Hyperuricemia is Associated with Immunoglobulin G N -Glycosylation: A Community-Based Study of Glycan Biomarkers

Abstract

The increasing prevalence of hyperuricemia has been recognized as an emerging public health concern in both developed and developing countries. Hyperuricemia is a metabolic condition characterized by an elevated serum uric acid, and associated with renal damage, diabetes, autoimmune disorders, and cardiovascular diseases. Although human genetic variation has been recognized as a factor, posttranslational cellular processes and glycan biomarkers have not been studied extensively for susceptibility to hyperuricemia. We evaluated whether immunoglobulin (Ig)G N-glycans play a role in hyperuricemia in the general population. This cross-sectional study enrolled 635 participants (208 men and 427 women), ages ≥18 years, from a community-based population in Beijing, China. The IgG N-glycan composition of serum was analyzed by an ultraperformance liquid chromatography method. The prevalence of hyperuricemia observed in this sample was 5.98% (14.9% in men and 1.6% in women). Serum uric acid level was positively correlated with glycan peaks (GP)1, GP2, GP4, GP6, GP10, and GP11, whereas it was negatively correlated with GP12, GP13, GP14, GP15, GP18, and GP20. The combination of GP9, GP10, body mass index, and gender distinguished individuals with hyperuricemia from subjects without hyperuricemia, with an area under the curve value of 0.849 (95% confidence interval: 0.784–0.915). These findings collectively suggest a possible link between hyperuricemia and IgG N-glycans, which might be potentially mediated through inflammation-related mechanisms. Additional research on glycan biomarkers in independent and community-based population samples might allow the development of glycan diagnostics for hyperuricemia and gout in the future.

Introduction

Hyperuricemia is a metabolic disease with an elevated serum uric acid, resulting from an increased uric acid formation, or reduced renal uric acid excretion, or a combination of both processes. Hyperuricemia not only leads to gout but also paves the way for potential renal damage, diabetes, autoimmune disorders, and cardiovascular diseases (Borghi et al., 2015; Dai et al., 2013; Fang and Alderman, 2000; Feig et al., 2008; Keenan et al., 2012; Sluijs et al., 2013; Xu et al., 2017). Along with the high risk of hyperuricemia worldwide (Kim et al., 2018), its prevalence in China has been increasing rapidly, reported from 1.4% to 13.3%, over the past three decades (Chen et al., 1998; Liu et al., 2014, 2015). Hence, the discovery of etiology, determinants, and indicators of hyperuricemia is urgent for handling the burden of hyperuricemia and associated diseases.

Genetics and dietary factors have been recognized as susceptible elements that contribute enormously to hyperuricemia (Kim et al., 2018; Qiu et al., 2013). On the contrary, posttranslational cellular processes and glycan biomarkers have not been studied extensively for susceptibility to hyperuricemia. Glycosylation is a complex posttranslational modification during gene expression, involved in more than half of all mammalian proteins (Arnold et al., 2007). Glycan binding in functional proteins plays important roles in the biological processes of molecular recognition and adhesion, cell signaling, and immunological response (Molinari, 2007). Protein glycosylation may vary between subjects in different conditions, but N-glycan profiles have been shown to be remarkably stable for a specific organism in a period of time (Gornik et al., 2009).

Researches on glycan biomarkers have given rise to prospects for glycan diagnostics for common and rare human diseases and provided new vistas on disease pathogenesis.

N-glycans also bind immunoglobulin (Ig)G, one of the large number of plasma glycoproteins, at asparagine 297 in the C_H2 domains of the fragment crystallization (Fc) region (Krištić et al., 2018; Liu et al., 2018). These N-glycans are found to widely modulate anti-inflammatory and proinflammatory functions of IgG and influence the development of diseases (e.g., hypertension, systemic lupus erythematosus, rheumatoid arthritis, dyslipidemia, diabetes, inflammatory bowel disease, Alzheimer's disease, and Parkinson's disease) (Bermingham et al., 2018; Liu et al., 2018; Wang et al., 2016). However, the relationship between hyperuricemia and IgG N-glycans has not been investigated from the aspect of epidemiological analysis.

