BIOMARKERS IN MEDICINE
2024, VOL. 18, NO. 24, 1113–1122
https://doi.org/10.1080/17520363.2024.2432309
RESEARCH ARTICLE
N-glycan as new potential biomarker for predicting treatment response in patients
with type 2 diabetes mellitus
Hui-Jun Donga*, Xiao-Hui Lib*, Qi-Xin Gua*, Chi-Fa Mab, Ming-Xia Yuanb, Zhen-Zi Wangc, Jian-Rong Suc, Lei Xud, Cui-Ying Chend,
Qiqige Ebulea, Hui Zhuanga and Xue-En Liua
a
Department of Microbiology and Center of Infectious Diseases, School of Basic Medical Sciences, Peking University Health Science Center, Beijing,
China; bDepartment of Endocrinology, Beijing Friendship Hospital, Capital Medical University, Beijing, China; cDepartment of laboratory medicine,
Beijing Friendship Hospital, Capital Medical University, Beijing, China; dDepartment of Research and Development, Sysdiagno (Nanjing) Biotech
Co., Ltd, Nanjing, Jiangsu Province, China
ABSTRACT
Aims: To investigate the N-glycans related to the metformin efficacy in patients with type 2 diabetes
mellitus (T2DM).
Materials and methods: We enrolled 141 healthy controls and 195 newly diagnosed T2DM patients
treated with metformin for 3 months. Serum N-glycan profile was determined by DNA sequencer –
assisted fluorophore-assisted carbohydrate electrophoresis (DSA-FACE). The N-glycan model was estab­
lished by logistic regression analysis. Receiver operating characteristic curve (ROC) analysis was used to
analyze the predictive power of the N-glycan model for metformin efficacy.
Results: The abundances of several N-glycans in serum of T2DM patients at baseline were significantly
different from those of healthy controls and tended to recover the N-glycan of controls after 3 months
treatment. Serum N-glycans changes were more significant in the good response group (FPG <7 mmol/L) after
metformin treatment. In addition, the abundance of peak9 at baseline had an opposite tendency between
HbA1c increased and decreased groups post-treatment, which could be a biomarker for predicting metformin
efficacy. Peak9 combined with other 11 N-glycans at baseline was used to establish the predictive model to
distinguish non-response from response patients (AUROC = 0.780, sensitivity = 70.6% and specificity = 77.5%).
Conclusions: Serum N-glycans may have potential value as biomarkers for indicating the efficacy of metformin.
1. Introduction
Diabetes mellitus has become one of the major public health
challenges due to its constantly growing prevalence in the
population year by year [1,2]. Among the different types of
diabetes mellitus, type 2 diabetes mellitus (T2DM) accounts for
more than 90% of all patients with diabetes. Also, T2DM is a
multifactorial disease resulting from the interplay between envir­
onmental and genetic factors, bringing incessantly increasing
personal and economic burden [3,4]. From a clinical perspective,
early identification and intervention with effective glucose-low­
ering agents are crucial for patients with T2DM to stabilize blood
glucose and delay the development of micro- and macrovascular
complications [1,5,6]. Currently, there are few effective biomar­
kers for early diagnosis and prognosis of T2DM, while N-glycans
may have potential ability in these aspects [7].
N-glycosylation is one of the most common post-transla­
tional modifications on proteins, which participates in broad
biological processes such as cell adhesion and receptor activa­
tion, and is of great significance to the function of protein [8].
ARTICLE HISTORY
Received 13 September
2024
Accepted 18 November
2024
KEYWORDS
N-glycan biomarker; type 2
diabetes mellitus;
metformin; treatment
response; clinical indicators
Different N-glycosylation modifications of the antibody IgG Fc
segment can regulate or even reverse the function of IgG [9,10].
In general, N-glycan is a promising biomarker. The process of Nglycan synthesis is easily affected by various factors such as
genetics and environment, so the changes in its structure and
quantity can reflect the dynamic process of physiological or
pathological changes in organisms in real time. N-glycan is
relatively stable molecule both in human body and in external
environment, especially in vitro, and will not be significantly
affected by experiencing repeated freezing and thawing [8]. In
the meanwhile, high-throughput methods make the qualitative
and quantitative analysis of N-glycan possible [8,11].
Several studies have reported that N-glycan changes are
associated with various diseases including T2DM [12–15]. In
T2DM, some N-glycans and derived traits are highly correlated
with the disease course [12,13]. Metformin is recommended as
a first-line drug for the treatment of T2DM by many guidelines
such as the American Diabetes Association (ADA) “Standards
of Care in Diabetes,” American Association of Clinical
CONTACT Ming-Xia Yuan
mx.yuan@ccmu.edu.cn
Department of Endocrinology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050,
China; Xue-En Liu
xueenliu@bjmu.edu.cn
Department of Microbiology and Center of Infectious Diseases, School of Basic Medical Sciences, Peking University
Health Science Center, Beijing 100191 China
*
Hui-jun Dong, Xiao-Hui Li and Qi-Xin Gu contributed equally to this work.
This article was originally published with errors, which have now been corrected in the online version. Please see Correction (http://dx.doi.org/10.1080/17520363.
2024.2437843)
Supplemental data for this article can be accessed online at https://doi.org/10.1080/17520363.2024.2432309.
© 2024 Peking University Health Science Center
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H.-J. DONG ET AL.
Article highlights
Serum N-glycan profile between T2DM patient and control was
markedly different.
● N-glycan profile of the patient changed significantly after 3 months
metformin therapy.
● Dynamic changes of N-glycan profile were associated with the effi­
cacy of metformin.
● Baseline N-glycan peak9 (NA3Fb) was correlated with the efficacy of
metformin.
● Serum N-glycan as a potential biomarker can predict the efficacy of
metformin.
the informed consent was obtained from all patients and
controls. The approval number is IRB00001052–21108.
