Development and Validation of Two Self-Reported Tools for Insulin Resistance and Hypertension Risk Assessment in A European Cohort: The Feel4Diabetes-Study

Anthropometric cut-points for discriminating diabetes and the metabolic syndrome among Arabs and Asians: the Kuwait Diabetes Epidemiology Program

This study aimed to determine anthropometric cut-points for screening diabetes and the metabolic syndrome (MetS) in Arab and South Asian ethnic groups in Kuwait and to compare the prevalence of the MetS based on the ethnic-specific waist circumference (WC) cut-point and the International Diabetes Federation (IDF) and American Heart Association/National Heart, Lung, and Blood Institute WC criteria. The national population-based survey data set of diabetes and obesity in Kuwait adults aged 18-60 years was analysed. Age-adjusted logistic regression and receiver operating characteristic (ROC) analyses were conducted to evaluate for 3589 individuals the utility of WC, waist:height ratio (WHtR) and BMI to discriminate both diabetes and ≥3 CVD risk factors. Areas under the ROC curve were similar for WC, WHtR and BMI. In Arab men, WC, WHtR and BMI cut-offs for diabetes were 106 cm, 0·55 and 28 kg/m2 and for ≥3 CVD risk factors, 97 cm, 0·55 and 28 kg/m2, respectively. In Arab women, cut-offs for diabetes were 107 cm, 0·65 and 33 kg/m2 and for ≥3 CVD risk factors, 93 cm, 0·60 and 30 kg/m2, respectively. WC cut-offs were higher for South Asian women than men. IDF-based WC cut-offs corresponded to a higher prevalence of the MetS across sex and ethnic groups, compared with Kuwait-specific cut-offs. Any of the assessed anthropometric indices can be used in screening of diabetes and ≥3 CVD risk factors in Kuwaiti Arab and Asian populations. ROC values were similar. The WC threshold for screening the MetS in Kuwaiti Arabs and South Asians is higher for women.

Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Diabetes mellitus (DM) is a severe chronic disease that affects human health and has a high prevalence worldwide. Research has shown that half of the diabetic people throughout the world are unaware that they have DM and its complications are increasing, which presents new research challenges and opportunities. In this paper, we propose a preemptive diagnosis method for diabetes mellitus (DM) to assist or complement the early recognition of the disease in countries with low medical expert densities. Diabetes data are collected from the Zewditu Memorial Hospital (ZMHDD) in Addis Ababa, Ethiopia. Light Gradient Boosting Machine (LightGBM) is one of the most recent successful research findings for the gradient boosting framework that uses tree-based learning algorithms. It has low computational complexity and, therefore, is suited for applications in limited capacity regions such as Ethiopia. Thus, in this study, we apply the principle of LightGBM to develop an accurate model for the diagnosis of diabetes. The experimental results show that the prepared diabetes dataset is informative to predict the condition of diabetes mellitus. With accuracy, AUC, sensitivity, and specificity of 98.1%, 98.1%, 99.9%, and 96.3%, respectively, the LightGBM model outperformed KNN, SVM, NB, Bagging, RF, and XGBoost in the case of the ZMHDD dataset.

Metformin protects against insulin resistance induced by high uric acid in cardiomyocytes via AMPK signalling pathways in vitro and in vivo

High uric acid (HUA) is associated with insulin resistance (IR) in cardiomyocytes. We investigated whether metformin protects against HUA-induced IR in cardiomyocytes. We exposed primary cardiomyocytes to HUA, and cellular glucose uptake was quantified by measuring the uptake of 2-NBDG, a fluorescent glucose analog. Western blot was used to examine the levels of signalling protein. Membrane of glucose transporter type 4 (GLUT4) was analysed by immunofluorescence. We monitored the impact of metformin on HUA-induced IR and in myocardial tissue of an acute hyperuricaemia mouse model established by potassium oxonate treatment. Treatment with metformin protected against HUA-reduced glucose uptake induced by insulin in cardiomyocytes. HUA directly inhibited the phosphorylation of Akt and the translocation of GLUT4 induced by insulin, which was blocked by metformin. Metformin promoted phosphorylation of AMP-activated protein kinase (AMPK) and restored the insulin-stimulated glucose uptake in HUA-induced IR cardiomyocytes. As a result of these effects, in a mouse model of acute hyperuricaemia, metformin improved insulin tolerance and glucose tolerance, accompanied by increased AMPK phosphorylation, Akt phosphorylation and translocation of GLUT4 in myocardial tissues. As expected, AICAR, another AMPK activator, had similar effects to metformin, demonstrating the important role of AMPK activation in protecting against IR induced by HUA in cardiomyocytes. Metformin protects against IR induced by HUA in cardiomyocytes and improves insulin tolerance and glucose tolerance in an acute hyperuricaemic mouse model, along with the activation of AMPK. Consequently, metformin may be an important potential new treatment strategy for hyperuricaemia-related cardiovascular disease.

Risk of Typical Diabetes-Associated Complications in Different Clusters of Diabetic Patients: Analysis of Nine Risk Factors

Objectives: Diabetic patients are often diagnosed with several comorbidities. The aim of the present study was to investigate the relationship between different combinations of risk factors and complications in diabetic patients. Research design and methods: We used a longitudinal, population-wide dataset of patients with hospital diagnoses and identified all patients (n = 195,575) receiving a diagnosis of diabetes in the observation period from 2003–2014. We defined nine ICD-10-codes as risk factors and 16 ICD-10 codes as complications. Using a computational algorithm, cohort patients were assigned to clusters based on the risk factors they were diagnosed with. The clusters were defined so that the patients assigned to them developed similar complications. Complication risk was quantified in terms of relative risk (RR) compared with healthy control patients. Results: We identified five clusters associated with an increased risk of complications. A combined diagnosis of arterial hypertension (aHTN) and dyslipidemia was shared by all clusters and expressed a baseline of increased risk. Additional diagnosis of (1) smoking, (2) depression, (3) liver disease, or (4) obesity made up the other four clusters and further increased the risk of complications. Cluster 9 (aHTN, dyslipidemia and depression) represented diabetic patients at high risk of angina pectoris “AP” (RR: 7.35, CI: 6.74–8.01), kidney disease (RR: 3.18, CI: 3.04–3.32), polyneuropathy (RR: 4.80, CI: 4.23–5.45), and stroke (RR: 4.32, CI: 3.95–4.71), whereas cluster 10 (aHTN, dyslipidemia and smoking) identified patients with the highest risk of AP (RR: 10.10, CI: 9.28–10.98), atherosclerosis (RR: 4.07, CI: 3.84–4.31), and loss of extremities (RR: 4.21, CI: 1.5–11.84) compared to the controls. Conclusions: A comorbidity of aHTN and dyslipidemia was shown to be associated with diabetic complications across all risk-clusters. This effect was amplified by a combination with either depression, smoking, obesity, or non-specific liver disease.