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PubMed2026

Aptamer-Based Platforms for Human Aging Biomarkers: Multiplexed Proteomics, Biosensors and Translational Perspectives.

Aptamer-based multiplexed proteomic platforms, especially the SOMAmer-based SomaScan assay, are widely used for large-scale discovery of circulating biomarkers relevant to human aging. This review summarizes 42 original research articles published from 2020 through 2026 in which aptamers or aptamer-derived biosensors were used to characterize aging-related biomarkers in human samples or clinically relevant human-disease contexts. The eligible literature falls into several thematic areas: whole-plasma and organ-specific proteomic aging clocks; inflammaging and senescence-associated secretory phenotype (SASP) markers; cardiovascular, metabolic, renal, hepatic, musculoskeletal and neurodegenerative biomarker panels; and aptasensor platforms for detection of individual analytes. Only a small number of studies have compared aptamer- and antibody-based platforms in the same specimens; we tabulate these and show that median between-platform agreement is low to moderate, which constrains the pooling of findings across technologies. We also make explicit an interpretive point that is usually left implicit: because proteomic clocks are trained against chronological age, their correlation with chronological age measures fit to the training target rather than biological validity, and the informative quantity is the residual age gap. In the reviewed literature, SomaScan-based studies are concentrated in cardiovascular, neurodegenerative, frailty, and proteomic aging-clock research, whereas de novo SELEX campaigns targeting aging-specific epitopes and longitudinal human validation of wearable aptasensors were not identified. The main barriers to translation are cross-platform discordance, limited replication across ancestries, under-reported pre-analytical variability, cost, and the research-use-only status of most assays.

Humans↗

PubMed2026

Cross-Platform Proteomics and Machine Learning Algorithms Nominate Plasma Biomarkers of Stroke Diagnosis.

BACKGROUND: Blood-based biomarkers for stroke subtyping could improve triage in emergency settings. We used cross-platform proteomics to identify plasma biomarkers differentiating major stroke diagnostic groups. METHODS: We conducted a case-control study using 2 biorepositories. Plasma was collected in the emergency department from adults with suspected stroke before therapeutic intervention. Differentially enriched proteins were identified across acute ischemic stroke, intracerebral hemorrhage, transient ischemic attack, and stroke mimics using SomaScan discovery proteomics (Grady). Differentially enriched proteins were nominated using pairwise and multigroup comparisons and adjusted for clinical covariates. Protein panels were created using least absolute shrinkage and selection operator logistic regression. Internal validation used repeated nested cross-validation (rCV) and targeted mass spectrometry (MS), while external validation used data-independent acquisition  mass spectrometry in an independent cohort (Yale). RESULTS: We included 100 subjects (40 with acute ischemic stroke, 20 with intracerebral hemorrhage, 20 with transient ischemic attack, 20 with stroke mimics) in discovery and 80 subjects (20 per group) in external validation cohorts. SomaScan quantified 7307 proteins, of which 61 differentiated stroke subtypes. We identified 7 protein classifiers for acute ischemic stroke (rCV-area under the curve, 0.82 [95% CI, 0.78-0.86]), 6 for intracerebral hemorrhage (rCV-area under the curve, 0.70 [95% CI, 0.64-0.76]), 8 for transient ischemic attack (rCV-area under the curve, 0.78 [95% CI, 0.73-0.84]), and 7 for stroke mimics (rCV-area under the curve, 0.81 [95% CI, 0.77-0.86]). Targeted proteomics internally validated 11 proteins, and data-independent acquisition-mass spectrometry externally validated 32 proteins, including VTN (vitronectin), PLG (plasminogen), and S100A9 as top stroke mimics, transient ischemic attack, and intracerebral hemorrhage classifiers. CONCLUSIONS: This study highlights plasma proteomics as a valuable tool for discovering protein biomarkers of stroke diagnosis. These findings support further validation in larger, multicenter cohorts to facilitate biomarker-guided stroke diagnosis in acute care.

Humans↗

PubMed2026

Serum Proteomic Signatures of Rheumatoid Arthritis Risk and Response: Analysis of a Rheumatoid Arthritis Interception Trial.

