Evaluation of MARC1 Variants and Extracellular Vesicle Cytokeratin-18 as Predictive Biomarkers in a group of Egyptian MASLD and MASH patients

Evaluation of MARC1 Variants and Extracellular Vesicle Cytokeratin-18 as Predictive Biomarkers in a group of Egyptian MASLD and MASH patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of MARC1 Variants and Extracellular Vesicle Cytokeratin-18 as Predictive Biomarkers in a group of Egyptian MASLD and MASH patients Asmaa Mohamed Fteah, Doaa Mamdouh Aly, Mohamed A Elrefaiy, Nagwa Elkhafif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8796724/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive inflammatory phenotype, metabolic dysfunction-associated steatohepatitis (MASH), represent an increasing global health challenge. Disease progression reflects a complex interaction between metabolic stress and genetic susceptibility. Variants in the mitochondrial amidoxime reducing component 1 (MARC1) gene have been implicated in hepatic lipid handling and hepatocellular injury, and hepatic outcomes. In parallel, hepatocyte-derived extracellular vesicles (EVs), particularly those carrying cytokeratin-18 (CK-18), have emerged as promising non-invasive indicators of liver cell damage. Aims: To investigate whether integrating MARC1 genetic variants with metabolic traits, conventional biochemical markers, and circulating EV-bound CK-18 improves the diagnostic and predictive performance for MASLD and MASH. Methodology: This case–control study comprised 450 participants, including 150 with fibroscan-confirmed MASH, and 150 healthy controls. TaqMan real-time PCR was used for genotyping of rs2642438 G>A in MARC1. While total, filtered, and EV-bound CK-18 levels were quantified using enzyme-linked immunosorbent assays. Multivariate regression and receiver operating characteristic (ROC) analyses were applied to evaluate genotype–phenotype associations and diagnostic performance. Results: Carriers of the MARC1 A allele exhibited a significantly lower risk of MASLD and MASH, with lower odds of MASLD diagnosis, suggesting a hepatoprotective genetic profile. Furthermore, circulating CK-18 levels, including the EV-bound fraction, increased progressively from controls to MASLD and were highest in MASH patients, correlating with disease severity. Conclusions: A multimodal approach that combines MARC1 genetic profiling with EV-bound CK-18 and conventional biochemical markers significantly improves non-invasive prediction and risk stratification across the MASLD–MASH spectrum. cytokeratin 18 hepatic extracellular vesicles Genetics MARC1 MAFLD MASH 1. Introduction Metabolic dysfunction–associated steatotic liver disease (MASLD) has emerged as the most prevalent chronic liver disorder worldwide, affecting approximately 25-30% of the global population. Its progressive phenotype, metabolic dysfunction–associated steatohepatitis (MASH), is characterized by hepatic inflammation, and varying degrees of fibrosis, significantly increasing the risk of cirrhosis and hepatocellular carcinoma (HCC) (Younossi et al., 2023a). Despite its clinical significance, the gold standard for diagnosing MASH remains the invasive liver biopsy, which is limited by sampling errors, cost, and potential procedural complications. Consequently, there is an urgent clinical need for accurate, non-invasive biomarkers to facilitate early diagnosis and risk stratification. The pathogenesis of MASLD and its progression to MASH are driven by a complex "multi-hit" mechanism involving metabolic dysfunction, oxidative stress, and genetic predisposition (Paik et al., 2023). In this context, genetic variants in mitochondrial amidoxime reducing component 1 (MARC1) have emerged as key determinants influencing lipid metabolism, hepatocellular stress responses, and disease progression. Genome-wide association studies (GWAS) have identified the p.Ala165Thr (rs2642438) G>A variant in the MARC1 gene as a potent hepatoprotective factor. While the exact physiological role of MARC1 remains under investigation, early evidence suggests that MARC1 is implicated in mitochondrial redox balance and detoxification pathways, suggesting that genetic variability may confer resilience against oxidative stress and lipotoxic injury, reducing hepatic fat accumulation, thereby protecting against inflammation and fibrosis (Hudert et al., 2022). In parallel with genetic insights, the study of extracellular vesicles (EVs) has opened new frontiers in liquid biopsy. EVs are nano-sized membrane particles released by cells, carrying a diverse molecular cargo (proteins, lipids, and nucleic acids) that reflects the physiological state of the parent cell (Szabo and Momen-Heravi, 2017). In the context of liver disease, hepatocyte-derived EVs carry cytokeratin-18 (CK-18) fragments, a hallmark protein of hepatocyte apoptosis and necroinflammation (Boccatonda and Piscaglia, 2025). Unlike total soluble CK-18, which can be influenced by extrahepatic factors, CK-18 encapsulated within EVs (EV-bound CK-18) is thought to provide a more specific and stable reflection of ongoing hepatocellular injury. Hepatocyte-derived EVs have been shown to propagate lipotoxic signals, activate Kupffer cells and hepatic stellate cells, and amplify inflammatory and fibrogenic pathways. Consequently, EV-bound biomarkers such as CK-18 represent a promising bridge between molecular pathogenesis and clinical application, offering a window into both the extent of hepatocellular injury and the underlying mechanisms driving disease progression (Li and Yu, 2024). Elkrief L et al., in their elaborative work in 2023, stated that combining hepatocyte-derived cytokeratin-18 with FibroTest or MELD scores in patients with Child-Pugh class A alcohol-related cirrhosis can identify those at high risk of liver-related events at 2 years (Elkrief et al., 2023). In this context, the present study aimed to evaluate the predictive utility of an integrative model that combines MARC1 genetic variants and EV-bound CK-18 levels with traditional biochemical markers across the MASLD–MASH spectrum. By bridging genetic susceptibility with dynamic markers of liver injury, this work seeks to provide a more robust and personalized non-invasive diagnostic framework for the identification of MASH and the assessment of MASLD severity with particular relevance to healthcare systems where access to invasive or high-cost diagnostic tools is limited. 2. Materials and Methods Study population and design This case–control study included 450 age- and sex-matched individuals, recruited between October 2023 and September 2024 from inpatient hepatology and gastroenterology units as well as outpatient clinics at TBRI. Participants were categorized into three groups: MASLD (n = 150), MASH (n = 150), and apparently healthy controls (n = 150) with no clinical, biochemical, or imaging evidence of fatty liver disease. Diagnosis of hepatic steatosis was based on a combination of clinical assessment, laboratory investigations, and imaging findings consistent with the most recent multi-society nomenclature for steatotic liver disease. Because these modalities alone cannot reliably distinguish inflammatory disease, fibrosis severity and steatohepatitis were further evaluated using transient elastography (FibroScan) in accordance with current American Association guidelines for MASH ( Cusi et al., 2022 ). Exclusion Criteria Participants were excluded if they had significant alcohol consumption (≥ 30 g/day for men, ≥ 20 g/day for women), viral hepatitis, autoimmune hepatitis, or other chronic liver diseases, history of malignancy, recreational drug abuse, pregnancy and those who are below 18 years old. Clinical, Anthropometric, and Metabolic Assessment All enrolled participants underwent standardized baseline evaluation including medical history, physical examination, and anthropometric measurements (height, weight, body mass index, and waist circumference). Obesity was defined according to World Health Organization criteria as BMI ≥ 30 kg/m², and morbid obesity as BMI ≥ 40 kg/m² (Adam et al., 2025). Associated metabolic comorbidities such as type 2 diabetes mellitus, hypertension, and dyslipidemia were documented. Routine biochemical analyses included liver function tests: alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-glutamyl transferase (GGT), total and direct bilirubin and renal function markers: urea and creatinine and lipid profile: total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides. All measurements were performed using automated enzymatic assays on the Beckman Coulter AU 480 autoanalyzer (Beckman Coulter Ireland Inc., Brea, CA, USA) in the Chemical Pathology Department at TBRI. Fasting plasma glucose and fasting insulin levels were determined, and insulin resistance was estimated using the homeostatic model assessment (HOMA-IR) according to the formula ( Horáková et al., 2019 ): HOMA-IR = Fasting plasma glucose (mmol/L) Ⅹ Fasting serum insulin (mIU/L) / 22.5 Serological testing for hepatitis B and C viruses, as well as insulin quantification, was performed using chemiluminescence immunoassays (Siemens Healthcare Diagnostics, Tarrytown, NY, USA). Genotyping of MARC1 G165A (rs2642438) Variants Genomic deoxyribonucleic acid (DNA) was isolated from peripheral blood leukocytes using the GeneJET Whole Blood Genomic DNA Purification Mini Kit (ThermoFisher Scientific, USA) according to the manufacturer’s protocol. The DNA purity and concentration were assessed by Qubit fluorometric quantification (ThermoFisher Scientific, USA), and samples were standardized to a working concentration of 20 ng/µL. Participants were genotyped using allele-specific TaqMan real-time quantitative polymerase chain reaction assays for MARC1 rs2642438 G > A (Assay ID: C_118610580_10) according to the protocol proposed by Kristiansen et al., 2001. Amplification and allelic discrimination were conducted on the Applied Biosystems ABI 7500 platform, using the manufacturer’s dedicated software in the Chemical Pathology Department, Cairo University Hospitals. Isolation of Extracellular Vesicles Following overnight fasting, venous blood samples were collected under standardized preanalytical conditions into plain and sodium citrate tubes. Plasma was separated by centrifugation at 2500 × g for 10 minutes at 4°C, aliquoted, and stored at − 80°C until further analysis. Large extracellular vesicles were isolated from platelet-free plasma by sequential filtration, as previously described in methodological guidelines for EV research by Théry et al., 2018 . This approach allows efficient separation of soluble plasma components from vesicle-associated fractions, ensuring reliable downstream biomarker quantification. Thietart and Rautou 2020 , stated that EV filtration can be used to separate smaller-sized soluble components from large extracellular vesicles which are retained on the filter. Quantification of Soluble and EV-Bound Cytokeratin-18 Levels of cytokeratin-18 (CK-18) were measured using a high-sensitivity commercially available enzyme-linked immunosorbent assay (ELISA) targeting the M65 antigen (BT LAB, R&D Systems), following the manufacturer’s instructions. CK-18 concentrations were determined in platelet-free plasma; before filtration (total soluble CK-18) and after two successive 0.2 µm filtrations (Ceveron MFU 500; Technoclone, Vienna, Austria). The difference between CK-18 levels measured in initial and in filtrated platelet-free plasma was used to estimate the extracellular vesicle–bound fraction, representing hepatocyte-derived microparticle–associated CK-18. Statistical analysis Data were coded and entered using the statistical package for the Social Sciences (SPSS) version 28 (IBM Corp., Armonk, NY, USA). Continuous variables expressed as mean and standard deviation for normally distributed quantitative variables or median and interquartile range for non-normally distributed quantitative variables and frequencies (number of cases) and relative frequencies (percentages) for categorical variables. Comparisons between groups were done using analysis of variance (ANOVA) with multiple comparisons post hoc test in normally distributed quantitative variables while non-parametric Kruskal-Wallis test and Mann-Whitney test were used for non-normally distributed quantitative variables ( Chan, 2003a ) . Categorical variables compared with chi-square (χ2) test. Exact test was used instead when the expected frequency is less than 5 ( Chan, 2003b ) . Multivariate regression for genotype-phenotype correlations, gene-gene interactions, and biochemical associations. Odds ratio (OR) with 95% confidence intervals were calculated. Correlations between quantitative variables were done using Spearman correlation coefficient ( Chan, 2003c ) . ROC curve was constructed with area under curve analysis performed to detect best cutoff value of CK-18 for detection of diseased liver. Logistic regression was done to detect independent predictors of diseased liver ( Chan, 2004 ) . P-values less than 0.05 were considered as statistically significant. 3. Results Baseline Clinical and Biochemical Characteristics A total of 450 age or sex matched participants were enrolled in this study, categorized into three groups: healthy controls, MASLD, and MASH. As summarized in Table 1, patients in the MASH group exhibited significantly higher BMI, waist circumference, HbA1c %, and homeostatic model assessment for insulin resistance (HOMA-IR) compared to both the MASLD and control groups (all p < 0.001). Liver enzymes (ALT and AST), GGT, and bilirubin showed a progressive increase corresponding to the severity of liver injury. Distribution of MARC1 G165A rs2642438 Genotypes and alleles The genotype distribution of the MARC1 G165A (rs2642438) polymorphism followed Hardy-Weinberg equilibrium. Our analysis revealed a significant association between the A allele and reduced susceptibility to advanced liver disease. Specifically, the GG genotype was significantly more prevalent in the control group compared to the MASH group (p < 0.01), indicating a pronounced reduction in disease risk (table 2). Furthermore, carriers of the GG genotype demonstrated lower fasting insulin levels and improved lipid profiles compared to those with the AA genotype, suggesting a potential protective role of the A allele against metabolic dysfunction, supporting its hepatoprotective and metabolically favorable profile (table 3). Soluble versus EV-bound cytokeratin -18 Fractions across the disease spectrum Consistent with our hypothesis, both soluble and EV-bound CK-18 fractions exhibited a marked and stepwise elevation across the disease spectrum (Control < MASLD < MASH) (p < 0.001). Specifically, EV-bound CK-18 levels were significantly higher in patients with MASH compared to those with simple steatosis (MASLD) (p < 0.001), whereas soluble CK-18 showed more overlap between the groups. This suggests that the encapsulation of CK-18 within extracellular vesicles better reflects active necroinflammatory processes in the liver, confirming increased hepatocellular injury with advancing disease (table 4). Receiver operating characteristic (ROC) curve analyses demonstrated excellent diagnostic performance of CK-18 fractions in discriminating MASLD and MASH from healthy controls. For differentiating MASH from MASLD, native and filtered CK-18 exhibited outstanding accuracy, with area under the curve (AUC) of 0.904 and 0.907, respectively. The EV-bound CK-18 fraction showed moderate diagnostic power (AUC = 0.740), indicating added but complementary value. Furthermore, CK-18 fractions effectively differentiated MASH from MASLD, underscoring their utility in identifying progressive inflammatory disease (tables 5–7). Correlation analysis revealed significant associations between circulating CK-18 levels and several metabolic and biochemical parameters. In control subjects, native and filtered CK-18 levels were negatively correlated with lipid profile components, including triglycerides, total cholesterol, and LDL-C, and positively correlated with total bilirubin and creatinine. In patients with MASLD and MASH, CK-18 fractions showed stronger correlations with markers of hepatic injury and metabolic dysfunction, supporting their role as sensitive indicators of hepatocellular damage in the context of metabolic stress. The EV-bound CK-18 fraction, in particular, demonstrated associations with uric acid levels, suggesting a potential link between oxidative stress, metabolic derangements, and hepatocyte-derived vesicle release (table 8). Discussion This study advances the understanding of MASLD by proposing an integrated multidimensional framework that integrates genetic susceptibility with dynamic indicators of