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Subclass-specific IgG glycosylation is associated with markers of inflammation and metabolic health


This study indicates that glycosylation of immunoglobulin G, the most abundant antibody in human blood, may convey useful information with regard to inflammation and metabolic health. IgG occurs in the form of different subclasses, of which the effector functions show significant variation. Our method provides subclass-specific IgG glycosylation profiling, while previous large-scale studies neglected to measure IgG2-specific glycosylation. We analysed the plasma Fc glycosylation profiles of IgG1, IgG2 and IgG4 in a cohort of 1826 individuals by liquid chromatography-mass spectrometry. For all subclasses, a low level of galactosylation and sialylation and a high degree of core fucosylation associated with poor metabolic health, i.e. increased inflammation as assessed by C-reactive protein, low serum high-density lipoprotein cholesterol and high triglycerides, which are all known to indicate increased risk of cardiovascular disease. IgG2 consistently showed weaker associations of its galactosylation and sialylation with the metabolic markers, compared to IgG1 and IgG4, while the direction of the associations were overall similar for the different IgG subclasses. These findings demonstrate the potential of IgG glycosylation as a biomarker for inflammation and metabolic health, and further research is required to determine the additive value of IgG glycosylation on top of biomarkers which are currently used.


Glycosylation is known to reflect the physiological state of an organism and changes thereof1. For immunoglobulin G (IgG), which occupies a central role in the immune system, it is known that the conserved N-glycan located at asparagine 297 on the fragment crystallisable (Fc) part of IgG can modulate inflammatory responses: a lack of core fucose, galactose and N-acetylneuraminic (sialic) acid increases the ability of IgG to induce antibody-dependent cell-mediated cytotoxicity (ADCC) in mice2,3,4. Furthermore, glycosylation of IgG is associated with various pathologies. Autoimmune diseases are generally associated with decreased galactosylation and sialylation of IgG Fc, which is a hallmark of the inflammatory state of these pathologies5,6,7,8,9,10. Different types of cancer11,12,13 and viral infections14,15 have been shown to exhibit low galactosylation and sialylation of IgG N-glycans similar to inflammatory conditions. IgG glycosylation, specifically low galactosylation and sialylation, has also been proposed as a biomarker of inflammageing, the state of chronic weak inflammation in elderly individuals16, or more generally as a marker of immune activation17.

Since IgG glycosylation is known to be altered during a state of inflammation, it is not surprising that associations between certain glycoforms and the inflammatory marker C-reactive protein (CRP) have been reported18,19,20,21. Inflammation often goes hand in hand with ageing and poor metabolic health, which are all associated with a higher risk for cardiovascular disease22. However, an overview is lacking of the associations between IgG glycosylation and an assortment of clinical markers related to metabolic health in healthy individuals. Furthermore, other large cohort studies (>100 healthy participants) on IgG glycosylation report either a released glycan profile23,24, which does not contain any subclass-specific information, or a joint profile for IgG2 and IgG325,26. This is due to the shared peptide sequence of the tryptic glycopeptides of these two subclasses, which prevents separate analysis by mass spectrometry, and thereby prevents separate examination of IgG2 glycosylation. From in vitro assays it is known that IgG2 exhibits a lower overall binding affinity to Fc gamma receptors (FcγRs)27,28,29 and ADCC capacity30,31.

In order to evaluate the merit of IgG Fc N-glycosylation as a systemic biomarker of metabolic health, we here analysed glycosylation of IgG1, 2 and 4 in a cohort of 1826 healthy individuals to investigate associations with known markers related to inflammation and metabolic health. The population analysed in this study is part of the Leiden Longevity Study, aimed at determining the biological foundation of healthy ageing. In this study we investigate the glycosylation of IgG in relation to measurements of inflammation, such as plasma levels of C-reactive protein (CRP) and interleukin-6 (IL-6), and of metabolism, such as lipids and thyroid hormone. IgG glycosylation analysis has previously been performed on this cohort with MALDI-MS to facilitate the measurement of 6 N-glycopeptides for IgG1 and IgG2 each, allowing only the assessment of galactosylation and bisection18. The current analysis is performed with nanoLC-ESI-QTOF-MS, which offers enhanced sensitivity and a more extensive glycoprofiling of the samples: 20 N-glycopeptides were identified for IgG1 and IgG2 and 10 for IgG4, providing novel information on the sialylation and fucosylation of these samples.


Cohort participants and study design

This study was performed on 1995 available plasma samples from 1170 offspring of nonagenarian siblings of the Leiden Longevity cohort and 656 of their partners as controls32. Families were included if the parents were over 91 (for females) or over 89 (for males) years of age. Previously we have shown that comparison of the middle-aged offspring of long-lived parents and their partners as controls reveals parameters associating with familial metabolic health, thereby demonstrating that offspring have a beneficial immune-metabolic health33,34,35. An overview of the age and sex distribution of the participants can be seen in Table 1. The prevalence of type 2 diabetes and myocardial infarction was 5.2% and 2.9%, respectively, and prescriptions for lipid-lowering medication and anti-hypertensives were being taken by 11.3% and 21.7% of the individuals in our sample set36. Venous blood was collected under non-fasting conditions. All participants have given informed consent prior to sample collection, in accordance with the Declaration of Helsinki. The study design and protocols were approved by the Ethical Committee of the Leiden University Medical Center. All experiments were carried out in accordance with relevant guidelines and regulations.

Table 1 Distribution of age, sex, glycosylation features and metabolic parameters within the Leiden Longevity study population.

