Introduction: Several recent research studies show high performance of blood biomarkers to identify Alzheimer’s disease also in the pre-dementia mild cognitive impairment (MCI) stage, but data from the routine clinical care memory clinic setting are needed. Methods: We examined plasma samples of 144 memory clinic patients, including dementia of Alzheimer type (DAT, n = 54), MCI (n = 57), and subjective cognitive decline (SCD, n = 33), who either presented as self-referrals or were referred by general practitioners or neurologists or psychiatrists. The plasma biomarkers, amyloid-beta42 (Aß42), amyloid-beta40 (Aß40), phospho-Tau181 (pTau181), total-tau (tTau), and neurofilament light (NFL), as well as different ratios, were measured using the ultrasensitive single molecule array (Simoa) immunoassay technology. Statistical analysis including Kruskal-Wallis test, linear regression, and receiver operating characteristics analyses was performed. Results: Of the single markers, we observed statistically significant group effects of pTau181 (H(2) = 34.43, p < 0.001) and NFL (H(2) = 27.66, p < 0.001). All individual group comparisons of pTau181 were significant, while the contrast of SCD versus MCI for NFL was not significant. In addition, the ratios of Aß42/Aß40 (H(2) = 7.50, p = 0.02) and pTau181/Aß42 (H(2) = 25.26, p < 0.001) showed significant group effects with significant difference between all groups for pTau181/Aß42 and an SCD versus MCI difference for Aß42/Aß40. PTau181 showed the highest area under the curve of 0.85 for the discrimination of SCD and DAT with a sensitivity of 80% and a specificity of 79% at a cut-off of 12.2 pg/mL. Age influenced Aß42, Aß40, and NFL concentrations. Conclusion: Plasma pTau181 and NFL, as well as the ratios Aß42/Aß40 and pTau181/Aß42, are biomarkers, which can differentiate diagnostic groups in a memory clinic setting outside of research studies.

© 2022 The Author(s). Published by S. Karger AG, Basel

Introduction

Dementia of Alzheimer’s type (DAT) is a major health care challenge of our times [1]. Alzheimer’s disease (AD) is neuropathologically characterized by the accumulation of neurofibrillary tangles composed of aggregated tau protein and amyloid deposition [2]. There is evidence that these pathological processes begin more than two decades before the onset of symptoms. Hence, biomarkers of amyloid deposition and tau aggregation can detect the disease in patients already in early stages [3]. Subjective cognitive decline (SCD) occurs at the late preclinical stage of AD, recently labeled as stage 2 [4], and is also a risk factor for the development of mild cognitive impairment (MCI) and DAT. SCD is not specific to AD and can also be caused by normal aging, depression, and other psychiatric and neurologic disorders [5]. In addition, a recent meta-analysis reported substantial variation of the proportion of SCD cases with amyloid pathology among individual samples, depending on the specific recruitment criteria and settings [6]. In those SCD cases, who progress to dementia, DAT is the most common, but other dementia types also occur [7].

The detection of neuropathological changes in patients currently requires amyloid-positron emission tomography (amyloid-PET) or biomarkers obtained from cerebrospinal fluid (CSF) [8-10]. Core biomarkers in CSF include amyloid-beta42 (Aß42), total-tau protein (ttau), and phosphorylated tau protein (pTau) [11]. Both the use of amyloid-PET and CSF biomarkers are limited concerning access and are either costly or invasive, and rare complications can occur [12, 13]. The recent evolution of plasma biomarkers provides potentially novel opportunities in the future regarding improved accessibility as well as lower risk [14, 15]. Since the early diagnosis of AD before the stage of DAT is most likely critical for the success of future therapies, accessible tests for the identification of pre-dementia stages of AD are becoming increasingly important [16, 17].

There was early inconsistent evidence about potential changes of plasma Aß42 throughout the disease course [18], with some studies not being able to differentiate between DAT and controls in a cross-sectional setting [16, 19]. Stable lower effects in DAT are found from 2018 onward using new measurement techniques, with the Aß42/amyloid-beta40 (Aß40) ratio providing promising results [20-26].

