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Non-Invasive Liver Fibrosis Markers Decrease During Weight Loss in Individuals with Obesity, Non-Alcoholic Fatty Liver Disease, and the Metabolic Syndrome

  † These authors contributed equally to this work.

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16 July 2026

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17 July 2026

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Abstract
Liver fibrosis is a serious complication of non-alcoholic fatty liver disease (NAFLD) and the metabolic syndrome (MetS), conditions closely linked to obesity. We investigated the levels and dynamics of liver fibrosis biomarkers and liver fat content during personalized weight loss intervention in individuals with obesity, NAFLD, and MetS (n=30), with assessments at baseline, 1, and 5 months. Liver fat content decreased significantly (p < 0.0001), corresponding with complete resolution of steatosis in 36.7% of the participants, after 1 month of weight loss. Similarily, the fibrosis markers: T1 (p < 0.001), CK18 (p < 0.01), PIIINP (p < 0.05), TIMP1 (p < 0.001), and MACK3 (p < 0.001) dropped after 1 month. CK18 (p < 0.01), PIIINP (p < 0.0001), and MACK3 (p < 0.001) further changed from 1 to 5 months, while FNI decreased after 5 months (p < 0.01). Except for CK18, the dynamics of these changes were more pronounced from baseline to 1 month compared with 1 to 5 months. Finally, MACK3 showed promise for explaining T1. Our study underlines the importance of weight loss in swiftly mitigating liver steatosis and supports the integration of non-invasive biomarkers to monitor fibrosis risk in individuals with obesity, NAFLD, and MetS.
Keywords: 
obesity
;  
intervention
;  
MASLD
;  
liver fibrosis risk
;  
MACK3
;  
T1 relaxation time
;  
CK18
;  
TIMP1
Subject: 
Medicine and Pharmacology  -   Gastroenterology and Hepatology

Introduction

Liver fibrosis is a pathological condition characterized by the excessive accumulation of extracellular matrix proteins in the liver, resulting from chronic liver injury and inflammation [1]. This condition represents a critical turning point in the progression of liver diseases, as it significantly increases the risk of cirrhosis, hepatocellular carcinoma, and liver failure [2,3]. The early identification and management of liver fibrosis are essential to mitigate these adverse outcomes and improve patient prognosis. However, effective early diagnostic strategies remain a challenge, particularly in populations at high risk due to obesity, non-alcoholic fatty liver disease (NAFLD), and the metabolic syndrome (MetS) [4].
The gold standard for diagnosis of liver fibrosis relies on liver biopsy and histological evaluation. However, this procedure is limited by its invasiveness, high cost, sampling variability, and associated risks, such as bleeding and infection, rendering it unsuitable for routine screening or longitudinal monitoring [5,6]. Especially for advanced liver fibrosis, transient elastography (FibroScan) and the fibrosis-4 (FIB-4) index represent non-invasive tools, although their use in individuals with obesity or for early fibrosis and risk assessment are restricted [7,8,9]. These limitations have driven the development of new non-invasive screening methods, which include imaging modalities, circulating biomarkers, and composite algorithms.
Among imaging-based techniques, magnetic resonance imaging (MRI) and magnetic resonance elastography (MRE) are promising tools. MRI-based T1 relaxation time (T1 mapping) assesses liver fibrosis, while MRE measures liver stiffness, a surrogate for fibrosis [7,10]. Both methods have shown potential for detecting early fibrotic changes but are limited by high costs and the need for specialized equipment [11,12]. Circulating biomarkers such as cytokeratin-18 (CK18), procollagen III N-terminal peptide (PIIINP), and tissue inhibitor of metalloproteinases 1 (TIMP1) offer less invasive alternatives by reflecting hepatocyte apoptosis, extracellular matrix remodeling, and fibrogenesis, respectivly [13,14,15,16]. Composite algorithms like the FIB-4 index, Forns score, fibrosis non-invasive index (FNI), and MACK3 score combine clinical and laboratory parameters to estimate fibrosis risk, enhancing their potential diagnostic accuracy while reducing reliance on single parameters [8,17,18,19]. However, these tools face challenges, including variability in sensitivity, specificity, and applicability across diverse patient populations.
In a previous study [20], we combined imaging techniques, circulating biomarkers, and algorithms for early fibrosis detection. This study demonstrated that T1 relaxation time, FNI, CK18, and MACK3 scores were significantly elevated in individuals with obesity, NAFLD, and MetS compared with an obesity control group, in the absence of overt liver disease. These findings underscore the importance of early detection strategies in high-risk populations and provide a foundation for refining screening approaches.
Building on these insights, the present study aimed to investigate the dynamical changes in liver fibrosis biomarkers during personalized weight loss intervention in individuals with obesity, NAFLD, and MetS. Weight loss is a well-established strategy for reducing liver steatosis and improving metabolic health [21]. However, its effects on the progression or regression of liver fibrosis remain less explored. We thus hypothesize that markers of liver fibrosis decrease during weight loss intervention in individuals with obesity, NAFLD, and MetS. By monitoring changes in T1 relaxation time, liver stiffness (MRE), CK18, PIIINP, TIMP1, FIB-4 index, Forns score, FNI, and MACK3 score throughout the intervention, we aimed to extend the findings of our previous study [20], by assessing the effect and its timing during weight loss. This research seeks to establish a temporal relationship between weight loss and dynamical changes of fibrosis biomarkers, offering new insights into the early screening to prevent liver fibrosis developing in individuals with obesity, NALFD, and MetS.

