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PubMed2026

Identification of Immune Response-Related Proteomic Biomarkers in Moyamoya Disease Using Serum Olink Proteomics.

Moyamoya disease, a rare chronic cerebrovascular disorder, requires invasive digital subtraction angiography (DSA) for diagnosis. This study employed high-throughput proteomics to identify plasma biomarkers for Moyamoya disease diagnosis. We conducted immunopanel analysis using the Olink platform to evaluate 92 immune-related proteins in plasma samples from 88 Moyamoya disease patients and 88 healthy controls. Key proteins were identified through differential expression analysis, GO, and KEGG enrichment analysis. A diagnostic model was constructed using LASSO regression, Boruta algorithm, and machine learning models including random forest and XGBoost. Validation of these proteins was performed using GEO external data sets, followed by prediction of potential therapeutic drugs and molecular docking validation through pharmacogenomic databases. A total of 44 differentially expressed proteins were identified through the Olink immunopanel, with 12 downregulated and 32 upregulated. GO and KEGG analyses revealed significant enrichment of these proteins in innate immune responses and signaling pathways such as NF-kB and MAPK. Through LASSO, random forest, and protein under-area analysis, four potential biomarkers for Moyamoya disease (MGMT, SIT1, PRDX1, TRAF2) were identified. A diagnostic model using these proteins showed the highest AUC value with the XGBoost model. Additionally, TRAF2 and PRDX1 exhibited significant expression differences in Moyamoya disease patients within the GEO data set. Our study revealed the immune landscape of Moyamoya disease, identified four biomarkers, and established a variety of diagnostic models.

Humans↗

PubMed2026

Serum Olink Proteomics Reveals Novel Biomarkers for Early Diagnosis of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly prevalent malignant tumor in China, and early diagnosis critically affects the prognosis. Current imaging and pathological biopsy techniques have limitations, including high invasiveness and limited accessibility, while the insufficient sensitivity of serum biomarkers (such as AFP) restricts their use in early screening. In this study, using the Olink proteomics platform based on the proximity extension assay (PEA), we screened for hepatocellular carcinoma-related differentially expressed proteins (DEPs) and constructed a multiprotein diagnostic model. In the discovery cohort, we included 15 patients with newly diagnosed HCCs and 16 healthy controls. DEPs were identified using Olink, and their diagnostic performance was analyzed to identify the candidate biomarkers. In an independent validation cohort, including 116 HCC patients (50 early stage, 66 late stage) and 83 healthy controls, we further validated the expression levels and diagnostic performance of identified proteins─C1QA and GFER. The C1QA and GFER expression levels were significantly higher in the serum of patients with early and late HCC stages compared to healthy controls. By constructing a multiprotein diagnostic model, we identified C1QA, GFER, and AFP as the optimal diagnostic combination, demonstrating a combined diagnostic AUC of 0.92 and 0.99 for early-stage and advanced-stage HCC, respectively.

Humans↗

PubMed2026

Inflammatory Serum Olink Proteomics in Cancer-Related Pain Treated with Opioids: A Pilot Cross-Sectional and Longitudinal Study.

Opioid analgesia shows substantial interindividual variability in cancer patients, yet the underlying serum inflammatory alterations remain poorly characterized. This study collected plasma samples from 44 cancer pain patients before and after opioid initiation, quantifying 92 immunoinflammation proteins by Olink proteomics. Cross-sectional analysis identified nine differentially expressed proteins between responders and nonresponders. A five-protein nomogram involving TGF-α, EN-RAGE, CASP-8, ST1A1, and IL-10RA demonstrated superior predictive performance for opioid efficacy (AUC 0.902) compared to traditional CRP (AUC 0.625). Longitudinal analysis of this population revealed upregulation of β-NGF, MCP-4, IL-1alpha, and IL-13, and downregulation of CD6, IL-12beta, and SCF after treatment. STRING analysis clustered these proteins into three functional groups: efficacy-related (NGF), bowel-inflammation-related (IL-12/IL-13), and CD6-related. Notably, expression of IL-12β showed a significant efficacy-constipation interaction: constipation completely reversed the efficacy-IL-12 association, and higher IL-12 levels predicted favorable response only in nonconstipated patients. These findings established a pretreatment protein signature for predicting opioid efficacy and revealed systemic immune reprogramming following opioid therapy.

Humans↗

PubMed2026

Candidate biomarker identification for blood stasis syndrome among coronary artery disease patients using the Olink proteomics platform.

