Blood markers vs transient elastography for liver stiffness and steatosis in metabolic dysfunction-associated steatotic liver disease

Abstract

BACKGROUND

Liver biopsy, once the gold standard for evaluating liver fibrosis and steatosis, has been largely replaced in routine clinical practice by non-invasive tools like Fibroscan^®, which evaluate liver stiffness measurement (LSM) and controlled attenuation parameter (CAP). While Fibroscan^® is well-validated, cost and accessibility challenges limit its use for regular follow-up, especially in primary care.

AIM

To investigate the diagnostic accuracy and correlation of blood-based parameters fibrosis 4 (FIB-4) score, aspartate transaminase to platelet ratio index (APRI), neutrophil-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and neutrophil percentage-to-albumin ratio (NPAR) with LSM and CAP values in metabolic dysfunction-associated steatotic liver disease (MASLD) patients.

METHODS

In a cross-sectional study of 300 MASLD patients we compared FIB-4, APRI, NLR, PLR, and NPAR with LSM and CAP values. Patients were categorized based on LSM into less fibro-progressed (F0-F2) and advanced fibro-progressed (F3-F4) groups, and by CAP into S1, S2 and S3 categories. Sensitivity, specificity, positive predictive value, and negative predictive value of the markers were analyzed, and receiver operating characteristic curves were plotted.

RESULTS

FIB-4 [r = 0.537, P < 0.001; area under curve (AUC) = 0.806; diagnostic accuracy = 75.63%] and APRI (r = 0.513, P < 0.001; AUC = 0.772) showed strong correlations with LSM, confirming their reliability for LSM. APRI and FIB-4 are validated against fibrosis in liver biopsy, our results demonstrate comparable performance between these scores and LSM by Fibroscan^®. PLR exhibited high specificity (98.0%) but showed negative correlation with LSM (r = -0.317, P < 0.01). For CAP, NPAR demonstrated the highest specificity (97.67%) and positive predictive value (91.31%), followed by NLR (specificity 92.77%, positive predictive value 91.58%), though AUC values were modest (0.562 and 0.540, respectively).

CONCLUSION

FIB-4 and APRI which are robust non-invasive markers for fibrosis, correlates well with LSM as well. NPAR shows potential for steatosis assessment using CAP, warranting further validation. Negative correlation of PLR might suggest its role in liver stiffness evaluation. These markers both conventional and novel, can be used for repeated measurements during follow-up in primary care settings.

Key Words: Metabolic dysfunction-associated steatotic liver disease; Non-alcoholic fatty liver disease; Transient elastography; Fibrosis-4 index; Aspartate transaminase to platelet ratio index; Neutrophil percentage to albumin ratio; Liver stiffness; Steatosis; Fibrosis

Core Tip: This study demonstrates that blood-based markers are cost-effective adjuvants to FibroScan for metabolic dysfunction-associated steatotic liver disease (MASLD) assessment. Fibrosis 4 and aspartate transaminase to platelet ratio index demonstrated strong correlations with liver stiffness measurement (LSM), confirming their reliability for fibrosis evaluation. Notably, neutrophil percentage-to-albumin ratio emerged as a novel marker with a specificity (97.67%) for steatosis assessment, while platelet-to-lymphocyte ratio showed an inverse correlation with LSM. These findings support integrating simple blood markers with imaging techniques for enhanced diagnostic precision, particularly valuable in primary care settings where repeated FibroScan measurements are financially or logistically challenging, addressing critical gaps in MASLD management.

INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a major global health concern, with recent epidemiological studies indicating a significant rise in its prevalence across diverse populations it is thus imperative to develop cost-effective diagnostic strategies that can be widely implemented in diverse healthcare settings[1]. Hepatic steatosis is strongly associated with cardiometabolic risk factors, particularly abdominal obesity, dyslipidemia, hyperglycaemia and insulin resistance[2]. The main determinant of the prognosis of these diseases is liver fibrosis, which can be assessed using various methods, including invasive liver biopsy and non-invasive techniques such as serum biomarkers and elastography[3,4]. Liver biopsy is considered the gold standard for assessment of hepatic inflammation and fibrosis, but due to the invasive nature and risk of complications, patient acceptability is low[5]. Post Delphi consensus on MASLD in 2023, liver biopsy is no longer considered as a mandatory requirement for the diagnosis of MASLD[6]. The European Association for the Study of the Liver and the American Association for the Study of Liver Diseases have recommended using Fibroscan^® for the evaluation of liver stiffness as an alternative. Fibroscan^® is a non-invasive, easy-to-use modality that can assess hepatic fat deposition and fibrosis with high accuracy[6,7]. Controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) can be used for steatosis quantification and fibrosis assessment because of its advantages: FibroScan^® assesses a liver volume of approximately 100 times larger than biopsy, thus reducing sampling error, this is especially significant, since fibroprogression in earlier stages can be patchy[5].