In the present study, we evaluated whether IgG N-glycans play a role in hyperuricemia in the general population. This cross-sectional study enrolled 635 participants (208 men and 427 women), ages ≥18 years, from a community-based population in Beijing, China.

Materials and Methods

Participants

A community-based sample of the Chinese population was recruited at Xicheng District in Beijing, from January to April of 2012. The subjects who met the following criteria were enrolled: (1) age ≥18 years, (2) no history of somatic and psychiatric abnormalities, and (3) no medication in the past 2 weeks. Participants with the following diseases were excluded: cardiovascular disease, respiratory disease, genitourinary disease, digestive disease, and hematic disease. This study was approved by the Ethics Committee of the Capital Medical University, Beijing, China. The study carried out on humans was in compliance with the Declaration of Helsinki (World Medical Association General Assembly, 2004). Written informed consents were obtained from all participants before the commencement of the study.

Data collection and measurements

Demographic characteristics (e.g., gender, age, and ethnicity) as well as history of medications were obtained using a questionnaire. Anthropometric parameters, including weight, height, waist circumference, hip circumference, body mass index (BMI), and blood pressure (BP), were measured as reported previously (Wang et al., 2016). Hypertension was defined by average systolic BP ≥140 mmHg/average and diastolic BP ≥90 mmHg, or self-reported current hypertension (Gu et al., 2002).

Fasting blood samples of subjects were collected by venipuncture. Hematology and biochemical parameters [total cholesterol (TC), low density lipoprotein cholesterol (LDL), high density lipoprotein cholesterol (HDL), triglyceride (TG), fasting blood glucose (FBG), serum creatinine, and serum uric acid] were measured by an automatic analyzer (Hitachi, Tokyo, Japan) in Affiliated Xuanwu Hospital of Capital Medical University (Wang et al., 2016).

Hyperuricemia was defined as elevated serum uric acid level >7.0 mg/dL (417 μmol/L) for men and 6.0 mg/dL (357 μmol/L) for women (Keenan et al., 2013).

IgG N-glycan analysis

IgG isolation and IgG glycan label were performed by an “in solution” method (Yu et al., 2016; Zhao et al., 2018). IgG N-glycans were initially analyzed by hydrophilic interaction liquid chromatography on an ultraperformance liquid chromatography (HILIC-UPLC) instrument (Walters, MA). Through this assay, 24 chromatographic glycan peaks (GPs) were achieved that represented all N-glycans (GP1-24) (Menni et al., 2013). For a specific sample, each peak area was divided by the sum of areas. As such, a GP value was quantitative as a proportion of all GPs. GP3 was excluded from the measured GPs since it did not pass quality control standards (Novokmet et al., 2014).

Along with the initial glycans directly measured with the abovementioned method, 54 derived traits were calculated to represent the composite glycosylation features, in terms of sialylation, galactosylation, and fucosylation (Vučković et al., 2015). Normalization and batch correction of the glycan data were undertaken as detailed previously (Lu et al., 2011).

Statistical analyses

The Kolmogorov–Smirnov test was utilized to examine whether quantitative data of glycan measurement fit into statistically normal distribution. As these data were not normally distributed, median (M) with interquartile range was figured out for descriptive statistics. The Mann–Whitney U test, a nonparametric statistic, was carried out for comparison of un-normal-distributed data, while the independent t-test was performed to compare normal-distributed quantitative data. Chi-squared test was used for comparisons of qualitative data.

Spearman correlations were performed for the calculation of correlation coefficient (r_s) between variables. Partial correlation analyses were undertaken to calculate the correlation coefficients adjusted for gender, age, BMI, and other covariables. Bonferroni correction was applied for covariance and partial correlation analyses, for which the significance level 0.05 was divided by 23 (number of initial glycans) instead of 77 (total of 23 initial and 54 derived glycan traits). Logistic regression, followed by receiver operating characteristic (ROC) curve analysis, was used to screen glycan biomarkers for hyperuricemia. All statistical analyses were performed with SPSS 24.0 (IBM, NY) and R packages 3.3.2 (R Core Team). All the reported p-values were two sided, and a p-value <0.05 was considered statistically significant (if not stated otherwise).