Metabolic parameter analysis

Various parameters related to metabolic health were determined for the participants of the Leiden Longevity cohort. High-sensitivity measurements of CRP, glucose, total cholesterol (TC), high-density lipoprotein cholesterol (HDLC) and triglycerides (TG) in blood samples were performed on the Hitachi Modular P-800, while free triiodothyronine (T3) levels were assessed using a Modular E-170 (both Roche Diagnostics, Almere, the Netherlands)37,38,39. Low-density lipoprotein cholesterol (LDLC) was derived from these measurements using the Friedewald formula (LDLC = TC − HDLC − TG/5)40. If the level of TG exceeded 4.52 mmol/L, LDLC was listed as missing. The level of insulin in blood was assessed with an Immulite 2500 (DPC, Los Angeles, CA)39. Interleukin 6 (IL-6) levels were determined with an enzyme-linked immunosorbent assay (ELISA) (Sanquin Reagents, Amsterdam, The Netherlands). Furthermore, the self-reported current smoking status of the participants was registered. An ELISA was done on serum to determine if the participants had antibodies against cytomegalovirus (PKS Assay, Medac, Wedel, Germany). The distributions of these metabolic parameters within the cohort are listed in Table 1.

IgG glycopeptide sample preparation

IgGs were purified in 96-well format as described previously18. An amount of 2 µL of plasma was incubated with 15 µL Protein A Sepharose Fast Flow beads (GE Healthcare, Uppsala, Sweden) and 150 µL phosphate buffered saline (PBS) on a 96-well filter plate for 1 hour at room temperature while shaking. The IgG samples were washed three times with PBS and three times with MilliQ-purified water, followed by elution with 100 µL 100 mM formic acid. The samples were then dried in a vacuum concentrator for 2 hours at 60 °C and resuspended in 40 µL 25 mM ammonium bicarbonate with 200 ng of trypsin (sequencing grade modified trypsin, Promega, Madison, WI). Digestion took place overnight at 37 °C. Two of the 96-well plates were prepared twice to assess interbatch variation.

NanoLC-ESI-QTOF-MS analysis

The IgG glycopeptide samples were analysed using liquid chromatography coupled to mass spectrometry (LC-MS), in a setup described previously41. An amount of 2.5 µL of the samples was injected in an Ultimate 3000 RSLCnano liquid chromatography system (Dionex, Sunnyvale, CA). The samples were first washed on an Acclaim PepMap100 C18 trap column (5 mm × 300 µm i.d., Dionex, Sunnyvale, CA), and subsequently separated on an Ascentis Express C18 nanoLC column (50 mm × 75 µm i.d., 2.7 µm HALO fused core particles; Supelco, Bellefonte, PA) with a flow rate of 0.9 µL/min. The following linear gradient was used, with solvent A consisting of 0.1% trifluoroacetic acid and B of 95% acetonitrile (ACN): t = 0, 3% solvent B; t = 2, 6%; t = 4.5, 18%; t = 5, 30%; t = 7, 30%; t = 8, 0%; t = 11, 0%.

Via a sheath-flow electrospray (ESI) interface (Agilent Technologies, Santa Clara, CA), the LC was coupled to a Maxis Impact quadrupole time-of-flight (QTOF)-MS system (micrOTOF-Q; Bruker Daltonics, Bremen, Germany). A sheath-flow consisting of 50% isopropanol, 20% propionic acid (Merck) and 30% MilliQ-purified water was applied at 2 µL/min, and nitrogen gas was applied at 4 L/min. MS1 spectra were acquired with a frequency of 0.5 Hz and within an m/z range of 600-2000. An IgG standard and two blank injections were run in between every 12 runs.

Glycosylation data processing

Glycosylation profiles were extracted from the data files as described previously41. The three subclass-specific types of tryptic glycopeptides (IgG1: EEQYNSTYR, IgG2: EEQFNSTFR, IgG4: EEQFNSTYR) eluted at different time points, with the retention times of differentially glycosylated variants of each subclass clustered closely together, since the secondary interaction between sialic acids and the silica column which usually leads to later elution of sialylated glycopeptides was negated by the use of TFA in the mobile phase (Supplemental Figure S1). Based on their mass and previous characterizations of IgG N-glycan structures42,43,44, 20 N-glycans were identified for both IgG1 and IgG2 (Supplemental Table S1). For IgG4, only 10 glycopeptides could be identified, as the mass of the minor afucosylated species overlapped with the much more abundant IgG1 fucosylated glycopeptides, and could thus not be reliably distinguished from tailing of the latter (Supplemental Table S1).

LC-MS data was examined and calibrated in Compass Data Analysis 4.2 (Bruker Daltonics), based on a list of four of the most abundant N-glycopeptides (G0F, G1F, G2F, G2FS) in both double and triple charge state. The data was then exported in mzXML file format. Alignment of the retention times was done using MSalign. Next, the in-house developed software tool Xtractor 2D45 was used to extract the signal intensity of the first three isotopic peaks of various glycopeptides in both double and triple charge state, within an m/z window of ±0.04 Thompson and a time window of ±10 s surrounding the retention time. These N-glycopeptides were inputted from a list of m/z values (Supplemental Table S1) encompassing 20 IgG1 glycopeptides, 20 IgG2 glycopeptides and 10 IgG4 glycopeptides (afucosylated IgG4 species overlapped with IgG1 glycopeptides and thus could not be properly analysed).

For each N-glycopeptide, the signal intensity of the three isotopic peaks in double and triple charge state were background-corrected and summed. IgG2-G1FNS1 (the IgG2 glycan carrying 1 galactose (G1), 1 sialic acid (S1), a core fucose (F) and a bisecting N-acetylglucosamine) and IgG2-G2FNS1, which each represented less than 0.4% of the total IgG2 glycopeptide abundance, were excluded from the data due to a severe batch effect. The N-glycopeptide signals were normalised by dividing each by the total signal intensity of all N-glycopeptides belonging to that subclass, yielding percentage data amounting to 100% per subclass.

An intensity threshold was determined based on the percentage of the signal intensity which was derived from N-glycopeptides with a signal-to-background ratio larger than 3. If the total intensity per subclass did not exceed the threshold, data from that subclass was excluded. After this exclusion, IgG1 data remained for 1825 individuals, IgG2 for 1826 and IgG4 for 1742.

