Ichikawa T, Miuma S, Yamashima M, Yamamichi S, Koike M, Nakano Y, Yajima H, Miyazaki O, Ikeda T, Okamura T, Komatsu N, Yoshino M, Miyaaki H. Cytokeratin 18 fragment is associated with steatosis-associated fibrosis estimator score and lipid in patients with steatotic liver disease. World J Hepatol 2025; 17(11): 110698 [DOI: 10.4254/wjh.v17.i11.110698]
Corresponding Author of This Article
Tatsuki Ichikawa, MD, Department of Gastroenterology, Nagasaki Harbor Medical Center, 1-7-1 Sakamoto, Nagasaki 850-8555, Japan. ichikawa@nagasaki-u.ac.jp
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Gastroenterology & Hepatology
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Case Control Study
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Nov 27, 2025 (publication date) through Dec 4, 2025
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World Journal of Hepatology
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Ichikawa T, Miuma S, Yamashima M, Yamamichi S, Koike M, Nakano Y, Yajima H, Miyazaki O, Ikeda T, Okamura T, Komatsu N, Yoshino M, Miyaaki H. Cytokeratin 18 fragment is associated with steatosis-associated fibrosis estimator score and lipid in patients with steatotic liver disease. World J Hepatol 2025; 17(11): 110698 [DOI: 10.4254/wjh.v17.i11.110698]
Author contributions: Ichikawa T wrote the manuscript and designed the study; Ichikawa T and Miuma S confirmed the authenticity of the raw data; Ichikawa T, Yamashima M, Yamamichi S, Koike M, Nakano Y, Yajima H, Miyazaki O, Ikeda T, Okamura T, Komatsu N, Yoshino M, and Miyaaki H collected the data; Nakao Y analyzed the data; All authors have read and approved the final manuscript.
Institutional review board statement: The protocol for this research project has been approved by a suitably constituted Ethics Committee of the Institution (Committee of Nagasaki Harbor Medical Center, No. H30-057) and it conforms to the provisions of the Declaration of Helsinki.
Informed consent statement: Informed consent was obtained from each patient included in the study and they were guaranteed the right to leave the study if desired (opt out).
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
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: The data generated in the present study are not publicly available (compelling reason exists owing to which the data are not public), but may be requested from the 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: Tatsuki Ichikawa, MD, Department of Gastroenterology, Nagasaki Harbor Medical Center, 1-7-1 Sakamoto, Nagasaki 850-8555, Japan. ichikawa@nagasaki-u.ac.jp
Received: June 13, 2025 Revised: July 3, 2025 Accepted: September 25, 2025 Published online: November 27, 2025 Processing time: 167 Days and 23.1 Hours
Abstract
BACKGROUND
Serum cytokeratin 18 fragment (CK18F) has been developed as a new non-invasive test (NIT) for risk assessment of steatotic liver disease (SLD); however, there are few reports on its relationship with existing NITs and association with cardiometabolic risk factors (CMRFs).
AIM
To clarify the relationship among CK18F, NITs, and CMRF.
METHODS
We included 125 patients who were assessed for SLD and had CK18F measured in cross-sectional study. The fibrosis-4 index (FIB-4), steatosis-associated fibrosis estimator (SAFE) score, liver stiffness (LS), controlled attenuation parameter, and FibroScan-aspartate aminotransferase (FAST) score were compared with CK18F as existing NITs.
RESULTS
CK18F was associated with aspartate aminotransferase, alanine aminotransferase, and triglyceride (TG). FAST and SAFE score were associated with high CK18F (> 260 U/L), but not FIB-4 or LS. The cut-off values for TG and high-density lipoprotein (HDL) cholesterol used to determine high CK18F using receiver operating characteristics analysis were 126 mg/dL and 56 mg/dL respectively. High TG (> 126 mg/dL) and low HDL (< 56 mg/dL) were associated with high CK18F. The risk of high CK18F was higher when high TG and low HDL were combined than when each was present alone. CMRF was higher in the high CK18F group, but was not associated with CK18F levels. However, when the TG and HDL criteria for CMRF were replaced by TG > 126 mg/mL and HDL < 56 mg/dL, modified CMRF (mCMRF) was associated with CK18F levels, with a higher risk of high CK18F than CMRF.
CONCLUSION
CK18F is a new NIT associated with SAFE score and FAST. High TG, low HDL, and mCMRF are associated with high CK18F.
Core Tip: Serum cytokeratin 18 fragment (CK18F) has been developed as a new non-invasive test for risk assessment of steatotic liver disease (SLD). We included 125 patients who were assessed for SLD and had CK18F measured. Steatosis-associated fibrosis estimator (SAFE) score, liver stiffness (LS), and FibroScan-aspartate aminotransferase (FAST) score were compared with CK18F. CK18F was associated with aspartate aminotransferase, alanine aminotransferase, and triglycerides (TGs). FAST and SAFE score were associated with high CK18F (> 260 U/L), but not fibrosis-4 or LS. Risk of high CK18F was higher when high TG and low high-density lipoprotein were combined than when each was present alone.
Citation: Ichikawa T, Miuma S, Yamashima M, Yamamichi S, Koike M, Nakano Y, Yajima H, Miyazaki O, Ikeda T, Okamura T, Komatsu N, Yoshino M, Miyaaki H. Cytokeratin 18 fragment is associated with steatosis-associated fibrosis estimator score and lipid in patients with steatotic liver disease. World J Hepatol 2025; 17(11): 110698
Cytokeratin 18 fragment (CK18F) predicts the histological disease severity markers in non-alcoholic steatohepatitis[1]. Hepatocellular ballooning correlates with increased CK18F detection, and is associated with CK18 depletion with CK18F-positive Mallory–Denk bodies[2]. Hepatocyte ballooning is the only independent variable associated with CK18F[3]. As a result of its association with histological progression, CK18F is associated with liver disease prognosis[4]. Changes in histological score in metabolic dysfunction associated with steatohepatitis (MASH) is associated with changes in serum CK18F[5]. From 2024, measurement of serum CK18F has been routinely performed for MASH assessment in Japan. No resolution has been reached on the use of CK18F vs existing non-invasive tests (NITs) for fibrosis.
In recent years, groups at high risk of MASH, such as patients with type 2 diabetes mellitus (T2DM), pre-diabetes, and obesity, have been assessed in a two-stage NIT. The first screening was with the fibrosis-4 index (FIB-4), including aspartate aminotransferase (AST), alanine aminotransferase (ALT), platelet, and age, and then with liver stiffness (LS) using FibroScan[6-8]. As a versatile NIT, the FibroScan-AST (FAST) score is very useful in determining elevated metabolic dysfunction-associated steatotic liver disease (MASLD) activity score (≥ 4) and diagnosis of advanced fibrosis (stage ≥ 2 [F ≥ 2])[9]. FAST is calculated using LS, controlled attenuation parameter (CAP), and AST, and the cut-off value is 0.35. FAST is widely used as a NIT for MASH[10,11], and is also associated with prognosis[11]. NITs that do not use FibroScan and only use general blood tests have also been developed. The steatosis-associated fibrosis estimator (SAFE) score was developed as a tool to detect low-risk MASLD in primary care[12]. SAFE score is calculated using age, body mass index (BMI), DM, AST, ALT, globulin (total protein [TP]-albumin [ALB]), and platelets. The SAFE score was created to discriminate liver fibrosis above F2, but survival can be stratified according to low (< 0), moderate (0-100), and high (> 100) risks. The SAFE score is also reportedly superior to FIB-4 as a first NIT for MASLD and has attracted attention[13-15]. There are no reports of an association between CK18F and NITs such as FIB-4, LS, FAST, and SAFE score.
When MASLD was chosen as an alternative name for MASLD, there was consensus to change the definition to include the presence of at least one of five cardiometabolic risk factors (CMRFs)[16]. Diagnostic criteria for CMRF were chosen to be consistent with insulin resistance, which are already well established and validated in the context of cardiovascular disease[17]. If three of the five items in the CMRF are abnormal, the patient is considered to have metabolic syndrome. In an MASLD group, the number of CMRFs correlated positively with cardiovascular disease risk[18]. The rate of major adverse liver outcomes increased progressively with increasing numbers of CMRF traits at baseline in patients with T2DM, and hypertension was significantly associated with adverse events[19]. In MASLD with very low alcohol consumption, CMRF counts are associated with a FAST score of ≥ 0.35[20]. Almost all individuals with excessive alcohol consumption also have CMRF, and patients with metabolic dysfunction and alcohol-related liver disease (MetALD) have an increased risk of FIB-4 ≥ 2.67 if they have more than three CMRF items; additionally, obesity, hypertension, and diabetes mellitus are associated with liver fibrosis[21]. The relative importance of each CMRF item to the severity of liver disease is not reflected. However, assessment of the association between CK18F and CMRF is lacking.
In the present study, we evaluated the role of serum CK18F measurement in the routine management of MASLD. Additionally, we focused on the association of CK18F with other NITs and CMRF.
MATERIALS AND METHODS
Patients
This cross-sectional study included 125 patients with MASLD and MetALD who visited our liver disease outpatient clinic between April 2024 and February 2025 for the first time. These participants did not include patients who were positive for anti-hepatitis C virus antibody or hepatitis B surface antigen positive (Supplementary Figure 1). Diagnostic criteria for MASLD and MetALD were established as previously described[16]. Medical records of the 125 patients were retrospectively reviewed. All laboratory measurements were obtained from the medical records. Each of the CMRF was defined as follows: (1) Obesity was BMI > 23 kg/m2; (2) Dysglycemia or T2DM was hemoglobin A1c (HbA1c) > 5.7% or fasting plasma glucose > 100 mg/dL or treatment for T2DM; (3) High plasma triglycerides (TGs) was TG > 150 mg/dL or lipid-lowering treatment; (4) Low high-density lipoprotein (HDL) cholesterol was HDL < 39 mg/dL in men and < 50 mg/dL in women or lipid-lowering treatment; and (5) High blood pressure was > 130 mmHg/85 mmHg or treatment for hypertension. CMRF was calculated as the sum of the corresponding number of the five items.
Laboratory measurements
Measurement range for CK18F was 125-2001 U/L, with < 125 U/L being 124 U/L and > 2001 U/L being 2002 U/L for consideration. The normal range for CK18F was < 260 U/L, and those with higher CK18F values were included in a high CK18F group. Normal ranges for other laboratory tests are as follows: (1) Total bilirubin (0.3-1.2 mg/dL); (2) TP (6.7-8.3 g/dL); (3) ALB (3.8-5.2 g/dL); (4) Prothrombin time (70%-130%); (5) International normalized ratio (0.9-1.1); (6) Creatinine (men [M]: 0.61-1.04 and women [W]: 0.47-0.79 mg/dL); (7) Platelet count (M: [13.1-26.2] × 104/μL and W: [13-36.9] × 104/μL); (8) AST (10-40 U/L); (9) ALT (5-40 U/L); (10) Alkaline phosphatase (38-113 U/L); (11) γ-glutamyl transpeptidase (GTP) (M: < 70 U/L and W: < 30 U/L); (12) Ferritin (M: 39.4-340 ng/mL and W: 3.6-114 ng/mL); (13) Immunoglobulin G (IgG) (870-1700 mg/dL); (14) IgM (M: 33-190 mg/dL and W: 46-260 mg/dL); (15) IgA (110-410 mg/dL); (16) Ammonia (30-80 μg/dL); (17) TG (50-149 mg/dL); (18) Low-density lipoprotein (LDL) cholesterol (70-139 mg/dL), HDL (M: 40-80 mg/dL and W: 40-90 mg/dL); (19) Small dense LDL (sdLDL) (> 35 mg/dL); and (20) HbA1c (4.8%-6%). Blood samples were collected in the morning on the day of outpatient visits, after at least 8 hours of fasting. All biochemical analyses except CK18F were performed on the same day in the biochemical laboratory of our facility. Serum CK18F levels were measured using an enzyme immunoassay at a commercial laboratory (BML Inc., Tokyo, Japan).
LS measurement (Kpa) and CAP (dB/m) were measured using FibroScan (Echosens, France). NITs using blood tests included the following: (1) FIB-4[22]; (2) SAFE score; (3) FAST; (4) Model for end-stage liver disease[23]; (5) ALB-bilirubin[24]; and (6) Child–Pugh scores[25].
The difference in mean HDL levels between the two groups, high CK18F and normal, was 10 mg/dL, and the standard deviation for the two groups combined was 16.2. With an alpha error of 0.05, a statistical power of 0.8, and a sample size ratio of 1:3.25, 27 cases were required in the high CK18F group and 87 cases in the control group, and the number of cases in this study was appropriate.
Statistical analyses
Data were analyzed using StatFlex (version 6.0; Artech, Osaka, Japan), and are presented as median and 95% confidence interval (CI). Laboratory variables were compared using the Mann–Whitney U test (for differences between the two groups), analysis of variance (ANOVA) (for difference between ≥ 3 groups), Dunn test (intra-group comparison), and χ2 tests. Detection level was analyzed using receiver operating characteristic (ROC) curves. A multiple regression analysis was performed, and a standardized partial regression coefficient, β, was employed. Multivariate analyses were performed using logistic regression. Correlations were evaluated using the Pearson’s correlation coefficient (R). Statistical significance was set at P < 0.05.
RESULTS
AST, ALT, FAST, SAFE score, HbA1c, TG, and HDL were associated with CK18F
CK18F was found to be below the measured sensitivity in 76 patients and above in 2, with a median value of 124 U/L (quartile 1:124 U/L and quartile 3:230.8 U/L). The high CK18F group (> 260 U/L) included 29 patients. Clinical factors were compared between high and normal CK18F groups (Tables 1-4). TP, AST, ALT, GTP, ferritin, TG, TG/HDL, and HbA1c were higher in the high CK18F group than in the normal group, and AST/ALT and HDL were lower in the high CK18F group than in the normal group (Table 1). By contrast, there were no changes in HDL or TG in the high and low LS 10 Kpa groups (Supplementary Table 1). In the NITs, SAFE score, CAP, and FAST scores were higher in the high CK18F group than in the normal group. In low, moderate, and high-risk SAFE groups, there were more patients with high CK18F in the high-risk SAFE group. The low-risk SAFE group had no patients with high CK18F, and the high-risk FAST group (> 0.35) had more patients with high CK18F than low-risk FAST group (Table 2). There was no difference in LS and FIB-4 scores between the CK18F groups (Table 2). Regarding CMRF, there were no differences in obesity, DM, or hypertension between the two groups; however, low HDL and high TG were more common in the CK18F group. Consequently, CMRF was higher in the high CK18F group (Table 3). The correlation between CK18F and items that differed significantly between the high and normal CK18F groups was evaluated (Tables 5 and 6). AST, ALT, ferritin, TG, TG/HDL, HbA1c, SAFE score, CAP, and FAST were positively correlated with CK18F, whereas HDL was negatively correlated.
SAFE and FAST high-risk groups were associated with high CK18F levels, but not high LS levels
Multiple regression analysis was used to examine the relationship between CK18F and AST, ALT, ferritin, TG, HDL, and HbA1c (Figure 1A). AST, ALT, and TG were contributing factors to CK18F levels. When TG and HDL were changed to TG/HDL and a similar study was conducted, TG/HDL also remained a significant factor (Figure 1B). A logistic analysis was performed to examine the contribution of high-risk, moderate-risk, and low-risk SAFE groups and high-risk and low-risk groups to the high CK18F group, and both NITs were contributing factors (Figure 1C). Significant differences in CK18F values were found between the high-risk, moderate-risk, and low-risk SAFE groups (ANOVA, P = 0.02639), with the high group having higher values than the low and moderate groups (Figure 2A). Similarly, differences in CK18F values were found between the high and low FAST groups (P = 0.02639) (Figure 2B). By contrast, LS score was elevated in the high-risk SAFE and FAST groups (Supplementary Table 1 and Supplementary Figure 2); however, there was no elevated CK18F in the high LS score group (Figure 2C and D).
Figure 1 Relationship between cytokeratin 18 fragment and clinical factors.
A: Aspartate aminotransferase (AST), alanine aminotransferase (ALT), ferritin, triglycerides (TG), high-density lipoprotein (HDL), and hemoglobin A1c (HbA1c), which were noted to correlate with cytokeratin 18 fragment (CK18F), were used in a multiple regression analysis. AST, ALT, and triglyceride (TG) were associated with CK18F; B: In the combined Figure 1A, TG and HDL were changed to TG/HDL and the same analysis was performed. AST, ALT, and TG/HDL were associated with CK18F; C: Multiple logistic regression analysis for CK18F > 260 U/L (high CK18F). In non-invasive tests, steatosis-associated fibrosis estimator (SAFE) score and FibroScan-aspartate aminotransferase (FAST) were associated with CK18F. SAFE score was divided as follows: (1) High (> 100); (2) Moderate (0-100); and (3) Low (< 0). FAST was divided as follows: (1) High (> 0.35); and (2) Low (< 0.35). Low-risk, moderate-risk, and high-risk (LMH) SAFE and low-risk and high-risk (LMH) FAST corresponded with CK18F.
Figure 2 Relationship between severity classification according to non-invasive test and cytokeratin 18 fragment values.
A: Classification according to steatosis-associated fibrosis estimator score. P value for analysis of variance is indicted in the figure. Cytokeratin 18 fragment (CK18F) was higher in the high group than those in the low and moderate groups (Dunn test); B: Classification according to FibroScan-aspartate aminotransferase score. P value for the Mann–Whitney U test is indicated in the figure; C: Classification according to liver stiffness (LS). High LS group is > 8 Kpa; D: High LS group is > 10 Kpa. Y axis is CK18F (U/L). ANOVA: Analysis of variance; SAFE: Steatosis associated fibrosis estimator; FAST: FibroScan-aspartate aminotransferase.
TG > 126 mg/dL and HDL < 56 mg/dL were associated with high CK18F
HDL was not associated with CK18F (multiple regression analysis) (Figure 1A); however, TG/HDL was associated with CK18F (Figure 1B). Therefore, the cut-off values for TG and HDL that resulted in high CK18F were determined using ROC analysis (Supplementary Figure 3 and Supplementary Tables 2 and 3). The point at which sensitivity and specificity coincided was used as the cut-off value. The cut-off values for high CK18F levels were ≥ 126 mg/dL for TG (sensitivity of 0.656), ≤ 56 mg/dL for HDL (0.675), and ≥ 2.24 for TG/HDL (0.655), regardless of lipid-lowering treatment. High/treatment TG (TG > 150 mg/dL or lipid-lowering treatment) and low/treatment HDL (HDL for M: < 39 mg/dL and W: < 50 mg/dL or lipid-lowering treatment), as defined in the CMRF, did not contribute to the high CK18F group according to a logistic analysis, but high TG (> 126 mg/dL) and low HDL (< 46 mg/dL) did contribute (Figures 3 and 4A). The combined high TG (> 126 mg/dL) and low HDL (< 46 mg/dL) (high TG and low HDL) group was more significantly associated with high CK18F than each of these alone (Figure 4B), but TG/HDL was not a significant factor (Supplementary Figure 3B). Each of the CMRF factors (obesity, DM, hypertension, high/treatment TG [> 150 mg/dL or lipid-lowering treatment] and low/treatment HDL [M: < 39 mg/dL and W: < 50 mg/dL or lipid-lowering treatment]) did not contribute to high CK18F, but if TG and HDL were changed to > 126 mg/dL and < 56 mg/dL respectively, high TG and low HDL became factors contributing to high CK18F (Supplementary Table 4).
Figure 3 Triglycerides and high-density lipoprotein influenced cytokeratin 18 fragment levels.
A: High triglycerides (TGs) were > 126 mg/dL. Figures in brackets were the number of patients. P value for the Mann–Whitney U test is indicated in the figure. Y axis is cytokeratin 18 fragment (CK18F) (U/L); B: High CK18F rate in the normal and high TG groups. P value for the χ2 test is shown in the figure; C: Low high-density lipoprotein (HDL) group with values < 56 mg/dL; D: High CK18F rate in the normal and low HDL groups; E: High TG and low HDL scores were calculated with high TG as 1 point and low HDL as 1 point. P value for the analysis of variance test is shown on the figure. P values between groups for the Dunn test; F: High CK18F rate for high TG and low HDL score. ANOVA: Analysis of variance; TGHHDLL: Triglycerides high (> 126 mg/dL) and high-density lipoprotein low (≤ 56 mg/dL).
Figure 4 Criteria for high triglycerides (≥ 126 mg/dL) and low high-density lipoprotein (≤ 56 mg/dL) have more effect on high cytokeratin 18 fragment than the settings for high/treatment triglycerides (≥ 150 mg/dL or lipid-lowering treatment) and low/treatment high-density lipoprotein (≤ 39 mg/dL for men and 50 mg/dL for women or lipid-lowering treatment).
A: Effect of high triglycerides (TGs) (≥ 126 mg/dL), low high-density lipoprotein (HDL) (≤ 56 mg/dL), high/treatment TG (≥ 150 mg/dL or lipid-lowering treatment), and low/treatment HDL (39 mg/dL in men and ≤ 50 mg/dL in women or lipid-lowering treatment) on high cytokeratin 18 fragment (CK18F) was investigated using multivariate logistic regression analysis; B: Effect of high TG (≥ 126 mg/dL), low HDL (≤ 56 mg/dL), and high TG and low HDL on high CK18F was evaluated using multivariate logistic regression analysis; C: The criteria for high TG and low HDL in cardiometabolic risk factor (CMRF) were replaced by TG ≥ 126 mg/dL and HDL < 56 mg/dL in modified CMRF (mCMRF), and the effect of CMRF and mCMRF on high CK18F was evaluated using multivariate logistic regression analysis. OR: Odds ratio; TGHHDLL: Triglycerides high (> 126 mg/dL) and high-density lipoprotein low (≤ 56 mg/dL).
Modified CMRF was significantly correlated with CK18F than CMRF and not with LS
CMRF did not correlate with CK18F, but modified CMRF (mCMRF), replacing the criteria of TG > 126 mg/dL and HDL < 56 mg/dL, did correlate with CK18F (Table 6). The mCMRF was a contributing factor in the high CK18F group, while CMRF was not (Figure 4C). The mCMRF was higher in the high CK18F group, and both CMRF and mCMRF were not significantly different in the group with > 10 Kpa LS (Supplementary Table 5). LS and CK18F between groups were compared using CMRF and mCMRF, with each score as a group. Groups with 0 point, 1 point, 4 points, and 5 points were the least number of patients in any classification, and 0 point and 1 point were combined into groups 0/1 and 4/5 as well. There were no significant differences between the groups in the percentage of > 10 Kpa LS and LS values (Kpa) in both CMRF and mCMRF (Supplementary Figure 4A-D). However, there were significant differences between groups in the proportion of high CK18F values in both CMRF and mCMRF, and there were differences in CK18F values (U/L) in each group of the mCMRF, but not in the CMRF (Supplementary Figure 4E-H). The association between lipid-lowering drugs (fibrates and statins) and TG and HDL was investigated. There was no association between lipid-lowering drugs (fibrates and statins) with high TG (≥ 126 mg/dL) and HDL (≤ 56 mg/dL) (Supplementary Table 6); there was no difference between the use of either fibrates or statins and no treatment (Supplementary Table 7). Among the CMRF criteria, when only BMI was changed to 24 or higher, the new CMRF (BMI24CMRF) classification was 26 patients in group 0/1, 34 patients in group 2, 16 patients in group 3, and 29 patients in group 4/5. When comparing the proportion of > 10 Kpa LS between each BMI24CMRF group, no significant difference was observed, and there was also no significant difference in LS values (Kpa) between each score group (Supplementary Figure 5A and B). The high CK18F high group showed a significant difference between each BMI24CMRF score group, but the 3 points group had more high value groups than the 4/5 points group (Supplementary Figure 5C). There was no significant difference in CK18F values (U/L) between each score group (Supplementary Figure 5D).
DISCUSSION
Among clinical factors, AST, ALT, and TG were contributing factors to CK18F. In NITs, the high-risk SAFE and FAST groups were associated with high CK18F, but not the high LS group. In CMRFs, TG > 126 mg/dL and HDL < 56 mg/dL were associated with high CK18F. The mCMRF correlated more with CK18F than CMRF.
CK18F was related to SAFE score and FAST but not LS. Reports on the histological association between CK18F and MASLD/MASH was not associated with liver fibrosis. In a population with a median BMI of 34 kg/m2, CK18F was associated with all histological indicators of the liver: (1) Steatosis; (2) Lobular inflammation; (3) Ballooning; (4) Fibrosis[1]; and (5) In a population with a median BMI of 40 kg/m2, CK18F was associated with LS[26]. By contrast, a common feature was the association between ballooning and CK18F[1,3,27]. Due to the close relationship between hepatocyte ballooning and CK18F in hepatocytes, serum CK18F is a marker of hepatocyte damage[2]. In this study, the association between CK18F with FAST and SAFE score, which are associated not only with diagnosis but also with prognosis, were demonstrated. LS was associated with liver-related risks in MASLD[28-30]. However, because CK18F was not associated with LS in our study, CK18F independent of LS also reflects hepatocellular damage with a background of ballooning, indicating that it may be useful not only in the diagnosis of MASH but also in determining prognosis. Future studies are needed to investigate the possibility of stratifying MASH prognosis by CK18F and in combination with LS. In this study, the median BMI in the high CK18F group was 26.6, and 75% of the subjects had a BMI of 23 or higher. No significant difference in LS was observed between this group and the normal CK18F group. Previous studies have shown an association between liver fibrosis and CK18F in the high BMI group (1.27). In this study, since many cases with low BMI were included, it is possible that no association was observed between CK18F and LS. It is necessary to increase the number of cases in the future and examine the relationship between high BMI and CK18F.
CK18F was associated with high TG and low HDL. By contrast, there were no changes in HDL or TG in the high and low LS 10 Kpa groups. High TG and low HDL were more strongly associated with high CK18F than each alone. De novo lipid (DNL) synthesis in the liver is important in the development of MASLD[31]. Serum TG is known to be high and HDL low, reflecting hepatic DNL[32,33]. Additionally, hepatic DNL has been implicated not only in steatosis but also in hepatocyte ballooning[34]. The lipid profile feature of high TG and low HDL in the high CK18F group, but not in LS, suggests that the hepatocellular damage may be due to DNL-induced lipotoxicity. The involvement of lipotoxicity caused by DNL in MASH progression has attracted attention, and DNL interventions are being explored for the treatment of MASH[35,36]. When MASLD cases improved with dietary therapy, it was reported that DNL decreased and TG and other lipids decreased. This study shows that improvements in lipids such as TG may serve as biomarkers for MASLD treatment[37]. When liver DNL is the treatment target, CK18F may be a better indicator than LS. In the present study, sdLDL was not associated with CK18F. In diabetes mellitus with high TG and low HDL, an association with high sdLDL has been suggested[32,33]. The relationship between sdLDL, TG, HDL, and CK18F in patients with MASLD/MASH should continue to be investigated.
CK18 M65 fragment has been reported as an indicator of cardiometabolic disorders[38]. According to the steatotic liver disease (SLD) criteria, each CMRF has equal weight and may not accurately reflect differences in relative importance[21,39]. In the present study, CMRF was higher in the high CK18F group, but no correlation was observed. However, no relationship was found between LS and CMRF. Each of the CMRFs was not a contributing factor to high CK18F in the logistic regression analysis. However, when the criteria for dyslipidemia were changed to TG ≥ 126 mg/dL and HDL ≤ 56 mg/dL, these two items became factors contributing to high CK18F. The mCMRF using the criteria of TG ≥ 126 mg/dL and HDL ≤ 56 mg/dL was not related to LS, but correlated with CK18F, and when mCMRF was divided into 0/1-point, 2-point, 3-point and 4/5-point groups, an association was observed in the ratio of high CK18F and CK18F values. When CMRFs are considered as NITs indexed to CK18F, the setting of each CRMF may not be optimal. Although the CMRF criteria included patients on lipid-lowering drugs, statins and fibrates, this may not have affected TG and HDL in the present study. The relationship between CK18F and lipids needs to be re-examined in the future when drugs are used with the aim of aggressively lowering TG (< 126 mg/dL) and increasing HDL (> 56 mg/dL). In future studies, it will be necessary to focus on the relationship between triglyceride reduction, HDL elevation, and CK18F reduction to evaluate the effects of lipid-lowering agents on MASLD/MASH.
This study had some limitations. This was a single-center, small, retrospective study. As the hospital is a specialized facility for the treatment of liver diseases, all of the patients were referrals and CMRFs were already being treated at the time of their first visit. In the future, long-term observation should be conducted to investigate the association of CK18F and CMRF with the prognosis of liver disease and development of cardiovascular disease. Due to the small sample size of this study, we believe that future studies should include more cases or conduct verification cohorts from other institutions. Alcohol consumption (MetALD and MASLD), use of glucagon-like peptide-1 receptor agonist, and use of statins were not associated with CK18F in this study, but further investigation with a larger sample size is necessary.
CONCLUSION
CK18F may be a NIT that reflects lipid metabolism abnormalities in this cohort and may be associated with SLD prognosis apart from LS. A reassessment of the CMRF as NIT targeting CK18F is needed. CK18F should be measured as appropriate during exacerbation of liver injury in patients with SLD having high TG and low HDL to assess risk and attempt the management of dyslipidemias.
