Non-invasive blood biomarkers for assessment of liver fibrosis in metabolic dysfunction-associated steatotic liver disease

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) requires accurate liver fibrosis assessment for management. While liver biopsy remains the gold standard, its invasiveness drives demand for non-invasive biomarkers. This review evaluates blood biomarkers for MASLD fibrosis staging. Established scores (fibrosis-4, non-alcoholic fatty liver disease fibrosis score) offer accessible screening but exhibit variable performance influenced by age, obesity, and comorbidities. Patented panels (e.g., enhanced liver fibrosis test, FibroMeter) improve accuracy by integrating extracellular matrix or metabolic markers, though context-specific thresholds are essential. Emerging biomarkers like propeptide of type 3 collagen, Mac-2 binding protein glycosylation isomer, epigenetic markers (proliferator-activated receptor-γ methylation), and angiopoietin-like proteins a family of eight glycoproteins show promise but require large-scale validation. Genetic risk scores and multi-omics approaches face generalizability challenges. Integration strategies, such as combining serum biomarkers with liver stiffness measurement via Agile scores, enhance diagnostic precision and reduce indeterminate classifications. Current tools aid risk stratification, but no single biomarker replicates biopsy-level precision. Future efforts must prioritize MASLD-specific diagnostic frameworks, standardized protocols, and multi-modal integration to enhance clinical utility and address MASLD’s growing burden.

Key Words: Metabolic dysfunction-associated steatotic liver disease; Non-invasive test; Blood biomarkers; Liver fibrosis; Patented panel

Core Tip: Current biomarkers (fibrosis-4, non-alcoholic fatty liver disease fibrosis score) provide accessible first-line screening for metabolic dysfunction-associated steatotic liver disease (MASLD) fibrosis but require age/obesity-adjusted thresholds. Patented panels (enhanced liver fibrosis, FibroMeter) enhance precision through metabolic/extracellular matrix markers yet need MASLD-specific validation. Emerging biomarkers (propeptide of type 3 collagen, Mac-2 binding protein glycosylation isomer, epigenetic regulators like proliferator-activated receptor-γ methylation, angiopoietin-like proteins such as angiopoietin-like protein a family of eight glycoproteins) show monitoring potential but need validation. No single biomarker replaces biopsy. Combining existing tools with novel multi-omics approaches and MASLD-specific diagnostic frameworks is critical for improving clinical outcomes in this epidemic.

INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), has emerged as one of the most prevalent chronic liver diseases globally, with an estimated prevalence exceeding 30% of the general population[1-3]. This alarming rise is closely associated with the increasing incidence of obesity and type 2 diabetes (T2DM), key components of the metabolic syndrome[4]. The global burden of MASLD is further exacerbated by its significant association with severe liver-related outcomes, including cirrhosis, hepatocellular carcinoma, and liver failure[5]. Liver fibrosis is a pivotal factor influencing the prognosis of MASLD[6]. The extent of fibrosis correlates strongly with disease severity, the incidence of complications, and overall patient survival[7]. Early identification and staging of liver fibrosis are therefore crucial for the clinical management of MASLD. Timely intervention can slow disease progression, reduce the risk of complications, and improve patient outcomes[8].

Currently, liver biopsy is considered the “gold standard” for assessing liver fibrosis[9]. However, this invasive procedure is associated with several limitations, including procedural risks, sampling variability, and significant costs[10,11]. Moreover, the invasiveness and potential complications of liver biopsy render it unsuitable for widespread application, particularly in large-scale screening programs[9]. As a result, there is a pressing need for non-invasive diagnostic methods that can accurately assess liver fibrosis[12].

According to MASLD clinical-pathological staging, fibrosis is classified as none/mild fibrosis (F0-1), significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (F4)[13]. Although the ultimate goal is for non-invasive fibrosis tests to provide information comparable to liver biopsy, none of these tests currently achieves equivalent accuracy to liver biopsy in assessing hepatic fibrosis when used alone. MASLD affects a substantial proportion of the general population, but only a minority progress to advanced hepatic fibrosis[14]. Patients with advanced fibrosis face an elevated risk of liver-related complications and mortality[15,16]. However, even in the presence of advanced fibrosis, individuals with MASLD often remain asymptomatic. Their first clinical presentation may coincide with hepatic decompensation, thereby missing opportunities for preventive interventions[17]. Currently, widespread screening for MAFLD in the general population, followed by treatment, is not deemed justified unless further evidence emerges. Therefore, proactive case-finding in high-risk populations is more appropriate. In current clinical practice, ultrasonography is the most common method for diagnosing hepatic steatosis and serves as a primary screening tool to aid in the diagnosis of MASLD. Among patients with T2DM, the prevalence of MASLD is significantly higher, and these individuals are at increased risk of developing steatohepatitis and advanced fibrosis[18,19]. Therefore, patients with metabolic syndrome, particularly those with T2DM, represent a key target population for identifying MASLD and advanced fibrosis. In this context, this can be achieved through simple blood tests and blood-based biomarkers or risk scores.