●
Endocrinology consensus statement, and a consensus report
by the American Diabetes Association (ADA) and the European
Association for the Study of Diabetes (EASD) [16–18]. At pre­
sent, a considerable number of T2DM patients do not respond
well to metformin in clinic. However, there is still a lack of
biomarkers related to efficacy of metformin therapy, and there
is also a lack of prospective studies on the correlation between
efficacy of metformin and serum N-glycan changes in T2DM
patients. Evaluation and prediction of metformin treatment
efficacy is as important as diagnosis of T2DM. In the present
study, we enrolled treatment naïve T2DM patients, admini­
strated metformin therapy and followed up for 3 months,
investigated the dynamic changes in serum N-glycans during
treatment, and tried to find the N-glycan biomarker associated
with the efficacy of metformin.
2. Materials and methods
2.1. Study design
In this study, we recruited 195 treatment-naïve patients with
T2DM and 141 healthy individuals who matched to T2DM
patients according to demographic characters such as age
and sex. T2DM patients were recruited from the Department
of Endocrinology at Beijing Friendship Hospital, while healthy
controls were enrolled from the physical examination center.
The T2DM patients met the following inclusion criteria: (1)
between 18 and 70 years old; (2) patients with initial diagnosis
of T2DM based on the international classification of disease 10
(ICD-10) criteria; (3) glycated hemoglobin A1c (HbA1c) ranging
from 6.5% to 12% in baseline venous blood. Individuals were
excluded based on the following criteria: (1) patients with
other types of diabetes mellitus; (2) history of severe hepatic,
nephritic and cardiogenic diseases; (3) cancer patients under­
going treatment; (4) mental illness patients receiving treat­
ment or others who cannot sign the informed consent due
to various reasons. T2DM patients were administrated metfor­
min therapy at a dose of 1500 mg ~2000 mg per day and were
followed up for 3 months. Serum samples and clinical data
were collected from T2DM patients at baseline and 3-month,
and the same procedure for healthy controls. All samples were
stored in a refrigerator set at −80°C. The research was
approved by the ethics committees of Peking University
Health Science Center and Beijing Friendship Hospital, and
2.2. N-glycan detection method
The DNA sequencer – assisted fluorophore-assisted carbohy­
drate electrophoresis (DSA-FACE) method was used to detect
and analyze the serum N-glycan profiles of individuals. The Nglycans in serum glycoprotein were separated and labeled
with fluorescent substance using the N-glycomics analysis kit
(developed by Jiangsu SysDiagno Biotechnology Co., Ltd.).
Briefly, 5 μL serum sample was added to a 96-well plate,
denatured with 10 mmol/L NH4HCO3 buffer (2 μL +3 μL H2O),
and then 3 μL glucosidase PNGaseF (2.2 U/μL) was added to
release the N-glycan from the N-glycoprotein. The N-glycan
released was then labeled by adding 1 μL APTS (8-aminonaph­
talene-1, 3, 6-trisulphonic acid) (20 mmol/L) mixed solution. A
mixture of 5 μL NH4Ac containing sialidase (5 mmol/L) was
added to remove sialic acid, and finally, the N-glycan solution
of 10 μL was used to detect N-glycan profiles by ABI3500dx
genetic analyzer (Applied Biosystems, Foster City, CA). Twelve
specific N-glycan peaks were obtained in each sample. The
data of N-glycan profiles were analyzed using GeneMapper
software version 4.1. The abundance of each peak was quan­
tified by normalizing its height to the sum of the heights of
twelve peaks [14].
2.3. Statistical analysis
Matched clinical data comparison was conducted through the
Wilcoxon test, while the Mann-Whitney U test was applied in
two unpaired groups. The relative abundance of N-glycans
between two groups was compared by the student’s t test
and paired t test between matched groups. The correlation
between baseline serum N-glycans and demography charac­
teristics in T2DM patients was assessed by Spearman’s correla­
tion analysis. N-glycans models were developed by logistic
regression analysis to differentiate T2DM patients from healthy
controls, and to differentiate response patients from no
response based on HbA1c decrease or increase after 3month therapy. The discriminant ability of the models was
evaluated by receiver operating characteristic curve (ROC)
analysis. Area under ROC (AUROC), sensitivity and specificity
was calculated for assessing diagnostic power. All statistical
analysis was conducted using SPSS (Statistical Package for
Social Sciences) software 24.0. Two-tailed tests were used in
all cases, and P-value <0.05 was considered statistically
significant.
3. Results
3.1. Subject characteristics
There were no significant differences in age and sex ratio
between the healthy control subjects and the T2DM patients
(Table 1), although there were more males than females in
both two groups (male/female: 97/44 vs. 122/73). Therefore,
the confounding effects caused by factors such as age and sex
BIOMARKERS IN MEDICINE
1115
Table 1. Comparison of demographic characteristics and clinical data between the T2DM and healthy control.
