Petersen, Ronald

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 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 Anti‐amyloid therapy trials have reported changes in some Alzheimer's disease (AD) blood biomarkers (BBMs) suggesting that these BBMs may be useful minimally invasive measures of amyloid clearance and treatment outcomes. However, a better understanding of how AD BBMs changes at the individual level reflect treatment response in relation to other relevant clinical endpoints is necessary before they can be adopted for treatment monitoring. Here, several AD BBMs were assessed longitudinally in patients undergoing lecanemab therapy. Method Patients undergoing lecanemab therapy at Mayo Clinic, Rochester, MN were recruited to participate in this study. Patients were asked to provide an EDTA‐plasma sample before treatment initiation during the first infusion visit and thereafter at 3‐, 6‐, 12‐, and 18 months post‐treatment. p ‐tau217, p ‐tau181, GFAP, NfL, Aβ42, Aβ40, Aβ42/40, and p ‐tau217/Aβ42 measurements were obtained using Fujirebio Lumipulse assays. Biomarker median differences were assessed between all time points. Patients were grouped into three categories: a decreased biomarker (median decrease of at least 10%), an increased biomarker (median increase of at least 10%), and a stable biomarker (median change < +/−10%.) Biomarker data up to 6 months after treatment is presented. Result Of forty‐one patients enrolled since November 2023, twenty had time points up to 6 months. None of the biomarkers showed a statistically significant median change from baseline at 3 months ( p >0.05). At 6 months, only Aβ42/40 showed a statistically significant median change (decrease) between 0 and 6 months ( n = 20, p = 0.0058). Table 1 shows patients grouped by median biomarker changes from baseline to 6 months. GFAP, p ‐tau217, and Aβ42/40 exhibited the greatest number of patients with decreased biomarker concentrations (80%, 60%, and 60% of patients with median decreases of ‐25%, ‐26%, and ‐18% respectively), followed by p ‐tau181 (50% of patients; median decrease ‐22%), p ‐tau217/Aβ42 (45% of patients; median decrease ‐40%) and Aβ42 (45% of patients; median decrease ‐18%). None of the patients showed a decreased NfL or Aβ40 concentration. Conclusion Preliminarily analysis revealed that GFAP, p ‐tau217, and Aβ42/40 had the most frequent post‐lecanemab biomarker decreases. Additional sample collection is ongoing to assess the association of AD BBM changes with Amyloid PET over time.

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 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.

Abstract Background Alzheimer’s Disease (AD) progression is conceptualized as a cascade of sigmoidal (S‐shaped) curves, with biomarkers shifting sequentially from normal to abnormal states. Estimating these curves requires sophisticated modeling, with challenges including (1) selecting appropriate sigmoidal‐type functions, (2) achieving algorithmic convergence, and (3) producing clinically useful parameters. We compare commonly used sigmoidal‐functions and effects on novel estimates of early and late AD‐biomarker progression ages. Method Using 3,999 Amyloid PET (PiB SUVR) measurements from 1,367 Mayo Clinic Study of Aging participants, we compared model fits (AIC, BIC) between logistic and hyperbolic functions using Non‐Linear Mixed Effect (NLME) models with subject‐specific age‐shifts. We defined novel “initial” and “maximum” acceleration points (IAP and MAP) to compare clinically “earlier” and “later” AD‐progression ages between sigmoidal‐curves. The MAP uses the maximum second‐derivative point, reflecting the age of fastest acceleration in biomarker accumulation. The IAP is defined as the first point where the second derivative of the sigmoidal‐curve exceeds a clinical threshold (here, 5% velocity increase per year), indicating onset age of early accelerated biomarker accumulation. Estimates for C 2 N plasma p ‐tau217 were also attempted (1,306 measurements on 457 participants). Models adjusted for sex and APOE4‐positivity. Result In the larger PET sample, the hyperbola had lower AIC/BIC (better fit), but showed a decreasing early PiB‐SUVR trend (Table 1A, Figure 1). Ages of late MAP were comparable, as were sex and APOE4+ related PiB‐SUVR‐curve shifts. Age of early AD‐onset was estimated 1.5 years earlier for logistic (IAP=63.6) versus hyperbola (IAP=65.1) functions. The logistic function always converged, while the hyperbolic function did not converge for p ‐tau217 in the smaller C 2 N sample. Early‐onset and late‐progression ages were 7‐8 years later for p ‐tau217 versus PiB‐SUVR using logistic‐curves. Conclusion Late AD‐progression and covariate‐related shifts in progression were comparable across models, suggesting robustness to sigmoid choice. In contrast, estimates of early AD‐onset may be more dependent on functional form. While logistic‐sigmoids inherently increase monotonically, hyperbolas do not, here estimating a decline in initial amyloid trend. Future work may explore adding biologically informed constraints to flexible sigmoidal‐functions, and alternative Bayesian approaches, to optimize both model fit and estimation of early AD‐onset age.