Materials and Methods

Study Design and Population

This study was designed as an interventional study (Figure 1A) to investigate the effects of a personalized weight loss intervention in individuals with obesity, NAFLD and MetS. The study organization, management, recruitment, and data acquisition took place at Aalborg University Hospital (Aalborg, Denmark) while the personalized weight loss intervention was conducted by “Diætisthuset Aalborg” (Aalborg, Denmark). This specific study was conducted between October 2020 to July 2022 and is part of the MULTISITE study [20].
The study started with a baseline/screening study visit to Aalborg University Hospital that included blood sample collection, magnetic resonance imaging (MRI), Dual-energy X-Ray absorptiometry (DXA) and collection of clinical data (anthropometry and blood pressure). Following this baseline visit, eligible individuals were included and began the personalized weight loss intervention. The inclusion criteria for participating in the weight loss intervention were: a medical evaluation confirming the presence of NAFLD (≥ 5% liver steatosis, measured by MRI, see below) and MetS (IDF criteria [22]). Participants were excluded based on previously diagnosed diabetes or glycated hemoglobin (HbA1c) above 48 mmol/mol (6.5%); endocrine or malignant disease; pregnancy; alcohol abuse (men: ≥ 21 standardized units per week; women ≥ 14 standardized units per week (12g alcohol per unit)); drug abuse; contraindications to MRI scanning; ongoing medical treatment with corticosteroids, antibiotics, chemotherapy, and antibiotic treatment within two months prior to inclusion. The final study population consisted of 34 participants (Figure 2B). All participants provided signed informed consent prior to inclusion.
The intervention (duration 5) was conducted by “Diætisthuset Aalborg” and consisted of one initial consultation with a dietician of 1 hour and 15 minutes where participants received a personalized diet plan customized to the client’s everyday life. After the initial consultation, participants attended 10 follow-up visits to discuss progress and potential personalized changes to the program. During the intervention period, participants attended two additional study visits at Aalborg University Hospital with blood sample collection, MRI, DXA, and collection of clinical data (anthropometry and blood pressure) after 1 and 5 months. For all study visits, participants were requested to fast overnight (> 8 hours) and remained fasting for the duration of the study visit. All assessments were completed in a single day to minimize confounding factors related to sample collection times. The study was registered on ClinicalTrials.gov (registration no. NCT05699863, 26/01/2023) and conducted in accordance with the Helsinki Declaration. The study was approved by The North Denmark Region Committee on Health Research Ethics (Den Videnskabsetiske Komité for Region Nordjylland, registration no. N-20200013).

Clinical Data

Weight was assessed with participants wearing a hospital gown. Height was measured using a stadiometer. Waist circumference, hip circumference, and waist-hip ratio (WHR) was measured and calculated in accordance with the world health organization (WHO) guidelines [23]. Blood pressure was measured a minimum of three times after a 5-10 minutes period of seated rest and reported as the average of measurement two and three.

Circulating Biomarkers

Blood samples were collected from human participants at Aalborg University Hospital (Aalborg, Denmark) during clinical visits. Participant characteristics, including age and sex distribution, are provided in Table 1. All participants provided written informed consent prior to inclusion, and ethical approval was obtained as described above.
Routine blood analysis was performed immediately after sample collection at an accredited hospital laboratory (DS/EN ISO 15189). Briefly, plasma glucose, alanine transaminase (ALT), aspartate transaminase (AST), gamma glutamyl transferase (GGT), cholesterol, high-density lipoproteins (HDL), triglycerides (TG) were measured on Cobas 6000c (Roche, Mannheim, Germany). Low-density lipoproteins (LDL) were calculated using the Friedewald equation [24]. Serum insulin and C-peptide levels were measured on Cobas 601E (Roche, Mannheim, Germany). Platelet concentration was measured on Sysmex XN-9000 (Sysmex Co., Kobe, Japan). HbA1c was measured on Sebia Capillarys 3 (Sebia, Lisses, France). Insulin resistance was estimated using the homeostasis model assessment for insulin resistance (HOMA2-IR), and was calculated from specific insulin and fasting plasma glucose levels using the iterative structural model using the HOMA2 calculator [25,26].
TIMP1 was measured using Quantikine ELISA kit (R&D Systems Inc., Minneapolis, MN, USA, Cat. #DTM100, Lot. #651-170619) with inter-assay CVs of 4.6% (97ng/mL; plasma pool), 2.2% (6ng/mL; high control), 4.3% (3ng/mL; medium control), and 4.1% (1ng/mL; low control). CK-18 was measured using the M30 Apoptosense and CK18 ELISA Kit (VLVbio, Stockholm, Sweden, Cat. #P10011, Lot. #10011-014) following manufacturer’s instructions with inter-assay CVs of 10.7% (137U/L; plasma pool), 1.4% (664U/L; high control), and 4.9% (122U/L; low control). Serum PIIINP was measured in an accredited routine hospital laboratory (DS/EN ISO 15189) by the Atellica IM analysis (Siemens Healthineers, Erlangen, Germany) with inter-assay CVs of 5.6% (11.4μg/L; high control) and 10.3% (2.24μg/L; low control).

