Objective: To investigate the mechanism of liraglutide affecting lipid metabolism by regulating lipolysis and lipogenesis in cells and ob/ob mice. Methods: 3 T3-L1 cells were treated with liraglutide in vitro, and differentially expressed genes were screened by RNA sequencing. Gene Ontology (GO) and KEGG (Kvoto Encyclopedia of Genes and Genomes) enrichment analyses identified target genes for lipid regulation of liraglutide. 3 T3-L1 preadipocytes were induced to differentiate into adipocytes using a "cocktail method". Western blot and immunofluorescence were used to detect the expression of target genes and the lipid regulatory effect of liraglutide. 3 T3-L1 preadipocytes were transfected with lentivirus overexpressing Zbtb20 to study its role in adipogenesis, and gene expression was analyzed by RTqPCR and Western blot. In vivo, ob/ob mice were subcutaneously injected with liraglutide or saline for 4 weeks. Blood lipids, adipose tissue volume and adipocyte size were detected. Immunohistochemical analysis and RTqPCR were used to detect the expression of target genes in adipose tissue. Results: Liraglutide reduced lipid droplets and TG levels and altered the expression of genes related to fatty acid metabolism, lipogenesis, fatty acid oxidation, and adipocyte browning. The results of PCR, Western blot and immunofluorescence confirmed that liraglutide could regulate the adipogenesis by downregulating the transcriptional suppressor ZBTB20, and overexpression of Zbtb20 inhibited the expression of LPL, the key enzyme for lipohydrolysis. Conclusions: Liraglutide regulates lipid metabolism through ZBTB20-LPL pathway to reveal its molecular mechanism.

ObjectiveTo investigate the effect of calcium channel blockers (CCBs) on tacrolimus blood concentrations in renal transplant recipients with different CYP3A5 genotypes.MethodsThis retrospective cohort study included renal transplant recipients receiving tacrolimus-based immunosuppressive therapy with or without CCBs in combination. Patients were divided into combination and control groups based on whether or not they were combined with CCBs, and then further analyzed according to the type of CCBs (nifedipine/amlodipine/felodipine). Propensity score matching was conducted for the combination and the control groups using SPSS 22.0 software to reduce the impact of confounding factors. The effect of different CCBs on tacrolimus blood concentrations was evaluated, and subgroup analysis was performed according to the patients’ CYP3A5 genotypes to explore the role of CYP3A5 genotypes in drug-drug interactions between tacrolimus and CCBs.ResultsA total of 164 patients combined with CCBs were included in the combination groups. After propensity score matching, 83 patients with nifedipine were matched 1:1 with the control group, 63 patients with felodipine were matched 1:2 with 126 controls, and 18 patients with amlodipine were matched 1:3 with 54 controls. Compared with the controls, the three CCBs increased the dose-adjusted trough concentration (C0/D) levels of tacrolimus by 41.61%–45.57% (P < 0.001). For both CYP3A5 expressers (CYP3A5*1*1 or CYP3A5*1*3) and non-expressers (CYP3A5*3*3), there were significant differences in tacrolimus C0/D between patients using felodipine/nifedipine and those without CCBs (P < 0.001). However, among CYP3A5 non-expressers, C0/D values of tacrolimus were significantly higher in patients combined with amlodipine compared to the controls (P = 0.001), while for CYP3A5 expressers, the difference in tacrolimus C0/D values between patients with amlodipine and without was not statistically significant (P = 0.065).ConclusionCCBs (felodipine/nifedipine/amlodipine) can affect tacrolimus blood concentration levels by inhibiting its metabolism. The CYP3A5 genotype may play a role in the drug interaction between tacrolimus and amlodipine. Therefore, genetic testing for tacrolimus and therapeutic drug monitoring are needed when renal transplant recipients are concurrently using CCBs.

Background: The effect of drug–drug interaction between tacrolimus and caspofungin on the pharmacokinetics of tacrolimus in different CYP3A5 genotypes has not been reported in previous studies. Objectives: To investigate the effect of caspofungin on the blood concentration and dose of tacrolimus under different CYP3A5 genotypes. Design: We conducted a retrospective cohort study in The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital from January 2015 to December 2022. All kidney transplant patients were divided into the combination or non-combination group based on whether tacrolimus was combined with caspofungin or not. Patients were subdivided into CYP3A5 expressers ( CYP3A5*1/*1 or CYP3A5*1/*3) and CYP3A5 non-expressers ( CYP3A5*3/*3). Methods: Data from the combination and the non-combination groups were matched with propensity scores to reduce confounding by SPSS 22.0. A total of 200 kidney transplant patients receiving tacrolimus combined with caspofungin or not were enrolled in this study. Statistical analysis was conducted on the dose-corrected trough concentrations ( C0/ D) and dose requirements ( D) of tacrolimus using independent sample two-sided t-test and nonparametric tests to investigate the impact on patients with different. Results: In this study, the C0/ D values of tacrolimus were not significantly different between the combination and non-combination groups ( p = 0.054). For CYP3A5 expressers, there was no significant difference in tacrolimus C0/ D or D values between the combination and non-combination groups ( p = 0.359; p = 0.851). In CYP3A5 nonexpressers, the C0/ D values of tacrolimus were significantly lower in the combination than in the non-combination groups ( p = 0.039), and the required daily dose of tacrolimus was increased by 11.11% in the combination group. Conclusion: Co-administration of caspofungin reduced tacrolimus blood levels and elevated the required daily dose of tacrolimus. In CYP3A5 non-expressers, co-administration of caspofungin had a significant effect on tacrolimus C0/ D values. An approximate 10% increase in the weight-adjusted daily dose of tacrolimus in CYP3A5 non-expressers is recommended to ensure the safety of tacrolimus administration.