Footnotes
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: Japan
Peer-review report’s classification
Scientific Quality: Grade A, Grade B, Grade B, Grade C
Novelty: Grade B, Grade B, Grade B, Grade B
Creativity or Innovation: Grade A, Grade B, Grade C, Grade C
Scientific Significance: Grade B, Grade B, Grade B, Grade C
P-Reviewer: Luo Y, PhD, Associate Professor, China; Yao JY, PhD, Associate Professor, China; Zhang XF, PhD, Chief Physician, China S-Editor: Luo ML L-Editor: Filipodia P-Editor: Zhang YL
Eguchi A, Iwasa M, Yamada M, Tamai Y, Shigefuku R, Hasegawa H, Hirokawa Y, Hayashi A, Okuno K, Matsushita Y, Nakatsuka T, Enooku K, Sakaguchi K, Kobayashi Y, Yamaguchi T, Watanabe M, Takei Y, Nakagawa H. A new detection system for serum fragmented cytokeratin 18 as a biomarker reflecting histologic activities of human nonalcoholic steatohepatitis.Hepatol Commun. 2022;6:1987-1999.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 11][Cited by in RCA: 13][Article Influence: 4.3][Reference Citation Analysis (0)]
Kawanaka M, Kamada Y, Takahashi H, Iwaki M, Nishino K, Zhao W, Seko Y, Yoneda M, Kubotsu Y, Fujii H, Sumida Y, Kawamoto H, Itoh Y, Nakajima A; Japan Study Group of NAFLD (JSG-NAFLD). Serum Cytokeratin 18 Fragment Is an Indicator for Treating Metabolic Dysfunction-Associated Steatotic Liver Disease.Gastro Hep Adv. 2024;3:1120-1128.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 3][Reference Citation Analysis (0)]
European Association for the Study of the Liver (EASL); European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD).J Hepatol. 2024;81:492-542.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 70][Cited by in RCA: 712][Article Influence: 712.0][Reference Citation Analysis (1)]
Newsome PN, Sasso M, Deeks JJ, Paredes A, Boursier J, Chan WK, Yilmaz Y, Czernichow S, Zheng MH, Wong VW, Allison M, Tsochatzis E, Anstee QM, Sheridan DA, Eddowes PJ, Guha IN, Cobbold JF, Paradis V, Bedossa P, Miette V, Fournier-Poizat C, Sandrin L, Harrison SA. FibroScan-AST (FAST) score for the non-invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: a prospective derivation and global validation study.Lancet Gastroenterol Hepatol. 2020;5:362-373.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 556][Cited by in RCA: 580][Article Influence: 116.0][Reference Citation Analysis (0)]
Mózes FE, Lee JA, Vali Y, Selvaraj EA, Jayaswal ANA, Boursier J, de Lédinghen V, Lupșor-Platon M, Yilmaz Y, Chan WK, Mahadeva S, Karlas T, Wiegand J, Shalimar, Tsochatzis E, Liguori A, Wong VW, Lee DH, Holleboom AG, van Dijk AM, Mak AL, Hagström H, Akbari C, Hirooka M, Lee DH, Kim W, Okanoue T, Shima T, Nakajima A, Yoneda M, Thuluvath PJ, Li F, Berzigotti A, Mendoza YP, Noureddin M, Truong E, Fournier-Poizat C, Geier A, Tuthill T, Yunis C, Anstee QM, Harrison SA, Bossuyt PM, Pavlides M. Diagnostic accuracy of non-invasive tests to screen for at-risk MASH-An individual participant data meta-analysis.Liver Int. 2024;44:1872-1885.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 1][Cited by in RCA: 9][Article Influence: 9.0][Reference Citation Analysis (0)]
Macias J, Frias M, Pineda JA, Corona-Mata D, Corma-Gomez A, Rivero-Juarez A, Santos M, García-Deltoro M, Rivero A, Ricart-Olmos C, Gonzalez-Serna A, Real LM. Impact of Nonalcoholic Fatty Liver Disease on the Survival of People Living With HIV.Aliment Pharmacol Ther. 2025;61:550-557.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 4][Reference Citation Analysis (0)]
Verma N, Duseja A, Mehta M, De A, Lin H, Wong VW, Wong GL, Rajaram RB, Chan WK, Mahadeva S, Zheng MH, Liu WY, Treeprasertsuk S, Prasoppokakorn T, Kakizaki S, Seki Y, Kasama K, Charatcharoenwitthaya P, Sathirawich P, Kulkarni A, Purnomo HD, Kamani L, Lee YY, Wong MS, Tan EXX, Young DY. Machine learning improves the prediction of significant fibrosis in Asian patients with metabolic dysfunction-associated steatotic liver disease - The Gut and Obesity in Asia (GO-ASIA) Study.Aliment Pharmacol Ther. 2024;59:774-788.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 7][Cited by in RCA: 9][Article Influence: 9.0][Reference Citation Analysis (0)]