Imaging

Liver steatosis was determined by MRI using proton density fat fraction (PDFF) maps and liver fibrosis was evaluated by MRI using T1 mapping and MRE, as previously described [20]. In short, participants were scanned in a 3T MRI scanner (Signa Premier, General Electric, Milwaukee, WI, USA) using a flexible 30-channel coil (AIR) and a 60-channel in-bed coil in the supine position. PDFF maps were obtained using breath-hold multi-point Dixon (IDEAL IQ) sequences using fat- and water-only images to generate PDFF maps [27]. Liver fat content was calculated from PDFF map images using Vitrea Read (v.8.3.53-55, Canon Medical Informatics Inc., Minnetonka, Minnesota, USA, RRID: SCR_023418) as the mean of four circular regions of interest (ROIs); (mean size: 10cm2).
Liver fibrosis was estimated using T1 mapping (T1 relaxation time) and MRE (liver stiffness). For T1 mapping, a pulse-triggered modified Look-Locker inversion recovery (MOLLI) 2D imaging sequence was done as described [27]. Assessment of T1 relaxation time was performed on the T1 maps using GE Volume Viewer (v. 15.0 Ext. 6, General Electrics, Milwaukee, WI, USA), and calculated as the mean of four circular ROIs (mean size: 7cm2). MRE was performed using mechanical waves centred on the xiphisternum and generated using a rigid, round, active pneumatic driver fastened to the upper abdomen and vibrated at 40 Hz. MRE was determined from stiffness maps generated using a direct inversion algorithm on the MRI scanner. Measurements were performed using GE Volume Viewer (v. 3.0, Ext. 2.3, General Electrics, Milwaukee, WI, USA), and calculated as the mean of four circular ROIs (mean size: 9cm2). ROIs for all measurements were placed in the anterior, posterior, medial, and lateral segments of the liver to include as much liver parenchyma as possible within the segments, excluding large vessels, the liver border, and artifacts, as recommended [28,29]. Images were processed by a single skilled operator.
For visceral fat, custom software (BodyComposition) was used to calculate the cross-sectional area of visceral fat content (cm2) from a single slice at the L3 level [30].
Total percent body fat and subcutaneous fat were determined with a Hologic 4500 (Hologic Inc., Marlborough, MA, USA) ® Dual-energy X-Ray absorptiometry (DXA) scanner. Daily quality control measures were in place using a standard phantom [31].

Liver Fibrosis Algorithms

Liver fibrosis algorithms were calculated for the FIB-4 index, Forns score, and FNI. The FIB-4 index was calculated using Equation 1 [32]:
F I B − 4 = A g e   y e a r s ∗ A S T   U / L P l a t e l e t s   10 9 / L ∗ A L T   U / L
The Forns score was calculated using Equation 2 [19]:
F o r n s   s c o r e = 7.811 − 3.131 ∗ l n P l a t e l e t s   10 9 / L + 0.781 ∗ l n G G T   U / L + 3.467 ∗ l n A g e   y e a r s ) − 0.014 ∗ C h o l e s t e r o l   m g / d L
FNI was calculated using Equation 3 [33]:
F N I = e − 10.33 + 2.54 ∗ l n A S T   U / L + 3.86 ∗ l n H b A 1 c   % − 1.66 ∗ l n H D L   m g / d L 1 − e − 10.33 + 2.54 ∗ l n A S T   U / L + 3.86 ∗ l n H b A 1 c   % − 1.66 ∗ l n H D L   m g / d L ∗ 100
MACK3 was calculated according to [17], using the online MACK3 calculator [34].

Data Analysis

Data analysis was performed in RStudio 2024.09.1 using different methods depending on the hypothesis being tested. Differences across the three time points (baseline, 1 month, and 5 months) were first evaluated using the non-parametric Friedman test to assess overall global differences, followed by paired Wilcoxon signed-rank tests for post-hoc comparisons. For binary or categorical traits measured longitudinally, changes in proportions over time were assessed using Cochran’s Q test. Statistical significance was defined as a p-value < 0.05. Variable estimations in the experimental groups are reported as mean values ± SD and changes in variables during the intervention are presented as mean change and the 95% confidence interval (CI). Correlations between variables were analysed using Spearman’s rank-order correlation method, with results presented as the correlation coefficient and the corresponding p-value.
Relative percentage changes per week of variables in baseline to 1 month and 1 month to 5 months were calculated as follows:
W e e k l y   c h a n g e =   Y f − Y 0 Y o   ×   N   ×   100 ,
Where Y0 represents the initial value of the variable, Yf represents the value of the variable after 1 or 5 months and N represents the number of weeks.
L1 regularization (LASSO regression) was used to assess the importance of fibrotic indexes and circulating markers in predicting liver fibrosis (T1). The model included fibrotic indexes (Forns, FIB-4, MACK3, and FNI), and PDFF and MRE values as predictors.
Furthermore, the model was constructed using both baseline/1 month data and baseline/5 months data to evaluate whether predictive power changes across different intervention stages. Each dataset was split into training (75%) and testing (25%) subsets, with hyperparameter tuning performed using 10-fold cross-validation. The penalty parameter was optimized by selecting the value that minimized the root mean square error. Predictor importance was assessed based on the absolute values of the regression coefficients obtained during model training.
Finally, model performance was evaluated using the test datasets, and the Pearson correlation coefficient was calculated by comparing predicted and actual T1 values.

Results

Study Participants

Participants underwent personalized weight loss intervention for 5 months with study visits at baseline, 1 month, and 5 months (Figure 1A). A total of 34 participants were included in the study. Out of these, 32 individuals were subject to the intervention. During the intervention, 2 participants withdrew from the study, resulting in a final study group of 30 (Figure 1B). In the study group, participants had a mean age of 48 years and a sex distribution of 60% females and 40% male (Table 1). The average duration of the intervention was 34 ± 5 days from baseline to first follow-up (1 month) and 165 ± 20 days from baseline to last follow-up (5 months).

