Biology subjects

Abdul-Ghani, M.

Publications and source records attributed to Abdul-Ghani, M..

2 recordsLinked to original sources

bioRxiv2019

The Metabolic Effects of Angiopoietin-like protein 8 (ANGPLT8) are Differentially Regulated by Insulin and Glucose in Adipose Tissue and Liver and are Controlled by AMPK Signaling

ObjectiveThe angiopoietin-like protein (ANGPTL) family represents a promising therapeutic target for dyslipidemia, which is a feature of obesity and type 2 diabetes (T2DM). The aim of the present study was to determine the metabolic role of ANGPTL8 and to investigate its nutritional, hormonal and molecular regulation in key metabolic tissues.\n\nMethodsThe metabolism of ANGPTL8 knockout mice (ANGPTL8-/-) was examined in mice following chow and high-fat diets (HFD). The regulation of ANGPTL8 expression by insulin and glucose was quantified using a combination of in vivo insulin clamp experiments in mice and in vitro experiments in hepatocytes and adipocytes. The role of AMPK signaling was examined, and the transcriptional control of ANGPTL8 was determined using bioinformatic and luciferase reporter approaches.\n\nResultsThe ANGPTL8-/-mice had improved glucose tolerance and displayed reduced fed and fasted plasma triglycerides. However, there was no reduction in steatosis in ANGPTL8-/-mice after the HFD. Insulin acutely activated ANGPTL8 expression in liver and adipose tissue, which was mediated by C/EBP{beta}. Using insulin clamp experiments we observed that glucose further enhanced ANGPTL8 expression in the presence of insulin in adipocytes only. The activation of AMPK signaling potently suppressed the effect of insulin on ANGPTL8 expression in hepatocytes.\n\nConclusionThese data show that ANGPTL8 plays an important metabolic role in mice that may extend beyond triglyceride metabolism. The finding that insulin and glucose have distinct roles in regulating ANGPTL8 expression in liver and adipose tissue may provide important clues about the function of ANGPTL8 in these tissues.

physiology↗

bioRxiv2019

Predicting long-term Type 2 Diabetes with Support Vector Machine using Oral Glucose Tolerance Test

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the dataset. Using 11 oral glucose tolerance test (OGTT) measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using Minimum Redundancy Maximum Relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individuals plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80 % and a sensitivity of 80.09 % obtained on a holdout set.

scientific communication and education↗