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PubMed2025

Relationships between childhood adversity, resilience, and inflammatory profiles in Taiwanese young adults.

Psychological resilience is the capacity to withstand and bounce back from stressors, trauma, and negative life events, such as childhood adverse experiences (ACEs). Yet, little is known about the biological mechanisms by which resilience mitigates the psychological effects of ACEs. We aimed to identify differentially expressed proteins (DEPs) that reflect the combined effects of early life stress and psychological resilience by using an inflammatory proteomics panel. Three different resilience and ACE questionnaires were employed to classify participants into four groups according to high vs. low levels of resilience and ACEs. Forty-five age-matched and sex-matched participants were selected for proteomics profiling with Olink's 92-protein inflammatory panel. Of these, only 32 passed quality control filtering for analysis. Results showed that CD274 emerged as a protein hub in resilient profiles, while CXCL5 was central to ACE-related profiles. Network co-expression analysis revealed group-specific protein rewiring, suggesting dysregulated inflammation in individuals with high ACE. In contrast, high-resilience profiles showed stronger immune checkpoint co-expression, indicating more effective inflammatory resolution as a key trait of resilience. These findings suggest that resilience maintains an adaptive immune network architecture that may be leveraged to promote resilience after early adversity.

Humans↗

PubMed2026

Elevated 5-oxoproline levels and adverse outcomes in heart failure: Association with renal cortical OPLAH loss in a multi-comorbidity model.

BACKGROUND: Heart failure (HF) progression is closely linked to oxidative stress. 5-Oxoproline (5-OP), a product of glutathione degradation, is normally metabolized by 5-oxoprolinase (OPLAH) but accumulates when the gamma-glutamyl cycle is disrupted. Here, we investigated the clinical characteristics of circulating 5-OP, its proteomic correlates, and the associations to outcome in HF. METHODS: In serum of 823 BIOSTAT-CHF patients, 5-OP was quantified by validated liquid chromatography-mass spectrometry and analyzed for associations with clinical outcomes. Proteomic correlates were identified across 355 OLINK proteins using stability selection with Minimax Concave Penalty regression. Mechanistic context was evaluated in a multi-comorbidity, large-animal cardio-kidney-metabolic (CKM) model with regional OPLAH assessment. RESULTS: Higher 5-OP was associated with worse renal function (eGFR declining across 5-OP tertiles, 67.9 to 60.2 mL/min/1.73 m2; p = 0.0012) and higher all-cause mortality (HR 1.55, 95% CI 1.11-2.17, p = 0.010). Per SD increase in log-5-OP, risk for the 2-year composite endpoint increased (HR 1.27, 95% CI 1.05-1.53), with broadly similar associations across CKD strata (interaction p = 0.63). TGF-α was the most robust proteomic correlate (π = 0.70; empirical permutation p = 0.001). In CKM swine, circulating 5-OP was elevated and renal cortical OPLAH protein, but not cardiac OPLAH, was selectively reduced, consistent with a renal contribution to systemic 5-OP elevation. CONCLUSION: Circulating 5-OP identifies HF patients at higher risk and is robustly associated with TGF-α. In a translational swine model, selective loss of renal cortical OPLAH provides tissue context supporting a renal contribution to systemic 5-OP elevation in cardiorenal syndrome.

Heart Failure↗

PubMed1976

Purification of the glycoprotein lectin from the broad bean (Vicia faba) and a comparison of its properties with lectins of similar specificity.

1. The lectin from the broad bean (Vicia faba) was purified by affinity chromatography by using 3-O-methylglucosamine covalently attached through the amino group to CH-Sepharose (an omega-hexanoic acid derivative of agarose). Its composition and the nature of its subunits were compared with concanavalin A and the lectins from pea and lentil. 2. Unlike the other three lectins, broad-bean lectin is a glycoprotein; a glycopeptide containing glucosamine and mannose was isolated from a proteolytic digest. 3. The mol.wt. is about 47500; the glycoprotein consists of two apprently identical subunits, held together by non-covalent forces. Fragments of the subunits, similar to those found in concanavalin A and soya-bean agglutinin, were found in active preparations. 4. Broad-bean lectin was compared with concanavalin A and the lectins from pea and lentil in an investigation of the inhibition of their action by a number of monosaccharides, methyl ethers of monosaccharides, disaccharides and glycopeptides. The most striking differences concern 3-O-substituted monosaccharides, which are strong inhibitors of the action of broad-bean, pea and lentil lectins but not of the action of concanavalin A. There is, however, no strong inhibition of the action of these lectins by 3-Olinked disaccharides.

