Hao Wang, Edith Cowan UniversityFollow
Enoch Odame Anto, Edith Cowan UniversityFollow
Xingang Li, Edith Cowan UnviersityFollow
Xueqing Wang, Edith Cowan UniversityFollow
Yulu Zheng, Edith Cowan UniversityFollow
Zheng Guo, Edith Cowan UniversityFollow
Manshu Song, Edith Cowan UniversityFollow
Wei Wang, Edith Cowan UniversityFollow
European Association for Predictive, Preventive and Personalised Medicine (EPMA) Journal
School of Medical and Health Sciences
This work was partially supported by National Natural Science Foundation of China (Grant Numbers: 81673247 & 81773527) and China- Australia International Collaborative Grant (NSFC 81561128020, NHMRC APP1112767). HW and XW were supported by the China Scholarship Council (CSC 201708110200 and CSC 201608230108).
NHMRC Number : APP1112767
Background: Suboptimal health status (SHS) is a subclinical stage of chronic diseases, and the identification of SHS provides an opportunity for the predictive, preventive, and personalized medicine (PPPM) of chronic diseases. Previous studies have reported the associations between metabolic signatures and early signs of chronic diseases. Methods: This study aimed to detect the metabolic biomarkers for the identification of SHS in a case-control study. SHS questionnaire-25 (SHSQ-25) was used in a population-based health survey to measure the SHS levels of participants. The liquid chromatography-mass spectrometry-based untargeted metabolomics analysis was conducted on plasma samples collected from 50 SHS participants and 50 age- and sex-matched healthy controls. Results: After adjusting for the confounders, 24 significantly differential metabolites, such as sphingomyelin, sphingosine, sphinganine, progesterone, pregnanolone, and bilirubin, were identified as the candidate biomarkers for SHS. Pathway analysis revealed that sphingolipid metabolism, taurine metabolism, and steroid hormone biosynthesis are the disturbed metabolic pathways related to SHS. A combination of four metabolic biomarkers (sphingosine, pregnanolone, taurolithocholate sulfate, cervonyl carnitine) can distinguish SHS individuals from the controls with a sensitivity of 94.0%, a specificity of 90.0%, and an area under the receiver operating characteristic curve of 0.977. Conclusion: Plasma metabolites are valuable biomarkers for SHS identification, and meanwhile, SHSQ-25 can be used as an alternative health screening tool in the population-based health survey. SHS-related metabolic disturbances could be detected at the early onset of SHS, and SHS-related metabolites could create a window opportunity for PPPM of chronic diseases.