Head-to-head comparison of plasma p-tau217 immunoassays for incipient Alzheimer’s disease in community cohorts

Head-to-head comparison of plasma p-tau217 immunoassays for incipient Alzheimer’s disease in community cohorts | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Head-to-head comparison of plasma p-tau217 immunoassays for incipient Alzheimer’s disease in community cohorts Rebecca A. Deek , Wasiu G. Balogun , Xuemei Zeng , Gallen Triana-Baltzer , Tharick A. Pascoal , Hartmuth C. Kolb , Beth Snitz , Ann D. Cohen , View ORCID Profile Thomas K. Karikari doi: https://doi.org/10.1101/2025.10.07.25337434 Rebecca A. Deek 1 Department of Biostatistics and Health Data Science, School of Public Health, University of Pittsburgh , Pittsburgh, Pennsylvania, USA 3 Alzheimer’s Disease Research Center, University of Pittsburgh , Pittsburgh, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wasiu G. Balogun 2 Department of Psychiatry, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania, USA 3 Alzheimer’s Disease Research Center, University of Pittsburgh , Pittsburgh, PA, USA 4 Biofluid Biomarker Laboratory, Western Psychiatric Hospital, University of Pittsburgh Medical Center , Pittsburgh PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xuemei Zeng 2 Department of Psychiatry, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania, USA 3 Alzheimer’s Disease Research Center, University of Pittsburgh , Pittsburgh, PA, USA 4 Biofluid Biomarker Laboratory, Western Psychiatric Hospital, University of Pittsburgh Medical Center , Pittsburgh PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gallen Triana-Baltzer 5 Johnson & Johnson , La Jolla, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tharick A. Pascoal 2 Department of Psychiatry, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania, USA 3 Alzheimer’s Disease Research Center, University of Pittsburgh , Pittsburgh, PA, USA 6 Department of Neurology, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hartmuth C. Kolb 5 Johnson & Johnson , La Jolla, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Beth Snitz 3 Alzheimer’s Disease Research Center, University of Pittsburgh , Pittsburgh, PA, USA 6 Department of Neurology, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ann D. Cohen 2 Department of Psychiatry, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania, USA 3 Alzheimer’s Disease Research Center, University of Pittsburgh , Pittsburgh, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas K. Karikari 2 Department of Psychiatry, School of Medicine, University of Pittsburgh , Pittsburgh, Pennsylvania, USA 3 Alzheimer’s Disease Research Center, University of Pittsburgh , Pittsburgh, PA, USA 4 Biofluid Biomarker Laboratory, Western Psychiatric Hospital, University of Pittsburgh Medical Center , Pittsburgh PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas K. Karikari For correspondence: Karikaritk{at}upmc.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Background Plasma p-tau217 is a promising biomarker for detecting incipient AD pathology, but direct comparison of different p-tau217 assays in community-based cohorts are limited. Methods We evaluated two cohorts from southwestern Pennsylvania, USA; the MYHAT-NI sub-study, which included two-year longitudinal follow-up neuroimaging assessments of Aβ, tau, and cortical thickness; and the Human Connectome Project/CoBRA, targeting a 50:50 split of self-identified Black and non-Hispanic White individuals. Plasma p-tau217 was measured using four different assays: Lumipulse, Johnson&Johnson, ALZpath, and NULISA. Aβ and tau pathologies were assessed with [ 11 C]PiB PET and [ 18 F]Flortaucipir PET, respectively. Clinical Dementia Rating (CDR) and Montreal Cognitive Assessment were used to assess cognitive performance. Results We included 344 participants (MYHAT-NI: n=111, median age 76 [IQR: 72-80], 54% female; HCP/CoBRA: n=234, median age 62 [IQR: 52-70], 65% female). All four p-tau217 assays exhibited moderate to strong cross-platform correlations (Spearman correlations of 0.40 – 0.86), and statistically equivalent AUCs (of 0.84-0.90) for determining Aβ positivity. Conclusions Our findings showed strong equivalent performances of plasma p-tau217 assays to identify amyloid positivity across two highly diverse cohorts of community-dwelling older adults. Background Several biofluid biomarkers have emerged as promising tools for diagnosing and prognosing Alzheimer’s disease (AD).; their non-invasiveness, cost-effectiveness, accessibility, and accuracy have led to their growing acceptance as biological indicators of AD pathology. 1 – 3 Among biofluid biomarkers, there is particularly increasing interest in plasma biomarkers, as they can support treatment and management, particularly for patients undergoing disease-modifying therapies such as anti-amyloid medications. 4 – 6 , reduce recruitment time and enhance representation during clinical trials 7 while being more accessible and cost-effective than their cerebrospinal fluid and neuroimaging alternatives 1 . Several plasma biomarkers have been developed that show clinical utility across the AD continuum; these include p-tau217 8 – 11 , p-tau181 12 , 13 , p-tau231 7 , 9 , 14 , GFAP 15 , 16 , and Aβ 42 / 40 ratio 9 , 17 , 18 . Out of these, plasma p-tau217 has demonstrated perhaps the most significant promise for early detection of AD pathophysiology, including in older adults without cognitive concerns. 