Association between the lymphocyte-to-high-density lipoprotein ratio and metabolic dysfunction-associated steatotic liver disease among US adults: a cross-sectional study from NHANES 2017 to 2020

Abstract

Background

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a liver disease that is strongly associated with chronic low-grade inflammation. Stage 3 of MASLD is characterized by excessive formation of connective tissues, commonly referred to as liver fibrosis. Although numerous inflammatory markers have been identified and extensively studied, including the tumor necrosis factor-α and interleukin-6 have been studied [Byrne CD, Targher G. NAFLD: a multisystem disease. J Hepatol. 2015;62(1 Suppl):S47–64], the lymphocyte-to-high-density lipoprotein ratio (LHR) as a new biomarker that has not been sufficiently studied. This study aims to investigate the relationship between LHR levels and MASLD, determine its potential as a predictive marker for steatosis and fibrosis stages.

Methods

This was a population-based study using data from 15,560 participants in the 2017–2020 National Health and Nutrition Examination Survey (NHANES) database. The study aimed to explore the relationship between LHR and MASLD. The disease progression was tracked by continuously measuring CAP and liver stiffness measurements. Participants who exhibited a median Controlled Attenuation Parameter (CAP) of 248 dB/m or higher were deemed to have hepatic steatosis. The LHR was calculated by dividing the lymphocyte count by the high-density lipoprotein cholesterol (HDL-C) level. Multivariate linear regression models were employed to explore the linear association between LHR and MASLD. Fitted smoothing curves and threshold effect analysis were employed to display nonlinear relationships. A two-part linear regression model was employed to estimate threshold effects. Subgroup analyses were conducted to determine the consistency of this association across various demographic groups.

Results

A total of 6,950 adults aged 18 years and older were enrolled in the study, with an average age of 48.15 ± 17.10 years (49.14% male, 50.86% female). The adjusted multiple logistic regression analysis revealed a significant positive correlation between LHR and MASLD (OR: 1.64, 95% CI: 1.40–1.92). Using the complex two-piece linear regression model, we observed an inverted L-shaped association between LHR and CAP, suggesting a critical inflection point at -2.58. Subgroup analyses indicated a pronounced association of the LHR index with obese individuals (OR: 1.96, 95% CI: 1.66–2.32) and females (OR: 1.76, 95% CI: 1.25–2.46). There was no significant association between LHR and clinically significant fibrosis.

Conclusion

The LHR index is positively correlated with MASLD among US adults. Therefore, LHR may be a robust marker for early screening, diagnosis, and monitoring of treatment efficacy in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-024-03565-5.

Introduction

The Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disorder globally, affecting nearly 2 billion people with a prevalence rate of 25%. This condition has emerged as a significant challenge to healthcare systems worldwide [1–3]. Clinically, MASLD includes a wide range of hepatic pathologies, ranging from simple steatosis to more advanced stages marked by infiltration of leukocyte, ballooning of hepatocytes, severe cirrhosis and fibrosis [4–6]. Beyond hepatic complications, MASLD also associates with diverse extrahepatic conditions, including obesity, insulin resistance, type 2 diabetes mellitus (T2DM), hypertension, and dyslipidemia [7–9]. These metabolic disorders increase the risk of cardiovascular disease (CVD) and mortality rates among MASLD patients [10]. Therefore, identifying risk factors and developing novel therapeutic targets is crucial for the effective management of patients.

The immune system (including the innate and adaptive immunity) play a crucial role in the development of MASLD [11, 12]. Studies have shown that during the development of NASH, the composition of immune cells in the liver is altered, causing potential damage to the parenchymal cells [13]. Case-control studies have shown that elevated IL-17 A + lymphocyte counts increase the risk of liver damage in MASLD patients [14]. MASLD patients often exhibit a lipid profile that promotes atherosclerosis, characterized by elevated triglyceride levels, decreased concentrations of HDL-C, and impaired glucose metabolism [15]. Impaired antioxidant capacity of HDL in MASLD patients correlates with elevated MASLD biomarker levels [16]. This suggests that decreased HDL-C levels may suppress the HDL’s antioxidant function, exacerbating MASLD progression. These imply that combining lymphocyte count with HDL-C levels may be a better marker for assessing the MASLD risk compared with either parameter applied alone. Chen et al. identified the LHR as a novel inflammatory marker that influence the severity of metabolic syndrome (MetS) [17]. It has also been reported that LHR modifies the odds ratios for MetS [18]. Given that MASLD is a manifestation of MetS, LHR can potentially predict the MASLD severity. This deduction is supported by evidence from clinical trials demonstrating that liver fat accumulation in MASLD directly correlates with post-prandial HDL-TG enrichment and HDL dysfunction [20]. Moreover, the prediction potential of lymphocyte counts for predicting MASLD risk has been validated as well [19].

