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Population-based case-control study revealed metabolomic biomarkers of suboptimal health status in Chinese population—potential utility for innovative approach by predictive, preventive, and personalized medicine

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Abstract

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.

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Abbreviations

ALP:

alkaline phosphatase

ALT:

alanine transaminase

AST:

aspartate transaminase

AUC:

area under the curve

BMI:

body mass index

BUN:

blood urea nitrogen

CI:

confidence interval

CK-MB:

creatine kinase-MB

CVD:

cardiovascular disease

DBP:

diastolic blood pressure

FDR:

false discovery rate

FPG:

fasting plasma glucose

HC:

hip circumference

HDL-C:

high-density lipoprotein cholesterol

HMDB:

human metabolome database

IPAQ:

international physical activity questionnaire

IQR:

interquartile ranges

KEGG:

Kyoto encyclopedia of genes and genomes

LC-MS:

liquid chromatography-mass spectrometry

LDH:

lactate dehydrogenase

LDL-C:

low-density lipoprotein cholesterol

m/z :

mass-to-change ratio

OPLS-DA:

orthogonal partial least squares projection-discriminant analysis

PCA:

principal component analysis

PPPM:

predictive preventive personalized medicine

QC:

quality control

ROC:

receiver operating characteristic

SBP:

systolic blood pressure

SD:

standard deviation

SHS:

suboptimal health status

SHSQ-25:

suboptimal health status questionnaire-25

TC:

total cholesterol

TG:

triglycerides

T2DM:

type 2 diabetes mellitus

UA:

uric acid

VIP:

variable importance on projection

WC:

waist circumference

WHR:

waist-to-hip ratio

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Acknowledgments

The authors acknowledge the participants and their families who donated their time and effort in helping to make this study possible.

Funding

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).

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Authors and Affiliations

  1. Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China

    Hao Wang, Qiuyue Tian, Jie Zhang, Xiaoyu Zhang, Weijie Cao, Jinxia Zhang, Di Liu, Lijuan Wu, Manshu Song, Youxin Wang & Wei Wang

  2. School of Medical and Health Sciences, Edith Cowan University, Perth, WA, Australia

    Hao Wang, Enoch Odame Anto, Xingang Li, Xueqing Wang, Yulu Zheng, Zheng Guo, Manshu Song & Wei Wang

  3. Student Health Center, Weifang University, Weifang, China

    Hongqi Liu

  4. School of Public Health, Shandong First Medical University, Taian, China

    Wei Wang

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  1. Hao Wang
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Contributions

HW, YW, and WW participated in the design of the study. HW, JZ, HL, XZ, WC, JXZ, DL, LW, and MS performed participant enrollment and collected the samples. HW, EOA, XL, XW, YZ, and ZG performed the metabolomics analysis. HW performed the statistical analysis and drafted the manuscript. YW and WW revised the manuscript.

Corresponding author

Correspondence to Wei Wang.

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Ethical approval and consent to participate

This study was approved by the Ethics Committee of the Weifang University, Weifang, China. Written informed consent was obtained from each participant at the beginning of the study. The ethics approval was given in compliance with the Declaration of Helsinki.

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Wang, H., Tian, Q., Zhang, J. et al. Population-based case-control study revealed metabolomic biomarkers of suboptimal health status in Chinese population—potential utility for innovative approach by predictive, preventive, and personalized medicine. EPMA Journal 11, 147–160 (2020). https://doi.org/10.1007/s13167-020-00200-7

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