Ranking factors involved in diabetes remission after bariatric surgery using machine-learning integrating clinical and genomic biomarkers.

ABSTRACT: As weight-loss surgery is an effective treatment for the glycaemic control of type 2 diabetes in obese patients, yet not all patients benefit, it is valuable to find predictive factors for this diabetic remission. This will help elucidating possible mechanistic insights and form the basis for prioritising obese patients with dysregulated diabetes for surgery where diabetes remission is of interest. In this study, we combine both clinical and genomic factors using heuristic methods, informed by prior biological knowledge in order to rank factors that would have a role in predicting diabetes remission, and indeed in identifying patients who may have low likelihood in responding to bariatric surgery for improved glycaemic control. Genetic variants from the Illumina CardioMetaboChip were prior

SUBMITTER: Pedersen HK 

PROVIDER: S-EPMC5685313 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature