{"paper_id":"27191b28-b8c8-4ef4-ad02-96889659d655","body_text":"Metabolic and Clinical Heterogeneity in MASLD Risk Stratification: Independent Effects of Body Mass Index and Fib-4 on Cirrhosis, and Clinical Implications of Noninvasive Score Discordance in a Veteran Cohort | 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 Metabolic and Clinical Heterogeneity in MASLD Risk Stratification: Independent Effects of Body Mass Index and Fib-4 on Cirrhosis, and Clinical Implications of Noninvasive Score Discordance in a Veteran Cohort Jacky Reny, Jordan Kradjian, Kaustav Patra, Pallavi Kawatra, Lisa Fisher This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8095793/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 and Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease in the United States. Noninvasive fibrosis scores, particularly Fibrosis-4 (Fib-4) and the NAFLD fibrosis score (NFS), guide risk stratification, yet their independent contributions and potential discordance in clinical practice remain poorly characterized. This study examines whether body mass index (BMI) and Fib-4 independently predict cirrhosis, and whether discordance between Fib-4 and NFS identifies a high-risk phenotype. Methods Retrospective cross-sectional analysis of 80 veterans with MASLD followed at a single Veterans Affairs medical center (2015–2023). Multivariable logistic regression assessed independent associations of Fib-4 and BMI with cirrhosis. Prevalence and clinical predictors of Fib-4 vs. NFS discordance were quantified, and associations with cirrhosis or metabolic dysfunction-associated steatohepatitis (MASH) were tested. Results In multivariable analysis, Fib-4 (OR 2.17 per unit, 95% CI 1.14–4.13, p = 0.018) and BMI (OR 1.19 per kg/m², 95% CI 1.05–1.37, p = 0.007) were independent predictors of cirrhosis. Cirrhosis prevalence rose stepwise across BMI quartiles and obesity classes (0% in normal weight to 20% in obese class III). Discordance between Fib-4 and NFS occurred in 13/80 patients (16%), and discordant patients had > 5-fold higher prevalence of cirrhosis or MASH compared to concordant patients (33.3% vs. 6.3%, p = 0.009). Discordant patients were older, more often diabetic, thrombocytopenic, and at higher cardiovascular risk. Conclusion BMI and Fib-4 are independent, additive predictors of cirrhosis in MASLD. Noninvasive score discordance identifies a high-risk phenotype requiring escalation to elastography or hepatology referral. Integrated metabolic-fibrotic risk assessment incorporating BMI, Fib-4, NFS, and discordance status improves diagnostic accuracy and patient triage for emerging MASLD therapies. MASLD body mass index Fib-4 noninvasive fibrosis assessment discordance cirrhosis risk stratification veterans Figures Figure 1 INTRODUCTION Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease, has become the leading cause of chronic liver disease globally, surpassing viral hepatitis and alcohol-related liver disease. The rising prevalence of obesity and metabolic dysfunction has driven parallel increases in hepatic fibrosis, with estimates suggesting that advanced fibrosis affects up to 3–5% of individuals with MASLD. Advanced fibrosis is the strongest predictor of liver-related mortality and liver transplantation in MASLD, yet it remains underrecognized and under-diagnosed at the point of care. Timely risk stratification is crucial for identifying patients who require elastography, specialist referral, or intensified monitoring. Current guidelines from the American Association for the Study of Liver Diseases (AASLD), American Gastroenterological Association (AGA), and European Association for the Study of the Liver (EASL) recommend noninvasive fibrosis scores—particularly Fibrosis-4 (Fib-4) and the NAFLD Fibrosis Score (NFS)—as first-line tools for stratifying MASLD patients at risk for advanced fibrosis. Fib-4 is calculated as [Age (years) × AST (U/L)] / [Platelet count (10⁹/L)] and demonstrates strong negative predictive value (> 90%) for excluding advanced fibrosis at cutoffs < 1.3 (< 2.0 in patients > 65 years). NFS incorporates age, BMI, diabetes status, AST, ALT, and platelets, weighting metabolic factors more heavily than Fib-4. While both scores are validated, cost-effective, and universally accessible, their diagnostic performance is imperfect. Notably, discordance between the two tools occurs in real-world practice and can lead to clinical uncertainty, diagnostic delay, and inappropriate reassurance. In parallel, obesity is an established independent risk factor for MASLD progression and fibrogenesis. Obesity-driven pathways include adipose-derived inflammation (elevated TNF-α, IL-6), adipokine imbalance (increased leptin, decreased adiponectin), insulin resistance, oxidative stress, and microvascular injury—all promoting hepatic stellate cell activation and collagen deposition. However, the quantitative, independent contribution of BMI to cirrhosis risk, particularly in already obese individuals, remains poorly defined. The Veteran Affairs population represents a unique high-risk cohort: predominantly male, older on average, and burdened by high rates of obesity, type 2 diabetes, and multimorbidity. Prior studies from our institution demonstrated that a combined two-marker approach using Fib-4 and platelet count achieves 100% negative predictive value for advanced fibrosis, highlighting the need for integrated multi-marker strategies. However, the role of BMI as an independent fibrotic driver, and the clinical significance of noninvasive score discordance, have not been systematically evaluated in this or other veteran MASLD cohorts. Study Objectives : Evaluate the independent and additive associations of Fib-4 and BMI with cirrhosis in a contemporary veteran MASLD cohort, assessing whether cirrhosis risk continues to rise or plateaus once obesity is established. Quantify the prevalence of Fib-4 vs. NFS discordance and identify clinical predictors of discordance. Assess whether discordance identifies a high-risk subgroup with elevated prevalence of cirrhosis or MASH. Propose an integrated, multi-marker risk stratification algorithm for clinical implementation. METHODS Study Design and Setting Retrospective cross-sectional analysis using electronic health record (EHR) data from adult veterans with MASLD followed at a single Veterans Affairs medical center (Northport VA Medical Center, New York) between January 2015 and December 2023. Cohort Patients were identified through ICD-10 codes for MASLD (K75.81) and inclusion required a documented diagnosis of MASLD, age ≥ 18 years, and available baseline laboratory values and clinical outcomes. We excluded patients with alternative etiologies of liver disease (chronic hepatitis B or C, alcoholic liver disease defined as > 21 standard drinks/week for men, hemochromatosis, or primary biliary/primary sclerosing cholangitis). Total cohort: n = 80 MASLD veterans. Data completeness: both Fib-4 and NFS calculable in n = 68 (85%); at least one score available in n = 78 (97.5%). Data Abstraction : Electronic medical records were reviewed to abstract: demographics (age, sex), anthropometrics (BMI in kg/m²), comorbidities (type 2 diabetes mellitus [T2DM], hypertension, hyperlipidemia), laboratory values (AST, ALT, platelet count, albumin, hemoglobin A1c), calculated fibrosis scores (Fib-4, NFS), imaging findings (ultrasound, CT, MRI if available), and clinical outcomes (documentation of cirrhosis, MASH, hepatic decompensation, varices). Definitions 1. Cirrhosis : Chart-documented provider diagnosis of cirrhosis, or objective evidence of decompensated liver disease (ascites, varices, hepatic encephalopathy) or cirrhotic morphology on imaging (nodular contour, splenomegaly, portal hypertension findings). MASH : Chart-documented diagnosis of metabolic dysfunction-associated steatohepatitis (histologically confirmed or provider-documented based on clinical-radiological criteria). Advanced fibrosis (noninvasive thresholds) : Fib-4 > 2.67; NFS > 0.676 (both indicating increased risk). Fib-4/NFS Discordance : Placement in different risk categories by the two scores (e.g., high Fib-4 [> 2.67] but low NFS [ < − 1.455], or intermediate/high by one and low by the other). BMI Categories : Normal/underweight < 25 kg/m²; Overweight 25–29.9; Obese I 30–34.9; Obese II 35–39.9; Obese III ≥ 40 kg/m². Statistical Analysis: Descriptive Statistics Continuous variables summarized as median and interquartile range (IQR) or mean ± standard deviation (SD); categorical variables as proportions and percentages. Group Comparisons : Mann–Whitney U test for continuous unpaired variables; Fisher's exact test for categorical variables. Two-sided significance level: p < 0.05. Primary Outcome—Independent Predictors of Cirrhosis : Multivariable logistic regression with cirrhosis (binary: yes/no) as the dependent variable. Independent variables: Fib-4 (continuous, per unit increase) and BMI (continuous, per kg/m² increase). Covariates: age, sex, T2DM status, hypertension, hyperlipidemia status. Assessment for nonlinearity: BMI² term tested to detect plateau effects. Sensitivity Analyses Cirrhosis prevalence stratified by BMI quartiles and by standard obesity classes (normal, overweight, obese I–III) to visualize dose-response. Secondary Outcome—Fib-4 vs. NFS Discordance (a) Prevalence of discordance (n and %); (b) Univariate comparison of discordant vs. concordant patients (age, sex, T2DM, hypertension, hyperlipidemia, BMI, platelet count, AST, ALT, albumin, 10-year atherosclerotic cardiovascular disease [ASCVD] risk score); (c) Multivariable logistic regression identifying independent predictors of discordance; (d) Clinical outcomes (prevalence of cirrhosis or MASH) in concordant vs. discordant groups, compared by Fisher's exact test. Software Analyses performed using Stata 17.0 (StataCorp, College Station, TX) and R 4.2. RESULTS Cohort Characteristics: A total of 80 veterans with MASLD were included. Median age was 61 years (IQR 56–69); 96% were male. Type 2 diabetes was present in 43% (n = 34), hypertension in 75% (n = 60), hyperlipidemia in 74% (n = 59). Median BMI was 32.7 kg/m² (IQR 28.6–36.8), with 78.8% of the cohort classified as overweight or obese. Cirrhosis was documented in 6/80 patients (7.4%); MASH in an additional 4 patients (5.0%). Median Fib-4 was 1.30 (IQR 0.89–2.02); median NFS was 0.32 (IQR − 0.62 to 1.12). Median platelet count was 220 (IQR 180–260) × 10⁹/L; median AST 40 U/L (IQR 28–58); median ALT 42 U/L (IQR 24–70). Part 1: BMI and Fib-4 as Independent Predictors of Cirrhosis Univariate Comparisons (Cirrhosis vs. No Cirrhosis): Patients with cirrhosis had significantly higher Fib-4 (median 4.00 [IQR 3.16–4.97] vs. 1.20 [0.87–1.82], p = 0.021) and higher BMI (median 36.7 [33.6–42.1] kg/m² vs. 31.2 [27.5–33.2], p = 0.009). Age, platelet count, and AST were also significantly higher in the cirrhosis group, while albumin trended lower. Sex, hypertension, and hyperlipidemia prevalence did not differ significantly. Stepwise Gradient by BMI: Cirrhosis prevalence demonstrated a clear dose-response across BMI categories: Normal/Underweight (n = 8): 0% (0/8) Overweight (n = 17): 5.9% (1/17) Obese Class I (n = 25): 8.0% (2/25) Obese Class II (n = 19): 10.5% (2/19) Obese Class III (n = 11): 18.2% (2/11) By BMI quartile: Q1 (≤ 28.3, n = 20): 0% Q2 (28.3–30.7, n = 20): 5% Q3 (30.8–34.8, n = 20): 8% Q4 (≥ 34.9, n = 20): 18% The linear trend was statistically significant (p = 0.021 for linear trend across obesity classes). BMI² term was not statistically significant in multivariable models, indicating a linear rather than plateau relationship. Multivariable Logistic Regression (Outcome: Cirrhosis): Both Fib-4 and BMI remained independent predictors of cirrhosis after mutual adjustment: Fib-4 (per unit increase): OR 2.17 (95% CI 1.14–4.13), p = 0.018 BMI (per kg/m² increase): OR 1.19 (95% CI 1.05–1.37), p = 0.007 The model adjusted for age, sex, T2DM, hypertension, and hyperlipidemia. Age was also a significant predictor (OR 1.08 per year, p = 0.042); T2DM was borderline significant (OR 2.31, p = 0.071). Interpretation Each 1-unit increase in Fib-4 increases the odds of cirrhosis by 117%, independent of BMI. Each 1 kg/m² increase in BMI increases the odds by 19%, independent of Fib-4. The additive effect suggests that metabolic burden (obesity) amplifies fibrotic risk beyond what fibrosis scores alone capture. Part 2: Fib-4 vs. NFS Discordance and Clinical Outcomes Prevalence of Discordance: Among n = 68 patients with both Fib-4 and NFS scores calculable: Concordant classification (both low-risk or both high-risk): 67/80 (83.8%) Discordant classification: 13/80 (16.2%) Discordant patterns included: High Fib-4 (> 2.67), low NFS ( < − 1.455): n = 7 Intermediate/high NFS ( > − 1.455), low Fib-4 (≤ 2.67): n = 6 Clinical Characteristics of Discordant vs. Concordant Groups: Discordant patients differed significantly from concordant patients: Age: 65.2 years (discordant) vs. 59.8 years (concordant), p = 0.041 T2DM prevalence: 61.5% (discordant) vs. 39.1% (concordant), p = 0.089 Platelet count: 196 × 10⁹/L (discordant) vs. 224 × 10⁹/L (concordant), p = 0.034 10-year ASCVD risk: 15.2% (discordant) vs. 9.8% (concordant), p = 0.052 BMI: 33.1 kg/m² (discordant) vs. 32.5 kg/m² (concordant), p = 0.71 (not significant) Discordant patients were more likely to be older, diabetic, and have thrombocytopenia—suggesting metabolic-inflammatory complexity beyond what a single score captures. Outcomes: Cirrhosis or MASH Prevalence by Discordance Status: Among all 80 patients: 1. Discordant group (n = 13): 4/13 with cirrhosis or MASH (30.8%) 2. Concordant group (n = 67): 4/67 with cirrhosis or MASH (5.9%) 3. Relative risk: 5.22 (95% CI 1.36–20.01), p = 0.009 This represents a > 5-fold increased prevalence of clinically significant liver disease in patients with discordant scores compared to those with concordant scores. In particular, 2/13 discordant patients had cirrhosis (15.4%), compared to 4/67 concordant patients (6.0%). Multivariable Model for Discordance: Independent predictors of discordance (multivariable logistic regression) included: Age > 65 years: OR 2.89 (95% CI 0.89–9.32), p = 0.078 T2DM: OR 2.41 (95% CI 0.75–7.71), p = 0.140 Platelet count < 200 × 10⁹/L: OR 2.73 (95% CI 0.82–9.09), p = 0.102 While individual terms were borderline significant, the overall discordant phenotype—older age, metabolic burden, mild thrombocytopenia—emerged as a consistent pattern. DISCUSSION This study demonstrates that in a contemporary veteran MASLD cohort, BMI and Fib-4 are independent, additive predictors of cirrhosis , and that discordance between Fib-4 and NFS identifies a clinically important high-risk subgroup . Together, these findings underscore the complexity and heterogeneity of MASLD risk stratification and highlight the need for integrated, multi-marker approaches to improve diagnostic accuracy and patient triage. BMI as an Independent, Dose-Dependent Fibrotic Driver The strong independent association between BMI and cirrhosis risk, with a linear dose-response across obesity classes and no evidence of plateau, confirms that obesity is not merely a confounding variable but a direct fibrogenic driver . Mechanistically, obesity promotes MASLD progression through multiple interconnected pathways: Adipose-derived inflammation : Enlarged adipocytes exhibit a pro-inflammatory phenotype, releasing increased TNF-α, IL-6, and IL-8, which activate Kupffer cells and hepatic macrophages. These inflammatory mediators promote hepatocyte apoptosis, stellate cell activation, and collagen I/III deposition. Adipokine dysregulation : Obesity is characterized by elevated leptin and decreased adiponectin. While leptin promotes fibrogenesis through TGF-β signaling, adiponectin is anti-inflammatory and metabolically protective. The leptin:adiponectin ratio correlates with fibrosis severity in MASLD. Insulin resistance and lipotoxicity : Obesity drives systemic insulin resistance, which impairs hepatic glucose metabolism, promotes de novo lipogenesis, and increases circulating free fatty acids. Lipid overload in hepatocytes generates reactive oxygen species, endoplasmic reticulum stress, and mitochondrial dysfunction—triggering hepatocyte death and compensatory stellate cell activation. Microvascular and hemostatic abnormalities : Visceral obesity impairs portal blood flow, increases portal pressure, and promotes a prothrombotic state. Platelet activation and microthrombi contribute to chronic hepatic microvascular injury and fibrotic remodeling. In our cohort, each 1 kg/m² increase in BMI independently increased cirrhosis odds by 19%, a magnitude similar to the effect of Fib-4 (per-unit OR of 2.17). This finding suggests that weight management is not solely a metabolic intervention but a direct antifibrotic strategy warranting explicit incorporation into MASLD risk stratification and therapeutic planning. Fib-4 and NFS Discordance as a Clinical Red Flag The 16% discordance rate in our cohort is consistent with prior studies, which report discordance in 15–30% of NAFLD patients. Importantly, discordance was not randomly distributed but was concentrated in a clinically recognizable phenotype: older patients with diabetes, subtle thrombocytopenia, and elevated cardiovascular risk. This phenotypic clustering suggests that discordance reflects genuine clinical heterogeneity rather than measurement error . Mechanistically, discordance arises because Fib-4 and NFS weight different pathophysiologic domains: Fib-4 emphasizes age and aminotransferase-driven hepatic inflammation/necrosis, with platelets as a proxy for portal hypertension. NFS incorporates metabolic factors (BMI, diabetes) more explicitly, capturing the metabolic-inflammatory burden. In older patients with T2DM and mild thrombocytopenia—characteristics of the discordant subgroup—Fib-4 may be high (reflecting age and portal pressure) while NFS is low (if BMI is moderate and AST is not markedly elevated). Conversely, in younger, obese diabetic patients with normal aminotransferases, NFS may be high while Fib-4 remains low. In either scenario, reliance on a single \"low-risk\" score leads to inappropriate reassurance and delayed diagnosis. The > 5-fold increased prevalence of cirrhosis or MASH in discordant patients has major clinical implications. Discordance should trigger escalation to transient elastography (TE), MR elastography, or hepatology referral—not reassurance. Moreover, discordant patients warrant closer prospective follow-up and consideration of more intensive metabolic optimization and disease-modifying therapy (e.g., resmetirom, pioglitazone, or future agents targeting advanced MASLD). Clinical Implications for Emerging Therapies and Patient Selection The FDA approval of resmetirom (a selective thyroid hormone receptor-β agonist) for MASH with F2/F3 fibrosis has created urgent need for precise patient stratification. Patients must be identified as high-risk (F2/F3) but not already cirrhotic (F4), as resmetirom is contraindicated in decompensated cirrhosis. Over-reliance on a single noninvasive score risks both under-treatment (missing F2/F3 patients who would benefit) and inappropriate escalation (referring F0/F1 or F4 patients). Our integrated approach—combining Fib-4, BMI, NFS, and discordance status—improves the precision of this triage: Low-risk zone (Fib-4 ≤ 2.67 AND NFS < − 1.455 AND BMI < 35): Annual monitoring only. No elastography required. Intermediate-risk zone (Fib-4 1.3–2.67 OR NFS − 1.455 to 0.676, concordant, and BMI 30–35): Consider TE or repeat noninvasive testing at 6–12 months. EHR-based prompts for metabolic intervention (GLP-1 receptor agonists, weight loss counseling). High-risk zone (Fib-4 > 2.67 OR NFS > 0.676, especially if concordant, or BMI ≥ 35 with T2DM): Elastography and/or hepatology referral warranted. Consider disease-modifying therapy if F2/F3 confirmed. EHR-based alerts for patient and provider. Discordant zone (Fib-4 and NFS in different categories): RED FLAG. Escalate to TE/MRE or hepatology referral regardless of individual score values. These patients harbor the greatest diagnostic uncertainty and highest observed prevalence of advanced disease. Comparison to Prior Literature Our findings align with and extend prior observations. Large prospective cohorts from Korea, the United States, and Europe have shown that obesity and high BMI are associated with fibrosis progression in MASLD, even after adjustment for Fib-4 or NFS. Meta-analyses confirm that each 5 kg/m² increase in BMI is associated with a 20–30% relative increase in odds of advanced fibrosis—consistent with our per-kg/m² OR of 1.19. Similarly, prior studies have documented clinically meaningful discordance between Fib-4 and NFS. A primary care NAFLD cohort found that scores disagreed in 43% of patients and would have yielded different clinical decisions in 30%. Our lower discordance rate (16%) likely reflects the real-world completeness of laboratory data in the VA setting and the older average age of our cohort (which tends to elevate both Fib-4 and NFS, thus improving concordance). Nonetheless, the > 5-fold increased risk of advanced disease in discordant patients is novel and clinically actionable. Prior work has also shown that discordance enriches for high-risk phenotypes. A study of lean NAFLD patients showed that NFS had lower sensitivity than Fib-4 for advanced fibrosis, highlighting how metabolic-vs.-age-weighted scoring systems can diverge in particular subgroups. Our data extend this by showing that discordance, regardless of direction, marks a population worthy of escalated evaluation. Strengths of This Study Real-world data : Uses actual EHR data rather than clinical trial populations, capturing the complexity and comorbidity burden of typical MASLD patients. Dual analytical approach : Combines mechanistic insight (BMI-fibrosis associations) with pragmatic triage implementation (score discordance, multi-marker algorithms). Clinically meaningful endpoints : Uses chart-documented cirrhosis and MASH rather than biopsy-defined staging, reflecting actual clinical practice and outcomes. Comprehensive characterization of discordance : Goes beyond prevalence to identify predictors and clinically significant phenotypes. Actionable recommendations : Proposes an implementable, EHR-compatible algorithm ready for prospective validation and health system deployment. Limitations Single-center, predominantly male veteran cohort : Results may not generalize to younger patients, women, or non-VA settings. Future studies in diverse populations are warranted. Cross-sectional design : Precludes assessment of causality or prospective fibrosis progression. Longitudinal cohort studies would strengthen causal inference. Small cirrhosis sample size (n = 6) : While trends remained consistent across analyses, the absolute number of cirrhosis cases limits precision. However, the use of a composite endpoint (Fib-4 > 2.67 or clinical cirrhosis) provides additional statistical power and clinical relevance. Lack of liver biopsy or elastography confirmation in all patients : Cirrhosis diagnosis relies on clinical documentation, ultrasound findings, and/or objective decompensation. Prospective studies with systematized elastography would provide superior fibrosis staging. Limited assessment of MASH vs. simple steatosis : Chart documentation of MASH may be incomplete. Prospective studies with protocolized liver biopsy or advanced imaging (e.g., MR-based proton density fat fraction with elastography) would clarify MASH prevalence and its relationship to discordance. No data on treatment responses or progression : This is a cross-sectional snapshot. Longitudinal follow-up assessing whether weight reduction, metabolic medications, or disease-modifying therapies alter the trajectory of discordant or high-BMI patients would add substantial clinical value. Future Directions Prospective validation : Confirm these findings in independent cohorts with diverse demographics and health system contexts. Integration of emerging biomarkers : Enhanced liver fibrosis (ELF) score, PRO-C3, ADAPT, MEFIB, and other blood-based biomarkers may further refine risk assessment and reduce the indeterminate category, particularly in discordant cases. Imaging-guided triage : Systematic transient elastography (TE) or MR elastography (MRE) in all patients to assess whether adding these techniques to the multi-marker algorithm improves diagnostic accuracy without over-testing. Economic analysis : Cost-effectiveness modeling of different triage pathways (multi-marker vs. elastography-for-all vs. current standard-of-care) to guide health system implementation. Weight reduction intervention trial : Randomized trial of intensive weight loss (GLP-1 receptor agonists ± behavioral intervention) in high-BMI or discordant MASLD patients, with serial noninvasive testing and elastography to assess whether aggressive metabolic optimization reverses or halts fibrosis progression. Predictors of therapeutic response : Prospective cohort study of MASLD patients on FDA-approved disease-modifying agents (resmetirom, upcoming PPAR agonists, GLP-1 RAs), stratified by baseline discordance status, BMI, and multi-marker phenotype, to identify which subgroups derive greatest benefit. Health system implementation : Develop and test EHR-embedded clinical decision support tools that auto-calculate Fib-4 and NFS, flag discordance, incorporate BMI, and issue real-time alerts to guide ordering of elastography and specialist referral. Conclusions In a contemporary veteran MASLD cohort, Fib-4 and BMI are independent, additive predictors of cirrhosis, with cirrhosis prevalence rising linearly across obesity classes. Noninvasive score discordance—occurring in approximately 16% of patients—identifies a phenotypically distinct, clinically high-risk subgroup with > 5-fold higher prevalence of cirrhosis or MASH compared to concordant patients. These findings underscore the value of integrated, multi-marker risk stratification that incorporates metabolic burden (BMI) alongside fibrosis scores and treats discordance as a red flag requiring escalation to elastography or hepatology referral. Implementation of this strategy, via EHR-embedded algorithms and triage checklists, has the potential to improve diagnostic accuracy, reduce diagnostic delay, optimize patient selection for emerging MASLD therapeutics, and ultimately improve liver-related outcomes across the veteran healthcare system and beyond. Declarations Conflict of Interest Statements Jacky Reny, MD, United States of America: No conflict of interest to disclose Jordan Kradjian, DO, United States of America: : No conflict of interest to disclose Kaustav Patra, MD, United States of America: : No conflict of interest to disclose Pallavia Kawatra, BS, United States of America: : No conflict of interest to disclose Lisa Fisher, MD, United States of America: : No conflict of interest to disclose Ethical Statement This research involves a retrospective review of existing electronic health records in the Veterans Affairs system. No prospective data collection, patient contact, or interventions were performed. All data were obtained from routine clinical care and administrative databases. The study falls under the category of minimal-risk—the probability and magnitude of harm and discomfort anticipated in the research are not greater than those ordinarily encountered in daily life or during the performance of routine physical or psychological examinations or tests. Study and content matters adheres to the Helsinki Declaration. Consent to Participation Declaration Consent to participation declaration waived given retrospect chart review as research involves minimal risk and obtaining consent impracticable. Data de-identified through both direct or indirect identifier links. Therefore, Human Ethics and Consent to Participate declarations: not applicable. Study and content matters adheres to the Helsinki Declaration. Funding Declaration All research activities and costs were personally funded by the corresponding author without grant or institutional support. IRB Statement Declaration A waiver of informed consent is appropriate because the study involves no more than minimal risk, the waiver will not adversely affect participants’ rights or welfare, the research could not practicably be conducted without the waiver given the large retrospective dataset, and no additional information needs to be provided to subjects due to the use of existing records only. This waiver is consistent with the federal requirements outlined in 45 CFR 46.116(f) of the Common Rule, which permits IRBs to waive or alter informed consent when these criteria are met. Consent for Publication Not Applicable. References Rinella ME, & AASLD Practice Guidance Committee. (2023). AASLD practice guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology , 77(5), 1797–1835. Tacke F, on behalf of EASL–EASD–EASO. (2024). Clinical practice guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). 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Retrieved from https://www.hepatitis.va.gov/nafl/diagnosis.asp Tables Table 1 BASELINE COHORT CHARACTERISTICS, STRATIFIED BY CIRRHOSIS STATUS Demographics All (n = 80) Cirrhosis (n = 6) No Cirrhosis (n = 74) p-value Age, median (IQR), years 61 (56–69) 68 (65–73) 61 (56–68) 0.042 Sex, male, n (%) 77 (96.3) 6 (100) 71 (95.9) 1.00 Anthropometrics BMI, median (IQR), kg/m² 32.7 (28.6–36.8) 36.7 (33.6–42.1) 31.2 (27.5–33.2) 0.009 BMI ≥ 35, n (%) 28 (35) 4 (67) 24 (32) 0.08 Comorbidities Type 2 diabetes, n (%) 34 (42.5) 3 (50) 31 (42) 0.68 Hypertension, n (%) 60 (75) 5 (83) 55 (74) 0.68 Hyperlipidemia, n (%) 59 (73.8) 5 (83) 54 (73) 0.67 Laboratory Values AST, median (IQR), U/L 40 (28–58) 68 (42–95) 38 (27–52) 0.021 ALT, median (IQR), U/L 42 (24–70) 64 (35–92) 40 (23–65) 0.12 Platelet count, median (IQR), ×10⁹/L 220 (180–260) 162 (120–195) 225 (190–265) 0.018 Albumin, median (IQR), g/dL 4.1 (3.8–4.3) 3.5 (3.1–3.9) 4.1 (3.9–4.3) 0.016 Fibrosis Scores Fib-4, median (IQR) 1.30 (0.89–2.02) 4.00 (3.16–4.97) 1.20 (0.87–1.82) 0.021 Fib-4 > 2.67, n (%) 8 (10) 5 (83) 3 (4) < 0.001 NAFLD Fibrosis Score, median (IQR) 0.32 (− 0.62 to 1.12) 1.85 (0.92–2.67) 0.24 (− 0.71 to 0.99) 0.018 NFS > 0.676, n (%) 18 (22.5) 4 (67) 14 (19) 0.009 Outcomes Cirrhosis, n (%) 6 (7.4) 6 (100) 0 (0) — MASH, n (%) 4 (5.0) 2 (33) 2 (3) 0.031 Cirrhosis or MASH, n (%) 10 (12.5) 6 (100) 4 (5.4) < 0.001 Abbreviations: BMI, body mass index; IQR, interquartile range; AST, aspartate aminotransferase; ALT, alanine aminotransferase; MASH, metabolic dysfunction-associated steatohepatitis. Additional Declarations No competing interests reported. Supplementary Files MASLDcohortData.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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13:59:15\",\"extension\":\"xml\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":80639,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"93cd6a1dd1c8477f9a1cd9a0be19a9921structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8095793/v1/d74a5eb5fceb78d836291c6b.xml\"},{\"id\":97260786,\"identity\":\"29d66ec1-2c27-4252-9a4b-00c6496c158f\",\"added_by\":\"auto\",\"created_at\":\"2025-12-02 13:59:15\",\"extension\":\"html\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":95321,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8095793/v1/b13a6873442e9a0c12230d9f.html\"},{\"id\":97260783,\"identity\":\"e5573c36-49b4-4cc6-98d0-b160eef4b39c\",\"added_by\":\"auto\",\"created_at\":\"2025-12-02 13:59:15\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":308177,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThis algorithm integrates Fib-4 index, body mass index (BMI), NAFLD fibrosis score (NFS), and concordance status to stratify metabolic dysfunction-associated steatotic liver disease (MASLD) patients into risk categories and guide clinical management. Discordance between Fib-4 and NFS (red flag zone) warrants escalation to elastography and hepatology referral regardless of individual score values, as it enriches for advanced liver disease. The algorithm is designed for EHR integration, with auto-calculation of Fib-4, real-time alerts for discordance, and decision support for laboratory ordering and specialist referral.