Objective This study aimed to explore the risk factors for gastrointestinal side effects (GISEs) in patients with type 2 diabetes mellitus (T2DM) during treatment with glucagon-like peptide-1 receptor agonists (GLP-1RAs) based on real-world data and to develop a prediction model for GLP-1RA-related GISEs. Methods A total of 855 patients who attended the First Affiliated Hospital of Shandong First Medical University from January 2020 to May 2023 were selected as the study participants, who were divided into the training set (598 cases) and the validation set (297 cases) using a simple random sampling method at a ratio of 7:3. The general information and biochemical indicators of the participants were collected to assess the risk factors for GLP-1RA-related GISEs, and multifactorial logistic regression analysis was used to obtain the best predictors. A nomogram prediction model was constructed. The Hosmer-Lemeshow test was used to assess the differentiation and calibration of the nomogram model, and decision curve analysis (DCA) was used to evaluate the clinical utility of the model. Results Age, gender, history of gastrointestinal disorders, and number of combined oral medications were found as risk factors for the occurrence of GISEs in patients with T2DM using GLP-1RAs (p < 0.05). The nomogram prediction model based on these four factors had good discriminability (AUC values of the training and validation sets of 0.855 and 0.836, respectively) and accuracy (Hosmer-Lemeshow test: p > 0.05 for the validation set). DCA showed that the prediction model curve had clinical utility in the threshold probability interval of >5%. Conclusions The established nomogram model has an excellent predictive effect on GISEs induced by GLP-1RAs in patients with T2DM.

Background: The effect of drug–drug interaction between tacrolimus and caspofungin on the pharmacokinetics of tacrolimus in different CYP3A5 genotypes has not been reported in previous studies. Objectives: To investigate the effect of caspofungin on the blood concentration and dose of tacrolimus under different CYP3A5 genotypes. Design: We conducted a retrospective cohort study in The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital from January 2015 to December 2022. All kidney transplant patients were divided into the combination or non-combination group based on whether tacrolimus was combined with caspofungin or not. Patients were subdivided into CYP3A5 expressers ( CYP3A5*1/*1 or CYP3A5*1/*3 ) and CYP3A5 non-expressers ( CYP3A5*3/*3 ). Methods: Data from the combination and the non-combination groups were matched with propensity scores to reduce confounding by SPSS 22.0. A total of 200 kidney transplant patients receiving tacrolimus combined with caspofungin or not were enrolled in this study. Statistical analysis was conducted on the dose-corrected trough concentrations ( C 0 / D ) and dose requirements ( D ) of tacrolimus using independent sample two-sided t- test and nonparametric tests to investigate the impact on patients with different. Results: In this study, the C 0 / D values of tacrolimus were not significantly different between the combination and non-combination groups ( p = 0.054). For CYP3A5 expressers, there was no significant difference in tacrolimus C 0 / D or D values between the combination and non-combination groups ( p = 0.359; p = 0.851). In CYP3A5 nonexpressers, the C 0 / D values of tacrolimus were significantly lower in the combination than in the non-combination groups ( p = 0.039), and the required daily dose of tacrolimus was increased by 11.11% in the combination group. Conclusion: Co-administration of caspofungin reduced tacrolimus blood levels and elevated the required daily dose of tacrolimus. In CYP3A5 non-expressers, co-administration of caspofungin had a significant effect on tacrolimus C 0 / D values. An approximate 10% increase in the weight-adjusted daily dose of tacrolimus in CYP3A5 non-expressers is recommended to ensure the safety of tacrolimus administration. Keywords caspofungin , / , CYP3A5 , kidney transplant , tacrolimus

Objective: To evaluate the relationship between GLP-1R gene polymorphisms and type 2 diabetes mellitus with dyslipidemia and without dyslipidemia in China.Methods: A total of 200 patients with Type 2 Diabetes Mellitus (T2DM) were included in this study, including 115 with dyslipidemia and 85 without dyslipidemia. We used Sanger double deoxygenation terminal assay and PCRRFLP to detect genotype of the GLP-1R rs10305420 and rs3765467 loci. T-test was used to analyze the association between gene polymorphisms and lipid indicators. SHEsis online analysis software was used to analyze the linkage balance effect of loci, and SPSS 26 was used to calculate the gene interaction by dominant model.Results: The genotype distribution of the two loci in the sample of this study was in accordance with Hardyweinberg equilibrium. There were significant differences in the genotype distribution and allele frequency of rs3765467 between T2DM patients with and without dyslipidemia (GG 52.9%, GA + AA 47.1% vs. GG 69.6%, GA + AA 30.4%; P = 0.017). Under the dominant model, the effects of rs3765467 A allele and rs10305420 T allele on dyslipidemia had multiplicative interactions (P = 0.016) and additive interactions (RERI = 0.403, 95% CI [-2.708 to 3.514]; AP = 0.376, 95% CI [-2.041, 2.793]). Meanwhile, HbA1c levels in rs3765467 A allele carriers (GA + AA) were found to be significantly lower than those in patients with GG genotype (P = 0.006).Conclusion: The rs3765467 (G/A) variant is associated with the incidence of dyslipidemia, and G allele may be a risk factor for dyslipidemia.

