Longitudinal Change in Blood-Based Biomarkers and the Association With MRI-Measured Neurodegeneration in Cognitively Unimpaired Individuals.

Longitudinal Change in Blood-Based Biomarkers and the Association With MRI-Measured Neurodegeneration in Cognitively Unimpaired Individuals - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Neurology . 2026 Jun 1;106(12):e218119. doi: 10.1212/WNL.0000000000218119 Longitudinal Change in Blood-Based Biomarkers and the Association With MRI-Measured Neurodegeneration in Cognitively Unimpaired Individuals Calvin Trieu 1, 2, 3, ✉ , Ellen Dicks 1, 2, 4 , Diederick de Leeuw 1, 2 , Karlijn DM de Wit 1, 2 , Mardou SSA Van Leeuwenstijn 1, 2 , Lisa-Marie Schlüter 1, 2 , Lynn Boonkamp 3 , Frederik Barkhof 4, 5 , Mara Ten Kate 4 , Elsmarieke van de Giessen 6 , Wiesje M Van Der Flier 2, 7, 8, 9 , Argonde C Van Harten 1, 2 , Charlotte E Teunissen 3 Author information Article notes Copyright and License information 1 Alzheimer Center Amsterdam, Department of Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, the Netherlands; 2 Amsterdam Neuroscience, Neurodegeneration, the Netherlands; 3 Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, Program Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam, the Netherlands; 4 Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, the Netherlands; 5 Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, United Kingdom; 6 Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; 7 Alzheimer Nederland, Amersfoort, the Netherlands; 8 Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, the Netherlands; and 9 Amsterdam Public Health, the Netherlands. ✉ Correspondence Mr. Trieu c.trieu@amsterdamumc.nl The Article Processing Charge was funded by the authors. Submitted and externally peer reviewed. The handling editor was Associate Editor Linda Hershey, MD, PhD, FAAN. ✉ Corresponding author. Received 2025 Nov 4; Accepted 2026 Mar 25; Issue date 2026 Jun 23. Copyright © 2026 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. PMC Copyright notice PMCID: PMC13239427  PMID: 42224636 Abstract Background and Objectives Blood-based biomarkers are reliable indicators of Alzheimer disease (AD)–related pathology in cognitively normal individuals. However, it remains unclear how changes in these biomarkers relate to emerging brain atrophy. We aimed to investigate longitudinal associations between blood-based biomarkers and brain atrophy, and the temporal sequence of these processes. Methods In the prospective observational Subjective Cognitive Impairment Cohort based in Amsterdam, individuals with subjective cognitive decline recruited from a memory clinic underwent repeated blood sampling (484 samples, follow-up 5 ± 3 years) and MRI scans (457 scans, follow-up 5 ± 3 years). We measured blood-based biomarkers (Aβ42/40 ratio [Aβ 42/40 ], phosphorylated tau [pTau217], glial fibrillary acidic protein [GFAP], and neurofilament light [NfL]) using the SIMOA platform. AD-signature brain volumes (5 temporal, 4 parietal, and 2 frontal regions) were determined using the FreeSurfer pipeline. We used linear mixed models to examine associations between baseline or slope of biomarker and brain volume changes. In an exploratory analysis, extrapolated pTau217 trajectories were compared with hippocampal volume trajectories to estimate the temporal gap between these changes. Results A total of 167 individuals were included (49 amyloid-positive and 118 amyloid-negative), with amyloid-positive individuals being older (age: 66 ± 8 years, 57.1% female) than amyloid-negative individuals (61 ± 8 years, 33.9% female). Baseline levels and longitudinal increases in GFAP showed associations with higher rates of atrophy across nearly all AD-signature regions (β range −0.02 to −0.00, 95% CI range −0.04 to −0.00, p FDR < 0.05). Baseline pTau217 was associated with atrophy across several medial temporal regions (β range −0.03 to −0.01, 95% CI range −0.04 to −0.00, p FDR < 0.05), whereas increases in pTau217 were only associated with atrophy primarily in temporal regions (β range −0.02 to −0.01, 95% CI range −0.02 to −0.00), although the latter associations were only trend-level after FDR correction ( p FDR = 0.077). Baseline concentrations, but not increases in NfL, showed associations with temporal regions (β range −0.02 to −0.01, 95% CI range −0.04 to −0.00, p FDR < 0.05). By contrast, baseline Aβ 42/40 showed only associations with hippocampal atrophy (0.01 [0.00–0.03]), but not after FDR correction. Using baseline and (extrapolated) trajectories over time, we estimated that changes in pTau217 preceded hippocampal atrophy by 19.8 (10.7–43.7) years. Discussion Blood-based biomarkers capture distinct aspects of brain atrophy, with pTau217 primarily indicating atrophy in medial temporal regions and GFAP more widespread neurodegeneration. This supports the complementary use of both markers for early identification and monitoring in preventive trials and clinical care. Introduction Alzheimer disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. The pathologic processes of AD, including the accumulation of β-amyloid (Aβ) plaques and tau tangles, neuronal loss, neuroinflammation, and synaptic dysfunction, begin many years before the onset of clinical symptoms. 1-4 This long preclinical phase, during which amyloid pathology starts to accumulate, offers a critical window for early identification of individuals at risk of decline and for intervention. Individuals with subjective cognitive decline (SCD), who report cognitive complaints without objective cognitive impairment, are at increased risk of clinical progression, particularly those who are amyloid positive. 5,6 As such, SCD represents a particularly relevant target population for research into early markers of disease progression and may also serve as a potential recruitment group for future preventive trials. Recent advances in blood-based biomarkers enable detection of AD pathology in vivo and the study of how brain pathology develops over time. 7-9 Blood-based biomarkers, including the Aβ42/40 ratio (Aβ 42/40 ), phosphorylated tau (pTau217), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL), offer a minimally invasive and cost-effective alternative to CSF or PET. Our previous work, together with previous literature, demonstrated that both baseline levels and longitudinal changes in blood-based biomarkers are associated with clinical progression, including cognitive decline and progression from SCD to mild cognitive impairment (MCI) or dementia. 10-12 To date, relatively less is known about how the earliest changes in blood-based biomarkers relate to brain volume changes over time as measured by MRI. Although previous literature has reported associations between blood-based biomarkers and MRI measures, these have been observed primarily in more advanced stages of the disease. 13,14 By contrast, studies in earlier preclinical stages have generally reported inconsistent findings or no associations. 15-18 Moreover, most studies have been cross-sectional and focused on baseline biomarker levels, limiting insight into the temporal dynamics between biomarkers and neurodegeneration. 