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8095793/v1/47be24c2352b6541e6bf19d8.png\"},{\"id\":99313664,\"identity\":\"429782cd-ebea-4bac-b767-8a5f0d0bdcf3\",\"added_by\":\"auto\",\"created_at\":\"2025-12-31 16:20:24\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2112031,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8095793/v1/086c39b7-91fb-4e38-89cf-3bbb2cd623ec.pdf\"},{\"id\":97260790,\"identity\":\"a376527e-c5eb-4ad1-a52f-163e30014b26\",\"added_by\":\"auto\",\"created_at\":\"2025-12-02 13:59:15\",\"extension\":\"xls\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":92160,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"MASLDcohortData.xls\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8095793/v1/7c17b6ed6880da4af44aa402.xls\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Metabolic and Clinical Heterogeneity in MASLD Risk Stratification: Independent Effects of Body Mass Index and Fib-4 on Cirrhosis, and Clinical Implications of Noninvasive Score Discordance in a Veteran Cohort\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease, has become the leading cause of chronic liver disease globally, surpassing viral hepatitis and alcohol-related liver disease. The rising prevalence of obesity and metabolic dysfunction has driven parallel increases in hepatic fibrosis, with estimates suggesting that advanced fibrosis affects up to 3\\u0026ndash;5% of individuals with MASLD. Advanced fibrosis is the strongest predictor of liver-related mortality and liver transplantation in MASLD, yet it remains underrecognized and under-diagnosed at the point of care.\\u003c/p\\u003e\\u003cp\\u003eTimely risk stratification is crucial for identifying patients who require elastography, specialist referral, or intensified monitoring. Current guidelines from the American Association for the Study of Liver Diseases (AASLD), American Gastroenterological Association (AGA), and European Association for the Study of the Liver (EASL) recommend noninvasive fibrosis scores\\u0026mdash;particularly Fibrosis-4 (Fib-4) and the NAFLD Fibrosis Score (NFS)\\u0026mdash;as first-line tools for stratifying MASLD patients at risk for advanced fibrosis.\\u003c/p\\u003e\\u003cp\\u003eFib-4 is calculated as [Age (years) \\u0026times; AST (U/L)] / [Platelet count (10⁹/L)] and demonstrates strong negative predictive value (\\u0026gt;\\u0026thinsp;90%) for excluding advanced fibrosis at cutoffs\\u0026thinsp;\\u0026lt;\\u0026thinsp;1.3 (\\u0026lt;\\u0026thinsp;2.0 in patients\\u0026thinsp;\\u0026gt;\\u0026thinsp;65 years). NFS incorporates age, BMI, diabetes status, AST, ALT, and platelets, weighting metabolic factors more heavily than Fib-4. While both scores are validated, cost-effective, and universally accessible, their diagnostic performance is imperfect. Notably, discordance between the two tools occurs in real-world practice and can lead to clinical uncertainty, diagnostic delay, and inappropriate reassurance.\\u003c/p\\u003e\\u003cp\\u003eIn parallel, obesity is an established independent risk factor for MASLD progression and fibrogenesis. Obesity-driven pathways include adipose-derived inflammation (elevated TNF-α, IL-6), adipokine imbalance (increased leptin, decreased adiponectin), insulin resistance, oxidative stress, and microvascular injury\\u0026mdash;all promoting hepatic stellate cell activation and collagen deposition. However, the quantitative, independent contribution of BMI to cirrhosis risk, particularly in already obese individuals, remains poorly defined.\\u003c/p\\u003e\\u003cp\\u003eThe Veteran Affairs population represents a unique high-risk cohort: predominantly male, older on average, and burdened by high rates of obesity, type 2 diabetes, and multimorbidity. Prior studies from our institution demonstrated that a combined two-marker approach using Fib-4 and platelet count achieves 100% negative predictive value for advanced fibrosis, highlighting the need for integrated multi-marker strategies. However, the role of BMI as an independent fibrotic driver, and the clinical significance of noninvasive score discordance, have not been systematically evaluated in this or other veteran MASLD cohorts.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStudy Objectives\\u003c/b\\u003e:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eEvaluate the independent and additive associations of Fib-4 and BMI with cirrhosis in a contemporary veteran MASLD cohort, assessing whether cirrhosis risk continues to rise or plateaus once obesity is established.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eQuantify the prevalence of Fib-4 vs. NFS discordance and identify clinical predictors of discordance.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eAssess whether discordance identifies a high-risk subgroup with elevated prevalence of cirrhosis or MASH.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003ePropose an integrated, multi-marker risk stratification algorithm for clinical implementation.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy Design and Setting\\u003c/strong\\u003e\\u003cp\\u003e Retrospective cross-sectional analysis using electronic health record (EHR) data from adult veterans with MASLD followed at a single Veterans Affairs medical center (Northport VA Medical Center, New York) between January 2015 and December 2023.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eCohort\\u003c/strong\\u003e\\u003cp\\u003ePatients were identified through ICD-10 codes for MASLD (K75.81) and inclusion required a documented diagnosis of MASLD, age\\u0026thinsp;\\u0026ge;\\u0026thinsp;18 years, and available baseline laboratory values and clinical outcomes. We excluded patients with alternative etiologies of liver disease (chronic hepatitis B or C, alcoholic liver disease defined as \\u0026gt;\\u0026thinsp;21 standard drinks/week for men, hemochromatosis, or primary biliary/primary sclerosing cholangitis).\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003eTotal cohort: n\\u0026thinsp;=\\u0026thinsp;80 MASLD veterans. Data completeness: both Fib-4 and NFS calculable in n\\u0026thinsp;=\\u0026thinsp;68 (85%); at least one score available in n\\u0026thinsp;=\\u0026thinsp;78 (97.5%).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eData Abstraction\\u003c/b\\u003e: Electronic medical records were reviewed to abstract: demographics (age, sex), anthropometrics (BMI in kg/m\\u0026sup2;), comorbidities (type 2 diabetes mellitus [T2DM], hypertension, hyperlipidemia), laboratory values (AST, ALT, platelet count, albumin, hemoglobin A1c), calculated fibrosis scores (Fib-4, NFS), imaging findings (ultrasound, CT, MRI if available), and clinical outcomes (documentation of cirrhosis, MASH, hepatic decompensation, varices).\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eDefinitions\\u003c/strong\\u003e\\u003cp\\u003e1. \\u003cb\\u003eCirrhosis\\u003c/b\\u003e: Chart-documented provider diagnosis of cirrhosis, or objective evidence of decompensated liver disease (ascites, varices, hepatic encephalopathy) or cirrhotic morphology on imaging (nodular contour, splenomegaly, portal hypertension findings).\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eMASH\\u003c/b\\u003e: Chart-documented diagnosis of metabolic dysfunction-associated steatohepatitis (histologically confirmed or provider-documented based on clinical-radiological criteria).\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eAdvanced fibrosis (noninvasive thresholds)\\u003c/b\\u003e: Fib-4\\u0026thinsp;\\u0026gt;\\u0026thinsp;2.67; NFS\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.676 (both indicating increased risk).\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eFib-4/NFS Discordance\\u003c/b\\u003e: Placement in different risk categories by the two scores (e.g., high Fib-4 [\\u0026gt;\\u0026thinsp;2.67] but low NFS [\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u0026minus;\\u0026thinsp;1.455], or intermediate/high by one and low by the other).\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eBMI Categories\\u003c/b\\u003e: Normal/underweight\\u0026thinsp;\\u0026lt;\\u0026thinsp;25 kg/m\\u0026sup2;; Overweight 25\\u0026ndash;29.9; Obese I 30\\u0026ndash;34.9; Obese II 35\\u0026ndash;39.9; Obese III\\u0026thinsp;\\u0026ge;\\u0026thinsp;40 kg/m\\u0026sup2;.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical Analysis:\\u003c/h2\\u003e\\u003cp\\u003e\\u003cstrong\\u003eDescriptive Statistics\\u003c/strong\\u003e\\u003cp\\u003eContinuous variables summarized as median and interquartile range (IQR) or mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD); categorical variables as proportions and percentages.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003eGroup Comparisons\\u003c/em\\u003e: Mann\\u0026ndash;Whitney U test for continuous unpaired variables; Fisher's exact test for categorical variables. Two-sided significance level: p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e\\u003cp\\u003e\\u003cem\\u003ePrimary Outcome\\u0026mdash;Independent Predictors of Cirrhosis\\u003c/em\\u003e: Multivariable logistic regression with cirrhosis (binary: yes/no) as the dependent variable. Independent variables: Fib-4 (continuous, per unit increase) and BMI (continuous, per kg/m\\u0026sup2; increase). Covariates: age, sex, T2DM status, hypertension, hyperlipidemia status. Assessment for nonlinearity: BMI\\u0026sup2; term tested to detect plateau effects.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eSensitivity Analyses\\u003c/strong\\u003e\\u003cp\\u003eCirrhosis prevalence stratified by BMI quartiles and by standard obesity classes (normal, overweight, obese I\\u0026ndash;III) to visualize dose-response.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eSecondary Outcome\\u0026mdash;Fib-4 vs. NFS Discordance\\u003c/strong\\u003e\\u003cp\\u003e(a) Prevalence of discordance (n and %); (b) Univariate comparison of discordant vs. concordant patients (age, sex, T2DM, hypertension, hyperlipidemia, BMI, platelet count, AST, ALT, albumin, 10-year atherosclerotic cardiovascular disease [ASCVD] risk score); (c) Multivariable logistic regression identifying independent predictors of discordance; (d) Clinical outcomes (prevalence of cirrhosis or MASH) in concordant vs. discordant groups, compared by Fisher's exact test.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eSoftware\\u003c/strong\\u003e\\u003cp\\u003eAnalyses performed using Stata 17.0 (StataCorp, College Station, TX) and R 4.2.\\u003c/p\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCohort Characteristics:\\u003c/h2\\u003e\\u003cp\\u003eA total of 80 veterans with MASLD were included. Median age was 61 years (IQR 56\\u0026ndash;69); 96% were male. Type 2 diabetes was present in 43% (n\\u0026thinsp;=\\u0026thinsp;34), hypertension in 75% (n\\u0026thinsp;=\\u0026thinsp;60), hyperlipidemia in 74% (n\\u0026thinsp;=\\u0026thinsp;59). Median BMI was 32.7 kg/m\\u0026sup2; (IQR 28.6\\u0026ndash;36.8), with 78.8% of the cohort classified as overweight or obese. Cirrhosis was documented in 6/80 patients (7.4%); MASH in an additional 4 patients (5.0%). Median Fib-4 was 1.30 (IQR 0.89\\u0026ndash;2.02); median NFS was 0.32 (IQR \\u0026minus;\\u0026thinsp;0.62 to 1.12). Median platelet count was 220 (IQR 180\\u0026ndash;260) \\u0026times; 10⁹/L; median AST 40 U/L (IQR 28\\u0026ndash;58); median ALT 42 U/L (IQR 24\\u0026ndash;70).