hepatocellular injury. Rather than relying solely on traditional biochemical markers, our approach highlights how genetic background and extracellular vesicle–associated biomarkers jointly shape disease heterogeneity across the MASLD–MASH continuum. One of the key insights emerging from the present study is the identification of the MARC1 G165A (rs2642438) variant as a modifier of disease progression across the MASLD–MASH spectrum rather than a primary determinant of disease susceptibility. While metabolic dysfunction provides the permissive background for hepatic steatosis, our data indicate that carriers of the A allele exhibit a substantially lower propensity to develop inflammatory and fibrotic phenotypes. This finding supports the notion that progression from MASLD to MASH is not merely a linear consequence of metabolic burden, but is also shaped by intrinsic hepatic resilience mechanisms. The biological plausibility of this observation is underscored by the established role of MARC1 in mitochondrial redox homeostasis and detoxification pathways. Enhanced redox buffering capacity among A-allele carriers may confer protection against oxidative stress and lipotoxic injury, thereby limiting the activation of downstream inflammatory and fibrogenic cascades. In this context, our findings align with recent genetic studies reporting that MARC1 downregulation is associated with reduced hepatocellular neutral lipid accumulation and more favorable hepatic outcomes, including lower risks of fibrosis and cirrhosis ( Smagris et al., 2024 ) and ( Ciociola et al., 2025 ). In contrast, individuals harboring the GG genotype appear to represent a genetically vulnerable subgroup in whom metabolic stress is more readily translated into clinically meaningful liver injury. This observation is consistent with experimental data demonstrating that loss of mARC1 alters hepatocyte responses to lipotoxic stress and protects against diet-induced MASH and liver fibrosis in murine models ( Coyne et al., 2025 ). Furthermore, the observed association between MARC1 genotype and fasting insulin levels in our study reinforces the hypothesis that mitochondrial redox regulation may constitute a mechanistic link between systemic insulin resistance and hepatic inflammatory susceptibility. Beyond genetic determinants, the present study emphasizes the added value of extracellular vesicle–bound cytokeratin-18 (EV–CK-18) as a biologically informative biomarker in MASLD and MASH. Although total circulating CK-18 has long been used as a surrogate of hepatocyte apoptosis, its clinical utility is often limited by extrahepatic contributions and rapid degradation. In contrast, CK-18 encapsulated within hepatocyte-derived extracellular vesicles may better reflect active necroinflammatory signaling and intercellular communication within the hepatic microenvironment. The progressive increase in EV–CK-18 levels observed from controls to MASLD and MASH likely mirrors not only the extent of hepatocellular injury but also the intensity of pathogenic signaling that promotes inflammation and fibrogenesis. This distinction has important clinical implications. While traditional serum biomarkers provide a snapshot of tissue damage, EV-associated biomarkers may offer dynamic insight into the mechanistic momentum of disease, capturing ongoing biological processes rather than merely cumulative tissue damage. Our diagnostic analyses further support this concept; while both soluble and EV-bound CK-18 fractions demonstrated strong discriminatory capacity, the inclusion of the EV fraction added complementary value, particularly in distinguishing patients with progressive inflammatory disease. These findings are consistent with prior work in alcoholic hepatitis and advanced cirrhosis by Julien et al. , where microvesicle-associated CK-18 outperformed soluble markers in reflecting histological severity and predicting clinical outcomes. Similarly, Povero and colleagues showed that circulating EVs increase progressively from MASH to MASH-related cirrhosis, correlating closely with fibrosis severity. In such a framework, a single-time assessment of MARC1 genetic variants may identify individuals with reduced hepatic resilience, whereas serial monitoring of EV–CK-18 could provide a dynamic measure of ongoing necroinflammatory activity. Together, these layers of information enable more refined risk stratification than conventional algorithms based solely on static biochemical thresholds or imaging findings. This approach is particularly relevant in resource-limited settings, where access to invasive procedures and advanced imaging remains restricted. In line with our study focusing on combining genetic with hepatic EVs biomarker data to refine diagnostics; Boonkaew et al, 2025 stated that this integrated approach leverages EVs' role in intercellular communication during lipotoxicity and inflammation, alongside genetic variants influencing lipid metabolism and fibrosis, with hepatic markers like ALT, AST, and HDL-C indicating injury severity. Study limitations and future directions Despite the promising results, the present study should be interpreted in light of certain limitations, including its relatively small sample size. Longitudinal studies are required to determine whether this integrative model can predict long-term clinical outcomes, such as the development of cirrhosis or hepatocellular carcinoma (HCC). Furthermore, functional studies are needed to further elucidate the exact molecular mechanism by which MARC1 influence liver fat accumulation. Conclusion In summary, this study demonstrates that integrating MARC1 G165A genetic profiling with extracellular vesicle–bound cytokeratin-18 measurement provides a robust non-invasive strategy for the diagnosis and risk stratification of MASLD and MASH. By capturing both inherited susceptibility and real-time hepatocellular injury, this multimodal approach offers superior predictive accuracy compared with conventional biomarkers alone. Abbreviations MASLD Metabolic dysfunction–associated steatotic liver disease MASH Metabolic dysfunction–associated steatohepatitis ALT Alanine aminotransferase AST Aspartate aminotransaminase ALP Alkaline phosphatase GGT ᵞ-glutamyl transferase HDL-C High‐density lipoprotein cholesterol LDL‐C Low‐density lipoprotein cholesterol HOMA-IR Homeostatic model assessment of insulin resistance BMI Body mass index q-PCR quantitative polymerase chain reaction MARC1 mitochondrial amidoxime reducing component 1 CK18 cytokeratin-18 ELISA Enzyme-linked immunosorbent assay AUC Area under the curve ROC Receiver operating characteristic CI confidence intervals OR odds ratio Declarations Ethics approval and consent to participate Prior to initiation of our case control study; the study protocol was reviewed and approved by the Institutional Review Board (IRB) of Theodor Bilharz Research Institute (TBRI) under approval number PT 784, following the ethical principles described by the 1975 Declaration of Helsinki and its later amendments. Consent to participate This study was conducted in accordance with the ethical principles outlined in the 1975 Declaration of Helsinki and its later amendments. Approval was obtained from the Institutional Review Board (IRB) of Theodor Bilharz Research Institute (TBRI) under approval number (PT 784). Informed written consent was secured from all participants prior to enrollment. Participants were recruited between October 2023 to September 2024 from the inpatient hepatology and gastroenterology departments, as well as outpatient clinics at TBRI. Clinical trial registration Not applicable. Data availability statement The data is available upon reasonable request from the corresponding author. Competing interests The authors declare that they have no competing interests. Authors’ contributions AMF and DMA conceived and designed the study. ME collected the clinical data. AMF and DMA performed the laboratory analyses and conducted the statistical analysis. AMF interpreted the data. AMF and ME drafted the manuscript. All authors critically revised the manuscript and approved the final version. Funding This work was conducted as part of an internally funded research project under project number (127) at Theodor Bilharz Research Institute and this study was supported and fully financed by the Institute. References Boccatonda A and