Revised: June 2, 2026
Accepted: June 24, 2026
Published online: July 27, 2026
Processing time: 116 Days and 20.4 Hours
Nonalcoholic steatohepatitis (NASH) has become the leading chronic liver disease worldwide. Within its spectrum, liver fibrosis is the only histological feature independently linked to long-term mortality and liver-related events. While traditional risk factors such as obesity and diabetes are known, most existing studies rely on noninvasive markers rather than the gold standard for liver biopsy. This study included 122 patients with NASH diagnosed by biopsy to analyze their clinicopathological features and risk factors for fibrosis severity based on the stratification of liver fibrosis.
To investigate the clinicopathological features and risk factors for fibrosis severity in NASH patients based on pathological stratification.
A total of 122 patients with biopsy-proven NASH at Tianjin Second People’s Hospital between December 2014 and December 2024 were included, with their clinical, laboratory, and pathological data collected. Patients were stratified according to the degree of pathological fibrosis. Univariate analysis, ordinal logistic regression, kappa statistic, and receiver operating characteristic (ROC) curves were applied to analyze their clinicopathological features and identify independent risk factors for fibrosis severity.
(1) Twenty-one patients (17.2%) were in the F1 stage, 86 (70.5%) were in the F2 stage, and 15 (12.3%) were in the F3 stage; (2) liver pathology revealed that as fibrosis severity advanced, hepatocellular steatosis and lobular inflammation worsened, with statistically significant differences (P < 0.05); (3) the overall agreement rate between the liver stiffness measurement (LSM) assessment and pathological staging of liver fibrosis was 48.4%; (4) ROC curve analysis revealed that the area under the curve values for the LSM in the diagnosis of stage F2 and F3 liver fibrosis were 0.761 and 0.829, respectively; and (5) ordinal logistic regression revealed that diabetes [odds ratio (OR) = 3.76, 95% confidence intervals (95%CI): 1.30-10.84, P = 0.014] and low-density lipoprotein (LDL) levels (OR = 1.52, 95%CI: 1.10-2.34, P = 0.049) were independent risk factors for liver fibrosis severity in patients with NASH.
Diabetes and LDL levels are independent risk factors associated with the severity of liver fibrosis in patients with NASH.
Core Tip: This retrospective study included 122 patients diagnosed with nonalcoholic steatohepatitis (NASH) via liver biopsy. SPSS 27.0 and R 4.5.2 software was used to analyze patient data. Patients were stratified according to the degree of pathological fibrosis, their clinicopathological features were analyzed, and risk factors associated with the severity of liver fibrosis were further explored. We revealed that the presence of diabetes and elevated low-density lipoprotein levels are risk factors associated with the severity of liver fibrosis in patients with NASH.
- Citation: Pan PY, Zheng WW, Guo YQ, Liu YG, Zhang XJ, Wu YY, Li JZ, Qi JG, Wang CY. Clinicopathological characteristics and risk factors in nonalcoholic steatohepatitis patients with different stages of liver fibrosis. World J Hepatol 2026; 18(7): 121661
- URL: https://www.wjgnet.com/1948-5182/full/v18/i7/121661.htm
- DOI: https://dx.doi.org/10.4254/wjh.121661
Non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease worldwide. Data from 1990 to 2019 revealed a global prevalence of 30.05%. Furthermore, this prevalence increased from 25.26% from 1990-2006 to 38.00% from 2016-2019[1]. NAFLD covers a wide disease spectrum, ranging from nonalcoholic fatty liver to nonalcoholic steatohepatitis (NASH), cirrhosis, and, in some cases, hepatocellular carcinoma. Current data estimate the global prevalence of NASH to be 5.27%, while the prevalence in the Asia-Pacific region is 4.49%[1].
Among the histological features of NAFLD, liver fibrosis plays a central role in disease progression and prognosis. Liver fibrosis is the only histological factor independently associated with all-cause mortality, liver transplantation requirements, and liver-related complications[2]. Patients with liver fibrosis face an increased risk of death as fibrosis progresses, and this risk is significantly greater in patients with liver fibrosis than in patients without fibrosis[2]. Therefore, identifying factors that contribute to liver fibrosis progression is clinically important for slowing disease progression and improving patient outcomes.
