Cerebrospinal fluid proteomics identification of biomarkers for amyloid and tau PET stages

Zhibo Wang; Yuhan Chen; Katherine Gong; Bote Zhao; Yuye Ning; Meilin Chen; Yan Li; Muhammad Ali; Jigyasha Timsina; Menghan Liu; Carlos Cruchaga; Jianping Jia

doi:10.1016/j.xcrm.2025.102031

. 2025 Mar 20;6(4):102031. doi: 10.1016/j.xcrm.2025.102031

Cerebrospinal fluid proteomics identification of biomarkers for amyloid and tau PET stages

Zhibo Wang ¹, Yuhan Chen ², Katherine Gong ^3,⁴, Bote Zhao ¹, Yuye Ning ¹, Meilin Chen ¹, Yan Li ¹, Muhammad Ali ^3,⁴, Jigyasha Timsina ^3,⁴, Menghan Liu ^3,⁴, Carlos Cruchaga ^3,^4,^5,^6,^7,^∗, Jianping Jia ^1,^8,^9,^10,^11,^12,^∗∗

PMCID: PMC12047519 PMID: 40118053

Summary

Accurate staging of Alzheimer’s disease (AD) pathology is crucial for therapeutic trials and prognosis, but existing fluid biomarkers lack specificity, especially for assessing tau deposition severity, in amyloid-beta (Aβ)-positive patients. We analyze cerebrospinal fluid (CSF) samples from 136 participants in the Alzheimer’s Disease Neuroimaging Initiative using more than 6,000 proteins. We apply machine learning to predict AD pathological stages defined by amyloid and tau positron emission tomography (PET). We identify two distinct protein panels: 16 proteins, including neurofilament heavy chain (NEFH) and SPARC-related modular calcium-binding protein 1 (SMOC1), that distinguished Aβ-negative/tau-negative (A−T−) from A+ individuals and nine proteins, such as HCLS1-associated protein X-1 (HAX1) and glucose-6-phosphate isomerase (GPI), that differentiated A+T+ from A+T− stages. These signatures outperform the established CSF biomarkers (area under the curve [AUC]: 0.92 versus 0.67–0.70) and accurately predicted disease progression over a decade. The findings are validated in both internal and external cohorts. These results underscore the potential of proteomic-based signatures to refine AD diagnostic criteria and improve patient stratification in clinical trials.

Keywords: proteomics, amyloid PET, tau PET, Alzheimer’s disease, CSF biomarkers, Aβ, p-tau, dementia, Alzheimer's disease continuum, biological staging

Graphical abstract

Highlights

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The early-stage protein panel distinguishes amyloid PET-positive and negative cases
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The late-stage protein panel identifies tau PET status in amyloid-positive individuals
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The protein panel predicts dementia progression and cognitive decline over 10 years
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Identified proteins are implicated in synaptic plasticity and metabolic dysfunction

Wang et al. apply machine learning to proteomic profiles to identify proteins predictive of Alzheimer’s disease pathology stages. They develop two models: one for early-stage amyloid positivity and another for assessing tau pathology severity in amyloid-positive patients. Both models can predict dementia progression and cognitive decline over 10 years.

Introduction

Alzheimer’s disease (AD) is the primary cause of dementia, characterized by heightened aggregated amyloid-β (Aβ) peptide and tau neurofibrillary tangles (NFTs) in the brain, with or without cognitive impairment.¹ Currently, assessing Aβ plaques and NFT neuropathological burden relies on positron emission tomography (PET) imaging with radioactive tracers or molecular protein biomarkers in cerebrospinal fluid (CSF) and, recently, blood, available at clinical or research levels.¹ PET imaging, the most accurate AD pathology diagnostic marker validated by autopsy data,²^,³ is limited by high cost and complex infrastructure to specialized centers. Conversely, fluid biomarkers are more affordable and clinically accessible; however, current established AD biomarkers (i.e., Aβ42, phosphorylated tau [p-tau]181, and total tau [t-tau]) mainly focus on early AD pathology, responding to Aβ plaques but lacking sensitivity for moderate to late tau pathology.⁴^,⁵

Staging the severity of Aβ and tau pathology is crucial for several reasons. Firstly, accurately discriminating the severity of AD pathology better reflects future cognitive function. Evidence suggests that cognitively normal individuals with amyloid PET-positive (A+) and tau PET-positive (T+) face a higher risk of cognitive decline than those with A+T−.⁶ Secondly, precise AD pathological staging aids in establishing inclusion and exclusion criteria; for instance, the Trailblazer-Alz study used amyloid PET stage as an inclusion criterion in which the doses were adjusted based on tau PET stage.⁷ Moreover, the results of the Trailblazer-Alz study revealed benefits in A+ individuals with low/medium tau, not high tau pathology,⁷ suggesting that capturing tau pathology stage can offer clinical guidance for improved treatment responses in patients.

CSF proteins, directly linked to the brain and less affected by peripheral factors, offer unique insights into normal and abnormal brain pathophysiology compared to plasma biomarkers. Moreover, CSF proteomics, spanning various biological processes, holds promise for advancing etiological understanding and refining promising biomarkers. Most previous CSF proteomic studies have focused on asymptomatic and symptomatic AD⁸^,⁹ or CSF-based biologically defined AD¹⁰ or sporadic and genetic AD¹¹; however, few studies have investigated potential CSF biomarkers for identifying specific pathological stages of AD based on PET scans. In addition, with the advent of modified nucleic acid aptamer (SomaScan)-targeted assay platforms,¹² large-scale CSF proteomic profiling (>7,000 proteins) at a population scale has become feasible; however, few studies have explored proteomic profiling in AD across this platform.

Here, we employed large-scale CSF proteomic profiling combined with machine learning to identify candidate biomarkers for discriminating amyloid PET and tau PET stages and compared their performance with established AD biomarkers. We developed two protein panels to differentiate early and late disease stages as defined by PET imaging. These models outperformed established CSF AD biomarkers (i.e., Aβ42 and p-tau181) in distinguishing disease stages. Furthermore, they were associated with dementia progression and cognitive decline over the subsequent decades. The proteins in the early-stage panel were enriched for synaptic damage and compensatory processes, while those in the late-stage panel were linked to metabolic dysfunction pathways. These findings not only provide more sensitive and specific protein markers for tracking disease progression but also offer insights into the temporal evolution of pathophysiological mechanisms underlying AD progression.

Results

Study design and participants

The study design is depicted in Figure 1. The primary objective was to identify CSF proteins exhibiting changes in early and late stages of AD as assessed by amyloid PET and tau PET. A total of 6,164 proteins meeting quality control criteria were included in the analysis (STAR Methods). Established AD biomarkers, including CSF Aβ42, p-tau181, t-tau, and the p-tau181/Aβ₄₂ ratio, were included for comparison. Machine learning techniques were employed to pinpoint a subset of proteins capable of accurately distinguishing between early and late AD stages. These proteins were further analyzed in relation to dementia progression and cognitive decline. Validation was performed using both external and internal cohorts.

Study workflow

This workflow outlines the primary analytical process of the study. Proteomic analysis identified proteins associated with amyloid PET or tau PET. LASSO models integrated with machine learning selected target proteins. The performance of protein panels in differentiating AT stages was compared with established CSF AD biomarkers. Additionally, the relationship between protein panels and dementia progression and cognitive decline was evaluated. Finally, findings were validated in external and internal cohorts

The primary cohort included 136 non-demented individuals (Table 1), with a mean age of 71.50 ± 2.98 years, 41.20% female, and 34.60% carrying at least one APOE ε4 allele. Participants were followed for an average of 4.4 years (SD: 2.98). During this period, 14 participants (11.80%) transitioned from amyloid PET negative (A−) to positive (A+), and 31 participants (22.80%) progressed to dementia. For autopsy validation, an independent cohort of 54 participants was analyzed, including 11 with none/low AD neuropathological changes (ADNCs) and 43 with intermediate/high ADNCs (Table S1). Additionally, a longitudinal population of 354 non-demented individuals was evaluated for clinical progression over a mean duration of 3.70 years (SD: 2.25) (Table S2).

Table 1.

