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Steatotic liver disease: non-invasive assessment and biomarkers across metabolic dysfunction-associated steatotic liver disease, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption and alcohol-associated liver disease

Monica Tincopa, Rohit Loombaorcid
Clinical and Molecular Hepatology 2026;32(Suppl):e2026.0415.
Published online: July 15, 2026

MASLD Research Center, Division of Gastroenterology and Hepatology, University of California at San Diego, La Jolla, CA, USA

Corresponding author : Rohit Loomba MASLD Research Center, University of California at San Diego, Altman Clinical and Translational Research Institute, 9500 Gilman Drive, La Jolla, CA 92093‐0887, USA Tel: +1-619-543-5415, Fax: +1-619-543-5715, E-mail: roloomba@ucsd.edu

Editor: Vincent Wai-Sun Wong, The Chinese University of Hong Kong, Hong Kong

• Received: April 3, 2026   • Revised: June 23, 2026   • Accepted: July 6, 2026

Copyright © 2026 by The Korean Association for the Study of the Liver

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Steatotic liver disease (SLD) is a leading cause of chronic liver disease worldwide impacting more than 30% of the adult population. With the rise of obesity, metabolic syndrome and alcohol use disorder, the prevalence of the two main subtypes of SLD - metabolic dysfunction-associated steatotic liver disease (MASLD) and alcohol-associated liver disease (ALD) - continues to increase. There has concurrently been an acknowledgement of a third subtype of SLD-metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption (MetALD)- wherein an individual has both components of metabolic dysfunction and regular alcohol intake. The public health significance of the SLD epidemic is substantial given risk for progression to cirrhosis, end-stage liver disease and development of hepatocellular carcinoma. Individuals with stage two fibrosis or above and those with active inflammation with hepatocyte injury are at highest risk for adverse liver-related outcomes and overall mortality. Current screening and risk stratification recommendations highlight the importance of identifying individuals at highest risk of clinical outcomes using non-invasive testing (NIT) as these individuals would benefit from liver-directed pharmacotherapy. Importantly, the performance of NITs can vary substantially based on NIT selected, cut points applied and patient population evaluated. The evidence base for NIT performance is strongest in MASLD with comparatively limited data in ALD and emerging data in MetALD. This review discusses NIT diagnostic performance in SLD and their role in chronic disease management.
  • Keywords: MASH; MASLD; Alcohol associated liver disease; MetALD; Biomarkers
Steatotic liver disease (SLD) encompasses a spectrum of chronic liver disease characterized by accumulation of hepatic steatosis (≥5% on imaging or biopsy) in the absence of other specific etiologies of liver disease. SLD accounts for leading etiologies of liver disease worldwide [1]. Subcategories of SLD are derived based on underlying pathogenic factors including metabolic dysfunction-associated steatotic liver disease (MASLD), alcohol-associated liver disease (ALD), and the overlap category of MetALD [1]. MASLD is defined by the presence of hepatic steatosis with cardiometabolic risk factors in the absence of significant alcohol use (females <20 g/day, males <30 g/day). ALD is defined by hepatic steatosis with alcohol use >50 g/day for females and >60 g/day for males in the absence of metabolic risk factors. The new overlap category, MetALD, applies to individuals with at least one cardiometabolic risk factor and alcohol consumption between 20–50 g/day for females and 30–60 g/day for males [1].
Accurate subcategorization of SLD subtypes can be challenging based on self-report alone. The blood/whole-blood biomarker phosphatidylethanol (PETH) provides a quantitative objective assessment of alcohol use with high sensitivity and specificity and has been shown to enhance SLD subclassification [2]. A PETH cut point of 35 ng/mL approximated the alcohol intake thresholds defining SLD subtypes [3,4]. PETH is not currently widely used in clinical practice; however, and thus alternative biomarkers such as the MetALD Prediction Index can also be used to help more accurately classify SLD (Fig. 1) [5].
MASLD prevalence is estimated to be over 30% in adults worldwide [6]. The global prevalence of ALD is estimated to be 3.5% in the general population, though estimates vary substantially by risk group and geographic region [7]. Disease burden of MetALD is less well characterized as a new entity, but current data suggests prevalence rates between 2–8% in the general population and between 8–20% within SLD populations [8,9]. In SLD, obesity and ongoing alcohol use are major drivers of cirrhosis, hepatocellular carcinoma (HCC) and liver-related mortality [10].
Fortunately, in recent years there has been substantial advancement in therapeutics for metabolic dysfunction-associated steatohepatitis (MASH). Presently, there are two Food and Drug Administration (FDA) approved pharmacologic treatment options for the more aggressive subtype of MASLD, MASH, for individuals with stage 2–3 fibrosis without cirrhosis. There are currently no FDA approved therapies specifically for ALD or MetALD. Given the high prevalence of SLD, the dynamic nature of disease progression, and the availability of liver-directed pharmacotherapy, it is critical to identify patients at high risk of clinical outcomes and individuals who may benefit from treatment. Higher rates of adverse liver-related outcomes and overall mortality are seen with increasing hepatic fibrosis stage and in those with “at-risk MASH”, defined in the presence of a NAFLD activity score (NAS) of ≥4 and fibrosis stage of ≥2 [11]. Active alcohol use also adds to the dynamic nature of disease progression and clinical risk in ALD and MetALD. Due to the invasive nature of liver biopsy and concerns regarding cost, sample variability and risks for complications, non-invasive tests (NITs) are the primary mechanism for risk assessment and longitudinal monitoring. Serum, imaging-based and composite biomarkers are currently embedded in clinical care pathways for SLD [12-14]. NITs have demonstrated accuracy to detect significant and advanced fibrosis, though identification of at-risk MASH is more challenging [15]. There are important caveats of NIT performance based on individual patient characteristics, patient subpopulations and SLD subtypes. Of note, the majority of available data are derived from MASLD cohorts. Data specific to ALD are comparatively limited and data from MetALD cohorts are emerging. This review evaluates the current evidence on diagnostic performance of NITs across the spectrum of SLD (Tables 1, 2), provides an overview of NIT cut points used in SLD in clinical practice (Table 3), and highlights key gaps for future research and clinical implementation (Fig. 2).
Fibrosis staging
