Open AccessAmino Acid Profiling Study of Psidium guajava L. Leaves as an Effective Treatment for Type 2 Diabetic Rats
Author(s) -
Chang Xu,
Xin Li,
Debin Zeng,
Ying Li,
Yu-Hang Gao,
Makoto Tsunoda,
Shi-Ming Deng,
Xi Xie,
Rong Wang,
Lu-shuang Li,
Yanting Song,
Yingxia Zhang
Publication year - 2020
Publication title -
evidence-based complementary and alternative medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.552
H-Index - 90
eISSN - 1741-4288
pISSN - 1741-427X
DOI - 10.1155/2020/9784382
Subject(s) - psidium , valine , diabetes mellitus , leucine , streptozotocin , amino acid , isoleucine , phenylalanine , type 2 diabetes , pharmacology , tyrosine , type 2 diabetes mellitus , medicine , chemistry , biochemistry , biology , endocrinology , botany
Type 2 diabetes mellitus (T2DM) has become a major disease threatening human health worldwide. At present, the treatment of T2DM cannot cure diabetes and is prone to many side effects. Psidium guajava L. leaves have been reported to possess hypoglycemic activity, and they have been widely used in diabetes treatment in the folk. However, the antidiabetic mechanism has not been clearly explained. Also, the change in amino acid profile can reflect a metabolic disorder and provide insights into system-wide changes in response to physiological challenges or disease processes. The study found that P. guajava L. leaves can decrease fasting blood glucose and lipid levels in type 2 diabetic rats induced by streptozotocin. Through the analysis of amino acid profiling following 20 days of gavage administration, the concentration data were modeled by principal component analysis and orthogonal partial least squares discriminant analysis to find the different metabolites and related metabolic pathways (including cysteine and methionine metabolism, valine, leucine, and isoleucine biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis) for the explanation of the hypoglycemic mechanism of P. guajava L., which provides an experimental and theoretical basis for diabetes prediction and for the development of new drugs for the treatment of diabetes.