Currently, the precise relationship between the LHR and the progression of hepatic steatosis and fibrosis is not well understood.

The National Health and Nutrition Examination Survey (www.cdc.gov/nchs/nhanes) was designed to investigate the health and nutritional status of adults and children in the United States [20]. The survey involved physical examinations and interviews. Several cross-sectional, nationally representative health examination surveys were included in the NHANES program. Questions about demographics, health insurance status, dietary habits, acute and chronic medical issues, mental health, and prescription drug were employed in the health interview. The examination components may change between survey cycles but typically include blood pressure, dental exams, vision, hearing, dermatology, fitness, balance and strength testing, respiratory testing, taste and smell, and body measurements. Hematology, organ and endocrine function, environmental exposure, dietary biomarkers, metabolic and cardiovascular health, and infectious disease are among the laboratory components obtained in the study.

To address the current knowledge gap in MASLD, we performed this cross-sectional investigation using data from the NHANES 2017–2020 to assess the association between LHR and MASLD among the adult US population. The aim was to determine whether LHR can predict MASLD, in the initial stages of hepatic steatosis and the more advanced stages of fibrosis.

Materials & methods

Study participants

Data from the 2017–2020 NHANES dataset were analyzed in this study, which included liver ultrasound transient elastography to assess liver conditions. Detailed information about the NHANES study design and dataset can be accessed at https://www.cdc.gov/nchs/nhanes/. NHANES collects data on potential health risk factors and nutritional status of the U.S. non-institutionalized civilian population, using a complex, stratified, multistage probability cluster sampling design to achieve a nationally representative sample. In this study, we enrolled data from the 2017–2018 and 2019–2020 NHANES cycles.

Definition of LHR

The Beckman Coulter MAXM automatic analyzer, developed by Beckman Coulter Inc., was utilized to measure lymphocyte counts, which were expressed in units of 10^3 cells per microliter (10^³ cells/μL). The counts were used to calculate the LHR, which was the primary exposure variable in the analysis. The LHR was obtained by dividing the lymphocyte count by the HDL level. This method provided valuable insights into the potential interplay between immune function and lipid metabolism. Recent research has demonstrated the significance of this ratio in understanding various health conditions, such as cardiovascular diseases and metabolic disorders [21].

Definition of MASLD and CSF

The NHANES Mobile Examination Centers (MECs) employ large or medium probes fitted with the 502 V2 Touch model FibroScan^® (Echosens, Paris, France) to conduct elastography examinations operated by experienced technicians. A comprehensive examination must satisfy several criteria: a minimum fasting period of three hours, completion of at least 10 measurements, and an interquartile range to median ratio of LSM values below 30%. Diagnosis of MASLD is based on the CAP, with a diagnostic threshold set at 248 dB/m [22]. Furthermore, CSF was defined based on LSM values, with a critical value of 8.6 kPa [23].

Covariates

To investigate the association between the LHR and MASLD, our multivariable-adjusted models incorporated several potential covariates. The covariates included demographic factors such as age, gender, and race, as well as socioeconomic factors like education level and household income. The enrolled clinical variables comprised diabetes status, hypertension status, smoking habits, and body mass index (BMI). Moreover, numerous biochemical markers were included, specifically aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), total bilirubin, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and total cholesterol levels. These covariates were meticulously selected considering their potential to confound the relationship between LHR and MASLD. The detailed methods used to measure the variables in the survey are described on the NHANES website [24].

