Proteomic Signatures of Protected APOE-ε4 Carriers Reveal Causal Pathways Associated with Delayed Alzheimer’s Disease Onset

Abstract

INTRODUCTION:

APOE-ε4 is the strongest common genetic risk factor for Alzheimer’s disease (AD), yet many carriers remain cognitively unimpaired into late life. We tested whether a protected-ε4-first proteomic approach could identify plasma proteins associated with delayed clinical onset among ε4 carriers.

METHODS:

We analyzed harmonized plasma proteomics from the Global Neurodegeneration Proteomics Consortium. Protected ε4 carriers (ε3/ε4 aged ≥75 years; ε4/ε4 aged ≥65 years; CDR=0; n=456) were compared with ε4 carriers with AD (n=1,096). Protein-wise linear models adjusted for age, sex, ε4 dosage, and plasma proteomic principal components. Top signals were integrated with high-confidence loss-of-function burden testing and plasma/CSF Mendelian randomization.

RESULTS:

ε4 protected was associated with 721 protein levels. Integrated analyses prioritized proteins linked to ε4-modified disease biology, including LILRA5, DBI, BPNT1, PTEN, EPHA1, and PCDH10, and proteins aligned with broader AD-related change, including OMG, SELENOW, VAT1, and TPPP3. TREM2 and ACE were also identified, providing internal biological validation of the approach.

DISCUSSION:

A protected-ε4-first plasma proteomic strategy highlights immune, synaptic, metabolic-stress, and myelin/axonal pathways that may delay AD onset and helps prioritize candidate ε4-specific modifiers for prevention-focused therapeutics.

Background

Alzheimer’s disease (AD) is the most common cause of dementia and a major global public health challenge. Recent estimates indicate that tens of millions of people worldwide are living with dementia, and the number of individuals across the AD continuum is expected to rise sharply with population aging [1,2]. Contemporary biological frameworks define AD by its underlying pathologic processes rather than by symptoms alone, emphasizing that amyloid-β deposition, tau pathology, and neurodegeneration accumulate over a prolonged preclinical period before overt dementia emerges [3,4]. This extended interval between biological onset and clinical diagnosis creates an important opportunity for prevention-oriented discovery, particularly if mechanisms that delay symptom onset can be identified in living humans.

Genetic risk for AD is substantial, but not deterministic. The APOE-ε4 allele remains the strongest common genetic risk factor for late-onset AD, with dose-dependent effects on risk and age at onset [5,6]. However, APOE-ε4 is neither necessary nor sufficient for AD: many ε4 carriers never develop dementia, and estimated absolute risks vary by age, sex and ancestry [5]. Studies of APOE ε4 carriers therefore provide a particularly informative setting in which to search for protective factors that modify the relationship between inherited risk and clinical outcome [7–9].

Conceptually, this strategy draws on the literature distinguishing resistance from resilience in AD. Resistance refers to lower-than-expected pathology given risk, whereas resilience refers to better-than-expected cognitive or clinical function despite pathology [7]. In a research setting where biomarker is unavailable and subjects cannot be classified as either resistant or resilient, we favor the more agnostic term “protected”. In many observational cohorts, however, complete amyloid and tau biomarker data are unavailable, making delayed clinical onset among genetically high-risk individuals a pragmatic phenotype for enrichment of protective biology. In the APOE-ε4 context, defining cognitively unimpaired older ε4 carriers as resilient therefore provides an operational framework to identify biological factors associated with delayed dementia onset [7,8].

Blood-based proteomics offers a scalable strategy for discovering such biology in living humans. Large affinity-based platforms, including aptamer-based assays such as SomaScan, can quantify thousands of plasma proteins simultaneously and have shown suitable reproducibility for population-scale biomarker discovery studies [10,11]. These technologies enable systems-level analyses in cohorts that are substantially larger than those typically available for cerebrospinal fluid or neuroimaging studies [10,12,13]. The Global Neurodegeneration Proteomics Consortium (GNPC) recently harmonized multi-cohort plasma proteomic data at unprecedented scale, improving power for biomarker and target discovery across neurodegenerative diseases and aging [12]. However, an important limitation of conventional case–control proteomic analyses is that many proteins associated with AD diagnosis or cognitive decline may reflect downstream responses to neurodegeneration, inflammation, frailty, medication, or comorbidity rather than upstream modifiers of disease onset [7,12,13].

