Biomarker-integrated prognostic stagings for Alzheimer’s Disease

Domain: neuroscience
Published: 2026-02-02
Editorial outcome: accept

Abstract
Accurately predicting disease progression remains a major challenge in Alzheimer’s disease (AD). Here we show that a biomarker-integrated prognostic staging system can stratify progression risk across the disease course by jointly incorporating cognitive status, established risk factors, plasma biomarkers, and neuroimaging measures. In the K-ROAD cohort (N = 1,263), the dominant prognostic contributors varied by clinical context—GFAP in cognitively unimpaired individuals, hippocampal volume in mild cognitive impairment, and age in dementia—while plasma phosphorylated tau-217 provided consistent secondary prognostic information across stages. Outcome-specific staging captured clinically meaningful gradients of progression risk and informed construction of a unified six-stage framework (Stage 0–IVB) with distinct inflection points of accelerated decline. External validation in the ADNI cohort (N = 290) demonstrated consistent patterns of worsening prognosis, particularly in early and intermediate stages. This system provides a clinically interpretable approach to risk stratification and may serve as an exploratory framework for biomarker-integrated prognostic stratification in AD.

Introduction
Alzheimer’s disease (AD) has a multifactorial etiology, with advanced age as the strongest risk factor, while genetic predisposition (apolipoprotein [APOE] ε4 carrier status) and female sex also significantly increase susceptibility1,2. These risk factors may influence not only the likelihood of developing AD but also the rate of clinical progression3,4. Clinically, AD is characterized along a continuum of cognitive stages ranging from cognitively unimpaired (CU) to mild cognitive impairment (MCI) and ultimately dementia5. Importantly, the rate of progression along this continuum can vary according to the specific cognitive status6 and underlying risk profile7–12.

Neuropathologically, AD features amyloid-β (Aβ) plaques, tau tangles, and neurodegeneration such as hippocampal atrophy13. These define the A/T/N (amyloid/tau/neurodegeneration) biomarker framework, but the cost and invasiveness of these methods drive the need for scalable plasma biomarkers14. To overcome these limitations, substantial efforts have been devoted to developing blood-based biomarkers that can capture A/T/N categories15–17. Core plasma biomarkers include Aβ42/40 ratio and phosphorylated tau species such as pTau181, pTau217, and pTau231, while non-specific markers—such as glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL)—reflect astrocytic activation/inflammation and axonal injury/neurodegeneration, respectively14. These plasma biomarkers have shown high concordance with their PET or MRI counterparts and have demonstrated utility not only for diagnosing AD but also for predicting prognosis, including rates of cognitive decline and progression across the disease continuum18–20. Among them, pTau217 stands out for its exceptional performance in detecting both amyloid and tau pathology18,21,22. In this study, preclinical AD was operationally defined as Aβ+ CU individuals, consistent with current secondary prevention trials23. Plasma pTau217 was used as an indirect correlate of tau pathology rather than a direct measurement18; therefore, the proposed framework does not constitute a biological staging system based on combined amyloid and tau positivity.

To date, most studies have focused on identifying prognostic factors within individual cognitive stages of AD. However, as clinical trials and therapeutic options continue to advance, there is a growing need for a unified prognostic framework to provide stage-consistent and biologically informed risk stratification across the disease continuum24,25. Such a system would enable more consistent communication about prognosis, harmonized interpretation of biomarker profiles, and standardized comparison of disease trajectories across studies26.

In this work, we develop and externally validate a prognostic staging system for AD, integrating traditional cognitive status with risk factors and multiple plasma and imaging markers to stratify dementia progression risk. To achieve this, we first identify prognostic subgroups using survival prediction modeling within each cognitive status, then develop outcome-specific stages by merging similar-risk subgroups for each of dementia progression outcomes, and finally integrate them into a comprehensive staging framework across the Alzheimer’s spectrum.

Results
Demographics of study participants
The study included 1263 participants from the Korea-Registries to Overcome dementia and Accelerate Dementia research (K-ROAD) cohort, comprising 224 CU individuals, 779 with MCI, and 260 with dementia. As detailed in Table 1, mean ± standard deviation (SD) of age was 71.8 ± 8.1 years, with female participants constituting 62.5%. Aβ PET positivity demonstrated incremental elevation across initial cognitive status (36.2%, 59.8%, and 84.2%, respectively). Longitudinal CDR-SB scores were obtained with a median of 2 assessments per participant (interquartile range [IQR] 2–3) over a median follow-up duration of 2.1 years (95% confidence interval [CI] 2.0–2.2). Clinically meaningful decline was defined using clinical dementia rating sum of boxes (CDR-SB) thresholds: ≥3 for very mild dementia, ≥4.5 for mild dementia, and ≥9.5 for moderate dementia.

Phase 1. Identifying prognostic risk subgroups through survival prediction modeling
Participants were classified into prognostic subgroups within each baseline cognitive status (CU, MCI, and dementia), with adjacent categories merged based on clinical and statistical validation (Fig. 1a). This resulted in 2 subgroups for CU, 3 for MCI, and 2 for dementia, totaling 7 refined subgroups (Fig. 2). In CU, participants were grouped by GFAP and pTau217: C1 (GFAP ≤ 195 pg/ml and pTau217 ≤ 0.62 pg/ml) and C2 (GFAP ≤ 195 pg/ml and pTau217 > 0.62 pg/ml or GFAP > 195 pg/ml) (Fig. 2a). In MCI, hippocampal volume (HV) and pTau217 were the main discriminators: M1 (HV > 2815 mm3 and pTau217 ≤ 0.68 pg/ml), M2 (HV > 2815 mm3 with pTau217 > 0.68 pg/ml or HV ≤ 2815 mm3 with GFAP ≤ 122 pg/ml), and M3 (HV ≤ 2815 mm3 with GFAP > 122 pg/ml) (Fig. 2b). In dementia, subgroups were defined by age and pTau217: D1 (> 60 years with pTau217 ≤ 1.1 pg/ml) and D2 (> 60 years with pTau217 > 1.1 pg/ml or ≤ 60 years regardless of pTau217) (Fig. 2c). Adjusted survival curves showed significant differences in progression risk across subgroups for CU (P = 0.011), MCI (P < 0.001), and dementia (P < 0.001), with incidence rates and 3-year cumulative incidences supporting discriminative validity (Supplementary Table 1).

