Abstract: 【Objective】 This study aims to search the relational metabolites in plasma of cows with clinical and subclinical ketosis by the method of metabolomics based on the technology of gas chromatography (GC) / mass spectrometry (MS) techniques, find out molecular markers for endogenous metabolism for early diagnosis and prognosis of ketosis, and reveal the essence of ketosis. 【Method】 Total 24 dairy cows with clinical ketosis (CK), 33 dairy cows with subclinical ketosis (SK) and 23 healthy controls (C) were selected for the research. Venous blood of cows was collected, for the preparation of plasma samples, and the biochemical criterions such as β-hydroxybutyric acid, glucose and so on were detected. After the plasma samples were pretreated, cows plasma metabolites were detected by GC/MS technique. The detected metabolites were identified by mass spectral database. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of multivariate statistical methods were used for recognizing different metabolic patterns among clinical ketosis, subclinical ketosis and healthy controls. After establishing the diagnosis models of disease by partial least squares discriminant analysis, potential biomarkers of disease were selected. 【Result】 A stable and reliable GC/MS analytical method was established to obtain the metabolite profiles of the plasma samples. A commercial mass spectral database (NIST2008) was used for the rapid identification of detected metabolites and totally 267 variables were detected. The metabolomics data were imported into Simca-P 11.0 software for PCA and PLS-DA . Ketosis groups and healthy groups could be clustered and distinguished by the metabolomics data, and 40 types of metabolites without difference among 3 groups were found out. Results showed that compared with the healthy control group, different metabolites obtained in subclinical and clinical ketosis groups were 32, different metabolites obtained between clinical ketosis group and subclinical ketosis group were 13. Through seeking the KEGG database and analysis of metabolites, these metabolites primarily were related to amino acid metabolism, fat metabolism and carbohydrate metabolism.【Conclusion】 The present study is an integrated detection of dairy cattle ketosis based on plasma metabolomic profiling by GC/MS combined with multivariate statistical analysis. It was firstly discovered that 40 metabolites (i.e. fatty acids, amino acids, carbohydrates, et. al) were differentially found among CK, SK and C. It proved that the plasma GC/MS metabolite profiles can effectively distinguish clinical and subclinical ketosis groups from healthy control group. This further proved that metabolomics technology, to some extent, can reveal the development and progression of clinical and subclinical ketosis, while the substances of different content and contributing to classification may be the potential metabolic marker and objective indicators for diagnosing ketosis. To research ketosis, in the process of the occurrence and development of clinical and subclinical ketosis, parts of metabolic patterns and metabolic pathways in plasma changed. Furthermore, new potential metabolites could shed light on new strategies for the diagnosis, prognosis, and prevention of ketosis .

Key words: gas chromatography/mass spectrometry , metabonomics , ketosis , multivariate statistical analysis