Clinical trials

The main objective is to determine if in-home portable air cleaners provide persistent reductions in PM2.5 exposures and improvements in systolic blood pressure and biochemical parameters over 4-weeks in patients with metabolic syndrome residing in Qatar.

Our objective is to create a cardiometabolic cohort that could be representative of the local population, consisting of Qataris and long-term residents, in order to identify the prevalence, risk factors, and clinical characteristics of cardiometabolic disorders, as well as the incidence of atherosclerotic cardiovascular disease events.

This study aims to isolate endothelial progenitor cells (EPCs) from participants with type 2 diabetes (T2D) and cardiovascular complications and to comprehensively characterize EPC dysfunction. Specifically, the study will evaluate maladaptive angiocrine signaling, calcium signaling pathways, and the role of inflammation in EPC function and the progression of atherosclerosis during T2D development. A sub-study will assess EPC functionality by examining endothelial nitric oxide synthase (eNOS) expression and activity, as well as the effectiveness of in vitro eNOS gene enhancement.

Cardiovascular disease is the leading cause of death worldwide, and individuals with diabetes or other cardiometabolic conditions are at increased risk of adverse cardiovascular outcomes. Although advances in prevention and treatment have reduced cardiovascular events globally, cardiometabolic disease continues to represent a significant health burden, particularly in regions with high diabetes prevalence. In Qatar and other Gulf Cooperation Council countries, the prevalence of diabetes and obesity is increasing, contributing to a high proportion of participants presenting with acute coronary syndrome who have type 2 diabetes or prediabetes. This observational study will use electronic medical record data from patients hospitalized at the Heart Hospital with acute coronary syndrome and a concomitant diagnosis of diabetes or prediabetes. The study will assess trends in cardiovascular risk factors and cardiovascular events, including readmission and mortality. An artificial intelligence component will be used to develop and validate machine learning based risk prediction models to forecast adverse cardiovascular outcomes in participants with cardiometabolic disease. These models will integrate clinical, biochemical, imaging, and other non-invasive data routinely collected during participants care to identify predictors of cardiovascular events.