Observational studies revealed changes in Immunoglobulin G (IgG) N-glycosylation during the aging process. However, it lacks causal insights and remains unclear in which direction causal relationships exist. The two-sample bidirectional Mendelian randomization (MR) design was adopted to explore causal associations between IgG N-glycans and the senescence-associated secretory phenotype (SASP). Inverse variance weighted (IVW) and Wald ratio methods were used as the main analyses, supplemented by sensitivity analyses. Forward MR analyses revealed causal associations between the glycan peak (GP) and SASP, including GP6 (odds ratio [OR] = 0.428, 95% confidence interval [CI] = 0.189-0.969) and GP17 (OR = 0.709, 95%CI = 0.504-0.995) with growth/differentiation factor 15 (GDF15), GP19 with an advanced glycosylation end-product-specific receptor (RAGE) (OR = 2.142, 95%  CI  = 1.384-3.316), and GP15 with matrix metalloproteinase 2 (MMP2) (OR = 1.136, 95%  CI =1.008-1.282). The reverse MR indicated that genetic liability to RAGE was associated with increased levels of GP17 (OR = 1.125, 95%  CI  = 1.003-1.261) and GP24 (OR = 1.222, 95%  CI  = 1.046-1.428), while pulmonary and activation-regulated chemokines (PARC) exhibited causal associations with GP10 (OR = 1.269, 95%  CI  = 1.048-1.537) and GP15 (OR = 1.297, 95%  CI = 1.072-1.570). The findings provided suggested evidence on the bidirectional causality between IgG N-glycans and SASP, which might reveal potential regulatory mechanisms.

Ageing is a complex biological process with variations among individuals, leading to the development of ageing clocks to estimate biological age. Glycans, particularly in immunoglobulin G (IgG), have emerged as potential biomarkers of ageing, with changes in glycosylation patterns correlating with chronological age.For precision analysis, three different plasma pools were analysed over 26 days in tetraplicates, 312 samples in total. In short-term variability analysis, two cohorts were analysed: AstraZeneca MFO cohort of 26 healthy individuals (median age 20) and a cohort of 70 premenopausal Chinese women (median age 22.5) cohort monitored over 3 months. Long-term variability analysis involved two adult men aged 47 and 57, monitored for 5 and 10 years, respectively. Samples were collected every 3 months and 3 weeks, respectively. IgG N-glycan analysis followed a standardized approach by isolating IgG, its subsequent denaturation and deglycosylation followed by glycan cleanup and labelling. Capillary gel electrophoresis with laser-induced fluorescence (CGE-LIF) and ultra-performance liquid chromatography analyses were employed for glycan profiling. Statistical analysis involved normalization, batch correction, and linear mixed models to assess time effects on derived glycan traits.The intermediate precision results consistently exhibited very low coefficient of variation values across all three test samples. This consistent pattern underscores the high level of precision inherent in the CGE method for analysing the glycan clock of ageing. The AstraZeneca MFO cohort did not show any statistically significant trends, whereas the menstrual cycle cohort exhibited statistically significant trends in digalactosylated (G2), agalactosylated (G0) and fucosylation (F). These trends were attributed to the effects of the menstrual cycle. Long-term stability analysis identified enduring age-related trends in both subjects, showing a positive time effect in G0 and bisected N-acetylglucosamine, as well as a negative time effect in G2 and sialylation, aligning with earlier findings. Time effects measured for monogalactosylation, and F remained substantially lower than ones observed for other traits.The study found that IgG N-glycome analysis using CGE-LIF exhibited remarkably high intermediate precision. Moreover, the study highlights the short- and long-term stability of IgG glycome composition, coupled with a notable capacity to adapt and respond to physiological changes and environmental influences such as hormonal changes, disease, and interventions. The discoveries from this study propel personalized medicine forward by deepening our understanding of how IgG glycome relates to age-related health concerns. This study underscores the reliability of glycans as a biomarker for tracking age-related changes and individual health paths.

BACKGROUND: Immunoglobulin G (IgG) N-glycosylation is considered a potential biomarker for aging and various pathological conditions. However, whether these changes in IgG N-glycosylation are a consequence or a contributor to the aging process remains unclear. This study aims to investigate the causality between IgG N-glycosylation and aging using Mendelian randomization (MR) analysis.
METHODS: We utilized genetic variants associated with IgG N-glycosylation traits, the frailty index (FI), and leukocyte telomere length (LTL) from a previous genome-wide association study (GWAS) on individuals of European ancestry. Two-sample and multivariable MR analyses were conducted, employing the inverse-variance weighted (IVW) method. Sensitivity analyses were performed to assess potential confounding factors.
RESULTS: Using the IVW method, we found suggestive evidence of a causal association between GP14 and FI (β 0.026, 95% CI 0.003 to 0.050, p = 0.027) and LTL (β -0.020, 95% CI -0.037 to -0.002, p = 0.029) in the two-sample MR analysis. In the multivariable MR analysis, suggestive evidence was found for GP23 and FI (β -0.119, 95% CI -0.219 to -0.019, p = 0.019) and GP2 and LTL (β 0.140, 95% CI 0.020 to 0.260, p = 0.023).
CONCLUSIONS: In conclusion, our results supported a potentially causal effect of lower GP23 levels on an advanced aging state. Additional verification is required to further substantiate the causal relationship between glycosylation and aging.

