Sun Y, Huang P, Zhao XQ, Tang ZQ, Xu TT, Wang XW, Qi ZX, Lin WR, Li MY, Gu YJ. Relationship between glucagon and metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus. World J Hepatol 2025; 17(6): 104693 [DOI: 10.4254/wjh.v17.i6.104693]
Corresponding Author of This Article
Yun-Juan Gu, Department of Endocrinology and Metabolism, Affiliated Hospital of Nantong University, No. 20 Xisi Road, Nantong 226000, Jiangsu Province, China. desette@ntu.edu.cn
Research Domain of This Article
Medical Laboratory Technology
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Yi Sun, Ping Huang, Xiao-Qin Zhao, Zhu-Qi Tang, Tong-Tong Xu, Xin-Wei Wang, Yun-Juan Gu, Department of Endocrinology and Metabolism, Affiliated Hospital of Nantong University, Nantong 226000, Jiangsu Province, China
Zong-Xian Qi, Wei-Rong Lin, Min-You Li, R&D Department, Guangzhou Jinde Biotech Company Limited, Guangzhou 510000, Guangdong Province, China
Author contributions: Sun Y and Huang P contribute equally to this study as co-first authors; Sun Y wrote the first draft (including substantive translation); Huang P analyzed or synthesized the study data; Zhao XQ and Tang ZQ validated the results; Xu TT and Wang XW performed the experiments and data collection; Qi ZX and Lin WR provided the study materials, instrumentation, and analytical tools; Li MY managed and coordinated the planning and implementation of the research activities; Gu YJ designed the study.
Supported by Nantong Municipal Science and Technology Project, No. MS22019005, No. MSZ2023155 and No. JCZ2023004; Nantong University Hospital Research Hospital Construction Project, No. YJXYY202204-XKB09; and Guangzhou Jinde Biotechnology Company Self-selected Clinical Research Projects, No. HXKT20221024.
Institutional review board statement: The study was reviewed and approved by the ethics committee of Nantong University Affiliated Hospital (Approval No. 2018-k016).
Informed consent statement: All study participants provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All authors disclosed no potential conflict of interest relevant to this article.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement:
Dataset available from the corresponding author at desette@ntu.edu.cn. Participants gave informed consent for data sharing and the presented data are anonymized and risk of identification is low.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yun-Juan Gu, Department of Endocrinology and Metabolism, Affiliated Hospital of Nantong University, No. 20 Xisi Road, Nantong 226000, Jiangsu Province, China. desette@ntu.edu.cn
Received: December 31, 2024 Revised: March 18, 2025 Accepted: May 26, 2025 Published online: June 27, 2025 Processing time: 177 Days and 19.7 Hours
Abstract
BACKGROUND
Glucagon (GCG) plays an important role in both diabetes and metabolic dysfunction-associated steatotic liver disease (MASLD).
AIM
To investigate the relationship between GCG and the development of MASLD in patients with type 2 diabetes mellitus (T2DM) and the possible influencing factors.
METHODS
A total of 212 T2DM patients were enrolled. GCG concentrations were measured using the chemiluminescence method. Fibro touch ultra sound attention parameter was used to determine the occurrence of MASLD. Multivariate logistic regression analyses were employed to assess the correlation between GCG levels and MASLD severity in T2DM patients.
RESULTS
The ultrasound attenuation parameter of T2DM patients was positively correlated with GCG, insulin (INS), C-peptide (CP), INS resistance, obesity related indicators (body mass index, waist circumference, percent body fat, basal metabolic rate, visceral fat area, fat free mass index, fat mass index, skeletal muscle index), liver cirrhosis related indicators [liver stiffness measurement (LSM), gamma glutamyl transpeptidase to platelet ratio, alanine aminotransferase], serum uric acid, diastolic blood pressure and triglyceride, while were negative correlated with age, fibrosis 4 score and high-density lipoprotein cholesterol (all P < 0.05). According to the multivariate logistic regression model, the T2DM patients with fasting GCG concentrations above the cut-off value had a significant increased risk of MASLD (OR: 3.068; 95%CI: 1.333-7.064; P = 0.008). Also, an increased concentration of fasting CP (OR: 1.965; 95%CI: 1.323-2.918; P = 0.001) and LSM (OR: 1.422; 95%CI: 1.16-1.743; P = 0.001) were significantly associated with a higher risk of MASLD in T2DM patients.
CONCLUSION
Fasting GCG, fasting CP and LSM are risk factors for MASLD in T2DM patients.
