Life’s essential 8 and non-alcoholic fatty liver disease: unmasking depressive symptoms’ mediating role

Abstract

Objectives

Aimed to reveal the complex associations between Life's Essential 8 (LE8), depressive symptoms and nonalcoholic fatty liver disease (NAFLD), and to explore the mediating role of depressive symptoms in the pathways of LE8 components affecting NAFLD.

Methods

Based on nationally representative data of 8908 adults ≥ 20 years from the 2005–2018 National Health and Nutrition Examination Survey (NHANES), weighted logistic, regression, restricted cubic spline(RCS), threshold effect and bootstrap mediated-effects analyses were used to assess the association between LE8, NAFLD and depressive symptoms associations, and stratified analysis reveals the heterogeneity of the association in population.

Results

Each one-point increase in the LE8 was associated with a reduced risk of NAFLD, OR (95% CI) = 0.19(0.16, 0.23), with health factor score showing particularly protective effect, OR (95% CI) = 0.09 (0.07, 0.10). These associations were stronger among women, older, and PIR (poverty-to-income ratio) > 3.5. A dose–response relationship was evident, with a positive correlation between severe depression and NAFLD, OR (95% CI) = 2.01(1.05, 3.85). Crucially, depressive symptoms constituted a significant mediating pathway in health behaviors, accounting for 46.78%, 17.74%, and 5.79% of the protective effects of optimal sleep health, adequate physical activity, and diet on NAFLD, respectively. Regarding nicotine exposure, depressive symptoms exerted a partial inhibitory effect, with the mediating effect accounting for -27.55%. However, for the association between health factors and NAFLD, depressive symptoms do not play a mediating role in the association.

Conclusions

This study is the first to confirm that depressive symptoms mediate the relationships between specific LE8 components and NAFLD. LE8 components are significantly correlated with NAFLD, possibly via depression—supporting a "physiological–psychological" integrated approach to NAFLD management. Targeted interventions for depressive symptoms may augment the benefits of optimized LE8 in high-risk populations.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-025-03707-9.

Research Insights

What is currently known about this topic?

1. Ideal cardiovascular health exposure is associated with a reduction in non-alcoholic fatty liver disease (NAFLD) development and an increase in NAFLD remission.

2. The depressive symptoms measured by the PHQ-9 scale was independently associated with NAFLD.

What is the key research question?

• How does Life’s Essential 8 (LE8) influence the risk of metabolic-associated NAFLD and what is the mediating role of depressive symptoms in the pathway between LE8 components？

What is new?

1. LE8 inversely correlates with NAFLD risk, health factors exhibit stronger protection.

2. LE8 and LE8 components benefit vary by gender, age, and socioeconomic status.

3. Depressive symptoms mediate LE8 and LE8 components -NAFLD pathways, acting as key psychological bridge.

How might this study influence clinical practice?

• We recommend integrating routine depression screening (using tools like PHQ-9) into routine care for patients with poor LE8 components or early NAFLD signs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-025-03707-9.

Introduction

Nonalcoholic fatty liver disease (NAFLD), a hepatic manifestation of metabolic syndrome, has emerged as a global health burden, affecting 25–30% of the adult worldwide [1]. NAFLD is characterized by metabolic dysfunction within the liver, and the onset and progression of NAFLD are closely linked to insulin resistance, chronic inflammation, and metabolic dysregulation, influenced by genetic predisposition, lifestyle factors, and comorbidities, such as abdominal obesity, hypertension, and atherogenic dyslipidemia [2–4]. Notably, these metabolic abnormalities are also well-established risk factors for cardiovascular disease (CVD) [5].In the state of CVD, endothelial dysfunction and inflammatory mediators released by atherosclerotic plaques [such as C-reactive protein (CRP) and IL-1β] can activate liver Kupffer cells, exacerbate oxidative stress of liver cells, lead to mitochondrial dysfunction and lipid peroxidation, and accelerate the progression of NAFLD [6–8].

The American Heart Association's Life's Essential 8 (LE8) integrates multidimensional cardiovascular health (CVH) indicators, including 4 health behaviors and 4 health factors, providing a framework for managing the comorbidities of NAFLD and depression [9]. It has been shown that the higher CVH, the lower the all-cause mortality rate and cardiovascular disease-specific mortality rate [10]. A large cohort study further showed that individuals with the highest LE8 quartile had a 76% reduced risk of NAFLD compared to the lowest quartile, and 469 of the NAFLD cohort showed an improvement in NAFLD during a median follow-up period of 2.4 years, suggesting that sustained good CVH is associated with a reduction in the incidence rate of NAFLD and an increase in the remission rate [11]. In addition, population study had found that good CVH was found to reduce the odds of depression well [11], LE8 scores were linearly and negatively correlated with depression, with a 17% reduction in the risk of depression for every ten-point increase in LE8 scores [12, 13]. While a 2019 mouse experiment showed that a high-fat diet promotes depression like behavior by inhibiting hypothalamic PKA signaling [14]. These findings collectively support the protective effects of LE8 on NAFLD and depression, possibly through metabolic regulation, inflammation inhibition, and neuroendocrine regulation, and depression may as a plausible mediator for LE8's behavioral influences on liver health.