Human genetics can help address this limitation through causal triangulation. Large pQTL resources have now mapped genetic regulators of plasma and cerebrospinal fluid protein levels, providing instruments for causal inference and target prioritization [14–16]. Two-sample Mendelian randomization using pQTLs can strengthen inference regarding whether altered protein abundance is more likely to lie upstream of disease risk, particularly when combined with complementary evidence from colocalization and disease genetics [15–17]. In parallel, rare-variant gene-based analyses in large sequencing cohorts can identify genes in which predicted loss-of-function variants are enriched in AD, offering an independent line of evidence for biological relevance [18,19]. Integrating proteomic associations with orthogonal human genetic evidence therefore increases confidence that prioritized proteins are not merely correlates of disease state but candidate drivers or modifiers of disease onset [15,16,18,19].

Here, we applied a protected APOE ε4-focused framework to large-scale plasma proteomics. We enriched for older ε4 carriers who remained cognitively normal at ages when dementia risk is high in ε4 carriers, identified plasma proteins associated with this protected phenotype, and prioritized proteins supported by independent human genetic evidence. We hypothesized that proteins supported across proteomic and genetic analyses would highlight biological processes that actively delay clinical onset of AD.

Methods

Study design overview

We applied a protected-ε4-first plasma proteomic framework in the Global Neurodegeneration Proteomics Consortium (GNPC) to identify proteins associated with delayed clinical onset among APOE-ε4 carriers (Figure 1). The primary GNPC analysis compared older cognitively unimpaired ε4 carriers (“protected” ε4 carriers) with ε4 carriers affected by Alzheimer’s disease (AD). To contextualize ε4-specific findings within broader AD-related proteomic biology, we also performed population-wide plasma proteomic analyses across all APOE genotypes, modeling diagnostic categories together with APOE ε2 and ε4 dosage and APOE ε4-by-diagnosis interaction terms. Candidate proteins from the protected-ε4 analysis were subsequently integrated with external human genetic evidence from gene-based loss-of-function burden testing and Mendelian randomization.

Figure 1. Protected-ε4-first plasma proteomics and genetic triangulation framework.

Harmonized GNPC EDTA plasma proteomic data were used to compare clinically protected older APOE-ε4 carriers with APOE-ε4 carriers with Alzheimer’s disease. Protein-wise linear models identified protected-ε4-associated proteins, which were then interpreted in the context of population-wide diagnosis and APOE ε4-by-diagnosis analyses and prioritized through orthogonal human genetic evidence, including high-confidence loss-of-function burden testing and plasma/cerebrospinal fluid Mendelian randomization. GNPC, Global Neurodegeneration Proteomics Consortium.

GNPC plasma proteomic cohorts and preprocessing

We used harmonized plasma proteomic data from multiple contributing GNPC cohorts. Analyses were restricted to EDTA plasma study samples. Protein measurements generated on SomaLogic SomaScan platforms were assembled into a unified plasma proteomic matrix, with removal of duplicate analyte columns and recoding −1 to missing. Based on the number of available analytes, samples were classified according to the SomaScan panel version (v3, v4, or v4.1).

Clinical variable harmonization and diagnostic definitions

To improve comparability across contributing GNPC cohorts, we harmonized measures of clinical severity using Clinical Dementia Rating (CDR) whenever available. For participants without a recorded CDR value, we derived an approximate CDR category from available cognitive screening data using prespecified mappings for the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). MMSE scores of 27–30, 21–26, 11–20, and 0–10 were mapped to CDR 0, 0.5, 1, and 2, respectively; the corresponding MoCA ranges were 26–30, 18–25, 11–17, and 0–10. Values coded as −1 were treated as missing.

To harmonize diagnoses across contributing cohorts, we used a rule-based approach centered on clinical severity while retaining a distinct category for non-AD neurodegenerative disorders. Participants with Parkinson’s disease, frontotemporal dementia, or amyotrophic lateral sclerosis were classified as non-AD neurodegenerative disease. For all other participants, final diagnostic assignment was based primarily on Clinical Dementia Rating (CDR): CDR = 0 defined cognitively normal controls, CDR = 0.5 defined mild cognitive impairment / subjective cognitive impairment (MCI/SCI), and CDR > 0.5 defined AD. If CDR was unavailable, we used the original cohort diagnosis variables, assigning AD when an AD diagnosis flag was present, MCI/SCI when an MCI/SCI flag was present, and control only when no disease flag was present and the available clinical information was consistent with normal cognition. This approach reduced between-cohort diagnostic heterogeneity, prioritized clinically interpretable severity categories, and preserved non-AD neurodegenerative disorders for secondary analyses.

