Association between monocyte-to-lymphocyte ratio and metabolic dysfunction-associated steatotic liver disease and hepatic steatosis: evidence from NHANES 2017–2020

Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a metabolic liver disorder characterized by abnormal hepatic fat accumulation, predominantly affecting individuals with cardiometabolic risk factors such as obesity, insulin resistance, type 2 diabetes, and metabolic syndrome (1). The pathological spectrum of MASLD ranges from simple steatosis (fat accumulation only) to steatohepatitis characterized by hepatocellular inflammation, and may further progress to liver fibrosis, cirrhosis, and even hepatocellular carcinoma (HCC) (2,3). In recent years, with changes in lifestyle and increasing prevalence of obesity, the incidence of MASLD has risen markedly. The global prevalence is estimated at approximately 25–30%, making it one of the leading causes of end-stage liver disease and liver transplantation. MASLD significantly impairs patients’ quality of life and imposes a substantial burden on public health systems (4,5).

Chronic inflammation is considered a key driver in the pathogenesis of MASLD. As an emerging inflammatory immune biomarker, the monocyte-to-lymphocyte ratio (MLR) has gained considerable attention in recent years. Monocytes, as important effector cells in inflammatory responses, promote disease progression by phagocytosing lipids and secreting inflammatory cytokines (6,7); lymphocytes, on the other hand, are essential regulators of immune responses, and changes in their numbers reflect the activity of the immune system (8). The MLR integrates inflammatory and immune information and provides a more comprehensive reflection of systemic inflammatory status and immune balance. It has demonstrated promising predictive value in a variety of chronic conditions. Existing studies have shown that MLR plays an important role in prognostic evaluation of cardiovascular diseases (9-11), malignant tumors (12,13), and infections (14,15). However, the relationship between MLR and MASLD—particularly hepatic steatosis and liver fibrosis—remains insufficiently elucidated.

In this context, the present study conducted a cross-sectional analysis based on the large population dataset from the National Health and Nutrition Examination Survey (NHANES) in the United States. The aim was to systematically evaluate the association between MLR and MASLD, as well as its core pathological features—hepatic steatosis and liver fibrosis. By adjusting for multiple potential confounders, this study seeks to clarify the potential role of MLR in early diagnosis and risk stratification of MASLD, thereby providing scientific evidence for future clinical research and intervention strategies. We present this article in accordance with the STROBE reporting checklist (available at https://tgh.amegroups.com/article/view/10.21037/tgh-25-110/rc).

Methods

Study population and design

NHANES is a nationally representative survey conducted in the United States using a complex, multistage probability sampling design, aimed at comprehensively collecting information on dietary habits and overall health status of U.S. residents (16). The survey is carried out every two years, with each cycle recruiting a different set of participants. The study protocol was approved by the Ethics Review Board of the Centers for Disease Control and Prevention (CDC), and written informed consent was obtained from all participants. It was also approved by the Ethics Review Board of the National Center for Health Statistics (NCHS). Details regarding the study design and data collection procedures of NHANES can be found on the CDC official website (https://www.cdc.gov/nchs/nhanes/). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

This analysis utilized continuous NHANES data from the 2017–2020 cycles, involving 6,801 participants. Exclusion criteria were as follows: missing data required for calculating MLR (n=3,409); missing or incomplete data on median liver stiffness/CAP or elastography (n=2,941); 42 participants positive for hepatitis B surface antigen; 177 participants positive for hepatitis C antibody or hepatitis C virus (HCV) RNA; 953 heavy drinkers (defined as consuming ≥4–5 alcoholic drinks per day); and 1,237 participants under 18 years of age. Ultimately, 6,801 individuals were included in the final analysis. Figure 1 presents the flowchart of sample selection.

Figure 1 Flowchart of participant selection. CAP, controlled attenuation parameter; HBV, hepatitis B virus; HCV, hepatitis C virus; LSM, liver stiffness measurement; MLR, monocyte-to-lymphocyte ratio; NHANES, National Health and Nutrition Examination Survey.