Blood biomarkers have garnered considerable attention due to their non-invasive nature, ease of acquisition, and cost-effectiveness. These biomarkers offer a practical alternative for large-scale screening and monitoring of liver fibrosis in MASLD[20,21]. By measuring specific proteins, enzymes, and other molecules in the blood, serum biomarkers can provide valuable insights into the presence and severity of liver fibrosis. Their ability to be repeatedly measured and their relatively low cost make them particularly suitable for longitudinal monitoring and risk stratification in clinical practice[22]. Despite the potential of serum biomarkers, no single marker has yet been able to completely replace liver biopsy. The accuracy and reliability of these biomarkers vary, and their performance can be influenced by factors such as age, obesity, and the presence of other metabolic conditions[20]. These tools help exclude advanced fibrosis in affected populations and identify which patients require further risk stratification. Therefore, this review aims to provide a comprehensive evaluation of the current landscape of blood biomarkers for the assessment of liver fibrosis in MASLD. We will discuss the strengths and limitations of existing biomarkers, their clinical applications, and future directions for research and development.

SERUM BIOMARKER SCORES FOR LIVER FIBROSIS

Aspartate aminotransferase to alanine aminotransferase ratio and aspartate aminotransferase-to-platelet ratio index

Elevated serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels should prompt suspicion of liver injury and warrant further investigation to determine the underlying cause. However, normal serum ALT and AST levels do not exclude underlying liver disease. Patients with MASLD, advanced fibrosis, or cirrhosis may have serum ALT and AST levels within the normal range[23,24]. Therefore, relying solely on elevated liver enzyme levels for risk stratification in MASLD patients is insufficient.

The earliest serum-based biomarker scores to emerge were the AST/ALT ratio and the AST-to-platelet ratio index (APRI)[25,26]. Originally developed and validated for fibrosis staging in patients with chronic hepatitis C, these indices demonstrate moderate accuracy in predicting advanced fibrosis in individuals with MASLD. The AST/ALT ratio achieved an area under the receiver operating characteristic curve (AUROC) of 0.66-0.90 for excluding advanced fibrosis, while APRI showed AUROC values of 0.67-0.83 for advanced fibrosis detection[27-32]. Although APRI demonstrated high diagnostic efficacy for advanced fibrosis in MASLD patients (AUROC: 0.85, specificity 99%), its limited sensitivity (16%) and potential confounding effects from high-risk cohort sampling raise concerns about the generalizability of these findings to broader MASLD populations at risk (Table 1)[33].

Table 1 Serum biomakers for the assessment of fibrosis in metabolic dysfunction-associated steatotic liver disease.

Serum biomaker	Components	Interpretation	Strength of recommendation
AST-to-ALT ratio[25,26]	ALT, ALT	Advanced fibrosis > 1.0	One star
AST-to-platelet ratio index[27-33]	AST, platelet count	Rules out fibrosis < 0.5	Two stars
		Significant fibrosis >1.0
Fibrosis-4[29,33-38]	Age, platelet count, AST, and ALT	Rules out fibrosis < 1.3	Three stars
		Advanced fibrosis > 2.671
NAFLD fibrosis score[22,28,39-44]	Body mass index, age	Rules out fibrosis < -1.455	Three stars
	AST/ALT ratio, platelet count, albumin, hyperglycemia	Advanced fibrosis > 0.675
BARD score[28,45-48]	BMI > 28 kg/m2, AST/ALT ratio > 0.8, diabetes	Low risk for advanced fibrosis 0-1	Two stars
		High risk for advanced fibrosis 0-1
Hepascore fibrosis score[32,49-51]	Age, diabetes, sex, AST, albumin, platelet count, HOMA-IR	Significant fibrosis > 0.50	Two stars
		Advanced fibrosis > 0.65
N-terminal PRO-C3[52-58]	Reflect true synthesis of type III collagen	Advanced fibrosis >25 U/L	Two stars
ADAPT[55]	Age, diabetes status, PRO-C3, and thrombocyte count	Rules out advanced fibrosis ≤ 6.3287	Three stars
		Advanced fibrosis > 9.8
Mac-2 binding protein glycosylation isomer[59-65]	A glycoprotein expressed by activated hepatic stellate cells	Advanced fibrosis > 0.71 COI	Two stars
Enhanced liver fibrosis score[22,28,43,66-68]	PIIINP, HA, TIMP-1	Rules out fibrosis < 7.7	Three stars
		Advanced fibrosis > 9.8
FibroTest[22,69,70]	GGT, total bilirubin	Rules out fibrosis < 0.3	Three stars
	α2-macroglobulin
	Apolipoprotein A-I	Advanced fibrosis > 0.7
	Haptoglobin, cholinesterase
FibroMeter[21,22,71-73]	Age, weight, AST, ALT, platelet count, blood glucose, and ferritin	Rules out fibrosis < 0.31	Three stars
		Advanced fibrosis > 0.45
Agile 3+ score[74,75]	LSM, platelets, AST, ALT, diabetes status, sex, and age	Rules out fibrosis < 0.351	Three stars
		Advanced fibrosis > 0.679