Recent studies have found a high correlation between NAFLD and depression, with interactions and a high risk of co-morbidity. Depressive symptoms, as assessed by the Patient Health Questionnaire-9 (PHQ-9), is independently associated with NAFLD in the U.S. population [15, 16]. The prevalence of depression among individuals with NAFLD was 1.13 times higher than in those without NAFLD. Furthermore, after adjusting for comorbidities, NAFLD has been identified as an independent risk factor for developing depression and anxiety [17]. At the same time, depressive symptoms exacerbates hepatic steatosis and fibrosis [18] through dysregulation of the neuroendocrine immune axis, with potential mechanisms involving interleukin-6 (IL-6)-mediated neuroinflammation, disruption of blood–brain barrier integrity, impairment of hippocampal synaptic plasticity, and activation of hepatic stellate cells [19–21]. This indicates that depressive symptoms can also promote the occurrence of NAFLD. Despite growing mechanistic research, targeted strategies for NAFLD-depression co-morbidity still need further refinement based on clearer pathway insights.

Notably, depressive symptoms may play a mediating role in the association between LE8 and NAFLD. While Mendelian randomization (MR) study had established depression's causal role in NAFLD through metabolic intermediates often mediated through metabolic intermediate, such as increased waist-to-hip ratio, hypertension (mediation: 35.8%) [22] but it cannot capture how modifiable lifestyle factors within LE8 modulate this pathway. Cohort study has clearly demonstrated that overall CVH is associated with the risk of NAFLD [11], but it did not consider whether depression symptoms played a certain role in this relationship. Specifically, the extent to which depressive symptoms mediate the protective effect of the LE8 and LE8 components on NAFLD has not been quantified, and the relative contribution of specific LE8 components in this pathway, such as sleep health and blood glucose control, are not yet clear. This gap impedes the translation of composite health scores into targeted behavioral interventions.

In this study, based on previous studies, we used data from the National Health and Nutrition Examination Survey (NHANES) to validate the association between LE8 and LE8 components and NAFLD in American adults, and attempted to quantify the mediating role of depressive symptoms in this relationship, and further identified the specific LE8 components that have the most significant impact on this depression mediated pathway, providing deeper insights into the interaction between CVH and NAFLD, providing a data basis for research on strategies based on LE8 to alleviate depressive symptoms and play a role in preventing NAFLD.

Materials and methods

Data source and study population

This study included participants ≥ 20 years from a total 7 survey cycles in NHANES 2005–2018. NHANES utilized a stratified multistage probability sampling method to select a series of nationally representative samples (http:// www.cdc.go/nchs/nhanes.htm).The NCHS Research Ethics Review Board approved the NHANES study protocol and all participants provided written informed consent. To better reflect the association between LE8, NAFLD, and depressive symptoms, and to involve as many research participants as possible in the study, we used the following nadir criteria for NAFLD: (i) exclusion of those < 20 years; (ii) exclusion of men who consumed > 3 drinks in a day, and women who consumed > 2 drinks in a day [23]; (iii) exclusion of participants who had positive for Hepatitis B or Hepatitis C [24]; (iv) exclusion of participants who were unable to calculate the FLI; (v) excluded participants who did not have data on any of the LE8 factors; (vi) exclude individuals with a PHQ-9 score > 27; and (vii) any participant who included data on any of the LE8 components and NAFLD was able to be included in the study.

Definitions of LE8 scores

The LE8 score assessed 4 health behaviors (diet, physical activity, nicotine exposure, and sleep) and 4 health factors (body mass index (BMI), blood lipids (non-HDL cholesterol), blood glucose, and blood pressure).The calculation method for LE8 scores for various indicators in NHANES data has been elaborated in detail in the previous section (Supplementary Table 1) [25, 26]. In this study, all participants had a physical exam at a mobile checkup center to obtain height, weight, waist circumference (WC), and blood pressure measurements. Their blood samples were taken during fasting and transported to a laboratory at the University of Minnesota Fairview Medical Center for analysis to obtain information on glucose, lipids, and nicotine. Dietary indicators were assessed using the 2015 Healthy Eating Index score, and participants' dietary intake (obtained from two 24-h dietary reviews) was combined with USDA Food Pattern Equivalency data to calculate scores. Self-reported questionnaires were used to obtain information on the frequency and duration of vigorous or moderate physical activity in the past 30 days. LE8 score = (HEI-2015 diet score + physical activity score + nicotine exposure score + sleep health score + BMI score + blood lipids score + blood glucose score + blood pressure score)/8. Health behavior score = (HEI-2015 diet score + physical activity score + nicotine exposure score + sleep health score)/4. Health factor score = (BMI score + blood lipids score + blood glucose score + blood pressure score)/4. All of the above data were calculated using the complete data set for each element. LE8 score, health behavior score, health factor score, and the eight LE8 components were categorized into high, moderate and low levels based on participants in the 80–100, 50–79.9, and 0–49.9 categories, respectively.

Diagnosis of NAFLD

Fatty liver index (FLI) is used to diagnose NAFLD, a diagnostic method originally proposed by Bedogni et al. Although liver biopsy remains the gold standard, FLI provided a viable alternative in large cohorts lacking imaging data [23, 27]. If the FLI value was < 60, it was judged to be a healthy population, and if the FLI was ≥ 60 it was judged to be a patient with NAFLD [23, 26]. As a non-invasive detection method, FLI mainly evaluates the prevalence of NAFLD based on common physical measurement indicators and serological parameters, including BMI, WC, triglycerides (TG), and gamma glutamyl transferase (GGT). Multiple epidemiological studies have confirmed that FLI has high accuracy and clinical value in screening and diagnosing NAFLD [24, 28]. The FLI formula is: [29]

$$\text{FLI =}\left\{(\text{e}_{}^{0.953\ast \text{loge(triglycerides)}}+0.139{\text{*BMI + 0.708*Log}}_{\text{e}}(\text{GGT})+0.053\ast \text{waistcircumference}-15.745)/1+\text{e}_{}^{0.953\ast \text{loge(triglycerides)}}+0.139{\text{*BMI + 0.718*log}}_{\text{e}}(\text{GGT})+0.053\ast \text{waistcircumference}-15.745)\right\}\ast 100$$

Diagnosis of depressive symptoms

Current depressive symptoms were measured by PHQ-9, which was used to assess depressive symptoms over the past 2 weeks and had moderate agreement with the Clinical Psychiatry Interview, and has been validated and widely used as a depression screening tool in large-scale epidemiological studies [30–32]. The PHQ-9 questionnaire consists of 9 items, each of which was assessed on a four-point Likert scale of 0 (“not at all”) to 1 (“several days”), 2 (“more than half the days”), and 3 (“nearly every day”), set the population's depressive symptoms to missing if any of the questions were not answered. The scores were differentiated, so that 0–4 (no depression), 5–9 (may have mild depression), 10–14 (may have moderate depression), 15–19 (may have moderately severe depression), and 20–27 (may have severe depression) [30, 32, 33].

Defining covariates

Self-reported questionnaires were used to obtain information on gender, age group (20–39, 40–59, and ≥ 60), education level (< high school, high school or ≥ high school), race (Mexican–American, other hispanic, non-hispanic white, non-hispanic black, and other race), marital status (coupled and single/separated). Poverty-to-income ratio (PIR) (≤ 1.3, 1.3–1.85, 1.85–3.50, and > 3.50). Alcohol use (yes, no), chronic disease drug use was defined as diabetes/hypertension/hyperlipidemia drug use (yes, no), and CVD (yes, no) were included as demographic and co-morbidity data in the covariates to be adjusted. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), direct HDL-Cholesterol, and energy intake, as confounding experimental test variables that may affect NAFLD, were also included in the covariate study. Due to the large number of cycles included in this study, to control the impact of sampling bias, updated testing methods, or changes in population characteristics between different survey cycles on the results, survey cycles were also included as a covariate in the study [34]. PIR denotes the ratio of household income to the federal poverty level after adjusting for household size. The higher the ratio, the higher the income level.

Statistical analysis

All analyses were performed using Stata 15.0 and R 4.0.1. To account for the complex, multi-stage probability sampling design of NHANES, all analyses were conducted in the environment of the survey package. Zstats 1.0 platform (www.zstats.net) was using for generating statistical tables. A two-sided P < 0.05 considered statistically significant.

Descriptive statistics and handling of missing data

For continuous variables, normality was confirmed using the Shapiro–Wilk test. Normal distribution data are presented in the form of mean ± standard deviation (SE), while non-normally distributed data are presented in the form of median (M) combined with interquartile range (1st quartile Q₁, 3rd quartile Q₃) to more accurately reflect their distribution characteristics. While Mann–Whitney test was used to compare the level of NAFLD group with the non-NAFLD group. Categorical variables were expressed as the number of cases and the composition ratio [n (%)]; the chi-square test was used to compare the percentages of these variables in the different groups. Missing data were handled using complete-case analysis for each specific model. Observations with missing values for any variable included in a given model were excluded from that particular analysis.

Regression models and confounder adjustment

Since NAFLD is a binary variable, survey-weighted multivariable logistic and regression models (svyglm function with family = quasibinomial) were employed to examine the associations. To comprehensively control potential confounding factors, we constructed a model with five sequentially adjusted covariates gradually included by category. Model 1 (Crude), included only the exposure (LE8 components/depressive symptoms) and the NAFLD. Model 2, adjusted for basic demographics and socioeconomic status (alcohol use, gender, race, marital status, education level, age group, PIR, and survey cycle). Model 3, further adjusted for clinical history (cardiovascular disease and chronic diseases drug use). Model 4, additionally adjusted for total energy intake to account for dietary confounding. Model 5, further incorporated metabolic and liver function markers (ALT, AST, GGT, and direct HDL-cholesterol).

Assessment of model assumptions and multicollinearity

The linearity assumption in the logit for continuous exposures was tested using restricted cubic splines (RCS). For variables exhibiting nonlinear relationships, RCS was formally adopted to characterize the dose–response pattern. Multicollinearity among all independent variables in the logistic regression models was assessed by calculating the Variance Inflation Factor (VIF). The maximum VIF across all models was < 5, indicating no substantial multicollinearity that would bias the estimates.