Study variables

In this study, we adopted the newly established nomenclature system for MASLD, in which hepatic steatosis is considered one of the core diagnostic features. Because hepatic steatosis in the NHANES dataset is assessed using the controlled attenuation parameter (CAP) obtained from transient elastography, we defined hepatic steatosis as CAP ≥285 dB/m. This threshold has been validated in U.S. populations, demonstrating approximately 80% sensitivity and 77% specificity for detecting hepatic steatosis, and thus serves as the foundational criterion for defining MASLD in the present analysis (17). In addition, according to the diagnostic consensus for MASLD, participants were classified as having MASLD only if hepatic steatosis was present and at least one cardiometabolic criterion (e.g., obesity, type 2 diabetes, or other metabolic abnormalities) was met (18).

In this study, MLR was analyzed as the exposure variable. The calculation of MLR was performed by dividing the monocyte count (unit: 1,000 cells/µL) by the lymphocyte count (unit: 1,000 cells/µL). Monocyte and lymphocyte counts were obtained from complete blood count measurements performed using the Coulter^® DxH 800 automated hematology analyzer [at the NHANES Mobile Examination Center (MEC)]. Because the distribution of MLR as a continuous marker was skewed among participants, log transformation was applied for subsequent statistical analyses. Liver status was assessed using vibration-controlled transient elastography (VCTE), conducted by trained NHANES personnel using the FibroScan^® 502 V2 Touch device. This device measures ultrasound attenuation to determine the CAP value, which reflects the degree of hepatic steatosis. Liver fibrosis assessment also relied on FibroScan, combining ultrasound and VCTE to evaluate liver stiffness. Liver stiffness measurement (LSM) was used as an indicator of the severity of hepatic fibrosis.

In this study, we selected covariates encompassing demographic characteristics, lifestyle factors, and metabolic indicators. Specifically, these included age, sex, race/ethnicity (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, and other races), smoking status (defined as having smoked more than 100 cigarettes in one’s lifetime), educational level, marital status, diabetes, hypertension, poverty-income ratio, body mass index (BMI), waist circumference (cm), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), fasting blood glucose (FBG), glycohemoglobin, alanine aminotransferase (ALT), and aspartate aminotransferase (AST). BMI was calculated as weight (kg) divided by the square of height (m), and participants were categorized into three groups based on BMI values: normal weight (<25 kg/m²), overweight (≥25 and <30 kg/m²), and obesity (≥30 kg/m²) (19).

Statistical analysis

Statistical analyses were performed using R software (version 4.4.1) and EmpowerStats (version 4.2). Continuous variables were summarized as means and standard deviations (SDs) and were described using weighted linear regression models. To examine the associations of MLR with MASLD, CAP, and LSM, multivariable regression analyses were conducted to calculate odds ratio (OR)/β estimates and their corresponding 95% confidence intervals (CIs). Three models were constructed to control for potential confounding factors: Model 1 was unadjusted; Model 2 adjusted for sex, age, and race/ethnicity; and Model 3 further adjusted for all potential covariates.

We first employed a threshold-effect analysis to identify potential nonlinear associations and detect possible inflection points in the relationships between MLR and MASLD, CAP, and LSM. Subsequently, we applied smooth curve fitting supplemented with restricted cubic spline (RCS) functions to provide a more refined and intuitive depiction of these associations. Furthermore, comprehensive subgroup analyses were conducted to assess whether age, sex, race, BMI, and other covariates modified the association between MLR and MASLD.

A P value <0.05 was considered statistically significant. To minimize the influence of sampling variability and enhance the robustness of estimates, weighted analyses were applied, providing more accurate and reliable results.

Results

Baseline characteristics

A total of 6,801 participants were included in this study, of whom 4,351 were in the non-MASLD group and 2,450 in the MASLD group. Significant differences were observed between the two groups in baseline characteristics (Table 1). Regarding demographic features, participants in the MASLD group were significantly older than those in the non-MASLD group (51.95±16.53 vs. 46.73±19.29 years, P<0.001), and the proportion of males was higher (51.18%). Significant differences were also noted between the groups in race/ethnicity distribution, education level, marital status, and smoking history (all P<0.001), whereas poverty-income ratio did not differ significantly (P=0.91).

