Plasma Biomarkers for Early Detection and Staging of Alzheimer’s Disease: A Cross-Sectional Study in a Japanese Cohort

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This cross-sectional study assessed the utility of multiple plasma biomarkers in a Japanese cohort, including healthy controls (HC), individuals on the AD continuum, and those with non-AD cognitive impairment. Methods Participants were classified using Aβ PET imaging and neuropsychological tests into HC, the AD continuum (preclinical [preAD], mild cognitive impairment [AD-MCI], and mild dementia [AD-D]), and non-AD cognitive impairment groups. We conducted ROC analyses to predict Aβ PET status, correlation analyses with Centiloid (CL) values and cognitive scores, and biomarker comparisons across AD stages. Plasma biomarkers assessed included Aβ42/40, phosphorylated tau (p-tau181, p-tau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), individually and in combination. Aβ42/40 was measured via High-Sensitivity Chemiluminescence Enzyme Immunoassay (HISCL), while all other biomarkers were measured using the Single Molecule Array (Simoa) platform. Results A total of 69 HC, 13 preAD, 37 AD-MCI, 44 AD-D, and 80 non-AD cognitive impairment participants were analyzed. AUCs for predicting Aβ PET status were 0.931 (Aβ42/40), 0.924 (p-tau217), and 0.944 (p-tau217/Aβ42). In the cognitively normal group, AUCs were 0.968 (Aβ42/40), 0.958 (p-tau217), and 0.979 (p-tau217/Aβ42), while in the cognitively impaired group, they were 0.907 (Aβ42/40), 0.890 (p-tau217), and 0.921 (p-tau217/Aβ42). Among HC and AD continuum participants, CL correlations were 0.75 (Aβ42/40), 0.81 (p-tau217), and 0.83 (p-tau217/Aβ42). All biomarkers correlated strongly with Logical Memory scores. Aβ42/40 levels declined sharply from HC to preAD, transitioning at a CL threshold of 19.3, while the Aβ PET positivity threshold was 32.9. P-tau217 exhibited a linear increase with disease progression. Conclusions Plasma biomarkers, Aβ42/40, p-tau217, and particularly their ratio (p-tau217/Aβ42), show strong potential as Aβ PET alternatives for AD diagnosis. HISCL-based plasma Aβ42/40 detects Aβ accumulation earlier (CL = 20) than Aβ PET visual reading threshold (CL = 32.9), underscoring its utility as an early diagnostic marker. P-tau217 consistently tracks disease progression, reinforcing its value in AD staging. Longitudinal validation of these findings is needed. Alzheimer’s disease plasma biomarkers Aβ42/40 p-tau217 p-tau217/Aβ42 HISCL Simoa Centiloid Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Plasma biomarkers have recently gained significant attention for diagnosing Alzheimer’s disease (AD). Although amyloid beta (Aβ) PET and cerebrospinal fluid (CSF) analyses remain the gold standard for confirming AD pathology [ 1 ], these methods are costly, invasive, and impractical outside specialized facilities. In contrast, plasma biomarker tests are simpler, minimally invasive, and feasible even in primary care settings, making them a promising tool for aiding AD diagnosis in broader clinical environments [ 2 ]. Key plasma biomarkers reflecting AD pathology include the Aβ42/40 ratio, phosphorylated tau (p-tau), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The Aβ42/40 ratio indicates Aβ deposition in the brain and declines in AD because of a selective reduction in Aβ42, which has a higher propensity for aggregation. While plasma Aβ measurement was once challenging, technological advances—such as the HISCL platform, an automated chemiluminescence immunoassay system with high stability and accuracy, have significantly improved its reliability [ 3 – 7 ]. P-tau reflects neurofibrillary tangles, a hallmark of tau pathology in AD, with different phosphorylation sites denoted by numeric markers. Among these, p-tau217 has demonstrated superior diagnostic accuracy and specificity for AD compared to p-tau181 [ 8 ]. GFAP serves as a marker of astrogliosis and increases AD because of astrocytic activation in response to Aβ plaque formation [ 9 ]. NfL indicates axonal degeneration and is elevated in various neurodegenerative diseases, including AD [ 10 ]. In our previous study [ 7 ], we found that plasma HISCL-based Aβ42/40 exhibited exceptional performance in detecting Aβ accumulation, surpassing p-tau181, GFAP, and NfL in diagnostic accuracy. In the present study, we expanded the sample size, incorporated the well-established biomarker p-tau217, and re-evaluated our findings. Additionally, we examined biomarker levels in relation to neuropsychological test outcomes and AD staging. Methods Participants This study was conducted between July 2018 and May 2024. Participants included cognitively impaired patients who attended the Memory Center at Keio University Hospital, and cognitively normal volunteers recruited through external organizations, as described in our previous papers [ 7 , 11 , 12 ]All participants were aged 40–85 years and had at least 12 years of education (YOE). Inclusion criteria Cognitively normal participants: Mini-Mental State Examination (MMSE) score ≥ 24 Clinical Dementia Rating Global Score (CDR) = 0 Wechsler Memory Scale (WMS) Logical Memory II Delayed Recall (LM-D) score ≥ 5 for < 16 YOE and ≥ 9 for ≥ 16 YOE Geriatric Depression Scale score < 6 Cognitively impaired participants: AD: MMSE score < 24 CDR = 0.5 or1 MCI: MMSE score ≥ 24 CDR = 0.5 with memory domain ≥ 0.5 WMS LM-D score < 9 for < 16 YOE or < 11 for ≥ 16 YOE Other dementias (corticobasal syndrome (CBS), Frontotemporal Lobar Degeneration (FTLD), traumatic brain injury (TBI), dementia with Lewy bodies, or others) Exclusion criteria Presence of any coexisting other neurological disorder Diagnosis of major depression or bipolar disorder within the past year Diagnosis of a substance-related or addictive disorder within the past 2 years Lifetime diagnosis of schizophrenia All diagnoses were established based on standard clinical criteria [ 13 – 19 ]. After enrollment, all participants underwent a comprehensive neuropsychological assessment, Aβ and tau PET imaging, plasma biomarker measurements, and Apolipoprotein E (APOE) genotyping. Ethical Approval Registration, and Informed Consent This study was approved by the Certified Review Board of Keio University (approval number N20170237) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants or their representatives when necessary. The study was registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR; ID: UMIN000032027, the Registration Date, 2018/3/31) and the Japan Registry of Clinical Trials (jRCT; ID: jRCTs031180225). Aβ and Tau PET Imaging Details of the PET scan protocol have been described previously [ 11 ]. Aβ PET imaging was performed using 18F-Florbetaben (FBB) [ 20 , 21 ]. Images were reconstructed using ordered-subsets expectation maximization. Following standardized guidelines, trained neuroradiologists and a dementia specialist classified scans as Aβ-positive (Aβ+) or Aβ-negative (Aβ–). Quantitative Aβ assessment was conducted using Amyquant, a semiautomated software tool [ 22 ] to calculate the Centiloid (CL) value [ 23 ]. The CL scale, a 100-point standardized system, facilitates data comparison across institutions and PET tracers. Regions of interest included the posterior cingulate cortex/precuneus, frontal lobe, temporal lobe, parietal lobe, and putamen, with the whole cerebellum serving as the reference region [ 24 ]. Tau PET imaging was performed using 18F-florzolotau [ 25 ] in most participants and 18F-PI-2620 [ 26 , 27 ] in 22 participants. Imaging procedures and evaluation methods followed previously reported protocols, and scan positivity or negativity was determined during a joint neurology and psychiatry specialist conference [ 11 ]. Plasma Biomarker Measurements Plasma concentrations of p-tau181, p-tau217, GFAP, and NfL were measured using the Single Molecule Array (Simoa) platform (Quanterix, Billerica, MA, USA). Plasma Aβ40 and Aβ42 concentrations were measured using the High-Sensitivity Chemiluminescence Enzyme Immunoassay (HISCL) (Sysmex, Kobe, Japan) [ 28 ]. APOE Genotyping Genomic DNA was extracted using the Magnetic Nanoparticles DNA Extraction kit. APOE genotypes (ε2, ε3, and ε4) were determined by real-time PCR with TaqMan probes [ 29 ]. Cognitive Assessment We administered the following comprehensive neuropsychological tests to assess cognitive function: CDR, MMSE, Alzheimer's Disease Assessment Scale–Cognitive Subscale (ADAS-Cog), WMS Logical Memory I Immediate Recall (LM-I) and LM-D, Word Fluency Test Category (WF-C) and Initial Letter (WF-I), and the Japanese version of the Trail Making Test Parts A (TMTJ-A) and B (TMTJ-B). Standard procedures were followed as described in a previous study [ 30 ]. If a participant could not be scored due to communication difficulties, we assigned the lowest possible scores: 70 for ADAS-Cog, 600 for TMTJ, and 0 for all other tests. Statistical Analysis At the time of study enrollment, clinical diagnoses of AD and MCI were re-confirmed using neuropsychological assessments. Participants who were Aβ + were classified as having mild AD dementia (AD-D) or MCI due to AD (AD-MCI), depending on cognitive impairment severity. Those with CDR = 0 or 0.5 who did not meet MCI criteria and showed no evidence of neuropsychiatric disorders were classified as cognitively normal. Among them, Aβ + participants were defined as preclinical AD (preAD), while Aβ- participants were designated as healthy controls (HC). Aβ- individuals who did not qualify as HC were categorized as having non-AD cognitive impairment. Using Python 3.10.16, analyses were conducted on 3 groups: the AD continuum (preAD, AD-MCI, and AD-D), the HC group, and the non-AD cognitive impairment group. For all analyses, a significance level of 0.05 was used. ROC Analyses for Amyloid PET Prediction We used ROC analyses to evaluate each biomarker’s ability to distinguish Aβ + from Aβ- in the following groups: (1) Overall dataset: AD continuum vs. HC + non-AD cognitive impairment (2) Cognitively impaired group: AD-MCI + AD-D vs. non-AD cognitive impairment (3) Cognitively normal group: preAD vs. HC Additionally, we examined 66 combinations derived from multiplying any two of 12 elements, including Aβ42, Aβ40, p-tau181, p-tau217, GFAP, NfL, and their reciprocals. For each combination, we identified the top-performing pairs based on the AUC for (1), (2), and (3). The optimal cut-off values were determined using the Youden Index (YI). Correlations with Centiloid and Cognitive Scores In participants classified as HC or in the AD continuum, Spearman’s correlation coefficients were used to analyze relationships between each biomarker and Centiloid (CL) values, as well as between each biomarker and cognitive scores. Cognitive scores were adjusted for sex, age, and YOE using a linear regression model. Changes Across AD Stages Biomarker values were compared among the HC, preAD, AD-MCI, and AD-D groups. In HC and preAD, comparisons were also performed based on CL level. Following criteria from a lecanemab trial in preAD [ 31 ], CL values were divided into 3 groups: low Aβ (< 20), intermediate Aβ (20–40), and elevated Aβ (≥ 40). Between-group comparisons were conducted using the Kruskal–Wallis test. When significant differences were detected, Dunn’s post-hoc test was applied to determine which specific group differences were significant. Additionally, linear trends were evaluated using a polynomial contrast test, and equivalence was assessed using a Two One-Sided Test (TOST). Results Descriptive statistics are presented in Table 1. After excluding participants with coexisting neurological disorders (1 CBS, 1 LBD, and 1 history of dura mater transplant due to a skull tumor) from the AD continuum group because of difficulties in interpreting the results, the final analysis included 69 HC, 13 preAD, 37 AD-MCI, 44 AD-D, and 80 non-AD cognitive impairment participants. The non-AD cognitive impairment group clinically consisted of 15 FTLD, 7 PSP, 6 CBS, 3 LBD, 3 TBI, 1 NPH, 1 encephalitis, 1 myotonic dystrophy, and 43 other cognitive impairments without a specific clinical diagnosis. Missing data were identified for tau PET, p-tau181, NfL, and CL in 2, 4, 4, and 4 participants, respectively. These missing values were excluded from analyses involving these variables. No significant differences in sex or YOE were observed between the HC and AD continuum groups. As shown in Fig. 1, Aβ42/40 and p-tau217 demonstrated high AUCs as single biomarkers, whereas p-tau217/Aβ42 was the best-performing biomarker pair. The AUCs for Aβ42/40 and p-tau217 were 0.931 and 0.924 (Fig. 1 A; AD continuum vs. HC + non-AD cognitive impairment), 0.907 and 0.890 (Fig. 1 B; AD-MCI + AD-D vs. non-AD cognitive impairment), and 0.968 and 0.958 (Fig. 1 C; preAD vs. HC), respectively. Among the top 5 biomarker pairs with the highest AUC values, p-tau217/Aβ42 and p-tau217/Aβ40 were consistently selected. The AUCs for p-tau217/Aβ42 were 0.944 (Fig. 1 D), 0.921 (Fig. 1 E), and 0.979 (Fig. 1 F), surpassing those of any single biomarker. The AUC for p-tau217/(Aβ42/ Aβ40) was also calculated, but it did not exceed that of p-tau217/Aβ42 (Fig. S1 ). Table S1 provides the cutoff, AUC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for single biomarkers and biomarker combinations. As shown in Fig. 2 , p-tau217/Aβ42 exhibited the strongest correlation with Centiloid (CL), followed by p-tau217 and Aβ42/40, with correlation coefficients of 0.83, 0.81, and 0.75, respectively. As shown in Fig. 3 , Logical Memory (LM-I and LM-D) had the highest correlations with biomarkers, followed by ADAS-Cog. Among the biomarkers, p-tau217, p-tau217/Aβ42, and CL demonstrated particularly strong correlations, with absolute correlation coefficients around 0.7 for LM (Table S2 ). Additionally, Word Fluency (WF) correlated more strongly with WF-C than with WF-I, while TMTJ correlated more strongly with TMTJ-B than with TMTJ-A. Figure 4 compares biomarker changes across AD stages, and detailed statistical test results are summarized in Table S3 . Although Aβ42/40 decreased markedly from HC to preAD, it remained relatively stable in later disease stages; specifically, TOST demonstrated significant equivalence only between AD-MCI and AD-D. In contrast, p-tau181, p-tau217, and p-tau217/Aβ42 continued increasing as the disease progressed, beginning from preAD; notably, significant linear contrasts were observed for p-tau217 and p-tau217/Aβ42 in the polynomial contrast test. While GFAP and NfL were significantly different between HC and the AD continuum, they did not follow a consistent trend of change with further AD progression. When examining CL and biomarkers limited to cognitively normal participants (HC + preAD) (Fig. 5 ) to focus on preclinical Aβ pathology, Aβ42/40 significantly differed between low and intermediate Aβ groups but not between intermediate and high Aβ groups. In contrast, p-tau217 and p-tau217/Aβ42 exhibited a stepwise increase across low, intermediate, and high Aβ groups. Based on these findings, we concluded that Aβ42/40 effectively differentiates Aβ accumulation qualitatively. Therefore, additional analyses were conducted to compare it with Aβ PET CL. The optimal CL threshold for differentiating Aβ PET positivity from negativity, determined by YI, was 32.90 (sensitivity 0.968, specificity 0.957; Fig. 6 A). As shown in Fig. 6 B, the Aβ42/40 distribution was bimodal (Ashman's D > 2). Using the intersection (0.096) of two Gaussian mixture components as the cutoff, participants were clearly separated into low and high Aβ42/40 groups. The optimal CL threshold distinguishing these groups was 19.35 (sensitivity 0.969, specificity 0.954; Fig. 6 C), which was substantially lower than the visual reading threshold for Aβ PET. Discussion In this study, we demonstrated that plasma Aβ42/40 and p-tau217 accurately predict Aβ PET positivity and negativity in individuals ranging from cognitively normal to mild dementia, with Aβ42/40 performing slightly better than p-tau217 (AUC 0.931 vs. 0.924). Notably, Aβ42/40 achieved high specificity (sensitivity 0.884, specificity 1.000; Table S1 ) in the cognitively normal group, highlighting its strong utility in identifying preAD. Plasma Aβ42/40 has already been validated in multiple studies [ 3 – 5 , 7 , 32 , 33 ], establishing its clinical usefulness. Although measurement techniques can influence biomarker performance, the inexpensive and simple HISCL platform used in this study has been reported to offer superior stability and accuracy compared with other methods [ 6 , 7 ]. P-tau217 outperformed p-tau181, GFAP, and NfL, consistent with findings from previous research [ 34 ]. When we evaluated biomarker combinations, the p-tau217/Aβ42 ratio exceeded the predictive performance of Aβ42/40 alone (AUC 0.944 vs. 0.931) using HISCL-based Aβ42 and SIMOA-based p-tau217. Previous studies have shown that p-tau217/Aβ42, using immunoprecipitation-mass spectrometry (IP-MS), achieves high predictive accuracy for Aβ positivity based on Aβ PET (AUC 0.91–0.95) [ 35 , 36 ]. Using HISCL, we demonstrated even higher performance (AUC 0.979) in the cognitively normal group. Since Aβ42/40 tends to have higher specificity, while p-tau217 exhibits greater sensitivity (Table S1 ), p-tau217/Aβ42 emerges as a promising biomarker index that integrates the strengths of both markers. Aβ42/40 showed little change after the preAD stage, particularly once CL values exceeded 20 (Figs. 4 and 5 ). This finding suggests that Aβ42/40 qualitatively reflects Aβ deposition but does not track further disease progression along the AD continuum. This pattern aligns with the established understanding that Aβ deposition reaches a plateau in the early phase of AD and remains relatively stable after clinical symptoms emerge [ 37 ]. Therefore, Aβ42/40 appears to be an accurate marker of early Aβ pathology. In contrast, p-tau217 steadily increased from the early stages onward, paralleling disease progression, consistent with findings from previous research [ 38 ]. Overall, Aβ42/40 functions as a qualitative indicator of Aβ status, whereas p-tau217 serves as a quantitative marker of AD progression. Among neuropsychological tests, the Logical Memory (LM) test showed the strongest correlations with both plasma biomarkers and CL, with no significant differences between immediate recall (LM-I) and delayed recall (LM-D). Although delayed recall is often emphasized in the clinical evaluation of AD, previous studies indicate that immediate recall can also decline in early preclinical stages [ 39 ], highlighting it as another key measure to monitor. In the WF test, the category version correlated more strongly with biomarkers than the initial letter version. This likely reflects a characteristic decline in semantic retrieval, which is primarily associated with the temporal lobe, rather than executive functioning, which is more closely linked to the frontal lobe [ 40 ]. Although previous studies reported that the fold difference between individuals with and without Aβ pathologic change is low (only 10%) for plasma Aβ42/40 compared to CSF assays [ 41 – 43 ], in this study, HISCL-based Aβ42/40 serves as a qualitative indicator of Aβ pathology and exhibits a clear bimodal distribution at a cutoff of 0.096, separating participants into low and high groups (Fig. 6 ). The corresponding CL threshold for this division is 19.35, which is lower than the typical threshold for Aβ PET positivity (CL 32.9). This suggests that Aβ42/40 changes occur earlier and can detect low levels of Aβ accumulation before the PET positivity threshold is reached. Notably, a threshold of approximately CL 20 has been reported to capture moderate to extensive Aβ plaque accumulation in post-mortem findings [ 44 ], aligning with the Aβ42/40 cutoff identified here. These findings suggest that Aβ42/40 reflects underlying pathological changes more sensitively than visual reading PET-based thresholds. Compared with Aβ PET, plasma biomarkers offer advantages such as lower physical burden, reduced cost, and greater overall availability. They show promise