Multiple risk factors, such as age, obesity, and type 2 diabetes, promote the progression of fibrosis in patients with NAFLD/NASH[3-5]. However, most of these investigations are based on large-scale epidemiological data using noninvasive diagnostics, primarily those that rely on noninvasive markers such as laboratory tests to analyze factors associated with liver fibrosis progression. Current research based on liver biopsy is relatively limited.
Therefore, in this study, the clinical data of 122 patients with NASH diagnosed by liver biopsy were retrospectively analyzed, liver fibrosis was stratified on the basis of pathological results, and their clinical, biochemical, and histopathological characteristics (steatosis, lobular inflammation and ballooning) were comprehensively evaluated. After adjusting for confounding factors, an ordered logistic regression model was used to explore and screen for independent risk factors associated with the severity of liver fibrosis in patients with NASH, providing a basis for the early identification of high-risk patients and the development of intervention strategies.
This single-center retrospective cohort study included patients who underwent liver biopsy at Tianjin Second People’s Hospital between December 2014 and December 2024 and who received a pathologically confirmed diagnosis of NASH. All patients underwent liver pathological examination because of unexplained abnormal liver function. The inclusion criteria were as follows: (1) Age ≥ 18 years; (2) Confirmed diagnosis of NASH via liver biopsy; and (3) Complete clinical data and laboratory test results. The exclusion criteria were as follows: (1) History of alcohol consumption (males >
Baseline clinical characteristics of the subjects, including age, sex, body mass index (BMI), and the prevalence of hypertension and diabetes, were meticulously documented. Laboratory tests included the following indicators: Estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), creatinine (CRE), uric acid (UA), total protein, albumin (ALB), total bilirubin (T-BIL), direct bilirubin (D-BIL), indirect bilirubin (I-BIL), fasting plasma glucose (FPG), total cholesterol (TC), triglyceride (TG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bile acids, high-density lipoprotein (HDL), low-density lipoprotein (LDL), white blood cell (WBC), red blood cell (RBC), hemoglobin (Hb), platelet (PLT), immunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin A (IgA), prothrombin time, and globulin. Liver stiffness measurements included controlled attenuation parameter (CAP) values and LSM values. To match the histological findings in time, all biochemical tests and liver examinations were completed within seven days before liver biopsy.
Transient elasticity imaging (FibroScan 502, Echosens, France) was used to measure the CAP and LSM values. Patients fasted for at least 2 hours before the examination. During the measurement, the physician stood to the right of the subject. The probe was placed in the 7th, 8th, and 9th intercostal spaces and was positioned as perpendicular as possible to the plane of the intercostal spaces. Measurements were taken using an M-type probe (3.5 MHz). The CAP (unit: DB/m) and LSM (unit: KPa) were measured and recorded. The data from 10 measurements were recorded, and the median was taken as the final result. A measurement was considered valid only if the deviation is less than one-third of the median value and if the success rate was ≥ 60%. All the operating physicians had undergone formal training in liver elastography and obtained certification for practice. In accordance with the reference ranges recommended by the Guidelines for the Prevention and Treatment of Metabolic-Associated (Nonalcoholic) Fatty Liver Disease (2024 Edition)[6], the staging criteria for liver fibrosis based on the LSM were defined as follows: F0-F1 for a LSM < 8.0 kPa; F2 for 8.0 kPa ≤ the LSM < 12.0 kPa; F3 for 12.0 kPa ≤ the LSM < 20.0 kPa; and F4 for an LSM ≥ 20.0 kPa.
After collection, liver tissue samples were fixed in formalin and embedded in paraffin. The sections were then stained with hematoxylin-eosin, reticular fibers, and Masson’s trichrome. Histological assessment was performed independently by two senior pathologists, each with more than 10 years of clinical experience. If disagreement occurred, the slides were reviewed again by a third senior pathologist. NASH was diagnosed based on the presence of hepatic steatosis, lobular inflammation, and hepatocyte ballooning. Disease activity was evaluated using the NAFLD Activity Score, an 8-point system that includes steatosis (0-3), ballooning (0-2), and lobular inflammation (0-3)[7,8]. The fibrosis stage was determined according to the Brunt criteria[9].
The data were analyzed using SPSS 27.0 and R 4.5.2 software. For continuous variables that conformed to a normal distribution, the independent samples t test was used for comparisons between two groups, and one-way analysis of variance was used for comparisons between three or more groups. The results are expressed as the mean ± SD. For continuous variables that did not conform to a normal distribution, the Mann-Whitney U test was used for comparisons between two groups, and the Kruskal-Wallis H test was used for comparisons between three or more groups. The results are expressed as M (P25, P75). Count data are expressed as the number and percentage of participants, and the χ2 test/Fisher’s exact test was used for comparison. The consistency between the LSM findings and histological fibrosis staging was evaluated using the kappa statistic. The diagnostic efficacy of the LSM for the diagnosis of liver fibrosis was evaluated by receiver operating characteristic (ROC) curve analysis. To identify independent risk factors for fibrosis severity in patients with NASH, we performed a backward stepwise ordered logistic regression analysis. The selection of candidate variables was based on biological plausibility and included demographic characteristics (age and sex), comorbidities (such as diabetes and hypertension) and liver injury biochemical markers. We described these associations using odds ratio (OR) and 95% confidence intervals (95%CI). Before constructing the multivariate model, we performed a multicollinearity diagnosis using the variance inflation factor to rule out multicollinearity among the variables. We used the parallel lines test to assess the proportional odds assumption and employed Pearson’s and deviance tests to evaluate the overall goodness of fit of the model. Given the specific skewed distribution of fibrosis in this cohort, we used the bootstrapping method (1000 bootstrap samples) to estimate the ORs and their 95%CI for the independent factors in the final model. Statistical significance was defined as a P value less than 0.05.
The final cohort consisted of 122 patients with biopsy-confirmed NASH, stratified by fibrosis severity into stages F1 (n = 21, 17.2%), F2 (n = 86, 70.5%), and F3 (n = 15, 12.3%). In total, 66 males (54.1%) and 56 females (45.9%) were included, and the mean age was 41.6 ± 14.2 years, and the mean BMI was 28.3 ± 3.7 kg/m2. The sex, age, and BMI variables did not significantly differ across the three groups (P > 0.05). There were 24 patients (19.7%) with diabetes, 0 (0%) in F1, 18 (75%) in F2, and 6 (25%) in F3. The difference in the prevalence of diabetes among the three groups was statistically significant (P = 0.009). Post hoc analysis confirmed that the diabetic prevalence in F3 significantly eclipsed that in F1 (P = 0.001). Hypertension, identified in 39 patients (32%), reached only marginal statistical significance in its distribution across groups (P = 0.052) (Table 1).
| Total (n = 122) | Stage F1 (n = 21) | Stage F2 (n = 86) | Stage F3 (n = 15) | P value | |
| Male | 66 (54.1) | 11 (52.3) | 47 (54.7) | 8 (53.3) | 0.981 |
| Age (years) | 41.6 ± 14.2 | 42 (30, 57) | 39.5 (28, 52) | 53 (35, 62) | 0.142 |
| BMI (kg/m2) | 28.3 ± 3.7 | 28.3 (26.4, 30.1) | 28.13 (25.4, 30.8) | 28.7 (25.1, 31) | 0.89 |
| Hypertension | 39 (32) | 9 (42.9) | 22 (25.6) | 8 (53.3) | 0.052 |
| Diabetes | 24 (19.7)a | 0 (0) | 18 (20.9) | 6 (40) | 0.009 |
There was no statistically significant difference in the CAP values among the three groups (P > 0.05). However, there was a statistically significant difference in the LSM among the three groups (P < 0.001). Post hoc analysis confirmed that the LSM in group F3 was significantly greater than that in groups F1 and F2 (P < 0.001), whereas the LSM in group F2 was significantly greater than that in group F1 (P = 0.002) (Table 2).
Statistically significant differences were observed among the three patient groups in terms of the eGFR and the serum ALB, AST, and LDL levels (P < 0.05). Intergroup comparisons revealed statistically significant differences in the serum ALB, AST, and LDL levels between groups F1 and F3 (P < 0.05) and statistically significant differences in the eGFR and serum ALB levels between groups F2 and F3 (P < 0.05). There were no statistically significant differences among the three groups in TC, TG, D-BIL, I-BIL, T-BIL, ALT, ALP, GGT, FPG, BUN, CRE, UA, or HDL. There were no statistically significant differences in blood counts (WBC, RBC, Hb, and PLT) or immunoglobulin levels (IgA, IgM, IgG, and Glo) among the three patient groups (P > 0.05) (Table 3).
| Variable | Total (n = 122) | Stage F1 (n = 21) | Stage F2 (n = 86) | Stage F3 (n = 15) | P value |
| ALT (U/L) | 85 (53.4, 141.7) | 70.2 (39, 121) | 85.5 (55.7, 143.5) | 93.2 (59, 158.3) | 0.351 |
| AST (U/L) | 49.5 (36.2, 76) | 47 (29.1, 66.9)a | 49 (36.2, 71.5) | 80 (43.9, 100.4) | 0.031 |
| ALP (U/L) | 76.1 (63, 89.3) | 71(58, 89.6) | 77.3 (63.9, 88) | 86 (54.9, 98.4) | 0.498 |
| GGT (U/L) | 70.4 (46.8, 105.2) | 72 (39, 115.2) | 70.4 (46, 106.1) | 70 (58, 97.5) | 0.9 |
| ALB (g/L) | 47.6 (45.5, 49.8) | 47.5 (44.7, 49) | 47.8 (46, 50.3)b | 45.5 (42.2, 47.9) | 0.008 |
| TBA (µmol/L) | 3.2 (2, 5.3) | 3 (2, 6) | 3 (2, 5) | 4 (3, 6) | 0.367 |
| TP (g/L) | 75.5 ± 5.1 | 75.2 ± 5.9 | 75.9 ± 4.7 | 73.2 ± 5.9 | 0.163 |
| TBIL (µmol/L) | 14 (11.6, 17.4) | 14 (10.5, 18.7) | 13.4 (11.5, 17.2) | 15.2 (13.1, 18.8) | 0.241 |
| DBIL (µmol/L) | 2.9 (1.9, 4.5) | 2.4 (1.7, 6.2) | 3 (1.7, 4.4) | 2.4 (1.9, 4.7) | 0.972 |
| IBIL (μmol/L) | 11 (8.8, 13.2) | 11.1 (7.6, 14.2) | 10.8 (8.8, 12.5) | 12.8 (10, 13.5) | 0.251 |
| BUN (g/L) | 4.5 (3.9, 5.2) | 6.6 (3.9, 5) | 4.5 (3.8, 5.2) | 4.9 (4.2, 5.9) | 0.195 |
| CRE (µmol/L) | 62.9 ± 14.4 | 62.6 ± 13.8 | 62.6 ± 14.0 | 65.5 ± 18.3 | 0.766 |
| UA (µmol/L) | 375 (334.4, 466) | 379 (346.8, 490.5) | 375 (320.9, 462.6) | 374 (363, 491) | 0.646 |
| eGFR (mL/minute/1.73 m2) | 110.8 ± 13.7 | 110.6 ± 14.3 | 113.3 ± 12.1c | 102.5 ± 18.6 | 0.036 |
| FPG (mmol/L) | 5.9 (5.5, 6.7) | 5.7 (5.4, 6.2) | 6 (5.5, 6.8) | 6.2 (5.3, 6.9) | 0.169 |
| TC (mmol/L) | 5.4 ± 1.2 | 5.2 ± 1.3 | 5.4 ± 1.2 | 6.0 ± 1.3 | 0.159 |
| TG (mmol/L) | 1.8 (1.4, 2.5) | 1.9 (1.7, 3) | 1.8 (1.3, 2.4) | 1.9 (1.5, 2.5) | 0.231 |
| HDL (mmol/L) | 1.3 ± 0.3 | 1.3 ± 0.3 | 1.3 ± 0.3 | 1.3 ± 0.2 | 0.599 |
| LDL (mmol/L) | 3.3 ± 1 | 2.9 ± 0.9d | 3.3 ± 0.9 | 3.8 ± 1 | 0.034 |
| Glo (%) | 15.3 (13.8, 16.8) | 15.7 (13.8, 16.5) | 15.1 (13.9, 17.1) | 15.2 (12.8, 17.4) | 0.942 |
| IgG (g/L) | 11.95 (10.31, 14.14) | 11.8 (9.44, 14.07) | 12.33 (10.68, 14.28) | 11.2 (10, 13.45) | 0.29 |
| IgM (g/L) | 0.95 (0.69, 1.19) | 0.97 (0.73, 1.18) | 0.9 (0.66, 1.17) | 1.13 (0.73, 1.72) | 0.267 |
| IgA (g/L) | 2.43 (1.81, 3.21) | 2.32 (1.83, 3.11) | 2.5 (1.82, 3.18) | 2.23 (1.54, 3.49) | 0.949 |
| WBC (109/L) | 6.3 ± 1.5 | 6.3 ± 1.6 | 6.3 ± 1.5 | 6.5 ± 1.2 | 0.856 |
| RBC (1012/L) | 4.91 (4.59, 5.29) | 4.84 (4.47, 5.12) | 4.95 (4.59, 5.36) | 4.88 (4.56, 5.29) | 0.351 |
| Hb (g/L) | 146 ± 14.6 | 144 ± 10.7 | 146.9 ± 15.7 | 145.9 ± 12.4 | 0.72 |
| PLT (109/L) | 248 ± 65 | 247.6 ± 78.6 | 252.2 ± 66.0 | 224.7 ± 31.1 | 0.309 |
| PT (s) | 11.1 (10.6, 12.53) | 12.1 (10.4, 12.95) | 11.1 (10.6, 11.95) | 11.6 (10.8, 13) | 0.272 |
Concomitant with the advancement of liver fibrosis, an intensification in both hepatic steatosis and lobular inflammation was observed across the cohorts. Statistical differences among the three groups were significant for both degrees of hepatic steatosis and lobular inflammation (P < 0.05). Specifically, compared with their F1 counterparts, F3 patients presented with markedly more pronounced hepatic steatosis (P = 0.049). Moreover, the severity of lobular inflammation in the F1 group remained significantly lower than that recorded in stages F2 and F3 (P < 0.05). However, no statistically significant difference was found in the degree of ballooning across the three groups (Table 4).
A crosstabulation analysis was conducted to compare the LSM-determined liver fibrosis results with the liver biopsy pathology findings. The results revealed an overall concordance rate of 48.4% between the LSM and pathological fibrosis staging, with an underestimation rate of 37.7%. Kappa consistency testing yielded a kappa value of 0.206 (P = 0.001) (Table 5).
| Pathological staging | LSM-F1 (n = 59) | LSM-F2 (n = 43) | LSM-F3 (n = 18) | LSM-F4 (n = 2) | Total |
| Path-F1 (n = 21) | 17 | 3 | 1 | 0 | 21 |
| Path-F2 (n = 86) | 39 | 36 | 11 | 0 | 86 |
| Path-F3 (n = 15) | 3 | 4 | 6 | 2 | 15 |
| Total | 59 | 43 | 18 | 2 | 122 |
To further explore the factors leading to LSM misjudgment, according to the consistency between the LSM results and pathological results, the patients were divided into a concordant group and a discordant group. The results revealed that there were no significant differences between the two groups in terms of BMI, CAP values, degree of steatosis or ballooning. The difference in lobular inflammation between the two groups was marginally significant (P = 0.097) (Table 6).
| Variable | Concordant group (n = 59) | Discordant group (n = 63) | P value | |
| BMI (kg/m2) | 27.9 ± 3.2 | 28.6 ± 4.1 | 0.105 | |
| CAP (dB/m) | 312.6 ± 30.1 | 310.5 ± 38.7 | 0.167 | |
| Steatosis, n | Grade 1 | 3 | 8 | 0.298 |
| Grade 2 | 27 | 28 | ||
| Grade 3 | 29 | 27 | ||
| Lobular inflammation, n | Grade 1 | 16 | 11 | 0.097 |
| Grade 2 | 41 | 46 | ||
| Grade 3 | 2 | 6 | ||
| Ballooning degeneration, n | Grade 1 | 23 | 18 | 0.226 |
| Grade 2 | 36 | 45 | ||
In this study, the diagnostic value of the LSM for the noninvasive staging of liver fibrosis in patients with NASH was evaluated using ROC curves (Figure 2). The results revealed that the area under the receiver operating characteristic curve (AUROC) for LSM in the diagnosis of liver fibrosis at stage F2 or higher was 0.761 (95%CI: 0.653-0.870). On the basis of the maximum Jodan index principle, the optimal cutoff value was determined to be 6.45 kPa, with a corresponding diagnostic sensitivity of 78.21% and specificity of 71.42%. Furthermore, the AUROC for LSM in diagnosing liver fibrosis at stage F3 was 0.829 (95%CI: 0.718-0.940), with an optimal cutoff value of 9.75 kPa, corresponding to a sensitivity of 80% and a specificity of 81.31% (Table 7).