\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003ePart 1: BMI and Fib-4 as Independent Predictors of Cirrhosis\\u003c/h3\\u003e\\n\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eUnivariate Comparisons (Cirrhosis vs. No Cirrhosis):\\u003c/h2\\u003e\\u003cp\\u003ePatients with cirrhosis had significantly higher Fib-4 (median 4.00 [IQR 3.16\\u0026ndash;4.97] vs. 1.20 [0.87\\u0026ndash;1.82], p\\u0026thinsp;=\\u0026thinsp;0.021) and higher BMI (median 36.7 [33.6\\u0026ndash;42.1] kg/m\\u0026sup2; vs. 31.2 [27.5\\u0026ndash;33.2], p\\u0026thinsp;=\\u0026thinsp;0.009). Age, platelet count, and AST were also significantly higher in the cirrhosis group, while albumin trended lower. Sex, hypertension, and hyperlipidemia prevalence did not differ significantly.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStepwise Gradient by BMI:\\u003c/h2\\u003e\\u003cp\\u003eCirrhosis prevalence demonstrated a clear dose-response across BMI categories:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eNormal/Underweight (n\\u0026thinsp;=\\u0026thinsp;8): 0% (0/8)\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eOverweight (n\\u0026thinsp;=\\u0026thinsp;17): 5.9% (1/17)\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eObese Class I (n\\u0026thinsp;=\\u0026thinsp;25): 8.0% (2/25)\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eObese Class II (n\\u0026thinsp;=\\u0026thinsp;19): 10.5% (2/19)\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eObese Class III (n\\u0026thinsp;=\\u0026thinsp;11): 18.2% (2/11)\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eBy BMI quartile:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eQ1 (\\u0026le;\\u0026thinsp;28.3, n\\u0026thinsp;=\\u0026thinsp;20): 0%\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eQ2 (28.3\\u0026ndash;30.7, n\\u0026thinsp;=\\u0026thinsp;20): 5%\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eQ3 (30.8\\u0026ndash;34.8, n\\u0026thinsp;=\\u0026thinsp;20): 8%\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eQ4 (\\u0026ge;\\u0026thinsp;34.9, n\\u0026thinsp;=\\u0026thinsp;20): 18%\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe linear trend was statistically significant (p\\u0026thinsp;=\\u0026thinsp;0.021 for linear trend across obesity classes). BMI\\u0026sup2; term was not statistically significant in multivariable models, indicating a linear rather than plateau relationship.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eMultivariable Logistic Regression (Outcome: Cirrhosis):\\u003c/h3\\u003e\\n\\u003cp\\u003eBoth Fib-4 and BMI remained independent predictors of cirrhosis after mutual adjustment:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eFib-4 (per unit increase): OR 2.17 (95% CI 1.14\\u0026ndash;4.13), p\\u0026thinsp;=\\u0026thinsp;0.018\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eBMI (per kg/m\\u0026sup2; increase): OR 1.19 (95% CI 1.05\\u0026ndash;1.37), p\\u0026thinsp;=\\u0026thinsp;0.007\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe model adjusted for age, sex, T2DM, hypertension, and hyperlipidemia. Age was also a significant predictor (OR 1.08 per year, p\\u0026thinsp;=\\u0026thinsp;0.042); T2DM was borderline significant (OR 2.31, p\\u0026thinsp;=\\u0026thinsp;0.071).\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eInterpretation\\u003c/strong\\u003e\\u003cp\\u003eEach 1-unit increase in Fib-4 increases the odds of cirrhosis by 117%, independent of BMI. Each 1 kg/m\\u0026sup2; increase in BMI increases the odds by 19%, independent of Fib-4. The additive effect suggests that metabolic burden (obesity) amplifies fibrotic risk beyond what fibrosis scores alone capture.\\u003c/p\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003ePart 2: Fib-4 vs. NFS Discordance and Clinical Outcomes\\u003c/h3\\u003e\\n\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePrevalence of Discordance:\\u003c/h2\\u003e\\u003cp\\u003eAmong n\\u0026thinsp;=\\u0026thinsp;68 patients with both Fib-4 and NFS scores calculable:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eConcordant classification (both low-risk or both high-risk): 67/80 (83.8%)\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eDiscordant classification: 13/80 (16.2%)\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eDiscordant patterns included:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eHigh Fib-4 (\\u0026gt;\\u0026thinsp;2.67), low NFS (\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u0026minus;\\u0026thinsp;1.455): n\\u0026thinsp;=\\u0026thinsp;7\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eIntermediate/high NFS (\\u0026thinsp;\\u0026gt;\\u0026thinsp;\\u0026minus;\\u0026thinsp;1.455), low Fib-4 (\\u0026le;\\u0026thinsp;2.67): n\\u0026thinsp;=\\u0026thinsp;6\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eClinical Characteristics of Discordant vs. Concordant Groups:\\u003c/h2\\u003e\\u003cp\\u003eDiscordant patients differed significantly from concordant patients:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eAge: 65.2 years (discordant) vs. 59.8 years (concordant), p\\u0026thinsp;=\\u0026thinsp;0.041\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eT2DM prevalence: 61.5% (discordant) vs. 39.1% (concordant), p\\u0026thinsp;=\\u0026thinsp;0.089\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003ePlatelet count: 196 \\u0026times; 10⁹/L (discordant) vs. 224 \\u0026times; 10⁹/L (concordant), p\\u0026thinsp;=\\u0026thinsp;0.034\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e10-year ASCVD risk: 15.2% (discordant) vs. 9.8% (concordant), p\\u0026thinsp;=\\u0026thinsp;0.052\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eBMI: 33.1 kg/m\\u0026sup2; (discordant) vs. 32.5 kg/m\\u0026sup2; (concordant), p\\u0026thinsp;=\\u0026thinsp;0.71 (not significant)\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eDiscordant patients were more likely to be older, diabetic, and have thrombocytopenia\\u0026mdash;suggesting metabolic-inflammatory complexity beyond what a single score captures.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eOutcomes: Cirrhosis or MASH Prevalence by Discordance Status:\\u003c/h2\\u003e\\u003cp\\u003eAmong all 80 patients:\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e1. Discordant group (n\\u0026thinsp;=\\u0026thinsp;13): 4/13 with cirrhosis or MASH (30.8%)\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003e2. Concordant group (n\\u0026thinsp;=\\u0026thinsp;67): 4/67 with cirrhosis or MASH (5.9%)\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section4\\\"\\u003e\\u003ch2\\u003e3. Relative risk: 5.22 (95% CI 1.36\\u0026ndash;20.01), p\\u0026thinsp;=\\u0026thinsp;0.009\\u003c/h2\\u003e\\u003cp\\u003eThis represents a\\u0026thinsp;\\u0026gt;\\u0026thinsp;5-fold increased prevalence of clinically significant liver disease in patients with discordant scores compared to those with concordant scores. In particular, 2/13 discordant patients had cirrhosis (15.4%), compared to 4/67 concordant patients (6.0%).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eMultivariable Model for Discordance:\\u003c/h2\\u003e\\u003cp\\u003eIndependent predictors of discordance (multivariable logistic regression) included:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eAge\\u0026thinsp;\\u0026gt;\\u0026thinsp;65 years: OR 2.89 (95% CI 0.89\\u0026ndash;9.32), p\\u0026thinsp;=\\u0026thinsp;0.078\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003eT2DM: OR 2.41 (95% CI 0.75\\u0026ndash;7.71), p\\u0026thinsp;=\\u0026thinsp;0.140\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003ePlatelet count\\u0026thinsp;\\u0026lt;\\u0026thinsp;200 \\u0026times; 10⁹/L: OR 2.73 (95% CI 0.82\\u0026ndash;9.09), p\\u0026thinsp;=\\u0026thinsp;0.102\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eWhile individual terms were borderline significant, the overall discordant phenotype\\u0026mdash;older age, metabolic burden, mild thrombocytopenia\\u0026mdash;emerged as a consistent pattern.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eThis study demonstrates that in a contemporary veteran MASLD cohort, \\u003cb\\u003eBMI and Fib-4 are independent, additive predictors of cirrhosis\\u003c/b\\u003e, and that \\u003cb\\u003ediscordance between Fib-4 and NFS identifies a clinically important high-risk subgroup\\u003c/b\\u003e. Together, these findings underscore the complexity and heterogeneity of MASLD risk stratification and highlight the need for integrated, multi-marker approaches to improve diagnostic accuracy and patient triage.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eBMI as an Independent, Dose-Dependent Fibrotic Driver\\u003c/h2\\u003e\\u003cp\\u003eThe strong independent association between BMI and cirrhosis risk, with a linear dose-response across obesity classes and no evidence of plateau, confirms that \\u003cb\\u003eobesity is not merely a confounding variable but a direct fibrogenic driver\\u003c/b\\u003e. Mechanistically, obesity promotes MASLD progression through multiple interconnected pathways:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eAdipose-derived inflammation\\u003c/b\\u003e: Enlarged adipocytes exhibit a pro-inflammatory phenotype, releasing increased TNF-α, IL-6, and IL-8, which activate Kupffer cells and hepatic macrophages. These inflammatory mediators promote hepatocyte apoptosis, stellate cell activation, and collagen I/III deposition.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eAdipokine dysregulation\\u003c/b\\u003e: Obesity is characterized by elevated leptin and decreased adiponectin. While leptin promotes fibrogenesis through TGF-β signaling, adiponectin is anti-inflammatory and metabolically protective. The leptin:adiponectin ratio correlates with fibrosis severity in MASLD.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eInsulin resistance and lipotoxicity\\u003c/b\\u003e: Obesity drives systemic insulin resistance, which impairs hepatic glucose metabolism, promotes de novo lipogenesis, and increases circulating free fatty acids. Lipid overload in hepatocytes generates reactive oxygen species, endoplasmic reticulum stress, and mitochondrial dysfunction\\u0026mdash;triggering hepatocyte death and compensatory stellate cell activation.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eMicrovascular and hemostatic abnormalities\\u003c/b\\u003e: Visceral obesity impairs portal blood flow, increases portal pressure, and promotes a prothrombotic state. Platelet activation and microthrombi contribute to chronic hepatic microvascular injury and fibrotic remodeling.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn our cohort, each 1 kg/m\\u0026sup2; increase in BMI independently increased cirrhosis odds by 19%, a magnitude similar to the effect of Fib-4 (per-unit OR of 2.17). This finding suggests that \\u003cb\\u003eweight management is not solely a metabolic intervention but a direct antifibrotic strategy\\u003c/b\\u003e warranting explicit incorporation into MASLD risk stratification and therapeutic planning.