Statistical analysis

All statistical analyses were conducted by the Centers for Disease Control and Prevention (CDC). The analyses incorporated the NHANES sampling weights to accommodate the intricate multiphase grouped study structure. This weighting ensures that the results are nationally representative and account for the stratification, clustering, and unequal probabilities of selection inherent in the NHANES data collection process. Continuous variables were presented as the means ± standard deviations, providing a clear measure of central tendency and variability, while categorical variables were expressed as percentages to illustrate the distribution of subjects in various categories. In contrast, categorical variables were presented as percentages to illustrated how respondents were distributed across various groups. To compare differences between groups divided by LHR quartiles, a weighted chi-square test or a weighted Student’s t-test was adopted. Weighted tests help to account for the NHANES complex survey design, yielding more accurate p-values and confidence intervals. To investigate the independent association between MASLD and LHR, multivariate logistic regression models were performed in three scenarios. Model 1 included no adjustments for covariates, and thus, served as the baseline model to determine the unadjusted association between LHR and MASLD. Model 2 was adjusted for basic demographic variables such as gender, age, and race, to reveal factors that may confound the relationship. Model 3 involved comprehensive adjustments for multiple variables: age, gender, race, education level, diabetes status, hypertension status, smoking habits, BMI, household income, and various biochemical markers including ALT, AST, GGT, total bilirubin, HDL, LDL, and total cholesterol levels. These modifications were performed to delineate the independent effect of LHR on MASLD and account for any possible confounding variables. To test the nonlinear association between LHR and MASLD, a weighted generalized additive model regression and the penalized spline approach were utilized. Smooth curve fitting with penalized splines allows for the flexible modeling of nonlinear relationships without imposing a specific functional form, while GAM regression combines the features of generalized linear models with additive models to dissect complex relationships. These methods provide a comprehensive and detailed analysis on the association of LHR with MASLD across different levels of the exposure variable. Additionally, a multivariate regression analysis that was stratified by gender, age, BMI, diabetes, hypertension, smoking status, and education level was performed in the subgroup analyses. Stratified analysis helped us to explore whether the relationship between LHR and MASLD varied across different subgroups, potentially revealing the effect modification or interaction among the variables. A P-value < 0.05 was considered statistically significant, implying that the likelihood of the observed association occurring by chance was less than 5%. This threshold is commonly used in biomedical research to control for Type I error, ensuring that the findings are not due to random variation. All analyses were performed using the R version 3.4.3 (http://www.R-project.org, The R Foundation) and Empower software (www.empowerstats.com; X&Y Solutions, Inc., Boston, MA, USA).

Results

Baseline characteristics of participants

An initial total of 15,560 participants were enrolled using the following inclusion and exclusion criteria: [1] exclusion of participants without LHR data (n = 1848); [2] individuals lacking LSM and CAP measurements (n = 4742); [3] those under 18 years of age (n = 2020). Following a strict screening procedure, a final sample of 6,950 participants was selected for the analysis. A detailed flowchart of the recruitment process is presented in Fig. 1. Table 1 [20] illustrates the weighted baseline demographic characteristics of the 15,560 participants in this study. The average age of the cohort was 48.15 ± 17.10 years, with 49.14% men and 50.86% females. The mean LHR was 0.46 ± 0.88, with the quartile ranges for LHR being 0.22 ± 0.05, 0.35 ± 0.03, 0.48 ± 0.04, and 0.78 ± 1.74, respectively. Notably, participants in Quartile 4 had higher rates of CAP and LSM relative to those in Quartile 1. It was also observed that LHR quartiles exhibited significant variations in terms of age, gender, race, educational level, diabetes, hypertension, smoking status, PIR, BMI, ALT, GGT, TBIL, HDL, LDL, CAP, and LSM (all P < 0.05).

Fig. 1.

Flowchart showing the sample selection procedure from the NHANES 2017–2020

Table 1.

