A gene expression signature for insulin resistance

Nicky Konstantopoulos, Victoria C. Foletta, David H. Segal, Katherine A. Shields, Andrew Sanigorski, Kelly Windmill, Courtney Swinton, Tim Connor, Stephen Wanyonyi, Thomas D. Dyer, Richard P. Fahey, Rose A. Watt, Joanne E. Curran, Juan Carlos Molero, Guy Krippner, Greg R. Collier, David E. James, John Blangero, Jeremy B. Jowett, Ken R. Walder

    Research output: Contribution to journalArticlepeer-review

    17 Scopus citations

    Abstract

    Insulin resistance is a heterogeneous disorder caused by a range of genetic and environmental factors, and we hypothesize that its etiology 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 largescale 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 tumor necrosis factor-α (TNF-α) and then reversed with aspirin and troglitazone ("resensitized"). The GES consisted of five genes whose expression levels best discriminated between the insulin-resistant and insulin-resensitized 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 with aspirin and troglitazone. This screen identified both known and new insulin-sensitizing compounds including nonsteroidal anti-inflammatory agents, β-adrenergic antagonists, β-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.

    Original languageEnglish (US)
    Pages (from-to)110-120
    Number of pages11
    JournalPhysiological Genomics
    Volume43
    Issue number3
    DOIs
    StatePublished - Feb 2011

    Keywords

    • Diabetes
    • Microarray
    • Personalized medicine
    • Screening

    ASJC Scopus subject areas

    • Physiology
    • Genetics

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