Healthy controls (A)
index
N = 141
Age(year)
51.3(22–71)
sex ratio (male/female)
97/44
BMI(Kg/m2)
26.57(19.83–38.06)
Waist to hip ratio
–
SBP(mmHg)
130.94(78–175)
DBP (mmHg)
77.95(17–115)
TG (mmol/L)
1.69(0.44–8.09)
CHOL (mmol/L)
5.01(1.33–7.60)
HDLC (mmol/L)
1.3(0.77–3.52)
LDLC (mmol/L)
3.15(1.63–5.18)
Blood glucose related indicators
HbA1c (%)
5.5(5.3–5.8)
FPG (mmol/L)
5.17(4.11–20.15)
PPG30min(mmol/L)
–
PPG1h(mmol/L)
–
PPG2h(mmol/L)
–
PPG3h(mmol/L)
–
FINS (mIU/L)
–
INS30min(mIU/L)
–
INS1h(mIU/L)
–
INS2h(mIU/L)
–
INS3h(mIU/L)
–
FCP(μg/L)
–
CP30min(μg/L)
–
CP1h(μg/L)
–
CP2h(μg/L)
–
CP3h(μg/L)
–
HOMA-IR
–
HOMA-β
–
Liver and kidney function related indexes
ALT(U/L)
26.49(8–91)
AST(U/L)
23.83(6.7–56.6)
ACR (mg/g)
–
Cr(μmol/L)
75.59(46.3–120.4)
T2DM baseline (B)
N = 195
53.1(19–84)
122/73
27.78(19.27–46.30)
0.95(0.73–1.22)
133.15(100.00–175.00)
82.59(59–114)
2.39(0.52–27.93)
5.26(2.31–10.15)
1.2(0.68–2.39)
3.32(0.75–6.83)
Post-treatment (C)
N = 195
53.1(19–84)
122/73
26.65(18.43–49.38)
0.94(0.59–1.27)
128.77(95.00–163.00)
80.13(32–103)
1.70(0.52–9.15)
4.57(2.17–6.95)
1.17(0.69–1.91)
2.85(1.13–4.58)
PAB
0.910
0.235
0.002
–
0.248
<0.001
0.003
0.053
0.006
0.064
PBC
–
–
<0.001
0.001
<0.001
0.001
<0.001
<0.001
0.018
<0.001
7.4(5.50–12.41)
8.37(5.40–19.62)
13.20(8.48–21.59)
16.49(10.40–23.60)
15.62(5.90–27.42)
11.03(3.63–24.91)
13.50(0.40–78.45)
39.33(0.85–271.90)
60.70(2.10–295.40)
77.44(7.40–424.70)
47.56(0.55–280.20)
2.77(0.27–9.30)
4.71(0.32–14.76)
7.05(0.45–16.85)
9.13(0.57–21.12)
7.44(0.50–16.91)
5.07(0.21–25.18)
64.16(0.99–512.71)
6.4(4.80–9.70)
6.80(4.50–13.88)
11.81(7.57–21.64)
14.79(5.00–25.55)
13.55(6.47–23.33)
8.76(2.80–18.43)
10.65(2.30–45.09)
38.58(5.60–447.60)
63.02(8.10–416.90)
73.57(10.10–310.00)
40.95(5.00–419.70)
2.49(0.79–6.07)
5.23(1.61–18.95)
8.11(2.45–18.88)
10.43(2.82–24.32)
7.98(2.74–21.10)
3.30(0.64–12.01)
75.72(14.05–530.47)
<0.001
<0.001
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.717
0.405
0.326
0.040
<0.001
<0.001
<0.001
<0.001
0.041
<0.001
0.001
35.15(7–140)
28.25(13.70–85.00)
23.46(0.77–644.04)
67.57(37.90–105.30)
26.36(7–111)
23.87(11.90–77.70)
25.09(1.08–653.68)
67.07(41.50–112.30)
<0.001
<0.001
–
<0.001
<0.001
<0.001
0.602
0.490
The Mann Whitney U test was used between A and B groups, and the Wilcoxon test was used between B and C groups. BMI: body mass index; TG: triglyceride;
CHOL: total cholesterol; HDLC: high-density lipoprotein; LDLC: low-density lipoprotein; HbA1c: glycated hemoglobin; FPG: fasting plasma glucose; PPG:
postprandial glucose; Fins: fasting insulin content; Ins30min: result of insulin release test 30 min; FCP: fasting C-peptide content; CP30min: C-peptide Release
Test 30 min results; HOMA-IR: insulin resistance index; HOMA- β: β Index of cell function; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ACR:
urinary microalbumin/creatinine; Cr: creatinine; T2DM: type 2 diabetes mellitus.
were excluded. Several clinical data were significantly changed
in T2DM patients at baseline compared to those in healthy
controls, as shown in Table 1.
decrease in the abundance of core α-1, 6 fucosylated N-gly­
cans and an increase in the abundance of non-core fucosy­
lated N-glycans. Our findings suggest that the serum N-glycan
profiles of T2DM patients differ significantly from those of
healthy subjects.
3.2. Differences in serum N-glycan profiles of T2DM
patients and healthy control subjects
Figure 1 shows representative serum N-glycan profiles of
healthy control and T2DM patients at baseline and at 3
months of treatment, and detailed data on the abundance of
N-glycan peaks were shown in Supplementary Table S1.
Compared with healthy controls, there were significant
changes in the abundance of several N-glycans in the serum
of T2DM patients, in which the abundance of peak0, 1, 2, 3
and 4 were decreased significantly, while the abundance of
peak5 and 8 were increased. Specifically, there was a decrease
in the abundance of simple N-glycans in the serum of T2DM
patients, including diantennary N-glycans, core fucosylated or
monogalactosylated modified diantennary N-glycans, whereas
an increase in the abundance of complex N-glycans, such as
non-fucosylated di- and tri-antennary N-glycans modified by
di- and tri-galactosylation. Additionally, we observed a
3.3. Distinguishing T2DM from healthy people by serum
N-glycan model
The binary logistic regression analysis was used to evaluate
the serum N-glycans as a biomarker to distinguish T2DM
patients from healthy controls. Finally, a total of four N-glycans
were incorporated to establish a T2DM diagnostic model (Y1),
the area under receiver operating characteristic curve (AUROC)
was 0.786 (95%CI: 0.735–0.838), with a sensitivity of 71.6% and
specificity of 71.1% for distinguishing T2DM patients from
healthy individuals (Supplementary Fig. S1.). Furthermore,
another diagnostic model was established by combining
T2DM-related clinical risk factors with serum N-glycans (Y2),
which had an AUROC of 0.892 (95% CI: 0.856-0.928), and its
sensitivity and specificity reaching 90.5% and 75.3%,
respectively.
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H.-J. DONG ET AL.
Figure 1. Serum N-glycan profiles of healthy controls and type 2 diabetes mellitus (T2DM) patients at baseline and 3-month of treatment. The legend shows the
specific structure of the detected glycans, and P/Peak indicates the N-glycan peak. Peak0: biantennary N-glycan (NGA2), peak1: agalacto core α1,6-fucosylated
biantennary N-glycan (NGA2F), peak2: agalacto core α1,6-fucosylated bisecting biantennary N-glycan (NGA2FB), peaks3 and 4: monogalacto core α1,6-fucosylated
biantennary N-glycan (NG1A2F), peak5: bigalacto biantennary N-glycan (NA2), peak6: bigalacto core α1,6-fucosylated biantennary N-glycan (NA2F), peak7: bigalacto
core α1,6-fucosylated bisecting biantennary N-glycan (NA2FB), peak8: triantennary N-glycan (NA3), peak9: branching-1,3-fucosylated triantennary N-glycan (NA3Fb),
peak10: tetragalacto tetraantennary N-glycan (NA4), and peak11: core α1,6-fucosylated tetraantennary N-glycan (NA4F). : galactose; : N-acetylglucosamine; :
mannose; : fucose.
