Diagnostic performance of plasma pTau 217, pTau 181, Aβ 1–42 and Aβ 1–40 in the LUMIPULSE automated platform for the detection of Alzheimer disease

BACKGROUND Recently developed blood markers for Alzheimer’s disease (AD) detection have high accuracy but usually require ultra-sensitive analytic tools not commonly available in clinical laboratories, and their performance in clinical practice is unknown. METHODS We analyzed plasma samples from 290 consecutive participants that underwent lumbar puncture in routine clinical practice in a specialized memory clinic (66 cognitively unimpaired, 130 participants with mild cognitive impairment, and 94 with dementia). Participants were classified as amyloid positive (A+) or negative (A−) according to CSF Aβ1–42/Aβ1–40 ratio. Plasma pTau217, pTau181, Aβ1–42 and Aβ1–40 were measured in the fully-automated LUMIPULSE platform. We used linear regression to compare plasma biomarkers concentrations between A + and A− groups, evaluated Spearman’s correlation between plasma and CSF and performed ROC analyses to assess their diagnostic accuracy to detect brain amyloidosis as determined by CSF Aβ1–42/Aβ1–40 ratio. We analyzed the potential of pTau217 to predict amyloidosis in CSF. RESULTS Plasma pTau217 and pTau181 concentration were higher in A + than A− while the plasma Aβ1–42/Aβ1–40 ratio was lower in A + compared to A−. pTau181 and the Aβ1–42/Aβ1–40 ratio showed moderate correlation between plasma and CSF (Rho = 0.66 and 0.69, respectively). The areas under the ROC curve to discriminate A + from A− participants were 0.94 (95% CI 0.92–0.97) for pTau217, and 0.88 (95% CI 0.84–0.92) for both pTau181 and Aβ1–42/Aβ1–40. Chronic kidney disease (CKD) was related to increased plasma biomarker concentrations, but ratios were less affected. Plasma pTau217 had the highest fold change (x4.2) and showed high predictive capability in discriminating A + from A−, having 4–7% misclassification rate. The global accuracy of plasma pTau217 using a two-threshold approach was robust in symptomatic groups, exceeding 90%. CONCLUSION The evaluation of blood biomarkers on an automated platform exhibited high diagnostic accuracy for AD pathophysiology, and pTau217 showed excellent diagnostic accuracy to identify participants with AD in a consecutive sample representing the routine clinical practice in a specialized memory unit.


CONCLUSION
The evaluation of blood biomarkers on an automated platform exhibited high diagnostic accuracy for AD pathophysiology, and pTau 217 showed excellent diagnostic accuracy to identify participants with AD in a consecutive sample representing the routine clinical practice in a specialized memory unit.

Introduction
Early and accurate diagnosis is becoming an increasing priority with the recent developments of diseasemodifying therapies for Alzheimer's Disease (AD).Pathophysiological biomarkers in cerebrospinal uid (CSF) and positron emission tomography (PET) imaging with amyloid and tau tracers have extensively proven to be useful to detect the disease pathophysiology but are either expensive and/or invasive [1], which can delay the diagnosis and access to treatment.
The measure of AD biomarkers in blood through reliable high-throughput platforms would simplify the diagnostic process.This is now technically possible thanks to the development of sensitive technologies that can consistently quantify brain-derived molecules that are present in blood in very low concentrations [2][3][4].Amyloid-β (Aβ) peptides and different isoforms of phosphorylated tau (pTau) in blood have shown high accuracy to detect AD pathophysiology in previous research studies [5][6][7][8][9][10][11][12].How all these plasma markers are affected by different comorbidities is also starting to be understood thanks to large well-characterized cohorts [13][14][15].Thus, blood-based markers have the potential to be of great use in the screening, early diagnosis, tracking progression, and ultimately, monitoring the e cacy of treatment [16][17][18][19][20].Of all the assessed plasma biomarkers evaluated, pTau 217 has demonstrated a promising pro le in identifying amyloidosis, showing the largest fold changes in symptomatic AD patients and the most predictive ability to identify cognitive decline [21][22][23][24][25][26][27][28][29].In previous immunoassay studies CSF pTau 217 showed better correlation with amyloid-PET and tau-PET than pTau 181 [30].
Subsequent research has revealed comparable e cacy of pTau 217 in both plasma and CSF for the identi cation of AD neuropathology and for distinguishing pathophysiological AD from other neurodegenerative diseases [31][32][33].However, most of the existing studies have assessed each of these plasma markers separately, have been measured in different platforms or through techniques not widely available in clinical laboratories, which limits their potential to be widely applied in the clinical routine.The implementation of blood AD markers in a fully-automated platform would facilitate their reproducibility and accessibility in clinical laboratories [34].
The fully-automated platform LUMIPULSE G, extensively used to measure CSF AD biomarkers in clinical laboratories world-wide, has recently launched developed assays to measure pTau 217 , pTau 181 , Aβ 1-42 and Aβ 1-40 in plasma.In this study, our aim was to assess the feasibility and diagnostic performance of pTau 217 , pTau 181 , Aβ 1-42 and Aβ 1-40 in plasma in the LUMIPULSE fully-automated platform in a cohort of well characterized consecutive individuals assessed in a memory clinic.