Effect of Weight Loss on Metabolic Parameters and Fat Depots

Assessing the metabolic characteristics, the average body mass index (BMI) was 35.8±2.8 kg/m2 at baseline, decreased to 34.2±2.9 kg/m2 at 1 month, and 32.7±3.5 kg/m2 at 5 months (Figure 2A), showing that BMI was decreased by 3.1 kg/m2 in average (CI: [3.8 – 2.4 kg/m2]; p < 0.0001) at the end of the intervention. Additional key metabolic parameters such as fasting plasma glucose, low density lipoprotein (LDL), high density lipoprotein (HDL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and insulin also decreased during weight loss (Table 1). Moreover, we found that insulin resistance assessed by homeostasis model assessment 2 for insulin resistance (HOMA2-IR) and glycated hemoglobin (HbA1c) were significantly reduced after 1 (CI: [1.6 – 0.3 mmol/mol]; p < 0.01) and 5 (CI: [2.0 – 0.5 mmol/mol]; p < 0.01) months (Figure 2B,C).
Investigating weight loss effects on accumulated fat showed that both liver and total body fat decreased significantly already after 1 month and further from 1 to 5 months (Figure 2D,E). Specifically, total body fat was decreased by 1.0% (CI: [1.6 – 0.5%]; p < 0.01) at 1 month and by 2.3% (CI: [3.0 to 1.5%]; p < 0.0001) at 5 months (Figure 2D), whereas liver steatosis (PDFF) was decreased at 1 month by 4.4% (CI: [5.7 – 3.0%]; p < 0.0001) and by 7.1% (CI: [9.6 – 4.6%]; p < 0.0001) after 5 months (Figure 2E,F). Also subcutaneous and visceral fat stores decreased significantly during the weight loss intervention, at 1 month by 30.8 cm2 (CI: [38.9 – 22.7 cm2]; p < 0.0001) and 24.6 cm2 (CI: [36.6 – 12.6 cm2]; p < 0.001) and by 70.2 cm2 (CI: [86.3 – 54.1 cm2 ]; p < 0.0001) and 41.6cm2 (CI: [58.4 – 24.9cm2]; p < 0.0001) at 5 months, respectively (Figure S1A,B).
Finally, after 1 month of weight loss intervention, 11 participants (36.7%) showed complete resolution of liver steatosis (< 5% liver fat content), while this increased to 17 participants (56.7%) after 5 months (Table 1). Additionally, after 1 and 5 months, 3 (10%) and 9 (30%) participants no longer presented with MetS, respectively (Table 1).

Changes in Liver Fibrosis Biomarkers During Weight Loss Intervention

Firstly, we evaluated the levels (and changes) of three different types of non-invasive liver fibrosis biomarkers during weight loss intervention. Firstly, the image-based T1 relaxation time decreased significantly from baseline to 1 month (CI: [48.4 – 15.6 ms]; p < 0.001) and baseline to 5 months (CI: [72.5 – 15.9 ms]; p < 0.01) non-significant differences were observed between 1 and 5 months (Figure 3A). In contrast, liver stiffness measures did not show changes during the intervention (Figure 3B).
Next, levels of the circulating biomarkers: CK18, PIIINP, and TIMP1 were evaluated. Significant reductions in CK18 levels were observed after both 1 and 5 months, from baseline (169 ± 114 U/L) to 1 month (147 ± 92 U/L; p<0.01) and to 5 months (124 ± 74 U/L; p<0.01), and from 1 month to 5 months (p<0.01; Figure 3C). Likewise, for PIIINP the circulating levels dropped from baseline (7.8 ± 1.9 µg/L) to 1 month (6.8 ± 1.3 µg/L; p<0.0001), and 5 months (7.3 ± 1.4 µg/L; p< 0.05), whereas an increase was seen between 1 and 5 months (p<0.05; Figure 3D). For TIMP1 levels, we found that levels decreased from baseline (102.1 ± 21.3 ng/mL) to 1 month (96.2 ± 17.5 ng/mL; p<0.001) and 5 months (96.8 ± 21.8 ng/mL; p<0.01), whereas no differences were observed between 1 and 5 months (Figure 3E).
Finally, we investigated the fibrosis algorithms the FIB-4 index, Forns score, FNI, and MACK3 score. The MACK3 score showed significant reductions between all three comparisons: from baseline (0.19 ± 0.16) to 1 month (0.13 ± 0.12; p<0.001) and to 5 months (0.07 ± 0.06; p<0.0001), and from 1 month to 5 months (p<0.001; Figure 3F). For FNI, significant differences were observed between baseline (0.15 ± 0.13) and 5 months (0.10 ± 0.07; p<0.01) and between 1 (0.15 ± 0.09) and 5 months (p<0.0001), whereas no difference was seen from baseline to 1 month (Figure 3G). For the FIB-4 index and Forns Score, measures did not show significant changes between timepoints during the intervention (Figure 3H,I).

Dynamics of Liver Fat Content and Liver Fibrosis Biomarkers During Weight Loss Intervention

When investigating the dynamics and temporality of the weight loss, we found that the relative percentage change in BMI occurred at a significantly faster rate from baseline to 1 month compared with the subsequent change from 1 to 5 months (-1.1 ± 0.6 vs -0.3 ± 0.3% per week; p < 0.0001; Figure 4A). This was also true for changes in liver fat content, changing from baseline to 1 month (-8.4 ± 5.3% per week) and from 1 to 5 months (-1.1± 3.8% per week; p < 0.0001; Figure 4B). Similar results were observed for total body fat (-0.6 ± 0.9 vs -0.2± 0.3% per week; p < 0.05; Figure 4C), subcutaneous fat (-1.5 ± 1.0 vs -0.5 ± 0.5% per week; p < 0.0001; Figure 4D), and visceral fat (-2.4 ± 3.2 vs -0.5 ± 1.0% per week; p < 0.05; Figure 4E).
For the liver fibrosis markers, T1 showed an increased rate of change during the first month of intervention (-0.9 ± 1.2 vs -0.1 ± 0.4% per week; p < 0.01) compared with the subsequent change from 1 to 5 months (Figure 5A). Again, liver stiffness did not show significant differences (Figure 5B). For the circulating biomarkers, the rates of changes in CK18 levels remained constant for both intervention periods (Figure 5C), while PIIIPN (Figure 5D) and TIMP1 (Figure 5E) decreased more rapidly from baseline to 1 month compared with 1 to 5 months (-3.0 ± 2.7 vs 0.6 ± 1.2% per week; p < 0.0001; and -1.3 ± 1.8 vs 0.02 ± 0.8% per week; p < 0.01, respectively). Finally, for the composite liver fibrosis scores, we found that the rate of changes of MACK3 were more pronounced between baseline and the first month (-4.7 ± 15.8 vs -2.2 ± 2.2% per week; p < 0.05; Figure 5F). This was also seen for FIB-4 (2.0 ± 4.1 vs 0.02 ± 1.8% per week; p < 0.05; Figure 5G), while the Forns score and FNI change stayed constant during the study (Figure 5H,I).

