Screening of multi-omics biomarkers for Type 2 Diabetes Mellitus among Chinese population

Author Identifiers

Siqi Ge
ORCID: 0000-0003-1030-6890

Date of Award


Degree Type


Degree Name

Doctor of Philosophy


School of Medical and Health Sciences

First Advisor

Professor Wei Wang

Second Advisor

Associate Professor Richard Brightwell


Type 2 diabetes mellitus (T2DM) is a major international health challenge which has a great worldwide impact on morbidity. Increased fasting plasma glucose (FPG) level is characterised as an independent risk factors for T2DM development and the development of predictive biomarkers for increased FPG level are crucial in Predictive, Preventive and Personalised medicine (PPPM) of T2DM. Established diagnostic tools for T2DM includes the oral glucose tolerance test (OGTT), fasting blood glucose (FBG) and glycated haemoglobin (HbA1c). These tests, however, provide “retrospective” markers for the diagnosis and prognosis of the disease, and therefore cannot be applied as predictive markers for from the perspectives of PPPM. A deeper understanding of the disease pathogenesis is urgently needed. Since blood is in direct contact with all tissues, pathological changes of T2DM are likely to be reflected in the genomic, proteomic and glycomic profiles of serum.

With a multi-omics design, Study I combined genomics and glycomics factors to investigate the candidate genes and the glycosylation patterns of IgG that lead to increased FPG level among Chinese populations. Study I identified 9 new SNPs located in 5 genetic loci (RPL7AP27, SNX30, SLC39A12, BACE2 and IGFL2), which considerably affect the increase of FPG level. Moreover, out of 24 glycan peaks (GPs), GPs 2, 8 and 11 were found present positive trends with increased FPG level, while GPs 4 and 14 presented negative trends. Study I implied the feasibility of our current multi-omics study design for T2DM and potential application of multi-omics approaches T2DM biomarkers screening at DNA and glycan levels, which led to Study II, the serumbased proteomic biomarkers research for T2DM at protein level.

In Study II, we analysed differences in serum peptide between T2DM patients and healthy controls, using magnetic bead-based fractionation, coupled with MALDI-TOF MS. Diagnostic models for T2DM based on a set of potential specific serum peptide biomarkers generated from a training cohort were further tested using an independent validation set of samples. Study II have found 6 peptides (peaks m/z 1452.9, 1692.8, 1946, 2115.1, 2211.0 and 4053.6) to be novel candidate biomarkers for T2DM diagnosis. The diagnostic performance of this peptide model indicated a high discriminatory power for T2DM, with an accuracy of 82.20%, sensitivity 82.50%, specificity 77.80% and an AUC value of 0.864. By combining Study I and Study II, we identified multiple biomarkers for T2DM across genomic, glycomic and proteomic level. Hence, it was necessary to complement it with robust subjective markers, and this led to Study III and Study IV.

Study III is a community-based, real-life environment, prospective study to investigate whether suboptimal health status (SHS), along with life-style and other socio-economic factors, contributes to the incidence of chronic disease in Chinese adults. Furthermore, Study III affords the opportunity to longitudinally analyse the genetic, lifestyle and environmental factors that may determine onset and etiology of targeted chronic disease.

Based on Study III, we further investigated the relationship between SHS and the onset of T2DM during the follow-up in Study IV. The main result of Study IV was that participants with higher levels of SHS had a considerably higher risk of T2DM. This finding in Study IV indicated that the risk will increase with the increasing SHS performance of an individual. These results provide the potential application of SHS as dynamic monitoring index for the development of T2DM.

In conclusion,

1) We found significant associations of 9 genetic loci located in 5 genes (RPL7AP27, SNX30, SLC39A12, BACE2 and IGFL2) with increased FPG level. We also found that IgG GPs 3, 8 and 11 presented positive trends with increased FPG level, whereas GPs 4 and 14 showed negative trends with increased FPG level.

2) We have characterised 6 peptides (peaks m/z 1452.9, 1692.8, 1946, 2115.1, 2211.0 and 4053.6) to be novel candidate biomarkers for T2DM diagnosis.

3) SHS is a novel predictive factor for T2DM onset, and a higher SHS score is associated with a higher incidence of T2DM. The evaluation of SHS combined with the analysis of modifiable risk factors for SHS allows the risk stratification of T2DM, which might consequently contribute to the prevention of T2DM.

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