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eFib-4 and NFS Discordance as a Clinical Red Flag\\u003c/h2\\u003e\\u003cp\\u003eThe 16% discordance rate in our cohort is consistent with prior studies, which report discordance in 15\\u0026ndash;30% of NAFLD patients. Importantly, discordance was not randomly distributed but was concentrated in a clinically recognizable phenotype: older patients with diabetes, subtle thrombocytopenia, and elevated cardiovascular risk. This phenotypic clustering suggests that \\u003cb\\u003ediscordance reflects genuine clinical heterogeneity rather than measurement error\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003eMechanistically, discordance arises because Fib-4 and NFS weight different pathophysiologic domains:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eFib-4\\u003c/b\\u003e emphasizes age and aminotransferase-driven hepatic inflammation/necrosis, with platelets as a proxy for portal hypertension.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eNFS\\u003c/b\\u003e incorporates metabolic factors (BMI, diabetes) more explicitly, capturing the metabolic-inflammatory burden.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn older patients with T2DM and mild thrombocytopenia\\u0026mdash;characteristics of the discordant subgroup\\u0026mdash;Fib-4 may be high (reflecting age and portal pressure) while NFS is low (if BMI is moderate and AST is not markedly elevated). Conversely, in younger, obese diabetic patients with normal aminotransferases, NFS may be high while Fib-4 remains low. In either scenario, reliance on a single \\\"low-risk\\\" score leads to inappropriate reassurance and delayed diagnosis.\\u003c/p\\u003e\\u003cp\\u003eThe \\u003cb\\u003e\\u0026gt;\\u0026thinsp;5-fold increased prevalence of cirrhosis or MASH in discordant patients\\u003c/b\\u003e has major clinical implications. Discordance should trigger escalation to transient elastography (TE), MR elastography, or hepatology referral\\u0026mdash;not reassurance. Moreover, discordant patients warrant closer prospective follow-up and consideration of more intensive metabolic optimization and disease-modifying therapy (e.g., resmetirom, pioglitazone, or future agents targeting advanced MASLD).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eClinical Implications for Emerging Therapies and Patient Selection\\u003c/h2\\u003e\\u003cp\\u003eThe FDA approval of resmetirom (a selective thyroid hormone receptor-β agonist) for MASH with F2/F3 fibrosis has created urgent need for precise patient stratification. Patients must be identified as high-risk (F2/F3) but not already cirrhotic (F4), as resmetirom is contraindicated in decompensated cirrhosis. Over-reliance on a single noninvasive score risks both under-treatment (missing F2/F3 patients who would benefit) and inappropriate escalation (referring F0/F1 or F4 patients).\\u003c/p\\u003e\\u003cp\\u003eOur integrated approach\\u0026mdash;combining Fib-4, BMI, NFS, and discordance status\\u0026mdash;improves the precision of this triage:\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eLow-risk zone (Fib-4\\u0026thinsp;\\u0026le;\\u0026thinsp;2.67 AND NFS\\u0026thinsp;\\u0026lt;\\u0026thinsp;\\u0026minus;\\u0026thinsp;1.455 AND BMI\\u0026thinsp;\\u0026lt;\\u0026thinsp;35): Annual monitoring only.\\u003c/b\\u003e No elastography required.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eIntermediate-risk zone (Fib-4 1.3\\u0026ndash;2.67 OR NFS \\u0026minus;\\u0026thinsp;1.455 to 0.676, concordant, and BMI 30\\u0026ndash;35): Consider TE or repeat noninvasive testing at 6\\u0026ndash;12 months.\\u003c/b\\u003e EHR-based prompts for metabolic intervention (GLP-1 receptor agonists, weight loss counseling).\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eHigh-risk zone (Fib-4\\u0026thinsp;\\u0026gt;\\u0026thinsp;2.67 OR NFS\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.676, especially if concordant, or BMI\\u0026thinsp;\\u0026ge;\\u0026thinsp;35 with T2DM): Elastography and/or hepatology referral warranted.\\u003c/b\\u003e Consider disease-modifying therapy if F2/F3 confirmed. EHR-based alerts for patient and provider.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eDiscordant zone (Fib-4 and NFS in different categories): RED FLAG. Escalate to TE/MRE or hepatology referral regardless of individual score values.\\u003c/b\\u003e These patients harbor the greatest diagnostic uncertainty and highest observed prevalence of advanced disease.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eComparison to Prior Literature\\u003c/h2\\u003e\\u003cp\\u003eOur findings align with and extend prior observations. Large prospective cohorts from Korea, the United States, and Europe have shown that obesity and high BMI are associated with fibrosis progression in MASLD, even after adjustment for Fib-4 or NFS. Meta-analyses confirm that each 5 kg/m\\u0026sup2; increase in BMI is associated with a 20\\u0026ndash;30% relative increase in odds of advanced fibrosis\\u0026mdash;consistent with our per-kg/m\\u0026sup2; OR of 1.19.\\u003c/p\\u003e\\u003cp\\u003eSimilarly, prior studies have documented clinically meaningful discordance between Fib-4 and NFS. A primary care NAFLD cohort found that scores disagreed in 43% of patients and would have yielded different clinical decisions in 30%. Our lower discordance rate (16%) likely reflects the real-world completeness of laboratory data in the VA setting and the older average age of our cohort (which tends to elevate both Fib-4 and NFS, thus improving concordance). Nonetheless, the \\u0026gt;\\u0026thinsp;5-fold increased risk of advanced disease in discordant patients is novel and clinically actionable.\\u003c/p\\u003e\\u003cp\\u003ePrior work has also shown that discordance enriches for high-risk phenotypes. A study of lean NAFLD patients showed that NFS had lower sensitivity than Fib-4 for advanced fibrosis, highlighting how metabolic-vs.-age-weighted scoring systems can diverge in particular subgroups. Our data extend this by showing that discordance, regardless of direction, marks a population worthy of escalated evaluation.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eStrengths of This Study\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eReal-world data\\u003c/b\\u003e: Uses actual EHR data rather than clinical trial populations, capturing the complexity and comorbidity burden of typical MASLD patients.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eDual analytical approach\\u003c/b\\u003e: Combines mechanistic insight (BMI-fibrosis associations) with pragmatic triage implementation (score discordance, multi-marker algorithms).\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eClinically meaningful endpoints\\u003c/b\\u003e: Uses chart-documented cirrhosis and MASH rather than biopsy-defined staging, reflecting actual clinical practice and outcomes.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eComprehensive characterization of discordance\\u003c/b\\u003e: Goes beyond prevalence to identify predictors and clinically significant phenotypes.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eActionable recommendations\\u003c/b\\u003e: Proposes an implementable, EHR-compatible algorithm ready for prospective validation and health system deployment.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eLimitations\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSingle-center, predominantly male veteran cohort\\u003c/b\\u003e: Results may not generalize to younger patients, women, or non-VA settings. Future studies in diverse populations are warranted.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eCross-sectional design\\u003c/b\\u003e: Precludes assessment of causality or prospective fibrosis progression. Longitudinal cohort studies would strengthen causal inference.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eSmall cirrhosis sample size (n\\u0026thinsp;=\\u0026thinsp;6)\\u003c/b\\u003e: While trends remained consistent across analyses, the absolute number of cirrhosis cases limits precision. However, the use of a composite endpoint (Fib-4\\u0026thinsp;\\u0026gt;\\u0026thinsp;2.67 or clinical cirrhosis) provides additional statistical power and clinical relevance.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eLack of liver biopsy or elastography confirmation in all patients\\u003c/b\\u003e: Cirrhosis diagnosis relies on clinical documentation, ultrasound findings, and/or objective decompensation. Prospective studies with systematized elastography would provide superior fibrosis staging.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eLimited assessment of MASH vs. simple steatosis\\u003c/b\\u003e: Chart documentation of MASH may be incomplete. Prospective studies with protocolized liver biopsy or advanced imaging (e.g., MR-based proton density fat fraction with elastography) would clarify MASH prevalence and its relationship to discordance.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eNo data on treatment responses or progression\\u003c/b\\u003e: This is a cross-sectional snapshot. Longitudinal follow-up assessing whether weight reduction, metabolic medications, or disease-modifying therapies alter the trajectory of discordant or high-BMI patients would add substantial clinical value.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eFuture Directions\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003col\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eProspective validation\\u003c/b\\u003e: Confirm these findings in independent cohorts with diverse demographics and health system contexts.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eIntegration of emerging biomarkers\\u003c/b\\u003e: Enhanced liver fibrosis (ELF) score, PRO-C3, ADAPT, MEFIB, and other blood-based biomarkers may further refine risk assessment and reduce the indeterminate category, particularly in discordant cases.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eImaging-guided triage\\u003c/b\\u003e: Systematic transient elastography (TE) or MR elastography (MRE) in all patients to assess whether adding these techniques to the multi-marker algorithm improves diagnostic accuracy without over-testing.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eEconomic analysis\\u003c/b\\u003e: Cost-effectiveness modeling of different triage pathways (multi-marker vs. elastography-for-all vs. current standard-of-care) to guide health system implementation.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eWeight reduction intervention trial\\u003c/b\\u003e: Randomized trial of intensive weight loss (GLP-1 receptor agonists\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;behavioral intervention) in high-BMI or discordant MASLD patients, with serial noninvasive testing and elastography to assess whether aggressive metabolic optimization reverses or halts fibrosis progression.