Automated Plasma Phospho-tau217 Assays for the diagnosis of Down Syndrome-Related Alzheimer's Disease

Automated Plasma Phospho-tau217 Assays for the diagnosis of Down Syndrome-Related Alzheimer's Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Automated Plasma Phospho-tau217 Assays for the diagnosis of Down Syndrome-Related Alzheimer's Disease Michael Rafii, Oliver Langford, Zinayida Schlachetzki, Matthew Zammit, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7359108/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Individuals with Down syndrome (DS) develop a genetic form of Alzheimer’s disease (AD) due to an extra copy of chromosome 21, which contains the APP gene. The widespread implementation of blood-based biomarkers for AD is imminent. These tests hold promise for diagnosing AD in DS once validated. In this study, we assessed plasma phospho-tau217 (p-tau217) in participants from the NIH Trial Ready Cohort – Down syndrome (TRC-DS), using predefined amyloid PET cutoffs. Plasma p-tau217 was measured via two fully automated assays: C2N Diagnostics’ PrecivityAD2 mass spectrometry and Fujirebio’s Lumipulse immunoassay. The primary outcome was AD pathology, defined by amyloid PET >18 centiloids. Both assays showed high accuracy: AUCs of 0.92 and 0.90, with sensitivities of 0.88 and specificities of 0.94 and 0.90, respectively. These results are comparable to composite plasma measures. In conclusion, automated p-tau217 tests offer strong potential for routine AD screening in individuals with DS. Health sciences/Neurology/Neurological disorders/Dementia Biological sciences/Neuroscience Figures Figure 1 Figure 2 Main Individuals with Down syndrome (DS) develop a genetic form of Alzheimer’s disease (AD) due to the extra copy of chromosome 21, which contains the APP gene 1 (Fortea et al., 2021). Several studies indicate that imaging and plasma biomarkers in DS demonstrate the same continuum of amyloid and tau pathology within the ATN framework 2 3 as in sporadic AD (Jack et al., 2024; Zammit et al., 2024). Plasma tau phosphorylated at threonine 217 (p-tau217) predicts Alzheimer’s disease (AD) pathology with high accuracy in the general population 4 (Palmqvist et al. 2020. Until recently, the use of FDA-approved amyloid-lowering treatments for early symptomatic AD required CSF or PET biomarker confirmation of elevated brain amyloid 3 (Jack et al. 2024). Accurate and scalable detection of AD pathology for people with DS are critical as treatments targeting AD-pathology become available for this population. Plasma p-tau217 tests provide broader accessibility, scalability and patient acceptability, and can help ensure equitable access to appropriate care and new medications. The Lumipulse G p-Tau217/ß-Amyloid 1–42 ratio is the only plasma biomarker test newly cleared by the U.S. Food and Drug Administration (FDA) for the early detection of amyloid plaques associated with AD in adults of 55 years and older 5 . However, this test has only limited application in DS, as AD pathology has earlier onset and faster progression in this population 2 (Zammit et al. 2024). In this study, we investigated the accuracy of plasma p-tau217 in predicting elevated brain amyloid pathology in individuals with DS. Specifically, we compared the performance of plasma p-tau217 (Lumipulse) with that of a high-performing mass-spectrometry (MS)-based method for plasma p-tau217 measures alone and the multi-analyte assay with algorithmic analysis that encompasses p-tau217 ratio (p-tau217:np-tau217 × 100) and the Aβ42/40 ratio (as defined by the Amyloid Probability Score 2 (APS2), PrecivityAD2) currently being used in clinical practice in the United States, along with other select regions of the world 6 (Meyer et al, 2024). Results Forty participants with DS in the Trial Ready Cohort – Down syndrome (TRC-DS) cohort were included. The mean age was 38.5 (s.d. 7.9) years, 60% were women and 22.5% had AD pathology as defined by a centiloid value of 18 or higher (Table 1 ). In this cohort, 28.2% were ApoE4 carriers. Plasma p-tau217 concentrations were higher in AD pathology-positive versus AD pathology-negative participants independent of the method used for measurements (Fig. 1 ). With a cutoff of 0.21 pg/mL (Lumipulse) and 1.77 pg/mL (MS; PrecivityAD2) for plasma p-tau217, areas under the receiver operating characteristic (ROC) curves (AUCs) were 0.94 (CI 0.84,1.00) and 0.91 (CI 0.77, 1.00), respectively, with lower AUCs observed for np-tau, Aβ42, Aβ40 (Fig. 2 , Table 2 , Suppl Fig. 2, Suppl. Table 1). In addition to p-tau217, similarly strong correlations with amyloid levels on PET were observed for the p-tau217 ratio (r = 0.79) and APS2 (r = 0.80), whereas plasma Aβ42, Aβ40 and Aβ42/40 ratio did not show any association with amyloid-PET results (r 2 range 0.04–0.22) (Fig. 1 , Suppl. Figure 1). The PrecivityAD2 measures of p-tau217 (0.21 pg/mL), %p-tau217 (3.08) and APS2 (20.5) had an AUC of 92%, a positive-predictive value (PPV) of 78% and a negative predictive value (NPV) of 97%, whereas p-tau217 of 0.21pg/mL measured by Lumipulse had only a slightly lower accuracy (90%) and PPV (70%), and the same NPV of 97% (Table 2 ). Summary statistics on the diagnostic ability of optimal biomarker thresholds for predicting amyloid-PET status are listed in descending order of Youden index and range from the highest of 0.91 for the C2N MS-determined cut-points of p-tau217 (0.21 pg/mL), %p-tau217 (3.08) and APS2 (20.5) to 0.19 for the Aβ42/40 ratio of 0.09 (Table 2 , Suppl. Table 1). For comparison, we evaluated diagnostic performance of APS2 and %ptau217 in predicting amyloid pathology using established thresholds of 47.5 and 4.2, respectively, as they are currently applied in clinical care for individuals with signs or symptoms of cognitive impairment undergoing evaluation for sporadic AD (sAD) 6 (PrecivityAD2, Meyer et al 2024). Using these cut-points, a specificity of 1.00 (95% CI 1.00–1.00) was reached for both parameters, while sensitivity is 0.75 (95% CI 0.50–1.00) for %p-tau217 and to 0.5 (95% CI 0.12–0.88) for APS2, with overall accuracy of 0.95 (95%CI 0.87–1.00) and 0.90 (95% CI 0.82–0.97), respectively (Suppl. Table 2). Comparing of C2N and Lumipulse p-tau217 assay results using DeLong’s test revealed no statistically significant difference (p = 0.42). This was consistent with their high correlation (r = 0.89, Suppl. Figure 3, Suppl. Figure 4) and similar diagnostic performance (Table 2 ). Discussion We found that the C2N PrecivityAD2 test and the fully automated Lumipulse immunoassay, which measures plasma p-tau217, both have excellent accuracy in differentiating individuals with DS with a significant amyloid burden. Our study suggests that both p-tau217 assessments provide an accurate and easily accessible diagnostic test to assess brain amyloid burden in adults with DS. Previous research in DS indicates variability in the age of onset of AD-pathology