APOE harmonization and analytic cohorts

APOE genotype was harmonized across GNPC cohorts and represented as ε2 and ε4 allele dosages for regression analyses. Sex was similarly harmonized for use as a covariate. The primary protected-ε4 analysis focused on APOE ε3/ε4 and ε4/ε4 carriers. Protected ε4 carriers were defined as cognitively unimpaired participants at ages when dementia risk is expected to be high among ε4 carriers, specifically ε3/ε4 individuals aged 75 years or older and ε4/ε4 individuals aged 65 years or older. Affected ε4 carriers were defined using the same genotypes but included participants classified as having AD. Accordingly, the primary analysis compared protected versus affected ε4 carriers, capturing delayed clinical onset across the AD spectrum rather than AD alone. For secondary population-wide analyses, we included all participants with available plasma proteomics, complete covariate data, and one of four harmonized diagnostic categories: cognitively normal control, AD, MCI/SCI, or non-AD neurodegenerative disease. When repeated visits were available, the last available visit was retained for analysis.

Plasma protein quality control, imputation, and principal components

Protein-level preprocessing was performed using the cleaned GNPC plasma proteomic matrix. Quality control followed an interquartile range (IQR)-based outlier detection strategy, in line with the GNPC plasma proteomics pipeline [20]. After outlier masking, samples and analytes were filtered according to call-rate thresholds, and remaining missing values were imputed using SoftImpute. Protein abundances were then transformed as log10(x+1). Principal component analysis was applied to the cleaned log-transformed matrix, with removal of principal component outliers followed by recomputation of the final proteomic principal components used as covariates in downstream models. All primary analyses reported here were based on the final IQR-QC, SoftImpute-completed, and PC-filtered plasma dataset.

GNPC proteomic association analyses

For the primary protected-ε4 analysis, we performed protein-wise linear regression using HC3 robust standard errors, with log10-transformed plasma protein abundance as the dependent variable. The primary comparison was between protected and affected ε4 carriers, where affected carriers included participants with AD. Models were adjusted for ε4 allele dosage, age at visit, sex, and the first three plasma proteomic principal components. Multiple testing was controlled using the Benjamini–Hochberg false discovery rate (FDR).

For secondary population-wide analyses, we performed protein-wise linear regression across all APOE genotypes, again using log10-transformed plasma protein abundance as the outcome. Covariates included ε4 dosage, ε2 dosage, age at visit, sex, and harmonized diagnostic categories for AD, MCI/SCI, and non-AD neurodegenerative disease, with cognitively normal controls as the reference group. To assess whether proteomic associations differed according to ε4 status, we additionally included ε4-by-AD and ε4-by-MCI/SCI interaction terms. The first three plasma proteomic principal components were included in all models. These analyses provided the broader diagnosis-related and ε4-modified proteomic signals used for subsequent integration with the protected-ε4 analysis.

Integration of protected-ε4 and population-wide proteomic signals

To identify the most robust candidate proteins, we integrated the protected-ε4 analysis with the broader GNPC population-wide proteomic analyses. Specifically, we considered three complementary domains of evidence: proteomic signals associated with AD-spectrum diagnosis, proteomic signals modified by ε4 status, and proteomic signals associated with protection among ε4 carriers. Within each analysis, statistical significance was defined using an FDR threshold of 0.05. We then evaluated concordance across analyses by examining whether proteins showed nominally significant associations in the same direction of effect across these complementary domains. This integration strategy was designed to prioritize proteins supported both by protected-ε4-specific and more general disease-related proteomic patterns.

Loss-of-function burden analyses

To provide orthogonal human genetic support, we incorporated gene-based high-confidence lossof-function burden results from a population-scale whole-genome sequencing analysis across UK Biobank, All of Us, and ADSP [19]. In that study, Alzheimer’s disease and dementia cases and controls were defined within each cohort using prespecified cohort-specific criteria [19], rare coding variants were annotated on GRCh38 using Ensembl VEP with LOFTEE [18] high-confidence loss-of-function classification, and gene-based burden testing was performed with REGENIE [21] with adjustment for age, sex, ancestry principal components, relatedness, and APOE ε4 and ε2 dosages. In the present manuscript, these results were used only as external gene-level support for prioritized GNPC proteins rather than as a primary discovery analysis.

Mendelian randomization analyses

To provide orthogonal genetic support for prioritized proteins, we performed two-sample Mendelian randomization (MR) using protein quantitative trait loci (pQTLs) as exposure instruments and Alzheimer’s disease GWAS summary statistics [MVP preprint to be posted] as the outcome. Plasma pQTL instruments were derived from large-scale proteogenomic resources including deCODE SomaScan and UK Biobank Olink datasets [16], whereas cerebrospinal fluid (CSF) pQTL instruments were obtained from a large CSF proteogenomic meta-analysis [15].