BIOMARKERS IN MEDICINE
The diagnostic models were represented as follows:
3.4. Altered N-glycan profiles in patients with T2DM
before and after metformin treatment
After a 3-month follow-up of metformin treatment in T2DM
patients, most patients showed a significant decrease in FPG
and HbA1c, and the improvement in results of OGTT and Cpeptide release tests (Table 1). At the same time, the abun­
dance of serum N-glycans peak2, peak4, peak9, and peak11
increased significantly while peak0, peak6, peak7, peak8 and
peak10 decreased, showing a trend of recovery toward Nglycan levels in the healthy control (Figure 2 and
Supplementary Table S1). In other words, compared with the
baseline N-glycan profiles in T2DM patients, the abundance of
fucosylated N-glycans increased while tri- and tetra-galacto Nglycans decreased after 3 months of treatment.
T2DM patients were stratified into three groups based on the
degree of FPG decline following treatment: those with a decrease
in FPG of less than 10%, a decrease of 10–20%, and a decrease of
1117
more than 20%. After 3-month metformin therapy, peak2
increased significantly in the group with < 10% FPG decline. In
the group of 10–20% FPG decline, the abundance of peak2 and
peak11 increased significantly, while peak6 and peak8
decreased. In the group with > 20% FPG decline, the abundance
of peak2, peak9 and peak11 increased significantly, whereas
peak6 and peak7 decreased (Supplementary Table S2). Results
demonstrated that the more obvious decline of the patients’
blood glucose, the more numbers of changed N-glycans.
T2DM patients were divided into the good response group
and the poor response group based on whether their FPG
decreased to normal level (FPG <7 mmol/L) after 3 months of
therapy. T2DM patient with FPG <7 mmol/L post-3 months
therapy was defined as a good response, while FPG ≥7
mmol/L was defined as a poor response. We observed that
the abundance of peak2 in both two groups were increased
after 3-month therapy (Supplementary Table S3 and
Supplementary Fig. S2.). However, the significantly changes
of peak0, peak6, peak7, peak8, peak9 and peak11 were only
observed in the good respond group.
3.5. Potential biomarkers and prediction models of
metformin treatment response of type 2 diabetes
mellitus
After 3 months of metformin treatment, 169 T2DM patients
had their HbA1c decreased, 7 unchanged and 2 missed.
Figure 2. Comparison of serum N-glycans between healthy controls and T2DM patients at baseline and at 3 months of treatment. HC: health control, pre-t: T2DM
patients at baseline, post-t: T2DM patients at 3 months of treatment. Each box represents the 25th to 75th percentiles. Lines inside the boxes represent the medians.
student’s t test is conduct between HC and Pre-t, paired t test is conduct between pre-t and post-t *p < 0.05, **p < 0.01, ***p < 0.001.
1118
H.-J. DONG ET AL.
Table 2. Comparison of N-glycan profiles between healthy control and T2DM patients in HbA1c decreased and increased groups.
N-glycans
peak0
peak1
peak2
peak3
peak4
peak5
peak6
peak7
peak8
peak9
peak10
peak11
Healthy control (A)
N = 141
1.01 ± 0.48
4.81 ± 1.45
1.11 ± 0.33
4.00 ± 0.81
3.69 ± 0.68
50.27 ± 3.29
15.24 ± 2.32
4.19 ± 1.01
9.20 ± 2.11
3.90 ± 1.41
1.76 ± 0.52
0.83 ± 0.39
HbA1c decreased group (B)
N = 169
0.90 ± 0.33
4.46 ± 1.67
0.86 ± 0.29
3.53 ± 0.84
3.29 ± 0.68
51.39 ± 3.71
14.86 ± 2.58
4.06 ± 0.99
10.31 ± 2.20
3.70 ± 1.73
1.78 ± 0.76
0.87 ± 0.50
HbA1c increased group (C)
N = 17
0.95 ± 0.50
4.30 ± 1.58
0.84 ± 0.27
3.71 ± 0.69
3.35 ± 0.54
51.08 ± 2.59
15.02 ± 2.00
4.03 ± 1.20
9.35 ± 2.43
4.83 ± 1.97
1.61 ± 0.73
0.94 ± 0.44
PBC
0.729
0.708
0.836
0.407
0.753
0.737
0.796
0.890
0.091
0.012
0.395
0.546
The HbA1c decreased group is defined as patients with HbA1c decreased after 3 months of treatment, the HbA1c increased group is defined as patients with HbA1c
increased after 3 months of treatment. After 3 months of metformin treatment, 7 T2DM patients had their HbA1c unchanged and 2 missed.
However, 17 patients had their HbA1c increased, indicating
these patients did not response well to metformin. In order to
explore the correlation between the efficacy of metformin and
serum N-glycans at baseline in T2DM patients, we further
analyzed differences of the clinical data and N-glycan profiles
at baseline between HbA1c decreased and increased groups.
As shown in Supplementary Table S4, there were no signifi­
cant differences in most clinical data at baseline (p > 0.05).
In addition, we observed a significant difference in the
abundance of peak9 at baseline (p < 0.05) between HbA1c
decreased and increased group (Table 2). The abundance of
peak9 at baseline in HbA1c increased group was significantly
higher than that in the decreased group.
The binary logistic regression analysis was used to evaluate
the serum N-glycan to predict metformin treatment response.
The abundance of N-glycan peak9 at baseline was used to
establish the prediction model (Y1), the AUROC was 0.681
(95% CI: 0.543–0.820), with a sensitivity of 76.5% and a speci­
ficity of 63.3%. Furthermore, when enrolled total of 12 serum
N-glycans at baseline in the model (Y2), the AUROC was 0.780
(95% CI: 0.682–0.878), with a sensitivity of 70.6% and specifi­
city of 77.5% (Figure 3). The prediction models were shown as
follows:
Figure 3. Receiver operating characteristic curve (ROC) curve plots for prediction of metformin treatment response. Green line: the model constructed by 12 baseline
serum N-glycans; blue line: the model constructed by baseline N-glycan peak9; yellow line: reference line.