The fibrosis 4 (FIB-4) index is an indirect serum fibrosis biomarker that is commonly used in clinical practice to assess risk of advanced fibrosis. FIB-4 is the initial NIT component of many current clinical care guidelines for risk stratification due to its ease of use and low cost [16,17]. FIB-4 calculation requires only age, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and platelet count. Using a rule-out cut point of <1.3 for advanced fibrosis in MASLD, FIB-4 has an area under the receiver operating curve (AUROC) of 0.71–0.79 (95% confidence interval [CI] 0.78–0.94) and an NPV of 88–95% [15,17-20]. The main drawback of FIB-4 stems from the high proportion (approximately 30%) of individuals who have indeterminant results and its modest positive predictive value (PPV) [21]. FIB-4 was also derived in predominantly middle-aged adults who had undergone liver biopsy [22]. As such, alternative cut points have been recommended based on age with a cut point of <2.0 for individuals older than 65 [12]. The accuracy of FIB-4 is also dependent on the prevalence and risk factors for SLD in the population studied which can lead to both over and under estimation of risk [23]. Performance characteristics of FIB-4 do not appear to differ substantially in MetALD compared to MASLD, though the proportion of patients with a result in an indeterminate zone may be somewhat higher for individuals with MetALD [24]. In an evaluation of 7,918 patients, FIB-4 had similar AUROC to detect advanced fibrosis in MetALD (0.85) versus MASLD (0.80) with similar sensitivity and specificity (77.3%) and NPV (99.4%) [25]. Performance of FIB-4 is less robust in ALD given liver enzyme elevation and reduction in platelets in the setting of alcohol use. Elevated AST seen in ALD can lead to overestimation of fibrosis risk, thereby reducing specificity and PPV for advanced fibrosis compared with MASLD populations. Comparing performance of FIB-4 in MASLD to ALD, FIB-4 maintained high sensitivity (80–90%) for excluding advanced fibrosis but inferior specificity (60–70%) [13,26]. Although AUROC for FIB-4 in ALD is numerically comparable to or higher than in MASLD (Table 1), this reflects a different balance of test characteristics - high sensitivity paired with reduced specificity and PPV - rather than equivalent overall clinical performance, underscoring that AUROC alone does not capture the impact of alcohol-related confounders on FIB-4 in this population. A cut point of <3.25 has been suggested to exclude advanced fibrosis or cirrhosis in ALD in the setting of higher amounts of inflammation with active alcohol intake [27].
Direct serum biomarkers of fibrosis that measure markers of extracellular matrix turnover and accumulation that occurs in fibrogenesis and are also used for non-invasive liver disease assessment in SLD. The enhanced liver fibrosis (ELF) panel is a proprietary send out test that is comprised of ECM components including hyaluronic acid, tissue inhibitor of metalloproteinase, and N-terminal procollagen III-peptide [28,29]. In a meta-analysis of 2,665 individuals with MASLD, a cut point of 7.7 yielded an AUROC of 0.83 (95% CI 0.71–0.90) with a sensitivity of 93% (95% CI 0.82–0.98) and specificity of 34% (95% CI 0.13–0.65) to detect advanced fibrosis [30,31]. Using the threshold of >10.51 as recommended by the UK NICE guidelines, the ELF panel yielded a specificity of 93% (95% CI 0.85–0.96) and sensitivity of 51% (95% CI 0.31–0.70) to detect advanced fibrosis in MASLD [30]. ELF is embedded in clinical practice guidelines for risk stratification among individuals with indeterminant FIB-4 scores [32-34]. These algorithms apply a cut point of 9.8 to identify high-risk patients in need of additional evaluation given that this cut point has been associated with advanced fibrosis in MASLD [35-37].
In a population screening study, ELF had fewer false positive tests compared to FIB-4 (11% vs. 35%, respectively) while maintaining a low false negative rate (8%) when using VCTE as the reference standard [38]. High inflammatory activity can impact ELF levels however [39]. ELF has been shown to have similar performance characteristics in MASLD compared to MetALD with an AUROC of 0.9 to detect advanced fibrosis [24]. ELF maintains robust diagnostic accuracy in ALD even in the setting of elevated aminotransferase levels with an AUROC of 0.92 (95% CI 0.89–0.96) to detect advanced fibrosis. Among a cohort of patients with ALD, using a cut point of <10.5, the negative predictive value (NPV) was 94–98% with sensitivity of 79% and specificity of 91% [40].
Therapeutic response and clinical outcomes assessment
There are limited data in support of the use of FIB-4 to assess treatment response in SLD, though improvement in FIB-4 over time has correlated with histologic fibrosis reduction [12]. Despite limitations in application of FIB-4 to assess short-term treatment response, there is utility in longitudinal FIB-4 changes for assessing fibrosis progression and risk of clinical outcomes. In a large multicohort study (N=52,453) of individuals with MASLD, dynamic FIB-4 changes correlated with fibrosis progression and clinical outcomes [41-45]. A systematic review of 12 studies demonstrates that FIB-4 had an AUROC of 0.65–0.81 to predict fibrosis progression and 0.71–0.89 for prediction of hepatic decompensation, HCC or liver transplant in MASLD [41]. In a study of 81,108 adults with MASLD and a median follow-up of 34.8 months, having a FIB-4 of ≥2.67 was independently associated with all-cause mortality (hazard ratio [HR] 2.49), end-stage liver disease (HR 1.86), HCC (HR 3.66) and liver transplantation (HR 7.98) [46]. FIB-4 has similarly shown prognostic utility in ALD. In a systematic review of four papers evaluating FIB-4 in ALD, FIB-4 had an AUROC of 0.64–0.83 to predict clinical outcomes [47]. A study of 463 patients with early, compensated ALD demonstrated a 6% risk for liver-related events (LRE) over 4 years for individuals with FIB-4 <1.3 compared to 10% for those with values 1.30–2.66 and 54% for those with FIB-4 ≥2.67 [48]. Data on the utility of FIB-4 to assess risk of clinical outcomes specifically in MetALD is more limited, though existing data suggests FIB-4 is associated with risk of decompensation-free survival in MetALD [24].
There is promising data regarding the role of ELF in assessing treatment response in SLD. In the phase 3 ESSENCE trial of semaglutide in MASH with moderate-to-advanced fibrosis, the ELF score decreased in parallel with histologic improvements [49]. ELF similarly tracked with histologic treatment response in the MAESTRO-NASH trial of resmetirom in individuals with MASH [24]. Current MASH treatment recommendation guidelines suggest that a reduction in baseline ELF ≥0.5 suggests significant improvement among those on semaglutide [50,51]. Further research is needed to assess the role of ELF to evaluate treatment response in ALD and MetALD. Changes in ELF over time have been evaluated as a tool to monitor disease progression [52]. ELF is approved by the FDA as a prognostic biomarker for patients with MASLD and advanced fibrosis [53]. Analysis of MASLD patients with 4.2 years of follow-up noted that a baseline ELF score of ≥9.8 was associated with an elevated risk of hepatic decompensation, HCC and portal hypertension [54]. ELF also demonstrated strong prognostic performance for predicting clinical outcomes in ALD. In a prospective study of 462 patients with early, compensated ALD followed for a median of 49 months, ELF had an AUROC of 0.85 (95% CI 0.82–0.89) to predict LRE [48]. There is less data on ELF for clinical prognostication in MetALD cohorts, though available data suggests that ELF similarly predicts decompensation-free survival in MetALD [24].