Objective:To compare the efficacy and safety of insulin degludec and insulin glargine U100 in patients with type 2 diabetes mellitus.Methods:This study was a retrospective cohort study. The subjects were patients with type 2 diabetes mellitus who were hospitalized in 13 3A-level general hospitals in Shandong Province from September 2018 to December 2021. According to the type of basal insulin used, the patients were divided into insulin degludec group and insulin glargine U100 group. The basic information and laboratory test results in patients in the 2 groups were collected, the differences of fasting blood glucose level and incidence of hypoglycemia between the 2 groups were compared. The patients with complete blood glucose monitoring data in the 2 groups were selected and their blood glucose fluctuations were compared.Results:A total of 1 152 patients were entered in the study, including 552 patients in the insulin degludec group and 600 patients in the insulin glargine U100 group. The difference in the basic conditions in patients in the 2 groups was not statistically significant (all P>0.05). After treatment, the fasting blood glucose levels in patients in the 2 groups were lower than those before treatment, with statistically significant differences [10.2 (8.8, 12.5) mmol/L vs. 7.5 (6.6, 8.7) mmol/L, Z=-19.443, P<0.001; 10.0 (8.6, 11.7) mmol/L vs. 7.8 (6.6, 9.0) mmol/L, Z=-15.449, P<0.001], but the difference in fasting blood glucose levels between the 2 groups after treatment was not statistically significant ( Z=-1.427, P>0.05). The incidence of hypoglycemia in the insulin degludec group was lower than that in the insulin glargine U100 group [1.09% (6/552) vs. 2.83% (17/600), Z=4.481, P=0.032]. The intraday blood glucose standard deviation, maximum blood glucose fluctuation range, postprandial blood glucose fluctuation range, and average blood glucose fluctuation range in patients with complete blood glucose monitoring data in the insulin degludec group were significantly lower than those in the insulin glargine U100 group [(1.7±0.6) mmol/L vs. (2.4±1.0) mmol/L, (4.5±1.6) mmol/L vs. (6.7±2.9) mmol/L, (1.8±1.0) mmol/L vs. (3.3±1.2) mmol/L, (2.9±1.3) mmol/L vs. (4.6±2.1) mmol/L; all P<0.001]. Conclusion:The efficacy of insulin degludec in the treatment of type 2 diabetes mellitus is equivalent to that of insulin glargine U100, but the risk of hypoglycemia and blood glucose fluctuation is lower.

Background: The effect of drug-drug interaction (DDI) between tacrolimus and voriconazole on the pharmacokinetics of tacrolimus in different CYP3A5 genotypes has not been reported in previous studies. Objective: The objective of this study was to investigate whether CYP3A5 genotype could influence tacrolimus-voriconazole DDI in Chinese kidney transplant patients.Methods: All kidney transplant patients were divided into combination and non-combination groups based on whether tacrolimus was combined with or without voriconazole. Each group was subdivided into CYP3A5 expresser (CYP3A5*1/*1 or CYP3A5*1/*3) and CYP3A5 nonexpresser (CYP3A5*3/*3). A retrospective analysis compared tacrolimus dose (D)-corrected trough concentrations (C-0) (C-0/D) between combination and non-combination groups, respectively. Tacrolimus C-0/D was also compared between CYP3A5 expresser and nonexpresser in both groups.Results: The C-0/D values of tacrolimus were significantly different between CYP3A5 expresser and nonexpresser in combination group (378.20 [219.38, 633.48] ng/mL/[mg/kg/d] vs 720.00 [595.35, 1681.50] ng/mL/[mg/kg/d], P = 0.0010). Either in CYP3A5 expresser or nonexpresser, we found a statistically significant difference in tacrolimus C-0/D between combination and non-combination group (P < 0.0001). The increase in CYP3A5 nonexpresser was 1.38 times higher than that in CYP3A5 expresser (320.93% vs 232.19%). Conclusion and Relevance: The median C-0/D values were 90.38% higher in kidney transplant recipients with CYP3A5*3/*3 genotype than in those with CYP3A5*1/*1 or CYP3A5*1/*3 genotype when treated with both tacrolimus and voriconazole. A CYP3A5 genotype-dependent DDI was found between tacrolimus and voriconazole. Therefore, personalized therapy accounting for CYP3A5 genotype detection and therapeutic drug monitoring is necessary for kidney transplant patients when treating with tacrolimus and voriconazole.