Phase 2. Developing a unified prognostic staging across the Alzheimer’s disease continuum
Prognostic subgroups from Phase 1 were used to generate outcome-specific stages for each of three progression outcomes (Fig. 1a): very mild dementia (N = 923), mild dementia (N = 1120), and moderate dementia (N = 1263). These outcome-specific stages were integrated into a six-stage unified prognostic system (Stage 0–IVB) reflecting increasing dementia severity (Fig. 3). Detailed procedures to develop a unified prognostic staging system is presented in Supplementary Method 3.

Adjusted survival curves confirmed effective separation between stages (Fig. 4a), and pairwise comparisons demonstrated significant differences in progression risk between all adjacent stages (all P < 0.05) (Fig. 4b). Notably, early to intermediate stages (Stages 0–III) were primarily distinguished by very mild and mild dementia outcomes, whereas advanced stages (Stage IVA–IVB) were driven by moderate dementia outcomes. Higher stages were consistently associated with increasing CDR-SB and decreasing MMSE scores (both P < 0.001) (Fig. 4c). Marked inflection points in incidence rates and 3-year cumulative incidence were observed at Stage 0→I, Stage I→II, Stage II→III, and Stage III→IVA (Table 2), highlighting clinically meaningful thresholds for prognosis and intervention. For clarity, we emphasize these key transitions, particularly between mid-level stages, as they represent sharp increases in progression risk and may serve as optimal points for clinical decision-making.

External validation of the unified staging system using the ADNI cohort
For external validation, we utilized an independent cohort from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), comprising 290 participants (160 CU, 118 MCI, and 12 dementia) with a median follow-up of 5.5 years (95% CI 5.1–6.0 years) (Table 1). Adjusted survival curves showed consistent patterns of worsening prognosis across stages for very mild and mild dementia outcomes, although moderate dementia differentiation was less distinct due to limited event occurrences in lower stages (Fig. 5a, b). Also, stages III, IVA, and IVB were excluded from analysis due to small sample sizes (N ≤ 10 each). Cognitive trajectories indicated progressive worsening, with increasing CDR-SB and decreasing MMSE scores at higher stages, aligning with severity escalation (Fig. 5c). Similar patterns were confirmed by 3-year cumulative incidence and incidence rates (Supplementary Table 2).

Methods
For the K-ROAD cohort, the study was approved by the institutional review board of Samsung Medical Center (No. 2021-02-135). All participants provided informed consent, and the study was conducted in accordance with the Declaration of Helsinki. The ADNI study was also approved by the institutional review boards of all participating sites, and written informed consent was obtained from all participants. All data were handled in accordance with relevant data protection and privacy regulations.

Participants selection
We included 1416 participants with CU, MCI and dementia from K-ROAD project, all of whom had available data on baseline demographics including comorbidities, plasma and imaging markers, and longitudinal CDR-SB assessments. The K-ROAD project is a nationwide initiative involving 25 university-affiliated hospitals across South Korea46. Recruitment was conducted both through memory disorder clinics and government-commissioned community dementia prevention centers, resulting in a hybrid cohort. However, we emphasize that this hybrid structure does not make the cohort representative of either the general population or a typical memory clinic population, as participants were selectively enrolled based on research-appropriate clinical evaluations and biomarker availability. Inclusion and exclusion criteria for this study have been described in detail elsewhere18. In summary, CU participants met the following conditions: (1) no major medical or psychiatric illness that could affect cognitive function, and (2) no objective cognitive impairment in any cognitive domain47,48. MCI participants met: (1) subjective cognitive complaints reported by the participants or caregiver; (2) objective cognitive impairment in one or more cognitive domains (defined as performance below −1.0 SD in memory and/or −1.5 SD in other domains, based on age- and education-adjusted norms); and (3) preserved instrumental activities of daily living49. Dementia participants fulfilled the National Institute on Aging-Alzheimer’s Association (NIA-AA) core clinical criteria for probable AD dementia50. Notably, baseline cognitive classifications (CU, MCI, and dementia) were determined independently of both CDR-SB and biomarker data (e.g., Aβ PET or plasma biomarkers), based solely on standard diagnostic procedures including neuropsychological testing, structured interviews, and functional assessments. As a result, each group may include individuals with or without biomarker positivity. The comprehensive participant selection flow is detailed in Supplementary Fig. 1.

Plasma biomarker measurements
Plasma Aβ40, Aβ42, GFAP, and NfL concentrations were measured using the commercial Neurology 4-Plex E kit (Quanterix, Billerica, MA, USA). Plasma pTau181 and pTau231 concentrations were measured using in-house Simoa assays developed at the University of Gothenburg, and pTau217 concentration was measured using the commercial ALZpath pTau217 assay kit. All samples were analyzed in a single run with one batch of reagents, and the intra-assay coefficient of variation was below 10%. All measurements were performed by analysts blinded to clinical data.

Brain MRI and hippocampal volume measurements
Three-dimensional T1-weighted turbo field echo imaging was performed for all participants, with a sagittal slice thickness of 1.0 mm and 50% overlap. As previously described, hippocampal volume was quantified using an automated segmentation method that combines a graph cut algorithm with atlas-based segmentation and morphological opening51.

Aβ PET acquisition and quantification
Aβ PET scans were performed using either 18F-Florbetaben (FBB) or 18F-Flutemetamol (FMM), following each manufacturer’s standardized imaging protocols. Aβ PET binding was quantified using the regional direct comparison centiloid (rdcCL) method, with the whole cerebellum as a reference region52. This method allows harmonization of FBB and FMM tracers without requiring 11C-labeled Pittsburgh compound B images. Aβ PET positivity was defined using a global MRI-based rdcCL threshold of 25.5, derived via Gaussian mixture modeling39. All imaging data were processed at the Samsung Medical Center laboratory, which served as the core center. The median time interval between plasma sampling and Aβ PET imaging was 4 days (IQR 0–69 days).

Physical comorbidities
Information on vascular risk factors, including hypertension and diabetes mellitus, was obtained from self-reported medical history or from records of current use of antihypertensive or antidiabetic medications.

Longitudinal cognitive assessments
The CDR-SB score is a measure of cognition and function, obtained by interviewing both patient and care partner. Its longitudinal assessments (i.e., CDR-SB profile) are used to track dementia progression over time. In this study, baseline CDR-SB was defined as the first assessment conducted within ±1 year of either blood sampling or Aβ PET imaging. All participants had at least two CDR-SB assessments with a minimum interval of 3 months between visits.