Correlations Between Independent Fibrosis Markers

When correlating independent liver fibrosis markers, we found that at baseline T1 relaxation time correlated positively with the MACK3 score (r = 0.382; p < 0.05), FNI (r = 377; p < 0.05), and FIB-4 (r = 0.380; p < 0.05), while T1 relaxation time correlated negatively with FNI at 5 months (r = -0.382, p < 0.05; Table 2).
To evaluate which non-invasive fibrosis algorithms and imaging parameters contribute most to explaining variation in liver T1 relaxation time during weight loss intervention, we applied LASSO regression as a variable-selection approach. The model included fibrosis scores (Forns, FIB-4, MACK3, and FNI) and MRI-based measures (liver steatosis: PDFF and liver stiffness: MRE) to determine their relative contribution within a shared multivariable context. MACK3, and to a lesser extent FIB-4, emerged as the parameters most strongly associated with T1 relaxation time, providing the clearest independent signal in the model. In contrast, Forns and FNI remained in the model but contributed less, reflecting that much of the information they capture overlaps with that of the stronger predictors. The BL–1M model showed the highest predictive accuracy (R2 = 0.80, Figure 6B), whereas the BL–5M (R2 = 0.508, Figure 6C) and the all-timepoint model (R2 = 0.50, Figure 6D) exhibited more modest fits.

