ABSTRACT Purpose Stroke prediction remains suboptimal. We aimed to identify plasma proteins associated with stroke risk and evaluate their potential as clinical biomarkers and causal mediators. Experimental design We used a multitiered proteomic approach in the UK Biobank (UKB). In a matched nested case‐control study (672 cases, 2,688 controls), 2,920 proteins were analyzed. Findings were internally validated in the UKB‐PPP cohort and cross‐validated using genetic instruments from seven large‐scale protein GWAS meta‐analyses. Mendelian randomization (MR) and mediation analyses were employed to assess causality. Results We identified 122 proteins significantly associated with stroke risk. A 122‐protein panel improved the C‐index from 0.610 to 0.693. MR analysis provided genetic evidence supporting an association between higher genetically predicted Hepatocyte Growth Factor (HGF) levels and stroke risk (OR 1.18, 95% CI 1.09–1.28). Mediation analysis indicated that HGF mediated 39.15% of the effect of weight and 37.28% of waist circumference on stroke risk. Conclusions and clinical relevance Our findings support HGF as a potential mediator linking metabolic risk factors to stroke. The identified protein panel offers a robust tool for stroke risk stratification and highlights HGF as a biologically plausible candidate for therapeutic intervention.
ABSTRACT Purpose Diabetic polyneuropathy (DPN) is a debilitating complication of diabetes that affects motor nerves and may impair skeletal muscle health. This study investigated skeletal muscle proteomic alterations associated with DPN compared with type 2 diabetes without DPN (T2D) and healthy controls (CON). Experimental Design An exploratory proteomic profiling was performed in 15 participants divided into three groups based on clinical and neurophysiological diagnosis (DPN, n = 5; T2D, n = 5; CON, n = 5). Biopsies from the gastrocnemius lateralis muscle were analyzed using LC‐MS/MS–based proteomics and pathway enrichment analyses. Results A total of 265 and 444 proteins were differentially expressed in DPN compared with T2D and CON, respectively (q < 0.05). Enrichment analyses revealed marked alterations in mitochondrial‐related pathways, including cellular respiration, distinguishing DPN from both comparison groups. Differences between T2D and CON were present but less pronounced. DPN was characterized by downregulation of glycogen‐regulating proteins (phosphorylase kinase G1 and A1) and upregulation of ferritin light chain (FTL), alpha actin 2, and myosin‐binding protein H. Conclusions and clinical relevance DPN is associated with a distinct and progressive skeletal muscle proteomic phenotype, supporting mitochondrial dysfunction as a key feature and providing a basis for future mechanistic and therapeutic studies.
OBJECTIVE:Systemic lupus erythematosus (SLE) is an autoimmune disease closely associated with enzyme dysfunction, yet its underlying molecular mechanisms remain incompletely understood. This study aims to characterize enzyme-network alterations associated with SLE status and disease activity and to identify candidate molecules with potential clinical relevance. METHODS:We integrated proteomic and phosphoproteomic data from peripheral blood mononuclear cells (PBMCs) of 130 SLE patients and 90 healthy controls (HC), along with transcriptomic data from 1461 SLE patients. Through systematic analysis of key enzyme phosphorylation sites, upstream transcription factors (TFs), and computationally prioritized candidate compounds, we sought to characterize enzyme-centered regulatory associations. RESULTS:Integrated proteomic and phosphoproteomic analyses revealed significant metabolic and signaling pathway disturbances, along with distinct phosphorylation patterns in SLE immune cells. Multiple SLE-associated and disease-activity-associated candidate molecules were identified. Regulatory network analysis uncovered an upstream transcription factor cluster centered around STAT1. Computational drug screening identified computationally prioritized candidate compounds with multi-gene DSigDB associations, which require further clinical safety evaluation and experimental validation. CONCLUSIONS:This study constructs a molecular map of SLE, highlighting associations between enzyme-network alterations, catalytic dysregulation, and SLE-related immune molecular signatures, and identifies candidate molecules for future clinical and functional evaluation.
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.
ABSTRACT Purpose The growing prevalence of obesity, MetS, and T2DM highlights the need to better understand host–microbiota interactions. While gut microbiota has been widely investigated, interactions between the oral microbiota and human salivary proteins remain largely unexplored. Experimental Design This study investigated the integrated human salivary proteome and bacterial secreted metaproteome in Brazilian individuals spanning different metabolic states: normal weight/control, overweight, obesity, MetS, and T2DM. Saliva samples were analyzed using mass spectrometry‐based proteomics to identify differential protein profiles. Results Key findings revealed significant downregulation of human proteins MYSM1 (eta2 = 0.710, 95% CI: [0.642, 0.825], qadj< 0.001) and GAD65 ( η 2 = 0.478, 95% CI: [0.368, 0.681], qadj < 0.001) in obese, MetS, and T2DM groups, correlating negatively with BMI, waist circumference, and HOMA‐IR, suggesting impaired anti‐inflammatory and endocrine functions that exacerbate metabolic dysregulation. Conversely, carbonic anhydrase VI (CA6) was markedly upregulated ( η 2 = 0.374, 95% CI: [0.274, 0.570], qadj < 0.001), showing positive correlations with systolic blood pressure and glucose levels, indicative of an acidic, inflammatory oral microenvironment linked to chronic low‐grade inflammation. In the bacterial secreted metaproteome, TrxC‐2 ( η 2 = 0.557, 95% CI: [0.501, 0.727], qadj < 0.001), UMPK ( η 2 = 0.629, 95% CI: [0.571, 0.781], qadj < 0.001), and RsmH ( η 2 = 0.772, 95% CI: [0.718, 0.862], qadj 0.001) were significantly elevated in obesity, MetS, and T2DM, positively associated with anthropometric and insulin resistance markers, reflecting microbial adaptations to oxidative stress and enhanced virulence through biofilm formation and RNA biosynthesis. Interactome analysis demonstrated strong negative correlations between bacterial proteins and human proteins (MYSM1, GAD65), alongside positive correlations with CA6, unveiling a vicious cycle, where oral dysbiosis amplifies host inflammation and metabolic dysfunction, while altered human proteins perpetuate microbial imbalance. Conclusions and Clinical Relevance These findings underscore the pivotal role of host–microbiota interactions in the oral cavity during the pathogenesis of obesity, MetS, and T2DM, highlighting the importance of further elucidating the molecular and functional mechanisms underlying microbiota–host crosstalk in metabolic diseases.