Study populations

	Overall	A−T−	A+T−	A+T+	A−T+
N	136	66	30	33	7
Age, mean (SD)	71.5 (6.9)	69.7 (6.8)	73.5 (6.4)	73.6 (7.0)	70.0 (5.6)
Gender, n (%) female	56 (41.2)	30 (45.5)	10 (33.3)	12 (36.4)	4 (57.1)
APOE ε4 carriers (%)	47 (34.6)	11 (16.7)	9 (30.0)	24 (72.7)	3 (42.9)
Years of education, mean (SD)	16.5 (2.6)	16.8 (2.7)	16.9 (2.7)	15.6 (2.2)	17.0 (4.2)
MMSE score at baseline, mean (SD)	28.5 (1.6)	28.9 (1.3)	28.2 (2.1)	28.1 (1.6)	28.9 (1.3)
CDRSB score at baseline, mean (SD)	0.7 (0.9)	0.6 (0.8)	0.6 (0.9)	1.1 (1.1)	0.6 (0.6)
ADAS13 score at baseline, mean (SD)	11.8 (6.2)	9.8 (4.9)	10.3 (4.2)	16.8 (7.4)	13.6 (5.7)
Diagnosis, n (%) MCI	69 (50.7)	30 (45.5)	12 (40.0)	22 (66.7)	5 (71.4)
Centiloid, mean (SD)	33.8 (47.9)	−2.8 (10.1)	66.5 (38.4)	84.7 (37.2)	−1.7 (11.2)
Tau PET Braak I-IV SUVR, mean (SD)	1.3 (0.3)	1.1 (0.1)	1.2 (0.1)	1.7 (0.3)	1.4 (0.2)
Follow-up months, mean (SD)	52.7 (35.8)	57.3 (34.5)	46.2 (35.7)	48.3 (35.8)	58.1 (48.8)
Progression from A− to A+, n (%)^a	14 (11.8%)	0 (0%)	12 (46.2%)	2 (7.7%)	0 (0%)
Progression to dementia, n (%)	31 (22.8)	4 (6.1)	4 (13.3)	21 (63.6)	2 (28.6)

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AD, Alzheimer’s disease; ADAS13, Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13; CDRSB, Clinical Dementia Rating Scale-Sum of Boxes; CSF, cerebrospinal fluid; MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination; PET, positron emission tomography.

Of the participants, 119 had available amyloid PET scans within 1 year of CSF proteomic assessments and had two or more amyloid PET scans, with some transitioning from amyloid PET-negative (A−) to amyloid PET-positive (A+) between the initial and final PET examination.

Proteomic profiling associated with amyloid PET

We identified proteins correlated with amyloid PET, quantified using Centiloids instead of standardized uptake value ratios (SUVRs) (STAR Methods). We conducted a multivariate linear regression analysis of 6,164 proteins and CSF AD biomarkers (Aβ42, p-tau181, and t-tau), adjusted for age, gender, and time interval between CSF collection and PET scanning. This analysis identified 262 proteins significantly associated with Centiloid values (p < 0.05). After false discovery rate (FDR) correction, 10 proteins remained significant (see also Figure 2A and Table S3). Notably, CSF p-tau181 showed the strongest positive correlation with Centiloid (β = 20.67, FDR p = 6.91 × 10⁻⁴),¹³ followed by C-C motif chemokine 25 (CCL25) (β = 20.01, FDR p = 7.24 × 10⁻⁴), kinetochore protein Spc25 (SPC25) (β = 19.01, FDR p = 2.00 × 10⁻³), coiled-coil-helix-coiled-coil-helix domain-containing protein 7 (CHCHD7) (β = 18.69, FDR p value = 4.00 × 10⁻³), and SPARC-related modular calcium-binding protein 1 (SMOC1) (β = 17.87, FDR p = 0.01). SMOC1, a protein that co-localizes with Aβ plaques, has been shown to be an early biomarker of AD, and its CSF levels are elevated many years prior to the onset of symptoms.¹⁴^,¹⁵^,¹⁶ CCL25 and SPC25 are associated with neuroinflammation and have been less frequently reported in AD.¹⁷^,¹⁸ CHCHD7 is associated with mitochondrial function, and its dysfunction may contribute to neurodegenerative diseases.¹⁹^,²⁰ Conversely, CSF Aβ42 was negatively associated with Centiloid,¹³ alongside by tubulin-specific chaperone A (TBCA) (β = −17.41, FDR p = 0.02), a protein involved in the microtubule protein folding pathway.

The 10 significant proteins demonstrated moderate to strong discriminatory power between A+ and A− statuses (Figure 2B). CSF Aβ42 exhibited the highest area under the curve (AUC) of 0.90, followed by SMOC1 (AUC = 0.76) and p-tau181 (AUC = 0.74). Logistic regression indicated that CSF Aβ42, SMOC1, and p-tau181 were the strongest predictors of A+ status (Figure 2C). We next used locally estimated scatterplot smoothing (LOESS) model to track the trajectory of proteins along the amyloid PET. Protein trajectories analyzed via LOESS models (Figure 2D) revealed that CSF Aβ42 levels sharply decreased up to 100 Centiloid units before plateauing, while TBCA levels declined post 20 Centiloid units. The remaining eight proteins showed gradual increases until 100 Centiloid units, followed by rapid ascents.

Gene Ontology (GO) enrichment analysis indicated significant involvement of these proteins in chemokine receptor binding and oxidoreductase activity-related processes, particularly those utilizing NAD/NADP as acceptors in oxidation-reduction reactions (Figure 2E). Additionally, enrichment was observed in cellular metabolic pathways, including nucleoside and glycosyl compound metabolism, glycolysis, and vesicular transport-related pathways such as the secretory granule lumen and cytoplasmic vesicle lumen.

Proteomic profiling associated with tau PET

Using multivariate linear regression, we identified 949 proteins significantly associated with tau PET (p < 0.05), with 101 proteins remaining significant after FDR correction (see also Figure 3A and Table S4). Key proteins included p-tau181 (β = 0.17, FDR p = 1.01 × 10⁻⁹), t-tau (β = 0.15, FDR p = 6.57 × 10⁻⁸), 14-3-3 protein gamma (YWHAG) (β = 0.15, FDR p = 4.19 × 10⁻⁷), and glucose-6-phosphate isomerase (GPI) (β = 0.14, FDR p = 6.12 × 10⁻⁶). YWHAG, a member of the 14-3-3 protein family, is strongly correlated with AD pathology and cognitive function,²¹^,²² while GPI is involved in brain energy metabolism and has been implicated in mild cognitive impairment and AD.²³

Receiver operating characteristic analysis demonstrated that HCLS1-associated protein X-1 (HAX1) (AUC = 0.78), YWHAG (AUC = 0.77), and N-chimerin (CHN1) (AUC = 0.76) were the strongest predictors for distinguishing tau PET-positive (T+) from negative (T−) statuses (Figure 3B). HAX1 is a multifunctional protein involved in apoptosis regulation, mitochondrial membrane potential maintenance, and calcium homeostasis.²⁴ CHN1 is a GTPase-activating protein related to neuronal axon guidance.²⁵ However, the role of these two proteins in AD remains unclear. Moreover, logistic regression revealed that each standard deviation increase in HAX1 and YWHAG levels was associated with a 22% increased risk of T+ status (Figure 3C). Protein trajectories indicated progressive increases in these proteins with rising tau PET SUVR, particularly for p-tau181, and the calcineurin (PPP3CA|PPP3R1) (Figure 3D). Calcineurin, a calcium/calmodulin-dependent protein phosphatase, has been implicated in AD through its role in tau hyperphosphorylation and NFT formation.²⁶

GO enrichment analysis of tau PET-associated proteins highlighted significant enrichment in kinase-related pathways (e.g., calcium-dependent protein kinase activity, serine/threonine kinase activity), protein degradation pathways (e.g., proteasome core complex, ubiquitin-like protein conjugating enzyme activity), and glucose metabolic processes (Figure 3E). These findings suggest a complex interplay between protein post-translational modifications, protein quality control, and energy metabolism in tau-mediated neurodegeneration.

Proteomic signatures for distinguishing amyloid and tau PET stages

Among the 111 proteins associated with amyloid and tau PET, including three CSF AD-specific biomarkers (e.g., Aβ42, p-tau181, and t-tau), 99 distinct proteins were selected for feature selection. We employed a robust feature selection process involving 100 iterations of random sampling, extracting 80% of the data in each iteration. Utilizing the least absolute shrinkage and selection operator (LASSO) model, we identified the most informative proteins across iterations. Proteins consistently selected in over 40% of bootstrap samples (16 proteins) constituted the early-stage discriminative panel (A+ versus A−T−) (Figure S1A), while those selected in more than 20% of samples (9 proteins) formed the late-stage panel (A+T+ versus A+T−) (Figure S1B). Notably, 13 proteins were exclusive to the early-stage panel, six to the late-stage panel, and three were common to both, highlighting distinct proteomic signatures at different AD stages.