Three survival outcomes of dementia progression were defined using CDR-SB cutoffs of ≥ 3.0 (very mild dementia), ≥ 4.5 (mild dementia), and ≥ 9.5 (moderate dementia), supported by both prior literature53–55 and validation within our cohort. Staging based on CDR-SB cutoffs of 0.5, 4.5, and 9.5 showed high concordance with global CDR scores (Cohen’s κ = 0.923 [95% CI, 0.910–0.936]; Supplementary Fig. 2). To refine early-stage classification, we compared individuals with CDR-SB scores of 0.5–2.5 versus 3.0–4.0. The two groups showed differences in baseline MMSE, plasma biomarker levels, and Aβ PET positivity (Supplementary Fig. 3a). They also exhibited distinct longitudinal MMSE trajectories with least squares mean plot using generalized estimating equation model (Supplementary Fig. 3b), supporting the use of 3.0 as the cutoff for very mild dementia.

For each dementia progression outcome, the time-to-progression was defined as the interval from the baseline date to the first visit when CDR-SB exceeded the corresponding cutoff. Participants with CDR-SB above the cutoff at the baseline were excluded from the analysis of that outcome.

Development process of a unified prognostic staging system
Phase 1. Identifying prognostic risk subgroups through survival prediction modeling. In Phase 1, the entire cohort was divided into three sub-cohorts by initial cognitive status (CU, MCI, and dementia) (Fig. 1a). Stratification within each sub-cohort was conducted independently by developing a survival prediction model for the corresponding dementia progression outcome: very mild dementia for CU, mild dementia for MCI, and moderate dementia for dementia. Candidate prognostic features included risk factors such as age, sex, education years, APOE ε4 carrier status, the presence of hypertension and diabetes mellitus, plasma biomarkers such as Aβ42/40 ratio, pTau181, pTau217, pTau231, GFAP, and NfL, and imaging markers including Aβ PET positivity and MRI hippocampal volume.

Random survival forest (RSF) was utilized to identify prognostically similar subgroups within each cognitive status based on survival outcomes of dementia progression56 (Fig. 1b). Modifications on RSF were made to select optimal prognostic features with their robust cutoff points. Firstly, the tree structure was restricted to a maximum depth of one (i.e., performing only a single split of each parent node). An optimal cutoff point was determined to enable bifurcation based on a numeric feature. For each node, we generated 1000 bootstrap samples to create 1000 single-split trees, each of which was represented by a best-splitting feature with its optimal cutoff point. The optimal prognostic feature was selected as the most frequently chosen feature across all single-split trees. When the selected feature was numeric, we employed weighted averaging of all identified cutoff points to determine the robust cutoff point. Among all unique values of the identified cutoff points, we chose the 5 or 10 most frequently identified values and averaged them. Following binary partitioning of a parent node, this algorithmic process was applied recursively to each resultant child node. The stopping rule for tree expansion was established by limiting the numbers of events and participants within a candidate parent node, so that a node cannot be split if it has less than the specified numbers. The cumulative hazard function served as the risk score of RSF56, and log-rank splitting57,58 or global non-quantile Brier score splitting59 were employed for splitting rule. Detailed process was described in Supplementary Method 2, and modified RSF was applied only in Phase I.

Phase 2. Developing a unified prognostic staging across the Alzheimer’s disease continuum. The second phase employed a three-step process to develop a unified prognostic staging system (Fig. 1a). Initially, outcome-specific staging was developed by merging prognostic risk subgroups with similar risk of each dementia progression based on clinical evaluation: visually overlapped adjusted survival curves with non-significant differences in pairwise comparison, and similar incidence metrics. Prognostic risk subgroups remained separate if their curves were visually distinct and incidence metrics were notably differed, even without statistical significance. In the second step, we integrated these outcome-specific categorizations into a unified prognostic staging system encompassing dementia progression and cognitive status information. Specifically, early stages were derived by focusing on the very mild dementia-specific categorization; intermediate stages were formed by considering the mild dementia-specific categorization; and advanced stages were created based on the moderate dementia-specific categorization. In the final step, the recombined stages were labeled from stage 0 to stage IV, with each progressive stage representing increasing disease severity. Adjacent stages with overlapping 95% CI for three-year cumulative incidence or incidence rates were assigned to the same primary stage, with further refinement into subcategories (A or B) within primary stages. Supplementary Method 3 illustrates the detailed process for developing the unified prognostic staging system.

The unified prognostic stages were clinically validated by comparing survival curves for dementia progression outcomes, and longitudinal cognitive trajectories for CDR-SB and MMSE with adjustment for age, sex, education, and apolipoprotein E (APOE) ε4 status.

External validation with the ADNI dataset
The unified staging system underwent external validation using ADNI dataset, with detailed procedures described in the Supplementary Method 1. A total of 290 ADNI participants with available plasma pTau217, GFAP, hippocampal volume on MRI, and longitudinal CDR-SB data were included to match the derivation criteria and minimize selection bias. After applying the proposed staging criteria to classify ADNI participants, we assessed whether three-year cumulative incidence and incidence rates demonstrated clear differentiation between adjacent stages in this independent cohort. Similarly, adjusted survival curves were generated and pairwise comparisons using multivariable Cox proportional hazard regression models were conducted to evaluate stage differentiation. Finally, the cognitive trajectory according to follow-up duration was also assessed whether the magnitude and progression rate of cognitive impairment differed between stages in this validation cohort, thereby confirming the generalizability of the staging system.

Statistical methods
Descriptive statistics were calculated for each cohort and proposed staging category, with continuous variables presented as mean (SD) and categorical variables as frequencies (proportion). Median follow-up duration with corresponding 95% CIs was determined using the reverse Kaplan-Meier method60.

All proposed subgroups, categories, and the unified staging system were evaluated through multiple measures. Incidence rates of dementia per 100 person-years with exact Poisson 95% CIs were calculated61, along with three-year cumulative incidence of dementia with 95% CIs (derived as one minus the Kaplan-Meier estimated survival probability at 3 years)62. We assessed whether incidence rates and 3-year cumulative incidence demonstrated sufficient discrimination between adjacent stages. Adjusted survival curves were drawn using inverse probability weights to evaluate potential overlap in dementia progression between stages63. These adjusted survival curves for all groups were compared using a log-rank test corrected for weighting to check whether all proposed categories have the same curves about probability of non-dementia across follow-up time64. Additionally, all pairwise comparisons were conducted using multivariable Cox proportional hazards regression models. The P values for all pairwise comparisons were adjusted using the Benjamini–Hochberg procedure to account for multiplicity, thereby controlling the false discovery rate65. Firth’s bias correction was adopted when some groups or categories contain very small number of events66. The longitudinal trajectories were estimated using least squares mean plot derived from generalized estimating equations67,68. Age, sex, the period of education, and APOE ε4 carrier status were considered as the covariates for adjustment.