For anthropometric and metabolic indicators, both BMI and waist circumference were significantly higher in the MASLD group compared with the non-MASLD group (P<0.001). BMI classification showed a markedly higher prevalence of obesity (BMI ≥30 kg/m²) in the MASLD group (67.74% vs. 26.60%, P<0.001). Regarding lipid profiles, LDL levels did not differ between groups (P=0.52), whereas HDL, TG, and TC were all significantly higher in the MASLD group (P<0.001; P=0.008; P<0.001, respectively). For glucose metabolism markers, FBG (6.94±2.52 vs. 5.84±1.58 mmol/L) and glycohemoglobin (6.19%±1.34% vs. 5.60%±0.85%) were significantly elevated in the MASLD group (both P<0.001).

With respect to liver-related indicators, the MASLD group showed markedly higher CAP values (329.81±33.03 vs. 224.59±39.66 dB/m, P<0.001), and LSM was also significantly increased (7.00±5.88 vs. 5.19±4.03 kPa, P<0.001). In addition, both MLR and Ln(MLR) were significantly higher in the MASLD group (P<0.001), suggesting elevated inflammatory levels.

Association between Ln(MLR) and MASLD

Multivariable logistic regression analyses showed that Ln(MLR) was significantly associated with the occurrence of MASLD. In the continuous variable analysis, Ln(MLR) demonstrated a stable positive association with MASLD. In the unadjusted model, each 1-unit increase in Ln(MLR) was associated with a 3.24-fold higher risk of MASLD (OR =3.24, 95% CI: 2.83–3.71, P<0.001). This association remained robust after adjustment for age, sex, and race in Model 2 (OR =3.19, 95% CI: 2.78–3.67, P<0.001). Further adjustment for demographic characteristics, lipid parameters, smoking status, and liver function indicators (Model 3) did not attenuate the association, which remained statistically significant (OR =2.58, 95% CI: 1.99–3.35, P<0.001), indicating that Ln(MLR) is an independent risk factor for MASLD.

In categorical analyses, using the lowest tertile of Ln(MLR) (T1) as the reference, the association between T2 and MASLD was significantly elevated across all models. In the fully adjusted Model 3, T2 was associated with a 30% increased risk of MASLD (OR =1.30, 95% CI: 1.02–1.66, P=0.04). The highest tertile (T3) exhibited a stronger risk effect, with MASLD risk increasing to 2.17-fold after full adjustment (OR =2.17, 95% CI: 1.70–2.77, P<0.001) (Table 2).

To further explore the relationship between Ln(MLR) and MASLD, two-piecewise linear regression models, smooth curve fitting, and RCS analyses were performed. The two-piecewise linear model (Table 3) suggested an inflection point at −1.32 for Ln(MLR). Below this threshold, the association between Ln(MLR) and MASLD was weaker but remained significant (OR =1.74, 95% CI: 1.07–2.83, P=0.02). Above −1.32, the association strengthened considerably (OR =3.78, 95% CI: 2.34–6.11, P<0.001), suggesting an accelerated increase in MASLD risk at higher inflammation levels. However, the log-likelihood ratio test comparing the single linear model with the two-piecewise model did not reach statistical significance (P=0.06), indicating that the segmented model was not significantly superior to the linear model. Smooth curve fitting (Figure 2A) showed a continuously increasing MASLD risk with rising Ln(MLR), reflecting a stable dose-response relationship. The RCS analysis (Figure 2B) showed a similar trend: although the overall association between Ln(MLR) and MASLD was significant, tests for nonlinearity did not reach statistical significance (P>0.05). Collectively, although segmented modeling and curve patterns suggested a potential risk amplification point near −1.32, the statistical evidence was insufficient to confirm a definitive threshold effect. Overall, the association between Ln(MLR) and MASLD appeared to follow a predominantly linear positive relationship, with a more pronounced risk increase at higher levels of Ln(MLR).

Figure 2 Association between Ln(MLR) and MASLD. (A) Adjusted smooth curve showing the association between Ln(MLR) and MASLD. The red line represents the fitted curve, and the blue dashed lines indicate the 95% confidence intervals. (B) RCS curve showing the dose-response association between Ln(MLR) and MASLD risk. Blue lines indicate the estimated effects, and shaded areas represent 95% CI. MLR, monocyte-to-lymphocyte ratio; MASLD, metabolic dysfunction-associated steatotic liver disease; RCS, restricted cubic spline; CI, confidence interval.