Fibrosis staging
Imaging biomarkers use elastography-based techniques to calculate liver stiffness measurements (LSM) as a surrogate measure of hepatic fibrosis [55]. Shear wave elastography (SWE) is widely available given that it can be integrated into a conventional ultrasound (US). SWE applies acoustic radiation force impulse (ARFI) to generate shear waves and measures wave propagation velocity to estimate liver stiffness. The AUROC of 2D SWE among 156 patients with biopsy-proven MASLD was 0.85 to detect significant fibrosis and 0.91 to detect cirrhosis [56]. There remain concerns regarding standardization of the liver region of interest and measurement protocols for 2D SWE. Accuracy is impacted by operator experience as well as obesity and active inflammation. Compared to vibration controlled transient elastography (VCTE), 2D SWE has a larger indeterminate zone to detect advanced fibrosis (24.6% vs. 15.3%, P<0.001) [57]. Compared to MASLD, performance of 2D SWE in ALD has similar diagnostic accuracy, though cut points are higher in ALD (16.4 kPa vs. 11.5 kPa for cirrhosis in ALD vs. MASLD, respectively) [58]. Active alcohol use and significant aminotransferase elevation can confound 2D SWE measurements [14]. Data on the performance of 2D SWE in MetALD for fibrosis staging are lacking.
VCTE is a widely used method to assess fibrosis stage in SLD. VCTE generates LSM via application of an external mechanical vibration to detect the velocity of shear wave propagation through the liver [59,60]. However, several key factors can confound VCTE results. These include active alcohol use, hepatic congestion, active hepatitis with an ALT >100 IU/L, recent food consumption, and those with class II obesity or higher [61]. Like other US based techniques, VCTE requires operator expertise to ensure accurate measurements. VCTE has been extensively validated in MASLD. In an individual patient data meta-analysis of 5,735 MASLD patients, VCTE demonstrated a superior AUROC (0.85) to detect advanced fibrosis compared to FIB-4 (AUROC 0.76) [20]. When a prevalence of advanced fibrosis was set at <30%, a VCTE cut off of 7.4 kPa or less yielded an NPV of 95% to detect advanced fibrosis among individuals with MASLD [60]. Comparing VCTE in MASLD and ALD, diagnostic accuracy remains high, though inflammation in the setting of active alcohol use is a critical confounder, thereby necessitating comparatively higher cut points. While there is a lack of consensus regarding differential cut points to use to rule out advanced fibrosis specifically in an LSM of ≥12–15 kPa has been suggested compared to ≥12 in MASLD [26,27,62]. A meta-analysis of 834 patients with ALD found sensitivity and specificity of 92% and 70% to detect advanced fibrosis and 95% and 71% for cirrhosis, respectively [63]. Validation of VCTE in MetALD is needed. VCTE is used as a secondary risk stratification NIT in clinical practice guidelines [12,32].
Magnetic resonance (MR)-based imaging techniques provide more comprehensive assessments of hepatic fibrosis. MR elastography (MRE) uses an external vibration source to generate low frequency mechanical waves through the liver with computation of quantitative maps of liver stiffness. MR methods are not prone to many of the confounding factors that can reduce reliability of US-based fibrosis assessments [18,64]. One potential exception is in cases of severe iron overload where MRE assessments may be susceptible to artifact [65]. MRE has consistently been shown to have superior accuracy to VCTE for identifying significant and advanced fibrosis [66]. In an analysis of 142 individuals with biopsy-proven MASLD, MRE yielded an AUROC of 0.91 compared to 0.82 for VCTE to detect ≥F2 [18]. In a cohort of 177 patients with biopsy-proven MASLD, using a threshold of >3.63 kPa on MRE yielded a PPV of 68%, NPV of 97%, sensitivity of 0.86 and specificity of 0.91 to detect advanced fibrosis [67]. A 2-step risk stratification algorithm wherein individuals initially complete a FIB-4 calculation and those with a FIB-4 of ≥ 1.3 have additional testing with MRE yielded an NPV of 88.6% for ruling out advanced fibrosis in MASLD [68]. MRE performance retains excellent diagnostic accuracy in ALD with a sensitivity of 89–100% and specificity of 79–95% with an AUROC of 0.88 to detect cirrhosis [27,69]. Data on performance of MRE in MetALD specifically is lacking but hypothesized to retain excellent diagnostic accuracy in this SLD subpopulation.
Additional MR-based biomarkers have demonstrated value for fibrosis staging for fibrosis staging. These include 3D-MRE protocols that provide shear-wave fields of the entire liver in 3D and application of different shear-wave frequencies. A 3D MRE protocol at 60 Hz (standard shearwave frequency) and 40 Hz was compared to 2D MRE in 100 individuals with biopsy-proven MASLD. In this analysis, 3D-MRE at 40 Hz had superior accuracy to detect advanced fibrosis [70]. At a cut point of 2.43 kPa, 3D MRE had a PPV of 72%, NPV of 100%, sensitivity of 1.0 and specificity of 0.94. A different MR based imaging biomarker, MRI cT1, has shown promise in identifying at-risk MASH [71]. An analysis of 264 patients demonstrated that cT1 was correlated with both NAS features and fibrosis with a difference of 88 ms in cT1 correlated with a two-point difference in NAS [72]. Among 543 clinical trial participants, cT1 identified at-risk MASH with an AUROC of 0.78 [73]. Data is lacking for validation in MetALD and ALD for cT1 however.
Therapeutic response and clinical outcomes assessment
For therapeutic treatment response, 2D SWE has not been validated in SLD. There is substantial data assessing 2D SWE for risk of clinical outcomes in SLD, however. 2D SWE has been associated with risk of overall mortality among individuals with advanced chronic liver disease [74]. Among a prospective cohort of 279 adults with MASLD, 2D SWE LSM was independently associated with life-threatening events (HR 1.20, 95% CI 1.09–1.32) [75]. Prognostic significance of 2D-SWE is retained in ALD cohorts, where the AUROC to predict LRE in patients followed for a median of 49 months was 0.87 [48]. Presently there is no direct data validating 2D-SWE for risk of clinical outcomes specifically in MetALD.