Results

Description of characteristics of study participants

The demographic descriptions and anthropometric parameters of 635 participants (38 individuals with hyperuricemia and 597 controls) are shown in Table 1. A total of 16 traits were compared between the control group without hyperuricemia and the hyperuricemic cases. Height, weight, waist circumference, serum triglycerides, serum glucose, and serum creatinine of hyperuricemia cases were significantly higher than those with normal uric acid, while serum HDL was significantly lower in hyperuricemia participants than the controls (p < 0.05).

Table 1.

Characteristics of the Study Participants

Variables	Cases (n = 38)	Controls (n = 597)	t/χ²	p
Age	46.00 ± 10.61	47.43 ± 6.11	0.824	0.415
Gender (% male)	31 (81.6)	177 (29.6)	43.741	<0.001
Height	169.06 ± 7.83	163.29 ± 7.79	4.299	<0.001
Weight	76.28 ± 16.99	65.26 ± 10.52	3.843	<0.001
BMI	26.44 ± 4.71	24.37 ± 3.25	0.384	0.701
Systolic BP	121.86 ± 15.25	117.53 ± 14.72	1.730	0.084
Diastolic BP	83.30 ± 11.72	78.64 ± 10.35	2.629	0.009
TC	5.38 ± 1.28	5.11 ± 0.89	1.276	0.210
HDL	1.47 ± 0.58	1.63 ± 0.33	2.728	0.007
LDL	2.97 ± 0.89	2.78 ± 0.71	1.298	0.202
TG	2.35 ± 1.28	1.50 ± 1.38	3.704	<0.001
Waist circumference	86.94 ± 12.13	78.41 ± 8.51	5.590	<0.001
Hip circumference	96.46 ± 7.11	95.69 ± 5.77	0.749	0.454
FBG	5.76 ± 0.97	5.34 ± 0.83	2.577	0.014
Serum creatinine	71.76 ± 13.31	55.82 ± 11.78	8.021	<0.001

BMI, body mass index; BP, blood pressure; FBG, fasting blood glucose; HDL, high density lipoprotein cholesterol; LDL, low density lipoprotein cholesterol; TG, triglyceride; TC, total cholesterol.

Prevalence of hyperuricemia among the participants

As shown in Supplementary Table S1, the prevalence of hyperuricemia observed in this study was 5.98%. Subgroup analyses revealed that the prevalence was 14.9% in men, which was significantly higher than that in women (1.6%). Meanwhile, the prevalence was 12.8% among obese individuals, 11.5% among hypertensive subjects, 16.7% among hyperglycemic subjects, and 8.1% among individuals with dyslipidemia, which were higher than that of normal individuals, respectively.

Descriptive statistics for the GPs and derived traits

The composition of the IgG glycome was analyzed by a previously established HILIC-UPLC method (Lu et al., 2011), and 54 derived traits were calculated. As shown in Table 2 and Supplementary Table S2, the results of initial and derived traits of IgG N-glycans were presented with medians and quartile ranges.

Table 2.