as measured by amyloid-PET, such that relying on age alone for clinical trial recruitment will lead to a high screening failure rate 7 (Zammit et al 2021). Compared to sAD, individuals with DS who have elevated amyloid levels show a significantly faster progression to tau pathology, and a more rapid decline in their cognitive functions 8 9 (Schworeret al, 2024, Krasny et al 2024). This underscores the necessity for timely inclusion of people with DS in AD clinical trials as only a narrow window of opportunity exists when they can benefit from early disease-targeting interventions. Plasma biomarkers, in particular p-tau217 have emerged as excellent predictors of brain amyloid pathology in sAD. Plasma p-tau217 composite measures, including the PrecivityAD2 APS2 score have been successfully used to enrich for amyloid-PET positive individuals during screening in sAD clinical trials 10 (Rissman et al. 2024). However, the critically needed data on thresholds to discriminate “amyloid-positive” from “amyloid-negative individuals with DS is still missing. The recently FDA-approved Lumipulse G plasma test that measures p-Tau217/ß-Amyloid 1–42 ratio is based on the same technology that was used in this study to collect plasma p-tau217 (Lumipulse) values in individuals with DS. Moreover, using a MS test (C2N), this study showed limited value of Aβ42/40 ratio in detection of amyloid-PET positivity, suggesting that plasma p-tau217 alone may be as useful as ptau-217/ß-Amyloid 1–42 ratio in people with DS, and may allow for scalable implementation in clinical research and care. Our data aligns with previous studies in DS-AD showing that a strong association exists between plasma p-tau217 and brain pathology, specifically the amyloid- and tau-PET biomarkers 11 (Janelidze et al, 2022). For this study, we observed high AUCs using the C2N mass spectrometry and Lumipulse immunoassay methodologies, which can serve as a starting point for future studies to collect more evidence and refine the cut points for detecting brain amyloid burden in DS. Accuracies of plasma p-tau217 Lumipulse and MS assays in this study reached 90% and 92% (AUC 0.94 and 0.91), respectively, which are considered excellent for detecting amyloid pathology and match or outperform the CSF-tests used in clinical practice 12 13 (Schindler et al 2018; Kaplow et al 2020). Our data also highlight the robustness of p-tau217 as an early AD-biomarker as both the MS and the immunoassay in this study demonstrated excellent overall accuracy of this biomarker in determining amyloid-PET positivity, though with different cut-offs. It is noteworthy that the thresholds for p-tau217 measures determined through MS in this study (p-tau217/Np-tau217 = 3.08 and APS2 = 20.5) are only somewhat different from the thresholds established in the sAD population, p-tau217/Np-tau217 = 4.2 and APS2 = 47.5 used in clinical practice 6 (Meyer et al 2024). This difference may be attributed to the distinct timelines for onset and progression of AD pathology in DS and sAD. Applying the PrecivityAD2 cutoffs in our study resulted in improved specificity and accuracy for %p-tau217 but lost on sensitivity, highlighting that thresholds established in sAD may not be directly transferrable to the genetic forms of AD. Establishing population and indication-specific cut-points or careful interpretation of the results may be warranted. In contrast to p-tau217, we did not observe any relationship of plasma amyloid-beta measures, including Aβ42, Aβ40 and Aβ42/40 ratio, with amyloid-PET status. Moreover, the addition of the Aβ42/40 ratio to the p-tau217 and %-ptau217 (i.e., APS2 values) did not improve accuracy in predicting amyloid status in this study, in contrast to its relevance in sAD 10 (Rissman et al 2024). These data further support the results from the prior studies that did not detect differences in Aβ40 and Aβ42 biomarkers between cognitively stable and symptomatic DS participants, though demonstrating consistently elevated plasma Aβ42 and Aβ40 across age and diagnostic groups in DS compared to the age-matched controls without DS 14 15 (Montoliu-Gaya et al 2021, Fortea et al., 2020) Our study provides valuable insights into the accuracy, sensitivity, and specificity of several clinically available plasma biomarkers in predicting amyloid pathology in DS, including p-tau217 measures using the same technology as for p-tau217/ ß-Amyloid 1–42 ratio from the FDA-approved Lumipulse plasma-based immunoassay. Nevertheless, our findings should be interpreted in the context of our study’s limited sample size, relatively low pre-test probability of disease among the cohort participants, and cross-sectional design. Using the cut-offs determined in this cohort to design prospective large clinical trials will further improve and refine the proposed thresholds. Methods Participants Participants were adults with a genetically confirmed DS, aged 25 to 55 years old, who completed a baseline assessment at TRC-DS study sites and had available MRI and amyloid PET imaging. Institutional Review Board approval and informed consent were obtained prior to study enrollment into the TRC-DS study by the participant or legally designated caregiver according to the Declaration of Helsinki. Participant demographics grouped by brain amyloid status are provided in Table 1. Inclusion criteria for the TRC-DS study were diagnosis of DS confirmed by genetic testing or medical record review, age of 25-55 years, estimated IQ ≥ 40, participant’s and study partner’s cooperation, and no conditions precluding from study participation or imaging. Imaging All participants underwent MRI imaging, as well as PET imaging using either PiB, Florbetapir (FBP), NAV4694 (NAV) or Flutemetamol (FMM). Global Aβ burden was determined using the Centiloid (CL) method 16 (Klunk et al, 2015) with the whole cerebellum serving as the reference region for PiB, NAV and FMM, while an eroded white matter reference was used for FBP. Briefly, PET images were spatially normalized to MNI152 template space. Standardized uptake value ratio (SUVr) images were then generated by voxel normalization to the mean activity in the reference tissue, and the mean cortical SUVr was extracted from the CL cortex ROI. Cortical SUVr was then converted to units of CL using the appropriate conversion equations for PiB 16 (Klunk et al, 2015), FBP 17 (Navitski et al, 2015), NAV 18 (Rowe et al, 2016) and FMM 19 (Battle et al, 2018). Participants were classified as amyloid-positive (A+) using an a priori threshold of 18 CL 2 (Zammit et al, 2024). Plasma Assays Plasma p-tau217 was analyzed using the Lumipulse immunoassay (Fujirebio) at the USC ATRI Biomarker Lab. In parallel, plasma p-tau217, np-tau217, Aβ40 and Aβ42 were analyzed using MS-based assays at C2N Diagnostics (St. Louis, MO), as previously described (Meyer et al, 2024). In addition to p-tau217, %p-tau217 (p-tau217:np-tau217 × 100) and a composite amyloid-prediction score (APS2) that incorporates p-tau217, np-tau217, Aβ40 and Aβ42 were used in the analysis. Statistical analysis Characteristics of the analysis population were summarized by brain amyloid status. Data is presented as means (standard deviations) for continuous variables, and counts (percentages) for categorical variables. Correlations between amyloid levels and the blood plasma biomarkers were assessed using Pearson’s correlation coefficient. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of the blood plasma biomarkers in predicting amyloid status. Performance was summarized with area under the curve (AUC), and ‘optimal’ thresholds were determined by maximizing the Youden index 20 . As an exploratory analysis, we applied thresholds 47.5 and 4.2 to C2Ns APS2 and %p-tau217. These thresholds were developed in symptomatic individuals 6 (PrecivityAD2, Meyer et al 2024). All thresholds are reported with corresponding sensitivity, specificity, accuracy, positive and negative predictive values, along with stratified bootstrap confidence intervals (N = 2000 and confidence level = 0.95). To compare the overall diagnostic performance of C2N and Lumipulse p-tau217 assays, we used DeLong’s test for correlated AUCs 21 . All statistical analyses were conducted using R (version 4.4.1). Table 1. Participant demographic grouped by brain amyloid status (positive if centiloid value ≥18.1). Category Negative (N=31) (18 Cl) Total (N=39) Age Mean (SD) 36.5 (7.0) 45.9 (7.5) 38.4 (8.0) Range 25.5 -55.1 33.1 – 53.1 25.5 – 55.1 Sex Female 10 (32.3%) 5 (62.5%) 15 (38.5%) Male 21 (67.7%) 3 (37.5%) 24 (61.5%) Ethnicity Hispanic 8 (25.8%) 2 (25.0%) 10 (25.6%) Non-Hispanic 23 (74.2%) 6 (75.0%) 29 (74.4%) APOE4 carrier No 21 (67.7%) 6 (85.7%) 27 (71.1%) Yes 10 (32.2%) 1 (14.3%) 11 (28.9%) APOE4 allele count 0 21 (67.7%) 6 (85.7%) 27 (71.1%) 1 9 (29.0%) 1 (14.3%) 10 (26.6%) 2 1 (3.2%) 0 (0.0%) 1 (2.6%) Table 2. Summary statistics on the diagnostic ability of the biomarkers using maximized Youden index cutpoints for predicting amyloid status. The table is in descending order according the Youden index value. Biomarkers Threshold Accuracy (95% CI) AUC (95% CI) Sensitivity (95% CI) Specificity (95% CI) Youden PPV NPV p-tau217 1.77 pg/mL 0.92 (0.82, 1.00_ 0.91 (0.77, 1.00) 0.88 (0.62, 1.00) 0.94 (0.84, 1.00) 0.81 0.78 0.97 p-tau217/Np-tau217 3.08 0.92 (0.82, 1.00) 0.89 (0.69,1.00) 0.88 (0.62, 1.00) 0.94 (0.84, 1.00) 0.81 0.78 0.97 APS2 20.50 0.92 (0.82, 1.00) 0.90 (0.74,1.00) 0.88 (0.62, 1 00) 0.94 (0.84, 1.00) 0.81 0.78 0.97 p-tau217 (Lumipulse) 0.21 pg/mL 0.90 (0.79, 0.97) 0.94 (0.84,1.00) 0.88 (0.62, 1.00) 0.90 (0.77, 1.00) 0.78 0.70 0.97 Aβ42/40 ratio 0.09 0.36 (0.26, 0.49) 0.57 (0.36,0.79) 1.00 (1.00, 1.00) 0.19 (0.06, 0.32) 0.19 0.24 1.00 References Fortea, J. , et al. Alzheimer's disease associated with Down syndrome: a genetic form of dementia. Lancet Neurol 20 , 930-942 (2021). Zammit, M.D. , et al. Characterizing the emergence of amyloid and tau burden in Down syndrome. Alzheimers Dement 20 , 388-398 (2024). Jack, C.R. , et al. Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimers Dement (2024). Palmqvist, S. , et al. Discriminative Accuracy of Plasma Phospho-tau217 for Alzheimer Disease vs Other Neurodegenerative Disorders. JAMA 324 , 772-781 (2020). Administration, U.S.F.a.D. FDA Clears First Blood Test Used in Diagnosing Alzheimer’s Disease. (https://www.fda.gov/news-events/press-announcements/fda-clears-first-blood-test-used-diagnosing-alzheimers-disease). (2025). Meyer, M.R. , et al. Clinical validation of the PrecivityAD2 blood test: A mass spectrometry-based test with algorithm combining %p-tau217 and Abeta42/40 ratio to identify presence of brain amyloid. Alzheimers Dement 20 , 3179-3192 (2024). Zammit, M.D. , et al. PET measurement of longitudinal amyloid load identifies the earliest stages of amyloid-beta accumulation during Alzheimer's disease progression in Down syndrome. Neuroimage 228 , 117728 (2021). Schworer, E.K. , et al. Timeline to symptomatic Alzheimer's disease in people with Down syndrome as assessed by amyloid-PET and tau-PET: a longitudinal cohort study. Lancet Neurol 23 , 1214-1224 (2024). Krasny, S. , et al. Assessing amyloid PET positivity and cognitive function in Down syndrome to guide clinical trials targeting amyloid. Alzheimers Dement 20 , 5570-5577 (2024). Rissman, R.A. , et al. Plasma Abeta42/Abeta40 and phospho-tau217 concentration ratios increase the accuracy of amyloid PET classification in preclinical Alzheimer's disease. Alzheimers Dement 20 , 1214-1224 (2024). Janelidze, S. , et al. Detection of Brain Tau Pathology in Down Syndrome Using Plasma Biomarkers. JAMA Neurol 79 , 797-807 (2022). Schindler, S.E. , et al. Cerebrospinal fluid biomarkers measured by Elecsys assays compared to amyloid imaging. Alzheimers Dement 14 , 1460-1469 (2018). Kaplow, J. , et al. Concordance of Lumipulse cerebrospinal fluid t-tau/Abeta42 ratio with amyloid PET status. Alzheimers Dement 16 , 144-152 (2020). Montoliu-Gaya, L., Strydom, A., Blennow, K., Zetterberg, H. & Ashton, N.J. Blood Biomarkers for Alzheimer's Disease in Down Syndrome. J Clin Med 10 (2021). Fortea, J. , et al. Clinical and biomarker changes of Alzheimer's disease in adults with Down syndrome: a cross-sectional study. Lancet 395 , 1988-1997 (2020). Klunk, W.E. , et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement 11 , 1-15 e11-14 (2015). Navitsky, M. , et al. Standardization of amyloid quantitation with florbetapir standardized uptake value ratios to the Centiloid scale. Alzheimers Dement 14 , 1565-1571 (2018). Rowe, C.C. , et al. Standardized Expression of 18F-NAV4694 and 11C-PiB beta-Amyloid PET Results with the Centiloid Scale. J Nucl Med 57 , 1233-1237 (2016). Battle, M.R. , et al. Centiloid scaling for quantification of brain amyloid with [(18)F]flutemetamol using multiple processing methods. EJNMMI Res 8 , 107 (2018). Youden, W.J. Index for rating diagnostic tests. Cancer 3 , 32-35 (1950). DeLong, E.R., DeLong, D.M. & Clarke-Pearson, D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 , 837-845 (1988). Additional Declarations Yes there is potential Competing Interest. MSR received grants from National Institutes of Health (NIH), Eisai and Lilly. He is a consultant for Ionis, Alnylam, and AC Immune and serves on the DSMB for Biohaven and Alzheon and on the Scientific Advisory Board for Helicon, Prescient Imaging, Positrigo, and Embic. JBB is an employee of C2N Diagnostics.RR has received research support from the National Institutes of Health (NIH), the Alzheimer’s Association, American Heart Association, Eli Lilly and Eisai. