PET scan with the associations being similar to amyloid‐PET.

Abstract Background Some data suggest that neuroinflammation may be associated with pro‐inflammatory states in the body. Systemic, proinflammatory cytokines (TNFα, IL‐1, IL‐6, etc.) are produced by a variety of comorbidities. Neuroinflammation has been shown to be associated with the accumulation of amyloid in the brain (Becher, Heneka). We hypothesized that systemic cytokine production could be associated with neuroinflammation and thereby be related to brain amyloid accumulation. This study aims to evaluate the relationship between neuroinflammation, amyloid accumulation and systemic cytokines. Method 90 participants from the Mayo Clinic Study of Aging (MCSA) received a fasting cytokine panel over a 5‐year span where IL‐1, IL‐6, IL‐10, TNFa levels were measured. Participants were divided into four groups: cognitively unimpaired with normal levels of amyloid (CU A‐, n = 25), cognitively unimpaired with elevated amyloid (CU A+, n = 25), and mild cognitively impaired (MCI) with amyloid positive (MCI A+, n = 25), and Alzheimer's disease (AD) with elevated amyloid (AD A+, n = 15). To compare regional standardized uptake value ratio (SUVr) from neuroinflammation PET based on amyloid status (A+ vs A‐) and the four cytokines, Spearman's rank correlation and Fishers r‐to‐z transformations were used to assess pairwise group differences. Result Using Spearman's rank correlation and Fishers r‐to‐z transformations, no strong relationship was found between systemic inflammation and neuroinflammation. While comparing CUA‐ vs CUA+ in Figure 1, three regions (supplementary motor area, frontal superior medial lobe, and middle cingulate cortex) showed a significant difference ( p < 0.05). However, Figure 2 showed an overarching trend bias towards significance across most regions in IL10, IL6, and TNFa. Conclusion Although significant relationships between systemic inflammation, brain amyloid and neuroinflammation were limited, the data show an overarching trend bias in broad brain regions, suggesting that further analyses are needed, possibly with larger sample sizes, to assess the relationship of regional neuroinflammation and amyloid to systemic inflammation. Future analyses could also regress out factors like sex, age, and genotype to further proinflammatory cytokine research.

Abstract Background Cognitive resilience is the discrepancy in individuals with intermediate to high Alzheimer's disease neuropathologic change (ADNC) and normal cognition. Currently, the role of microglial activation in cognitive resilience is understudied. Method In the MCSA brain bank, high‐cognitively unimpaired (high‐CU) participants were defined as those performing in the upper quartile of their respective age groups based upon antemortem global cognition z‐scores, computed from memory, language, executive function, and visuospatial domain z‐scores. From the high‐CU group ( N = 118, global z‐scores from 1.55 to 1.78), cognitively resilient (CR) individuals were identified as those with intermediate to high ADNC upon neuropathologic evaluation. First, regression models were fit to evaluate global cognition as a function of age, sex, amyloid PET, and microglial‐specific genes of interest, including MS4A gene cluster SNPs. Second, a subset of CR participants was analyzed for postmortem ARM expression in the dorsolateral prefrontal cortex. Single immunohistochemistry was performed on formalin‐fixed, paraffin‐embedded sections using antibodies against beta‐amyloid (6F3D) and CD163 (10D6). Digital image analysis was performed on scanned sections (40x, Leica Aperio GT 450), and ARM (CD163%area) and beta‐amyloid (%area) measurements were obtained using QuPath. Cognitively impaired (CI) control group was matched on age, sex, ADNC level, and APOE4 status. Spearman correlations were performed between postmortem ARM and antemortem MRI, PET, plasma, and global cognition metrics. Result A third (39/118) of high‐CU cases showed intermediate/high ADNC (CR group). Furthermore, 28% of CR individuals harbored an MS4A6A SNP, which showed a protective effect on cognition via amyloid‐PET ( p = 0.043, Figure 1). Moreover, CR individuals harbored significantly higher ARM:amyloid than the CI group ( p <0.001, Figure 2) and fewer neuritic plaques ( p = 0.03, Figure 3). Antemortem white matter hyperintensities and global cognition were significantly correlated to ARM expression (r=0.556 [p=0.042] and r=0.648 [p=0.02], respectively). Conclusion Microglial specific MS4A6A SNP, which modulates microglial activation, was seen in a third of CR individuals. Postmortem analyses in CR individuals revealed increased ARM:amyloid, which was also associated with higher antemortem cognitive performance. These findings underscore the role of microglial activation in cognitive resilience. Future work is needed to investigate microglial protective mechanisms and links between vascular disease and microglial activation.

Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.