There is a substantial body of literature evaluating VCTE as a mechanism to assess treatment response in MASLD. American Association for the Study of Liver Diseases (AASLD) Practice Guidance for semaglutide and resmetirom recommends using a ≥30% and ≥25% reduction in LSM from VCTE as a marker of fibrosis improvement, respectively [50,51]. Data on VCTE-LSM to assess treatment response in ALD and MetALD are emerging. In evaluation of individuals with ALD with subsequent alcohol abstinence, VCTE-LSM decreased significantly in 80% of abstainers (–2.6±5.5 kPa) [76]. Importantly, VCTE-LSM has also been consistently shown to predict risk of disease progression, LRE and overall mortality in SLD [77-80]. Analysis of clinical trials data among individuals with MASH and bridging fibrosis found that a baseline LSM of ≥16.6 kPa or a change in VCTE-LSM ≥5 kPa with at least a 20% increase in kPa was associated with risk of progression to cirrhosis [81]. In a study of 533 patients with MASLD, change in VCTE-LSM was associated with hepatic decompensation (HR 1.56), HCC (HR 1.72), liver-related mortality (HR 1.96) and overall mortality (HR 1.73) [82]. VCTE-derived LSM also has robust performance for predicting clinical outcomes in ALD. In an observational cohort study of 536 patients with 3,008 patient years of follow-up, VCTE-LSM had an AUROC of 0.85–0.98 for predicting hepatic decompensation and 0.70–0.74 for predicting overall mortality for adults at-risk of ALD [82]. For longitudinal monitoring, an increase in VCTE-LSM of ≥20% was associated with an HR of 4.39 for hepatic decompensation. 82 There is emerging data in support of VCTE-LSM for clinical prognosis in MetALD. VCTE-LSM predicted decompensation-free survival in MetALD, though this data is from a single center study of 102 individuals with MetALD [24].
Changes in MRE-LSM over time are associated with histologic response, though there is a lack of consensus regarding thresholds to apply. Current AASLD practice guidelines recommend a ≥20% reduction in LSM as a marker of beneficial treatment response with semaglutide or resmetirom [50,51]. A recent expert Delphi consensus on surrogate endpoints for treatment assessment in MASH stated that a ≥30% reduction in LSM on MRE was a meaningful indicator of treatment response [83]. In the MAESTRO-NASH trial with resmetirom, a ≥20% reduction in LSM on MRE identified histologic response similar to liver biopsy [84]. Overall, a change of ≥19% in MRE-LSM has been associated with fibrosis progression or regression [85-87]. Treatment response data of MRE-LSM in ALD is lacking with no clinical trial data specifically evaluating MRE to monitor ALD treatment response. Similarly, there is a need for data monitoring changes in MRE-LSM in MetALD for treatment response. LSM on MRE has been closely associated with risk of LREs in SLD including risk of hepatic decompensation, HCC and overall mortality [88]. In an individual participant data meta-analysis of 2018 patients with MASLD, compared to individuals with an MRE of <5 kPa, those with an MRE 5–8 and ≥8 had an HR of 11 and 15.9 for hepatic decompensation [88]. The role of MRE-LSM in assessing risk of clinical outcomes in ALD is less robust. Among a cohort of 182 patients with ALD, MRE-LSM was independently predictive of development of cirrhosis (HR 2.0–2.2 per 1 kPa increase) and hepatic decompensation (HR 1.2–1.3 per 1 kPa increase) [89]. The role of MRE-LSM in assessing risk of clinical outcomes in MetALD is under investigation.
MRI cT1 has been evaluated as a marker of histologic treatment response in MASLD, though data in ALD and MetALD is not available. In a pooled analysis of three interventional trials in MASLD, a ≥80 ms reduction in cT1 was predictive of resolution of steatohepatitis without worsening of fibrosis (sensitivity 0.68, specificity 0.70) [90]. MRI cT1 data has also shown prognostic significance. Among a subgroup of individuals with SLD, cT1 had an AUROC of 0.71 to predict disease progression [91]. A cT1 of >825 ms was predictive of risk of clinical outcomes (ascites, variceal bleeding, HCC, need for liver transplant and overall mortality) with an HR of 9.9 (95% CI 1.29–76.4) among a cohort of 197 patients with chronic liver disease with 693 patient years of follow-up [92].
Fibrosis staging
Multiple composite NITs have been developed with an aim of enhancing diagnostic performance compared to individual NITs alone [93-95]. These composite scores have been designed and validated in MASLD, though data in ALD and MetALD is limited. The FAST score uses LSM and controlled attenuation parameter from VCTE with AST. Among 17,792 patients with MASLD, FAST had an AUROC of 0.75 to detect advanced fibrosis [96]. The MAST score uses LSM from MRE and MRI-proton density fat fraction with AST. Using a high cut point of 0.242 and a low cut point of 0.165, the MAST score had an NPV of 96–98% with PPV 29–50% [97]. The MEFIB index uses LSM on MRE with FIB-4. Evaluation of 563 individuals with biopsy-proven MASLD found that MEFIB had superior accuracy compared to MAST and FAST to detect at-risk MASH using one set of rule-in parameters (≥3.3 kPa on MRE and ≥1.6 FIB-4 for MEFIB, >0.242 for MAST, and ≥0.67 for FAST) and one set of rule-out parameters (<3.3 kPa on MRE and FIB-4 <1.6 for MEFIB, <0.165 for MAST, and ≤0.35 for FAST). MEFIB yielded an AUROC of 0.76 compared with 0.72 for MAST and 0.69 for FAST [98]. The Agile-3 and Agile-4 scores use LSM from VCTE with AST/ALT ratio, platelet count, diabetes status and biological sex to assess risk of advanced fibrosis and cirrhosis, with Agile-3 also including age [99-102]. Agile scores have an AUROC of 0.85–0.92 to detect advanced fibrosis and 0.96–0.97 to detect cirrhosis in MASLD. Agile 3 and 4 have demonstrated accuracy in ALD populations, though validation in ALD is much more limited compared to MASLD [103]. Validation of Agile scores in MetALD is lacking currently. The performance of these composite NITs compared to their individual components to detect advanced fibrosis, and their respective strengths and limitations are outlined in Table 1.
Therapeutic response and clinical outcomes assessment
Composite NITs have been evaluated for their utility to assess treatment response in SLD. Data from the phase 2b semaglutide trial noted that FAST score reduction correlated with MASH resolution, though not with fibrosis improvement [104]. There are no published data evaluating FAST to assess treatment response in ALD or MetALD. Data supporting the use of the MAST, MEFIB and Agile scores for assessing treatment response in SLD is currently absent. The MASH resolution index, a composite score developed and validated to detect histologic resolution of MASH without worsening of fibrosis, achieved an AUROC of 0.83 in its validation cohort [105]. For risk assessment for clinical outcomes, FAST has not demonstrated strong performance. The MAST score however did demonstrate good prognostic performance for predicting clinical outcomes in MASLD. Among 346 MASLD patients, for each 1 logit unit increase in MAST, the HR was 2.01 (95% CI 1.59–2.54) for adverse LRE [106]. MAST has not been evaluated for risk of clinical outcomes in ALD or MetALD. MEFIB has superior performance compared to MAST for risk assessment for clinical outcomes in MASLD. A multicenter individual participant data meta-analysis of 454 patients with a median of 4.2 years of follow-up demonstrated that MEFIB had an AUROC of 0.89 compared to 0.81 for MAST to predict hepatic decompensation [107]. MEFIB has not been validated for clinical outcomes risk prediction in ALD or MetALD as of yet. Both Agile-3 and Agile-4 scores are associated with LRE and overall mortality in MASLD [78,102]. In a study of 16,603 MASLD patients with serial Agile scores followed over a median of 51.7 months, Agile 3 and Agile 4 had an AUROC of 0.87–0.91 for predicting LRE at 3 and 5 years [78]. Clinical outcomes assessment in ALD or MetALD with Agile scores have not been completed. Lastly, the ANTICIPATE and ANTICIPATE-NASH models are composite scores used to predict clinically significant portal hypertension and LRE in patients with compensated advanced chronic liver disease. These models combine LSM from VCTE with platelet count (ANTICIPATE-NASH) with BMI added in the ANTICIPATE model [108-110]. In a multicenter cohort of patients with MASLD and F3-4, the ANTICIPATE-NASH score had an AUROC to predict LRE of 0.93 in cohort 1 and 0.84 in the validation cohort [108]. When evaluated in a more heterogeneous cohort of patients including those with ALD and MetALD, the ANTICIPATE±NASH score yielded an AUROC of 0.77–0.82 when combined with albumin [110].