The advantage of Fibroscan^® is that the CAP provides a non-invasive assessment of steatosis simultaneously with LSM which is used to stage hepatic fibrosis[8,9]. Despite these advantages, Fibroscan^® has some limitations, such as high cost, limited accessibility, operator dependency, failure rate in obese patients, inability to perform in pregnancy and in patients with implanted devices and recurring expenses due to repeated measurements during follow-up[10]. Non-invasive markers in blood are considered widely accepted and reliable for monitoring disease development and progression. So while looking for ideal surrogate blood based tests correlating with LSM and CAP, those must be widely available, sensitive, specific, reproducible and inexpensive. Integrating these serum-based parameters with FibroScan^® metrics similar to FibroScan-AST (aspartate aminotransferase) score (FAST) and AGILE score could bridge current diagnostic gaps by combining the high specificity of imaging with the low cost and wide accessibility of blood tests[11]. In this study we chose the following markers - aspartate transaminase to platelet ratio index (APRI), fibrosis 4 (FIB-4), neutrophil-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and neutrophil percentage-to-albumin ratio (NPAR). FIB-4 is a time honoured marker used in conjunction with Fibroscan^® to enhance diagnostic accuracy and combining FIB-4 with Fibroscan^® has shown reduction in the need for liver biopsy[12]. Although APRI tends to have lower accuracy compared to Fibroscan^® for diagnosing significant fibrosis, it can still be effective in large population screenings for excluding advanced fibrosis[13]. In terms of the other markers, a study in 2024 on MASLD explored the correlation of NLR with liver fibrosis stages, indicating a potential link between systemic inflammation and fibrosis severity, albeit not directly comparing it with Fibroscan^®[14]. Regarding other exploratory markers like PLR and NPAR there is a noticeable lack of data comparing these markers with Fibroscan^®. These markers can be advantageous in primary or secondary care settings where Fibroscan^® is not available; we evaluate them here as newer markers that may complement conventional indices (FIB-4, APRI) in resource-limited settings for screening and follow-up. Also these blood based markers may be useful in deriving scoring systems in conjunction with Fibroscan^®, akin to FAST (FibroScan-AST) and AGILE score[11]. There is a gap in the literature to evaluate these markers thus potentially expanding the diagnostic arsenal for MASLD and ultimately enhancing non-invasive diagnosis and improving clinical decision-making and follow up, particularly in resource-limited settings.

MATERIALS AND METHODS

Our study aimed at comparing the diagnostic accuracy and comparability between Fibroscan^® and non-invasive blood based parameters (FIB-4, APRI, NLR, NPAR and PLR) for assessing liver stiffness and CAP in patients with MASLD. This was a cross-sectional study involving adult patients with MASLD. The sample size was based on previous studies, assuming a correlation coefficient of 0.6 between Fibroscan^® and APRI for liver stiffness. The study recruited 300 patients with MASLD in the age group of 30 years to 70 years. Patients included had confirmed MASLD based on imaging evidence of hepatic steatosis and presence of metabolic dysfunction criteria according to recent nomenclature guidelines[6]. Exclusion criteria included significant alcohol consumption (> 20 g/day for women and > 30 g/day for men). Viral hepatitis, autoimmune liver disease, Wilson's disease, hemochromatosis, alpha-1 antitrypsin deficiency and current use of hepatotoxic medications were also evaluated to find out if there are additional etiologies other than MASLD. This study was conducted following recommendations of the ethics research committee and ethical clearance (ethical approval ref. no. is 20^th ECM I A/P22) from our institution and the research was conducted in accordance with the Declaration of Helsinki. Vibration controlled transient elastography, showing the amount of liver stiffness and CAP were determined using Fibroscan^® 502 Touch (Echosense, France) which was done with the patient in supine position, right arm fully extended and placed behind the head to expose the right side of the chest. The patient was instructed to take a deep breath in and hold it briefly during the measurement so as to minimize motion artifacts and obtain accurate results. The Fibroscan^® results were classified as: F0: 1-6 kPa, F1: 6.1-7 kPa, F2: 7.1-9 kPa, F3: 9.1-10.3 kPa, F4: ≥ 10.4 kPa. CAP score, representing the quantification of steatosis, was also determined. With results classified as follows: S0: 150-248 dB/m, S1: 249-260 dB/m, S2: 261-280 dB/m, S3: > 280 dB/m. CAP values obtained from FibroScan^® were used as the imaging reference for steatosis in this cross-sectional study. The research question and the iterative loop of the study is primarily to explore correlation between liver stiffness and CAP representing liver fibrosis and hepatic steatosis respectively, with blood based markers. With the newer terminology of MASLD post Delphi consensus 2023, liver biopsy is no longer a mandatory requirement for diagnosing and managing MASLD[6]. Liver biopsy was not performed for histological confirmation due to ethical and logistic considerations. Thus CAP (rather than histology) served as the comparator for blood-based steatosis analyses. Comprehensive baseline data were collected including body mass index, presence of diabetes mellitus, hypertension, dyslipidemia, and components of metabolic syndrome. The following measurements were done from blood samples of all patients who were included in the study: Serum alanine aminotransferase (ALT) with 45 U/L as upper limit of normal: Serum aspartate transaminase (AST) with 32 U/L as upper limit of normal; absolute lymphocyte count with 1000-4800 as the normal range; absolute neutrophil count with 2500-7000 as the normal range; platelet count with 150000-400000/µL as the normal range.

