The Association Between Normal BMI With Central Adiposity And Proinflammatory Potential Immunoglobulin G N-Glycosylation

Regulators of placental antibody transfer through a modeling lens

Infants are vulnerable to infections owing to a limited ability to mount a humoral immune response and their tolerogenic immune phenotype, which has impeded the success of newborn vaccination. Transplacental transfer of IgG from mother to fetus provides crucial protection in the first weeks of life, and maternal immunization has recently been implemented as a public health strategy to protect newborns against serious infections. Despite their early success, current maternal vaccines do not provide comparable protection across pregnancies with varying gestational lengths and placental and maternal immune features, and they do not account for the dynamic interplay between the maternal immune response and placental transfer. Moreover, progress toward the rational design of maternal vaccines has been hindered by inadequacies of existing experimental models and safety challenges of investigating longitudinal dynamics of IgG transfer in pregnant humans. Alternatively, in silico mechanistic models are a logical framework to disentangle the processes regulating placental antibody transfer. This Review synthesizes current literature through a mechanistic modeling lens to identify placental and maternal regulators of antibody transfer, their clinical covariates, and knowledge gaps to guide future research. We also describe opportunities to use integrated modeling and experimental approaches toward the rational design of vaccines against existing and emerging neonatal pathogen threats.

Clinical glycoproteomics: methods and diseases

Glycoproteins, representing a significant proportion of posttranslational products, play pivotal roles in various biological processes, such as signal transduction and immune response. Abnormal glycosylation may lead to structural and functional changes of glycoprotein, which is closely related to the occurrence and development of various diseases. Consequently, exploring protein glycosylation can shed light on the mechanisms behind disease manifestation and pave the way for innovative diagnostic and therapeutic strategies. Nonetheless, the study of clinical glycoproteomics is fraught with challenges due to the low abundance and intricate structures of glycosylation. Recent advancements in mass spectrometry-based clinical glycoproteomics have improved our ability to identify abnormal glycoproteins in clinical samples. In this review, we aim to provide a comprehensive overview of the foundational principles and recent advancements in clinical glycoproteomic methodologies and applications. Furthermore, we discussed the typical characteristics, underlying functions, and mechanisms of glycoproteins in various diseases, such as brain diseases, cardiovascular diseases, cancers, kidney diseases, and metabolic diseases. Additionally, we highlighted potential avenues for future development in clinical glycoproteomics. These insights provided in this review will enhance the comprehension of clinical glycoproteomic methods and diseases and promote the elucidation of pathogenesis and the discovery of novel diagnostic biomarkers and therapeutic targets.

Predicting Biochemical and Physiological Parameters: Deep Learning from IgG Glycome Composition

In immunoglobulin G (IgG), N-glycosylation plays a pivotal role in structure and function. It is often altered in different diseases, suggesting that it could be a promising health biomarker. Studies indicate that IgG glycosylation not only associates with various diseases but also has predictive capabilities. Additionally, changes in IgG glycosylation correlate with physiological and biochemical traits known to reflect overall health state. This study aimed to investigate the power of IgG glycans to predict physiological and biochemical parameters. We developed two models using IgG N-glycan data as an input: a regression model using elastic net and a machine learning model using deep learning. Data were obtained from the Korčula and Vis cohorts. The Korčula cohort data were used to train both models, while the Vis cohort was used exclusively for validation. Our results demonstrated that IgG glycome composition effectively predicts several biochemical and physiological parameters, especially those related to lipid and glucose metabolism and cardiovascular events. Both models performed similarly on the Korčula cohort; however, the deep learning model showed a higher potential for generalization when validated on the Vis cohort. This study reinforces the idea that IgG glycosylation reflects individuals' health state and brings us one step closer to implementing glycan-based diagnostics in personalized medicine. Additionally, it shows that the predictive power of IgG glycans can be used for imputing missing covariate data in deep learning frameworks.