Association between Ln(MLR) and CAP

Multivariable linear regression analysis demonstrated a significant association between Ln(MLR) and CAP (Table 4). In the continuous variable analysis, each 1-unit increase in Ln(MLR) was associated with an average increase of 36.58 dB/m in CAP in the unadjusted model (Model 1) (β=36.58, 95% CI: 32.87–40.29, P<0.001). This association remained robust after adjusting for age, sex, and race in Model 2 (β=34.71, 95% CI: 31.11–38.31, P<0.001). After further adjustment for demographic characteristics, lipid profiles, smoking status, and liver function indicators (Model 3), the positive association persisted (β=24.33, 95% CI: 19.46–29.19, P<0.001), indicating that Ln(MLR) is an independent determinant of elevated CAP. In the tertile-based categorical analysis, compared with the lowest tertile of Ln(MLR) (T1), both the middle (T2) and highest tertiles (T3) were significantly associated with higher CAP values. In Model 3, T2 was associated with a 6.95 dB/m increase in CAP (β=6.95, 95% CI: 2.40–11.51, P=0.003), while T3 showed a markedly stronger association, with CAP increasing by 21.31 dB/m (β=21.31, 95% CI: 16.71–25.92, P<0.001). Trend tests across tertiles reached statistical significance in all models (P for trend <0.001), indicating a clear dose–response relationship between Ln(MLR) and CAP.

In further threshold effect analyses (Table 5), the single linear model revealed a significant positive linear association between Ln(MLR) and CAP (β=24.33, 95% CI: 19.46–29.19, P<0.001). The two-piecewise linear model suggested a potential inflection point at Ln(MLR) =−1.58. When Ln(MLR) was below this inflection point, its association with CAP was not significant (β=−1.83, 95% CI: −16.00 to 12.33, P=0.80). However, when Ln(MLR) exceeded −1.58, the association strengthened substantially (β=33.20, 95% CI: 26.57–39.83, P<0.001). The log-likelihood ratio test (P<0.001) indicated that the two-piecewise model provided a significantly better fit than the single linear model, suggesting a possible acceleration point for CAP elevation around Ln(MLR) =−1.58.

Smooth curve fitting (Figure 3A) demonstrated a continuously increasing trend, with CAP rising progressively as Ln(MLR) increased. RCS analyses (Figure 3B) also supported this upward trend, and the curve shape further revealed a steeper slope beyond the identified inflection point, consistent with a potential threshold effect. Collectively, these findings indicate a significant positive association between Ln(MLR) and CAP, with the relationship becoming stronger at higher Ln(MLR) levels, suggesting that elevated inflammatory status is closely linked to greater severity of hepatic steatosis.

Figure 3 Association between Ln(MLR) and CAP. (A) Adjusted smooth curve showing the association between Ln(MLR) and CAP. The red line represents the fitted curve, and the blue dashed lines indicate the 95% CIs. (B) RCS curve showing the association between Ln(MLR) and CAP. Blue lines indicate the estimated effects, and shaded areas represent 95% CIs. CAP, controlled attenuation parameter; CI, confidence interval; MLR, monocyte-to-lymphocyte ratio.

Association between Ln(MLR) and LSM

Multivariable linear regression results showed that in Model 3, which fully adjusted for sociodemographic variables, lipid profiles, smoking status, and liver enzyme levels, the association between Ln(MLR) and LSM was not significant (β=−0.01, 95% CI: −0.50 to 0.48, P=0.90) (Table 6). Threshold effect analysis (Table S1) similarly indicated no significant association in the single linear model (β=−0.01, P=0.97). The two-piecewise linear model suggested a potential inflection point at −1.92; below this value, Ln(MLR) showed a marginal negative association with LSM (β=−2.85, P=0.059), whereas above this threshold, the association again remained nonsignificant (β=0.20, P=0.47). Moreover, the log-likelihood ratio test did not reach statistical significance (P=0.055), indicating that the two-piecewise model did not offer a clear advantage. The smooth curve in Figure S1A showed a mild upward trend between Ln(MLR) and LSM, though with only minimal magnitude. Likewise, the RCS curve in Figure S1B did not reveal any distinct nonlinearity or inflection. Overall, both plots indicate a relatively stable relationship between Ln(MLR) and LSM, with neither linear nor nonlinear analyses demonstrating strong associations. Unlike its pronounced association with CAP, Ln(MLR) exerts only a weak effect on LSM, suggesting that this inflammatory marker has limited explanatory value for early-stage liver fibrosis.

