Purpose: This study aimed to investigate how resveratrol (RSV) improves high-fat diet (HFD)-induced hepatic insulin resistance in mice via microRNA (miRNA) mmu-miR-363-3p in vitro and in vivo. Materials and Methods: C57BL/6J mice were fed a HFD for 8 weeks to establish an insulin resistance model. The model mice were treated or not with RSV for 6 weeks. Differential miRNA expression in mouse liver tissues was analyzed by high-throughput sequencing. Mouse HepG2 cells were treated with palmitic acid (PA) to establish a cell model of insulin resistance. HepG2 cells were transfected with mmu-miR-363-3p inhibitor or mimic, and the expression of PI3K-Akt signaling pathway-related proteins was analyzed. Results: Based on the high-throughput sequencing analysis, mmu-miR-363-3p was identified as a major miRNA involved in the action of RSV on insulin resistance. Based on KEGG pathway enrichment analysis, PI3K-Akt signaling was found to be significantly enriched among differentially expressed miRNAs, and this pathway is closely related to insulin resistance. RSV treatment reduced the expression of FOXO1 and G6PC, which are downstream of the PI3K-Akt pathway. In the cell model, mmu-miR-363-3p inhibitor significantly suppressed p-Akt and p-PI3K levels, but enhanced those of FOXO1 and G6PC. In contrast, mmu-miR-363-3p mimic significantly enhanced the p-Akt and p-PI3K levels, but suppressed FOXO1 and G6PC expression, which was similar to the effect of RSV. Conclusion: RSV improves insulin resistance by upregulating miRNA mmu-miR-363-3p via the PI3K-Akt pathway.

Background and Aims. Previous studies on the effects of resveratrol on metabolic indicators reported contradictory findings, and these indicators have not been frequently studied in patients with type 2 diabetes. In this study, we aimed to examine the effects of resveratrol on metabolic indicators in a specific group of people with type 2 diabetes using the most recent literature. Methods. We used RevMan 5.4 and Stata 14.0 software to identify randomized controlled studies on the impact of resveratrol on metabolic indicators in patients with type 2 diabetes using relevant search terms and keywords such as “resveratrol” and “type 2 diabetes” in the China National Knowledge Infrastructure, PubMed, Cochrane, and Embase. Data were expressed as the weighted mean difference (WMD) and 95% confidence interval (CI). Results. This meta‐analysis included 19 studies involving 1151 patients with type 2 diabetes, including 584 patients treated with resveratrol and 567 patients who received placebo. Compared with the control data, large doses of resveratrol (≥1000 mg) reduced fasting blood glucose levels (WMD: −18.76 mg/dL, 95% CI: −23.43, −14.09; P < 0.00001). Additionally, resveratrol reduced systolic blood pressure (WMD: −7.97 mmHg, 95% CI: −10.63, −5.31; P < 0.00001) and diastolic blood pressure (WMD: −3.55 mmHg, 95% CI: −5.18, −1.93; P < 0.00001) in patients with type 2 diabetes but did not improve waist circumference (WMD: 0.05 cm, 95% CI: −1.77, 1.88; P = 0.95), triglyceride levels (WMD: −4.49 mg/dL, 95% CI: −24.23, 15.25; P = 0.66), or high‐density lipoprotein cholesterol levels (WMD: −1.05 mg/dL, 95% CI: −2.44, 0.33; P = 0.14) in patients with type 2 diabetes. Conclusion. This systematic review and meta‐analysis updated the most recent literature and provided new evidence, proving that resveratrol treatment can reduce systolic blood pressure and diastolic blood pressure. High‐dose resveratrol can reduce fasting blood glucose in patients with type 2 diabetes, although it has no effect on waist circumference, triglyceride, and high‐density lipoprotein cholesterol.

Purpose: The aims of this study were to determine the relationship between 25-hydroxyvitamin D [25(OH) D] and glycated hemoglobin (HbA1c) levels in male and female patients with type 2 diabetes mellitus (T2DM). Patients and Methods: The participants were adults diagnosed with T2DM recruited from Hebei General Hospital. Patient information and information regarding blood indicators were collected. The subjects were divided into no vitamin D deficiency group [25(OH) D > 20 ng/mL] and vitamin D deficiency group [25(OH) D < 20 ng/mL], and these groups were then further subdivided into male-only or female-only subgroups. And then, the subjects were divided into male group and female group in different 25(OH) D levels. Results: HbA1c levels in the vitamin D deficiency group were significantly higher than those in the no vitamin D deficiency group for all subjects. The same was true for female patients but not for male patients. There was no difference in HbA1c levels between male and female patients with T2DM, regardless of 25(OH) D deficiency. A negative correlation existed between 25(OH) D and HbA1c in all subjects, as well as in the male-only and female-only subgroups. Vitamin D deficiency was associated with high HbA1c levels before and after adjusting for confounding factors in all participants and in the female-only subgroup, but not in the male-only subgroup. Conclusion: This study confirmed that vitamin D deficiency was related with high HbA1c levels in patients with T2DM, and this relationship differs between female and male patients.

Purpose: The triglyceride–glucose index (TyG) is positively correlated with serum uric acid (SUA) in patients with type 2 diabetes mellitus (T2DM). However, whether this relationship exists in non-obese T2DM patients remains unknown. The study investigated the relationship between TyG and SUA in Chinese non-obese T2DM patients and examined the prognostic value of TyG in hyperuricemia (HUA). Patients and Methods: In total, 719 T2DM patients who were not obese were enrolled from among those who visited the Hebei General Hospital. The patients were categorized into groups according to their SUA levels. The relationship between TyG and clinical parameters was examined through correlation analysis. To consider covariates and examine the independent impact of TyG on HUA, logistic regression was performed. The receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of TyG and homeostasis model assessment of insulin resistance (HOMA-IR) for HUA. Results: The HUA prevalence was 12.10%. TyG was statistically different among the four SUA groups, with lower TyG levels in the Q1, Q2, and Q3 groups than that in the Q4 group. TyG was positively correlated with SUA (r = 0.176, P < 0.001). Logistic regression exhibited that TyG and SUA were independently correlated (OR = 2.427, 95% CI = 1.134-5.195, P = 0.022) even after adjustment for confounding factors. The ROC curve showed that the predictive value of TyG for HUA was higher than that of HOMA-IR (AUROC = 0.613, P = 0.001). Conclusion: TyG was positively correlated with SUA in non-obese T2DM patients. TyG may better predict HUA in non-obese T2DM patients than HOMA-IR.

Purpose. This study was conducted to investigate whether high body mass index (BMI) and triglycerides (TGs) were protective factors for reducing osteoporosis (OP) in patients with type 2 diabetes mellitus (T2DM). Participants and Methods. Seventy-nine patients (aged 20 to 81) with T2DM were included in the study. Basic information and blood indicators were collected. Bone mineral density was used to diagnose OP. Participants were grouped according to BMI (normal weight vs. overweight/obese participants), TG (normal TG vs. hypertriglyceridemia), and OP (non-OP vs. OP), and differences were compared between groups. Regression analysis was used to explore whether BMI or TG were independent factors affecting OP. Results. The proportions of OP in the overweight/obese and hypertriglyceridemic groups were significantly lower than those in the normal weight (30.0% vs. 69.0%; ) and normal TG (27.3% vs. 56.5%; ) groups. In the OP group, the BMI ( vs. ; ) was significantly lower than that in the non-OP group, and TG showed the same trend (1.30 (0.81) vs. 1.71 (1.1) mmol/L; ). Logistic regression in the crude model showed that the odds ratios (ORs) of OP in the overweight/obese and hypertriglyceridemic groups were 0.193 (95% CI: 0.071, 0.520) and 0.315 (95% CI: 0.119, 0.830) compared with those of the normal weight and normal TG groups. After adjusting for sex and smoking, the ORs were 0.204 (95% CI: 0.074, 0.567) and 0.242 (95% CI: 0.082, 0.709) for the overweight/obese and hypertriglyceridemic groups, respectively. After adjusting for all confounding factors, the ORs for these groups were 0.248 (95% CI: 0.083, 0.746) and 0.299 (95% CI: 0.091, 0.989), respectively. Conclusion. BMI and TG are independent protective factors against OP in patients with T2DM.

Purpose: We aimed to determine the relationship between the levels of glycated hemoglobin (HbA1c) and biomarkers of bone metabolism in patients with type 2 diabetes mellitus (T2DM), and whether HbA1c independently influences any of these biomarkers. Patients and Methods: A cohort study of 240 patients with T2DM was performed. Serum was obtained and used to measure HbA1c, total cholesterol (TC), triglycerides, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol (LDL-C), very-low-density lipoprotein-cholesterol, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), total protein, albumin, blood urea nitrogen (BUN), creatinine, serum 25-hydroxyvitamin D (25OHD), osteocalcin (OC), β-C-terminal cross-linked telopeptide of type I collagen (β-CTX), procollagen type 1 N-terminal propeptide (P1NP), or parathyroid hormone (PTH) concentrations. The participants were divided into three study groups according to HbA1c level: < 7%, 7– 9% and ≥ 9%. Chi-square testing and one-way analysis of variance were used to compare groups. The relationships between HbA1c and bone metabolism biomarker values were analyzed using linear correlation analysis and multiple linear regression analysis. Results: Age, duration of T2DM, and the concentrations of TC, LDL-C, apolipoprotein B, albumin, and BUN showed significant difference among the < 7%, 7– 9% and ≥ 9% HbA1c groups. Of the bone metabolism biomarkers, there were significant differences in serum 25-hydroxyvitamin D (25OHD) and osteocalcin (OC) among the groups. The correlation coefficients (r) for the relationships of HbA1c with 25OHD and OC were − 0.200 and − 0.183, respectively (P < 0.05). Regardless of adjustment for none, some, or all of the confounding factors (age, sex, and duration of T2DM), the 25OHD and OC concentrations were significantly lower in the HbA1c ≥ 9% group than in the HbA1c < 7% group. HbA1c showed no relationship with β-CTX, PINP, or PTH. Conclusion: T2DM patients with poorer glycemic control had lower concentrations of serum 25OHD and OC, suggesting that HbA1c is an independent risk factor for low 25OHD and OC.