Liver enzymes- AST and ALT, absolute neutrophil count and absolute lymphocyte count were measured using the Nihon Kohden Fully Automatic CellTac Hematology Analyzer (inc. Japan) NLR, PLR, NPAR, APRI and FIB-4 were calculated for each patient.

The Statistical Package for the Social Sciences software version 27 was used for data analysis. Mean, standard deviation, median and percentages were obtained. Distribution normality of quantitative variables were determined using the Kolmogorov-Smirnov normality test. Spearman’s Rho correlation was used to determine their correlations and the Mann-Whitney test was used for comparison by gender. The receiver operating characteristic (ROC) curves were drawn to determine the diagnostic value of FIB-4 and APRI for the differentiation of less fibro-progressed (F1, F2) from advanced fibro-progression (F3, F4). The area under the ROC curve was calculated for each non-invasive test along with scatter plots to visualize the relationships between blood-based markers and LSM values. The optimal cut-off of all these indices were also determined for this purpose using the ROC curves. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy were calculated for the Non-Invasive parameters as well. P value ≤ 0.05 was regarded as statistically significant. For established markers, we compared our results against validated literature thresholds: FIB-4: Cut-off > 1.45 for excluding advanced fibrosis (NPV > 90%) and > 3.25 for ruling in advanced fibrosis (PPV > 65%) based on Sterling et al[15] and Shah et al[16] studies; APRI: Cut-off > 0.7 for significant fibrosis and > 1.0 for advanced fibrosis based on Wai et al[17] validation studies; For novel markers (PLR and NPAR), optimal cut-offs were determined using: Youden’s index (Sensitivity + Specificity - 1) from ROC curve analysis; maximum diagnostic accuracy approach; clinical relevance assessment.

Multiple testing correction: Given the simultaneous evaluation of five biomarkers, we applied Bonferroni correction for multiple comparisons. The significance level was adjusted from α = 0.05 to α = 0.01 (0.05/5 = 0.01) for correlation analyses and diagnostic performance comparisons. For individual marker performance against established cut-offs, we maintained α = 0.05 as these represent validation studies rather than exploratory analyses.

Sensitivity analysis: We performed sensitivity analysis using both literature-based and ROC-derived cut-offs for established markers to ensure robustness of our findings.

RESULTS

General characteristics of the study participants

A total of 300 MASLD patients were included. LSM using FibroScan^® categorized 37.7% of patients as F0, 10.0% as F1, 12.7% as F2, 5.0% as F3, and 34.6% as F4 (Table 1). We recorded baseline clinical variables including body mass index, presence of type 2 diabetes mellitus, dyslipidemia and systemic hypertension. These variables are summarized in Table 2. Gender-based comparisons of all indices revealed no significant differences except for LSM (Table 3).

Table 1 Frequencies of liver stiffness from FibroScan.

E	Counts	% of total
F0	113	37.7
F1	30	10.0
F2	38	12.7
F3	15	5.0
F4	104	34.6

Table 2 Baseline characteristics of participants, n (%).