Study participants and clinical classi cation
We included all consecutive individuals who underwent lumbar puncture for the analysis of AD CSF biomarkers assessed at the Sant Pau Memory Unit (SPIN cohort, Barcelona, Spain) as part of their diagnostic work-up [35] between January 2021 and December 2021.The study was approved by the Sant Pau Ethics Committee (Protocol code: EC/22/202/6880) following the standards for medical research in humans recommended by the Declaration of Helsinki.All participants or their legally authorized representative gave written informed consent to participate in biomarkers research studies.
At the time of CSF and plasma acquisition, participants had a diagnosis of dementia, mild cognitive impairment (MCI), or were cognitively unimpaired (CU).The clinical diagnosis was established after a thorough neurological and neuropsychological evaluation [35].To assess the impact of vascular risk factors and comorbidities, we collected information about the presence of high blood pressure, dyslipidemia, diabetes, obstructive sleep apnea and history of stroke.Participants were also classi ed according to the estimated glomerular ltrate rate (eGFR) in different stages (1-5) of chronic kidney disease (CKD) using CKD-EPI formula.
After a full evaluation that included analysis of AD CSF biomarkers, participants were classi ed according to their etiologic diagnosis, as Alzheimer disease (AD), other neurodegenerative diseases (OtherDem), not neurodegenerative diseases (OtherNotDeg) or CU.A proportion of participants' diagnosis was classi ed as "uncertain" as they had an unclear etiological diagnosis after a full initial evaluation and required clinical follow-up.
Cognitively unimpaired participants were patients with no evidence of cognitive impairment after a thorough neuropsychological evaluation and healthy volunteers interested in research.

Sample collection and analysis
Blood samples were collected in EDTA-K2 tubes and subsequently centrifuged (2000rpm x 10 mins, 4ºC) within 2 hours after extraction.Plasma was aliquoted and stored at -80ºC until analysis.CSF samples were obtained through lumbar puncture, and were also centrifuged, aliquoted and stored at -80ºC until analysis.Blood and CSF samples were collected simultaneously.Full protocol for CSF and blood sample collection in our center has been previously reported [35,36].
All plasma samples were measured in the Lumipulse fully-automated platform G600II using commercially available kits (Fujirebio Europe, Ghent, Belgium) for pTau 181 , Aβ 1-42 and Aβ 1-40 between July and August 2022 with the same lot of reagents.Plasma pTau 217 was analyzed between August and September 2023 in another aliquot of the same samples using a novel assay recently developed by Fujirebio.On the day of the analysis, plasma samples were brought to room temperature, mixed thoroughly, centrifuged for 5 minutes at 2000g, and subsequently transferred to speci c cuvettes for analysis in the Lumipulse platform.
DNA was extracted from full blood using standard procedures, and APOE was genotyped following previously reported methods [35].Brie y, direct DNA sequencing of exon 4 was performed routinely for all participants in the SPIN cohort, followed by visual analysis of the resulting electropherogram to identify the two coding polymorphisms that encode the three possible APOE isoforms.