OBJECTIVE: To identify candidate biomarkers of blood stasis syndrome (BSS) associated with coronary artery disease (CAD) and explore the underlying inflammatory mechanisms. METHODS: Using the Olink Target 96 Inflammation panel, we identified plasma proteins in a group of 88 patients comprised of healthy controls (HCs), those with CAD and BSS (CAD-BSS), those with CAD without BSS (CAD-non-BSS), and those with BSS without CAD (non-CAD-BSS) (n = 22 in each group). Protein molecules that were specifically expressed in CAD or BSS were identified by differential expression analyses. Subsequently, potential protein biomarkers were identified using least absolute shrinkage and selection operator regression to enable CAD and BSS differentiation. The potential functional mechanisms of identified proteins were then determined by Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. RESULTS: Patients with CAD had 31/92 upregulated and 4/92 downregulated proteins compared with those without. Chemokine (C-C motif) ligand 11 (CCL11), CUB domain-containing protein 1, hepatocyte growth factor, sirtuin 2 (SIRT2), eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1), CCL25, and tumor necrosis factor (TNF) showed the strongest upregulation (all P <0.0001). Patients with BSS had 8/92 downregulated proteins, specifically CCL28, CCL11, cystatin D, STAM-binding protein, 4E-BP1, matrix metalloproteinase-10, SIRT2, and monocyte chemotactic protein 4, compared with those without (all P < 0.05). The CAD-BSS group had one interleukin-17 (IL-17) upregulated and 10/92 downregulated proteins compared with the CAD-non-BSS group. When compared with the non-CAD-BSS group, the CAD-BSS group had 8 upregulated proteins but only 2 downregulated proteins, namely interleukin-10 receptor subunit alpha (IL-10RA) and TNF-related activation-induced cytokine (both P < 0.05). Totally 10 proteins were identified as potential candidate biomarkers of BSS in CAD patients. After least absolute shrinkage and selection operator regression analysis, two proteins that distinguished between BSS and non-BSS individuals among CAD patients were identified (SIRT2 and 4E-BP1). These proteins are primarily associated with the mechanistic target of rapamycin signaling pathway, which regulates inflammation and oxidative stress. CONCLUSIONS: Results suggest that the inflammatory response and mechanistic target of rapamycin signaling pathway participate in CAD and BSS development, and that SIRT2 and 4E-BP1 are prospective protein biomarkers for patients with CAD and BSS.

Humans↗

PubMed2026

DNA Methylation and Proteomic Profiling of Postmortem Brain Tissue Reveals Epigenetic Dysregulation and Neuroinflammatory in Fragile X-associated Tremor/Ataxia Syndrome (FXTAS).

BACKGROUND: Fragile X-associated Tremor/Ataxia Syndrome (FXTAS) is a late-onset neurodegenerative disorder caused by FMR1 premutation CGG repeat expansions (55-200 repeats). The epigenetic landscape of the FXTAS brain remains uncharacterized. We performed genome-wide DNA methylation profiling of postmortem prefrontal cortex tissue to identify differentially methylated positions (DMPs) and candidate genes, and sought protein-level support for a neuroinflammatory signal. METHODS: DNA methylation was profiled in postmortem prefrontal cortex (Brodmann area 9) from 27 male FXTAS cases and 29 male controls using the Illumina MethylationEPIC array (EPICv1 and EPICv2 platforms), merging 721,802 common probes. Surrogate variable analysis (SVA) controlled for confounders. DMPs were defined by |Δβ| > 0.10 and FDR < 0.05; exploratory Reactome 2024 pathway analysis was performed on the DMP-associated gene list. Targeted proteomic profiling was performed in the same brain region using the Olink (proximity extension assay) Inflammation panel in 9 FXTAS cases and 12 controls, with SVA-adjusted differential abundance analysis, and concordance assessment against a prior mass spectrometry dataset. RESULTS: We identified 108 significant cg-type DMPs mapping to 80 genes (50 hypermethylated, 58 hypomethylated in FXTAS). The strongest signal was CYP2E1 (7 concordant hypomethylated DMPs, mean Δβ = -0.143), an oxidative stress gene also implicated in Parkinson's disease. FTCD, a one-carbon cycle enzyme, carried 5 hypermethylated DMPs (mean Δβ = +0.210). A cluster of DMP-associated genes with established roles in innate immune and NF-κB signaling, TRAF3 (the single most significant DMP among the inflammation genes, hypermethylated), BATF, RCOR1, and MSI2; they pointed toward neuroinflammatory dysregulation. Additional genes included LINGO1 (myelination inhibitor), SYT3 (synaptic vesicle), and SLC39A4 (zinc transporter). Exploratory Reactome enrichment using the DMP-associated gene set nominated themes including neuroinflammation resolution, axonal growth inhibition, zinc homeostasis, and CYP2E1 metabolism at nominal significance (p<0.05); however, the gene-to-pathway mapping rate was low and no pathway survived correction for multiple testing. Olink proteomic analysis independently identified 60 significantly altered inflammation proteins (59 downregulated), including CXCL8, CXCL10, IL6, IL15, IL18, TLR3, IRAK1/4, and complement C1QA, which were directionally concordant with prior mass spectrometry data. CONCLUSIONS: This integrated study reveals a genome-wide epigenetic signature in the FXTAS prefrontal cortex implicating oxidative stress, myelination failure, zinc dysregulation, one-carbon cycle disruption, and most notably a coordinated set of epigenetically altered genes governing innate immune and NF-κB signaling. Convergence of TRAF3 hypermethylation with independent downregulation of TLR3 and NF-κB-pathway proteins at the protein level supports a coherent, cross-platform model of dysregulated neuroinflammatory signaling in FXTAS, identified here through individual gene- and protein-level convergence rather than formal pathway enrichment. FTCD hypermethylation proposes a self-reinforcing epigenetic loop via SAM depletion. These multi-omic findings establish FXTAS as a disorder of pervasive epigenetic reprogramming and nominate candidate genes for future mechanistic and therapeutic investigation.

CYP2E1↗

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

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

Comparison of endothelin-1 levels in plasma from human coronary arteries measured by enzyme linked immunosorbent assay and Olink high-throughput proteomics platform.

Endothelin-1 (ET-1) antagonists are increasingly being approved for new treatments for cardiovascular disease, where elevated ET-1 levels contribute to increased vasoconstriction. Further therapeutic targets, including coronary artery disease, are under investigation. The Olink Explore 3072 Proximity Extension Assay platform enables multiplexed high-throughput measurement of ~3000 plasma proteins, from minimal (≤6 µL) sample volumes. However, it is not known if the two oligonucleotide-tagged antibodies raised against preproET-11-212, used in this Olink assay, specifically measure biologically active ET-1 or the other inactive EDN1-encoded peptides, also secreted by human endothelial cells. Paired plasma samples from 29 patients with coronary artery disease were obtained, using a specialised intra-coronary sampling catheter, designed to obtain site specific biochemical information from within coronary arteries. We compared ET-1 concentrations measured with an ET-1 specific ELISA, demonstrated to have no cross-reactivity with other EDN1-encoded peptides versus values obtained using Olink Explore platform. Olink-measured ET-1 correlated significantly with ELISA-derived ET-1 levels (r = 0.53, p = 0.003), and Olink values predicted ELISA results. Olink ET-1 concentrations also correlated with ETB receptor levels (r = 0.40, p < 0.05). These findings indicate that the Olink Explore platform can detect relative changes in biologically active ET-1, supporting its use as a biomarker tool in clinical and translational studies.

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↗

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

Height variation independent of known genetic variants and health in later life: a cohort study.

BACKGROUND: Adult-attained height is associated with later-life health, but it reflects both genetic and nongenetic influences. The health implications of height variation not explained by known common height-associated genetic variants remain unclear. OBJECTIVES: This study aimed to examine associations of residual height (height variation independent of known genetic variants) with multiple disease incidence and all-cause mortality in later life. METHODS: In this cohort study of 407,366 adults of European ancestry (aged 40-70 y) in the United Kingdom Biobank (2006-2010), sex- and age-specific genetically predicted height was estimated from 9863 height-associated variants, adjusted for 30 principal components of ancestry. Residual height was calculated as the difference between observed and genetically predicted height. Plasma proteomics (2054 proteins; Olink Explore) were profiled. Deaths and 49 incident diseases were ascertained through national registries. Multivariable Cox models estimated associations of residual height and related proteins with disease incidence and mortality. RESULTS: Higher residual height [mean (standard deviation, SD), 0.0 (4.8)] was associated with more favorable self-reported preadulthood exposures (e.g., later birth years, no maternal smoking around birth, being breastfed as an infant, no adoption experience, and lower childhood adversity scores) and lower hazard ratios (HRs) of 32 out of 49 diseases (median follow-up = ∼12.5 y). Using participants with residual height within ±0.5 SDs from the mean as reference, those with residual height < -2 SDs had higher adjusted HRs of mortality [1.61; 95% confidence interval (CI): 1.50, 1.72], multimorbidity (1.28; 95% CI: 1.12, 1.46), cardiovascular disease (1.45; 95% CI: 1.32, 1.60), psychiatric/neurological disease (1.38; 95% CI: 1.28, 1.48), and other disease categories (e.g., diabetes, digestive, and musculoskeletal diseases). In contrast, higher genetically predicted height was associated with a higher incidence of 19 diseases, including subtypes of cancer, non-atherosclerotic cardiovascular diseases, and musculoskeletal diseases, as well as higher all-cause mortality. We identified 806 plasma proteins related to inflammation, immune response, and autophagy via tumor necrosis factor, Nuclear factor-kappa B, phosphoinositide-3 kinase/protein kinase B, and Janus kinase/signal transducer and activator of transcription signaling pathways, which were associated with residual height and multiple diseases and mortality. CONCLUSIONS: Higher residual height is associated with lower disease incidence and mortality, with associations that are distinct from those for genetically predicted height.