Dose–response and threshold effect analysis

To systematically evaluate the shape of the associations, we utilized survey-weighted RCS regression with 4 knots placed at recommended percentiles. The reference points for RCS were set at statistically identified inflection points derived from a two-piecewise linear regression model, allowing for objective detection of potential thresholds.

Mediation analysis

The severity of depressive symptoms, classified by PHQ-9 into an ordinal variable, was introduced as the mediator. The mediating role of depressive symptoms in the association between LE8 components and NAFLD was examined using the mediation (5000 simulations), adapted to respect the complex survey design. The analysis quantified the proportion of the total association that was statistically explained by the pathway through depressive symptoms. It is important to note that due to the cross-sectional nature of the data, this analysis demonstrates analysis demonstrates statistical mediation rather than establishing causal mediation with definitive temporal sequence.

Results

Characteristics of the study population

A total of 8908 participants ≥ 20 years from 2005–2018 NHANES were included in this study, 4220 (48.45%) were men with a mean age of 51.3 years. The prevalence of NAFLD was 3906 (43.85%). As shown in Table 1, the NAFLD prevalence in population was statistically different (P < 0.05) in all variables except for survey cycles and marital status.

Table 1.

Distribution of characteristics of the study population

Variables	Total (n = 8908)	Non-NAFLD (n = 5002)	NAFLD (n = 3906)	Statistic	P
Survey cycles, n (%)				χ2 = 24.43	0.064
2005–2006	1197 (13.67)	651 (13.15)	546 (14.34)
2007–2008	1377 (14.14)	731 (13.83)	646 (14.54)
2009–2010	1465 (14.49)	830 (15.32)	635 (13.42)
2011–2012	1282 (15.21)	761 (15.77)	521 (14.49)
2012–2013	1280 (13.98)	748 (14.81)	532 (12.91)
2014–2015	1129 (14.16)	628 (13.47)	501 (15.06)
2016–2017	1178 (14.35)	653 (13.65)	525 (15.25)
Age, Mean ± SE	51.30 ± 0.29	49.96 ± 0.38	53.04 ± 0.36	t = 6.71	< 0.001
Age group, n (%)				χ2 = 123.05	< 0.001
20–39	2376 (27.85)	1541 (32.48)	835 (21.86)
40–59	2838 (37.66)	1496 (35.12)	1342 (40.93)
≥ 60	3694 (34.49)	1965 (32.40)	1729 (37.20)
Gender, n (%)				χ2 = 69.41	< 0.001
Men	4220 (48.45)	2236 (44.56)	1984 (53.46)
Women	4688 (51.55)	2766 (55.44)	1922 (46.54)
Race/Ethnicity, n (%)				χ2 = 66.51	< 0.001
Mexican American	1282 (7.12)	644 (6.56)	638 (7.84)
Other Hispanic	797 (4.85)	446 (4.97)	351 (4.69)
Non-Hispanic white	3985 (69.84)	2193 (68.79)	1792 (71.20)
Non-Hispanic black	1828 (10.48)	968 (10.01)	860 (11.08)
Other race	1016 (7.71)	751 (9.67)	265 (5.19)
Marital status, n (%)				χ2 = 1.52	0.468
Coupled	5722 (69.42)	3185 (68.89)	2537 (70.10)
Single/Separated	3185 (30.58)	1817 (31.11)	1368 (29.90)
Education level, n (%)				χ2 = 50.53	< 0.001
< High school	2115 (15.20)	1059 (13.41)	1056 (17.51)
High school	1947 (22.35)	1051 (21.05)	896 (24.02)
> High school	4839 (62.45)	2886 (65.55)	1953 (58.46)
PIR, n (%)				χ2 = 27.78	< 0.001
≤ 1.3	2386 (17.83)	1243 (16.81)	1143 (19.15)
1.3–1.85	1075 (9.57)	581 (8.77)	494 (10.60)
1.85–3.50	2097 (24.68)	1184 (24.23)	913 (25.26)
> 3.50	3350 (47.93)	1994 (50.20)	1356 (44.99)
Alcohol use status, n (%)				χ2 = 72.28	< 0.001
No	4567 (43.70)	2435 (40.92)	2132 (47.29)
Yes	4341 (56.30)	2567 (59.08)	1774 (52.71)
Cardiovascular disease, n (%)				χ2 = 80.66	< 0.001
No	7771 (89.36)	4512 (91.94)	3259 (86.03)
Yes	1137 (10.64)	490 (8.06)	647 (13.97)
Chronic disease drugs use, n (%)				χ2 = 470.25	< 0.001
No	5258 (62.30)	3412 (72.10)	1846 (49.65)
Yes	3650 (37.70)	1590 (27.90)	2060 (50.35)
ALT (U/L), M (Q₁, Q₃)	21.00 (16.00, 28.00)	19.00 (15.00, 24.00)	24.00 (18.00, 32.00)	Z = -12.93	< 0.001
AST (U/L), M (Q₁, Q₃)	22.00 (19.00, 27.00)	22.00 (19.00, 26.00)	23.00 (19.00, 27.00)	Z = -5.03	< 0.001
GGT (U/L), M (Q₁, Q₃)	19.00 (14.00, 27.00)	15.00 (12.00, 21.00)	24.00 (18.00, 36.00)	Z = -15.50	< 0.001
Direct HDL-Cholesterol (mg/dL), M (Q₁, Q₃)	51.00 (43.00, 62.00)	57.00 (48.00, 68.00)	45.00 (39.00, 54.00)	Z = -16.49	< 0.001
Energy intake (kcal), Mean ± SE	2055.66 ± 13.79	2037.92 ± 14.63	2078.54 ± 19.25	t = 2.09	0.039

t t test, M Median, Q₁ 1st Quartile, Q₃ 3st Quartile; Z Mann–Whitney test, χ² chi-square test

Correlation analysis of LE8 components and depressive symptoms with NAFLD

As shown in Table 2, there were differences in NAFLD prevalence among LE8 components, depressive symptoms.