For each protein, candidate pQTL instruments were selected from variants associated with protein abundance and present in the Alzheimer’s disease GWAS dataset. In the plasma analyses, instruments were required to meet a significance threshold of P < 5 × 10; in the CSF analyses, a more permissive threshold of P < 5 × 10 was used because of the smaller sample size of the CSF pQTL studies. To reduce redundancy and limit bias from correlated signals, instruments were restricted to one variant per 1 Mb region.

Exposure and outcome datasets were harmonized by effect allele before MR analysis. We focused primarily on inverse-variance weighted (IVW) estimates after exclusion of highly pleiotropic instruments, where pleiotropy was operationally defined as instrument variants associated with more than three proteins across the instrument-selection framework. When multiple instruments were available, MR results were generated using IVW together with sensitivity methods including weighted median and MR-Egger. When only a single instrument was available, the causal estimate was obtained using the Wald ratio.

For the current study, we prioritized IVW and Wald ratio results derived from non-pleiotropic instruments. These MR findings were then integrated with the GNPC proteomic and loss-of-function burden results to help prioritize proteins with convergent evidence for a potential role in delaying Alzheimer’s disease onset.

Ethics and approvals

Each contributing cohort obtained approval from its local Institutional Review Board or Ethics Committee, and all participants gave written informed consent. Human studies were conducted in accordance with the Declaration of Helsinki. The current study protocol was granted an exemption by the Stanford University institutional review board because the analyses were carried out on deidentified data; therefore, additional informed consent was not required.

Results

Study population and analytic cohorts

After preprocessing and retention of participants present in the final IQR-based analytic datasets, the population-wide analysis included 9,734 individuals, comprising 4,659 cognitively unimpaired participants, 1,968 with MCI/SCI, 2,251 with AD, and 856 with non-AD neurodegenerative disease (Supplementary Table S1). In the protected-ε4 analysis, the current final uploaded analytic set included 456 protected ε4 carriers and 1,096 affected ε4 carriers with AD (Table 1). Across the broader GNPC sample, ε3/ε3 was the most common genotype, whereas ε4/ε4 carriers were enriched among AD cases relative to cognitively unimpaired participants. In the protected-ε4, unaffected participants were older than affected ε4 carriers by design, while the sex distribution was similar between groups.

Table 1.

Demographics of the protected-ε4 analysis

	Resilient ε4			Affected ε4
APOE genotype	Carriers, N	Female, N (%)	Age, mean (SD)	Carriers, N	Female, N (%)	Age, mean (SD)
All	456	271 (59.4%)	79.7 (5.3)	1,096	649 (59.2%)	74.7 (11.5)
ε3/ε4	392 (86.0%)	233 (59.4%)	80.6 (4.5)	888 (81.0%)	527 (59.3%)	75.4 (11.6)
ε4/ε4	64 (14.0%)	38 (59.4%)	73.9 (6.2)	208 (19.0%)	122 (58.7%)	71.5 (10.4)

Proteome-wide landscape of protected-ε4-analysis

The protected-ε4 analysis identified a broad plasma proteomic signature associated with preserved cognition among older APOE ε4 carriers (Figure 2; Supplementary Table S2). In total, 721 of 7,588 assayed protein measures were associated with protected-ε4 at FDR < 0.05, including 386 proteins that were relatively higher in protected ε4 carriers and 335 that were relatively higher in affected ε4 carriers (Supplementary Table S2). Among the strongest signals enriched in protected ε4 carriers were NPTXR, SELENOW, ENO3, ENO2, LUM, NT5C, and NRCAM, whereas PLA2G7, SNAP23, TINAGL1, ODC1, AGRP, CD81, and APOB were among the strongest signals enriched in affected carriers.

Figure 2. Proteome-wide landscape of protected-ε4.

Volcano plot of the primary protected-ε4 analysis comparing protected APOE ε4 carriers with APOE ε4 carriers with Alzheimer’s disease. The x-axis shows the model effect estimate and the y-axis shows −log10(p). Negative β values indicate proteins relatively higher in protected ε4 carriers, whereas positive β values indicate proteins relatively higher in affected ε4 carriers. The horizontal dashed line denotes the false discovery rate threshold, and selected prioritized proteins are annotated.

Taken together, these results suggest that protected-ε4 is associated with widespread plasma protein differences rather than a limited set of isolated analytes. Broadly, proteins relatively higher in protected ε4 carriers included neuronal, synaptic, and structural candidates, whereas proteins relatively higher in affected ε4 carriers more often reflected inflammatory, lipid-related, and injury-associated biology.

