The association between cigarette smoking and diabetic nephropathy in Chinese male patients

Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM

Introduction

To investigate the predictors of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients and establish a nomogram model for predicting the risk of DKD.

Methods

The clinical data of T2DM patients, admitted to the Endocrinology Department of Chengde Central Hospital from October 2019 to September 2020 and divided into a case group or a control group based on whether they had DKD, were collected. The predictive factors of DKD were screened by univariate and multivariate analysis, and a nomogram prediction model was constructed for the risk of DKD in T2DM. Bootstrapping was used for model validation, receiver operating characteristic (ROC) curve and GiViTI calibration curve were used for evaluating the discrimination and calibration of prediction model, and decision analysis curve (DCA) was used for evaluating the practicality of model.

Results

Predictors for DKD are diabetic retinopathy (DR), hypertension, history of gout, smoking history, using insulin, elevation of body mass index (BMI), triglyceride (TG), cystatin C (Cys-C), and reduction of 25 (OH) D. The nomogram prediction model based on the above nine predictors had good representativeness (Bootstrap method: precision: 0.866, Kappa: 0.334), differentiation [the area under curve (AUC) value: 0.868], and accuracy (GiViTI-corrected curved bands, P = 0.836); the DAC curve analysis showed that the prediction model, whose threshold probability was in the range of 0.10 to 0.70, had clinical practical value.

Conclusion

The risk of DKD in T2DM could be predicted accurately by DR, hypertension, history of gout, smoking history, using insulin, elevation of BMI, TG, Cys-C, and reduction of 25 (OH) D.

Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms

Objective

Microalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk of cardiovascular events and mortality. Hence, the present study aimed to establish a risk model for MAU by applying machine learning algorithms.

Methods

This cross-sectional study included 3,294 participants ranging in age from 16 to 93 years. R software was used to analyze missing values and to perform multiple imputation. The observed population was divided into a training set and a validation set according to a ratio of 7:3. The first risk model was constructed using the prepared data, following which variables with P <0.1 were extracted to build the second risk model. The second-stage model was then analyzed using a chi-square test, in which a P ≥ 0.05 was considered to indicate no difference in the fit of the models. Variables with P <0.05 in the second-stage model were considered important features related to the prevalence of MAU. A confusion matrix and calibration curve were used to evaluate the validity and reliability of the model. A series of risk prediction scores were established based on machine learning algorithms.

Results

Systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG) levels, sex, age, and smoking were identified as predictors of MAU prevalence. Verification using a chi-square test, confusion matrix, and calibration curve indicated that the risk of MAU could be predicted based on the risk score.

Conclusion

Based on the ability of our machine learning algorithm to establish an effective risk score, we propose that comprehensive assessments of SBP, DBP, FBG, TG, gender, age, and smoking should be included in the screening process for MAU.

Transcription factor 7-like 2 gene- smoking interaction on the risk of diabetic nephropathy in Chinese Han population

Objectives

To evaluate the relationship between transcription factor 7-like 2 (TCF7L2) gene polymorphism and diabetic nephropathy (DN) risk, as well as the effect of gene-environment interactions on DN risk in Chinese Han population.

Methods

The Hardy-Weinberg equilibrium (HWE) and the relationship between TCF7L2 gene single nucleotide polymorphism (SNPs) and DN susceptibility were evaluated by SNPStats. The interaction among four SNPs and environmental factors were tested by generalized multifactor dimensionality reduction (GMDR). The consistency of cross validation, accuracy of test balance and sign test were calculated to evaluate the interaction of each selection. The logistic regression was used to test the interaction between rs7903146 and current smoking by stratified analysis.

Results

Logistic regression analysis indicated that the DN risk of rs7903146-T allele carriers were obviously higher than that in CC genotype carriers (CT + TT versus CC), adjusted OR (95 %CI) = 1.64 (1.24–2.06). However, we also discovered that people with rs12255372, rs11196205 and rs290487 minor allele had non-significant difference risk of DN compared with people with major allele. The GMDR model found a significant two-locus model (p = 0.0100) including rs7903146 and current smoking, suggesting a potential gene–environment interaction between rs7903146 and current smoking. Compared with never smokers with rs7903146- CC genotype, current smokers with rs7903146- CT or TT genotype had the highest DN risk. After covariate adjustment, OR (95 %CI) was 2.15 (1.58–2.78).

Conclusions

We found a significant relationship of rs7903146-T alleles, and the interaction between rs7903146-T and current smoking with increased DN risk.

Spatial analysis of metformin use compared with nicotine and caffeine consumption through wastewater-based epidemiology in China

Monitoring the consumption of pharmaceuticals and licit drugs is important for assessing the needs of public health owing to the impact on individuals as well as society. The present work applied wastewater-based epidemiology to profile the spatial patterns of metformin, nicotine, and caffeine use and their correlations. Influent wastewater samples were collected from 27 wastewater treatment plants in 22 typical Chinese cities that covered all geographic regions of the country. The consumption of metformin ranged from 0.02 g/d/1000 inh to 8.92 g/d/1000 inh, whereas caffeine and nicotine consumption ranged from 4.33 g/d/1000 inh to 394 g/d/1000 inh and 0.17 g/d/1000 inh to 1.88 g/d/1000 inh, respectively. There were significant regional differences in the consumption of caffeine, with the highest consumption in East China and the lowest consumption in Northeast China. The consumption and concentration of caffeine were related to the gross domestic product and per capita disposable income of urban residents, respectively. There was a correlation between the concentrations of caffeine and cotinine (a nicotine metabolite), thereby indicating that individuals that use one of these substances are likely to use the other substance. A significant relationship was found between the concentration of metformin and cotinine, thereby implying that the use of tobacco may be correlated with type 2 diabetes. Co-analysis of these substances in wastewater may provide a more accurate picture of substance use situations within different communities and provide more information on human health, human behavior, and the economy. This report describes the newest study related to the consumption of metformin among the general population in China.