Descriptive Statistics for the Glycan Peaks Identified in Chromatography

Glycans	Cases	Controls	Between-group difference
Glycans	M (IQR)	M (IQR)	W-statistic	p
GP1	0.08 (0.06–0.10)	0.06 (0.04–0.09)	185058.0	0.001
GP2	0.36 (0.24–0.51)	0.32 (0.22–0.46)	187027.5	0.146
GP4	16.15 (13.58–22.85)	15.39 (12.51–18.19)	186372.0	0.040
GP5	0.24 (0.21–0.27)	0.23 (0.19–0.28)	187964.0	0.551
GP6	4.37 (3.65–5.03)	3.71 (3.09–4.53)	185718.5	0.008
GP7	0.48 (0.34–0.65)	0.58 (0.42–0.75)	9305.5	0.012
GP8	18.58 (17.71–19.94)	18.51 (17.33–19.62)	187987.5	0.566
GP9	9.47 (8.46–10.35)	9.68 (8.82–10.68)	10339.5	0.118
GP10	5.21 (4.19–5.91)	4.96 (4.41–5.60)	188516.5	0.928
GP11	0.65 (0.58–0.79)	0.68 (0.60–0.77)	11267.5	0.476
GP12	0.69 (0.51–1.04)	1.00 (0.71–1.31)	8293.5	0.001
GP13	0.42 (0.32–0.55)	0.47 (0.40–0.55)	9654.0	0.029
GP14	16.53 (13.58–19.84)	18.34 (16.01–20.8)	9101.0	0.007
GP15	1.90 (1.50–2.29)	1.99 (1.70–2.29)	10246.0	0.100
GP16	3.12 (2.72–3.58)	3.06 (2.71–3.39)	187965.0	0.552
GP17	0.96 (0.82–1.02)	1.00 (0.85–1.19)	9928.0	0.053
GP18	11.07 (9.13–13.13)	12.30 (10.44–14.32)	9682.5	0.031
GP19	1.92 (1.69–2.18)	1.88 (1.67–2.11)	187994.5	0.570
GP20	0.34 (0.25–0.46)	0.34 (0.27–0.42)	188332.0	0.796
GP21	0.72 (0.65–0.82)	0.68 (0.56–0.79)	186888.0	0.114
GP22	0.10 (0.08–0.13)	0.10 (0.07–0.12)	187733.5	0.418
GP23	1.71 (1.29–2.33)	1.65 (1.30–2.09)	187575.5	0.342
GP24	1.82 (1.44–2.08)	1.58 (1.29–1.89)	186048.0	0.019

M, median; IQR, interquartile range; GPs, glycan peaks.

There were 9 initial glycans and 11 derived traits that were significantly different between the hyperuricemia group and control group. Four glycans (GP1, GP4, GP6, and GP24) in the hyperuricemia group were significantly higher than that in the control group, while the other five glycans (GP7, GP12, GP13, GP14, and GP18) were lower in hyperuricemic individuals. Six derived traits [FG2S2/(FG2 + FG2S1 + FG2S2), FBG2S2/(FBG2 + FBG2S1 + FBG2S2), FBS^total/FS^total, G0ⁿ, F^{n total}, and FG1^{n total/}G1ⁿ] were higher in the case group than control group. Meanwhile, five derived traits [F^totalS1/F^totalS2, FS1/FS2, FBS1/FBS2, G2ⁿ, and Bⁿ/(Fⁿ + FBⁿ)] were lower in hyperuricemic individuals.

Correlation analyses of IgG N-glycans with uric acid

As the Spearman correlation analyses show in Supplementary Table S3, GP1, GP2, GP4, GP6, GP10, and GP11 were positively correlated with the uric acid level. However, GP12, GP13, GP14, GP15, GP18, and GP20 were negatively correlated with uric acid level. Subgroup analyses showed that GP4 was positively correlated with uric acid, while GP12, GP13, GP14, GP17, and GP20 were negatively correlated with uric acid level among females. Moreover, 11 derived glycan traits were positively correlated with the uric acid level and 18 presented traits showed negative correlation.

Partial correlation analyses were performed to measure the correlation between glycans and uric acid, for which age, BMI, TC, TG, HDL, LDL, and FBG were adjusted. As shown in Table 3 and Supplementary Table S4, the glycan of GP4 was positively correlated with uric acid, while GP12 and GP14 were negatively correlated with uric acid level.

Table 3.