To ensure the robustness of our predictive models and mitigate overfitting, we compared multiple machine learning algorithms, including Random Forest (RF), XGBoost (XGB), Support Vector Machine, LASSO, and generalized linear model (GLM) (A+ versus A−T−: Table S5; A+T+ versus A+T−: Table S6). Although RF and XGB achieved perfect discrimination (AUC = 1.0), their performance suggested potential overfitting given the current sample size.²⁷ Therefore, we selected the GLM for its optimal balance of predictive performance, interpretability, and ability to avoid overfitting.

In the early-stage protein panel, synaptotagmin-12 (SYT12), tropomodulin-2 (TMOD2), calcium/calmodulin-dependent protein kinase type II subunit beta (CAMK2B), and microtubule-associated proteins 1A/1B light chain 3A (MAP1LC3A) were the most important proteins in the models (Figure S1C). These proteins are primarily involved in synaptic function and plasticity, with SYT12 serving as a presynaptic protein and CAMK2B playing a critical role in calcium signaling and synaptic plasticity.²⁸^,²⁹ Additionally, TMOD2 and MAP1LC3A are associated with cytoskeletal regulation and autophagy pathways, respectively,³⁰^,³¹ indicating multifaceted cellular maintenance mechanisms in AD pathology. For the late-stage panel, TMOD2, BLOC-1-related complex subunit 5 (BORCS5), and CAMK2B were identified as key proteins (Figure S1D). BORCS5 is integral to cellular degradation and autophagic processes,³² although its specific association with AD requires further investigation. The selection of these proteins underscores the progression of AD-related proteomic alterations from synaptic and structural dysfunctions to impaired cellular degradation mechanisms.

To elucidate the biological functions of the identified protein panels, we performed enrichment analysis (Figure S1E). Our enrichment analysis revealed that, during the early stages, there is a significant upregulation of proteins involved in synaptic function and plasticity pathways. This suggests a compensatory mechanism by which neurons enhance synaptic transmission and plasticity to counteract early Aβ toxicity. In contrast, the late stages of AD show protein enrichment in downregulated pathways related to energy metabolism, calcium homeostasis, and insulin signaling, indicating metabolic dysfunction and cellular stress contributing to neurodegeneration. These findings offer insights into the temporal progression of AD pathology, emphasizing early neuronal compensatory upregulation of synaptic function in response to Aβ accumulation and the subsequent metabolic dysfunction and calcium homeostasis disturbances associated with advanced stages characterized by both Aβ and tau pathologies.

Predictive performance for early-stage and late-stage protein panels

Next, we compared the accuracy of protein panels, established AD biomarkers, and baseline models (including age, gender, and APOE ε4 status) in distinguishing AT stages. The early-stage protein panel demonstrated superior accuracy (AUC = 0.95) in distinguishing A+ from A−T−, outperforming CSF Aβ42 (AUC = 0.90, p = 0.08) and baseline models (AUC = 0.78, p < 1.78 × 10⁻⁵) (Figure 4A). Similarly, the late-stage protein panel achieved an AUC of 0.92 in distinguishing A+T+ from A+T−, exceeding baseline models (AUC = 0.74, p = 0.001) and CSF Aβ42 (AUC = 0.70, p = 0.005) (Figure 4B). Sensitivity analyses excluding participants with missing proteomic data confirmed the consistency of these AUC values (Figure S2).

To evaluate the relative performance of our CSF-based predictive models against plasma-based AD biomarkers, we analyzed a subset of 57 participants from the ADNI cohort with available plasma data (STAR Methods). The early-stage protein panel achieved an AUC of 0.94 for distinguishing A+ from A−T−, and the late-stage panel reached an AUC of 1.00 for differentiating A+T+ from A+T−. These results were superior to plasma %p-tau217 (the ratio between phosphorylated and non-phosphorylated p-tau217)³³ (AUC = 0.85 and 0.88, respectively) and plasma p-tau217 alone (AUC = 0.82 and 0.87, respectively) (Figure S3).

We further investigated whether incorporating additional proteins could enhance the performance of existing AD biomarkers and baseline models. The p-tau181/Aβ42 ratio (AUC = 0.958) achieved an AUC of 0.958 in distinguishing A+ from A−T−, while adding four proteins (neurofilament heavy chain [NEFH], CCL25, TMOD2, and SMOC1) to CSF Aβ42 yielded a comparable AUC of 0.95 (p = 0.42) (Figure 4C). NEFH, a marker of neurodegeneration, was closely related to cognitive function.³⁴ For differentiating A+T+ from A+T−, combining CSF Aβ42 with four proteins (HAX1, BORCS5, CAMK2B, and GPI) resulted in an AUC of 0.91, which outperformed baseline models and the CSF p-tau181/Aβ42 ratio (Figure 4D).

Comprehensive analysis of 22 CSF proteins alongside AD biomarkers across AT stages revealed significant alterations in CSF Aβ42 and SMOC1 across all comparisons. Additionally, levels of CSF p-tau181, t-tau, CCL25, and NEFH varied within the AD continuum, although no significant differences were observed between A+ and A−T+ stages (Figure 4E). In addition, we compared the accuracy of 22 proteins and AD biomarkers individually in distinguishing AT status. The results showed that CSF Aβ42 (AUC = 0.895) performed best in distinguishing A+ from A−T−, followed by NEFH (AUC = 0.758), CSF p-tau181 (AUC = 0.752), and SMOC1 (AUC = 0.747) (Figure 4F). While HAX1 (AUC = 0.777), GPI (AUC = 0.739), and BORCS5 (AUC = 0.725) showed superior performance in distinguishing A+T+ from A+T−, the established AD biomarkers showed normal performance (AUC = 0.67–0.704) (Figure 4G).

To validate the robustness and generalizability of our findings, we conducted multiple sensitivity analyses. First, we examined subpopulations with assessment intervals between CSF proteomics and PET scans within 6 (Figure S4), 8 (Figure S5), and 10 years (Figure S6). In these subgroups, both protein panels and the 22 proteins maintained discriminative performance consistent with our primary results. Second, excluding 14 participants who transitioned from A− to A+ between initial and final PET assessments did not alter the outcomes of the protein panels and individual proteins (Figure S7). Third, restricting amyloid PET assessments to within 1 year of CSF proteomics resulted in 471 participants (Table S7), where protein panels and all proteins remained significantly associated with amyloid PET after FDR correction (Table S8). Additionally, the early protein panels continued to accurately discriminate A+ from A− (Table S9). Lastly, in the subset of 324 participants with available repeated amyloid PET measurements, we examined the longitudinal association between proteins and amyloid PET (Table S10). Utilizing a linear mixed model to explore longitudinal changes in amyloid PET, 12 of the 22 proteins and both protein panels were significantly associated with these changes (Table S11). These consistent results across various analyses underscore the robustness and clinical relevance of our CSF proteomic signatures in predicting AD PET staging.

Association with progression to dementia and rate of cognitive decline

We conducted a time-to-event analysis to evaluate the predictive capacity of two CSF protein panels for dementia onset in non-demented individuals over a 10-year follow-up period. Participants were categorized into high and low expression groups based on the median panel scores. Kaplan-Meier survival analysis demonstrated significantly different dementia-free survival probabilities between the high and low expression groups (see also Figure 5A and Table S12). Cox proportional hazard models revealed that individuals with high panel scores had a markedly increased risk of developing dementia compared to those with low scores. Specifically, early-stage panel exhibited a hazard ratio (HR) of 4.992 (95% confidence interval [CI]: 2.0–12.4, p = 5.49 × 10⁻⁴) and late-stage panel an HR of 5.965 (95% CI: 2.3–15.6, p = 2.80 × 10⁻⁴) in model 1, which adjusted for age, gender, and education. These associations remained robust after further adjustment for APOE genotype (model 2) and baseline clinical diagnosis (model 3) (Table S12).

Association with dementia progression and cognitive decline at 10-year follow-up

(A) Kaplan-Meier survival curves comparing dementia progression between low and high scores for the early-stage and late-stage protein panels. p values and hazard ratios (HRs) were obtained from Cox proportional hazards regression models, adjusted for age, gender, and years of education. The data underlying this figure are found in Table S12.

(B) Cognitive trajectories of MMSE scores between low and high scores for the early-stage and late-stage protein panels. Average regression lines for each group were fitted, with error bars representing 95% confidence intervals (CIs). Interaction p values for time were derived from linear mixed-effects models with random effects, adjusted for age, gender, and years of education. The data underlying this figure are found in Table S14.

