ObjectiveTo investigate the relationship between plasma vitamin D2(VD2) and type 2 diabetes(T2DM).MethodData from electronic medical records of 797 inpatients treated at Sun Yat Sen Memorial Hospital, Sun Yat-sen University between June 24, 2019 and December 24, 2020 were collected, and a total of 596 patients were enrolled after screening based on inclusion and exclusion criteria. Patients were divided into diabetic and non-diabetic groups according to whether they had T2DM. The Wilcoxon rank sum test was finally selected for the analysis of differences between groups according to the distribution of patients’ plasma VD2, and logistic regression models were used to find the corresponding influencing factors.ResultOf the 596 hospitalized patients, 138 (23.15%) were diagnosed with T2DM. The Wilcoxon test showed no statistically significant difference in plasma VD2 concentrations between the T2DM and non-T2DM groups (p=0.833). After adjustment for confounders by multivariate logistic regression, there was still no significant difference in plasma VD2 concentrations between the two groups (P=0.316, OR: 1.15 (0.88,1.49)). The uncorrelated relationship between VD2 and T2DM was not found to change after incorporating 12 indicators, including demographic characteristics, laboratory indicators and complications, into the logistic regression model by 3 steps, even the OR (1.08 (0.92,1.26)) did not change in the 3 models. Similarly, the adjusted ORs agreed that there was no statistical association between VD2 and T2DM.ConclusionVD2 levels are similar in patients with T2DM compared to those without T2DM. Clinical caution should be exercised in giving VD2 supplementation to patients with T2DM unless other diseases requiring VD2 supplementation (e.g., rickets, osteoporosis) are present.

ObjectiveTo investigate the association between amino acids related to the urea cycle and diabetic nephropathy (DN) in two independent cross-sectional studies.MethodsWe obtained the medical records of 145 individuals with DN and 596 individuals without DN who attended an annual health examination at Liaoning Medical University First Affiliated Hospital (LMUFAH), China, from May 2015 to August 2016. From April 2018 to April 2019, we collected medical records of another 741 individuals: 338 individuals with DN and 403 individuals without DN from the Second Affiliated Hospital of Dalian Medical University (DALIAN), China. Binary logistic regression was used to obtain the odds ratio (OR) and 95% confidence interval (CI).ResultsIn two independent cross-sectional studies, we observed that citrulline was consistently associated with DN risk [OR (95% CI) of per standard deviation (SD) increase for citrulline in the LMUFAH population: 1.200 (1.006, 1.432); OR (95% CI) of per SD increase for citrulline in the DALIAN population: 1.189 (1.012, 1.396); pooled effect size for citrulline: 1.194 (1.060, 1.345)]. However, ornithine, arginine, and the ratio of arginine to ornithine were consistently unrelated to DN risk, and the ratios of other amino acids in the urea cycle were inconsistently associated with DN risk. ConclusionsCitrulline was consistently associated with DN risk in two independent cross-sectional studies in Chinese adults.

ObjectiveTo explore the association between serum leucine (leu) and diabetic retinopathy (DR) in patients with type 2 diabetes (T2D) and then to analyze the influence of gender on the association.MethodThe electronic medical records of 1,149 T2D patients who met inclusion and exclusion criteria were retrieved from the Second Affiliated Hospital of Dalian Medical University and the First Affiliated Hospital of Jinzhou Medical University. Serum leu levels of all subjects were measured by liquid chromatography–mass spectrometry. Logistic regression was used to obtain the odds ratio (OR) and CI of leu–DR risk in multiple models. When using these models, restricted cubic spline (RCS) was used to test the potential non-linear relationship between multiple continuous independent variables, such as leu and DR (classification), and dependent variables. We also used the additive interaction method to evaluate the interaction effect between leu and gender on DR.ResultsLeu was a protective factor of DR [0.78 (0.66, 0.92)]. When gender was divided into male and female, the above relationship was statistically significant only in men [0.73 (0.58, 0.94)]. Three indicators of additive interaction—RERI, AP, and S—suggested that there is no interaction between gender and leu on the risk of DR.ConclusionsMale T2D patients with high leu levels may have a lower risk of DR.

ObjectiveThis study established a model to predict the risk of diabetic retinopathy (DR) with amino acids selected by partial least squares (PLS) method, and evaluated the effect of metformin on the effect of amino acids on DR in the model.MethodsIn Jinzhou, Liaoning Province, China, we retrieved 1031 patients with type 2 diabetes (T2D) from the First Affiliated Hospital of Liaoning Medical University. After sorting the amino acids using the PLS method, the top 10 amino acids were included in the model. Multivariate logistic regression was used to analyze the relationship between different amino acids and DR. And then the effects of metformin on amino acids were explored through interaction. Finally, Spearman’s rank correlation analysis was used to analyze the correlation between different amino acids.ResultsAfter sorting by PLS, Gly, Pro, Leu, Lyr, Glu, Phe, Tyr, His, Val and Ser were finally included in the DR risk prediction model. The predictive model after adding amino acids was statistically different from the model that only included traditional risk factors (p=0.001). Metformin had a significant effect on the relationship between DR and 7 amino acids (Gly, Glu, Phe, Tyr, His, Val, Ser, p<0.05), and the population who are not using metformin and have high levels of Glu (OR: 0.44, 95%CI: 0.27-0.71) had an additive protection effect for the occurrence of DR. And the similar results can be seen in high levels of Gly (OR: 0.46, 95%CI: 0.29-0.75), Leu (OR: 0.48, 95%CI: 0.29-0.8), His (OR: 0.46, 95%CI: 0.29-0.75), Phe (OR: 0.24, 95%CI: 0.14-0.42) and Tyr (OR: 0.41, 95%CI: 0.24 -0.68) in population who are not using metformin.ConclusionsWe established a prediction model of DR by amino acids and found that the use of metformin reduced the protective effect of amino acids on DR developing, suggesting that amino acids as biomarkers for predicting DR would be affected by metformin use.