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Statins, the first-line medication for dyslipidemia, are linked to an increased risk of type 2 diabetes. But exactly how statins cause diabetes is yet unknown. In this study, a developed short-term statin therapy on hyperlipidemia mice show that hepatic insulin resistance is a cause of statin-induced diabetes. Statin medication raises the expression of progesterone and adiponectin receptor 9 (PAQR9) in liver, which inhibits insulin signaling through degradation of protein phosphatase, Mg 2+ /Mn 2+ dependent 1 (PPM1α) to activate ERK pathway. STIP1 homology and U-box containing protein 1 (STUB1) is found to mediate ubiquitination of PPM1α promoted by PAQR9. On the other hand, decreased activity of hepatocyte nuclear factor 4 alpha (HNF4α) seems to be the cause of PAQR9 expression under statin therapy. The interventions on PAQR9, including deletion of PAQR9, caloric restriction and HNF4α activation, are all effective treatments for statin-induced diabetes, while liver specific over-expression of PPM1α is another possible tactic. The results reveal the importance of HNF4α-PAQR9-STUB1-PPM1α axis in controlling the statin-induced hepatic insulin resistance, offering a fresh insight into the molecular mechanisms underlying statin therapy., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

We aimed to identify the key potential insulin resistance (IR)-related genes and investigate their correlation with immune cell infiltration in type 2 diabetes (T2D). The GSE78721 dataset (68 diabetic patients and 62 controls) was downloaded from the Gene Expression Omnibus database and utilized for single-sample gene set enrichment analysis. IR-related genes were obtained from the Comparative Toxicology Genetics Database, and the final IR-differentially expressed genes (DEGs) were screened by intersecting with the DEGs obtained from the GSE78721 datasets. Functional enrichment analysis was performed, and the networks of the target gene with microRNA, transcription factor, and drug were constructed. Hub genes were identified based on a protein-protein interaction network. Least absolute shrinkage and selection operator regression and Random Forest and Boruta analysis were combined to screen diagnostic biomarkers in T2D, which were validated using the GSE76894 (19 diabetic patients and 84 controls) and GSE9006 (12 diabetic patients and 24 controls) datasets. Quantitative real-time polymerase chain reaction was performed to validate the biomarker expression in IR mice and control mice. In addition, infiltration of immune cells in T2D and their correlation with the identified markers were computed using CIBERSORT. We identified differential immune gene set regulatory T-cells in the GSE78721 dataset, and T2D samples were assigned into three clusters based on immune infiltration. A total of 2094 IR-DEGs were primarily enriched in response to endoplasmic reticulum stress. Importantly, HDAC9 and ARRDC4 were identified as markers of T2D and associated with different levels of immune cell infiltration. HDAC9 mRNA level were higher in the IR mice than in control mice, while ARRDC4 showed the opposite trend. In summary, we discovered potential vital biomarkers that contribute to immune cell infiltration associated with IR, which offers a new sight of immunotherapy for T2D., (© 2024. The Author(s).)