Luo Y, Li R, Kang J, Zhao BN, Lan LJ, Gao FJ, Ren XX, Zhu YF, Liu DF. Chronic hepatitis B with type 2 diabetes mellitus: Association between glycemic control and liver fibrosis. World J Diabetes 2025; 16(7): 107256 [DOI: 10.4239/wjd.v16.i7.107256]
Corresponding Author of This Article
Da-Feng Liu, MD, PhD, Chief Physician, Professor, The First Ward of Internal Medicine, Public Health Clinical Center of Chengdu, No. 18 Jingjushi Road, Jinjiang District, Chengdu 610066, Sichuan Province, China. ldf312@126.com
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Retrospective 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/
Yan Luo, Yan-Feng Zhu, School of Public Health, Chengdu Medical College, Chengdu 610500, Sichuan Province, China
Rui Li, Jun Kang, Feng-Jiao Gao, Xiao-Xia Ren, Da-Feng Liu, The First Ward of Internal Medicine, Public Health Clinical Center of Chengdu, Chengdu 610066, Sichuan Province, China
Ben-Nan Zhao, Li-Juan Lan, Department of Internal Medicine, Public Health Clinical Center of Chengdu, Chengdu 610066, Sichuan Province, China
Co-corresponding authors: Yan-Feng Zhu and Da-Feng Liu.
Author contributions: Luo Y and Li R contributed equally to this manuscript as co-first authors. Luo Y and Liu DF designed the research study; Zhu YF and Liu DF supervised the study and made equal contributions as co-corresponding authors. Luo Y, Li R, Kang J, Zhao BN, Lan LJ, Gao FJ, Ren XX, Zhu YF, and Liu DF acquired the data, analyzed and interpreted the data, drafted the manuscript, provided administrative, technical, or material support. All authors approved the final manuscript.
Supported by Natural Science Foundation of Sichuan Province, No. 2023NSFSC0682.
Institutional review board statement: This study was approved by the Ethics Committee of the Public Health Clinical Center of Chengdu, No. YJ-K2024-15-01.
Informed consent statement: As this is a retrospective study and the majority of patients could not be contacted, the requirement of written informed consent was waived by the hospital ethics committee.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: All the data, models, or codes generated or used during the study are available from the corresponding author by request corresponding author.
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: Da-Feng Liu, MD, PhD, Chief Physician, Professor, The First Ward of Internal Medicine, Public Health Clinical Center of Chengdu, No. 18 Jingjushi Road, Jinjiang District, Chengdu 610066, Sichuan Province, China. ldf312@126.com
Received: March 20, 2025 Revised: April 30, 2025 Accepted: June 16, 2025 Published online: July 15, 2025 Processing time: 118 Days and 2.9 Hours
Abstract
BACKGROUND
The interplay between abnormal glucose metabolism and the progression of liver fibrosis in patients with both chronic hepatitis B (CHB) and type 2 diabetes mellitus (T2DM) remains unclear. Previous studies have suggested that the coexistence of these conditions may exacerbate liver inflammation and fibrosis; however, the impacts of dynamic changes in glucose metabolism indicators, hypoglycemic medication regimens, and glycemic control status on liver fibrosis require further elucidation.
AIM
To explore the effect of glycemic control on hepatic fibrosis in patients with CHB and T2DM.
METHODS
A total of 420 patients with CHB and T2DM admitted to the Public Health Clinical Center of Chengdu between October 2018 and January 2022 were retrospectively included and classified according to liver stiffness measurement and glycemic control for between-group comparisons.
RESULTS
Significant differences were observed in the alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase, AST/ALT ratio, total bilirubin, direct bilirubin, diabetes treatment program, and thrombin time values among the liver fibrosis groups (adjusted P < 0.05). Significant differences in albumin and gamma-glutamyl transferase levels were observed among the groups categorized by glucose status at admission (adjusted P < 0.05). A positive correlation between fasting plasma glucose (FPG) and liver stiffness measurement was found to be mediated by ALT and AST. Fibrinogen and the international normalized ratio were positively correlated with glycated hemoglobin A1c, while the fibrosis-4 score, ALT, AST/ALT ratio, type III procollagen N-terminal peptide, ferritin, and activated partial thromboplastin time were correlated with FPG at admission. Additionally, AST was positively correlated with FPG at discharge (P < 0.05).
CONCLUSION
Specific glucose metabolic parameters, hypoglycemic agents, and glycemic control status markers are associated with hepatic fibrosis in patients with both CHB and T2DM. Close blood glucose monitoring, optimized use of hypoglycemic agents, and continuous maintenance of good glycemic control may slow the progression of liver fibrosis in patients with CHB and T2DM.
Core Tip: This study found that fasting blood glucose significantly mediated the progression of hepatic fibrosis in patients with both chronic hepatitis B and type 2 diabetes mellitus, via the alanine aminotransferase/aspartate aminotransferase pathway. Albumin and gamma-glutamyl transferase levels varied among groups with differing glycemic status, while fibrinogen, the international normalized ratio, serum ferritin, type III procollagen N-terminal peptide, aspartate aminotransferase, prothrombin time, and thromboplastin activity were associated with glycemic levels. These findings indicate that both hepatic impairment and coagulation dysfunction are influenced by glycemic control, and that stringent glycemic management along with the administration of hypoglycemic agents may lower the risk of fibrosis.
Citation: Luo Y, Li R, Kang J, Zhao BN, Lan LJ, Gao FJ, Ren XX, Zhu YF, Liu DF. Chronic hepatitis B with type 2 diabetes mellitus: Association between glycemic control and liver fibrosis. World J Diabetes 2025; 16(7): 107256
Hepatitis B poses a major threat to public health due to its high transmissibility and the high incidence of its chronicity and adverse outcomes[1]. According to a World Health Organization report, approximately 254 million people globally are living with chronic hepatitis B (CHB), which results in about 1.1 million deaths annually[2]. China is classified as a medium-high hepatitis B endemic area, with 70 million hepatitis B surface antigen-positive infections[3], accounting for almost one-third of the global burden of hepatitis B virus (HBV) infections[4], with approximately 28 million cases of CHB[5]. The latest estimates indicate that the global prevalence of diabetes mellitus (DM) among adults was 537 million in 2021 with an expected increase to 783 million by 2045, with type 2 DM (T2DM) accounting for more than 90% of the total DM prevalence[6].