Rinella ME, Lazarus JV, Ratziu V, Francque SM, Sanyal AJ, Kanwal F, Romero D, Abdelmalek MF, Anstee QM, Arab JP, Arrese M, Bataller R, Beuers U, Boursier J, Bugianesi E, Byrne CD, Castro Narro GE, Chowdhury A, Cortez-Pinto H, Cryer DR, Cusi K, El-Kassas M, Klein S, Eskridge W, Fan J, Gawrieh S, Guy CD, Harrison SA, Kim SU, Koot BG, Korenjak M, Kowdley KV, Lacaille F, Loomba R, Mitchell-Thain R, Morgan TR, Powell EE, Roden M, Romero-Gómez M, Silva M, Singh SP, Sookoian SC, Spearman CW, Tiniakos D, Valenti L, Vos MB, Wong VW, Xanthakos S, Yilmaz Y, Younossi Z, Hobbs A, Villota-Rivas M, Newsome PN; NAFLD Nomenclature consensus group. A multisociety Delphi consensus statement on new fatty liver disease nomenclature.J Hepatol. 2023;79:1542-1556.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 1676][Cited by in RCA: 1551][Article Influence: 775.5][Reference Citation Analysis (1)]
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr; International Diabetes Federation Task Force on Epidemiology and Prevention; Hational Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; International Association for the Study of Obesity. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity.Circulation. 2009;120:1640-1645.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 8720][Cited by in RCA: 10758][Article Influence: 672.4][Reference Citation Analysis (0)]
Johnson PJ, Berhane S, Kagebayashi C, Satomura S, Teng M, Reeves HL, O'Beirne J, Fox R, Skowronska A, Palmer D, Yeo W, Mo F, Lai P, Iñarrairaegui M, Chan SL, Sangro B, Miksad R, Tada T, Kumada T, Toyoda H. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade.J Clin Oncol. 2015;33:550-558.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 1962][Cited by in RCA: 2087][Article Influence: 208.7][Reference Citation Analysis (0)]
de Alteriis G, Pugliese G, Di Sarno A, Muscogiuri G, Barrea L, Cossiga V, Perruolo G, Di Tolla MF, Zumbolo F, Formisano P, Morisco F, Savastano S. Visceral Obesity and Cytokeratin-18 Antigens as Early Biomarkers of Liver Damage.Int J Mol Sci. 2023;24:10885.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 3][Reference Citation Analysis (0)]
Zhang X, Zheng MH, Liu D, Lin Y, Song SJ, Chu ES, Liu D, Singh S, Berman M, Lau HC, Gou H, Wong GL, Zhang N, Yuan HY, Loomba R, Wong VW, Yu J. A blood-based biomarker panel for non-invasive diagnosis of metabolic dysfunction-associated steatohepatitis.Cell Metab. 2025;37:59-68.e3.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 8][Cited by in RCA: 20][Article Influence: 20.0][Reference Citation Analysis (0)]
Gawrieh S, Vilar-Gomez E, Wilson LA, Pike F, Kleiner DE, Neuschwander-Tetri BA, Diehl AM, Dasarathy S, Kowdley KV, Hameed B, Tonascia J, Loomba R, Sanyal AJ, Chalasani N; NASH Clinical Research Network. Increases and decreases in liver stiffness measurement are independently associated with the risk of liver-related events in NAFLD.J Hepatol. 2024;81:600-608.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 25][Cited by in RCA: 35][Article Influence: 35.0][Reference Citation Analysis (0)]
Mózes FE, Lee JA, Vali Y, Alzoubi O, Staufer K, Trauner M, Paternostro R, Stauber RE, Holleboom AG, van Dijk AM, Mak AL, Boursier J, de Saint Loup M, Shima T, Bugianesi E, Gaia S, Armandi A, Shalimar, Lupșor-Platon M, Wong VW, Li G, Wong GL, Cobbold J, Karlas T, Wiegand J, Sebastiani G, Tsochatzis E, Liguori A, Yoneda M, Nakajima A, Hagström H, Akbari C, Hirooka M, Chan WK, Mahadeva S, Rajaram R, Zheng MH, George J, Eslam M, Petta S, Pennisi G, Viganò M, Ridolfo S, Aithal GP, Palaniyappan N, Lee DH, Ekstedt M, Nasr P, Cassinotto C, de Lédinghen V, Berzigotti A, Mendoza YP, Noureddin M, Truong E, Fournier-Poizat C, Geier A, Martic M, Tuthill T, Anstee QM, Harrison SA, Bossuyt PM, Pavlides M; LITMUS investigators. Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis.Lancet Gastroenterol Hepatol. 2023;8:704-713.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 102][Cited by in RCA: 114][Article Influence: 57.0][Reference Citation Analysis (0)]
Grenier-Larouche T, Coulter Kwee L, Deleye Y, Leon-Mimila P, Walejko JM, McGarrah RW, Marceau S, Trahan S, Racine C, Carpentier AC, Lusis AJ, Ilkayeva O, Vohl MC, Huertas-Vazquez A, Tchernof A, Shah SH, Newgard CB, White PJ. Altered branched-chain α-keto acid metabolism is a feature of NAFLD in individuals with severe obesity.JCI Insight. 2022;7:e159204.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in RCA: 27][Reference Citation Analysis (0)]