Core Tip: Our study aims to explore the relationship between glucagon (GCG) and the development of metabolic dysfunction-associated steatotic liver disease (MASLD) in type 2 diabetes mellitus (T2DM) patients, and the possible influencing factors. A total of 212 patients with T2DM were enrolled. We categorized the study subjects into two groups with and without MASLD by the ultrasound attenuation parameter (244 dB/m as cut-off value). We find fasting GCG, fasting C-peptide and liver stiffness measurement are risk factors for MASLD in patients with T2DM.
Citation: Sun Y, Huang P, Zhao XQ, Tang ZQ, Xu TT, Wang XW, Qi ZX, Lin WR, Li MY, Gu YJ. Relationship between glucagon and metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus. World J Hepatol 2025; 17(6): 104693
According to the IDF Diabetes Atlas, by 2030 and 2045, the number of people have diabetes is projected to reach 643 million and 783 million, with the prevalence rates of 11.3% and 12.2%, respectively[1]. Notably, more than 90% patients are type 2 diabetes mellitus (T2DM). In recent years, a complex bidirectional interaction between T2DM and metabolic dysfunction-associated steatotic liver disease (MASLD) has been identified[2], and T2DM is an important risk factor for the development of MASLD[3], with a prevalence rate of MASLD of about 55% in patients with T2DM[4]. While, MASLD also increases the risk of developing T2DM[5]. These two diseases and their complications place a heavy disease burden on society[6].
Glucagon (GCG) is a 29 amino acid peptide produced by pancreatic alpha cells that acts by binding to the GCG receptor (GCGR)[7]. GCGR is widely distributed in the human body and is found in tissues such as kidney, heart, adipocytes, brain, and gastrointestinal tract, with the highest expression in hepatocytes[8]. As early as 1975, Unger and Orci[9] proposed the "bihormonal hypothesis" to emphasize the importance of GCG in diabetes[10]. Recently, it has been found that patients with liver diseases such as fatty liver[11], chronic hepatitis[12] and cirrhosis[13] may develop hyperglucagonemia. GCG has a wide-ranging effects on liver-mediated metabolic homeostasis and plays key roles in hepatic amino acid and ketone body metabolism, mitochondrial metabolism and function, and lipid metabolism[14]. GCG may influence hepatic lipid metabolism by participating in the regulation of the dynamic balance between de novo lipogenesis, lipolysis, and fatty acid oxidation (β-oxidation)[14].
However, few studies have examined the changes in GCG secretion levels in patients with T2DM combined with MASLD. This investigation sought to determine whether GCG levels correlate with MASLD risk in T2DM patients and evaluate factors that might affect this association.
MATERIALS AND METHODS
Study population
A total of 212 patients hospitalized for T2DM, whose median duration of diabetes was 7 years, were consecutively recruited from August to October 2023 in the present study at Affiliated Hospital of Nantong University. Diabetes mellitus was diagnosed according to the 2024 American Diabetes Association diagnostic criteria[15,16]. The exclusion criteria were as follows: T1MD, secondary diabetes, acute complications of diabetes, use history of dipeptidyl peptidase 4 inhibitors, GCG-like peptide-1 analogues or receptor agonists, severe cardiopulmonary impairment, chronic liver or renal diseases, chronic or acute inflammation, malignancy, or previous surgical or trauma history within the past three months. Each participant was informed of the purpose of the study, and all subjects provided written consent. The study was approved by the ethics committee of Nantong University Affiliated Hospital (2018-k016) and was conducted in accordance with the principles of the Declaration of Helsinki.
Anthropometric and biochemical measurements
At study initiation, detailed participant information was documented, encompassing: identity and demographics (name, age, sex), physical measurements [weight, height, waist-to-hip ratio (WHR), waist circumference (WC)], hemodynamic profiles [systolic blood pressure (SBP), diastolic blood pressure (DBP)], disease-specific metrics (illness duration) and historical health data (past medical history). WC was measured with a fiber measuring tape at the level of the umbilicus under fasting conditions, with the subject in a standing position. Dividing hip (cm) by WC (cm) equals WHR. Blood pressure was measured twice, and the mean value was calculated and then included. The body mass index (BMI) is obtained by dividing a person's weight (kg) by his height squared (m²).