| AUROC (95%CI) | Optimal cut-off (kPa) | Sensitivity (%) | Specificity (%) | Youden index | |
| ≥ F2 | 0.761 (0.653-0.870) | 6.45 | 78.21 | 71.42 | 0.496 |
| ≥ F3 | 0.829 (0.718-0.94) | 9.75 | 80 | 81.31 | 0.613 |
To identify the independent risk factors for liver fibrosis severity in the NASH cohort, we constructed an ordered logistic regression model. Univariate ordered logistic regression revealed that diabetes, LDL levels, and AST levels were significantly associated with the severity of fibrosis (P < 0.05). Variables showing statistical significance in the univariate analysis, along with confounding factors (age, BMI, and sex), were incorporated into a multivariate regression model, and variables were excluded using stepwise backward regression. The results of the parallel lines test for the final model were P = 0.209, and the results of the Pearson goodness-of-fit test were P = 0.527. In addition, the collinearity tests revealed that the variance inflation factor values for all the variables in the final model were less than 5. The final model revealed that diabetes (OR = 3.76, 95%CI: 1.30-10.84, P = 0.014) and LDL level (OR = 1.52, 95%CI: 1.10-2.34, P = 0.049) were independent risk factors for liver fibrosis severity in patients with NASH (Table 8).
| Univariate analysis | Multivariate analysis | |||
| Variables | OR (95%CI) | P value | OR (95%CI) | P value |
| Male | 1.034 (0.48, 2.232) | 0.931 | ||
| Age | 1.012 (0.986, 1.04) | 0.36 | ||
| BMI | 1.013 (0.914, 1.122) | 0.81 | ||
| Hypertension | 1.306 (0.582, 2.93) | 0.52 | ||
| Diabetes | 4.358 (1.589, 11.953) | 0.004 | 3.76 (1.30, 10.84) | 0.014 |
| FPG | 1.16 (0.93, 1.45) | 0.186 | ||
| AST | 1.012 (1.002, 1.021) | 0.021 | ||
| LDL | 1.706 (1.134, 2.565) | 0.01 | 1.52 (1.10, 2.34) | 0.049 |
With the global prevalence of obesity and diabetes, the prevalence of NASH is increasing. Approximately 16.02% of patients with NAFLD also have NASH[1]. Liver fibrosis is among the key causes of NASH progression, and liver fibrosis progresses significantly faster in patients with NASH than in those with NAFLD[10]. If liver fibrosis continues to progress, it can lead to cirrhosis or even hepatocellular carcinoma[2]. Identification of risk factors associated with liver fibrosis severity is thus important for preventing fibrosis and improving patient outcomes. In this study, we included 122 patients with biopsy-proven NASH, explored their clinicopathological characteristics according to the stratification of liver fibrosis, and further identified the risk factors of fibrosis severity.
The clinical features of this cohort reflect the features of NASH as a systemic disease. The median BMI of the patients was 28.3 kg/m2, suggesting that overweight and obesity are commonly observed in NASH. Although hypertension was not an independent risk factor for liver fibrosis progression in this study, other studies have shown that hypertension is important for fibrosis progression[3]. Hypertension was also marginally important in univariate analyses, suggesting that other factors may mask the role of hypertension.
The results of this study suggest that, on the basis of liver pathology results, the degree of lobular inflammation increases significantly with the aggravation of liver fibrosis, confirming the results of Younossi et al[11]. These findings revealed a positive association between the severity of lobular inflammation and the risk of progression to cirrhosis in patients with NASH. Inflammation-induced macrophages in liver lobules activate hepatic stellate cells and promote liver fibrosis[12]. However, we found that the degree of hepatic steatosis also increased significantly with the aggravation of liver fibrosis, but Younossi et al[11] did not find a link between hepatic steatosis and cirrhosis. This disagreement may be because of differences in the study populations: The cohort studied by Younossi et al[11] included patients with cirrhosis (F4), whereas our study focused on patients with fibrosis stages F1-F3. Intrahepatic fatty acid accumulation may trigger YAP activation by activating the p38 MAPK pathway and promoting fibrosis through paracrine activation of hepatic stellate cells[13,14]. In clinical practice, early intervention targeting lipid accumulation and inflammation may delay disease progression to advanced cirrhosis.