The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.
We conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran\'s Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.
We found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.
Our study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels.

We previously demonstrated that N-glycosylation of plasma proteins and IgGs is different in children with recent-onset type 1 diabetes compared with their healthy siblings. To search for genetic variants contributing to these changes, we undertook a genetic association study of the plasma protein and IgG N-glycome in type 1 diabetes.
A total of 1105 recent-onset type 1 diabetes patients from the Danish Registry of Childhood and Adolescent Diabetes were genotyped at 183,546 genetic markers, testing these for genetic association with variable levels of 24 IgG and 39 plasma protein N-glycan traits. In the follow-up study, significant associations were validated in 455 samples.
This study confirmed previously known plasma protein and/or IgG N-glycosylation loci (candidate genes MGAT3, MGAT5 and ST6GAL1, encoding beta-1,4-mannosyl-glycoprotein 4-beta-N-acetylglucosaminyltransferase, alpha-1,6-mannosylglycoprotein 6-beta-N-acetylglucosaminyltransferase and ST6 beta-galactoside alpha-2,6-sialyltransferase 1 gene, respectively) and identified novel associations that were not previously reported for the general European population. First, novel genetic associations of IgG-bound glycans were found with SNPs on chromosome 22 residing in two genomic intervals close to candidate gene MGAT3; these include core fucosylated digalactosylated disialylated IgG N-glycan with bisecting N-acetylglucosamine (GlcNAc) (pdiscovery=7.65 × 10-12, preplication=8.33 × 10-6 for the top associated SNP rs5757680) and core fucosylated digalactosylated glycan with bisecting GlcNAc (pdiscovery=2.88 × 10-10, preplication=3.03 × 10-3 for the top associated SNP rs137702). The most significant genetic associations of IgG-bound glycans were those with MGAT3. Second, two SNPs in high linkage disequilibrium (missense rs1047286 and synonymous rs2230203) located on chromosome 19 within the protein coding region of the complement C3 gene (C3) showed association with the oligomannose plasma protein N-glycan (pdiscovery=2.43 × 10-11, preplication=8.66 × 10-4 for the top associated SNP rs1047286).
This study identified novel genetic associations driving the distinct N-glycosylation of plasma proteins and IgGs identified previously at type 1 diabetes onset. Our results highlight the importance of further exploring the potential role of N-glycosylation and its influence on complement activation and type 1 diabetes susceptibility.

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

Previous prospective studies highlighted aberrant immunoglobulin G (IgG) N-glycosylation as a risk factor for dementia [such as Alzheimer\'s disease (AD) and vascular dementia (VaD)]. It is unclear whether this association is causal or explained by confounding or reverse causation.
The aim is to examine the association of genetically predicted IgG N-glycosylation with dementia using 2-sample Mendelian randomization (MR).
Independent genetic variants for IgG N-glycosylation traits were selected as instrument variables from published genome-wide association studies (GWAS) among individuals of European ancestry. We extracted their corresponding summary statistics from large-scale clinically diagnosed AD GWAS dataset and FinnGen biobank VaD GWAS dataset. The inverse variance weighted (IVW) was performed to calculate the effect estimates. Meanwhile, multiple sensitivity analyses were used to assess horizontal pleiotropy and outliers.
There were no associations of genetically predicted IgG N-glycosylation traits with the risk of AD and VaD using the IVW method (all Bonferroni corrected p > 0.0013). These estimates of four additional sensitivity analyses methods were consistent with the IVW estimates in terms of direction and magnitude. Additionally, the MR-PRESSO global test and the intercept of MR-Egger regression indicated no evidence of horizontal pleiotropy. Meanwhile, the heterogeneity test showed no significant heterogeneity using the Cochran Q statistic. The leave-one-out sensitivity analyses also did not detect any significant change.
Our MR study did not support evidence for the hypothesis that IgG N-glycosylation level may be causally associated with the risk of dementia.