(C) Kaplan-Meier survival curves comparing dementia progression between low and high levels of five specific proteins. p values and HRs were obtained from Cox proportional hazards regression models, adjusted for age, gender, and years of education. The data underlying this figure are found in Table S12.

(D) Association between 22 proteins and cognitive scores. FDR-adjusted p values and beta values were derived from multiple linear regression models, adjusted for age, gender, and years of education. Green, proteins selected in early and late stages; blue, early-stage proteins; red, late-stage proteins.

(E) Cognitive trajectories of MMSE scores between low and high levels of five proteins. Average regression lines for each group were fitted, with error bars representing 95% CIs. Interaction p values for time were obtained from linear mixed-effects models with random effects, adjusted for age, gender, and years of education. The data underlying this figure are found in Table S14.

To determine whether the identified protein panels could differentiate between slow and rapid cognitive decline, we employed linear mixed-effects models adjusted for age, gender, and education. Participants were stratified into high and low protein expression groups based on median levels. Cognitive decline was assessed using the Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale-Sum of Boxes (CDRSB), and Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS13). Significant differences in the regression slopes were observed between high and low expression groups for all cognitive measures (MMSE: p < 0.001, Figure 5B; CDRSB: p < 0.001, Figure S8; ADAS13: p < 0.001, Figure S9). Individuals with high protein panel scores exhibited accelerated cognitive decline over the 10-year period. These findings persisted across models 2 and 3, which included adjustments for APOE genotype and baseline diagnosis (ADAS13: Table S13; MMSE: Table S14; CDRSB: Table S15).

Further analysis focused on individual proteins within the panels to identify specific markers associated with dementia risk. Elevated levels of NEFH, YWHAG, SMOC1, CHN1, and CAMK2B were significantly linked to an increased risk of dementia, with HRs ranging from 3.271 to 5.131 (p < 0.005) (Figure 5C). Additionally, six of the remaining 17 proteins demonstrated significant risk differences between high and low expression groups (Table S12). Based on subsequent analysis using multiple regression analysis adjusted for age, gender, and years of education, we found the cross-sectional relationship between these 22 proteins and cognitive function (Figure 5D). Notably, five proteins (NEFH, YWHAG, SMOC1, CHN1, and CAMK2B) and nine of the remaining 17 proteins were significantly associated with the rate of cognitive decline (see also Figure 5E and Table S14). Interestingly, proteins SMOC1 and TMOD2 did not correlate with baseline MMSE scores but were linked to longitudinal declines in MMSE scores (Table S14). Consistent associations were observed when cognitive decline was measured using ADAS13 scores (Table S13) and CDRSB (Table S15), reinforcing the relevance of these proteins as biomarkers for AD progression.

Validation in external cohort and internal cohorts

To validate the predictive performance of our identified CSF protein panels, we analyzed data from the external Knight Alzheimer Disease Research Center (ADRC) cohort (N = 146) (Table S16). In this cohort, the interval between CSF collection and PET scan assessment was restricted to less than 6 months (STAR Methods). As expected, the early protein panel showed an AUC of 0.99 for distinguishing A+ individuals from A−T− controls. This performance significantly surpassed that of established CSF AD biomarkers (AUC range: 0.91–0.97, p < 0.002) and baseline models (AUC = 0.84, p = 3.26 × 10⁻⁵) (Figure 6A). Similarly, the late-stage protein panel effectively classified A+T+ individuals from A+T− subjects, attaining an AUC that outperformed both CSF AD biomarkers (AUC range: 0.55–0.69, p < 0.001 for p-tau181 and t-tau; p = 6.22 × 10⁻² for Aβ42) and baseline models (AUC = 0.83, p = 3.26 × 10⁻¹) (Figure 6B). These findings underscore the robustness of our protein panels in external validation settings, highlighting their superior performance over conventional biomarkers.

To further assess the clinical utility of our proteomic signatures, we validated our findings within an internal autopsy-confirmed cohort (N = 54). This cohort allowed for the evaluation of protein panel accuracy in differentiating stages of autopsy-confirmed AD pathology, the gold standard for AD diagnosis. ADNC was categorized as none/low (A−T−), intermediate/high (A+), intermediate (A+T−), and high (A+T+), focusing primarily on intermediate/high stages pertinent to AD pathology diagnosis.² The early-stage protein panel achieved perfect discrimination between ADNC none/low and ADNC intermediate/high stages (AUC = 1), outperforming the CSF p-tau181/Aβ42 ratio (AUC = 0.97) and a combination of the top four proteins from the early-stage panel (AUC = 0.96) (Figure 6C). Incorporating the top four proteins significantly enhanced the discriminative accuracy of CSF Aβ42 from an AUC of 0.89–1, compared to AD biomarkers and baseline models. Similarly, the late-stage protein panel excelled in distinguishing between ADNC intermediate and high stages, with significant improvements observed upon adding the top four proteins from this panel, particularly for CSF Aβ42 and baseline models (Figure 6D). Comprehensive correlation analyses revealed significant associations between individual proteins and the severity of AD neuropathological scores, including Thal, Braak, CERAD, and overall ADNC, after FDR correction. Specifically, CCL25, YWHAG, CHCHD7, HAX1, and TBCA were significantly correlated with all four neuropathological scores. Notably, translin (TSN) and CHN1 from the late-stage panel were exclusively associated with Braak score severity (Figure S8). TSN, a multifunctional protein involved in nucleic acid metabolism such as DNA repair and RNA processing, may contribute to neurodegeneration underlying AD pathogenesis.³⁵

To evaluate the prognostic value of our protein panels, we examined an independent non-demented cohort (N = 354) for dementia progression and cognitive decline. Individuals with elevated scores in either the early or late-stage protein panels exhibited a significantly higher risk of progression to dementia (see also Figure 6E and Table S17) and demonstrated accelerated cognitive decline over time (ADAS13: Table S18; MMSE: see also Figure 6F and Table S19; CDRSB: Table S20). Analysis of individual proteins within both panels indicated that participants with protein levels above the median had an increased risk of dementia progression, with NEFH and YWHAG showing the highest HR among the 22 proteins assessed (see also Figure 6E and Table S17). Additionally, in contrast to prior findings where 12 out of 22 proteins were associated with at least one cognitive scale, the larger sample size revealed that 21 out of 22 proteins correlated with at least one baseline cognitive score (Figure S9). Longitudinal analyses further demonstrated that individuals with high levels of 20 out of these 22 proteins experienced faster cognitive decline over a decade compared to those with lower levels (ADAS13: Table S18; MMSE: see also Figure 6F and Table S19; CDRSB: Table S20). These associations remained robust after adjusting for APOE genotype (model 2) and clinical diagnosis (model 3), underscoring the independent predictive value of both the protein panels and the individual proteins for dementia progression (Table S17) and cognitive decline (ADAS13: Table S18; MMSE: Table S19; CDRSB: Table S20).

Discussion

In this study, we integrated comprehensive CSF proteomic profiling with advanced machine learning techniques to identify two distinct protein panels capable of accurately discriminating the pathological stages of AD as determined by PET imaging. Specifically, we developed an early-stage protein panel to assess Aβ deposition and a late-stage panel to evaluate tau pathology severity. These protein signatures not only enhance the stratification of participants in clinical trials but also demonstrate potential as predictive markers for dementia progression and cognitive decline over a decade.