Fasting blood samples were collected on the morning of the second day of admission. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDH), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum uric acid (SUA) and serum creatinine (Scr) levels were determined by a fully automatic biochemical analyzer (Hitachi 7600-020; Hitachi, Tokyo, Japan). The measurement of glycosylated hemoglobin (HbA1C) was performed via high-performance liquid chromatography (Bio-Rad Laboratories, Hercules, CA). Urinary creatinine and albumin concentrations were measured by immunoinsulin assay and BN II analyzer (Siemens Diagnostics). Urinary albumin to creatinine ratio (UACR) was calculated as a division of urinary albumin levels with urinary creatinine levels. The Chronic Kidney Disease Epidemiology Collaboration equation (2009) is expressed as follows: Estimated glomerular filtration rate (eGFR) = 141 × min (Scr/κ, 1) α × max (Scr/κ, 1) - 1.209 × 0.993 age (× 1.018 if female; × 1.159 if black), where κ is 0.7 for females and 0.9 for males, α is -0.329 for females and -0.411 for males, min indicates the minimum of Scr/κ or 1, and max indicates the maximum of Scr/κ or 1[17].
Calculation of noninvasive liver fibrosis diagnostic assessment indicators: AST to platelet ratio index (APRI) = [AST/ULN/PLT (109/L)] × 100 (ULN: Upper limit of normal for AST); gamma glutamyl transpeptidase to platelet ratio (GPR) = (GGT/ULN) /PLT (109/L) × 100 (ULN: Upper limit of normal for GGT); fibrosis 4 score (FIB-4) = [age (years) × AST] /[PLT (109/L) × ALT1/2].
Oral glucose tolerance test and measurement of plasma GCG concentration
The patients underwent 3-hour oral glucose tolerance tests (OGTTs) and ingested 75 g of glucose. Before the OGTT implementations, all patients stopped taking hypoglycemic drugs for 3 days. Glucose, INS, C-peptide (CP) and GCG levels were determined using venous blood specimens acquired at 0, 30, 60, 120 and 180 minutes. Plasma glucose was examined using a standard laboratory procedure (Siemens ADVIA® 2400). INS levels and CP concentrations were determined using chemiluminescent methods (Cobas e411; Roche, Switzerland).
GCG concentrations were measured using a fully automated analyzer based on chemiluminescence technology (HomoG 100, JINDE BIOTECH, Guangzhou, China). The lowest detection concentration for GCG was less than 2.0 pmol/L. The coefficient of variation of GCG measurements was < 10%.
The Homeostatic model assessment of INS resistance (HOMA-IR) was computed using the following formula: HOMA-IR = [fasting plasma glucose (mmol/L) × fasting INS (μU/mL)]/22.5.
Transient elastography FibroTouch measurement
Transient elastography FibroTouch (FT; FibroTouch-FT5000, iLivTouch series, Wuxi Hisky Medical Technologies, China) measures the degree of hepatic fibrosis by detecting the liver stiffness measurement (LSM) based on the vibration-controlled instantaneous elastography. Liver steatosis is quantitatively assessed by measuring the extent of attenuation of ultrasound signal occurs in liver, referred as the ultrasound attenuation parameter (UAP).
The UAP of 244 dB/m was used as the cut-off value for the diagnosis of hepatic steatosis[18].
Measurement of body composition indicators
Patients' body composition indicators were measured through bioelec trical impedance analysis (BIA; InBody 770, Korea). The BIA device is a universal, convenient, instantaneous, non-invasive, and highly accurate bioelectrical impedance analyzer. We assessed the participants' percent body fat (PBF), basal metabolic rate (BMR), visceral fat area (VFA), fat-free mass index (FFMI), fat mass index (FMI), and skeletal muscle index (SMI) by means of this instrument.
Statistical analysis
Continuous variables were presented as mean ± SD. Non-normally distributed data were log-transformed prior to analysis and were presented as median (interquartile range). If the transformed data remained non-normally distributed, the Mann-Whitney U test was used for analysis. For categorical and qualitative variables expressed numerically, the χ2 test was employed. An independent t-test was used to compare normally distributed quantitative data between two independent groups. Pearson correlation analysis was conducted to identify specific factors that may be associated with UAP. Binary logistic regression analysis was performed to determine the independent influence of MASLD in T2DM patients. Analysis was adjusted for Age, WC, GPR, FIB4, ALT, DBP, UA, TG and HDL. All analyses were conducted using the SPSS statistical package (version 26.0; SPSS, Chicago, IL, United States). Statistical significance was set at P < 0.05.
RESULTS
We categorized the study population into two groups with and without MASLD by UAP (244 dB/m as cut-off value).
