Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as the most common chronic liver disorder worldwide, with an estimated global prevalence of 25%1. MASLD represents a spectrum of conditions characterized by excessive fat accumulation in the liver of individuals without significant alcohol consumption, ranging from simple steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, and cirrhosis2. Accumulating evidence has revealed MASLD as a multisystem disease closely associated with obesity, insulin resistance, hypertension, and dyslipidemia, which can increase risks of cardiovascular disease, diabetes, chronic kidney disease, and malignancy3.

Lifestyle modification focusing on weight loss, dietary changes, and increased physical activity is recommended as first-line therapy for MASLD management4. However, the specific lifestyle factors that are most influential on MASLD incidence are not fully understood. The American Heart Association (AHA) recently proposed an 8-factor cardiovascular health score (Life’s Essential 8, LE8 score) comprising health factors (body mass index, blood glucose, blood pressure, total cholesterol) and health behaviors (physical activity, diet, smoking, sleep duration) to comprehensively assess cardiovascular health5. Research examining the LE8 score in relation to MASLD risk can elucidate the lifestyle components that are most protective against MASLD onset.

In this study, we aimed to investigate the association between LE8 score, encompassing 4 health factors and 4 health behaviors, with risk of MASLD in a nationally representative sample of US adults. We further evaluated the individual contributions of the 8 factors to MASLD through weighted quantile sum regression. Findings from this study can enhance our understanding of optimal lifestyle recommendations for MASLD prevention.

Methods

Study population

The National Health and Nutrition Examination Survey (NHANES) is a large cross-sectional survey conducted by the National Center for Health Statistics (NCHS) in the United States, aimed at assessing the health and nutritional status of adults and children in the country. The survey encompasses multiple components, including questionnaire interviews, physical examinations, and laboratory tests. This study was approved by the NCHS institutional review board (Protocol #98-12, #2005-06, #2011-17, #2018-01), all methods were performed in accordance with the relevant guidelines and regulations, and informed consent was obtained from all participants and/or their legal guardians. More information about NHANES can be obtained from the official website (https://www.cdc.gov/nchs/nhanes/index.htm).

This study collected data from a total of eight cycles spanning from 2003 to 2018, including a sample size of 80,132 participants. Among them, 63,132 participants were excluded due to an inability to diagnose MASLD, 6085 participants were excluded due to an inability to calculate LE8 scores, and 1641 participants were excluded due to missing covariate data. Ultimately, a total of 9454 participants were included in this study, with 4536 male participants and 4918 female participants. More specific details can be seen in Fig. 1

Figure 1
figure 1

The flowchart of study and excluded participants, from NHANE 2003–2018.

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Ethical approval

The NHANES agreement has been reviewed and approved by the NCHS Research Ethics Committee. All participants provided written informed consent before participating.

Measurement of LE8

The LE8 scoring algorithm consists of 4 health factors (blood glucose, blood pressure, body mass index (BMI), non-high-density lipoprotein cholesterol) and 4 health behaviors (diet, physical activity, nicotine exposure, sleep duration)6,7. We calculated the LE8 score for 8 cardiovascular health (CVH) indicators based on the published detailed algorithms for each CVH indicator, using the definition provided by the AHA. Each of the 8 CVH indicators has a score range of 0–100, and the overall LE8 score is calculated as the arithmetic mean of the scores for the 8 indicators. Participants with an LE8 score between 80 and 100 are considered to have a high CVH level, those with an LE8 score between 50 and 79 are classified as having moderate CVH, while participants with an LE8 score between 0 and 49 are categorized as having low CVH6.

Dietary indicators were assessed using the Healthy Eating Index-2015 (HEI-2015)8. The HEI-2015 score was developed jointly by the U.S. Department of Agriculture (USDA) and the U.S. Department of Health and Human Services (HHS) to assess the overall dietary quality of U.S. residents. The calculation and construction of the HEI-2015 score were based on the dietary intake data collected from two 24-h dietary recall questionnaires and the food pattern equivalent data from the USDA. We utilized the SAS code provided by the National Cancer Institute of the United States to apply a straightforward HEI scoring algorithm, thereby calculating the scores for HEI-2015. Detailed components and scoring criteria of the HEI-2015 can be found in Supplementary Table 1. Self-reported health questionnaires were used to obtain information on diabetes history, smoking status, duration of sleep, medication history, as well as frequency and duration of vigorous or moderate-intensity physical activity in the past 30 days. Height, weight, blood pressure (average of three consecutive measurements), and body mass index (BMI) calculated as weight (kg) divided by height squared (m2) were obtained from medical examinations. Blood samples from participants were sent to the central laboratory for further assessment of blood lipids, glucose, glycated hemoglobin (HbA1c), and other indicators. In this study, we classified the LE8 score using the same definitions and cut-off points.