Immunoglobulin G glycosylation and its alterations in aging-related diseases

Immunoglobulin G (IgG) is an important serum glycoprotein and a major component of antibodies. Glycans on IgG affect the binding of IgG to the Fc receptor or complement C1q, which in turn affects the biological activity and biological function of IgG. Altered glycosylation patterns on IgG emerge as important biomarkers in the aging process and age-related diseases. Key aging-related alterations observed in IgG glycosylation include reductions in galactosylation and sialylation, alongside increases in agalactosylation, and bisecting GlcNAc. Understanding the role of IgG glycosylation in aging-related diseases offers insights into disease mechanisms and provides opportunities for the development of diagnostic and therapeutic strategies. This review summarizes five aspects of IgG: an overview of IgG, IgG glycosylation, IgG glycosylation with inflammation mediation, IgG glycan changes with normal aging, as well as the relevance of IgG glycan changes to aging-related diseases. This review provides a reference for further investigation of the regulatory mechanisms of IgG glycosylation in aging-related diseases, as well as for evaluating the potential of IgG glycosylation changes as markers of aging and aging-related diseases.

IgM N-glycosylation correlates with COVID-19 severity and rate of complement deposition

The glycosylation of IgG plays a critical role during human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, activating immune cells and inducing cytokine production. However, the role of IgM N-glycosylation has not been studied during human acute viral infection. The analysis of IgM N-glycosylation from healthy controls and hospitalized coronavirus disease 2019 (COVID-19) patients reveals increased high-mannose and sialylation that correlates with COVID-19 severity. These trends are confirmed within SARS-CoV-2-specific immunoglobulin N-glycan profiles. Moreover, the degree of total IgM mannosylation and sialylation correlate significantly with markers of disease severity. We link the changes of IgM N-glycosylation with the expression of Golgi glycosyltransferases. Lastly, we observe antigen-specific IgM antibody-dependent complement deposition is elevated in severe COVID-19 patients and modulated by exoglycosidase digestion. Taken together, this work links the IgM N-glycosylation with COVID-19 severity and highlights the need to understand IgM glycosylation and downstream immune function during human disease.

Perturbation of 3D nuclear architecture, epigenomic aging and dysregulation, and cannabinoid synaptopathy reconfigures conceptualization of cannabinoid pathophysiology: part 2-Metabolome, immunome, synaptome

The second part of this paper builds upon and expands the epigenomic-aging perspective presented in Part 1 to describe the metabolomic and immunomic bases of the epigenomic-aging changes and then considers in some detail the application of these insights to neurotoxicity, neuronal epigenotoxicity, and synaptopathy. Cannabinoids are well-known to have bidirectional immunomodulatory activities on numerous parts of the immune system. Immune perturbations are well-known to impact the aging process, the epigenome, and intermediate metabolism. Cannabinoids also impact metabolism via many pathways. Metabolism directly impacts immune, genetic, and epigenetic processes. Synaptic activity, synaptic pruning, and, thus, the sculpting of neural circuits are based upon metabolic, immune, and epigenomic networks at the synapse, around the synapse, and in the cell body. Many neuropsychiatric disorders including depression, anxiety, schizophrenia, bipolar affective disorder, and autistic spectrum disorder have been linked with cannabis. Therefore, it is important to consider these features and their complex interrelationships in reaching a comprehensive understanding of cannabinoid dependence. Together these findings indicate that cannabinoid perturbations of the immunome and metabolome are important to consider alongside the well-recognized genomic and epigenomic perturbations and it is important to understand their interdependence and interconnectedness in reaching a comprehensive appreciation of the true nature of cannabinoid pathophysiology. For these reasons, a comprehensive appreciation of cannabinoid pathophysiology necessitates a coordinated multiomics investigation of cannabinoid genome-epigenome-transcriptome-metabolome-immunome, chromatin conformation, and 3D nuclear architecture which therefore form the proper mechanistic underpinning for major new and concerning epidemiological findings relating to cannabis exposure.

IgG glycans in health and disease: Prediction, intervention, prognosis, and therapy

Immunoglobulin (IgG) glycosylation is a complex enzymatically controlled process, essential for the structure and function of IgG. IgG glycome is relatively stable in the state of homeostasis, yet its alterations have been associated with aging, pollution and toxic exposure, as well as various diseases, including autoimmune and inflammatory diseases, cardiometabolic diseases, infectious diseases and cancer. IgG is also an effector molecule directly involved in the inflammation processes included in the pathogenesis of many diseases. Numerous recently published studies support the idea that IgG N-glycosylation fine-tunes the immune response and plays a significant role in chronic inflammation. This makes it a promising novel biomarker of biological age, and a prognostic, diagnostic and treatment evaluation tool. Here we provide an overview of the current state of knowledge regarding the IgG glycosylation in health and disease, and its potential applications in pro-active prevention and monitoring of various health interventions.

