Title

Screening multi-omics biomarkers for suboptimal health status

Author

Hao Wang

Author Identifiers

Hao Wang
ORCID: 0000-0002-9131-0199

Date of Award

2021

Degree Type

Thesis

Degree Name

Doctor of Philosophy

School

School of Medical & Health Sciences

First Advisor

Professor Wei Wang

Second Advisor

Dr Rachel Alexander

Abstract

Background

Suboptimal health status (SHS) is an intermediate health status between ideal health and diseases. It is characterized by chronic fatigue, perception of health complaints and a cluster of physical symptoms lasting for more than three months. SHS is recognised as a subclinical, reversible stage of chronic diseases.

Objectives

Study I. To investigate the prevalence of SHS in a cross-sectional study.
Study II. To screen transcriptomic biomarkers for SHS in a case-control study.
Study III. To screen metabolomics biomarkers for SHS in a case-control study.

Materials and Methods

Study I. A cross-sectional study was conducted from September 2017 to November 2017. SHS questionnaire-25 was used to assess the SHS levels of the participants.

Study II. The RNA sequencing (RNA-Seq)-based transcriptome analysis was firstly conducted on buffy coat samples collected from 30 participants with SHS and 30 age- and sex-matched healthy controls.

Study III. The liquid chromatography-mass spectrometry (LC-MS)- based untargeted metabolomics analysis was conducted on plasma samples collected from 50 SHS participants and 50 age- and sex-matched healthy controls.

Result

In Study I, a total of 4839 Chinese university students enrolled in this study. The prevalence of SHS was 8.39%, with the prevalence of 6.57% among males and 9.60% among females. The multivariate logistic regression results showed that SHS was significantly associated with age (Odd ratio (OR) = 1.193, P = 0.019), female (OR = 1.437, P = 0.002), sleep duration (OR = 0.609, P < 0.001), insomnia symptoms (OR = 1.238, P < 0.001), anxiety symptoms (OR = 1.025, P = 0.019), and depression symptoms (OR = 1.082, P < 0.001).

In study II, transcriptome analysis identified a total of 46 differentially expressed genes, in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that several biological processes were related to SHS, such as ATP-binding cassette transporter and neurodegeneration. A combination of transcripts can distinguish SHS individuals from the healthy controls with a sensitivity of 83.3%, a specificity of 90.0%, and an area under the receiver operating characteristic curve (AUC) of 0.938.

In study III, metabolomics analysis identified a total of 24 significantly altered metabolites 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 AUC of 0.977.

Conclusion SHS is prevalent in Chinese university students. Older age, female, insomnia, depression, and anxiety symptoms are risk factors for SHS, while longer sleep duration is a protective factor for SHS. Blood transcripts and metabolites are valuable biomarkers for SHS identification. These findings suggest the potential utility of SHS-related transcriptomic and metabolomic biomarkers for the Predictive, Preventive, and Personalized Medicine (PPPM) of chronic diseases.

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