Assessment of MASLD

The initial stage of MASLD is considered as the process of abnormal fat deposition in the liver. Data on liver transient elastography was lacking during the NHANES database cycle from 2003 to 2006. Therefore, in this study, we introduced an indicator, namely the Fatty Liver Index (FLI), which can effectively assess the state and severity of MASLD. The algorithm includes several clinically easily obtainable indices such as triglycerides, waist circumference, BMI, gamma-glutamyltransferase (GGT), etc. The detailed calculation formula is as followed Eq. (1):

$$ {\mathbf{FLI}} = \frac{{{\mathbf{e}}^{{0.953 \times {\mathbf{ln}}({\mathbf{triglycerides}}) + 0.139 \times {\mathbf{BMI}} + 0.718 \times {\mathbf{ln}}({\mathbf{gamma}} - {\mathbf{glutamyltransferase}}) + 0.053 \times {\mathbf{waist}}\;{\mathbf{circumference}} - 15.745}} }}{{1 + {\mathbf{e}}^{{0.953 \times {\mathbf{ln}}({\mathbf{triglycerides}}) + 0.139 \times {\mathbf{BMI}} + 0.718 \times {\mathbf{ln}}({\mathbf{gamma}} - {\mathbf{glutamyltransferase}}) + 0.053 \times {\mathbf{waist}} {\mathbf{circumference}} - 15.745}} }} \times 100 $$
(1)

After excluding other liver diseases related to the above factors, we defined participants with FLI ≥ 30 as suffering from MASLD9,10.

Covariates

To more accurately assess the relationship between LE8 and MASLD, we adjusted for the following confounding factors: gender (male, female), age, race/ethnicity (Mexican American, Non-Hispanic Black, Non-Hispanic White, Other Hispanic, Other Race -Including Multi-Racial), education level (low high school, high school, College or above), Poverty income ratio(< 1, 1–3,  ≥ 3), drinking status(former drinker(individuals who used to drink but have now stopped), never drinker(those who have never consumed any alcoholic beverages), mild drinker(those who consume between 1 and 2 standard drink units per day), moderate drinker(those who consume up to 4 standard drink units per day for males, and up to 3 for females), and heavy drinker(those who consume more than 4 standard drink units per day for males, and more than 3 for females).

Statistical analyses

Considering the complex stratified sampling characteristics of the NHANES database, we followed the guidelines of the NCHS and conducted a weighted analysis according to the recommendations of the database. Weighting factors including 1-day dietary interview weights (WTDRD1), 2-day dietary interview weights (WTDR2D), strata (SDMVSTRA), and primary sampling units (SDMVPSU) were taken into account to accommodate the complex survey design. Continuous variables are represented by weighted means (± standard deviation), and statistical differences are described by weighted t-tests. Categorical variables are represented by the number of sample cases (weighted percentage), and statistical differences are described using a weighted chi-square test. In this study, we built weighted univariate and multivariate logistic regression models to evaluate the correlation between LE8 scores and MASLD. We plotted restrictive cubic spline graphs to explore the dose–response relationship between LE8 and MASLD. Additionally, we constructed a weighted quantile sum regression (WQS) model to analyze the relationship between mixed exposure of various LE8 indicators and MASLD, as well as the weight proportion of each indicator11. The basic weighted index model is as follows, more specific details can be seen in Eqs. (2) and (3)

$$ {\text{g}}\left( {\upmu } \right) = \beta_{o} + \beta_{1} \left( {\mathop \sum \limits_{i = 0}^{c} \omega_{i} \varphi_{i} } \right) + z^{\prime}\Phi $$
(2)
$$ WQS = \mathop \sum \limits_{i = 1}^{c} \overline{\omega }_{i} \varphi_{i} $$
(3)

