Objective To construct a nomogram prediction model for predicting hemoglobin A1c(HbA1c)failure in type 2 diabetes mellitus(T2DM)patients.Methods A total of 936 inpatients with T2DM admitted to the Department of Endocrinology of the Affiliated Hospital of Shandong Second Medical University from January 2021 to January 2022 were selected as the research objects and divided into the non-standard group(HbA1c≥7%,n=801)and the standard group(HbA1c<7%,n=135).Univariate analysis was used to screen the related factors of HbA1c failure.Logistic regression multivariate model was used to analyze the influencing factors of HbA1c failure in T2DM patients.The R language was used to construct a nomogram,and the area under the receiver operating characteristic(ROC)curve(AUC)was used to evaluate the predictive ability of the model.The C-index and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model.Results There were statistically significant differences in triglyceride(TG),low-density lipoprotein cholesterol,direct bilirubin,urinary albumin/creatinine ratio(UACR),self-monitoring of blood glucose(SMBG),meat and vegetable pairing,hot pot,whole grain and animal viscera consumption between the two groups(P<0.05).Logistic regression analysis showed that TG(OR 1.699,95%CI 1.298～2.222),UACR(OR 1.003,95%CI 1.001～1.005),SMGB(OR 0.480,95%CI 0.313～0.735),more meat and less vegetables(OR 1.432,95%CI 1.062～1.931)were the influencing factors of HbA1c failure.The AUC of the nomogram prediction model based on the influencing factors was 0.711,with C-index 0.710(95%CI 0.663～0.758)and good calibration(χ2=11.185,P=0.191).Conclusions The nomogram prediction model for HbA1c failure in T2DM patients established based on TG,UACR,SMGB,meat and vegetarian mix has good discrimination and calibration,which can provide certain reference value for warning of poor blood glucose control.

Objective To analyze the disease burden of chronic kidney diseases (CKD) attributed to high body mass index (BMI) in China from 1992 to 2021 and predict the disease burden for the next decade, and to provide evidence for the prevention and treatment of CKD. Methods Using the Global Burden of Disease (GBD) database and the Joinpoint model, the average annual percentage rate change (AAPC) of the mortality rate and disability-adjusted life year (DALY) rate was calculated to describe and analyze the CKD disease burden attributed to high BMI in China from 1992 to 2021. The ARIMA model was employed to predict and analyze the change trend of the CKD disease burden. Results From 1992 to 2021, the mortality rate and DALY rate attributed to high BMI-induced chronic kidney disease showed an upward trend. Compared to 1992, the attributed number of deaths increased by 324.38%, and DALYs increased by 268.56%; the mortality rate increased by 64.00%, and the DALY rate grew by 51.62%. From 1992 to 2021, the mortality rate and DALY rate for males were lower than those for females, but the growth rate for males exceeded that of females. From 1992 to 2021, the mortality rate and DALY rate of chronic kidney disease attributed to high BMI in China increased with age. The average annual change rate of chronic kidney disease attributed to high BMI in China from 1992 to 2021 (mortality rate: 1.40 per 100,000 (95% CI: 1.04–1.76), DALY rate: 1.43 per 100 000 (95% CI: 1.17–1.70)) was higher than thHuaiyin Normal University, Huai'anher social demographic index (SDI) regions. The ARIMA model predicted that the age-standardized mortality rate increased from 2.91 per 100 000 in 2022 to 3.05 per 100 000 in 2026, and the age-standardized DALY rate increased from 69.65 per 100 000 in 2022 to 73.58 per 100 000 in 2026. Conclusion Chronic kidney disease attributed to high BMI in China is on the rise, and it will continue to grow in the future. The focus of CKD prevention and control should be on males and the elderly, while active measures should be taken to reduce the occurrence and progression of chronic kidney disease.