13,17,19 Investigating whether baseline levels and longitudinal changes in these biomarkers reflect subsequent brain volume changes could clarify their value as surrogate markers of neurodegeneration, supporting their use in early identification, disease monitoring, and stratification in preventive trials or clinical care. In this study, we investigated the association between longitudinal blood-based biomarkers and brain atrophy in individuals with SCD over 5 years of follow-up. Specifically, we examined (1) how both baseline levels and longitudinal changes in blood-based biomarkers are associated with regional brain volume changes over time, (2) the temporal sequence of changes in blood-based biomarkers and measurable brain atrophy, and (3) how pTau217 and hippocampal volume relate to clinical progression. Methods Participants Participants were recruited between 2014 and 2020 from the prospective and longitudinal Subjective Cognitive Impairment Cohort (SCIENCe), a memory-clinic-based cohort at the Alzheimer Center Amsterdam. 20 These participants were primarily referred by general physicians, neurologists, or geriatricians for cognitive assessment. Inclusion required subjective cognitive complaints without objective impairment, determined through neuropsychological assessment and multidisciplinary evaluation, with eligibility restricted to individuals aged 45 years or older. Exclusion criteria included cognitive impairment due to dementia, psychiatric disorders, or other neurologic diseases, as well as substance abuse and language barriers that could interfere with assessment. 20 Each participant underwent a standardized baseline workup, including medical history, physical and neurologic examination, neuropsychological assessment, blood sampling, and either CSF analysis or an amyloid PET scan. CSF analysis was performed as the routine assessment, whereas most of the amyloid PET scans were performed within a research setting. Participants were reassessed annually using a standardized protocol, including medical history, neuropsychological assessment (Mini-Mental State Examination [MMSE], Montreal Cognitive Assessment, and tests across cognitive domains, eMethods), blood sampling (biennial), and diagnostic re-evaluation to determine progression from SCD to MCI, AD dementia, or other types of dementia, such as vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. 20 Progression was defined as MCI/AD dementia determined by a neurologist based on clinical evaluation and neuropsychological testing in accordance with the National Institute on Aging and Alzheimer's Association criteria, 21 with progression time defined as the date of first diagnosis, MCI if it preceded dementia, otherwise dementia. This study included only participants who had completed at least 2 MRI scans and repeated blood samples. The mean follow-up duration for the study involving repeated blood samples was 5.3 ± 2.8 years (median 4.3 years, maximum 15.6 years), MRI scans was 4.6 ± 2.7 years (median 4.0 years, maximum of 13.8 years), and 6.1 ± 3.2 years for clinical follow-up, with an average of 3 blood samples and MRI scans per participant. Over the course of the study, 21 (12.6%) participants progressed from SCD to MCI or dementia. Standard Protocol Approvals, Registrations, and Patient Consents Written informed consent was obtained from all participants before enrollment. The study was approved by the Medical Ethical Committee of the Amsterdam UMC and was in compliance with the ethical standards set by the Helsinki Declaration of 1975. This study was reported following the Strengthening the Reporting of Observational Studies in Epidemiology guideline for cohort studies (eMethods). Amyloid Status Amyloid status was assessed using amyloid PET scans (n = 142) or CSF (n = 25) measurements, with PET results used when both were available. Amyloid PET scans were acquired on Gemini TF PET/CT, Ingenuity TF PET/CT, and Ingenuity PET/MRI (Philips Healthcare, Best, The Netherlands), with the following tracers: [ 18 F]florbetapir (n = 100), [ 18 F]florbetaben (n = 32), [ 18 F]flutemetamol (n = 6), or [ 11 C]Pittsburgh compound-B (n = 4). Each scan was visually rated by a trained nuclear medicine physician and categorized as amyloid-positive (A+) or amyloid-negative (A−), following company-specific guidelines or previously published methods for [ 11 C]Pittsburgh compound-B. 22 A more detailed PET protocol of the SCIENCe cohort has been published elsewhere. 20,23 CSF samples were obtained through lumbar puncture at the L3/L4, L4/L5, or L5/S1 intervertebral space using an atraumatic 25-gauge needle. Samples were collected in polypropylene tubes, centrifuged, aliquoted into 0.5 mL volumes, and stored at −80°C until further analysis. CSF biomarkers were analyzed using either Innotest ELISA (Innogenetics-Fujirebio, Ghent, Belgium) or Elecsys electrochemiluminescence immunoassays (Roche Diagnostics GmbH, Mannheim, Germany). Innotest Aβ 42 was adjusted to correct for drift over the years. 24 Amyloid status was determined as A+ for Innotest Aβ 42 below 813 pg/mL and Elecsys pTau/Aβ 42 ratio above 0.02, while other values were classified as A−. 25 Blood-Based Biomarkers Blood samples were collected at baseline and biennially during the follow-up period. Samples were obtained by venipuncture in EDTA plasma tubes, centrifuged at 1,800 g for 10 minutes at room temperature, and subsequently aliquoted into 0.5 mL volumes in polypropylene tubes (Sarsedt, Germany) for storage at −80°C. Before analysis, all samples were shortly thawed at room temperature and centrifuged at 10,000 g for 10 minutes. A Single Molecule Array (Simoa HDX analyzer) technique was used to analyze the plasma samples, measuring Aβ 1–40 , Aβ 1–42 , GFAP, and NfL with the Simoa Neurology 4-plex E kit (Quanterix, Billerica, MA) and pTau217 with the Simoa pTau217 kit (Janssen Sciences Ireland UC, Cork, Ireland). Analyses were performed in single replicates for the Neurology 4-plex E kit and in duplicates for the pTau217 kit. Measurements were performed according to the manufacturer's guidelines, including a 1:4 automated on-board sample dilution. All longitudinal samples from each participant were quantified within the same analytical run to minimize technical variability and potential batch effects. The intra-assay and interassay coefficients of variation were below 10% for all biomarkers. Biomarker values exceeding 3 SDs from the mean, and suspected to result from measurement error, were excluded from the analyses. In total, 2 measurements were excluded: one for the Aβ 42/40 and the other for NfL. MRI Acquisition, Data Processing, and Analysis T1-weighted MRI scans were acquired at baseline and at multiple, nonstandardized time points throughout the follow-up period. Scanners included Philips Ingenuity (n = 206), GE Discovery MR750 (n = 122), Toshiba Vantage Titan (n = 49), Siemens MAGNETOM Vida (n = 41), GE Signa HDxt (n = 37), and Siemens MAGNETOM Impact Expert (n = 2). Most of the MRI scans were performed on a 3T scanner (99.6%), with only 1 participant scanned at 1.5T. For participants with the MRI scan at multiple field strengths, only the most frequently used field strength was retained in the analyses. Further details on MRI acquisition parameters are provided in the eMethods. MRI scans were processed using the longitudinal processing stream in FreeSurfer version 7.1.1, which creates a within-participant template to which each scan is registered. This method improves the reliability of longitudinal analyses and minimizes bias in the resulting estimates. 