Practical framework for treatment response monitoring
With the availability of FDA-approved pharmacotherapy in MASH, there is an emerging evidence-based framework for monitoring treatment response. Data on treatment response thresholds for NITs are overviewed in the preceding sections throughout this review and highlighted in Table 2. In accordance with society guidelines, NITs (ALT, ELF, and VCTE-LSM) are obtained at baseline prior to starting therapy. Patients are monitored for safety and treatment-related adverse events with treatment response assessment occurring at approximately 1 year after treatment initiation. A >30% reduction in VCTE-LSM or a >0.5-point reduction in ELF are suggested benchmarks to define treatment response, though specific thresholds and timelines for response assessment vary across guidelines and by specific treatment agent (semaglutide vs. resmetirom) [50,51,83,111,112].
Given the underlying pathophysiologic contributors to SLD, there is ongoing work evaluating proteomic, lipidomic, metabolomic and genomic biomarkers for diagnosis and prognosis. Proteomic profiling identified serum protein signatures that correspond to histologic features of MASLD. In a study of 636 patients with biopsy-proven MASLD, a dichotomized protein-phenotype model identified fibrosis (AUROC 0.92), ballooning (AUROC 0.87), inflammation (0.83), and at-risk MASH (0.93) [113]. These models were also sensitive to treatment response. Other proteomic analyses have similarly demonstrated good diagnostic accuracy for fibrosis staging in MASLD and detection of MASH [114,115]. Proteomic biomarkers have also distinguished disease severity in ALD [116]. B2M, IGFALS and IGFBP3 were identified as having excellent diagnostic performance with AUROCs >0.9 to distinguish patients with severe ALD from healthy controls [117]. Data from the UK Biobank used proteomics to distinguish SLD subtypes and identify individuals at higher risk of cirrhosis and overall mortality [118]. Lipidomic profiling has shown capability in identifying individuals with SLD with more advanced disease. Among 151 individuals with paired biopsies, lipidomic profiles achieved an AUROC of 0.91 for identifying at-risk MASH and 0.95 for advanced fibrosis [119]. Lipidomic signatures have also demonstrated utility in distinguishing MASLD from MetALD and ALD [120]. Expectedly, metabolomic signatures have correlated with disease severity, prognosis and treatment response in SLD. In a study of adults with biopsy-proven MASLD and two validation cohorts, a combination of 10 serum metabolites achieved an AUROC of 0.94 to detect advanced fibrosis [121]. Metabolomic signatures may help distinguish MetALD from MASLD, though proteomic signatures performed more robustly [118]. Serum metabolite profiles also distinguished individuals with progressive ALD and highest risk of adverse clinical outcomes [122,123]. Lastly, there is interest in integrating genomic markers as part of diagnostic and clinical risk prediction in SLD. Polygenic risk scores improve prediction of severe liver disease when combined with clinical fibrosis score [124-126].
Despite promising diagnostic and prognostic performance, these emerging biomarkers face substantial barriers to clinical implementation. Most lack external validation and were derived from homogenous, small cohorts. Cost and availability are also key barriers as these platforms require specialized laboratory infrastructure and are not available outside of research settings. Reimbursement pathways are not established for clinical care either. Moreover, it is necessary to demonstrate the clinical utility of these emerging biomarkers beyond existing, lower cost NITs.
Despite substantial progress in NIT for SLD for diagnosis, treatment response and clinical prognostication, severe critical gaps limit implementation in real world clinical practice and precision medicine approaches. Cost, accessibility and global availability differ substantially across NITs. ELF is a proprietary send-out laboratory test with variable reimbursement and turnaround time and is not available in many regions. VCTE requires a dedicated device and trained operator but is comparatively portable and lower cost than MRI-based imaging. Though VCTE is becoming increasingly more available in primary care and community settings in higher-resourced countries, access remains limited in many low and middle-income settings. MRE and MRI cT1 require MRI infrastructure and dedicated post-processing software, carry the highest per-test cost, and are largely confined to academic or specialty centers, limiting their feasibility for population-level screening or serial monitoring even where MRI is otherwise available. NIT for fibrosis staging at set cut points has variable diagnostic performance across subpopulations given differences in disease prevalence and risk factors [23]. The substantial proportion of patients with initial risk stratification test with an indeterminate zone result is also a challenge, as these individuals require additional testing. Sequential testing improves diagnostic accuracy but increases cost and complexity. Clinical care algorithms that employ sequential testing strategies remain undefined for ALD and MetALD. The lack of validated etiology-specific cut-points for ALD and MetALD is also problematic for delivering disease specific care. Most NITs were developed and validated in MASLD with limited data on their performance in other SLD subgroups where confounders such as acute alcohol intake, inflammation and differential fibrosis patterns may impact test characteristics. Treatment response monitoring represents another key unmet need in SLD. Most NITs were developed for cross-sectional diagnosis rather than longitudinal assessment. While VCTE and MRE show promise for detecting response to treatment, the minimal clinically important difference and optimal monitoring intervals remain undefined. Artificial intelligence and other machine learning approaches may improve diagnostic performance and facilitate more personalized risk prediction. Integration of mechanistic biomarkers like omics may outperform existing biomarkers, though clinical implementation remains challenging. Mechanisms to provide point-of-care and decentralized testing that expands availability of NIT beyond hepatology clinics to primary care are critical to allow for real world uptake of screening recommendations. Defining optimal NITs, associated cut points and intervals for longitudinal outcome prediction and disease progression assessments are also necessary for optimization of clinical care.

