Citations

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 Alzheimer's disease (AD) blood biomarkers (BBMs) are increasingly used in clinical trials and clinical practice. However, widespread adoption of BBMs requires further understanding of factors that might influence the interpretation of these biomarkers which would allow for the development of standardized sample collection protocols. For example, studies of the effect of fasting on AD BBM concentrations have yielded inconsistent findings. In this study, we evaluated plasma concentrations of amyloid‐beta(Aβ) 42, Aβ40, phosphorylated Tau ( p ‐tau) 181, p ‐tau217, and neurofilament light chain (NfL) in cognitively normal individuals under fasting and non‐fasting conditions. Method EDTA‐plasma was collected from healthy donors ( n = 14; age >55yrs old) two weeks apart, at the same time of day. The first sample collection was under fasting conditions in which donors did not eat or drink (except water) for at least 12 hours prior to collection. The second sample collection was under non‐fasting conditions in which donors were instructed to eat a meal in the morning, approximately half an hour prior to collection. Plasma Aβ42, Aβ40, p ‐tau181, p ‐tau217, and NfL concentrations were measured on the Fujirebio Lumipulse G1200 analyzer. Wilcoxon signed‐rank test was used to compare values between the fasting and non‐fasting results. Spearman's rank correlation was used to assess biomarker correlation between fasting status. Result Moderate to strong correlation was observed between the fasting and non‐fasting measurements: p ‐tau217 (r s : 0.86), p ‐tau181 (r s : 0.89), NfL (r s : 0.94), p ‐tau217/Aβ42 (r s : 0.80), Aβ42/40 (r s : 0.66), Aβ42 (r s : 0.77), and Aβ40 (r s : 0.92). Average percent difference between the 2 conditions was: p ‐tau217 27%, p ‐tau181 6%, NfL 9%, p ‐tau217/ Aβ42 25%, Aβ42/40 4%, Aβ42 2%, and Aβ40 ‐6%. Statistically significant differences ( p ≤ 0.05) between fasting and non‐fasting measurements were observed for p ‐tau217, NfL, p ‐tau217/Aβ42, Aβ42/Aβ40, and Aβ42; but not for Aβ40 or p ‐tau181. Conclusion This data suggests that for some AD BBMs, there may be statistically significant differences in concentrations of samples collected in fasting versus non‐fasting conditions. However, further studies are needed to determine if the observed differences may affect the clinical interpretation of these BBMs in individuals with amyloid related pathology.

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 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.

Abstract Background Cognitive impairment (CI) has diverse neurological etiologies, including Alzheimer’s Disease (AD), non-degenerative (ND) conditions, and cerebrospinal fluid (CSF) dynamic disorders, such as normal pressure hydrocephalus (NPH). The etiology of CI may be different to diagnose due to the overlap of non-specific symptoms. Various CSF biomarkers, such as amyloid beta (Aβ) 42, Aβ40, total Tau (t-Tau), and phosphorylated Tau 181 (p-Tau181), have been evaluated for their diagnostic utility in identifying the underlying cause for CI. Previous studies have suggested an isolated abnormal Aβ42 levels may be indicative of NPH in the appropriate clinical context. This study aims to further explore the clinical utility of AD-related CSF biomarkers to distinguish NPH from AD and ND causes of CI. Methods 240 patients were chart reviewed and categorized as either AD (n = 124), ND (n = 68), or NPH (n = 47) based on the etiologies of CI. Concentrations of Aβ40, Aβ42, t-Tau, p-Tau181 were measured in CSF (Cobas e601, Roche). Receiver operating characteristic (ROC) curves were plotted and optimal Youden cutoffs and areas under curve (AUCs) were calculated for each biomarker, and for selected ratios of biomarker combinations, for their ability to differentiate NPH from either AD or ND. Additionally, qualitative (abnormal/normal) result patterns for Aβ42, t-Tau, p-Tau181, and p-Tau181/Aβ42 ratio were reviewed for each patient, using established cut-offs for identifying AD (Aβ42≤1026 pg/mL, t-Tau&gt;238 pg/mL, p-Tau181&gt;21.7 pg/mL, p-Tau181/Aβ42&gt;0.023). Results p-Tau181/Aβ42 ratio demonstrated one of the highest AUCs (0.931) to separate NPH from AD (sensitivity=84.8%, specificity= 91.9%), and Aβ42 showed the highest AUC (0.856) to separate NPH from ND (sensitivity=71.6%, specificity=89.7%). When using previously established AD cut-offs, the combination of an abnormally low Aβ42 along with a normal p-Tau181/Aβ42 ratio yielded a sensitivity of 40.4% and a specificity of 96.4% in distinguishing NPH from other CI etiologies in this cohort. In contrast, abnormal results for both Aβ42 and p-Tau181/Aβ42 ratio had a sensitivity of 89.5% and a specificity of 87.0% in identifying AD, while normal results for both biomarkers had a sensitivity of 91.2% and a specificity of 90.1% in identifying ND. Incorporating the qualitative results for t-Tau and p-Tau181 into the evaluation did not enhance the diagnostic performance for distinguishing NPH, AD or ND in this cohort. Conclusion ROC analysis indicated that p-Tau181/Aβ42 and Aβ42 may help differentiate NPH from AD and ND, respectively. However, the combination of an abnormally low Aβ42 and a normal p-Tau181/Aβ42 ratio, using current AD cut-offs, demonstrated a lower sensitivity but high specificity in distinguishing NPH from the other CI etiologies in this cohort, suggesting this result pattern may be used for NPH rule-in in patients with CI.