Humans↗

PubMed2026

Proteomic Profile in Retinopathy of Prematurity: A Secondary Analysis of the Mega Donna Mega Randomized Clinical Trial.

IMPORTANCE: Identifying early proteomic profiles in infants who develop severe retinopathy of prematurity (ROP) may reveal targets for preventive interventions to reduce retinal vessel loss and the subsequent risk of severe ROP. OBJECTIVE: To assess early longitudinal profiles of blood protein levels in preterm infants with or without severe ROP and the effect of arachidonic acid (AA) and docosahexaenoic acid (DHA) supplementation. DESIGN, SETTING, AND PARTICIPANTS: This was an exploratory, post hoc analysis of serum proteome profiles in preterm infants in the double-masked Mega Donna Mega (MDM) randomized clinical trial using targeted Olink Proximity Extension Assay proteomics covering 538 analytes. The setting was 3 university hospitals in Sweden and included extremely preterm infants born before 28 weeks of gestational age (GA), from 2016 to 2019. Data were analyzed from January to March 2025. EXPOSURES: All infants received standard nutrition; additionally, half received enteral lipid supplementation with AA/DHA (100/50 mg/kg per day) from birth to term equivalent age. MAIN OUTCOMES AND MEASURES: Longitudinal protein profiles during the first month of life were examined using mixed models for repeated measures, adjusted for GA, study center, and AA/DHA supplementation, and tested for the interaction between severe ROP (stage ≥3 and/or treated) and postnatal age. RESULTS: A total of 177 extremely preterm infants (mean [SD] GA, 25.6 [1.4] weeks; 100 male [56.5%]) were included, of whom 50 (28.2%) developed severe ROP. Of 538 longitudinal analyzed proteins, 109 protein profiles in the first month of life associated with severe ROP, proteins related to immune response, apoptotic processes, blood coagulation, and lipid metabolism. The most pronounced association with severe ROP was a fast rise in fibroblast growth factor 21 (FGF-21; β = 0.68; 95% CI, 0.39-0.97; Q =.002) and tissue plasminogen activator (tPA; β = 0.21; 95% CI, 0.13-0.29; Q <.001) during the first postnatal days. The increase in serum FGF-21 level in the first week of life was associated with lower GA, lower birth weight, low enteral energy intake, and more days receiving mechanical ventilation. No association was observed between AA/DHA supplementation and the proteome. CONCLUSIONS AND RELEVANCE: In this post hoc exploratory analysis of data from the MDM randomized clinical trial, a fast rise in FGF-21 levels, a metabolic stress-induced hormone, during the first postnatal days was strongly associated with the development of severe ROP in extremely preterm infants. These findings suggest that early interventions improving bioenergetic status may help prevent severe ROP. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03201588.

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↗

PubMed2026

Machine learning-assisted plasma PEA proteomics enables differential diagnosis of melancholic depression and bipolar disorder.

Differentiating bipolar disorder (BD) from major depressive disorder (MDD) remains a critical unmet need in psychiatry due to overlapping clinical presentations and the absence of reliable biological markers. In this study, we assessed the capacity of multivariate machine learning models to accurately differentiate BD from MDD with melancholic features using plasma proteomic profiles obtained via Proximity Extension Assay (PEA) technology. A total of 67 participants were included (23 BD, 20 MDD, and 24 HC), and plasma protein expression was assessed using the Olink Target 96 Neurology panel. Differential proteomic analysis revealed distinct disorder-specific expression patterns, identifying 21 differentially expressed proteins in BD versus MDD, 18 in BD versus healthy controls, and 7 in MDD versus healthy controls. Using a stepwise feature reduction strategy, machine learning models were trained on three feature sets comprising all proteins, the top 20 most informative proteins, and the top 5 most beneficial proteins, and evaluated across BD-MDD, BD-HC, and MDD-HC classification tasks using five algorithms. For BD-MDD discrimination, the Random Forest model achieved the highest performance when trained on the top 5 protein set (LXN, HAGH, MATN3, PLXNB1, and CTSC), yielding an AUC of 0.905, with similarly strong performance observed using the top 20 protein set. Feature importance analysis highlighted proteins involved in neurodevelopmental processes, immune regulation, and extracellular matrix organization. Overall, these findings demonstrate that integrating plasma proteomics with machine learning enables robust differentiation between BD and MDD with melancholic features, supporting the development of scalable and biologically informed diagnostic tools for precision psychiatry.

Bipolar disorder↗

PubMed2024

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology.

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

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↗

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↗