Characteristics of the study population based on Log2-LHR quartiles

Variables	Total	Q1	Q2	Q3	Q4	P-value
		N = 1735	N = 1736	N = 1737	N = 1742
Age(years)	48.15 ± 17.10	53.93 ± 17.26	48.68 ± 17.05	46.21 ± 16.24	43.65 ± 16.10	< 0.0001
Gender, n(%)						< 0.0001
Male	49.14	37.71	48.07	53.02	57.98
Female	50.86	62.29	51.93	46.98	42.02
Race, n(%)						< 0.0001
Mexican American	8.56	4.95	8.15	8.94	12.29
Other Hispanic	7.65	5.67	7.22	8.06	9.71
Non-Hispanic White	63.29	70.25	63.21	62.48	57.07
Non-Hispanic Black	10.67	11.75	10.92	9.90	10.11
Other Race	9.82	7.38	10.49	10.62	10.
Education Level, n(%)						< 0.0001
Less than high school	10.79	8.04	9.94	11.72	13.55
High school	26.94	21.50	24.23	28.09	34.14
More than high school	62.27	70.46	65.83	60.20	52.30
Diabetes, n(%)						< 0.0001
Yes	13.50	9.69	12.20	13.54	18.71
No	86.50	90.31	87.80	86.46	81.29
Hypertension, n(%)						0.0019
Yes	32.00	31.86	29.10	32.13	35.02
No	68.00	68.14	70.90	67.87	64.98
Smoking, n(%)						< 0.0001
Yes	42.75	38.51	39.84	43.13	49.73
No	57.25	61.49	60.16	56.87	50.27
Coronary heart disease, n(%)						0.1097
Yes	4.31	5.33	4.02	3.94	3.86
No	95.69	94.67	95.98	96.06	96.11
Stroke, n(%)						0.0013
Yes	3.81	4.96	4.46	2.85	2.97
No	96.19	95.04	95.54	97.15	97.03
Cancer, n(%)						< 0.0001
Yes	11.69	17.41	12.06	10.10	6.90
No	88.31	82.59	87.94	89.90	93.10
Body mass index(kg/m2)	29.74 ± 7.23	26.84 ± 6.16	28.57 ± 6.55	31.01 ± 7.22	32.62 ± 7.53	< 0.0001
Household income, n (%)						< 0.0001
PIR < 1	12.57	8.82	11.65	13.77	16.14
PIR 1 to < 3	33.64	30.42	32.18	33.82	38.32
PIR ≥ 3	53.79	60.76	56.17	52.41	45.53
ALT, IU/L	22.83 ± 17.35	19.78 ± 18.07	20.92 ± 13.58	24.19 ± 16.05	26.55 ± 20.29	< 0.0001
AST, IU/L	21.87 ± 12.76	22.18 ± 16.61	21.43 ± 11.19	21.70 ± 10.12	22.19 ± 12.16	0.1978
GGT, IU/L	29.54 ± 43.14	27.01 ± 56.00	28.10 ± 42.90	30.31 ± 35.01	32.82 ± 34.75	0.0002
TBIL, mg/dL	0.47 ± 0.29	0.51 ± 0.30	0.48 ± 0.32	0.47 ± 0.28	0.42 ± 0.24	< 0.0001
HDL-cholesterol, mg/dL	53.66 ± 15.69	68.83 ± 16.68	55.95 ± 10.94	48.93 ± 9.58	40.58 ± 8.22	< 0.0001
LDL-cholesterol, mg/dL	109.81 ± 34.98	107.42 ± 34.58	110.07 ± 33.96	112.57 ± 33.82	109.47 ± 38.14	0.0121
Total cholesterol, mg/dL	187.50 ± 40.14	192.26 ± 40.09	186.17 ± 37.54	187.04 ± 39.90	184.47 ± 42.57	< 0.0001
CAP, dB/m	264.85 ± 63.05	238.84 ± 56.36	254.97 ± 59.08	275.11 ± 61.06	291.27 ± 62.87	< 0.0001
LSM, kPa	5.89 ± 4.93	5.56 ± 4.77	5.39 ± 3.19	6.05 ± 4.94	6.60 ± 6.30	< 0.0001
Lymphocyte count, 109/L	22.06 ± 34.43	14.64 ± 3.91	19.62 ± 3.82	23.19 ± 4.62	31.04 ± 68.19	< 0.0001
Lymphocyte to HDL-C ratio	0.46 ± 0.88	0.22 ± 0.05	0.35 ± 0.03	0.48 ± 0.04	0.78 ± 1.74	< 0.0001

Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression model; (%) for categorical variables: the P value was calculated by the weighted chi-square test

Abbreviations: MASLD Metabolic dysfunction-associated steatotic liver disease; LHR lymphocyte to HDL-C ratio; CAP controlled attenuation parameter; PIR Ratio of family income to poverty; LSM liver stiffness measure. ALT alanine aminotransferase; AST aspartate aminotransferase; GGT gamma Glutamyl Transferase; TBIL total Bilirubin; HDL high-density lipoprotein; LDL low-density lipoprotein;