Discussion

With this longitudinal study, assessing the dynamics of liver fat content and non-invasive liver fibrosis markers in individuals with obesity, NAFLD, and MetS that undergo personalized weight loss, we show rapid changes in levels of both liver fat content and fibrosis markers. Specifically, already after 1 month liver steatosis dropped below 5% in nearly 37% of the participants, while the fibrosis markers: T1 relaxation time, CK18, PIIINP, TIMP1, and the MACK3 score, also decreased. Moreover, liver fat content, CK18, and MACK3 continued to decrease towards 5 months were also FNI had dropped significantly. In contrast, PIIINP increased between 1 and 5 months.
We thus, build on our recent findings that liver fibrosis markers (T1 relaxation time, CK18, MACK3) are elevated in individuals with obesity, NAFLD, and MetS compared with an obesity control group [20]. As such, our study is currently the first reported presentation of data on liver fat content as well as multiple, independent measures of liver fibrosis markers early (after 1 month) during weight loss intervention in a population of individuals with obesity, NAFLD, and MetS without overt liver disease.
A principal observation from our intervention study is that T1 relaxation time declined significantly within the first month of weight loss intervention, corresponding to a rapid decrease in hepatic fat content. Interestingly, while liver fat content, BMI, and total body fat continued to decrease from baseline to 1 month and further from 1 to 5 months, T1 relaxation time did not change after 1 month, possibly indicating that levels normalize already after the first month of weight loss. T1 mapping has emerged as a highly reliable non-invasive imaging method for fibrosis staging and progression [11]. It directly reflects the liver’s extracellular matrix composition including water content, thereby correlating with fibrotic remodelling [11,35]. However, T1 mapping is both cost- and resource-intensive, limiting its accessibility in many clinical settings. Identifying more practical alternatives that parallel T1 changes is therefore of high importance to clinicians and researchers alike.
Among the examined biomarkers, the MACK3 score continued to decrease throughout the weight loss intervention. Because MACK3 incorporates AST, HOMA, and CK18, markers that are closely tied to hepatocyte injury and metabolic stress, this score has been proposed to capture processes associated with lipotoxicity and inflammation in NAFLD [17,18]. Moreover, given that MACK3 has validated refence values for the spectrum of NASH/fibrosis, our previous findings suggested that MACK3 could differentiate the participants in our study at baseline [20]. Finally, we found that MACK3 showed promise for explaining T1 mapping. Although, the observed differences should be interpreted primarily as reflections of dataset structure and model fitting, rather than true biological sensitivity of the included markers. The comparatively stronger performance of the baseline to 1 Month model raises the hypothesis that non-invasive fibrosis markers, particularly MACK3, may capture variability in T1 relaxation time more effectively during earlier stages of the intervention. Collectively, our findings, support the MACK3 score for risk assessment and monitoring weight loss effect in individuals with obesity, NAFLD, and MetS, although these interpretations are exploratory and requires validation in larger cohorts.
The temporal dynamics of T1 relaxation time, TIMP1, and PIIINP during the weight loss intervention reveal distinct phases of metabolic and potential fibrotic improvement. The most pronounced reductions in T1 relaxation time, TIMP1, and PIIINP occurred within the first month, coinciding with rapid declines in liver fat content as well as subcutaneous and visceral fat. This rapid response underlines the dynamic nature of and link between hepatic lipotoxicity, inflammation, and fibrosis risk, early in NAFLD. Moreover, like T1 relaxation time, TIMP1 reductions plateaued after the first month with PIIINP even slightly increasing between month 1 to 5, collectedly indicating that the remodeling of extracellular matrix components is most active early in the intervention phase, with slower progression towards month 5. CK18, a well-established biomarker of hepatocyte apoptosis and indicator of fibrosis, continued to decline throughout the study, reinforcing its potential as a dynamic marker of fibrotic activity.
The concurrent reduction in visceral fat is particularly relevant, as it is a major source of pro-inflammatory cytokines and adipokines that contribute to hepatic fibrogenesis [36]. The sustained improvements observed in liver fibrosis markers are consistent with prior weight-loss and bariatric studies showing that metabolic interventions elicit the largest changes in steatosis and non-invasive fibrosis markers early, whereas fibrosis regression and extracellular matrix remodeling often progress more gradually over longer follow-up [21,37,38,39]. Our findings thus, underscore the importance of monitoring biomarker dynamics in response to weight loss interventions, as short-term changes may better capture metabolic and fibrotic shifts than baseline measures alone.
Collectively, these findings expand on our previous research in which T1 relaxation time, CK18, MACK3, and FNI were found to be elevated in individuals with obesity, NAFLD, and MetS compared with individuals with obesity alone prior to clinical signs of liver disease [20].
FIB-4 and Forns, originally developed to detect advanced fibrosis in chronic viral hepatitis cohorts, remain widely used as an inexpensive screening tool to rule out severe fibrosis and have proven clinical utility for stratifying long-term fibrosis risk [8,19]. However, as expected due to our previous findings in our cohort, which did not include patients with clinical signs of liver disease including fibrosis, advanced cirrhosis, or high-baseline FIB-4 values, neither indices changed during the 5-month intervention. Indeed, on average, both remained below the conventional “rule-out” thresholds for advanced fibrosis and this biomarker is likely not applicable in our early NAFLD population, as previously discussed [20], and are, as expected, not seemingly suitable for tracking changes during weight loss in obesity/early NAFLD.
Similarly, MRE did not track short-term changes in our study and did not exhibit a robust correlation with T1 relaxation time or the composite indices, even as liver fat decreased. Although elastography-based techniques are widely used to quantify liver stiffness as a surrogate for fibrosis [40], MRE values can be confounded by high hepatic fat content, which can make small improvements in early-stage fibrosis difficult to detect [41]. We thus, speculate that our findings are linked to 1) the loss of liver fat content during weight loss, alone increasing stiffness in the tissue and/or, 2) the potential reduction in fibrotic changes, reducing stiffness in the tissue. Combined these two events would not confer changes in MRE during intervention.
Collectively, these results indicate that while FIB-4, Forns, and MRE provide important data for advanced disease staging or risk stratification, they may not be the most useful markers for monitoring changes in early NAFLD.
Despite its strengths, our study has several limitations. First and foremost, since we aimed to study changes in liver fibrosis markers along with liver fat content early in developing NAFLD, liver biopsy data were not collected, since this would not be ethically feasible. This prevents direct histopathological confirmation of potential fibrosis stage and thus, regression. While T1 mapping is a robust marker of tissue composition, it is not yet considered an absolute replacement for biopsy in all clinical or research contexts, especially in early-stage fibrosis. Second, hepatic steatosis can influence MRI-based parameters [42]. Although we accounted for fat content, there may still be residual confounding effects. Advanced or iron-corrected T1 techniques might enhance specificity for fibrosis by factoring out the impact of hepatic iron and fat. Third, our cohort size was relatively small, limiting statistical power and generalizability. Larger trials, ideally with repeated imaging and histological sampling, will be necessary to validate and extend these findings. Despite the strong performance of CK18, universal assay standardization remains a challenge for mainstream clinical implementation. While the M30 Apoptosense assay we employed is one of the most validated, laboratories use different kits and cutoffs, complicating cross-study comparisons.
Finally, despite the recent change in terminology, replacing NAFLD with the metabolic dysfunction-associated steatotic liver disease (MASLD) [43], we chose to keep the original grouping strategy in this study. We thus, refer to individuals with obesity, NAFLD, and MetS (NAFLD+MetS), since our study was designed and registered prior to release of the MASLD criteria [43]. If we change the terminology in our study from NAFLD+MetS to MASLD, three individuals in the obesity control group (individuals with obesity and either NAFLD or MetS or none) presented with MASLD. These individuals were thus, not offered weight loss intervention, why changing the grouping to MASLD would introduce a bias in our intervention group. Importantly, we previously showed similar results for fibrosis markers at baseline, using either grouping strategy (NAFLD+MetS or MASLD), compared with the obesity control groups [20].
Overall, our results suggest that MACK3 and CK18 hold promise as more accessible alternatives to T1 relaxation time for routine monitoring of liver fibrosis risk, especially in resource-limited settings. Their consistent declines in tandem with liver fat content and T1 relaxation time at 1 month, highlight their capacity to capture the early metabolic and inflammatory shifts that accompany weight loss. Going forward, long-term studies should confirm whether these biomarker trends persist and whether composite algorithms that integrate both dynamic metabolic and collagen-turnover markers can further enhance diagnostic accuracy.
In conclusion, this work underlines that especially MACK3 and CK18, along with T1 mapping, have potential as sensitive non-invasive measure for assessing dynamics of early fibrosis (risk) during weight loss intervention in individuals with obesity, NAFLD, and MetS. Thus, these markers could provide pragmatic and cost-effective proxies for clinical care and research. A combined approach, featuring both advanced imaging in specialized centers and accessible biomarker-based tools for community settings, may ultimately offer the most comprehensive strategy for tracking liver fibrosis risk in individuals with obesity.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org. Supplementary Figure S1: Effect of weight loss intervention on subcutaneous and visceral fat.