OBJECTIVE: Our study objective was to identify serum protein signatures associated with progression to rheumatoid arthritis (RA) and response to abatacept in at-risk individuals. METHODS: A total of 440 serum samples from 118 APIPPRA (Arthritis Prevention In the Preclinical Phase of RA with Abatacept) study participants were selected from baseline to RA onset for 46 progressors of RA or to study end for 72 participants who did not develop RA. Samples were analyzed using the SomaScan 7k assay platform. Differential expression analysis was assessed by progression to RA (three pre-RA time intervals to RA, progressors of RA vs nonprogressors, baseline to RA), and by treatment allocation (abatacept vs placebo). Risk and response signatures were identified in the full 7k panel and two prespecified subpanels defined as Inflammatory Mediators and Adaptive Immune Cell panel. RESULTS: We observed significant changes in 80 proteins (68 down-regulated and 12 up-regulated) occurring between RA onset and 6 to 24 months before developing disease. Progression to RA was associated with increased levels of acute-phase reactants SAA1 and SAA2 and reductions in CTLA4, when compared to nonprogressors at the end of treatment. Two up-regulated proteins (CTLA4 and CD86) and seven down-regulated proteins (CXCL13, FCRL4, FCER2, CCL21, LTA|LTB, FDCSP, and IL22RA2) were observed in participants receiving abatacept compared to placebo regardless of RA outcome. CONCLUSION: Protein signatures dominated by acute-phase proteins define progression to RA, whereas changes associated with abatacept therapy highlight potential mechanisms of treatment response. Such signatures provide a better understanding of the immune landscape of the at-risk phase, opening up the possibility of new treatment modalities for RA prevention.

Adult↗

PubMed2026

A Multifaceted Interplay Among Hemophagocytosis, Interleukin-18, and Type I Interferon Distinguishes Still Disease From Other Autoinflammatory Diseases.

OBJECTIVE: The unknown pathophysiology and the lack of specific features for systemic juvenile idiopathic arthritis and adult-onset Still disease (collectively known as Still disease; SD) delay diagnosis and appropriate treatment. The goal of this study was to identify features and mechanisms that distinguish SD from other systemic autoinflammatory diseases (SAID). METHODS: Using the SomaScan assay and RNA sequencing (RNA-Seq), we determined the plasma proteomes and immune cell microRNA (miRNA) and RNA transcriptomes of 372 patients with SAID, respectively. Proteomic findings were validated by enzyme-linked immunosorbent assays. SD (n = 72) and non-SD SAIDs (n = 300) were compared to identify distinguishing features of SD. We performed integrated and unbiased analyses of all data sets using weighted gene correlation network analysis to identify feature modules that characterize SD and stratify patients. RESULTS: Elevated plasma heme oxygenase 1 (HO-1) and interleukin-18 (IL-18) strongly correlate and characterize SD but do not associate with general inflammation. SD was characterized by ferroptosis in plasma, type I interferon (IFN) signaling in monocyte transcriptomes, and elevated natural killer cell miRNA-146a-5p, which is an IL-18 induced miRNA. Finally, we identified feature modules that distinguish SD from other SAIDs and stratified patients with SD into two distinct subgroups not attributable to disease activity or inflammation but hemophagocytosis. CONCLUSION: This unprecedented large omics data set of SAIDs revealed that complex interactions among hemophagocytosis, IL-18, and type I IFN signaling characterize SD. Furthermore, two distinct subgroups in patients with SD were distinguished by the degree of hemophagocytic activity. Finally, the large proteomics and RNA-Seq data sets generated in this study can serve as an invaluable resource for the further investigation of SD and other SAIDs.

Humans↗

PubMed2026

Proteomics as a theranostic compass in BCR::ABL1-negative myeloproliferative neoplasms: Integrating biomarker discovery with therapeutic stratification.