Amino Acids↗

PubMed2026

Proteomic patterns according to ejection fraction: an EMPEROR-programme analysis.

AIMS: Left ventricular ejection fraction (LVEF) has been incorporated as an inclusion criterion in HF trials. Patient's characteristics, event risk, and treatment response vary according to LVEF. A better understanding of the biological processes across LVEF is warranted. To study proteomic biomarker expression across LVEF using data from the EMPEROR-Programme. METHODS: Two thousand two hundred and fifty-four patients who had proteomic measurements available using 1134 proteins overlapping between the Explore 1536 and 3072 Olink® platforms were included. Main analyses were performed within the EMPEROR-Preserved dataset due to differences in entry criteria between EMPEROR-Preserved and EMPEROR-Reduced with higher entry N-terminal pro B-type natriuretic peptide (NT-proBNP) levels that varied by LVEF cut-offs in the latter. Protein concentrations were compared using ordinal logistic regression across LVEF categories: 41%-49%, 50%-59%, and ≥60%. The resulting β-coefficient indicates the change in the log-odds for the outcome of being in a lower LVEF category for every NPX unit in log2 scale. Analyses were adjusted for covariates and a false-discovery-rate (FDR) correction was applied. RESULTS: A total of 297 proteins exhibited a trend of expression across LVEF categories in EMPEROR-Preserved after adjustment for potential confounders and correction for test multiplicity. Of these, the top 10 proteins were: NT-pro BNP (β = 0.18, 95% CI 0.09-0.27), Wnt inhibitory factor-1 (β = 0.40, 95% CI 0.19-0.61), sialomucin core protein 24 (β = 0.48, 95% CI 0.22-0.74), phospholipid transfer protein (β = 0.38, 95% CI 0.17-0.59), natriuretic peptides B (β = 0.13, 95% CI 0.06-0.20), intercellular adhesion molecule 5 (β = 0.31, 95% CI 0.14-0.49), neural cell adhesion molecule 2 (β = 0.45, 95% CI 0.19-0.70), neural cell adhesion molecule L1-like protein (β = 0.45, 95% CI 0.19-0.71), interactor protein for cytohesin exchange factors 1 (β = 0.12, 95% CI 0.05-0.19), and 3-ketoacyl-CoA thiolase, peroxisomal (β = 0.17, 95% CI 0.07-0.26). The correlation between these proteins and LVEF was generally weak (Rho ≤0.2). CONCLUSIONS: Within EMPEROR-Preserved, the top differentially expressed circulating proteins suggest that pathways related to natriuretic peptides, cell-adhesion, and clonal haematopoiesis are overexpressed at mildly-reduced ejection fraction, but none of the proteins passed the 5%FDR cut-off, and the correlation between circulating proteins and LVEF was weak. These findings suggest that circulating proteins may not be a good discriminant of ejection fraction.

Humans↗

PubMed2026

Cardiac remodelling and dysfunction in cancer patients receiving cardiotoxic therapies: proteomic and metabolomic profiling.