Author Contributions

Conceptualization: AH, AA, KH, MM; Methodology: AA, AH, JBF, PV, RWR, MG, MM; Data curation: JDQ; Visualization: JDQ; Formal Analysis: AA, AH, MM JBF, PV, MG, KH, JDQ, MU, NAG; Original Draft Preparation: AA, AH, MM, JDQ, MU, NAG; Funding Acquisition: AH, MM; Writing – Review & Editing: All authors.

Funding

This work was supported by a research grant from the Danish Diabetes Academy, which is funded by the Novo Nordisk Foundation, Grant No. NNF17SA0031406, the Independent Research Fund Denmark, Grant No. 10.46540/3101-00394B, Project in Clinical and Translational Medicine, funded by the Novo Nordisk Foundation, Grant No. NNF22OC0080036, and research grant from the Region North’s Health Sciences Research Fund, Grant No. 2024-0069.

Data Availability Statement

Restrictions apply to the availability of some, or all data generated or analysed during this study to preserve patient confidentiality. The corresponding author will on request detail the restrictions and any conditions under which access to some data may be provided.

Acknowledgments

The authors would like to thank and acknowledge Anne-Mette Haubro Christensen, Birthe H. Thomsen, Rikke Bülow Eschen, and Zuzana Valnickova Hansen for collecting blood samples. Kenneth Krogh Jensen for assisting in planning and performing the MRI examinations. Merete Grothe Christensen, Mette Brodersen, Line Rosengreen Kaldahl, and Katrine Bruhn Vogensen for performing height and weight measurements, and Anne Lone Larsen for performing ELISA for TIMP1 and CK18.

Conflicts of Interest Statement

The authors declare no conflicts of interest.