Plasma phospho-Tau181 (pTau181) distinguishes between DAT and controls and is elevated to a lesser extent in patients with MCI [14, 27-32]. There is also evidence that plasma pTau181 differentiates between DAT and other neurodegenerative diseases [29, 30]. Other plasma pTau markers, such as pTau217, show similar results [33, 34]. The ratio of pTau181/Aß42 in CSF predicted greater clinical decline in MCI patients and showed a comparable accuracy as PET imaging [35].

Some studies showed increased levels of tTau in DAT compared to MCI and controls and provided evidence for an association of tTau with poor cognition and brain atrophy [20, 36, 37]. Some findings suggest that the overlap between normal aging and AD is large, implying that tTau may not be a suitable biomarker when measured in plasma [36]. Increased tTau values were also detected in other neurodegenerative diseases suggesting that tTau is a nonspecific biomarker of neuronal damage [38, 39].

The biomarker neurofilament light (NFL) is considered an unspecific marker for neurodegeneration which can be measured in CSF, but also in plasma [40]. NFL concentration differentiates between patients with DAT and controls [41]. Changes in plasma NFL were associated with amyloid deposition in amyloid-PET [42]. NFL was associated with cognitive deficits and magnetic resonance imaging (MRI) characteristics of DAT at early stages and throughout the disease course [41]. Higher baseline plasma levels were associated with poorer longitudinal cognition [41, 43]. The goal of this study is to test how these plasma biomarkers perform with regard to the differentiation of the diagnostic groups of patients with DAT, MCI, and SCD in a routine care memory clinic setting outside of a highly standardized research setting.

Methods

We included samples and data from patients of the memory clinic of the Centre for Memory Disorders (ZfG) at the University Hospital of Cologne who gave written consent to provide blood samples for research purposes. The study was approved by the Ethics Commission of the medical faculty of the University of Cologne. The study complied with the Declaration of Helsinki.

Study Sample

Our study included 144 patients who were clinically diagnosed with SCD (n = 33), MCI (n = 57), or DAT (n = 54) at the memory clinic between 2016 and 2018. Patients either presented as self-referrals or were referred by general practitioners or neurologists or psychiatrists.

Clinical Examination and Diagnosis

All patients underwent a comprehensive clinical examination, including medical history, caregiver report, psychopathological and physical examination, the Mini-Mental State Examination (MMSE), extended neuropsychological testing, standard blood laboratory measures, and MRI. CSF for diagnostic purposes was obtained in 31 DAT and 17 MCI cases. Note that according to the current guidelines, CSF biomarker sampling is not recommended in SCD outside of research due to the yet unclear meaning in determining the individual cause and prognosis of SCD. For this reason, CSF biomarkers were only obtained in 2 SCD cases, who specifically demanded AD biomarker assessment.

The syndromal diagnosis of DAT, MCI, and SCD was established based on all available information by the treating physician. Patients with DAT and MCI met the clinical National Institute on Aging and Alzheimer’s Association (NIA-AA) criteria [4]. Both were defined by cognitive impairment as documented by the MMSE and by extended neuropsychological testing. The groups were discriminated by the presence of impairment in activities of daily living, which interfere with independence based on clinical judgment in the case of DAT and the absence thereof in the case of MCI. Thirty-five MCI patients (61%) were multi-domain MCI. Of these, 34 patients were amnestic multi-domain. One was nonamnestic multi-domain (language and executive function domain affected). Twenty-one patients (37%) were single-domain MCI of which 18 were single-domain amnestic MCI, two were single-domain visuo-construction, and one single-domain language. Following proposed criteria [5], SCD was defined by a complaint of cognitive decline and age-, sex-, and education-adjusted unimpaired cognitive performance in extended neuropsychological testing.

The clinical and neuropsychological work-up together with the laboratory and MRI information was used in all groups to exclude non-AD causes of cognitive impairment as good as possible, including psychiatric disorders. Patients fulfilling clinical criteria of a depressive episode, or any other detectable non-AD cause of cognitive impairment, were not included. The DAT group comprised early- and late-onset cases. Note that the definition of groups in this study is based on the clinical diagnoses of DAT, MCI, and SCD only and does not incorporate CSF biomarker information as inclusion or exclusion criterion.