Unravelling the genetic causality of immunoglobulin G N-glycans in ischemic stroke

BACKGROUND

Evidence suggests that immunoglobulin G (IgG) N-glycosylation is associated with ischemic stroke (IS). However, the causality of IgG N-glycosylation for IS remains unknown.

METHODS

Two-sample Mendelian randomization (MR) analyses were performed to investigate the potential causal effects of genetically determined IgG N-glycans on IS using publicly available summarized genetic data from East Asian and European populations. Genetic instruments were used as proxies for IgG N-glycan traits. IgG N-glycans were analysed using ultra-performance liquid chromatography. Four complementary MR methods were performed, including the inverse variance weighted method (IVW), MR‒Egger, weighted median and penalized weighted median. Furthermore, to further test the robustness of the results, MR based on Bayesian model averaging (MR-BMA) was then applied to select and prioritize IgG N-glycan traits as risk factors for IS.

RESULTS

After correcting for multiple testing, in two-sample MR analyses, genetically predicted IgG N-glycans were unrelated to IS in both East Asian and European populations, and the results remained consistent and robust in the sensitivity analysis. Moreover, MR-BMA also showed consistent results in both East Asian and European populations.

CONCLUSIONS

Contrary to observational studies, the study did not provide enough genetic evidence to support the causal associations of genetically predicted IgG N-glycan traits and IS, suggesting that N-glycosylation of IgG might not directly involve in the pathogenesis of IS.

IgM N-glycosylation correlates with COVID-19 severity and rate of complement deposition

The glycosylation of IgG plays a critical role during human SARS-CoV-2, activating immune cells and inducing cytokine production. However, the role of IgM N-glycosylation has not been studied during acute viral infection in humans. In vitro evidence suggests that the glycosylation of IgM inhibits T cell proliferation and alters complement activation rates. The analysis of IgM N-glycosylation from healthy controls and hospitalized COVID-19 patients reveals that mannosylation and sialyation levels associate with COVID-19 severity. Specifically, we find increased di- and tri-sialylated glycans and altered mannose glycans in total serum IgM in severe COVID-19 patients when compared to moderate COVID-19 patients. This is in direct contrast with the decrease of sialic acid found on the serum IgG from the same cohorts. Moreover, the degree of mannosylation and sialylation correlated significantly with markers of disease severity: D-dimer, BUN, creatinine, potassium, and early anti-COVID-19 amounts of IgG, IgA, and IgM. Further, IL-16 and IL-18 cytokines showed similar trends with the amount of mannose and sialic acid present on IgM, implicating these cytokines' potential to impact glycosyltransferase expression during IgM production. When examining PBMC mRNA transcripts, we observe a decrease in the expression of Golgi mannosidases that correlates with the overall reduction in mannose processing we detect in the IgM N-glycosylation profile. Importantly, we found that IgM contains alpha-2,3 linked sialic acids in addition to the previously reported alpha-2,6 linkage. We also report that antigen-specific IgM antibody-dependent complement deposition is elevated in severe COVID-19 patients. Taken together, this work links the immunoglobulin M N-glycosylation with COVID-19 severity and highlights the need to understand the connection between IgM glycosylation and downstream immune function during human disease.

Identification and validation of IgG N-glycosylation biomarkers of esophageal carcinoma

Introduction

Altered Immunoglobulin G (IgG) N-glycosylation is associated with aging, inflammation, and diseases status, while its effect on esophageal squamous cell carcinoma (ESCC) remains unknown. As far as we know, this is the first study to explore and validate the association of IgG N-glycosylation and the carcinogenesis progression of ESCC, providing innovative biomarkers for the predictive identification and targeted prevention of ESCC.

Methods

In total, 496 individuals of ESCC (n=114), precancerosis (n=187) and controls (n=195) from the discovery population (n=348) and validation population (n=148) were recruited in the study. IgG N-glycosylation profile was analyzed and an ESCC-related glycan score was composed by a stepwise ordinal logistic model in the discovery population. The receiver operating characteristic (ROC) curve with the bootstrapping procedure was used to assess the performance of the glycan score.