Subgroup analysis

Figure 4 illustrates the associations between Ln(MLR) and MASLD risk across various subgroups. All subgroups showed a highly significant positive association (all P<0.001), indicating that elevated Ln(MLR) is consistently and strongly linked to increased MASLD risk across the entire study population. Among these, non-Hispanic Black participants exhibited the strongest association (OR =4.05, 95% CI: 3.04–5.41). Interaction analyses revealed significant interactions for BMI (P=0.02) and smoking status (P=0.04). The association was relatively attenuated among individuals who were overweight (OR =2.12), whereas it was strengthened among smokers (OR =3.76). No significant interactions were observed for other characteristics, including sex, marital status, race/ethnicity, and education level (all P>0.05), suggesting that these factors did not alter the overall strength or direction of the association between Ln(MLR) and MASLD.

Figure 4 Subgroup analyses of the association between Ln(MLR) and MASLD risk. AA degree, Associate of Arts degree; BMI, body mass index; CI, confidence interval; GED, general educational development; MASLD, metabolic dysfunction-associated steatotic liver disease; MLR, monocyte-to-lymphocyte ratio; OR, odds ratio.

Discussion

In this cross-sectional study of 6,801 U.S. adults, we systematically evaluated the associations between MLR and MASLD as well as key hepatic indicators. Our findings demonstrated a significant and robust positive relationship between MLR and MASLD risk, which remained independent even after comprehensive multivariable adjustments. In addition, the association between MLR and hepatic steatosis, reflected by CAP, was also highly significant, showing a clear dose-response pattern and an evident threshold effect, with a more pronounced increase in risk at higher inflammatory levels. In contrast, the relationship between MLR and liver stiffness (LSM) was not significant after full adjustment, and neither RCS nor threshold analyses supported a definitive nonlinear pattern, suggesting that the influence of inflammation on liver fibrosis may be weaker or confounded by other metabolic factors. Notably, our subgroup analyses revealed a high degree of consistency across all major population subgroups, with significant interactions observed for BMI and smoking status, and the strongest risk elevation detected among non-Hispanic Black participants. Collectively, these findings indicate that MLR is more likely a specific and sensitive inflammatory marker for MASLD and hepatic steatosis rather than an effective indicator of liver fibrosis, providing new evidence for its potential application in the early identification and risk stratification of MASLD.

Previous studies have extensively explored the clinical value of MLR in various liver diseases, including liver failure and HCC. Li et al. conducted a retrospective analysis of clinical indicators and demonstrated that MLR had significant prognostic value in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). Their study identified an optimal cutoff value of 0.399, and patients in the high-MLR group had significantly lower 90-day survival rates than those in the low-MLR group. After multivariable adjustment, MLR remained an independent prognostic predictor for HBV-ACLF (20). Similarly, Wang et al. analyzed 347 patients with HBV-ACLF and found that non-survivors had significantly higher median MLR levels than survivors. MLR was significantly associated with 90-day mortality, further supporting its potential value as a prognostic biomarker (21). MLR has also drawn increasing attention in the context of HCC. In a prospective cohort of 606 HCC patients, Wang et al. reported that elevated MLR was strongly associated with tumor recurrence, early recurrence, overall survival, and adverse outcomes. Moreover, baseline MLR and its dynamic changes during follow-up might predict long-term survival after recurrence (22). He et al. also suggested that preoperative MLR could serve as a promising biomarker for early recurrence and prognosis in HCC patients undergoing hepatic resection (23). Additionally, in a single-center prospective cohort, Wu et al. identified MLR >0.4 as an independent predictor of early malnutrition risk in patients with liver cirrhosis (24). Lashen et al. further demonstrated that MLR could predict clinical outcomes in patients with acute decompensation (AD) and ACLF (25), while a recently developed inflammation-based scoring system incorporating MLR was validated as an effective tool for predicting long-term mortality in HBV-ACLF patients (26). Notably, earlier studies have also shown that MLR correlates strongly with liver function biomarkers and varies significantly with increasing severity of hepatic steatosis (27). Our findings provide an additional perspective on its clinical utility, further supporting the potential diagnostic and prognostic value of MLR in fatty liver disease and related hepatic conditions.