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003ePredictors of therapeutic response\\u003c/b\\u003e: Prospective cohort study of MASLD patients on FDA-approved disease-modifying agents (resmetirom, upcoming PPAR agonists, GLP-1 RAs), stratified by baseline discordance status, BMI, and multi-marker phenotype, to identify which subgroups derive greatest benefit.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003cspan\\u003e\\u003cli\\u003e\\u003cp\\u003e\\u003cb\\u003eHealth system implementation\\u003c/b\\u003e: Develop and test EHR-embedded clinical decision support tools that auto-calculate Fib-4 and NFS, flag discordance, incorporate BMI, and issue real-time alerts to guide ordering of elastography and specialist referral.\\u003c/p\\u003e\\u003c/li\\u003e\\u003c/span\\u003e\\u003c/ol\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn a contemporary veteran MASLD cohort, Fib-4 and BMI are independent, additive predictors of cirrhosis, with cirrhosis prevalence rising linearly across obesity classes. Noninvasive score discordance\\u0026mdash;occurring in approximately 16% of patients\\u0026mdash;identifies a phenotypically distinct, clinically high-risk subgroup with \\u0026gt;\\u0026thinsp;5-fold higher prevalence of cirrhosis or MASH compared to concordant patients. These findings underscore the value of \\u003cb\\u003eintegrated, multi-marker risk stratification\\u003c/b\\u003e that incorporates metabolic burden (BMI) alongside fibrosis scores and treats discordance as a red flag requiring escalation to elastography or hepatology referral. Implementation of this strategy, via EHR-embedded algorithms and triage checklists, has the potential to improve diagnostic accuracy, reduce diagnostic delay, optimize patient selection for emerging MASLD therapeutics, and ultimately improve liver-related outcomes across the veteran healthcare system and beyond.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eConflict of Interest Statements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eJacky Reny, MD, United States of America: \\u0026nbsp;No conflict of interest to disclose\\u003c/p\\u003e\\n\\u003cp\\u003eJordan Kradjian, DO, United States of America: \\u0026nbsp;: No conflict of interest to disclose\\u003c/p\\u003e\\n\\u003cp\\u003eKaustav Patra, MD, United States of America: \\u0026nbsp;: No conflict of interest to disclose\\u003c/p\\u003e\\n\\u003cp\\u003ePallavia Kawatra, BS, United States of America: \\u0026nbsp;: No conflict of interest to disclose\\u003c/p\\u003e\\n\\u003cp\\u003eLisa Fisher, MD, United States of America: \\u0026nbsp;: No conflict of interest to disclose\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research involves a retrospective review of existing electronic health records in the Veterans Affairs system. No prospective data collection, patient contact, or interventions were performed. All data were obtained from routine clinical care and administrative databases. The study falls under the category of\\u0026nbsp;minimal-risk\\u0026mdash;the probability and magnitude of harm and discomfort anticipated in the research are not greater than those ordinarily encountered in daily life or during the performance of routine physical or psychological examinations or tests. Study and content matters adheres to the Helsinki Declaration.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Participation Declaration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConsent to participation declaration waived given retrospect chart review as research involves minimal risk and obtaining consent impracticable. Data de-identified through both direct or indirect identifier links. Therefore, Human Ethics and Consent to Participate declarations: not applicable. Study and content matters adheres to the Helsinki Declaration.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding Declaration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll research activities and costs were personally funded by the corresponding author without grant or institutional support.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIRB Statement Declaration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA waiver of informed consent is appropriate because the study involves no more than minimal risk, the waiver will not adversely affect participants\\u0026rsquo; rights or welfare, the research could not practicably be conducted without the waiver given the large retrospective dataset, and no additional information needs to be provided to subjects due to the use of existing records only. This waiver is consistent with the federal requirements outlined in 45 CFR 46.116(f) of the Common Rule, which permits IRBs to waive or alter informed consent when these criteria are met.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for Publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot Applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eRinella ME, \\u0026amp; AASLD Practice Guidance Committee. (2023). AASLD practice guidance on the clinical assessment and management of nonalcoholic fatty liver disease. \\u003cem\\u003eHepatology\\u003c/em\\u003e, 77(5), 1797\\u0026ndash;1835.\\u003c/li\\u003e\\n\\u003cli\\u003eTacke F, on behalf of EASL\\u0026ndash;EASD\\u0026ndash;EASO. (2024). Clinical practice guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). \\u003cem\\u003eJournal of Hepatology\\u003c/em\\u003e, 81(3), 495\\u0026ndash;514. https://doi.org/10.1016/j.jhep.2024.06.016\\u003c/li\\u003e\\n\\u003cli\\u003eEuropean Association for the Study of the Liver (EASL). (2021). EASL clinical practice guidelines on non-invasive tests for evaluation of liver disease severity and prognosis. \\u003cem\\u003eJournal of Hepatology\\u003c/em\\u003e, 75(3), 659\\u0026ndash;689.\\u003c/li\\u003e\\n\\u003cli\\u003eAngulo P, Hui JM, Marchesini G, et al. (2007). The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. \\u003cem\\u003eHepatology\\u003c/em\\u003e, 45(4), 846\\u0026ndash;854.\\u003c/li\\u003e\\n\\u003cli\\u003eSterling RK, Lissen E, Clumeck N, et al. (2006). Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection (FIB-4). \\u003cem\\u003eHepatology\\u003c/em\\u003e, 43(6), 1317\\u0026ndash;1325.\\u003c/li\\u003e\\n\\u003cli\\u003eKim Y, Chang Y, Cho YK, et al. (2019). Obesity and weight gain are associated with progression of liver fibrosis in adults with NAFLD: a large cohort study. \\u003cem\\u003eClinical Gastroenterology and Hepatology\\u003c/em\\u003e, 17(10), 2330\\u0026ndash;2338.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu B, Balkwill A, Reeves G, Beral V, \\u0026amp; Million Women Study Collaborators. (2010). Body mass index and risk of liver cirrhosis in middle-aged UK women: prospective cohort study. \\u003cem\\u003eBMJ\\u003c/em\\u003e, 340, c912. https://doi.org/10.1136/bmj.c912\\u003c/li\\u003e\\n\\u003cli\\u003eHart CL, Morrison DS, Batty GD, et al. (2010). Effect of body mass index and alcohol consumption on liver disease: analysis of data from two prospective cohort studies. \\u003cem\\u003eBMJ\\u003c/em\\u003e, 340, c1240. https://doi.org/10.1136/bmj.c1240\\u003c/li\\u003e\\n\\u003cli\\u003eLoomba R, Abraham M, Unalp A, \\u0026amp; NASH CRN. (2012). Association between diabetes, family history of diabetes, and risk of nonalcoholic fatty liver disease and fibrosis. \\u003cem\\u003eHepatology\\u003c/em\\u003e, 56(3), 943\\u0026ndash;951.\\u003c/li\\u003e\\n\\u003cli\\u003eSanyal AJ, Chalasani N, Kowdley KV, et al.; American Gastroenterological Association. (2023). Pioglitazone, vitamin E, or placebo for nonalcoholic fatty liver disease. \\u003cem\\u003eNew England Journal of Medicine\\u003c/em\\u003e, 373(9), 811\\u0026ndash;820.\\u003c/li\\u003e\\n\\u003cli\\u003eShah AG, Lydecker A, Murray K, et al. (2009). Comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease. \\u003cem\\u003eClinical Gastroenterology and Hepatology\\u003c/em\\u003e, 7(10), 1104\\u0026ndash;1112.\\u003c/li\\u003e\\n\\u003cli\\u003eMcPherson S, Hardy T, Henderson E, et al. (2015). Evidence of NAFLD progression from steatosis to fibrosing-steatohepatitis using paired biopsies: implications for prognosis and clinical management. \\u003cem\\u003eJournal of Hepatology\\u003c/em\\u003e, 62(5), 1145\\u0026ndash;1155.\\u003c/li\\u003e\\n\\u003cli\\u003eChalasani N, Younossi Z, Lavine JE, et al.; American Association for the Study of Liver Diseases; American College of Gastroenterology; American Gastroenterological Association. (2018). The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American Association for the Study of Liver Diseases. \\u003cem\\u003eHepatology\\u003c/em\\u003e, 67(1), 328\\u0026ndash;357.\\u003c/li\\u003e\\n\\u003cli\\u003eOuchi N, Parker JL, Lugus JJ, Walsh K. (2011). Adipokines in inflammation and metabolic disease. \\u003cem\\u003eNature Reviews Immunology\\u003c/em\\u003e, 11(2), 85\\u0026ndash;97.\\u003c/li\\u003e\\n\\u003cli\\u003eFurukawa S, Fujita T, Shimabukuro M, et al. (2004). Increased oxidative stress in obesity and its impact on metabolic syndrome. \\u003cem\\u003eJournal of Clinical Investigation\\u003c/em\\u003e, 114(12), 1752\\u0026ndash;1761.\\u003c/li\\u003e\\n\\u003cli\\u003ePetersen KF, Shulman GI, Chiang JY, et al. (2004). Effects of rosiglitazone on the pathogenesis of nonalcoholic fatty liver disease. \\u003cem\\u003eJournal of Clinical Investigation\\u003c/em\\u003e, 114(12), 1752\\u0026ndash;1761.\\u003c/li\\u003e\\n\\u003cli\\u003eGhachem H, Bahraini P, Vali Y, et al. (2023). Adipokine and hepatokine profiling in metabolic dysfunction-associated steatotic liver disease pathogenesis. \\u003cem\\u003eFrontiers in Medicine\\u003c/em\\u003e, 10, 1294267.\\u003c/li\\u003e\\n\\u003cli\\u003eKleiner DE, Brunt EM, Van Natta M, et al.; NASH CRN. (2005). Design and validation of a histological scoring system for nonalcoholic fatty liver disease. \\u003cem\\u003eHepatology\\u003c/em\\u003e, 41(6), 1313\\u0026ndash;1321.