Statistical significance was defined as two-sided P values less than 0.05. All statistical analyses and RSF modeling were conducted with SAS version 9.4 (SAS Institute, Cary, NC, USA) and R version 4.4.1 (The R Foundation, www.R-project.org).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have been very responsive to my concerns regarding the manuscript. In general, they have responded and qualified many of their comments throughout the manuscript to accommodate some of my concerns. However, the primary concern regarding the complexity of the analyses and their descriptions in the manuscript remain a challenge.\n1. With respect to my first comment, the authors have noted that this is a proof-of-concept prognostics staging framework and, as such, is not ready for clinical application. I think that is laudable but still embodies a great deal of the complexity.\n2. They clarify that they did not use the CDR-SB scores in their staging scheme and, in fact, that they are independent of the other data. This is satisfactory.\n3. They have simplified their description of the key transitions between mid-level stages and the increases in progression risk. They acknowledge the complexity that this introduced.\n4. They have noted that the ADNI sample was quite small and that the conclusions regarding this would be quite speculative. They explained the subselection of the ADNI participants with regard to having relevant biomarkers, and that is reasonable.\n5. They have acknowledged that both the K-ROAD and the ADNI samples are quite selective. However, they note that the Korean sample, while ascertained in a memory clinic, does reflect somewhat a community population due to the nature of the clinic structure in Korea. That softens some of this concern, but not totally.\n6. The labeling issue of CU, MCI and DAT was explained. I think it would be preferable to just characterize the DAT group as dementia. A reader will be confused as to whether or not DAT represents dementia with biomarker confirmation or not. The authors do state this in the Methods section, but just clarifying the term and using “dementia” would be preferable.\n7. The authors have been very responsive to my concerns, and I appreciate their diligence.", "reviewer_3": "Overall, I am not really satisfied with the authors' responses. Most of their responses do not seek to understand the substance of my criticism and merely reiterate their previous position. I would like to emphasize that my criticism was based on the assumption that the authors shared the view that it is more relevant to produce a clinically and scientifically significant article than to publish anything in a leading journal.\n1. Usefulness of the paper to personalise the delivery of anti-amyloid monoclonal antibodies. The authors reply that to address this point they conducted additional analyses incorporating major physical comorbidities (e.g., hypertension, diabetes), other plasma biomarkers (Aβ42/40 ratio, pTau181, pTau231, GFAP, NfL), and hippocampal volume on MRI. Actually, to address this point they should have suppressed the claim that this staging scheme can help personalise the delivery of anti-amyloid monoclonal antibodies. The core issue here is that the K-ROAD cohort is not suitable to this aim.\n2. Secondary prevention in CU persons. Again, the answer that additional predictors were incorporated fails to address the issue. The authors’ answer does not realise that secondary prevention in CU persons requires assessment, among others, of modifiable lifestyle risk factors. Again, the core issue here is that the K-ROAD cohort is not suitable to this aim.\nBoth answers bring us to the authors claim of representativeness of the K-ROAD cohort. They claim that “Recruitment was conducted both through memory disorder clinics and government commissioned community dementia prevention centers, resulting in a hybrid cohort combining clinic-based and community-based sources. This approach enhanced the representativeness of the sample relative to conventional memory clinic–only cohorts.” I believe that, on the contrary, the selection criteria make K-ROAD neither representative of the general nor the memory clinic population. This may be the reason why the authors were not able to replicate some well-established findings in memory clinic cohorts (i.e. that the best predictors of progression in MCI patients are biomarkers of Alzheimer’s pathology rather than neuroinflammation) and in the general population (i.e. that modifiable lifestyle risk factors are predictors of cognitive impairment and dementia).\nI believe that the authors should tone down their claims and acknowledge these key limitations.", "reviewer_4": "The authors propose a prognostic staging system for Alzheimer's disease (AD) that integrates cognitive status, traditional risk factors, and multiple plasma and imaging biomarkers to stratify progression risk. They identified prognostic subgroups using survival prediction modeling within each cognitive status, mapped them to specific outcome stages, and unified them into a comprehensive staging framework. Key discriminators varied by stage: GFAP for cognitively unimpaired (CU) individuals, hippocampal volume for those with mild cognitive impairment (MCI), and age for those with dementia due to Alzheimer's disease (DAT). Plasma pTau217 consistently served as a secondary discriminator across all groups. This integrated staging system preserves the strengths of individual outcome-specific stages while providing a single, clinically interpretable scale. The paper emphasizes the importance of stage-specific prognostic factors for precise risk stratification and highlights the potential of a comprehensive, biomarker-integrated staging system. While very interesting there were several points that limited enthusiasm\n1) Preclinical AD Stage: It seems the authors missed addressing the significant preclinical stage of Alzheimer's disease (AD).Biological diagnosis and pathological confirmation of AD typically involve amyloid and tau markers, yet the study only indirectly considers these through pTau 217. The complexity of the proposed staging system may hinder its intuitive understanding and clinical application.\n2) Non-Specific Markers: Both GFAP and nFL are markers not exclusive to AD and are influenced by age and other diseases. A detailed discussion about the limitations of these non-specific biomarkers should be included.\n3) Staging System Analogy: The authors draw an analogy to a TNM system for AD, but the existing ATN criteria already serve this purpose. The spatial distribution and magnitude of these biomarkers need proper consideration, which the authors' model lacks.\n4) Age as a Predictor: Using age alone as the best predictor of dementia due to Alzheimer's disease (DAT) is questionable, as it suggests all elderly individuals would have DAT, which is not accurate. Many individuals do very well at older ages and these are not considered in the current model.\n5) Mixed Pathology: The authors should address that the diagnosis in their samples often includes mixed pathology. There is no consideration of vascular measures, genetics (APOE), or social determinants of health, all of which can impact AD progression. This significantly limits the generalizability of the results.\n6) Sample Representation: The ADNI subgroup used (290 participants) may not be representative of the broader cohort. More details on the selection process are needed.\n7) Comparison with Existing Models: The authors should compare their model against current models that include amyloid and tau biomarkers to show it is superior.