2 , 7 – 9 , 19 Plasma p-tau217 has consistently shown high performance in differentiating biomarker-positive AD from non-AD neurodegenerative diseases. 6 Additionally, plasma p-tau217 has proven effective to distinguish amyloid-beta PET positive (Aβ+) populations 20 , achieving high accuracies in differentiating between Aβ+ PET and Aβ-PET at baseline and at follow-up. This provides empirical evidence for p-tau217 as an early indicator of AD, incident dementia, and Aβ pathology in community-based populations. 20 – 22 This accumulation of evidence positions plasma p-tau217 as a leading blood biomarker for diagnosing and prognosticating AD, particularly regarding Aβ pathology as evidenced by the FDA approval of the Lumipulse G pTau217/β-Amyloid 1-42 Plasma Ratio as the first blood biomarker for the early detection of brain Aβ plaques. Nonetheless, plasma p-tau217 evaluation has primarily taken place in clinic-oriented cohorts and settings 19 . However, community-based studies are limited, with only a few such cohort studies conducted18,21 including several from our group10,23. In effect, head-to-head comparison studies of different plasma p-tau217 studies have excluded community-based cohorts. Clinic-based cohorts tend to have strict eligibility criteria (e.g., concerning previous/current neurological and other comorbid conditions) that often eliminate participation of community-dwelling older adults. Focused investigation of community-based cohorts could provide new insights into which plasma p-tau217 assays are fit for purpose in these settings that tend to include mostly cognitively normal individuals. We performed a head-to-head comparison of four different plasma p-tau217 immunoassays in two community-based cohorts, comparing their performances to identify Aβ and tau pathology defined by neuroimaging. Methods Study participants Study participants were from two separate community-based cohorts from southwestern Pennsylvania, USA, described below. Population-based cohort: The Monongahela-Youghiogheny Healthy Aging Team Neuroimaging (MYHAT-NI) cohort, which enrolled a subset of the parent MYHAT study participants for a two-year longitudinal neuroimaging follow-up of Aβ, tau, and neurodegeneration. 24 MYHAT-NI participants had Clinical Dementia Rating (CDR) sum-of-boxes scores of <1.0 at enrollment. Sociodemographic variables including age, level of education and race were collected at the baseline neuroimaging visit. Blood sample collection, neuropsychological assessment, and neuroimaging (Aβ and Tau PET) were performed at both the baseline and two-year follow-up. For this study, only baseline samples were analyzed. Diversity cohort The Human Connectome Project (HCP) cohort, is a community-based longitudinal study that aimed to recruit an equal number of self-identified Black/African American and non-Hispanic White participants aged 50-89 years old. 25 Sociodemographic information gathering, blood sample collection, neuropsychological assessment and neuroimaging were conducted over three days. In both cohorts, Aβ positivity using Pittsburgh Compound B (PiB) PET was defined as a mean standardized uptake value ratio (SUVR) >1.346. Participant characteristic for each cohort is shown in Table 1 . Assay comparisons were limited to the participants with data for the comparisons of interest. Aβ and Tau PET and Magnetic Resonance Imaging (MRI) procedures for both cohorts were described below. Plasma p-tau217 assays Plasma p-tau217 assays from four sources were evaluated: Lumipulse, ALZpath, NULISA, and Johnson & Johnson (formerly known as Janssen). The Lumipulse p-tau217 assay is a chemiluminescent enzyme immunoassay (CLEIA) and was conducted on a LUMIPULSE G1200 analyzer using Lumipulse G pTau217 Immunoreaction Cartridges (Fujirebio, Cat # 00003765) according to the manufacturer’s recommendation. The assay had a mean repeatability of 1.8% according to two Quality Control (QC) samples measured in duplicate alongside the experimental samples. The ALZpath, p-tau217 and the Johnson & Johnson p-tau217+ assays are Single Molecule Array (Simoa)-based methods. The ALZpath assay was performed on an HD-X analyzer using Simoa® ALZpath pTau-217 CARe Advantage Kit (ACCALPT217) from Quanterix. The intra- and inter-assay coefficients of variation (CVs) were 3.7% and 11.4%, respectively. The Johnson & Johnson p-tau217+ assay was analyzed at Quanterix, USA, as described by Groot et al. 2 This assay employs the anti-p-tau217 antibody PT3, which has enhanced affinity when threonine 212 is also phosphorylated, as the capture antibody, and HT43 (anti-tau) as the detector. The intra-assay and inter-assay CVs for the Johnson & Johnson p-tau217+ assay were 3.9% and 7.3%, respectively. The NULISA p-tau217 assay, a proximity ligation assay, was performed on an Alamar ARGO TM system (Alamar Biosciences, CA, USA) following published protocols as part of the multiplex NULISAseq CNS Disease Panel 120. 26 , 27 The intra-assay and inter-assay CVs for NULISA p-tau217 were 2.0% and 5.3% respectively. Amyloid and tau PET imaging The detailed procedures for PET imaging were described in prior publications. 