Risk factors associated with the development of diabetic kidney disease in Sudanese patients with type 2 diabetes mellitus: A case-control study

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.

Prevalence and associated factors of overweight and obesity among persons with type 2 diabetes in Africa: a systematic review and meta-analysis

BACKGROUND

Type 2 diabetes and obesity are serious public health concerns globally and a growing burden in Africa. Both conditions have serious repercussions on health when they co-occur, yet the extent of their co-occurrence in Africa remains unknown. Therefore, this review aimed to identify the prevalence and associated factors of overweight and obesity among persons with type 2 diabetes in Africa.

METHOD

A systematic search was conducted on PubMed, MEDLINE, Embase, African Index Medicus (AIM), and African Journals Online (AJOL) for observational studies that reported the prevalence of overweight and/or obesity among type 2 diabetes patients in Africa. The prevalence data from individual studies were aggregated through a random-effects meta-analysis. The I² statistic was used to evaluate between-studies heterogeneity, while subgroup analysis and mixed-effects meta-regression were performed to identify sources of heterogeneity. We assessed publication bias using funnel plots and Egger's test. This review adhered to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.

RESULTS

Of 1753 records retrieved, 80 articles were eligible for this review, with 74 cross-sectional studies included in the meta-analysis. The pooled prevalence of overweight and obesity was 35.6% and 25.6% respectively, while the overall prevalence of both overweight and obesity was 61.4%. Also, the pooled prevalence of both overweight and obesity across the five geographical areas in Africa ranged from 56.9% in East Africa to 88.5% in Southern Africa. Nineteen factors were significantly associated with overweight and obesity among patients with type 2 diabetes.

CONCLUSION

The high prevalence of overweight and obesity among patients with type 2 diabetes is a significant public health concern that transcends geographical boundaries within Africa. The findings from this review highlight the need for innovative weight management interventions that are tailored to the cultural context of the African setting.KEY MESSAGESThere was a high prevalence of overweight and obesity among the type 2 diabetes patients.Nineteen factors were identified to be significantly associated with overweight and obesity among type 2 diabetes patients.Only 12 out of the 80 included studies primarily focused on the prevalence of overweight and/or obesity which reflects a dearth of interest in this topic.

Early detection system of risk factors for diabetes mellitus type 2 utilization of machine learning-random forest

BACKGROUND

The prevalence of morbidity and mortality for type 2 diabetes mellitus (DM) is still increasing because of changing lifestyles. There needs to be a means of controlling the rise in the incidence of the disease. Many researchers have utilized technological advances such as machine learning for disease prevention and control, especially in noncommunicable conditions. Researchers are, therefore, interested in creating an early detection system for risk factors of type 2 diabetes.

MATERIALS AND METHODS

The study was conducted in February 2022, utilizing secondary surveillance data from Puskesmas Johar Baru, Jakarta, in 2019, 2020, and 2021. Data was analyzed utilizing various bivariate and multivariate statistical methods at 5% significance level and machine learning methods (random forest algorithm) with an accuracy rate of >80%. The data for the three years was cleaned, normalized, and merged.

RESULTS

The final population was 65,533 visits out of the initial data of 196,949, and the final number of DM 2 population was 2766 out of the initial data of 9903. Age, gender, family history of DM, family history of hypertension, hypertension, high blood sugar levels, obesity, and central obesity were significantly associated with type 2 DM. Family history was the strongest risk factor of all independent variables, odds ratio of 15.101. The classification results of feature importance, with an accuracy rate of 84%, obtained in order were age, blood sugar level, and body mass index.

CONCLUSION

Blood sugar level is the most influential factor in the incidence of DM in Puskesmas Johar Baru. In other words, a person with a family history of type 2 diabetes, at unproductive age, of female gender, and of excessive weight can avoid type 2 diabetes if they can regularly maintain their blood sugar levels.