\n8) Non-Specific Markers and Pathology Alignment: Even pTau 217 levels increase with age; overall, the study's reliance on non-specific markers complicates their use for classification aligned with AD pathology.\n9) Clinical Application and Validation: The claim that the model could guide monoclonal antibody treatment is doubtful, as current therapies depend on amyloid presence, which the model doesn't fully capture or evaluate for treatment response.\n10) External validation from diverse, population-based cohorts is missing, limiting generalizability. The exploratory nature and lack of formal sensitivity analyses suggest further validation studies are necessary to confirm long-term predictive accuracy."}, "author_response": "Response to Reviewer 1:\nWe thank the reviewer for raising this important point regarding the complexity of the analytical framework. We agree that the two-step structure of Phase 2—deriving outcome-specific progression stages and then integrating them into a unified system—may have contributed to this complexity. To enhance clarity, we substantially reorganized the Results section. We now present only the final unified prognostic staging system in the main text (corresponding to the original Fig. 3b), and we removed the intermediate steps of Phase 2 from the Results. The detailed procedures and stage-integration process have been moved to Supplementary Method 3, where they are described with full transparency for readers who wish to follow the complete modeling workflow. Figure 3 has also been simplified to improve interpretability. We believe these revisions significantly improve readability and reduce cognitive burden while maintaining methodological rigor.\n“These outcome-specific stages were integrated into a six-stage unified prognostic system (Stage 0–IVB) reflecting increasing dementia severity (Fig. 3). Detailed procedures to develop a unified prognostic staging system is presented in Supplementary Method 3. (Results)”\n“Supplementary method 3 illustrates the detailed process for developing the unified prognostic staging system. (Methods)”\n“Supplementary Method 3. Detailed process for developing a unified prognostic staging system across the Alzheimer’s disease continuum\nStep 1. Development of outcome-specific progression stages\nAll prognostic subgroups identified in Phase 1 were evaluated to determine whether adjacent subgroups could be merged based on statistical similarity of dementia progression risk for each of three outcomes: very mild, mild, and moderate dementia. The seven subgroups comprised C1 and C2 for cognitively unimpaired (CU), M1-M3 for mild cognitive impairment (MCI), and D1 and D2 for dementia of Alzheimer's type. Merging criteria were applied separately for each outcome, yielding five stages each for very mild and mild dementia outcomes, and four stages for the moderate dementia outcome, as shown below:\nDetailed procedures for developing outcome-specific stages for each progression outcome are provided below.\nVery mild dementia outcome\nFor the very mild dementia progression outcome, all seven prognostic subgroups (left panel) were assessed using adjusted survival curves with pairwise log-rank comparisons and incidence rates (IRs) per 100 person-years with 95% confidence intervals (CIs). Subgroups C2 and M1 showed visually overlapping adjusted survival curves with non-significant differences in pairwise comparison (P = 0.413), and their 95% CIs of IR overlapped substantially (4.69 to 13.92 in C2 and 2.67 to 6.56 in M1). Likewise, subgroups D1 and D2 exhibited similar progression risk (pairwise comparison between survival curves, P = 0.867; 95% CI of IR, 43.72 to 113.14 in D1 and 44.06 to 130.69 in D2). Consequently, C2 and M1 were merged into a single category, as were D1 and D2. Following these combinations, adjusted survival curves (right panel) demonstrated clear separation among five stages, with non-overlapping 95% CIs of IRs between adjacent stages confirming adequate discriminative capacity.\nMild dementia outcome\nThe same evaluation criteria as described above were applied to assess adjacent subgroups for mild dementia. Based on overlapping survival curves and 95% CIs of IRs, C2 and M1 were merged (pairwise comparison between survival curves, P = 0.972; 95% CI of IR, 2.38 to 9.52 in C2 and 2.49 to 6.11 in M1), as were D1 and D2 (95% CI of IR, 35.39 to 60.32 in D1 and 43.21 to 78.03 in D2), yielding five stages. After merging, adjusted survival curves and 95% CIs of IRs demonstrated clear distinction among five stages.\nModerate dementia outcome\nFor the moderate dementia outcome, statistical evaluation indicated that subgroups C1, C2, and M1 could be merged (pairwise comparison between survival curves, P = 0.994; 95% CIs of IRs, no event was observed in C1, 0.08 to 4.18 in C2, and 0.30 to 2.13 in M1), as could M3 and D1 (P = 0.125; 4.36 to 7.82 in M3 and 6.66 to 13.63 in D1), due to overlapping survival curves and 95% CIs of IRs. These combinations yielded four stages with distinct adjusted survival curves and non-overlapping IRs among adjacent stages.\nStep 2: Integration based on outcome-specific progression stages\nThe outcome-specific categorizations were integrated into a unified prognostic staging system by utilizing different outcomes for different severity levels. Early stages were determined using the very mild dementia-specific categorization (subgroups C1 and C2&M1), intermediate stages using the mild dementia-specific categorization (M2 and M3), and advanced stages using the moderate dementia-specific categorization (D1 and D2). This integration strategy ensured that each unified stage reflected clinically meaningful progression across the Alzheimer's disease continuum, yielding a total of six stages.\nStep 3: Labeling from Stage 0 to Stage IV\nThe integrated stages were assigned labels from Stage 0 to Stage IVB. Early stages (0-I) were distinguished primarily by the very mild dementia outcome, intermediate stages (II-III) by the mild dementia outcome, and advanced stages (IV) by the moderate dementia outcome. Stage IV was subdivided into IVA and IVB to provide refined risk stratification at the most advanced stage, reflecting the overlapping progression patterns observed in the very mild and mild dementia outcomes for the highest-risk subgroups (D1 and D2; see Table 2). (Supplementary Methods)”\n\nWe appreciate the reviewer’s clarification. As noted, CDR-SB was not incorporated into the prognostic staging scheme, and its use was limited solely to outcome assessment and remained independent of the predictive modeling process.\n\nWe thank the reviewer for this comment. As noted, we have simplified the description of mid-level transitions and clarified how these transitions relate to increases in progression risk. We appreciate that the revised presentation has improved clarity while acknowledging the inherent complexity of the framework.\n\nWe appreciate the reviewer’s comment. As noted, the ADNI subgroup was necessarily limited due to the availability of corresponding biomarker data, and we agree that conclusions drawn from this subset should be interpreted with caution. We are grateful that the reviewer finds our explanation and justification for the ADNI subselection reasonable.\n\nWe appreciate the reviewer’s thoughtful perspective. We agree that both the K-ROAD and ADNI cohorts are selective in nature, and we have clarified this more explicitly in the revised manuscript. While the hybrid recruitment structure of K-ROAD incorporates community dementia prevention centers and partially reflects a broader population, we fully acknowledge that it does not eliminate selection bias. We have therefore tempered statements regarding representativeness and further emphasized this limitation in the Discussion.