Data preprocessing

From the individual N-glycan percentage data, five glycosylation features were determined for each IgG subclass (calculations can be found in Supplemental Table S2): fucosylation (% of fucosylated N-glycopeptides), bisection (% of N-glycopeptides carrying a bisecting N-acetylglucosamine (GlcNAc)), galactosylation (% of galactoses per antennae), sialylation (% of sialic acids per antennae) and sialic acid per galactose (% of sialic acids per galactose). Fucosylation could not be determined for IgG4, since no afucosylated IgG4 glycopeptides were measured due to overlap with IgG1 glycopeptides.

The three most abundant glycopeptides (G0F, G1F, G2F) of the standard IgG samples run in between every twelve samples showed a relative standard deviation (RSD) of 4.7%. The average intraplate RSD was 1.9%, based on standard IgG samples run during the measurement of each plate, and the interplate RSD was 3.3%. Therefore, batch correction was done using the ComBat package46 in R 3.0.1, with the 96-well plates as batches47. Furthermore, any glycosylation feature value which deviated more than 5 standard deviations (SD) from the mean was replaced by 5 × SD from the mean.

To not violate the assumption of linear regression that variables are normally distributed, we transformed the glycosylation features and several of the metabolic parameters (CRP, IL-6, glucose, insulin, TG, free T3) using a natural logarithm.

Reproducibility of the analysis

Two 96-well plates with samples were purified and analysed twice, to assess the reproducibility of our method. To assess the level of correlation between these sample plates, a Pearson’s correlation test was performed in R using the function cor.test(). The glycosylation features on the different plates showed a high level of correlation, i.e. Pearson’s correlation coefficients (r) of 0.97 (fucosylation), 0.95 (bisection), 0.98 (galactosylation), 0.93 (sialylation) and 0.80 (SA per gal) for IgG1. From the two plates which were prepared twice, the plate which showed the highest signals was used for further data analysis.

Statistical association analysis

Statistical analyses were done in R 3.0.147. A paired t-test from the R stats package was performed to see if there was a significant difference in glycosylation features between different subclasses. Regression analysis was done using the generalised estimating equation package (geepack)48 to account for family relationship. A linear model with formula ‘glycosylation feature Y ~ β1*metabolic parameter X + β2*age + β3*sex + β4*(age*sex) + error’ was fitted to the data, to assess the association between each glycosylation feature and metabolic parameter, while correcting for confounders, taking into account within-family relatedness and under the assumption of an exchangeable correlation structure. The output of this regression analysis consisted of a p 1-value, β1 coefficient and its standard error (SE(β1)) describing the association between each glycosylation feature and metabolic parameter, and from this the t statistic was derived using t = β1/SE(β1). To assess the relation of IgG glycosylation with familial metabolic health, a logistic regression model was applied using the same software, with familial metabolic health as dependent variable Y, coded as 0 for controls and 1 for members of long-lived families, and IgG glycosylation features as independent variable X, while correcting for the same confounders and within-family relatedness, and under the assumption of an independent correlation structure.

Significance was defined as a p-value below the Bonferroni-corrected threshold α = 0.000714 (7.14 × 10−4). This threshold was derived from the standard threshold α = 0.05 divided by 70, which is the number of analyses between 14 variables (metabolic parameters + age + sex) and 5 glycosylation features (subclass-specific versions of the same glycosylation feature were disregarded since they are highly intercorrelated).

Data visualization

Before plotting, glycosylation data was corrected for either age-specific differences or for both age- and sex-specific differences, by fitting the linear model (geepack) ‘glycosylation feature ~ age’ or ‘glycosylation feature ~ age + sex’, and then taking the residuals. The ggplot2 package in R was used for visualization of the data49. A linear trendline was fitted to plots with the geom_smooth function. A heatmap was generated in R with the weighted correlation network analysis (WGCNA) package50.


Tryptic N-glycopeptides of IgG2 and IgG3 share the same peptide sequence in Caucasians51,52, and so the commonly used protein G enrichment of IgG gives a joint profile for both IgG2 and IgG3. In this study we use Protein A for IgG purification, which has been shown to capture IgG1, 2 and 4, but has a much lower binding affinity for IgG3 under the enrichment conditions we use53, thus allowing for near-separate glycoprofiling of IgG2.

By performing nanoLC-ESI-QTOF-MS analysis on tryptic IgG glycopeptides, Fc glycosylation profiles of IgG1, IgG2 and IgG4 were determined for nearly 2000 participants of the Leiden Longevity study. After data curation, IgG1, IgG2 and IgG4 data remained for respectively 1825, 1826 and 1742 participants. From the individual N-glycan data five glycosylation features were derived (the calculations can be found in Supplemental Table S2): fucosylation, bisection, galactosylation, sialylation and sialic acid per galactose (SA per gal). While the type of sialic acid linkage could not be determined by our methods, it is known from literature that the vast majority of sialic acids from plasma-derived Fc-IgG are 2,6-linked54,55,56.

