N-Glycosylation Profiling of Type II Diabetes Mellitus from Baseline to Follow Up: An Observational Study in a Ghanaian Population

Session: 
PS1 Poster session 1 Odd numbers
Code: 
P153
Location (hall): 
Foyer
Start/end time: 
Monday, July 1, 2019 - 15:45 to 17:15
Elham
Memariam

Elham Memarian1,2, Eric Adua3, Alyce Russell3, Irena Trbojević-Akmačić2, Ivan Gudelj2, Julija Jurić2, Peter Roberts3, Gordan Lauc2,4, Wei Wang3,5,6

1Leiden University Medical Center, Leiden, The Netherlands, 2Genos Glycoscience Research Laboratory, Zagreb, Croatia, 3School of Medical and Health Sciences, Edith Cowan University, , Australia , 4University of Zagreb, Faculty of Pharmacy and Biochemistry, Zagreb, Croatia, 5School of Public Health, Taishan Medical University, Shandong, China, 6Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, , China

Type II diabetes mellitus (T2DM) is a well-recognised cause of death worldwide[1]. In fact, over one million deaths in 2015 were attributed to T2DM while the disease prevalence is still rising[2]. This is because, despite considerable efforts, we still do not have reliable diagnostic and prognostic biomarkers to anticipate effective treatments, support better clinical outcomes, and move towards personalised medicine. Recently, N-glycosylation profiles have emerged as robust and field tested candidates for improving patient stratification and revolutionising T2DM therapeutics[3]. Glycans bind to protein backbone in a process termed glycosylation and thus far, it is regarded as the most complex and abundant co- and post-translational process in the cell. N-glycans which are a subclass of glycan types bind to asparagine side chains of proteins in the consensus sequence Asn-X-Thr/Ser (where X is any amino acid except proline).

Although these structures are fairly stable within an individual they change under influence of an external perturbation[4], with different physiological parameters such as age[5], and pathophysiological conditions such as metabolic syndrome[6] and T2DM[7].

In this study, we undertook an observational study to determine plasma N-glycosylation patterns longitudinally among 253 T2DM patients and the correlations between environmental factors and derived plasma N-glycan traits over a 6-month period.

From January to April 2016, 253 Ghanaian T2DM individuals were recruited (in Ghana, Western Africa) and depending on the date of first visit, each participant was called back after 6 months.

Demographic and anthropometric information were obtained while fasting blood samples were collected for clinical assessments. After which plasma N-glycans were freed, fluorescently labelled and analyzed by ultra-performance liquid chromatography (UPLC).

Followed by Wisconsin’s Ranked Sum (paired) tests and Spearman correlations for data analyses. Two tailed q-value <0.05 after adjusting for false discovery rate was considered significant.

There were statistical differences between males and females at baseline: body mass index (BMI) (χ2=13.26, q=0.0080), education (χ2=20.3, q=0.0003), occupation (χ2=23.08, q=0.0003), waist to height ratio (U=4733.5, q=0.0001), triglycerides (U=5919, q=0.0458) and high density lipoproteins (HDL-c) (U=4485, q=0.0001). Significant differences were observed between baseline and follow up: HDL-c (W=526, q=0.000), low density lipoprotein cholesterol (LDL-c) (W=6488, q=0.002) and TG (W=2513, q=0.000). However, fasting plasma glucose (FPG) and total cholesterol (TC) were not statistically different at baseline and follow up. Similarly, plasma N-glycosylation profiles were unchanged when comparing paired data at baseline and follow up. 

Our results showed moderate differences in biochemical measures at baseline compared to follow up. However, these did not cause a significant change in plasma N-glycosylation profiles. The study makes a significant contribution to our understanding of the stability or constancy of plasma N-glycosylation profiles in the absence of co-morbidities. 

References: 
  1. Tao Z, Shi A, Zhao J. Epidemiological Perspectives of Diabetes. Cell Biochem Biophys. 2015:1-5.
  2. International Diabetes Federation. IDF Diabetes Atlas. http://www.diabetesatlas.org/resources/2015-atlas.html, accessed, 04/10/2016. 2015
  3. Russell A, Adua E, Ugrina I, Laws S, Wang W. Unravelling Immunoglobulin G Fc N-Glycosylation: A Dynamic Marker Potentiating Predictive, Preventive and Personalised Medicine. International Journal of Molecular Sciences. 2018;19(2):390
  4. Gornik O, Wagner J, Pučić M, Knežević A, Redžić I, Lauc G. Stability of N-glycan profiles in human plasma. Glycobiology. 2009;19(12):1547-53
  5. Yu X, Wang Y, Kristic J, Dong J, Chu X, Ge S, et al. Profiling IgG N-glycans as potential biomarker of chronological and biological ages: A community-based study in a Han Chinese population. Medicine. 2016;95(28):e4112
  6. Lu JP, Knezevic A, Wang YX, Rudan I, Campbell H, Zou ZK, et al. Screening novel biomarkers for metabolic syndrome by profiling human plasma N-glycans in Chinese Han and Croatian populations. J Proteome Res. 2011;10(11):4959-69
  7. Lauc G. Precision medicine that transcends genomics: Glycans as integrators of genes and environment. Biochim Biophys Acta. 2016;1860(8):1571-3

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