Abstract Peanut allergy is a leading cause of food-induced allergy (prevalence of ∼1%). The diagnosis relies on evaluating clinical symptoms consistent with peanut exposure, supported by the detection of total peanut specific IgE (sIgE) and peanut component IgE antibodies (Ara h1, Ara h2, Ara h3, Ara h6, Ara h8, Ara h9 and Bet v2) through serum or skin testing. However, sensitization patterns across different ages and regions in the United States remain largely unknown. In this study, we investigated regional and demographic patterns of peanut component sensitization in a large U.S. population tested for suspected peanut allergy. Retrospective data were collected from 13,702 individuals tested at Mayo Clinic Laboratories. sIgEs were measured using the ImmunoCAP system (Thermo Fisher Scientific). Samples with detectable total peanut sIgE (≥0.1 kU/L) were reflexed to testing for seven peanut component IgEs. Statistical analyses including linear regression and correlation matrix analysis were conducted using R Studio. Regional differences were assessed using care provider zip code information. Among the study population, 92.0% (12,604 of 13,702) had detectable total peanut sIgE, with the Western U.S. region exhibiting the highest detectable rate (95.6%), and the Southeast the lowest (85.6%) (p &lt; 0.001). On average, 3.38 different component IgEs were detectable per person with a detectable total peanut sIgE; 7.1% (892) had no detectable component IgEs, possibly due to antibodies against other peanut components. Correlation analysis revealed strong associations between Ara h2 and Ara h6 (r = 0.94), with moderate correlations observed between Ara h2, Ara h3, and Ara h6 to Ara h1 (r ranging from 0.72 to 0.77). Age-stratified analysis indicated higher total peanut sIgE detectable rates in children and adolescents (95.2%, ≤18 years) compared to adults (76.5%, &gt;18 years). Interestingly, adults were more likely than children to have negative results for all component IgEs. Sensitization rates to different peanut components varied, with Ara h2 (77.5%) and Ara h6 (73.2%) being the most frequently detected, followed by Ara h1 (56%), and Ara h3 (47.3%). Lower detectable rates were observed for Ara h8, Ara h9, and Bet v2 (34.8%, 26.1% and 22.7%, respectively). Regional analysis showed consistently high detectable rates for Ara h2 and Ara h6 (&gt;70%) across most areas (p &lt; 0.001) with slightly lower rates in the Southwest (&lt;63%). Bet v2, despite its overall low prevalence (&lt;24%), had a relatively higher detectable rate in the Southwest (29%). This study provides valuable insights into potential demographic and geographic factors influencing peanut allergy sensitization. Our study demonstrates that peanut sensitization patterns vary by age and geographic location. Children and adolescents exhibit higher detectable rates for total peanut sIgE and component IgEs compared to adults, with Ara h2 and Ara h6 as the most frequently detected and highly correlated components.

