ABSTRACT
-
Background/Aims
Iron-corrected T1 (cT1) is a magnetic resonance imaging (MRI)-derived biomarker of hepatic fibro-inflammation. This study aimed to investigate whether high cT1 values are associated with an increased risk of extrahepatic malignancy in a large prospective cohort.
-
Methods
We included 24,003 cancer-free participants from the United Kingdom (UK) Biobank with liver MRI and complete covariate data. Incident extrahepatic malignancy was assessed over a median follow-up of 4.28 years. Multivariable Fine–Gray subdistribution hazard models were used to evaluate the association between cT1 and extrahepatic cancer risk, accounting for competing risks and adjusting for demographic, metabolic, and liver-related factors.
-
Results
During follow-up, 1,144 participants developed extrahepatic malignancies. In the multivariable analysis, higher cT1 was independently associated with increased extrahepatic cancer risk (hazard ratio [HR] 1.116; 95% confidence interval [CI] 1.033–1.205; per standard deviation increase; P=0.005). Older age (HR 1.512; 95% CI 1.402–1.631) and male sex (HR 1.388; 95% CI 1.220–1.578) were also significant predictors (P<0.001). The fibrosis-4 score showed a modest positive association (HR 1.032; 95% CI 1.004–1.062; P=0.026), whereas MRI-proton density fat fraction demonstrated an inverse association (HR 0.906; 95% CI 0.832–0.987; P=0.024). When dichotomized at 771 ms, elevated cT1 was associated with a higher cumulative incidence of extrahepatic malignancy (6.1% vs. 4.6%, P=0.002).
-
Conclusions
Elevated cT1 is independently associated with an increased risk of future extrahepatic malignancy, suggesting that it may reflect systemic disease processes related to cancer risk. These findings highlight the potential relevance of cT1 beyond liver-specific outcomes; however, further validation is required before clinical implementation.
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Keywords: Magnetic resonance imaging; Biomarkers; Neoplasms; Prospective studies; Liver diseases
Study Highlights
• This study demonstrates that iron-corrected T1 (cT1), a quantitative MRI marker of hepatic fibro-inflammation, is independently associated with future extrahepatic malignancy risk. These findings highlight the broader systemic significance of liver inflammation, extending its clinical relevance beyond liver disease. For clinicians and researchers, cT1 may be useful for identifying individuals with increased systemic disease risk. Further validation is warranted before integrating cT1-based risk assessment into clinical decision-making.
Graphical Abstract
INTRODUCTION
Iron-corrected T1 (cT1), a liver magnetic resonance imaging (MRI)-derived biomarker, has been increasingly used in research settings to assess hepatic inflammation and fibrosis, providing a noninvasive measure of liver disease severity [
1,
2]. Current guidelines recognize cT1 as a marker for identifying at-risk metabolic dysfunction-associated steatohepatitis (MASH) [
3,
4]. Although cT1 has been extensively studied in the assessment of liver disease, its potential relevance to extrahepatic outcomes, including malignancies, remains largely unexplored.
Chronic liver diseases, particularly metabolic dysfunction-associated steatotic liver disease (MASLD), have been strongly linked to an increased risk of extrahepatic malignancies, including colorectal, gastric, pancreatic, breast, prostate, and bladder cancers [
5–
7]. This association is thought to be mediated by chronic systemic inflammation, oxidative stress, and gut-liver axis dysregulation, which may contribute to tumorigenesis [
8]. However, although MASLD and its systemic complications have been widely studied, biomarkers that reflect hepatic inflammation and their association with extrahepatic malignancies remain poorly understood.
A recent study has suggested an association between cT1 and cardiovascular disease risk, highlighting its potential as an indicator of systemic disease burden [
9]. Notably, this study demonstrated that higher cT1 values are independently associated with an increased risk of major cardiovascular events and all-cause mortality, independent of traditional metabolic risk factors [
9]. This suggests that hepatic fibro-inflammation, as assessed by cT1, may play a broad role in systemic disease processes beyond liver pathology.
Given the interplay between hepatic fibro-inflammation, metabolic stress, and malignancy, elevated cT1 values may be associated with an increased risk of extrahepatic cancers. However, no previous large-scale prospective cohort study has systematically examined whether cT1 serves as a predictor of extrahepatic malignancy. Therefore, this study aimed to investigate whether higher cT1 values are associated with an increased risk of extrahepatic malignancies, using data from the United Kingdom (UK) Biobank.
MATERIALS AND METHODS
Data source
This study used data from the UK Biobank (application 85037) cohort, which is a large-scale prospective study with over 500,000 participants aged 40–69 years recruited between 2006 and 2010 [
10]. The UK Biobank has accumulated vast data regarding environmental, lifestyle, biological, and genetic information. Participants who resided in England, Scotland, or Wales were first assessed, between March 2006 and July 2010. A total of 100,000 randomly selected individuals were being recruited for an imaging-focused sub-study and have been undergoing multimodal imaging covering the brain, cardiovascular system, abdomen, bone, and carotid arteries since 2014 [
11]. Approval for the UK Biobank cohort study was obtained from the North West Multi-Center Research Ethics Committee (16/NW/0274). The study followed the ethical principles outlined in the Declaration of Helsinki, and all participants provided written informed consent.
At the time of this investigation, the UK Biobank included 502,369 participants. Among these, 65,655 participants attended the imaging visit. Of these, cT1 measurements were available for 34,003 participants in whom the Liver-MultiScan® sequence had been acquired. We excluded participants with a diagnosis of cancer prior to the imaging visit (n=5,030) and those with missing covariate data (n=4,970). The final analytic cohort comprised 24,003 participants (
Fig. 1). The follow-up period was defined as the interval from the imaging visit to the date of cancer diagnosis or last follow-up, whichever occurred first.
