Our Research Members
Program Director, Knowledge Translation and Optimizing Care Models, Banting & Best Diabetes Centre
Assistant Professor, Leslie Dan Faculty of Pharmacy
Knowledge Translation Projects particularly in improving the care of patients with diabetes, innovative pharmacy interventions, using multimedia for patient education, and educating patients on risks and benefits of medications.
Associate Professor, Chemical Engineering and Applied Chemistry, Institute of Biomaterials and Biomedical Engineering
Assistant Professor, Faculty of Medicine
Division of Pediatric Endocrinology, The Hospital for Sick Children
Division of Endocrinology
555 University Avenue
Toronto, ON M5G 1X8
As a pediatric endocrinologist who cares for children with diabetes, obesity and other chronic medical conditions on a daily basis, I am concerned about the prospects for their future health. This has driven me to seek answers to some of the meaningful questions that are going to benefit their health and this has prompted me to pursue a clinical research program. My research interests include the study of conditions associated with type 1 diabetes, such as celiac disease and the evaluation of early atherosclerotic risk factors in young patients with endocrine conditions who are at high risk of cardiovascular disease including both type 1 and type 2 diabetes, insulin resistance and obesity. I have conducted clinical studies to identify patients at risk and study the impact of this process on the health of these children.
These studies are also evaluating interrelated risk factors for atherosclerosis in these high risk populations and the effectiveness of lifestyle, diet and drug therapy treatment strategies. Specifically we evaluated dietary intake in patients with type 1 diabetes as well as standardized activity assessments which showed high rates of dietary fat intake and inactivity, which worsened during adolescence. As part of my research, I adapted an innovative method to study endothelial function as a marker of early atherosclerosis which we able to evaluate as a reversible indicator of disease with the potential to assess the effectiveness of different treatment strategies on reducing early atherosclerotic changes.
Longer term, the findings of this research will be used to develop guidelines for clinical practice, prevention and treatment strategies. It is hoped that the effective translation of this research into clinical practice will help to manage cardiovascular disease risk in children with the ultimate goal of cardiovascular disease prevention.
Professor, Institute of Health Policy, Management, and Evaluation, Faculty of Medicine and the Leslie Dan Faculty of Pharmacy
Department of Medicine, Faculty of Medicine
Director, Applied Health Research Centre, Li Ka Shing Knowledge Institute, St. Michael’s Hospital
I currently lead the Li Ka Shing Centre for Healthcare Analytics Research and Training (LKS-CHART) of St. Michael’s Hospital. The LKS-CHART is composed of a team of data scientists with expertise in biostatistics, computer science (with a focus on machine learning, deep learning, and natural language processing), and operations research engineering (with a focus on simulation modeling and optimization). Our team works closely with clinicians and hospital management to solve ‘real world’ problems with advanced analytics. For example, the LKS-CHART team has recently developed a machine learning algorithm that constantly monitors inpatients with diabetes to predict hypoglycemic events in advance so clinicians can proactively manage patients more effectively.
- Professor, University of Toronto, Faculty of Dentistry
- Cross-appointed to Department of Laboratory Medicine and Pathobiology, Faculty of Medicine
- Canada Research Chair in Matrix Dynamics
- Director, Matrix Dynamics Group, Faculty of Dentistry
- Full Member, School of Graduate Studies
- Director, CIHR STIHR, “Cell Signalling in Mucosal Health and Disease”
- Consultant, Royal College of Dental Surgeons of Ontario (RCDSO), Toronto, ON
My laboratory is focused on the signals arising from the extracellular matrix that regulate the metabolic activities of fibroblasts. In the context of diabetes, I am interested in the notion that diabetes-induced glycation of collagen may impact the function and differentiation of fibroblasts in the cardiac interstitium. Accordingly, I cardiac fibroblasts may convert to myofibroblasts, cells that contribute to the fibrosis that is observed in diabetic cardiomyopathy.
Associate Professor, Department of Immunology
Senior Scientist, Princess Margaret Cancer Centre, Tumor Immunotherapy Program
We are interested in the endoplasmic reticulum stress/unfolded protein response (UPR) and integrated stress response (ISR) signals as drivers of macrophage (MF) phenotype and tissue destruction in autoimmune diabetes (T1D).
MF infiltration in the islets is a well-established mechanistic driver of insulitis and b cell death in T1D. Functionally, infiltrating MF in pancreatic lesions exhibit an inflammatory phenotype with NADPH-driven superoxide and ROS production and TNF-a and IL-1b secretion (in a STAT1-dependent mechanism). Via production of these effector molecules, MF act in a synergistic fashion with infiltrating dendritic cells and T cells to drive islet cell dysfunction and death. Conversely, modulating MF phenotype has a dominant impact on the course of disease in experimental models of spontaneous and induced T1D, reducing the onset and severity of insulitis. Given this, manipulation of MF function becomes an attractive therapeutic target in T1D to control autoimmune pathology and as a tool to promote transplant tolerance.
Cellular stress signals play a key role in myeloid inflammatory potential. UPR stress can drive JNK and STAT1 activation, promote TNFa secretion, and inflammasome maturation/IL-1b production in MF and DC. Likewise the ISR plays a dominant role in controlling MF phenotype and inflammatory potential. For example, our lab demonstrated the ISR kinase GCN2 regulates IL-6 and IL-12 production promoting endotoxemic mortality. However, stress signals can also suppress inflammation as reactive oxygen species driven ER stress promotes myeloid-dependent immune suppression in tumors. Further, we have found GCN2 suppresses inflammatory T cell proliferation and systemic autoimmunity. Importantly modification of stress signaling by pharmacologic approaches has a potent impact on outcomes in inflammation, transplantation, autoimmunity and cancer. These striking findings show stress signaling is a cell and context-dependent modifier of immune function providing novel druggable targets; however, it is paramount to clearly delineate the role (either pro- or anti-inflammatory) of stress signaling modules in the pathologic process.