Our proteomic analysis revealed 262 proteins associated with amyloid PET and 949 proteins linked to tau PET, such as HAX1 and CCL25.³⁶^,³⁷^,³⁸^,³⁹ Pathway enrichment analysis highlighted distinct molecular signatures for amyloid and tau pathologies. Proteins associated with amyloid PET were predominantly involved in neuroinflammatory pathways, particularly chemokine receptor binding and cytokine activity, aligning with the current understanding of neuroinflammation’s role in Aβ-mediated AD pathology.⁴⁰^,⁴¹ Additionally, the enrichment of oxidoreductase activity and nucleoside metabolic processes suggests disruptions in cellular energy homeostasis and mitochondrial function, which are critical aspects of AD pathogenesis.¹⁹ Conversely, proteins linked to tau PET were enriched in pathways related to protein quality control and post-translational modifications, including ubiquitin-like protein conjugating enzyme activity and proteasomal degradation. These findings are consistent with the established mechanisms of tau protein clearance and hyperphosphorylation.⁴² Furthermore, the involvement of calcium-dependent protein kinase activity and mitogen-activated protein kinase signaling pathways in tau pathology indicates potential regulatory mechanisms that contribute to synaptic dysfunction and neuronal loss.⁴³ Notably, both Aβ- and tau-associated proteins showed convergent enrichment in glucose metabolic processes, highlighting a shared metabolic vulnerability that may represent a critical point in AD pathogenesis.⁴⁴

Leveraging these proteomic findings, we developed two discrete predictive models characterizing distinct stages of AD progression. Through machine learning approaches, we derived a 16-protein signature for early-stage prediction (A−T− vs. A+) and a 9-protein signature for late-stage differentiation (A+T− vs. A+T+), revealing stage-specific molecular landscapes that extend our understanding of AD pathogenesis. The early-stage signature exhibited significant enrichment in pathways governing synaptic function and plasticity, indicating that synaptic dysfunction may serve as an early response to the pathological changes occurring in AD.⁴⁵ Notably, we observed a significant involvement of microtubule-based transport processes, suggesting the activation of compensatory mechanisms in the early stages of the disease.⁴⁵ This compensatory response may help explain the preservation of cognitive function in some individuals despite the presence of underlying Aβ pathology.⁴⁶ Conversely, the late-stage signature demonstrated enrichment in pathways characteristic of advanced AD, encompassing disruptions in energy metabolism, calcium homeostasis, and insulin signaling. The coordinated alterations in these metabolic, calcium, and insulin pathways provide molecular evidence for the multi-factorial deterioration seen in advanced AD.⁴⁷^,⁴⁸^,⁴⁹ This complex pathway perturbation may underlie increased neuronal vulnerability at this stage, where compensatory mechanisms become overwhelmed, culminating in cognitive decline. These findings yield several key insights: (1) a comprehensive molecular map of disease progression, (2) potential mechanisms governing the transition from compensation to pathological deterioration, and (3) the significance of system-wide alterations in AD. The stage-specific molecular signatures suggest that therapeutic strategies should be tailored according to disease stage—targeting compensatory mechanisms early while employing multi-target approaches in advanced disease.

A key methodological strength of our study lies in utilizing PET staging as the primary outcome measure. PET and CSF biomarkers represent distinct approaches to measuring AD pathology, with PET providing direct visualization of aggregated amyloid plaques and tau deposits, while CSF biomarkers (e.g., Aβ42, p-tau181, and t-tau) reflect soluble forms of these pathologies. Autopsy studies have demonstrated that although CSF biomarkers effectively discriminate AD pathological stages, their accuracy remains lower than PET imaging.² Therefore, our study is based on amyloid and tau PET stages as the study outcome and head-to-head comparison with CSF AD biomarkers and final autopsy pathology validation to improve the robustness and reliability of the findings. The accuracy of the two protein panels identified in this study surpasses that of existing CSF AD biomarkers, and we further demonstrate that four proteins can significantly improve established CSF AD biomarkers and baseline models. Notably, the late-stage protein panel we discovered accurately captures amyloid and tau pathologies detected by PET, addressing a key limitation of current CSF biomarkers, which primarily reflect amyloid plaque changes but show reduced sensitivity to tau accumulation.⁵⁰ This limitation is further evidenced by recent phase 3 trials of gantenerumab, where anti-Aβ treatment affected amyloid PET and CSF/plasma biomarkers but not tau PET signals.⁵¹ Furthermore, the latest diagnostic criteria for AD proposed by the National Institute on Aging and the Alzheimer’s Association (NIA-AA) working group classify AD into four pathological stages based on Aβ and tau PET.¹³ However, corresponding CSF biomarkers for advanced PET-based disease stages are currently lacking. Previous studies have indicated that the microtubule-binding region of tau containing the residue 243 is a promising biomarker for tau PET with higher correlation than other established AD biomarkers,⁴ yet its accuracy in AT status discrimination requires further investigation. Moreover, plasma p-tau217 is another promising biomarker for identifying tau pathology stages,⁵²^,⁵³^,⁵⁴ with studies showing that plasma %p-tau217 performs comparably or even superior to CSF p-tau181/Aβ42.⁵⁴ In the present study, the protein panel we identified outperformed plasma %p-tau217 and p-tau217 in distinguishing PET stages. However, the limited number of participants in this subgroup (n = 57) may have reduced the statistical power, limiting the robustness of this conclusion. Overall, our findings suggest that the protein panels identified for late-stage disease hold promise in overcoming the limitations of existing AD biomarkers in identifying high tau pathology in amyloid-positive individuals.

The identified protein panels demonstrate significant potential for advancing clinical trials and patient care in AD. Their implementation in CSF offers distinct advantages, as CSF directly interfaces with brain pathophysiology and provides real-time insights into disease progression with minimal interference from peripheral factors.⁵⁵^,⁵⁶ In early disease stages, the panel enables sensitive detection of Aβ deposition, facilitating timely intervention for individuals at the onset of AD pathology. For later stages, the panel provides assessment of tau pathology as a cost-effective alternative to PET imaging, increasing accessibility for broader patient populations. Given the established relationship between anti-Aβ treatment efficacy and tau burden,⁷^,⁵⁷ these panels can enhance clinical trial stratification by identifying participants more likely to benefit from anti-Aβ therapeutics, thereby optimizing intervention outcomes. Moreover, the protein markers serve as reliable pharmacodynamic indicators, making them particularly valuable for assessing drug target engagement and therapeutic efficacy in clinical trials. This application is especially crucial given CSF biomarkers’ ability to reflect subtle changes in brain protein homeostasis that may not be detectable through other means.⁵⁸ Furthermore, the panels’ validated ability to predict cognitive decline and dementia progression over a decade positions them as valuable prognostic tools for individual risk stratification. These protein signatures could enable clinicians to identify high-risk individuals who may benefit from more intensive monitoring and early intervention strategies before significant symptoms manifest. In addition, the panels could facilitate clinical trial recruitment by identifying individuals at higher risk of cognitive decline and dementia progression, potentially reducing trial costs and increasing success rates through enriched participant selection. These characteristics, combined with the panel’s robust performance in distinguishing pathological stages, suggest significant potential for broader implementation in both clinical practice and therapeutic development programs.

Limitations of the study

Several limitations of this study warrant consideration. First, the median interval of 6.2 years between CSF sampling and PET assessment may affect protein panel performance due to potential transitions in amyloid and tau PET status. Although we addressed this through methodological approaches and sensitivity analyses, including validation in an independent cohort with CSF-PET intervals under 6 months, this temporal gap remains a consideration for interpretation. Second, while our analyses demonstrated robust protein associations with cognitive decline and dementia progression over 10 years, the predictive validity beyond this time frame requires investigation. Third, the promising comparison with plasma p-tau217 is limited by sample size (n = 57), necessitating validation in larger cohorts. Fourth, while blood-based tests would offer significant clinical advantages over CSF sampling due to their minimal invasiveness and greater feasibility for widespread implementation, the predictive performance of our identified CSF protein signatures when translated to blood markers was not investigated in this study. Fifth, our machine learning approach utilized the entire dataset for training due to sample size constraints, potentially affecting model generalizability. We implemented bootstrapping (100 iterations) and nested 10-fold cross-validation to mitigate overfitting risks. Sixth, our proteomics profiling relied solely on the SomaScan platform, lacking orthogonal validation with alternative proteomic technologies, which is important given potential measurement biases from varying aptamer reagent affinities.⁵⁹ Seventh, our current analysis used a median-based dichotomization to determine protein thresholds in a single cohort. While this approach effectively demonstrated the proteins’ prognostic potential, establishing clinically applicable thresholds will require validation in larger independent cohorts to ensure result robustness and generalizability. Eighth, the specificity of our panels for AD versus other neurodegenerative conditions remains to be established. Finally, validation across diverse ethnicities using various measurement technologies in large independent cohorts is necessary to enhance result robustness. Future studies incorporating larger samples and longitudinal biomarker measurements will be crucial for understanding the temporal dynamics and clinical utility of these proteins.

Resource availability

Lead contact

Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Jianping Jia (jjp@ccmu.edu.cn).

Materials availability

This study did not generate new materials.