¹The cutoff value of fibrosis-4 for individuals ≥ 65 years old is adjusted to 2.0.

The strength of recommendation reflects the graded evidence level supporting whether primary risk assessment or secondary risk assessment using serum biomarkers is recommended in clinical practice for patients with metabolic dysfunction-associated steatotic liver disease. The highest strength of recommendation is designated as three stars. AST: Aspartate aminotransferase; ALT: Alanine aminotransferase; NAFLD: Non-alcoholic fatty liver disease; BMI: Body mass index; HOMA-IR: Homeostatic model assessment of insulin resistance; PRO-C3: Propeptide of type 3 collagen; ADAPT: Age, diabetes status, propeptide of type 3 collagen, and thrombocyte; COI: Cot-off index; PIIINP: N-terminal propeptide of type III procollagen; HA: Hyaluronic acid; TIMP-1: Tissue inhibitor of metalloproteinase 1; GGT: Gamma-glutamyl transferase; LSM: Liver stiffness measurement.

Fibrosis-4 index

The fibrosis-4 (FIB-4) index, incorporating age, platelet count, AST, and ALT, is recommended as a first-line screening tool for fibrosis assessment in general clinical practice[34]. Validation studies defined cutoff values for advanced fibrosis risk stratification as follows: < 1.45 for low risk, > 3.25 for high risk, and values between these thresholds classified as intermediate risk[34]. It exhibits distinct diagnostic performance in MASLD compared to NAFLD. Studies on NAFLD focus optimizing thresholds [e.g., > 3.25 for high specificity 98%, positive predictive value (PPV) 75% vs > 1.3 for sensitivity 85%][29], while MASLD cohorts using the conventional cutoff (> 2.67) show strong specificity (97%-99%) and AUROCs of 0.80-0.82 for advanced fibrosis[33,35]. However, MASLD-specific analyses reveal reduced diagnostic precision, particularly in severely obese patients, where the > 2.67 threshold yielded an AUROC of 0.57; adjusting to > 1.53 marginally improved AUROC to 0.69 but remained inferior to NAFLD benchmarks[36]. Age significantly impacts FIB-4 reliability, with elevated false-positive rates in patients ≥ 65 years, necessitating age-adjusted thresholds (> 2.0 for patients ≥ 65 years old) to improve specificity (Table 1)[37,38]. These disparities highlight the need for MASLD-specific diagnostic frameworks to address metabolic comorbidities and age-related variability.

While FIB-4 effectively distinguishes advanced fibrosis from no/mild fibrosis in MASLD, it demonstrates limited discriminatory power for intermediate fibrosis stages. The score’s simplicity and reproducibility render it a valuable first-line screening tool, particularly advantageous in resource-limited clinical environments. Despite remaining a cornerstone of non-invasive fibrosis evaluation in MASLD, FIB-4 exhibits significant divergence in optimal diagnostic thresholds and performance characteristics compared to established NAFLD benchmarks. Crucially, age-specific cutoff adjustments are imperative to mitigate age-related false-positive rates and enhance diagnostic precision in elderly MASLD cohorts (Figure 1).

Figure 1 A suggested simplified algorithm for the use of non-invasive serum biomarkers for the assessment of patients with metabolic dysfunction-associated steatotic liver disease. ¹The cutoff value of fibrosis-4 for individuals ≥ 65 years old is adjusted to 2.0. MASLD: Metabolic dysfunction-associated steatotic liver disease; T2DM: Type 2 diabetes; FIB-4: Fibrosis-4; NFS: Non-alcoholic fatty liver disease fibrosis score; ELF: Enhanced liver fibrosis; APRI: Aspartate aminotransferase-to-platelet ratio index; ADAPT: Age, diabetes status, propeptide of type 3 collagen, and thrombocyte.