Table 2.

Distribution of LE8 components and depressive symptoms in NAFLD

Variable	Total (n = 8908)	non-NAFLD (n = 5002)	NAFLD (n = 3906)	Statistic	P
LE8 score, n (%)				χ2 = 1624.76	< 0.001
Low (0–49)	1012 (10.10)	190 (3.03)	822 (19.46)
Moderate (50–79)	5516 (65.29)	2928 (57.50)	2588 (75.60)
High (80–100)	1737 (24.62)	1589 (39.47)	148 (4.94)
LE8 score, M (Q₁, Q₃)	69.38(59.38, 79.38)	76.25 (66.88, 84.38)	61.25 (52.50, 69.38)	Z = -18.58	< 0.001
Health behavior score, n (%)				χ2 = 225.10	< 0.001
Low (0–49)	1698 (16.73)	817 (14.02)	881 (20.24)
Moderate (50–79)	4538 (50.26)	2424 (46.56)	2114 (55.04)
High (80–100)	2646 (33.01)	1750 (39.43)	896 (24.72)
Health behavior score, M (Q₁, Q₃)	70.00 (55.00, 81.25)	73.75 (57.50, 86.25)	67.50 (51.25, 78.75)	Z = -8.64	< 0.001
Health factor score, n (%)				χ2 = 2834.25	< 0.001
Low (0–49)	1451 (14.76)	195 (2.80)	1256 (30.56)
Moderate (50–79)	4358 (51.43)	2196 (41.43)	2162 (64.63)
High (80–100)	2479 (33.81)	2326 (55.76)	153 (4.81)
Health factor score, M (Q₁, Q₃)	70.00 (56.25, 82.50)	82.50 (70.00, 92.50)	57.50 (46.25, 67.50)	Z = -18.58	< 0.001
HEI-2015 diet score, n (%)				χ2 = 183.82	< 0.001
Low (0–49)	4278 (48.98)	2174 (43.73)	2104 (55.76)
Moderate (50–79)	2223 (24.46)	1254 (24.46)	969 (24.45)
High (80–100)	2407 (26.56)	1574 (31.80)	833 (19.79)
HEI-2015 diet score, M (Q₁, Q₃)	50.00 (25.00, 80.00)	50.00 (25.00, 80.00)	25.00 (0.00, 50.00)	Z = -8.01	< 0.001
Physical activity score, n (%)				χ2 = 61.64	< 0.001
Low (0–49)	3014 (29.47)	1524 (26.26)	1490 (33.62)
Moderate (50–79)	404 (4.90)	229 (4.70)	175 (5.15)
High (80–100)	5490 (65.63)	3249 (69.04)	2241 (61.23)
Physical activity score, M (Q₁, Q₃)	100.00 (20.00, 100.00)	100.00 (40.00, 100.00)	100.00 (0.00, 100.00)	Z = -5.71	< 0.001
Nicotine exposure score, n (%)				χ2 = 90.64	< 0.001
Low (0–49)	1426 (15.41)	809 (15.67)	617 (15.06)
Moderate (50–79)	2068 (23.33)	981 (19.62)	1087 (28.12)
High (80–100)	5411 (61.27)	3210 (64.71)	2201 (56.82)
Nicotine exposure score, M (Q₁, Q₃)	100.00 (75.00, 100.00)	100.00 (75.00, 100.00)	100.00 (75.00, 100.00)	Z = -5.10	< 0.001
Sleep health score, n (%)				χ2 = 53.91	< 0.001
Low (0–49)	1568 (14.66)	792 (12.36)	776 (17.63)
Moderate (50–79)	1820 (19.61)	1015 (19.29)	805 (20.02)
High (80–100)	5497 (65.74)	3186 (68.35)	2311 (62.36)
Sleep health score, M (Q₁, Q₃)	100.00 (70.00, 100.00)	100.00 (70.00, 100.00)	100.00 (70.00, 100.00)	Z = -4.77	< 0.001
Body mass index score, n (%)				χ2 = 5052.97	< 0.001
Low (0–49)	3429 (37.69)	385 (6.92)	3044 (77.40)
Moderate (50–79)	2945 (33.08)	2116 (41.90)	829 (21.69)
High (80–100)	2534 (29.23)	2501 (51.18)	33 (0.91)
Body mass index score, M (Q₁, Q₃)	70.00 (30.00, 100.00)	100.00 (70.00, 100.00)	30.00 (15.00, 30.00)	Z = -20.63	< 0.001
Blood lipids score, n (%)				χ2 = 359.77	< 0.001
Low (0–49)	2950 (33.22)	1277 (25.43)	1673 (43.27)
Moderate (50–79)	2002 (23.26)	1135 (23.52)	867 (22.92)
High (80–100)	3956 (43.52)	2590 (51.05)	1366 (33.81)
Blood lipids score, M (Q₁, Q₃)	60.00 (40.00, 100.00)	80.00 (40.00, 100.00)	60.00 (40.00, 80.00)	Z = -12.03	< 0.001
Blood glucose score, n (%)				χ2 = 621.30	< 0.001
Low (0–49)	1295 (11.45)	420 (5.84)	875 (18.87)
Moderate (50–79)	1701 (17.24)	762 (12.56)	939 (23.44)
High (80–100)	5527 (71.31)	3672 (81.60)	1855 (57.69)
Blood glucose score, M (Q₁, Q₃)	100.00 (60.00, 100.00)	100.00 (100.00, 100.00)	100.00 (60.00, 100.00)	Z = -12.97	< 0.001
Blood pressure score, n (%)				χ2 = 412.85	< 0.001
Low (0–49)	2259 (21.55)	1012 (15.84)	1247 (28.89)
Moderate (50–79)	2575 (30.60)	1308 (27.06)	1267 (35.16)
High (80–100)	3821 (47.85)	2539 (57.10)	1282 (35.95)
Blood pressure score, M (Q₁, Q₃)	75.00 (50.00, 100.00)	80.00 (50.00, 100.00)	55.00 (30.00, 80.00)	Z = -12.13	< 0.001
Depressive symptoms, n (%)				χ2 = 99.11	< 0.001
Normal	6469 (79.94)	3785 (83.64)	2684 (75.24)
Mild depression	1178 (13.80)	572 (11.87)	606 (16.26)
Moderate depression	391 (4.07)	171 (3.00)	220 (5.43)
Severe depression	147 (1.61)	60 (1.12)	87 (2.24)
Very severe depression	57 (0.57)	24 (0.37)	33 (0.83)
Depressive symptoms, M (Q₁, Q₃)	1.00 (0.00, 4.00)	1.00 (0.00, 3.00)	2.00 (0.00, 4.00)	Z = -5.19	< 0.001