The baseline of this study was defined as the third visit (imaging visit) for participants in the UK Biobank cohort. Blood tests were not included in the imaging visit protocol; therefore, we used data from the first or second visit, selecting the visit closest to the imaging visit. The interval between the blood biomarker assessment and the imaging study was a median of 9.2 (interquartile range [IQR] 7.6–10.3) years. Demographic variables (age, sex, race, smoking history, and alcohol consumption history), comorbidities (diabetes mellitus, hypertension, and dyslipidemia), anthropometric variables (body mass index and waist circumference), and blood pressure were evaluated as covariates. Alcohol consumption was defined as moderate if it was <210 g per week for men and <140 g per week for women, and as significant if it exceeded these amounts [
4]. Comorbidities at baseline were defined as self-reported or physician diagnosis (
Supplementary Table 1). Blood tests for liver function and comorbidities were also performed. The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI 2021 formula [
12] and the fibrosis-4 (FIB-4) score, a simple and useful noninvasive screening tool for hepatic fibrosis, was calculated as follows:
Covariates were selected a priori based on their established associations with cancer risk, metabolic dysfunction, and chronic liver disease, as well as their potential to confound the relationship between hepatic fibro-inflammatory activity and extrahepatic malignancy [
6,
14,
15].
All measurements were performed using Siemens 1.5T MAGNETOM Aera. A single transverse slice at the level of the porta hepatis was used to represent the liver. Quantitative liver metrics, including liver iron content, liver fat, and liver inflammatory factors, were generated according to the Liver
MultiScan® protocol (Perspectum Diagnostics, Oxford, UK). Liver T1 mapping was performed using a cardiac-gated ShMOLLI sequence. Concurrently, a multi-echo spoiled gradient echo sequence was used to quantify liver iron levels and proton density fat fraction (PDFF). The measured T1 values were adjusted for the confounding effect of iron deposition to derive cT1 values, which reflected hepatic fibro-inflammatory changes [
16]. PDFF maps were generated by applying a three-point Dixon technique to the second, fourth, and sixth echoes from the ten-echo MR sequence [
17].
The primary endpoint was the development of extrahepatic cancer. Participants of the UK Biobank cohort provided consent for their health to be monitored over time using linked electronic medical records. So far, linkage with the UK National Cancer Registry has been completed. The cancer registry offers validated information on cancer diagnosis. The International Classification of Diseases, 10th Revision (ICD-10) codes were used to categorize primary endpoints.
Statistical analysis
Continuous variables are presented as the mean± standard deviation, and categorical variables as numbers (percentages). Between-group differences were assessed using independent
t-tests for continuous variables and chi-square tests for categorical variables. In the survival analysis, extrahepatic malignancy development was defined as the primary endpoint, with all-cause mortality as a competing risk. Cumulative incidence functions were estimated and compared between groups using Gray’s test. The Fine–Gray subdistribution hazard model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Continuous variables were analyzed both as standardized Z-scores to permit direct comparison across differently scaled variables, and as categorical variables to reveal nonlinear relationships and establish clinically relevant thresholds. Accordingly, HRs for continuous variables represent the relative risk per one standard deviation increase. Variance inflation factors were calculated for all covariates in the multivariable models; all values were below the conventional threshold, indicating no evidence of multicollinearity (
Supplementary Table 2). False discovery rate (FDR) correction was applied to adjust for multiple comparisons across cancer sites. The optimal cut-off for MRI cT1 for the development of extrahepatic malignancy was set to the value that maximizes the partial log-likelihood of the Fine–Gray subdistribution hazard model. We performed internal validation using bootstrap resampling (1,000 iterations). To assess the potential impact of reverse causality, we performed 1-year and 2-year landmark analyses, excluding participants who developed cancer within the first 1 or 2 years after MRI and re-initiating follow-up from each respective landmark time point. Potential nonlinear associations of MRI cT1 and MRI-PDFF with extrahepatic malignancy development were examined using restricted cubic splines with three knots placed at 10th, 50th, and 90th percentiles of cT1, with HRs expressed relative to the median cT1 value. Cancer type-specific analyses were performed by constructing separate Fine–Gray subdistribution hazard models to identify distinct risk profiles. In addition, robust (sandwich) variance estimators were used for inference in all Fine–Gray models. Additional sensitivity analyses were performed using alternative covariate specifications, including models excluding selected blood-based biomarkers and instead adjusting for clinically relevant comorbidities and medication use, given differences in measurement timing between laboratory variables and imaging. In addition, inverse probability of treatment weighting (IPTW) analyses were conducted as sensitivity analyses using the predefined cT1 threshold (≥771 ms). Propensity scores were estimated using logistic regression incorporating all covariates included in the multivariable model, and stabilized weights were applied. Covariate balance was assessed using standardized mean differences, with values <0.1 indicating adequate balance, which all covariates achieved after IPTW. To evaluate potential effect modification by sex, sex-stratified analyses were additionally performed. All statistical analyses were performed using R software (version 4.4.2, R Foundation for Statistical Computing, Vienna, Austria), and a two-sided
P-value <0.05 was considered statistically significant.