NAFLD fibrosis score

NAFLD fibrosis score (NFS), incorporating body mass index (BMI), age, impaired glucose metabolism, AST/ALT ratio, albumin, and platelet count, was originally developed from a cohort of NAFLD patients[39]. The cutoffs validated for this score are < -1.455 and > 0.675, with the lower cutoff having an negative predictive value (NPV) of 88% and the highest cutoff a PPV of 82%[39]. Jointly endorsed with FIB-4 by the European Association for the Study of the Liver, NFS remains one of the most widely utilized scoring systems for fibrosis severity assessment in clinical practice (Figure 1)[22]. It has been demonstrated reproducible accuracy in stratifying low-risk and high-risk groups for advanced fibrosis[29,40-42]. However, recent studies in MASLD cohorts reveal significantly reduced diagnostic performance compared to its established utility in NAFLD. Key limitations include elevated false-positive rates driven by BMI- and age-related score inflation[28], as well as compromised accuracy in patients with extreme platelet count abnormalities (e.g., asplenia or post-transjugular intrahepatic portosystemic shunt)[43]. Notably, emerging evidence suggests that in MASLD patients with low BMI, NFS (cutoff > -1.455) outperforms FIB-4 in specificity for advanced fibrosis prediction (AUROC 0.85 vs 0.79)[44], highlighting its context-dependent utility in specific subpopulations.

BARD score

The BARD score is calculated by summing three parameters: BMI > 28 kg/m² (1 point), AST/ALT ratio > 0.8 (2 points), and diabetes (1 point), yielding a total range of 0-4 points[45]. Scores of 0-1 indicate low risk for advanced fibrosis, while 2-4 denote high risk. Initial validation studies reported promising performance (AUROC: 0.81%, NPV: 96%), though with limited PPV (43%)[45]. However, subsequent external validations revealed significantly reduced accuracy, with AUROCs of 0.59-0.61, PPVs of 27%-53%, and NPVs of 69%-89%[46,47]. Key limitations include poor reproducibility across cohorts and restricted applicability in obese populations. Additionally, the score’s performance may be confounded in ethnic groups predisposed to obesity-related complications at lower BMI thresholds, such as South Asian populations with hepatic steatosis development at relatively lean body weights[28,48].

Hepamet fibrosis score

Hepamet fibrosis score incorporates variables such as age, presence of diabetes, AST levels, albumin, platelet count, sex, and insulin resistance assessed via the homeostasis model assessment of insulin resistance in non-diabetic individuals[49]. It demonstrates strong performance in ruling out advanced fibrosis, with sensitivity and NPV ranging from 74%-90% and 90%-98%, respectively[32,50,51]. However, its clinical utility is constrained in primary care settings due to the requirement for serum insulin measurement-a barrier particularly relevant in resource-limited regions where insulin assays are less accessible.

SEREM BIOMARKERS FOR LIVER FIBROSIS

N-terminal propeptide of type 3 collagen

N-terminal propeptide of type 3 collagen (PRO-C3) is a biomarker reflecting collagen synthesis during fibrogenesis. As a byproduct of extracellular matrix (ECM) remodeling, PRO-C3 is released during the cleavage of type 3 collagen precursors and serves as a surrogate marker for active fibrogenesis[52]. Its utility in evaluating hepatic fibrosis in MASLD has been extensively investigated. In diagnostic studies, PRO-C3 demonstrates robust performance in distinguishing advanced fibrosis stages. A multicenter study reported an area under AUROC of 0.73 for advanced fibrosis, with strong correlations observed between PRO-C3 levels and histological fibrosis severity in MASLD patients[53]. These findings are corroborated by subsequent validation studies, where PRO-C3 achieved AUROCs ranging from 0.75 to 0.83 for advanced fibrosis and 0.76 for cirrhosis detection[54,55]. Notably, in cohorts with concurrent metabolic risk factors such as diabetes, PRO-C3 has emerged as an independent predictor of fibrotic progression, capable of identifying rapid disease progression and therapeutic responsiveness to antimetabolic interventions[56,57]. This positions PRO-C3 as a promising tool for stratifying high-risk populations and monitoring anti-fibrotic treatment efficacy in clinical trials.