Objective To construct a nomogram prediction model for predicting hemoglobin A1c(HbA1c)failure in type 2 diabetes mellitus(T2DM)patients.Methods A total of 936 inpatients with T2DM admitted to the Department of Endocrinology of the Affiliated Hospital of Shandong Second Medical University from January 2021 to January 2022 were selected as the research objects and divided into the non-standard group(HbA1c≥7%,n=801)and the standard group(HbA1c<7%,n=135).Univariate analysis was used to screen the related factors of HbA1c failure.Logistic regression multivariate model was used to analyze the influencing factors of HbA1c failure in T2DM patients.The R language was used to construct a nomogram,and the area under the receiver operating characteristic(ROC)curve(AUC)was used to evaluate the predictive ability of the model.The C-index and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model.Results There were statistically significant differences in triglyceride(TG),low-density lipoprotein cholesterol,direct bilirubin,urinary albumin/creatinine ratio(UACR),self-monitoring of blood glucose(SMBG),meat and vegetable pairing,hot pot,whole grain and animal viscera consumption between the two groups(P<0.05).Logistic regression analysis showed that TG(OR 1.699,95%CI 1.298～2.222),UACR(OR 1.003,95%CI 1.001～1.005),SMGB(OR 0.480,95%CI 0.313～0.735),more meat and less vegetables(OR 1.432,95%CI 1.062～1.931)were the influencing factors of HbA1c failure.The AUC of the nomogram prediction model based on the influencing factors was 0.711,with C-index 0.710(95%CI 0.663～0.758)and good calibration(χ2=11.185,P=0.191).Conclusions The nomogram prediction model for HbA1c failure in T2DM patients established based on TG,UACR,SMGB,meat and vegetarian mix has good discrimination and calibration,which can provide certain reference value for warning of poor blood glucose control.

Objective : To identify the factors affecting microvascular complications among patients with type 2 diabetes (T2DM), so as to provide insights into the management of microvascular complications of T2DM. Methods: T2DM patients hospitalized in the Department of Endocrinology of a tertiary hospital in Weifang City, Shandong Province from January 2021 to January 2022 were enrolled, and subjects' basic information, lifestyle and medical history were collected using questionnaire surveys. Fasting insulin, fasting blood glucose and glycated hemoglobin were measured, and factors affecting microvascular complications were identified among T2DM patients using a multivariable logistic regression model and a decision tree model. Results: Totally 1 003 T2DM inpatients were enrolled, including 515 men (51.35%) and 488 women (48.65%), and the prevalence of microvascular complications was 40.18%. Multivariable logistic regression analysis showed that age of 60 years and older (OR=2.510, 95%CI: 1.441-4.374), T2DM duration of 10 years and longer (OR=3.205, 95%CI: 2.242-4.581), fasting insulin of lower than 3.21 μIU/mL (OR=1.749, 95%CI: 1.239-2.469), using of agents or insulin to control blood glucose (OR=1.880, 95%CI: 1.143-3.092), glycated hemoglobin level of 7% and higher (OR=1.751, 95%CI: 1.172-2.615) as factors affecting microvascular complications among T2DM patients. Decision tree analysis identified course of T2DM as a major factor affecting the risk of microvascular complications among T2DM patients, and the prevalence of microvascular complications was 70.22% among T2DM patients with disease course of 10 years and longer and fasting insulin of lower than 3.21 μIU/mL or 16.32 μIU/mL and higher, 44.23% among T2DM patients with disease course of 5 to 10 years and at ages of 60 years and older, and 43.10% among T2DM patients with disease course of less than 5 years and fasting insulin of lower than 3.21 μIU/mL. Conclusion Advanced age, long course of T2DM, low fasting insulin and high glycated hemoglobin may increase the risk of microvascular complications among T2DM patients.

Objective To construct cecropin A-thanatin combinant gene engineering antimicrobial peptide gene CA(1-7)-T(4-19) for expression in Pichia pastoris.Methods The combinant antimicrobial peptide gene was artificially synthesized via gene splicing by overlap extension (SOE).The gene was cloned into the pPICZαA vector and transformed into Pichia pastoris X-33 by electroporation.The positive clones obtained by the screening of bleomycin resistance were induced by methanol ,and the antibacterial activity of the products was detected and the antimicrobial spectrum was established.Results The combinant peptide gene CA (1-7)-T (4-19) was successfully cloned on the carrier pPICZαA.The identification results were consistent with the pre-designed gene sequence.The combinant peptide gene was expressed under the induction of methanol ,and the minimum inhibitory concentration of 76 strains of Gram-egative and Gram-positive pathogenic bacteria isolated from the clinic was obtained ,and the minimum inhibitory concentration was up to 5 μg/mL.Conclusion A combinant genetic engineering antimicrobial peptide with antibacterial activity was obtained successfully and it had obvious inhibition effect on clinical common multidrug-resistant strains.