Classic BCR::ABL1-negative myeloproliferative neoplasms (MPNs)-polycythaemia vera, essential thrombocythaemia, and primary myelofibrosis-are clonal haematopoietic stem cell disorders with marked heterogeneity in clinical phenotype, disease trajectory, and therapeutic response. Genomic stratification by driver and cooperating mutations only partially accounts for this variability, leaving gaps in predicting thrombotic risk, fibrotic progression, leukaemic transformation, and treatment benefit. Proteomics bridges this gap by providing function-proximal readouts of protein abundance, post-translational modifications, pathway activity, and intercellular signalling that genomics and transcriptomics cannot capture, positioning it as a theranostic platform in which the same molecular readouts simultaneously inform diagnostic stratification and therapeutic decision-making. We propose a five-stage translational framework spanning from discovery-scale mass spectrometry and affinity-based plasma profiling to targeted validation, multicentre standardisation, and machine learning-integrated clinical panels. Proteomic evidence is synthesised across the following four disease axes: clonal fitness in haematopoietic stem and progenitor cells; bone marrow microenvironmental remodelling and fibrosis; chronic inflammation and thrombosis; and leukaemic transformation. We further describe how phosphoproteomics reveals resistance mechanisms to JAK inhibitors, including AXL-MAPK bypass and PP2A-autophagy-mediated tolerance, and how protein-level biomarkers (BCL2-BCL-XL, RAS-ERK, CAMK2G, and ROCK1/2) can guide individualised therapeutic selection. Affinity-based platforms (Olink PEA and SomaScan) and spatially resolved technologies (CODEX and single-cell proteomics) complement discovery proteomics. At present, however, this evidence base is constrained by small and heterogeneous cohorts, limited cross-platform reproducibility, and a scarcity of independent external validation for candidate protein panels. Realising this vision will require multicentre standardisation, analytically validated panel assays, and prospective clinical studies that translate molecular findings into decision-grade tools for patients with MPNs.

Humans↗

PubMed2026

From prediction to mechanism: Explainable AI uncovers plasma and CSF proteomic signatures of Alzheimer's disease.

Alzheimer's disease (AD) plasma and cerebrospinal fluid (CSF) proteomics can distinguish AD from cognitively normal controls, but the generalizability of machine learning performance and the recurrence of biological signals across datasets require cautious interpretation. We developed an explainable artificial intelligence framework spanning two fluids and four ADNI proteomic datasets, covering 2082 modality specific samples, all analysed internally within ADNI. Phase 1 analysed plasma using a 119 analyte NULISA and targeted UPENN panel (n = 727; 216 CE, 511 controls). Phase 2 extended the analysis to CSF using SOMAscan7k, TMT-MS and targeted SET2, with Elecsys Aβ42, Aβ40, total tau and p-tau181 as anchor biomarkers. Only SOMAscan was subject-independent relative to Phase 1 plasma; TMT-MS and SET2 overlapped with Phase 1 for 96.0% and 97.7% of subjects and therefore are not independent replication cohorts. Under subject-level splits with fold internal preprocessing, we compared Elastic Net, Explainable Boosting Machines and gradient boosted trees with SHAP-based explanations. Among the candidate pipelines, we selected the pipeline with the highest held-out test ROC AUC for each platform; the selected values were 0.927 in plasma and 0.954-0.973 across the three CSF datasets. Because the same held out test performance was used for pipeline selection and headline reporting, these are optimistically selected single-holdout estimates, not unbiased estimates of generalizable or clinical performance. Explanations identified five recurring biological axes within ADNI: cholinergic (ACHE), tau/14-3-3 (YWHAG, YWHAZ, YWHAB, YWHAE), neuro-axonal (NEFL, NEFH), microglial/complement (CHIT1, SMOC1, CHI3L1, C7, CFH) and synaptic (NPTXR, NPTX2, DLG4, SYT5, VSNL1, ELAVL2). CSF analyses showed synaptic vesicle-cycle enrichment (q = 2 × 10-6), and CSF YWHAG correlated strongly with total tau (ρ = 0.87). Cross-fluid directional concordance was modest overall (54-57%) but increased to 73-80% among mapped analyte/protein rows reaching q < 0.05 in CSF. These findings provide hypothesis-generating, internally supported evidence within ADNI. Independent external cohorts with locked pipelines are required to evaluate generalizable performance and biological reproducibility; the overlapping TMT-MS and SET2 analyses should not be interpreted as independent replication.

Alzheimer Disease↗

PubMed2025

Brain aging rejuvenation factors in adults with genetic and sporadic neurodegenerative disease.