\n“First, although the K-ROAD cohort used a hybrid recruitment strategy from both memory clinics and community dementia prevention centers, it nonetheless represents a selectively ascertained research sample rather than a fully population-based or memory-clinic cohort.”\n\nWe thank the reviewer for this helpful suggestion. We agree that the term “DAT” may be confusing to readers regarding whether biomarker confirmation is implied. In accordance with the reviewer’s recommendation, we have replaced “DAT” with “dementia” throughout the manuscript for greater clarity, while preserving the original clinical diagnostic criteria as described in the Methods section.\n\nWe sincerely appreciate the reviewer’s positive assessment and thank them for their thoughtful and constructive feedback.\n\nResponse to Reviewer 3:\nWe appreciate the reviewer’s candid feedback and fully understand the underlying concern that our previous responses did not sufficiently acknowledge the substance of the reviewer’s critique. We agree that scientific and clinical significance must take precedence over publication goals, and we have carefully re-evaluated the manuscript with this principle in mind. In the revised version, we have substantially toned down claims that may have overstated the applicability of the framework, clarified limitations related to cohort characteristics and generalizability, and revised several sections to more directly address the reviewer’s core concerns. We are grateful for the reviewer’s guidance, which has strengthened the rigor and clarity of the manuscript.\n\n1. We thank the reviewer for this important clarification. We fully agree that the K-ROAD cohort is not appropriate for evaluating the personalized delivery or stratified use of anti-amyloid monoclonal antibodies, and we acknowledge that our previous statements may have implied a level of clinical applicability that is not supported by the data. In accordance with the reviewer’s recommendation, we have removed all phrasing suggesting potential use of the staging scheme for guiding monoclonal antibody treatment. We now emphasize explicitly that the framework is exploratory and designed solely to examine biomarker-integrated prognostic stratification, rather than to inform therapeutic decision-making. We are grateful for the reviewer’s guidance, which has allowed us to more accurately align the manuscript with the scope and limitations of the available data.\n“However, this framework is not designed for therapeutic decision-making. In particular, eligibility for monoclonal antibody therapy requires confirmed amyloid positivity and treatment-response biomarkers—elements that the present system does not assess. Therefore, this model should be used strictly for prognostic stratification rather than for guiding treatment selection. (Discussion)”\n\n2. We thank the reviewer for this important point. We fully agree that true secondary prevention in cognitively unimpaired (CU) individuals requires comprehensive assessment of modifiable lifestyle risk factors, longitudinal lifestyle tracking, and population-based sampling. As the reviewer correctly notes, the K-ROAD cohort does not include systematic measurements of lifestyle factors such as diet, physical activity, sleep, social engagement, or cardiovascular health behaviors, and therefore our dataset is not suitable for constructing or evaluating a secondary prevention model in CU individuals.\n“Second, the dataset did not include systematic measurements of modifiable lifestyle risk factors (e.g., diet, physical activity, sleep, cardiovascular behaviors), precluding evaluation of lifestyle-related predictors commonly observed in population-based cohorts. Because such assessments are required to model prevention in cognitively unimpaired individuals, the present framework should not be interpreted as a prevention model. (Discussion)”\n\n3. We thank the reviewer for this thoughtful and important comment. We agree that the K-ROAD cohort is not representative of either a general population cohort or a typical memory clinic cohort, as it was assembled based on specific research-oriented inclusion and exclusion criteria and depended on the availability of biomarker assessments. We have now revised the manuscript to clearly state that K-ROAD should not be considered population-based, prevention-oriented, or fully reflective of real-world memory clinic populations.\nThe intention of our original wording was not to imply representativeness in an epidemiological sense, but rather to indicate that in contrast to conventional single-site, tertiary-care memory clinic cohorts, K-ROAD includes individuals recruited from both specialty clinics and community dementia prevention centers. However, we fully agree with the reviewer that this hybrid structure does not overcome the fundamental selection biases inherent to research cohorts.\nIn the Methods section,\n“However, we emphasize that this hybrid structure does not make the cohort representative of either the general population or a typical memory clinic population, as participants were selectively enrolled based on research-appropriate clinical evaluations and biomarker availability.”\nIn the Discussion section,\n“First, although the K-ROAD cohort used a hybrid recruitment strategy from both memory clinics and community dementia prevention centers, it nonetheless represents a selectively ascertained research sample rather than a fully population-based or memory-clinic cohort.”\n\nResponse to Reviewer 4:\n1. We appreciate the reviewer’s emphasis on the biological definition of preclinical AD. In the revised manuscript, we now clarify that our study adopts an operational definition aligned with major secondary prevention trials, in which preclinical AD refers to cognitively unimpaired individuals with abnormal amyloid biomarkers (Aβ+), irrespective of tau status. This approach reflects current clinical frameworks and is consistent with recent evidence demonstrating that plasma pTau217 robustly predicts both Aβ and tau PET positivity and captures early disease-related changes. Indeed, our separate study published in European Journal of Nuclear Medicine and Molecular Imaging (2025) showed that plasma pTau217 achieved high accuracy for predicting Aβ (AUC 0.96) and tau (AUC 0.90) PET positivity and effectively distinguished longitudinal cognitive decline across plasma-based AT profiles. These findings support the use of plasma pTau217 as a sensitive marker of early AD-related pathology even in the absence of direct tau PET measures. Nonetheless, we agree that pTau217 represents an indirect proxy rather than a direct measure of tau pathology, and we now explicitly caution that the proposed staging system does not constitute a biological staging framework and should not be interpreted as a substitute for AT(N)-based classification. We have expanded the Discussion and Limitations sections accordingly to emphasize this point and to acknowledge that the complexity of the framework may limit intuitive clinical interpretability, particularly in preclinical stages.\nIn the Background section,\n“In this study, preclinical AD was operationally defined as Aβ+ CU individuals, consistent with current secondary prevention trials. Plasma pTau217 was used as an indirect correlate of tau pathology rather than a direct measurement; therefore, the proposed framework does not constitute a biological staging system based on combined amyloid and tau positivity.”