Background: Type 2 diabetes (T2D) is a prevalent chronic disease with elusive. Combining transcriptome and single-cell sequencing data to explore biomarkers of T2D could provide new insights into the in-depth understanding of the molecular mechanisms and diagnosis of T2D. Methods: The GSE41762 dataset including RNA-seq data for healthy and T2D patients, was obtained from the Gene Expression Omnibus (GEO) database. The potential functions of the differentially expressed genes (DEGs) were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Moreover, biomarkers were screened out by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and receiver operating characteristic (ROC) analysis. Furthermore, single-cell RNA (sc-RNA)-seq data in the “E-MTAB-5061” dataset was downloaded from the ArrayExpress (European Bioinformatics Institute, EBI) database. Principal components analysis (PCA) and t-distributed stochastic neighbor embedding (tSNE) were used for dimensionality reduction analysis and cell clustering. The FindAllMarkers function was used annotate different cell clusters, and key cell clusters were screened by the expression levels of the biomarkers. Finally, the transcription factors (TFs) of the biomarkers were recognized. Results: A total of 111 DEGs were screened in the GSE41762 dataset, which were mainly related to hormone secretion, specialized postsynaptic membrane, pancreatic secretion, JAK-STAT signaling pathway, and Ras signaling pathway. In addition, SLC2A2, SERPINF1, RASGRP1, and CHL1 were screened out as biomarkers of T2D, which possessed potential diagnostic value as AUC value greater than 0.8. A total of 1,515 T2D group cells and 1,817 healthy cohort cells were screened as core cells in the “E-MTAB-5061” dataset. Following tSNE dimensionality reduction cluster analysis, the core cells were divided into 13 cell clusters. According to the marker genes, the 13 cell clusters were annotated into six types of cells. Notably, SERPINF1 was highly expressed in fibroblasts and might be regulated by NR2F2 (nuclear receptor subfamily2, group F, and member 2). Conclusions: This study identified four biomarkers (SLC2A2, SERPINF1, RASGRP1, and CHL1) for T2D, which provided new markers for the clinical diagnosis of T2D. Among them, SERPINF1 might be regulated by NR2F2, which provides valuable insight into the pathogensis of T2D.

Hepatic steatosis, a hallmark of non-alcoholic fatty liver disease (NAFLD), represents a significant global health issue. Liver lipidomics has garnered increased focus recently, highlighting Traditional Chinese Medicine's (TCM) role in mitigating such conditions through lipid metabolism regulation. The Zuogui Jiangtang Qinggan Formula (ZGJTQGF), a longstanding TCM regimen for treating Type 2 Diabetes Mellitus (T2DM) with NAFLD, lacks a definitive mechanism for its lipid metabolism regulatory effects. This research aims to elucidate ZGJTQGF's mechanism on lipid metabolism in T2DM with NAFLD. The study, utilized db/db mice to establish T2DM with NAFLD models. Evaluations included Hematoxylin-Eosin (HE) and Oil Red O stainedstaining of liver tissues, alongside biochemical lipid parameter analysis. Liver lipidomics and Western blotting further substantiated the findings, systematically uncovering the mechanism of action mechanism. ZGJTQGF notably reduced body weight, and Fasting Blood Glucose (FBG) , enhancing glucose tolerance in db/db mice. HE, and Oil Red O staining, complemented by biochemical and liver lipidomics analyses, confirmed ZGJTQGF's efficacy in ameliorating liver steatosis and lipid metabolism anomalies. Lipidomics identified 1571 significantly altered lipid species in the model group, primarily through the upregulation of triglycerides (TG) and diglycerides (DG), and the downregulation of phosphatidylcholine (PC) and phosphatidylethanolamine (PE). Post-ZGJTQGF treatment, 496 lipid species were modulated, with increased PC and PE levels and decreased TG and DG, showcasing significant lipid metabolism improvement in T2DM with NAFLD. Moreover, ZGJTQGF's influence on lipid synthesis-related proteins was observed, underscoring its anti-steatotic impact through liver lipidomic alterations and offering novel insights into hepatic steatosis pathogenesis. Liver lipidomics analysis combined with protein verification further demonstrated that ZGJTQGF could ameliorate the lipid disturbance of TG, DG, PC, PE in T2DM with NAFLD, as well as improve fatty acid and cholesterol synthesis and metabolism through De novo lipogenesis pathway.