Data and code availability

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This study does not generate new original data. All data used in this study were obtained from the ADNI and Knight ADRC cohorts. For ADNI, all variables used tables in this study are provided at https://zenodo.org/records/14866417, and the full datasets are available through ADNI upon reasonable request. For ADNI data access, please visit the ADNI website at www.adni-info.org, and request approval for access.
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For the Knight ADRC cohort used in the validation of this study, individual-level data and proteomics can be requested from the NeuroGenomics and Informatics Center (NGI) at https://neurogenomics.wustl.edu/open-science/resource-sharing-hub/ or from Database: NIAGADS, NG00130 .
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This paper does not report the original code. The code for analyses conducted in this study was derived from two previously published studies.⁶⁰^,⁶¹ The machine learning code employed is publicly accessible at https://zenodo.org/records/14866417.
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All software utilized in this study is publicly available. Summary results generated from this study are provided in Tables S3 and S4. A full list of open-source software used is available in the key resources table.
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Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Acknowledgments

The data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). Data used to prepare CSF SomaScan 7K proteomics were generated by the NeuroGenomics and Informatics Center at Washington University (https://neurogenomics.wustl.edu/). As such, the investigators within the NGI provided data but did not participate in analysis or writing of this report. A complete listing of NGI investigators can be found at https://neurogenomics.wustl.edu/about-us/leadership/. CSF Somalogic (7K) and Metabolomics (Metabolon HD4) data generation and QC was supported by grants from the National Institutes of Health (RF1AG074007 [PI: Yun Ju Sung], R01AG044546 [PI: C.C.], P01AG003991 [PI: John Morris and C.C.], RF1AG053303 [PI: C.C.], RF1AG058501 [PI: C.C.], and U01AG058922 [PI: C.C.]), the Chan Zuckerberg Initiative (CZI), and the Alzheimer’s Association Zenith Fellows Award (ZEN-22-848604, awarded to C.C.). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P30AG066444 (PI: John Morris and C.C.), P01AG03991 (PI: John Morris and C.C.), and P01AG026276 (PI: John Morris and C.C.). This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/), and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

We thank all the researchers and participants in the ADNI initiative. As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or in the writing of this paper. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. We express appreciation to contributors of Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer, Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Author contributions

Z.W. and J.J. conceived and designed the study. C.C. provided resources. Z.W., Y.C., K.G., M.A., J.T., and M.L. performed data acquisition and preprocessing. Z.W., Y.C., and J.J. analyzed and interpreted the data. Z.W. and Y.C. conducted the statistical analysis. K.G., M.A., J.T., and M.L. carried out the validation. Z.W. wrote the manuscript. B.Z., Y.N., M.C., Y.L., and J.J. revised the manuscript.

Declaration of interests

C.C. has received research support from GSK and Eisai. C.C. is a member of the scientific advisory board of Circular Genomics and owns stocks. C.C. is a member of the scientific advisory board of ADmit.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Deposited data

Analyzed summary statistics	This paper	Tables S3 and S4
ADNI data	ADNI	https://adni.loni.usc.edu/
Knight ADRC data	Knight ADRC data	https://dss.niagads.org/datasets/ng00130/

Software and algorithms

R 4.3.1	Open-source	https://cran.r-project.org/
ggplot2	Open-source	https://cran.r-project.org/web/packages/ggplot2/index.html
clusterProfiler 4.0	Yu et al.¹	https://doi.org/10.1016/j.xinn.2021.100141
tidyverse 2.0	Open-source	https://cran.r-project.org/web/packages/tidyverse/index.html
survival 3.5–7	Open-source	https://cran.r-project.org/web/packages/survival/index.html
survminer 0.4.9	Open-source	https://cran.r-project.org/web/packages/survminer/index.html
ggsci 3.0	Xiao et al.	https://github.com/nanxstats/ggsci
missRanger	Stekhoven et al.²	https://doi.org/10.1093/bioinformatics/btr597
pROC 1.18.4	Robin et al.³	https://doi.org/10.1186/1471-2105-12-77

Other

Code	This paper	https://zenodo.org/records/14866417
Table used in ADNI	This paper	https://zenodo.org/records/14866417

Open in a new tab

Experimental model and study participant details

Human subject characteristics

The ADNI cohort

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort is a global research initiative actively supporting investigations and treatments to impede Alzheimer’s Disease progression. The protocol of ADNI was approved by the Institutional Review Boards of all participating institutions. All participants provided written informed consent. ADNI was aimed to validate biomarkers for application in AD clinical treatment trials. Detailed information on recruitment and exclusion/inclusion criteria is available in the previous study.⁴¹ All participants were aged from 55 to 90 and underwent the standardized diagnostic assessment that rendered a clinical diagnosis of cognitively unimpaired (CN), mild cognitive impairment (MCI), or dementia. Further details on clinical conditions and ADNI criteria are accessible at https://adni.loni.usc.edu/wpcontent/uploads/2010/09/ADNI_GeneralProceduresManual.pdf.

The primary criteria for inclusion in this study were the availability of CSF proteomics, AD biomarkers, and PET data. The initial step involved evaluating whether participants had CSF proteomics and AD biomarker (Aβ42, p-tau181, and t-tau) data tested at the same time, enabling a direct comparison of their performance. Additionally, we evaluated the availability of amyloid PET and tau PET data in this cohort. Given that the ADNI cohort provides amyloid PET scans within a year of proteomic assessments but lacks corresponding tau PET scans within this same period, we included all available tau PET scans regardless of the interval between these scans and the CSF examination. For participants with more than three time points, we selected the sample corresponding to the most recent time point of the CSF examination. To ensure consistency, we restricted the analysis to amyloid PET scans performed within one year before or after the tau PET assessment. Eligible participants were also required to have clinical diagnoses available at each follow-up visit and to have completed at least three cognitive assessments within a maximum follow-up duration of ten years. Cognitive assessments included the Mini-Mental State Examination (MMSE), the Clinical Dementia Rating Scale-Sum of Boxes (CDRSB), and the Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS13). The discovery population comprised 136 non-demented individuals (CN and MCI participants), including 56 females (41.2%). The cohort was predominantly White (97.1%), with 34.60% carrying at least one APOE ε4 allele. Detailed participant characteristics are provided in Table 1. To account for potential gender-based differences, all necessary analyses were adjusted for gender or included gender as a model variable. The median interval time between the dates of CSF proteomics and PET scans was 6.2 years (IQR: 5.8–8.2 years). Of the participants, 119 had available amyloid PET scans within one year of CSF proteomic assessments and had two or more amyloid PET scans, with 14 participants transitioning from amyloid PET-negative (A-) to amyloid PET-positive (A+) between the initial and final PET examination. Furthermore, a subset of 57 participants from the FNIH Biomarkers Consortium project had both plasma biomarkers and CSF proteomics data available within a one-year window, enabling comparison of CSF biomarker performance with the promising biomarker, p-tau217.⁵⁴

Two independent subsets from the ADNI cohort were utilized to validate our findings. For pathological stage classification, we replaced PET imaging with postmortem data from the ADNI cohort. Within this cohort, 58 individuals underwent postmortem neuropathological assessments, with pre-mortem CSF proteomics and AD biomarker data available. Of these, four participants were part of our discovery cohort, thus the validation was performed using a separate group of 54 individuals. For the validation of prognostic findings, we included 490 non-demented individuals who had undergone at least three cognitive assessments over a 10-year follow-up period and had available CSF proteomics data. Of these, 136 were included in our discovery cohort. Consequently, the remaining 354 non-demented participants were used for validation of clinical progression.

The Knight ADRC cohort

We validated our findings with a cohort of 146 participants from the Knight Alzheimer Disease Research Center (ADRC). This cohort included 102 individuals classified as A-T-, 8 as A + T-, and 36 as A + T+. The average age of the participants was 69.14 years (SD 8.56), with 76 females (52.1%) and 58 individuals (39.7%) being carriers of the APOE ε4 allele (Table S16). The Knight ADRC at Washington University School of Medicine has conducted longitudinal assessments of community-dwelling adults aged 45 and older since 1979.³⁶ All studies were approved by the Human Research Protection Office at Washington University, and written informed consent was obtained from all participants. Participants in this study undergo comprehensive annual evaluations, which include clinical assessments, neuropsychological testing, neuroimaging, and biofluid sampling. The center generates multi-omics data—covering genetics, epigenomics, transcriptomics, proteomics, metabolomics, and lipidomics—from brain, CSF, and plasma samples. This extensive data collection aims to identify novel risk factors for dementia, protective genetic variants, and potential therapeutic targets.