BIOMARKERS IN MEDICINE
3.6. Multiple serum N-glycans at baseline were
associated with insulin, C-peptide levels after 3 months
of treatment
Several serum N-glycans were significantly correlated with age
and gender at baseline in T2DM patients. Therefore, in sub­
sequent analysis of the correlation between N-glycans and
clinical indicators, age and gender were used as control vari­
ables to exclude confounding effects. And multiple baseline
serum N-glycans exhibited a significant correlation with clin­
ical indicators such as insulin levels and C-peptide levels in
T2DM patients after 3 months of treatment (Supplementary
Table S5). Among them, peak0 and peak10, which represents
biantennary N-glycan and tetragalacto tetra-antennary N-gly­
can, displayed negative correlation, while peak4, which
represents bi-antennary N-glycans modified by mono-galacto­
sylated core fucosylation, showed a positive correlation. In
other words, the abnormal increase in the abundance of
peak0 and peak10 at baseline may indicate insufficient
secretion of insulin and C-peptide after treatment.
4. Discussion
In this study, 195 patients with T2DM were treated with met­
formin and followed up for 3 months, and the serum N-glycan
profiles at baseline and 3 months of treatment were detected.
This is the first prospective study on the dynamic changes of
serum N-glycans in T2DM patients during the treatment of
metformin. The DSA-FACE method was used for the detection
of serum N-glycans, and we obtained the serum N-glycan
profile of each sample, which included the relative abundance
of 12 main N-glycan peaks. Comparing DSA-FACE to high
performance liquid chromatography and mass spectrometry,
DSA-FACE has the advantages of easy operation, simple
experimental pretreatment, and fast procedure [11,19]. In
addition, N-glycans are correlated with characteristics such as
age and gender [20], our findings suggest differences in the
relationship between N-glycan and metformin efficacy by con­
sidering age and gender as factors (Supplementary Table S6
and Supplementary Table S7). Therefore, we performed a
rigorous matching of demographic characteristics between
T2DM patients and healthy controls to avoid the effects of
confounding factors.
Serum N-glycan profiles in T2DM patients were signifi­
cantly altered compared to healthy controls. We observed
that the abundance of core α1,6-fucosylated N-glycans with
simple biantennary structures (peak1, 2, 3 and 4) had
declined, while the abundance of complex bi- and tri-galacto­
sylated N-glycans without the core fucosylated structure
(peak5 and 8) were elevated. Similar results have been
reported in other studies, such as the discoveries from Keser
and Adua [13,21].
Many studies have performed a comparative analysis of Nglycan profiles between T2DM patients and healthy people,
and found that N-glycans are able to distinguish T2DM
patients from healthy people. Meng and Rudmen reported
that the diagnostic models, established using N-glycans com­
bined with clinical indicators, have a higher diagnostic effi­
ciency with AUROC 0.9 [22,23]. Another study established the
1119
IgG N-glycans model to discriminate T2DM patients from
healthy population with a complex procedure [24]. In the
present study, we established the N-glycan model to discrimi­
nate T2DM patients from healthy people through directly
detecting and analyzing serum N-glycans with AUROC of
0.786 and 0.892 when combined with clinical indexes. The
majority of T2DM patients enrolled in this study are asympto­
matic patients who need repeated examinations to be diag­
nosed. The N-glycan diagnostic model is helpful in assisting
such asymptomatic patients to achieve one-time diagnosis
and to avoid repeated laboratory tests.
After 3 months of metformin treatment, improvements in
various clinical parameters occurred in T2DM patients and the
levels of peak0, 2, 4, 6, 7, 8, 9, 10, and 11 changed significantly
after therapy (p < 0.05). Moreover, multiple serum N-glycans
changes detected in the good response groups after metfor­
min treatment (Supplementary Table S3) suggested that the
drug efficacy was associated with the significant changes of Nglycans. Serum N-glycan profiles may have potential value in
indicating the efficacy of metformin treatment.
T2DM patients were stratified by the magnitude of FPG
decline (percentage decline) after 3 months of treatment in
order to further investigate the correlation between the effi­
cacy of the drug and changes of serum N-glycan. We found
that the number of significant changed N-glycan peaks
increased while the magnitude of FPG decline increased.
And in the group with the greatest decline of FPG (>20%
decreased), the complex more multi-branched N-glycans
showed significant changes, whereas, in the group with slight
decline of FPG (<10% decreased), only two N-glycans showed
significant change. Such a correlation was also reported in a
cross-sectional study, which found a correlation between met­
formin treatment and increase of agalacto core α1, 6-fucosy­
lated bisecting biantennary N-glycan (peak2) [25]. In addition,
T2DM patients were classified into the poor response and
good response groups according to whether FPG levels
returned to normal after 3 months of treatment. We found
that peak2 was significantly altered in both two groups; how­
ever, the significant changes of peak0, peak6, peak7, peak8,
peak9 and peak11 were only observed in the good respond
group. This result illustrated again that the more significant
changes of N-glycans accompanied the better treatment out­
comes. N-glycan changes have a certain correlation with met­
formin efficacy, although the relevance between N-glycans
and metformin efficacy has not yet been explained and
requires further study [26,27].