Characteristic	Total (n = 300)	Male (n = 237)	Female (n = 63)
Age (years)	52.4 ± 11.2	52.1 ± 11.0	53.2 ± 11.8
BMI (kg/m2)	28.7 ± 4.3	28.9 ± 4.1	28.1 ± 4.8
Diabetes mellitus	187 (62.3)	149 (62.9)	38 (60.3)
Hypertension	203 (67.7)	162 (68.4)	41 (65.1)
Dyslipidemia	245 (81.7)	195 (82.3)	50 (79.4)

BMI: Body mass index.

Table 3 Comparison of different indices by gender.

Indices	Male	Female	P value
FIB-4	1.64 (0.83-3.15)	1.59 (0.94-2.54)	0.560
NLR	1.90 (1.54-2.81)	2.19 (1.12-3.21)	0.632
PLR	142.5 (98.2-189.7)	139.7 (102.4-178.3)	0.781
NPAR	14.13 (12.20-17.14)	14.04 (11.80-16.56)	0.793
APRI	1.23 (0.25-2.21)	1.09 (0.22-1.96)	0.297
CAP	270.00 (223-302)	273.00 (243.00-302.00)	0.642
Liver stiffness	9.30 (5.40-26.80)	4.80 (4.40-7.60)	0.002

FIB-4: Fibrosis index based on four factors; APRI: Aspartate transaminase to platelet ratio index; NLR: Neutrophil-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; NPAR: Neutrophil percentage-to-albumin ratio; CAP: Controlled attenuation parameter.

Correlation analysis

Correlation analysis revealed significant positive associations between established fibrosis markers and LSM, with FIB-4 demonstrating the strongest correlation (r = 0.537, P < 0.001), followed by APRI (r = 0.513, P < 0.001). Among novel markers, NPAR showed moderate positive correlation (r = 0.340, P = 0.001), while PLR exhibited a significant inverse relationship with LSM (r = -0.317, P = 0.002). NLR demonstrated weak correlation that did not reach statistical significance (r = 0.120, P = 0.259). The diagnostic accuracy of the non-invasive markers were assessed using ROC curve analysis (Figure 1). FIB-4 exhibited the highest diagnostic performance for differentiating advanced fibrosis (F3 and F4) from lower stages (F0-F2), with an area under curve (AUC) of 0.806 (P < 0.001), sensitivity of 69.2%, and specificity of 80.4%. APRI followed closely with an AUC of 0.772 (P < 0.001), sensitivity of 82.0%, and specificity of 62.7%. NLR and PLR had limited utility for fibrosis detection, with AUC values of 0.540 and 0.301, respectively. Table 4 summarizes the diagnostic performance metrics for differentiating advanced fibrosis (F3-F4) from lower stages, while Table 5 provides the detailed diagnostic performance for LSM assessment. The correlation patterns between individual blood-based markers and liver stiffness are visualized in scatter plots (Figure 2), demonstrating the relationship between FIB-4 (Figure 2A), APRI (Figure 2B), PLR (Figure 2C), NLR (Figure 2D), and NPAR (Figure 2E) with LSM values. For CAP, NPAR demonstrated the highest specificity (97.67%) and PPV (92.31%) for differentiating between S2 and S3, though its overall diagnostic accuracy remained moderate (AUC = 0.562, P = 0.172). The diagnostic performance for CAP assessment is presented in Table 6, demonstrating NPAR’s superior specificity for steatosis detection. The diagnostic performance for steatosis showing ROC curves for differentiating S2 and S3 steatosis grades are illustrated (Figure 3). APRI and FIB-4 did not show any correlation in CAP stratification, with AUCs of 0.478 and 0.459, respectively. Our study demonstrates good agreement between literature-established cut-offs and ROC-derived thresholds for FIB-4 and APRI, supporting the validity of our methodology. The close alignment of our cut-offs with established thresholds (Table 7).

Figure 1 Receiver operating characteristic curves for blood-based markers in differentiating advanced fibrosis (F3-F4) from lower stages (F0-F2). FIB-4: Fibrosis 4; APRI: Aspartate transaminase to platelet ratio index; NLR: Neutrophil-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; NPAR: Neutrophil percentage-to-albumin ratio; AUC: Area under curve; ROC: Receiver operating characteristic.