RESULTS
Baseline characteristics
The baseline characteristics are summarized in
Table 1. Deaths during follow-up were 173 (0.72%). The mean age was 63.77 years, and 48.3% (n=11,598) of the participants were men. White participants (n=23,203; 96.7%) made up the majority of the study population. Never, previous, and current smokers were 63.5% (n=15,247), 33.2% (n=7,961), and 3.3% (n=795), respectively. Nearly half of the participants reported moderate alcohol consumption (n=11,891; 49.5%), followed by those who did not drink at all (n=6,719; 28.0%), and those who were significant drinkers (n=5,393; 22.5%). The prevalence of diabetes mellitus, hypertension, and dyslipidemia was 5.0% (n=1,212), 30.4% (n=7,287), and 28.0% (n=6,715), respectively. The mean MRI-PDFF was 4.88%, and MRI cT1 was 700.28 ms. The mean FIB-4 score was 1.52.
Among 24,003 participants, 1,144 developed extrahepatic malignancy. The median follow-up period was 4.28 years (IQR: 3.61–5.28). Baseline characteristics of participants with and without extrahepatic malignancy are compared in
Supplementary Table 3. Participants with extrahepatic malignancy were older (mean 66.39
vs. 63.64 years) and had a higher proportion of men (58.1%
vs. 47.8%; all
P<0.001) than those without extrahepatic malignancy. The prevalence of comorbidities such as hypertension (37.3%
vs. 30.0%) and dyslipidemia (36.2%
vs. 27.6%) was also higher in patients who developed extrahepatic malignancies (all
P<0.001). In contrast, participants without extrahepatic malignancy had better-preserved hepatic function, as indicated by higher albumin levels (mean 45.53
vs. 45.34 g/L,
P=0.016) and platelet counts (mean: 249.22
vs. 245.37, ×10
9/L;
P=0.043), and better renal function (mean serum creatinine: 74.23
vs. 72.52 μmol/L,
P<0.001) than those with extrahepatic malignancy. In addition, both the FIB-4 score (mean: 1.65
vs. 1.51,
P<0.001) and MRI cT1 (mean: 705.29
vs. 700.02 ms,
P=0.002) were significantly elevated in participants with extrahepatic malignancy.
Competing risk analyses using Fine–Gray subdistribution hazard models were performed to identify risk factors, as shown in
Table 2. Multivariable analysis revealed that older age (HR 1.512; 95% CI 1.402–1.631) and male sex (HR 1.388; 95% CI 1.220–1.578) were associated with increased risks of extrahepatic malignancy (all
P<0.001). Higher systolic blood pressure (SBP) was inversely associated with extrahepatic malignancy in the multivariable model (HR 0.929; 95% CI 0.873–0.989;
P=0.022). In exploratory analyses modeling SBP as a continuous variable with additional adjustment for age, the direction of association was attenuated and reversed, supporting the presence of substantial age-related confounding (data not shown). While the FIB-4 score (HR 1.032; 95% CI 1.004–1.062;
P=0.026) and MRI cT1 (HR 1.116; 95% CI 1.033–1.205;
P=0.005) were positively related to extrahepatic malignancy, MRI-PDFF (HR 0.906; 95% CI 0.832–0.987;
P=0.024) showed an inverse relationship. In the landmark analyses, after excluding participants who developed cancer within the first 1 or 2 years after MRI, the direction and magnitude of the association between cT1 and the risk of extrahepatic malignancy were broadly consistent (
Supplementary Table 4). The multivariable-adjusted restricted cubic spline analysis demonstrated a gradual increase in the risk of extrahepatic malignancy with higher cT1 values (
P for overall association= 0.007), suggesting a positive association between hepatic fibro-inflammation and extrahepatic cancer risk (
Supplementary Fig. 1). In contrast, multivariable-adjusted spline analysis of MRI-PDFF showed a linear inverse association with extrahepatic malignancy risk, with no evidence of nonlinearity (
P for nonlinearity=0.421;
Supplementary Fig. 2). Sensitivity analyses using alternative covariate specifications yielded consistent results, with cT1 demonstrating a similar magnitude and direction of association with extrahepatic malignancy in continuous analyses (
Supplementary Table 5).
To complement the analysis of cT1 as a continuous variable, we evaluated its predictive value using a thresholdbased approach. A cT1 value of 771 ms was selected as the optimal cut-off for predicting extrahepatic malignancy. Additionally, we assessed previously reported cT1 cut-offs (800, 825, 875 ms) together with the 771 ms threshold using bootstrap resampling (
Supplementary Table 6). The 771 ms cut-off was selected in 906 of 1,000 bootstrap iterations, supporting strong internal stability. In the multivariable analysis, age ≥60 (HR 1.999; 95% CI 1.716–2.328; P<0.001), male sex (HR 1.470; 95% CI 1.302–1.661; P<0.001), FIB-4 ≥2.67 (HR 1.359; 95% CI 1.046–1.765; P=0.022), and MRI cT1 ≥771 ms (HR 1.404; 95% CI 1.152–1.711; P<0.001) were associated with an increased risk of extrahepatic malignancy, whereas MRI-PDFF ≥5% was associated with a reduced risk (HR 0.786; 95% CI 0.670–0.923; P=0.003) (
Supplementary Table 7). In the landmark analyses, consistent results were observed (
Supplementary Table 8). The cumulative incidence rate of extrahepatic malignancy was significantly higher in patients with MRI cT1 ≥771 ms than in those with MRI cT1 <771 ms (
Fig. 2,
P=0.002). Similar results were observed in sensitivity analyses using alternative covariate specifications with the predefined threshold of cT1 ≥771 ms (
Supplementary Table 9). We performed additional analyses stratified by the interval between the blood biomarker assessment and the imaging data to evaluate the sensitivity of MRI cT1. The positive association of the HRs remained consistent; however, in some strata, statistical significance was lost due to reduced power associated with smaller sample sizes (
Supplementary Table 10).