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7359108","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":507533356,"identity":"937dcfb0-f60a-4641-b777-8b5a0b096896","order_by":0,"name":"Michael Rafii","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYJCCDwkGNhAWD5E6GGckGKSRqoWB4TAJWnRnH37Y8KDgfOL89gOMD962EaHF7FyaYUOCwe3EDWcSmA3nEqXlDIP5A7AWCQY2aV7itLB/BNpyLnH+DAb230Rq4QE57EBiww0GNmZitRQCtSQbbziT2Cw55xxxDtvY+OOPnez89sMHP7wpI0ILEmBsIE39KBgFo2AUjALcAADZ7jhl1KdetAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2640-2094","institution":"Alzheimer's Therapeutic Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"","lastName":"Rafii","suffix":""},{"id":507533357,"identity":"f8d66dd0-4dd6-499a-8d46-4af5c2996ebc","order_by":1,"name":"Oliver Langford","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Langford","suffix":""},{"id":507533358,"identity":"50d3c2d8-4d2b-4ba8-9214-3e2aa75a82c1","order_by":2,"name":"Zinayida Schlachetzki","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Zinayida","middleName":"","lastName":"Schlachetzki","suffix":""},{"id":507533359,"identity":"7e3197c8-cf9a-40e0-9fe0-922c32972ad8","order_by":3,"name":"Matthew Zammit","email":"","orcid":"https://orcid.org/0000-0001-8966-8397","institution":"University of Wisconsin-Madison","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Zammit","suffix":""},{"id":507533360,"identity":"22f62c8f-0cf3-44ba-b6cc-4e30f547521f","order_by":4,"name":"Michael Donohue","email":"","orcid":"https://orcid.org/0000-0001-6026-2238","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Donohue","suffix":""},{"id":507533361,"identity":"9c779bef-4d31-47d8-9333-c52ea9b3773a","order_by":5,"name":"Xiaoyu Zhou","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Zhou","suffix":""},{"id":507533362,"identity":"13c169fe-5734-4527-b4ff-51b35271acdb","order_by":6,"name":"Sonal Sukreet","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Sonal","middleName":"","lastName":"Sukreet","suffix":""},{"id":507533363,"identity":"3df74736-8e20-4401-ad0a-1fa31b732510","order_by":7,"name":"Sara Abdel-Latif","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Abdel-Latif","suffix":""},{"id":507533364,"identity":"132447ec-2143-496b-8b11-83f5ea9a815f","order_by":8,"name":"Joel Braunstein","email":"","orcid":"","institution":"C2N","correspondingAuthor":false,"prefix":"","firstName":"Joel","middleName":"","lastName":"Braunstein","suffix":""},{"id":507533365,"identity":"70b5b174-c006-4582-9cb3-b1645182c403","order_by":9,"name":"Robert Rissman","email":"","orcid":"","institution":"USC","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Rissman","suffix":""}],"badges":[],"createdAt":"2025-08-12 21:00:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7359108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7359108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90321806,"identity":"7fb1726c-1be3-4cc2-a4bf-8d094993782e","added_by":"auto","created_at":"2025-09-01 11:00:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179674,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of plasma biomarkers with standardized centiloids on amyloid PET. APS2- the score from the PrecivityAD2 test.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7359108/v1/9b4178a93ddb222b9dc0a44b.png"},{"id":90320360,"identity":"868c573d-16ce-4c99-8600-63f5da9046f5","added_by":"auto","created_at":"2025-09-01 10:44:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89170,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for plasma biomarkers with amyloid status as the outcome variable. All biomarker measures were collected using MS except for the p-tau217 LUMI, which was determined through Lumipulse immunoassay.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7359108/v1/59dc52ae09cf19f0e3d0b5a4.png"},{"id":93158928,"identity":"4ec50ef4-542c-4e0f-93b3-b8ac90a77c10","added_by":"auto","created_at":"2025-10-09 16:13:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":799938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7359108/v1/b2b8f92e-758a-4f0f-8c9a-488a0d131ce3.pdf"},{"id":90320358,"identity":"d72bccb4-522b-43d2-8e13-1df511f55cd5","added_by":"auto","created_at":"2025-09-01 10:44:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":135226,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.docx","url":"https://assets-eu.researchsquare.com/files/rs-7359108/v1/1c3f04932c2fca81794ab66e.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nMSR received grants from National Institutes of Health (NIH), Eisai and Lilly. He is a consultant for Ionis, Alnylam, and AC Immune and serves on the DSMB for Biohaven and Alzheon and on the Scientific Advisory Board for Helicon, Prescient Imaging, Positrigo, and Embic. JBB is an employee of C2N Diagnostics.RR has received research support from the National Institutes of Health (NIH), the Alzheimer’s Association, American Heart Association, Eli Lilly and Eisai.","formattedTitle":"Automated Plasma Phospho-tau217 Assays for the diagnosis of Down Syndrome-Related Alzheimer's Disease","fulltext":[{"header":"Main","content":"\u003cp\u003eIndividuals with Down syndrome (DS) develop a genetic form of Alzheimer\u0026rsquo;s disease (AD) due to the extra copy of chromosome 21, which contains the \u003cem\u003eAPP\u003c/em\u003e gene \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (Fortea et al., 2021). Several studies indicate that imaging and plasma biomarkers in DS demonstrate the same continuum of amyloid and tau pathology within the ATN framework \u003csup\u003e2 3\u003c/sup\u003e as in sporadic AD (Jack et al., 2024; Zammit et al., 2024). Plasma tau phosphorylated at threonine 217 (p-tau217) predicts Alzheimer\u0026rsquo;s disease (AD) pathology with high accuracy in the general population \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e (Palmqvist et al. 2020.\u003c/p\u003e\u003cp\u003eUntil recently, the use of FDA-approved amyloid-lowering treatments for early symptomatic AD required CSF or PET biomarker confirmation of elevated brain amyloid \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e (Jack et al. 2024). Accurate and scalable detection of AD pathology for people with DS are critical as treatments targeting AD-pathology become available for this population. Plasma p-tau217 tests provide broader accessibility, scalability and patient acceptability, and can help ensure equitable access to appropriate care and new medications. The Lumipulse G p-Tau217/\u0026szlig;-Amyloid 1\u0026ndash;42 ratio is the only plasma biomarker test newly cleared by the U.S. Food and Drug Administration (FDA) for the early detection of amyloid plaques associated with AD in adults of 55 years and older \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, this test has only limited application in DS, as AD pathology has earlier onset and faster progression in this population \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (Zammit et al. 2024).