Results

In the discovery population, the adjusted OR of GP20 (digalactosylated monosialylated biantennary with core and antennary fucose), IGP33 (the ratio of all fucosylated monosyalilated and disialylated structures), IGP44 (the proportion of high mannose glycan structures in total neutral IgG glycans), IGP58 (the percentage of all fucosylated structures in total neutral IgG glycans), IGP75 (the incidence of bisecting GlcNAc in all fucosylated digalactosylated structures in total neutral IgG glycans), and the glycan score are 4.03 (95% CI: 3.03-5.36, P<0.001), 0.69 (95% CI: 0.55-0.87, P<0.001), 0.56 (95% CI: 0.45-0.69, P<0.001), 0.52 (95% CI: 0.41-0.65, P<0.001), 7.17 (95% CI: 4.77-10.79, P<0.001), and 2.86 (95% CI: 2.33-3.53, P<0.001), respectively. Individuals in the highest tertile of the glycan score own an increased risk (OR: 11.41), compared with those in the lowest. The average multi-class AUC are 0.822 (95% CI: 0.786-0.849). Findings are verified in the validation population, with an average AUC of 0.807 (95% CI: 0.758-0.864).

Discussion

Our study demonstrated that IgG N-glycans and the proposed glycan score appear to be promising predictive markers for ESCC, contributing to the early prevention of esophageal cancer. From the perspective of biological mechanism, IgG fucosylation and mannosylation might involve in the carcinogenesis progression of ESCC, and provide potential therapeutic targets for personalized interventions of cancer progression.

IgG N-glycan Signatures as Potential Diagnostic and Prognostic Biomarkers

IgG N-glycans are an emerging source of disease-specific biomarkers. Over the last decade, the continued development of glycomic databases and the evolution of glyco-analytic methods have resulted in increased throughput, resolution, and sensitivity. IgG N-glycans promote adaptive immune responses through antibody-dependent cellular cytotoxicity (ADCC) and complement activation to combat infection or cancer and promote autoimmunity. In addition to the functional assays, researchers are examining the ability of protein-specific glycosylation to serve as biomarkers of disease. This literature review demonstrates that IgG N-glycans can discriminate between healthy controls, autoimmune disease, infectious disease, and cancer with high sensitivity. The literature also indicates that the IgG glycosylation patterns vary across disease state, thereby supporting their role as specific biomarkers. In addition, IgG N-glycans can be collected longitudinally from patients to track treatment responses or predict disease reoccurrence. This review focuses on IgG N-glycan profiles applied as diagnostics, cohort discriminators, and prognostics. Recent successes, remaining challenges, and upcoming approaches are critically discussed.

Association of IgG N-glycomics with prevalent and incident type 2 diabetes mellitus from the paradigm of predictive, preventive, and personalized medicine standpoint

Objectives

Type 2 diabetes mellitus (T2DM), a major metabolic disorder, is expanding at a rapidly rising worldwide prevalence and has emerged as one of the most common chronic diseases. Suboptimal health status (SHS) is considered a reversible intermediate state between health and diagnosable disease. We hypothesized that the time frame between the onset of SHS and the clinical manifestation of T2DM is the operational area for the application of reliable risk assessment tools, such as immunoglobulin G (IgG) N-glycans. From the viewpoint of predictive, preventive, and personalized medicine (PPPM/3PM), the early detection of SHS and dynamic monitoring by glycan biomarkers could provide a window of opportunity for targeted prevention and personalized treatment of T2DM.

Methods

Case-control and nested case-control studies were performed and consisted of 138 and 308 participants, respectively. The IgG N-glycan profiles of all plasma samples were detected by an ultra-performance liquid chromatography instrument.

Results

After adjustment for confounders, 22, five, and three IgG N-glycan traits were significantly associated with T2DM in the case-control setting, baseline SHS, and baseline optimal health participants from the nested case-control setting, respectively. Adding the IgG N-glycans to the clinical trait models, the average area under the receiver operating characteristic curves (AUCs) of the combined models based on repeated 400 times fivefold cross-validation differentiating T2DM from healthy individuals were 0.807 in the case-control setting and 0.563, 0.645, and 0.604 in the pooled samples, baseline SHS, and baseline optimal health samples of nested case-control setting, respectively, which presented moderate discriminative ability and were generally better than models with either glycans or clinical features alone.

