DNA methylation is closely related to the occurrence and development of many diseases, but its role in obesity is still unclear. This study aimed to find the potential differentially methylated genes associated with obesity occurrence and development. By combining methylation and transcriptome analysis, we identified the key genes in adipose tissue affecting the occurrence and development of obesity and revealed the possible molecular mechanisms involved in obesity pathogenesis. We first screened 14 methylation-related differential genes and verified their expression in adipose tissue by quantitative polymerase chain reaction (qPCR). Seven genes with the same expression pattern were identified as key genes, namely, CCRL2, GPT, LGALS12, PC, SLC27A2, SLC4A4, and TTC36. Then, the immune microenvironment of adipose tissue was quantified by CIBERSORT, and we found that the content of M0 macrophages and T follicular helper cells in adipose tissue was significantly increased and decreased, respectively, in the obese group. Furthermore, the relationship between key genes and the immune microenvironment was analyzed. Additionally, the metabolic pathway activity of each sample was calculated based on the ssGSEA algorithm, and the key gene–metabolic network was constructed. Moreover, we performed a CMAP analysis based on the differential genes in adipose tissue to screen out drugs potentially effective in obesity treatment. In conclusion, we identified seven methylation-related key genes closely related to obesity pathogenesis and explored the potential mechanism of their role in obesity. This study provided novel insights into the molecular mechanisms and management of obesity.

We measure the voltage or electric field (EF) modulated change in anisotropy using two methods on the same nanometer sized device: 1) Directly using the area of the hard axis magnetization loop and 2) Indirectly using the switching field distribution method. Both methods yield similar values of efficiency. With the indirect method, the efficiency derived from the thermal stability was found to be more consistent than that from the anisotropy field. Our data also suggests that memory devices that rely solely on EF effects may benefit from larger device sizes.

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Introduction Diabetic kidney disease (DKD) has become the leading cause of end-stage kidney disease (ESKD) in most countries. Recently, long noncoding RNA XIST has been found involved in the development of DKD. Methods A total of 1184 hospitalized patients with diabetes were included and divided into four groups based on their estimated glomerular filtration rate (eGFR) and urinary albumin to creatinine ratio (UACR): normal control group (nDKD), DKD with normoalbuminuric and reduced eGFR (NA-DKD), DKD with albuminuria but without reduced eGFR (A-DKD), and DKD with albuminuria and reduced eGFR (Mixed), and then their clinical characteristics were analyzed. Peripheral blood mononuclear cells (PBMCs) of patients with DKD were isolated, and lncRNA XIST expression was detected by real-time quantitative PCR. Results The prevalence of DKD in hospitalized patients with diabetes mellutus (DM) was 39.9%, and the prevalence of albuminuria and decreased eGFR was 36.6% and 16.2%, respectively. NA-DKD, A-DKD, and Mixed groups accounted for 3.3%,23.7%, and 12.9%, respectively. Women with DKD had considerably lower levels of lncRNA XIST expression in their PBMCs compared to nDKD. There was a significant correlation between eGFR level and lncRNA XIST expression ( R = 0.390, P = 0.036) as well as a negative correlation between HbA1c and lncRNA XIST expression ( R = − 0.425, P = 0.027) in female patients with DKD. Conclusions Our study revealed that 39.9% of DM inpatients who were admitted to the hospital had DKD. Importantly, lncRNA XIST expression in PBMCs of female patients with DKD was significantly correlated with eGFR and HbA1c. Supplementary Information The online version contains supplementary material available at 10.1007/s13300-023-01439-9.