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Aim: The study sought to determine the patterns of N-glycan profiles among Type 2 diabetes mellitus (T2DM) patients over a 6-month period. Materials & methods: Biochemical and clinical data were obtained from 253 T2DM patients at baseline and follow-up. Ultra-performance liquid chromatography and statistical methods were applied for N-glycan profiling. Results: The coefficients of variation were 28% and 29% at baseline and follow-up, respectively, whereas the range of N-glycan variability was from 11% to 56%. Apart from GP1 (FA2) and GP29 (FA3G3S [3,3,3]3), the intra-individual variations of N-glycan peaks were not statistically significant. Conclusion: N-glycan profiles were stable over 6-month period in T2DM patients and could be used to monitor biochemical changes in relation with T2DM comorbidities.

Type 2 diabetes mellitus (T2DM) is a common complex trait arising from interactions among multiple environmental, genomic, and postgenomic factors. We report here the first attempt to investigate the association between immunoglobulin G (IgG) N-glycan patterns, T2DM, and their clinical risk factors in an Australian population. N-glycosylation of proteins is one of the most frequently observed co- and post-translational modifications, reflecting, importantly, the real-time status of the interplay between the genomic and postgenomic factors. In a community-based case-control study, 849 participants (217 cases and 632 controls) were recruited from an urban community in Busselton, Western Australia. We applied the ultraperformance liquid chromatography method to analyze the composition of IgG N-glycans. We then conducted Spearman's correlation analyses to explore the association between glycan biomarker candidates and clinical risk factors. We performed area under the curve (AUC) analysis of the receiver operating characteristic curves by fivefold cross-validation for clinical risk factors, IgG glycans, and their combination. Two directly measured and four derived glycan peaks were significantly associated with T2DM, after correction for extensive clinical confounders and false discovery rate, thus suggesting that IgG N-glycan traits are highly correlated with T2DM clinical risk factors. Moreover, adding the IgG glycan profiles to fasting blood glucose in the logistic regression model increased the AUC from 0.799 to 0.859. The AUC for IgG glycans alone was 0.623 with a 95% confidence interval 0.580-0.666. In addition, our study provided new evidence of diversity in T2DM complex trait by IgG N-glycan stratification. Six IgG glycan traits were firmly associated with T2DM, which reflects an increased proinflammatory and biological aging status. In summary, our study reports novel associations between the IgG N-glycome and T2DM in an Australian population and the putative role of proinflammatory mechanisms. Furthermore, IgG N-glycomic alterations offer future prospects as inflammatory biomarker candidates for T2DM diagnosis, and monitoring of T2DM progression to cardiovascular disease or renal failure.

Despite decades of investment in biomarker research, we still do not have robust and field-tested biomarkers for many chronic diseases so as to anticipate clinical outcomes and thus move toward personalized medicine. Biomarker innovations have tended to focus on genomics, but next-generation biomarkers from the nascent field of glycomics now offer fresh vistas for innovation in chronic disease biomarkers and systems diagnostics. Glycosylation, regarded as a complex enzymatic process where sugars (glycans) bind to proteins and lipids, affects many human biological functions, including cell signaling, adhesion, and motility. Notably, and contrary to proteins, glycan biosynthesis does not require a template; rather its final structure is catalyzed by a repertoire of enzymes that attach or detach monosaccharides in the glycosylation pathway, making glycomics research more challenging than proteomics or genomics. Yet, given glycans' biological significance, alterations in their processing may be detrimental to human health and also offer insights for preventive medicine and wellness interventions. Therefore, studying glycans' structure and understanding their function and molecular interactions in the emerging field of glycomics are key to unraveling the pathogenesis of various common chronic diseases. This review summarizes the major concepts in glycomics, including glycan release methods, techniques for large-scale glycan analysis, and glycoinformatic tools for data handling and storage. In all, this analysis on glycomics offers strategies to build a robust postgenomic innovation roadmap for glycan-driven biomarkers as the field is anticipated to mature further and gain greater prominence in the near future.

