Deakin University

File(s) under permanent embargo

A gene expression signature for insulin resistance

journal contribution
posted on 2011-11-16, 00:00 authored by Nicky Konstantopoulos, Victoria Foletta, D Segal, K Shields, A Sanigorski, Kelly WindmillKelly Windmill, Courtney SwintonCourtney Swinton, Timothy ConnorTimothy Connor, Stephen Wanyonyi, T Dyer, Richard Fahey, R Watt, J Curran, Juan Molero Navajas, G Krippner, Gregory Collier, D James, J Blangero, Jeremy Jowett, Ken WalderKen Walder
Insulin resistance is a heterogeneous disorder caused by a range of genetic and environmental factors, and we hypothesize that its aetiology varies considerably between individuals. This heterogeneity provides significant challenges to the development of effective therapeutic regimes for long-term management of type 2 diabetes. We describe a novel strategy, using large-scale gene expression profiling, to develop a Gene Expression Signature (GES) that reflects the overall state of insulin resistance in cells and patients. The GES was developed from 3T3-L1 adipocytes that were made ‘insulin resistant’ by treatment with tumour necrosis factor-alpha (TNFα) and then reversed with aspirin and troglitazone (‘re-sensitized’). The GES consisted of five genes whose expression levels best discriminated between the insulin resistant and insulin re-sensitized states. We then used this GES to screen a compound library for agents that affected the GES genes in 3T3- L1 adipocytes in a way that most closely resembled the changes seen when insulin resistance was successfully reversed using aspirin and troglitazone. This screen identified both known and new insulin sensitizing compounds including non-steroidal anti inflammatory agents, β-adrenergic antagonists, beta-lactams and sodium channel blockers. We tested the biological relevance of this GES in participants in the San Antonio Family Heart Study (n = 1,240) and showed that patients with the lowest GES scores were more insulin resistant (according to HOMA_IR and fasting plasma insulin levels, P < 0.001). These findings show that GES technology can be used for both the discovery of insulin sensitizing compounds and the characterization of patients into subtypes of insulin resistance according to GES scores, opening the possibility of developing a personalized medicine approach to type 2 diabetes.



Physiological genomics






110 - 120


American Physiological Society


Bethesda, Md.







Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2010, American Physiological Society.