In recent years, a noteworthy correlation between HBV infection and DM has been observed, sparking considerable interest. In HBV-endemic areas, the prevalence of DM among individuals infected with CHB ranges from 6%-14% across different countries[7-9]. Some studies have also observed a higher rate of HBV infection among patients with T2DM than among those without DM[10]. Liver fibrosis is a common stage in the progression of all chronic liver diseases towards cirrhosis. HBV infection increases the overall risk of liver fibrosis[11], and patients with CHB are more susceptible to developing cirrhosis, with DM exacerbating these adverse outcomes[12]. Furthermore, a study by Watt et al[13] indicated that elevated glycated hemoglobin A1c (HbA1c) levels are a key indicator that necessitates further screening for susceptibility to liver fibrosis as well as hepatocellular carcinoma. Therefore, clinical strategies to delay the progression of liver fibrosis are critical to reducing the incidence of cirrhosis. Previous research has shown that the use of hypoglycemic agents may reduce the risk of liver-related events[14], decrease cirrhosis, and improve liver fibrosis[15]. Moreover, the mortality rate from primary liver cancer in T2DM patients has risen by approximately 70% in most countries and regions worldwide in recent years[16], indicating that T2DM might be a risk factor for the progression of liver disease.
Currently, a liver biopsy is considered the gold standard for assessing liver fibrosis; however, its invasive nature has limited its use in clinical settings. In recent years, transient elastography, a noninvasive, cutting-edge technique for diagnosing liver fibrosis, has been increasingly utilized in clinical practice. In the present study, we utilized liver ultrasound and transient elastography to obtain liver stiffness measurement (LSM) values and examined the correlations of general clinical characteristics, DM-related laboratory indices, and DM treatment regimens with the degree of liver fibrosis in patients with CHB combined with T2DM. The aim of this study was to provide a foundation for the development of strategies to slow the progression of liver fibrosis in this patient population.
MATERIALS AND METHODS
Study population
This retrospective analysis involved screening of patients with both CHB and T2DM who were admitted to the Public Health Clinical Center of Chengdu between October 27, 2018, and January 25, 2022. After screening, 420 patients with both CHB and T2DM were included in this study for analysis. The sample size of this study was larger than the minimum sample size of 306 cases calculated using the OpenEpi website (https://www.openepi.com).
Inclusion and exclusion criteria
The study enrolled participants based on the following criteria: (1) Diagnoses of both CHB and T2DM; (2) Voluntary agreement to noninvasive liver stiffness assessments via ultrasound; (3) Age 18 years or older; and (4) Hospitalization. The criteria for exclusion included: (1) Type 1 DM; and (2) Infection with a different hepatitis virus.
Diagnostic criteria
The diagnostic criteria and staging of T2DM were based on the 2017 edition of the Chinese Guidelines for Prevention and Control of T2DM[17], and the diagnosis of CHB was based on the 2015 edition of the Guidelines for Prevention and Control of CHB[18].
Grouping standards
In accordance with the Expert Consensus on Diagnosis of Liver Fibrosis by Transient Elastography (2018 update)[19], the 420 included patients were categorized into three groups based on LSM values, with LSM < 6.0 kPa considered exclusion of liver fibrosis, LSM ≥ 6.0 kPa and < 12.0 kPa considered with liver fibrosis, and LSM > 12.0 kPa considered cirrhosis. For those with available data, patients were also divided into three glycemic control categories based on fasting plasma glucose (FPG) and HbA1c levels upon admission. The well-managed group had FPG levels between 4.4-6.1 mmol/L and HbA1c ≤ 7.0%, while the moderately controlled group had FPG levels in the range of 6.1-7.8 mmol/L FPG and HbA1c of 7.0%-8.0%. The poorly controlled group exhibited FPG > 7.8 mmol/L and HbA1c > 8.0%. In cases where the FPG and HbA1c ranges indicated different groups, the HbA1c values were used for classification. Patients with FPG data at discharge were also divided into groups with optimal glucose control (FPG < 7.0 mmol/L) and suboptimal control (FPG ≥ 7.0 mmol/L).
Data collection
Data for demographic (age, gender, ethnicity) and clinical (history of DM, history of CHB, height, weight, hospitalization duration, history of smoking, history of alcohol consumption, liver function markers, liver fibrosis serologic markers, coagulation markers, DM markers, LSM, DM treatment regimen, and platelet counts) characteristics were collected for the study population. Body mass index (BMI) was calculated as weight (kg)/[height (m)]2, and the fibrosis-4 (FIB-4) score was calculated using age, aspartate aminotransferase (AST) level, platelet count, and alanine aminotransferase (ALT) level, as follows: Age (years) × AST (U/L)/[platelet count (× 109/L) × ALT (U/L)1/2]. Databases were created in accordance with research needs. Researchers strictly ensured the accuracy, completeness, and authenticity of all data.
