Postprandial plasma lipid levels are influenced by the interaction of functional polymorphisms in the microsome triglyceride transfer protein and beta3 adrenergic receptor genes

• Published in: Medical science monitor Vol. 13; no. 5; p. BR112
• Main Authors: Ueno, Takahiro, Takahashi, Yumiko, Matsumoto, Taro, Tsunemi, Akiko, Watanabe, Hideyuki, Tahira, Kazunobu, Fukuda, Noboru, Soma, Masayoshi, Matsumoto, Koichi
• Format: Journal Article
• Language: English
• Published: United States 01.05.2007
• Subjects: Adult; Animals; Carrier Proteins - genetics; Carrier Proteins - metabolism; Genotype; Glucose - metabolism; Humans; Middle Aged; Polymorphism, Genetic; Postprandial Period; Rats; Receptors, Adrenergic, beta-3 - genetics; Receptors, Adrenergic, beta-3 - metabolism; Triglycerides - blood
• ISSN: 1234-1010

The effects of polymorphisms in the genes encoding microsome triglycerides transfer protein (MTP) and beta3-adrenergic receptor (beta3-AR) on lipid and glucose metabolism were investigated. Clinical phenotypes related to lipid and glucose metabolism were evaluated during dietary loading (17 g of fat, 750 Cal) and glucose loading (75 g glucose). MTP and beta3-AR genotypes were determined by restriction fragment length polymorphism. Subjects with the Arg64 beta3-AR gene (Arg+) polymorphism showed significantly higher fasting (FTG) and postprandial (PTG) triglyceride levels, fasting plasma glucose (FPG), fasting plasma immuno-reactive insulin (FIRI) and HOMA-R in comparison with Trp64 homozygotes. Subjects with the T allele (T +) of MTP -164T/G polymorphism (with T allele) showed significantly lower levels of FPG, FIRI, HOMA-R and PTG than did subjects without the T allele (T-). To evaluate the interaction of the polymorphisms, we divided our subjects into four groups. T-/Arg-, T-/Arg+, T+/Arg- and T+/Arg+. In these four groups, only T-/Arg+ showed significantly higher PTG levels. Plasma glucose levels were significantly higher at 60 and 120 min after oral glucose loading in in the T-/Arg+ subjects. In this study, we identified an example of genotypic interactions that influence the clinical phenotype in multi-factorial diseases.