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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

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

Immune Markers and Risk of Pancreatic Cancer in the European EPIC Cohort.

The immune system is a major driver in pancreatic cancer development. Several prospective cohort studies have found associations for single immune system-derived proteins such as IL6 or CRP, but results are inconclusive, and Omics-based research is scarce. Hence, we aimed to investigate associations of a comprehensive protein panel with the risk of pancreatic cancer. Within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, 92 immune proteins were measured in baseline blood samples of 406 incident pancreatic cancer cases and 406 sex- and age-matched controls, using the Olink Immuno-Oncology panel. Multivariable adjusted conditional logistic regression was used to estimate odds ratios (OR, 95% CI) for protein levels in association with pancreatic cancer risk. Eight biomarkers were associated with pancreatic cancer risk (MMP12, LAMP3, CD28, IL-6, IL-12, FASLG, PD-L2, and PDCD1) but only MMP12 was significantly associated after multivariable adjustments for confounders and the seven proteins, with OR = 1.56 (95% CI: 1.20-2.03) for a doubling in protein concentration. After correction for multiple testing, none of the proteins were associated with risk. Restricting analyses to cases diagnosed within the first 4 years and 4-8 years after recruitment resulted in OR of 1.89 (95% CI: 1.28-2.80) and 1.37 (95% CI: 1.01-1.86) for MMP12, respectively. Higher levels of MMP12 were associated with pancreatic cancer risk specifically in those diagnosed shortly after recruitment, while other immune-related factors were not associated with risk. Further cohort studies are needed to confirm our initial findings.

Humans↗

PubMed2025

Proteomics-enabled learning machine algorithms enhance the prediction of cardiovascular diseases in patients with type 2 diabetes mellitus.

BACKGROUND AND AIMS: Estimating the risk of cardiovascular disease (CVD) complications in type 2 diabetes mellitus (T2DM) patients is critical in the medical decision-making process. This study aimed to use a machine learning technique combined with proteomics to develop personalized models for predicting CVD in patients with T2DM. METHODS AND RESULTS: In total, 874 patients with T2DM and 2,920 Olink proteins obtained from the UK Biobank were used in this study. Proteins were screened using Cox regression and LASSO regression. A basic model containing clinical features and a full model combining proteome and clinical features were constructed using the random survival forest algorithm. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive performance of the models and compare them with other CVD predictive models. Compared with the basic model, the full model performed better in predicting CVD, with time-dependent AUCs of 0.81 (3 years), 0.74 (5 years) and 0.74 (10 years) (0.77, 0.69 and 0.67). We calculated the risk scores of the Framingham, ASCVD and Score2-Diabetes models. The results revealed that the prediction performance of the full model was also better than that of the abovementioned models. In terms of differentiation accuracy, the results of the net reclassification improvement index and integrated discrimination improvement index showed that the full model can identify high-risk individuals more accurately (accuracy rate: 79% vs. 69%). CONCLUSIONS: Proteomics can be used to predict cardiovascular complications in diabetic patients. It is also necessary to consider the applicability of the model due to the limitations of the sample size and the constraints of proteomics in clinical applications.

Humans↗

PubMed2026

Protein Biomarkers in Risk and Prognosis of Amyotrophic Lateral Sclerosis.

BACKGROUND: Plasma and cerebrospinal fluid (CSF) protein biomarkers in amyotrophic lateral sclerosis (ALS) may provide insight into disease mechanisms and yield clinically useful biomarkers. METHODS: Overall, 363 proteins in plasma and CSF from 198 patients with ALS and 125 matched controls were profiled using Olink assays. Associations with disease status, survival, and functional decline, as well as longitudinal biomarker stability across the disease course were assessed, together with network and enrichment analyses. ALS risk-associated biomarkers were externally validated in the UK Biobank (UKB). RESULTS: Overall, 125 proteins were significantly associated with at least one outcome (i.e., case status, risk, survival, or functional decline), and 21 were associated with three or more outcomes. NEFL was the most robust biomarker in plasma and CSF, alongside TNFRSF12A in plasma and CSF, EDA2R in plasma, and FABP4 in plasma and CSF. Most biomarkers remained stable longitudinally across the disease course. ALS risk-associated biomarkers were replicated in UKB, in which > 3000 plasma proteins were measured in 52,990 participants, including 298 with ALS. Network and enrichment analyses highlighted their roles in immune response and extracellular-matrix remodeling, and their enrichments in the brain and T-cell subsets. Construction of an ALS risk-prediction model achieved an ROC-AUC of 0.72 in the UKB validation cohort. CONCLUSIONS: These findings suggest candidate protein biomarkers for ALS risk stratification, early detection, and clinical therapeutic monitoring.