Authors’ contribution

Study concept and design: MT, RL. Literature search and data synthesis: MT, RL. Drafting of the manuscript: MT. Critical revision of the manuscript for important intellectual content: MT, RL. Study supervision: RL. Both authors read and approved the final manuscript.

Acknowledgements

RL receives funding support from NCATS (5UL1TR001442), NIDDK (U01DK061734, U01DK130190, R01DK121378, P30DK120515 and T32DK142622), and John C Martin Foundation (RP124).

Conflicts of Interest

Professor Loomba serves as a consultant to Altimmune, Arrowhead Pharmaceuticals, AstraZeneca, Cascade Pharmaceuticals, Eccogene, Eli Lilly, Gilead, GSK, Hepta- Bio, Inipharma, Intercept, Inventiva, Ionis, Madrigal, Metavi, Neurobo, Novo Nordisk, Sagimet, Sourcebio, 89 bio, Roche, Takeda, Terns Pharmaceuticals and Viking Therapeutics. He has stock options in Sagimet biosciences. In addition, his institution received research grants from Arrowhead Pharmaceuticals, AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Eccogene, Eli Lilly, Gilead, GSK, Hanmi, Intercept, Inventiva, Ionis, Janssen, Madrigal Pharmaceuticals, Merck, Novo Nordisk, Roche, Sonic Incytes, TensorBio and Terns Pharmaceuticals. He is also the co-founder of LipoNexus Inc.

Figure 1.
Approach to subclassification among individuals at-risk for SLD. ALD, alcohol-associated liver disease; AUDIT, alcohol use disorders identification test; BMI, body mass index; BP, blood pressure; CAP, controlled attenuation parameter; HDL, high density lipoprotein; MAPI, MetALD prediction index; MASH, metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; MRI, magnetic resonance imaging; PDFF, proton density fat fraction; PETH, phosphatidylethanol; SLD, steatotic liver disease; T2DM, type II diabetes mellitus; TG, triglycerides; US, ultrasound; VCTE, vibration controlled transient elastography; WC, waist circumferences.
cmh-2026-0415f1.jpg
Figure 2.
Gaps, challenges and future directions for application of NIT in SLD. AI, artificial intelligence; ALD, alcohol-associated liver disease; ALT, alanine aminotransferase; ELF, enhanced liver fibrosis; FIB-4, fibrosis 4; MASH, metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; MRE, magnetic resonance elastography; NIT, non-invasive test; PDFF, proton density fat fraction; PETH, phosphatidylethanol; SLD, steatotic liver disease; US, ultrasound; VCTE, vibration controlled transient elastography.
cmh-2026-0415f2.jpg
Table 1.
NIT Performance for fibrosis staging across SLD subtypes
Table 1.
NIT Components AUROC for advanced fibrosis
 Strengths and limitations
MASLD MetALD ALD
FIB-4 Age, AST, ALT, platelets 0.71–0.79 [15,20] 0.80–0.85 [25] 0.85 [13,26,27,40] Low cost; widely available
Large indeterminate zones (30%)
Age specific cut points
Higher cut point needed in ALD due to alcohol impact on AST/ALT/platelets
ELF HA, PIIINP, TIMP-1 0.80–0.83 [15,30,31] 0.90 [24] 0.85 [40] Less impact from alcohol use
Proprietary send out test
Active inflammation can elevate scores
2D-SWE Shear wave velocity measurement on standard US 0.72–0.92 [56,57] Not validated 0.92* [55,58] More widely available
Less validated than VCTE
Operator dependent
Impacted by obesity and inflammation
Limited data in ALD
VCTE LSM via mechanical vibration 0.83–0.88 [15,93] 0.85* 0.90 [63] Extensive validation in MASLD
Limited data in MetALD and ALD
Multiple confounders including BMI, inflammation, alcohol use, cardiac dysfunction
MRE LSM via mechanical vibration 0.92 [66] Not validated 0.88 [27,69] Affected by fewer confounders than VCTE
Less widely available
High cost
MRI cT1 Iron-corrected T1 relaxation time (ms) 0.71*, [71] Not validated Not validated Requires specialized MRI
Limited availability
Needs validation in MetALD and ALD
FAST VCTE-LSM, CAP, AST 0.75 [96] Not validated Not validated Designed to detect at-risk MASH
Needs validation in MetALD and ALD
MAST MRE-LSM, MRI-PDFF, AST 0.77*, [98] Not validated Not validated Requires MRE
Needs validation in MetALD and ALD
MEFIB MRE-LSM, FIB-4 0.90*, [98] Not validated Not validated Requires MRE
Needs validation in MetALD and ALD
Agile 3 VCTE-LSM, sex, AST, ALT, platelet, T2DM status, age 0.86–0.87 [99,102] Not validated Not validated Needs validation in MetALD and ALD
Agile 4 VCTE-LSM, sex, AST, ALT, platelet, T2DM status 0.90†, [101] Not validated Not validated Needs validation in MetALD and ALD

Unless otherwise specified, AUROC and diagnostic accuracy values in this table are derived from cohorts using liver biopsy as the reference standard; one population screening study cited in the text used VCTE as the reference standard for ELF performance and is noted separately where discussed.

ALD, alcohol-associated liver disease; ALT, alanine aminotransferase; AST, aspartate aminotransferase; AUROC, area under the receiver operating curve; CAP, controlled attenuation parameter; ELF, enhanced liver fibrosis; FAST, Fibroscan AST; FIB-4, fibrosis 4; kPa, kilopascals; HA, hyaluronic acid; LSM, liver stiffness measurements; MASH, metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease; MAST, MRI AST; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; MRE, magnetic resonance elastography; NIT, non-invasive test; PDFF, proton density fat fraction; SLD, steatotic liver disease; SWE, shear wave elastography; T2DM, type II diabetes mellitus; TIMP-1, tissue inhibitor of metalloproteinase; VCTE, vibration controlled transient elastography.

*Significant fibrosis.

†Cirrhosis.