Changes in serum N-glycan profiles are associated with the
expression levels of glycosyltransferases in the body. In the
present study, we found that the abundance of core α1, 6fucosylated N-glycans (peak1, peak2, peak3 and peak4) was
significantly decreased in T2DM patients, which was consis­
tent with the results of several studies [21,28]. This phenom­
enon may be associated with reduced expression of
fucosyltransferases. The abundance of peak5 and peak8
(non-core α1, 6-fucosylated bi- and tri-galacto bi- and triantennary N-glycans) were elevated in the serum of T2DM
patients, and the increased level of multi-branched N-glycans
was also correlated with glycosyltransferase levels. When
patients were chronically suffered from hyperglycemia, the
1120
H.-J. DONG ET AL.
activity of glutamine-fructose-6-phosphate aminotransferase
(GFAT) was increased. GFAT acts as a key enzyme in the
hexosamine biosynthetic pathway (HBP), leading to an
increase in the proportion of glucose that is metabolized via
the HBP, which in turn produces more metabolites such as
uridine diphosphate N-acetylglucosamine (UDP-GlcNAc). Nacetylglucosamine transferase (GnT)-Ⅳ or GnT-V, a type of
glycosyltransferases, synthesize the multi-branched structure
of N-glycan by adding N-acetylglucosamine. Its synthesis effi­
ciency significantly increased when the concentration of the
glucose metabolite UDP-GlcNAc increased [29].
It is well known that T2DM patients are in a low-grade
chronic inflammatory state for a long time, and the circulating
levels of inflammation-related proteins are inextricably related
to the clinical manifestations of T2DM [30]. The alteration of
serum N-glycans in T2DM patients may also be associated with
inflammatory status. In inflammatory diseases such as chronic
pancreatitis and rheumatoid arthritis, N-glycans show a ten­
dency toward multi-branching and sialylated modification
[31,32]. N-glycans of serum glycoproteins are mainly derived
from hepatic acute phase proteins and IgG [33].
Proinflammatory cytokines cause an increase in polybranched
modified glycans of serum liver derived glycoproteins [34,35].
This may also be the reason for that serum bi-, tri-antennary Nglycans were increased in T2DM patients. Core fucosylated Nglycan changes are also related to the body’s inflammatory
status, bisecting N-GlcNAc structure inhibits the expression of
core fucosylation, and indirectly promote IgG to FC γ Receptor
III binding, which in turn enhances antibody dependent cellmediated cytotoxicity (ADCC) and pro-inflammatory status [36].
It was speculated that the decreased abundance of core fuco­
sylated N-glycans in the serum of T2DM patients may be asso­
ciated with T2DM pathogenicity. In addition, increased
modification of galactosylation is associated with the hypergly­
cemic state in T2DM patients, and we found that the abun­
dance of bi-, tri-galacto N-glycans were significantly increased
in the serum of patients. Altered serum N-glycan profiles may
contribute to T2DM disease progression; however, the causal
relationship still needs to be further studied. Several evidences
now suggest that aberrant glycosylation may play a role in the
pathogenic mechanisms of T2DM, such as an important role for
protein glycosylation in immune cell migration, recognition of
pathogen associated molecular patterns (PAMPs), and other
processes involved in the development of inflammation [37].
A significant finding in the present study is that N-glycan
peak9 at baseline was significant different between HbA1c
increased and decreased groups indicating the potential
value to predict the response of metformin treatment in
T2DM patients. There were still no recommended methods
for individualizing therapy by any guidelines. It is reported
that insulin secretion received much more attention in recent
years. Abdelgani performed a controlled trial to compare the
efficacy of 2 therapeutic approaches for the management of
patients with new-onset T2DM. The ratio of C-peptide release
in 120 mins and 0 min (C-Pep120/C-Pep0) during OGTT test was
found the strongest predictor of response to metformin ther­
apy (AUROC is 0.724 with sensitivity of 51% and specificity of
80%) [38]. Compared to the ratio of C-Pep120/C-Pep0, the 12
baseline N-glycans to predict the response of metformin ther­
apy represented higher AUROC (0.780) and sensitivity (70.6%).
Furthermore, a larger, multicenter, multiethnic study is war­
ranted to confirm the generalizability of the present results.
The advantages of N-glycans test are small sample require­
ment (2 ~ 5 μL) and short duration (6 hours) of testing. Our
finding provided evidence that N-glycans at baseline could be
potential biomarkers for predicting the efficacy of metformin
treatment.
In addition, our study found that several serum N-glycans
at baseline were associated with insulin levels and C-peptide
release at 3 months of metformin treatment. It is speculated
that the increased abundance of galactosylated and multibranched N-glycans in the baseline serum of T2DM patients
may be a dangerous signal for predicting insufficient insulin
secretion after treatment.
This study is the first prospective study to observe the
correlation between the therapeutic efficacy of metformin
and serum N-glycans change in T2DM patients. And we dis­
covered N-glycans are closely related to metformin treatment
response. However, our study has some limitations. First,
because the sialic acid modification of N-glycans was enzyma­
tically digested using DSA-FACE method, we did not perform
the analysis of the sialylation traits. We are establishing a
dynamic data system to explore the potential N-glycan bio­
markers and prediction models in T2DM with metformin treat­
ment. Second, we did not delve into the underlying
mechanisms of how N-glycans interact with metformin or
influence glucose. Understanding the metabolism would pro­
vide valuable insights and strengthen the clinical relevance of
the findings. We will explore the possible mechanisms of
glycan changes by analyzing the expression of glycosidases
and glycosyltransferases trough constructing animal models in
the future. In addition, this study is the results of 3 months
follow-up of metformin therapy in newly diagnosed T2DM
patients. It is essential to consider whether the results can be
obtained in patients with different disease durations and
treatment regimens.
5. Conclusion
In conclusion, we obtained serum N-glycan profiles of T2DM
patients and healthy controls and found significant differences
in the abundance of N-glycans between the two populations.
Multiple N-glycans were also significantly altered in serum of
T2DM patients after three months of metformin treatment, in
parallel with the improvement in clinical indicators, showing a
trend of recovery toward the N-glycan profiles of healthy
individuals. Furthermore, T2DM patients with better therapeu­
tic efficacy had more significant changes in serum N-glycans.
In addition, N-glycans in serum of pre-treatment T2DM
patients were correlated with clinical indicators such as insulin
levels in patients after three months of metformin treatment,
and baseline N-glycans were found possibility to predict the
response of T2DM patients to metformin treatment. Serum Nglycans may have potential value as biomarkers to indicate
drug efficacy in T2DM patients.
BIOMARKERS IN MEDICINE
Acknowledgments
Authors thank the participants in the present study.
