Hyperlipidemia refers to a chronic disease caused by systemic metabolic disorder, and its pathophysiology is very complex. Shanmei capsule (SM) is a famous preparation with a long tradition of use for anti-hyperlipidemia treatment in China. However, the regulation mechanism of SM on hyperlipidemia has not been elucidated so far. In this study, a combination of UPLC-Q-TOF/MS techniques and 16S rDNA gene sequencing was performed to investigate the effects of SM treatment on plasma metabolism-mediated change and intestinal homeostasis. The results indicated that SM potently ameliorated high-fat diet-induced glucose and lipid metabolic disorders and reduced the histopathological injury. Pathway analysis indicated that alterations of differential metabolites were mainly involved in glycerophospholipid metabolism, linolenic acid metabolism, α-linoleic acid metabolism, and arachidonic acid metabolism. These changes were accompanied by a significant perturbation of intestinal microbiota characterized by marked increased microbial richness and changed microbiota composition. There were many genera illustrating strong correlations with hyperlipidemia-related markers (e.g., weight gains, GLU, and total cholesterol), including the Lachnospiraceae NK4A136 group and the Lachnospiraceae NK4B4 group. Overall, this study initially confirmed that hyperlipidemia is associated with metabolic disturbance and intestinal microbiota disorders, and SM can be employed to help decrease hyperlipidemia risk, including improving the abnormal metabolic profile and maintaining the gut microbial environment.

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Gastric cardia adenocarcinoma (GCA) has a high mortality rate worldwide; however, current early diagnostic methods lack efficacy. Therefore, the aim of the present study was to identify potential biomarkers for the early diagnosis of GCA. Global metabolic profiles were obtained from plasma samples collected from 21 patients with GCA and 48 healthy controls using ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry. The orthogonal partial least squares discrimination analysis model was applied to distinguish patients with GCA from healthy controls and to identify potential biomarkers. Metabolic pathway analysis was performed using MetaboAnalyst (version 4.0) and revealed that ‘glycerophospholipid metabolism’, ‘linoleic acid metabolism’, ‘fatty acid biosynthesis’ and ‘primary bile acid biosynthesis’ were significantly associated with GCA. In addition, an early diagnostic model for GCA was established based on the relative levels of four key biomarkers, including phosphorylcholine, glycocholic acid, L-acetylcarnitine and arachidonic acid. The area under the receiver operating characteristic curve revealed that the diagnostic model had a sensitivity and specificity of 0.977 and 0.952, respectively. The present study demonstrated that metabolomics may aid the identification of the mechanisms underlying the pathogenesis of GCA. In addition, the proposed diagnostic method may serve as a promising approach for the early diagnosis of GCA.

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