\nIn the Discussion section,\n“Indeed, plasma pTau217 has demonstrated independent associations with both amyloid and tau pathology.18,38 Notably, our recent work39 demonstrated that pTau217 predicts both Aβ (AUC 0.96) and tau (AUC 0.90) PET positivity and distinguishes longitudinal cognitive decline across plasma-defined AT profiles, underscoring its utility for prognostic stratification even without tau PET.”\n\n2. We thank the reviewer for highlighting this important point. We agree that GFAP and NfL are not specific to AD pathology and may be influenced by age, vascular comorbidities, and other neurodegenerative conditions. In the revised manuscript, we have added a detailed discussion acknowledging that these markers reflect broader astrocytic activation and axonal injury, and therefore cannot be interpreted as disease-specific indicators of AD biology. We now explicitly state that their prognostic value in our framework should be viewed within this context and that future studies incorporating more AD-specific tau biomarkers and multimodal measures are needed to enhance biological specificity.\n“However, GFAP and NfL are not disease-specific, and even pTau217 partly reflects non–AD processes—including ageing, cerebrovascular burden, and other neurodegenerative conditions—so these markers should be interpreted as indicators of broader neural vulnerability rather than AD-specific pathology. (Discussion)”\n\n3. We appreciate the reviewer’s insightful comment. We agree that the A/T/N framework already provides a biological classification system that parallels the role of TNM in oncology, and we do not intend to propose a replacement for, or duplicate of, the existing A/T/N criteria. Our staging framework differs in purpose and structure: while A/T/N captures the biological state of amyloid, tau, and neurodegeneration—including the spatial distribution and magnitude of these pathologies—our proposed system is a prognostic framework that integrates cognitive stage with multimodal biomarkers to stratify future clinical progression risk. To address this concern, we now clarify in the manuscript that our model does not incorporate regional tau patterns or a hierarchical biological ordering like A/T/N, and should therefore not be interpreted as a biological staging system. Its intent is to complement—not replace—biological frameworks by providing a survival-based, outcome-oriented structure. We have also tempered the analogy to the TNM system and added text to explicitly acknowledge that spatial distribution and burden of tau pathology, which are critical for biological staging, were not modeled due to the lack of tau PET in the derivation cohort. Future studies incorporating tau PET, regional measures, and T2 biomarkers will be necessary to connect our prognostic framework more directly to pathobiological staging.\n“Unlike biological staging frameworks such as A/T/N24, our model does not incorporate regional tau PET or quantitative pathological burden and therefore should not be viewed as a biological classification system. Instead, it is an outcome-driven, prognostic framework built from T1 biomarkers, non-specific plasma markers, and known clinical risk factors—intended to complement, not replace, biological staging approaches. Because T2 biomarkers such as tau PET are not yet widely available in routine research or clinical settings, the present framework necessarily emphasizes prognostic rather than biological classification. Within each cognitive stage, individuals exhibited substantial heterogeneity in progression risk7,8,12,42, and this variability was captured by the combined contribution of plasma biomarkers, neurodegeneration measures, and clinical risk factors. (Discussion)”\n\n4. We thank the reviewer for raising this important point. We fully agree that age alone cannot be interpreted as a general predictor of dementia due to Alzheimer’s disease (DAT), nor does it imply that all older adults are at elevated risk. In our study, age emerged as the strongest discriminator within the dementia subgroup, but this reflects well-established pathophysiological characteristics of early-onset AD rather than a universal effect of aging. Specifically, numerous studies have shown that individuals with early-onset AD exhibit:\n• markedly higher amyloid positivity rates,\n• greater tau burden on PET, and\n• more severe and widespread cortical atrophy\ncompared with late-onset AD. These biological features—not age per se—are strongly linked to a more aggressive clinical course, which explains why younger individuals with dementia showed faster progression in our cohort.\n“In dementia, younger age emerged as the strongest predictor of faster progression3, a pattern consistent with extensive evidence that early-onset AD follows a more aggressive clinical course. Individuals with early-onset AD typically show markedly higher amyloid positivity rates31, greater tau burden on PET32, and more severe and widespread cortical atrophy33 compared with late-onset AD. These biological differences—rather than chronological age itself—likely account for the accelerated progression observed in younger patients34,35. (Discussion)”\n\n5. We appreciate the reviewer’s insightful comment regarding mixed pathology and additional determinants of AD progression. In our study, we attempted to reduce the influence of severe non-AD pathology by excluding individuals with extensive cerebrovascular disease. Furthermore, among the candidate prognostic features, we incorporated several key vascular and genetic factors—including age, hypertension, diabetes mellitus, and APOE ε4 carrier status—as well as education level, which is a well-established proxy for cognitive reserve. However, we acknowledge that the present framework does not include more detailed social determinants of health (e.g., socioeconomic status, environmental factors, occupational history, or quality of education) nor comprehensive vascular imaging markers such as white matter hyperintensity burden, lacunes, microbleeds, or cerebral perfusion indices. The absence of these broader contributors limits the model’s ability to fully capture mixed pathology and the multifactorial nature of cognitive decline, thus constraining generalizability to more diverse populations. We have revised the manuscript to clearly note these limitations and to emphasize that future work integrating detailed vascular imaging features, broader genetic contributors, and multidimensional social determinants will be essential to enhance the robustness and clinical validity of biomarker-integrated prognostic staging.\n“Although individuals with extensive cerebrovascular disease were excluded, only limited vascular and genetic variables (age, hypertension, diabetes mellitus, APOE ε4 carrier status, and education) were included, and detailed vascular imaging markers or social determinants of health were not available—factors that may contribute to mixed pathology. (Discussion)”\n\n6. In this study, the ADNI cohort within the FNIH Biomarkers Consortium dataset included individuals with CU, MCI, and DAT who had plasma pTau217, GFAP, hippocampal volume on MRI, and longitudinal CDR assessments available. Plasma pTau217 (ALZpath) and GFAP (Neurology 4-Plex E) were measured on the Quanterix Simoa platform, and hippocampal volumes were derived from UCSF FreeSurfer longitudinal processing. A total of 378 participants (CU, n = 209; MCI, n = 148; DAT, n = 21) had available plasma pTau217 measurements (filename: All_Subjects_FNIHBC_BLOOD_BIOMARKER_TRAJECTORIES_10Oct2024). For the present validation analysis, we included 290 participants who had complete data across all required modalities—plasma pTau217, GFAP, hippocampal volume, Aβ PET, and longitudinal CDR scores. Participants missing any of these data elements were excluded, resulting in a biomarker-enriched subset suitable for consistent prognostic validation.