Building on the diagnostic potential of PRO-CC3, the algorithm integrates this biomarker with clinically accessible variables - age, diabetes status, PRO-C3, and thrombocyte (platelet) count (ADAPT) - to enhance fibrosis risk stratification[55]. In the ADAPT validation study, this composite score demonstrated superior diagnostic accuracy for advanced fibrosis in MASLD compared to established non-invasive tests (NITs). Across derivation and validation cohorts (431 biopsy-proven patients), ADAPT achieved robust AUROCs of 0.86 and 0.87, respectively, significantly outperforming APRI (AUROC: 0.73-0.78), FIB-4 (AUROC: 0.78-0.85), and NFS (AUROC: 0.78-0.79) in most comparisons. Further supporting its reliability, ADAPT maintained a standardized AUROC (0.89) and high NPV (> 90% across subpopulations). Critically, it eliminated the “indeterminate zone” plaguing other scores, correctly classifying 92% of advanced fibrosis cases and 74% of non-advanced cases using a single cutoff (6.3287)[55]. Unlike FIB-4 and NFS, ADAPT’s exclusion of liver enzymes reduced age-related confounding and enhanced applicability in normo-enzymatic patients, solidifying its position as a precise, clinically practical tool for first-line MASLD risk stratification.

However, PRO-C3 interpretation requires clinical contextualization. A critical limitation arises in advanced fibrotic stages, where reduced collagen synthetic activity in “burnt-out” fibrotic nodules may paradoxically normalize PRO-C3 levels, potentially leading to false-negative results[58]. Future studies should focus on longitudinal PRO-C3 monitoring to elucidate its role in tracking fibrosis regression during therapeutic interventions.

Mac-2 binding protein glycosylation isomer

Mac-2 binding protein glycosylation isomer (M2BPGi), a glycoprotein predominantly expressed by activated hepatic stellate cells during liver injury, drives fibrogenesis through modulation of transforming growth factor β signaling, promoting ECM deposition. It further contributes to fibrosis by dysregulating matrix metalloproteinases, impairing ECM degradation and accelerating collagen accumulation. M2BPGi also exacerbates inflammation and fibrosis by upregulating pro-inflammatory cytokines (e.g., interleukin-6, tumor necrosis factor α) and suppressing the antifibrotic adipokine adiponectin[59].

In MASLD, elevated serum M2BPGi levels correlate with fibrosis severity, insulin resistance, and metabolic dysregulation, reflecting disease progression. At a cutoff of 0.71 cut-off index, M2BPGi demonstrates 78% sensitivity and 74% specificity with an AUROC of 0.823 for detecting advanced fibrosis[60]. Its diagnostic performance is particularly notable in pediatric MASLD, where it achieves an AUROC of 0.742 with a cutoff of 1.35 cut-off index for advanced fibrosis[61]. M2BPGi consistently outperforms conventional NITs for detecting advanced fibrosis. For example, in the study by Nah et al[62] (≥ F3), M2BPGi had an AUROC of 0.836, compared to 0.766 for FIB-4 and 0.735 for APRI[62]. Similarly, in the study by Jang et al[63] (≥ F3), M2BPGi had an AUROC of 0.900, compared to 0.641 for FIB-4, 0.894 for APRI, and 0.926 for MASLD[63]. Seko et al[64] conducted a comparative analysis of M2BPGi, the enhanced liver fibrosis (ELF), and FIB-4 for assessing fibrosis severity, emphasizing the potential clinical advantages of M2BPGi. Combination panels, such as M2BPGi + FIB-4, can further enhance diagnostic sensitivity, reaching a sensitivity of 87.1%, specificity of 82.5% for advanced fibrosis[65].

However, standardization of M2BPGi assays is needed, as inter-laboratory variability necessitates harmonized protocols. The cost-effectiveness of routine M2BPGi testing also requires validation. Future research should focus on validating M2BPGi in diverse cohorts (e.g., non-Asian populations, pediatric patients) and developing multi-marker algorithms (e.g., combining M2BPGi with the ELF).

PATENTED SEREM BIOMARKERS FOR LIVER FIBROSIS

ELF

The ELF test, a patented serum-based panel, integrates three biomarkers: Hyaluronic acid, tissue inhibitor of metalloproteinase 1, and the N-terminal propeptide of type III procollagen is used to non-invasively assess hepatic fibrosis[66]. Originally derived from the European Liver Fibrosis test, this FDA-approved prognostic tool has demonstrated superior diagnostic accuracy in MASLD compared to other liver diseases, reflecting its ability to quantify ECM remodeling dynamics[66]. The ELF test exhibits robust diagnostic utility for advanced fibrosis in MASLD. A meta-analysis reported a sensitivity of 0.93 and specificity of 0.34 at a low cutoff value of 7.7 for excluding significant fibrosis, with NPVs ranging from 0.82 to 0.99 in low-prevalence (< 50%) cohorts. For advanced fibrosis detection, the recommended threshold of 9.8 achieves an AUROC of 0.83, with 65% sensitivity and 86% specificity (Table 1)[67]. Notably, in a Danish population-based study, ELF demonstrated a lower false-positive rate than FIB-4, positioning it as a reliable first-line screening tool to optimize referrals and reduce unnecessary biopsies[68].

