Oral Presentation ANZOS-Breakthrough Discoveries Joint Annual Scientific Meeting 2018

Novel circulating biomarkers identify insulin resistance phenotypes in obesity (#87)

Yen Chin Koay 1 2 , Pengyi Yang 3 , Daniel Chen 4 , Arthur B Jenkins 4 5 , Jerry Greenfield 4 6 7 , John F O'Sullvan 1 2 8 , Dorit Samocha-Bonet 4 5
  1. Heart Research Institute, Camperdown, NEW SOUTH WALES, Australia
  2. Sydney Medical School, University of Sydney, Camperdown, New South Wales, Australia
  3. School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
  4. Diabetes and Metabolism Division, Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia
  5. School of Medicine, University of Wollongong, Keiraville, New South Wales, Australia
  6. St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
  7. Department of Endocrinology and Diabetes Centre, St Vincent's Hospital, Sydney, New South Wales, Australia
  8. Royal Prince Alfred Hospital, Department of Cardiology, Sydney, New South Wales, Australia

Objective: Measurement of insulin resistance may ultimately assist in guiding the most effective therapy in type 2 diabetes (T2D). We aimed to identify circulating biomarkers of muscle and liver insulin resistance in obesity to guide treatment in the clinical setting.

Research Design and Methods: Metabolomics and lipidomics were combined with a specialized machine-learning algorithm to identify plasma biomarkers that characterize muscle and liver insulin resistance in a cohort of 62 individuals with obesity (BMI range 31-48 kg/m2) phenotyped using the gold-standard 2-step hyperinsulinaemic-euglycaemic clamp with deuterated glucose to evaluate glucose regulation in muscle and liver.

Results: Comprehensive metabolomic and lipidomic profiling by LC/MS revealed that a total of fourteen circulating metabolites and lipids were closely correlated with muscle insulin resistance (Spearman rho > 0.2, p < 0.05) while nineteen were associated with hepatic insulin resistance (Spearman rho > 0.3, p < 0.05). A hybrid learning model that combines clustering-based prototype selection and random forest-based feature analysis identified two triacylglycerols (TAGs) and a phosphatidylcholine (PC) in plasma as the best classifiers differentiating between the liver and muscle insulin resistance phenotypes, followed by select metabolites, clinical features, and biochemical parameters. The three lipids identified by the hybrid learning model far out-performed standard clinical measures, including fasting plasma glucose, 2-h plasma glucose post 75 g oral glucose load, glycosylated haemoglobin (HbA1c), and homeostatic model assessment of insulin resistance (HOMA-IR), classifying 61 of 62 subjects correctly.

Conclusions: We provide a simple novel tool based on circulating lipids and metabolites to guide physicians to the most effective insulin-sensitising treatment in individuals with obesity. Future studies are necessary to validate these findings and to compare the efficacy of the biomarker-guided therapy with the traditional treatment.

 

O’Sullivan and Samocha-Bonet contributed equally to this work