for widespread clinical use in diagnosing AD, including the preclinical stage. By considering the distinct characteristics of each biomarker and developing a panel that integrates multiple markers, both early diagnosis and accurate disease progression assessment may become feasible in routine clinical practice. Limitations This study has several limitations. First, the sample size remains small, limiting the generalizability of the results. Second, the recently highlighted biomarker p-tau231, which reached abnormal levels at the lowest Aβ burden [ 45 ], was not measured. Finally, the absence of an independent validation cohort and longitudinal data underscores the urgent need for replication in independent samples and longitudinal validation studies. Conclusion Our findings demonstrate that plasma Aβ42/40, p-tau217, and particularly their ratio (p-tau217/Aβ42) effectively detect and monitor AD pathology. Notably, HISCL-based Aβ42/40 identifies Aβ accumulation at an earlier stage than the conventional PET threshold, while p-tau217 clearly tracks disease progression. The p-tau217/Aβ42 ratio surpasses Aβ42/40 alone in predicting Aβ positivity, emphasizing its diagnostic superiority. These results underscore the potential of plasma assays for large-scale screening and more accessible disease monitoring, particularly as AD-modifying therapies continue to emerge. Abbreviations Aβ Amyloid Beta Aβ42/40 Amyloid Beta 42/40 ratio AD Alzheimer’s Disease AD-D Alzheimer’s Disease Dementia AD-MCI Mild Cognitive Impairment due to Alzheimer’s Disease CL Centiloid CDR Clinical Dementia Rating FTLD Frontotemporal Lobar Degeneration GFAP Glial Fibrillary Acidic Protein HISCL High-Sensitivity Chemiluminescence Enzyme Immunoassay IP-MS Immunoprecipitation-Mass Spectrometry LM-D Logical Memory Delayed Recall MCI Mild Cognitive Impairment MMSE Mini-Mental State Examination NfL Neurofilament Light Chain p-tau Phosphorylated Tau p-tau181 Phosphorylated Tau at Threonine 181 p-tau217 Phosphorylated Tau at Threonine 217 PPV Positive Predictive Value ROC Receiver Operating Characteristic TOST Two One-Sided Test WF-C Word Fluency Category WF-I Word Fluency Initial Letter WMS Wechsler Memory Scale YI Youden Index Declarations Ethics approval and consent to participate : The Certified Review Board of Keio University (#N20170237) approved the study design and protocol. The study was conducted in accordance with the Declaration of Helsinki. All participants (plus their proxies as needed) provided written informed consent for participation in the study. The study was registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR; https://www.umin.ac.jp/ctr/index.htm, ID# UMIN000032027) and Japan Registry of Clinical Trials (jRCT; https://jrct.niph.go.jp/, ID# jRCTs031180225). Consent for publication: Not applicable. Availability of data and materials: The datasets used and analyzed during the current study will be available from the corresponding author upon reasonable request. Competing interests: DI has received honorariums from Daiichi Sankyo, Nihon Medi-Physics, Kowa, PDRadiopharma, Otsuka Pharmaceutical, Lilly and Eisai and has a joint research agreement with Sysmex. There are no other relationships or activities that could appear to have influenced the submitted work. Funding: This research received support from the Japan Agency for Medical Research and Development (AMED) under Grant Number JP17pc0101006. The initial grant (number JP17pc0101006) was awarded by AMED to EISAI Co., Ltd. (https://www.eisai.com/index.html), and a portion was subsequently allocated to Keio University School of Medicine. Neither AMED nor EISAI participated in the study design, data collection, analysis, or manuscript preparation. However, both organizations reserve the right to review the manuscript for intellectual property potential and may withhold publication if it contains significant new intellectual property. Authors' contributions: MK, SB, HT, and DI contributed to study conception (lead contributor was DI). SB, MK, YMomota, YI, TT, MS, YY, RS, SK, YM, and SS contributed to participant recruitment. KT and TH KT, HA, YS contributed to data curation, including activities to clean and maintain research data. NS, AM, AO, and YH contributed to administrative, technical, or material support. All authors interpreted the results and critically reviewed the manuscript. JK and JN, MM supervised the study. Acknowledgements: The authors would like to thank Mr. Ryo Ueda, Mr. Yoshiki Oowaki, Dr. Masahiro Jinzaki and the staff of the Division of Nuclear Medicine and the Department of Radiology and Aprinoia Therapeutics Inc. for their help in PET examinations and image processing. We thank the following individuals for the analysis of tau imaging: Dr. Kenji Tagai, Dr. Hitoshi Shimada, and Dr. Makoto Higuchi at the Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, and National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan. 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Evaluation of [18F]PI-2620, a second-generation selective tau tracer, for assessing four-repeat tauopathies. Brain communications. 2021;3. doi:10.1093/braincomms/fcab190 Yamashita K, Watanabe S, Ishiki K, Miura M, Irino Y, Kubo T, et al. Fully automated chemiluminescence enzyme immunoassays showing high correlation with immunoprecipitation mass spectrometry assays for β-amyloid (1-40) and (1-42) in plasma samples. Biochem Biophys Res Commun. 2021;576: 22–26. Yi L, Wu T, Luo W, Zhou W, Wu J. A non-invasive, rapid method to genotype late-onset Alzheimer’s disease-related apolipoprotein E gene polymorphisms. Neural Regeneration Res. 2014;9: 69–75. Yagi T, Ito D, Sugiyama D. Diagnostic accuracy of neuropsychological tests for classification of dementia. Neurol Asia. 2016. Rafii MS, Sperling RA, Donohue MC, Zhou J, Roberts C, Irizarry MC, et al. The AHEAD 3-45 Study: Design of a prevention trial for Alzheimer’s disease. Alzheimers Dement. 2023;19: 1227–1233. 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Alzheimers Res Ther. 2024;16: 115. Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9: 119–128. Ashton NJ, Janelidze S, Mattsson-Carlgren N, Binette AP, Strandberg O, Brum WS, et al. Differential roles of Aβ42/40, p-tau231 and p-tau217 for Alzheimer’s trial selection and disease monitoring. Nat Med. 2022;28: 2555–2562. Bilgel M, An Y, Lang A, Prince J, Ferrucci L, Jedynak B, et al. Trajectories of Alzheimer disease-related cognitive measures in a longitudinal sample. Alzheimers Dement. 2014;10: 735-742.e4. Henry JD, Crawford JR, Phillips LH. Verbal fluency performance in dementia of the Alzheimer’s type: a meta-analysis. Neuropsychologia. 2004;42: 1212–1222. Brand AL, Lawler PE, Bollinger JG, Li Y, Schindler SE, Li M, et al. The performance of plasma amyloid beta measurements in identifying amyloid plaques in Alzheimer’s disease: a literature review. Alzheimers Res Ther. 2022;14: 195. Rabe C, Bittner T, Jethwa A, Suridjan I, Manuilova E, Friesenhahn M, et al. Clinical performance and robustness evaluation of plasma amyloid-β42/40 prescreening. Alzheimers Dement. 2023;19: 1393–1402. Cullen NC, Janelidze S, Mattsson-Carlgren N, Palmqvist S, Bittner T, Suridjan I, et al. Test-retest variability of plasma biomarkers in Alzheimer’s disease and its effects on clinical prediction models. Alzheimers Dement. 2023;19: 797–806. Amadoru S, Doré V, McLean CA, Hinton F, Shepherd CE, Halliday GM, et al. Comparison of amyloid PET measured in Centiloid units with neuropathological findings in Alzheimer’s disease. Alzheimers Res Ther. 2020;12: 22. Milà-Alomà M, Ashton NJ, Shekari M, Salvadó G, Ortiz-Romero P, Montoliu-Gaya L, et al. Plasma p-tau231 and p-tau217 as state markers of amyloid-β pathology in preclinical Alzheimer’s disease. Nat Med. 2022 [cited 26 Mar 2023]. doi:10.1038/s41591-022-01925-w Additional Declarations Competing interest reported. DI has received honorariums from Daiichi Sankyo, Nihon Medi-Physics, Kowa, PDRadiopharma, Otsuka Pharmaceutical, Lilly and Eisai and has a joint research agreement with Sysmex. There are no other relationships or activities that could appear to have influenced the submitted work. Supplementary Files AdditionalFile1.docx Additional file 1: Fig. S1 File format: docx Title: ROC analysis of p-tau217/(Aβ42/Aβ40) vs. tau217/Aβ42 Description: ROC analysis comparing p-tau217/(Aβ42/Aβ40) and p-tau217/Aβ42 across different comparison groups (AD continuum vs. HC + non-AD cognitive impairment, AD-MCI + AD-D vs. non-AD cognitive impairment, preAD vs. HC). The AUC values for each comparison group are presented. AdditionalFile2.xlsx Additional file 2: Table S1 File format: xlsx Title: Performance metrics of single biomarkers and biomarker combinations Description: This table presents performance metrics, including AUC, sensitivity, specificity, PPV, and NPV for various biomarkers and combinations across different clinical groups. AdditionalFile3.xlsx Additional file 3: Table S2 File format: xlsx Title: Spearman’s correlation coefficients between each biomarker and cognitive scores Description: This table shows the correlation between biomarkers such as Aβ42/40, p-tau181, p-tau217, and cognitive scores including MMSE, ADAS-Cog, LM-I, LM-D, and others. AdditionalFile4.xlsx Additional file 4: Table S3 File format: xlsx Title: Statistical Tests Comparing Biomarkers Across AD Stages Description: This table shows the results of Kruskal-Wallis test, polynomial contrast test, and two one-sided test among HC, preAD, AD-MCI,and AD-D. Cite Share Download PDF Status: Published Journal Publication published 07 Jun, 2025 Read the published version in Alzheimer's Research & Therapy → Version 1 posted Editorial decision: Revision requested 03 Apr, 2025 Reviews received at journal 03 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviews received at journal 30 Mar, 2025 Reviews received at journal 27 Mar, 2025 Reviewers agreed at journal 20 Mar, 2025 Reviewers agreed at journal 20 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers agreed at journal 18 Mar, 2025 