Plasma Proteomic Profiling Reveals Divergent Molecular Pathways in Aging and Alzheimer’s Disease

Technical Summary: Plasma Proteomic Profiling Reveals Divergent Molecular Pathways in Aging and Alzheimer's Disease

Problem Statement
Dementia, particularly Alzheimer's disease (AD), represents a progressive neurodegenerative disorder imposing significant global burdens. While aging is the primary risk factor, the systemic molecular mechanisms distinguishing physiological aging from pathological cognitive decline remain incompletely understood. Previous research has largely focused on central nervous system pathology or specific brain-derived biomarkers (e.g., plasma p-tau181), potentially overlooking broader systemic physiological alterations. Furthermore, existing proteomic studies often focus on disease cohorts without adequately distinguishing pathological changes from the non-linear, wave-like proteomic remodeling that occurs during normal aging. This study addresses the gap in understanding whether plasma proteomic signatures associated with cognitive decline are merely an extension of normal aging or represent distinct systemic processes.

Methodology
The study employed a mass spectrometry (MS)-based plasma proteomic approach across two independent cohorts:

1. Healthy Aging Cohort (TMM): 119 plasma samples from healthy male individuals aged 20–79 years, stratified into six decade-based groups.
2. Cognitive Decline Cohort (NCNP): 60 plasma samples comprising non-demented controls (NC, n=20), mild cognitive impairment (MCI, n=20), and AD patients (n=20), diagnosed according to National Institute on Aging–Alzheimer's Association (NIA-AA) criteria. This cohort included a balanced male-to-female ratio.

Experimental Workflow:

• Sample Preparation: High-abundance plasma proteins (accounting for ~90–95% of total mass) were depleted using High Select™ Depletion Spin Columns to enrich low-abundance proteins.
• Digestion and LC-MS: Purified proteins underwent tryptic digestion (SP3 protocol) and were analyzed via Liquid Chromatography coupled with Data-Independent Acquisition (DIA) mass spectrometry (Orbitrap Fusion).
• Data Processing: DIA data were processed using DIA-NN with an in silico spectral library derived from the UniProtKB human database.
• Statistical Analysis: Differential protein expression was assessed using two-sample t-tests with permutation-based false discovery rate (FDR) correction (FDR < 0.05). Missing values were not imputed; proteins with excessive missingness were excluded. Functional enrichment and network analyses were conducted using Metascape (GO, KEGG, Reactome). Hierarchical clustering (Euclidean distance, complete linkage) was used to identify stage-specific expression patterns.

Key Results

• Proteomic Changes in Healthy Aging:

  • Analysis of 383 retained proteins revealed gradual, age-associated changes.
  • Comparisons between younger (20–39) and older (60–79) groups identified 94 significantly altered proteins (36 upregulated, 58 downregulated).
  • Functional enrichment highlighted immune and inflammatory pathways, specifically complement activation and inflammatory signaling.
  • Clustering identified six distinct temporal patterns. Notably, Cluster 1 (progressive increase with age) included lipid-associated apolipoproteins (APOB, APOD, APOE), while Cluster 3 (gradual decrease) contained extracellular matrix and cell adhesion proteins (TNC, NCAM1).

• Proteomic Changes in Cognitive Decline:

  • Analysis of 408 retained proteins in the NCNP cohort identified distinct alterations compared to healthy aging.
  • MCI vs. NC: 70 altered proteins (60 up, 10 down).
  • AD vs. NC: 160 altered proteins (121 up, 39 down).
  • Overlap: A significant overlap existed between MCI and AD alterations (56 of 60 upregulated and 7 of 10 downregulated proteins in MCI were also altered in AD), suggesting early molecular onset.
  • Functional Enrichment: Unlike the aging cohort, the cognitive decline cohort showed strong enrichment in actin- and platelet-related pathways (e.g., platelet aggregation, degranulation, axon guidance).
  • Novelty: Among the 160 proteins altered in AD, 80 proteins (63 upregulated, 17 downregulated) had not been previously reported in plasma dementia studies.

• Divergent Trajectories (Integrated Analysis):

  • Integrated comparison revealed that aging and AD follow divergent proteomic trajectories.
  • Opposing Trends: Nine proteins (e.g., AHSG, ECM1) increased with age but were reduced in AD. Conversely, six proteins (e.g., NCAM1, TNC, PDIA3) decreased with age but increased in AD.
  • Convergent Decline: Seven proteins (e.g., BCHE, KNG1, TNXB) decreased with age and showed further reduction in AD, indicating a progressive decline.
  • Stage-Specific Signatures: Clustering revealed that proteins in Cluster 3 (cell adhesion, platelet function) and Cluster 6 (lipid metabolism) changed during the transition from NC to MCI and persisted in AD. In contrast, Cluster 1 (platelet/vascular/cytoskeleton) and Cluster 4 (lipid/immune) showed substantial changes specifically in the AD stage.

Significance and Claims
The authors claim that plasma alterations associated with cognitive decline are not merely an extension of normal aging but reflect distinct systemic processes involving immune regulation, actin dynamics, and platelet function.

• Differentiation: The study provides evidence that pathological cognitive decline involves molecular signatures that diverge from physiological aging trajectories, particularly regarding the regulation of complement components and cytoskeletal proteins.
• Biomarker Discovery: The identification of 80 previously unreported proteins altered in AD suggests a new set of candidate blood-based biomarkers and potential therapeutic targets.
• Mechanistic Insight: The findings support the concept that circulating factors, including platelet-derived components and inflammatory mediators, play a role in the systemic physiology of dementia, potentially distinct from the "inflammaging" observed in healthy older adults.

Limitations and Modesty
The paper explicitly acknowledges several limitations:

• Cohort Imbalance: The healthy aging cohort consisted entirely of males, while the cognitive decline cohort was sex-balanced, potentially confounding cross-cohort comparisons (e.g., SHBG levels).
• Technical Variability: Independent processing of the two cohorts without explicit batch effect correction introduces potential technical variability.
• Depletion Bias: The use of high-abundance protein depletion may have co-depleted low-abundance proteins bound to carriers like albumin.
• Sample Size: The cognitive decline cohort (n=20 per group) is modest, potentially limiting statistical power to detect moderate effect sizes or differences between MCI and AD stages.
• Exploratory Nature: The 80 novel proteins are presented as exploratory findings from a discovery cohort requiring validation in larger, longitudinal studies with standardized procedures. The authors do not claim these proteins are definitive diagnostic tools at this stage but rather candidates for further investigation.