The Partial Correlation Coefficients of Uric Acid with Glycans

Glycans	Total		Men		Women
Glycans	r	p	r	p	r	p
GP4	0.19	5.17E-06	0.09	2.31E-01	0.25	7.74E-07
GP12	−0.17	2.96E-05	−0.05	5.21E-01	−0.24	2.27E-06
GP14	−0.18	1.38E-05	−0.08	2.67E-01	−0.23	4.66E-06
FG2S2/(FG2 + FG2S1 + FG2S2)	0.16	1.26E-04	0.13	8.21E-02	0.18	4.33E-04
GP4ⁿ	0.20	2.03E-06	0.10	1.70E-01	0.25	4.63E-07
GP12ⁿ	−0.17	3.33E-05	−0.04	5.50E-01	−0.24	2.86E-06
GP14ⁿ	−0.17	5.33E-05	−0.05	4.94E-01	−0.23	7.05E-06
G0ⁿ	0.19	3.29E-06	0.10	1.90E-01	0.25	8.39E-07
G2ⁿ	−0.18	1.63E-05	−0.04	5.60E-01	−0.25	1.06E-06

The following covariates were controlled for age: BMI, body mass index; TC, total cholesterol; TG, triglyceride; HDL, high density lipoprotein cholesterol; LDL, low density lipoprotein cholesterol; BP, blood pressure, and fasting blood glucose.

Furthermore, the analyses of derived IgG N-glycans indicated that FG2S2/(FG2 + FG2S1 + FG2S2), GP4ⁿ, and G0ⁿ were positively correlated with uric acid level, whereas GP12ⁿ, GP14ⁿ, and G2ⁿ showed negative correlation. Subgroup analyses showed that FG2S2/(FG2 + FG2S1 + FG2S2), GP4ⁿ, G0ⁿ, and F^{n total} were positively correlated with uric acid level, while GP12ⁿ, GP14ⁿ, GP15ⁿ G2ⁿ, and Bⁿ/(Fⁿ + FBⁿ) were negatively correlated with uric acid level among females. However, neither initial IgG N-glycans nor their derived traits were noted to be correlated with the uric acid level despite adjustment for the covariables.

Classification of hyperuricemia using IgG N-glycan

The univariate logistic regression analyses were performed to identify whether each of IgG N-glycans was associated with hyperuricemia. As shown in Supplementary Table S5, GP1, GP2, GP3, GP12, FBGS/(FBG + FBGS), FBGS/(FB + FBG + FBGS), FG2S2/(FG2 + FG2S1 + FG2S2), FBS1/FS1, and FG0ⁿ/G0ⁿ were associated with hyperuricemia.

Furthermore, to test whether IgG N-glycans could be utilized to distinguish hyperuricemic individuals from those without hyperuricemia, we performed multiple logistic regression analyses to establish a classification model, in which age, BMI, TC, TG, HDL, LDL, BP, and fasting blood glucose were controlled. As shown in Table 4, BMI, gender, GP9, and GP10 were included in this model.

Table 4.

Multiple Logistic Regression Analyses of the Association of Glycans with Hyperuricemia

Variables	B	SE	Walds	p	OR	95% CI of OR
Variables	B	SE	Walds	p	OR	LCI	UCI
BMI	0.920	0.284	10.475	0.001	2.508	1.437	4.377
Gender	−3.132	0.583	28.828	0.000	0.044	0.014	0.137
GP9	−0.523	0.159	10.820	0.001	0.593	0.434	0.809
GP10	−0.440	0.196	5.052	0.025	0.644	0.439	0.945

B, regression coefficient; SE, standard error; OR, odds ratio; CI, confidence interval; LCI, lower confidence interval; UCI, upper confidence interval.

A ROC curve analysis was carried out to identify the value of the model on the classification of hyperuricemia. The area under the curve (AUC) value of the model consisting of GP9, GP10, BMI, and gender was determined as 0.849 (95% confidence interval [CI]: 0.784–0.915), while the other model composed of BMI and gender yielded an AUC value of 0.772 (95% CI: 0.681–0.862) (Fig. 1). The results showed that the classification model, incorporating GP9 and GP10, was able to distinguish hyperuricemia from subjects without hyperuricemia. A cutoff value of 9.4 was used to distinguish hyperuricemia in this model, yielding a sensitivity of 80.6% and a specificity of 80.4%.