Author contribution
Ming-Xia Yuan, Xue-en Liu and Hui Zhuang were responsible for the
design, funding acquisition, project administration of the work and
reviewed the manuscript. Hui-jun Dong, and Qi-Xin Gu wrote the first
draft of the manuscript and contributed to data and N-glycan profiles
analysis. Xiao-Hui Li, Chi-Fa Ma, Zhen-Zi Wang and Jian-Rong Su per­
formed clinical sample collecting and clinical case enrollment. Lei Xu,
Cui-Ying Chen and Qiqige Ebule contributed to investigation, N-glycan
profiles analysis. All authors edited, reviewed, and approved the final
version of the manuscript.
Disclosure statement
The authors have no relevant affiliations or financial involvement with any
organization or entity with a financial interest in or financial conflict with
the subject matter or materials discussed in the manuscript. This includes
employment, consultancies, honoraria, stock ownership or options, expert
testimony, grants or patents received or pending, or royalties.
Funding
This work was supported by the Major Science and Technology Special
Project of China Thirteenth Five-year Plan ([2018ZX10732401-003-015] and
the project of Peking University - SysDiagno Biotech Joint Laboratory of
Glycomics.
Writing disclosure
No writing assistance was utilized in the production of this manuscript.
Ethical conduct of research
The authors state that they have obtained appropriate institutional review
board approval () and/or have followed the principles outlined in the
Declaration of Helsinki for all human or animal experimental investiga­
tions. In addition, for investigations involving human subjects, informed
consent has been obtained from the participants involved.
References
Papers of special note have been highlighted as either of interest (•)
or of considerable interest (••) to readers.
1. Ogurtsova K, Guariguata L, Barengo NC, et al. IDF diabetes Atlas:
global estimates of undiagnosed diabetes in adults for 2021.
Diabetes Res Clin Pract. 2022;183:109118. doi: 10.1016/j.diabres.
2021.109118
2. Sun H, Saeedi P, Karuranga S, et al. IDF diabetes atlas: global,
regional and country-level diabetes prevalence estimates for 2021
and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119.
doi: 10.1016/j.diabres.2021.109119
• Diabetes prevalence estimates in 2021 and projections in 2045.
3. Crandall JP, Knowler WC, Kahn SE, et al. The prevention of type 2
diabetes. Nat Clin Pract Endocrinol Metab. 2008;4(7):382–393. doi:
10.1038/ncpendmet0843
4. Memarian E, T Hart LM, Slieker RC, et al. Plasma protein N-glycosy­
lation is associated with cardiovascular disease, nephropathy, and
retinopathy in type 2 diabetes. BMJ Open Diabetes Res Care.
2021;9(1):e002345. doi: 10.1136/bmjdrc-2021-002345
5. Brown JB, Nichols GA, Perry A, et al. The burden of treatment
failure in type 2 diabetes. Diabetes Care. 2004;27(7):1535–40. doi:
10.2337/diacare.27.7.1535
1121
6. Piliponiene L, Velickiene D, Kregzdyte R. Microvascular complica­
tions, peripheral artery disease and mortality in patients with type
2 diabetes mellitus, in two counties of southern lithuania over 13
years: analysis using a cohort database of the national health
insurance. Medicina (Kaunas). 2021;57(12):1380. doi: 10.3390/
medicina57121380
7. Peng W, Zhao J, Dong X, et al. Clinical application of quantitative
glycomics. Expert Rev Proteomics. 2018;15(12):1007–1031. doi: 10.
1080/14789450.2018.1543594
•• Comprehensively describes how quantitative glycomics has
been applied in glycan biomarker discoveries to improve clin­
ical prognosis and diagnosis of many diseases.
8. Xiao K, Han Y, Yang H, et al. Mass spectrometry-based qualitative
and quantitative N-glycomics: an update of 2017-2018. Anal Chim
Acta. 2019;1091:1–22. doi: 10.1016/j.aca.2019.10.007
9. Kaneko Y, Nimmerjahn F, Ravetch JV. Anti-inflammatory activity of
immunoglobulin G resulting from fc sialylation. Science. 2006;313
(5787):670–673. doi: 10.1126/science.1129594
10. Reily C, Stewart TJ, Renfrow MB, et al. Glycosylation in health and
disease. Nat Rev Nephrol. 2019;15(6):346–366. doi: 10.1038/s41581019-0129-4
•• Comprehensively describes how the secreted and cell-surface
glycomes reflect overall cellular status in health and disease.
11. Lageveen-Kammeijer GSM, Kuster B, Reusch D, et al. High sensitiv­
ity glycomics in biomedicine. Mass Spectrom Rev. 2022;41(6):1014–
1039. doi: 10.1002/mas.21730
12. Dotz V, Lemmers RFH, Reiding KR, et al. Plasma protein N-glycan
signatures of type 2 diabetes. Biochim Biophys Acta Gen Subj.
2018;1862(12):2613–2622. doi: 10.1016/j.bbagen.2018.08.005
• Comprehensively describes glycosylation features in type 2
diabetes patients.
13. Keser T, Gornik I, Vuckovic F, et al. Increased plasma N-glycome
complexity is associated with higher risk of type 2 diabetes.
Diabetologia. 2017;60(12):2352–2360. doi: 10.1007/s00125-0174426-9
•• Increased complexity of plasma N-glycan structures is asso­
ciated with higher risk of developing type 2 diabetes and
poorer regulation of blood glucose levels.
14. Liu XE, Desmyter L, Cf G, et al. N-glycomic changes in hepatocel­
lular carcinoma patients with liver cirrhosis induced by hepatitis B
virus. Hepatol. 2007;46(5):1426–1435. doi: 10.1002/hep.21855
15. Vreeker GCM, Vangangelt KMH, Bladergroen MR, et al. Serum Nglycan profiles differ for various breast cancer subtypes. Glycoconj
J. 2021;38(3):387–395. doi: 10.1007/s10719-021-10001-3
16. Buse JB, Wexler DJ, Tsapas A, et al. 2019 update to: management of
hyper glycemia in type 2 diabetes, 2018. A Consensus Report by
the American Diabetes Association (ADA) and the European
Association for the Study of Diabetes (EASD). Diabetes Care.
