Genetic Variants and Their Associations to Type 2 Diabetes Mellitus Complications in the United Arab Emirates

AimType 2 Diabetes Mellitus (T2DM) is associated with microvascular complications, including diabetic retinopathy (DR), diabetic nephropathy (DNp), and diabetic peripheral neuropathy (DPN). In this study, we investigated genetic variations and Single Nucleotide Polymorphisms (SNPs) associated with DR, DNp, DPN and their combinations among T2DM patients of Arab origin from the United Arab Emirates, to establish the role of genes in the progression of microvascular diabetes complications.MethodsA total of 158 Emirati patients with T2DM were recruited in this study. The study population was divided into 8 groups based on the presence of single, dual, or all three complications. SNPs were selected for association analyses through a search of publicly available databases, specifically genome-wide association study (GWAS) catalog, infinome genome interpretation platform, and GWAS Central database. A multivariate logistic regression analysis and association test were performed to evaluate the association between 83 SNPs and DR, DNp, DPN, and their combinations.ResultsEighty-three SNPs were identified as being associated with T2DM and 18 SNPs had significant associations to one or more diabetes complications. The most strongly significant association for DR was rs3024997 SNP in the VEGFA gene. The top-ranked SNP for DPN was rs4496877 in the NOS3 gene. A trend towards association was detected at rs833068 and rs3024998 in the VEGFA gene with DR and rs743507 and rs1808593 in the NOS3 gene with DNp. For dual complications, the rs833061, rs833068 and rs3024997 in the VEGFA gene and the rs4149263 SNP in the ABCA1 gene were also with borderline association with DR/DNp and DPN/DNp, respectively. Diabetic with all of the complications was significantly associated with rs2230806 in the ABCA1 gene. In addition, the highly associated SNPs rs3024997 of the VEGFA gene and rs4496877 of the NOS3 gene were linked to DR and DPN after adjusting for the effects of other associated markers, respectively.ConclusionsThe present study reports associations of different genetic polymorphisms with microvascular complications and their combinations in Emirati T2DM patients, reporting new associations, and corroborating previous findings. Of interest is that some SNPs/genes were only present if multiple comorbidities were present and not associated with any single complication.

Effect of Clinical Inertia on Diabetes Complications among Individuals with Type 2 Diabetes: A Retrospective Cohort Study

Background and Objectives: Clinical inertia is a key obstacle that leads to suboptimal care in patients with type 2 diabetes mellitus (T2DM). It can occur at any stage of T2DM treatment. However, the effect of clinical inertia on diabetes complications has not been studied sufficiently. This study aimed to evaluate the effect of clinical inertia on the risk of diabetes complications among patients with T2DM. Materials and Methods: A retrospective cohort study was conducted at a tertiary teaching hospital in Thailand between 2011 and 2017. Outpatients with T2DM, aged 40–65 years, presenting an HbA1c greater than 7% were included in this study. Clinical inertia was identified when patients did not get treatment intensification at the index date and a subsequent prescription. The association between clinical inertia and diabetes complications, including a composite of macrovascular complications and a composite of microvascular complications, was determined using a Cox proportional hazard model. Propensity score methods were applied, to control confounding by indication. Results: Of 686 patients with T2DM, 165 (24.0%) experienced clinical inertia. Baseline low-density lipoprotein cholesterol, blood pressure, body mass index, the estimated glomerular filtration rate, and medication between the two groups did not differ significantly. Our study found that clinical inertia was associated with a significantly increased risk of diabetic nephropathy (adjusted HR 1.51, 95% CI 1.01–2.27). The results remained the same as when using propensity score methods. According to the post hoc analysis, lowering the HbA1c levels by 1% results in a significant decrease in the rate of diabetic complications (adjusted HR 0.92, 95% CI 0.86–0.99), the composite of microvascular complications (adjusted HR 0.91, 95%CI 0.84–0.98) and diabetic nephropathy (adjusted HR 0.89, 95% CI 0.80–0.98). Conclusions: Our results demonstrated a significant effect of clinical inertia on diabetic nephropathy. Patients with an HbA1c level over the target range should have their medication intensified to reduce the risk of diabetic nephropathy.