\u003c/p\u003e\u003cp\u003eIn this study, we investigated the accuracy of plasma p-tau217 in predicting elevated brain amyloid pathology in individuals with DS. Specifically, we compared the performance of plasma p-tau217 (Lumipulse) with that of a high-performing mass-spectrometry (MS)-based method for plasma p-tau217 measures alone and the multi-analyte assay with algorithmic analysis that encompasses p-tau217 ratio (p-tau217:np-tau217 \u0026times; 100) and the Aβ42/40 ratio (as defined by the Amyloid Probability Score 2 (APS2), PrecivityAD2) currently being used in clinical practice in the United States, along with other select regions of the world \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e (Meyer et al, 2024).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eForty participants with DS in the Trial Ready Cohort \u0026ndash; Down syndrome (TRC-DS) cohort were included. The mean age was 38.5 (s.d. 7.9) years, 60% were women and 22.5% had AD pathology as defined by a centiloid value of 18 or higher (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In this cohort, 28.2% were ApoE4 carriers.\u003c/p\u003e\u003cp\u003ePlasma p-tau217 concentrations were higher in AD pathology-positive versus AD pathology-negative participants independent of the method used for measurements (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). With a cutoff of 0.21 pg/mL (Lumipulse) and 1.77 pg/mL (MS; PrecivityAD2) for plasma p-tau217, areas under the receiver operating characteristic (ROC) curves (AUCs) were 0.94 (CI 0.84,1.00) and 0.91 (CI 0.77, 1.00), respectively, with lower AUCs observed for np-tau, Aβ42, Aβ40 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Suppl Fig.\u0026nbsp;2, Suppl. Table\u0026nbsp;1). In addition to p-tau217, similarly strong correlations with amyloid levels on PET were observed for the p-tau217 ratio (r\u0026thinsp;=\u0026thinsp;0.79) and APS2 (r\u0026thinsp;=\u0026thinsp;0.80), whereas plasma Aβ42, Aβ40 and Aβ42/40 ratio did not show any association with amyloid-PET results (r\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e range 0.04\u0026ndash;0.22) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Suppl. Figure\u0026nbsp;1). The PrecivityAD2 measures of p-tau217 (0.21 pg/mL), %p-tau217 (3.08) and APS2 (20.5) had an AUC of 92%, a positive-predictive value (PPV) of 78% and a negative predictive value (NPV) of 97%, whereas p-tau217 of 0.21pg/mL measured by Lumipulse had only a slightly lower accuracy (90%) and PPV (70%), and the same NPV of 97% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Summary statistics on the diagnostic ability of optimal biomarker thresholds for predicting amyloid-PET status are listed in descending order of Youden index and range from the highest of 0.91 for the C2N MS-determined cut-points of p-tau217 (0.21 pg/mL), %p-tau217 (3.08) and APS2 (20.5) to 0.19 for the Aβ42/40 ratio of 0.09 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Suppl. Table\u0026nbsp;1). For comparison, we evaluated diagnostic performance of APS2 and %ptau217 in predicting amyloid pathology using established thresholds of 47.5 and 4.2, respectively, as they are currently applied in clinical care for individuals with signs or symptoms of cognitive impairment undergoing evaluation for sporadic AD (sAD) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e (PrecivityAD2, Meyer et al 2024). Using these cut-points, a specificity of 1.00 (95% CI 1.00\u0026ndash;1.00) was reached for both parameters, while sensitivity is 0.75 (95% CI 0.50\u0026ndash;1.00) for %p-tau217 and to 0.5 (95% CI 0.12\u0026ndash;0.88) for APS2, with overall accuracy of 0.95 (95%CI 0.87\u0026ndash;1.00) and 0.90 (95% CI 0.82\u0026ndash;0.97), respectively (Suppl. Table\u0026nbsp;2). Comparing of C2N and Lumipulse p-tau217 assay results using DeLong\u0026rsquo;s test revealed no statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.42). This was consistent with their high correlation (r\u0026thinsp;=\u0026thinsp;0.89, Suppl. Figure\u0026nbsp;3, Suppl. Figure\u0026nbsp;4) and similar diagnostic performance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found that the C2N PrecivityAD2 test and the fully automated Lumipulse immunoassay, which measures plasma p-tau217, both have excellent accuracy in differentiating individuals with DS with a significant amyloid burden. Our study suggests that both p-tau217 assessments provide an accurate and easily accessible diagnostic test to assess brain amyloid burden in adults with DS. Previous research in DS indicates variability in the age of onset of AD-pathology as measured by amyloid-PET, such that relying on age alone for clinical trial recruitment will lead to a high screening failure rate \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e (Zammit et al 2021). Compared to sAD, individuals with DS who have elevated amyloid levels show a significantly faster progression to tau pathology, and a more rapid decline in their cognitive functions \u003csup\u003e8 9\u003c/sup\u003e (Schworeret al, 2024, Krasny et al 2024). This underscores the necessity for timely inclusion of people with DS in AD clinical trials as only a narrow window of opportunity exists when they can benefit from early disease-targeting interventions.\u003c/p\u003e\u003cp\u003ePlasma biomarkers, in particular p-tau217 have emerged as excellent predictors of brain amyloid pathology in sAD. Plasma p-tau217 composite measures, including the PrecivityAD2 APS2 score have been successfully used to enrich for amyloid-PET positive individuals during screening in sAD clinical trials \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e (Rissman et al. 2024). However, the critically needed data on thresholds to discriminate \u0026ldquo;amyloid-positive\u0026rdquo; from \u0026ldquo;amyloid-negative individuals with DS is still missing. The recently FDA-approved Lumipulse G plasma test that measures p-Tau217/\u0026szlig;-Amyloid 1\u0026ndash;42 ratio is based on the same technology that was used in this study to collect plasma p-tau217 (Lumipulse) values in individuals with DS. Moreover, using a MS test (C2N), this study showed limited value of Aβ42/40 ratio in detection of amyloid-PET positivity, suggesting that plasma p-tau217 alone may be as useful as ptau-217/\u0026szlig;-Amyloid 1\u0026ndash;42 ratio in people with DS, and may allow for scalable implementation in clinical research and care. Our data aligns with previous studies in DS-AD showing that a strong association exists between plasma p-tau217 and brain pathology, specifically the amyloid- and tau-PET biomarkers \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e (Janelidze et al, 2022). For this study, we observed high AUCs using the C2N mass spectrometry and Lumipulse immunoassay methodologies, which can serve as a starting point for future studies to collect more evidence and refine the cut points for detecting brain amyloid burden in DS.