Aberrant protein glycosylation may reflect changes in cell metabolism of type II diabetes mellitus (T2DM) and offers fresh vistas for discovering potential biomarkers. However, the functional significance of T2DM N-glycan alterations is underexplored, since to date, N- glycan profiling studies have been performed in selected populations. Geographically and genetically isolated populations are needed for validation of specific biomarkers. This age-sex matched cross sectional study comprising 232 T2DM patients and 219 controls was conducted in Ghana, Western Africa. Blood plasma samples were collected for clinical assessment after which plasma N-glycans were freed and analysed by ultra-performance liquid chromatography (UPLC). High branching (HB) [W = 46328 ; q = 0.00072], tri-galactosylated (G3) [W = 44076 ; q = 0.00096], antennary fucosylated (FUC_A) [W = 43055 ; q = 0.0000763], and triantennary (TRIA) [W = 44624 ; q = 0.0025], N-glycan structures were increased in T2DM whereas low branching (LB) [W = 46328 ; q = 0.00072], non- sialylated (S0) [W = 46929 ; q = 0.00292], monogalactosylation (G1) [W = 44091 ; q = 0.0000763], core fucosylation (FUC_C), [W = 46497 ; q = 0.00096], biantennary galactosylation (A2G) [W = 45663 ; q = 0.000763], and biantennary (BA) [W = 46376 ; q = 0.00072], structures were decreased compared to controls. Nine N-glycan peaks (GPs (GP1, GP4, GP7, GP11, GP17, GP19, GP22, GP26, GP29)) were found to predict case status based on Akaike's information criterion (AIC) and Bayesian information criterion (BIC) model selection. Adjusting for age, sex and other co- variates in this model yielded an area under the curve (AUC) of 80.5% with sensitivity of 79% and specificity of 73%, indicating the predicting power of N-glycans as robust biomarkers. Our results show that hyperglycemia influences N-glycan complexities among Ghanaians. N-glycan profiling in distinct populations has affirmed the potentiality of N- glycan profiles as generic biomarkers which may facilitate better prognosis for T2DM.

Why Not Use the Immunoglobulin G N-Glycans as Predictor Variables in Disease Biomarker-Phenotype Association Studies? A Multivariate Analysis. A letter to the Editor.

Background Increased body fat may be associated with an increased risk of developing an underlying pro-inflammatory state, thus leading to greater risk of developing certain chronic conditions. Immunoglobulin G has the ability to exert both anti- and pro-inflammatory effects, and the N-glycosylation of the fragment crystallisable portion is involved in mediating this process. Body mass index, a rudimentary yet gold standard indication for body fat, has been shown to be associated with agalactosylated immunoglobulin G N-glycans. Aim We aimed to determine the association between increased body fat and the immunoglobulin G glycosylation features, comparing body mass index to other measures of body fat distribution. Methods We investigated a sample of 637 community-based 45–69 year olds, with mixed phenotypes, residing in Busselton, Western Australia. Body mass index and the waist-to-hip and waist-to-height ratios were calculated using anthropometry, while dual-energy x-ray absorptiometry was performed to gain an accurate measure of total and area specific body fat. Serum immunoglobulin GN-glycans were analysed by ultra-performance liquid chromatography. Results Twenty-two N-glycan peaks were found to be associated with at least one of the fat measures. While the previous association of body mass index to agalactosylated immunoglobulin G was replicated, measures of central adiposity explained the most variation in the immunoglobulin G glycome. Conclusion Central adiposity is associated with an increased pro-inflammatory fraction of immunoglobulin G, suggesting that the android/gynoid ratio or waist-to-height ratio instead be considered when controlling for adiposity in immunoglobulin G glycome biomarker studies.