24 , 25 In brief, PET imaging was conducted on a Siemens Biograph mCT Flow 64–4R PET/CT scanner for MYHAT-NI and Siemens/CTI ECAT HR C scanner for HCP. [ 11 C] PiB was used as the tracer for Aβ PET (both MYHAT-NI and HCP) and [18F]-flortaucipir (AV-1451) for tau PET (MYHAT-NI only). Both tracers were injected via the antecubital vein using slow bolus injections. For MYHAT-NI, PET imaging data were collected with 5-minute frames, spanning 50 to 70 minutes after injection for Aβ PET and 80 to 100 minutes for tau PET. For the HCP cohort, Aβ PET images were collected in six 5-minute frames for 30 minutes. Collected images were processed into T1 MR images using PMOD software (PMOD Technologies, Zurich, Switzerland) and analyzed with FreeSurfer v5.3 to derive composite scores for regions of interest (ROIs). For Aβ PET, a global [ 11 C] PiB standardized uptake value ratio ( 11 C-PiB SUVR) was computed by volume-weighted average of nine composite regional outcomes (anterior cingulate, posterior cingulate, insula, superior frontal cortex, orbitofrontal cortex, lateral temporal cortex, parietal, precuneus, and ventral striatum). Aβ positivity (A+) was defined as ( 11 C-PiB SUVR) > 1.346. 28 , 29 For tau PET, a composite SUVR (AV-1451 SUVR) was computed from the volume-weighted average of three composite regional outcomes reflecting Braak pathologic staging (Braak 1, Braak 3/4, and Braak 5/6). Tau positivity was defined as AV-1451 SUVR > 1.18. 30 Structural MRI measures Structural MRI was conducted on a Siemens Prisma 3-Tesla 64-channel system equipped with Connectome level gradients operating at 80mT/m. T1-weighted structural MRI series (MPRAGE) were collected for MYHAT NI. HCP collected the following additional scans: T2-weighted SPACE image, FLAIR, susceptibility weighted imaging, diffusion tensor imaging, task-free functional MRI, task-based fMRI, and arterial spin labeling. A composite thickness (CT) score based on critical temporal lobe areas including the fusiform gyrus, entorhinal cortex, and the inferior and middle temporal gyri, was computed. Neurodegeneration positivity (N+) was defined as using two criteria: Mayo N positivity by CT < 2.7, and Pitt N positivity by CT < 2.8. 31 Statistical analysis Demographic characteristics were presented for each study. Continuous variables were reported as median (interquartile range, IQR) and categorical variables were reported as counts (%). Significance testing comparing Aβ-PET+ vs. Aβ-PET-groups for the MYHAT-NI and HCP studies were performed via the Wilcoxon rank sum test and Pearson’s Chi-squared or Fisher’s exact test for continuous and categorical variables, respectively. Spearman’s correlations between plasma p-tau217 measurements from different platforms were also reported for all participants, as well as subsets stratified by Aβ-PET status. Correlations between plasma p-tau217 levels and Aβ/tau/cortical thickness status were also reported using continuous 11 C-PiB SUVR, AV-1451 SUVR, and cortical thickness composite score, respectively. We built logistic regression models to predict CDR, Aβ-PET status, and tau-PET status using plasma p-tau217 measurements and demographic variables age, sex, and APOE ε4 carrier status as predictors. Models were fit for each assay platform and cohort separately. We trained and validated our models using five-fold cross-validation to avoid “double dipping” in statistical inference, which occurs when the same data is used to train and evaluate the model, potentially leading to erroneous findings. Under this cross-validation framework, the logistic regression models were trained using 80% of the data and validated on the remaining 20%. This process was repeated five times such that a different 20% was “left out” for validation each time. Receiver operating characteristic (ROC) curves were constructed for each cross-validation set, and the out-of-sample predictive performance of the models was evaluated using the area under the curve (AUC) for each of the cross-validated ROC curves. The overall AUC values were calculated as the averages across the five cross-validated AUCs. Confidence intervals were estimated using a computationally efficient influence curve approach. 32 Sensitivity analyses of the predictive performance were run using only participants that had biomarker measures for all assays in a study. Three-fold cross validation was used in sensitivity analyses due to the smaller sample sizes. All statistical analysis was performed in R studio using R (version 4.4.2). Results Participant characteristics We included a total of 344 participants from the MYHAT-NI (n=111) and HCP (n=234) cohorts. The MYHAT-NI cohort had a median (IQR) age of 76 (72, 80) years, 60 (54%) were females, 18 (16%) had an APOE ε4 allele, 28 (25%) were Aβ-PET positive, 39 (35%) were tau-PET positive, and 101 (91%) had a CDR global score of 0 (i.e. cognitive normal; Table 1a ). Regarding MRI-derived neurodegeneration (i.e., cortical thickness), 70 (63%) and 33 (30%) had abnormal (N+) profiles according to the Pittsburgh and Mayo criteria, respectively. One hundred and five (95%) participants self-identified as non-Hispanic White, and six (5%) as Black/African American. The average length of education was 12 (12-16) years. There were no age (p=0.052), sex (p=0.2) or education (p=0.7) differences between the Aβ-PET groups, but there was a significant difference in APOE ε4 carriership (p<0.001), with a higher proportion in the Aβ-PET positive vs. negative groups. Additionally, the Aβ-PET positive group was associated with higher tau-PET positivity (p < 0.001) but not N positivity (p = 0.10 for Pitt N status and 0.5 for Mayo N status). View this table: View inline View popup Table 1a: Participant Characteristics for the MYHAT-NI Cohort according to Aβ-PET status The HCP cohort had a median (IQR) age of 62 (56-70) years, 149 (65%) female participants, 69 (30%) APOE ε4 allele carriers, 35 (15%) Aβ-PET positive and 85 (36%) were N-negative participants (Mayo N) ( Table 1b ). The average length of education was 14 (12-17) years. This study did not report CDR scores but used MOCA; the average MOCA score was 25. One hundred twenty (52%) participants self-identified as Black/African American, 3 (1.3%) as Asian, 106 (46%) non-Hispanic White and 1 other. The Aβ-PET positive group was older (p<0.001), had fewer female (p=0.002) and non-Hispanic White participants (p=0.001), and had received more advanced formal education (p<0.001) than the Aβ-PET negative group. However, the two groups had no difference in APOE ε4 carriership ( Table 1b ). View this table: View inline View popup Table 1b: Participant Characteristics for the HCP Cohort according to Aβ-PET positivity Cross-platform Correlation of plasma p-tau217 assays Plasma p-tau217 concentration values measured with different immunoassays showed moderate to strong correlations across the two cohorts, with Spearman correlation coefficients (ρ) ranging from 0.55 to 0.87 ( Figure 1 ). Download figure Open in new tab Figure 1: Correlations between ptau217 assays and radiotracers across all participants split by cohort. Overall, better inter-assay correlation was observed in the MYHAT-NI cohort, with ρ-values ranging between 0.78 and 0.87 among the four different assays (Johnson & Johnson, ALZpath, Lumipulse, and NULISA). The strongest correlation was observed between the ALZpath and NULISA assays (ρ=0.87, p<0.001), followed by ALZpath and Johnson & Johnson (ρ=0.86, p<0.001). P-tau217 levels in the HCP cohort were measured using the ALZpath and Johnson & Johnson assays, which showed a ρ of 0.55 (p<0.001). Cross-platform correlations were overall stronger in the Aβ-PET positive subgroup (Supplementary Fig. 1-2). In the MYHAT-NI cohort, four out of six possible pairwise platform comparisons gave stronger correlation coefficients when examined in the Aβ-PET positive group, with the strongest correlation observed between ALZpath and NULISA (ρ=0.86, p<0.001). For the HCP cohort, ALZpath and Johnson & Johnson showed a ρ value of 0.50 in Aβ-PET+ participants compared to 0.44 in Aβ-PET-individuals. Correlation of plasma p-tau217 with AD pathology burden Aβ burden assessed by PiB PET All plasma p-tau217 measurements in the MYHAT-NI and HCP cohorts showed significant positive correlations with PiB SUVR across all participants (all p<0.001, Figures 1 and 2 ). Stronger correlations were observed among Aβ-PET positive individuals (Supplementary Figures 1-2). Download figure Open in new tab Figure 2: Scatterplots of ptau217 assays with global PiB SUVR for the MYHAT NI colored by centiloid group. In MYHAT-NI, the ρ values ranged from 0.34 to 0.51 for the four assays, with the weakest being Johnson & Johnson and the strongest being NULISA ( Figure 1 ). Stratification by Aβ-PET status revealed that the overall correlations were driven mostly by the Aβ-PET+ group, which showed moderate to strong associations (ρ: 0.43-0.68, p: 0.001-0.04). In contrast, for the Aβ-PET-group, the correlations between the p-tau217 assays and PiB SUVR were not significant (ρ: −0.14-0.08, p: 0.26-0.95) (Supplementary Figure 1). In the HCP cohort, both ALZpath and Johnson & Johnson correlated with PiB SUVR, with a ρ value of 0.33 (both p < 0.001) ( Figure 1 ). When stratified by Aβ-PET status, the only significant correlation was observed with the ALZpath assay in the Aβ-PET+ group (ρ=0.60, p=0.003). The Johnson & Johnson assay had a positive but non-significant coefficient (ρ=0.34, p=0.11) for the Aβ-PET+ participants. Both assays showed no significant correlation with PiB SUVR in the Aβ-PET-participants (Supplementary Figure 2). Tau pathology burden by tau-PET For the MYHAT-NI cohort, plasma p-tau217 measurements by Lumipulse, ALZpath, and NULISA were significantly associated with tau-PET SUVR in the full cohort (ρ: 0.28-0.38, p<0.001 for all), with the strongest association observed with the Lumipulse assay. The Johnson & Johnson assay showed a marginally significant association (ρ=0.20, p=0.06). Similar to their association with PiB SUVR, the observed correlations were stronger in the Aβ-PET+ (ρ: 0.35-0.46, p: 0.02-0.10) than the Aβ-PET- (ρ: −0.09-0.09, p: 0.42-0.75) group. Neurodegeneration by cortical thickness In the MYHAT-NI cohort, none of the p-tau217 assays showed strong correlation with cortical thickness when evaluated in the entire cohort (ρ: −0.14-−0.06, p: 0.18-0.56). When stratified by Aβ-PET status, Lumipulse showed a significant association in the Aβ-PET-group, with a correlation coefficient of −0.25 (p = 0.02). None of the remaining associations was significant. In the HCP cohort, Johnson & Johnson p-tau217+ had a significant negative correlation with cortical thickness (ρ = −0.15, p = 0.03), while ALZpath showed a