As shown in Table 1, there were no significant differences in sex, diabetes duration, SBP, DBP, AST, SUA, CR, eGFR, UACR, GLU0 min, GLU120 min, area under the curve (AUC)GLU0-180 min, HBA1c and APRI between the two groups. In patients with T2DM, patients with MASLD were younger than those without MASLD (P = 0.003). HDL and FIB4 were lower in T2DM patients with MASLD than in those without MASLD (P < 0.05). When compared with the participants without MASLD, the T2DM patients with MASLD had significantly higher levels of BMI, WC, WHR, ALT, TC, TG, LDL, INS0 min, INS120 min, AUCINS0-180 min, HOMA-IR, CP0 min, CP120 min, AUCCP0-180 min, GCG0 min, GCGpeak, AUCGCG0-180 min, LSM, GPR, PBF, BMR, VFA, FFMI, FMI and SMI (all P < 0.05).
Table 1 Anthropometric parameters and biochemical indexes among subjects with or without metabolic dysfunction-associated steatotic liver disease.
Variables
UAP ≤ 244 dB/m (n = 68)
UAP > 244 dB/m (n = 144)
P value
Male/female
42/26
96/48
0.538
Age (years)
59 (54, 69)
54 (39, 66)
0.003
Diabetes duration (years)
8.5 (3, 12.75)
5 (1, 10)
0.1
BMI (kg/m2)
22.14 (20.22, 24.21)
26.1 (23.67, 28.65)
< 0.001
WC (cm)
84.32 ± 9.9
92.49 ± 11.24
< 0.001
WHR
0.9 (0.86, 0.95)
0.95 (0.9, 1.0)
< 0.001
SBP (kPa)
18.53 (16.13, 20.13)
18 (16.67, 19.73)
0.706
DBP (kPa)
10.67 ± 1.57
11 ± 1.53
0.143
ALT (U/L)
17 (10.25, 28.5)
24 (15, 39.75)
0.001
AST (U/L)
20 (16, 27.75)
22 (18, 29.75)
0.067
SUA (μmol/L)
314.5 (254, 401)
342 (271.75, 443)
0.074
Scr (μmol/L)
63.5 (52.5, 79.75)
62 (52, 77)
0.702
eGFR (mL/min/1.73 m2)
106.55 (87, 126.35)
116.95 (92.65, 139.78)
0.162
UACR (mg/g)
2.48 (0.97, 6.27)
2.11 (1.09, 7.82)
0.315
TC (mmol/L)
4.45 (3.62, 5.2)
4.8 (4.17, 5.6)
0.031
TG (mmol/L)
1.14 (0.94, 1.47)
1.81 (1.14, 2.61)
< 0.001
HDL (mmol/L)
1.18 (0.97, 1.3)
1.06 (0.91, 1.2)
0.012
LDL (mmol/L)
2.91 (2.3, 3.5)
3.25 (2.65, 3.91)
0.036
GLU0 min (mmol/L)
8.86 ± 2.48
9.11 ± 2.37
0.468
GLU120 min (mmol/L)
19.94 ± 5.71
19.75 ± 4.04
0.81
AUCGLU 0-180 min
3109.48 ± 804.22
3100.06 ± 590.39
0.931
HBA1c (%)
9.55 (7.23, 11.3)
8.9 (7.7, 10.73)
0.367
INS0 min (mIU/L)
0.59 (0.28, 0.76)
0.86 (0.53, 1.1)
< 0.001
INS120 min (mIU/L)
1.19 ± 0.41
1.41 ± 0.44
0.001
AUCINS 0-180 min
3.38 ± 0.4
3.6 ± 0.41
< 0.001
HOMA-IR
0.17 (-0.14, 0.35)
0.46 (0.13, 0.46)
< 0.001
CP0 min (μg/L)
1.37 (0.79, 2.04)
2.14 (1.37, 2.98)
< 0.001
CP120 min (μg/L)
3.75 (2.81, 5.47)
5.32 (3.54, 7.89)
0.001
AUCCP 0-180 min
569.48 (418.24, 745.2)
834.68 (521.36, 1155)
< 0.001
GCG0 min (pmol/L)
0.97 ± 0.21
1.11 ± 0.26
< 0.001
GCGpeak (pmol/L)
1.0 6± 0.2
1.2 ± 0.24
< 0.001
AUCGCG 0-180 min
3.1 ± 0.24
3.3 ± 0.26
< 0.001
LSM (kPa)
6 (4.08, 7)
7 (6, 8.33)
< 0.001
APRI
-0.59 ± 0.29
-0.51 ± 0.29
0.224
GPR
-0.65 ± 0.37
-0.52 ± 0.34
0.014
FIB4
0.18 ± 0.2
0.10 ± 0.27
0.016
PBF (%)
25.57 ± 8.46
30.64 ± 6.73
< 0.001
BMR (kcal)
1350.15 ± 173.24
1486.33 ± 204.92
< 0.001
VFA (cm2)
75.35 (57.18, 94.9)
98.05 (81.4, 146.15)
< 0.001