As a simple hematological indicator reflecting the immune and inflammatory status of the body, the MLR has increasingly been recognized for its role in various metabolic and inflammatory diseases. Monocytes, as key immune cells, participate primarily in innate immune responses by recognizing and eliminating invading pathogens while playing a central role in pro-inflammatory processes (28). During both acute and chronic inflammation, monocytes secrete cytokines such as tumor necrosis factor-α and interleukin-1β to amplify local and systemic inflammatory responses, thereby contributing to tissue injury and disease progression (29,30). At sites of inflammation, monocytes accumulate and differentiate into M1 and M2 macrophages. M1 macrophages exhibit a pro-inflammatory phenotype and rely predominantly on glycolysis for energy. In contrast, M2 macrophages, characterized by anti-inflammatory functions, derive energy mainly from oxidative phosphorylation (OXPHOS) and fatty acid oxidation (FAO). Shifting macrophage metabolism from glycolysis toward OXPHOS suppresses M1 activation while promoting M2 polarization, ultimately exerting anti-inflammatory and reparative effects (31). Notably, monocyte-derived macrophages with distinct pro- and anti-inflammatory properties have been shown to influence both the severity and clinical course of liver diseases (32). Lymphocytes, on the other hand, are critical components of adaptive immunity. By recognizing specific antigens and initiating targeted immune responses, they play essential roles in immune tolerance and immunological memory (33). A reduction in lymphocyte proportion often indicates impaired immune function, which may lead to decreased immune tolerance or immune evasion (34,35). Thus, as a marker integrating information on immune and inflammatory dynamics, MLR reflects the balance between monocytes and lymphocytes and thereby mirrors alterations in systemic immune responses and inflammatory activity.

Chronic low-grade inflammation is widely recognized as a key driver in the pathophysiological progression of MASLD, promoting the transition from simple lipid accumulation to hepatocellular injury, inflammation, and fibrosis. In the early stages of MASLD, hepatic lipid deposition predominates; however, as the disease advances, inflammatory activity and fibrosis gradually intensify, potentially culminating in cirrhosis or HCC. Monocytes play a pivotal role throughout this process, not only by participating in inflammatory responses but also by contributing to tissue repair and fibrogenesis. Evidence indicates that monocyte infiltration and activation represent early features in the progression of MASLD to metabolic dysfunction-associated steatohepatitis (MASH) (36). Likewise, aberrant lymphocyte activation may further promote hepatic inflammation and accelerate disease deterioration (37). In our study, the significant association between MLR and MASLD reinforces the central role of immune-mediated inflammation in the initiation and progression of MASLD. Elevated MLR not only reflects heightened systemic inflammatory status but may also indicate a critical transition from simple steatosis to inflammation and early fibrotic changes.

This study is the first to systematically confirm MLR as an independent inflammatory marker of MASLD. After comprehensive adjustment for potential confounders, increased Ln(MLR) remained strongly associated with a higher risk of MASLD, with consistently positive associations observed across all subgroups, among which non-Hispanic Black participants exhibited the greatest risk elevation. Furthermore, we observed an independent and significant positive relationship between Ln(MLR) and hepatic steatosis measured by CAP, accompanied by a clear dose-response pattern. RCS and threshold analyses further revealed that this association was more pronounced at higher Ln(MLR) levels, suggesting that inflammatory activation may play a critical role in exacerbating hepatic fat accumulation. In contrast with its clear association with CAP, Ln(MLR) showed no significant relationship with liver stiffness (LSM), and neither linear nor nonlinear analyses identified any meaningful threshold or strong association. These findings suggest that MLR more likely reflects early steatosis and inflammatory processes in MASLD rather than hepatic fibrosis. The development of liver fibrosis involves complex interactions among hepatic stellate cell activation, extracellular matrix deposition, and multiple signaling pathways; thus, a single inflammatory marker may be insufficient to fully capture this highly dynamic process, a notion consistent with prior literature reporting weak correlations between inflammatory biomarkers and fibrosis (38,39). This highlights the need for future studies incorporating more refined immune cell profiling and fibrosis-specific biomarkers to comprehensively elucidate the mechanisms linking immune inflammation to different stages of MASLD.