In our research project we are examining the role of metabolism and stress signaling in inflammatory pathology in T1D using mouse models of disease. Moreover we are examining the follow-on prediction that manipulation of MF phenotype via antagonists or agonists of UPR stress and/or the ISR will modulate the phenotype of MF; thereby reducing autoimmune pathophysiology and/or promoting islet transplant acceptance.
Associate Professor of Psychiatry and Pharmacology
Head, Mood Disorders Psychopharmacology Unit (MDPU), University Health Network
399 Bathurst Street
Toronto, ON M5T 2S9
Individuals with mood disorders are differentially affected by abnormalities in glucose handling, hyperglycemia, and diabetes mellitus. Evidence indicates that both mood disorders and diabetes are highly associated with neurocognitive impairment as well as changes in brain volume and structure. Points of pathophysiological commonality have been implicated between mood disorders and diabetes and include alterations in metabolic effector systems, immunoinflammatory dysregulation, oxidative stress, and incretin systems. We have coined the moniker “Metabolic Syndrome Type II” to characterize these points of commonality. The Mood Disorder Psychopharmacology Unit (MDPU) is engaged in a plethora of both descriptive and interventional studies that aim to identify the effect of abnormal glucose homeostasis, insulin resistance, and incretin dysregulation on brain structure and function. The MDPU welcomes multidiscipline centre of excellence and research characterizing the psychiatry and endocrinology interface.
Professor, Department of Medical Imaging
Associate Professor, Department of Medical Biophysics
Full member Institute of Medical Sciences
Chair, Department of Medical Imaging
Senior Scientist, Sunnybrook Research Institute
Staff Radiologist, Sunnybrook Health Sciences Centre
Diabetic patients are known to suffer an excess risk of cardiovascular disease due to atherosclerosis. Atherosclerosis is a blood vessel wall disease caused by the build-up of fatty substances called plaques. The blockage of blood flow eventually leads to serious heart and circulatory problems such as heart attack or stroke. However, diagnosis of diabetic cardiovascular disease is commonly not undertaken unless and until the patient presents with symptoms. By then, the disease is already far advanced and the potential for stabilization or reversal is limited. Although numerous imaging techniques for visualization of the diabetic blood vessel wall are available, early-stage diseases may not be easily detected. The need for developing new techniques to detect and monitor diabetic vascular disease is becoming more important as diabetes becomes more prevalent. We aim to develop MRI and ultrasound techniques for high-resolution and contrast imaging, specifically for improved characterization of vessel-wall disease in the early stages of diabetes. Our studies also aim to provide an improved understanding of the biology and interactions within the atherosclerotic plaque. This may provide a more rational and targeted approach to identify early signs of cardiovascular disease, and allow early treatment prior to significant tissue damage.
Associate Professor, Faculty of Dentistry
Associate Professor, Faculty of Medicine (Laboratory Medicine & Pathobiology)
We study systemic dissemination mechanisms of bloodborne bacterial pathogens, with a focus on the Lyme disease pathogen, Borrelia burgdorferi. Lyme disease is the most common vector-borne infection in the industrialized world, and its incidence is increasing rapidly, in parallel with rising rates of obesity and diabetes. Systemic dissemination of pathogens causes most of the mortality due to bacterial infection, but remains poorly understood. One critical step in dissemination is microbe adhesion to blood vessel surfaces in the face of fluid shear force. Vascular adhesion enables pathogens to decelerate and transmigrate through vessels to reach extravascular tissues in joints, heart and brain where secondary infection is established. B. burgdorferi adheres more readily to sites of turbulent, altered blood flow (Moriarty et al., 2008). This observation, together with the epidemiological profile of Lyme disease, prompted us to examine the effect of blood flow-altering conditions such as diet-induced obesity on B. burgdorferi dissemination in mice. We have found that diet-induced obesity significantly enhances host susceptibility to disseminated Borrelia infection, and are currently investigating the mechanisms underlying this increased susceptibility, as well as the role of diabetes in host susceptibility to Lyme disease.
Associate Profressor, Deapartment of Psychiatry
Head, Pharmacogenetics Research Clinic, Centre for Addiction and Mental Health
The overarching goal of my research is to improve psychiatric drug treatment by implementation of personalized medicine using genetic information.
One major focus of my research is to study the genetics of antipsychotic-induced weight gain (AIWG). AIWG frequently leads to obesity and secondary conditions such as diabetes mellitus, hypertension and cardiovascular events. Our research has revealed significant associations between AIWG and polymorphisms of the Cannabinoid-1-receptor (Twari et al., 2010) gene, the melanocortin-4-receptor gene (Chowdhury et al., 2013), the Neurpeptide-Y gene (Tiwari et al., 2013) and mitochondrial genes (Goncalves et al., 2014). We are currently developing an algorithm that will incorporate these genes along with clinical and demographic risk factors which will result in a genetic risk model for clinical application. Such algorithm will help to identify patients at higher risk for AIWG and diabetes, in order to select a medication panel with low risk. In addition, high risk patients will receive frequent monitoring and encouraged to enroll in diet and excercise programs.