The largest risk factor for dementia is age. Heterochronic blood exchange studies have uncovered age-related blood factors that demonstrate 'pro-aging' or 'pro-youthful' effects on the mouse brain. The clinical relevance and combined effects of these factors for humans is unclear. We examined five previously identified brain rejuvenation factors in cerebrospinal fluid of adults with autosomal dominant forms of frontotemporal dementia and sporadic Alzheimer's disease. Our frontotemporal dementia cohort included 100 observationally followed adults carrying autosomal dominant frontotemporal dementia mutations (Mage = 49.6; 50% female; 43% C9orf72, 24% GRN, 33% MAPT) and 62 non-carriers (Mage = 52.6; 45% female) with cerebrospinal fluid analysed on Somascan, and longitudinal (Mvisits = 3 years, range 1-7 years) neuropsychological and functional assessments and plasma neurofilament light chain. Our Alzheimer's disease cohort included 35 adults with sporadic Alzheimer's disease (Mage = 69.4; 60% female) and 56 controls (Mage = 68.8, 50% female) who completed the same cerebrospinal fluid and clinical outcome measures cross-sectionally. Levels of C-C motif chemokine ligand 11, C-C motif chemokine ligand 2, beta-2-micorglobulin, bone gamma-carboxyglutamate protein (aka Osteocalcin) and colony stimulating factor 2 in cerebrospinal fluid were linearly combined into a composite score, with higher values reflecting 'pro-youthful' levels. In genetic frontotemporal dementia, higher baseline cerebrospinal fluid rejuvenation proteins predicted slower decline across cognitive, functional, and neurofilament light chain trajectories; estimates were similar across genotypes. In transdiagnostic analyses, higher cerebrospinal fluid rejuvenation proteins associated with better functional, cognitive, and neurofilament light chain outcomes in adults with sporadic Alzheimer's disease. Proteins with pre-clinical evidence for brain rejuvenation show translational clinical relevance in adults with Alzheimer's disease and related dementias and warrant further investigation.

Alzheimer’s disease↗

PubMed2026

Proteomics Analysis of Plasma for Risk of Sepsis: Findings from the Atherosclerosis Risk in Communities Study.

BACKGROUND: Sepsis is a life-threatening complication of infection with high mortality. A high-throughput analysis of circulating blood proteins may provide mechanistic insight and potent therapeutic targets for the prevention of sepsis. METHODS: We used multivariable Cox regression analysis to examine the association of 4955 plasma proteins, measured by SomaScan, with the risk of incident sepsis among 11 065 participants of the Atherosclerosis Risk in Communities (ARIC) Study (visit 3 in 1993 to 1995; mean age, 60.1 years, 54.4% female, 21.0% Black). Proteins (false discovery rate [FDR] of P < 0.05) discovered at visit 3 were replicated using data at visit 5 (n = 4869 in 2011 to 2013: mean age, 75.5 years) and in the Cardiovascular Health Study (CHS) (n = 3512 in 1992 to 1993; mean age, 74.5 years). Canonical pathways were identified by enrichment analyses. RESULTS: At ARIC visit three, 669 proteins were associated with the risk of sepsis; 175 were replicated at visit 5. Of these, 90 were validated in the CHS. The top 20 proteins ranked by P value were relevant to acute inflammatory signaling in innate immunity. Pathway analyses implicated activation of pro-inflammatory pathways (e.g., cytokine storm signaling) as well as inhibition of anti-inflammatory pathways (e.g., liver X receptor/retinoid X receptor [LXR/RXR] activation), which also play relevant roles in lipid metabolism. CONCLUSIONS: In this analysis, levels of acute inflammatory proteins measured during routine visits were associated with the subsequent incidence of sepsis. An increased risk of sepsis associated with the inhibition of anti-inflammatory pathways, such as LXR/RXR warrants further mechanistic investigation.

Humans↗

PubMed2025

Sex differences in cerebrospinal fluid proteomics of patients with restless legs syndrome.