nonsignificant correlation of −0.12 (p = 0.07). When stratified by Aβ-PET status neither assay was significantly associated with cortical thickness in the Aβ-PET-group (ρ: −0.08-−0.03, p: 0.28-0.72). For the Aβ-PET+ group, ALZpath was significantly associated with cortical thickness (ρ = −0.46, p = 0.01), but Johnson & Johnson was not (ρ = −0.18, p = 0.34). Accuracy in identifying A β -PET positivity Predictive models including plasma p-tau217 and demographic variables — age, sex, and APOE ε4 carrier status — had high AUCs, close to 90% to distinguish normal from abnormal Aβ-PET scans irrespective of the assay used ( Figure 3 ). In MYHAT-NI, the AUCs were as follows: NULISA (0.89, 95% CI= [0.82, 0.96]), Johnson & Johnson (0.89, 95% CI= [0.81, 0.96]), ALZpath (0.89, 95% CI= [0.82, 0.95]), and Lumipulse (AUC = 0.87, 95% CI= [0.80, 0.95]). All assays performed similar to one another, with overlapping confidence intervals ( Figure 3 ). The largest fold change difference between the Aβ-PET+ and Aβ-PET-groups was observed for ALZpath p-tau217, which had a large fold change of 2.072, followed by fold changes of 1.93 for Lumipulse, 1.86 for Johnson & Johnson, and 1.06 for NULISA ( Figure 4a ). Download figure Open in new tab Figure 3: AUC and confidence intervals for Abeta prediction using several ptau217 assays across cohorts. Download figure Open in new tab Figure 4: Scatter and boxplots of the distribution of ptau217 assays across cohorts split by Aβ-PET status. (A) MYHAT-NI and (B) HCP In the HCP cohort, the AUCs were 0.89 (95% CI= [0.83, 0.95]) for Johnson & Johnson and 0.87 (95% CI= [0.82, 0.92]) for ALZpath. The ALZpath assay was associated with a fold change of 2.25, and Johnson & Johnson reported a fold change of 2.11 ( Figure 4b ). Accuracy in identifying tau-PET positivity Concerning differentiating normal from abnormal tau-PET scans in MYHAT-NI, all predictive models exhibited poor performance, regardless of the assay used ( Figure 5 ). The Lumipulse assay had the highest predictive capability (AUC= 0.61, 95% CI= [0.50 0.73]) followed by NULISA (0.59, 95% CI= [0.47, 0.70]), ALZpath (0.58, 95% CI= [0.46, 0.69]), and Johnson & Johnson (0.52, 95% CI= [0.39 0.66]). All confidence intervals crossed 0.5, indicating predictive performance on par with random chance. Download figure Open in new tab Figure 5: AUC and confidence intervals for tau-PET prediction using several ptau217 assays in the MYHAT-NI cohort. Sensitivity analyses We restricted our analysis to only participants who had measures for all assays in each of the two cohorts. This reduced the sample sizes to 92 and 201 participants in the MYHAT-NI and HCP cohorts respectively. We found that, in this subset, the predictive performance of the assays for Aβ-PET status was similar to the overall performance, likely due to the overall low level of missingness. Briefly, the best performing assay for MYHAT-NI was Johnson & Johnson followed by NULISA (AUC range: 0.83-0.87). Johnson & Johnson performed slightly better than ALZpath in HCP (AUC: 0.89 vs. 0.88). This indicates that the assays performed comparably, as in the original analysis using the larger data set. Discussion In this study, we have demonstrated that plasma p-tau217 assays from four different sources have equivalent classification accuracies for identifying in vivo brain amyloid and tau pathologies in predominantly cognitively normal older adults recruited from community settings. Our results suggest that plasma p-tau217 assays can potentially be used for population screening exercises and prevention trial eligibility programs aimed toward the identification of community-dwelling individuals with subtle but emerging AD pathophysiology. We extend previous head-to-head studies that focused on participants recruited from various sources including memory clinics, research registry and other clinical sources 19 , 33 , 34 . Various analytical techniques have been developed to measure p-tau217 in plasma. Among the four assays examined, while the measured absolute concentrations values were different for each assay, inter-assay correlations were moderate in the full cohorts and high when focusing on the Aβ-PET+ subgroup. Notably, the ALZPath assay displayed the strongest relationship with NULISA, followed by Johnson & Johnson. This finding aligns with several results which identified a strong correlation between these p-tau217 assays. 27 , 34 , 35 The results can be explained by the fact that both the ALZpath and NULISA assays use the same p-tau217 antibody – the ALZpath p-tau217 antibody - while the Johnson & Johnson and Lumipulse assays each uses a separate p-tau217 capture antibody. In this study, each plasma p-tau217 assays demonstrated moderate to strong correlation with the Aβ-PET SUVR, suggesting that they can be used to assess brain Aβ plaque burden. The results show that plasma p-tau217 can effectively differentiate between Aβ-PET+ and Aβ-PET-participants even among mostly CDR = 0 participants. Our study revealed a high area under the curve (AUC) ranging from 88% to 90% for identifying abnormal Aβ-PET scans, regardless of the assay or cohort used, in agreement with recommendations by the Alzheimer’s Association expert workgroup. 