Furthermore, although Younossi et al’s study[11] indicated that severe ballooning degeneration significantly increased the risk of patients with NASH progressing to cirrhosis, this phenomenon was not observed in our study, and the results showed that ballooning degeneration was not related to the degree of liver fibrosis. Although hepatocellular ballooning is an important histological indicator for assessing NASH activity, Ribback et al[15] noted that ballooning may not cause irreversible lethal cell damage. Hepatocellular cells that have ballooned are actually in an adaptive survival state and have not entered the apoptotic phase, during which they continuously release profibrotic signals. Consequently, in clinical practice, ballooning should primarily be utilized to define disease activity rather than being directly employed to assess the progression of fibrosis.
Cross-tabulation analysis revealed low agreement between LSM-assessed fibrosis staging and pathological results (Kappa = 0.206), and 37.7% of patients’ liver fibrosis stages were underestimated. Previous studies have indicated that BIM, CAP, and the degree of hepatic steatosis can affect the LSM value[16,17]. Mendoza et al[18] also reported that inflammatory activity affects the accuracy of transient elastography in determining liver stiffness. However, in this study, there were no statistically significant differences between the concordant group and discordant groups in terms of BMI, CAP, or liver pathological characteristics. We found that the degree of lobular inflammation in the discordant group tended to be greater than that in the concordant group, but this difference did not reach the level of conventional statistical significance (P = 0.097). Although the influencing factors leading to LSM underestimation were not clearly identified in this study, the underestimation of LSM observed in this study cohort should not be ignored. Future large-scale, multicenter prospective studies are necessary to further confirm the causes of this underestimation. Further ROC curve analysis revealed that the AUROC values for the LSM for the diagnosis of F2- and F3-stage liver fibrosis were 0.761 and 0.829 with optimal cutoff values of 6.45 kPa and 9.75 kPa, respectively. Therefore, despite the limitations mentioned above, importantly, LSM still demonstrates good diagnostic efficacy for advanced fibrosis.
This study revealed that diabetes is an independent risk factor for liver fibrosis progression in patients with NASH. This has been confirmed in previous studies. Shaikh et al[3] reported that diabetes increased the risk of liver fibrosis progression by 67%. Moreover, compared patients without diabetes, with diabetes patients experience significantly faster progression of fibrosis, and the proportion of these patients who develop advanced fibrosis is three times greater than that of patients without diabetes[19,20]. Liver fibrosis is associated with hyperglycemia and insulin resistance in patients with diabetes[21]. Hence, more aggressive monitoring of fibrosis and metabolic intervention should be implemented for NASH patients with diabetes.
Low-density lipoprotein cholesterol is commonly used in clinical practice to assess the risk of cardiovascular disease. In our study, high LDL levels were found to be an independent risk factor for liver fibrosis in patients with NASH. Notably, none of the patients in this cohort had received lipid-lowering therapy, such as statins, thereby effectively ruling out residual confounding factors associated with such treatment. Yang et al[22] reported that in a metabolic dysfunction-associated steatohepatitis mouse model, circulating LDL levels increased with the progression of fibrosis. However, elevated LDL levels should not be overinterpreted as a single, direct cause of disease; rather, they may reflect broad systemic metabolic dysfunction. A study by Li et al[23] confirmed that metabolic syndrome, including dyslipidemia, is closely associated with significant liver fibrosis and severe steatosis in patients with NASH, with elevated LDL levels being a particularly significant risk factor for the development of intrahepatic inflammation. Cholesterol can trigger an inflammatory response by stimulating Kupffer cells and hepatic stellate cells, thereby exacerbating microcirculatory dysfunction and activating liver fibrosis. Clinically, controlling LDL levels can be therapeutically important for preventing the progression of NASH-associated liver fibrosis[24].
The main strength of our study is that all of the participants had liver biopsy-induced NASH diagnoses and were stratified according to different degrees of fibrosis. This provides reliable clinical evidence for the risk factors associated with NASH fibrosis severity, but as this was a cross-sectional study with a small sample size, it does not meet the time requirements for causality inference. The conclusions must be validated in large-scale cohort studies.
This study revealed that diabetes and elevated LDL cholesterol are independent risk factors for liver fibrosis severity in patients with NASH. In clinical practice, early intervention to address metabolic disorders and control blood glucose may help slow progression to advanced cirrhosis and improve patient outcomes.
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