The distribution of the age and sex of the study population, as well as the average measurements of metabolic parameters and IgG glycosylation features, can be found in Table 1. IgG glycosylation was observed to be age-dependent and differences were seen between males and females (Supplemental Figure S2), as has been described in earlier reports26,57,58,59. We observe significant differences between the glycosylation features of the three IgG subclasses: t-tests revealed a p-value below 1.0 × 10−10 for all comparisons of glycosylation features between IgG subclasses (1 vs 2, 1 vs 4 and 2 vs 4). IgG2 exhibits a higher level of fucosylation (IgG1: 91.2 ± 4.1% (mean ± SD); IgG2: 97.4 ± 0.9%; IgG4 fucosylation could not be determined), but a lower level of bisection (IgG1: 19.1 ± 3.3%, IgG2: 13.9 ± 2.7%, IgG4: 19.6 ± 4.4%) and galactosylation (IgG1: 50.1 ± 6.4%, IgG2: 39.5 ± 6.2%, IgG4: 43.7 ± 6.9%) as well as sialylation (IgG1: 6.8 ± 1.4%, IgG2: 6.2 ± 1.4%, IgG4: 9.1 ± 2.0%). It should be noted that galactosylation and sialylation here are defined as the percentage of galactoses/sialic acids per antenna. If defined instead as the percentage of galactoses/sialic acids per N-glycan, the percentages of N-glycans carrying at least one galactose would lie at 72.3 ± 7.0% (IgG1), 59.9 ± 7.6% (IgG2), and 62.6 ± 8.1% (IgG4), while the percentage of N-glyans carrying at least one sialic acid consists of 13.7 ± 2.9% (IgG1), 12.3 ± 2.7% (IgG2), and 18.0 ± 3.9% (IgG4).

Association between IgG glycosylation and immune-metabolic parameters

To investigate the relationship between IgG glycosylation and metabolic health parameters, we performed regression analysis. The resulting associations between IgG glycosylation features and metabolic parameters are visualised in a heatmap (Fig. 1, Supplemental Table S3).

Figure 1

Associations between glycosylation features (y-axis) and metabolic parameters (x-axis). The colors indicate the magnitude of the association as represented by the t statistic (t1 = β1/SE(β1)), with positive associations shown in red and negative in blue. Associations with a p 1-value below 0.05 are marked with a point, while associations with a p 1-value below 7.14 × 10−4, which are significant after Bonferroni correction, are marked with an X.

Inflammation markers

Low galactosylation and high fucosylation of IgG were associated with low-grade inflammation, a state of chronic inflammation characterized by elevated levels of inflammation markers CRP and IL-6, which is associated with morbidity and mortality in elderly people60,61 (Fig. 2) (p 1 < 1.0 × 10−4 for all but IgG1 fucosylation, for which p 1 = 2.0 × 10−3). IgG sialylation was also found to be significantly increased at higher levels of CRP and IL-6 (p 1,IgG1 < 1.0 × 10−4), but sialic acid per galactose was not (p 1,IgG1 = 6.2 × 10−1, p 1,IgG2 = 1.9 × 10−2 for CRP). Notably, CRP only has a small contribution to the variance of IgG galactosylation (R2 = 0.0569 with a linear model for IgG1).

Figure 2

IgG1 galactosylation and fucosylation are associated with CRP. Both parameters were log transformed, and the glycosylation features were subsequently adjusted for age. Males are shown in blue and females in pink. A trend line is shown with a 95% confidence interval. P 1 -values are shown for the total population (males and females).

Metabolic markers

An increase in IgG galactosylation and sialylation (p 1,IgG1 < 1.0 × 10−4), as well as a non-significant decrease in fucosylation (p 1,IgG1 = 5.2 × 10−2; p 1,IgG2 = 1.3 × 10−2) was observed at high levels of HDLC and low levels of triglycerides (TG), which together form beneficial risk profiles for cardiovascular disease, type 2 diabetes and obesity62,63. A negative association of galactosylation and a positive association of fucosylation were seen for insulin and glucose, which are markers for diabetes type 2 and are also known to associate with inflammation64. Interestingly, IgG bisection was increased at high levels of glucose and insulin (p 1,IgG4 = 3.1 × 10−4 for glucose). Total cholesterol (TC) and LDLC did not significantly influence IgG glycosylation.

CMV and smoking

The presence of antibodies against cytomegalovirus (CMV), a common herpes virus which remains latent in many individuals after infection, was found to be associated with low levels of IgG fucosylation. Fucosylation generally shows little variation between individuals (91.2 ± 4.1% for IgG1), so while the difference in IgG1 fucosylation between seronegative and seropositive individuals was minor (91.7 ± 3.7% versus 90.6 ± 4.3%), it was nonetheless statistically significant (p 1,IgG1 < 1.0 × 10−4, Fig. 3A). The other glycosylation features did not show any association with CMV serostatus.

Figure 3

(A) IgG1 fucosylation of individuals with and without cytomegalovirus infection. (B) IgG1 and IgG2 bisection of smokers/non-smokers. The interquartile range is shown. Glycosylation data was log transformed and adjusted for age and sex.

Furthermore, smokers showed slightly higher levels of bisection compared to non-smokers (p 1 < 1.0 × 10−4 for all IgG subclasses, Fig. 3B), as well as a lower level of IgG2 fucosylation. Individuals who smoked at the time of sample collection showed a mean IgG1 bisection of 20.1 ± 3.4% versus 18.9 ± 3.2% in non-smokers.

While these differences appear to be too minor to have a significant biological influence, they might point towards larger differences in subpopulations of IgG, as is theorized in the Discussion.

Familial metabolic health

Since we observed various associations between IgG glycosylation and parameters related to metabolic health, we were interested to know whether metabolically healthier middle-aged offspring from nonagenarians differed from controls. None of the glycosylation features showed, however, a significant difference between these groups (Supplemental Table S4, all p 1 -values > 0.02).


In the current study we were able to measure IgG2-specific glycosylation features, in addition to those of IgG1 and IgG4. IgG2-specific glycosylation features showed similar associations with inflammation and metabolic health markers compared to IgG1 and 4, though IgG2 glycosylation levels showed in general weaker associations for galactosylation and sialylation. Low levels of galactosylation and sialylation, as well as a high degree of fucosylation, appear to reflect an inflammatory state, poor metabolic health and potentially cardiovascular disease risk. In contrast, sialic acid per galactose was not associated with any of the metabolic markers. Furthermore, fucosylation is decreased in individuals infected with cytomegalovirus, reflecting a low-grade inflammatory state, and current smokers show higher bisecting GlcNAc levels.

