Amino acids signatures in relation to polycystic ovary syndrome and increased risk of different metabolic disturbances

作者
Zhenhong Ye,Chunmei Zhang,Siyu Wang,Yurong Zhang,Rong Li,Yue Zhao,Jie Qiao
出处
期刊:Reproductive Biomedicine Online [Elsevier]
标识
DOI:10.1016/j.rbmo.2021.11.012
摘要

ABSTRACT Research question Polycystic ovary syndrome (PCOS) is the most common reproductive endocrine disorder accompanied by a series of metabolic dysfunctions, but the amino acids profile in PCOS has not been systematically studied. Our objective was to investigate amino acids signatures related to PCOS, as well as for the detection of potential amino acid markers for different metabolic risks of PCOS. Design A total of 380 Chinese Han women (190 PCOS patients and 190 age and BMI matching control subjects) were recruited and basal state plasma was collected. The concentrations of 50 amino acids were quantitatively determined by the high-performance liquid chromatography-tandem mass spectrometry. Results There were 31 amino acids exhibited abnormal levels in PCOS group compared with the controls, indicating the dysregulated amino acids metabolism in PCOS. 28 amino acids were up-regulated, while only 3 amino acids were down-regulated. The combination of ten mostly significant amino acids achieved better ability for diagnosing PCOS than clinical endocrine parameters. After adjustment for age, specific amino acids were associated with the increased odds of obesity, insulin resistance and metabolic syndrome in PCOS, respectively. Alterations of tyrosine, glutamic acid, lysine, GABA and glycine were related to the obesity in PCOS women specifically, but not in control group; enhanced levels of branched-chain amino acids, tyrosine, alanine and lysine were correlated to insulin resistance in PCOS group; and a combination of alanine, valine, leucine, tyrosine, glutamic acid, cysteine and glycine indicated the predictive potential of MetS risk in PCOS women. Conclusions PCOS patients suffered from severe metabolic dysfunction of amino acids, and specific amino acid signatures could be used as powerful markers for PCOS diagnosis and prediction of metabolic disturbances.
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