36 Unfortunately, we could not replicate this level of performance for tau-PET scans, which showed AUCs only ranging from 47% to 61%. This inability to differentiate abnormal Tau-PET scans from normal ones is in agreement with previous results 34 , 37 and could be because p-tau217 is known to be an early AD biomarker and may be unable to effectively detect tau aggregate pathology that is visible with tau PET imaging 37 . Other biomarkers that seem to be more aligned with tau aggregate pathology – including p-tau262 and p-tau356 for soluble tau assemblies 38 and MTBR tau for fibrils and tangles 38 – 40 – should be investigated in future studies. The key takeaway from our study is that the plasma p-tau217 assays evaluated, regardless of the measurement platform, demonstrates a strong correlation with Aβ pathology and has strong accuracies to identify an abnormal Aβ PET scan. A significant strength of this study is the focus on community-based cohorts recruiting individuals across various educational backgrounds and APOE ε4 carrier statuses, potentially enhancing the applicability of our findings to real-life settings. Furthermore, we focused on mostly cognitively unimpaired individuals, providing a window of opportunity into early disease phases before symptoms appear. However, the study also has limitations, such as the lack of a cohort that represents a broader spectrum of AD continuum. Furthermore, we were unable to evaluate longitudinal changes, as the study only included baseline visits with no short-term or long-term follow-ups, which would be essential for validating the clinical utility of the assays. Conclusions In this study that compared the performances of four different plasma p-tau217 immunoassays in two predominantly cognitively normal cohorts, we found equivalent accuracies of all assays to identify an abnormal Aβ PET scan. These results underscore the utility of plasma p-tau217 from different sources to serve as a biomarker for AD pathology. Data Availability All data produced in the present study are available upon reasonable request to the authors Declarations Ethical approval and consent to participate All studies were approved by the University of Pittsburgh Institutional Review Board. Written informed consent of all participants in the cohort was obtained. This study was performed in accordance with the Declaration of Helsinki. Consent for publication All authors have consented for this paper to be published. Availability of data and materials Data generated from this study are available from the corresponding author on reasonable request. Competing interests XZ is listed inventors on the University of Pittsburgh provisional patent #63/672,952. GTB and HK are employees of Johnson & Johnson Pharmaceuticals. They did not play any role in the data analysis but provided comments on the manuscript and provided approval for submission of the manuscript. TKK has consulted for Quanterix Corporation, SpearBio Inc., Neurogen Biomarking LLC., and Alzheon, has served on advisory boards for Siemens Healthineers and Neurogen Biomarking LLC., outside the submitted work. Over the last two years, he has received in-kind research support from Janssen Research Laboratories, SpearBio Inc., and Alamar Biosciences, as well as meeting travel support from the Alzheimer’s Association and Neurogen Biomarking LLC., outside the submitted work. TKK has received royalties from Bioventix for the transfer of specific antibodies and assays to third party organizations. TKK is an inventor on patents and provisional patents regarding biofluid biomarker methods, targets and reagents/compositions, that may generate income for the institution and/or self should they be licensed and/or transferred to another organization. Funding The MYHAT, MYHAT-NI and HCP studies were funded by R37AG023651, R01 AG052521 and R01 AG072641-02 respectively. TKK and other members of the Karikari Laboratory were TKK was supported by NIH/NIA (R01 AG083874, U24AG082930, P30 AG066468, RF1 AG077474, R01 AG083156, R37 AG023651, R01 AG025516, R01 AG073267, R01 AG075336, R01 AG072641, P01 AG025204, R01 AG052521), NIH/NINDS (U01 NS131740, U01 NS141777), NIH/NIMH (R01 MH108509), Aging Mind Foundation (DAF2255207), DoD (HT94252320064), the Anbridge Charitable Fund, and a professorial endowment from the Department of Psychiatry, University of Pittsburgh. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Authors’ contributions RAD: conceptualization, methodology, software, data curation, investigation, validation, formal analysis, visualization, project administration, resources, writing – original draft, writing – review and editing WGB: validation, formal analysis, visualization, project administration, resources, writing – original draft, writing – review and editing XZ: conceptualization, methodology, validation, supervision, project administration, writing – original draft, writing – review and editing. GT-B: validation, formal analysis, visualization, project administration, resources, writing – original draft, writing – review and editing TAP: Imaging, funding acquisition, project administration, writing – review and editing. HK: validation, formal analysis, visualization, project administration, resources, writing – original draft, writing – review and editing BS: Imaging, funding acquisition, project administration, writing – review and editing. ADC: funding acquisition, project administration, writing – review and