STUDY OBJECTIVES: The pathobiology of restless legs syndrome (RLS) remains poorly understood, complicating effective treatment. This observational cross-sectional study aimed to identify a cerebrospinal fluid proteomic signature of RLS and to explore sex-specific differences in cerebrospinal fluid proteomics. METHODS: Cerebrospinal fluid samples were collected from 22 untreated RLS patients and 18 controls, matched for age, body mass index, and sex. Proteomic analysis was conducted using the SOMAscan platform, assessing over 7000 peptides. RESULTS: Eight proteins were differentially abundant between patients and controls, with CRP and JAML increased, and TAPBPL and IL1RL1 decreased. Pathway analysis highlighted significant involvement in immune response, coagulation, and cytoskeletal regulation. Analyses were then carried out using sex stratification, comparing men and women separately. Sex-specific analyses revealed more pronounced proteomic alterations in males (68 differentially abundant proteins vs. control males) than in females (17 proteins). Gene enrichment analysis revealed that men with RLS had more involvement in gene regulation and epigenetic factors than control males and women with restless legs syndrome had greater involvement in systemic inflammatory and vascular processes than control females. CONCLUSIONS: This study identified a cerebrospinal fluid proteomic signature in RLS, implicating immune and inflammatory pathways in the disease's pathophysiology. Significant sex differences in protein level suggest potential sex-specific mechanisms in RLS, warranting further investigation. These findings contribute to the current understanding of RLS and could inform future therapeutic strategies.

Humans↗

PubMed2025

Predictors of response to terlipressin therapy in hepatorenal syndrome: Metabolomic and proteomic analysis from the CONFIRM trial.

BACKGROUND: Terlipressin is the only FDA-approved vasoconstrictor for hepatorenal syndrome (HRS). The CONFIRM study is the largest trial of terlipressin versus placebo. Novel predictors of HRS response are required to enrich patient selection and optimize outcomes. METHODS: Samples at treatment initiation were tested using (a) liquid chromatography-mass spectrometry of 1594 plasma/1420 urine metabolites (Metabolon Inc.), (b) aptamer-based array of 7289 plasma proteins (SomaScan), and (c) 14 plasma/urine pre-specified assays. The CONFIRM trial's original definition of HRS response [2 serum creatinine (SCr) <1.5 mg/dL separated by >2 h] was used as the primary outcome. RESULTS: In all, 115 patients [79 terlipressin-treated (TT) and 36 placebo-treated (PT)] provided samples. Baseline characteristics, outcomes, and 2:1 TT:PT allocation were preserved from the original 300-patient trial. A total of 36 out of 116 (31.0%) patients achieved HRS reversal. HRS reversal was associated with lower SCr (p=0.001), cystatin C (p=0.005), angiopoietin-2 (p=0.04), and beta-2 microglobulin (p=0.006). In metabolite analysis, PT had the most significant differences in HRS reversal [n=26 plasma, n=50 urine, including lower urine levels of those centered on sulfated secondary bile acids (microbiome-derived), N-acetylated amino acids, catechols (both uremic toxins), and phosphocholines (cell membrane integrity)], with fewer in TT (n=1 plasma, n=2 urine), and in all patients (n=3 plasma, n=7 urine). There were no significant aptamers associated with HRS reversal after false-discovery correction. CONCLUSIONS: SCr, cystatin C, angiopoietin-2, and beta-2 microglobulin were associated with HRS reversal. Protein and metabolite signals centered on microbiome function and uremic toxins appeared more robust in PT patients, likely selecting a subgroup that may recover without terlipressin. Use of novel biomarkers may enrich for terlipressin response.

Humans↗

PubMed2025

Redefining ALS: Large-scale proteomic profiling reveals a prolonged pre-diagnostic phase with immune, muscular, metabolic, and brain involvement.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder with a largely unknown duration and pathophysiology of the pre-diagnostic phase, especially for the common non-monogenic form. METHODS: We leveraged the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort with up to 30 years of follow-up to identify incident ALS cases across five European countries. Pre-diagnostic plasma samples from initially healthy participants underwent high-throughput proteomic profiling (7,285 protein markers, SomaScan). Cox proportional hazards models based on 4,567 participants (including 172 incident ALS cases) were used to identify protein biomarkers associated with future ALS diagnosis. Top results were indirectly validated in two independent case-control studies of prevalent ALS (n=417 ALS, 852 controls). Functional annotation included cross-disease comparisons, gene set and tissue enrichment testing, organ-specific proteomic clocks, and the application of large-language models (LLM). FINDINGS: Five proteins (SECTM1, CA3, THAP4, KLHL41, SLC26A7) were identified as significant pre-diagnostic ALS biomarkers (FDR=0.05), detectable approximately two decades before diagnosis. Of these, all except SECTM1 were indirectly validated in independent cohorts of prevalent ALS cases, supporting their clinical significance. Additionally, 22 nominally significant (p<0.05) pre-diagnostic biomarkers were FDR-significant in prevalent ALS with consistent effect directions. Cross-disease comparisons with pre-diagnostic Parkinson's and Alzheimer's disease suggested a largely specific pre-diagnostic ALS biomarker signature. Gene ontology and tissue enrichment highlighted early involvement of immune, muscle, metabolic, and digestive processes. Furthermore, analyses of proteomic clocks revealed accelerated aging in brain-cognition, immune, and muscle tissues before clinical diagnosis. Druggability and LLM analyses revealed possible therapeutic targets and novel strategies, emphasizing translational relevance. INTERPRETATION: Our study provides first evidence of ultra-early molecular changes in common ALS up to two decades prior to clinical onset, mainly affecting immune, muscle, metabolic, digestive, and cognitive systems. Our study nominates several compelling candidates for risk stratification studies and novel therapeutic targets for early intervention. FUNDING: Clinical Research in ALS and Related Disorders for Therapeutic Development (CreATe) Consortium, Cure Alzheimer's Fund, Michael J Fox Foundation, Interdisciplinary Centre for Clinical Research, University Münster.