BACKGROUND AND AIMS: The objective of this study was to define the relationships between the circulating proteome and metabolome with cardiac structure and function in patients with breast cancer receiving cardiotoxic therapies. METHODS: Proteomics and metabolomics profiling was performed in a longitudinal, prospective cohort study of breast cancer patients receiving anthracyclines and/or trastuzumab, using the Olink Explore 3072 platform and rapid liquid chromatography-mass spectrometry, respectively. Multivariable linear mixed-effect models evaluated the contemporaneous (same visit) and lagged (subsequent visit) associations between repeated measures of individual proteins or metabolites with quantitative echocardiographic measures of cardiac structure [left ventricular (LV) mass and left atrial volume index] and function [LV ejection fraction (LVEF), longitudinal and circumferential strain, E/e', and ventricular-arterial coupling]. Cox regression and pathway enrichment analyses were conducted for biomarkers demonstrating significant associations with cardiac function. RESULTS: Across 547 breast cancer participants (median age 50 years), 203 unique proteins and 16 unique metabolites were significantly associated with measures of cardiac structure and function in contemporaneous and lagged analyses. Notably, cathepsin C was associated with LVEF [false discovery rate (FDR), P = .017], longitudinal strain (FDR, P = .046), left atrial volume index (FDR, P = .035), and incident cardiac dysfunction, defined by an LVEF decline ≥10% to <50% (hazard ratio .61, 95% confidence interval .41, .90). The 147 proteins associated with cardiac function were enriched in biological processes reflective of protein deubiquitination, protein modification by small protein removal, macromolecule catabolic processes, and global metabolic pathways. Individual metabolites significantly associated with cardiac function (LVEF, longitudinal strain) included n-acetylglutamine, aspartic acid, acetylasparagine, alanyl-alanine, and prolyl-glycine (FDR, P-value < .001), and belonged to amino acids and derivatives and peptides. CONCLUSIONS: These findings provide translational insights into cancer therapy-related cardiac dysfunction and remodelling and identify potential new biomarkers of cardiotoxicity. There is an important need for validation of these findings and a deeper understanding of the biology of these biomarkers.

Humans↗

PubMed2026

Proteomic Profiling Captures Residual Cardiovascular Risk Beyond the PREVENT Model in Individuals With Cardiovascular-Kidney-Metabolic Syndrome Stages 2-3.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome reflects complex pathobiological interactions among metabolic disorders, kidney injury, and cardiovascular disease (CVD). Stages 2 and 3 represent critical phases of disease progression characterised by high pathological heterogeneity. This study aimed to develop a CVD protein risk score (PRS) for this population and evaluate its incremental predictive value over the PREVENT model. METHODS: This study included 24 017 participants with CKM Stages 2-3 from the UK Biobank. Using 2923 plasma proteins measured via the Olink platform, a PRS was developed in a training set (n = 19 218) using the LASSO method. In the validation set (n = 4799), the incremental predictive performance of this score over the PREVENT model was assessed using Harrell's C-statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS: A risk score comprising 63 proteins was constructed, primarily reflecting inflammation, kidney injury and matrix remodelling. Key proteins included growth differentiation factor 15 (GDF15), hepatitis A virus cellular receptor 1 (HAVCR1), matrix metallopeptidase 12 (MMP12) and NT-proBNP. In the validation set, after adjusting for PREVENT risk factors, individuals in the high PRS group had a 2.56-fold higher risk of CVD compared to those in the low score group (HR: 2.56, 95% CI: 1.96-3.37). Integrating the score into the PREVENT model improved the C-statistic by 0.034 (0.672-0.706) and achieved a 10-year NRI of 15.8% (95% CI: 9.5%-20.9%) and an IDI of 2.2% (95% CI: 1.3%-3.3%). CONCLUSION: Combining the PREVENT model with the PRS developed in this study enhances the prediction of future CVD events in the CKM Stages 2-3 population. This approach facilitates the capture of residual risk and supports precision risk stratification and management for this high-risk group.

Humans↗

PubMed2026

Proteomics identify disease-associated variants in patients with rare diseases undiagnosed after genome sequencing.