\({\upbeta }_{\text{o}}\) represents the intercept, \({\upbeta }_{1}\) represents the regression coefficient, \(c\) denotes the number of LE8 indicators included in the analysis. \({\text{z}}^{{^{\prime}}}\) and Φ respectively represent the matrix of covariates and the coefficients, \({\upomega }_{\text{i}}\) represents the weighted index, each index ranges from 0–1 (0 <  = \({\upomega }_{\text{i}}\)< = 1), and the sum of the entire weighted index equals 1. \({{\upvarphi }}_{\text{i}}\) epresents the quartile of each LE8 indicator, where (\({{\upvarphi }}_{\text{i}}\)=0, 1, 2, 3) correspond to the 1st, 2nd, 3rd, or 4th quartile, respectively. \(\left( {\mathop \sum \nolimits_{i = 0}^{{\text{c}}} \omega_{i} \varphi_{i} } \right)\) is the sum of the weighted quartiles of c indicators, g \((\upmu )\) represents any differentiable link function. We assumed a linear function fitting a Gaussian distribution and randomly divided the data into a training set (60%) and a validation set (40%), estimating the weights of the 8 LE8 indicators within the training set12. Furthermore, we employed subgroup analyses to explore the relationship between LE8 scores and MASLD across different populations categorized by age, sex, race, educational level, and poverty income ratio.

All statistical tests were two-sided, with p value < 0.05 considered meaningful. All analyses and plotting were performed using R Project for Statistical Computing (version 4.2.3) and Rstudio software.

Results

Baseline characteristics

A total of 9454 participants were classified into the MASLD group and the non-MASLD group based on the presence of MASLD, The MASLD group comprised 2188 individuals, while the non-MASLD group consisted of 7266 individuals. Notably, the non-MASLD group had a higher proportion of female participants, accounting for approximately 54.53%. The MASLD group had a higher proportion of male participants, accounting for approximately 56.51%. The participants in the MASLD group were more likely to be male, aged under 65, non-Hispanic white, have a college education or above, and moderate alcohol drinkers. Compared to the non-MASLD group, the MASLD group had higher levels of tobacco/nicotine exposure, shorter sleep duration, lower BMI, blood pressure, blood lipids, blood glucose, and HEI-2015, as well as less physical activity. For more detailed baseline information, please refer to Table 1.

Table 1 Survey-weighted baseline characteristics of the study population (values are presented as percentages (%) or mean (standard deviation, SD)).
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Association between weighted LE8 Score and MASLD

The single-factor weighted logistic regression model showed that compared to the low LE8 Score group, the moderate and high LE8 Score groups had a 71% reduction in the risk of MASLD (OR 0.29, 95%CI 0.24–0.36, P < 0.001) and a 97% reduction in the risk of MASLD (OR 0.03, 95%CI 0.02–0.04, P < 0.001). In the multi-factor weighted logistic regression model, after adjusting for gender, race, education level, poverty-income ratio, and alcohol consumption level, the results showed that compared to the low LE8 Score group, the moderate and high LE8 Score groups had a 72% reduction in the risk of MASLD (OR 0.28, 95%CI 0.22–0.36, P < 0.001) and a 98% reduction in the risk of MASLD (OR 0.02, 95%CI 0.02–0.03, P < 0.001). For more specific details, please refer to Table 2.

Table 2 Association between Weighted Life’s Essential 8 Scores and metabolic dysfunction-associated steatotic liver disease (MASLD) (values are presented as odds ratios (OR) with 95% Confidence Intervals (CI) and P-values).
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Nonlinearity analysis using RCS

The dose–response relationship between LE8 Score and MASLD was evaluated using the RCS model. Nodes were established at the 5th, 35th, 65th, and 95th percentiles of LE8 Score, with the 5th percentile used as the reference value. The analysis results showed that there was a correlation between LE8 Score and MASLD in the univariate model, and they exhibited a significant nonlinear relationship (p for overall association < 0.001, p for nonlinear association < 0.001). In the multivariable model, after adjusting for gender, race, education level, poverty income ratio, and alcohol consumption level, there was still a correlation between LE8 Score and MASLD, and they exhibited a significant nonlinear relationship (p for overall association < 0.001, p for nonlinear association < 0.001). (Fig. 2).