t t test, M Median, Q₁ 1st Quartile, Q₃ 3st Quartile; Z Mann–Whitney test, χ² chi-square test

Weight logistics analysis of LE8 components and depressive symptoms with NAFLD

Supplementary Table 2 shows that weighted multivariate logistics analysis and regression analysis result in model 5. High level of health behavior score was significantly negatively correlated with NAFLD, OR (95% CI) = 0.74 (0.60,0.91). In addition, in those with higher health factor score was also have a significant protective association with NAFLD, OR (95% CI) = 0.09(0.07,0.10). Compared with the normal population, there was a significant positive correlation between severe depression and NAFLD, OR (95% CI) = 2.01 (1.05,3.85). LE8 components are associated with NAFLD, except for sleep and physical activity.

Subgroup analysis of LE8 components, depressive symptoms, and NAFLD

Stratified analysis showed significant heterogeneity in the association between LE8 components, depressive symptoms, and NAFLD (Supplementary Fig. 1). The LE8 score was strongly negatively correlated with NAFLD and with a more significant impact on women (OR = 0.16 for man OR = 0.23, P-interaction = 0.038). The health behavior score was negatively correlated with NAFLD, especially in women (P-interaction = 0.006). The health factor score showed age dependent protection with greater benefits for older adults (≥ 60 years, OR = 0.14 for 20–39 years, OR = 0.03, P-interaction < 0.001). Subgroup-specific associations for LE8 components are detailed in Supplementary Fig. 1.

Weight RCS and threshold effect analysis of LE8 components and depressive symptoms with NAFLD

Weighted RCS and threshold effect analysis revealed a non-linear correlation between LE8 components, depressive symptoms and NAFLD are shown in Fig. 1. The LE8 score showed a significant segmented effect at the threshold of 78.743. Below this value, for every one-point increase, the corresponding OR (95% CI) = 0.94 (0.93, 0.94), P < 0.001. After exceeding this value, the protective effect was further enhanced, OR (95% CI) = 0.82 (0.78, 0.86), P < 0.001.

Fig. 1.

Weight RCS and threshold effect analysis of LE8 components and depressive symptoms with NAFLD

The health behavior score showed significant segmentation at inflection point 75 (likelihood ratio test P < 0.001). When the score is below 75, there is no significant correlation between the score and NAFLD. When the score ≥ 75, it showed a stable protective effect, OR (95% CI) = 0.98 (0.96, 0.99). There was a significant segmentation in the health factor score at 82.5 (likelihood ratio test P < 0.001). In the low segment (< 82.5), each additional point corresponding OR (95% CI) = 0.91 (0.90,0.92). In the high segment (≥ 82.5), the protective effect was stronger, OR (95% CI) = 0.73 (0.68,0.78).