Statistical analysis
The SPSS 26.0 software was utilized for statistical analysis. Measurement data were subjected to the Kolmogorov-Smirnov test, and those exhibiting a normal distribution are presented as mean ± SD. Group comparisons were conducted using independent samples t-tests and analysis of variance. Non-normally distributed measures were described using the median and the 25th and 75th percentiles [M (Q1-Q3)], and group comparisons were conducted using the Mann-Whitney U test and the Kruskal-Wallis H rank sum test. Rates or constituent ratios were employed to represent count data, and group comparisons were made using the χ2 test and the exact probability method, with P < 0.05 indicating a statistically significant difference. One-way analysis of variance was followed by the least significant difference method for two-way comparisons of sample means, if significant, the Wilcoxon rank sum test for two-way comparisons, if significant on the Kruskal-Wallis H rank sum test, and the χ2 split for significance on the χ2 test. The process plug-in of SPSS was used to apply the bootstrap method to mediate the effect analysis of those with meaningful comparisons of liver fibrosis groupings with glycemic indices and LSM, and P < 0.05 indicated a statistically significant difference. A generalized linear regression model was used to investigate the effect of liver fibrosis on patients’ blood glucose levels. To reduce bias, the model was adjusted for confounding factors, including age, sex, ethnicity, smoking history, alcohol consumption, and BMI. P < 0.05 was considered statistically significant.
RESULTS
Baseline characteristics of patients with CHB and T2DM
The study included 420 patients with both CHB and T2DM, which constituted 28.9% (420/1452) of the total number of patients hospitalized with CHB and DM during the study period (Figure 1). The majority of the patients were male, representing 76.4% of the total, with a male-to-female ratio of 3.24:1 (Table 1). Age grouping revealed that 54.8% of patients were between 45-59 years of age, while those aged 75-89 years accounted for the lowest percentage (3.3%, Table 1). The mean BMI of the patients was 24.09 ± 3.52 kg/m2, with 82.6% falling within the normal and overweight range (Table 1). Additionally, 51.9% had a history of smoking, 49% had a history of alcohol consumption, and the majority of hospitalizations lasted less than 1 month (Table 1). Of the 420 patients, 272 (64.8%) had LSM values meeting the diagnostic criteria for cirrhosis (LSM > 12.0 kPa), while the groups with (LSM ≥ 6.0 kPa and < 12.0 kPa) and without liver fibrosis (LSM < 6.0 kPa) included 121 and 27 patients, respectively (Figure 1 and Table 1).
Figure 1 Patient categorization according to liver stiffness measurement and glycemic control.
Table 1 General clinical data of patients with chronic hepatitis B combined with type 2 diabetes mellitus, n (%).
Variable
Classifications
Cases
mean ± SD/mean (Q1-Q3)
Age (years)
18-44
59 (14.0)
54 (48, 62)
45-59
230 (54.8)
60-74
117 (27.9)
75-89
14 (3.3)
Number of days of hospitalization (days)
17 (12-24)
FIB-4
1.04 (0.58-2.04)
ALT (U/L)
40 (26-65)
AST (U/L)
40 (29-57)
ALP (U/L)
100 (77-144)
GGT (U/L)
64 (30.5-115.5)
S/L
1 (0.7-1.4)
TP (g/L)
67.63 ± 8.28
ALB (g/L)
37.27 ± 6.62
Glo (g/L)
29.9 (26.8-34)
A/G
1.25 (1.03-1.5)
TBIL (μmol/L)
16 (9.8-27.65)
Bc-Tr (μmol/L)
1.59 (0.82-4.17)
Fer (ng/mL)
222 (94-447)
NEFA (mmol/L)
0.52 (0.36-0.7)
PIIIP (ng/mL)
56.25 (15.97-148.53)
CG (μg/mL)
4.75 (1.78-15.72)
CIV (ng/mL)
49.11 (22.28-113.97)
LN (ng/mL)
87.95 (58.37-125.66)
HA (ng/mL)
118.03 (75.56-260.73)
FBG (g/L)
2.3 (1.85-2.81)
PT (s)
13.4 (12.4-14.6)
Pa (%)
79.3 (67-90.5)
PT-INR
1.12 (1.04-1.25)
APTT (seconds)
30.1 (27.2-34.35)
TT (seconds)
17.6 (16.6-18.95)
Admission glucose status
HbA1c (%)
Favorable
155 (36.9)
7.3 (6.2-9)
Normal
92 (21.9)
Admission FPG (mmol/L)
Poor
168 (40.0)
7.57 (6.32-10.19)
Defective
5 (1.2)
BMI (kg/m2)
Thin (BMI < 18.5 kg/m2)
11 (2.6)
24.13 ± 3.5
Normal (18.5-24 kg/m2)
187 (44.5)
Overweight (24-28 kg/m2)
160 (38.1)
Obese (BMI ≥ 28 kg/m2)
47 (11.2)
Defective
15 (3.6)
Duration of diabetes mellitus (years)
2 (0.01-6)
Duration of liver disease (years)
7 (0.5-20)
LSM (kPa)
Liver fibrosis exclusion group
27 (6.4)
16.6 (9.13-26.98)
Liver fibrosis group
121 (28.8)
Cirrhosis group
272 (64.8)
Discharge FPG (mmol/L)
Standard
224 (53.3)
6.42 (5.47-8)
Non standard
146 (34.8)
Not examined
50 (11.9)
Ethnicity
The Han nationality
407 (96.9)
Tibetan nationality
12 (2.9)
Yi nationality
1 (0.2)
Smoking history
Chronic smoking
148 (35.2)
Occasional smoker
16 (3.8)
Quit smoking
54 (12.9)
Never smoked
202 (48.1)
Drinking history
Drinking alcohol for a long time
76 (18.1)
Occasional drinker
62 (14.8)
Quit drinking
68 (16.2)
Never drank alcohol
214 (51)
Gender
Female
99 (23.6)
Male
321 (76.4)
Diabetes treatment program
Insulin
172 (41)
Drugs
71 (16.9)
Insulin + drugs
34 (8.1)
Untreated
143 (34)
The admission FPG data available for 415 patients indicated that 155 (37.3%) had good glycemic control and 92 (22.1%) had normoglycemia, while 168 (40.0%) exhibited poor glycemic control (Table 1). Additionally, of the 370 patients evaluated at discharge, 244 (65.9%) patients showed optimal glucose control and 146 (39.5%) patients exhibited suboptimal control (Figure 1 and Table 1). More than one-third of all 420 patients were untreated 143 (34.0%), and 172 (41.0%) used insulin only for glycemic control (Table 1).
