Background Diabetes mellitus is a serious metabolic disorder which becomes common in middle and low incomes countries since few decades. Microvascular complications include retinopathy, neuropathy and nephropathy all of which can lead to disability, dependency, accelerate their morbidity, and mortality. In Ethiopia, there is paucity data regarding this topic. Hence, this study aimed to assess prevalence of microvascular complications and its predictors among type 2 diabetes mellitus patients. Methods Cross-sectional study was conducted from February to March 2020 at Dessie town hospitals. We used simple random sampling to recruit study participants and pre-tested interviewer administered questionnaire to collect the data. Data was entered into Epi-Data 3.1 and exported to SPSS-23 for analysis. Binary logistic regression was done to select potential variables to be adjusted at p ≤ 0.25. After running multivariable regression, variables with a p-value ≤ 0.05 were declared as statistically significant. Results Three hundred and thirty-five type 2 DM patients participated in the study, of which 54.6% were males. One hundred and twenty-seven [37.9% (95% CI 32.5%-43.3%)] of diabetes mellitus had at least one microvascular complications. These were retinopathy 24.8%, nephropathy 16.1%, and neuropathy 8.1%. Age 60-87 years (AOR = 2.76, 95% CI 1.02-7.46), duration of diabetes > 5 years (AOR = 4.09, 95% CI 2.40-6.96), mellitus and co-morbid hypertension (AOR = 3.52, 95% CI 2.09-5.95), were statistically significant. Conclusions In this study, diabetic microvascular complications are prevalent. Increasing the age of participants, longer duration of diabetes mellitus and co-morbid hypertension were independent predictors. Health workers should give emphasis for diabetes mellitus through early screening and health education, abrupt medication for aged patients with long duration of diabetes mellitus, and hypertension, and also early detection and management of microvascular complication.

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Microvascular complications and its predictors among type 2 diabetes mellitus patients at Dessie town hospitals, Ethiopia.

Background: Since chronic hyperglycemia-related damage to small blood vessels results in complications, patients with longer durations of type 2 diabetes mellitus (T2DM) are more likely to develop microvascular complications, such as retinopathy, neuropathy and nephropathy, which are very harmful to the health of humans Therefore, this study aimed to assess the prevalence of diabetes-related microvascular complications and to explore their risk factors in patients with T2DM in Tianjin, China
 Methods: This observational, cross-sectional study was conducted at 8 hospitals in urban and suburban regions of Tianjin, China The information collected from the subjects mainly included demographic characteristics, anthropometric measurements and clinical information Univariate and multivariate logistic regression was used to identify the possible risk factors for microvascular complications (retinopathy, neuropathy and nephropathy)
 Results: A total of 4,490 patients with T2DM from 8 hospitals in Tianjin, China were selected from November 2015 to January 2016 Of the study subjects, 2,270 (506%) were males The median age was 640±130 years The percentage of patients with T2DM who had at least one microvascular complication was 345% The prevalence rates of neuropathy, retinopathy, and nephropathy were 235%, 174%, and 108%, respectively The results of the multivariate logistic regression showed that the duration of diabetes, insulin use, and the presence of hypertension and dyslipidemia were the main risk factors for developing microvascular complications of T2DM
 Conclusions: The incidence of diabetes complications in Tianjin is high Increasing the control of risk factors can reduce the occurrence of complications to reduce the disease burden and improve the quality of life of patients

/pdf/prevalence-of-and-factors-related-to-microvascular-4luqptc57e.pdf

Prevalence of and factors related to microvascular complications in patients with type 2 diabetes mellitus in Tianjin, China: a cross-sectional study

Purpose This study aimed to develop a diabetic nephropathy (DN) or diabetic retinopathy (DR) incidence risk nomogram in China's population with type 2 diabetes mellitus (T2DM) based on a community-based sample. Methods We carried out questionnaire evaluations, physical examinations and biochemical tests among 4219 T2DM patients in Shanghai. According to the incidence of DN and DR, 4219 patients in our study were divided into groups of T2DM patients with DN or DR, patients with both, and patients without any complications. We successively used least absolute shrinkage and selection operator regression analysis and logistic regression analysis to optimize the feature selection for DN and DR. To ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data for both DN and DR. The selected features were included to establish a prediction model. The C-index, calibration plot, curve analysis and internal validation were used to validate the distinction, calibration, and clinical practicality of the model. Results The predictors in the prediction model included disease course, body mass index (BMI), total triglycerides (TGs), systolic blood pressure (SBP), postprandial blood glucose (PBG), haemoglobin A1C (HbA1c) and blood urea nitrogen (BUN). The model displayed moderate predictive power with a C-index of 0.807 and an area under the receiver operating characteristic curve of 0.807. In internal verification, the C-index reached 0.804. The risk threshold was 16-75% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice. Conclusion This DN or DR incidence risk nomogram incorporating disease course, BMI, TGs, SBP, PBG, HbA1c and BUN can be used to predict DN or DR incidence risk in T2DM patients. The research team has developed an online app based on a clinical prediction model incorporating risk factors for rapid and simple prediction.

https://www.dovepress.com/getfile.php?fileID=57496

Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study

Although there are several studies to investigate the association between blood lipids and microvascular complications, these studies reported conflicting results. The aim of the current study was to explore the association between blood lipid parameters and the risk of microvascular complications, especially the dose-response association between them, among community patients with type 2 diabetes mellitus (T2DM) in Shanghai, China. The cross-sectional study was conducted in 6 community health service centers in Shanghai between December 2014 and December 2016.The associations between blood lipids and diabetic kidney disease (DKD) or diabetic retinopathy (DR) were assessed using multiple logistic regression. Restricted cubic spline (RCS) was employed to estimate the dose-response relation of blood lipids and the risk of microvascular complications. A total of 3698 participants were included in the final analysis to study the association between blood lipids and DKD, wherein 33.2% of participants had DKD and 1374 were included for the analysis of the association between blood lipids and DR, wherein 23.2% of participants had DR. DKD odds ratio was increased by 1.16(95%CI,1.08–1.25), 1.21(95%CI,1.13–1.30), 1.18(95%CI,1.10–1.26) for comparing fourth to first quartiles of triglycerides (TG), TG/high-density lipoprotein cholesterol (HDL-C), non-HDL-C/HDL-C, respectively, and decreased by 0.83(95%CI,0.78–0.89) for comparing fourth to first quartiles of HDL-C. Furthermore, the dose-response association between TG, HDL-C, TG/HDL-C, non-HDL-C/HDL-C and the risk of DKD demonstrated turning points in TG of 1.90 mmol/L, HDL-C of 1.62 mmol/L, TG/HDL-C of 2.00, non-HDL-C/HDL-C of 3.09, respectively. However, no significant association was found between blood lipid parameters and DR. This community-based study indicated that TG, HDL-C, TG/HDL-C, non-HDL-C/HDL-C were independently associated with DKD but not DR.

/pdf/are-blood-lipids-associated-with-microvascular-complications-59imulbadp.pdf

Are blood lipids associated with microvascular complications among type 2 diabetes mellitus patients? A cross-sectional study in Shanghai, China

Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D). Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People's Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set. Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively. Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care.

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

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Prevalence of and factors related to microvascular complications in patients with type 2 diabetes mellitus in Tianjin, China: a cross-sectional study