FIG. 1.

The receiver operating characteristic curve analysis of the logistic regression model for prediction of hyperuricemia. Determinant 1 consists of GP9, GP10, BMI, and gender. Determinant 2 consists of BMI and gender. BMI, body mass index.

Discussion

This study revealed that the prevalence of hyperuricemia in our community-based population sample is 5.98%, while obese, hypertensive, hyperglycemic, or dyslipidemic individuals had a higher prevalence. To the best of our knowledge, this is the first study investigating the association of IgG N-glycans with hyperuricemia in this population. The results demonstrate that IgG N-glycosylation profiles are different between individuals with hyperuricemia and controls. Through our classification model based on GP9 and GP10, individuals with hyperuricemia could be distinguished from subjects without hyperuricemia, while future studies with larger sample sizes are still warranted.

Hyperuricemia plays a key role in the development of gout, which leads to a large proportion of inflammatory arthritis in men (Kim et al., 2018). In addition, hyperuricemia contributes to the occurrence of hypertension, diabetes mellitus, kidney injury, and mortality (Chen et al., 2009; Dehghan et al., 2008; Li et al., 2014). The increasing prevalence of hyperuricemia has been recognized as an emerging public health concern in both developed and developing regions (Kim et al., 2018).

A systematic review reported that the pooled prevalence rate of hyperuricemia is 13.3% (95% CI: 11.9–14.6) in China (Liu et al., 2015). The hyperuricemia rate in this study was 5.98% (38/635), which is significantly lower than the previous studies. This might be attributed to our approach of population sampling. That is, we excluded individuals with defined diseases such as cardiovascular, respiratory, genitourinary, digestive, and hematological diseases.

Gender-related differences of hyperuricemia have been reported worldwide. In Mainland China, from 2000 to 2014, the pooled prevalence of hyperuricemia is 19.4% in men, which is substantially higher than that of women (7.9%) (Liu et al., 2015). However, divergent results were observed in a North American investigation where hyperuricemia rates are 21.2% in men and 21.6% in women, respectively (Zhu et al., 2011). The current study also observed a higher hyperuricemia prevalence in men, consistent with most studies in Asia (Kim et al., 2018). Serum uric acid level is positively associated with obesity and is suggested as an obesity-related indicator (Oyama et al., 2006).

The positive association between fasting glucose and hyperuricemia has been observed in a previous study (Kim et al., 2018). We also detected higher serum glucose in hyperuricemic cases in this study. In addition, a subgroup analysis illustrated the higher prevalence of hyperuricemia among hyperglycemic individuals. This finding is consistent with the investigation that suggested hyperuricemia as an independent determinant of diabetes mellitus (Dehghan et al., 2008). Moreover, we noted that hypertensive individuals and dyslipidemic cases suffered from more hyperuricemia than the normal population.

Although there is an argument for the direction of temporality with regard to the relationship between inflammatory conditions and hyperuricemia, that is, whether high serum uric acid is caused by inflammation or a marker of a proinflammatory state, it has been proved that increased proinflammatory cytokine levels were associated with incidence of hyperuricemia in a longitudinal study (Ryu et al., 2012). Inflammatory molecules can upregulate uric acid generation through increasing the activity of xanthine oxidase (Komaki et al., 2005). Meanwhile, low-grade inflammation is considered a risk factor associated with hyperuricemia (Ryu et al., 2012).

Cumulative evidence indicates that variations in IgG Fc N-glycome play an important role in the proinflammatory or anti-inflammatory process, leading to various diseases such as rheumatoid arthritis, metabolic syndrome, hypertension, inflammatory bowel disease, systemic lupus erythematous, cancers, multiple sclerosis, and dementia (Bermingham etal., 2018; Liu et al., 2018; Wang et al., 2016), as well as the process of aging (Dall'Olio et al., 2013; Yu et al., 2016). In the present study, several deviations in IgG N-glycans were detected in the hyperuricemia cases compared with the controls. Four initial glycans (GP1, GP4, GP6, and GP24) were enriched in hyperuricemia individuals, while five initial glycans (GP7, GP12, GP13, GP14, and GP18) were detected with a decreased level.