Reviewers invited by journal 18 Mar, 2025 Editor assigned by journal 17 Mar, 2025 Submission checks completed at journal 17 Mar, 2025 First submitted to journal 16 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6239779","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431687018,"identity":"644815df-706b-4590-a541-3b8e54f2a2bb","order_by":0,"name":"Masahito Kubota","email":"","orcid":"","institution":"Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Masahito","middleName":"","lastName":"Kubota","suffix":""},{"id":431687019,"identity":"991781b9-8bb8-4fbc-8c4e-6eca8088a927","order_by":1,"name":"Shogyoku Bun","email":"","orcid":"","institution":"Keio University School of 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Ito","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie2RMWrDMBSGfxF4XhK7o0R6CAWDoWB6lkIgU6Frh1BkDMrYtYHSY4SMDwKefIfYBDo7ByitXFJohqgZC9EHAkno09P/BAQC/xEWBngknRztSr9SGNSklfl92K9AGGGhNZ9x/zcxDxbN/i1ONUdtI9b5E8Yl42Z9WlHuYcVyRZniYapFPZO4ru6g6tOK3rZFOVpRHvOQxh92IyHvNZT1KK5KOXqlHBy9S2E/nfLQnaEYyq4YmVO4rwKv0mdZvlSUqrLPYqfKyplmX5aYo6br5tXkOVq4jtnbJJHTdqc8HTtQAYPDlNzY/Hysh/nxUuz/VgKBQOBy+ALxR0sHbVdi5AAAAABJRU5ErkJggg==","orcid":"","institution":"Keio University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Ito","suffix":""}],"badges":[],"createdAt":"2025-03-16 23:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6239779/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6239779/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13195-025-01778-8","type":"published","date":"2025-06-07T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79339513,"identity":"858ed3a5-8c3e-45b5-aae5-7b0a4be5300e","added_by":"auto","created_at":"2025-03-27 08:24:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":454512,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of single biomarkers (A–C) and their combinations (D–F). The combinations represent the top 5 biomarker pairs with the highest AUC, calculated using multiplication or division. The comparison groups are as follows: A, D: AD continuum (n=94) vs. HC + non-AD cognitive impairment (n=149); B, E: AD-MCI + AD-D (n=81) vs. non-AD cognitive impairment (n=80); C, F: preAD (n=13) vs. HC (n=69).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/0701a2acf1b9b0bfe359690b.png"},{"id":79339514,"identity":"f9cccdd8-4254-4cd0-a786-4bdcc08cedfc","added_by":"auto","created_at":"2025-03-27 08:24:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":969963,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots between CL and each biomarker (including p-tau217/Aβ42), with Spearman’s correlation coefficients (ρ) and the best-fit linear regressions.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/d473f5a90756b7760e3b23fe.png"},{"id":79339519,"identity":"aa6312cf-202c-4b41-932b-1d5a608e16cc","added_by":"auto","created_at":"2025-03-27 08:24:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95889,"visible":true,"origin":"","legend":"\u003cp\u003eAbsolute values of Spearman’s correlation coefficients between biomarkers (including p-tau217/Aβ42 and CL) and cognitive scores, adjusted for sex, age, and years of education using a linear regression model.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/b99ef95632d5a42a4befc78e.png"},{"id":79340168,"identity":"85efd5f5-4f66-4678-81ed-c418ce97a283","added_by":"auto","created_at":"2025-03-27 08:32:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":283295,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of biomarker values at each AD stage in HC (n=69) + the AD continuum (preAD [n=13], AD-MCI [n=37], and AD-D [n=44]). *\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/667208c6de5757dd197010d4.png"},{"id":79341179,"identity":"75ddddaf-894b-42b9-8563-62796d8f6d9d","added_by":"auto","created_at":"2025-03-27 08:40:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":244690,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of biomarker values at each CL level in HC (n=69) + preAD (n=13). *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/bb37431e761c06fba2d92cd0.png"},{"id":79339530,"identity":"2ad3c850-a5e6-4364-ae2e-d77acdeb7ed0","added_by":"auto","created_at":"2025-03-27 08:24:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":243506,"visible":true,"origin":"","legend":"\u003cp\u003eA: Comparison of CL values and threshold based on visual reading Aβ PET status. B: Histogram of Aβ42/40 values and the probability density functions (blue and green curves) estimated by a two-component Gaussian mixture model. The intersection of the 2 component distributions served as the threshold for classifying Aβ42/40 values into low and high groups. C: Comparison of CL values and threshold between the low and high Aβ42/40 groups.\u003c/p\u003e\n\u003cp\u003eTargets were HC (n=69) + the AD continuum (preAD [n=13], AD-MCI [n=37], and AD-D [n=44]). The optimal CL threshold was determined using the Youden Index.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/b31bdafff023ff82752af9fd.png"},{"id":84242711,"identity":"3a0e2d90-d629-48a7-942b-e2869573afaa","added_by":"auto","created_at":"2025-06-09 16:11:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2689311,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/4fc50982-6474-4281-b51e-3fa510bd219b.pdf"},{"id":79341176,"identity":"c6a04795-834f-40c9-afbb-efc19c2e1c0c","added_by":"auto","created_at":"2025-03-27 08:40:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":78443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1\u003c/strong\u003e: Fig. S1\u003c/p\u003e\n\u003cp\u003eFile format: docx\u003c/p\u003e\n\u003cp\u003eTitle: ROC analysis of p-tau217/(Aβ42/Aβ40) vs. tau217/Aβ42\u003c/p\u003e\n\u003cp\u003eDescription: ROC analysis comparing p-tau217/(Aβ42/Aβ40) and p-tau217/Aβ42 across different comparison groups (AD continuum vs. HC + non-AD cognitive impairment, AD-MCI + AD-D vs. non-AD cognitive impairment, preAD vs. HC). The AUC values for each comparison group are presented.\u003c/p\u003e","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/f790a201f561527388c2a26d.docx"},{"id":79342799,"identity":"ade134a6-46d1-45e3-9e5a-fb18bd9e9100","added_by":"auto","created_at":"2025-03-27 08:56:59","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2\u003c/strong\u003e: Table S1\u003c/p\u003e\n\u003cp\u003eFile format: xlsx\u003c/p\u003e\n\u003cp\u003eTitle: Performance metrics of single biomarkers and biomarker combinations\u003c/p\u003e\n\u003cp\u003eDescription: This table presents performance metrics, including AUC, sensitivity, specificity, PPV, and NPV for various biomarkers and combinations across different clinical groups.\u003c/p\u003e","description":"","filename":"AdditionalFile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/aa31c11ead710ff0bab80029.xlsx"},{"id":79341505,"identity":"61432955-e6f9-45aa-a674-d7f6b42469c3","added_by":"auto","created_at":"2025-03-27 08:48:59","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3\u003c/strong\u003e: Table S2\u003c/p\u003e\n\u003cp\u003eFile format: xlsx\u003c/p\u003e\n\u003cp\u003eTitle: Spearman’s correlation coefficients between each biomarker and cognitive scores\u003c/p\u003e\n\u003cp\u003eDescription: This table shows the correlation between biomarkers such as Aβ42/40, p-tau181, p-tau217, and cognitive scores including MMSE, ADAS-Cog, LM-I, LM-D, and others.\u003c/p\u003e","description":"","filename":"AdditionalFile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/2be2649eb6b9132e85d62cdf.xlsx"},{"id":79340165,"identity":"5986fe03-cfea-4b79-bf95-8df8c9a91812","added_by":"auto","created_at":"2025-03-27 08:32:59","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12174,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 4\u003c/strong\u003e: Table S3\u003c/p\u003e\n\u003cp\u003eFile format: xlsx\u003c/p\u003e\n\u003cp\u003eTitle: Statistical Tests Comparing Biomarkers Across AD Stages\u003c/p\u003e\n\u003cp\u003eDescription: This table shows the results of Kruskal-Wallis test, polynomial contrast test, and two one-sided test among HC, preAD, AD-MCI,and AD-D.\u003c/p\u003e","description":"","filename":"AdditionalFile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6239779/v1/d63d868dd899fd71d0104bd1.xlsx"}],"financialInterests":"Competing interest reported. DI has received honorariums from Daiichi Sankyo, Nihon Medi-Physics, Kowa, PDRadiopharma, Otsuka Pharmaceutical, Lilly and Eisai and has a joint research agreement with Sysmex. There are no other relationships or activities that could appear to have influenced the submitted work.","formattedTitle":"Plasma Biomarkers for Early Detection and Staging of Alzheimer’s Disease: A Cross-Sectional Study in a Japanese Cohort","fulltext":[{"header":"Background","content":"\u003cp\u003ePlasma biomarkers have recently gained significant attention for diagnosing Alzheimer\u0026rsquo;s disease (AD). Although amyloid beta (Aβ) PET and cerebrospinal fluid (CSF) analyses remain the gold standard for confirming AD pathology [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], these methods are costly, invasive, and impractical outside specialized facilities. In contrast, plasma biomarker tests are simpler, minimally invasive, and feasible even in primary care settings, making them a promising tool for aiding AD diagnosis in broader clinical environments [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKey plasma biomarkers reflecting AD pathology include the Aβ42/40 ratio, phosphorylated tau (p-tau), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The Aβ42/40 ratio indicates Aβ deposition in the brain and declines in AD because of a selective reduction in Aβ42, which has a higher propensity for aggregation. While plasma Aβ measurement was once challenging, technological advances\u0026mdash;such as the HISCL platform, an automated chemiluminescence immunoassay system with high stability and accuracy, have significantly improved its reliability [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. P-tau reflects neurofibrillary tangles, a hallmark of tau pathology in AD, with different phosphorylation sites denoted by numeric markers. Among these, p-tau217 has demonstrated superior diagnostic accuracy and specificity for AD compared to p-tau181 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. GFAP serves as a marker of astrogliosis and increases AD because of astrocytic activation in response to Aβ plaque formation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. NfL indicates axonal degeneration and is elevated in various neurodegenerative diseases, including AD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our previous study [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], we found that plasma HISCL-based Aβ42/40 exhibited exceptional performance in detecting Aβ accumulation, surpassing p-tau181, GFAP, and NfL in diagnostic accuracy. In the present study, we expanded the sample size, incorporated the well-established biomarker p-tau217, and re-evaluated our findings. Additionally, we examined biomarker levels in relation to neuropsychological test outcomes and AD staging.