Table 2.
NIT performance for treatment response and risk of clinical outcomes across SLD subtypes
Table 2.
NIT Treatment response
 Clinical outcomes
MASLD MetALD ALD MASLD MetALD ALD
FIB-4 Limited data Not validated Improves with abstinence fibrosis progression and risk of clinical outcomes [41-46] Limited data Stratifies decompensation risk [47,48]
Associated with decompensation-free survival [24]
ELF ↓ ≥0.5 associated with histologic response [50,51] Not validated Not validated ELF≥11.3 increased risk hepatic decompensation [54] Limited data Predicts decompensation [48]
FDA approved prognostic biomarker Predicts decompensation-free survival [24]
2D-SWE Not validated Not validated Not validated Limited data Not validated Limited data
Associated with risk of life-threatening events [75] Predicts decompensation [48]
VCTE ↓ ≥25–30% associated with fibrosis improvement [50,51] Not validated ↓ LSM with alcohol abstinence [76] Associated with risk of LRE and overall mortality [77-82] Limited data Predicts decompensation and overall survival [82]
Predicts decompensation-free survival [24]
MRE ↓ ≥20% associated with fibrosis improvement [50,51] Not validated Not validated Associated with risk of LRE and overall mortality [88] Not validated Limited data
Predicts decompensation
MRI cT1 ↓ ≥80 ms associated with MASH resolution [90] Not validated Not validated >825 ms predictive of decompensation [92] Not validated Not validated
FAST ↓ associated with MASH resolution [104] Not validated Not validated Limited data Not validated Not validated
Lacking robust performance
MAST Not validated Not validated Not validated Associated with LRE. [106] Not validated Not validated
MEFIB Not validated Not validated Not validated Predicts decompensation [107] Not validated Not validated
Agile 3 Not validated Not validated Not validated Predicts decompensation and overall mortality. [78,102] Not validated Not validated
Agile 4 Not validated Not validated Not validated Predicts decompensation and overall mortality. [78,102] Not validated Not validated
ANTICIPATE±NASH Not validated Not validated Not validated Predicts LRE [108] Predicts LRE [108] Predicts LRE [108]
MASH resolution index AUROC 0.83 to detect MASH resolution without worsening of fibrosis [105] Not validated Not validated Not validated Not validated Not validated

ALD, alcohol-associated liver disease; ALT, alanine aminotransferase; AST, aspartate aminotransferase; AUROC, area under the receiver operating curve; BMI, body mass index; CAP, controlled attenuation parameter; ELF, enhanced liver fibrosis; FAST, Fibroscan AST; FDA, Food and Drug Administration; FIB-4, fibrosis 4; kPa, kilopascals; LRE, liver-related events; LSM, liver stiffness measurements; MASH, metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease; MAST MRI AST; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; MRE, magnetic resonance elastography; NASH, non-alcoholic steatohepatitis; NIT, non-invasive test; SLD, steatotic liver disease; SWE, Shear wave elastography; T2DM, type II diabetes mellitus; VCTE, vibration controlled transient elastography.

Table 3.
Rule-out, indeterminate, and rule-in thresholds for first and second line NITs in MASLD
Table 3.
NIT Rule out cut point Indeterminate Rule in cut point Caveats
FIB-4 <1.3 1.3–2.67 >2.67 Age-adjusted rule-out <2.0 for age >65
Higher cut point likely needed in setting of active alcohol use
ELF <7.7 7.7–9.8 >9.8 UK NICE guidance uses higher-specificity threshold of >10.51
VCTE (kPa) <8 8–12 >12 Higher cut point likely needed in setting of active alcohol use

Thresholds shown are for advanced fibrosis in MASLD as reported in the body of this review; validated, etiology-specific rule-out/rule-in cut points for ALD and MetALD are not yet established (see Gaps, Challenges and Future Directions).

ALD, alcohol-associated liver disease; ELF, enhanced liver fibrosis; FIB-4, fibrosis 4; kPa, kilopascals; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; NIT, non-invasive test; VCTE, vibration controlled transient elastography.

ALD

alcohol-associated liver disease

ALT

alanine aminotransferase

AST

aspartate aminotransferase

AUROC

area under the receiver operating characteristic curve

CAP

controlled attenuation parameter

CI

confidence interval

CSPH

clinically significant portal hypertension

ELF

enhanced liver fibrosis

FAST

Fibroscan AST

FDA

Food and Drug Administration

FIB-4

fibrosis 4

HCC

hepatocellular carcinoma

HR

hazard ratio

kPa

kilopascals

LRE

liver-related events

LSM

liver stiffness measurements

MAPI

MetALD Prediction Index

MASH

metabolic dysfunction-associated steatohepatitis

MASLD

metabolic dysfunction-associated steatotic liver disease

MAST

MRI AST

MetALD

metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption

MRE

magnetic resonance elastography

NIT

non-invasive test

NPV

negative predictive value

PDFF

proton density fat fraction

PETH

Phosphatidylethanol

PPV

positive predictive value

SLD

steatotic liver disease

SWE

Shear wave elastography

VCTE

vibration controlled transient elastography
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Steatotic liver disease: non-invasive assessment and biomarkers across metabolic dysfunction-associated steatotic liver disease, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption and alcohol-associated liver disease
Clin Mol Hepatol. 2026;32(Suppl):e2026.0415  Published online July 15, 2026
DOI: https://doi.org/10.3350/cmh.2026.0415
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Steatotic liver disease: non-invasive assessment and biomarkers across metabolic dysfunction-associated steatotic liver disease, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption and alcohol-associated liver disease
Image Image
Figure 1. Approach to subclassification among individuals at-risk for SLD. ALD, alcohol-associated liver disease; AUDIT, alcohol use disorders identification test; BMI, body mass index; BP, blood pressure; CAP, controlled attenuation parameter; HDL, high density lipoprotein; MAPI, MetALD prediction index; MASH, metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; MRI, magnetic resonance imaging; PDFF, proton density fat fraction; PETH, phosphatidylethanol; SLD, steatotic liver disease; T2DM, type II diabetes mellitus; TG, triglycerides; US, ultrasound; VCTE, vibration controlled transient elastography; WC, waist circumferences.
Figure 2. Gaps, challenges and future directions for application of NIT in SLD. AI, artificial intelligence; ALD, alcohol-associated liver disease; ALT, alanine aminotransferase; ELF, enhanced liver fibrosis; FIB-4, fibrosis 4; MASH, metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; MRE, magnetic resonance elastography; NIT, non-invasive test; PDFF, proton density fat fraction; PETH, phosphatidylethanol; SLD, steatotic liver disease; US, ultrasound; VCTE, vibration controlled transient elastography.

Figure 1.

Figure 2.