Integration with population-wide GNPC analyses

In the population-wide analysis, plasma proteomic differences associated with each of three clinical diagnoses versus controls were widespread (Figure 3; Supplementary Tables S3 and S4). AD was associated with 3,245 protein measures at FDR < 0.05, MCI/SCI with 3,153, and non-AD neurodegenerative disease with 3,857. In contrast, APOE ε4-by-diagnosis interaction effects were much more selective, with 130 significant protein measures for ε4-by-AD and only 13 for ε4-by-MCI/SCI (Supplementary Tables S3 and S4). These findings indicate that broad diagnosis-related proteomic differences are common in plasma, whereas APOE ε4-modified disease effects are comparatively restricted, supporting the value of the protected-ε4-first framework for enriching more specific biology.

Figure 3. Population-wide diagnosis and APOE-ε4 interaction analyses.

Left, volcano plot of the population-wide AD versus cognitively unimpaired comparison across the full GNPC sample. Right, volcano plot of the APOE ε4-by-AD interaction analysis. The x-axis shows the model effect estimate and the y-axis shows −log10(p). Negative β values indicate lower protein levels in AD or in the APOE ε4-by-AD interaction term, and positive β values indicate higher levels. Selected prioritized proteins are annotated. AD, Alzheimer’s disease.

Among the strongest AD-associated proteins in the population-wide models, SERPINA3 and SMOC1 were increased in AD, whereas VAT1, NPTXR, ENO3, and ANTXR2 were decreased. The strongest ε4-by-AD interaction signals included TBCA, PEA15, TCEAL5, PFKM, SPC25, and NPTXR. Taken together, the population-wide analysis recapitulated broad AD-related plasma proteomic remodeling while also identifying a smaller subset of proteins whose disease associations differed according to APOE-ε4 status.

Integration of protected-ε4 and population-wide signals

To place the protected-ε4 findings in the context of the broader GNPC disease-associated proteomic landscape, we examined overlap between proteins associated with protected-ε4 and those associated with AD or with ε4-by-AD interaction effects in the population-wide analysis. Of the 721 protein measures associated with protected-ε4 at FDR < 0.05, 458 were also significant in the population-wide AD model, and 440 of these showed concordant direction of effect (Supplementary Table S5). In addition, 49 were significant in the ε4-by-AD interaction model, and all 49 were directionally concordant with the protected-ε4 association. When false-discovery-rate correction was recalculated within the subset of protected-ε4-associated proteins, 474 remained significant for AD and 169 remained significant for the ε4-by-AD interaction, again with predominantly concordant directionality. Together, these findings indicate that a substantial proportion of the protected-ε4 signal overlaps with broader AD-related proteomic change, whereas a smaller subset appears to capture more selective ε4-modified disease biology (Figure S1).

This integrated analysis also helped distinguish proteins associated with general AD-related biology from those showing more specific evidence of ε4-modified disease association. For example, NPTXR, VAT1, ENO3, and SERPINA3 were identified both in the protected-ε4 analysis and in the broader AD case–control analysis, consistent with a relationship to general disease-associated proteomic change. By contrast, DBI, BPNT1, PCDH10, and one LILRA5 aptamer showed evidence of ε4-by-AD interaction, suggesting that their disease associations may be modified by ε4 status. These results provided the basis for subsequent prioritization of proteins supported across complementary lines of evidence.

Orthogonal genetic support and candidate prioritization

Among proteins associated with protected-ε4, several showed convergent support across complementary analytic frameworks, including the population-wide GNPC models, gene-based high-confidence loss-of-function burden testing, and plasma or cerebrospinal fluid Mendelian randomization (Figure 4; Table 2). This convergence strengthens prioritization by identifying proteins supported not only by the protected-versus-affected ε4 comparison, but also by broader disease-related proteomic patterns and independent human genetic evidence.

Figure 4. Integrated evidence across proteomic and genetic analyses.

Heatmap summarizing direction and strength of evidence for prioritized proteins across the protected-ε4 analysis, the population-wide AD analysis, the APOE ε4-by-AD interaction analysis, high-confidence loss-of-function burden testing, and plasma/cerebrospinal fluid Mendelian randomization. Color indicates signed evidence score based on direction of effect and statistical strength. In-cell significance symbols denote nominal evidence thresholds: * P < 0.05, ** P < 0.01, and *** P < 0.001. For the proteomic analyses, false discovery rate significance is reported separately in the corresponding results tables and volcano plots. AD, Alzheimer’s disease; MR, Mendelian randomization.

Table 2. Detailed integrated summary of prioritized proteins across proteomic and genetic analyses.