Plasma Biomarkers

All patients agreed to plasma sampling for research purposes when they first presented in the clinic. Research plasma samples were obtained within the diagnostic process of the respective participant and stored directly at −20° and permanently at −80°C. None of the samples was thawed and refrozen before this study. The samples were shipped on dry ice to and analyzed in Gothenburg (Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, Sahlgrenska Academy). Biomarker measurements were performed through ultrasensitive single molecule array (Simoa) immunoassay technology (Quanterix, Billerica, MA, USA) [44]. The laboratory was blinded to patient diagnoses and other clinical data. The following numbers of measurements were obtained: Aß42 – n = 142; Aß40 – n = 144; pTau181 – n = 143; tTau – n = 144; NFL – n = 144. Calibrators were run in duplicates, and samples were diluted 4-fold and run in singlicates. Results were compensated for the dilution. Two QC levels were run in duplicates at the beginning and the end of each run. Intra-assay coefficients of variation were below 10%.

Statistical Analysis

Differences between diagnostic groups in age, years of education, and plasma biomarkers were assessed using the nonparametric Kruskal-Wallis test, χ2 test, and post hoc comparison. Linear regression was used to examine the correlation between plasma biomarkers, age, sex, and years of education. In the case of significant associations, the group difference analyses were performed with ANCOVAs adjusted for respective covariates. To determine the diagnostic accuracy, receiver operating characteristic (ROC) analysis was used to calculate areas under the curve (AUCs) for the comparisons of SCD versus (vs.) MCI plus DAT, SCD vs. MCI, SCD vs. DAT, and MCI vs. DAT. Potential cut-off values are proposed.

In addition, the plasma biomarkers across all groups of those cases with pathological CSF biomarkers were plotted against those with normal CSF biomarkers. Note that for this step, the clinical routine CSF and the respective cut-off data obtained from the central laboratory of the University Hospital of Cologne were used. The following local cut-offs were applied to define CSF positivity: Aß42 <629 pg/mL, pTau >61 pg/mL, tTau <290 pg/mL, and Aß42/Aß40 <0.095 pg/mL. In addition to plotting, the plasma measures of CSF-positive and CSF-negative cases across all groups were compared with t tests. All analysis and statistics were performed in SPSS (Version 26.0, IBM, Armonk, NY, USA).

ResultsSample Characteristics

The age ranged from 42 to 90 years with a mean age of 69.7 (standard deviation [SD] = 10.1) years (Table 1) with 31% of the patients being under the age of 65 (55% of SCD, 32% of MCI, and 15% of DAT patients). Statistically significant group differences in age (p < 0.001) and years of education (p < 0.001) were observed (Table 1). Post hoc tests showed group effects of age for SCD-MCI (p = 0.01), SCD-DAT (p < 0.001), and MCI-DAT (p = 0.01) and of years of education for SCD-MCI (p = 0.03) and SCD-DAT (p < 0.001), but not for MCI-DAT (p = 0.08). There were no significant group differences for sex (p = 0.12).

Table 1.

Characteristics of the diagnostic groups and plasma biomarker measures

Association of Plasma Biomarkers, Age, Years of Education, and Sex

There was no association of any plasma biomarker with years of education or sex. The age of participants affected the concentration of Aß42 (p < 0.001), of Aß40 (p < 0.001), and of NFL (p < 0.001). Older patients showed increased plasma values. Other biomarkers or their ratios were not correlated with age.

There was an association of Aß42 with age also in all individual diagnostic groups: SCD (p = 0.01), MCI (p = 0.02), and DAT (p = 0.02), and of Aß40 with age in patients with SCD (p = 0.02) and DAT (p = 0.01), but not in patients with MCI (p = 0.12). Furthermore, an increase of NFL with age was observed for SCD (p < 0.001), MCI (p < 0.001), and DAT (p = 0.01).

Group Differences of Plasma Biomarkers

All plasma biomarker measures for all groups are listed in Table 1. The AUCs of all comparisons are displayed in Table 2. In Table 3, cut-offs are proposed for individual markers at a sensitivity level of 80%. Figure 1 shows the distribution of the biomarker data separated by the diagnostic groups.

Table 2.

ROC performance of plasma biomarkers

Table 3.

Possible cut-off points SCD versus MCI and SCD versus DAT

Fig. 1.