Steatotic liver disease: non-invasive assessment and biomarkers across metabolic dysfunction-associated steatotic liver disease, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption and alcohol-associated liver disease
NIT Components AUROC for advanced fibrosis
 Strengths and limitations
MASLD MetALD ALD
FIB-4 Age, AST, ALT, platelets 0.71–0.79 [15,20] 0.80–0.85 [25] 0.85 [13,26,27,40] Low cost; widely available
Large indeterminate zones (30%)
Age specific cut points
Higher cut point needed in ALD due to alcohol impact on AST/ALT/platelets
ELF HA, PIIINP, TIMP-1 0.80–0.83 [15,30,31] 0.90 [24] 0.85 [40] Less impact from alcohol use
Proprietary send out test
Active inflammation can elevate scores
2D-SWE Shear wave velocity measurement on standard US 0.72–0.92 [56,57] Not validated 0.92* [55,58] More widely available
Less validated than VCTE
Operator dependent
Impacted by obesity and inflammation
Limited data in ALD
VCTE LSM via mechanical vibration 0.83–0.88 [15,93] 0.85* 0.90 [63] Extensive validation in MASLD
Limited data in MetALD and ALD
Multiple confounders including BMI, inflammation, alcohol use, cardiac dysfunction
MRE LSM via mechanical vibration 0.92 [66] Not validated 0.88 [27,69] Affected by fewer confounders than VCTE
Less widely available
High cost
MRI cT1 Iron-corrected T1 relaxation time (ms) 0.71*, [71] Not validated Not validated Requires specialized MRI
Limited availability
Needs validation in MetALD and ALD
FAST VCTE-LSM, CAP, AST 0.75 [96] Not validated Not validated Designed to detect at-risk MASH
Needs validation in MetALD and ALD
MAST MRE-LSM, MRI-PDFF, AST 0.77*, [98] Not validated Not validated Requires MRE
Needs validation in MetALD and ALD
MEFIB MRE-LSM, FIB-4 0.90*, [98] Not validated Not validated Requires MRE
Needs validation in MetALD and ALD
Agile 3 VCTE-LSM, sex, AST, ALT, platelet, T2DM status, age 0.86–0.87 [99,102] Not validated Not validated Needs validation in MetALD and ALD
Agile 4 VCTE-LSM, sex, AST, ALT, platelet, T2DM status 0.90†, [101] Not validated Not validated Needs validation in MetALD and ALD
NIT Treatment response
 Clinical outcomes
MASLD MetALD ALD MASLD MetALD ALD
FIB-4 Limited data Not validated Improves with abstinence fibrosis progression and risk of clinical outcomes [41-46] Limited data Stratifies decompensation risk [47,48]
Associated with decompensation-free survival [24]
ELF ↓ ≥0.5 associated with histologic response [50,51] Not validated Not validated ELF≥11.3 increased risk hepatic decompensation [54] Limited data Predicts decompensation [48]
FDA approved prognostic biomarker Predicts decompensation-free survival [24]
2D-SWE Not validated Not validated Not validated Limited data Not validated Limited data
Associated with risk of life-threatening events [75] Predicts decompensation [48]
VCTE ↓ ≥25–30% associated with fibrosis improvement [50,51] Not validated ↓ LSM with alcohol abstinence [76] Associated with risk of LRE and overall mortality [77-82] Limited data Predicts decompensation and overall survival [82]
Predicts decompensation-free survival [24]
MRE ↓ ≥20% associated with fibrosis improvement [50,51] Not validated Not validated Associated with risk of LRE and overall mortality [88] Not validated Limited data
Predicts decompensation
MRI cT1 ↓ ≥80 ms associated with MASH resolution [90] Not validated Not validated >825 ms predictive of decompensation [92] Not validated Not validated
FAST ↓ associated with MASH resolution [104] Not validated Not validated Limited data Not validated Not validated
Lacking robust performance
MAST Not validated Not validated Not validated Associated with LRE. [106] Not validated Not validated
MEFIB Not validated Not validated Not validated Predicts decompensation [107] Not validated Not validated
Agile 3 Not validated Not validated Not validated Predicts decompensation and overall mortality. [78,102] Not validated Not validated
Agile 4 Not validated Not validated Not validated Predicts decompensation and overall mortality. [78,102] Not validated Not validated
ANTICIPATE±NASH Not validated Not validated Not validated Predicts LRE [108] Predicts LRE [108] Predicts LRE [108]
MASH resolution index AUROC 0.83 to detect MASH resolution without worsening of fibrosis [105] Not validated Not validated Not validated Not validated Not validated
NIT Rule out cut point Indeterminate Rule in cut point Caveats
FIB-4 <1.3 1.3–2.67 >2.67 Age-adjusted rule-out <2.0 for age >65
Higher cut point likely needed in setting of active alcohol use
ELF <7.7 7.7–9.8 >9.8 UK NICE guidance uses higher-specificity threshold of >10.51
VCTE (kPa) <8 8–12 >12 Higher cut point likely needed in setting of active alcohol use
Table 1. NIT Performance for fibrosis staging across SLD subtypes

Unless otherwise specified, AUROC and diagnostic accuracy values in this table are derived from cohorts using liver biopsy as the reference standard; one population screening study cited in the text used VCTE as the reference standard for ELF performance and is noted separately where discussed.

ALD, alcohol-associated liver disease; ALT, alanine aminotransferase; AST, aspartate aminotransferase; AUROC, area under the receiver operating curve; CAP, controlled attenuation parameter; ELF, enhanced liver fibrosis; FAST, Fibroscan AST; FIB-4, fibrosis 4; kPa, kilopascals; HA, hyaluronic acid; LSM, liver stiffness measurements; MASH, metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease; MAST, MRI AST; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; MRE, magnetic resonance elastography; NIT, non-invasive test; PDFF, proton density fat fraction; SLD, steatotic liver disease; SWE, shear wave elastography; T2DM, type II diabetes mellitus; TIMP-1, tissue inhibitor of metalloproteinase; VCTE, vibration controlled transient elastography.

Significant fibrosis.

Cirrhosis.

Table 2. NIT performance for treatment response and risk of clinical outcomes across SLD subtypes

ALD, alcohol-associated liver disease; ALT, alanine aminotransferase; AST, aspartate aminotransferase; AUROC, area under the receiver operating curve; BMI, body mass index; CAP, controlled attenuation parameter; ELF, enhanced liver fibrosis; FAST, Fibroscan AST; FDA, Food and Drug Administration; FIB-4, fibrosis 4; kPa, kilopascals; LRE, liver-related events; LSM, liver stiffness measurements; MASH, metabolic dysfunction-associated steatohepatitis; MASLD, metabolic dysfunction-associated steatotic liver disease; MAST MRI AST; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; MRE, magnetic resonance elastography; NASH, non-alcoholic steatohepatitis; NIT, non-invasive test; SLD, steatotic liver disease; SWE, Shear wave elastography; T2DM, type II diabetes mellitus; VCTE, vibration controlled transient elastography.

Table 3. Rule-out, indeterminate, and rule-in thresholds for first and second line NITs in MASLD

Thresholds shown are for advanced fibrosis in MASLD as reported in the body of this review; validated, etiology-specific rule-out/rule-in cut points for ALD and MetALD are not yet established (see Gaps, Challenges and Future Directions).

ALD, alcohol-associated liver disease; ELF, enhanced liver fibrosis; FIB-4, fibrosis 4; kPa, kilopascals; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption; NIT, non-invasive test; VCTE, vibration controlled transient elastography.

Table 1.

Table 2.

Table 3.

Clin Mol Hepatol : Clinical and Molecular Hepatology
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