For each prioritized protein, the table reports the corresponding proteomic assay, effect estimates and p-values from the protected-ε4, AD, and ε4-by-AD analyses, high-confidence loss-of-function burden testing results, and the best available plasma and cerebrospinal fluid Mendelian randomization results.

Protein	ε4-Resilicncc	AD	ε4*AD	LoF Burden	MR plasma	MR CSF
DBI	−0.020 (p=1.36e,–04)	0.008 (p=0.015)	−0.015 (p=1.52e–05)	0.712 (p=0.004)	Wald: −0.016 (p=0.478)	IVW: 0.020 (p=0.154)
BPNT1	−0.028 (p=0.001)	−0.000 (p=0.960)	−0.025 (p=5.93e–05)	0.524 (p=0.001)	IVW: −0.006 (p=0.961)	IVW: 0.016 (p=0.220)
PTEN	−0.021 (p=5.56e–04)	−0.006 (p=0.103)	−0.012 (p=0.003)	1.235 (p=0.002)	Wald: 0.163 (p=0.253)	IVW: 0.006 (p=0.733)
LILRA5	−0.028 (p=5.20e–06)	0.002 (p=0.666)	−0.017 (p=3.79e–04)	−0.258 (p=0.109)	IVW: −0.047 (p=0.013)	IVW: 0.031 (p=0.010)
OMG	−0.031 (p=0.002)	−0.040 (p=1.65e–l 1)	−0.010 (p=0.145)	0.104 (p=0.812)	IVW: −0.213 (p=0.003)	IVW: −0.010 (p=0.362)
EPHA1	−0.029 (p=0.002)	−0.004 (p=0.454)	−0.015 (p=0.018)	−0.045 (p=0.509)	IVW: −0.119 (p=0.017)	IVW: 0.019 (p=0.012)
PCDH10	−0.023 (p=6.53e–04)	0.002 (p=0.719)	−0.018 (p=2.83e–04)	−0.386 (p=0.407)	IVW: −0.017 (p=0.026)	IVW: 0.011 (p=0.584)
TREM2	−0.025 (p=0.002)	0.018 (p=0.001)	−0.006 (p=0.346)	0.931 (p=1.27e–09)	IVW: −0.142 (p=3.75e–23)	IVW:−0.125 (p=4.86e–05)
ACE	−0.018 (p=0.003)	−0.014 (p=2.57e–04)	−0.007 (p=0.111)	0.171 (p=0.031)	IVW: −0.145 (p=2.65e–12)	IVW: −0.080 (p=0.008)
SELENOW	−0.068 (p=9.23e–13)	−0.024 (p=6.24e–05)	−0.026 (p=1.13e–04)	0.045 (p=0.833)		Wald: 0.086 (p=0.044)
NRBP1	0.020 (p=4.29e–05)	0.007 (p=0.019)	0.015 (p=1.80e–05)	−1.213 (p=0.077)	IVW: −0.088 (p=7.11e–04)	IVW: −0.010 (p=0.596)
TPPP3	−0.019 (p=1.95e–04)	−0.013 (p=9.20e–05)	0.001 (p=0.736)	−0.515 (p=0.041)	IVW: −0.028 (p=0.745)	IVW: 0.049 (p=0.002)
CD247	0.018 (p=0.002)	0.012 (p=6.73e–04)	0.009 (p=0.024)	−0.434 (p=0.003)	Wald: 0.251 (p=0.019)	IVW: 0.008 (p=0.539)
F2	0.008 (p=0.005)	−0.003 (p=0.085)	0.005 (p=0.020)	0.550 (p=0.015)	IVW: 0.083 (p=0.011)	IVW: 0.008 (p=0.605)
VAT1	−0.052 (p=3.83e–07)	−0.074 (p=1.33e–31)	0.015 (p=0.040)	0.189 (p=0.441)	IVW: −0.097 (p=0.003)	IVW: 0.002 (p=0.887)

A first group of proteins combined protected-ε4 association with evidence of ε4-modified disease effects in the population-wide models. These included DBI, BPNT1, PTEN, LILRA5, EPHA1, PCDH10, NRBP1, CD247, and F2, suggesting that their associations are not limited to general case–control differences but may be particularly relevant to ε4-associated disease biology. Within this set, DBI, BPNT1, and PTEN also showed supportive high-confidence loss-of-function burden results, whereas LILRA5, EPHA1, PCDH10, NRBP1, CD247, and F2 had additional support from plasma and/or CSF Mendelian randomization. These proteins were not uniform in direction: DBI, BPNT1, PTEN, LILRA5, EPHA1, and PCDH10 were relatively higher in protected ε4 carriers, whereas NRBP1, CD247, and F2 were relatively higher in affected ε4 carriers.

