Title

Adverse hepatic and cardiac responses to rosiglitazone in a new mouse model of type 2 diabetes: relation to dysregulated phosphatidylcholine metabolism.

Document Type

Article

Publication Date

2006

Keywords

Cardiolipins, Cardiovascular-System, Choline-Phosphate-Cytidylyltransferase, Crosses-Genetic, Diabetes-Mellitus-Type-2, Disease-Models-Animal, Fatty-Liver, Gene-Expression-Regulation, Hypoglycemic-Agents, Liver, Male, Mice-Inbred-NOD, Mice-Obese, Mitochondria-Heart, PPAR-gamma, Phosphatidylcholines, Phosphatidylethanolamine-N-Methyltransferase, Thiazolidinediones

JAX Location

see Reprint Collection

JAX Source

Vascul Pharmacol 2006 Jul; 45(1):65-71.

Abstract

Given the heterogeneous nature of metabolic dysfunctions associated with insulin resistance and type 2 diabetes (T2D), a single pharmaceutical cannot be expected to provide complication-free therapy in all patients. Thiazolidinediones (TZD) increase insulin sensitivity, reduce blood glucose and improve cardiovascular parameters. However, in addition to increasing fat mass, TZD have the potential in certain individuals to exacerbate underlying hepatosteatosis and diabetic cardiomyopathy. Pharmacogenetics should allow patient selection to maximize therapy and minimize risk. To this end, we have combined two genetically diverse inbred strains, NON/Lt and NZO/Lt, to produce a "negative heterosis" increasing the frequency of T2D in F1 males. As in humans with T2D, treatment of diabetic and hyperlipemic F1 males with rosiglitazone (Rosi), an agonist of peroxisome proliferator-activated gamma receptor (PPARgamma), reverses these disease phenotypes. However, the hybrid genome perturbed both major pathways for phosphatidylcholine (PC) biosynthesis in the liver, and effected remarkable alterations in the composition of cardiolipin in heart mitochondria. These metabolic defects severely exacerbated an underlying hepatosteatosis and increased levels of the adipokine, plasminogen activator inhibitor-1 (PAI-1), a risk factor for cardiovascular events. This model system demonstrates how the power of mouse genetics can be used to identify the metabolic signatures of individuals who may be prone to drug side effects.

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