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N6-methyladenosine (m6A) methylation has been reported to play a role in type 2 diabetes (T2D). However, the key component of m6A methylation has not been well explored in T2D. This study investigates the biological role and the underlying mechanism of m6A methylation genes in T2D. The Gene Expression Omnibus (GEO) database combined with the m6A methylation and transcriptome data of T2D patients were used to identify m6A methylation differentially expressed genes (mMDEGs). Ingenuity pathway analysis (IPA) was used to predict T2D-related differentially expressed genes (DEGs). Gene ontology (GO) term enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to determine the biological functions of mMDEGs. Gene set enrichment analysis (GSEA) was performed to further confirm the functional enrichment of mMDEGs and determine candidate hub genes. The least absolute shrinkage and selection operator (LASSO) regression analysis was carried out to screen for the best predictors of T2D, and RT-PCR and Western blot were used to verify the expression of the predictors. A total of 194 overlapping mMDEGs were detected. GO, KEGG, and GSEA analysis showed that mMDEGs were enriched in T2D and insulin signaling pathways, where the insulin gene (INS), the type 2 membranal glycoprotein gene (MAFA), and hexokinase 2 (HK2) gene were found. The LASSO regression analysis of candidate hub genes showed that the INS gene could be invoked as a predictive hub gene for T2D. INS, MAFA,and HK2 genes participate in the T2D disease process, but INS can better predict the occurrence of T2D.

Diabetic retinopathy (DR), the most common microvascular complication of diabetes and leading cause of visual impairment in adults worldwide, is suggested to be linked to abnormal lipid metabolism. The present study aims to comprehensively investigate the relationship between n-6 polyunsaturated fatty acids (PUFAs) and DR. This was a propensity score matching based case–control study, including 69 pairs of DR patients and type 2 diabetic patients without DR with mean age of 56.7 ± 9.2 years. Five n-6 PUFAs were determined by UPLC-ESI-MS/MS system. Principle component regression (PCR) and multiple conditional logistic regression models were used to investigate the association of DR risk with n-6 PUFAs depending on independent training and testing sets, respectively. According to locally weighted regression model, we observed obvious negative correlation between levels of five n-6 PUFAs (linoleic acid, γ-linolenic acid, eicosadienoic acid, dihomo-γ-linolenic acid and arachidonicacid) and DR. Based on multiple PCR model, we also observed significant negative association between the five n-6 PUFAs and DR with adjusted OR (95% CI) as 0.62 (0.43,0.87). When being evaluated depending on the testing set, the association was still existed, and PCR model had excellent classification performance, in which area under the curve (AUC) was 0.88 (95% CI: 0.78, 0.99). In addition, the model also had valid calibration with a non-significant Hosmer–Lemeshow Chi-square of 9.44 (P = 0.307) in the testing set. n-6 PUFAs were inversely associated with the presence of DR, and the principle component could be potential indicator in distinguishing DR from other T2D patients.

We aimed to assess the association between fasting plasma glucose (FPG) change trajectory and incident hypertension among Chinese population. This cohort study included 11,791 adults aged 18–80 years without hypertension at first entry and who completed at least four follow-ups between 2009 and 2016. Logistic regression was used to estimate odds ratios (ORs) and 95% CIs for the association between FPG change trajectory and probability of hypertension. During a median follow-up of 5.10 years (total person–years 61,887.76), hypertension developed in 2177 participants. After adjusting for baseline potential confounders, the probability of hypertension increased with the increasing FPG change trajectory (adjusted OR (aOR) 1.22, 95% CI 1.07–1.40), bell-shape trajectory (aOR 1.15, 95% CI 1.02–1.30) and other-shape trajectory (aOR 1.13, 95% CI 1.02–1.25) which showed a higher variability of FPG compared to the decreasing group. In addition, the increasing FPG change trajectory was associated with a higher probability of hypertension compared with the decreasing group regardless of age and BMI but was only significant in males and in those with normal FPG at baseline. Our study indicates that the increasing FPG change trajectory determines the highest risk of hypertension, demonstrating the importance of maintaining low and stable levels of FPG, especially in males and in those with normal FPG.