Influence of the degree of liver fibrosis on various indicators in patients with CHB and T2DM
A comparative analysis of the three groups with differing liver fibrosis status revealed statistically significant differences (P < 0.05) for multiple parameters, including ALT, AST, alkaline phosphatase (ALP), AST/ALT ratio (S/L), total bilirubin (TBIL), true conjugated bilirubin (Bc-Tr) level, thrombin time (TT), and DM treatment protocol (Table 2). In pairwise comparisons, ALT, AST, and S/L differed significantly between the no liver fibrosis group and the cirrhosis group, as well as between the liver fibrosis group and the cirrhosis group (adjusted P < 0.05). The ALT and AST levels were lower in the no liver fibrosis group and the cirrhosis group, whereas the S/L was higher in the no liver fibrosis group than in the cirrhosis group. ALP levels differed significantly between the liver fibrosis group and cirrhosis group (adjusted P < 0.05), with a lower level observed in the liver fibrosis group compared with the cirrhosis group (Figure 2). Additionally, TBIL, Bc-Tr, and TT differed between the no liver fibrosis group and the cirrhosis group (adjusted P < 0.05), with lower values in the no liver fibrosis group than in the cirrhosis group (Figure 3A-C). DM treatment program differed significantly between the liver fibrosis group and cirrhosis group (adjusted P < 0.05, Figure 3D). In this study, outliers were defined as values that fell outside the interval (Q1 - 3 × interquartile range, Q3 + 3 × interquartile range). After removal of these outliers, the same differences were observed in the pair-wise group comparisons, indicating the robustness of the results (Supplementary Figures 1 and 2).
Figure 2 Pair-wise comparisons among groups with differing fibrosis status (total n = 420, no fibrosis group n = 27, liver fibrosis group n = 121, and cirrhosis group n = 272).
A: Alanine aminotransferase; B: Aspartate aminotransferase; C: Alkaline phosphatase; D: Aspartate aminotransferase/alanine aminotransferase ratio. The Wilcoxon rank-sum test was used for intergroup comparisons of alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase and aspartate aminotransferase/alanine aminotransferase ratio. ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; ALP: Alkaline phosphatase; S/L: Aspartate aminotransferase/alanine aminotransferase ratio. aP < 0.05, bP < 0.001.
Figure 3 Pairwise comparisons among those with differing fibrosis status (total n = 420, no fibrosis group n = 27, liver fibrosis group n = 121, and cirrhosis group n = 272).
A: Total bilirubin; B: True conjugated bilirubin; C: Thrombin time; D: Diabetes mellitus treatment program. The Wilcoxon rank-sum test was used for intergroup comparisons of total bilirubin, true conjugated bilirubin and thrombin time, and the χ2 test was used for intergroup comparisons of the diabetes mellitus regimens. TBIL: Total bilirubin; Bc-Tr: True conjugated bilirubin; TT: Thrombin time; DM: Diabetes mellitus. aP < 0.05.
Table 2 Comparison between groups in various stages of liver fibrosis, n (%).
Variable
Excluded liver fibrosis group
Hepatic fibrosis group
Cirrhosis group
F/H/χ2
P value
TP (g/L)
65.96 ± 8.33
66.87 ± 7.8
68.14 ± 8.46
1.555
0.212
ALB (g/L)
35.46 ± 6.35
37.1 ± 5.78
37.53 ± 6.97
1.248
0.288
BMI (kg/m2)
23.72 ± 3.44
24.33 ± 3.98
24.07 ± 3.28
0.392
0.676
Age (years)
55 (50, 63)
54 (48, 62)
54.5 (48, 62.75)
0.173
0.917
Days of hospitalization (days)
15 (10, 21)
17 (12.5, 26)
17 (11.25, 23)
2.067
0.356
FIB-4
1.49 (0.7, 2.71)
1.12 (0.62, 2.48)
1 (0.52, 1.96)
5.097
0.078
ALT (U/L)
12 (10, 42)
23 (18.5, 50)
47.5 (29.75, 79)
70.894
< 0.001
AST (U/L)
29 (19, 39)
32 (23.5, 50.5)
43.5 (33, 64)
40.724
< 0.001
ALP (U/L)
108 (82, 130)
89 (71, 114.5)
104 (80, 149)
10.275
0.006
GGT (U/L)
65 (30, 97)
53 (26, 118)
66 (33, 121)
1.906
0.386
S/L
1.5 (0.9, 2.4)
1.2 (0.9, 1.55)
0.9 (0.7, 1.3)
26.964
< 0.001
Glo (g/L)
29.3 (25.5, 34.4)
29.2 (26.35, 33.05)
30.4 (27.2, 34.1)
3.613
0.164
A/G
1.29 (1.02, 1.47)
1.26 (1.02, 1.51)
1.23 (1.03, 1.5)
0.27
0.872
TBIL (μmol/L)
10.4 (7, 19.4)
14.5 (9.1, 28.35)
16.7 (10.53, 28.8)
8.975
0.011
Bc-Tr (μmol/L)
0.87 (0.38, 1.9)
1.52 (0.75, 3.28)
1.67 (0.95, 4.73)