\u003c/p\u003e\u003cp\u003eAccuracies of plasma p-tau217 Lumipulse and MS assays in this study reached 90% and 92% (AUC 0.94 and 0.91), respectively, which are considered excellent for detecting amyloid pathology and match or outperform the CSF-tests used in clinical practice \u003csup\u003e12 13\u003c/sup\u003e (Schindler et al 2018; Kaplow et al 2020). Our data also highlight the robustness of p-tau217 as an early AD-biomarker as both the MS and the immunoassay in this study demonstrated excellent overall accuracy of this biomarker in determining amyloid-PET positivity, though with different cut-offs.\u003c/p\u003e\u003cp\u003eIt is noteworthy that the thresholds for p-tau217 measures determined through MS in this study (p-tau217/Np-tau217\u0026thinsp;=\u0026thinsp;3.08 and APS2\u0026thinsp;=\u0026thinsp;20.5) are only somewhat different from the thresholds established in the sAD population, p-tau217/Np-tau217\u0026thinsp;=\u0026thinsp;4.2 and APS2\u0026thinsp;=\u0026thinsp;47.5 used in clinical practice \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e (Meyer et al 2024). This difference may be attributed to the distinct timelines for onset and progression of AD pathology in DS and sAD. Applying the PrecivityAD2 cutoffs in our study resulted in improved specificity and accuracy for %p-tau217 but lost on sensitivity, highlighting that thresholds established in sAD may not be directly transferrable to the genetic forms of AD. Establishing population and indication-specific cut-points or careful interpretation of the results may be warranted.\u003c/p\u003e\u003cp\u003eIn contrast to p-tau217, we did not observe any relationship of plasma amyloid-beta measures, including Aβ42, Aβ40 and Aβ42/40 ratio, with amyloid-PET status. Moreover, the addition of the Aβ42/40 ratio to the p-tau217 and %-ptau217 (i.e., APS2 values) did not improve accuracy in predicting amyloid status in this study, in contrast to its relevance in sAD \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e (Rissman et al 2024). These data further support the results from the prior studies that did not detect differences in Aβ40 and Aβ42 biomarkers between cognitively stable and symptomatic DS participants, though demonstrating consistently elevated plasma Aβ42 and Aβ40 across age and diagnostic groups in DS compared to the age-matched controls without DS \u003csup\u003e14 15\u003c/sup\u003e (Montoliu-Gaya et al 2021, Fortea et al., 2020)\u003c/p\u003e\u003cp\u003e Our study provides valuable insights into the accuracy, sensitivity, and specificity of several clinically available plasma biomarkers in predicting amyloid pathology in DS, including p-tau217 measures using the same technology as for p-tau217/ \u0026szlig;-Amyloid 1\u0026ndash;42 ratio from the FDA-approved Lumipulse plasma-based immunoassay. Nevertheless, our findings should be interpreted in the context of our study\u0026rsquo;s limited sample size, relatively low pre-test probability of disease among the cohort participants, and cross-sectional design. Using the cut-offs determined in this cohort to design prospective large clinical trials will further improve and refine the proposed thresholds.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were adults with a genetically confirmed DS, aged 25 to 55 \u0026nbsp;years old, who completed a baseline assessment at TRC-DS study sites and had available MRI and amyloid PET imaging. Institutional Review Board approval and informed consent were obtained prior to study enrollment into the TRC-DS study by the participant or legally designated caregiver according to the Declaration of Helsinki. Participant demographics grouped by brain amyloid status are provided in Table 1. Inclusion criteria for the TRC-DS study were diagnosis of DS confirmed by genetic testing or medical record review, age of 25-55 years, estimated IQ \u0026ge; 40, participant\u0026rsquo;s and study partner\u0026rsquo;s cooperation, and no conditions precluding from study participation or imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants underwent MRI imaging, as well as PET imaging using either PiB, Florbetapir (FBP), NAV4694 (NAV) or Flutemetamol (FMM). Global A\u0026beta; burden was determined using the Centiloid (CL) method \u003csup\u003e16\u003c/sup\u003e(Klunk et al, 2015) with the whole cerebellum serving as the reference region for PiB, NAV and FMM, while an eroded white matter reference was used for FBP. Briefly, PET images were spatially normalized to MNI152 template space. Standardized uptake value ratio (SUVr) images were then generated by voxel normalization to the mean activity in the reference tissue, and the mean cortical SUVr was extracted from the CL cortex ROI. Cortical SUVr was then converted to units of CL using the appropriate conversion equations for PiB \u003csup\u003e16\u003c/sup\u003e (Klunk et al, 2015), FBP \u003csup\u003e17\u003c/sup\u003e (Navitski et al, 2015), NAV \u003csup\u003e18\u003c/sup\u003e (Rowe et al, 2016) and FMM \u003csup\u003e19\u003c/sup\u003e (Battle et al, 2018). Participants were classified as amyloid-positive (A+) using an a priori threshold of 18 CL \u003csup\u003e2\u003c/sup\u003e (Zammit et al, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlasma Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma p-tau217 was analyzed using the Lumipulse immunoassay (Fujirebio) at the USC ATRI Biomarker Lab. \u0026nbsp;In parallel, plasma p-tau217, np-tau217, A\u0026beta;40 and A\u0026beta;42 were analyzed using MS-based assays at C2N Diagnostics (St. Louis, MO), as previously described (Meyer et al, 2024). In addition to p-tau217, %p-tau217 (p-tau217:np-tau217 \u0026times; 100) and a composite amyloid-prediction score (APS2) that incorporates p-tau217, np-tau217, A\u0026beta;40 and A\u0026beta;42 were used in the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCharacteristics of the analysis population were summarized by brain amyloid status. \u0026nbsp;Data is presented as means (standard deviations) for continuous variables, and counts (percentages) for categorical variables. \u0026nbsp; Correlations between amyloid levels and the blood plasma biomarkers were assessed using Pearson\u0026rsquo;s correlation coefficient. \u0026nbsp;Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of the blood plasma biomarkers in predicting amyloid status. \u0026nbsp;Performance was summarized with area under the curve (AUC), and \u0026lsquo;optimal\u0026rsquo; thresholds were determined by maximizing the Youden index \u003csup\u003e20\u003c/sup\u003e. \u0026nbsp; As an exploratory analysis, we applied thresholds 47.5 and 4.2 to C2Ns APS2 and %p-tau217. \u0026nbsp;These thresholds were developed in symptomatic individuals \u003csup\u003e6\u003c/sup\u003e (PrecivityAD2, Meyer et al 2024). \u0026nbsp;All thresholds are reported with corresponding sensitivity, specificity, accuracy, positive and negative predictive values, along with stratified bootstrap confidence intervals (N = 2000 and confidence level = 0.95). \u0026nbsp;To compare the overall diagnostic performance of C2N and Lumipulse p-tau217 assays, we used DeLong\u0026rsquo;s test for correlated AUCs\u003csup\u003e21\u003c/sup\u003e. \u0026nbsp;All statistical analyses were conducted using R (version 4.4.