editing. TKK: conceptualization, methodology, investigation, funding acquisition, project administration, writing – original draft, writing – review and editing, resources, validation, supervision. Acknowledgements We thank the study participants, their families and caregivers for their contributions to this study. List of abbreviations AD Alzheimer’s dementia p-tau217 phospho-tau 217 HCP Human connectome project MYHAT-NI The Monongahela-Youghiogheny Healthy Aging Team Neuroimaging PET Positron Emission Tomography Aβ Amyloid beta MOCA Montreal Cognitive Assessment AUC area under the curve CV Coefficient of variation SUVR Standardized Uptake Value Ratio MRI Magnetic resonance imaging MMSE Mini-mental state examination References 1. ↵ Teunissen CE , Verberk IMW , Thijssen EH , et al. Blood-based biomarkers for Alzheimer’s disease: towards clinical implementation . Lancet Neurol . 2022 ; 21 ( 1 ): 66 – 77 . OpenUrl CrossRef PubMed 2. ↵ Groot C , Cicognola C , Bali D , et al. Diagnostic and prognostic performance to detect Alzheimer’s disease and clinical progression of a novel assay for plasma p-tau217 [published correction appears in Alzheimers Res Ther . 2022 ; 14 ( 1 ): 82 . OpenUrl CrossRef PubMed 3. ↵ Grande , G. , Valletta , M. , Rizzuto , D. et al. Blood-based biomarkers of Alzheimer’s disease and incident dementia in the community . Nat Med . 2025 ; 31 : 2027 – 2035 . OpenUrl CrossRef PubMed 4. ↵ Sevigny J , Chiao P , Bussière T , et al. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease . Nature . 2016 ; 537 ( 7618 ): 50 – 56 . OpenUrl CrossRef PubMed 5. Mintun MA , Lo AC , Duggan Evans C , et al. Donanemab in Early Alzheimer’s Disease . N Engl J Med . 2021 ; 384 ( 18 ): 1691 – 1704 . OpenUrl CrossRef PubMed 6. ↵ Ashton NJ , Brum WS , Di Molfetta G , et al. Diagnostic Accuracy of a Plasma Phosphorylated Tau 217 Immunoassay for Alzheimer Disease Pathology . JAMA Neurol . 2024 ; 81 ( 3 ): 255 – 263 . OpenUrl PubMed 7. ↵ Milà-Alomà , M. , Ashton , N.J. , Shekari , M. et al. Plasma p-tau231 and p-tau217 as state markers of amyloid-β pathology in preclinical Alzheimer’s disease . Nat Med 28 , 1797 – 1801 ( 2022 ). OpenUrl CrossRef PubMed 8. ↵ Palmqvist S , Janelidze S , Quiroz YT , et al. Discriminative Accuracy of Plasma Phospho-tau217 for Alzheimer Disease vs Other Neurodegenerative Disorders . JAMA . 2020 ; 324 ( 8 ): 772 – 781 . OpenUrl CrossRef PubMed 9. ↵ Ashton , N.J. , Janelidze , S. , Mattsson-Carlgren , N. et al. Differential roles of Aβ42/40, p-tau231 and p-tau217 for Alzheimer’s trial selection and disease monitoring . Nat Med 28 , 2555 – 2562 ( 2022 ) OpenUrl CrossRef PubMed 10. Sehrawat A , Zeng X , Abrahamson EE , et al. Pittsburgh plasma p-tau217: Classification accuracies for autosomal dominant and sporadic Alzheimer’s disease in the community . Alzheimers Dement . 2025 ; 21 ( 7 ): e70409 . OpenUrl PubMed 11. ↵ Gonzalez-Ortiz F , Ferreira PCL , González-Escalante A , et al. A novel ultrasensitive assay for plasma p-tau217: Performance in individuals with subjective cognitive decline and early Alzheimer’s disease . Alzheimers Dement . 2024 ; 20 ( 2 ): 1239 – 1249 . OpenUrl CrossRef PubMed 12. ↵ Karikari TK , Pascoal TA , Ashton NJ , et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts . Lancet Neurol . 2020 ; 19 ( 5 ): 422 – 433 . OpenUrl CrossRef PubMed 13. ↵ Karikari , T.K. , Benedet , A.L. , Ashton , N.J. et al. Diagnostic performance and prediction of clinical progression of plasma phospho-tau181 in the Alzheimer’s Disease Neuroimaging Initiative . Mol Psychiatry 26 , 429 – 442 ( 2021 ). OpenUrl CrossRef PubMed 14. ↵ Ashton NJ , Pascoal TA , Karikari TK , et al. Plasma p-tau231: a new biomarker for incipient Alzheimer’s disease pathology . Acta Neuropathol . 2021 ; 141 ( 5 ): 709 – 724 . OpenUrl CrossRef PubMed 15. ↵ Bellaver B , Povala G , Ferreira PCL , et al. Plasma GFAP for populational enrichment of clinical trials in preclinical Alzheimer’s disease . Alzheimers Dement . 2025 ; 21 ( 5 ): e70209 . OpenUrl PubMed 16. ↵ Chatterjee , P. , Pedrini , S. , Stoops , E. et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer’s disease . Transl Psychiatry 11 , 27 ( 2021 ). OpenUrl PubMed 17. ↵ Schindler SE , Bollinger JG , Ovod V , et al. High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis . Neurology . 2019 ; 93 ( 17 ): e1647 – e1659 . OpenUrl CrossRef PubMed 18. ↵ Bun , S. , Ito , D. , Tezuka , T. et al. Performance of plasma Aβ42/40, measured using a fully automated immunoassay, across a broad patient population in identifying amyloid status . Alz Res Therapy 15 , 149 ( 2023 ). OpenUrl 19. ↵ Mendes AJ , Ribaldi F , Lathuiliere A , et al. Head-to-head study of diagnostic accuracy of plasma and cerebrospinal fluid p-tau217 versus p-tau181 and p-tau231 in a memory clinic cohort . J Neurol . 2024 ; 271 ( 4 ): 2053 – 2066 . OpenUrl CrossRef PubMed 20. ↵ Gildengers A , Weinstein AM , Gujral S , et al. Where Do Plasma Biomarkers fit in With Current Alzheimer’s Disease Detection? . Am J Geriatr Psychiatry . 2025 ; 33 ( 4 ): 428 – 437 . OpenUrl PubMed 21. Xiao Z , Wu W , Ma X , et al. Plasma p-tau217, p-tau181, and NfL as early indicators of dementia risk in a community cohort: The Shanghai Aging Study . Alzheimers Dement (Amst) . 2023 ; 15 ( 4 ): e12514 . OpenUrl CrossRef 22. ↵ Jonaitis EM , Janelidze S , Cody KA , et al. Plasma phosphorylated tau 217 in preclinical Alzheimer’s disease . Brain Commun . 