FFMI (kg/m2)
16.7 (14.93, 17.85)
18.05 (16.4, 19.48)
< 0.001
FMI (kg/m2)
5.85 (4.45, 7.23)
7.35 (6.13, 9.98)
< 0.001
SMI (kg/m2)
6.9 (5.85, 7.5)
7.5 (6.7, 8.08)
< 0.001
The UAP of T2DM patients were positively correlated with GCG (0 min, peak, AUC0-180 min), INS (0 min, 120 min, AUC0-180 min), CP (0 min, 120 min, AUC0-180 min), HOMA-IR, obesity related indicators (BMI, WC, PBF, BMR, VFA, FFMI, FMI, SMI), liver cirrhosis related indicators (LSM, GPR, ALT), SUA, DBP and TG, while were negative correlated with age, FIB-4 and HDL(all P < 0.05; Table 2).
Table 2 Correlation analysis between ultrasound attenuation parameter and other parameters.
We performed an analysis of covariance for factors associated with UAP. As shown in Table 3, severe multicollinearity was observed between BMI, INS0 min, INS120 min, AUCINS 0-180 min, HOMA-IR, CP120 min, AUCCP0-180 min, GCGpeak, AUCGCG 0-180 min, PBF, BMR, VFA, FFMI, FMI and SMI.
Table 3 Covariance analysis of ultrasound attenuation parameter-related factors.
Collinearity statistics
Tolerance
VIF
Age
0.457
2.191
BMI
0.085
11.734
WC
0.367
2.726
DBP
0.770
1.298
ALT
0.580
1.723
SUA
0.698
1.432
TG
0.785
1.273
HDL
0.787
1.271
INS0 min
0.013
75.684
INS120 min
0.008
131.244
AUCINS 0-180 min
0.005
207.055
HOMA-IR
0.029
34.293
CP0 min
0.126
7.945
CP120 min
0.006
154.348
AUCCP 0-180 min
0.005
185.228
GCG0 min
0.156
6.404
GCGpeak
0.054
18.686
AUCGCG 0-180 min
0.075
13.308
LSM
0.607
1.647
GPR
0.423
2.363
FIB4
0.422
2.368
PBF
0.055
18.162
BMR
0.064
15.617
VFA
0.042
23.946
FFMI
0.060
16.617
FMI
0.024
42.368
SMI
0.029
34.632
Next, we excluded these factors with strong covariates and performed a multiple linear regression analysis, which revealed that UAP was still positively correlated with GCG0 min, CP0 min, and LSM after adjusting for age, weight, DBP, ALT, SUA, TG, HDL, GPR, and FIB-4 (Table 4, all P < 0.05).
Table 4 Multivariate linear regression analysis of various biomarkers with ultrasound attenuation parameter.
Standard β
t value
P value
Age
0.078
0.991
0.323
WC
-0.005
-0.067
0.947
DBP
-0.048
-0.756
0.451
ALT
0.093
1.258
0.210
SUA
0.034
0.512
0.609
TG
0.033
0.526
0.599
HDL
0.050
0.794
0.428
CP0 min
0.245
3.566
< 0.001
GCG0 min
0.182
2.020
0.045
LSM
0.320
5.144
< 0.001
GPR
-0.094
-1.088
0.278
FIB4
0.085
0.977
0.330
We then generated a receiver operating characteristic (ROC) curve of GCG0 min levels to evaluate its diagnostic accuracy for detecting MASLD in patients with T2DM. As illustrated in Figure 1, the ROC curve analysis identified an optimal cutoff value of 10.04 pmol/L for diagnosing MASLD. At this threshold, the sensitivity was 57.35%, and the specificity was 71.53%. The AUC was 0.665 (P = 0.001), indicating a statistically significant diagnostic performance.
Figure 1 Diagnostic performance of glucagon0min in identifying metabolic dysfunction-associated steatotic liver disease in type 2 diabetes mellitus patients.