Statistical analysis
Data normality was assessed with the Shapiro-Wilk test.Non-normally distributed variables were logtransformed when necessary.Linear regression models and ANCOVA adjusted by age and sex were performed for group comparison.We used Chi Square test to assess differences in categorical variables and Spearman test to assess the correlation between plasma and CSF markers.Diagnostic accuracy of plasma biomarkers was assessed through receiver operating characteristic (ROC) analysis.We calculated the areas under the curve (AUC) of individual markers and that of logistic regression models that combined them with each other and with clinical variables.A basic model that included Age, Sex and APOEε4 status was used as a reference to assess the added diagnostic value of plasma markers.We compared the accuracy of individual markers and regression models using DeLong's test adjusted by multiple comparisons using Bonferroni method.We evaluated the sensitivity, speci city, and Youden's J index of a range of cutoffs to discriminate A + from A-participants.We followed a previously reported approach[38] to stratify our cohort in low, medium, and high risk of having CSF amyloidosis.Using predictive models, according to the risk of the participants of being A+, we selected conservative (97.5% sensitivity/speci city) and more lenient (95% sens/spec) cutoffs.We bootstrapped to assess the cutoff robustness.All tests were performed in R statistical software version 4.2.1.Alpha threshold was set at 0.05 for all analysis.

Study participants and clinical classi cation
We included 290 participants that were syndromically classi ed as cognitively unimpaired (CU, n = 66), had mild cognitive impairment (MCI, n = 130) or a clinical diagnosis of dementia (n = 94).Table 1 shows the etiologic diagnoses in each category, main demographic characteristics, and biomarker measures in each group.CU participants were younger than those with MCI (p < 0.001) and those with dementia (p < 0.001).There were more female participants (62%).The proportion of A+, A + T + and APOEε4 positive increased according to the clinical stage.More extensive demographics details, including strati cation by clinical diagnosis or A status, can be found in Supplementary Material (Tables 1 and 2).

Correlation between plasma and CSF biomarkers
As per inclusion criteria, all participants had CSF biomarkers measures, and we explored the correlation between both matrices.The correlation between plasma and CSF was moderate for pTau 181 (Rho = 0.66, p < 0.001) and low for Aβ  3 and 4).We found no signi cant in uence of these observations in our analysis.

Effect of other variables on plasma biomarkers
We assessed whether plasma pTau 217 , pTau 181 , Aβ 1-42 and Aβ 1-40 were affected by other variables in the multivariate model.As seen in Fig. 2, amyloid positivity was the variable with the largest effect on all plasma markers.We also observed that decreased renal function was associated with higher concentrations of pTau 217 (p = 0.019) and pTau 181 (p < 0.001) and higher Aβ To further investigate the association of pTau 217 and pTau 181 with renal function, we performed a subanalysis stratifying by estimated glomerular ltration rate.We found that pTau 181 concentration in plasma was higher as renal function decreased (< 60 vs 60-90mL/min/1.73m 2 and < 60 vs. >90 mL/min/1.73m 2 , p < 0.001).Aβ 1-42 and Aβ 1-40 concentrations in plasma were also higher as renal function decreased (p < 0.001).We also observed marginally signi cant differences in pTau 217 concentrations in plasma samples from patients with low renal function (< 60 mL/min/1.73m 2 ) compared to those with normal renal function (> 90 mL/min/1.73m 2 , p = 0.047).However, those differences were lost when using the Aβ 1-42 /Aβ 1-40 or the pTau 217 /Aβ 1-42 ratios.

Diagnostic accuracy of plasma biomarkers and their combinations for the discrimination of A + from A-
In the whole sample, the AUC to discriminate A + from A-participants were 0.94 (95% Cl 0.92-0.97)for pTau 217 , and 0.88 (95% CI 0.84-0.92)for both pTau 181 and Aβ 1-42 /Aβ 1-40 (Fig. 3).The diagnostic accuracy of pTau 217 to detect amyloid positivity was not outperformed by any other individual plasma biomarker, their ratios or their combinations.Aβ 1-42 and Aβ 1-40 individually had poor diagnostic accuracy, yielding AUCs below 0.70.Detailed two-by-two comparisons can be found as Supplementary Table 3. Sensitivity, speci city and Youden indices yielded by individual plasma markers are shown in Supplementary Fig. 5.