2023 ; 5 ( 2 ): fcad057 . OpenUrl 23. Dresse , M.T. , Ferreira , P.C.L. , Prasadan , A. et al. Plasma biomarkers identify brain ATN abnormalities in a dementia-free population-based cohort . Alz Res Therapy 17 , 173 ( 2025 ). OpenUrl 24. ↵ Sullivan KJ , Liu A , Chang CH , et al. Alzheimer’s disease pathology in a community-based sample of older adults without dementia: The MYHAT neuroimaging study . Brain Imaging Behav . 2021 ; 15 ( 3 ): 1355 – 1363 . OpenUrl PubMed 25. ↵ Cohen AD , Bruña R , Chang YF , et al. Connectomics in Brain Aging and Dementia - The Background and Design of a Study of a Connectome Related to Human Disease . Front Aging Neurosci . 2021 ; 13 : 669490 . OpenUrl PubMed 26. ↵ Feng , W. , Beer , J.C. , Hao , Q. et al. NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing . Nat Commun 14 , 7238 ( 2023 ). OpenUrl CrossRef PubMed 27. ↵ Zeng , X. , Lafferty , T.K. , Sehrawat , A. et al. Multi-analyte proteomic analysis identifies blood-based neuroinflammation, cerebrovascular and synaptic biomarkers in preclinical Alzheimer’s disease . Mol Neurodegeneration 19 , 68 ( 2024 ). OpenUrl 28. ↵ Lopez OL , Becker JT , Chang Y , et al. Amyloid deposition and brain structure as long-term predictors of MCI, dementia, and mortality . Neurology . 2018 ; 90 ( 21 ): e1920 – e1928 . OpenUrl CrossRef PubMed 29. ↵ Snitz BE , Tudorascu DL , Yu Z , et al. Associations between NIH Toolbox Cognition Battery and in vivo brain amyloid and tau pathology in non-demented older adults . Alzheimers Dement (Amst ). 2020 ; 12 ( 1 ): e12018 . OpenUrl CrossRef PubMed 30. ↵ Gogola A , Lopresti BJ , Tudorascu D , et al. Biostatistical Estimation of Tau Threshold Hallmarks (BETTH) Algorithm for Human Tau PET Imaging Studies . J Nucl Med . 2023 ; 64 ( 11 ): 1798 – 1805 . OpenUrl Abstract / FREE Full Text 31. ↵ Jack CR Jr . , Wiste HJ , Weigand SD , et al. Defining imaging biomarker cut points for brain aging and Alzheimer’s disease . Alzheimers Dement . 2017 ; 13 ( 3 ): 205 – 216 . OpenUrl CrossRef PubMed 32. ↵ LeDell E , Petersen M , van der Laan M . Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates . Electron J Stat . 2015 ; 9 ( 1 ): 1583 – 1607 . OpenUrl CrossRef PubMed 33. ↵ Janelidze S , Bali D , Ashton NJ , et al. Head-to-head comparison of 10 plasma phospho-tau assays in prodromal Alzheimer’s disease . Brain . 2023 ; 146 ( 4 ): 1592 – 1601 . OpenUrl CrossRef PubMed 34. ↵ Warmenhoven N , Salvadó G , Janelidze S , et al. A comprehensive head-to-head comparison of key plasma phosphorylated tau 217 biomarker tests . Brain 2025 ; 148 ( 2 ): 416 – 431 . OpenUrl PubMed 35. ↵ Rea Reyes RE , Wilson RE , Langhough RE , et al. Targeted proteomic biomarker profiling using NULISA in a cohort enriched with risk for Alzheimer’s disease and related dementias . Alzheimers Dement . 2025 ; 21 ( 5 ): e70166 . OpenUrl PubMed 36. ↵ Palmqvist S , Whitson HE , Allen LA , et al. Alzheimer’s Association Clinical Practice Guideline on the use of blood-based biomarkers in the diagnostic workup of suspected Alzheimer’s disease within specialized care settings . Alzheimers Dement . 2025 ; 21 ( 7 ): e70535 . OpenUrl PubMed 37. ↵ Mattsson-Carlgren N , Janelidze S , Palmqvist S , et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer’s disease . Brain . 2020 ; 143 ( 11 ): 3234 – 3241 . OpenUrl CrossRef PubMed 38. ↵ Islam T , Hill E , Abrahamson EE , et al. Phospho-tau serine-262 and serine-356 as biomarkers of pre-tangle soluble tau assemblies in Alzheimer’s disease . Nat Med . 2025 ; 31 ( 2 ): 574 – 588 . OpenUrl CrossRef PubMed 39. Horie K , Salvadó G , Barthélemy NR , et al. CSF MTBR-tau243 is a specific biomarker of tau tangle pathology in Alzheimer’s disease . Nat Med . 2023 ; 29 ( 8 ): 1954 – 1963 . OpenUrl CrossRef PubMed 40. ↵ Horie K , Salvadó G , Koppisetti RK , et al. Plasma MTBR-tau243 biomarker identifies tau tangle pathology in Alzheimer’s disease . Nat Med . 2025 ; 31 ( 6 ): 2044 – 2053 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted October 09, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Head-to-head comparison of plasma p-tau217 immunoassays for incipient Alzheimer’s disease in community cohorts Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Head-to-head comparison of plasma p-tau217 immunoassays for incipient Alzheimer’s disease in community cohorts Rebecca A. Deek , Wasiu G. Balogun , Xuemei Zeng , Gallen Triana-Baltzer , Tharick A. Pascoal , Hartmuth C. Kolb , Beth Snitz , Ann D. Cohen , Thomas K. Karikari medRxiv 2025.10.07.25337434; doi: https://doi.org/10.1101/2025.10.07.25337434 Share This Article: Copy Citation Tools Head-to-head comparison of plasma p-tau217 immunoassays for incipient Alzheimer’s disease in community cohorts Rebecca A. Deek , Wasiu G. Balogun , Xuemei Zeng , Gallen Triana-Baltzer , Tharick A. Pascoal , Hartmuth C. Kolb , Beth Snitz , Ann D. Cohen , Thomas K. Karikari medRxiv 2025.10.07.25337434; doi: https://doi.org/10.1101/2025.10.07.25337434 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15228) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6599) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9231) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a005e1e6c85cb3d6',t:'MTc3OTU1ODQxMQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();