The predictive performance of cT1 for individual extrahepatic cancer risk is summarized in
Table 3. FDR-adjusted
P-values were additionally provided to account for multiple comparisons across cancer sites. In the multivariable analysis, after adjusting for age, male sex, smoking, alcohol consumption, body mass index, SBP, low-density lipoprotein (LDL)-cholesterol, eGFR, HbA1c, FIB-4 score, and MRI-PDFF, higher cT1 levels were independently associated with an increased risk of cancer of respiratory and intrathoracic organs (HR 1.543; 95% CI 1.047–2.275;
P=0.028), urinary tract cancer (HR 1.760; 95% CI 1.315–2.359;
P<0.001), and cancer of the primary lymphoid, hematopoietic, and related tissues (HR 1.405; 95% CI 1.016–1.945;
P=0.040). After FDR correction, the association remained statistically significant only for urinary tract cancer. In multivariable analyses using the predefined threshold of cT1 ≥771 ms (
Supplementary Table 11), elevated cT1 was associated with increased risks of colorectal cancer (HR 2.251; 95% CI 1.048–4.835;
P=0.038) and urinary tract cancer (HR 4.693; 95% CI 2.128–10.346;
P<0.001), and only urinary tract cancer remained significant after FDR adjustment. In sensitivity analyses using IPTW with the predefined cT1 threshold (≥771 ms), the overall findings were consistent with the primary multivariable analyses. Elevated cT1 remained significantly associated with an increased risk of extrahepatic malignancy (HR 1.524; 95% CI 1.177–1.974;
P<0.001). For site-specific cancers, the general pattern and direction of associations were broadly similar (
Supplementary Table 12).
Given the potential for effect modification by sex, we conducted additional sex-stratified analyses. The direction of the association between cT1 and extrahepatic malignancy was generally consistent across both males and females. When cT1 was modeled as a continuous variable, the association reached statistical significance only in females (
Supplementary Table 13); however, the effect estimates were comparable in magnitude between sexes. When using the predefined threshold (cT1 ≥771 ms), the association was significant in both males and females (
Supplementary Table 14), with a larger effect size observed in females. Similar patterns were observed in site-specific analyses (
Supplementary Tables 15,
16), where the direction of associations was generally consistent across sexes despite variability in statistical significance, likely reflecting reduced statistical power and heterogeneity in cancer incidence. Overall, the direction and magnitude of associations were broadly similar across sexes.
DISCUSSION
We analyzed data from the UK Biobank cohort to evaluate the relationship between cT1 and the risk of extrahepatic malignancies. Our findings showed that individuals with elevated cT1 values had an independently increased risk of extrahepatic cancers. These associations remained robust after adjusting for demographic, metabolic, and hepatic confounders, including the FIB-4 score and MRI-PDFF. Together, these findings suggest that cT1, a noninvasive MRI biomarker reflecting liver inflammation and fibrosis, may not only reflect hepatic injury but also be associated with systemic processes relevant to carcinogenesis. However, these findings should be interpreted as associations rather than evidence of causality.
The biological plausibility of this association is increasingly supported by mechanistic and epidemiological data. Hepatic fibro-inflammatory activity, as quantified by cT1, may reflect microenvironmental alterations that contribute to tumorigenesis, potentially through mechanisms such as systemic inflammation, immune modulation, or altered metabolic signaling [
1,
8,
18]. These pathophysiological links may explain why elevated cT1 is associated with cancers beyond the liver even after accounting for established risk factors. Notably, a growing body of evidence supports the role of liver disease as a systemic disease that influences extrahepatic organ systems [
5–
7,
9]. Nevertheless, cT1 and several extrahepatic malignancies share common upstream risk factors, such as obesity and insulin resistance, and residual confounding due to these shared pathways cannot be fully excluded despite multivariable adjustment. Although reverse causality cannot be entirely excluded, landmark analyses excluding early events showed consistent directions of association. These findings provide some reassurance against, but do not exclude, the possibility of reverse causation. In addition, the relatively short median follow-up duration (4.28 years) may limit the ability to fully capture long-term cancer risk, particularly for malignancies with longer latency periods. Therefore, these findings should be interpreted as associations rather than evidence of a causal relationship.
As a magnetic resonance biomarker, cT1 has emerged as a clinically feasible and reproducible tool for noninvasive assessment of liver pathology [
3,
4]. Unlike liver biopsy, which is subject to sampling error, procedural complication risk, and interobserver variability [
19], cT1 provides a quantitative MRI-derived estimate of hepatic fibro-inflammation, typically averaged over the entire liver, with high reproducibility [
20]. cT1 values have been shown to reflect hepatic inflammation and fibrosis, the key drivers of progression in MASLD, viral hepatitis, and other chronic liver diseases [
21]. Furthermore, according to a recent systematic review, cT1 has shown clinical feasibility in reflecting therapeutic effects in MASLD, supporting its use for longitudinal treatment monitoring [
22]. In addition, cT1-derived liver disease activity correlated with an increased risk of cardiovascular outcomes and all-cause mortality, irrespective of the presence of liver fat, fibrosis, or metabolic syndrome [
9]. In the present study, a one standard deviation increase in cT1 (approximately 55 ms) was associated with an approximately 11% relative increase in the risk of extrahepatic malignancy. Although modest at the individual level, this magnitude warrants further investigation in the context of population-based risk prediction frameworks. cT1 may therefore have potential value as a component of multivariable risk prediction models; however, external validation is required before any clinical application can be considered. In addition, cT1 is a potentially time-varying biomarker, and the use of a single baseline MRI measurement in this study may not fully capture longitudinal changes in hepatic fibro-inflammatory activity. Future studies incorporating repeated imaging assessments are required to better characterize the temporal dynamics of cT1 and their relationship with cancer risk.
Our multivariable analysis revealed that older age, male sex, and higher FIB-4 scores were associated with increased risks of extrahepatic cancer, whereas higher SBP and greater hepatic fat content, as measured using MRIPDFF, were inversely associated. These findings highlight the differential clinical implications of liver steatosis and fibro-inflammation. Notably, both the elevated FIB-4 score and cT1 remained significant even after mutual adjustment, suggesting that hepatic inflammation and fibrosis may be relevant correlates of systemic oncogenesis, though causal inference cannot be established from this observational study [
23]. In contrast, hepatic steatosis alone, without accompanying fibrosis or inflammation, may not carry the same risk and may even be inversely associated with extrahepatic cancer. The observed protective association between higher SBP and extrahepatic malignancy in the multivariable Fine–Gray model is counterintuitive, lacks clear biological plausibility, and should not be interpreted as a causal effect. In exploratory analyses modeling SBP as a continuous variable, the association changed direction after additional adjustment for age, indicating substantial confounding by age and related clinical factors. Overall, these findings indicate that SBP is strongly confounded by age and treatment status and should be interpreted as a clinical correlate rather than an independent risk factor for extrahepatic malignancy.
To complement the continuous analysis, we assessed cT1 using a threshold-based approach. Since this threshold was derived within the same dataset, it should be interpreted as exploratory and hypothesis-generating rather than confirmatory. Although we observed strong internal stability of this cut-off in bootstrap resampling analyses, external validation in independent cohorts is required to confirm its generalizability. Individuals with cT1 ≥771 ms exhibited an independently increased risk of developing extrahepatic cancers in our cohort. Although previous studies have validated higher thresholds in the context of at-risk MASH (
e.g., 825 ms and 875 ms) [
3,
4] or cardiovascular disease risk stratification (
e.g., 800 ms) [
9], our data are consistent with the possibility that oncological risk may be detectable at relatively lower degrees of hepatic fibro-inflammation, though this hypothesis requires prospective validation in independent cohorts. This discrepancy may reflect differences in the pathophysiological processes underlying systemic carcinogenesis, which may involve broader immune, inflammatory, or metabolic dysregulation, rather than the more advanced hepatic injury associated with defining at-risk MASH or cardiovascular events [
24].
The relationship between chronic liver disease and increased risk of extrahepatic cancers has been extensively explored in previous studies [
25]. In individuals with MASLD, extrahepatic cancers account for a higher mortality burden than hepatocellular carcinoma [
25,
26]. The presence of MASLD is associated with a substantially higher risk of gastrointestinal cancers with odds increased by 1.5 to 2 times [
6]. It is also linked to a 1.2–1.5-fold higher risk of non-gastrointestinal cancers including lung, breast, gynecological, and urinary system cancers, independent of conventional risk factors [
6]. In addition, with the advent of antiviral therapy, hepatocellular carcinoma-related mortality has diminished, whereas mortality from extrahepatic cancers has increased in patients with chronic hepatitis B infection [
27,
28]. Notably, extrahepatic malignancies now constitute the leading cause of death in patients with chronic hepatitis B without cirrhosis [
28]. Moreover, a meta-analysis demonstrated that patients with chronic liver disease have an increased risk of colorectal cancer compared with the general population, and this elevated risk persists after liver transplantation [
29].
In our study, elevated cT1 was associated with an increased risk of several site-specific extrahepatic malignancies. When analyzed as a continuous variable, higher cT1 was independently linked to cancers of the respiratory and intrathoracic organs; urinary tract; and lymphoid, hematopoietic, and related tissues. In addition, when applying the predefined threshold of cT1 ≥771 ms, significant associations were observed for colorectal and urinary tract cancers. However, the pattern of associations was not entirely consistent across modeling approaches. Some cancer types showed associations only when cT1 was analyzed continuously, whereas others were observed only in threshold-based analyses. These discrepancies likely reflect differences in model specification, limited numbers of site-specific cancer events, and the exploratory nature of these analyses. In addition, only the association with urinary tract cancer remained statistically significant after FDR correction; therefore, site-specific results should be interpreted cautiously, taking into account the FDR-adjusted significance levels. Therefore, findings for individual cancer sites—particularly those identified in thresholdbased analyses—should be interpreted cautiously and considered hypothesis-generating, requiring external validation in independent cohorts before definitive conclusions can be drawn. These findings suggest that cT1 may capture systemic processes relevant to oncogenesis beyond the liver, highlighting its potential as a biomarker for extra-hepatic cancer risk stratification. Some of these cancers, particularly colorectal cancer, are amenable to early detection through established surveillance protocols [
30], whereas others currently lack standardized screening approaches. Whether individuals with chronic liver disease have a sufficiently elevated risk to justify intensified cancer surveillance beyond that of the general population remains unclear. Therefore, our results should be considered hypothesis-generating. While our study demonstrates an association between cT1 and extrahepatic cancer, the current evidence does not support its use as a standalone biomarker for immediate clinical use. Instead, cT1 may be better positioned as a component within multivariable risk prediction frameworks. We also acknowledge that the relatively high cost and limited availability of MRI may restrict its widespread use in routine clinical practice. Therefore, cT1 may be more appropriately applied in a selective manner, particularly among individuals at increased risk, rather than as a population-wide surveillance tool. Given the wide availability of the FIB-4 score, a stepwise approach may be feasible, in which MRI-based cT1 measurement could be selectively applied to patients with indeterminate or intermediate FIB-4 results. Future studies are needed to determine whether incorporating cT1 into existing risk stratification frameworks improves cancer risk prediction in this population and to evaluate its cost-effectiveness.