Conclusions

This study comprehensively illustrated that the observed altered IgG N-glycosylation, i.e., decreased galactosylation and fucosylation/sialylation without bisecting GlcNAc, as well as increased galactosylation and fucosylation/sialylation with bisecting GlcNAc, reflects a pro-inflammatory state of T2DM. SHS is an important window period of early intervention for individuals at risk for T2DM; glycomic biosignatures as dynamic biomarkers have the ability to identify populations at risk for T2DM early, and the combination of evidence could provide suggestive ideas and valuable insight for the PPPM of T2DM.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-022-00311-3.

Aberrant Cellular Glycosylation May Increase the Ability of Influenza Viruses to Escape Host Immune Responses through Modification of the Viral Glycome

Individuals with metabolic dysregulation of cellular glycosylation often experience severe influenza disease, with a poor immune response to the virus and low vaccine efficacy. Here, we investigate the consequences of aberrant cellular glycosylation for the glycome and the biology of influenza virus. We transiently induced aberrant N-linked glycosylation in cultured cells with an oligosaccharyltransferase inhibitor, NGI-1. Cells treated with NGI-1 produced morphologically unaltered viable influenza virus with sequence-neutral glycosylation changes (primarily reduced site occupancy) in the hemagglutinin and neuraminidase proteins. Hemagglutinin with reduced glycan occupancy required a higher concentration of surfactant protein D (an important innate immunity respiratory tract collectin) for inhibition compared to that with normal glycan occupancy. Immunization of mice with NGI-1-treated virus significantly reduced antihemagglutinin and antineuraminidase titers of total serum antibody and reduced hemagglutinin protective antibody responses. Our data suggest that aberrant cellular glycosylation may increase the risk of severe influenza as a result of the increased ability of glycome-modified influenza viruses to evade the immune response.

Impact of Adiposity and Fat Distribution, Rather Than Obesity, on Antibodies as an Illustration of Weight-Loss-Independent Exercise Benefits

Obesity represents a risk factor for a variety of diseases because of its inflammatory component, among other biological patterns. Recently, with the ongoing COVID-19 crisis, a special focus has been put on obesity as a status in which antibody production, among other immune functions, is impaired, which would impact both disease pathogenesis and vaccine efficacy. Within this piece of writing, we illustrate that such patterns would be due to the increased adiposity and fat distribution pattern rather than obesity (as defined by the body mass index) itself. Within this context, we also highlight the importance of the weight-loss-independent effects of exercise.

Variation of IgG N-linked glycosylation profile in diabetic retinopathy

BACKGROUND

The relationship of immunoglobulin G (IgG) glycosylation with diabetes and diabetic nephropathy has been reported, but its role in diabetic retinopathy (DR) remains unclear. We aimed to investigate and validate the association of IgG glycosylation with DR.

METHODS

We analyzed the IgG N-linked glycosylation profile and primarily selected candidate glycans by lasso (least absolute shrinkage and selection operator) regression analysis in the discovery population. The findings were validated in the replication population using a binary logistics model. The association between the significant glycosylation panel and clinical features was illustrated with Spearman's coefficient. The results were confirmed by sensitivity analyses.

RESULTS

Among 16 selected glycan candidates using lasso, two IgG glycans (GP15, GP20) and two derived traits (IGP32, IGP54) were identified and validated to be significantly associated with DR (P < .05), and the combined adjusted odds ratios (ORs) were 0.587, 0.613, 1.970, and 0.593, respectively. The glycosylation panel showed a weak correlation with clinical features, except for age. In addition, the results remained consistent when the subjects with prediabetes were excluded from the controls, and the adjusted ORs were 0.677, 0.738, 1.597, and 0.678 in the whole population. Furthermore, in the 1:3 rematched population, a significant association was observed, apart from GP20.

CONCLUSIONS

The IgG glycosylation profile, reflecting an aging and pro-inflammatory status, was significantly associated with DR. The variation in the IgG glycome deserves more attention in diabetic complications.