26 For each scan, regional gray matter volumes (in mm 3 ) were derived according to the Desikan-Killiany atlas. 27 FreeSurfer segmentation outputs were visually inspected for artifacts using the ENIGMA QC guidelines (C.T. and K.D.M.d.W.). 28,29 If only minor artifacts were detected, the affected region and surrounding areas were excluded from analysis, whereas scans with more significant artifacts were excluded in their entirety. Before analysis, we examined the distribution of scanner models and assessed whether scanner changes influenced outcome measures. Scanner models were evenly distributed between A+ and A− groups (eTable 1). Participants who switched scanner models during follow-up were identified, and their scans checked for unusually large changes in volume. Regions with >0.5 SD change within a 1-year interval were excluded from further analysis. For the analyses, we selected AD-signature regions of interest based on previous literature, including the medial temporal (hippocampal, entorhinal, and parahippocampal), inferior temporal, temporal pole, angular (inferior parietal), precuneus, supramarginal, superior parietal, superior frontal, and caudal middle frontal regions. 30 Statistical Analysis Data analyses were performed in R version 4.4.1, using the “lme4” package for linear mixed models (LMMs) and the “survival” package for Cox regression models. 31,32 Surface plots were visualized in Python version 3.8.18 using the “enigmatoolbox” package. 33 We assessed differences in demographic characteristics using unpaired t tests for continuous variables and χ 2 or Fisher exacts tests for categorical data. Biomarker and gray matter volume values were z -transformed for comparability. pTau217 and NfL had a nonnormal distribution and were therefore log-transformed. All models were adjusted for age, sex, and MRI scanner model, with total intracranial volume included as a covariate in models including volume measures to account for interparticipant variability in head size. MRI and blood-based biomarker measurements during follow-up were paired when acquisition dates were within 1 year of each other because MRI scans were acquired at nonstandardized time points, whereas at baseline, MRI and blood sample collection occurred during the same study visit. Because not all participants had both modalities collected within this time window, 23%–27% of MRI or biomarker measurements could not be paired, resulting in missing data. Missing data were addressed using multiple imputation (“MICE” package with the 2l.glm.norm method), suitable for multilevel data. This approach preserved the integrity of the analyses while maintaining baseline and trend characteristics, as confirmed through sensitivity checks comparing imputed and original data. LMMs with random intercepts and random slopes were used to examine the trajectory of brain volumes over time, with time as a predictor in separate models for each brain region. The same approach was applied to blood-based biomarkers, with separate models for each biomarker. Individual annual biomarker slopes were estimated using LMMs and entered as predictors in subsequent models. To account for slope-estimation uncertainty, we applied a participant-level cluster bootstrap (500 replicates) in which slopes were re-estimated within each replicate and subsequently included in second-stage LMMs assessing associations with longitudinal brain volume change. In all models including biomarker slopes, the baseline value of the corresponding biomarker was included as a covariate to isolate the independent effect of the slope. Subsequently, we assessed the association between amyloid status and regional brain volume changes at baseline and over time, using models including terms for amyloid status, time, and their interaction term, as an initial step to determine whether MRI measures showed longitudinal change related to AD pathology. We applied the same approach to blood-based biomarkers. To investigate the association between blood-based biomarkers and regional brain volume changes, we first used LMMs with time-varying biomarker levels to capture their overall relationship across the study period (eTable 2). We then assessed whether baseline biomarker levels were associated with brain volume change over time using LMMs that included an interaction between baseline values and time. Finally, we examined whether the rate of change in biomarker levels (individual biomarker slopes) was associated with brain volume change, using LMMs with terms for biomarker slope, time, and their interaction. To account for multiple testing across models, false discovery rate (FDR) correction was applied separately for each biomarker across the tested brain regions (11 regions per biomarker). As an exploratory, model-based approach, the temporal gap between blood-based biomarker changes and hippocampal volume changes was estimated by identifying divergence points, defined as the timepoint at which trajectories of biomarker levels or hippocampal volume begin to separate between A+ and A− individuals. By comparing the divergence point of blood-based biomarker levels with that of hippocampal volume, this approach provides an interpretable estimate of how much earlier biomarker abnormalities may emerge relative to the onset of detectable hippocampal atrophy. The divergence point of hippocampal volume was derived directly from the LMMs, whereas for blood-based biomarkers, the points were extrapolated from estimated baselines and longitudinal slopes. Divergence time was calculated as the baseline gap divided by the difference in slopes (crossing time = baseline gap/difference in slopes), marking the point where the 2 modeled trajectories intersect under the assumption of linear progression. To assess the robustness of this assumption, we performed sensitivity analyses using models that included a quadratic time term, which are reported in the eMethods. To quantify uncertainty in these model-based estimates, 95% CIs for divergence times and their temporal differences were derived using a participant-level bootstrap of the full estimation procedure (1,000 bootstrap replicates), with pooling across imputed data sets within each bootstrap replicate. The temporal difference between biomarker and hippocampal divergence points was then used to estimate how many years earlier biomarker changes emerged relative to hippocampal atrophy. Although given the exploratory, model-based nature of this approach, the order of magnitude rather than the exact estimate should be appreciated. As pTau217 showed the strongest longitudinal association with