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Model selection reveals selective regulation of blood amino acid and lipid metabolism by insulin in humans.
Fujita, Suguru; Hironaka, Ken-Ichi; Karasawa, Yasuaki; Kuroda, Shinya.
Afiliación
  • Fujita S; Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
  • Hironaka KI; Department of Biotechnology, Graduate School of Agricultual and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan.
  • Karasawa Y; Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
  • Kuroda S; Department of Neurosurgery, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan.
iScience ; 27(6): 109833, 2024 Jun 21.
Article en En | MEDLINE | ID: mdl-39055606
ABSTRACT
Insulin plays a crucial role in regulating the metabolism of blood glucose, amino acids (aa), and lipids in humans. However, the mechanisms by which insulin selectively regulates these metabolites are not fully understood. To address this question, we used mathematical modeling to identify the selective regulatory mechanisms of insulin on blood aa and lipids. Our study revealed that insulin negatively regulates the influx and positively regulates the efflux of lipids, consistent with previous findings. By contrast, we did not observe the previously reported insulin's negative regulation of branched-chain aa (BCAA) influx; instead, we found that insulin positively regulates BCAA efflux. We observed that the earlier peak time of lipids compared to BCAA is dependent on insulin's negative regulation of their influx. Overall, our findings shed new light on how insulin selectively regulates the levels of different metabolites in human blood, providing insights into the metabolic disorder pathogenesis and potential therapies.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IScience Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IScience Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Estados Unidos