Figure 2 Scatter plot demonstrating. A: Scatter plot demonstrating correlation between fibrosis 4 and liver stiffness measurement; B: Scatter plot demonstrating correlation between aspartate transaminase to platelet ratio index and liver stiffness measurement; C: Scatter plot demonstrating correlation between platelet-to-lymphocyte ratio and liver stiffness measurement; D: Scatter plot demonstrating correlation between neutrophil-lymphocyte ratio and liver stiffness measurement; E: Scatter plot demonstrating correlation between neutrophil percentage-to-albumin ratio and liver stiffness measurement. FIB-4: Fibrosis 4; APRI: Aspartate transaminase to platelet ratio index; LSM: Liver stiffness measurement; E: Elasticity measurement in kilopascals; PLR: Platelet-to-lymphocyte ratio; NLR: Neutrophil-lymphocyte ratio; NPAR: Neutrophil percentage-to-albumin ratio.

Figure 3 Receiver operating characteristic curves for blood-based markers in differentiating severe steatosis (S3) from lower grades (S2 and below). FIB-4: Fibrosis index based on four factors; APRI: Aspartate transaminase to platelet ratio index; NLR: Neutrophil-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; NPAR: Neutrophil percentage-to-albumin ratio; ROC: Receiver operating characteristic.

Table 4 Diagnostic performance of the indices for the differentiation of F3 and F4 from lower stages.

Indices	AUC	P value	Sensitivity	Specificity	PPV (%)	NPV (%)	DA (%)
FIB-4	0.806	0.000	0.692	0.804	72.97	77.36	75.63
APRI	0.772	0.000	0.820	0.627	62.75	82.05	71.21
NLR	0.540	0.512	0.462	0.706	54.55	63.16	60.00
PLR	0.301	0.001	0.051	0.980	66.67	57.47	58.43
NPAR	0.673	0.005	0.615	0.745	64.86	71.7	68.88

FIB-4: Fibrosis index based on four factors; APRI: Aspartate transaminase to platelet ratio index; NLR: Neutrophil-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; NPAR: Neutrophil percentage-to-albumin ratio; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value; DA: Diagnostic accuracy.

Table 5 Diagnostic performance of the indices for liver stiffness measurement.

Indices	AUC	P value	Sensitivity	Specificity	PPV (%)	NPV (%)
FIB-4	0.806	0	0.692	0.804	72.97	77.36
APRI	0.772	0	0.82	0.627	62.75	82.05
NLR	0.54	0.512	0.462	0.706	54.55	63.16
PLR	0.301	0.001	0.051	0.98	66.67	57.47
NPAR	0.673	0.005	0.615	0.745	64.86	71.7

FIB-4: Fibrosis index based on four factors; APRI: Aspartate transaminase to platelet ratio index; NLR: Neutrophil-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.

Table 6 Diagnostic performance of the indices for controlled attenuation parameter.

Indices	AUC	P value	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)
FIB-4	0.516	0.769	40.54	72.09	71.43	41.33
APRI	0.566	0.236	50.08	32.56	67.42	50
NPAR	0.467	0.553	16.22	97.67	92.31	40.38
NLR	0.474	0.57	45.34	92.77	91.58	40.38
PLR	0.54	0.388	58.14	54.05	59.52	52.63

FIB-4: Fibrosis index based on four factors; APRI: Aspartate transaminase to platelet ratio index; NLR: Neutrophil-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; NPAR: Neutrophil percentage-to-albumin ratio; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.

Table 7 Cut-off validation against established thresholds.

Marker	Established cut-off	Our cut-off	Clinical utility
FIB-4	> 1.45 (rule-out)	> 1.42	Equivalent rule-out
APRI	> 0.7 (significant)	> 0.68	Validated threshold
PLR	Novel marker	> 142.5	Exploratory
NPAR	Novel marker	> 14.13	Exploratory

FIB-4: Fibrosis index based on four factors; APRI: Aspartate transaminase to platelet ratio index; PLR: Platelet-to-lymphocyte ratio; NPAR: Neutrophil percentage-to-albumin ratio.

For novel markers (PLR and NPAR), we acknowledge that our ROC-derived cut-offs require external validation before clinical implementation. The application of Bonferroni correction strengthens our findings by controlling for multiple comparisons, with FIB-4, APRI, PLR, and NPAR maintaining statistical significance even after adjustment.

In summary, FIB-4 and APRI which are time honoured blood based markers for fibrosis assessment, demonstrates high diagnostic accuracy and significant correlation with LSM as well. FIB-4 and APRI, known to correlate with liver biopsy, seem to correlate with fibroscan based liver stiffness as well. The novel marker of PLR showed higher specificity more than FIB-4 and APRI. NPAR shows potential as a specific marker for steatosis assessment but requires further validation. Even though NLR showed a modest correlation with LSM, its overall diagnostic utility was low and was not as useful as the other markers for fibrosis and steatosis stratification in this cohort. Overall these findings emphasize the potential for integrating non-invasive markers with existing diagnostic workflows in the management of MASLD.

DISCUSSION

The rising prevalence of MASLD in diverse populations and across a wide spectrum of socio-economic strata has underscored the need for reliable, cost-effective, and widely accessible diagnostic tools to assess hepatic fibrosis and steatosis[15]. There is great interest in developing and validating blood based markers to detect hepatic steatosis and liver fibrosis. These non-invasive methods are particularly useful when FibroScan^® is not readily accessible or in cases where repeated measurements are not feasible due to logistical or financial reasons. Deriving formulae and indices combining fibroscan values and blood based markers is also being explored. Our findings confirm that established blood-based fibrosis markers maintain their diagnostic utility when compared against transient elastography-derived LSM. The strong correlation observed between FIB-4 (r = 0.537) and APRI (r = 0.513) with LSM validates their continued relevance in the era of elastography-based assessment. These results are consistent with previous validation studies that demonstrated comparable performance between these serum markers and elastographic fibrosis staging[16]. APRI also offers comparable sensitivity and could serve as an effective screening tool. In contrast, while PLR exhibited an inverse relationship with LSM, its high specificity suggests that it could serve as an ancillary indicator of fibrosis severity. This was proposed in a previous study which mentioned that PLR ≥ 42.29 might be a protective factor of MASLD[17], however its comparison with FibroScan^® was not attempted. The high specificity (98.0%) of PLR suggests potential utility as a rule-in test, but the clinical significance of this finding requires validation in larger, multicenter studies. The promising performance of NPAR which demonstrates high specificity and positive predictive value for steatosis assessment could serve its potential as a practical tool in clinical settings although further studies are needed to validate the findings. Given that NPAR can be derived from routine blood tests, it offers a cost-effective and accessible option for early identification and stratification of MASLD patients, especially in resource-limited settings. However, it should be interpreted with caution, and we acknowledge that current results are preliminary and require external validation before clinical implementation. Although NLR seems to have a role, being a systemic inflammatory marker[18], its utility as LSM and CAP correlate may be constrained due to the multifactorial nature of inflammation in MASLD that may not directly reflect fibrotic or steatotic changes. The inverse correlation between PLR and LSM observed in our study aligns with prior studies linking thrombocytopenia and lymphopenia to advanced fibrosis[19]. Another reason could be the chronic inflammation in advanced liver disease causing thrombocytopenia, further lowering PLR as seen in hepatocellular carcinoma[20-22]. An approach for utilising these markers is integration of the markers with imaging techniques such as FibroScan^® so as to create a synergistic framework which enhances diagnostic precision, akin to FAST (FibroScan-AST) and AGILE score[14]. Moreover, the complementary use of these markers addresses the limitations of any single marker alone. However despite these encouraging findings and revelations the study has some inherent limitations. The single-center setting may limit the generalizability of these findings to broader populations with varying demographic and clinical profiles. The predominantly male composition of our cohort (78.9%) represents a significant limitation that may affect the generalizability of our findings. Additionally, even though the sample size was sufficient to detect statistically significant correlations, further multicentric studies with larger cohorts are needed to validate these findings and to set an optimal cut-off value for these markers in diverse clinical scenarios across various geographic regions. Another possible area to focus is to elucidate the dynamic changes of the non-invasive parameters over time, particularly in response to therapeutic interventions. This may ultimately lead to personalized management strategies in MASLD, reducing reliance on invasive procedures and in resource-limited settings where repeated transient elastography measurements are challenging, these markers may serve as valuable screening tools and monitoring aids.

CONCLUSION

To conclude, our study highlights that while FIB-4 and APRI remain as mainstay LSM correlates, newer markers such as PLR hold promise. NPAR can be considered for non-invasive evaluation of hepatic steatosis, although further studies are needed to validate it. Future multicenter studies with longitudinal follow-up are essential to establish the clinical role of these accessible biomarkers as adjuncts to, rather than replacements for, transient elastography in MASLD management.