Current guidelines endorse ELF as part of sequential diagnostic algorithms (Figure 1). The European Association for the Study of the Liver advocates combining ELF > 9.8 with FIB-4 > 1.30 and liver stiffness measurement (LSM) > 8 kPa to confirm high-risk advanced fibrosis[4,22]. Conversely, the American Association for the Study of Liver Diseases incorporates the 7.7 cutoff to rule out advanced fibrosis, emphasizing its role in risk stratification[4,22]. Despite its strengths, ELF interpretation requires cautious clinical contextualization. Age-dependent variability in biomarker levels necessitates age-adjusted reference ranges, and concurrent conditions associated with elevated collagen turnover, such as interstitial lung disease, may yield false-positive results[28,43]. Furthermore, ELF’s validation in MASLD-specific cohorts remains limited. These gaps highlight the need for prospective studies to refine cutoff values and validate its prognostic utility in diverse MASLD subpopulations.

FibroTest

FibroTest is a multi-parametric serum assay combining age, gender, and six biochemical markers: Α2-macroglobulin, haptoglobin, apolipoprotein A-I, gamma-glutamyl transferase, total bilirubin, and cholinesterase to non-invasively evaluate liver fibrosis. This algorithm quantifies fibrotic activity by integrating markers reflecting hepatic inflammation, synthetic dysfunction, and cholestasis[69]. In MASLD cohorts, FibroTest demonstrates clinically relevant accuracy for fibrosis staging. At a low cutoff of 0.3 (90% sensitivity), it effectively excludes significant fibrosis, while a high cutoff of 0.7 (90% specificity) confirms its presence, aligning with guidelines recommending dual thresholds for rule-out and rule-in strategies. For intermediate risk stratification, a cutoff range of 0.30-0.48 achieves balanced performance, with 72% sensitivity and 85% specificity for detecting significant fibrosis[70]. Notably, at the 0.48 threshold, FibroTest exhibits utility in excluding advanced fibrosis, though its validation in MASLD remains less extensive compared to ELF and FibroMeter[22].

FibroTest leverages routine biochemical parameters, enabling integration into standard clinical workflows without specialized assays. However, its interpretation requires caution in conditions affecting non-hepatic markers (e.g., hemolysis altering haptoglobin levels or Gilbert’s syndrome elevating bilirubin), which may confound results[69]. Additionally, while FibroTest has demonstrated cross-etiology validity, MASLD-specific data are limited, necessitating further studies to optimize thresholds for metabolic-driven fibrosis phenotypes.

FibroMeter

FibroMeter is an algorithm-based serum test specifically designed for MASLD, integrating parameters such as age, weight, AST, ALT, platelet count, blood glucose, and ferritin to systematically evaluate liver fibrosis[71]. This test demonstrates excellent diagnostic performance in MASLD, with an AUROC of 0.80-0.94 for diagnosing advanced fibrosis. A cutoff value of 0.45 is recommended to exclude advanced fibrosis, while a cutoff of 0.31 maximizes sensitivity and specificity in high-risk populations[71,72]. In a cross-sectional study of MASLD patients, FibroMeter showed diagnostic accuracy comparable to liver stiffness measurement, with enhanced predictive capability for advanced fibrosis[21]. Its unique advantage lies in incorporating metabolic markers such as glucose and ferritin, which directly reflect MASLD’s core pathological mechanisms-insulin resistance and iron overload-distinguishing it from tests focused solely on ECM remodeling (e.g., ELF)[73]. However, ferritin levels may be confounded by inflammation or hereditary hemochromatosis, and validation across diverse ethnic populations remains incomplete[73]. Current guidelines recommend combining FibroMeter with FIB-4 and LSM in a stepwise diagnostic workflow, optimizing referral decisions through a dual-threshold strategy (0.31 for initial screening and 0.45 for exclusion) to reduce unnecessary biopsies[22]. Although cost-effective in routine clinical practice, future studies should refine thresholds for metabolic-specific phenotypes and conduct longitudinal research to validate its utility in monitoring therapeutic responses.