\n“In this study, the ADNI cohort within the FNIH Biomarkers Consortium dataset included individuals with cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia due to AD (DAT) who had available plasma pTau217, GFAP, hippocampal volume on MRI, and longitudinal CDR assessments. Plasma pTau217 (ALZpath) and GFAP (Neurology 4-Plex E) were measured on the Quanterix Simoa platform, and hippocampal volumes were derived from the UCSF FreeSurfer longitudinal processing pipeline. A total of 378 participants (CU, n = 209; MCI, n = 148; DAT, n = 21) had available plasma pTau217 measurements (filename: All_Subjects_FNIHBC_BLOOD_BIOMARKER_TRAJECTORIES_10Oct2024). For the present validation analysis, we included 290 individuals who had complete data across all required modalities—plasma pTau217, GFAP, hippocampal volume, Aβ PET, and longitudinal CDR scores. Participants missing any of these variables were excluded to ensure consistent modeling, resulting in a biomarker-enriched subset appropriate for prognostic validation.”\n\n7. We thank the reviewer for this important comment. Direct statistical comparison with existing amyloid– and tau–based biological staging models was not feasible because tau PET (T2 biomarkers) was not available in the K-ROAD cohort, and CSF tau measures were not collected. As such, our framework was developed using T1 biomarkers (Aβ PET and plasma pTau epitopes), non-specific plasma markers, and established prognostic factors, reflecting the data modalities currently accessible in large clinical cohorts. To address the reviewer’s concern, we have now clarified in the Discussion that our system is conceptually distinct from biological staging frameworks such as ATN or amyloid/tau PET–based staging. Rather than serving as a biological staging system, our model is a prognostic staging framework designed to capture heterogeneity in progression risk within each cognitive stage by integrating plasma biomarkers, neurodegeneration measures, and clinical risk factors. We have also emphasized that the proposed framework is intended to complement—rather than replace—biological staging systems. These clarifications have been added to the revised Discussion section.\n“Unlike biological staging frameworks such as A/T/N, our model does not incorporate regional tau PET or quantitative pathological burden and therefore should not be viewed as a biological classification system. Instead, it is an outcome-driven, prognostic framework built from T1 biomarkers, non-specific plasma markers, and known clinical risk factors—intended to complement, not replace, biological staging approaches. Because T2 biomarkers such as tau PET are not yet widely available in routine research or clinical settings, the present framework necessarily emphasizes prognostic rather than biological classification. Within each cognitive stage, individuals exhibited substantial heterogeneity in progression risk, and this variability was captured by the combined contribution of plasma biomarkers, neurodegeneration measures, and clinical risk factors. (Discussion)”\n\n8. We appreciate the reviewer’s thoughtful comment. We agree that several of the biomarkers used in our prognostic framework—including GFAP, NfL, and to a lesser extent plasma pTau217—are influenced by age and may reflect neuroinflammatory or neurodegenerative processes that are not specific to AD pathology. Our goal in this study, however, was not to construct a biological staging system aligned with amyloid or tau pathology, but rather to develop a prognostic framework capable of stratifying clinical progression risk using the multimodal measures available in large, real-world cohorts. To address this point, we have revised the Discussion to explicitly acknowledge that (i) non-specific markers likely capture global vulnerability or mixed pathological processes rather than AD-specific mechanisms, (ii) age contributes to elevations in pTau217 and must be interpreted within a broader clinical and biomarker context, and (iii) the absence of T2 biomarkers such as tau PET limits biological specificity. We also clarify that our model is designed as a prognostic—rather than biological—staging system and is intended to complement rather than replace amyloid/tau-based staging frameworks. These clarifications have been incorporated into the revised manuscript.\n“However, GFAP and NfL are not disease-specific, and even pTau217 partly reflects non–AD processes—including ageing, cerebrovascular burden, and other neurodegenerative conditions—so these markers should be interpreted as indicators of broader neural vulnerability rather than AD-specific pathology. (Discussion)”\n\n9. We thank the reviewer for raising this important point. We agree that monoclonal antibody therapies require confirmed amyloid positivity and that our prognostic framework—while incorporating Aβ PET as a T1 biomarker—does not fully evaluate amyloid burden or treatment response. Our intention was not to propose the model as a tool for selecting monoclonal antibody therapy, but to suggest that stage-dependent prognosis may eventually help contextualize treatment decisions when used alongside established biological criteria.To address this concern, we have revised the Discussion to remove any implication that the framework could currently guide monoclonal antibody treatment. We now explicitly state that the model is a prognostic, rather than therapeutic or biological, staging system and that determining suitability for anti-amyloid therapies requires dedicated amyloid- and tau-specific biomarkers as well as prospective treatment-response studies. These clarifications have been incorporated into the revised manuscript.\n“However, this framework is not designed for therapeutic decision-making. In particular, eligibility for monoclonal antibody therapy requires confirmed amyloid positivity and treatment-response biomarkers—elements that the present system does not assess. Therefore, this model should be used strictly for prognostic stratification rather than for guiding treatment selection. (Discussion)”\n\n10. We fully agree with the reviewer that broader external validation is essential to establish the generalizability and long-term predictive utility of our prognostic staging framework. In the present study, external validation was performed using the ADNI cohort; however, ADNI is not population-based and includes a predominance of early-stage participants. As a result, later dementia stages were underrepresented, limiting our ability to evaluate prognostic performance across the full disease spectrum. Furthermore, because this work represents a proof-of-concept exploratory framework, we did not conduct formal sensitivity analyses. We acknowledge that additional validation in diverse, population-based cohorts—together with systematic sensitivity analyses—will be crucial to confirm the stability, robustness, and long-term predictive accuracy of the proposed staging system. These elements are now noted explicitly in the revised manuscript.\n“External validation using the ADNI cohort supported stage-dependent prognostic patterns, particularly for early and intermediate stages. However, only 290 of the 378 eligible participants could be analyzed because complete plasma pTau217, GFAP, hippocampal volume, Aβ PET, and longitudinal CDR data were required for consistent modeling. This resulted in a biomarker-enriched subset that underrepresented advanced dementia and limited evaluation of later-stage performance. Broader validation using population-based and clinically heterogeneous cohorts will therefore be necessary to establish generalizability. (Discussion)”"}]

Decision letter