Cutoffs application
Table 2 shows the accuracy of different thresholds for pTau 217 , pTau 181 and Aβ 1-42 /Aβ 1-40 to detect amyloid positivity with a sensitivity and speci city of 97.5%, 95% and 90%.As pTau 217 was the individual biomarker with highest diagnostic accuracy, we assessed the performance of selected cutoffs for this marker in different clinical groups, stratifying by decade.We found that the cutoff that had a global sensitivity of 95% (0.186 pg/mL) yielded accuracies above 84% across all decades and clinical groups when the amyloidosis prevalence was above 20%.In groups with lower amyloidosis prevalence (i.e.CN) the cutoff with speci city of 95% (0.388 pg/mL) showed higher accuracies.We observed that the cutoff that had a speci city of 95% showed a progressive decrease in accuracy with age in the MCI group (accuracy 84% in 60-69, 75% in 70-79 and 62% over 80 years).The negative predictive value of this cutoff was low over 70 years (58%).Detailed information on the accuracy of cutoffs in distinct clinical groups and decades can be found in Supplementary Material (tables 4, 5, 6 & 7).
We conducted a supervised decision tree analysis to determine the potential of various biomarkers and demographic factors in correctly identifying individuals with amyloidosis.This analysis incorporated plasma pTau 217 , pTau 181 , ratio Aβ 1-42 / Aβ 1-40, Age, Sex, APOE ε4 allele presence, and clinical diagnosis group (cognitively unimpaired, mild cognitive impairment, and dementia).The most effective discriminators for amyloidosis were plasma pTau 217 followed by the Aβ 1-42 / Aβ 1-40 ratio when pTau 217 was high (Supplementary Fig. 8).Other variables were deemed less critical for amyloidosis detection and thus excluded from the decision tree.This algorithm exhibited a misclassi cation rate of 7.4%, with a sensitivity of 91%, speci city of 94%, overall accuracy of 93%, positive predictive value (PPV) of 94%, and negative predictive value (NPV) of 91%, accompanied by a false-negative rate (FNR) of 9.4% and a falsepositive rate (FPR) of 5.6%. .We bootstrapped to obtain robust predictive cuttoffs.We found no differences between the initial prediction and the mean of the 1000 predictive iterations, with a perfect correlation between them (Rho = 1, p < 0.001) thus reinforcing the robustness of our initial predictions.
To facilitate the clinical implementation of plasma biomarkers while ensuring accuracy, we applied a twothreshold approach to classify participants into three groups, those with high, medium, and low likelihood of being CSF amyloid positive.Following this approach, those with a medium risk would bene t from a con rmation with gold standard tests like CSF or Amyloid PET. Figure 4 shows the thresholds in two different scenarios based on two levels of restrictiveness in sensitivity and speci city (97.5% and 95%).
Using a highly accurate combination of cutoffs (one for 97.5% Sens and another for 97.5% Spec), only 41.9% of patients in the whole sample would require an additional test, with a global misclassi cation rate of 4.2%.Using less restrictive cutoffs (95% Sens/Spec), the proportion could be reduced to 19% with a global misclassi cation rate of 6%.Similar results were found in the subgroup of patients with CU, MCI and Dementia.
We nally assessed the robustness of those cutoffs in the whole sample, considering the increase of prevalence of amyloidosis with age in our population.In Fig. 5, we show the NPV, PPV, and global accuracy of pTau 217 to detect A positivity by decades using cutoff combinations with 95% and 97.5% sensitivity and speci city in different clinical groups.We found high global accuracy (75%-100%) of the two-threshold application in all the scenarios, with variations of PPV and NPV according to CSF amyloid positive prevalence in our sample.