Piscaglia F (2025): Predictive role of microvesicles in cirrhotic patients: A promised land or a land of confusion? A narrative review. Annals of Hepatology;30: 101563. Boonkaew B, Charoenthanakitkul D, Suntornnont N, Ariyachet C, Atngkijvanich P, et al (2025): Extracellular vesicles in metabolic dysfunction-associated steatotic liver disease: From intercellular signaling to clinical translation. World J Hepatol; 17(9): 108259. Chan YH (2003a): Biostatistics102: Quantitative Data – Parametric & Non-parametric Tests. Singapore Med J.;44(8): 391-396. Chan YH (2003b): Biostatistics103: Quantitative Data –Tests of Independence. Singapore Med J.;44(10): 498-503. Chan YH (2003c): Biostatistics104: Correlational Analysis. Singapore Med J.;44(12): 614-619. Chan YH (2004): Biostatistics202: logistic regression analysis. Singapore Med J.;45(4): 149-153. Ciociola E, Dutta T, Sasidharan K, Kovooru L, Noto F, et al (2025): Downregulation of the MARC1 p.A165 risk allele reduces hepatocyte lipid content by increasing beta oxidation. Clinical and Molecular Hepatology; 31(2): 12-23. Coyne E, Nie Y, Lee D, Pandovski S, Yang T, Zhou H, et al (2025): Loss of mitochondrial amidoxime-reducing component 1 (mARC1) prevents disease progression by reducing fibrosis in multiple mouse models of chronic liver disease. Hepatology Communications; 9: e0637. Cusi K, Isaacs S, Barb D, et al (2022): American Association of Clinical Endocrinology clinical practice guideline for the diagnosis and management of NAFLD in Primary Care and Endocrinology Clinical Settings. Endocr Pract.;28(5): 528–562. Elkrief L, Ganne-Carrié N, Manceau H, GAnguy M, et al (2023): Hepatocyte-derived biomarkers predict liver-related events at 2 years in Child-Pugh class A alcohol-related cirrhosis. Journal of Hepatology;79(4): 910-923, ISSN 0168-8278. Horáková D, Štěpánek L, Janout V, et al (2019): Optimal HOMA-IR cut-offs in the Czech population. Medicina (Kaunas);55(5): 158. doi:10.3390/medicina55050158 Hudert C, Alisi A, Anstee Q, Crudele A, Draijer L, et al (2022): Variants in MARC1 and HSD17B13 reduce severity of NAFLD in children, perturb phospholipid metabolism, and suppress fibrotic pathways Short title: MARC1 and HSD17B13 in pediatric NAFLD Hepatology Communications;6:1934–1948. Julien B, José A, Cécile D, Olivier R, Audrey P, et al (2017): A prospective study of the utility of plasma biomarkers to diagnose alcoholic hepatitis. Hepatology 66(2): 555-563. Kristensen V, Kelefiotis D, Kristensen T, et al (2001): High-throughput methods for detection of genetic variation. Biotechniques;30: 318–322. Li W and Yu L (2024): Role and therapeutic perspectives of extracellular vesicles derived from liver and adipose tissue in metabolic dysfunction-associated steatotic liver disease. Artificial Cells, Nanomedicine, and Biotechnology;52(1): 355–369. Paik J, Henry L, Younossi Z, et al (2023): The burden of metabolic dysfunction–associated steatotic liver disease is rapidly growing worldwide from 1990 to 2019. Hepatol Commun;7: e0251. Potter AW, Chin GC, Looney DP, Friedl KE (2025) : Defining overweight and obesity by percent body fat instead of BMI. J Clin Endocrinol Metab.;110(4): e1103–e1107. Povero D, YamashiGA H, Ren W, Subramanian M, Myers R, et al (2020): Characterization and Proteome of Circulating Extracellular Vesicles as Potential Biomarkers for NASH. Hepatol.Commun;4: 1263–1278. Smagris E, Shihanian LM, MinGAh IJ, Bigdelou P, Livson Y, Brown H, et al, (2024): Divergent role of Mitochondrial Amidoxime Reducing Component 1 (MARC1) in human and mouse. PLoS Genet 20(3): e1011179. Szabo G and Momen-Heravi F (2017): Extracellular vesicles in liver disease and potential as biomarkers and therapeutic targets. Nat Rev Gastroenterol Hepatol. 2017 Aug;14(8):455-466. Théry C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, et al (2018): Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV guidelines. J Extracell Vesicles;7: 1535750. Thietart S and Rautou PE (2020): Extracellular vesicles as biomarkers in liver diseases: A clinician's point of view, Journal of Hepatology;1: 19. Younossi Z, Golabi P, Paik J, et al (2023a): Global epidemiology of metabolic dysfunction–associated steatotic liver disease and steatohepatitis. Hepatology;77: 1335–1347. Tables Tables 1 to 8 are available in the supplementary files section Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8796724","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591310974,"identity":"9af6bafe-7a3c-4557-9e55-ce2d2c39d105","order_by":0,"name":"Asmaa Mohamed Fteah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYPACOSDmYXzAwHCAaC3GEkAtzAYka2GTIEoLf//pxA8/GAzq+NnPHqvmqbkjx8/A/PDRDTxaJG7kbpbsYTCQkOzJS7vNc+yZsWQDm7FxDj5rbvBukOBh+CNhcCDH7DYP2+HEDQd42KTxaZE/f3bzzz9AW+zPvzEr5vlHhBaDA7nbpHmAWgwkcsyYeduI0GJ4I3ebtYyBgeSMG2+MJef2HTaWbCbgFzmgw26+qTDg5+/PMfzw5tthOX725oeP8Xof4jwIxcQDIpkJKkcCjD9IUT0KRsEoGAUjBgAA4O1J6xGsFr4AAAAASUVORK5CYII=","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Asmaa","middleName":"Mohamed","lastName":"Fteah","suffix":""},{"id":591310975,"identity":"de890ed0-5f9c-450b-9cac-5fd2d338b254","order_by":1,"name":"Doaa Mamdouh Aly","email":"","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Doaa","middleName":"Mamdouh","lastName":"Aly","suffix":""},{"id":591310976,"identity":"3d8a3fd0-1350-4598-ae36-eae387bc8bbe","order_by":2,"name":"Mohamed A Elrefaiy","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"A","lastName":"Elrefaiy","suffix":""},{"id":591310977,"identity":"afa84ba5-8ed3-4212-bf87-4f056e9fb611","order_by":3,"name":"Nagwa Elkhafif","email":"","orcid":"","institution":"Theodor Bilharz Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Nagwa","middleName":"","lastName":"Elkhafif","suffix":""}],"badges":[],"createdAt":"2026-02-05 12:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8796724/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8796724/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102912526,"identity":"991c52db-2952-4389-ab06-13ac06dffe7f","added_by":"auto","created_at":"2026-02-18 10:26:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1160189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8796724/v1/2fdb37f6-9d8e-42ee-9df0-591aff5069b9.pdf"},{"id":102912407,"identity":"ca9239f5-60bc-46b9-a8e3-4c5575b90cd0","added_by":"auto","created_at":"2026-02-18 10:25:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":89984,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8796724/v1/d2a26d7e0fd18ad77deab58c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of MARC1 Variants and Extracellular Vesicle Cytokeratin-18 as Predictive Biomarkers in a group of Egyptian MASLD and MASH patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetabolic dysfunction–associated steatotic liver disease (MASLD) has emerged as the most prevalent chronic liver disorder worldwide, affecting approximately 25-30% of the global population. Its progressive phenotype, metabolic dysfunction–associated steatohepatitis (MASH), is characterized by hepatic inflammation, and varying degrees of fibrosis, significantly increasing the risk of cirrhosis and hepatocellular carcinoma (HCC) \u003cstrong\u003e\u003cem\u003e(Younossi et al., 2023a).\u003c/em\u003e\u003c/strong\u003e Despite its clinical significance, the gold standard for diagnosing MASH remains the invasive liver biopsy, which is limited by sampling errors, cost, and potential procedural complications. Consequently, there is an urgent clinical need for accurate, non-invasive biomarkers to facilitate early diagnosis and risk stratification. The pathogenesis of MASLD and its progression to MASH are driven by a complex \"multi-hit\" mechanism involving metabolic dysfunction, oxidative stress, and genetic predisposition \u003cstrong\u003e\u003cem\u003e(Paik et al., 2023).\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this context, genetic variants in mitochondrial amidoxime reducing component 1 (MARC1) have emerged as key determinants influencing lipid metabolism, hepatocellular stress responses, and disease progression. Genome-wide association studies (GWAS) have identified the p.Ala165Thr (rs2642438)\u0026nbsp;G\u0026gt;A\u0026nbsp;variant in the MARC1 gene as a potent hepatoprotective factor. While the exact physiological role of MARC1 remains under investigation, early evidence suggests that MARC1 is implicated in mitochondrial redox balance and detoxification pathways, suggesting that genetic variability may confer resilience against oxidative stress and lipotoxic injury, reducing hepatic fat accumulation, thereby protecting against inflammation and fibrosis \u003cstrong\u003e\u003cem\u003e(Hudert et al., 2022).