Kaberi Dasgupta, Professor of Medicine and Physician Scientist, McGill University Health Cente

McGill University

Kaberi Dasgupta is a Physician, Scientist, and Professor of Medicine at McGill University and the McGill University Health Centre. Her research focus is prevention, reversal, and self-management support in diabetes. Her research is funded by the CIHR, Heart & Stroke Foundation, Lawson Foundation, and Diabetes Canada. She has published over 120 papers and her research has received a high level of media coverage because of its direct relevance to patients, practitioners, and policy makers. She is the Director of the Centre for Ourcomes Research and Evaluation (CORE) at the Research Institute of the McGill University Health Centre Please note: Slides with results will be presented but will not be posted as these results have not yet been published in their final form.

Associations of free sugars with overweight and gestational diabetes: solid vs liquid sources

We will discuss our analyses of data from the 2004-2005 Canadian Community Health Survey nutrition data linked with hospital discharge diagnoses over 13 years. Our aim was to examine associations of free sugars from solid sources and liquid sources with the presence of overweight at baseline and the occurrence of gestational diabetes mellitus among those who became pregnant during the follow-up period. We will describe our use of multivariate logistic regression through a nested case control design to evaluate associations between free sugars and GDM while maintaining an appropriate comparator (women who delivered but did not have GDM). We will discuss the methods used to estimate free sugars from information on food type and total sugars. We will discuss our intriguing findings, namely that free sugar intake from solid (food) sources above 5% total energy was associated with lower odds of GDM than intake below this threshold. Similarly, in a sensitivity analysis, intakes between 5 to 10% were also associated with lower odds than intakes below 5%. We will discuss the limitations of our analyses, interpretations of our findings, and their implications.