CONCURRENT UTILIZATION OF SERUM BIOMARKERS AND LSM

Given the suboptimal performance of standalone NITs, combining serum biomarkers with LSM enhances diagnostic precision for liver fibrosis staging. Agile scores are composite biomarkers integrating LSM with routine clinical/Laboratory parameters (platelets, AST, ALT, diabetes, sex ± age) to stage fibrosis in MASLD. The Agile 3+ score diagnoses advanced fibrosis, while Agile 4 identifies cirrhosis (F4)[74]. External validation confirms their diagnostic accuracy equals LSM alone in MASLD (Agile 3+ AUC: 0.86 vs LSM: 0.86; Agile 4 AUC: 0.89 vs LSM: 0.90), but significantly reduces indeterminate classifications by 25%-55% when using dual thresholds (90% sensitivity/specificity). Agile 3+ cut-offs (< 0.351 rule-out; > 0.679 rule-in) correctly classify 67% of patients (vs 61% for LSM), with only 21% indeterminates (vs 28% for LSM). Agile 4 further optimizes cirrhosis diagnosis (79% correct classification; 11% indeterminates). Sequential use with FIB-4 (FIB-4 < 1.3 and Agile 3+) boosts correct classification to 75% and cuts indeterminates to 12%. Concurrently, concordant high-risk results (e.g., Agile 3+ > 0.679 + LSM > 12 kPa) increase specificity to 93% for advanced fibrosis[75]. Agile scores thus refine MASLD fibrosis staging by minimizing diagnostic uncertainty and enhancing integration into risk-stratification algorithms, though they require elastography access.

POTENTIAL NOVEL BLOOD BIOMARKERS FOR LIVER FIBROSIS

Epigenetic and immunological biomarkers

Given the dynamic nature of epigenetic markers and their critical role in mediating gene-environment interactions, multiple epigenetic markers have emerged as potential biomarkers for MASLD[76,77]. Emerging biomarkers for advanced fibrosis evaluation in MASLD demonstrate multi-modal diagnostic potential. Epigenetic markers, exemplified by peroxisome proliferator-activated receptor-γ plasma DNA methylation, exhibit promising diagnostic accuracy with an AUROC of 0.91, achieving 91% PPV and 87% NPV in distinguishing advanced fibrosis[78]. Immunological pathways also show utility, where macrophage activation markers such as soluble CD163 achieve an AUROC of 0.83 when combined with NFS. Similarly, macrophage-derived deaminase markers independently predict advanced fibrosis with an AUROC of 0.82[79,80]. While these novel biomarkers exhibit strong initial performance, current evidence predominantly derives from small-scale cross-sectional cohorts. Further validation through large-scale prospective studies and external cohorts is essential to confirm their diagnostic robustness and establish standardized clinical thresholds.

Genetic biomarkers and polygenic risk scores

Genetic variants regulating core pathophysiological pathways in MASLD, including lipid metabolism, insulin/adipokine signaling, and inflammatory modulation, play pivotal roles in disease progression[76,81]. Among fibrosis-associated polymorphisms, the most extensively studied single-nucleotide polymorphisms involve the PNPLA3, TM6SF2, and MBOAT7 genes[82-84]. A cohort study evaluated 515 NAFLD patients demonstrated significant associations between these loci and hepatic injury: PNPLA3 variants concurrently elevate risks of steatosis and fibrosis, TM6SF2 primarily correlates with steatosis severity, while MBOAT7 specifically drives fibrogenesis[85]. Combining PNPLA3 and TM6SF2 genotypes with the FIB-4 slope facilitates the identification of patients at high risk for accelerated fibrosis progression, especially those with a baseline FIB-4 < 2.67, guiding early interventions such as lifestyle modifications and pharmacotherapy[86].

Emerging research focuses on constructing polygenic risk scores integrating clinical parameters (e.g., BMI, glycemic status) with genetic data to enhance predictive accuracy for fibrosis trajectories[76]. However, persisting challenges hinder clinical translation: (1) Ambiguous boundaries between risk stratification and prognostic prediction in polygenic risk scores applications; and (2) Limited generalizability due to Eurocentric bias in existing genome-wide association studies, necessitating validation in ethnically diverse cohorts[76].

Metabolomic and proteomic biomarkers

The intimate linkage between MASLD and metabolic dysregulation has driven growing interest in leveraging metabolomic and proteomic approaches to stratify hepatic fibrosis. Among candidate proteomic biomarkers, angiopoietin-like proteins (ANGPTLs)-a family of eight glycoproteins sharing structural homology but exhibiting distinct tissue-specific expression and regulatory roles-have emerged as critical mediators bridging metabolic syndrome and liver pathology[87]. Functionally, ANGPTLs modulate lipid metabolism, insulin sensitivity, and hormonal signaling, positioning them as potential molecular connectors between systemic metabolic dysfunction and MASLD progression[87].