10.182
0.006
Fer (ng/mL)
110 (29, 479.3)
196.5 (65.75, 415.75)
230.5 (119, 468.43)
5.686
0.058
NEFA (mmol/L)
0.59 (0.39, 0.7)
0.48 (0.3, 0.67)
0.54 (0.38, 0.72)
4.379
0.112
PIIIP (ng/mL)
61.8 (6.45, 142.33)
64.48 (13.1, 157.77)
52.9 (18.1, 146.41)
0.708
0.702
CG (μg/mL)
2.54 (1.39, 11.39)
4.47 (1.7, 21.19)
4.99 (1.83, 14.84)
1.249
0.535
CIV (ng/mL)
46.47 (29.1, 84.15)
53.12 (18.62, 115.61)
48.89 (22.64, 114.62)
0.039
0.981
LN (ng/mL)
79.15 (56.39, 152.71)
91.78 (61.28, 125.74)
86.91 (57.66, 123.61)
0.537
0.765
HA (ng/mL)
104.85 (81.46, 286.29)
117.28 (73.01, 264.51)
123.05 (73.25, 250.3)
0.246
0.884
FBG (g/L)
2.64 (1.9, 3.44)
2.32 (1.88, 2.76)
2.29 (1.83, 2.81)
3.242
0.198
PT (seconds)
13.1 (12.1, 15)
13.5 (12.6, 14.3)
13.4 (12.4, 14.7)
0.280
0.869
Pa (%)
79.1 (64.2, 97.2)
78 (67.4, 89.2)
80.3 (65.8, 91)
0.019
0.991
PT-INR
1.06 (1.02, 1.27)
1.12 (1.03,1.24)
1.12 (1.05, 1.25)
1.477
0.478
APTT (seconds)
30 (26.8, 40.5)
30.1 (27.1, 34.3)
30.2 (27.3, 33.9)
0.545
0.762
TT (seconds)
17 (15.6, 18.3)
17.7 (16.7, 18.8)
17.7 (16.6, 19)
6.327
0.042
HbA1c (%)
6.7 (6.6, 8.5)
7.3 (6.1, 9.5)
7.3 (6.2, 9)
0.162
0.922
Duration of diabetes mellitus (years)
2 (0.42, 7)
2 (0.05, 7.5)
2 (0.01, 6)
0.857
0.651
Duration of liver disease (years)
6 (0.08, 11)
8 (1, 20)
7 (0.44, 20)
1.214
0.545
Admission FPG (mmol/L)
6.81 (6.3, 9.9)
7.62 (6.28, 9.37)
7.63 (6.33, 10.62)
1.121
0.571
Discharge FPG (mmol/L)
6.11 (5.44, 7.47)
6.58 (5.76, 7.96)
6.42 (5.36, 8.29)
0.727
0.695
Ethnicity
The Han nationality
26 (96.3)
118 (97.5)
263 (96.7)
4.322
0.386
Tibetan nationality
1 (3.7)
2 (1.7)
9 (3.3)
Yi nationality
1 (0.8)
Smoking history
Chronic smoking
10 (37.0)
34 (28.1)
104 (38.2)
5.035
0.516
Occasional smoker
6 (5.0)
10 (3.7)
Quit smoking
3 (11.1)
19 (15.7)
32 (11.8)
Never smoked
14 (51.9)
62 (51.2)
126 (46.3)
Drinking history
Drinking alcohol for a long time
3 (11.1)
17 (14.0)
56 (20.6)
7.467
0.272
Occasional drinker
2 (7.4)
17 (14.0)
43 (15.8)
Quit drinking
8 (29.6)
21 (17.4)
39 (14.3)
Never drank alcohol
14 (51.9)
66 (54.5)
134 (49.3)
Gender
Female
8 (29.6)
31 (25.6)
60 (22.1)
1.177
0.587
Male
19 (70.4)
90 (74.4)
212 (77.9)
Diabetes treatment program
Insulin
9 (33.3)
36 (29.8)
127 (46.7)
13.892
0.026
Drugs
3 (11.1)
26 (21.5)
42 (15.4)
Insulin + drugs
1 (3.7)
12 (9.9)
21 (7.7)
Untreated
14 (51.9)
47 (38.8)
82 (30.1)
Impact of T2DM on liver fibrosis
Group comparisons revealed marked distinctions in gamma-glutamyl transferase (GGT) and albumin (ALB) levels between the good, moderate, and poor glycemic control groups (P < 0.05, Table 3). Pair-wise group comparisons showed that the GGT level was significantly lower in the group with good glycemic control than in the group with poor glycemic control (adjusted P < 0.05, Figure 4A) and the ALB level was significantly higher in the moderate glycemic control group than in the poor glycemic control group (adjusted P < 0.05, Figure 4B). Moreover, variations in LSM were observed among patients with CHB and T2DM who were treated with different DM management regimens. LSM values were significantly higher in the group treated with insulin only than in the group treated with drug only (P < 0.05, Figure 5). In addition, LSM values were significantly higher in the insulin-treated group than in the untreated group (P < 0.05, Figure 5).
Figure 4 Pair-wise comparisons among groups with differing glycemic control status at admission (total n = 415, good glycemic control n = 155, moderate glycemic control n = 92, and poor glycemic control n = 168).
A: Gamma-glutamyl transferase; B: Albumin. The least significant difference method was used for intergroup comparisons of gamma-glutamyl transferase, and the Wilcoxon rank-sum test was used for intergroup comparisons of albumin. GGT: Gamma-glutamyl transferase; ALB: Albumin. aP < 0.05.
Figure 5 Pair-wise comparisons of groups receiving different diabetes mellitus treatment regimens (total n = 420, untreated group n = 143, insulin group n = 172, drug group n = 71, and insulin + drug group n = 34).
The Wilcoxon rank sum test was used. LSM: Liver stiffness measurement. bP < 0.001.
Table 3 Comparison of liver-related characteristics of patients with different admission glycemic states.