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Participant demographic grouped by brain amyloid status (positive if centiloid value \u0026ge;18.1).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative (N=31)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026lt;18 Cl)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive (N=8)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026gt;18 Cl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (N=39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e36.5 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e45.9 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e38.4 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e25.5 -55.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e33.1 \u0026ndash; 53.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e25.5 \u0026ndash; 55.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e10 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e5 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e15 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e21 (67.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e3 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e24 (61.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHispanic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e8 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e10 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Hispanic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e23 (74.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e6 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e29 (74.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPOE4 carrier\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e21 (67.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e6 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e27 (71.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e10 (32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e11 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPOE4 allele count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e21 (67.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e6 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e27 (71.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e9 (29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e10 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Summary statistics on the diagnostic ability of \u0026nbsp;the biomarkers using maximized Youden index cutpoints for predicting amyloid status. \u0026nbsp; The table is in descending order according the Youden index value.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1066%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eBiomarkers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThreshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSensitivity (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSpecificity (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.46395%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYouden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePPV\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9655%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1066%;\"\u003e\n \u003cp\u003ep-tau217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e1.77 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003e0.92 (0.82, 1.00_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e0.91 (0.77, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e0.88 (0.62, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e0.94 (0.84, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.46395%;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5934%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9655%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1066%;\"\u003e\n \u003cp\u003ep-tau217/Np-tau217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003e0.92 (0.82, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e0.89 (0.69,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e0.88 (0.62, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e0.94 (0.84, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.46395%;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5934%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9655%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1066%;\"\u003e\n \u003cp\u003eAPS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e20.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003e0.92 (0.82, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e0.90 (0.74,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e0.88 (0.62, 1\u003c/p\u003e\n \u003cp\u003e00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e0.94 (0.84, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.46395%;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5934%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9655%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1066%;\"\u003e\n \u003cp\u003ep-tau217 (Lumipulse)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e0.21 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003e0.90 (0.79, 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e0.94 (0.84,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e0.88 (0.62, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e0.90 (0.77, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.46395%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5934%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9655%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1066%;\"\u003e\n \u003cp\u003eA\u0026beta;42/40 ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3448%;\"\u003e\n \u003cp\u003e0.36 (0.26, 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e0.57 (0.36,0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9875%;\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.2853%;\"\u003e\n \u003cp\u003e0.19 (0.06, 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.46395%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.5934%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9655%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFortea, J.\u003cem\u003e, et al.\u003c/em\u003e Alzheimer\u0026apos;s disease associated with Down syndrome: a genetic form of dementia. \u003cem\u003eLancet Neurol\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 930-942 (2021).\u003c/li\u003e\n\u003cli\u003eZammit, M.D.\u003cem\u003e, et al.\u003c/em\u003e Characterizing the emergence of amyloid and tau burden in Down syndrome. \u003cem\u003eAlzheimers Dement\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 388-398 (2024).\u003c/li\u003e\n\u003cli\u003eJack, C.R.\u003cem\u003e, et al.\u003c/em\u003e Revised criteria for diagnosis and staging of Alzheimer\u0026apos;s disease: Alzheimer\u0026apos;s Association Workgroup. \u003cem\u003eAlzheimers Dement\u003c/em\u003e (2024).