UNASSIGNED: Suboptimal health status (SHS), a reversible borderline condition between optimal health status and disease, has been recognized as a main risk factor for non-communicable diseases (NCDs). From the standpoint of predictive, preventive, and personalized medicine (PPPM/3PM), the early detection of SHS provides a window of opportunity for targeted prevention and personalized treatment of NCDs. Considering that immunoglobulin G (IgG) N-glycosylation levels are associated with NCDs, it can be speculated that IgG N-glycomic alteration might occur at the SHS stage.
UNASSIGNED: A case-control study was performed and it consisted of 124 SHS individuals and 124 age-, gender-, and body mass index-matched healthy controls. The IgG N-glycan profiles of 248 plasma samples were analyzed by ultra-performance liquid chromatography instrument.
UNASSIGNED: After adjustment for potential confounders (i.e., age, levels of education, physical activity, family income, depression score, fasting plasma glucose, and low-density lipoprotein cholesterol), SHS was significantly associated with 16 IgG N-glycan traits at 5% false discovery rate, reflecting decreased galactosylation and fucosylation with bisecting GlcNAc, as well as increased agalactosylation and fucosylation without bisecting GlcNAc. Canonical correlation analysis showed that glycan peak (GP) 20, GP9, and GP12 tended to be significantly associated with the 5 domains (fatigue, the cardiovascular system, the digestive system, the immune system, and mental status) of SHS. The logistic regression model including IgG N-glycans was of moderate performance in tenfold cross-validation, achieving an average area under the receiver operating characteristic curves of 0.703 (95% confidence interval: 0.637-0.768).
UNASSIGNED: The present findings indicated that SHS-related alteration of IgG N-glycans could be identified at the early onset of SHS, suggesting that IgG N-glycan profiles might be potential biomarker of SHS. The altered SHS-related IgG N-glycans are instrumental for SHS management, which could provide a window opportunity for PPPM in advanced treatment of NCDs and shed light on future studies investigating the pathogenesis of progression from SHS to NCDs.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13167-022-00278-1.

UNASSIGNED: Migraine is a common neurological disease, but its pathogenesis is still unclear. Previous studies suggested that migraine was related to immunoglobulin G (IgG). We intended to analyze the immune characteristics of migraine from the perspective of IgG glycosylation and provide theoretical assistance for exploring its pathogenesis.
UNASSIGNED: The differences in the serum level of IgG glycosylation and glycopeptides between patients with episodic migraine and healthy controls were analyzed by applying the poly(glycerol methacrylate)@chitosan (PGMA@CS) nanomaterial in combination with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). We constructed a binary classification model with a feedforward neural network using PyTorch 1.6.0 in Python 3.8.3 to classify the episodic migraine and healthy control groups.
UNASSIGNED: Twenty patients with migraine and 20 healthy controls were enrolled and the blood samples and clinical information were collected. Forty-nine IgG N-glycopeptides were detected in the serum of the subjects. The serum level of N-glycopeptide IgG1 G0-NF (p = 0.012) was increased in patients with migraine. The serum level of N-glycopeptide IgG3/4 G2FS (p = 0.041) was decreased in patients with migraine with family history of headache. It was found that the serum level of the IgG1 G1 (p = 0.004) and IgG2 G0 (p = 0.045) was increased in patients with migraine with aura, while the serum level of IgG2 G0N (p = 0.043) in patients with migraine with aura was significantly lower than that in patients with migraine without aura. In addition, a linear feedforward neural network (FFNN) was used to construct a binary classification model by detected IgG N-glycopeptides. The area under the curve (AUC) value of the binary classification model, which was constructed with 7 IgG N-glycopeptides, was 0.857, suggesting a good prediction performance. Among these IgG N-glycopeptides that were constructed the model, IgG1 G0-NF was overlapped with the differential IgG N-glycopeptide between patients with migraine and healthy controls detected with MALDI-TOF-MS.
UNASSIGNED: Our results indicated that the serum level of N-glycopeptides IgG1 G0-NF might be one of the important biomarkers for the diagnosis of migraine. To the best of our knowledge, this is the first study about the changes of IgG N-glycosylation in patients with migraine by the method of MALDI-TOF-MS. The results indicated a relationship between the migraine and immune response.

UNASSIGNED: Novel biomarkers are urgently needed to distinguish between benign and malignant thyroid nodules and detect thyroid cancer in the early stage. The associations between serum IgG N-glycosylation and thyroid cancer risk have been revealed. We aimed to explore the potential of IgG N-glycan traits as biomarkers in the differential diagnosis of thyroid cancer.
UNASSIGNED: Plasma IgG N-glycome analysis was applied to a discovery cohort followed by independent validation. IgG N-glycan profiles were obtained using a robust quantitative strategy based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. IgG N-glycans were relatively quantified, and classification performance was evaluated based on directly detected and derived glycan traits.
UNASSIGNED: Four directly detected glycans were significantly changed in thyroid cancer patients compared to that in non-cancer controls. Derived glycan traits and a classification glycol-panel were generated based on the directly detected glycan traits. In the discovery cohort, derived trait BN (bisecting type neutral N-glycans) and the glyco-panel showed potential in distinguishing between thyroid cancer and non-cancer controls with AUCs of 0.920 and 0.917, respectively. The diagnostic potential was further validated. Derived trait BN and the glycol-panel displayed \"accurate\" performance (AUC>0.8) in discriminating thyroid cancer from benign thyroid nodules and healthy controls in the validation cohort. Meanwhile, derived trait BN and the glycol-panel also showed diagnostic potential in detecting early-stage thyroid cancer.
UNASSIGNED: IgG N-glycome analysis revealed N-glycomic differences that allow classification of thyroid cancer from non-cancer controls. Our results suggested that derived trait BN and the classification glyco-panel rather than single N-glycans may serve as candidate biomarkers for further validation.