Figure 2
figure 2

Association between Life’s Essential 8 scores and metabolic dysfunction-associated steatotic liver disease (MASLD) using a Restricted Cubic Spline regression model. Legends: Data are presented as log (odds ratios) (y-axis) for the level of Life’s Essential 8 scores. (a) No covariates were adjusted, (b) Adjusted for sex, race/ethnicity, education attainment, and poverty income ratio.

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Weighted quantile sum (WQS) regression

The WQS model was employed to construct an index. The cumulative effects and weighted proportions of blood glucose, blood pressure, blood lipids, BMI, HEI-2015 diet score, physical activity, sleep health, and tobacco/nicotine exposure on MASLD were evaluated in the LE8 scores. The highest weighted proportions were observed for blood glucose (40.70%) and BMI (40.65%). The lowest weighted proportion was found for tobacco/nicotine exposure (< 0.01%). More detailed information can be found in Fig. 3.

Figure 3
figure 3

WQS model regression index weights for the metabolic dysfunction-associated steatotic liver disease (MASLD), adjusted for sex, race/ethnicity, education attainment, and poverty income ratio.

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Subgroup analysis and Interaction

A negative correlation was observed between the LE8 scores and MASLD across all subgroups. Subgroup analysis revealed significant interactions between the LE8 scores and age, education level, and PIR (p < 0.05). The negative correlation between the LE8 scores and MASLD were evident among younger participants (aged 18–65 years, OR 0.92, 95% CI (0.92, 0.93), participants with a university education or higher (OR 0.92, 95% CI (0.92, 0.93), and participants with a PIR ≥ 3 (OR 0.92, 95% CI (0.92, 0.93). No significant interactions were found between the LE8 scores and either gender or ethnicity. More detailed information can be found in Fig. 4.

Figure 4
figure 4

Subgroup analysis of the association of the Life’s Essential 8 scores and the presence of metabolic dysfunction-associated steatotic liver disease (MASLD).

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Discussion

In this nationally representative study of US adults, we found that higher LE8 scores were associated with lower odds of having MASLD after adjusting for potential confounders. We also observed a nonlinear dose–response relationship between LE8 score and MASLD, with the steepest decline in risk occurring at LE8 score above 40. The weighted quantile sum regression model suggested that blood glucose and BMI contributed most to the inverse association between LE8 score and MASLD. Subgroup analyses indicated that the association did not differ significantly across sex and race/ethnicity groups, but differed slightly by age, education level, and poverty income ratio.

Our findings are consistent with previous studies demonstrating that greater adherence to healthy lifestyle behaviors is associated with lower risk of NAFLD4. For example, a study by Zelber-Sagi et al. found that participants with higher Healthy Lifestyle Index scores had an over 60% lower risk of NAFLD compared to those with lower scores5. When comparing our results to the study, which reported an OR of 0.40 (95% CI 0.30, 0.53) for NAFLD among those with higher healthy lifestyle scores, we observed a similar trend with an OR of 0.28 (95% CI 0.22, 0.36) for moderate CVH and an OR of 0.02 (95% CI 0.02, 0.03) for high CVH in our study. This stronger association in our study might be due to differences in the scoring systems and population characteristics. Another study in middle-aged British men also reported that individuals adhering to more health behaviors had markedly lower prevalence of NAFLD13. The study mentioned above reported a prevalence ratio (PR) of 0.35 (95% CI 0.25, 0.49) for higher adherence to healthy behaviors, which is in line with our findings, reinforcing the consistency of the protective effects of healthy lifestyles across diverse populations. The dose–response relationship observed in our study aligns with previous evidence suggesting that each additional healthy lifestyle factor is associated with further reduction in NAFLD risk2.

Our finding that blood glucose and BMI contributed most to the inverse association between LE8 score and MASLD is supported by existing knowledge on the pathophysiology of NAFLD. Insulin resistance and visceral adiposity are major risk factors for NAFLD3,14. Addressing abnormalities in glucose metabolism and obesity through lifestyle changes are first-line therapeutic strategies and critical for NAFLD management15,16. The differential associations across sociodemographic subgroups may reflect disparities in access to resources needed for optimizing cardio-metabolic health, highlighting the need for targeted interventions among disadvantaged populations17,18.