Background Alleviating the burden of rare diseases requires research into new diagnostic and therapeutic strategies. We undertook a systematic review to identify and compare the impact of stand-alone registries, registries with biobanks, and rare disease biobanks on research outcomes in rare diseases. Methods A systematic review and meta-aggregation was conducted using the preferred reporting items for systematic reviews and meta-analyses (the PRISMA statement). English language publications were sourced from PubMed, Medline, Scopus, and Web of Science. Original research papers that reported clinical, epidemiological, basic or translational research findings derived from data contained in stand-alone registries, registries with biobanks, and rare disease biobanks were considered. Articles selected for inclusion were assessed using the critical appraisal instruments by JBI-QARI. Each article was read in its entirety and findings were extracted using the online data extraction software from JBI-QARI. Results Thirty studies including 28 rare disease resources were included in the review. Of those, 14 registries were not associated to biobank infrastructure, 9 registries were associated with biobank infrastructure, and 6 were rare disease biobank resources. Stand-alone registries had the capacity to uncover the natural history of disease and contributed to evidence-based practice. When annexed to biobank infrastructure, registries could also identify and validate biomarkers, uncover novel genes, elucidate pathogenesis at the Omics level, and develop new therapeutic strategies. Rare disease biobanks in this review had similar capacity for biological investigations, but in addition, had far greater sample numbers and higher quality laboratory techniques for quality assurance processes. Discussion We examined the research outcomes of three specific populations: stand-alone registries, registries with biobanks, and stand-alone rare disease biobanks and demonstrated that there are key differences among these resources. These differences are a function of the resources’ design, aims, and objectives, with each resource having a distinctive and important role in contributing to the body of knowledge for rare disease research. Whilst stand-alone registries had the capacity to uncover the natural history of disease, develop best practice, replace clinical trials, and improve patient outcomes, they were limited in their capacity to conduct basic research. The role of basic research in rare disease research is vital; scientists must first understand the pathways of disease before they can develop appropriate interventions. Rare disease biobanks, on the other hand (particularly larger biobanks), had the key infrastructure required to conduct basic research, making novel Omics discoveries, identify and validate biomarkers, uncover novel genes, and develop new therapeutic strategies. However, these stand-alone rare disease biobanks did not collect comprehensive data or impact on clinical observations like a rare disease registry. Rare disease research is important not only for rare diseases, but also for also common diseases. For example, research of low-density lipoprotein (LDL)-receptors in the rare disease known as familial hypercholesterolemia led to the discovery of statins, a drug therapy that is now used routinely to prevent heart disease. Conclusions Rare diseases are still under-researched worldwide. This review made the important observation that registries with biobanks had the function of both stand-alone registries (the capacity to collect comprehensive clinical and epidemiological data) and stand-alone rare disease biobanks (the ability to contribute to Omics research). We found registries with biobanks offer a unique, practical, cost-effective, and impactful solution for rare disease research. Linkage of stand-alone registries to rare disease biobanks will provide the appropriate resources required for the effective translation of basic research into clinical practice. Furthermore, facilitators such as collaboration, engagement, blended recruitment, pro-active marketing, broad consent, and “virtual biobank” online catalogues will, if utilised, add to the success of these resources. These important observations can serve to direct future rare diseases research efforts, ultimately improve patient outcomes and alleviate the significant burden associated with rare disease for clinicians, hospitals, society, and most importantly, the patients and their families. Electronic supplementary material The online version of this article (10.1186/s13023-018-0942-z) contains supplementary material, which is available to authorized users.

Hypertension results from the interaction of genetic and acquired factors. IgG occurs in the form of different subclasses, of which the effector functions show significant variation. The detailed differences between the glycosylation profiles of the individual IgG subclasses may be lost in a profiling method for total IgG N-glycosylation. In this study, subclass-specific IgG Fc glycosylation profile was investigated in the four northwestern Chinese minority populations, namely, Uygur (UIG), Kazak (KZK), Kirgiz (KGZ), and Tajik (TJK), composed of 274 hypertensive patients and 356 healthy controls. The results showed that ten directly measured IgG N-glycan traits (i.e., IgG1G0F, IgG2G0F, IgG2G1FN, IgG2G1FS, IgG2G2S, IgG4G0F, IgG4G1FS, IgG4G1S, IgG4G2FS, and IgG4G2N) representing galactosylation and sialylation are significantly associated with hypertension, with IgG4 consistently showing weaker associations of its sialylation, across the four ethnic groups. We observed a modest improvement on the AUC of ROC curve when the IgG Fc N-glycan traits are added into the glycan-based model (difference between AUCs, 0.044, 95% CI: 0.016–0.072, P = 0.002). The AUC of the diagnostic model indicated that the subclass-specific IgG Fc N-glycan profiles provide more information reinforcing current models utilizing age, gender, BMI, and ethnicity, and demonstrate the potential of subclass-specific IgG Fc N-glycosylation profiles to serve as a biomarker for hypertension. Further research is however required to determine the additive value of subclass-specific IgG Fc N-glycosylation on top of biomarkers, which are currently used.