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThis study was conducted between July 2018 and May 2024. Participants included cognitively impaired patients who attended the Memory Center at Keio University Hospital, and cognitively normal volunteers recruited through external organizations, as described in our previous papers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]All participants were aged 40\u0026ndash;85 years and had at least 12 years of education (YOE).\u003c/p\u003e \u003cp\u003eInclusion criteria\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCognitively normal participants:\u003c/p\u003e\u003cp\u003eMini-Mental State Examination (MMSE) score\u0026thinsp;\u0026ge;\u0026thinsp;24\u003c/p\u003e\u003cp\u003eClinical Dementia Rating Global Score (CDR)\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\u003cp\u003eWechsler Memory Scale (WMS) Logical Memory II Delayed Recall (LM-D) score\u0026thinsp;\u0026ge;\u0026thinsp;5 for \u0026lt;\u0026thinsp;16 YOE and \u0026ge;\u0026thinsp;9 for \u0026ge;\u0026thinsp;16 YOE\u003c/p\u003e\u003cp\u003eGeriatric Depression Scale score\u0026thinsp;\u0026lt;\u0026thinsp;6\u003c/p\u003e\u003cp\u003eCognitively impaired participants:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAD:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMMSE score\u0026thinsp;\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCDR\u0026thinsp;=\u0026thinsp;0.5 or1\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMCI:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMMSE score\u0026thinsp;\u0026ge;\u0026thinsp;24\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCDR\u0026thinsp;=\u0026thinsp;0.5 with memory domain\u0026thinsp;\u0026ge;\u0026thinsp;0.5\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWMS LM-D score\u0026thinsp;\u0026lt;\u0026thinsp;9 for \u0026lt;\u0026thinsp;16 YOE or \u0026lt;\u0026thinsp;11 for \u0026ge;\u0026thinsp;16 YOE\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOther dementias (corticobasal syndrome (CBS), Frontotemporal Lobar Degeneration (FTLD), traumatic brain injury (TBI), dementia with Lewy bodies, or others)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eExclusion criteria\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePresence of any coexisting other neurological disorder\u003c/p\u003e\u003cp\u003eDiagnosis of major depression or bipolar disorder within the past year\u003c/p\u003e\u003cp\u003eDiagnosis of a substance-related or addictive disorder within the past 2 years\u003c/p\u003e\u003cp\u003eLifetime diagnosis of schizophrenia\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAll diagnoses were established based on standard clinical criteria [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. After enrollment, all participants underwent a comprehensive neuropsychological assessment, Aβ and tau PET imaging, plasma biomarker measurements, and Apolipoprotein E (APOE) genotyping.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthical Approval Registration, and Informed Consent\u003c/b\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e This study was approved by the Certified Review Board of Keio University (approval number N20170237) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants or their representatives when necessary. The study was registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR; ID: UMIN000032027, the Registration Date, 2018/3/31) and the Japan Registry of Clinical Trials (jRCT; ID: jRCTs031180225).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAβ and Tau PET Imaging\u003c/h3\u003e\n\u003cp\u003eDetails of the PET scan protocol have been described previously [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAβ PET imaging was performed using 18F-Florbetaben (FBB) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Images were reconstructed using ordered-subsets expectation maximization. Following standardized guidelines, trained neuroradiologists and a dementia specialist classified scans as Aβ-positive (Aβ+) or Aβ-negative (Aβ\u0026ndash;).\u003c/p\u003e \u003cp\u003eQuantitative Aβ assessment was conducted using Amyquant, a semiautomated software tool [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] to calculate the Centiloid (CL) value [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The CL scale, a 100-point standardized system, facilitates data comparison across institutions and PET tracers. Regions of interest included the posterior cingulate cortex/precuneus, frontal lobe, temporal lobe, parietal lobe, and putamen, with the whole cerebellum serving as the reference region [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTau PET imaging was performed using 18F-florzolotau [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] in most participants and 18F-PI-2620 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] in 22 participants. Imaging procedures and evaluation methods followed previously reported protocols, and scan positivity or negativity was determined during a joint neurology and psychiatry specialist conference [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePlasma Biomarker Measurements\u003c/h3\u003e\n\u003cp\u003ePlasma concentrations of p-tau181, p-tau217, GFAP, and NfL were measured using the Single Molecule Array (Simoa) platform (Quanterix, Billerica, MA, USA). Plasma Aβ40 and Aβ42 concentrations were measured using the High-Sensitivity Chemiluminescence Enzyme Immunoassay (HISCL) (Sysmex, Kobe, Japan) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAPOE Genotyping\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted using the Magnetic Nanoparticles DNA Extraction kit. APOE genotypes (ε2, ε3, and ε4) were determined by real-time PCR with TaqMan probes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCognitive Assessment\u003c/h3\u003e\n\u003cp\u003eWe administered the following comprehensive neuropsychological tests to assess cognitive function: CDR, MMSE, Alzheimer's Disease Assessment Scale\u0026ndash;Cognitive Subscale (ADAS-Cog), WMS Logical Memory I Immediate Recall (LM-I) and LM-D, Word Fluency Test Category (WF-C) and Initial Letter (WF-I), and the Japanese version of the Trail Making Test Parts A (TMTJ-A) and B (TMTJ-B). Standard procedures were followed as described in a previous study [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. If a participant could not be scored due to communication difficulties, we assigned the lowest possible scores: 70 for ADAS-Cog, 600 for TMTJ, and 0 for all other tests.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAt the time of study enrollment, clinical diagnoses of AD and MCI were re-confirmed using neuropsychological assessments. Participants who were Aβ\u0026thinsp;+\u0026thinsp;were classified as having mild AD dementia (AD-D) or MCI due to AD (AD-MCI), depending on cognitive impairment severity. Those with CDR\u0026thinsp;=\u0026thinsp;0 or 0.5 who did not meet MCI criteria and showed no evidence of neuropsychiatric disorders were classified as cognitively normal. Among them, Aβ\u0026thinsp;+\u0026thinsp;participants were defined as preclinical AD (preAD), while Aβ- participants were designated as healthy controls (HC). Aβ- individuals who did not qualify as HC were categorized as having non-AD cognitive impairment.\u003c/p\u003e \u003cp\u003eUsing Python 3.10.16, analyses were conducted on 3 groups: the AD continuum (preAD, AD-MCI, and AD-D), the HC group, and the non-AD cognitive impairment group. For all analyses, a significance level of 0.05 was used.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eROC Analyses for Amyloid PET Prediction\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWe used ROC analyses to evaluate each biomarker\u0026rsquo;s ability to distinguish Aβ\u0026thinsp;+\u0026thinsp;from Aβ- in the following groups:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e(1) Overall dataset: AD continuum vs. HC\u0026thinsp;+\u0026thinsp;non-AD cognitive impairment\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e(2) Cognitively impaired group: AD-MCI\u0026thinsp;+\u0026thinsp;AD-D vs. non-AD cognitive impairment\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e(3) Cognitively normal group: preAD vs. HC\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdditionally, we examined 66 combinations derived from multiplying any two of 12 elements, including Aβ42, Aβ40, p-tau181, p-tau217, GFAP, NfL, and their reciprocals. For each combination, we identified the top-performing pairs based on the AUC for (1), (2), and (3). The optimal cut-off values were determined using the Youden Index (YI).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCorrelations with Centiloid and Cognitive Scores\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn participants classified as HC or in the AD continuum, Spearman\u0026rsquo;s correlation coefficients were used to analyze relationships between each biomarker and Centiloid (CL) values, as well as between each biomarker and cognitive scores. Cognitive scores were adjusted for sex, age, and YOE using a linear regression model.