hippocampal atrophy, this biomarker was selected for comparison. As exploratory analyses, we used Cox regression models to compare blood-based biomarkers and MRI measures in relation to clinical progression. Plasma p-tau217 and hippocampal volume were selected for these analyses based on their strong associations with AD-related pathology. We compared 4 models: (1) demographics only (age, sex, baseline MMSE, and education level), (2) demographics + baseline (and slope) of plasma p-tau217, (3) demographics + baseline (and slope) of hippocampal volume, and (4) a combined model including demographics, baseline and slope of plasma p-tau217, and slope of hippocampal volume. Discriminative ability of the models was assessed using the concordance index (c-index), with 95% CIs estimated through bootstrap resampling (100 replicates). Data Availability Anonymized data not published within this article may be made available on request to any qualified investigator. Results Demographics The study included 167 participants, comprising 49 A+ and 118 A− individuals. Participants in the A+ group were older and more frequently female than those in the A− group (65.6 vs 60.8 years, p < 0.001; 57.1% vs 33.9% female, p < 0.01, Table 1 ). In the total group, most temporal and parietal regions showed atrophy over time, including the hippocampal, entorhinal, parahippocampal, inferior temporal, temporal pole, angular, and precuneus regions (β Time −0.02 to −0.07, p < 0.05, eTable 3), whereas frontal regions did not. Table 1. Demographic Characteristics Total (n = 167) A− (n = 118) A+ (n = 49) p Value Age, mean (SD) 62.20 (8.22) 60.78 (8.05) 65.63 (7.64) <0.001 Female, n (%) 68 (40.7) 40 (33.9) 28 (57.1) 0.009 APOE e4 carriership, n (%) 68 (40.7) 33 (28.0) 35 (71.4) <0.001 MMSE, mean (SD) 28.35 (1.49) 28.34 (1.54) 28.37 (1.36) 0.898 Follow-up time, mean (SD) 6.19 (3.08) 6.73 (3.31) 4.88 (1.90) <0.001 Aβ 42/40 , mean (SD) 0.06 (0.01) 0.06 (0.01) 0.05 (0.01) <0.001 pTau217, mean (SD) 0.05 (0.03) 0.04 (0.02) 0.07 (0.04) <0.001 GFAP, mean (SD) 86.24 (46.21) 72.45 (32.81) 119.45 (56.28) <0.001 NfL, mean (SD) 13.27 (7.40) 11.60 (6.45) 17.29 (8.04) <0.001 Hippocampus volume, mean (SD) 7.83 (0.89) 7.87 (0.90) 7.73 (0.89) 0.361 Total intracranial volume, mean (SD) 1524.71 (161.80) 1533.24 (165.16) 1504.17 (153.08) 0.278 Open in a new tab Abbreviations: Aβ 42/40 = amyloid-β 42/40 ratio; GFAP = glial fibrillary acidic protein; IQR = interquartile range; MMSE = Mini-Mental State Examination; NfL = neurofilament light chain; N.S. = not significant; pTau217 = phosphorylated tau 217. This table summarizes demographic, cognitive, biomarker, and neuroimaging characteristics of the study cohort. Values are presented as mean (SD), median (IQR), or number (percentage). p Values were derived from independent t tests, χ 2 tests, or Fisher exact tests, as appropriate. Stratified by amyloid status, there were no baseline differences in any regional brain volume between A+ and A− participants. However, A+ showed greater rate of subsequent atrophy in the hippocampal, entorhinal, parahippocampal, temporal pole, angular, precuneus, and supramarginal volume regions (β Time × Amyloidstatus −0.02 to −0.07, p < 0.05, Table 2 , eFigure 1). Table 2. Associations Between Amyloid Status and Brain Volumes Region Measure A− A+ p Value Hippocampal volume Baseline 0.12 ± 0.87 0.18 ± 0.88 0.685 Longitudinal −0.05 ± 0.01 −0.13 ± 0.02 0.000 Entorhinal volume Baseline −0.28 ± 0.75 −0.02 ± 0.77 0.144 Longitudinal −0.02 ± 0.01 −0.06 ± 0.02 0.001 Parahippocampal volume Baseline −0.85 ± 0.87 −0.76 ± 0.88 0.637 Longitudinal −0.04 ± 0.01 −0.08 ± 0.02 0.003 Inferior temporal volume Baseline −0.02 ± 0.67 0.12 ± 0.68 0.276 Longitudinal −0.03 ± 0.01 −0.05 ± 0.02 0.068 Temporal pole volume Baseline −0.62 ± 0.92 −0.29 ± 0.94 0.058 Longitudinal −0.03 ± 0.01 −0.08 ± 0.02 0.018 Angular volume Baseline −0.34 ± 0.87 −0.18 ± 0.89 0.263 Longitudinal −0.02 ± 0.01 −0.05 ± 0.02 0.022 Precuneus volume Baseline −0.41 ± 1.00 −0.55 ± 1.01 0.339 Longitudinal −0.02 ± 0.01 −0.05 ± 0.01 0.001 Supramarginal volume Baseline −0.11 ± 0.87 −0.02 ± 0.88 0.527 Longitudinal −0.01 ± 0.01 −0.04 ± 0.01 0.009 Superior parietal volume Baseline −0.52 ± 0.89 −0.50 ± 0.90 0.893 Longitudinal −0.01 ± 0.01 −0.03 ± 0.02 0.071 Superior frontal volume Baseline −0.48 ± 0.80 −0.54 ± 0.81 0.648 Longitudinal −0.01 ± 0.01 −0.02 ± 0.01 0.478 Caudal middle frontal volume Baseline 0.46 ± 0.86 0.50 ± 0.87 0.770 Longitudinal −0.01 ± 0.01 −0.02 ± 0.01 0.778 Open in a new tab This table presents baseline and longitudinal associations between amyloid status (A− vs A+) and regional brain volumes. Values are estimated means ± standard errors from linear mixed-effects models, centered around mean age, sex, and total intracranial volume (for volumetric measures), and adjusted for model number. Baseline values represent estimated starting values (A− = intercept; A+ = intercept + amyloid status term). Longitudinal values represent estimated annualized change (A− = time effect; A+ = time effect + time × amyloid status interaction). p Values represent group differences (A+ vs A−). Unlike MRI measures, blood-based biomarkers (β Amyloidstatus (−)0.47 to 1.06, all p < 0.001) did show group differences by amyloid status at baseline, with pTau217 additionally showing longitudinal differences over time (β Time × Amyloidstatus 0.06 ± 0.02, p < 0.05). The trajectories of these biomarkers have been published previously and are included in the Supplement for completeness (eMethods; eTable 4; eFigure 2). 10 Blood-Based Biomarkers and MRI Measures In Figure 1 , blood-based biomarkers demonstrated distinct and region-specific patterns of association with subsequent rates of brain atrophy. Baseline GFAP showed widespread associations, with higher levels associated with higher rates of atrophy in multiple temporal and parietal regions, including the hippocampal, entorhinal, parahippocampal, inferior temporal, temporal pole, angular, precuneus, and supramarginal regions (β range −0.02 to −0.01, 95% CI range −0.04 to −0.00, all p < 0.05 and p FDR < 0.05, Table 3 , Figure 1 , eFigure 3). Baseline NfL showed a similar pattern, with associations in multiple temporal and parietal regions overlapping with GFAP (β range −0.02 to −0.01, 95% CI range −0.04 to −0.00, all p < 0.05), except for the precuneus and supramarginal regions. However, after FDR correction, the associations in the inferior temporal ( p FDR = 0.066) and angular ( p FDR = 0.070) regions no longer remained significant. Baseline pTau217 levels were only associated with rates of atrophy in hippocampal, parahippocampal, angular, precuneus and supramarginal regions (β range −0.03 to −0.01, 95% CI range −0.00 to −0.04, all p < 0.05). After FDR correction, associations in the angular ( p FDR = 0.095), precuneus ( p FDR = 0.095), and supramarginal ( p FDR = 0.066) regions were no longer significant. Baseline Aβ 42/40 was associated solely with atrophy in the hippocampus before FDR correction (β 0.01 [95% CI 0.00–0.03], p < 0.05, p FDR = 0.411), but this association was no longer significant after FDR adjustment. Figure 