The Impact of Incretin-Based Medications on Lipid Metabolism

Pathophysiological pathways that are induced by chronic hyperglycemia negatively impact lipid metabolism. Thus, diabetes is commonly accompanied by varying degrees of dyslipidemia which is itself a major risk factor for further macro- and microvascular diabetes complications such as atherosclerosis and nephropathy. Therefore, normalizing lipid metabolism is an attractive goal for therapy in patients with diabetes. Incretin-based medications are a novel group of antidiabetic agents with potent hypoglycemic effects. While the impact of incretins on glucose metabolism is clear, recent evidence indicates their positive modulatory roles on various aspects of lipid metabolism. Therefore, incretins may offer additional beneficial effects beyond that of glucose normalization. In the current review, how these antidiabetic medications can regulate lipid homeostasis and the possible cellular pathways involved are discussed, incorporating related clinical evidence about incretin effects on lipid homeostasis.

Novel enhancer for male fertility dysfunction in STZ diabetic rats

This new study evaluated effect of aqueous leaves extracts of Chrozophora tinctoria (L.) Rafin on male reproductive hormones and histological features of the testis diabetic rats. The results obtained showed remarkable increased of sperm count, viability and motility after treatment. Testosterone, FSH and LH hormones also increased with oral administration of the plant extract at dose 70 mg/kg of body weight. The results suggested the anti hyperglycemic property of the extract as well as its positive effect to maintain male hormones concentrations. Histological examination of the testis revealed improvement of spermatogenesis and Leydig cell proliferation with all doses of the extract. However, the histoarchitecture of the testis rats received 70 and 90 mg/kg of the plant extract, was found to be similar to the healthy group. These findings suggest that the extract can maintain the damage fertility in diabetic rats. These promising results may explain the use of this plant in the future for therapeutic treatment of diabetes complications associated subfertility.

Predictors of Rehospitalization and Mortality in Diabetes-Related Hospital Admissions

The risk factors of rehospitalization and death post-discharge in diabetes-related hospital admissions are not fully understood. To determine them, a population-based retrospective epidemiological survey was performed on diabetes-related admissions from the Polish national database. Logistic regression models were used, in which the dependent variables were rehospitalization due to diabetes complications and death within 90 days after the index hospitalization. In 2017, there were 74,248 hospitalizations related to diabetes. A total of 11.3% ended with readmission. Risk factors for rehospitalization were as follows: age < 35 years; male sex; prior hospitalization due to acute diabetic complications; weight loss; peripheral artery disease; iron deficiency anemia; kidney failure; alcohol abuse; heart failure; urgent, emergency, or weekend admission; length of hospitalization; and hospitalization in a teaching hospital with an endocrinology/diabetology unit. Furthermore, 7.3% of hospitalizations resulted in death within 90 days following discharge. Risk factors for death were as follows: age; neoplastic disease with/without metastases; weight loss; coagulopathy; alcohol abuse; acute diabetes complications; heart failure; kidney failure; iron deficiency anemia; peripheral artery disease; fluid, electrolytes, and acid–base balance disturbances; urgent or emergency and weekend admission; and length of hospitalization. We concluded that of all investigated factors, only hospitalization within an experienced specialist center may reduce the frequency of the assessed outcomes.

A Machine Learning Approach to Predicting Diabetes Complications

Diabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for building different models to predict and classify eight diabetes complications. The complications include metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy. For this study, a dataset collected by the Rashid Center for Diabetes and Research (RCDR) located in Ajman, UAE, was utilized. The dataset consists of 884 records with 79 features. Some essential preprocessing steps were applied to handle the missing values and unbalanced data problems. Furthermore, feature selection was performed to select the top five and ten features for each complication. The final number of records used to train and build the binary classifiers for each complication was as follows: 428—metabolic syndrome, 836—dyslipidemia, 223—neuropathy, 233—nephropathy, 240—diabetic foot, 586—hypertension, 498—obesity, 228—retinopathy. Repeated stratified k-fold cross-validation (with k = 10 and a total of 10 repetitions) was employed for a better estimation of the performance. Accuracy and F1-score were used to evaluate the models’ performance reaching a maximum of 97.8% and 97.7% for accuracy and F1-scores, respectively. Moreover, by comparing the performance achieved using different attributes’ sets, it was found that by using a selected number of features, we can still build adequate classifiers.