IgG subclass-specific glycosylation differences

The IgG subclasses display subtle differences in their Fc glycosylation, with IgG2 exhibiting a higher degree of fucosylation and a lower level of bisection and galactosylation compared to the other subclasses, confirming observations from a previous study which examined only four blood samples53. In addition, we find that galactosylation and sialylation of IgG2 generally display a weaker association (i.e. lower β coefficient) with markers of inflammation and metabolic health compared to IgG1 and IgG4. This corresponds with literature, in which it is described that IgG2 exhibits the lowest overall affinity for FcγRs27,28,29 and has the lowest ADCC capacity30 compared to other IgG subclasses. One may speculate that the lower capacity of IgG2 to trigger inflammatory responses, either pro-inflammatory via FcγRI, IIa, IIc, and IIIa, or anti-inflammatory via FcγRIIb, results in a weaker association with inflammation and metabolic health as compared to the other subclasses. Furthermore, it has been reported that IgG2 antibody production is primarily triggered during T-cell independent immune reactions28,31, which would also contribute to the weaker association with T-cell-secreted IL-6 and IL-6-stimulated CRP. Together this points towards a more limited role of IgG2 with regard to inflammation compared to the other IgG subclasses.

Galactosylation and sialylation

Low galactosylation and sialylation are associated with a state of inflammation, which is in concordance with previous work based on MALDI-MS measurements within the same cohort18,65 and in children17. Our observations also agree with observations of decreased galactosylation and sialylation in autoimmune diseases5,6,7,8,9,10,66,67,68 and with several experimental findings that IgG without galactose and/or sialic acid has a higher inflammatory capacity3,4,69.

Levels of sialylation and galactosylation are highly correlated, reflecting the fact that the attachment of a sialic acid requires the presence of a galactose. We find that galactosylation and sialylation exhibit very similar associations with metabolic parameters, but the association with sialylation is generally slightly weaker than with galactosylation. The presence of sialic acid on IgG N-glycans is thought to confer an anti-inflammatory effect3,69. However, we did not observe any significant association with sialic acid per galactose, i.e. the percentage of galactoses which carry a sialic acid, indicating that associations with sialylation may be mediated by its close correlation with galactosylation. This is in line with several previous studies which concluded that galactosylation of human IgG, rather than sialylation, appears to show a pronounced negative association with clinical parameters and infection5,70. Together, this supports the notion that IgG Fc galactosylation may be involved in modulating inflammation.


We find that IgG fucosylation is increased in individuals with a higher level of inflammation. Despite the fact that functional studies have shown that the lack of a core fucose in IgG glycosylation greatly increases the inflammatory capacity through increased binding to FcγRIIIa71,72,73,74, an increase in IgG fucosylation is sometimes observed in autoimmune patients66,67,68. Furthermore, a previous study using UHPLC found that high CRP levels were associated with high fucosylation in non-galactosylated IgG N-glycans75, further demonstrating that IgG fucosylation is increased during a state of inflammation.

In individuals who tested seropositive for cytomegalovirus (CMV), a common virus which can remain in the body in a latent state after infection, a slightly lower level of fucosylation of IgG was observed than in non-infected individuals. While the differences in fucosylation are minor, it should be noted that these are individuals with a latent, asymptomatic CMV infection, and it would be interesting to investigate if a larger decrease in fucosylation is seen during active disease. Furthermore, the changes in fucosylation of total IgG may reflect a larger shift towards afucosylation in the subpopulation of anti-CMV IgG. Low IgG fucosylation has also been observed in other antigen-specific IgG populations – in alloantibody reactions against platelets and red blood cells during pregnancy or blood transfusion (as low as 12%)76 and in anti-HIV antibodies (~75%) – and has been suggested to be a defense mechanism by B-cells to increase antiviral control77. It may be interesting to investigate autoantibodies against CMV, as well as other types of infectious agents, to determine whether low fucosylation of IgG might be a hallmark of viral infections.

Associations with longevity and metabolic health

Though we find that the IgG glycosylation reflects poor metabolic health, we were unable to find significant differences between middle-aged members of long-lived families and controls, who are known to differ on various metabolic measures33,36,38. Previous work which applied MALDI-MS to the same set of samples reported a significant decrease in the level of bisection of offspring of long-lived people, but only in individuals below 60 years of age18, and IgG glycosylation features contributed to the differentiation of controls and members of long-lived families78. A recent investigation into the released N-glycan profile of plasma samples within the Leiden Longevity cohort also could not find any association with longevity, while replicating several of the associations we find with N-glycans which likely originate from IgG65.Our study design does not allow for estimation of whether IgG glycosylation features offer a predictive value for cardiovascular disease in addition to traditional markers of inflammation. However, we did test whether IgG parameters associate with metabolic health markers and which of the subclasses features the strongest association. We show that low levels of galactosylation and sialylation and high levels of fucosylation correspond with a state of low-grade inflammation and a detrimental lipid profile, characterized by low HDLC and high TG. This is likely due to the fact that a chronic level of inflammation is a known risk factor for cardiovascular disease79. This high risk profile is also characteristic for ageing – in older individuals, galactosylation and sialylation decrease, while bisection increases (Supplemental Figure 2).

IgG glycosylation is known to be directly involved in the interaction with Fcγ receptors and complement, modulating the inflammatory capacity of the antibody3,80, whereas CRP influences opsonisation of pathogens and complement activation81. Considering the complexity of the immune system and the multitude of pathways therein, these regulatory mechanisms might well be (partially) additive rather than overlapping. Furthermore, the complexity of IgG glycosylation with its multiple features has the advantage that it could provide more information than the level of a single analyte such as CRP, as shown by the IgG glycosylation features exhibiting different associations with metabolic markers. For these reasons, subclass-specific glycosylation of IgG, independently or in combination with the traditional inflammation marker CRP, might be a more informative biomarker regarding inflammation and metabolic health than CRP on its own. Further studies are required to review the added value of IgG glycosylation as a biomarker in light of prospective data on cardiovascular incidence and morbidity.