The inverse association between LE8 score and MASLD may be explained by several mechanisms. Adhering to healthy lifestyles can reduce inflammatory stress19, improve insulin sensitivity15, prevent excess adiposity16, and lead to more favorable cardiovascular risk profiles20, which may ameliorate pathways contributing to NAFLD development. Specifically, healthy dietary patterns such as the Mediterranean diet and Dietary Approaches to Stop Hypertension (DASH) diet have been shown to reduce inflammation and improve insulin resistance through high content of monounsaturated fatty acids, fiber, phytochemicals and antioxidants21,22. Regular physical activity can also improve inflammatory status and insulin sensitivity by reducing immune cell activation and increasing glucose transporter type 4 translocation23,24. Avoiding adiposity prevents dysfunctional adipose tissue from releasing inflammatory cytokines and free fatty acids, reducing lipotoxicity and hepatic steatosis2,3. Maintaining optimal cardio-metabolic factors also minimizes systemic inflammation, endothelial dysfunction, and atherogenic dyslipidemia, further improving metabolic pathways underlying NAFLD25,26. In addition, diet and physical activity help maintain normal blood glucose levels and body weight15,16 by improving glucose homeostasis and promoting fat oxidation, thereby reducing excess hepatic triglyceride accumulation. Adequate sleep27 and smoking avoidance28 further optimize metabolic health through improving insulin sensitivity, reducing oxidative stress and inflammation. Hence, targeting multiple lifestyle factors may confer synergistic benefits for reducing NAFLD risk through improving various metabolic and inflammatory pathways20.

Our findings highlight the potential utility of the LE8 score for MASLD risk assessment and stratification in clinical practice. One of the innovative strengths of our study is the use of the LE8 score, a comprehensive measure that incorporates multiple lifestyle factors, providing a holistic view of cardiovascular health. The LE8 score's robustness in capturing the synergistic effects of lifestyle behaviors on MASLD risk sets it apart from traditional single-factor assessments. The simple LE8 algorithm based on lifestyle factors may help identify individuals at high risk of NAFLD who require more targeted screening and preventive interventions29. Furthermore, our application of the WQS regression model is a novel methodological approach in this context, allowing us to identify the most influential components of the LE8 score on MASLD risk. In addition,we utilized data from the NHANES, an ongoing large-scale cross-sectional survey, whose multistage probability sampling design and large sample size enhance the representativeness and generalizability of our study results to the U.S. population. Another advantage of our study is the detailed subgroup and interaction analysis conducted to investigate the impact of different population characteristics on the relationship between LE8 scores and MASLD risk. This approach enables us to gain a deeper understanding of the variations among different populations and highlights the need for targeted interventions for specific subgroups. Our study also reinforces the importance of promoting healthy lifestyles for MASLD prevention, especially optimizing diet quality, physical activity, and weight status30. Physicians should provide specific lifestyle recommendations and refer patients to nutritionists, exercise specialists, and behavioral counselors as needed. Population-level interventions and policies to improve cardio-metabolic health may help curb the rising burden of NAFLD20,31.

Several limitations should be acknowledged. One limitation of this study is its cross-sectional design, which prevents establishing causality between the LE8 score and MASLD. Secondly, misclassification of MASLD status based on FLI is possible. In addition, the FLI is an indirect estimation method based on biomarkers and may not be as accurate as direct imaging diagnosis9. Thirdly, the data on eating habits are based on 24-h recalls, which may be subject to biases. Recall methods may lead participants to underestimate or overestimate their actual dietary intake, affecting data accuracy. Besides, residual confounding by unmeasured factors cannot be excluded. Although we adjusted for various potential confounders, there may still be other unmeasured variables influencing the results. Generalizability to other populations requires further study. Generalizability to other populations requires further study.

Conclusion

In a nationally representative sample of US adults, we observed an inverse association between an 8-factor cardiovascular health lifestyle score and risk of MASLD. Our findings highlight the potential utility of the simple LE8 algorithm for MASLD risk assessment and lend further support to promoting healthy lifestyles for MASLD prevention.