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Figure 1. A) Overview of the study timeline from screening/baseline visit, introduction with dietician, intervention start, 1 month study visit, follow up visits at the dietician and 5 months study visit. Created with BioRender.com. B) Consort diagram of study population and exclusion information, showing number of participants that were: included (n=34), excluded (n=2), eligible (n=32), withdrew (n=2), and the final study population (n=30).
Figure 1. A) Overview of the study timeline from screening/baseline visit, introduction with dietician, intervention start, 1 month study visit, follow up visits at the dietician and 5 months study visit. Created with BioRender.com. B) Consort diagram of study population and exclusion information, showing number of participants that were: included (n=34), excluded (n=2), eligible (n=32), withdrew (n=2), and the final study population (n=30).
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Figure 2. Effect of weight loss on BMI, fat depots, and metabolic parameters assessed at baseline (BL), 1 month (1M), and 5 months (5M). Data presented as paired dot-plots showing changes during the intervention for A) BMI (kg/m2), B) HOMA2-IR, C) HbA1c (mmol/mol), D) total body fat (%), E) liver fat content (PDFF), F) representative PDFF images from one participant at baseline (~ 30% liver fat content), 1 month (~ 15% liver fat content), and 5 months (< 5% liver fat content). Error bars: Mean ± SD. Significance levels: * (p-value < 0.05), ** (p-value < 0.01), ***(p-value < 0.001), ****(p-value < 0.0001).
Figure 2. Effect of weight loss on BMI, fat depots, and metabolic parameters assessed at baseline (BL), 1 month (1M), and 5 months (5M). Data presented as paired dot-plots showing changes during the intervention for A) BMI (kg/m2), B) HOMA2-IR, C) HbA1c (mmol/mol), D) total body fat (%), E) liver fat content (PDFF), F) representative PDFF images from one participant at baseline (~ 30% liver fat content), 1 month (~ 15% liver fat content), and 5 months (< 5% liver fat content). Error bars: Mean ± SD. Significance levels: * (p-value < 0.05), ** (p-value < 0.01), ***(p-value < 0.001), ****(p-value < 0.0001).
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Figure 3. Effect of weight loss on liver fibrosis markers assessed at baseline (BL), 1 month (1M), and 5 months (5M). Paired box plots showing the effect of the intervention for A) T1 relaxation time, B) liver stiffness (MRE), C) CK18 (U/L), D) PIIINP (µg/L), E) TIMP1 (ng/mL), F) MACK3 score G) FNI index, H) FIB4 index, and I) Forns score. Error bars: Mean ± SD. Significance levels: * (p-value < 0.05), ** (p-value < 0.01), ***(p-value < 0.001), ****(p-value < 0.0001).
Figure 3. Effect of weight loss on liver fibrosis markers assessed at baseline (BL), 1 month (1M), and 5 months (5M). Paired box plots showing the effect of the intervention for A) T1 relaxation time, B) liver stiffness (MRE), C) CK18 (U/L), D) PIIINP (µg/L), E) TIMP1 (ng/mL), F) MACK3 score G) FNI index, H) FIB4 index, and I) Forns score. Error bars: Mean ± SD. Significance levels: * (p-value < 0.05), ** (p-value < 0.01), ***(p-value < 0.001), ****(p-value < 0.0001).
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Figure 4. Relative differences per week (%) in weight loss temporality between baseline to 1 month (BL to 1M) and 1 to 5 months (1M to 5M) for A) BMI, B) liver fat content, C) total body fat, D) subcutaneous fat, E) Visceral fat area. Error bars: Mean ± SD. Significance levels: * (p-value < 0.05), ** (p-value < 0.01), ***(p-value < 0.001), ****(p-value < 0.0001).
Figure 4. Relative differences per week (%) in weight loss temporality between baseline to 1 month (BL to 1M) and 1 to 5 months (1M to 5M) for A) BMI, B) liver fat content, C) total body fat, D) subcutaneous fat, E) Visceral fat area. Error bars: Mean ± SD. Significance levels: * (p-value < 0.05), ** (p-value < 0.01), ***(p-value < 0.001), ****(p-value < 0.0001).
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Figure 5. Relative differences per week (%) in weight loss temporality between baseline to 1 month (BL to 1M) and 1 to 5 months (1M to 5M) for A) T1 relaxation time, B) MRE, C) CK18, D) PIIINP, E) TIMP1, F) MACK3 score, G) FIB4, H) Forns score, and I) FNI index. Error bars: Mean ± SD. Significance levels: * (p-value < 0.05), ** (p-value < 0.01), ***(p-value < 0.001), ****(p-value < 0.0001).
Figure 5. Relative differences per week (%) in weight loss temporality between baseline to 1 month (BL to 1M) and 1 to 5 months (1M to 5M) for A) T1 relaxation time, B) MRE, C) CK18, D) PIIINP, E) TIMP1, F) MACK3 score, G) FIB4, H) Forns score, and I) FNI index. Error bars: Mean ± SD. Significance levels: * (p-value < 0.05), ** (p-value < 0.01), ***(p-value < 0.001), ****(p-value < 0.0001).
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Figure 6. Predictive performance of fibrosis algorithms and imaging parameters in estimating T1 relaxation time. A) Overview of LASSO regression models trained on all intervention data, BL and 1M data, and BL and 5M data. (B–D) Scatter plots show the correlation between predicted and observed T1 values for BL and 1M (B), BL and 5M (C), and all intervention data (D), with the corresponding R2 values representing the Pearson correlation coefficient. Higher R2 values indicate better predictive performance.
Figure 6. Predictive performance of fibrosis algorithms and imaging parameters in estimating T1 relaxation time. A) Overview of LASSO regression models trained on all intervention data, BL and 1M data, and BL and 5M data. (B–D) Scatter plots show the correlation between predicted and observed T1 values for BL and 1M (B), BL and 5M (C), and all intervention data (D), with the corresponding R2 values representing the Pearson correlation coefficient. Higher R2 values indicate better predictive performance.
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Table 1. Clinical and metabolic features of all participants according to study visit.
Table 1. Clinical and metabolic features of all participants according to study visit.
Characteristic Visit p-value
Baseline (n=30) 1 Month (n=30) 5 Months (n=30)
General
Age 48.1 (7.1)
Female2 18/30 (60%) 18/30 (60%) 18/30 (60%) 1b
Male2 12/30 (40%) 12/30 (40%) 12/30 (40%) 1b
BMI (kg/m2)1 35.8 (2.8) 34.2 (2.9) 32.7 (3.5) <0.0001a
NAFLD 30/30 (100%) 19/30 (63.3%) 13/30 (43.3%) <0.0001a
Liver enzymes
ALT (U/L)1 33.7 (16.5) 32.6 (13.2) 26.6 (10.6) 0.02a
AST (U/L)1 25.6 (7.6) 25.6 (6.4) 22.2 (5.5) 0.01a
Lipids
Triglycerides (mmol/L)1 1.6 (0.7) 1.6 (0.7) 1.4 (0.7) 0.01a
HDL (mmol/L)1 1.2 (0.2) 1.1 (0.2) 1.2 (0.2) 0.006a
LDL (mmol/L)1 3.1 (0.7) 2.7 (0.7) 2.9 (0.7) 0.02a
Cholesterol (mmol/L)1 4.9 (0.8) 4.5 (0.8) 4.7 (0.9) 0.02a
Metabolism
Glucose (mmol/L)1 5.9 (0.6) 5.6 (0.5) 5.5 (0.6) <0.001a
Insulin (pmol/L)1 159.7 (74.1) 106.4 (47.9) 92.7 (39.8) <0.0001a
GGT (U/L)1 38.3 (25.3) 29.8 (19.4) 29.9 (13.7) <0.0001a
HOMA2-IR1 3.4 (1.5) 2.3 (1.0) 2.0 (0.9) <0.0001a
HbA1C (mmol/mol)1 35.5 (3.4) 34.6 (3.1) 34.3 (3.6) 0.03a
MetS components
Blood pressure2 27/30 (90%) 23/30 (76.7%) 23/30 (76.7%) 0.17c
Central obesity2 30/30 (100%) 30/30 (100%) 30/30 (100%) 1c
Glucose2 24/30 (80%) 16/30 (53.5%) 11/30 (36.7%) <0.0001c
HDL2 19/30 (63.3%) 21/30 (70%) 16/30 (53.3%) 0.15c
Triglycerides2 10/30 (33.3%) 12/30 (40%) 21/30 (70%) 0.02c
MetS 30/30 (100%) 27/30 (90%) 21/30 (70%) 0.002c
Table 2. Spearman’s correlation factor of T1 with selected variables. Values are presented as Spearman’s correlation factor (p-value).
Table 2. Spearman’s correlation factor of T1 with selected variables. Values are presented as Spearman’s correlation factor (p-value).
T1 relaxation time
at baseline		 T1 relaxation time
at 1 month		 T1 relaxation time at 5 months
MRE -0.351 (0.057) -0.160 (0.415) 0.364 (0.057)
CK18 (U/L) 0.328 (0.077) 0.145 (0.452) 0.065 (0.748)
PIIINP (µg/L) -0.056 (0.768) -0.215 (0.263) 0.148 (0.451)
TIMP1 (ng/mL) 0.324 (0.081) 0.250 (0.190) 0.122 (0.544)
FORNS Score 0.233 (0.215) -0.032 (0.869) -0.151 (0.445)
FIB-4 index 0.380 (0.038)* -0.045 (0.817) -0.136 (0.489)
FNI index 0.377 (0.043)* -0.045 (0.818) -0.382 (0.049)*
MACK3 score 0.382 (0.041)* 0.138 (0.485) -0.228 (0.264)
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