\u003c/li\u003e\n\u003cli\u003ePalmqvist, S.\u003cem\u003e, et al.\u003c/em\u003e Discriminative Accuracy of Plasma Phospho-tau217 for Alzheimer Disease vs Other Neurodegenerative Disorders. \u003cem\u003eJAMA\u003c/em\u003e \u003cstrong\u003e324\u003c/strong\u003e, 772-781 (2020).\u003c/li\u003e\n\u003cli\u003eAdministration, U.S.F.a.D. 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(2025).\u003c/li\u003e\n\u003cli\u003eMeyer, M.R.\u003cem\u003e, et al.\u003c/em\u003e Clinical validation of the PrecivityAD2 blood test: A mass spectrometry-based test with algorithm combining %p-tau217 and Abeta42/40 ratio to identify presence of brain amyloid. \u003cem\u003eAlzheimers Dement\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 3179-3192 (2024).\u003c/li\u003e\n\u003cli\u003eZammit, M.D.\u003cem\u003e, et al.\u003c/em\u003e PET measurement of longitudinal amyloid load identifies the earliest stages of amyloid-beta accumulation during Alzheimer\u0026apos;s disease progression in Down syndrome. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e228\u003c/strong\u003e, 117728 (2021).\u003c/li\u003e\n\u003cli\u003eSchworer, E.K.\u003cem\u003e, et al.\u003c/em\u003e Timeline to symptomatic Alzheimer\u0026apos;s disease in people with Down syndrome as assessed by amyloid-PET and tau-PET: a longitudinal cohort study. \u003cem\u003eLancet Neurol\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 1214-1224 (2024).\u003c/li\u003e\n\u003cli\u003eKrasny, S.\u003cem\u003e, et al.\u003c/em\u003e Assessing amyloid PET positivity and cognitive function in Down syndrome to guide clinical trials targeting amyloid. \u003cem\u003eAlzheimers Dement\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 5570-5577 (2024).\u003c/li\u003e\n\u003cli\u003eRissman, R.A.\u003cem\u003e, et al.\u003c/em\u003e Plasma Abeta42/Abeta40 and phospho-tau217 concentration ratios increase the accuracy of amyloid PET classification in preclinical Alzheimer\u0026apos;s disease. \u003cem\u003eAlzheimers Dement\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1214-1224 (2024).\u003c/li\u003e\n\u003cli\u003eJanelidze, S.\u003cem\u003e, et al.\u003c/em\u003e Detection of Brain Tau Pathology in Down Syndrome Using Plasma Biomarkers. \u003cem\u003eJAMA Neurol\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 797-807 (2022).\u003c/li\u003e\n\u003cli\u003eSchindler, S.E.\u003cem\u003e, et al.\u003c/em\u003e Cerebrospinal fluid biomarkers measured by Elecsys assays compared to amyloid imaging. \u003cem\u003eAlzheimers Dement\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1460-1469 (2018).\u003c/li\u003e\n\u003cli\u003eKaplow, J.\u003cem\u003e, et al.\u003c/em\u003e Concordance of Lumipulse cerebrospinal fluid t-tau/Abeta42 ratio with amyloid PET status. \u003cem\u003eAlzheimers Dement\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 144-152 (2020).\u003c/li\u003e\n\u003cli\u003eMontoliu-Gaya, L., Strydom, A., Blennow, K., Zetterberg, H. \u0026amp; Ashton, N.J. Blood Biomarkers for Alzheimer\u0026apos;s Disease in Down Syndrome. \u003cem\u003eJ Clin Med\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e(2021).\u003c/li\u003e\n\u003cli\u003eFortea, J.\u003cem\u003e, et al.\u003c/em\u003e Clinical and biomarker changes of Alzheimer\u0026apos;s disease in adults with Down syndrome: a cross-sectional study. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e395\u003c/strong\u003e, 1988-1997 (2020).\u003c/li\u003e\n\u003cli\u003eKlunk, W.E.\u003cem\u003e, et al.\u003c/em\u003e The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. \u003cem\u003eAlzheimers Dement\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1-15 e11-14 (2015).\u003c/li\u003e\n\u003cli\u003eNavitsky, M.\u003cem\u003e, et al.\u003c/em\u003e Standardization of amyloid quantitation with florbetapir standardized uptake value ratios to the Centiloid scale. \u003cem\u003eAlzheimers Dement\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1565-1571 (2018).\u003c/li\u003e\n\u003cli\u003eRowe, C.C.\u003cem\u003e, et al.\u003c/em\u003e Standardized Expression of 18F-NAV4694 and 11C-PiB beta-Amyloid PET Results with the Centiloid Scale. \u003cem\u003eJ Nucl Med\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 1233-1237 (2016).\u003c/li\u003e\n\u003cli\u003eBattle, M.R.\u003cem\u003e, et al.\u003c/em\u003e Centiloid scaling for quantification of brain amyloid with [(18)F]flutemetamol using multiple processing methods. \u003cem\u003eEJNMMI Res\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 107 (2018).\u003c/li\u003e\n\u003cli\u003eYouden, W.J. Index for rating diagnostic tests. \u003cem\u003eCancer\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 32-35 (1950).\u003c/li\u003e\n\u003cli\u003eDeLong, E.R., DeLong, D.M. \u0026amp; Clarke-Pearson, D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. \u003cem\u003eBiometrics\u003c/em\u003e\u003cstrong\u003e44\u003c/strong\u003e, 837-845 (1988).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7359108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7359108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividuals with Down syndrome (DS) develop a genetic form of Alzheimer’s disease (AD) due to an extra copy of chromosome 21, which contains the APP gene. The widespread implementation of blood-based biomarkers for AD is imminent. These tests hold promise for diagnosing AD in DS once validated. In this study, we assessed plasma phospho-tau217 (p-tau217) in participants from the NIH Trial Ready Cohort – Down syndrome (TRC-DS), using predefined amyloid PET cutoffs. Plasma p-tau217 was measured via two fully automated assays: C2N Diagnostics’ PrecivityAD2 mass spectrometry and Fujirebio’s Lumipulse immunoassay. The primary outcome was AD pathology, defined by amyloid PET \u0026gt;18 centiloids. Both assays showed high accuracy: AUCs of 0.92 and 0.90, with sensitivities of 0.88 and specificities of 0.94 and 0.90, respectively. These results are comparable to composite plasma measures. In conclusion, automated p-tau217 tests offer strong potential for routine AD screening in individuals with DS.\u003c/p\u003e","manuscriptTitle":"Automated Plasma Phospho-tau217 Assays for the diagnosis of Down Syndrome-Related Alzheimer's Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:43:55","doi":"10.21203/rs.3.rs-7359108/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0ac4893-51b3-4d90-bc2b-c405c27148ae","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53913937,"name":"Health sciences/Neurology/Neurological disorders/Dementia"},{"id":53913938,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-01-07T14:25:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:43:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7359108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7359108","identity":"rs-7359108","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}