Insulin-like growth factor-II in adipocyte regulation: depot-specific actions suggest a potential role limiting excess visceral adiposity.

The IGF system has an important role in growth and development. IGF-II is a recognized fetal growth promoter. However, its physiological postnatal role remains uncertain, although it is maintained in the circulation at a substantially high level throughout life. IGF-II has been strongly linked to obesity in genetic studies, and more recent evidence suggests a metabolic role. We examined fat depot differences in IGF-II's action on differentiation and metabolism. We speculate a specific effect on visceral adipocytes in relation to the differential distribution of insulin receptors between visceral and subcutaneous fat depots. We used a previously established adipocyte, cell culture system of matched pairs of visceral and subcutaneous fat biopsies from 20 normal weight children undergoing routine surgery for nonmalignant, nonseptic conditions. Preadipocytes were differentiated for 14 days in the presence or absence of IGF-II. Oil Red O staining, Western blotting, and reverse transcription polymerase chain reaction techniques were employed to assess levels of adipogenesis markers and levels of the insulin receptor and insulin receptor isoforms. Our data indicate that IGF-II promotes preadipocyte differentiation in subcutaneous preadipocytes but showed a protective, opposing effect restricting visceral preadipocyte differentiation, confirmed by reductions in the differentiation markers peroxisome proliferator-activated receptor gamma and adiponectin and in triglyceride staining. Additionally, IGF-II reduced mRNA expression of the insulin receptor in adipocytes and downregulated insulin receptor isoform A and glucose transporter 4 abundance and corresponding glucose uptake in visceral adipocytes. In conclusion, IGF-II is a regulator of preadipocyte differentiation and metabolism by acting as a differential modulator of fat accumulation favoring less visceral fat deposition in children.

A novel stacking ensemble for detecting three types of diabetes mellitus using a Saudi Arabian dataset: Pre-diabetes, T1DM, and T2DM.

Glucose is the primary source of energy for cells, which are the building blocks of life. It is given to the body by insulin that carries out the metabolic tasks that keep people alive. Glucose level imbalance is a sign of diabetes mellitus (DM), a common type of chronic disease. It leads to long-term complications, such as blindness, kidney failure, and heart disease, having a negative impact on one's quality of life. In Saudi Arabia, a ten-fold increase in diabetic cases has been documented within the last three years. DM is broadly categorized as Type 1 Diabetes (T1DM), Type 2 Diabetes (T2DM), and Pre-diabetes. The diagnosis of the correct type is sometimes ambiguous to medical professionals causing difficulties in managing the illness progression. Intensive efforts have been made to predict T2DM. However, there is a lack of studies focusing on accurately identifying T1DM and Pre-diabetes. Therefore, this study aims to utilize Machine Learning (ML) to distinguish and predict the three types of diabetes based on a Saudi Arabian hospital dataset to control their progression. Four different experiments have been conducted to achieve the highest results, where several algorithms were used, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (K-NN), Decision Tree (DT), Bagging, and Stacking. In experiments 2, 3, and 4, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. The empirical results demonstrated promising results of the novel Stacking model that combined Bagging K-NN, Bagging DT, and K-NN, with a K-NN meta-classifier attaining an accuracy, weighted recall, weighted precision, and cohen's kappa score of 94.48%, 94.48%, 94.70%, and 0.9172, respectively. Five principal features were identified to significantly affect the model accuracy using the permutation feature importance, namely Education, AntiDiab, Insulin, Nutrition, and Sex.