A second group aligned more closely with broader AD-related proteomic change. OMG, ACE, SELENOW, VAT1, and TPPP3 were all associated with protected-ε4 and also showed lower levels in AD in the population-wide analysis. Within this set, ACE, OMG, VAT1, and TPPP3 had additional support from plasma or CSF Mendelian randomization, and TPPP3 also showed supportive loss-of-function burden evidence. In addition, TREM2 provided a strong positive-control signal, showing association with protected-ε4 together with robust support from loss-of-function burden testing and both plasma and CSF Mendelian randomization.

Taken together, these findings suggest that the protected-ε4-first framework captures at least two complementary patterns (Figure 4; Figure S2): proteins that may reflect broader AD-related biology and proteins that may be more informative for ε4-specific disease modification. Within the latter group, LILRA5 is particularly notable because it combined protected-ε4 association, evidence of ε4-by-AD interaction in the GNPC models, and supportive Mendelian randomization. Other proteins with a similar ε4-modified pattern, including DBI, BPNT1, PTEN, EPHA1, and PCDH10, may therefore be especially promising as candidate modifiers of delayed onset among ε4 carriers.

Discussion

The central contribution of this study is a protected-ε4-first design that focuses on biological mechanisms associated with preserved clinical status in older APOE-ε4 carriers, rather than on proteins that simply distinguish cases from controls [12]. This distinction matters in AD plasma proteomics because many case–control signals are likely to reflect downstream neurodegeneration, frailty, vascular comorbidity, or treatment exposure rather than mechanisms that actively delay symptom onset. The broader GNPC literature makes this framing biologically plausible, as carrying APOE-ε4 is associated with a conserved immune-related proteomic signature across neurodegenerative diseases, implying that ε4 creates a basal vulnerability state that still requires additional modifiers to shape clinical expression [12,22]. In this context, the main value of the present results lies in distinguishing proteins that primarily reflect broader AD-related biology from those that may be more informative for ε4-specific disease modification [22].

Within the ε4-modified tier, LILRA5 is the most immediately compelling hypothesis-generating candidate. In our data, LILRA5 combined protected-ε4 association, ε4-by-AD interaction, and supportive Mendelian randomization. The relevance of that pattern is strengthened by a recent large APOE-stratified GWAS, which identified a genome-wide significant signal at the LILRA5 locus in the ε4-positive stratum [23]. Together, these findings highlight convergence between proteomic ε4-by-AD interaction and genotype-stratified human genetics at this locus. Biologically, LILRA5 belongs to the leukocyte immunoglobulin-like receptor cluster on chromosome 19, and recent work shows that LILRA5 is expressed in naïve monocytes and neutrophils and can trigger reactive oxygen species generation and inflammatory signaling [24,25]. In a disease context in which APOE-ε4 already appears to prime chronic immune dysregulation, LILRA5 is therefore a plausible candidate for ε4-conditioned innate immune amplification rather than merely a generic inflammatory marker [22].

EPHA1 belongs close to LILRA5 in this interpretive tier, although the underlying biology is different. EPHA1 is an established AD susceptibility locus from large GWAS, and functional follow-up of the AD-associated P460L variant suggests altered receptor activity with consequences for endothelial behavior and blood–brain barrier function [26]. That is relevant here because BBB dysfunction is increasingly recognized as part of AD pathophysiology, and a protected-ε4-associated EPHA1 signal could plausibly index neurovascular protection rather than only amyloid- or tau-centered biology [27]. PTEN is also compelling because its biology sits at the intersection of synaptic signaling, neuronal energetics, and amyloid-related synaptic toxicity [28]. Experimental work has shown that Aβ can recruit PTEN to postsynaptic compartments and drive synaptic depression, while human tissue data suggest that aberrant synaptic PTEN increases with symptomatic AD progression [28]. In the present setting, PTEN therefore fits a model in which preserved signaling homeostasis and resistance to synaptic depression may help postpone threshold crossing in ε4 carriers.