To investigate the association of tag-SNPs and haplotype structures of the CIDEA gene with obesity in a Han Chinese population. Five single nucleotide polymorphisms (SNPs) (rs1154588/V115F, rs4796955/SNP1, rs8092502/SNP2, rs12962340/SNP3 and rs7230480/SNP4) in the CIDEA gene were genotyped in a case–control study. Genotyping was performed using the sequenom matrix-assisted laser desorption/ionization time-of-flight mass spectrometry iPLEX platform. There were significant differences between the obese and control groups in genotype distributions of V115F (P

Multiple factors influence immunoglobulin G glycosylation, which in turn affect the glycoproteins’ function on eliciting an anti- inflammatory or pro-inflammatory response. It is prudent to underscore these processes when considering the use of immunoglobulin G N-glycan moieties as an indication of disease presence, progress, or response to therapeutics. It has been demonstrated that the altered expression of genes that encode enzymes involved in the biosynthesis of immunoglobulin G N-glycans, receptors, or complement factors may significantly modify immunoglobulin G effector response, which is important for regulating the immune system. The immunoglobulin G N-glycome is highly heterogenous ; however, it is considered an interphenotype of disease (a link between genetic predisposition and environmental exposure) and so has the potential to be used as a dynamic biomarker from the perspective of predictive, preventive, and personalised medicine. Undoubtedly, a deeper understanding of how the multiple factors interact with each other to alter immunoglobulin G glycosylation is crucial. Herein we review the current literature on immunoglobulin G glycoprotein structure, immunoglobulin G Fc glycosylation, associated receptors, and complement factors, the downstream effector functions, and the factors associated with the heterogeneity of immunoglobulin G glycosylation.

It is critical to distinguish events that are temporarily associated with, but not caused by, vaccination from those caused by vaccination during mass immunization. We performed a literature search in China National Knowledge Infrastructure and Pubmed databases. The number of coincident events was calculated based on its incidence rate and periods after receipt of a dose of hypothesized vaccine. We included background incidences of Guillain-Barré syndrome, anaphylaxis, seizure, sudden adult death syndrome, sudden cardiac death, spontaneous abortion, and preterm labour or delivery. In a cohort of 10 million individuals, 7.71 cases of Guillain-Barré syndrome would be expected to occur within six weeks of vaccination as coincident background cases. Even for rare events, a large number of events can be expected in a short period because of the large population targeted for immunization. These findings may encourage health authorities to screen the safety of vaccines against unpredictable pathogens.

Background The premise of disease-related phenotypes is the definition of the counterpart normality in medical sciences. Contrary to clinical practices that can be carefully planned according to clinical needs, heterogeneity and uncontrollability is the essence of humans in carrying out health studies. Full characterization of consistent phenotypes that define the general population is the basis to individual difference normalization in personalized medicine. Self-claimed normal status may not represent health because asymptomatic subjects may carry chronic diseases at their early stage, such as cancer, diabetes mellitus and atherosclerosis. Currently, treatments for non-communicable chronic diseases (NCD) are implemented after disease onset, which is a very much delayed approach from the perspective of predictive, preventive and personalized medicine (PPPM). A NCD pandemic will develop and be accompanied by increased global economic burden for healthcare systems throughout both developed and developing countries. This paper examples the characterization of the suboptimal health status (SHS) which represents a new PPPM challenge in a population with ambiguous health complaints such as general weakness, unexplained medical syndrome (UMS), chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME), post-viral fatigue syndrome (PVFS) and chronic fatigue immune dysfunction syndrome (CFIDS). Methods We applied clinical informatic approaches and developed a questionnaire—suboptimal health status questionnaire-25 (SHSQ-25) for measuring SHS. The validity and reliability of this approach were evaluated in a small pilot study and then in a cross-sectional study of 3,405 participants in China. Results We found a correlation between SHS and systolic blood pressure, diastolic blood pressure, plasma glucose, total cholesterol and high-density lipoprotein (HDL) cholesterol among men, and a correlation between SHS and systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides and HDL cholesterol among women. Conclusions The SHSQ-25 is a self-rated questionnaire of perceived health complaints, which can be used as a new instrument for PPPM. An ongoing longitudinal SHS cohort survey (China Sub-optimal Health Cohort Study, COACS) consisting of 50,000 participants will provide a powerful health trial to use SHSQ-25 for its application to PPPM through patient stratification and therapy monitoring using innovative technologies of predictive diagnostics and prognosis: an effort of paradigm shift from reactive to predictive medicine.