2020;43(2):487-493. doi: 10.2337/dci19-0066
17. ElSayed NA, Aleppo G, Aroda VR, et al. 9. Pharmacologic
approaches to glycemic treatment: standards of care in diabetes2023. Diabetes Care. 2023;46(Suppl 1):S140–S157. doi: 10.2337/
dc23-S009
18. Samson SL, Vellanki P, Blonde L, et al. American association of
clinical endocrinology consensus statement: comprehensive type
2 diabetes management algorithm - 2023 update. Endocr Pract.
2023;29(5):305–340. doi: 10.1016/j.eprac.2023.02.001
19. Makrydaki E, Kotidis P, Polizzi KM, et al. Hitting the sweet spot with
capillary electrophoresis: advances in N-glycomics and glycopro­
teomics. Curr Opin Biotechnol. 2021;71:182–190. doi: 10.1016/j.cop
bio.2021.07.013
• Comprehensively describes advancements in capillary electro­
phoresis-based
applications
to
glycomics
and
glycoproteomics.
20. Vanhooren V, Laroy W, Libert C, et al. N-Glycan profiling in the
study of human aging. Biogerontol. 2008;9(5):351–356. doi: 10.
1007/s10522-008-9140-z
21. Adua E, Memarian E, Russell A, et al. High throughput profiling of
whole plasma N-glycans in type II diabetes mellitus patients and
healthy individuals: a perspective from a Ghanaian population.
1122
H.-J. DONG ET AL.
Arch Biochem Biophys. 2019;661:10–21. doi: 10.1016/j.abb.2018.10.
015
22. Meng X, Song M, Vilaj M, et al. Glycosylation of IgG associates with
hypertension and type 2 diabetes mellitus comorbidity in the
Chinese muslim ethnic minorities and the Han Chinese. J Pers
Med. 2021;11(7):614. doi: 10.3390/jpm11070614
23. Rudman N, Kifer D, Kaur S, et al. Children at onset of type 1
diabetes show altered N-glycosylation of plasma proteins and
IgG. Diabetologia. 2022;65(8):1315–1327. doi: 10.1007/s00125-02205703-8
24. Meng X, Wang F, Gao X, et al. Association of IgG N-glycomics with
prevalent and incident type 2 diabetes mellitus from the paradigm
of predictive, preventive, and personalized medicine standpoint.
Epma J. 2023;14(1):1–20. doi: 10.1007/s13167-022-00311-3
25. Singh SS, Naber A, Dotz V, et al. Metformin and statin use associate
with plasma protein N-glycosylation in people with type 2 dia­
betes. BMJ Open Diabetes Res Care. 2020;8(1):e001230. doi: 10.
1136/bmjdrc-2020-001230
•• Comprehensively describes metformin and statins associated
with decreased fucosylation and increased galactosylation and
sialylation in glycans.
26. Clerc F, Reiding KR, Jansen BC, et al. Human plasma protein Nglycosylation. Glycoconj J. 2016;33(3):309–343. doi: 10.1007/
s10719-015-9626-2
27. Gudelj I, Lauc G, Pezer M. Immunoglobulin G glycosylation in aging
and diseases. Cell Immunol. 2018;333:65–79. doi: 10.1016/j.cellimm.
2018.07.009
28. Lemmers RFH, Vilaj M, Urda D, et al. IgG glycan patterns are
associated with type 2 diabetes in independent European popula­
tions. Biochim Biophys Acta Gen Subj. 2017;1861(9):2240–2249. doi:
10.1016/j.bbagen.2017.06.020
29. Lau KS, Partridge EA, Grigorian A, et al. Complex N-glycan number
and degree of branching cooperate to regulate cell proliferation
and differentiation. Cell. 2007;129(1):123–134. doi: 10.1016/j.cell.
2007.01.049
30. Calle MC, Fernandez ML. Inflammation and type 2 diabetes.
Diabetes Metab. 2012;38(3):183–191. doi: 10.1016/j.diabet.2011.11.
006
31. Reiding KR, Vreeker GCM, Bondt A, et al. Serum protein N-glycosy­
lation changes with rheumatoid arthritis disease activity during
and after pregnancy. Front Med (Lausanne). 2018;4:241. doi: 10.
3389/fmed.2017.00241
32. Sarrats A, Saldova R, Pla E, et al. Glycosylation of liver acute-phase
proteins in pancreatic cancer and chronic pancreatitis. Proteomics
Clin Appl. 2010;4(4):432–448. doi: 10.1002/prca.200900150
33. Hamfjord J, Saldova R, Stöckmann H, et al. Serum N-glycome
characterization in patients with resectable periampullary adeno­
carcinoma. J Proteome Res. 2015;14(12):5144–5156. doi: 10.1021/
acs.jproteome.5b00395
34. Arnold JN, Saldova R, Hamid UM, et al. Evaluation of the serum Nlinked glycome for the diagnosis of cancer and chronic inflamma­
tion. Proteomics. 2008;8(16):3284–3293. doi: 10.1002/pmic.
200800163
35. Saldova R, Piccard H, Perez-Garay M, et al. Increase in sialylation
and branching in the mouse serum N-glycome correlates with
inflammation and ovarian tumour progression. PLOS ONE. 2013;8
(8):e71159. doi: 10.1371/journal.pone.0071159
36. Ma B, Simala-Grant JL, Taylor DE. Fucosylation in prokaryotes and
eukaryotes. Glycobiology. 2006;16(12):158R–184R. doi: 10.1093/gly
cob/cwl040
37. Irons EE, Lau JTY. Systemic ST6Gal-1 is a pro-survival factor for
murine transitional B cells. Front Immunol. 2018;9:2150. doi: 10.
3389/fimmu.2018.02150
38. Abdelgani S, Puckett C, Adams J, et al. Insulin secretion predicts the
response to antidiabetic therapy in patients with new-onset dia­
betes. J Clin Endocrinol Metab. 2021;106(12):3497–3504. doi: 10.
1210/clinem/dgab403
• A comprehensive description of the plasma C-peptide concen­
tration (C-Pep)120/C-Pep0 ratio during the OGTT was the
strongest predictor of response to therapy.