\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn parallel with genetic insights, the study of extracellular vesicles (EVs) has opened new frontiers in liquid biopsy. EVs are nano-sized membrane particles released by cells, carrying a diverse molecular cargo (proteins, lipids, and nucleic acids) that reflects the physiological state of the parent cell \u003cstrong\u003e\u003cem\u003e(Szabo and Momen-Heravi, 2017).\u003c/em\u003e\u003c/strong\u003e In the context of liver disease, hepatocyte-derived EVs carry cytokeratin-18 (CK-18) fragments, a hallmark protein of hepatocyte apoptosis and necroinflammation \u003cstrong\u003e\u003cem\u003e(Boccatonda and Piscaglia, 2025).\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eUnlike total soluble CK-18, which can be influenced by extrahepatic factors, CK-18 encapsulated within EVs (EV-bound CK-18) is thought to provide a more specific and stable reflection of ongoing hepatocellular injury.\u003c/p\u003e\n\u003cp\u003eHepatocyte-derived EVs have been shown to propagate lipotoxic signals, activate Kupffer cells and hepatic stellate cells, and amplify inflammatory and fibrogenic pathways. Consequently, EV-bound biomarkers such as CK-18 represent a promising bridge between molecular pathogenesis and clinical application, offering a window into both the extent of hepatocellular injury and the underlying mechanisms driving disease progression \u003cstrong\u003e\u003cem\u003e(Li and Yu, 2024).\u003c/em\u003e\u003c/strong\u003e Elkrief L et al., in their elaborative work in 2023, stated that combining hepatocyte-derived cytokeratin-18 with FibroTest or MELD scores in patients with Child-Pugh class A alcohol-related cirrhosis can identify those at high risk of liver-related events at 2 years \u003cstrong\u003e\u003cem\u003e(Elkrief et al., 2023).\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this context, the present study aimed to evaluate the predictive utility of an integrative model that combines MARC1 genetic variants and EV-bound CK-18 levels with traditional biochemical markers across the MASLD–MASH spectrum. By bridging genetic susceptibility with dynamic markers of liver injury, this work seeks to provide a more robust and personalized non-invasive diagnostic framework for the identification of MASH and the assessment of MASLD severity with particular relevance to healthcare systems where access to invasive or high-cost diagnostic tools is limited.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003ch3\u003eStudy population and design\u003c/h3\u003e\u003cp\u003eThis case–control study included 450 age- and sex-matched individuals, recruited between October 2023 and September 2024 from inpatient hepatology and gastroenterology units as well as outpatient clinics at TBRI. Participants were categorized into three groups: MASLD (n = 150), MASH (n = 150), and apparently healthy controls (n = 150) with no clinical, biochemical, or imaging evidence of fatty liver disease.\u003c/p\u003e\u003cp\u003eDiagnosis of hepatic steatosis was based on a combination of clinical assessment, laboratory investigations, and imaging findings consistent with the most recent multi-society nomenclature for steatotic liver disease. Because these modalities alone cannot reliably distinguish inflammatory disease, fibrosis severity and steatohepatitis were further evaluated using transient elastography (FibroScan) in accordance with current American Association guidelines for MASH \u003cb\u003e(\u003c/b\u003eCusi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eExclusion Criteria\u003c/h2\u003e\u003cp\u003eParticipants were excluded if they had significant alcohol consumption (≥ 30 g/day for men, ≥ 20 g/day for women), viral hepatitis, autoimmune hepatitis, or other chronic liver diseases, history of malignancy, recreational drug abuse, pregnancy and those who are below 18 years old.\u003c/p\u003e\u003ch3\u003eClinical, Anthropometric, and Metabolic Assessment\u003c/h3\u003e\u003cp\u003eAll enrolled participants underwent standardized baseline evaluation including medical history, physical examination, and anthropometric measurements (height, weight, body mass index, and waist circumference). Obesity was defined according to World Health Organization criteria as BMI ≥ 30 kg/m², and morbid obesity as BMI ≥ 40 kg/m² \u003cb\u003e(Adam et al., 2025).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAssociated metabolic comorbidities such as type 2 diabetes mellitus, hypertension, and dyslipidemia were documented. Routine biochemical analyses included liver function tests: alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-glutamyl transferase (GGT), total and direct bilirubin and renal function markers: urea and creatinine and lipid profile: total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides. All measurements were performed using automated enzymatic assays on the Beckman Coulter AU 480 autoanalyzer (Beckman Coulter Ireland Inc., Brea, CA, USA) in the Chemical Pathology Department at TBRI.\u003c/p\u003e\u003cp\u003eFasting plasma glucose and fasting insulin levels were determined, and insulin resistance was estimated using the homeostatic model assessment (HOMA-IR) according to the formula \u003cb\u003e(\u003c/b\u003eHoráková et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e):\u003c/p\u003e\u003cp\u003eHOMA-IR = Fasting plasma glucose (mmol/L) Ⅹ Fasting serum insulin (mIU/L) / 22.5\u003c/p\u003e\u003cp\u003eSerological testing for hepatitis B and C viruses, as well as insulin quantification, was performed using chemiluminescence immunoassays (Siemens Healthcare Diagnostics, Tarrytown, NY, USA).\u003c/p\u003e\u003ch3\u003eGenotyping of MARC1 G165A (rs2642438) Variants\u003c/h3\u003e\u003cp\u003e Genomic deoxyribonucleic acid (DNA) was isolated from peripheral blood leukocytes using the GeneJET Whole Blood Genomic DNA Purification Mini Kit (ThermoFisher Scientific, USA) according to the manufacturer’s protocol. The DNA purity and concentration were assessed by Qubit fluorometric quantification (ThermoFisher Scientific, USA), and samples were standardized to a working concentration of 20 ng/µL.\u003c/p\u003e\u003cp\u003eParticipants were genotyped using allele-specific TaqMan real-time quantitative polymerase chain reaction assays for MARC1 rs2642438 G \u0026gt; A (Assay ID: C_118610580_10) according to the protocol proposed by \u003cb\u003eKristiansen et al., 2001.\u003c/b\u003e Amplification and allelic discrimination were conducted on the Applied Biosystems ABI 7500 platform, using the manufacturer’s dedicated software in the Chemical Pathology Department, Cairo University Hospitals.\u003c/p\u003e\u003ch3\u003eIsolation of Extracellular Vesicles\u003c/h3\u003e\u003cp\u003eFollowing overnight fasting, venous blood samples were collected under standardized preanalytical conditions into plain and sodium citrate tubes. Plasma was separated by centrifugation at 2500 × g for 10 minutes at 4°C, aliquoted, and stored at − 80°C until further analysis. Large extracellular vesicles were isolated from platelet-free plasma by sequential filtration, as previously described in methodological guidelines for EV research by Théry et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e. This approach allows efficient separation of soluble plasma components from vesicle-associated fractions, ensuring reliable downstream biomarker quantification. Thietart and Rautou \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e, stated that EV filtration can be used to separate smaller-sized soluble components from large extracellular vesicles which are retained on the filter.\u003c/p\u003e\u003ch3\u003eQuantification of Soluble and EV-Bound Cytokeratin-18\u003c/h3\u003e\u003cp\u003eLevels of cytokeratin-18 (CK-18) were measured using a high-sensitivity commercially available enzyme-linked immunosorbent assay (ELISA) targeting the M65 antigen (BT LAB, R\u0026amp;D Systems), following the manufacturer’s instructions.