Document Type

Journal Article

Publication Title

Scientific Reports

Volume

12

Issue

1

PubMed ID

35768493

Publisher

Nature

School

School of Science / Centre for People, Place and Planet / School of Medical and Health Sciences / Centre for Precision Health

RAS ID

45166

Funders

Edith Cowan University

Comments

Adua, E., Afrifa-Yamoah, E., Peprah-Yamoah, E., Anto, E. O., Acheampong, E., Awuah-Mensah, K. A., & Wang, W. (2022). Multi-block data integration analysis for identifying and validating targeted N-glycans as biomarkers for type II diabetes mellitus. Scientific Reports, 12(1), 1-12. https://doi.org/10.1038/s41598-022-15172-z

Abstract

Plasma N-glycan profiles have been shown to be defective in type II diabetes Mellitus (T2DM) and holds a promise to discovering biomarkers. The study comprised 232 T2DM patients and 219 healthy individuals. N-glycans were analysed by high-performance liquid chromatography. The multivariate integrative framework, DIABLO was employed for the statistical analysis. N-glycan groups (GPs 34, 32, 26, 31, 36 and 30) were significantly expressed in T2DM in component 1 and GPs 38 and 20 were related to T2DM in component 2. Four clusters were observed based on the correlation of the expressive signatures of the 39 N-glycans across T2DM and controls. Cluster A, B, C and D had 16, 16, 4 and 3 N-glycans respectively, of which 11, 8, 1 and 1 were found to express differently between controls and T2DM in a univariate analysis (p< 0.05). Multi-block analysis revealed that trigalactosylated (G3), triantennary (TRIA), high branching (HB) and trisialylated (S3) expressed significantly highly in T2DM than healthy controls. A bipartite relevance network revealed that HB, monogalactosylated (G1) and G3 were central in the network and observed more connections, highlighting their importance in discriminating between T2DM and healthy controls. Investigation of these N-glycans can enhance the understanding of T2DM.

DOI

10.1038/s41598-022-15172-z

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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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