Glycosylation of IgG Associates with Hypertension and Type 2 Diabetes Mellitus Comorbidity in the Chinese Muslim Ethnic Minorities and the Han Chinese

Objectives: Hypertension and type 2 diabetes mellitus comorbidity (HDC) is common, which confers a higher risk of cardiovascular disease than the presence of either condition alone. Describing the underlying glycomic changes of immunoglobulin G (IgG) that predispose individuals to HDC may help develop novel protective immune-targeted and anti-inflammatory therapies. Therefore, we investigated glycosylation changes of IgG associated with HDC. Methods: The IgG N-glycan profiles of 883 plasma samples from the three northwestern Chinese Muslim ethnic minorities and the Han Chinese were analyzed by ultra-performance liquid chromatography instrument. Results: We found that 12 and six IgG N-glycan traits showed significant associations with HDC in the Chinese Muslim ethnic minorities and the Han Chinese, respectively, after adjustment for potential confounders and false discovery rate. Adding the IgG N-glycan traits to the baseline models, the area under the receiver operating characteristic curves (AUCs) of the combined models differentiating HDC from hypertension (HTN), type 2 diabetes mellitus (T2DM), and healthy individuals were 0.717, 0.747, and 0.786 in the pooled samples of Chinese Muslim ethnic minorities, and 0.828, 0.689, and 0.901 in the Han Chinese, respectively, showing improved discriminating performance than both the baseline models and the glycan-based models. Conclusion: Altered IgG N-glycan profiles were shown to associate with HDC, suggesting the involvement of inflammatory processes of IgG glycosylation. The alterations of IgG N-glycome, illustrated here for the first time in HDC, demonstrate a biomarker potential, which may shed light on future studies investigating their potential for monitoring or preventing the progression from HTN or T2DM towards HDC.

Associated Factors of Suboptimal Health Status Among Adolescents in China: A Cross-Sectional Study

PURPOSE

Suboptimal health status (SHS) is a state between health and disease, has several adverse effects, although, its main underlying mechanism is still unclear. This study aimed to investigate SHS and its associated factors of adolescents.

METHODS

A community-based cross-sectional study was conducted in the three different geographic locations of China (Shanxi, Guangzhou, and Tibet). A multidimensional sub-health questionnaire of adolescent (MSQA) is used to evaluate SHS. Independent two-sample K-S test was performed for the quantitative data as the non-parametric test, whereas Chi-square test method was applied to explore the difference of discrete variables data between groups. Then finally, multiple logistic regression analysis was applied to analyze the influential factors of SHS.

RESULTS

Among 1461 respondents (between 15 and 18 years old), females proportion (56.47%) was higher than males (43.53%) where SHS was higher in Shanxi followed by Tibet and then Guangdong. The rural area, grade, lack of sleep time, home visit in a week, lack of exercise, a heavy burden of study, smoking, drinking, and fewer friends were the risk factors of SHS, while families living status, seeking help and extroversion were the protective factors.

CONCLUSION

SHS is significantly associated with behavior and lifestyle-related factors. For comprehensively prevention and control of the SHS, it is urgently needed to reduce the risk factors and enhance the protective factors among adolescents.

COVID-19 and Preparing Planetary Health for Future Ecological Crises: Hopes from Glycomics for Vaccine Innovation

A key lesson emerging from COVID-19 is that pandemic proofing planetary health against future ecological crises calls for systems science and preventive medicine innovations. With greater proximity of the human and animal natural habitats in the 21st century, it is also noteworthy that zoonotic infections such as COVID-19 that jump from animals to humans are increasingly plausible in the coming decades. In this context, glycomics technologies and the third alphabet of life, the sugar code, offer veritable prospects to move omics systems science from discovery to diverse applications of relevance to global public health and preventive medicine. In this expert review, we discuss the science of glycomics, its importance in vaccine development, and the recent progress toward discoveries on the sugar code that can help prevent future infectious outbreaks that are looming on the horizon in the 21st century. Glycomics offers veritable prospects to boost planetary health, not to mention the global scientific capacity for vaccine innovation against novel and existing infectious agents.

Association of dementia with immunoglobulin G N-glycans in a Chinese Han Population