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eChanges Across AD Stages\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBiomarker values were compared among the HC, preAD, AD-MCI, and AD-D groups. In HC and preAD, comparisons were also performed based on CL level. Following criteria from a lecanemab trial in preAD [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], CL values were divided into 3 groups: low Aβ (\u0026lt;\u0026thinsp;20), intermediate Aβ (20\u0026ndash;40), and elevated Aβ (\u0026ge;\u0026thinsp;40). Between-group comparisons were conducted using the Kruskal\u0026ndash;Wallis test. When significant differences were detected, Dunn\u0026rsquo;s post-hoc test was applied to determine which specific group differences were significant. Additionally, linear trends were evaluated using a polynomial contrast test, and equivalence was assessed using a Two One-Sided Test (TOST).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics are presented in Table\u0026nbsp;1. After excluding participants with coexisting neurological disorders (1 CBS, 1 LBD, and 1 history of dura mater transplant due to a skull tumor) from the AD continuum group because of difficulties in interpreting the results, the final analysis included 69 HC, 13 preAD, 37 AD-MCI, 44 AD-D, and 80 non-AD cognitive impairment participants. The non-AD cognitive impairment group clinically consisted of 15 FTLD, 7 PSP, 6 CBS, 3 LBD, 3 TBI, 1 NPH, 1 encephalitis, 1 myotonic dystrophy, and 43 other cognitive impairments without a specific clinical diagnosis. Missing data were identified for tau PET, p-tau181, NfL, and CL in 2, 4, 4, and 4 participants, respectively. These missing values were excluded from analyses involving these variables. No significant differences in sex or YOE were observed between the HC and AD continuum groups.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;1, Aβ42/40 and p-tau217 demonstrated high AUCs as single biomarkers, whereas p-tau217/Aβ42 was the best-performing biomarker pair. The AUCs for Aβ42/40 and p-tau217 were 0.931 and 0.924 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; AD continuum vs. HC\u0026thinsp;+\u0026thinsp;non-AD cognitive impairment), 0.907 and 0.890 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; AD-MCI\u0026thinsp;+\u0026thinsp;AD-D vs. non-AD cognitive impairment), and 0.968 and 0.958 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC; preAD vs. HC), respectively. Among the top 5 biomarker pairs with the highest AUC values, p-tau217/Aβ42 and p-tau217/Aβ40 were consistently selected. The AUCs for p-tau217/Aβ42 were 0.944 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), 0.921 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), and 0.979 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), surpassing those of any single biomarker. The AUC for p-tau217/(Aβ42/ Aβ40) was also calculated, but it did not exceed that of p-tau217/Aβ42 (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e provides the cutoff, AUC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for single biomarkers and biomarker combinations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p-tau217/Aβ42 exhibited the strongest correlation with Centiloid (CL), followed by p-tau217 and Aβ42/40, with correlation coefficients of 0.83, 0.81, and 0.75, respectively. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Logical Memory (LM-I and LM-D) had the highest correlations with biomarkers, followed by ADAS-Cog. Among the biomarkers, p-tau217, p-tau217/Aβ42, and CL demonstrated particularly strong correlations, with absolute correlation coefficients around 0.7 for LM (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Additionally, Word Fluency (WF) correlated more strongly with WF-C than with WF-I, while TMTJ correlated more strongly with TMTJ-B than with TMTJ-A.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e compares biomarker changes across AD stages, and detailed statistical test results are summarized in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. Although Aβ42/40 decreased markedly from HC to preAD, it remained relatively stable in later disease stages; specifically, TOST demonstrated significant equivalence only between AD-MCI and AD-D. In contrast, p-tau181, p-tau217, and p-tau217/Aβ42 continued increasing as the disease progressed, beginning from preAD; notably, significant linear contrasts were observed for p-tau217 and p-tau217/Aβ42 in the polynomial contrast test. While GFAP and NfL were significantly different between HC and the AD continuum, they did not follow a consistent trend of change with further AD progression. When examining CL and biomarkers limited to cognitively normal participants (HC\u0026thinsp;+\u0026thinsp;preAD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) to focus on preclinical Aβ pathology, Aβ42/40 significantly differed between low and intermediate Aβ groups but not between intermediate and high Aβ groups. In contrast, p-tau217 and p-tau217/Aβ42 exhibited a stepwise increase across low, intermediate, and high Aβ groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on these findings, we concluded that Aβ42/40 effectively differentiates Aβ accumulation qualitatively. Therefore, additional analyses were conducted to compare it with Aβ PET CL. The optimal CL threshold for differentiating Aβ PET positivity from negativity, determined by YI, was 32.90 (sensitivity 0.968, specificity 0.957; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, the Aβ42/40 distribution was bimodal (Ashman's D\u0026thinsp;\u0026gt;\u0026thinsp;2). Using the intersection (0.096) of two Gaussian mixture components as the cutoff, participants were clearly separated into low and high Aβ42/40 groups. The optimal CL threshold distinguishing these groups was 19.35 (sensitivity 0.969, specificity 0.954; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), which was substantially lower than the visual reading threshold for Aβ PET.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrated that plasma Aβ42/40 and p-tau217 accurately predict Aβ PET positivity and negativity in individuals ranging from cognitively normal to mild dementia, with Aβ42/40 performing slightly better than p-tau217 (AUC 0.931 vs. 0.924). Notably, Aβ42/40 achieved high specificity (sensitivity 0.884, specificity 1.000; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) in the cognitively normal group, highlighting its strong utility in identifying preAD. Plasma Aβ42/40 has already been validated in multiple studies [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], establishing its clinical usefulness. Although measurement techniques can influence biomarker performance, the inexpensive and simple HISCL platform used in this study has been reported to offer superior stability and accuracy compared with other methods [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. P-tau217 outperformed p-tau181, GFAP, and NfL, consistent with findings from previous research [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen we evaluated biomarker combinations, the p-tau217/Aβ42 ratio exceeded the predictive performance of Aβ42/40 alone (AUC 0.944 vs. 0.931) using HISCL-based Aβ42 and SIMOA-based p-tau217. Previous studies have shown that p-tau217/Aβ42, using immunoprecipitation-mass spectrometry (IP-MS), achieves high predictive accuracy for Aβ positivity based on Aβ PET (AUC 0.91\u0026ndash;0.95) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Using HISCL, we demonstrated even higher performance (AUC 0.979) in the cognitively normal group. Since Aβ42/40 tends to have higher specificity, while p-tau217 exhibits greater sensitivity (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), p-tau217/Aβ42 emerges as a promising biomarker index that integrates the strengths of both markers.\u003c/p\u003e \u003cp\u003eAβ42/40 showed little change after the preAD stage, particularly once CL values exceeded 20 (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This finding suggests that Aβ42/40 qualitatively reflects Aβ deposition but does not track further disease progression along the AD continuum. This pattern aligns with the established understanding that Aβ deposition reaches a plateau in the early phase of AD and remains relatively stable after clinical symptoms emerge [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, Aβ42/40 appears to be an accurate marker of early Aβ pathology. In contrast, p-tau217 steadily increased from the early stages onward, paralleling disease progression, consistent with findings from previous research [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Overall, Aβ42/40 functions as a qualitative indicator of Aβ status, whereas p-tau217 serves as a quantitative marker of AD progression.\u003c/p\u003e \u003cp\u003eAmong neuropsychological tests, the Logical Memory (LM) test showed the strongest correlations with both plasma biomarkers and CL, with no significant differences between immediate recall (LM-I) and delayed recall (LM-D). Although delayed recall is often emphasized in the clinical evaluation of AD, previous studies indicate that immediate recall can also decline in early preclinical stages [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], highlighting it as another key measure to monitor. In the WF test, the category version correlated more strongly with biomarkers than the initial letter version. This likely reflects a characteristic decline in semantic retrieval, which is primarily associated with the temporal lobe, rather than executive functioning, which is more closely linked to the frontal lobe [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough previous studies reported that the fold difference between individuals with and without Aβ pathologic change is low (only 10%) for plasma Aβ42/40 compared to CSF assays [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], in this study, HISCL-based Aβ42/40 serves as a qualitative indicator of Aβ pathology and exhibits a clear bimodal distribution at a cutoff of 0.096, separating participants into low and high groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The corresponding CL threshold for this division is 19.35, which is lower than the typical threshold for Aβ PET positivity (CL 32.9). This suggests that Aβ42/40 changes occur earlier and can detect low levels of Aβ accumulation before the PET positivity threshold is reached. Notably, a threshold of approximately CL 20 has been reported to capture moderate to extensive Aβ plaque accumulation in post-mortem findings [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], aligning with the Aβ42/40 cutoff identified here. These findings suggest that Aβ42/40 reflects underlying pathological changes more sensitively than visual reading PET-based thresholds.