Nevertheless, several limitations of this study should be acknowledged. First, the cross-sectional design restricts our ability to infer causality. Although we observed a significant association between MLR and MASLD, the absence of temporal information prevents us from determining whether elevated MLR plays a causal role in the onset or progression of MASLD. Therefore, future prospective cohort studies are warranted to validate the predictive value of MLR in MASLD risk assessment and to further elucidate its potential mechanistic involvement in disease development. Second, the diagnosis of MASLD in this study relied primarily on non-invasive imaging and biochemical assessments, such as transient elastography (CAP/LSM), liver enzyme levels, and lipid profiles. Although these methods are widely applied in epidemiological and clinical settings, liver biopsy—considered the gold standard—was not included. Due to its invasive nature and limited feasibility in large population-based surveys, liver biopsy could not be incorporated, which may introduce diagnostic misclassification, particularly regarding the detection of early-stage steatosis. Additionally, the NHANES database does not include markers required for diagnosing specific liver diseases—such as autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), or drug-induced liver injury (DILI)—including antinuclear antibody (ANA), anti-smooth muscle antibody (SMA), anti-mitochondrial antibody (AMA), or detailed medication exposure data. Thus, complete exclusion of these conditions was not possible. However, given their extremely low prevalence in the general population, their impact on the overall results is likely minimal. Moreover, as a hematological marker, MLR may be influenced by multiple factors, including acute infections, medication use, and comorbid conditions. Therefore, its clinical interpretation should be integrated with patient history and other inflammatory biomarkers to ensure reliability. This study also did not incorporate lifestyle factors such as dietary patterns and physical activity, which are known to play critical roles in the development and progression of MASLD. The absence of these variables may introduce residual confounding. Future research should integrate these important lifestyle determinants to enhance the accuracy and interpretability of analytical models.

In summary, this study is the first to systematically reveal the significant association and potential non-linear pattern between MLR and MASLD, underscoring the critical role of immune-mediated inflammation in the early steatotic phase of MASLD. Our findings suggest that MLR holds potential clinical value as a risk assessment tool. Future research should incorporate longitudinal cohort studies and mechanistic experiments to further investigate the dynamic changes of MLR across different pathological stages of MASLD, and to evaluate its feasibility as a clinical early-warning biomarker and potential immunomodulatory target. Such efforts will help establish a more robust theoretical foundation for precision prevention of MASLD and provide new directions for optimizing related intervention strategies.

Conclusions

This study demonstrates a significant and independent association between the MLR and both MASLD and hepatic steatosis, whereas its relationship with liver fibrosis appears limited. These findings indicate that MLR more accurately reflects early steatosis and inflammatory activity in MASLD. Although our results support the potential utility of MLR as an inflammatory biomarker for MASLD, further longitudinal investigations are needed to confirm its predictive value.

Acknowledgments

We would like to thank all NHANES staff and participants.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tgh.amegroups.com/article/view/10.21037/tgh-25-110/rc

Peer Review File: Available at https://tgh.amegroups.com/article/view/10.21037/tgh-25-110/prf

Funding: This study was funded by the National Natural Science Foundation of China (No. 82205009), the Clinical Research Project of the Shanghai Health and Family Planning Commission (No. 20214Y0178), the Three-Year Action Plan for Accelerating the Inheritance and Innovation of Traditional Chinese Medicine in Shanghai (No. 1-2-3), and the Innovation and Transformation Capacity Training Program of the Three-Year Action Plan for Advancing Clinical Skills and Innovation in Municipal Hospitals in Shanghai (No. SHDC2022CRD009).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tgh.amegroups.com/article/view/10.21037/tgh-25-110/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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