Immunoglobulin G (IgG) functionality can drastically change from anti- to proinflammatory by alterations in the IgG N-glycan patterns. Our previous studies have demonstrated that IgG N-glycans associated with the risk factors of dementia, such as aging, dyslipidemia, type 2 diabetes mellitus, hypertension, and ischemic stroke. Therefore, the aim is to investigate whether the effects of IgG N-glycan profiles on dementia exists in a Chinese Han population. A case–control study, including 81 patients with dementia, 81 age- and gender-matched controls with normal cognitive functioning (NC) and 108 non-matched controls with mild cognitive impairment (MCI) was performed. Plasma IgG N-glycans were separated by ultra-performance liquid chromatography. Fourteen glycan peaks reflecting decreased of sialylation and core fucosylation, and increased bisecting N-acetylglucosamine (GlcNAc) N-glycan structures were of statistically significant differences between dementia and NC groups after controlling for confounders (p < 0.05; q < 0.05). Similarly, the differences for these 14 initial glycans were statistically significant between AD and NC groups after adjusting for the effects of confounders (p < 0.05; q < 0.05). The area under the receiver operating curve (AUC) value of the model consisting of GP8, GP9, and GP14 was determined to distinguish dementia from NC group as 0.876 [95% confidence interval (CI): 0.815–0.923] and distinguish AD from NC group as 0.887 (95% CI: 0.819–0.936). Patients with dementia were of an elevated proinflammatory activity via the significant changes of IgG glycome. Therefore, IgG N-glycans might contribute to be potential novel biomarkers for the neurodegenerative process risk assessment of dementia.

Glycosylation Profiling as a Biomarker of Suboptimal Health Status for Chronic Disease Stratification

WHO defines health as "a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity." We coined and defined suboptimal health status (SHS) as a subclinical, reversible stage of the pre-chronic disease. SHS is a physical state between health and disease, characterized by health complaints, general weakness, chronic fatigue, and low energy levels. We have developed an instrument to measure SHS, Suboptimal Health Status Questionnaire-25 (SHSQ-25), a self-reported survey assessing five health components that has been validated in various ethnical populations. Our studies suggest that SHS is associated with the major components of cardiovascular health and the early onset of metabolic diseases. Besides subjective measure of health (SHS), glycans are conceived as objective biomarkers of SHS. Glycans are complex and branching carbohydrate moieties attached to proteins, participating in inflammatory regulation and chronic disease pathogenesis. We have been investigating the role of glycans and SHS in multiple cardiometabolic diseases in different ethnical populations (African, Chinese, and Caucasian). Here we present case studies to prove that a combination of subjective health measure (SHS) with objective health measure (glycans) represents a window of opportunity to halt or reverse the progression of chronic diseases.

Immunoglobulin G Glycosylation Changes in Aging and Other Inflammatory Conditions

Among the multiple roles played by protein glycosylation, the fine regulation of biological interactions is one of the most important. The asparagine 297 (Asn₂₉₇) of IgG heavy chains is decorated by a diantennary glycan bearing a number of galactose and sialic acid residues on the branches ranging from 0 to 2. In addition, the structure can present core-linked fucose and/or a bisecting GlcNAc. In many inflammatory and autoimmune conditions, as well as in metabolic, cardiovascular, infectious, and neoplastic diseases, the IgG Asn₂₉₇-linked glycan becomes less sialylated and less galactosylated, leading to increased expression of glycans terminating with GlcNAc. These conditions alter also the presence of core-fucose and bisecting GlcNAc. Importantly, similar glycomic alterations are observed in aging. The common condition, shared by the above-mentioned pathological conditions and aging, is a low-grade, chronic, asymptomatic inflammatory state which, in the case of aging, is known as inflammaging. Glycomic alterations associated with inflammatory diseases often precede disease onset and follow remission. The aberrantly glycosylated IgG glycans associated with inflammation and aging can sustain inflammation through different mechanisms, fueling a vicious loop. These include complement activation, Fcγ receptor binding, binding to lectin receptors on antigen-presenting cells, and autoantibody reactivity. The complex molecular bases of the glycomic changes associated with inflammation and aging are still poorly understood.

Omics Biomarkers in Obesity: Novel Etiological Insights and Targets for Precision Prevention

PURPOSE OF REVIEW

Omics-based technologies were suggested to provide an advanced understanding of obesity etiology and its metabolic consequences. This review highlights the recent developments in "omics"-based research aimed to identify obesity-related biomarkers.

RECENT FINDINGS

Recent advances in obesity and metabolism research increasingly rely on new technologies to identify mechanisms in the development of obesity using various "omics" platforms. Genetic and epigenetic biomarkers that translate into changes in transcriptome, proteome, and metabolome could serve as targets for obesity prevention. Despite a number of promising candidate biomarkers, there is an increased demand for larger prospective cohort studies to validate findings and determine biomarker reproducibility before they can find applications in primary care and public health. "Omics" biomarkers have advanced our knowledge on the etiology of obesity and its links with chronic diseases. They bring substantial promise in identifying effective public health strategies that pave the way towards patient stratification and precision prevention.