1. Associations Between Blood-Based Biomarkers and Rate of Brain Atrophy. Open in a new tab This figure shows associations between blood-based biomarkers (Aβ 42/40 , pTau217, GFAP, and NfL) and rate of brain atrophy, split into (A) biomarker baseline levels and (B) biomarker slopes. Values are standardized regression coefficients (β) from linear mixed models (time × biomarker term), adjusted for age, sex, MRI model, and total intracranial volume (for volumetric measures). Blue shading encodes association strength (darker blue indicates a stronger association). Regions are shown in white if the association was not statistically significant based on FDR-corrected p -values, and in grey if they lie outside the AD-signature and were not analyzed. Cortical associations are mapped on the brain surface; the hippocampus (subcortical) is shown separately beneath the cortical maps. (A) Baseline panels represent estimated effects of biomarker baseline levels on the rate of atrophy (time × biomarker baseline interaction). (B) Slope panels represent estimated effects of biomarker slope (baseline-corrected) on the rate of atrophy (time × biomarker slope interaction). Aβ 42/40 = amyloid-β 42/40 ratio; AD = Alzheimer disease; FDR = false discovery rate; GFAP = glial fibrillary acidic protein; NfL = neurofilament light chain; pTau217 = phosphorylated tau 217. Table 3. Associations Between Baseline Blood-Based Biomarkers and Rate of Brain Atrophy Region Aβ42/40 pTau217 GFAP NfL Estimate ± SE p Value FDR q value Estimate ± SE p Value FDR q value Estimate ± SE p Value FDR q value Estimate ± SE p Value FDR q value Hippocampal volume 0.01 (0.00 to 0.03) 0.044 0.411 −0.03 (−0.04 to −0.01) 0.000 0.000 −0.02 (−0.04 to −0.01) 0.004 0.030 −0.02 (−0.03 to −0.01) 0.003 0.033 Entorhinal volume 0.00 (−0.01 to 0.01) 0.872 0.872 −0.01 (−0.02 to 0.00) 0.268 0.421 −0.01 (−0.02 to −0.00) 0.015 0.030 −0.01 (−0.02 to −0.00) 0.007 0.038 Parahippocampal volume −0.01 (−0.02 to 0.01) 0.292 0.803 −0.02 (−0.03 to −0.01) 0.001 0.005 −0.02 (−0.03 to −0.00) 0.009 0.030 −0.02 (−0.03 to −0.00) 0.011 0.040 Inferior temporal volume 0.00 (−0.01 to 0.01) 0.559 0.821 −0.01 (−0.01 to 0.00) 0.170 0.312 −0.01 (−0.02 to −0.00) 0.020 0.030 −0.01 (−0.02 to −0.00) 0.030 0.066 Temporal pole volume 0.00 (−0.02 to 0.02) 0.847 0.872 −0.01 (−0.02 to 0.01) 0.511 0.625 −0.02 (−0.04 to −0.00) 0.016 0.030 −0.02 (−0.04 to −0.00) 0.016 0.044 Angular volume 0.00 (−0.01 to 0.01) 0.597 0.821 −0.01 (−0.02 to −0.00) 0.040 0.095 −0.01 (−0.02 to −0.00) 0.019 0.030 −0.01 (−0.02 to −0.00) 0.038 0.070 Precuneus volume 0.01 (−0.00 to 0.01) 0.112 0.411 −0.01 (−0.02 to −0.00) 0.043 0.095 −0.01 (−0.02 to −0.00) 0.022 0.030 −0.01 (−0.01 to 0.00) 0.064 0.082 Supramarginal volume 0.00 (−0.00 to 0.01) 0.490 0.821 −0.01 (−0.02 to −0.00) 0.018 0.066 −0.01 (−0.02 to −0.00) 0.008 0.030 −0.01 (−0.01 to 0.00) 0.051 0.080 Superior parietal volume 0.01 (−0.00 to 0.02) 0.108 0.411 −0.00 (−0.01 to 0.01) 0.398 0.547 −0.01 (−0.02 to 0.00) 0.063 0.077 −0.01 (−0.02 to 0.00) 0.067 0.082 Superior frontal volume 0.00 (−0.00 to 0.01) 0.368 0.810 0.00 (−0.01 to 0.01) 0.749 0.749 −0.00 (−0.01 to 0.01) 0.622 0.622 −0.00 (−0.01 to 0.01) 0.681 0.744 Caudal middle frontal volume 0.00 (−0.01 to 0.01) 0.675 0.825 0.00 (−0.01 to 0.01) 0.658 0.724 −0.00 (−0.01 to 0.00) 0.447 0.492 −0.00 (−0.01 to 0.01) 0.744 0.744 Open in a new tab Abbreviations: Aβ 42/40 = amyloid-β 42/40 ratio; FDR = false discovery rate; GFAP = glial fibrillary acidic protein; NfL = neurofilament light chain; pTau217 = phosphorylated tau 217. This table shows associations between baseline blood-based biomarkers (Aβ 42/40 , pTau217, GFAP, and NfL) and rate of brain atrophy. Estimates (95% CI) represent estimated effects of biomarker baseline levels on the rate of atrophy (time × biomarker baseline interaction) from linear mixed models, adjusted for age, sex, MRI model, and total intracranial volume. Uncorrected p values represent the significance of the biomarker effect on the rate of atrophy, and corresponding FDR-adjusted p values are also reported. Longitudinally, changes in GFAP also showed the most widespread associations with rate of atrophy ( Table 4 ; Figure 1 ; eFigure 3). Steeper GFAP slopes were associated with higher rates of atrophy in the hippocampal, entorhinal, parahippocampal, inferior temporal, temporal pole, angular, precuneus, supramarginal, and superior parietal regions (β range −0.02 to −0.01, all p < 0.001 and p FDR < 0.001). Steeper pTau217 slopes were associated with the rate of atrophy in the entorhinal, inferior temporal, precuneus, superior parietal regions (β range −0.02 to −0.01, 95% CI range −0.04 to −0.00, p < 0.05), but these associations were reduced to trend-level significance after FDR correction (all p FDR = 0.077). NfL and Aβ 42/40 slopes were not associated with the rate of atrophy in any brain regions. Table 4. Associations Between Slope of Blood-Based Biomarkers and Rate of Brain Atrophy Region Aβ 42/40 pTau217 GFAP NfL Estimate ± SE p Value FDR q value Estimate ± SE p Value FDR q value Estimate ± SE p Value FDR q value Estimate ± SE p Value FDR q value Hippocampal volume −0.07 (−1.09 to 0.70) 0.484 0.840 −0.05 (−0.15 to 0.01) 0.056 0.077 −0.02 (−0.03 to −0.02) <0.001 <0.001 −0.00 (−0.05 to 0.02) 0.188 0.607 Entorhinal volume −0.02 (−0.12 to 0.23) 0.308 0.840 −0.02 (−0.04 to −0.00) 0.044 0.077 −0.02 (−0.02 to −0.01) <0.001 <0.001 −0.02 (−0.03 to 0.02) 0.520 0.780 Parahippocampal volume −0.02 (−0.44 to 0.23) 0.660 0.840 −0.04 (−0.08 to 0.00) 0.052 0.077 −0.02 (−0.02 to −0.02) <0.001 <0.001 0.00 (−0.04 to 0.02) 0.344 0.607 Inferior temporal volume 0.03 (−0.15 to 0.15) 0.912 0.912 −0.01 (−0.03 to −0.00) 0.028 0.077 −0.01 (−0.01 to −0.01) <0.001 <0.001 0.01 (−0.02 to 0.02) 0.208 0.607 Temporal pole volume −0.00 (−0.14 to 0.21) 0.364 0.840 −0.01 (−0.02 to 0.02) 0.148 0.148 −0.02 (−0.02 to −0.02) <0.001 <0.001 0.01 (−0.02 to 0.04) 0.260 0.607 Angular volume −0.09 (−0.16 to 0.14) 0.440 0.840 −0.03 (−0.04 to 0.00) 0.060 0.077 −0.01 (−0.01 to −0.01) <0.001 <0.001 0.00 (−0.01 to 0.01) 0.768 0.900 Precuneus volume 0.03 (−0.22 to 0.22) 0.796 0.853 −0.02 (−0.03 to −0.00) 0.036 0.077 −0.01 (−0.01 to −0.01) <0.001 <0.001 −0.00 (−0.01 to 0.01) 0.300 0.607 Supramarginal volume −0.02 (−0.17 to 0.14) 0.544 0.840 −0.01 (−0.03 to 0.00) 0.064 0.077 −0.01 (−0.01 to −0.01) <0.001 <0.001 0.00 (−0.01 to 0.01) 0.816 0.900 Superior parietal volume −0.02 (−0.28 to 0.21) 0.628 0.840 −0.02 (−0.03 to −0.01) 0.032 0.077 −0.01 (−0.01 to −0.01) <0.001 <0.001 −0.00 (−0.01 to 0.00) 0.364 0.607 Superior frontal volume −0.03 (−0.17 to 0.13) 0.332 0.840 −0.01 (−0.01 to 0.00) 0.072 0.077 −0.00 (−0.00 to −0.00) <0.001 <0.001 −0.00 (−0.01 to 0.00) 0.104 0.607 Caudal middle frontal volume −0.01 (−0.11 to 0.09) 0.396 0.840 −0.00 (−0.01 to 0.00) 0.072 0.077 −0.00 (−0.00 to −0.00) <0.001 <0.001 0.00 (−0.00 to 0.01) 0.848 0.900 Open in a new tab Abbreviations: Aβ 42/40 = amyloid-β 42/40 ratio; FDR = false discovery rate; GFAP = glial fibrillary acidic protein; NfL = neurofilament light chain; pTau217 = phosphorylated tau 217. This table shows associations between blood-based biomarker