Despite the introduction of genome sequencing (GS) for rare disease diagnostics, a genetic cause is not identified in most patients. Here, we explored the potential of proteomics to improve the diagnostic yield in 424 patients with rare diseases from the 100,000 Genomes Project (100kGP) without a genetic diagnosis. Serum proteomic profiling was performed using the Olink Explore 1536 assay (N = 1463 proteins). For 13 patients without genetic diagnoses, detection of lower serum protein "outliers" (z-score < -2) led to confirmed genetic diagnoses by resolving variants of uncertain significance or prioritizing genes for targeted GS reanalysis. For 23 additional patients without genetic diagnoses (64% of findings), we identified candidate gene-disease links and variants through convergent evidence from lower protein outliers and variants ranked through the variant prioritization tool Exomiser. For example, we identified a candidate heterozygous missense variant [Genome Aggregation Database (gnomAD) minor allele frequency = 0.006%] in tyrosine kinase with immunoglobulin-like and epidermal growth factor homology domains 1 (TIE1) that was only present in a patient with lower TIE1 serum abundance (z-score = -5.12) and their father, both of whom were affected by the same monogenic cardiac disorder, but in no other individuals from the 100kGP. Missense (52.5%) and splice region (27.5%) variants accounted for most diagnostic or candidate variants prioritized. This proof-of-principle study demonstrated that serum proteomics can support rare disease diagnosis and identify disease-causing genes in patients undiagnosed after GS, although successful implementation will likely depend on tissue specificity of protein expression, detectability in blood, proteomic platform coverage, and sensitivity.

Humans↗

PubMed2026

Markers of microvascular instability predict severity and survival in idiopathic pulmonary fibrosis.

INTRODUCTION: Most research on idiopathic pulmonary fibrosis (IPF) has focused on the interplay among fibroblasts, the immune system and epithelial cells. There is growing evidence that microvascular dysfunction also plays a role in disease progression, but large human translational studies are lacking. In this research, we aim to identify a proteomic signature of microvascular instability and assess the impact of current therapeutics on the microvasculature. METHODS: Olink proteomic data from patients with IPF were obtained from the Pulmonary Fibrosis Foundation Patient Registry (PFF-PR) (n=914) and an independent validation cohort (n=366). Among the PFF-PR, 640 patients also have whole-blood RNA sequencing data available. A subset of 79 microvascular-associated proteins was curated, and their associations with disease severity and transplant-free survival were examined. An adaptive least absolute shrinkage and selection operator was used to generate a novel microvascular risk score. RESULTS: Higher plasma levels of five microvascular-associated proteins (SDC1, MMP10, THBS2, HGF and SERPINA5) were associated with lung function and survival in both cohorts. Whole-blood RNA sequencing of patients with microvascular risk revealed enrichment of immune-mediated processes. Patients with higher microvascular risk who were subsequently put on nintedanib in the following year had significantly better 3-year transplant-free survival compared with patients who did not receive antifibrotic intervention (HR 0.56, 95% CI 0.35 to 0.89, p=0.0142). DISCUSSION: Integrative multi-omics analyses suggest that perturbations to microvascular remodelling contribute to disease severity and progression in IPF. This analysis offers a framework for a precision medicine approach for IPF.

Idiopathic pulmonary fibrosis↗

PubMed2026

Plasma von Willebrand Factor and ADAMTS13 Interact With APOE-ε4 in Predicting Longitudinal Brain Atrophy and Cognitive Decline Over a 9-Year Follow-Up.