Method details

CSF proteomics measures

CSF proteomics, including approximately 7000 proteins, was measured by SomaScan 7K (v4.1) in ADNI cohort. Detailed methodologies for the SomaScan 7K measurements within the ADNI are described in the previous study⁶²^,⁶³^,⁶⁴ and in the ADNI website (ADNI_CruchagaLab_Methods_CSF_SomaScan7K_Proteomic_Data_20230817.pdf). Briefly, all samples were sent to SomaLogic together to minimize batch effects and were randomly distributed across plates, as described previously.⁶⁵^,⁶⁶ SomaLogic conducted a randomization check on the samples, and no batch effects related to age, gender, or disease status were observed. Protein levels, reported as Relative Fluorescence Unit (RFU), underwent data normalization by SomaLogic. For quality control provided by SomaLogic according to an in-house protocol, aptamers were removed if they failed to meet either of the two criteria: the maximum absolute difference between the aptamer scale factor and median scale factor of any plate was ≥ 0.5, or the median cross-plate coefficient of variation (CV) was ≥ 0.15. Interquartile range (IQR) was computed for each aptamer based on log-10 transformed levels, and values beyond 1.5-fold of the IQR were replaced with NA values. Aptamers with <65% call rate or samples with the same rate were excluded. Call rates were recalculated, imposing a more stringent threshold of 85%. Additional information can be found at www.adni-info.org. For proteins with more than one peptide measured, we selected the peptides with the highest mean value of RFU, which left 6164 unique protein gene products for all subsequent analyses (Table S21). Missing data for CSF proteomics were imputed using the missRanger package,⁶⁷ employing a random forest approach.

In the Knight ADRC cohort, SomaScan 7K data processing followed similar procedures.³⁶ Briefly, raw protein expression values were used, with missing values imputed through random sampling with replacement. For proteins with duplicate analytes, we maintained consistency with ADNI selections (Table S21). Analysis included only samples with CSF collection and amyloid/tau-PET imaging within a 6-month interval.

CSF and plasma AD biomarker measures

In ADNI cohort, the levels of established CSF AD biomarkers (Aβ42, p-tau181, and t-tau) were measured using the electrochemiluminescence immunoassays Elecsys on a cobas 601 instrument. The ratio of CSF p-tau181/Aβ42 was calculated by dividing CSF p-tau181 by CSF Aβ42. The Knight ADRC cohort utilized the LumiPulse G platform for CSF AD biomarker measurements.

Plasma samples were collected, processed, and stored in accordance with ADNI protocols.³³ Plasma p-tau217 and the percentage of phosphorylated tau (%p-tau217) were quantified using mass spectrometry assays (C2N Diagnostics). The %p-tau217 was determined by dividing the p-tau217 concentration by the np-tau217 concentration and multiplying by 100, representing the percentage of phosphorylation occupancy.

Imaging acquisition and quantification

In ADNI cohort, amyloid-PET was acquired using [18F] Florbetaben (FBB) and [18F] Florbetapir (FBP) (referred to as AV45), and tau-PET using [18F] flortaucipir (FTP) (referred to as AV1451). The primary measurement for quantifying amyloid PET involved calculating the standard uptake value ratio (SUVR) in a cortical summary. This SUVR was determined by measuring uptake in the frontal, anterior/posterior, cingulate, lateral parietal, and lateral temporal regions, all normalized to the whole cerebellum reference region as defined by FreeSurfer. The cutoffs of Aβ positivity (A+) were set at SUVR >1.32 and 1.08 (or Centiloid >20) in FBP and FBB, respectively.⁶⁸ In this study, tracer-specific cortical summary SUVRs normalized to the whole cerebellum were converted to Centiloid for direct comparison of multiple tracers, as described in previous computational equations.⁶⁸^,⁶⁹

For tau-PET, the inferior cerebellar cortex was used as the reference region for calculating SUVRs. Early (Braak I) and intermediate (Braak III–IV) deposition regions were assessed, and the average value of Braak I and Braak III-IV was considered as the summary tau PET SUVR. The regions of interest (ROIs) corresponding to Braak stages can be found in the previous publication.⁷⁰ Tau positivity was calculated by if summary tau PET SUVR >1.3, as previous ADNI research reported.⁷¹ Based on amyloid and tau PET status, participants were categorized into four AT groups: A−T−, A + T−, A + T+, and A-T+.

In the Knight ADRC cohort, amyloid-PET imaging employed either florbetapir (FBP) or Pittsburgh Compound B (PiB), while tau-PET utilized AV-1451.⁷² The amyloid-PET analysis used the same summary regions, with SUVR values converted to Centiloids for standardization. The tau-PET analysis focused on Braak I and III-IV regions as well. Positivity thresholds were established at SUVR >1.49 for FBP and SUVR >1.21 for PiB for amyloid PET, and SUVR >1.22 for tau PET.

Assessment of clinical outcome

In our study, we employed both binary and continuous measurements to assess clinical progression. Initially, we investigated the progression from non-dementia (CN and MCI individuals) to dementia. For detailed information on clinical diagnosis, please refer to the previous publications.³ Subsequently, we utilized ADAS13, MMSE, and CDR scores to reflect overall cognitive trajectories.⁴¹ Higher scores on ADAS13 and CDR, and lower scores on MMSE, indicate poorer cognitive function.

Neuropathological examination

The neuropathological examinations followed established diagnostic criteria as previously detailed.²^,³ Adhering to NIA-AA guidelines, AD neuropathologic lesions were characterized by three histopathological scoring systems, including the Thal phase scoring system denoted Aβ plaque distribution (scores 0–5), the Braak stage scoring system depicted neurofibrillary tangle tau pathology distribution and density (scores 0–6), and the Consortium to Establish a Registry for AD (CERAD) scoring system indicated Aβ plaque density (scores 0–3). The composite AD neuropathological changes (ADNC) measure, integrating these aspects, was converted to a four-point scale: “none” (0), “low” (1), “intermediate” (2), and “high” (3). An ADNC score of 2 or higher signifies a diagnosis of autopsy-confirmed AD. For a detailed explanation of neuropathology scoring scales, please refer to the Neuropathology Data Sheet Coding Guidelines from the National Alzheimer’s Disease Coordinating Center.

Quantification and statistical analysis

All statistical analyses were conducted using R statistical software (version 4.3.1). A two-tailed p value of less than 0.05 was considered statistically significant. Multiple comparisons were controlled using the Benjamini-Hochberg false discovery rate (FDR) method. Confidence intervals (CIs) were calculated at the 95% level.

Association between proteomics and PET outcomes

CSF proteomic data were log₂-transformed to normalize distributions. Associations between CSF biomarkers and PET outcomes were evaluated using multiple linear regression (MLR) models, adjusting for age, gender, and the time interval between CSF collection and PET scanning. Proteins were Z-standardized to facilitate comparison of effect sizes, reported as standardized β coefficients. For binary PET outcomes (amyloid-PET A+/A- and tau-PET T+/T-), generalized linear models (GLMs) with logistic regression were employed, yielding odds ratios (OR) based on Z-standardized protein values. Protein trajectories across amyloid and tau PET progression stages were visualized using locally estimated scatterplot smoothing (LOESS) regression of Z-scored protein levels. To assess the association between proteins and longitudinal amyloid PET, linear mixed models with random intercepts and slopes were utilized, adjusting for age, gender, and years of education.

Discriminative performance of proteomics

The diagnostic performance of CSF biomarkers and multivariate models was evaluated using receiver operating characteristic (ROC) curve analysis for comparisons including A+ versus A-, T+ versus T-, A+ versus A-T-, and A+T+ versus A + T-. Differences between ROC curves were assessed using DeLong’s test. Effect sizes were quantified by the area under the curve (AUC) with corresponding 95% CIs.

Pathway analyses

To interpret the biological functions of the proteins, we performed pathway enrichment analysis using Gene Ontology (GO) annotations with the ClusterProfiler package in R.⁷³^,⁷⁴ Analysis parameters were set to include terms with p < 0.05 and a minimum count of 5 genes. The top five enriched pathways were selected from each GO category: Biological Process, Cellular Component, and Molecular Function.

Predictive modeling analysis

To identify proteomic signatures for A+ versus A-T- and A+T+ versus A + T-stages, LASSO regression was selected because of its capacity to identify a minimal number of predictors, computational efficiency allowing robust bootstrap resampling, and reduced susceptibility to overfitting, thus facilitating feature selection in separate subsets. Model training utilized the R packages caret⁷⁵ and glmnet⁷⁶ and the ROSE R package⁷⁷ was utilized to address case–control imbalance in the resampling. Nested 10-fold cross-validation (inner loop to determine regularization parameter, ʎ) was performed over 100 bootstrap samples (outer loop) drawn from the feature selection set. Each protein was scored based on its frequency of inclusion across 100 bootstrap models, with each bootstrap model randomly selecting 80% of the total population, resulting in scores from 0 to 100. Selected proteins were incorporated into multiple machine learning models (e.g., Random Forest [RF], XGBoost [XGB], Support Vector Machine [SVM], LASSO, and GLM) to generate probability scores for disease stage classification. Model performance was evaluated using ROC curve analysis.