IgG is observed to exert anti-inflammatory function by Fc sialylation. This differential sialylation may act as a switch from innate anti-inflammatory activity to generate adaptive proinflammatory effects under antigenic challenge (Anthony et al., 2008). The association between hyperuricemia and sialylation of IgG Fc was examined in the current study. The results should be noted that three derived traits of sialylation [FG2S2/(FG2 + FG2S1 + FG2S2), FBS^total/FS^total, and FBG2S2/(FBG2 + FBG2S1 + FBG2S2)] displayed higher abundance among hyperuricemic cases. On the contrary, three sialylation traits (FS1/FS2, F^totalS1/F^totalS2, and FBS1/FBS2) were lower in hyperuricemic individuals.

It is reported that high IgG galactosylation mediates anti-inflammatory response by elevating the binding of the inhibitory receptor FcγRIIB and a C-type lectin-like receptor dectin-1. The decreases of core fucosylation of anti-HPA-1a-specific IgG also upregulated the binding activity to FcγRIIIA and FcγRIIIB (Kapur et al., 2014; Karsten et al., 2012). Our results indicated that there was augmented abundance of galactosylation (G0ⁿ) and core fucosylation (Fn ^total, FG1ⁿ ^total/G1ⁿ) among hyperuricemic population. Meanwhile, another galactosylation (G2ⁿ) was lower in hyperuricemic individuals.

With a view to future glycan diagnostic development, and IgG N glycan as a putative biomarker for hyperuricemia, we aimed to develop a classification model based on the multiple logistic regression analysis. Two initial glycans, GP9 and GP10, were included in this model. GP9 and GP10 were not associated with either uric acid or hyperuricemia in univariate analyses, while they were found to be significantly associated with hyperuricemia in the multiple logistic regression analyses, where the following covariates were controlled: age, BMI, TC, TG, HDL, LDL, BP, and FBG. This suggests that the relationships between hyperuricemia and GP9 and GP10 are independent to commonly recognized determinants of uric acid, such as age, BMI, dyslipidemia, hypertension, and blood glucose.

We then tested the performance of this model by an ROC curve analysis, which showed that GP9 and GP10 could distinguished hyperuricemia cases from subjects without hyperuricemia.

Hyperuricemia, a common metabolism and inflammation disorder, may distort IgG Fc glycosylation (Liu et al., 2018). However, the causative effect between protein glycosylation and hyperuricemia and other diseases remains unclear. The relationship between hyperuricemia and IgG glycans could also be impacted by environmental factors, lifestyles, and characteristics of individuals. Thus, the effects of IgG N-glycans on hyperuricemia are probably underestimated or overestimated.

Although this study shows a plausible association between uric acid metabolism and glycome, several limitations of this work should be considered: (1) this study is a cross-sectional study, which cannot verify the cause/effect relationship of IgG glycome and hyperuricemia; and (2) the relatively small sample size of hyperuricemic cases limits the generalizability of this study and robustness of the conclusions. Therefore, larger sample size studies and multiple center studies are needed to further evaluate and validate our findings.

In conclusion, this study provides new insights into the pathogenesis of hyperuricemia even though further studies are required. IgG N-glycosylation may serve as a potential biomarker for identification of hyperuricemic cases, which could contribute to preventive, predictive and personalized medicine. Future studies that focus on the mechanisms of glycan-related diseases are called for.

Footnotes

Acknowledgments

The study was supported by grants from the National Natural Science Foundation of China (81673247, 81773527, and 81872682), Australia-China Collaborative Grant (NHMRC APP1112767–NSFC 81561128020), the National Key R&D Program of China (2017YFE0118800), European Commission Horizon 2020 (779238), and the Science and Technology Development Project in Taian (2018NS0114).

Author Disclosure Statement

The authors declare that no conflicting financial interests exist.

Supplementary Material

Abbreviations Used

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