Abstract Background Use of software to “de‐face” research brain images has grown as advances in automated face recognition have made it increasingly possible to re‐identify participants. Imaging studies of Alzheimer’s Disease and related diseases have included more participants from historically underrepresented groups, but de‐facing software have primarily been validated using images from mostly non‐Hispanic White participants, and its performance has not been directly compared across ethnoracial groups. Method From The Health and Aging Brain Study: Health Disparities (HABS‐HD) study, we constructed a sample of 305 T1‐weighted MRI from HABS‐HD baseline visits, matched across three ethnoracial groups (non‐Hispanic White (NHW, n = 104), non‐Hispanic African American (AA, n = 104), Mexican American (MA, n = 97)) according to age‐decade, sex, cognitive status (impaired/unimpaired), and MRI scanner model. We ran mri_reface 0.3.5 on each image, and we ran FreeSurfer 7.4.1 on each unmodified image and on each de‐faced output. We then compared regional gray matter (GM) volume and cortical thickness measurements by subtracting each de‐faced image measurement from the corresponding unmodified image measurement. We compared these differences between de‐faced and unmodified images across the three matched groups using GLM analyses adjusted for age, sex, and cognitive status, and Bonferroni corrected. Result When aggregating across all brain regions, differences in cortical thickness measures due to de‐facing were significantly different between the NHW and MA groups, and between AA and MA, but not between NHW and AA (Figure 1). Differences between these group means were <0.005mm. For GM volume, differences were significant between AA and MA (magnitude < 0.2%), but not between NHW and AA or NHW and MA (Figure 2). Examining each region individually (Figure 3), cortical thickness differences were significant for only 4 regions (all with magnitudes <0.03mm), and GM volume differences were significant for only 6 regions (all with magnitudes <2%). Conclusion Effects of mri_reface on FreeSurfer brain measurements were significantly different across ethnoracial groups. Our future work will optimize mri_reface to eliminate these differences. However, the magnitudes of these differences are sufficiently small that they are unlikely to have a practical effect on analyses of data using current versions.

Abstract Background Diffusion MRI (dMRI) measures are increasingly being used as biomarkers for aging and dementia in multi‐center studies. Measurements in longitudinal studies are often acquired on different systems, which can result in systematic differences of machine origin (e.g. vendor, hardware, algorithms). This may warrant harmonization prior to pooling multi‐site data. However, harmonization algorithms may introduce a risk of removing valuable information content and reducing sensitivity to physiological factors (e.g. sex, age, disease). Here, we tested the hypothesis that protocol standardization may minimize machine‐origin differences, obviating a need for post‐acquisition harmonization in a two‐site (Mississippi‐MS and Minnesota‐MN) cohort of the longitudinal Mayo Clinic Study of Aging (MCSA). Methods Two data sets were analyzed: first, a crossover dataset with 80 participants (scanned within 2 days) bridging two different protocols on GE and Siemens scanners; second, participants scanned using standardized protocols from the ongoing MCSA ( N MN‐MCSA =204; N MS‐MCSA =52). Parameters are listed in Figure 1. For crossover data, analyses were repeated with GE data resampled to 2‐mm isotropic resolution, and discarding Siemens‐only b‐shells to artificially reduce protocol disparities. Analysis encompassed visualization of overlain subjectwise histogram‐derived densities obtained from whole‐brain masks for dMRI measures; and median dMRI measures from within the genu of the corpus callosum (GCC) with nonparametric statistical testing. When available, both DTI and NODDI measures were compared. Results Pronounced visual distributional differences seen between MD and FA in crossover data were reduced with resampling of GE data and including only b‐shells shared across acquisitions. For the resampled/reshelled data, significant differences remained for all GCC‐FA comparisons, but not all GCC‐MD (Figure 2). For the ongoing MCSA data from two sites, MD and FA whole‐brain densities were more closely aligned, with GCC‐FA and GCC‐MD showing no significant differences (Figure 3). NODDI measures showed more discordant densities than DTI measures, including shifts in most probable values and differences in distributional arrangement. Conclusions Our work provides further evidence supporting protocol and processing standardization for minimizing machine‐origin differences prior to considering harmonization. Differences in dMRI metrics seen when comparing across unstandardized protocols were reduced when protocols were standardized. NODDI metrics had more apparent sensitivity to site than DTI measures.

