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

Author:

Adua Eric1,Memarian Elham2,Russell Alyce1,Trbojević-Akmačić Irena2,Gudelj Ivan2,Jurić Julija2,Roberts Peter1,Lauc Gordan23,Wang Wei145

Affiliation:

1. School of Medical & Health Sciences, Edith Cowan University, WA 6027, Australia

2. Genos Glycoscience Research Laboratory, Zagreb 10000, Croatia

3. University of Zagreb, Faculty of Pharmacy & Biochemistry, Zagreb 10000, Croatia

4. School of Public Health, Taishan Medical University, Shandong, Taian 271000, PR China

5. Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing 100069, PR China

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.

Publisher

Future Medicine Ltd

Subject

Biochemistry, medical,Clinical Biochemistry,Drug Discovery

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