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↗

PubMed2025

Plasma Proteomic Profiles Predict Individual Future Osteoarthritis Risk.

OBJECTIVE: Osteoarthritis (OA) is a widespread degenerative joint disease that causes a considerable socioeconomic burden. Despite progress in genetic and environmental insights, early diagnosis is still limited by the lack of evident symptoms during the initial phases and accurate biomarkers. This study aims to identify plasma proteins associated with future risk of OA and develop a predictive model. METHODS: We conducted a large-scale proteomic analysis of 45,307 participants from the UK Biobank, excluding those with baseline OA. Plasma samples were assayed using the Olink Explore Proximity Extension Assay targeting 1,463 unique proteins. Clinical variables and OA outcomes were extracted and linked to electronic health records. A predictive model was constructed using the LightGBM machine learning method, and SHapley Additive exPlanations (SHAP) were applied to evaluate the importance of variables. RESULTS: We identified a panel of proteins significantly associated with the risk of developing OA. Notably, after adjusting for multiple confounders, collagen type IX alpha 1 chain (COL9A1) and cartilage acidic protein 1 (CRTAC1) were the most significant predictors of incident OA, with hazard ratios of 1.54 (95% confidence interval [CI] 1.48-1.61) and 1.65 (95% CI 1.54-1.78), respectively. SHAP analysis allowed a profound interpretation of the contribution of each protein and clinical variable to the model, revealing the multifactorial nature of OA risk prediction. The temporal trajectories of plasma proteins indicated that the levels of COL9A1 and CRTAC1 began to deviate from normal for more than a decade before OA onset, suggesting their potential use in early detection strategies. The predictive model, developed using the LightGBM algorithm, integrated proteins with clinical covariates and demonstrated an area under the curve (AUC) of 0.729 for 5-year OA prediction, 0.721 for 10-year prediction, and 0.723 for all incident OA. The predictive accuracy of the model was further enhanced for hip and knee OA, achieving AUCs of 0.820 and 0.803 for 5-year predictions. CONCLUSION: Our study identified the role of plasma proteomics in predicting future OA risk, which could contribute to preemptive measures. The innovative model, which integrates proteomic biomarkers with clinical data, offers a potential tool for risk assessment, potentially optimizing OA management strategies and enhancing prevention efforts.

Humans↗

PubMed2026

Large-Scale Plasma Proteomics Reveals Preclinical Biomarkers of Incident Severe Liver Disease.

The absence of robust biomarkers for early detection of severe liver disease (SLD) highlights the critical need for high-throughput proteomics-driven discovery. In this prospective cohort study, we aimed to identify plasma protein signatures associated with incident SLD and assess their clinical utility. Using the large-scale Olink Explore 1536 platform, we quantified 1461 plasma proteins in 46951 participants from the UK Biobank community-based cohort without baseline liver disease. Over a median follow-up of 14.1 years, we identified 490 proteins significantly associated with incident SLD risk. Growth differentiation factor 15 (GDF15) emerged as the strongest predictor, achieving a C-index of 0.80 and outperforming conventional clinical indices (LiverRisk score: 0.75; FIB-4: 0.68; APRI: 0.68). Temporal trajectories revealed that GDF15 levels began increasing up to 10 years before diagnosis, with progressive elevation as the diagnosis timepoint approached. Mendelian randomization analysis supported genetic associations linking higher protein levels of GDF15, FABP1, SPON2, CHI3L1, and PIGR with SLD risk. Our large-scale proteome-wide study not only reveals significant proteomic changes preceding SLD diagnosis but also establishes GDF15 as both a promising preclinical biomarker, opening new avenues for early intervention in at-risk individuals.