Abstract Background Most existing biomarkers for Alzheimer’s disease (AD) inform on core AD pathologies in the central nervous system, namely amyloid, tau and neurodegeneration (ATN). However, molecular disease mechanisms underlying AD are complex and heterogeneous, highlighting the need for cost‐effective blood‐based biomarkers that reflect brain molecular changes in AD beyond ATN. Method Using peripheral blood transcriptome data from the Mayo Clinic Study of Aging(MCSA) and the Alzheimer's Disease Neuroimaging Initiative, we identified genes associated with AD or mild cognitive impairment (MCI) diagnosis, cognitive, and harmonized neuroimaging measures in two cohorts, followed by meta‐analysis. We identified consensus gene co‐expression modules in MCSA and ADNI using WGCNA and assessed the association between module eigengenes and aforementioned measures. Using seven bulk brain RNAseq cohorts, we confirmed the preservation of coexpression modules across blood and brain. Enrichment analyses of Gene Ontology (GO) terms and cell‐types were performed using significant and/or hub genes. The MCSA transcriptome was further deconvoluted to obtain cell‐type proportions and cell‐type specific expression. Result Blood transcriptome modules M5 and M8 are upregulated in AD/MCI and associate with worse cognition, whereas M1, M10, M13, M16, and M21 are downregulated in AD/MCI and associate with better cognition. M17 associates with more microhemorrhages. Cell‐type enrichment analysis revealed that M5 is enriched in basophils, monocytes, and neutrophils; M8 in monocytes and neutrophils; M13 in natural kills cells; M17 in megakaryocytes; M21 in B cells. GO analysis shows broad consistency with the cell‐type enrichment analysis. M1, 5, and 8 are preserved in the brain network modules. From these modules, we prioritized transcripts significantly associated with at least one AD‐related phenotype in both blood and brain. Cell‐type deconvolution analysis shows that prioritized transcripts in M1 associate with better cognition and higher metabolic activity in B cells. Conclusion We identified blood transcripts and modules associated with AD/MCI, cognition, or neuroimaging, with three preserved in brain networks. These three modules represent centrally linked peripheral molecular signatures that can reveal perturbed brain pathways and cell level transcriptional changes in AD/MCI. We aim to further investigate the prioritized transcripts in additional datasets to establish them as potential novel biomarkers and therapeutic targets.

Abstract Background Plasma neurofilament light chains (NfL) and glial fibrillary acidic protein (GFAP) are blood‐based biomarkers of axonal injury and astrocytic activation, respectively, that are elevated in those with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) relative to cognitively unimpaired (CU) individuals. Neurite orientation dispersion and density imaging (NODDI) is a multishell diffusion MRI method capable of evaluating tissue microstructure at the axonal and dendritic level. We evaluated associations between temporal cortex orientation dispersion index (ODI), neurite density index (NDI) and isotropic volume fraction (ISOVF) and plasma NfL and GFAP concentrations. Method We included Mayo Clinic Study of Aging (MCSA) participants (ages 60‐95 years) with diagnoses or CU ( n = 356) or MCI ( n = 60), and Alzheimer’s Dementia Research Center (ADRC) participants with dementia ( n = 51). NODDI imaging was obtained on a 3T Siemens Prisma scanner with 2 mm isotropic resolution. NfL and GFAP were measured using the Simoa® Neurology 4‐Plex E Advantage kit. We fit separate linear regression models within each MCALT_ADIR122 cortical ROI with regional NODDI as the primary predictor and log‐transformed plasma concentration as the outcome. Models were adjusted for age, sex, BMI, cortical thickness (or hippocampal & amygdalar volume) and total intracranial volume. Result The Table displays demographic data. Figures 1 & 2 show the relationship of NDI and ISOVF measures to plasma NfL concentrations across participants in individual ROIs (similar findings were noted with GFAP). A significant inverse relationship between NfL concentrations and NDI was observed in hippocampus, amygdala, entorhinal cortex, parahippocampal gyrus as well as all temporal neocortical regions except the superior temporal gyrus ( p <0.001) (Figure 1), whereas higher NfL concentrations were associated with higher ISOVF in all regions ( p <0.001) (Figure 2). Fewer significant associations were also seen with ODI. After adjusting for age, cortical thickness/volume and other confounds, significant associations with NDI remain, particularly in hippocampal and amygdalar regions. Conclusion NODDI‐derived measures (NDI and ISOVF) within the temporal lobe cortical regions demonstrate significant associations with plasma biomarkers of axonal and astrocytic dysfunction that are elevated during cognitive decline. This association provides preliminary validation of the utility of NODDI in assessing cortical microstructural changes associated with cognitive decline.