  1. 1.

    Varki, A. Biological roles of oligosaccharides: all of the theories are correct. Glycobiology 3, 97–130 (1993).

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Collin, M. & Ehlers, M. The carbohydrate switch between pathogenic and immunosuppressive antigen-specific antibodies. Exp Dermatol 22, 511–4 (2013).

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Kaneko, Y., Nimmerjahn, F. & Ravetch, J. V. Anti-inflammatory activity of immunoglobulin G resulting from Fc sialylation. Science 313, 670–3 (2006).

    ADS  CAS  Article  PubMed  Google Scholar 

  4. 4.

    Karsten, C. M. et al. Anti-inflammatory activity of IgG1 mediated by Fc galactosylation and association of FcgammaRIIB and dectin-1. Nat Med 18, 1401–6 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Bondt, A. et al. Association between galactosylation of immunoglobulin G and improvement of rheumatoid arthritis during pregnancy is independent of sialylation. J Proteome Res 12, 4522–31 (2013).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Matsumoto, A., Shikata, K., Takeuchi, F., Kojima, N. & Mizuochi, T. Autoantibody activity of IgG rheumatoid factor increases with decreasing levels of galactosylation and sialylation. J Biochem 128, 621–8 (2000).

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Holland, M. et al. Differential glycosylation of polyclonal IgG, IgG-Fc and IgG-Fab isolated from the sera of patients with ANCA-associated systemic vasculitis. Biochim Biophys Acta 1760, 669–77 (2006).

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Wuhrer, M. et al. Skewed Fc glycosylation profiles of anti-proteinase 3 immunoglobulin G1 autoantibodies from granulomatosis with polyangiitis patients show low levels of bisection, galactosylation, and sialylation. J Proteome Res 14, 1657–65 (2015).

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Vuckovic, F. et al. Association of systemic lupus erythematosus with decreased immunosuppressive potential of the IgG glycome. Arthritis Rheumatol 67, 2978–89 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Youinou, P. et al. Galactose terminating oligosaccharides of IgG in patients with primary Sjogren’s syndrome. J Autoimmun 5, 393–400 (1992).

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Theodoratou, E. et al. Glycosylation of plasma IgG in colorectal cancer prognosis. Sci Rep 6, 28098 (2016).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Saldova, R., Wormald, M. R., Dwek, R. A. & Rudd, P. M. Glycosylation changes on serum glycoproteins in ovarian cancer may contribute to disease pathogenesis. Dis Markers 25, 219–32 (2008).

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Ren, S. et al. Distribution of IgG galactosylation as a promising biomarker for cancer screening in multiple cancer types. Cell Res 26, 963–6 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Moore, J. S. et al. Increased levels of galactose-deficient IgG in sera of HIV-1-infected individuals. Aids 19, 381–9 (2005).

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Ho, C. H. et al. Aberrant serum immunoglobulin G glycosylation in chronic hepatitis B is associated with histological liver damage and reversible by antiviral therapy. J Infect Dis 211, 115–24 (2015).

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Dall’Oo, F. et al. N-glycomic biomarkers of biological aging and longevity: a link with inflammaging. Ageing Res Rev 12, 685–98 (2013).

    Article  Google Scholar 

  17. 17.

    de Jong, S. E. et al. IgG1 Fc N-glycan galactosylation as a biomarker for immune activation. Sci Rep 6, 28207 (2016).

    ADS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Ruhaak, L. R. et al. Decreased levels of bisecting GlcNAc glycoforms of IgG are associated with human longevity. PLoS One 5, e12566 (2010).

    ADS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Gardinassi, L. G. et al. Clinical severity of visceral leishmaniasis is associated with changes in immunoglobulin g fc N-glycosylation. MBio 5, e01844 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Dube, R. et al. Agalactosyl IgG in inflammatory bowel disease: correlation with C-reactive protein. Gut 31, 431–4 (1990).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Novokmet, M. et al. Changes in IgG and total plasma protein glycomes in acute systemic inflammation. Sci Rep 4, 4347 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Guarner, V. & Rubio-Ruiz, M. E. Low-grade systemic inflammation connects aging, metabolic syndrome and cardiovascular disease. Interdiscip Top Gerontol 40, 99–106 (2015).

    Article  PubMed  Google Scholar 

  23. 23.

    Kristic, J. et al. Glycans are a novel biomarker of chronological and biological ages. J Gerontol A Biol Sci Med Sci 69, 779–89 (2014).

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Pucic, M. et al. High throughput isolation and glycosylation analysis of IgG-variability and heritability of the IgG glycome in three isolated human populations. Mol Cell Proteomics 10, M111.010090 (2011).

  25. 25.

    Lauc, G. et al. Loci associated with N-glycosylation of human immunoglobulin G show pleiotropy with autoimmune diseases and haematological cancers. PLoS Genet 9, e1003225 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Bakovic, M. P. et al. High-throughput IgG Fc N-glycosylation profiling by mass spectrometry of glycopeptides. J Proteome Res 12, 821–31 (2013).

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Canfield, S. M. & Morrison, S. L. The binding affinity of human IgG for its high affinity Fc receptor is determined by multiple amino acids in the CH2 domain and is modulated by the hinge region. J Exp Med 173, 1483–91 (1991).

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Bruhns, P. et al. Specificity and affinity of human Fcgamma receptors and their polymorphic variants for human IgG subclasses. Blood 113, 3716–25 (2009).

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Hogarth, P. M. & Pietersz, G. A. Fc receptor-targeted therapies for the treatment of inflammation, cancer and beyond. Nat Rev Drug Discov 11, 311–31 (2012).

