A thorough examination of GWAS data on dysregulation of miRNA networks and lipid metabolism pathways in metabolic syndrome

Integrated bioinformatics and SEM analysis reveal GPAM as a key mediator of fibrosis in NAFLD with metabolic dysfunction

Proteomic Pathways Linking Type 2 Diabetes Genetic Clusters to Cardiovascular Diseases: A Network Mendelian Randomisation Study.

This network Mendelian randomisation study delineates protein-mediated pathways linking cluster-stratified T2D genetic predisposition to cardiovascular risk, which can inform preventive strategies by stratifying individuals based on distinct biological pathways represented by these genetic clusters.

Integrated gut microbiome and serum lipidomics reveals microbial–lipid interactions for predicting incident metabolic syndrome: a nested case–control study

Background Metabolic syndrome (MetS) is a multifactorial disorder characterized by obesity, dyslipidemia, hypertension, and insulin resistance. Although gut microbiota and lipid metabolism are both known to influence MetS development, their interactions remain incompletely characterized. Methods We conducted an exploratory nested case–control study within a prospective health examination cohort. We selected 100 participants (50 incident MetS cases and 50 matched controls) based on age, sex, and baseline MetS components. Gut microbial profiles were characterized by metagenomic sequencing, and serum lipid metabolites were measured using high-resolution mass spectrometry. Multi-omics integration was performed using correlation-based feature fusion. We constructed a support vector machine (SVM) model, optimized with recursive feature elimination (RFE) and five-fold cross-validation, to predict the incidence risk of MetS. Results MetS participants differed from controls in gut microbial composition, metabolic pathway activities, and lipidomic profiles. Circos analysis revealed positive associations between Blautia and sphingomyelins and negative associations between Bacteroides and triglycerides. The integrated model combining microbiota and lipidomic features demonstrated strong discrimination in the training set (AUC = 0.995, 95% CI: 0.987–0.999) and acceptable performance in the validation set (AUC = 0.722, 95% CI: 0.525–0.919). Conclusion Integration of baseline gut microbiota and lipidomic data revealed specific pre-disease microbial–lipid signatures, including positive Blautia–sphingomyelin and negative Bacteroides–triglyceride associations. A multi-omics model improved prediction of incident MetS over single-omics models, supporting the potential of microbiota–metabolite panels for early risk detection.

Integrative multi-omics analysis of metabolite–protein interaction networks across different stages of coronary heart disease

To elucidate the molecular characteristics of synergistic interactions across the clinical stages of coronary heart disease (CHD)—specifically stable angina pectoris (SAP), unstable angina pectoris (UAP), and acute myocardial infarction (AMI)—through integrated metabolomic and proteomic analyses. Based on a cohort including SAP, UAP, AMI, and healthy controls, metabolomic and proteomic analyses were performed to identify differentially expressed molecules, followed by KEGG pathway enrichment analysis. Pathways co-enriched across both omics platforms were selected to construct metabolite-protein interaction networks. The number of pathways co-enriched in both metabolomic and proteomic analyses increased markedly with disease stage. Only two pathways (histidine metabolism and arginine and proline metabolism) were identified in the SAP stage; this number increased to five in the UAP stage (including ferroptosis and efferocytosis) and expanded to 25 in the AMI stage, encompassing three major functional modules: immune inflammation, metabolic reprogramming, and cell signaling. The core network exhibited a stepwise increase in connectivity, shifting from a sparse structure in the SAP stage to a highly interconnected architecture in the AMI stage, with L-glutamate and KNG1 identified as the central hubs in this cross-sectional network. In addition, CNDP1 exhibited a stage-dependent functional transition, shifting from downregulation in SAP to upregulation in AMI. In this cross-sectional analysis, metabolic dysregulation and immune activation exhibited stepwise increases in interconnectivity across the SAP, UAP, and AMI groups, with the most extensive crosstalk observed in the AMI stage—a network configuration consistent with a tightly coupled “molecular storm”. These findings provide novel insights into stage-associated molecular signatures of CHD and identify candidate hub molecules for stage-oriented therapeutic investigation.

Serum metabolic characteristics of interstitial lung disease: a potential link between endocrine aging and gut-derived metabolites

Untargeted metabolomics has revealed significant systemic metabolic dysregulation in ILD and the biomarkers and “metabolic-immune-endocrine” interaction patterns identified offer potential leads for early diagnosis and targeted treatment, which require validation in larger cohorts.

Integrated multi-omics analyses identify an RAS-SLC11A2-associated molecular framework linking iron metabolism with PCOS-related cardiometabolic risk.

INTRODUCTION PCOS is a common endocrine disorder with elevated cardiometabolic risk, yet the role of the renin-angiotensin system (RAS)-iron metabolism axis in this comorbidity remains unclear. We explored its underlying mechanisms and evaluated the therapeutic potential of gentiopicroside. METHODS Integrated multi-omics analyses combining transcriptomics, single-cell RNA sequencing, Mendelian randomization, machine learning, molecular docking, and in vitro functional assays were performed to identify shared molecular pathways and therapeutic targets across PCOS, hypertension, NAFLD, and T2DM. RESULTS SLC11A2 was consistently dysregulated in PCOS transcriptomic datasets, and associated with iron metabolism, inflammatory response and oxidative stress pathways. Genetic analyses validated RAS-related regulation in hypertension susceptibility and revealed shared genetic architecture between PCOS and cardiometabolic traits. Network and single-cell analyses characterized SLC11A2-associated molecular patterns in disease-relevant cell types; machine learning identified disease-classifying molecular signatures. Gentiopicroside alleviated inflammatory and oxidative stress phenotypes, including reduced IL-6 expression and reactive oxygen species accumulation. CONCLUSION This study defines an RAS-SLC11A2 molecular framework linking iron metabolism dysregulation to PCOS-related cardiometabolic risk, elucidating the mechanisms connecting ovarian dysfunction, inflammation, oxidative stress and hypertension, and supports gentiopicroside as a promising therapeutic candidate.