The use of MRI-PDFF to quantify hepatic steatosis is now well established in clinical practice [
31]. MASLD encompasses a continuum of liver diseases, ranging from simple steatosis to MASH and cirrhosis [
32]. Beyond liver cancer, MASLD has also been linked to a wide range of extrahepatic malignancies [
33]. In a meta-analysis evaluating this association, the incidence rate of extrahepatic cancer was calculated to be 10.58 per 1,000 person-years, approximately eight times higher than that of hepatocellular carcinoma [
34]. In our study, MRI-PDFF was not associated with extrahepatic cancers in univariate analysis but showed an inverse association after multivariable adjustments. This counterintuitive finding likely reflects the statistical context of the multivariable model, in which cT1 and FIB-4—markers of hepatic fibro-inflammatory and fibrotic burden—were simultaneously included. Under these conditions, MRI-PDFF may represent the residual component of hepatic fat independent of fibro-inflammation and advanced metabolic dysfunction. This suggests that isolated steatosis, in the absence of significant fibro-inflammatory activity, may not confer the same cancer risk as steatohepatitis or fibrosis-driven liver disease [
23,
35]. Recently, a new conceptual framework for “clinical obesity” has been proposed, in which obesity is defined not only by excess adiposity but also by the presence of organ dysfunction attributable to adiposity [
36]. Within this framework, MASLD with liver fibrosis can be viewed as a form of obesity-related organ dysfunction. In addition, prior studies have reported a lower cancer risk in metabolically healthy obesity compared with metabolically unhealthy obesity [
37,
38]. Together, these concepts provide a useful framework for interpreting why simple steatosis, without broader metabolic or fibro-inflammatory derangements, may not be associated with a higher risk of extrahepatic malignancy.
Although this study provides meaningful clinical insights, several limitations should be acknowledged. First, the use of the UK Biobank cohort introduces healthy volunteer bias, as participants tend to be healthier than the general population. In addition, UK Biobank participants are primarily middle-aged to older British residents and predominantly of European ancestry, with limited representation from younger individuals, non-European ethnic groups, and other geographic regions. Therefore, the generalizability of our findings to more diverse populations may be limited, and external validation in independent cohorts is required. Second, the use of ICD-10 codes from linked healthcare records may introduce misclassification, incomplete case ascertainment, and limited diagnostic specificity. In addition, reliance on registry-based data may lead to delayed or missed diagnoses, particularly for early or asymptomatic tumors, which could result in underestimation of the true association. Third, only a subset of participants underwent abdominal MRI, which may have introduced selection bias in the imaging-based analyses. The imaging sub-study was not conducted based on clinical indication but was implemented as part of the UK Biobank imaging enhancement protocol. In addition, the absence of cT1 data among many imaging participants primarily reflects non-acquisition of the LiverMultiScan® sequence within the UK Biobank imaging protocol rather than systematic measurement failure; however, this feature of the imaging design may still limit the generalizability of our findings. Also, our study used a single MRI scanner vendor (Siemens); therefore, further validation across other vendors is warranted. Fourth, blood-based covariates (LDL-cholesterol, eGFR, HbA1c, and FIB-4 score) were measured at enrollment, preceding the MRI by a median of 9.2 years. As these biomarkers are inherently time-varying, residual confounding due to exposure–confounder misalignment cannot be excluded. This temporal mismatch represents an inherent structural constraint of the UK Biobank imaging sub-study design and cannot be fully addressed through additional analyses. In addition, cT1 itself is a potentially time-varying biomarker, and a single baseline MRI measurement may not fully capture longitudinal changes in hepatic fibro-inflammatory activity. Additionally, as cT1 and several extrahepatic cancers share common metabolic risk factors, residual confounding by shared upstream determinants cannot be fully excluded despite covariate adjustment. Therefore, the findings should be interpreted with caution. Fifth, the lack of detailed information on tumor molecular subtypes precluded evaluation of whether the observed associations differ across specific biological subtypes. Sixth, the number of events for several individual cancer types was relatively small; therefore, these results should be interpreted with caution. Seventh, we were unable to apply fat-suppressed or Dixon-based T1 mapping approaches that more directly isolate the water T1 signal and minimize potential fat-related signal contributions. In addition, cT1 was derived from a single standardized MRI slice, which may not fully capture regional heterogeneity in liver fibro-inflammatory activity. Future studies using fat-suppressed T1 techniques and multi-slice or volumetric MRI acquisitions will be required to better delineate the fibro-inflammatory component of cT1 and clarify its oncologic implications independent of hepatic steatosis. Eighth, the presence of pre-existing (occult) cancers at the time of the imaging visit could have influenced the observed association between cT1 and extrahepatic cancer risk. Although we performed landmark analyses to mitigate reverse causation, we could not completely exclude residual influence from incident cancers, particularly those diagnosed shortly after imaging. In addition, the relatively short median follow-up duration of 4.28 years may limit the ability to fully capture long-term cancer risk, particularly for malignancies with longer latency periods. Lastly, the observational study design inherently limits causal inference and remains susceptible to residual and unmeasured confounding.