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Michaelsen, T. E., Aase, A., Norderhaug, L. & Sandlie, I. Antibody dependent cell-mediated cytotoxicity induced by chimeric mouse-human IgG subclasses and IgG3 antibodies with altered hinge region. Mol Immunol 29, 319–26 (1992).

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Vidarsson, G., Dekkers, G. & Rispens, T. IgG subclasses and allotypes: from structure to effector functions. Front Immunol 5, 520 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Schoenmaker, M. et al. Evidence of genetic enrichment for exceptional survival using a family approach: the Leiden Longevity Study. Eur J Hum Genet 14, 79–84 (2006).

    PubMed  Google Scholar 

  33. 33.

    Rozing, M. P. et al. Favorable glucose tolerance and lower prevalence of metabolic syndrome in offspring without diabetes mellitus of nonagenarian siblings: the Leiden longevity study. J Am Geriatr Soc 58, 564–9 (2010).

    Article  PubMed  Google Scholar 

  34. 34.

    Postmus, I. et al. LDL cholesterol still a problem in old age? A Mendelian randomization study. Int J Epidemiol 44, 604–12 (2015).

    Article  PubMed  Google Scholar 

  35. 35.

    Derhovanessian, E. et al. Hallmark features of immunosenescence are absent in familial longevity. J Immunol 185, 4618–24 (2010).

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Westendorp, R. G. et al. Nonagenarian siblings and their offspring display lower risk of mortality and morbidity than sporadic nonagenarians: The Leiden Longevity Study. J Am Geriatr Soc 57, 1634–7 (2009).

    Article  PubMed  Google Scholar 

  37. 37.

    Rozing, M. P. et al. C-reactive protein and glucose regulation in familial longevity. Age (Dordr) 33, 623–30 (2011).

    CAS  Article  Google Scholar 

  38. 38.

    Rozing, M. P. et al. Low serum free triiodothyronine levels mark familial longevity: the Leiden Longevity Study. J Gerontol A Biol Sci Med Sci 65, 365–8 (2010).

    Article  PubMed  Google Scholar 

  39. 39.

    Rozing, M. P. et al. Human insulin/IGF-1 and familial longevity at middle age. Aging (Albany NY) 1, 714–22 (2009).

    CAS  Article  Google Scholar 

  40. 40.

    Friedewald, W. T., Levy, R. I. & Fredrickson, D. S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18, 499–502 (1972).

    CAS  PubMed  Google Scholar 

  41. 41.

    Selman, M. H. et al. Fc specific IgG glycosylation profiling by robust nano-reverse phase HPLC-MS using a sheath-flow ESI sprayer interface. J Proteomics 75, 1318–29 (2012).

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Parekh, R. B. et al. Association of rheumatoid arthritis and primary osteoarthritis with changes in the glycosylation pattern of total serum IgG. Nature 316, 452–7 (1985).

    ADS  CAS  Article  PubMed  Google Scholar 

  43. 43.

    Stadlmann, J., Pabst, M., Kolarich, D., Kunert, R. & Altmann, F. Analysis of immunoglobulin glycosylation by LC-ESI-MS of glycopeptides and oligosaccharides. Proteomics 8, 2858–71 (2008).

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Takahashi, N. et al. Comparative structural study of the N-linked oligosaccharides of human normal and pathological immunoglobulin G. Biochemistry 26, 1137–44 (1987).

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Palmblad, M. X. (Creative Commons Attribution, 2009).

  46. 46.

    Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–27 (2007).

    Article  PubMed  MATH  Google Scholar 

  47. 47.

    R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2013).

  48. 48.

    Hojsgaard, S., Halekoh, U. & Yan, J. The R Package geepack for Generalized Estimating Equations. Journal of Statistical Software 15, 1–11 (2006).

    Google Scholar 

  49. 49.

    Wickam, H. ggplot2: elegant graphics for data analysis, (2009).

  50. 50.

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Plomp, R., Bondt, A., de Haan, N., Rombouts, Y. & Wuhrer, M. Recent Advances in Clinical Glycoproteomics of Immunoglobulins (Igs). Mol Cell Proteomics 15, 2217–28 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Dard, P., Lefranc, M. P., Osipova, L. & Sanchez-Mazas, A. DNA sequence variability of IGHG3 alleles associated to the main G3 m haplotypes in human populations. Eur J Hum Genet 9, 765–72 (2001).

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Wuhrer, M. et al. Glycosylation profiling of immunoglobulin G (IgG) subclasses from human serum. Proteomics 7, 4070–81 (2007).

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    de Haan, N. et al. Linkage-specific sialic acid derivatization for MALDI-TOF-MS profiling of IgG glycopeptides. Anal Chem 87, 8284–91 (2015).

    Article  PubMed  Google Scholar 

  55. 55.

    Bondt, A. et al. Immunoglobulin G (IgG) Fab glycosylation analysis using a new mass spectrometric high-throughput profiling method reveals pregnancy-associated changes. Mol Cell Proteomics 13, 3029–39 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Anthony, R. M. et al. Recapitulation of IVIG anti-inflammatory activity with a recombinant IgG Fc. Science 320, 373–6 (2008).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Shikata, K. et al. Structural changes in the oligosaccharide moiety of human IgG with aging. Glycoconj J 15, 683–9 (1998).

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Parekh, R., Roitt, I., Isenberg, D., Dwek, R. & Rademacher, T. Age-related galactosylation of the N-linked oligosaccharides of human serum IgG. J Exp Med 167, 1731–6 (1988).

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    Yamada, E., Tsukamoto, Y., Sasaki, R., Yagyu, K. & Takahashi, N. Structural changes of immunoglobulin G oligosaccharides with age in healthy human serum. Glycoconj J 14, 401–5 (1997).

    CAS  Article  PubMed  Google Scholar 

  60. 60.

    Petersen, A. M. & Pedersen, B. K. The anti-inflammatory effect of exercise. J Appl Physiol (1985) 98, 1154–62 (2005).