Variable
Good glucose control group
Normal blood glucose group
Poor glycemic control group
F/H/χ2
P value
TP (g/L)
67.44 ± 7.09
68.9 ± 7.47
67.35 ± 8.05
1.403
0.247
ALB (g/L)
37.31 ± 6.17
38.29 ± 6.25
36.28 ± 6.39
3.127
0.045
FIB-4
1.49 (0.7, 2.71)
1.12 (0.62, 2.48)
1 (0.52, 1.96)
0.534
0.766
ALT (U/L)
12 (10, 42)
23 (18.5, 50)
47.5 (29.75, 79)
1.098
0.577
AST (U/L)
29 (19, 39)
32 (23.5, 50.5)
43.5 (33, 64)
2.419
0.298
ALP (U/L)
108 (82, 130)
89 (71, 114.5)
104 (80, 149)
5.128
0.077
GGT (U/L)
65 (30, 97)
53 (26, 118)
66 (33, 121)
6.439
0.040
S/L
1.5 (0.9, 2.4)
1.2 (0.9, 1.55)
0.9 (0.7, 1.3)
5.337
0.069
Glo (g/L)
29.3 (25.5, 34.4)
29.2 (26.35, 33.05)
30.4 (27.2, 34.1)
0.910
0.635
A/G
1.29 (1.02, 1.47)
1.26 (1.02, 1.51)
1.23 (1.03, 1.5)
2.672
0.263
TBIL (μmol/L)
10.4 (7, 19.4)
14.5 (9.1, 28.35)
16.7 (10.53, 28.8)
0.740
0.691
Bc-Tr (μmol/L)
0.87 (0.38, 1.9)
1.52 (0.75, 3.28)
1.67 (0.95, 4.73)
2.249
0.325
Fer (ng/mL)
110 (29, 479.3)
196.5 (65.75, 415.75)
230.5 (119, 468.43)
5.556
0.062
NEFA (mmol/L)
0.59 (0.39, 0.7)
0.48 (0.3, 0.67)
0.54 (0.38, 0.72)
3.798
0.150
PIIIP (ng/mL)
61.8 (6.45, 142.33)
64.48 (13.1, 157.77)
52.9 (18.1, 146.41)
1.725
0.422
CG (μg/mL)
2.54 (1.39, 11.39)
4.47 (1.7, 21.19)
4.99 (1.83, 14.84)
1.692
0.429
CIV (ng/mL)
46.47 (29.1, 84.15)
53.12 (18.62, 115.61)
48.89 (22.64, 114.62)
0.844
0.656
LN (ng/mL)
79.15 (56.39, 152.71)
91.78 (61.28, 125.74)
86.91 (57.66, 123.61)
1.037
0.595
HA (ng/mL)
104.85 (81.46, 286.29)
117.28 (73.01, 264.51)
123.05 (73.25, 250.3)
2.197
0.333
FBG (g/L)
2.64 (1.9, 3.44)
2.32 (1.88, 2.76)
2.29 (1.83, 2.81)
0.817
0.665
PT (seconds)
13.1 (12.1, 15)
13.5 (12.6, 14.3)
13.4 (12.4, 14.7)
2.433
0.296
Pa (%)
79.1 (64.2, 97.2)
78 (67.4, 89.2)
80.3 (65.8, 91)
2.440
0.295
PT-INR
1.06 (1.02, 1.27)
1.12 (1.03, 1.24)
1.12 (1.05, 1.25)
3.747
0.154
APTT (seconds)
30 (26.8, 40.5)
30.1 (27.1, 34.3)
30.2 (27.3, 33.9)
0.339
0.844
TT (seconds)
17 (15.6, 18.3)
17.7 (16.7, 18.8)
17.7 (16.6, 19)
2.271
0.321
LSM (kPa)
5.3 (4.8, 5.6)
8.3 (7.35, 9.95)
23.35 (16.95, 36.2)
0.147
0.929
Impact of blood glucose on liver fibrosis
Through the mediating effect of ALT, a positive correlation was observed between admission FPG and LSM (β = 0.60, P < 0.05), with an 80.0% direct effect (β = 0.48, P < 0.05) and a 20.0% indirect effect (β = 0.12, Pa < 0.05, Pb < 0.05, Supplementary Table 1, Figure 6). Through the mediating effect of ALT, no direct effect was detected between discharge FPG and LSM (P > 0.05), indicating a positive correlation between discharge FPG and LSM (β = 0.30, Pa < 0.05, Pb < 0.05, Supplementary Table 1). Similarly, a positive correlation was observed between discharge FPG and LSM (β = 0.35, Pa < 0.05, Pb < 0.05) through the mediating effect of AST, while no direct effect was found between discharge FPG and LSM (P > 0.05, Supplementary Table 1).
Figure 6 Schematic of the analysis of the mediating effects of independent, mediating, and dependent variables.
a: Regression coefficients for the effect of independent variables on the mediating variables; b: Regression coefficient for the effect of the mediating variable on the dependent variable; c: Total effect of the independent variable on the dependent variable without considering the mediating variable; e1: Residual.
An analysis of the mediating effects between admission FPG and AST, ALP, S/L, TBIL, Bc-Tr, TT, and other factors revealed positive correlations with LSM (β > 0, P < 0.05). However, no significant mediating effect or a weak mediating effect was found for AST, ALP, S/L, TBIL, Bc-Tr, TT, and other factors (Pa > 0.05 and Pb > 0.05, Supplementary Table 1). Similarly, an analysis of the mediating effects of discharge FPG on ALP, S/L, TBIL, and TT revealed a positive correlation between discharge FPG and LSM (β > 0, P < 0.05), while the indicators ALP, S/L, TBIL, and TT exhibited no mediating effect or a weak mediating effect (Pa > 0.05, Pb > 0.05, Supplementary Table 1).
Impact of liver fibrosis on blood glucose
A generalized linear regression model was constructed to investigate the effect of liver fibrosis on patients’ blood glucose levels, and after accounting for confounders including age, gender, ethnicity, smoking history, drinking history, and BMI, the model indicated that both FBG (β = 0.618, P < 0.05) and prothrombin time-international normalized ratio (PT-INR) (β = 4.871, P < 0.05) were positively correlated with HbA1c (Table 4). FIB-4 (β = 0.001, P < 0.05), ferritin (Fer) (β > 0, P < 0.05), and procollagen type III N-terminal peptide (β > 0, P < 0.05) were positively correlated with admission FPG, whereas ALT (β < 0, P < 0.05), S/L (β = -0.003, P < 0.05), and activated partial thromboplastin time (β < 0, P < 0.05) were negatively correlated with admission FPG (Table 4). AST (β = 0.016, P < 0.05) was positively correlated with discharge FPG (Table 4).