BACKGROUND: Von Willebrand factor (VWF) and ADAMTS13 (a disintegrin and metalloproteinase with thrombospondin type 1 motif, 13) are linked to dementia risk, and limited evidence suggests apolipoprotein E (APOE)-ε4 alters VWF release. This study assessed whether baseline VWF and ADAMTS13 levels predict neurodegeneration and cognitive decline and evaluated effect modification by APOE-ε4 carriership. METHODS: Vanderbilt Memory and Aging Project cohort participants (n=332, 73±7 years, 59% male) completed serial blood draw, neuropsychological assessment, and brain magnetic resonance imaging over 6.4 years (range 1.4-9.7 years). Baseline plasma VWF and ADAMTS13 levels were quantified using mass spectrometry and Olink. Fully adjusted linear mixed-effects models related protein×time and protein×APOE-ε4×time interaction terms to longitudinal brain magnetic resonance imaging and neuropsychological outcomes. RESULTS: Lower baseline ADAMTS13 predicted faster declines in language (β=0.11, P=0.01), information processing speed (β=0.27, P=0.001), executive function (β=0.01, P=0.03), episodic memory (β=0.01, P=0.03), and visuospatial ability (β=0.11, P=0.001) and faster increases in global (β=-0.29, P=0.01) and frontal (β=-0.17, P=0.01) white matter hyperintensity volumes. Associations between ADAMTS13 and faster rates of cognitive decline and white matter injury were driven by APOE-ε4 carriers. Models relating VWF to longitudinal outcomes were null. APOE-ε4 interacted with VWF on longitudinal gray matter volumetric outcomes, such that faster rates of global gray matter atrophy were observed with higher baseline VWF levels among APOE-ε4 noncarriers only (β=-1530.5, P<0.001). CONCLUSIONS: ADAMTS13 shows promise as a potential plasma biomarker for brain aging outcomes, but additional research is warranted to understand the performance of VWF in the presence versus absence of an APOE-ε4 allele.

Humans↗

PubMed2026

Proteomics-Based Soluble Urokinase Plasminogen Activator Receptor Levels Are Associated With Adverse Cardiovascular Outcomes in the General Population: Insights From the UK Biobank.

BACKGROUND: Elevated soluble urokinase plasminogen activator receptor (suPAR) levels are associated with inflammation, immune activation, and major adverse cardiovascular events in coronary artery disease. Encoded by the PLAUR gene, suPAR levels are influenced by the rs4760 genetic variant. Whether proteomics-based suPAR levels predict adverse outcomes in the general population remains unknown. METHODS: Proteomics-based suPAR levels were measured using the Olink Immunoassay in 33 963 UK Biobank participants without known coronary artery disease. Fine-Gray and Cox proportional hazards models assessed associations between suPAR and major adverse cardiovascular events (primary outcome: cardiovascular mortality, nonfatal myocardial infarction, or stroke), cardiovascular mortality, and all-cause mortality (secondary outcomes), after adjustment for demographic and clinical risk factors, hs-CRP (high-sensitivity C-reactive protein), and the rs4760 variant. Incremental discrimination was evaluated using C-statistics. RESULTS: Participants were aged 56.4 (SD, 8.2) years; 45% were men, and 93.4% were White. Over a median follow-up of 14 years (476 177 person-years), 10.7% experienced major adverse cardiovascular events, 2.6% experienced cardiovascular mortality, and 9.4% experienced all-cause mortality. Each 1-SD increment in proteomics-based suPAR was associated with significantly higher risk of major adverse cardiovascular events (hazard ratio [HR], 3.2 [95% CI, 2.9-4.5]), cardiovascular mortality (HR, 5.9 [95% CI, 5.1-6.9]), and all-cause mortality (HR, 5.0 [95% CI, 4.6-5.5]), independent of clinical risk factors and hs-CRP. Additional adjustment for rs4760 did not attenuate these associations. Proteomics-based suPAR significantly improved discrimination beyond clinical risk factors (C-statistic: 0.719 versus 0.732; P<0.001). CONCLUSIONS: Proteomics-based suPAR independently predicts adverse cardiovascular outcomes in the general population, beyond conventional risk factors, hs-CRP, and genetic predisposition to elevated suPAR levels.

Humans↗

PubMed2025

VEGFA sex-specific signature is associated to long COVID symptom persistence.