\u003c/p\u003e \u003cp\u003eCompared with Aβ PET, plasma biomarkers offer advantages such as lower physical burden, reduced cost, and greater overall availability. They show promise for widespread clinical use in diagnosing AD, including the preclinical stage. By considering the distinct characteristics of each biomarker and developing a panel that integrates multiple markers, both early diagnosis and accurate disease progression assessment may become feasible in routine clinical practice.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the sample size remains small, limiting the generalizability of the results. Second, the recently highlighted biomarker p-tau231, which reached abnormal levels at the lowest Aβ burden [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], was not measured. Finally, the absence of an independent validation cohort and longitudinal data underscores the urgent need for replication in independent samples and longitudinal validation studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings demonstrate that plasma Aβ42/40, p-tau217, and particularly their ratio (p-tau217/Aβ42) effectively detect and monitor AD pathology. Notably, HISCL-based Aβ42/40 identifies Aβ accumulation at an earlier stage than the conventional PET threshold, while p-tau217 clearly tracks disease progression. The p-tau217/Aβ42 ratio surpasses Aβ42/40 alone in predicting Aβ positivity, emphasizing its diagnostic superiority. These results underscore the potential of plasma assays for large-scale screening and more accessible disease monitoring, particularly as AD-modifying therapies continue to emerge.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAβ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmyloid Beta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAβ42/40\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmyloid Beta 42/40 ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlzheimer\u0026rsquo;s Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAD-D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlzheimer\u0026rsquo;s Disease Dementia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAD-MCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMild Cognitive Impairment due to Alzheimer\u0026rsquo;s Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentiloid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Dementia Rating\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFTLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFrontotemporal Lobar Degeneration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGFAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlial Fibrillary Acidic Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHISCL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Sensitivity Chemiluminescence Enzyme Immunoassay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIP-MS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmunoprecipitation-Mass Spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLM-D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLogical Memory Delayed Recall\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMild Cognitive Impairment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMini-Mental State Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNfL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeurofilament Light Chain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ep-tau\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphorylated Tau\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ep-tau181\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphorylated Tau at Threonine 181\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ep-tau217\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphorylated Tau at Threonine 217\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive Predictive Value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTOST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTwo One-Sided Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWF-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWord Fluency Category\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWF-I\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWord Fluency Initial Letter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWechsler Memory Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eYouden Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: The Certified Review Board of Keio University (#N20170237) approved the study design and protocol. The study was conducted in accordance with the Declaration of Helsinki. All participants (plus their proxies as needed) provided written informed consent for participation in the study. The study was registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR; https://www.umin.ac.jp/ctr/index.htm, ID# UMIN000032027) and Japan Registry of Clinical Trials (jRCT; https://jrct.niph.go.jp/, ID# jRCTs031180225).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and analyzed during the current study will be available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eDI has received honorariums from Daiichi Sankyo, Nihon Medi-Physics, Kowa, PDRadiopharma, Otsuka Pharmaceutical, Lilly and Eisai and has a joint research agreement with Sysmex. There are no other relationships or activities that could appear to have influenced the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received support from the Japan Agency for Medical Research and Development (AMED) under Grant Number JP17pc0101006. The initial grant (number JP17pc0101006) was awarded by AMED to EISAI Co., Ltd. (https://www.eisai.com/index.html), and a portion was subsequently allocated to Keio University School of Medicine. Neither AMED nor EISAI participated in the study design, data collection, analysis, or manuscript preparation. However, both organizations reserve the right to review the manuscript for intellectual property potential and may withhold publication if it contains significant new intellectual property.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eMK, SB, HT, and DI contributed to study conception (lead contributor was DI). SB, MK, YMomota, YI, TT, MS, YY, RS, SK, YM, and SS contributed to participant recruitment. KT and TH KT, HA, YS contributed to data curation, including activities to clean and maintain research data. NS, AM, AO, and YH contributed to administrative, technical, or material support. All authors interpreted the results and critically reviewed the manuscript. JK and JN, MM supervised the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank Mr. Ryo Ueda, Mr. Yoshiki Oowaki, Dr. Masahiro Jinzaki and the staff of the Division of Nuclear Medicine and the Department of Radiology and Aprinoia Therapeutics Inc. for their help in PET examinations and image processing. We thank the following individuals for the analysis of tau imaging: Dr. Kenji Tagai, Dr. Hitoshi Shimada, and Dr. Makoto Higuchi at the Department of Functional Brain Imaging Research, National Institute of Radiological Sciences, and National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan. We are also grateful to the following individuals for clinical assistance and participant entry: Dr. Yoshinori Nishimoto, Department of Neurology, Dr. Kei Funaki, Dr. Toshie Kitao, Department of Neuropsychiatry/Memory Center, Keio University School of Medicine, Japan.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer\u0026rsquo;s disease. Alzheimers Dement. 2018;14: 535\u0026ndash;562.\u003c/li\u003e\n\u003cli\u003eJack CR Jr, Andrews SJ, Beach TG, Buracchio T, Dunn B, Graf A, et al. Revised criteria for the diagnosis and staging of Alzheimer\u0026rsquo;s disease. Nat Med. 2024;30: 2121\u0026ndash;2124.\u003c/li\u003e\n\u003cli\u003eOvod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, et al. 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Beta-amyloid imaging with florbetaben. Clin Transl Imaging. 2015;3: 13\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eSabri O, Sabbagh MN, Seibyl J, Barthel H, Akatsu H, Ouchi Y, et al. Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer\u0026rsquo;s disease: phase 3 study. Alzheimers Dement. 2015;11: 964\u0026ndash;974.\u003c/li\u003e\n\u003cli\u003eMatsuda H, Yamao T. Software development for quantitative analysis of brain amyloid PET. Brain Behav. 2022;12: e2499.\u003c/li\u003e\n\u003cli\u003eKlunk WE, Koeppe RA, Price JC, Benzinger TL, Devous MD Sr, Jagust WJ, et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. 2015;11: 1-15.e1\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eCho SH, Choe YS, Park S, Kim YJ, Kim HJ, Jang H, et al. Appropriate reference region selection of 18F-florbetaben and 18F-flutemetamol beta-amyloid PET expressed in Centiloid. Sci Rep. 2020;10: 14950.\u003c/li\u003e\n\u003cli\u003eTagai K, Ono M, Kubota M, Kitamura S, Takahata K, Seki C, et al. High-Contrast In Vivo Imaging of Tau Pathologies in Alzheimer\u0026rsquo;s and Non-Alzheimer\u0026rsquo;s Disease Tauopathies. Neuron. 2021;109: 42-58.e8.