Table 4 Generalized linear regression model for liver-related indicators and diabetes mellitus indicators.
In the present study of patients with both CHB and T2DM, 64.8% of patients showed progression to cirrhosis (Table 1). The hypothesis that DM has a deleterious effect on cirrhosis has been associated with insulin resistance and hepatic steatosis[20,21]. Studies have shown that hepatic steatosis is more prevalent among patients with CHB than in the general population[22]. Consequently, DM may accelerate the progression of fibrosis in individuals with CHB. Consistently, Wu et al[23] previously reported that T2DM is linked to the development of progressive liver fibrosis in CHB patients. Research has also indicated that some patients with CHB exhibit poor glycemic control. However, in the present study, 34% of individuals with both CHB and T2DM were not receiving DM treatment (Table 1). Considering that poor glycemic control is known to be an important cause of poor patient prognosis[24], this population requires closer blood glucose monitoring and the selection of appropriate treatment regimens.
With stratification of liver pathology into three groups, significant differences in biochemical indices were observed between the fibrosis and cirrhosis groups, as well as between the fibrosis and no fibrosis groups, highlighting the progressive nature of liver dysfunction (Table 2, Figures 2 and 3). Notably, AST, ALT, and TBIL were previously shown to be strongly correlated with liver fibrosis assessment parameters such as AST to platelet ratio index, FibroTest, and the FIB-4 score[25]. Elevated values for these indices reflect hepatocellular injury and the destruction of hepatic lobular structure, as well as impairment of hepatic secretory and excretory functions. T2DM can exacerbate hepatic injury and further elevate ALT and AST levels through several pathways, including oxidative stress and inflammatory responses that increase hepatocellular injury[26]. This can lead to a chronic state of low-grade inflammation, which contributes to elevated levels of cytokines (tumor necrosis factor-α, interleukin-6, etc.) that promote hepatocellular necrosis[27]. Moreover, DM is accompanied by hyperlipidemia and metabolic disorders, which promote hepatic fibrosis and indirectly aggravate liver injury[28]. Prolonged hyperglycemia, insulin resistance, and impaired fat metabolism can damage hepatocytes and diminish their ability to process bilirubin, resulting in elevated TBIL levels[29].
In the present study, no statistically significant differences were observed in TBIL, Bc-Tr, and TT between the groups with and without liver fibrosis, although trends towards a gradual increase were observed. It is possible that our data inclusion criteria, which targeted the mild fibrosis stage[30], captured a transitional state where traditional biochemical markers lack differentiation ability, a phenomenon increasingly recognized in the noninvasive diagnostic literature.
Upon analyzing patients’ glycemic status upon admission, statistical differences in ALB and GGT levels were observed between groups (Table 3, Figure 4). The underlying mechanisms responsible for these differences are intriguing. The lower ALB level in the poor glycemic control group may indicate dual forms of hepatocyte injury: Compromised synthesis due to prolonged endoplasmic reticulum stress[31] and increased catabolism resulting from hyperglycemia-induced lysosomal activation[32]. This aligns with the negative correlation between the ALB level and glycemic status as proposed by Jiao[33]. Notably, the liver is the only organ capable of synthesizing ALB, and liver injury associated with CHB also results in impaired ALB synthesis.
The elevation of GGT in hyperglycemic CHB patients may involve the following mechanisms: Hyperglycemia itself leads to an increase in oxidative stress, which mobilizes the body’s antioxidant enzyme system and upregulates the expression of GGT to combat reactive oxygen species[34]; oxidative stress and chronic inflammation are increased in the liver of CHB patients, the superimposition of which aggravates damage to the hepatocytes and cholangiocytes, contributing to the entry of GGT into the bloodstream[35]; the insulin resistance due to T2DM and CHB is associated with hepatic steatosis[36]; and hepatic steatosis further activates GGT[37]. A study by Yuan et al[38] found that GGT is positively correlated with glycemic status, aligning with the findings of the present study. Consistently, GGT exhibits different characteristics in various liver diseases[39].
In the present study, although not statistically significant, an increasing trend for LSM was observed according to DM treatment in the following order: Drug-treated group < untreated group < insulin + drug group < insulin group (Figure 5). Several studies have shown that various glucose-lowering drugs, such as metformin[40], empagliflozin combined with ursodeoxycholic acid[41], and sodium-glucose cotransporter 2 inhibitors alone[42] can delay the progression of hepatic fibrosis. The primary mechanisms involve enhancing insulin sensitivity, decreasing oxidative stress, and suppressing inflammation. For instance, metformin activates the adenosine monophosphate-activated protein kinase pathway to inhibit hepatic lipid synthesis and reduce inflammatory factor expression[43], whereas sodium-glucose co-transporter 2 inhibitors can slow down fibrosis progression by reducing glucose toxicity[44] and ameliorating the fibrotic hepatic microenvironment[45]. Furthermore, in patients with T2DM and cirrhosis, the combination of liraglutide and insulin pump therapy can reduce hepatic lipid peroxidation, decrease hepatic fibrosis indices, and improve the degree of cirrhosis[46,47].