Differential analysis

Two-sample t-tests were performed to compare protein levels across AT stages in four key comparisons: A+ versus A-T+, A+T+ versus A + T-, A + T-versus A-T-, and A+ versus A-T-. Effect sizes were reported as t-statistics for all significant comparisons.

Survival analysis

Time-to-event analysis was performed to assess the impact of panel scores and individual proteins on progression to dementia using Cox proportional hazards regression models. Participants were stratified into high and low expression groups based on the median values of their panel scores or individual protein levels. Three nested models were constructed: Model 1 adjusted for age, gender, and years of education; Model 2 additionally adjusted for APOE genotype; and Model 3 further included baseline clinical diagnosis. For each model, we calculated hazard ratios (HRs), 95% confidence intervals (CIs), and p values. To visualize the cumulative risk of dementia development, we generated Kaplan-Meier survival curves for the high and low expression groups. Time-to-event was calculated from the baseline assessment to either the diagnosis of dementia or the last follow-up visit for participants who remained dementia-free (censored observations).

Cognitive association analysis

Multiple linear regression was used to assess cross-sectional relationships between protein levels and cognitive measures (MMSE, CDRSB, and ADAS13). Three models were used: Model 1 adjusted for age, gender, and years of education; Model 2 further adjusted APOE genotype; Model 3 further adjusted clinical diagnosis. Longitudinal cognitive trajectories were analyzed using linear mixed-effects models with random intercepts and slopes for each subject, adjusting for the same covariates. Proteins were dichotomized at their median expression levels for visualization of cognitive trajectories.

External validation

The selected two protein panels were validated in an external Knight ADRC cohort to differentiate AT stages, compared with established CSF biomarkers. A new GLM was fitted for the selected proteins and CSF AD biomarkers to generate probability scores for disease classification. ROC curve analysis was conducted, and differences between ROC curves were assessed using DeLong’s test.

Internal validation

Model validation was performed using an independent longitudinal cohort from ADNI. The predefined GLM models were applied to generate model scores for this validation dataset. Cross-sectional analyses employed multiple linear regression to examine associations between protein levels and cognitive measures (MMSE, CDRSB, and ADAS13) using three sequential models: Model 1 adjusted for age, gender, and educational attainment; Model 2 additionally incorporated APOE status; Model 3 further included clinical diagnosis as a covariate. Longitudinal analyses utilized Cox proportional hazards regression to assess the relationship between protein levels and progression to dementia, while linear mixed-effects models evaluated cognitive decline over time, maintaining consistent covariate adjustment across all analyses.

For autopsy validation, neuropathological scores were dichotomized into two comparative analyses: 1) ADNC none/low versus intermediate/high (analogous to A+ versus A-T-); and 2) ADNC intermediate versus high (analogous to A+T+ versus A + T-). The selected proteins were integrated into new GLMs to predict these dichotomized pathological stages. Model performance was assessed through ROC curve analysis, with AUC values and corresponding 95% confidence intervals calculated. Furthermore, multivariable ordinal logistic regression analyses were conducted to evaluate associations between identified proteins and specific neuropathological measures (Thal, Braak, CERAD, and ADNC scores), with adjustment for age, gender, and the temporal interval between CSF collection and death.

Sensitivity analyses

To assess the robustness of our findings, we conducted multiple sensitivity analyses. First, we excluded any missing proteomics data to reassess the performance of protein panels in determining AT status using receiver ROC analysis. Second, we examined the temporal stability of the predictive performance of both protein panels and individual proteins for AT status by performing ROC analysis across different time intervals (6, 8, and 10 years) between CSF and PET assessments. Third, we assessed the impact of transitions in amyloid status by conducting ROC analysis after excluding participants who converted from A-to A+ during the study period. Fourth, we conducted a ROC analysis on a subset of participants who received amyloid PET imaging within one year of CSF collection to evaluate the predictive performance of our findings over a shorter time frame. Finally, we investigated the longitudinal relationships between two protein panels or individual proteins and amyloid burden using linear mixed models in participants who had repeated amyloid PET measurements (with at least two scans), while adjusting for age, gender, and education.

Published: March 20, 2025

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2025.102031.

Contributor Information

Carlos Cruchaga, Email: cruchagac@wustl.edu.

Jianping Jia, Email: jjp@ccmu.edu.cn.

Supplemental information

Document S1. Figures S1–S9 and Tables S1, S2, and S5–S20

mmc1.pdf^{(1.8MB, pdf)}

Table S3. Associations between proteins and amyloid PET, related to Figure 2

mmc2.xlsx^{(354.4KB, xlsx)}

Table S4. Associations between proteins and tau PET, related to Figure 3

mmc3.xlsx^{(846.5KB, xlsx)}

Table S21. Biological characterization of the proteins used in this study, related to STAR Methods

mmc4.xlsx^{(881.2KB, xlsx)}

Document S2. Article plus supplemental information

mmc5.pdf^{(24.6MB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S9 and Tables S1, S2, and S5–S20

mmc1.pdf^{(1.8MB, pdf)}

Table S3. Associations between proteins and amyloid PET, related to Figure 2

mmc2.xlsx^{(354.4KB, xlsx)}

Table S4. Associations between proteins and tau PET, related to Figure 3

mmc3.xlsx^{(846.5KB, xlsx)}

Table S21. Biological characterization of the proteins used in this study, related to STAR Methods

mmc4.xlsx^{(881.2KB, xlsx)}

Document S2. Article plus supplemental information

mmc5.pdf^{(24.6MB, pdf)}

Data Availability Statement

•
This study does not generate new original data. All data used in this study were obtained from the ADNI and Knight ADRC cohorts. For ADNI, all variables used tables in this study are provided at https://zenodo.org/records/14866417, and the full datasets are available through ADNI upon reasonable request. For ADNI data access, please visit the ADNI website at www.adni-info.org, and request approval for access.
•
For the Knight ADRC cohort used in the validation of this study, individual-level data and proteomics can be requested from the NeuroGenomics and Informatics Center (NGI) at https://neurogenomics.wustl.edu/open-science/resource-sharing-hub/ or from Database: NIAGADS, NG00130 .
•
This paper does not report the original code. The code for analyses conducted in this study was derived from two previously published studies.⁶⁰^,⁶¹ The machine learning code employed is publicly accessible at https://zenodo.org/records/14866417.
•
All software utilized in this study is publicly available. Summary results generated from this study are provided in Tables S3 and S4. A full list of open-source software used is available in the key resources table.
•
Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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PERMALINK

Cerebrospinal fluid proteomics identification of biomarkers for amyloid and tau PET stages

Zhibo Wang

Yuhan Chen

Katherine Gong

Bote Zhao

Yuye Ning

Meilin Chen

Yan Li

Muhammad Ali

Jigyasha Timsina

Menghan Liu

Carlos Cruchaga

Jianping Jia

Summary

Graphical abstract

Highlights

Introduction

Results

Study design and participants

Figure 1.

Table 1.

Proteomic profiling associated with amyloid PET

Figure 2.

Proteomic profiling associated with tau PET

Figure 3.

Proteomic signatures for distinguishing amyloid and tau PET stages

Predictive performance for early-stage and late-stage protein panels

Figure 4.

Association with progression to dementia and rate of cognitive decline

Figure 5.

Validation in external cohort and internal cohorts

Figure 6.

Discussion

Limitations of the study

Resource availability

Lead contact

Materials availability

Data and code availability

Acknowledgments

Author contributions

Declaration of interests

STAR★Methods

Key resources table

Experimental model and study participant details

Human subject characteristics

The ADNI cohort

The Knight ADRC cohort

Method details

CSF proteomics measures

CSF and plasma AD biomarker measures

Imaging acquisition and quantification

Assessment of clinical outcome

Neuropathological examination

Quantification and statistical analysis

Association between proteomics and PET outcomes

Discriminative performance of proteomics

Pathway analyses

Predictive modeling analysis

Differential analysis

Survival analysis

Cognitive association analysis

External validation

Internal validation

Sensitivity analyses

Footnotes

Contributor Information

Supplemental information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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