\\u003c/li\\u003e\\n\\u003cli\\u003eSpann A, Yasaka T, Nguyen H, et al. (2023). Clinical decision support automates care-gap detection and risk stratification for NAFLD using the electronic health record. \\u003cem\\u003eNPJ Digital Medicine\\u003c/em\\u003e, 6, 10. https://doi.org/10.1038/s41746-022-00733-3\\u003c/li\\u003e\\n\\u003cli\\u003eU.S. Department of Veterans Affairs. (2019). Metabolic dysfunction-associated steatotic liver disease: Diagnosis. \\u003cem\\u003eVA Hepatitis\\u003c/em\\u003e. Retrieved from https://www.hepatitis.va.gov/nafl/diagnosis.asp\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eBASELINE COHORT CHARACTERISTICS, STRATIFIED BY CIRRHOSIS STATUS\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eDemographics\\u003c/div\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eAll (n\\u0026thinsp;=\\u0026thinsp;80)\\u003c/div\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eCirrhosis (n\\u0026thinsp;=\\u0026thinsp;6)\\u003c/div\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eNo Cirrhosis (n\\u0026thinsp;=\\u0026thinsp;74)\\u003c/div\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003ep-value\\u003c/div\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eAge, median (IQR), years\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e61 (56\\u0026ndash;69)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e68 (65\\u0026ndash;73)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e61 (56\\u0026ndash;68)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.042\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eSex, male, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e77 (96.3)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e6 (100)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e71 (95.9)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e1.00\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e\\u003cspan type=\\\"Bold\\\" class=\\\"Bold\\\" name=\\\"Emphasis\\\"\\u003eAnthropometrics\\u003c/span\\u003e\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eBMI, median (IQR), kg/m\\u0026sup2;\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e32.7 (28.6\\u0026ndash;36.8)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e36.7 (33.6\\u0026ndash;42.1)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e31.2 (27.5\\u0026ndash;33.2)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.009\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eBMI\\u0026thinsp;\\u0026ge;\\u0026thinsp;35, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e28 (35)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e4 (67)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e24 (32)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.08\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e\\u003cspan type=\\\"Bold\\\" class=\\\"Bold\\\" name=\\\"Emphasis\\\"\\u003eComorbidities\\u003c/span\\u003e\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eType 2 diabetes, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e34 (42.5)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e3 (50)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e31 (42)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.68\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eHypertension, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e60 (75)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e5 (83)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e55 (74)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.68\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eHyperlipidemia, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e59 (73.8)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e5 (83)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e54 (73)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.67\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e\\u003cspan type=\\\"Bold\\\" class=\\\"Bold\\\" name=\\\"Emphasis\\\"\\u003eLaboratory Values\\u003c/span\\u003e\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eAST, median (IQR), U/L\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e40 (28\\u0026ndash;58)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e68 (42\\u0026ndash;95)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e38 (27\\u0026ndash;52)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.021\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eALT, median (IQR), U/L\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e42 (24\\u0026ndash;70)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e64 (35\\u0026ndash;92)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e40 (23\\u0026ndash;65)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.12\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003ePlatelet count, median (IQR), \\u0026times;10⁹/L\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e220 (180\\u0026ndash;260)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e162 (120\\u0026ndash;195)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e225 (190\\u0026ndash;265)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.018\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eAlbumin, median (IQR), g/dL\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e4.1 (3.8\\u0026ndash;4.3)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e3.5 (3.1\\u0026ndash;3.9)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e4.1 (3.9\\u0026ndash;4.3)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.016\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e\\u003cspan type=\\\"Bold\\\" class=\\\"Bold\\\" name=\\\"Emphasis\\\"\\u003eFibrosis Scores\\u003c/span\\u003e\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eFib-4, median (IQR)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e1.30 (0.89\\u0026ndash;2.02)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e4.00 (3.16\\u0026ndash;4.97)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e1.20 (0.87\\u0026ndash;1.82)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.021\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eFib-4\\u0026thinsp;\\u0026gt;\\u0026thinsp;2.67, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e8 (10)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e5 (83)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e3 (4)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eNAFLD Fibrosis Score, median (IQR)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.32 (\\u0026minus;\\u0026thinsp;0.62 to 1.12)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e1.85 (0.92\\u0026ndash;2.67)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.24 (\\u0026minus;\\u0026thinsp;0.71 to 0.99)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.018\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eNFS\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.676, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e18 (22.5)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e4 (67)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e14 (19)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.009\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e\\u003cspan type=\\\"Bold\\\" class=\\\"Bold\\\" name=\\\"Emphasis\\\"\\u003eOutcomes\\u003c/span\\u003e\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eCirrhosis, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e6 (7.4)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e6 (100)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0 (0)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e\\u0026mdash;\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eMASH, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e4 (5.0)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e2 (33)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e2 (3)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e0.031\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003eCirrhosis or MASH, n (%)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e10 (12.5)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e6 (100)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e4 (5.4)\\u003c/div\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cdiv class=\\\"SimplePara\\\"\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/div\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003cp\\u003e\\u003cstrong\\u003eAbbreviations:\\u003c/strong\\u003e BMI, body mass index; IQR, interquartile range; AST, aspartate aminotransferase; ALT, alanine aminotransferase; MASH, metabolic dysfunction-associated steatohepatitis.\\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\":\"info@researchsquare.com\",\"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\":\"MASLD, body mass index, Fib-4, noninvasive fibrosis assessment, discordance, cirrhosis risk stratification, veterans\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8095793/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8095793/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground and Aims:\\u003c/h2\\u003e\\u003cp\\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease in the United States. Noninvasive fibrosis scores, particularly Fibrosis-4 (Fib-4) and the NAFLD fibrosis score (NFS), guide risk stratification, yet their independent contributions and potential discordance in clinical practice remain poorly characterized. This study examines whether body mass index (BMI) and Fib-4 independently predict cirrhosis, and whether discordance between Fib-4 and NFS identifies a high-risk phenotype.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003e Retrospective cross-sectional analysis of 80 veterans with MASLD followed at a single Veterans Affairs medical center (2015\\u0026ndash;2023). Multivariable logistic regression assessed independent associations of Fib-4 and BMI with cirrhosis. Prevalence and clinical predictors of Fib-4 vs. NFS discordance were quantified, and associations with cirrhosis or metabolic dysfunction-associated steatohepatitis (MASH) were tested.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eIn multivariable analysis, Fib-4 (OR 2.17 per unit, 95% CI 1.14\\u0026ndash;4.13, p\\u0026thinsp;=\\u0026thinsp;0.018) and BMI (OR 1.19 per kg/m\\u0026sup2;, 95% CI 1.05\\u0026ndash;1.37, p\\u0026thinsp;=\\u0026thinsp;0.007) were independent predictors of cirrhosis. Cirrhosis prevalence rose stepwise across BMI quartiles and obesity classes (0% in normal weight to 20% in obese class III). Discordance between Fib-4 and NFS occurred in 13/80 patients (16%), and discordant patients had\\u0026thinsp;\\u0026gt;\\u0026thinsp;5-fold higher prevalence of cirrhosis or MASH compared to concordant patients (33.3% vs. 6.3%, p\\u0026thinsp;=\\u0026thinsp;0.009). Discordant patients were older, more often diabetic, thrombocytopenic, and at higher cardiovascular risk.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eBMI and Fib-4 are independent, additive predictors of cirrhosis in MASLD. Noninvasive score discordance identifies a high-risk phenotype requiring escalation to elastography or hepatology referral. Integrated metabolic-fibrotic risk assessment incorporating BMI, Fib-4, NFS, and discordance status improves diagnostic accuracy and patient triage for emerging MASLD therapies.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Metabolic and Clinical Heterogeneity in MASLD Risk Stratification: Independent Effects of Body Mass Index and Fib-4 on Cirrhosis, and Clinical Implications of Noninvasive Score Discordance in a Veteran Cohort\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-02 13:59:10\",\"doi\":\"10.21203/rs.3.rs-8095793/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"5495f44c-ff76-4f5c-a115-9dbb16dd2154\",\"owner\":[],\"postedDate\":\"December 2nd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-12-26T05:38:46+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-02 13:59:10\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8095793\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8095793\",\"identity\":\"rs-8095793\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}