    CAS  Article  Google Scholar 

  61. 61.

    Franceschi, C. & Campisi, J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci 69(Suppl 1), S4–9 (2014).

    Article  PubMed  Google Scholar 

  62. 62.

    Rigotti, A. & Krieger, M. Getting a handle on “good” cholesterol with the high-density lipoprotein receptor. N Engl J Med 341, 2011–3 (1999).

    CAS  Article  PubMed  Google Scholar 

  63. 63.

    Smith, S. C. Jr. Multiple risk factors for cardiovascular disease and diabetes mellitus. Am J Med 120, S3–S11 (2007).

    Article  PubMed  Google Scholar 

  64. 64.

    Haffner, S. M. Insulin resistance, inflammation, and the prediabetic state. Am J Cardiol 92, 18–26 (2003).

    Article  Google Scholar 

  65. 65.

    Reiding, K. R. et al. Human plasma N-glycosylation as analyzed by MALDI-FTICR-MS associates with markers of inflammation and metabolic health. Mol Cell Proteomics 16, 228–242 (2016).

    Article  PubMed  Google Scholar 

  66. 66.

    Flogel, M., Lauc, G., Gornik, I. & Macek, B. Fucosylation and galactosylation of IgG heavy chains differ between acute and remission phases of juvenile chronic arthritis. Clin Chem Lab Med 36, 99–102 (1998).

    CAS  Article  PubMed  Google Scholar 

  67. 67.

    Rombouts, Y. et al. Anti-citrullinated protein antibodies acquire a pro-inflammatory Fc glycosylation phenotype prior to the onset of rheumatoid arthritis. Ann Rheum Dis 74, 234–41 (2015).

    CAS  Article  PubMed  Google Scholar 

  68. 68.

    Sjowall, C. et al. Altered glycosylation of complexed native IgG molecules is associated with disease activity of systemic lupus erythematosus. Lupus 24, 569–81 (2015).

    CAS  Article  PubMed  Google Scholar 

  69. 69.

    Scallon, B. J., Tam, S. H., McCarthy, S. G., Cai, A. N. & Raju, T. S. Higher levels of sialylated Fc glycans in immunoglobulin G molecules can adversely impact functionality. Mol Immunol 44, 1524–34 (2007).

    CAS  Article  PubMed  Google Scholar 

  70. 70.

    Kemna, M. J. et al. Galactosylation and Sialylation Levels of IgG Predict Relapse in Patients With PR3-ANCA Associated Vasculitis. EBioMedicine 17, 108–118 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Lu, J. et al. Structure of FcgammaRI in complex with Fc reveals the importance of glycan recognition for high-affinity IgG binding. Proc Natl Acad Sci USA 112, 833–8 (2015).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Niwa, R. et al. IgG subclass-independent improvement of antibody-dependent cellular cytotoxicity by fucose removal from Asn297-linked oligosaccharides. J Immunol Methods 306, 151–60 (2005).

    CAS  Article  PubMed  Google Scholar 

  73. 73.

    Ferrara, C. et al. Unique carbohydrate-carbohydrate interactions are required for high affinity binding between FcgammaRIII and antibodies lacking core fucose. Proc Natl Acad Sci USA 108, 12669–74 (2011).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Shields, R. L. et al. Lack of fucose on human IgG1 N-linked oligosaccharide improves binding to human Fcgamma RIII and antibody-dependent cellular toxicity. J Biol Chem 277, 26733–40 (2002).

    CAS  Article  PubMed  Google Scholar 

  75. 75.

    Menni, C. et al. Glycosylation of immunoglobulin g: role of genetic and epigenetic influences. PLoS One 8, e82558 (2013).

    ADS  Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Sonneveld, M. E., van der Schoot, C. E. & Vidarsson, G. The Elements Steering Pathogenesis in IgG-Mediated Alloimmune Diseases. J Clin Immunol 36(Suppl 1), 76–81 (2016).

    CAS  Article  PubMed  Google Scholar 

  77. 77.

    Ackerman, M. E. et al. Natural variation in Fc glycosylation of HIV-specific antibodies impacts antiviral activity. J Clin Invest 123, 2183–92 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Beekman, M. et al. Classification for Longevity Potential: The Use of Novel Biomarkers. Front Public Health 4, 233 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Koene, R. J., Prizment, A. E., Blaes, A. & Konety, S. H. Shared Risk Factors in Cardiovascular Disease and Cancer. Circulation 133, 1104–14 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Anthony, R. M. & Nimmerjahn, F. The role of differential IgG glycosylation in the interaction of antibodies with FcgammaRs in vivo. Curr Opin Organ Transplant 16, 7–14 (2011).

    CAS  Article  PubMed  Google Scholar 

  81. 81.

    Murphy, K. & Weaver, C. Janeway’s Immunobiology, 924 (Garland Science, New York, 2016).

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This work was supported by the European Union (Seventh Framework Programme HighGlycan project grant number 278535; IDEAL grant number 259679). The Leiden Longevity Study was financially supported by the Netherlands Consortium for Healthy Ageing (grant 050–060–810), in the framework of the Netherlands Genomics Initiative, Netherlands Organization for Scientific Research (NWO); by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007).

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The study was designed by P.E.S., M.W., J.J.H., H.U. and M.B. IgG capturing and LC-MS measurement was done by R.P., L.R.R. and M.S. Data processing was done by R.P. and M.S. Data analysis was carried out by R.P., M.B., H.U. and K.R.R. R.P. wrote the first draft of the manuscript. All authors contributed to interpretation of the data and writing of the manuscript.

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Correspondence to Manfred Wuhrer.

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Plomp, R., Ruhaak, L.R., Uh, HW. et al. Subclass-specific IgG glycosylation is associated with markers of inflammation and metabolic health. Sci Rep 7, 12325 (2017).

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