In addition, the HEI-2015 dietary score (inflection point 74.303), nicotine exposure score (inflection point 55.884), blood glucose score (inflection point 47.387), blood pressure score (inflection point 67.619), and BMI score (inflection point 15.661) all showed varying degrees of non-linear relationships. The weighted RCS non-linear test for physical activity score and sleep health score were not significant, and no significant correlation was found on both sides of the inflection point in the two-stage regression (P > 0.05) (Fig. 1).

Mediating role of depressive symptoms in the association of LE8 components with NAFLD

As shown in Fig. 2a, the LE8 score was significantly negatively associated with NAFLD, with a total effect β (95% CI) =  −0.27 (−0.29, −0.24), and after controlling for depressive symptoms, the direct effect of LE8 was still significant, β₃ (95% CI) =  −0.26 (−0.29, −0.23), and mediating percentage of LE8 score in the total effect of depressive symptoms on NAFLD = 1.39%, P = 0.040. Health behavior score (Fig. 2b) was significantly negatively associated with NAFLD, with a total effect β (95% CI) = −0.03 (−0.05, −0.01), and the direct effect of health behavior score remained significant after controlling for depressive symptoms, β₃ (95% CI) =  −0.02(−0.04, −0.01), with a mediating percentage in the total effect of depressive symptoms = 22.16%, P < 0.001. Health factor score (Fig. 2c) was significantly negatively associated with NAFLD, with a total effect β (95% CI) =  −0.37(−0.39, −0.35), and after controlling for depressive symptoms, the direct effect of health factor score remained significant, β₃ (95% CI) = 0.36 (−0.38, −0.34), the mediating percentage in the total effect of depressive symptoms was statistically significant, although it accounted for a relatively low (0.58%) P = 0.002.

Fig. 2.

Mediating role of depressive symptoms in the association of LE8 with NAFLD

The mediating effect of each component is shown in Table 3. For HEI-2015 diet score and BMI score, depressive symptoms had a partial mediating effect, with mediation effect percentages of 5.79% and 0.22%, respectively (P < 0.05). In physical activity and sleep health, depressive symptoms played a fully mediating role, and depressive symptoms was the main pathway affecting NAFLD, the mediating effect percentage was 17.74% and 46.78%, P < 0.001. The negative mediation ratio of nicotine exposure (-27.55%) may indicate the presence of inhibitory effects. For metabolic indexes blood lipids score, blood glucose score and blood pressure score, depressive symptoms did not mediate the effect between them and NAFLD, proportion mediation P > 0.05.

Table 3.

Mediation effects of depressive symptoms in LE8 components—NAFLD pathways (Adjusting for model 5’s covariates)

LE8 components	Path coefficients and significance	Total effect β (95% CI)	Direct effect β₃ (95% CI)	Mediating effect (β₁*β₂)	Mediation proportion	Mediation significance (P value)
HEI-2015 diet score	β₁ = − 0.04(− 0.06, − 0.03), P < 0.001; β₂ = 0.27(0.14,0.39), P < 0.001	− 0.03(− 0.05, − 0.02), P < 0.001	− 0.03(− 0.04, − 0.01), P < 0.001	− 0.0108	5.79%	< 0.001
Physical activity score	β₁ = − 0.04(− 0.06, − 0.02), P < 0.001; β₂ = 0.28(0.15,0.41), P < 0.001	− 0.01(− 0.02,0.00), P = 0.226	− 0.01(− 0.02,0.01), P = 0.342	− 0.0112	17.74%	< 0.001
Nicotine exposure score	β₁ = − 0.12(− 0.15, − 0.09), P < 0.001; β₂ = 0.30(0.17,0.43), P < 0.001	0.02(0.00,0.03), P = 0.040	0.02(0.01,0.04), P = 0.010	− 0.036	− 27.55%	< 0.001
Sleep health score	β₁ = − 0.14(− 0.16, − 0.11), P < 0.001; β₂ = 0.27(0.15,0.40), P < 0.001	− 0.01(− 0.03,0.01), P = 0.188	− 0.00(− 0.02,0.01), P = 0.558	− 0.0378	46.78%	< 0.001
BMI score	β₁ = − 0.04(− 0.07, − 0.02), P = 0.003; β₂ = 0.28(0.05,0.51), P = 0.019	− 0.52(− 0.55, − 0.50), P < 0.001	− 0.52(− 0.55, − 0.50), P < 0.001	− 0.0112	0.22%	0.024
Blood lipids score	β₁ = − 0.02(− 0.04, − 0.01), P = 0.032; β₂ = 0.27(0.15,0.40), P < 0.001	− 0.05(− 0.07, − 0.03), P < 0.001	− 0.05(− 0.07, − 0.03), P < 0.001	− 0.0054	1.53%	0.076
Blood glucose score	β₁ = − 0.02(− 0.05,0.01), P = 0.298; β₂ = 0.29(0.16,0.41), P < 0.001	− 0.07(− 0.09, − 0.05), P < 0.001	− 0.07(− 0.09, − 0.05), P < 0.001	− 0.0058	1.21%	0.152
Blood pressure score	β₁ =− 0.01(− 0.03,0.01), P = 0.418; β₂ = 0.29(0.16,0.41), P < 0.001	-0.05(-0.06, -0.03), P < 0.001	− 0.05(− 0.06, − 0.03), P < 0.001	− 0.0029	1.09%	0.324