Journal Article↗

PubMed2025

Shared and divergent acute cardiovascular risk protein responses to lipid infusion in women with and without PCOS.

AIMS: Elevated circulating lipids are linked to cardiovascular disease (CVD), especially in insulin-resistant states like polycystic ovary syndrome (PCOS), but their effects on cardiovascular risk proteins (CVRPs) remain unclear. This study used a two-step approach to examine acute cardiovascular proteomic responses to lipid-induced metabolic stress. We first identified proteins altered by lipids and insulin in healthy control (HC) women, then assessed whether these responses were similar or divergent in women with PCOS. METHODS: In a randomised cross-over study, 10 healthy controls and 12 women with PCOS underwent 5-h saline (control) or intralipid infusions. After 3 h, a 2-h hyperinsulinemic-euglycemic clamp was initiated. Plasma CVRP expression was assessed at baseline, post-lipid (180 min) and post-clamp (300 min) using SOMAscan proteomics. STRING and pathway enrichment analyses were performed to explore functional associations. RESULTS: In the HC group, lipid infusion altered the expression of 11 out of 54 CVRPs including increases in RANK, IL2RA, TACI, SLAF5 and DCN (p <0.05) and decreases in THPO, BOC, SOD2, FGF23, and AgRP (p <0.05). Most changes reversed with insulin, but BOC, SOD2, MMP12, FGF23, and DCN remained dysregulated. In PCOS, responses mirrored the HC group except for lower AgRP following lipid infusion (p <0.01), and persistent elevation of SLAF5 and DCN following insulin (p <0.05). Enrichment analysis linked altered proteins to immune activation, cell proliferation, and cytokine-receptor signalling. CONCLUSION: Acute lipid infusion revealed shared and phenotype-specific proteomic responses linked to early vascular stress. In PCOS, persistent dysregulation suggests reduced metabolic adaptability, with exploratory signals that may complement established biomarkers of early cardiovascular risk.

Humans↗

PubMed2026

Subphenogroups of acute heart failure with preserved ejection fraction: comprehensive proteomics and pathway analysis.

BACKGROUND: Heterogeneity of heart failure with preserved ejection fraction (HFpEF) results in significant challenges for treatment development. Identifying and characterising distinct HFpEF phenogroups may aid in tailoring therapeutic strategies for these patients. The objective of this study was to assess proteomic patterns of HFpEF phenogroups identified through a machine-learning-based clustering model, with the aim of uncovering specific biological pathways associated with each phenogroup. METHODS: This study represents a post-hoc analysis of the ongoing Prospective mUlticenteR obServational stUdy of patIenTs with Heart Failure with preserved Ejection Fraction (PURSUIT-HFpEF) study, which is a multicentre prospective observational study of hospitalised patients with acute decompensated HFpEF. Of the overall cohort (N=1238), this study analysed 198 patients with HFpEF with available proteomics data. These patients were classified into four phenogroups using the machine-learning-based clustering model. The SomaScan assay V.4.1 was used to measure levels of >7000 plasma proteins, and subsequent pathway analysis was conducted to determine the biological differences among the phenogroups. RESULTS: We identified four distinct phenogroups: Phenogroup 1 ('rhythm trouble'), Phenogroup 2 ('ventricular-arterial uncoupling'), Phenogroup 3 ('low output and systemic congestion') and Phenogroup 4 ('systemic failure'). The proteomics revealed distinct protein expression profiles among the phenogroups, with ribonuclease 4, tax1-binding protein 1, regenerating islet-derived protein 3-gamma and alpha-1-antichymotrypsin being the most significant markers to specific identified phenogroups. Pathway analysis suggested differences in immune response, autonomic activation, cellular homeostasis and tissue repair mechanisms across the phenogroups. CONCLUSIONS: Using a comprehensive plasma proteomics approach, our study identified distinct proteomic profiles of HFpEF phenogroups, which in turn suggest specific underlying biological processes. These profiles suggest the involvement of inflammatory activation, tissue injury and regenerative responses, immune modulation and systemic stress signalling as key components of HFpEF pathophysiology. TRIAL REGISTRATION NUMBER: UMIN-CTR ID: UMIN000021831.