Purpose: This study aimed to explore the mechanism of Zuogui Jiangtang Shuxin formula (ZGJTSXF) in the treatment of diabetic cardiomyopathy (DCM) by an integrative strategy combining serum pharmacochemistry, network pharmacology analysis, and experimental validation. Methods: An Ultra high performance liquid chromatography-high resolution mass spectrometry (UPLC-Q-Exactive-Orbitrap-MS) method was constructed to identify compounds in rat serum after oral administration of ZGJTSXF. A component-target network between the targets of ZGJTSXF ingredients and DCM was established using Cytoscape. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed to deduce ZGJTSXF-associated targets and pathways. The DCM model mice were treated with ZGJTSXF, and the predicted important signaling pathways were verified using quantitative PCR and Western blot. Results: We identified 78 compounds in serum of medicated rats, which mainly included flavonoids, small peptides, nucleosides, organic acids, phenylpropanoids, alkaloids, phenanthrenequinones, iridoids, phenols, and saponins. Network pharmacology analysis revealed that ZGJTSXF may regulate targets including ALB, TNF, AKT1, GAPDH, VEGFA, EGFR, SRC, CASP3, MAPK3, JUN, and PI3K/AKT signaling pathway in the treatment of DCM. ZGJTSXF administration improved blood sugar levels, heart function, and cardiac morphological changes in DCM mice. Notably, ZGJTSXF inhibited cardiomyocytes apoptosis, which was associated with restored PI3K/Akt signaling and upregulated Bcl-2 and Bcl-xL proteins expression. Conclusion: Our preliminary results proposed the material basis and possible mechanisms of ZGJTSXF in treating DCM, which is related to the activation of the PI3K/AKT signaling pathway and apoptosis inhibition. These findings shed new light in developing ZGJTSXF-based therapeutics in treating DCM.

Objective: This meta-analysis and trial sequential analysis (TSA) aimed to evaluate the effects of probiotics on lipid metabolism, oxidative stress, and inflammation in patients with coronary heart disease (CHD). Methods: Six databases were systematically searched for relevant studies published before May 1, 2025. Basic study characteristics, outcome data, and risk of bias were extracted. Meta-analyses were performed using RevMan 5.3, TSA was conducted using TSA 0.9.5.10 beta, and publication bias was assessed using Egger’s test. Results: Eight randomized controlled trials involving 296 patients were included. Compared with the placebo, probiotics significantly reduced the low-density lipoprotein cholesterol (LDL-C) [mean difference (MD) −11.07 mg/dL, 95% confidence interval (CI) −19.94 to −2.20], malondialdehyde [standardized mean difference (SMD) −0.57, 95% CI −1.01 to −0.13], high-sensitivity C-reactive protein (MD −0.81 ng/mL, 95% CI −1.31 to −0.30), Toll-like receptor 4 (MD −4.13 ng/mL, 95% CI −5.39 to −2.88), and interleukin-6 (MD −3.22 ng/mL, 95% CI −4.01 to −2.44). Meanwhile, they increased the high-density lipoprotein cholesterol (HDL-C) (MD 2.79 mg/dL, 95% CI 0.95 to 4.63), glutathione (MD 104.66 μmol/L, 95% CI 53.74 to 155.58), and total antioxidant capacity (MD 69.51 mmol/L, 95% CI 44.64 to 94.38). However, the effects on LDL-C and HDL-C were not robust in sensitivity analyses, and neither outcome reached the minimal clinically important difference, indicating that lipid modulation is not clinically meaningful. Additionally, no significant differences were observed in very low-density lipoprotein cholesterol, total cholesterol, triglycerides, tumor necrosis factor-alpha and adverse event rate. TSA confirmed the robustness of all significant outcomes except for malondialdehyde, and Egger’s tests revealed no significant publication bias. Conclusion: Probiotics exert moderate anti-inflammatory and antioxidative effects, supporting their potential as an complementary strategy for CHD. In contrast, their influence on lipid metabolism appears uncertain and clinically negligible. Given the limited certainty of evidence and the geographic concentration of available trials, high-quality multicenter studies are required to confirm these findings.