Discussion
In this study, we found that the concentration of plasma pTau 217 , plasma pTau 181 , and the ratio Aβ 1- 42 /Aβ 1-40 measured in a fully automated platform, yielded excellent accuracy to detect the AD pathophysiology in the setting of the routine clinical practice of a memory clinic.Of all plasma markers, the recently developed assay that measures pTau 217 was the most accurate followed by the Aβ 1-42 /Aβ 1- 40 ratio and pTau 181 .We also found that different comorbidities had a mild signi cant effect on plasma markers of AD, but the amyloid status was the single variable with the largest effect on their concentration.In patients with advanced chronic kidney disease, the use of ratios could reduce the impact of having higher plasma concentrations associated to low renal function.Furthermore, we applied predictive models to obtain strati cation pro les that showed the potential to reduce the need of more costly or invasive procedures by approximately 60 to 80%.
The performance of plasma markers to detect the AD pathophysiology has been assessed in previous studies using different analytical platforms, with AUCs ranging from 0.70 to 0.96 for pTau 181 [7,23,[39][40][41][42][43], and from 0.64 to 0.86 for Aβ 1-42 /Aβ 1-40 [5,8,10].Plasma pTau 231 and pTau 217 have shown to better capture the earliest cerebral Aβ changes in CU, before overt Aβ plaque pathology is present [26] For plasma pTau 217 , accuracies have varied depending on the platform, yet the performance has consistently been high in discriminating amyloidosis in MCI and predicting progression, typically outperforming that of other plasma pTau isoforms [7,23,33,44].Most research studies reported better accuracies with the use of composite measures that combined two or more markers and/or clinical or genetic information [45][46][47].In our study, plasma pTau 217, the Aβ 1-42 /Aβ 1-40 ratio and pTau 181 measured with a fully automated platform showed high diagnostic performance to detect amyloid positivity.Of these, pTau 217 was the marker that showed higher accuracy and, importantly, it was not outperformed by composite measures indicating that it is a good candidate for its implementation as a single biomarker.
Automated platforms have revolutionized CSF analysis by resolving critical analytical factors and, similarly, they hold promise for transforming plasma testing, enhancing both precision and e ciency in biomarker quanti cation.Recent studies have assessed the diagnostic performance of pTau 181 and Aβ 1- 42 /Aβ 1-40 in the Lumipulse platform.Janelidze et al. reported an AUC of 0.7 for pTau 181 for the identi cation of CSF amyloidosis in MCI [23]and of 0.74 to detect progression to dementia.However, Wilson et al. reported a higher accuracy of 0.96 [40] for the discrimination between Aβ-CU and Aβ + AD patients.Another recent study using the Lumipulse platform analyzed the accuracy to detect AD of plasma pTau 181 and Aβ 1-42 /Aβ 1-40 ratio in cognitively unimpaired participants and found that Aβ 1- 42 /Aβ 1-40 ratio was the most cost-effective (AUC 0.9 for A + and 0.89 for A + T+), followed by pTau 181 that showed an AUC of 0.76 for A + and 0.86 for A + T+ [48].Our results are in line with previous studies and show a global accuracy of 0.88 for plasma pTau 181 and for the plasma Aβ 1-42 /Aβ 1-40 ratio.A variety of reasons could explain the minor discrepancies between studies, including differences in preanalytical conditions [49], in the kits that were used, characteristics of the sample and cohorts, and the design of the studies.Our design including consecutive patients that underwent a lumbar puncture for routine diagnostic work-up in a memory clinic setting, provides information about the potential implementation of plasma markers in this context.and with neuropathology [30].The fact that it has shown greatest fold changes and effect sizes compared to other pTau isoforms, makes it a perfect candidate for its implementation in clinical settings, as small analytical variations (5-10%) would not substantially affect its diagnostic performance.Its implementation on a fully automated platform would not only simplify the process but also enhance accessibility for clinical laboratories.In our study using a fully automated platform, we found that A + patients had 3.2 times higher concentrations of plasma pTau 217 .This magnitude of effect, combined with the advantages of automation, makes this assay particularly promising for integration into standard clinical practices.
To facilitate a more rational utilization of plasma biomarkers, we followed the methodology delineated by Brum et al. stratifying the risk of having AD pathophysiology [38].By implementing this strategy, it is feasible to de ne threshold values that are highly sensitive and speci c to either detect or rule out amyloid pathology minimizing the probability of misclassi cation.This would also allow for selecting those patients that fall within an intermediate likelihood category or 'grey zone' and that would bene t of further etiological investigations (CSF biomarkers, amyloid or Tau PET).This strati cation framework represents a pragmatic and exible approach for the incorporation of plasma biomarkers in clinical settings.An open point of discussion is the optimal context of use -primary care, general neurology, or specialized memory clinics-and the management strategies for participants identi ed as high or low risk.Our study showed that following this approach, pTau 217 had an excellent performance in the context of a specialized memory clinic, but these classi cations will need to be contextualized within other clinical settings [4,34].The effect of comorbidities as CKD on plasma biomarker concentrations points in the same direction as recently published studies [15,50], in which the use of ratios could attenuate the effect of CKD.Moreover, we found that the effect of renal dysfunction on plasma pTau 217 concentrations was signi cantly less than that of the amyloid positivity status, suggesting that the actual impact of CKD on the diagnostic performance of this marker would be minimal.
One of the strengths of our study is that we included all consecutive participants from routine clinical practice that underwent lumbar puncture throughout one year in our memory clinic including a variety of diagnoses.This approach reduces the risk of selection biases and ensures a reliable representation of the population assessed in the setting of a specialized memory clinic, also providing relevant information on their potential implementation in the routine diagnostic work-up in this context.Other strengths in our study are the fact that all markers were measured using the same batch of reagents and that the clinical information available allowed us to analyze the potential impact of comorbidities and perform subanalyses within distinct clinical stages.