Humans↗

PubMed2026

Mapping the plasma proteomic architecture of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a heterogeneous systemic autoimmune disease, yet the molecular basis underlying this variability remains incompletely understood. We profiled the plasma proteome in 260 SLE patients and 86 healthy volunteers (HVs) using the SomaScan v4.1 platform, quantifying 7,288 analytes corresponding to 6,595 unique proteins. We identified 215 proteins that were robustly differentially abundant between SLE patients and HVs in both discovery (n = 207 SLE, n = 45 HVs) and validation sets (n = 53 SLE, n = 41 HVs). Within-cases analyses identified 421 proteins associated with disease activity. Network-based clustering delineated correlated protein modules, including an interferon-associated (IFN-associated) module and a kidney-associated module. Autoantibody-stratified analyses further uncovered distinct proteomic endotypes; positivity for antibodies targeting RNA-binding proteins (anti-Sm, anti-Ro-60, anti-RNP68, anti-RNP-A) was associated with increased IFN-stimulated protein levels (e.g., MX1, ISG15, and CXCL10), independent of disease activity. Anti-Sm, anti-RNP-A, and anti-Ro52 antibodies were associated with reduced plasma levels of their respective autoantigens. Anti-dsDNA antibodies were associated with elevated levels of CD40 ligand (CD40LG) and the neutrophil protease, proteinase-3. Moreover, we identified an association between CD40LG and disease activity specific to the anti-dsDNA-positive subgroup. Together, these data define plasma protein signatures of SLE and disease activity, highlight autoantibody-specific molecular phenotypes, and provide a basis for precision medicine.

Humans↗

PubMed2025

Proteomic discovery analysis of quantitatively assessed emphysema in the general population. The MESA Lung Study.

BACKGROUND: Pulmonary emphysema occurs frequently in older adults, often without airflow limitation. Its presence predicts symptoms, respiratory hospitalizations and deaths, and all-cause mortality. Proteomics may provide further insights into emphysema pathogenesis and inform therapeutic targets. OBJECTIVE: We performed a proteomic discovery analysis of percent emphysema on computed tomography (CT) in a population-based, multiethnic sample from the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study. Replication was performed in two chronic obstructive pulmonary disease (COPD)-based studies, the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) and the Genetic Epidemiology of COPD (COPDGene) Study. METHODS: MESA recruited participants from the general population in 2000-02. The MESA Lung Study performed full-lung CT scans in 2010-12. Percent emphysema was defined as the percentage of lung voxels < -950 Hounsfield units. Over 7,200 plasma aptamers were measured via SomaScan. Cross-sectional linear and least absolute shrinkage and selection operator (LASSO) regression models were adjusted for demographics, anthropometrics, smoking, renal function, and scanner parameters. Statistical significance was defined as a false discovery rate p-value < 0.05. Gene Ontology (GO)/Reactome enrichment analyses were performed. LASSO-selected proteins' predictive performance was evaluated. RESULTS: Among 2,504 participants in the MESA Lung Study, mean age was 69.4 years, 1,291 had ever smoked, and median percent emphysema-like lung was 1.4%. In total, 1,234 aptamers were significantly associated with percent emphysema in the MESA Lung Study, and 35 replicated in the SPIROMICS and COPDGene Studies. Novel associations included protein family with sequence similarity (FAM) 177A1, syntenin-2, ubiquitin carboxyl-terminal hydrolase 25, and uncharacterized protein C20orf173. Previously identified emphysema-associated proteins included soluble advanced glycosylation end product-specific receptor (sRAGE), protein S100-A12, high mobility group protein B1, and roundabout homolog 2. Enrichment analyses identified 40 GO biological processes, including chemokine production and regulation and cell-cell adhesion and regulation, and two Reactome pathways, including RAGE signaling. In tenfold cross-validation, novel proteins were largely retained by LASSO (R2 = 5.4%), improved overall model performance (R2 = 24.8%), and uniquely explained greater variance in percent emphysema. CONCLUSIONS: This analysis in a general population sample identified novel and previously characterized proteins whose functional roles were validated by GO/Reactome enriched pathways, offering new insights into emphysema pathophysiology and therapeutics.