BACKGROUND: Long COVID involves persistent symptoms after COVID-19 recovery, affecting multiple organ systems for months or years. Risk factors include female sex, prior chronic conditions, severe SARS-CoV-2 infection, reinfections, and lack of vaccination. As a major public health concern, ongoing research continues to investigate its causes, mechanisms, and long-term effects. METHODS: Proteomic expression analysis of 171 individuals, in two time points, with confirmed SARS-CoV-2 infection, including 133 long COVID patients from the deeply characterized COVICAT cohort, assessed 1395 protein biomarkers using Olink® technology. Statistical analyses with linear mixed models examined protein expression changes, long COVID status, and sex-specific differences. Functional analysis included gene set enrichment analysis and protein-protein interaction networks. RESULTS: Findings revealed VEGFA overexpression in long COVID patients (effect size 0.322, SE = 0.098, p = 0.0013), along with sex-specific expression patterns and the influence of sex-hormonal status in females, with significant overexpression of circulating VEGFA levels specifically in postmenopausal women (Mann-Whitney U test p value = 8.55 × 10-3). Network analysis identified 109 nodes and 274 edges, with VEGFA ranking highest in centrality. Dysregulated chemokine signaling, complement activation, and viral reactivation were also confirmed, consistent with prior studies. CONCLUSIONS: Using high-throughput proteomic profiling in a population-based cohort, we observed that vascular dysfunction, particularly involving VEGFA, is a key feature of long COVID, especially in milder cases, with significant overexpression of VEGFA in postmenopausal women. Sex-specific proteomic patterns suggest distinct recovery mechanisms, highlighting the need to consider sex, vascular health, and disease severity in the pathogenesis and management of long COVID.

Humans↗

PubMed2026

Social isolation and 59 common health conditions: insights from observational and genetics analyses.

BACKGROUND: The impacts of social isolation on diverse health conditions and how it contributes to health risks remain unclear. We aimed to investigate the associations of social isolation with 59 health conditions among older adults. METHODS: Participants from the UK Biobank without baseline diagnosis of the included diseases were selected. Social isolation was assessed with three questions. The 59 health conditions included all-cause mortality, 5 cause-specific mortalities, and 53 diseases. We used an instrumental variable from multivariable common factor GWAS in Mendelian randomization (MR) to explore causal links of social isolation with diseases. Omics analyses were conducted to assess the roles of Olink plasma proteins and metabolomics, and PERM was calculated to evaluate the influence of other factors. RESULTS: A total of 489,741 individuals [266,706 (54.5%) women; mean age 56.5 years (SD 8.1)] were included. During a median follow-up of 12.5 years, social isolation was uncorrelated with the majority of 59 health conditions. Significantly, it was associated with increased risks of all-cause [adjusted HR (aHR) 1.28, 95% CI 1.25-1.32], 5 cause-specific mortalities (aHR range, 1.18-1.38), and 11 specific diseases (aHR range, 1.08-1.17). Living alone was the strongest item of isolation in predicting mortality (aHR range, 1.18-1.45) and selected diseases. MR analyses offered little evidence to support a causal link between social isolation and these diseases. The proteins involved in these associations are predominantly related to "response to stimulus". Proteomic signatures (PERM, 36%-49%), health behaviours (32%-59%), and socioeconomic factors (22%-42%) were the main explanatory factors linking social isolation to 8 health outcomes. CONCLUSIONS: Social isolation is associated with elevated risks of 17 out of the 59 examined adverse health outcomes, predominantly mortality-related conditions; however, MR analyses indicate an absence of evidence supporting causality for these associations.

Humans↗

PubMed2026

Determinants of pro- and anti-inflammatory cytokine profiles across populations.