Emerging evidence highlights their specific associations with liver fibrosis staging. While circulating ANGPTL levels demonstrate inconsistent correlations with MASLD across studies[88,89], a meta-analysis of 13 clinical trials revealed that ANGPTL8 is significantly elevated in MASLD patients compared to healthy controls, with levels progressively increasing from mild to moderate-severe disease stages[88,90]. Notably, this gradient pattern suggests ANGPTL8’s potential utility in non-invasive fibrosis monitoring, particularly for distinguishing advanced fibrosis (F3-F4) from early-stage disease (F0-F2)[90]. Mechanistically, ANGPTL8 may exacerbate fibrogenesis by amplifying hepatic lipotoxicity and insulin resistance-key drivers of stellate cell activation. However, the fibrosis-specific regulatory roles of other ANGPTL family members (e.g., ANGPTL3 in lipoprotein remodeling, ANGPTL4 in adipose-liver crosstalk) remain underexplored, warranting longitudinal studies to dissect their stage-dependent contributions to fibrotic progression. Their dual role as metabolic regulators and fibrosis modulators underscores the need for standardized assays and multi-center validation to establish clinical thresholds aligned with histopathological staging criteria.

Targeting the underlying mechanisms of hepatic fibrogenesis to identify relevant biomarkers offers superior diagnostic value compared to conventional approaches. Verschuren et al[91] developed and validated a novel, mechanism-based blood biomarker panel (insulin-like growth factor-binding protein 7, systemic sclerosis 5-domain protein, semaphorin 4-domain protein) for non-invasive staging of hepatic fibrosis in MASLD. Utilizing a translational approach starting from a diet-induced fibrotic mouse model (LDLr-/-.Leiden) and progressing through human liver transcriptomics and serum protein analysis, the panel targets pathways linked to active collagen turnover. Evaluated using LightGBM modeling in a testing cohort (n = 128), the panel demonstrated high accuracy: AUCs of 0.82 for distinguishing no/mild fibrosis (F0/F1), 0.89 for significant fibrosis (F2), and 0.87 for advanced fibrosis/cirrhosis (F3/F4). This performance was validated in an independent cohort (n = 156), yielding AUCs of 0.84 for both F0/F1 and F3/F4 stages (though F2 prediction was more modest, AUC 0.62). Crucially, the overall diagnostic accuracy of the panel, termed timely liver model 3 model significantly outperformed established NITs including FIB-4, APRI, and FibroScan (transient elastography) in head-to-head comparisons within the validation cohort. The study highlights the superior predictive capability of this mechanism-driven, three-biomarker panel for identifying clinically relevant fibrosis stages in MASLD, offering a promising tool for non-invasive patient stratification.

Moreover, serum thrombospondin-2 (TSP2) levels are closely associated with the severity of liver fibrosis in patients with MASLD, particularly those with obesity. Levels increase progressively with higher fibrosis stages (F0 to F3), showing a significant positive correlation (after adjusting for confounders). Logistic regression analysis confirms that elevated serum TSP2 is an independent predictor of significant fibrosis (≥ F1: Odds ratio = 1.59; ≥ F2: Odds ratio = 1.93, after adjustment for sex, age, and BMI)[92]. The mechanism may be the high expression of TSP2 in hepatic stellate cells, which may promote collagen secretion and fibrosis progression by activating the ECM receptor interaction pathway and the phosphoinositide 3 - kinase and protein kinase B signaling pathway.

CONCLUSION

In summary, non-invasive blood biomarkers offer a promising alternative to liver biopsy for assessing liver fibrosis in MASLD. Established scores like FIB-4 and NFS, along with patented serum markers such as ELF and FibroTest, provide valuable tools for clinical diagnosis and risk stratification (Figure 1). Despite advances, barriers to widespread adoption persist, including inter-laboratory variability in proprietary assays (e.g., M2BPGi, ELF), insufficient validation in African and Latin American cohorts, and cost constraints limiting access to patented panels. Their performance, however, can vary across populations, necessitating further validation and refinement. While significant progress has been made in identifying novel diagnostics, including biomarkers and algorithms, none have yet achieved sufficient performance to fully replace liver biopsy. Future work should focus on developing and validating novel biomarkers, integrating multi-modal approaches, and standardizing protocols to enhance diagnostic accuracy and clinical utility. Future validation studies should prioritize ethnically diverse cohorts to optimize biomarker thresholds for MASLD subpopulations worldwide. While blood biomarkers provide accessible first-line tools, their integration with imaging optimizes risk stratification. Future studies should formalize cost-benefit analyses of multi-modal algorithms. Continued research is crucial to optimize patient outcomes and address the global burden of MASLD.