Associations between the LHR and MASLD

Table 2 displays the association between the LHR and MASLD based on the sample-weighted multivariate logistic regression analysis. Initially, LHR was treated as a continuous variable to determine its correlation with MASLD. In Model 1, LHR (OR 2.11, 95% CI 1.97–2.27, P < 0.0001) was positively correlated with the disease. The incidence of MASLD increased by 54% for every unit rise in LHR after considering for all relevant variables (95% CI: 1.30–1.82, P < 0.0001). LHR was categorized into quartiles to perform sensitivity analysis to test the robustness of results. Participants in the lowest quartile (Quartile 1) showed a 2.17-fold lower risk of developing MASLD compared with those in the highest quartile (Quartile 4) in the fully adjusted model (95% CI: 1.59–3.04, P < 0.0001). However, there was no significant relationship between LHR and CSF in the multivariate analysis on this population (Table 2). Furthermore, the potential nonlinear association between LHR and MASLD was explored using smooth curve fitting approaches. The study demonstrated a nonlinear relationship that a two-segment linear regression model best explains (Fig. 2). The association between LHR and MASLD was L-shaped as determined by the model, with a key transition point at an LHR value of -2.58 (Table 3).

Table 2.

The association between Log2-LHR and MASLD

Exposure	Model 1 [OR (95% CI)]	Model 2 [OR (95% CI)]	Model 3 [OR (95% CI)]
MASLD	2.11 (1.97, 2.27) < 0.0001	2.42 (2.24, 2.62) < 0.0001	1.54 (1.30, 1.82) < 0.0001
Quartiles of LHR
Q1	Reference	Reference	Reference
Q2	1.38 (1.21, 1.57) < 0.0001	1.56 (1.36, 1.79) < 0.0001	1.26 (1.07, 1.72) 0.0234
Q3	2.54 (2.22, 2.90) < 0.0001	3.07 (2.66, 3.54) < 0.0001	2.32 (1.63, 2.66) < 0.0001
Q4	3.91 (3.40, 4.49) < 0.0001	4.95 (4.26, 5.74) < 0.0001	2.17 (1.59, 3.04) < 0.0001
P for trend	< 0.0001	< 0.0001	< 0.0001

Exposure	Model 1 [OR (95% CI)]	Model 2 [OR (95% CI)]	Model 3 [OR (95% CI)]
CSF	1.39 (1.25, 1.55) < 0.0001	1.51 (1.35, 1.69) < 0.0001	0.89 (0.68, 1.36) 0.9766
Quartiles of LHR
Q1	Reference	Reference	Reference
Q2	0.89 (0.69, 1.15) 0.3718	1.01 (0.78, 1.31) 0.9461	0.76 (0.62, 1.30) 0.8906
Q3	1.56 (1.24, 1.96) 0.0001	1.86 (1.47, 2.35) < 0.0001	1.33 (0.83, 2.08) 0.1425
Q4	1.85 (1.49, 2.31) < 0.0001	2.30 (1.83, 2.90) < 0.0001	1.53 (0.98, 2.38) 0.2025
P for trend	< 0.0001	< 0.0001	0.5422

Model 1: no covariates were adjusted

Model 2: age, gender, and race were adjusted

Model 3: age, gender, race, education level, diabetes, hypertension, smoking, body mass index, household income, alanine aminotransferase, aspartate aminotransferase, gamma glutamyl transferase, total bilirubin, high-density lipoprotein, low-density lipoprotein, total cholesterol were adjusted

Abbreviations: MASLD Metabolic dysfunction-associated steatotic liver disease; LHR lymphocyte to HDL-C ratio; CAP controlled attenuation parameter;

Fig. 2.

The nonlinear relationship between LHR and MASLD

Table 3.

Threshold effect analysis of Log2-LHR on nonalcoholic fatty liver disease using the two-piecewise linear regression model

Nonalcoholic fatty liver disease	Adjusted β (95% CI) P value
Fitting by the standard linear model	1.64 (1.40, 1.92) < 0.0001
Fitting by the two-piecewise linear model
Inflection point(K)	-2.58
LHR< K effect	0.75 (0.35, 1.64) 0.4769
LHR> K effect	1.74 (1.47, 2.06) < 0.0001
Log likelihood ratio	0.046

Fully adjusted model: age, gender, race, education level, diabetes, hypertension, smoking, body mass index, household income, alanine aminotransferase, aspartate aminotransferase, gamma glutamyl transferase, total bilirubin, high-density lipoprotein, low-density lipoprotein, total cholesterol