BACKGROUND: Cytokine dysregulation contributes to chronic inflammation and immune-mediated diseases, yet population-level determinants of pro- and anti-inflammatory cytokines remain poorly characterized. We aimed to identify demographic and lifestyle determinants of plasma cytokine levels and evaluate reproducibility of inflammatory patterns across European populations. METHODS: In the population-based Rotterdam Study cohort (n = 3,456; mean age 57 years; 56% female), we examined associations between plasma levels of nine cytokines (Olink Inflammation Panel) and age, sex, smoking, body mass index (BMI), and alcohol consumption. Linear and non-linear regression models were applied, with stratified analyses where appropriate. Cytokine clustering was assessed using principal component analysis (PCA). Findings were replicated in two independent European cohorts using identical protocols. Additionally, associations between raw IL-6 levels and determinants were meta-analyzed across the two replication cohorts (total n > 4,000). RESULTS: Plasma IL-10, IL-6, IL-17 A, TNF, IFN-γ, IL-18, and IL-17 C increased with age; IL-18 and IL-17 C followed non-linear trends (P < 0.01). Females had lower IL-18 and IL-17 C levels (β: - 0.41 and - 0.32, respectively) but higher IFN-γ (β: 0.17). Smoking was associated with higher IL-10, IL-6, IL-18, and IL-17 C (β range: 0.10-0.54) and lower IFN-γ (β: -0.15) and IL-13 (β: - 0.10). BMI was positively associated with IL-6, IL-18, TNF, and IL-17 A levels (P < 0.05), and inversely with IL-10 (P < 0.001). Sex-specific associations were observed for alcohol use. PCA revealed stable pro-inflammatory cytokine clustering, consistent across cohorts. Replication in analyses confirmed a robust, shared pro-inflammatory signature, and meta-analysis supported consistent associations with older age, higher BMI, and current smoking. CONCLUSIONS: Older age, higher BMI, male sex, and current smoking are consistent and reproducible determinants of inflammatory cytokine profiles across European populations. These findings support the use of cytokine profiling in enhancing risk stratification for inflammation-related diseases.

Humans↗

PubMed2026

Exerkine dysregulation links visceral adiposity to skeletal muscle impairment in end-stage heart failure with reduced ejection fraction: proteomic evidence for a cardio-adipose-muscle axis.

BACKGROUND: Heart failure with reduced ejection fraction (HFrEF) is associated with profound alterations in body composition, skeletal muscle dysfunction, and impaired exercise capacity. Exerkines representing exercise-responsive signaling molecules released by skeletal muscle, adipose tissue, and other organs may mediate systemic metabolic communication between tissues. However, their role in advanced HFrEF and their relationship with adiposity and skeletal muscle characteristics remain poorly understood. METHODS: We studied 73 patients with end-stage HFrEF and 16 healthy controls. Body composition was assessed using computed tomography, including visceral (VAT), subcutaneous (SAT), and epicardial adipose tissue (EAT), as well as skeletal muscle quantity (psoas muscle index, PMI) and quality (psoas muscle density, PMD). Functional performance was evaluated using handgrip strength (HGT) and the 6-min walk test (6MWT). Circulating exerkines were quantified using the Olink technology. Associations between proteins and clinical variables were assessed using age- and creatinine-adjusted linear models with false discovery rate correction. RESULTS: Among patients with HFrEF, 36% were obese and 38% exhibited central obesity independent of BMI. Muscle strength and muscle quality were strongly associated with functional capacity. VAT correlated with muscle mass but not with muscle quality or performance. Compared with controls, HFrEF patients demonstrated elevated inflammatory and metabolic stress-related exerkines including CXCL8, CCL2, IL-6, TNF, IL-15, GDF15, FGF21, ANGPTL4, CTSB, DCN, and resistin. In contrast, proteins associated with muscle integrity and regenerative signaling (myostatin, BDNF, IL-7, SPARC) were significantly reduced. In HFrEF patients leptin strongly correlated with adiposity measures. Metabolic stress mediators (GDF15, IL-15, FGF21, CTSB) were inversely associated with muscle quality and functional performance, whereas myostatin positively correlated with muscle quality, strength, and exercise capacity. BDNF was inversely associated with frailty. CONCLUSIONS: Advanced HFrEF is characterized by a dysregulated exerkine network linking adiposity, skeletal muscle quality, and functional performance. Four biologically coherent axes were identified: a leptin-driven adiposity axis, a metabolic stress-muscle quality axis, a myostatin-related muscle function axis, and a neurotrophic frailty axis. These findings support the presence of a systemic cardio-adipose-muscle signaling network in end-stage HFrEF and identify candidate molecular mediators of sarcopenia and functional decline.