In conclusion, elevated cT1 may reflect systemic pathophysiological processes associated with extrahepatic cancer risk; however, causality cannot be inferred from this observational study. While cT1 may provide additional information for risk stratification, it should not be considered a standalone clinical biomarker based on the current evidence. Further prospective validation in diverse, independent cohorts with contemporaneous biochemical and imaging assessments is required before clinical implementation can be considered.
Data Availability
This study was conducted using the UK Biobank resource under application number (85037). The data that support the findings of this study are available from the UK Biobank (
www.ukbiobank.ac.uk) but restrictions apply to the availability of these data, which were used under license for the current study, and are therefore not publicly available. Data are, however, available from the authors upon reasonable request and with permission of the UK Biobank.
FOOTNOTES
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Authors’ Contribution
Study conception and design: Tae Seop Lim and Ja Kyung Kim; Data collection and analysis: SungA Bae and Seok-Jae Heo; Data interpretation: Hye Yeon Chon, Seok-Jae Heo, SungA Bae, and Tae Seop Lim; Drafting the manuscript: Hye Yeon Chon, Seok-Jae Heo, Tae Seop Lim, and Ja Kyung Kim. All authors have reviewed and approved the final version of the paper.
-
Acknowledgements
The authors thank the Center for Digital Health, Yongin Severance Hospital, Yonsei University Health System. The authors also thank MID (Medical Illustration & Design), a member of the Medical Research Support Services of Yonsei University College of Medicine, for providing excellent support with medical illustration.
This study was supported by a faculty research grant of Yonsei University College of Medicine (6-2025-1059).
-
Conflicts of Interest
The authors have no conflicts to disclose.
SUPPLEMENTARY MATERIAL
Supplementary material is available at Clinical and Molecular Hepatology website (
http://www.e-cmh.org).
Supplementary Figure 1.
Restricted cubic spline analysis showing the adjusted hazard ratio of cT1 for predicting extrahepatic malignancy. The hazard ratio is shown relative to the Reference value set at the median cT1 level (hazard ratio=1.0). The model was adjusted for the same covariates included in the multivariable model shown in Table 2. The restricted cubic spline model included three knots placed at the 10th, 50th, and 90th percentiles. Shaded areas represent 95% confidence intervals. P for overall association=0.007; P for nonlinearity=0.200. cT1, iron-corrected T1.
cmh-2025-1372-Supplementary-Fig-1.pdf
Supplementary Figure 2.
Multivariable-adjusted restricted cubic spline analysis of MRI-PDFF and risk of extrahepatic malignancy. The hazard ratio is shown relative to the Reference value set at the median MRI-PDFF values (hazard ratio=1.0). The model was adjusted for the same covariates included in the multivariable model shown in Table 2. The restricted cubic spline model included three knots placed at the 10th, 50th, and 90th percentiles. P for overall association=0.044; P for nonlinearity=0.421. Shaded areas represent 95% confidence intervals. MRI-PDFF, magnetic resonance imaging-proton density fat fraction.
cmh-2025-1372-Supplementary-Fig-2.pdf
Figure 1Selection of study population. cT1, iron-corrected T1.
Figure 2Cumulative incidence of extrahepatic malignancy stratified by cT1 ≥771 ms and cT1 <771 ms. P-value was calculated using Gray’s test. cT1, iron-corrected T1.
Table 1Baseline characteristics (n=24,003)
Table 1
|
Variables |
Value |
|
Age, yr |
63.77±7.55 |
|
Male sex |
11,598 (48.3) |
|
Race |
|
White |
23,203 (96.7) |
|
Black |
130 (0.5) |
|
Asian |
354 (1.5) |
|
Others |
310 (1.3) |
|
Smoking |
|
Never |
15,247 (63.5) |
|
Previous |
7,961 (33.2) |
|
Current |
795 (3.3) |
|
Alcohol consumption |
|
None |
6,719 (28.0) |
|
Moderate |
11,891 (49.5) |
|
Significant |
5,393 (22.5) |
|
Diabetes mellitus |
1,212 (5.0) |
|
Hypertension |
7,287 (30.4) |
|
Dyslipidemia |
6,715 (28.0) |
|
Body mass index, kg/m2
|
26.39±4.26 |
|
Waist circumference, cm |
88.15±12.56 |
|
Systolic blood pressure, mmHg |
138.86±18.62 |
|
Diastolic blood pressure, mmHg |
79.13±10.07 |
|