Plasma biomarker distribution across the diagnostic groups.

Aß42 concentrations were not significantly different between the diagnostic groups (p = 0.4). Neither were there any significant differences in Aß42 between groups when adjusted for age (p = 0.07). The AUC in ROC analysis was 0.58 for SCD vs. MCI (p = 0.20), 0.57 for SCD vs. MCI + DAT (p = 0.25), 0.55 for SCD vs. DAT (p = 0.44), and 0.54 for MCI vs. DAT (p = 0.47) indicating that the performance of Aß42 to discriminate between diagnostic groups is poor.

The ANOVA for Aß40 did not show a significant group effect (p = 0.09). Furthermore, there were no significant differences in Aß40 between groups when adjusting for age (p = 0.94). ROC analysis showed an AUC of 0.55 for SCD vs. MCI (p = 0.44), 0.59 for SCD vs. MCI + DAT (p = 0.10), and 0.57 for MCI vs. DAT (p = 0.18). Only the ROC for SCD vs. DAT with an AUC of 0.64 (p = 0.03) was significant.

Significant group effects for plasma pTau181 were observed (p < 0.001). Post hoc test showed differences in all diagnostic groups with pTau181 increasing with the severity of impairment: SCD-MCI (p = 0.01), SCD-DAT (p < 0.001), and MCI-DAT (p = 0.01). The AUC for SCD vs. MCI was 0.72 (p < 0.001), for SCD vs. MCI + DAT 0.78 (p < 0.001), for SCD vs. DAT 0.85 (p < 0.001), and for MCI vs. DAT 0.69 (p < 0.001) (Fig. 2). At a cut-off of 10.2 pg/mL, pTau181 had a sensitivity of 80% and a specificity of 79% to distinguish between SCD and DAT. Furthermore, at a cut-off point with a sensitivity of 80%, a specificity of 55% to distinguish between SCD and MCI was reached.

Fig. 2.

ROC performance of pTau181 for SCD versus MCI and SCD versus DAT.

tTau was not significantly different between diagnostic groups (p = 0.56). ROC analysis showed an AUC of 0.52 for SCD vs. MCI (p = 0.80), 0.51 for SCD vs. MCI + DAT (p = 0.48), 0.57 for SCD vs. DAT (p = 0.30), and 0.54 for MCI vs. DAT (p = 0.43).

Significant group effects for NFL were observed (p < 0.001). Post hoc test showed differences between SCD-DAT (p < 0.001) and MCI-DAT (p > 0.001) with NFL values increasing with the severity of impairment. The contrast SCD-MCI was not significant (p = 0.34). Significant differences in NFL between groups remained when adjusted for age (p = 0.04). ROC analysis was significant for SCD vs. MCI + DAT with an AUC of 0.7 (p < 0.001), for SCD vs. DAT with an AUC of 0.81 (p < 0.001), and for MCI vs. DAT with an AUC of 0.72 (p < 0.001), but not for SCD vs. MCI with an AUC of 0.61 (p = 0.09). A cut-off of 12.7 pg/mL for the differentiation of SCD and DAT showed a sensitivity of 80% and a specificity of 67%.

The plasma biomarkers in relation to CSF positivity or negativity of the respective marker are shown in Figure 3. Direct comparison of the plasma biomarkers between CSF-positive and CSF-negative cases revealed a significant group difference for plasma pTau181 when tested one-sided (p = 0.034, a priori hypothesis of increased concentration in CSF-positive cases) and a trend-level difference at two-sided testing (p = 0.069). None of the other plasma biomarkers showed a significant difference between the respective CSF-positive and CSF-negative cases.

Fig. 3.

Plasma biomarker values in relation to CSF biomarker classification as positive or negative according to the measurements and cut-off obtained and applied locally in routine care.

Biomarker Ratios

Significant group effects (p = 0.02) were observed for Aß42/Aß40. Post hoc test showed a decrease of the ratio between SCD-DAT (p = 0.01) and SCD-MCI (p = 0.03), but not between MCI-DAT (p = 0.61). The ability to discriminate between SCD vs. MCI with an AUC of 0.64 (p = 0.03), between SCD vs. MCI + DAT with an AUC of 0.66 (p = 0.01), and between SCD vs. DAT with an AUC of 0.67 (p = 0.01) indicated better diagnostic performance than Aß42 or Aß40 alone. The ability to discriminate between MCI vs. DAT with an AUC of 0.53 (p = 0.62) was poor. Cut-off points reached a specificity of 47 at a sensitivity of 80% for SCD vs. DAT and SCD vs. MCI.