ROC: Receiver operating characteristic; AUC: Area under curve.
To further explore the association between GCG0 min and MASLD, serum GCG0 min levels were categorized as a binary variable based on the cutoff value derived from the ROC curve. As a categorical independent variable (with the lower GCG0 min as reference), it was included in the multivariate logistic regression analysis (Table 5). We observed that GCG0 min concentrations surpassing the cut-off value were associated with a more than threefold elevated risk of MASLD (OR: 3.068; 95%CI: 1.333-7.064; P = 0.008). Also an increased concentration of CP0 min (OR: 1.965; 95%CI: 1.323-2.918; P = 0.001) and LSM (OR: 1.422; 95%CI: 1.16-1.743; P = 0.001) were significantly associated with a higher risk of MASLD in T2DM patients (Table 5).
Table 5 Multivariate logistic regression model with metabolic dysfunction-associated steatotic liver disease as a dependent variable.
OR (95%CI)
P value
GCG0 min
3.068 (1.333-7.064)
0.008
CP0 min
1.965 (1.323-2.918)
0.001
LSM
1.422 (1.16-1.743)
0.001
DISCUSSION
Our study found that the development of MASLD in T2DM patients was associated with elevated fasting GCG. The possible mechanisms include: First, hepatic steatosis results in decreased sensitivity of the liver to GCG, whereas feedback mechanisms acting at the level of pancreatic alpha cells elevate GCG levels[19]. Second, the presence of the hepatocyte axis[20], the reciprocal feedback loop between GCG and amino acids. Several studies[21-24] have confirmed that when hepatic steatosis induces GCG resistance, hyperaminoacidemia leads to hyperglucagonemia due to decreased urea production stimulated by GCG and increased circulating amino acids.
Several studies[25-27] have found that fasting CP is higher in patients with MASLD disease. Atsawarungruangkit et al[28] found that fasting CP was one of the major risk factors for MAFLD in the United States. In addition, fasting CP has been found to be positively associated with the development of non-alcoholic steatohepatitis (NASH) and the presence of fibrosis in studies of both T2DM[29] and overweight/obese[30] patients. The possible mechanisms include: Firstly, peripheral INS resistance as well as dysregulation of hepatic lipid metabolism in T2DM can explain the positive correlation between fasting CP and intra-hepatic fat content[26]. Previous studies have found that INS regulates important endocrine metabolic regulators such as follistatin, fibroblast growth factor 21 and leptin[31,32]. CPs can regulate leptin secretion by modulating the phosphoinositide 3-kinase or protein kinase B pathway, which in turn affects energy homeostasis processes[29,33].
In addition, regarding the relationship between fasting GCG levels and CP levels, it was found as early as 1987 that GCG could transiently stimulate an increase in CP concentrations through direct or indirect effects[34]. In 2019, Japanese clinical study found that fasting GCG concentration was positively correlated with fasting CP level in patients with T2DM[35]. Moore et al[36] also found a positive correlation between C peptide and GCG in animal studies.
In our study, hepatic steatosis was assessed using FibroTouch UAPs, which have high diagnostic performance and are suitable for clinical evaluation and monitoring of patients with MASLD[18]. MASLD is broadly categorized into non-alcoholic fatty liver and NASH. It is a gradual progression from steatosis, lobular inflammation to fibrosis and cirrhosis[37]. Not surprisingly, our regression results showed a correlation between fatty liver and LSM.
Our study had several limitations. First, the sample size was relatively small; Second, as a single-center, observational study, the causality between GCG and MASLD in patients with T2DM could not be determined; Third, we used the fibro touch UAP to determine fatty liver rather than the gold standard liver biopsy. Therefore, further research involving larger, multicenter cohorts should be conducted to verify the results and molecular studies are also needed to explore the mechanisms underlying these associations.
CONCLUSION
Fasting GCG, fasting CP and LSM are risk factors for MASLD in T2DM patients. Therefore, GCG agonists are being explored as a novel treatment strategy for common metabolic diseases such as fatty liver disease and T2DM.
ACKNOWLEDGEMENTS
The authors thank all the participants of the study.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade C, Grade D
Novelty: Grade B, Grade D, Grade D
Creativity or Innovation: Grade B, Grade C, Grade D
Scientific Significance: Grade B, Grade D, Grade D
P-Reviewer: JI F; Kumar D S-Editor: Lin C L-Editor: A P-Editor: Yu HG
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