\u003c/p\u003e\u003cp\u003eCK-18 concentrations were determined in platelet-free plasma; before filtration (total soluble CK-18) and after two successive 0.2 µm filtrations (Ceveron MFU 500; Technoclone, Vienna, Austria). The difference between CK-18 levels measured in initial and in filtrated platelet-free plasma was used to estimate the extracellular vesicle–bound fraction, representing hepatocyte-derived microparticle–associated CK-18.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData were coded and entered using the statistical package for the Social Sciences (SPSS) version 28 (IBM Corp., Armonk, NY, USA). Continuous variables expressed as mean and standard deviation for normally distributed quantitative variables or median and interquartile range for non-normally distributed quantitative variables and frequencies (number of cases) and relative frequencies (percentages) for categorical variables. Comparisons between groups were done using analysis of variance (ANOVA) with multiple comparisons post hoc test in normally distributed quantitative variables while non-parametric Kruskal-Wallis test and Mann-Whitney test were used for non-normally distributed quantitative variables \u003cb\u003e(\u003c/b\u003eChan, \u003cspan class=\"CitationRef\"\u003e2003a\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Categorical variables compared with chi-square (χ2) test. Exact test was used instead when the expected frequency is less than 5 \u003cb\u003e(\u003c/b\u003eChan, \u003cspan class=\"CitationRef\"\u003e2003b\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Multivariate regression for genotype-phenotype correlations, gene-gene interactions, and biochemical associations. Odds ratio (OR) with 95% confidence intervals were calculated. Correlations between quantitative variables were done using Spearman correlation coefficient \u003cb\u003e(\u003c/b\u003eChan, \u003cspan class=\"CitationRef\"\u003e2003c\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. ROC curve was constructed with area under curve analysis performed to detect best cutoff value of CK-18 for detection of diseased liver. Logistic regression was done to detect independent predictors of diseased liver \u003cb\u003e(\u003c/b\u003eChan, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. P-values less than 0.05 were considered as statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Clinical and Biochemical Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 450 age or sex matched participants were enrolled in this study, categorized into three groups: healthy controls, MASLD, and MASH. As summarized in Table 1, patients in the MASH group exhibited significantly higher BMI, waist circumference, HbA1c %, and homeostatic model assessment for insulin resistance (HOMA-IR) compared to both the MASLD and control groups (all p \u0026lt; 0.001). Liver enzymes (ALT and AST), GGT, and bilirubin showed a progressive increase corresponding to the severity of liver injury.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of MARC1 G165A rs2642438 Genotypes and alleles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genotype distribution of the MARC1 G165A (rs2642438) polymorphism followed Hardy-Weinberg equilibrium. Our analysis revealed a significant association between the A allele and reduced susceptibility to advanced liver disease. Specifically, the GG genotype was significantly more prevalent in the control group compared to the MASH group (p \u0026lt; 0.01), indicating a pronounced reduction in disease risk (table 2). Furthermore, carriers of the GG genotype demonstrated lower fasting insulin levels and improved lipid profiles compared to those with the AA genotype, suggesting a potential protective role of the A allele against metabolic dysfunction, supporting its hepatoprotective and metabolically favorable profile (table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoluble versus EV-bound cytokeratin -18 Fractions across the disease spectrum\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with our hypothesis, both soluble and EV-bound CK-18 fractions exhibited a marked and stepwise elevation across the disease spectrum (Control \u0026lt; MASLD \u0026lt; MASH) (p \u0026lt; 0.001). Specifically, EV-bound CK-18 levels were significantly higher in patients with MASH compared to those with simple steatosis (MASLD) (p \u0026lt; 0.001), whereas soluble CK-18 showed more overlap between the groups. This suggests that the encapsulation of CK-18 within extracellular vesicles better reflects active necroinflammatory processes in the liver, confirming increased hepatocellular injury with advancing disease (table 4). Receiver operating characteristic (ROC) curve analyses demonstrated excellent diagnostic performance of CK-18 fractions in discriminating MASLD and MASH from healthy controls. For differentiating MASH from MASLD, native and filtered CK-18 exhibited outstanding accuracy, with area under the curve (AUC) of 0.904 and 0.907, respectively. The EV-bound CK-18 fraction showed moderate diagnostic power (AUC = 0.740), indicating added but complementary value. Furthermore, CK-18 fractions effectively differentiated MASH from MASLD, underscoring their utility in identifying progressive inflammatory disease (tables 5\u0026ndash;7).\u003c/p\u003e\n\u003cp\u003eCorrelation analysis revealed significant associations between circulating CK-18 levels and several metabolic and biochemical parameters. In control subjects, native and filtered CK-18 levels were negatively correlated with lipid profile components, including triglycerides, total cholesterol, and LDL-C, and positively correlated with total bilirubin and creatinine. In patients with MASLD and MASH, CK-18 fractions showed stronger correlations with markers of hepatic injury and metabolic dysfunction, supporting their role as sensitive indicators of hepatocellular damage in the context of metabolic stress. The EV-bound CK-18 fraction, in particular, demonstrated associations with uric acid levels, suggesting a potential link between oxidative stress, metabolic derangements, and hepatocyte-derived vesicle release (table 8).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study advances the understanding of MASLD by proposing an integrated multidimensional framework that integrates genetic susceptibility with dynamic indicators of hepatocellular injury. Rather than relying solely on traditional biochemical markers, our approach highlights how genetic background and extracellular vesicle\u0026ndash;associated biomarkers jointly shape disease heterogeneity across the MASLD\u0026ndash;MASH continuum.\u003c/p\u003e \u003cp\u003eOne of the key insights emerging from the present study is the identification of the MARC1 G165A (rs2642438) variant as a modifier of disease progression across the MASLD\u0026ndash;MASH spectrum rather than a primary determinant of disease susceptibility. While metabolic dysfunction provides the permissive background for hepatic steatosis, our data indicate that carriers of the A allele exhibit a substantially lower propensity to develop inflammatory and fibrotic phenotypes. This finding supports the notion that progression from MASLD to MASH is not merely a linear consequence of metabolic burden, but is also shaped by intrinsic hepatic resilience mechanisms. The biological plausibility of this observation is underscored by the established role of MARC1 in mitochondrial redox homeostasis and detoxification pathways. Enhanced redox buffering capacity among A-allele carriers may confer protection against oxidative stress and lipotoxic injury, thereby limiting the activation of downstream inflammatory and fibrogenic cascades. In this context, our findings align with recent genetic studies reporting that MARC1 downregulation is associated with reduced hepatocellular neutral lipid accumulation and more favorable hepatic outcomes, including lower risks of fibrosis and cirrhosis \u003cb\u003e(\u003c/b\u003eSmagris et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) \u003cb\u003eand (\u003c/b\u003eCiociola et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, individuals harboring the GG genotype appear to represent a genetically vulnerable subgroup in whom metabolic stress is more readily translated into clinically meaningful liver injury. This observation is consistent with experimental data demonstrating that loss of mARC1 alters hepatocyte responses to lipotoxic stress and protects against diet-induced MASH and liver fibrosis in murine models \u003cb\u003e(\u003c/b\u003eCoyne et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the observed association between MARC1 genotype and fasting insulin levels in our study reinforces the hypothesis that mitochondrial redox regulation may constitute a mechanistic link between systemic insulin resistance and hepatic inflammatory susceptibility.