slopes (Aβ 42/40 , pTau217, GFAP, and NfL) and rate of brain atrophy. Estimates (95% CI) represent estimated effects of biomarker slopes on the rate of atrophy (time × biomarker slope interaction) from linear mixed models, adjusted for age, sex, MRI model, total intracranial volume, and baseline biomarker values. Uncorrected p values represent the significance of the biomarker effect on the rate of atrophy, and corresponding FDR-adjusted p values are also reported. Temporal Sequence of Blood-Based Biomarkers and MRI Measures To estimate the temporal relationship between changes in blood-based biomarker concentrations and brain volume changes, we compared the trajectories of A+ and A− individuals. Although baseline differences between A+ and A− were already present for blood-based biomarker concentrations, no such differences were observed in hippocampal volume or any other AD-signature region ( Table 1 ), suggesting that biomarker changes precede neurodegenerative changes as observed on MRI. To estimate the temporal gap between these processes, we extrapolated the longitudinal trajectories of blood-based biomarkers to the point at which trajectories for the A+ and A− groups began to separate. We estimated that the divergence point for hippocampal volume occurred at approximately 0.8 (95% CI −1.9 to 4.0) years relative to baseline, whereas the divergence point for pTau217 occurred at approximately −19.0 (95% CI −42.7 to −10.7) years relative to baseline ( Figure 2 ). This corresponded to pTau217 diverging approximately 19.8 (95% CI 10.7–43.7) years earlier than hippocampal volume, supporting a temporal sequence in which blood-based biomarker abnormalities precede detectable brain volume changes. As a sensitivity analysis, we additionally evaluated models including a quadratic time term (eTable 5, eFigure 4). Although the quadratic effects were not statistically significant, the resulting temporal estimates differed from those obtained under the linear model, indicating that the estimated timing depends on the assumed trajectory shape. This illustrates the model-dependent nature of the temporal estimates. Figure 2. Temporal Sequence of Blood-Based Biomarkers and Hippocampal Atrophy. Open in a new tab This figure shows modeled trajectories ( z -scores) over time for pTau217 (thin lines) and hippocampal atrophy (thick lines) stratified by amyloid status: A− (blue) and A+ (red). Predicted lines are derived from separate linear mixed models for pTau217 and hippocampus volume, each including terms for time × amyloid status interactions. Baseline values represent the model intercepts (A−) and intercept + amyloid status term (A+), and slopes represent the time effect (A−) and time + time × amyloid-status interaction (A+). Hippocampal trajectories are inverted, so upward trends reflect greater atrophy. Solid lines show the observed time window, and dashed lines indicate extrapolation to the estimated divergence point between A+ and A−. pTau217 = phosphorylated tau 217. Clinical Progression We conducted exploratory analyses of how blood biomarkers and MRI measures jointly relate to clinical progression. For baseline pTau217, each SD increase in concentration was associated with a 2.4-fold (95% CI 1.5–3.8, p = 0.001; Table 5 ) higher risk of progression, with a c-index 0.84 (0.75–0.91) compared with 0.72 (0.59–0.85) for demographics only. Every 0.05 SD per year increase in slope of pTau217 was associated with a 6.6-fold (0.8–51.3, p = 0.067) higher risk of progression. Adding the pTau217 slope to demographics and baseline pTau217 yielded a c-index of 0.88 (0.81–0.94). For hippocampal volume, baseline values were not associated with risk of progression (hazard ratio 1.4 [0.7–2.7], p = 0.308), with a c-index of 0.73 (0.62–0.85). By contrast, a 0.05 SD per year steeper decline in slope of hippocampal volume was associated with a 2.9-fold higher risk (1.4–6.1, p = 0.01), and adding the slope to the demographic model and baseline hippocampal volume yielded a c-index of 0.82 (0.71–0.91). Finally, a combined model adding the hippocampal slope to baseline and the slope of pTau217 yielded a c-index of 0.89 (0.83–0.95). Table 5. Prognostic Performance of Blood-Based Biomarkers and Brain Volumes for the Risk of Progression to MCI or Dementia Biomarker Condition HR (95% CI) C-index (95% CI) Demographics — 0.724 (0.594–0.848) pTau217 Baseline 2.4 (1.5–3.8)* 0.835 (0.753–0.908) Baseline + Slope 1.7 (0.8–3.6) 6.6 (0.8–51.3) 0.879 (0.812–0.940) Hippocampal volume Baseline 1.4 (0.7–2.7) 0.733 (0.617–0.847) Baseline + Slope 1.1 (0.5–2.4) 2.9 (1.4–6.1)* 0.821 (0.714–0.911) pTau217 + Hippocampal volume Baseline pTau217 + Slope pTau217 + Slope hippocampus 1.3 (0.5–3.3) 4.5 (0.4–54.9) 1.9 (0.9–4.1) 0.893 (0.827–0.950) Open in a new tab Abbreviations: HR = hazard ratio; MCI = mild cognitive impairment; MMSE = Mini-Mental State Examination; pTau217 = phosphorylated tau 217. This table shows HRs with 95% CIs from Cox proportional hazards models, examining the associations of baseline levels and longitudinal slopes of each biomarker or MRI measure with risk of progression. Each model included demographics (baseline age, sex, baseline MMSE, and education level), plus the biomarker or MRI measure of interest. Models were adjusted for baseline age, sex, baseline MMSE, and education level, while MRI-based models were additionally adjusted for the scanner model and total intracranial volume (for volumetric measures). For MRI measures, hazard ratios were inverted (reported as 1/HR) so that larger values consistently indicate higher risk of progression. Concordance indices (C-index) with 95% CI are shown to indicate prognostic performance. NE = CI could not be reliably estimated due to the unstable model. * p < 0.05, ** p < 0.001. Discussion This study provides new insights into the temporal relationship between AD-related blood-based biomarkers and brain volume changes in cognitively unimpaired individuals. Both higher baseline levels and steeper longitudinal increases in plasma concentrations of pTau217 and GFAP were associated with steeper rates of brain atrophy, while for NfL, only baseline levels were linked to the rate of atrophy. Changes in pTau217 were estimated to precede brain atrophy by many years, highlighting their potential as early indicators of neurodegeneration in preclinical AD. Associations between (changes in) pTau217, GFAP, and NfL and subsequent brain atrophy suggest that these biomarkers are indicative of future neurodegeneration. Aβ 42/40 was not useful for tracking neurodegeneration, showing only a weak baseline association with hippocampal atrophy consistent with previous literature. 11,19 Potentially, this could also be explained by lower diagnostic performance of plasma Aβ 42/40 measured using the Simoa platform compared with other assay platforms. 34 pTau217 primarily showed associations with atrophy in medial temporal regions rather than more widespread cortical areas, extending previous literature that has linked pTau217 to cortical thinning in AD-signature regions among cognitively unimpaired individuals. 