Previous research has shown that T2DM patients treated with insulin have a significantly greater insulin resistance index than those who do not receive insulin therapy[48]. This phenomenon can be explained by several mechanisms. Firstly, while enhancing glucose utilization, insulin may stimulate fibroblast activation and collagen deposition via direct or indirect pathways, thereby contributing to liver fibrosis[49]. Secondly, patients who are given insulin treatment often present with more severe conditions, including a higher degree of underlying liver injury. The possibility of disease severity acting as a confounding factor may contribute to their elevated LSM value. Furthermore, when insulin is combined with other hypoglycemic agents, pharmacokinetic or pharmacodynamic interactions between the drugs may impact hepatic tissue repair and fibrosis. Overall, the mechanisms by which various glucose-lowering regimens affect hepatic fibrosis are complex. Glucose-lowering drugs can improve the hepatic microenvironment and inhibit fibrosis. Subsequent prospective and large-sample studies should be conducted to further clarify the effects of different drug treatment strategies on the progression of liver fibrosis and the responsible mechanisms of action.
The mediation analysis in the present study indicated a positive correlation between FPG at both admission and discharge and LSM, with higher blood glucose levels associated with more severe liver fibrosis (Supplementary Table 1). A study by Mak et al[50] concurred with our findings that a high glycemic load is associated with fibrosis progression in patients with CHB and T2DM, emphasizing the importance of glycemic control in reducing liver-related complications. These observations indicate that glycemic control in patients with CHB and DM may be an important measure for delaying the progression of liver fibrosis.
Moreover, after adjustment for confounding factors in this study, multiple indicators related to metabolism and liver function demonstrated a significant correlation with glycemic control (Table 4). First, both FBG and PT-INR were positively correlated with HbA1c, which aligns with previous research findings[51,52]. Additional studies have reported that both FBG and PT-INR, as markers of coagulation, are not only closely associated with post-hepatitis B liver fibrosis but also that their elevation commonly signifies liver fibrosis and chronic inflammatory responses[53,54]. Past research has also shown that the degree of liver fibrosis is also positively correlated with HbA1c[13,55]. As liver fibrosis progresses, FBG, an acute-phase reactant protein, significantly increases. This reflects the patient’s inflammation and coagulation activation levels and also indirectly leads to an increase in HbA1c by affecting insulin resistance and glucose metabolism[56]. Thus, the severity of liver fibrosis can further affect a patient’s HbA1c level via FBG and PT-INR.
Additional studies have found that abnormalities in liver fibrosis markers, Fer, liver function, and coagulation function are all associated with FPG at admission and discharge. Andrade et al[57] reported that FPG is strongly correlated with hepatic fibrosis, and that patients with mild-to-moderate hepatic fibrosis exhibit lower glycemia compared with those with severe hepatic fibrosis and cirrhosis. Fer has been shown to be positively correlated with both T2DM and FPG[58], and serum Fer, which may affect the blood glucose level, has been widely used as a marker of iron status in epidemiologic studies. A study in Saudi Arabia concluded that the AST to platelet ratio index scores for AST and ALT represent cost-effective and novel FBG markers that can be used as supporting evidence for the diagnosis and management of hyperglycemia[59]. In the present study, hyperglycemia was associated with a hypercoagulable state of the blood, which may lead to increased renal injury in patients with T2DM. Additionally, anticoagulation may play a role in improving blood glucose levels in patients with CHB combined with T2DM.
The present study has certain limitations that should be considered. First, it has the common methodological shortcomings of all single-center, retrospective analyses. Additionally, the observation period was relatively brief, as it was limited to patients’ hospital stays, and did not encompass the full spectrum of liver disease management. Thirdly, the impacts of lipid levels and nutritional status on the progression of liver fibrosis were not examined. Additionally, the sample size of this study was limited, which may have introduced selection bias into the analysis. Finally, information regarding specific types of hypoglycemic agents used by patients who received hypoglycemic drug therapy was not available, which precludes an assessment of their role in the progression of liver fibrosis. Despite these limitations, our analysis revealed several noteworthy trends: Patients with CHB and T2DM were predominantly male, outnumbering female patients by a ratio of approximately 3:1. The majority fell within the age range of 45-59 years, and their mean BMI measured 24.09 kg/m2, with a standard deviation of 3.52 kg/m2. Overall, 64.8% of the patients had cirrhosis, 36.9% exhibited good fasting glycemic control upon admission, and 53.3% achieved glycemic control by discharge, and these patients all met the established standards. Patients whose liver disease had progressed to cirrhosis and those with poorer glycemic control exhibited more severe liver injury. Additionally, glucose-lowering drugs were found to slow the progression of liver fibrosis. Positive relationships were observed between admission FPG and LSM, which was mediated by ALT, and between discharge FPG and LSM, which was mediated by both ALT and AST. Liver injury, coagulation, and liver fibrosis may influence blood glucose levels.
CONCLUSION
In patients with CHB and T2DM, little difference in the degree of liver injury was observed until the liver disease progressed to cirrhosis, which had a greater impact on liver dysfunction. A hyperglycemic state may exacerbate liver injury in these patients, and glucose-lowering drugs can decelerate the progression of liver fibrosis in patients with CHB and T2DM. With poorer glycemic control, the degree of liver fibrosis is more severe. Also, a hypercoagulable blood state may exacerbate hyperglycemia. Consequently, effective glycemic management and the selection of an appropriate treatment regimen in patients with CHB combined with T2DM could delay the progression of liver fibrosis to cirrhosis. Early treatment of CHB may also support better glycemic control.
ACKNOWLEDGEMENTS
We sincerely acknowledge all the study patients for their cooperation in past data collection. We thank all the members of this study group for their valuable contributions.
Footnotes
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Endocrinology and metabolism
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade A, Grade B, Grade B, Grade B, Grade B, Grade B, Grade C
Novelty: Grade A, Grade B, Grade C, Grade D
Creativity or Innovation: Grade B, Grade B, Grade C, Grade D
Scientific Significance: Grade A, Grade B, Grade C, Grade D
P-Reviewer: Chen YX; Dabla PK; Li MZ; Liu YF; Pappachan JM; Tung TH S-Editor: Wu S L-Editor: A P-Editor: Zhang XD
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