Humans↗

PubMed2026

Targeted proteomics of extreme vascular phenotypes in type 1 diabetes: the ESCAPER study.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in Type 1 Diabetes (T1D), but a subset of individuals remains free from macrovascular or renal complications despite decades of hyperglycaemia and a significant risk factor burden. We used a targeted proteomic approach (Olink Cardiovascular panel III, targeting 92 proteins) to characterize the proteomic profile of cardiovascular resilience in T1D by comparing 92 patients with long-standing T1D (age 59.8 [53.2, 69.1], duration 40.0 [35.0, 45.2] years) free from macrovascular complications or nephropathy against a reference group of 57 T1D patients with accelerated vascular pathology (age 42.0 [32.0, 56.0], duration 22.0 [18.0, 27.0] years), proliferative retinopathy and/or nephropathy in relation to diabetes duration, termed Rapid Progressors (RP). Twenty proteins differed significantly between RP and Escapers (False Discovery Rate [FDR] < 0.05) after adjustment for age, sex, HbA1c, and eGFR: Caspase-3 was significantly higher in RP (Adjusted difference: + 2.12 Normalized Protein eXpression [NPX], p < 0.001). Proteins associated with platelet activation and leukocyte adhesion with increased levels in RP included Junctional Adhesion Molecule A (+ 1.40 NPX), Glycoprotein VI (GP6: + 1.29 NPX), and P-Selectin (+ 0.82 NPX) (all p < 0.001). PECAM-1 (+ 0.55 NPX) and TNFRSF14 (+ 0.43 NPX), were also elevated. RP also showed higher levels of metabolic and tissue-remodelling proteins; Transferrin Receptor (+ 0.53 NPX) and Fatty Acid Binding Protein 4 (+ 0.52 NPX), as well as higher Bleomycin Hydrolase, Trefoil Factor 3, GDF-15, U-PAR, and Cystatin B. Conversely, von Willebrand Factor (vWF) levels (- 1.35 NPX, p < 0.001) and Paraoxonase 3 (PON3) was lower in RP (- 0.34 NPX, p = 0.003). In conclusion, escaping complications in long-term T1D appears to be associated with active molecular mechanisms. Progression is marked by apoptosis (Caspase-3), fibrosis (CHI3L1) and platelet activation (GP6), whereas resilience is associated with a distinct signature involving higher vWF and PON3. These findings highlight a profound biological divergence between extreme T1D phenotypes and provide a foundation for further research into vascular resilience.

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↗

PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve–based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC = 0.844). The NODM cohort was stratified into high- (n = 2,362) and low-risk (n = 5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗

PubMed2026

Proteomic Signatures Related to Physical Activity Are Associated with Risks of Future Disease.

PURPOSE: Physical activity (PA) can lower the risk of developing chronic diseases. However, few studies have examined the proteomic signatures linked to PA, and the role of these signatures in the connection between PA levels and future disease risk remains unclear. This study aimed to investigate whether proteomic signatures indicative of PA are associated with the risk of developing common chronic diseases and to explore their role as statistical links in the relationship between PA levels and disease development. METHODS: We used data from a subcohort of UK Biobank participants. PA intensity data were collected from accelerometers worn by each participant. Plasma proteomics results were obtained through Olink analysis. The risks of developing each primary chronic disease were evaluated for types of PA and their associated proteomic signatures, adjusting for age, sex, ethnicity, socioeconomic status, lifestyle factors, and key measurement time-lag covariates. RESULTS: Based on the UK Biobank, we identified significant differences among the proteomic signatures of accelerometer-measured light PA, moderate-to-vigorous PA, and total PA. The main enriched pathways of these proteomic signatures included cell adhesion, cell migration, and immune response. Higher levels of accelerometer-measured PA and their associated proteomic signatures correlated with a lower risk of developing cardiometabolic disorders, cancers, psychological or neurological disorders, and respiratory diseases. CONCLUSIONS: Our findings show that PA and PA-related proteomic signatures are statistically associated with lower risks of chronic diseases. Further analyses identified proteins that were correlated with both PA and disease risk. These results need to be confirmed through longitudinal studies involving diverse populations.

Humans↗