Title

Utilization of N-glycosylation profiles as risk stratification biomarkers for suboptimal health status and metabolic syndrome in a Ghanaian population

Document Type

Journal Article

Publication Title

Biomarkers in Medicine

ISSN

1752-0371

Volume

13

Issue

15

First Page

1273

Last Page

1287

PubMed ID

31559833

Publisher

Future Medicine

School

School of Medical and Health Sciences

RAS ID

29824

Funders

This study was supported partially by the Joint Project of the Australian National Health & Medical Research Council and the National Natural Science Foundation of China (NHMRC APP1112767-NSFC 81561128020), National Natural Science Foundation of China (grant numbers 8177120753, 81673247 and 81573215), Edith Cowan University Collaboration Enhancement Scheme 2017 (round 1), the National Key Technology Support Program of China (2012BAI37B03), as well as by funding from the European Structural and Investments funds for ‘Croatian National Centre of Research Excellence in Personalized Healthcare’ (contract no.KK.01.1.1.01.0010) and funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska–Curie grant for project GlySign (contract no. 722095)

Grant Number

NHMRC Number : 1112767

Comments

Originally published as: Adua, E., Memarian, E., Russell, A., Trbojević-Akmačić, I., Gudelj, I., Jurić, J., ... & Wang, W. (2019). Utilization of N-glycosylation profiles as risk stratification biomarkers for suboptimal health status and metabolic syndrome in a Ghanaian population. Biomarkers in Medicine, 13(15), 1273-1287.

Original article available here.

Abstract

Aim: The study sought to apply N-glycosylation profiles to understand the interplay between suboptimal health status (SHS) and metabolic syndrome (MetS).

Materials & methods: In this study, 262 Ghanaians were recruited from May to July 2016. After completing a health survey, plasma samples were collected for clinical assessments while ultra performance liquid chromatography was used to measure plasma N-glycans.

Results: Four glycan peaks were found to predict case status (MetS and SHS) using a step-wise Akaike’s information criterion logistic regression model selection. This model yielded an area under the curve of MetS: 83.1% (95% CI: 78.0–88.1%) and SHS: 67.1% (60.6–73.7%).

Conclusion: Our results show that SHS is a significant, albeit modest, risk factor for MetS and N-glycan complexity was associated with MetS.

DOI

10.2217/bmm-2019-0005

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