Limitations
Our study also has some limitations.First, as the inclusion criteria required that participants had received a lumbar puncture for CSF biomarkers, the extrapolation to other contexts of use different than specialized memory units, such as primary care or population screening programs, should be made cautiously.Second, we could not compare plasma pTau 217 with its counterpart in CSF as the assay was speci cally developed for plasma.Another limitation is the lack of Amyloid/Tau PET or neuropathological con rmation in our participants, and although the CSF biomarker cutoffs in our center were validated against amyloid PET [37], we cannot be certain about how using a different gold-standard might affect our results.Finally, this is a single-center study, and even though this increases uniformity and we performed a bootstrapped cross-validation to ensure the predictive robustness of our models, the accuracy and true predictive power of the cutoffs derived from our results need to be veri ed in diverse datasets with comparable characteristics.

Conclusions
Our study provides evidence that plasma markers can reliably be measured in an automated platform, and highlights plasma pTau 217 as the most promising plasma biomarker, showing great potential for the detection of AD pathophysiology in the context of a memory clinic.With the arrival of disease-modifying treatments into clinical practice, it is urgent to have easily accessible and e cient diagnostic methods to identify patients that could bene t from these therapies.The implementation of plasma biomarkers in readily accessible fully automated platforms will streamline the diagnosis and enhance the accessibility of disease modifying therapies.The reagents necessary to complete the study were funded by Fujirebio-Europe.
The sponsors of the study did not take part in the design and conduct of the study; collection, management, analysis, and interpretation of the data; writing and review of the report; or the decision to submit the article for publication.

Together with the commercially available pTau 181 and Aβ 1 - 42 /
Aβ 1-40 , we evaluated the performance of plasma pTau 217 , recently developed for the same automated platform, and found that it outperformed pTau 181 and Aβ 1-42 /Aβ 1-40 in the detection of amyloid positivity.In previous research studies, plasma pTau 217 has consistently shown exceptionally high accuracy across different platforms and has demonstrated strong correlations with other markers of AD (CSF biomarkers, amyloid PET and Tau PET) Author's contribution DA and AL designed the study.JA, NZ, SR-G, IR-B, RF, MC-I, IB, II-G, MS-S, AL, JF, MT, DA acquired data relevant for the study.MC-I, II-G, JF, AL, MT, DA contributed vital reagents/tools/patents.MC-I, II-G, MS-S, JF, AL, DA obtained funding for the study.DA and JA performed statistical analysis.DA, JA contributed in analysis and interpretation of data.DA, AL, JF participated in study supervision or coordination.JA and DA drafted the rst version of the manuscript.

Figure 1 Levels
Figure 1

Figure 2 Effect
Figure 2

Figure 5 Negative
Figure 5
renal function measured by the estimated glomerular ltration rate (eGFR), vascular risk factors (presence of at least one of the following: high blood pressure, diabetes mellitus, dyslipidemia, history of stroke, obstructive sleep apnea with CPAP) and clinical status (CU, MCI and Dementia).As shown in Fig.1, the multivariate model con rmed that the A + group had higher plasma concentrations of pTau 217 (fold-change 4.24, p < 0.001) and pTau 181 (fold-change 1.72, p < 0.001) compared to the A-group.Similar results were seen using the ratios pTau 217 /Aβ 1-42 and Aβ 1-42 /Aβ 1-40 .
We assessed the differences in plasma biomarkers between CSF amyloid positive and amyloid negative individuals considering other variables in a multivariate model.We studied the effect of age, sex, APOE status (APOE ε4+),