\u003c/li\u003e\n\u003cli\u003eKroth H, Oden F, Molette J, Schieferstein H, Capotosti F, Mueller A, et al. Discovery and preclinical characterization of [18F]PI-2620, a next-generation tau PET tracer for the assessment of tau pathology in Alzheimer\u0026rsquo;s disease and other tauopathies. Eur J Nucl Med Mol Imaging. 2019;46: 2178\u0026ndash;2189.\u003c/li\u003e\n\u003cli\u003eTezuka T, Takahata K, Seki M, Tabuchi H, Momota Y, Shiraiwa M, et al. Evaluation of [18F]PI-2620, a second-generation selective tau tracer, for assessing four-repeat tauopathies. Brain communications. 2021;3. doi:10.1093/braincomms/fcab190\u003c/li\u003e\n\u003cli\u003eYamashita K, Watanabe S, Ishiki K, Miura M, Irino Y, Kubo T, et al. 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Plasma \u0026beta;-amyloid in Alzheimer\u0026rsquo;s disease and vascular disease. Sci Rep. 2016;6: 26801.\u003c/li\u003e\n\u003cli\u003eJanelidze S, Teunissen CE, Zetterberg H, Allu\u0026eacute; JA, Sarasa L, Eichenlaub U, et al. Head-to-Head Comparison of 8 Plasma Amyloid-\u0026beta; 42/40 Assays in Alzheimer Disease. JAMA Neurol. 2021;78: 1375\u0026ndash;1382.\u003c/li\u003e\n\u003cli\u003eTherriault J, Janelidze S, Benedet AL, Ashton NJ, Arranz Mart\u0026iacute;nez J, Gonzalez-Escalante A, et al. Diagnosis of Alzheimer\u0026rsquo;s disease using plasma biomarkers adjusted to clinical probability. Nat Aging. 2024;4: 1529\u0026ndash;1537.\u003c/li\u003e\n\u003cli\u003eOlvera-Rojas M, Sewell KR, Karikari TK, Huang H, Oberlin LE, Zeng X, et al. Influence of medical conditions on the diagnostic accuracy of plasma p-tau217 and p-tau217/A\u0026beta;42. Alzheimers Dement. 2024. doi:10.1002/alz.14430\u003c/li\u003e\n\u003cli\u003eNiimi Y, Janelidze S, Sato K, Tomita N, Tsukamoto T, Kato T, et al. Combining plasma A\u0026beta; and p-tau217 improves detection of brain amyloid in non-demented elderly. Alzheimers Res Ther. 2024;16: 115.\u003c/li\u003e\n\u003cli\u003eJack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer\u0026rsquo;s pathological cascade. Lancet Neurol. 2010;9: 119\u0026ndash;128.\u003c/li\u003e\n\u003cli\u003eAshton NJ, Janelidze S, Mattsson-Carlgren N, Binette AP, Strandberg O, Brum WS, et al. Differential roles of A\u0026beta;42/40, p-tau231 and p-tau217 for Alzheimer\u0026rsquo;s trial selection and disease monitoring. Nat Med. 2022;28: 2555\u0026ndash;2562.\u003c/li\u003e\n\u003cli\u003eBilgel M, An Y, Lang A, Prince J, Ferrucci L, Jedynak B, et al. Trajectories of Alzheimer disease-related cognitive measures in a longitudinal sample. Alzheimers Dement. 2014;10: 735-742.e4.\u003c/li\u003e\n\u003cli\u003eHenry JD, Crawford JR, Phillips LH. Verbal fluency performance in dementia of the Alzheimer\u0026rsquo;s type: a meta-analysis. Neuropsychologia. 2004;42: 1212\u0026ndash;1222.\u003c/li\u003e\n\u003cli\u003eBrand AL, Lawler PE, Bollinger JG, Li Y, Schindler SE, Li M, et al. The performance of plasma amyloid beta measurements in identifying amyloid plaques in Alzheimer\u0026rsquo;s disease: a literature review. Alzheimers Res Ther. 2022;14: 195.\u003c/li\u003e\n\u003cli\u003eRabe C, Bittner T, Jethwa A, Suridjan I, Manuilova E, Friesenhahn M, et al. Clinical performance and robustness evaluation of plasma amyloid-\u0026beta;42/40 prescreening. Alzheimers Dement. 2023;19: 1393\u0026ndash;1402.\u003c/li\u003e\n\u003cli\u003eCullen NC, Janelidze S, Mattsson-Carlgren N, Palmqvist S, Bittner T, Suridjan I, et al. Test-retest variability of plasma biomarkers in Alzheimer\u0026rsquo;s disease and its effects on clinical prediction models. Alzheimers Dement. 2023;19: 797\u0026ndash;806.\u003c/li\u003e\n\u003cli\u003eAmadoru S, Dor\u0026eacute; V, McLean CA, Hinton F, Shepherd CE, Halliday GM, et al. Comparison of amyloid PET measured in Centiloid units with neuropathological findings in Alzheimer\u0026rsquo;s disease. Alzheimers Res Ther. 2020;12: 22.\u003c/li\u003e\n\u003cli\u003eMil\u0026agrave;-Alom\u0026agrave; M, Ashton NJ, Shekari M, Salvad\u0026oacute; G, Ortiz-Romero P, Montoliu-Gaya L, et al. Plasma p-tau231 and p-tau217 as state markers of amyloid-\u0026beta; pathology in preclinical Alzheimer\u0026rsquo;s disease. Nat Med. 2022 [cited 26 Mar 2023]. doi:10.1038/s41591-022-01925-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"alzheimers-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"azrt","sideBox":"Learn more about [Alzheimer's Research and Therapy](http://alzres.biomedcentral.com/)","snPcode":"13195","submissionUrl":"https://submission.nature.com/new-submission/13195/3","title":"Alzheimer's Research \u0026 Therapy","twitterHandle":"@AlzheimersRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease, plasma biomarkers, Aβ42/40, p-tau217, p-tau217/Aβ42, HISCL, Simoa, Centiloid","lastPublishedDoi":"10.21203/rs.3.rs-6239779/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6239779/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePlasma biomarkers offer a promising alternative to amyloid beta (Aβ) PET or cerebrospinal fluid (CSF) biomarkers for diagnosing Alzheimer\u0026rsquo;s disease (AD). This cross-sectional study assessed the utility of multiple plasma biomarkers in a Japanese cohort, including healthy controls (HC), individuals on the AD continuum, and those with non-AD cognitive impairment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eParticipants were classified using Aβ PET imaging and neuropsychological tests into HC, the AD continuum (preclinical [preAD], mild cognitive impairment [AD-MCI], and mild dementia [AD-D]), and non-AD cognitive impairment groups. We conducted ROC analyses to predict Aβ PET status, correlation analyses with Centiloid (CL) values and cognitive scores, and biomarker comparisons across AD stages. Plasma biomarkers assessed included Aβ42/40, phosphorylated tau (p-tau181, p-tau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), individually and in combination. Aβ42/40 was measured via High-Sensitivity Chemiluminescence Enzyme Immunoassay (HISCL), while all other biomarkers were measured using the Single Molecule Array (Simoa) platform.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 69 HC, 13 preAD, 37 AD-MCI, 44 AD-D, and 80 non-AD cognitive impairment participants were analyzed. AUCs for predicting Aβ PET status were 0.931 (Aβ42/40), 0.924 (p-tau217), and 0.944 (p-tau217/Aβ42). In the cognitively normal group, AUCs were 0.968 (Aβ42/40), 0.958 (p-tau217), and 0.979 (p-tau217/Aβ42), while in the cognitively impaired group, they were 0.907 (Aβ42/40), 0.890 (p-tau217), and 0.921 (p-tau217/Aβ42). Among HC and AD continuum participants, CL correlations were 0.75 (Aβ42/40), 0.81 (p-tau217), and 0.83 (p-tau217/Aβ42). All biomarkers correlated strongly with Logical Memory scores. Aβ42/40 levels declined sharply from HC to preAD, transitioning at a CL threshold of 19.3, while the Aβ PET positivity threshold was 32.9. P-tau217 exhibited a linear increase with disease progression.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePlasma biomarkers, Aβ42/40, p-tau217, and particularly their ratio (p-tau217/Aβ42), show strong potential as Aβ PET alternatives for AD diagnosis. HISCL-based plasma Aβ42/40 detects Aβ accumulation earlier (CL\u0026thinsp;=\u0026thinsp;20) than Aβ PET visual reading threshold (CL\u0026thinsp;=\u0026thinsp;32.9), underscoring its utility as an early diagnostic marker. P-tau217 consistently tracks disease progression, reinforcing its value in AD staging. Longitudinal validation of these findings is needed.\u003c/p\u003e","manuscriptTitle":"Plasma Biomarkers for Early Detection and Staging of Alzheimer’s Disease: A Cross-Sectional Study in a Japanese Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 08:24:54","doi":"10.21203/rs.3.rs-6239779/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-03T12:45:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-03T12:32:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T17:37:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-31T13:30:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-31T01:49:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-27T10:09:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31316300645104280596017802741054367005","date":"2025-03-20T15:35:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177759371506288451762137367509059145830","date":"2025-03-20T09:41:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113018861199064290810868116596225192049","date":"2025-03-19T17:34:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325044519457597225931609868595153734898","date":"2025-03-19T08:29:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29767979579155948342877517429122065610","date":"2025-03-18T18:57:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-18T12:54:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T01:42:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T01:40:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Alzheimer's Research \u0026 Therapy","date":"2025-03-16T23:45:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"alzheimers-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"azrt","sideBox":"Learn more about [Alzheimer's Research and Therapy](http://alzres.biomedcentral.com/)","snPcode":"13195","submissionUrl":"https://submission.nature.com/new-submission/13195/3","title":"Alzheimer's Research \u0026 Therapy","twitterHandle":"@AlzheimersRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bad76f97-91cd-456c-9676-d1f3b208549f","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:06:25+00:00","versionOfRecord":{"articleIdentity":"rs-6239779","link":"https://doi.org/10.1186/s13195-025-01778-8","journal":{"identity":"alzheimers-research-and-therapy","isVorOnly":false,"title":"Alzheimer's Research \u0026 Therapy"},"publishedOn":"2025-06-07 15:57:25","publishedOnDateReadable":"June 7th, 2025"},"versionCreatedAt":"2025-03-27 08:24:54","video":"","vorDoi":"10.1186/s13195-025-01778-8","vorDoiUrl":"https://doi.org/10.1186/s13195-025-01778-8","workflowStages":[]},"version":"v1","identity":"rs-6239779","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6239779","identity":"rs-6239779","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}