Abstracts – Early Career
Risk Of Unintentional Severe Hypoglycemia In Hospital (rushh-ai)
Michael Fralick, MD, PhD, SM [1,2] David Dai, MSc [2] Chloe Pou-Prom, MSc [2] Amol A Verma, MD, MPhil [2-4] Muhammad Mamdani, PharmD, MPH, MA [2-5]
- Sinai Health System and the Department of Medicine, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Centre for Healthcare Analytics Research and Training, Unity Health, Toronto, Ontario
- Unity Health and the Department of Medicine, University of Toronto, Toronto
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto
Combined Associations Of Work And Leisure Time Physical Activity On Incident Diabetes Risk
Aviroop Biswas, PhD [1-2] Mahée Gilbert-Ouimet PhD [1-3] Cameron A. Mustard Sc.D [1-2] Richard H. Glazier MD, MPH [2-6] Peter M. Smith, PhD [1,2,7]
- Institute for Work & Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Health Sciences, Université du Québec à Rimouski, Québec, Canada
- ICES, Toronto, Ontario, Canada
- Department of Family and Community Medicine, University of Toronto and St Michael’s Hospital, Toronto, Ontario, Canada
- Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Ontario, Canada
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
Adipocyte Caspase 8 Regulates Metabolic Inflammation And Glucose Homeostasis
Cynthia T. Luk [1-4], Sally Yu Shi [3,5], Stephanie A. Schroer [5], Mansa Krishnamurthy [5], Rukhsana Aslam [2], Daniel Jong Jin Han [2-3], Minna Woo [3-6]
- Division of Endocrinology and Metabolism, Department of Medicine, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada
- Keenan Research Centre for Biomedical Science and Division of Endocrinology and Metabolism, St. Michael’s Hospital, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Banting and Best Diabetes Centre, University of Toronto, Toronto, ON, Canada
- Toronto General Research Institute, University Health Network, Toronto, ON, Canada
- Division of Endocrinology, Department of Medicine, University Health Network, Toronto, ON, Canada
Reduced Hepatic Gluconeogenesis Observed With Inhibition Of Dipeptidyl Peptidase 4 (dpp4) Is Mediated By Targeting Dpp4 Within The Hepatocyte
Natasha Trzaskalski [1,2], Branka Vulesevic [1], My- Anh Nguyen [1], Evgenia Fadzeyeva [1,2], Antonio Hanson [1,2], Nadya Morrow [1,2], Ilka Lorenzen-Schmidt [1], Conor O’Dwyer [2], Morgan Fullerton [2], Erin E. Mulvihill [1,2]
- University of Ottawa Heart Institute
- University of Ottawa Department of Biochemistry, Microbiology and Immunology
Attainment Of Glycemic Targets Among Adults With Diabetes In Canada: A Cross-sectional National Diabetes Repository Study
Alanna Weisman [1,2,3], Bruce Perkins [1,2,4]
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital. Toronto, Ontario, Canada
- ICES, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada
Liver Insulin Signaling Regulates Adipose Tissue Thermogenesis Via Circulating Fgf21 And Catecholamine-induced Lipolysis
Jaimarie Sostre-Colón [1], Kahealani Uehara [1], Matthew J. Gavin [1], Jeff Ishibashi [1], Anna E. Garcia Whitlock [1], Matthew J. Potthoff [2,3], Patrick Seale [1] and Paul M. Titchenell [1,4,5]*
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience and Pharmacology
- Fraternal Order of Eagles Diabetes Research Center, University of Iowa Carver College of Medicine, Iowa City, IA 522242, USA
- Department of Physiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Lead Contact
Beta-cell Mir-125b Targets Mitochondrial And Lysosomal Genes To Control Glucose Homeostasis
Rebecca Cheung* [1] ,Grazia Pizza* [1], Pauline Chabosseau, Marie-Sophie Nguyen-Tu [1],Delphine Rolando [2], Alejandra Tomas [1], Thomas Burgoyne [3] , Anna Salowska [1], Piero Marchetti [4], James Shapiro [5], Lorenzo Piemonti [6], Isabelle Leclerc [1], Kei Sakamoto [7], David M. Smith [8], Guy A. Rutter [1] and Aida Martinez-Sanchez [1] *Co-first authors
- Section of Cell Biology and Functional Genomics, Department of Medicine, Imperial College London, U.K.