Abstract Disclosure: D. Hamadi: None. A. Algeciras-Schimnich: None. M. Stan: None. Background: Thyroid hormone-binding proteins (TBPs)—thyroxine-binding globulin (TBG), transthyretin, and albumin—play a critical role in transporting thyroid hormones in the bloodstream. Rare genetic alterations in these proteins can lead to discordant thyroid function test (TFT) results, potentially mimicking thyroid dysfunction despite a clinically euthyroid state. This study retrospectively evaluates the clinical scenarios that warrant TBP testing. Methods: Data was extracted from the electronic medical record (EMR) at the Mayo Clinic in Rochester, MN. The study included patients who had Thyroid-binding protein electrophoresis (TBPE) ordered between 2016 to 2023. Results: A total of 138 patients were included (53% female; median age: 58 years) of which 42% had TBP abnormalities. TBP testing was frequently ordered by endocrinology (33%), followed by cardiology (9%) and family medicine (7%). Discrepancies between thyroid-stimulating hormone (TSH) and total T4 (TT4) were identified in 41 patients of which 75% had abnormal TBP. Among these, eleven were diagnosed with familial dysalbuminemic hyperthyroxinemia (FDH), including one with a known family history. Additionally, five patients were diagnosed with thyroid hormone resistance—one of whom had previously been misdiagnosed with Graves’ disease. Three patients were found to have TSH-secreting tumors, and three were receiving infusion therapy with patisiran with resulting TBP abnormalities. The remaining cases involved binding protein abnormalities, most commonly presenting as elevated TBG. Conclusion: This study identified varied clinical utilization for TBP testing, from very appropriate indications to cases of doubtful clinical utility. TBP abnormalities were clinically meaningful in 22% of cases, supporting the utility of testing in select scenarios. Specifically, TBP testing should be reserved for situations with a clear discrepancy between clinical presentation and TFT results—particularly discordant TSH and total T4 levels. In such contexts, TBP assessment is valuable in preventing misdiagnoses and avoiding unnecessary treatments, especially since individuals with TBP mutations are typically clinically euthyroid. Presentation: Sunday, July 13, 2025

Abstract As Alzheimer's disease (AD) research advances, tau has emerged as both a critical biomarker and a promising therapeutic target, central to understanding disease mechanisms, tracking progression, and guiding treatment development. The Fall 2024 Alzheimer's Association Research Roundtable convened experts from academia, industry, National Institutes of Health (NIH), and the United States Food and Drug Administration (FDA) to explore current progress and future directions in tau‐centered diagnostics and therapeutics. Discussions addressed the integration of tau biomarkers into the 2024 Revised Diagnostic Criteria for AD, updates to amyloid and tau positron emission tomography (PET) imaging, and modeling of biomarker trajectories. Presenters highlighted tau‐targeting therapeutics including antisense oligonucleotides, monoclonal antibodies, and small molecules, alongside innovations in drug delivery. The interplay between anti‐amyloid and anti‐tau therapies and strategic design of combination trials were key themes. Regulatory insights facilitated discussions on drug approval pathways. The meeting highlighted the rapid evolution of tau research and emphasized opportunities to improve diagnostics, trial design, and treatment strategies in AD. Highlights The Alzheimer's Association Research Roundtable (AARR) convened leaders from industry, academia, and government, to explore current progress and future directions in tau‐centered diagnostics and therapeutics. Tau has emerged as both a critical biomarker and a promising therapeutic target, central to understanding disease mechanisms, tracking progression, and guiding treatment development. Discussions addressed the integration of tau biomarkers into the 2024 revised diagnostic criteria for AD, updates to amyloid and tau PET imaging, modeling of biomarker trajectories, tau‐targeting therapeutics, and interplay between anti‐amyloid and anti‐tau therapies and strategic design of combination trials.