11 By contrast, both baseline levels and longitudinal increases in GFAP were associated with atrophy rates in a broader range of AD-signature regions, particularly in the temporal and parietal lobes. These distinct spatial patterns likely reflect the underlying biology of the markers: pTau217, as an AD-specific marker, may reflect localized neurofibrillary pathology concentrated in the medial temporal lobe, whereas GFAP, a marker of astroglial activation, may capture more diffuse neuroinflammatory processes occurring throughout the brain. By contrast, NfL appeared to reflect existing neurodegeneration rather than predict future neurodegeneration. Previous studies have reported mixed findings for GFAP and NfL, with some showing weak or no associations with brain atrophy measurements. 11,13,19,35,36 The stronger associations observed in our study may reflect differences in study design, including longer follow-up, inclusion of an enriched population of cognitively unimpaired individuals with SCD, and the use of repeated volumetric MRI and biomarker measurements. Our study supports the value of AD-related blood-based biomarkers as reliable indicators of future brain volume changes in individuals with SCD. Although cognitive and brain volume differences by amyloid status were not yet apparent at baseline, A+ participants already exhibited abnormal blood-based biomarker profiles. In particular, our results indicate that abnormalities in pTau217 precede detectable hippocampal atrophy by many years. Although no previous studies have examined the longitudinal relationship between blood-based biomarkers and hippocampal volume, several longitudinal studies using CSF have shown that amyloid-related and tau-related changes occur many years before hippocampal atrophy. 4,37 Although these studies reported a somewhat shorter interval between biomarker abnormalities and hippocampal volume loss than observed here, this difference likely reflects variation in disease stage, biomarker modality, and modeling approach while nonetheless confirming a prolonged temporal separation between amyloid-related pathology and hippocampal atrophy. This temporal gap highlights a considerable preclinical window for preventive intervention in sporadic AD, offering opportunities to preserve brain health and delay clinical symptoms before the onset structural brain changes. Exploratory analyses further indicated that both baseline levels and longitudinal changes in pTau217 were associated with clinical progression to MCI or dementia, showing stronger prognostic value than repeated measures of hippocampal volume. The modest additional benefit gained from repeated MRI measures suggests that blood-based biomarkers may already convey sufficient information about clinical risk at this early stage, with MRI-based measures contributing a less incremental value. However, further validation in larger cohorts will be needed to clarify their comparative predictive value. Taken together, these findings underscore the potential of blood-based biomarkers, particularly pTau217 and GFAP, as accessible tools for early identification, monitoring, and stratification in preclinical AD. Their complementary associations, with pTau217 indicating medial temporal regions and GFAP indicating more widespread neuroinflammatory degeneration, further support their combined use in tracking and potentially targeting early brain changes in AD. Strengths of our study include the careful phenotyping, extended follow-up period, and the inclusion of both a repeated comprehensive panel of blood-based biomarkers and detailed volumetric MRI across AD-signature regions. Despite these strengths, certain limitations should be acknowledged. First, the relatively limited sample size constrained several aspects of the analyses. It precluded stratification by amyloid status and, combined with the modest number of clinical progression events, limited statistical power and evaluation of temporal relationships. As a result, the Cox regression and temporal analyses were necessarily exploratory and should be considered hypothesis-generating. However, the absence of amyloid stratification may be less limiting biologically because amyloid status can change over time and blood-based biomarkers may precede PET abnormalities, suggesting that amyloid-based grouping may not fully capture the underlying disease process. 38 Second, imaging data were not collected at standardized intervals nor acquired on uniform scanners because scans were obtained in the context of routine clinical care and/or different studies. Nonetheless, irregular scan timing was accounted for using LMMs, and potential scanner-related bias was minimized by excluding regions with implausible changes, and longitudinal multiple imputation was used to improve comparability of follow-up between blood-based biomarkers and MRI measures. However, when estimated individual biomarker slopes were subsequently used as predictors of MRI change, these associations reflect concurrent longitudinal patterns derived from overlapping follow-up information rather than a strict temporal sequence, and should therefore not be interpreted causally. Third, estimation of the temporal sequence between blood-based biomarkers and brain volume changes required extrapolation of biomarker trajectories under the assumption of linear progression over time because direct observation of changes occurring decades before baseline is rarely feasible in longitudinal cohorts. Although biomarker change may not follow a perfectly linear pattern across the entire disease continuum, visual inspection, model diagnostics, and sensitivity analyses indicated that within our follow-up window, trajectories were approximately linear, supporting the validity of this assumption. Nevertheless, future research with longer follow-up and more frequent sampling across the disease spectrum is needed to better characterize potential nonlinear dynamics and validate these temporal estimates. Fourth, as our study population consisted of individuals with SCD referred to a tertiary memory clinic, the findings may not be fully generalizable to the broader population. Nonetheless, this group is highly relevant for investigating preclinical stages of AD because these are precisely the individuals who present to memory clinics with subjective complaints, for whom preventive treatments may be most appropriate. Moreover, this clinical population is likely somewhat enriched for underlying AD pathology, which may increase the ability to detect sufficient change over time to meaningfully characterize disease trajectories. In conclusion, our findings show that changes in blood-based biomarkers capture distinct aspects of neurodegeneration in cognitively unimpaired individuals: pTau217 primarily indicating atrophy in the medial temporal regions and GFAP indicating more widespread brain atrophy. Changes in blood-based biomarkers occur early in A+ individuals, long before brain volume loss becomes apparent. This underscores their complementary value for early identification and monitoring in preventive trials and clinical care, particularly during the preclinical stage when intervention may be most effective. Glossary Aβ β-amyloid Aβ 42/40 Aβ42/40 ratio AD Alzheimer disease c-index concordance index FDR false discovery rate GFAP glial fibrillary acidic protein LMM linear mixed model MCI mild cognitive impairment MMSE Mini-Mental State Examination NfL neurofilament light pTau217 phosphorylated tau SCD subjective cognitive decline Author Contributions C. Trieu: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. E. Dicks: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. D. de Leeuw: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data. K.D.M. de Wit: drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data. M.S.S.A. Van Leeuwenstijn: major role in the acquisition of data. L.