Integrating Mendelian randomization and single-cell RNA sequencing to identify therapeutic targets of baicalin for type 2 diabetes mellitus.

Background: Alternative and complementary therapies play an imperative role in the clinical management of Type 2 diabetes mellitus (T2DM), and exploring and utilizing natural products from a genetic perspective may yield novel insights into the mechanisms and interventions of the disorder. Methods: To identify the therapeutic target of baicalin for T2DM, we conducted a Mendelian randomization study. Druggable targets of baicalin were obtained by integrating multiple databases, and target-associated cis-expression quantitative trait loci (cis-eQTL) originated from the eQTLGen consortium. Summary statistics for T2DM were derived from two independent genome-wide association studies available through the DIAGRAM Consortium (74,124 cases vs. 824,006 controls) and the FinnGen R9 repository (9,978 cases vs. 12,348 controls). Network construction and enrichment analysis were applied to the therapeutic targets of baicalin. Colocalization analysis was utilized to assess the potential for the therapeutic targets and T2DM to share causative genetic variations. Molecular docking was performed to validate the potency of baicalin. Single-cell RNA sequencing was employed to seek evidence of therapeutic targets' involvement in islet function. Results: Eight baicalin-related targets proved to be significant in the discovery and validation cohorts. Genetic evidence indicated the expression of ANPEP, BECN1, HNF1A, and ST6GAL1 increased the risk of T2DM, and the expression of PGF, RXRA, SREBF1, and USP7 decreased the risk of T2DM. In particular, SREBF1 has significant interaction properties with other therapeutic targets and is supported by strong colocalization. Baicalin had favorable combination activity with eight therapeutic targets. The expression patterns of the therapeutic targets were characterized in cellular clusters of pancreatic tissues that exhibited a pseudo-temporal dependence on islet cell formation and development. Conclusion: This study identified eight potential targets of baicalin for treating T2DM from a genetic perspective, contributing an innovative analytical framework for the development of natural products. We have offered fresh insights into the connections between therapeutic targets and islet cells. Further, fundamental experiments and clinical research are warranted to delve deeper into the molecular mechanisms of T2DM.

Association of circulating inflammatory proteins with type 2 diabetes mellitus and its complications: a bidirectional Mendelian randomization study.

Background: Increasing evidence indicates that immune response underlies the pathology of type 2 diabetes (T2D). Nevertheless, the specific inflammatory regulators involved in this pathogenesis remain unclear. Methods: We systematically explored circulating inflammatory proteins that are causally associated with T2D via a bidirectional Mendelian randomization (MR) study and further investigated them in prevalent complications of T2D. Genetic instruments for 91 circulating inflammatory proteins were derived from a genome-wide association study (GWAS) that enrolled 14,824 predominantly European participants. Regarding the summary-level GWASs of type 2 diabetes, we adopted the largest meta-analysis of European population (74,124 cases vs. 824,006 controls) and a prospective nested case-cohort study in Europe (9,978 cases vs. 12,348 controls). Summary statistics for five complications of T2D were acquired from the FinnGen R9 repository. The inverse variance-weighted method was applied as the primary method for causal inference. MR-Egger, weighted median and maximum likelihood methods were employed as supplementary analyses. Results from the two T2D studies were combined in a meta-analysis. Sensitivity analyses and phenotype-wide association studies (PheWAS) were performed to detect heterogeneity and potential horizontal pleiotropy in the study. Results: Genetic evidence indicated that elevated levels of TGF-α (OR = 1.16, 95% CI = 1.15-1.17) and CX3CL1 (OR = 1.30, 95% CI = 1.04-1.63) promoted the occurrence of T2D, and increased concentrations of FGF-21 (OR = 0.87, 95% CI = 0.81-0.93) and hGDNF (OR = 0.96, 95% CI = 0.95-0.98) mitigated the risk of developing T2D, while type 2 diabetes did not exert a significant influence on said proteins. Elevated levels of TGF-α were associated with an increased risk of ketoacidosis, neurological complications, and ocular complications in patients with T2D, and increased concentrations of FGF-21 were potentially correlated with a diminished risk of T2D with neurological complications. Higher levels of hGDNF were associated with an increased risk of T2D with peripheral vascular complications, while CX3CL1 did not demonstrate a significant association with T2D complications. Sensitivity analyses and PheWAS further ensure the robustness of our findings. Conclusion: This study determined four circulating inflammatory proteins that affected the occurrence of T2D, providing opportunities for the early prevention and innovative therapy of type 2 diabetes and its complications.

Association between the Systemic Immune-Inflammation Index and Thyroid Function in U.S. Adults.

Background: The systemic immune-inflammation index (SII) is used as an indicator of prognosis for a wide range of diseases. Thyroid function has been found to be strongly associated with inflammation. The purpose of this investigation was to analyze the correlation between SII and various thyroid functions. Methods: This study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2007-2012. The association between SII and thyroid function was analyzed using weighted univariate and multivariate linear regression analyses. Subgroup analyses, interaction tests, and weighted restricted cubic spline (RCS) regression analyses were also employed to test this correlation. Results: Of the 6,875 participants (age ≥ 20 years), the mean age was 46.87 ± 0.40 years. The adjusted model showed that lnSII was negatively correlated with FT3 (ß = -0.0559, 95% CI -0.1060 to -0.0059,) and FT3/FT4 (ß = -0.0920, 95% CI -0.1667 to -0.0173,). There was a positive correlation between lnSII and TT4 (ß = 0.1499, 95% CI 0.0722-0.2276,). In subgroup analyses, lnSII still independently affected a wide range of thyroid functions. Weighted RCS analysis showed a nonlinear relationship between FT3 and lnSII. Conclusion: Close relationships exist between SII and a variety of thyroid functions. SII can be used as an indicator to predict thyroid dysfunction. Control of inflammatory activity may be a protective measure against thyroid dysfunction. More large-scale prospective studies are necessary to further explore the correlation between SII and thyroid function and the role of obesity in this.