PCDH10 adds a complementary synaptic and circuit-maintenance dimension. PCDH10 is a protocadherin involved in excitatory synapse development and circuit refinement [29], its association here is consistent with the possibility that protected-ε4 resilience may involve preservation of synaptic architecture in addition to modulation of inflammatory pathways. DBI and BPNT1 are less established in AD, but both are mechanistically plausible and therefore remain biologically relevant candidates in the present framework. DBI encodes diazepam-binding inhibitor/acyl-CoA-binding protein, a neuropeptide with links to GABAergic signaling and lipid metabolism, and older clinical work reported altered CSF DBI levels in AD and other dementias. BPNT1 is a lithium-sensitive 3′-phosphoadenosine 5′-phosphate phosphatase with roles in sulfation-related nucleotide metabolism [30]. Experimental work has linked BPNT1 inhibition to neuronal dysfunction and lithium-responsive biology [31]. DBI and BPNT1 are not established AD susceptibility genes, but their known biology supports their candidacy as markers of metabolic-stress and neuromodulatory pathways that may be underappreciated in current AD target prioritization.

A second set of proteins appears to align more closely with broader AD-related plasma remodeling, but several of them remain highly informative. OMG, reduced here in AD versus controls. is particularly important because a new multi-cohort study now supports it as a brain-specific proteomic determinant of neurodegenerative resiliency [32]. In that study, lower plasma OMG was associated with cortical amyloid deposition, compromised brain structure, dementia, and future dementia risk, and genetic analyses supported OMG as causally protective across several neurodegenerative outcomes [32]. This independent evidence strengthens the interpretation of the present OMG finding and supports its relevance to protected-ε4 resilience or resistance -related biology. SELENOW is also noteworthy because recent experimental work showed that selenoprotein W modulates tau homeostasis and synaptic maintenance in an AD mouse model [33]. VAT1 is less AD-specific, but it is a neuronal vesicle protein with oxidoreductase activity and links to vesicular transport, mitochondrial fusion, and phospholipid biology[34], making lower VAT1 in AD and relative preservation in protected ε4 carriers biologically plausible. TPPP3 remains a limited-evidence but intriguing candidate, as it belongs to a tubulin polymerization-promoting family and has been linked to axon regeneration [35] and microtubule biology [36], which is relevant to neurodegeneration even if direct AD literature is still sparse. Together, OMG, SELENOW, VAT1, and TPPP3 support the possibility that protected-ε4 resilience or resistance is partly mediated by preservation of axonal integrity, synaptic structure, and cytoskeletal stability [32–35].

Three proteins merit a more nuanced interpretation because they were relatively higher in affected rather than protected ε4 carriers: NRBP1, CD247, and F2. In our data, all three also showed interaction and/or orthogonal genetic support, arguing against a simplistic “lower in affected equals risk-increasing” model. NRBP1 is mechanistically relevant because it regulates degradation of BRI2 and BRI3, physiological inhibitors of APP processing and Aβ oligomerization, and NRBP1 depletion reduces Aβ production in neuronal cells [37]. CD247 is the T-cell receptor ζ-chain, and although it is not an established AD risk gene, its appearance fits a growing literature implicating adaptive immune [38] and CD8+ T-cell alterations in AD progression [39]. F2 encodes prothrombin, and thrombin is increasingly viewed as a neurovascular and inflammatory mediator in AD that can contribute to BBB dysfunction, microglial activation, and neurotoxicity [40–42]. These genes may therefore prove most useful as markers of ε4-conditioned vulnerability pathways, compensatory responses, or contextdependent amplifiers rather than straightforward protective mediators.

TREM2 and ACE occupy a different interpretive category from the less established candidates. In particular, TREM2 is already strongly anchored in AD pathobiology: rare coding variants in TREM2 were among the earliest non-APOE genetic associations and to implicate microglial lipid handling and phagocytic function in AD [43]. Subsequent CSF proteogenomic studies have reinforced the relevance of TREM2-related biology to disease mechanisms [44]. ACE plays a similar positive-control role it is a late-onset AD GWAS locus [45] and can convert Aβ42 to Aβ40 [46,47], with ACE inhibition enhancing amyloid deposition in experimental systems. The re-emergence of TREM2 and ACE is therefore reassuring, because it shows that the protected-ε4-first framework recovers established AD-relevant biology.

From a translational perspective, the most promising proteins are not necessarily those with the largest effect sizes, but those with the clearest convergence across protected-ε4 association, ε4-modified disease association, and human genetic support. On current evidence, LILRA5 stands out as the clearest ε4-specific hypothesis-generating candidate, with EPHA1, PTEN, DBI, BPNT1, and PCDH10 forming a second tier of plausible modifiers, while OMG, SELENOW, VAT1, and TPPP3 appear especially informative for a broader axonal and structural-resilience axis. The next steps should be replication in independent ε4-enriched cohorts, longitudinal validation that these proteins track delayed conversion rather than survivor bias, and integration with amyloid and tau biomarkers to distinguish resistance-like from resilience-like mechanisms of protection. More broadly, these data support a meaningful shift towards prevention-focused AD target discovery.