-M. Schlüter: major role in the acquisition of data. L. Boonkamp: major role in the acquisition of data. F. Barkhof: drafting/revision of the manuscript for content, including medical writing for content. M. Ten Kate: drafting/revision of the manuscript for content, including medical writing for content. E. van de Giessen: major role in the acquisition of data. W.M. Van Der Flier: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. A.C. Van Harten: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design; analysis or interpretation of data. C.E. Teunissen: drafting/revision of the manuscript for content, including medical writing for content; major role in the acquisition of data; study concept or design. Study Funding Research of Alzheimer Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting Steun Alzheimercentrum Amsterdam. Argonde van Harten is the recipient of funding from Stichting Alzheimer Nederland en ZonMW. The SCIENCe project receives funding from Stichting Dioraphte, Stichting Edwin Bouw Fonds, Stichting Pieter Houbolt Fonds, and the Noaber Foundation. Disclosure C. Trieu reports no disclosures relevant to the manuscript. E. Dicks is supported by the Race Against Dementia charity ( raceagainstdementia.com ); and consults and conducts analyses for Therini Bio, with all funding paid to Amsterdam UMC. D. de Leeuw, K.D.M. de Wit, M.M.S.S.A. van Leeuwenstijn, L. Schlüter, and L. Boonkamp report no disclosures relevant to the manuscript. F. Barkhof is supported by the NIHR Biomedical Research Centre at UCLH; serves on steering committees or data safety monitoring boards for Biogen, Merck, Eisai, and Prothena; serves on advisory boards for Combinostics, Scottish Brain Sciences, IXICO, and Alzheimer Europe; and has consulted for Roche, Celltrion, Merck, and Bracco; has research agreements with ADDI, Merck, Biogen, GE Healthcare, Icometrix, and Roche; and is a cofounder and shareholder of Queen Square Analytics Ltd. M. ten Kate reports no disclosures relevant to the manuscript. E. van de Giessen has received research support from NWO, ZonMw, Hersenstichting, Alzheimer Nederland, Health∼Holland, and KWF; has performed contract research for Heuron Inc., AC Immune, and Roche; and has a consultancy agreement with IXICO for reading PET scans. All support was paid to the institution. W.M. van der Flier is Executive Director at Alzheimer Nederland, Amersfoort, the Netherlands. Research programs of W.M. van der Flier have been funded by ZonMW, NWO, EU-JPND, EU-IHI, Alzheimer Nederland, Hersenstichting, CardioVasculair Onderzoek Nederland, Health∼Holland, Topsector Life Sciences & Health, Stichting Dioraphte, Noaber Foundation, Pieter Houbolt Fonds, Gieskes-Strijbis Fonds, Stichting Equilibrio, Edwin Bouw Fonds, Pasman Stichting, Philips, Biogen MA Inc., Novartis-NL, Life-MI, AVID, Roche BV, Eli Lilly-NL, Fujifilm, Eisai, and Combinostics. Additionally, W.M. van der Flier is a recipient of ABOARD, a public-private partnership receiving funding from ZonMW (73305095007) and Health∼Holland, Topsector Life Sciences & Health (PPP allowance LSHM20106); has been an invited speaker at Biogen MA Inc., Danone, Eisai, WebMD Neurology (Medscape), Novo Nordisk, Springer Healthcare, and European Brain Council; has served as a consultant to Oxford Health Policy Forum CIC, Roche, Biogen MA Inc., Eisai, Eli Lilly, Owkin France, and Nationale Nederlanden Ventures; has participated in advisory boards of Biogen MA Inc., Roche, and Eli Lilly; and has been a member of the steering committee of the phase 3 EVOKE/EVOKE+ studies (Novo Nordisk) and the phase 3 Trontinemab study (Roche). All funding has been paid to Amsterdam UMC. W.M. van der Flier was an associate editor of Alzheimer Research & Therapy in 2020–2021 and of Brain in 2021–2025; is a Chair of the Scientific Leadership Group of InRAD and a member of the Supervisory Board (Raad van Toezicht) of Trimbos Instituut. A. van Harten was part of the educational advisory board of Eli Lilly; and has been an invited speaker for WebMD Neurology (Medscape). Research programs of A. van Harten have been supported by Stichting Alzheimer Nederland, ZonMW, the Alzheimer Drug Discovery Foundation, and Roche Diagnostics. Research of C.E. Teunissen is supported by the European Commission (Marie Curie International Training Network, grant agreements 860197 [MIRIADE] and 101119596 [TAME]); Innovative Medicines Initiative 3 TR (Horizon 2020, grant 831434); EPND (IMI 2 Joint Undertaking [JU], grant 101034344); JPND (bPRIDE, CCAD); the European Partnership on Metrology, co-financed from the European Union's Horizon Europe Research and Innovation Programme and by the Participating States (22HLT07 NEuroBioStand); Horizon Europe (PREDICTFTD, 101156175); and the CANTATE project funded by the Alzheimer Drug Discovery Foundation, Alzheimer's Association, Michael J. Fox Foundation, Health Holland, the Dutch Research Council (ZonMW), Alzheimer Drug Discovery Foundation, The Selfridges Group Foundation, and Alzheimer Netherlands. C.E. Teunissen is a recipient of ABOARD, a public-private partnership receiving funding from ZonMW (73305095007) and Health∼Holland, Topsector Life Sciences & Health (PPP allowance LSHM20106), and of TAP-dementia, a ZonMW-funded project (10510032120003) within the Dutch National Dementia Strategy; has research contracts with Acumen, ADx Neurosciences, AC-Immune, Alamar, Aribio, Axon Neurosciences, Beckman-Coulter, BioConnect, Bioorchestra, Brainstorm Therapeutics, C2N Diagnostics, Celgene, Cognition Therapeutics, EIP Pharma, Eisai, Eli Lilly, Fujirebio, Instant Nano Biosensors, Merck, Muna, Nitrase Therapeutics, Novo Nordisk, Olink, PeopleBio, Quanterix, Roche, Sysmex, Toyama, Vaccinex, and Vivoryon; serves as Editor-in-Chief of Alzheimer Research and Therapy and is on the editorial boards of Molecular Neurodegeneration , Alzheimer's & Dementia , Neurology ® Neuroimmunology & Neuroinflammation , and Medidact Neurologie (Springer); is a committee member for guideline development for Cognitive Disturbances and Acute Neurology in the Netherlands; and has consultancy and/or speaker contracts with Aribio, Biogen, Beckman-Coulter, Cognition Therapeutics, Danaher, Eisai, Eli Lilly, Janssen, Merck, Neurogen Biomarking, Nordic Biosciences, Novo Nordisk, Novartis, Olink, Quanterix, Roche, Sanofi, and Veravas. 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