Humans↗

PubMed2026

Esketamine multi-omic biomarker evaluation in major depressive disorder (EMBER-MDD): concept, objectives and methodologies of a non-clinical investigator-initiated study.

Treatment resistance (TR) in major depressive disorder (MDD) affects a substantial minority of patients and is hard to recognize early, delaying intensified care. The Esketamine multi-omic biomarker evaluation in MDD (EMBER-MDD) is a non-interventional, investigator-initiated, in-vitro study within the EU Psych-STRATA programme, analyzing biospecimens collected in the randomized INTENSIFY study and the mirror OBS-TR cohort after participants complete treatment. EMBER-MDD aims to discover individual-omic and integrated multi-omic (hypothesis-free) biomarkers and signatures associated with TR risk, and molecular correlates of clinical response to esketamine nasal spray versus treatment as usual (TAU). Biomaterials will derive from approximately 420 adults with MDD (estimated n = 210 esketamine; n = 210 TAU) and include whole blood, RNA-stabilized whole blood, plasma and serum, sampled at baseline and, when feasible, during and after treatment (up to ~ 5,040 aliquots stored at - 80 °C). Genomics will use baseline DNA genotyping on Illumina Infinium GSA v3.0+MD arrays; epigenomics will profile genome-wide DNA methylation across time points using MethylationEPIC v2.0; transcriptomics will employ mRNA-seq (NovaSeq X/ X Plus); and proteomics/ metabolomics will be generated using high-throughput Olink and/ or Biocrates platforms. Each layer will undergo state-of-the-art preprocessing and analyses (e.g., GWAS/ PRS, EWAS, differential expression, WGCNA, pathway and network analyses), followed by integrative strategies including QTL mapping (meQTL/ eQTL/ pQTL/ mQTL) and intermediate-fusion machine learning with nested cross-validation, explainable AI (SHAP/ LIME) and treatment-effect modelling. All outputs are research-only and will not support individual efficacy, tolerability, or clinical decision-making. The study will deliver robust biosignatures and mechanistic hypotheses to guide future validation and inform stratified, molecularly guided intervention strategies in subsequent prospective trials. Trial registration number: 2023-506617-21-00 and 2025-178-f-S.

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↗

PubMed2025

Plasma proteomic markers of pain and emotional dysfunction in fibrous dysplasia/McCune-Albright syndrome.

Pain in Fibrous dysplasia/McCune-Albright syndrome (FD/MAS) remains poorly understood and inadequately managed due to uncertainties regarding clinical or biological drivers. This cross-sectional pilot study aimed to use plasma proteomics to identify markers that inform on molecular pathways associated with pain and emotional symptoms in FD/MAS. Seventeen individuals (15 females, 2 males), aged 16 to 63 years, with confirmed diagnoses of monostotic FD, polyostotic FD, or MAS participated in a single study visit conducted at Boston Children's Hospital and Massachusetts General Brigham. During the visit, participants completed validated questionnaires assessing neuropathic pain characteristics, pain interference, anxiety symptoms, depression symptoms, and perceived stress, and provided plasma samples. These samples were analyzed for 57 proteins using Olink proximity extension assay. Associations between protein concentrations and symptom scores were evaluated using Spearman's correlations with false discovery rate correction (|r| > 0.5, p < 0.05). After FDR correction, the concentrations of seven proteins (TNF-α, LTA, CCL19, CSF2, CCL2, CCL4, CCL7) significantly correlated with pain interference, HADS-depression scores, or perceived stress. Four protein concentrations (TNF-α, CCL19, CSF2, CCL7) significantly correlated with multiple clinical measures. This pilot study identified several pain-associated proteins in individuals with FD/MAS, suggesting that proteomic profiling may be a promising approach for discovering pain biomarkers. Larger, longitudinal studies are needed to validate these results and investigate whether targeting immune pathways can alleviate pain and improve emotional health in FD/MAS.

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

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↗

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↗