Significant group effects for pTau181/Aß42 were observed (p < 0.001). Post hoc test showed an increase in the ratio between SCD-MCI (p < 0.001), SCD-DAT (p < 0.001), and MCI-DAT (p = 0.04). Furthermore, pTau181/Aß42 showed an AUC of 0.72 (p < 0.001) for SCD vs. MCI, of 0.77 (p < 0.001) for SCD vs. MCI + DAT, of 0.81 (p < 0.001) for SCD vs. DAT, and of 0.62 (p = 0.03) for MCI vs. DAT. Cut-off points reached a specificity of 75% at a sensitivity of 80% for SCD vs. DAT and a specificity of 50% at a sensitivity of 80% for SCD vs. MCI.

Discussion/Conclusion

Plasma biomarkers analyzed with the Simoa technology can discriminate between diagnostic groups in a routine memory clinic setting outside of specific research studies. The ratio of Aß42/Aß40 was higher in SCD compared to MCI and DAT. This agrees with the model of amyloid level plateauing in the early phase of the disease [3]. In agreement with previous studies, we also found that neither plasma Aß42 nor Aß40 alone can distinguish between groups [16, 19]. Aß42/Aß40 achieved AUCs between 0.64 and 0.67 providing the best discrimination between SCD and DAT. With AUCs <0.7, these results are of low clinical relevance but may be meaningful in the future in combination with additional diagnostic markers. PTau181 differed between all groups and showed higher values in more advanced stages of impairment. It provided the best discrimination between SCD and DAT achieving an AUC of 0.85 and a specificity of over 79% at a sensitivity of 80%. With a slightly lower sensitivity and specificity, pTau181 also differentiates between SCD and MCI. Plasma pTau181 concentrations were higher in CSF pTau181-positive cases compared with negative cases. These findings agree with previous studies showing that pTau181 is increased in patients with DAT, but also discriminates between different early stages of the disease [28, 29]. We confirm that the ratio of plasma pTau181/Aß42 differs between the groups as previously shown in CSF [35]. The combination of pTau181/Aß42 was not superior to pTau181 alone, indicating that the combination does not improve the ability to discriminate between diagnostic groups. Our study indicates that tTau discriminates poorly between the diagnostic groups. Some previous studies showed a modest association of tTau with conversion to DAT [37], whereas others suggested that tTau is not a suitable biomarker for discriminating DAT from other groups, which is in agreement with our findings [36]. In contrast to pTau181, NFL did not differentiate between SCD and MCI. NFL differed, however, between SCD and MCI in comparison with DAT. NFL values increased with the severity of impairment and achieved a sensitivity of 80% and specificity of 67% for discriminating SCD and DAT. An association between higher NFL values and DAT diagnosis has been shown previously [41]. NFL was correlated with age; however, the overall statistically significant difference between the diagnostic groups remained with adjustment for age.

Our study has limitations. We did not include CSF biomarkers of the full sample, because the numbers of CSF samples in the SCD group and also in the MCI group were low due to the lack of clinical recommendation for standard use in these groups. Furthermore, CSF was measured locally in the clinical diagnostic context, whereas plasma biomarkers were measured in the research context in the laboratory in Gothenburg. We did not include a healthy control group as we restricted the sampling to memory patients attending the memory clinic only. Furthermore, longitudinal data were not available as these data are derived from routine care, which does not comprise systematic follow-up of patients. Finally, while highly promising for wider application in routine care in the future, at present, the Simoa technology is mainly used in research and has not yet been in clinical practice.

The strength of our study is the real-world design. All patients who presented with SCD, MCI, or DAT in clinical routine and who were willing to participate were included. There were no additional study-specific inclusion or exclusion criteria.