\u003c/p\u003e \u003cp\u003eBeyond genetic determinants, the present study emphasizes the added value of extracellular vesicle\u0026ndash;bound cytokeratin-18 (EV\u0026ndash;CK-18) as a biologically informative biomarker in MASLD and MASH. Although total circulating CK-18 has long been used as a surrogate of hepatocyte apoptosis, its clinical utility is often limited by extrahepatic contributions and rapid degradation. In contrast, CK-18 encapsulated within hepatocyte-derived extracellular vesicles may better reflect active necroinflammatory signaling and intercellular communication within the hepatic microenvironment. The progressive increase in EV\u0026ndash;CK-18 levels observed from controls to MASLD and MASH likely mirrors not only the extent of hepatocellular injury but also the intensity of pathogenic signaling that promotes inflammation and fibrogenesis. This distinction has important clinical implications. While traditional serum biomarkers provide a snapshot of tissue damage, EV-associated biomarkers may offer dynamic insight into the mechanistic momentum of disease, capturing ongoing biological processes rather than merely cumulative tissue damage. Our diagnostic analyses further support this concept; while both soluble and EV-bound CK-18 fractions demonstrated strong discriminatory capacity, the inclusion of the EV fraction added complementary value, particularly in distinguishing patients with progressive inflammatory disease. These findings are consistent with prior work in alcoholic hepatitis and advanced cirrhosis by \u003cb\u003eJulien et al.\u003c/b\u003e, where microvesicle-associated CK-18 outperformed soluble markers in reflecting histological severity and predicting clinical outcomes. Similarly, \u003cb\u003ePovero and colleagues\u003c/b\u003e showed that circulating EVs increase progressively from MASH to MASH-related cirrhosis, correlating closely with fibrosis severity.\u003c/p\u003e \u003cp\u003eIn such a framework, a single-time assessment of MARC1 genetic variants may identify individuals with reduced hepatic resilience, whereas serial monitoring of EV\u0026ndash;CK-18 could provide a dynamic measure of ongoing necroinflammatory activity. Together, these layers of information enable more refined risk stratification than conventional algorithms based solely on static biochemical thresholds or imaging findings. This approach is particularly relevant in resource-limited settings, where access to invasive procedures and advanced imaging remains restricted. In line with our study focusing on combining genetic with hepatic EVs biomarker data to refine diagnostics; Boonkaew et al, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e stated that this integrated approach leverages EVs' role in intercellular communication during lipotoxicity and inflammation, alongside genetic variants influencing lipid metabolism and fibrosis, with hepatic markers like ALT, AST, and HDL-C indicating injury severity.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations and future directions\u003c/h2\u003e \u003cp\u003eDespite the promising results, the present study should be interpreted in light of certain limitations, including its relatively small sample size. Longitudinal studies are required to determine whether this integrative model can predict long-term clinical outcomes, such as the development of cirrhosis or hepatocellular carcinoma (HCC). Furthermore, functional studies are needed to further elucidate the exact molecular mechanism by which MARC1 influence liver fat accumulation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study demonstrates that integrating MARC1 G165A genetic profiling with extracellular vesicle\u0026ndash;bound cytokeratin-18 measurement provides a robust non-invasive strategy for the diagnosis and risk stratification of MASLD and MASH. By capturing both inherited susceptibility and real-time hepatocellular injury, this multimodal approach offers superior predictive accuracy compared with conventional biomarkers alone.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMASLD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic dysfunction\u0026ndash;associated steatotic liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMASH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic dysfunction\u0026ndash;associated steatohepatitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAST\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate aminotransaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eALP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGGT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eᵞ-glutamyl transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh‐density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDL‐C\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow‐density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHOMA-IR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHomeostatic model assessment of insulin resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eq-PCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003equantitative polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMARC1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emitochondrial amidoxime reducing component 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCK18\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecytokeratin-18\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eELISA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnzyme-linked immunosorbent assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to initiation of our case control study; the study protocol was reviewed and approved by the Institutional Review Board (IRB) of Theodor Bilharz Research Institute (TBRI) under approval number PT 784, following the ethical principles described by the 1975 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the 1975 Declaration of Helsinki and its later amendments. Approval was obtained from the Institutional Review Board (IRB) of Theodor Bilharz Research Institute (TBRI) under approval number (PT 784). Informed written consent was secured from all participants prior to enrollment. Participants were recruited between October 2023 to September 2024 from the inpatient hepatology and gastroenterology departments, as well as outpatient clinics at TBRI. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data is available upon reasonable request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAMF and DMA conceived and designed the study. ME collected the clinical data. AMF and DMA performed the laboratory analyses and conducted the statistical analysis. AMF interpreted the data. AMF and ME drafted the manuscript. All authors critically revised the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was conducted as part of an internally funded research project under project number (127) at Theodor Bilharz Research Institute and this study was supported and fully financed by the Institute.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eBoccatonda A and Piscaglia F (2025):\u0026nbsp;\u003c/strong\u003ePredictive role of microvesicles in cirrhotic patients: A promised land or a land of confusion? A narrative review. Annals of Hepatology;30: 101563.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBoonkaew B, Charoenthanakitkul D, Suntornnont N, Ariyachet C, Atngkijvanich P, et al (2025):\u003c/strong\u003e Extracellular vesicles in metabolic dysfunction-associated steatotic liver disease: From intercellular signaling to clinical translation. World J Hepatol; 17(9): 108259.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChan YH (2003a):\u003c/strong\u003e Biostatistics102: Quantitative Data \u0026ndash; Parametric \u0026amp; Non-parametric Tests. Singapore Med J.;44(8): 391-396.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChan YH (2003b):\u003c/strong\u003e Biostatistics103: Quantitative Data \u0026ndash;Tests of Independence. Singapore Med J.;44(10): 498-503. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChan YH (2003c):\u003c/strong\u003e Biostatistics104: Correlational Analysis. Singapore Med J.;44(12): 614-619.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChan YH (2004):\u003c/strong\u003e Biostatistics202: logistic regression analysis. Singapore Med J.;45(4): 149-153.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCiociola E, Dutta T, Sasidharan K, Kovooru L, Noto F, et al (2025):\u003c/strong\u003e Downregulation of the MARC1 p.A165 risk allele reduces hepatocyte lipid content by increasing beta oxidation. Clinical and Molecular Hepatology; 31(2): 12-23.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCoyne E, Nie Y, Lee D, Pandovski S, Yang T, Zhou H, et al (2025):\u0026nbsp;\u003c/strong\u003eLoss of mitochondrial amidoxime-reducing component 1 (mARC1) prevents disease progression by reducing fibrosis in multiple mouse models of chronic liver disease. Hepatology Communications; 9: e0637.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCusi K, Isaacs S, Barb D, et al (2022):\u003c/strong\u003e American Association of Clinical Endocrinology clinical practice guideline for the diagnosis and management of NAFLD in Primary Care and Endocrinology Clinical Settings. Endocr Pract.;28(5): 528\u0026ndash;562.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eElkrief L, Ganne-Carri\u0026eacute; N, Manceau H, GAnguy M, et al (2023):\u003c/strong\u003e Hepatocyte-derived biomarkers predict liver-related events at 2 years in Child-Pugh class A alcohol-related cirrhosis. Journal of Hepatology;79(4): 910-923, ISSN 0168-8278.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHor\u0026aacute;kov\u0026aacute; D, \u0026Scaron;těp\u0026aacute;nek L, Janout V, et al (2019):\u0026nbsp;\u003c/strong\u003eOptimal HOMA-IR cut-offs in the Czech population. Medicina (Kaunas);55(5): 158. doi:10.3390/medicina55050158\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHudert C, Alisi A, Anstee Q, Crudele A, Draijer L, et al (2022):\u003c/strong\u003e Variants in MARC1 and HSD17B13 reduce severity of NAFLD in children, perturb phospholipid metabolism, and suppress fibrotic pathways Short title: MARC1 and HSD17B13 in pediatric NAFLD Hepatology Communications;6:1934\u0026ndash;1948.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eJulien B, Jos\u0026eacute; A, C\u0026eacute;cile D, Olivier R, Audrey P, et al (2017):\u003c/strong\u003e A prospective study of the utility of plasma biomarkers to diagnose alcoholic hepatitis. Hepatology\u0026nbsp;\u003ca href=\"https://journals.lww.com/hep/toc/2017/08000\"\u003e66(2): 555-563.\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eKristensen V, Kelefiotis D, Kristensen T, et al (2001):\u003c/strong\u003e High-throughput methods for detection of genetic variation. Biotechniques;30: 318\u0026ndash;322.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLi W and Yu L (2024):\u003c/strong\u003e Role and therapeutic perspectives of extracellular vesicles derived from liver and adipose tissue in metabolic dysfunction-associated steatotic liver disease. Artificial Cells, Nanomedicine, and Biotechnology;52(1): 355\u0026ndash;369.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePaik J, Henry L, Younossi Z, et al (2023):\u003c/strong\u003e The burden of metabolic dysfunction\u0026ndash;associated steatotic liver disease is rapidly growing worldwide from 1990 to 2019. Hepatol Commun;7: e0251.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePotter AW, Chin GC, Looney DP, Friedl KE (2025)\u003c/strong\u003e: Defining overweight and obesity by percent body fat instead of BMI. J Clin Endocrinol Metab.;110(4): e1103\u0026ndash;e1107.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePovero D, YamashiGA H, Ren W, Subramanian M, Myers R, et al (2020):\u0026nbsp;\u003c/strong\u003eCharacterization and Proteome of Circulating Extracellular Vesicles as Potential Biomarkers for NASH. Hepatol.Commun;4: 1263\u0026ndash;1278.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSmagris E, Shihanian LM, MinGAh IJ, Bigdelou P, Livson Y, Brown H, et al, (2024):\u003c/strong\u003e Divergent role of Mitochondrial Amidoxime Reducing Component 1 (MARC1) in human and mouse. PLoS Genet 20(3): e1011179.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSzabo G and Momen-Heravi F (2017):\u003c/strong\u003e Extracellular vesicles in liver disease and potential as biomarkers and therapeutic targets. Nat Rev Gastroenterol Hepatol. 2017 Aug;14(8):455-466.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTh\u0026eacute;ry C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, et al (2018):\u0026nbsp;\u003c/strong\u003eMinimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV guidelines. J Extracell Vesicles;7: 1535750.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eThietart S and Rautou PE (2020):\u003c/strong\u003e Extracellular vesicles as biomarkers in liver diseases: A clinician\u0026apos;s point of view, Journal of Hepatology;1: 19.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eYounossi Z, Golabi P, Paik J, et al (2023a):\u003c/strong\u003e Global epidemiology of metabolic dysfunction\u0026ndash;associated steatotic liver disease and steatohepatitis. Hepatology;77: 1335\u0026ndash;1347.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 8 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cytokeratin 18, hepatic extracellular vesicles, Genetics, MARC1, MAFLD, MASH","lastPublishedDoi":"10.21203/rs.3.rs-8796724/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8796724/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive inflammatory phenotype, metabolic dysfunction-associated steatohepatitis (MASH), represent an increasing global health challenge. Disease progression reflects a complex interaction between metabolic stress and genetic susceptibility. Variants in the mitochondrial amidoxime reducing component 1 (MARC1) gene have been implicated in hepatic lipid handling and hepatocellular injury, and hepatic outcomes. In parallel, hepatocyte-derived extracellular vesicles (EVs), particularly those carrying cytokeratin-18 (CK-18), have emerged as promising non-invasive indicators of liver cell damage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims:\u003c/strong\u003e To investigate whether integrating MARC1 genetic variants with metabolic traits, conventional biochemical markers, and circulating EV-bound CK-18 improves the diagnostic and predictive performance for MASLD and MASH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology: \u003c/strong\u003eThis case–control study comprised 450 participants, including 150 with fibroscan-confirmed MASH, and 150 healthy controls. TaqMan real-time PCR was used for genotyping of rs2642438 G\u0026gt;A in MARC1. While total, filtered, and EV-bound CK-18 levels were quantified using enzyme-linked immunosorbent assays. Multivariate regression and receiver operating characteristic (ROC) analyses were applied to evaluate genotype–phenotype associations and diagnostic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCarriers of the MARC1 A allele exhibited a significantly lower risk of MASLD and MASH, with lower odds of MASLD diagnosis, suggesting a hepatoprotective genetic profile. Furthermore, circulating CK-18 levels, including the EV-bound fraction, increased progressively from controls to MASLD and were highest in MASH patients, correlating with disease severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eA multimodal approach that combines MARC1 genetic profiling with EV-bound CK-18 and conventional biochemical markers significantly improves non-invasive prediction and risk stratification across the MASLD–MASH spectrum.\u003c/p\u003e","manuscriptTitle":"Evaluation of MARC1 Variants and Extracellular Vesicle Cytokeratin-18 as Predictive Biomarkers in a group of Egyptian MASLD and MASH patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 10:23:24","doi":"10.21203/rs.3.rs-8796724/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83ad4fe2-338e-4ece-a00f-f1c635f8a3bb","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T10:23:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 10:23:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8796724","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8796724","identity":"rs-8796724","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}