Abbreviations: MASLD Metabolic dysfunction-associated steatotic liver disease; LHR lymphocyte to HDL-C ratio;

Subgroup analysis

To assess the overall population’s consistency in the association between LHR and MASLD, and reveal any potential differences between other subpopulations, subgroup analyses were conducted in terms of gender, age, educational level, body mass index, diabetes, and smoking habits (Fig. 3). When participants were stratified by BMI, a persistent association was obtained between LHR and MASLD. This association was stronger in participants with a BMI < 25 (OR: 1.38, 95% CI: 1.06–1.81). When grouped by gender, the correlation remained significantly positive for males (OR: 1.55, 95% CI: 1.27–1.90). However, no significant correlations were observed in the subgroups divided by age, educational level, diabetes, hypertension, and smoking.

Fig. 3.

Subgroup analyses for the association between LHR and MASLD

Abbreviations: MASLD Metabolic dysfunction-associated steatotic liver disease; LHR lymphocyte to HDL-C ratio

Discussion

The global prevalence of MASLD has been on the rise, and therefore, understanding the effectiveness of LHR in predicting its prevalence of has become an important research topic [25]. This study is the first to assess the potential association between the LHR and MASLD in U.S. adults. A significant correlation between elevated LHR and increased risk of NAFLD was reported among 15,560 subjects. This relationship persisted even when the participants were stratified into subgroups based on gender, BMI, diabetes status, and hypertension. Through threshold effect analysis and smoothing curve fitting, we identified a nonlinear association between LHR and MASLD, which confirmed the robustness of our findings. An LHR value of -2.58 was determined to be a significant turning point. Increased frequency of MASLD was positively associated with increased score of LHR above this tipping threshold. Conversely, below this threshold, the correlation between LHR and MASLD was not significant.

The findings demonstrated a strong correlation between LHR and LSM. This may explain the findings obtained in recent epidemiological research, which showed that inflammation was associated with the progression of MASLD [26–28]. In a multinational study involving MASLD patients from the United States, Europe, and Thailand, biopsy samples did not differ in liver-related adverse outcomes between patients with NASH and fibrosis compared to those without these conditions [29]. Furthermore, a significant positive association was observed between LHR and CAP, suggesting a strong relationship between hepatic steatosis and inflammation. Through a review literature, we postulate that elevated levels of C-C motif chemokine ligand 2 (CCL2) or monocyte chemoattractant protein 1 (MCP1) contribute to the progression of NASH in 47 patients with MASLD. These patients exhibit low-grade systemic inflammation [30, 31]. The research identifies a robust positive correlation between CAP and LHR, reinforcing the significant relationship between inflammation and hepatic steatosis. Furthermore, the association between LHR and CAP appears to vary markedly by gender. Previous multicenter clinical studies have indicated that the prevalence of MASLD is higher in men compared to premenopausal women (or those aged ≤ 50–60 years), suggesting that both the incidence and severity of MASLD are influenced by gender differences [30, 31].

Inflammatory immune responses contribute to the development of MASLD [32, 33]. In this condition, dead hepatocytes release DAMPs such as mitochondrial DNA and ATP, which activate Kupffer cells, leading to the release of pro-inflammatory cytokines like IL-1β and IL-18 [34]. These cytokines exacerbate liver fibrosis and inflammation [35]. This is consistent with our findings that an elevated LHR was positively associated with high risk of MASLD. Nonetheless, the precise mechanisms linking inflammation to MASLD progression need to be further studied. Research have demonstrated that the gut microbiota composition in MASLD patients markedly differs from that of healthy individuals [36]. Metabolites produced by specific gut microbes can modify the occurrence of liver inflammation via the gut-liver axis. These metabolites enter the liver via the bloodstream, activating Kupffer cells and other immune cells, thereby promoting inflammation and fibrosis [37]. Furthermore, in MASLD, abnormal lipid metabolism causes accumulation of lipotoxic lipids, which damage hepatocytes and triggers inflammation via enhanced stress and activation of inflammasome pathways [38]. Diacylglycerol and fatty acids can potentially induce hepatocyte apoptosis and exacerbate inflammation and fibrosis by activating TLR and inflammasome pathways [39].