We found that the plasma measures of pTau181 and NFL, as well as the ratios Aß42/Aß40 and pTau181/Aß42, are sufficiently robust to differentiate diagnostic groups with limited sample size in a memory clinic setting. In agreement with recent research studies, pTau181 proved to be the most promising biomarker that distinguishes between all three groups of SCD, MCI, and DAT suggesting potential clinical use also in early symptomatic stages. The implementation of plasma biomarkers in memory clinic procedures and potentially even in nonspecialized and general practice settings will substantially increase accessibility to biomarker-based diagnostics.

Statement of Ethics

Our research complied with the guidelines for human studies and was conducted in accordance with the World Medical Association Declaration of Helsinki. Participants gave their written informed consent before participating in the study. The study was approved by the Ethics Commission of the Faculty of Medicine of Cologne University, approval number 20-1503. The study was registered in the German Clinical Trials Register under the clinical trial number DRKS00023471.

Conflict of Interest Statement

Henrik Zetterberg has served at scientific advisory boards and/or as a consultant for AbbVie, Alector, Eisai, Denali, Roche, Wave, Samumed, Siemens Healthineers, Pinteon Therapeutics, NervGen, AZTherapies, CogRx, and Red Abbey Labs, has given lectures in symposia sponsored by Cellectricon, Fujirebio, AlzeCure, and Biogen, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). Kaj Blennow has served as a consultant, at advisory boards or at data monitoring committees for Abcam, Axon, Biogen, JOMDD/Shimadzu, Julius Clinical, Lilly, MagQu, Novartis, Prothena, Roche Diagnostics, and Siemens Healthineers, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, all unrelated to the work presented in this paper. Frank Jessen has received fees for advice and presentations from AbbVie, AC Immune, Biogen, Böhringer, Danone, Eisai, Green Valley, Grifols, Janssen, MSD, Roche, and Vifor. Within the last 3 years, Oezguer A. Onur has received fees for advice from Biogen.

Funding Sources

The project was funded intramurally by the Department for Psychiatry and Psychotherapy of the University Hospital Cologne. Henrik Zetterberg is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712), Swedish State Support for Clinical Research (#ALFGBG-720931), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), the AD Strategic Fund and the Alzheimer’s Association (#ADSF-21-831376-C, #ADSF-21-831381-C, and #ADSF-21-831377-C), the Olav Thon Foundation, the Erling-Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2019-0228), the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 860197 (MIRIADE), and the UK Dementia Research Institute at UCL. Kaj Blennow is supported by the Swedish Research Council (#2017-00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017-0243), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF agreement (#ALFGBG-715986), the European Union Joint Program for Neurodegenerative Disorders (JPND2019-466-236), the National Institute of Health (NIH), USA (grant no. #1R01AG068398-01), and the Alzheimer’s Association 2021 Zenith Award (ZEN-21-848495). Oezguer A. Onur was supported by a grant from the Marga and Walter Boll Foundation.

Author Contributions

Michelle Gerards, Ann-Katrin Schild, Dix Meiberth, Ayda Rostamzadeh, Oezguer A. Onur, Franziska Maier, and Frank Jessen contributed to the overall design and the implementation of the study. Michelle Gerards, Ann-Katrin Schild, Dix Meiberth, Ayda Rostamzadeh, Franziska Maier, and Frank Jessen conducted the study. Jörg Janne Vehreschild, Sebastian Wingen-Heimann, Pamela Martino Adami, Ayda Rostamzadeh, Thomas K. Karikari, Nicholas J. Ashton, Henrik Zetterberg, and Kaj Blennow were responsible for methodological core central data management and data analyses. Michelle Gerards and Ann-Katrin Schild were responsible for the methodological core manuscript. Michelle Gerards, Ann-Katrin Schild, Dix Meiberth, Ayda Rostamzadeh, Jörg Janne Vehreschild, Sebastian Wingen-Heimann, Wibke Johannis, Pamela Martino Adami, Oezguer A. Onur, Alfredo Ramirez, Thomas K. Karikari, Nicholas J. Ashton, Henrik Zetterberg, Kaj Blennow, Franziska Maier, and Frank Jessen contributed to the interpretation of data, drafting of the manuscript, and approval of the final version. They agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Data Availability Statement

The data of this study are not publicly available but can be obtained upon request.

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