Humans↗

PubMed2026

Proteomics of multimorbidity progression across cardiometabolic diseases and cancer in a multinational cohort.

BACKGROUND: Multimorbidity, defined here as the co-occurrence of cardiovascular disease (CVD), type 2 diabetes (T2D), and/or cancer is a major public health challenge. However, its underlying biological mechanisms remain unclear, limiting progress toward identifying shared interventional targets. METHODS: We applied large-scale plasma proteomics (SomaScan 7k; 7,289 aptamers) in 13,270 European Prospective Investigation into Cancer and Nutrition (EPIC) participants to identify protein signatures of multimorbidity. We modelled multimorbidity progression as sequential disease transitions, i.e., from the disease-free state at baseline to a first disease and from the first disease to a second disease. Using weighted multivariable Cox regression, we estimated hazard ratios (HR) and 95% confidence intervals (CI) for risk of cancer, CVD, and T2D. Risk associations were replicated using Olink proteomics in UK Biobank (N = 44,567). RESULTS: We identified 422 aptamers associated with more than one disease (FDR-corrected P < 0.05), e.g., 265 aptamers were shared between CVD and T2D. Thirty-eight aptamers were associated with multimorbidity progression. Among these, 27 aptamers showed consistent positive associations across sequential disease transitions, including SEMA6A (disease-free to cancer HR: 1.14; 95% CI 1.05, 1.23; cancer to T2D HR: 2.61; 95% CI 1.76, 3.80). Four aptamers showed consistent inverse associations, including NLGN1 (disease-free to T2D HR: 0.72; 95% CI 0.61, 0.84; T2D to cancer HR: 0.57; 95% CI 0.43, 0.75). Nineteen of the identified proteins were also measured in UK Biobank, with broadly consistent associations. CONCLUSIONS: This study identifies candidate proteins that may indicate molecular pathways to multimorbidity of cardiometabolic diseases and cancer. Future studies should evaluate the causal roles of these proteins for targeted interventions and risk stratification.

Humans↗

PubMed2024

Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium.

OBJECTIVES: To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis. METHODS: Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis (STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from 1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into discovery (n = 1045) and replication (n = 701) datasets, were analysed by SomaScan Discovery Plex V4.1 (>7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical confounders and batch-effects were identified and adjusted for. Poorly performing SOMAmers and samples were excluded. Variance in the data was determined by principal component (PC) analysis. RESULTS: A synovial fluid standardised protocol was optimised that had good reliability (<20% co-efficient of variation for >80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. 1720 samples and >6290 SOMAmers met inclusion criteria. 48% of data variance (PC1) was strongly correlated with individual SOMAmer signal intensities, particularly with low abundance proteins (median correlation coefficient 0.70), and was enriched for nuclear and non-secreted proteins. We concluded that this component was predominantly intracellular proteins, and could be adjusted for using an 'intracellular protein score' (IPS). PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space. CONCLUSIONS: We have developed a robust method for analysing synovial fluid protein, creating a molecular and clinical dataset of unprecedented scale to explore potential patient subtypes and the molecular pathogenesis of OA. Such methodology underpins the development of new approaches to tackle this disease which remains a huge societal challenge.

Humans↗

PubMed2025

Redefining ALS: Large-scale proteomic profiling reveals a prolonged pre-diagnostic phase with immune, muscular, metabolic, and brain involvement.

Journal Article↗