Compared to other ratios including monocyte-to-high-density lipoprotein ratio (MHR) and neutrophil-to-high-density lipoprotein ratio (NHR), which are also influence the risk of MASLD, the LHR levels focus on the interplay between chronic low-grade inflammatory response and lipid metabolism [40, 41]. In contrast, markers such as MHR and NHR primarily indicate a stable, non-specific response rather than incorporating the more sustained effects of lymphocytes in the progression of MASLD [14]. Although some studies have highlighted the predictive value of platelet-related ratios for MASLD, these measures may not adequately capture the crucial roles of inflammation and liver disease progression. In this context, LHR offers a more targeted and informative perspective. Incorporating the LHR level test into the management of metabolic dysfunction-associated MASLD may be an attractive approach for improving early risk stratification and developing targeted preventive interventions. Considering the association of LHR with the initial steatosis stage of MASLD, LHR can serve as a practical biomarker for identifying at-risk individuals, particularly in populations with metabolic syndrome. Therefore, monitoring LHR levels may reflect changes in inflammatory status and potential liver dysfunction, enabling timely initiation of lifestyle modifications and therapeutic interventions to mitigate disease progression. In addition, it will also facilitate the implementation of personalized non-pharmacological preventive plans, such as, dietary adjustments and lifestyle improvements [42]. Notably, our findings suggest that an LHR value of nearly − 2.58 should be established as a risk indicator, individuals with LHR levels exceeding this threshold should be considered highly at risk of MASLD and require confirmation diagnosis.

Strengths and limitations

This study has significant strengths and limitations that should be acknowledged. By recruiting a nationally representative sample and incorporating results from transient elastography analysis, the data used in this study provide more accurate estimates of the true prevalence of MASLD compared to studies based solely on medical records. Moreover, the LHR, as a composite indicator, may more reliably reflect inflammation levels and immune status considering the complex interaction between lymphocytes and HDL-C, making it a promising marker for early disease screening and severity monitoring. Previous research by Yu et al. showed that LHR has higher accuracy than PLR in predicting newly diagnosed MetS. However, this study has several notable limitations. Being a cross-sectional analysis, we could not establish a causal relationship or temporal link between LHR and MASLD, highlighting the need for larger prospective studies to validate our findings. Although we adjusted for several confounding factors, the influence of other potential confounders cannot be excluded, and thus our results should be interpreted with caution. Lastly, while liver biopsy remains the gold standard for grading liver steatosis and fibrosis, the study used transient elastography for assessment, and the results may differ from those of biopsy-based evaluations.

Conclusion

Studies have demonstrated a positive correlation between LHR levels and the initial hepatic steatosis stage of MASLD among US adults, but no association has been reported for more advanced fibrosis stage. This suggests that LHR may serve as an effective predictive indicator for detecting early onset of MASLD and tracking liver fat accumulation in MASLD patients. Nevertheless, further prospective clinical trials are necessary to validate the role of LHR in the progression and management of MASLD.

Electronic Supplementary Material

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Abbreviations

ALT

Alanine aminotransferase

AST

Aspartate aminotransferase

CAP

Controlled attenuation parameter

GGT

Gamma Glutamyl Transferase

HDL

High-density lipoprotein

LDL

Low-density lipoprotein

LHR

Lymphocyte to HDL C ratio

LSM

Liver stiffness measure

MASLD

Metabolic dysfunction associated steatotic liver disease

TBIL

Total Bilirubin

Author contributions

Conceptualization, Z.H. and S.L.; methodology, C.T.; formal analysis, K.Z., M.G., and D.P.; data curation, K.Z., M.G., and D.P.;writing—original draft preparation, C.T.; writing—review and editing, Z.H., S.L., Z.W., and H.L.; supervision, Z.H., S.L., Z.W., and H.L.; funding acquisition, Z.W. and H.L.; All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported Chongqing science and health joint project (2024GGXM005), Natural Science Foundation of Chongqing (CSTB2022NSCQ-MSX0477) and Project on Teaching Reform of Graduate Education of Chongqing Medical University at School Level (XYJG230210).

Data availability

The survey data are publicly available on the internet for data users and researchers throughout the world (www.cdc.gov/nchs/nhanes/).

Declarations

Ethics approval and consent to participate

In compliance with the Declaration of Helsinki, every NHANES protocol was approved by Ethics Review Board of National Center for Health Statistics. Every participant signed the informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.