- Beta Cell Genome Regulation Laboratory, Department of Medicine, Imperial College London, U.K.
- UCL Institute of Ophthalmology, London, United Kingdom
- University of Pisa, Italy
- University of Alberta, Canada
- Vita-Salute San Raffaele University, Milan, Italy
- Nestle Institute of Health Sciences, Lausanne, Switzerland
- Astrazeneca, Cambridge, U.K.
Using Machine Learning To Predict Severe Hypoglycemia In Hospital
Michael Fralick, MD, PhD, SM [1,2] David Dai, MSc [2] Chloe Pou-Prom, MSc [2] Amol A Verma, MD, MPhil [2-4] Muhammad Mamdani, PharmD, MPH, MA [2-4,5]
- Sinai Health System and the Department of Medicine, University of Toronto, Toronto, ON, Canada
- Li Ka Shing Centre for Healthcare Analytics Research and Training, Unity Health, Toronto, Ontario
- Unity Health and the Department of Medicine, University of Toronto, Toronto
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto
Hypoglycaemia in-hospital is common, potentially life-threatening, and often preventable.
The objective of our study was to predict the risk of hypoglycemia using machine learning techniques in hospitalized patients.
Methods: We conducted a cohort study of patients hospitalized under general internal medicine (GIM) and cardiovascular surgery (CV). Three models were generated using supervised machine learning: Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, gradient boosted trees, and a recurrent neural network.
Each model included baseline patient data and time-varying data. Natural language processing was used to incorporate text data from physician and nursing notes.
Results: We included 8492 GIM admissions and 8044 CV admissions. Hypoglycemia occurred in 15% of GIM admissions and 13% of CV admissions. The area under the curve for the models in the held-out validation set was approximately 0.80 on the GIM ward and 0.82 on the CV ward. When the threshold for hypoglycemia was lowered to 2.9 mmol/L (52 mg/dL), similar results were observed.
Discussion: Machine learning approaches can accurately identify patients at high risk of hypoglycemia in hospital. Future work will involve evaluating whether implementing this model with targeted clinical interventions can improve clinical outcomes.
Identification Of An Epi-stable Subpopulation Of Beta Cells Regulated By Overnutrition
Steven J. Millership [1], Sanjay Khadayate [2], Dominic J. Withers [2], Guy A. Rutter [1]
- Department of Metabolism, Digestion and Reproduction, Imperial College London, UK
- MRC London Institute of Medical Sciences, London, UK
- Department of Health Sciences, Université du Québec à Rimouski, Québec, Canada
- ICES, Toronto, Ontario, Canada
- Department of Family and Community Medicine, University of Toronto and St Michael’s Hospital, Toronto, Ontario, Canada
- Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Ontario, Canada
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
Imprinted genes are characterised by monoallelic, parent of origin-specific expression resulting from epigenetic silencing of one allele1. Imprinted genes play key functional roles in beta cells and are deregulated in type 2 diabetes2, 3.
Transcriptional and functional heterogeneity between individual beta cells is known to be crucial for overall islet function4.
Using fluorescent reporter mouse lines5, 6 and 2D FACS analysis, we have shown that beta cells cluster into two distinct subtypes, with ‘on/off’ expression of the imprinted gene, neuronatin. Bisulphite sequencing analysis demonstrated that ‘on/off’ neuronatin expression was controlled by stable differences in DNA methylation at the neuronatin promoter. Beta cells represented by high neuronatin expression also displayed reduced expression of key functional beta cell markers.
Furthermore, beta cells with high neuronatin expression increased dramatically upon acute overnutrition (high fat diet) but were diminished after chronic overnutrition, suggesting that this subpopulation is both highly proliferative but may also be susceptible to dietary stress.
The well-characterised epigenetic regulation of imprinted loci will allow us to better understand how overnutrition negatively affects the beta cell epigenome, and targeted approaches using epigenetic-modifying drugs at specific genomic regions have the potential for preserving beta cell function in the face of nutrient excess.