Platelet count, 109/L |
249.03±56.73 |
|
Albumin, g/L |
45.52±2.52 |
|
Total bilirubin, μmol/L |
9.38±4.60 |
|
Aspartate aminotransferase, U/L |
25.77±10.67 |
|
Alanine aminotransferase, U/L |
22.98±13.82 |
|
Gamma glutamyl transferase, U/L |
33.30±33.81 |
|
Creatinine, μmol/L |
72.60±13.93 |
|
eGFR, mL/min/1.73 m2
|
93.06±9.61 |
|
HbA1c, mmol/mol |
35.03±5.09 |
|
Total cholesterol, mmol/L |
5.73±1.09 |
|
HDL-cholesterol, mmol/L |
1.48±0.38 |
|
LDL-cholesterol, mmol/L |
3.59±0.83 |
|
Triglyceride, mmol/L |
1.64±0.94 |
|
MRI-PDFF, % |
4.88±4.73 |
|
MRI cT1, ms |
700.28±54.73 |
|
FIB-4 score |
1.52±0.75 |
Table 2Risk factors related to the development of extrahepatic malignancy
Table 2
|
Variables |
Univariable |
Multivariable |
|
HR (95% CI) |
P-value |
HR (95% CI) |
P-value |
|
Age, yr |
1.468 (1.383–1.559) |
<0.001 |
1.512 (1.402–1.631) |
<0.001 |
|
Male sex |
1.498 (1.332–1.685) |
<0.001 |
1.388 (1.220–1.578) |
<0.001 |
|
Smoking |
|
None |
Reference |
|
Reference |
|
|
Past |
1.140 (1.009–1.288) |
0.035 |
1.007 (0.888–1.141) |
0.915 |
|
Current |
1.129 (0.824–1.546) |
0.451 |
1.164 (0.847–1.601) |
0.350 |
|
Alcohol consumption |
|
None |
Reference |
|
Reference |
|
|
Moderate |
1.057 (0.921–1.213) |
0.430 |
1.000 (0.869–1.150) |
0.998 |
|
Significant |
1.001 (0.848–1.182) |
0.987 |
1.014 (0.854–1.205) |
0.870 |
|
Body mass index, kg/m2
|
1.017 (0.962–1.076) |
0.550 |
1.035 (0.965–1.110) |
0.336 |
|
Systolic blood pressure, mmHg |
1.066 (1.009–1.128) |
0.024 |
0.929 (0.873–0.989) |
0.022 |
|
LDL-cholesterol, mmol/L |
0.973 (0.916–1.033) |
0.369 |
0.970 (0.915–1.029) |
0.318 |
|
eGFR, mL/min/1.73 m2
|
0.911 (0.868–0.956) |
<0.001 |
1.054 (0.973–1.142) |
0.199 |
|
HbA1c, mmol/mol |
1.052 (1.005–1.101) |
0.029 |
0.960 (0.906–1.017) |
0.163 |
|
FIB-4 score |
1.058 (1.036–1.081) |
<0.001 |
1.032 (1.004–1.062) |
0.026 |
|
MRI-PDFF, % |
0.989 (0.934–1.048) |
0.718 |
0.906 (0.832–0.987) |
0.024 |
|
MRI cT1, ms |
1.094 (1.036–1.155) |
0.001 |
1.116 (1.033–1.205) |
0.005 |
Table 3Association between cT1 (per one standard deviation increase) and risk of site-specific extrahepatic cancers
Table 3
|
Cancer site |
n (%) |
Unadjusted HR (95% CI) |
P-value |
FDR-adjusted P-value |
Adjusted HR* (95% CI) |
P-value |
FDR-adjusted P-value |
|
Lip, oral cavity, and pharynx (C0–14) |
13 (0.05) |
0.967 (0.607–1.539) |
0.886 |
0.886 |
1.050 (0.599–1.840) |
0.865 |
0.865 |
|
Digestive system (C15–26) |
122 (0.51) |
1.197 (1.019–1.407) |
0.028 |
0.091 |
1.169 (0.911–1.501) |
0.218 |
0.348 |
|
Upper gastrointestinal organs (C15–17) |
16 (0.07) |
1.352 (0.890–2.054) |
0.158 |
0.355 |
1.561 (0.741–3.289) |
0.241 |
0.348 |
|
Colorectum (C18–20) |
70 (0.29) |
1.158 (0.942–1.426) |
0.164 |
0.355 |
1.306 (0.947–1.801) |
0.103 |
0.335 |
|
Pancreas (C25) |
22 (0.09) |
1.254 (0.850–1.853) |
0.254 |
0.367 |
0.756 (0.437–1.306) |
0.315 |
0.410 |
|
Respiratory and intrathoracic organs (C30–39) |
30 (0.12) |
1.607 (1.271–2.035) |
<0.001 |
0.003 |
1.543 (1.047–2.275) |
0.028 |
0.173 |
|
Skin (C43–44) |
452 (1.88) |
0.990 (0.904–1.085) |
0.835 |
0.886 |
0.987 (0.873–1.115) |
0.828 |
0.865 |
|
Breast (C50)†
|
91 (0.73) |
0.951 (0.752–1.202) |
0.672 |
0.794 |
0.806 (0.583–1.113) |
0.189 |
0.348 |
|
Female genital organs (C51–58)†
|
26 (0.11) |
1.221 (0.867–1.726) |
0.250 |
0.367 |
1.558 (0.953–2.536) |
0.174 |
0.348 |
|
Male genital organs (C60–63)‡
|
183 (0.76) |
1.157 (1.013–1.327) |
0.025 |
0.091 |
1.090 (0.909–1.308) |
0.353 |
0.417 |
|
Urinary tract (C64–68) |
44 (0.18) |
1.650 (1.355–2.013) |
<0.001 |
0.003 |
1.760 (1.315–2.359) |
<0.001 |
0.007 |
|
Eye, brain, and other parts of the central nervous system (C69–72) |
18 (0.07) |
1.293 (0.874–1.914) |
0.197 |
0.366 |
1.434 (0.848–2.428) |
0.178 |
0.348 |
|
Stated or presumed to be primary, of lymphoid, hematopoietic, and related tissue (C81–96) |
54 (0.22) |
1.087 (0.870–1.358) |
0.462 |
0.601 |
1.405 (1.016–1.945) |
0.040 |
0.173 |
Abbreviations
aspartate aminotransferase
estimated glomerular filtration rate
International Classification of Diseases, 10th Revision
inverse probability of treatment weighting
metabolic dysfunction-associated steatohepatitis
metabolic dysfunction-associated steatotic liver disease
magnetic resonance imaging
proton density fat fraction
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