Discussion

This large-scale cross-sectional study provided new insights into the complex inter-relationships between Life's Essential 8, depressive symptoms, and non-alcoholic fatty liver disease. We have verified a significant negative correlation between the LE8 and NAFLD, and it was found that this effect exhibits group heterogeneity in factors, such as gender, age, and socioeconomic status. Compared to health behavior, health factors such as blood sugar, blood glucose, and BMI have a stronger protective effect. More importantly, we have found for the first time that depressive symptoms play an important and complete mediating role, accounting for 46.78% of the best sleep health protection effect, 17.74% of the appropriate physical activity effect and 27.55% of nicotine exposure inhibitory effects. While the effects on diet and BMI were partially mediated. These findings not only establish depressive symptoms as an independent risk factor, but also as a key psychosocial pathway through which lifestyle-related health behaviors can affect the risk of NAFLD.

Although previous studies have established a link between LE8 and NAFLD [8, 35, 36], our subgroup analysis further reveals clinically relevant heterogeneity, with the protective association of LE8 being significantly stronger in women, older adults, and populations with higher PIR. These findings are consistent with the principles of psychological neuroimmunology. The stronger protective association observed in women may be attributed to hormone interactions [37], which can explain why they benefit more from improving cardiovascular health. For age dependent effects, we observed that health factor scores brought greater benefits in the population aged ≥ 60 years. This may be related to age-related chronic low-grade inflammation, which creates a physiological environment that is highly susceptible to metabolic damage [38]. Therefore, directly offsetting this intensified inflammatory response such as improving core metabolic parameters (lipids, glucose, BMI) may result in disproportionately greater returns in this population. For individuals with high PIR, those with more socio-economic resources are more capable of utilizing LE8's comprehensive recommendations to reduce the risk of NAFLD [39].

Second, our work has driven the development of this field by systematically analyzing the different roles of health behavior and health factor within the LE8 framework, and by formally testing depressive symptoms as mediators. We confirm that health behavior and health factor are independent, but their impact sizes and potential pathways are significantly different. Health factor such as lipids, glucose, and BMI have significantly stronger protective effects, mainly through direct biological pathways such as increasing leptin resistance to reduced liver fat deposition [40] increasing insulin sensitivity, thereby inhibiting fat production and reducing pro-inflammatory cascade reactions [41–43].

In contrast, health behavior such as physical activity and sleep almost entirely work by improving depressive symptoms. Mechanistically, these findings are consistent with the "metabolic psychiatric comorbidity" hypothesis and extend this hypothesis. This indicates that mental health is not only a confounding factor, but also a functional mediator and provides a reasonable explanation for why interventions that only focus on lifestyle sometimes have limited effectiveness. 17.74% of physical activity mediators indicate that depression plays a significant role in this pathway. Depressive symptoms may promote NAFLD through overactive hypothalamic pituitary adrenal axis driven by glucocorticoids and chronic inflammation characterized by cytokines, such as IL-6 and tumor necrosis factor-alpha (TNF-α)[44, 45], but regular physical activity promotes hippocampal neurogenesis by upregulating brain-derived neurotrophic factor (BDNF), promoting hippocampal neurogenesis by improving the hypothalamic pituitary adrenal axis (HPA) axis [46, 47], thereby further alleviating MAFLD.

The mediating role of 46.78% in the relationship between sleep health and NAFLD implies that nearly half of the protective effect of good sleep on NAFLD is explained by the reduction of depressive symptoms, highlighting the importance of addressing sleep quality and depressive symptoms in NAFLD prevention strategies. Improving sleep quality can reduce cortisol circadian rhythm disorders and decrease glucocorticoid driven lipid accumulation in liver cells [48, 49]. The restoration of sleep quality can reduce the permeability of IL-6 to the blood–brain barrier, jointly alleviating the remote effects of neuroinflammation on the liver [50, 51].

While high scores on nicotine exposure should theoretically reduce the risk of NAFLD, this study found that its protective effect was further attenuated after adjusting for depression symptoms (OR from 1.49 to 2.10), and the mediator ratio for depression symptoms was negative (-27.55%), which may reflect inhibitory effects, where depressive states mask the true extent of the association between nicotine exposure and NAFLD. When controlling depressive symptoms, the direct effects of nicotine exposure become more apparent, indicating that depressive symptoms may be an inhibitory variable. This paradox may stem from two points. (1) The nicotine exposure includes a “quit state” and ex-smokers may experience short-term depressive exacerbation triggered by nicotine withdrawal [52, 53], thus partially counteracting the direct benefits of quitting. (2) Depression may indirectly exacerbate liver injury by facilitating relapse [54], resulting in an increased risk of liver damage creating a vicious cycle of depression–smoking–liver injury. This finding echoes the clinical observations of the recent hepatology study on the impact of depression management after smoking cessation on the prognosis of NAFLD [55]. This means that in clinical practice, implementing smoking cessation programs should simultaneously address potential depression, highlighting the complexity of managing addiction and mental health to achieve fully effective results.

In addition, the mediating role of depressive symptoms in the relationship between diet and NAFLD revealed in this study (5.79%) is partially achieved through the indirect pathway of improving mental health, highlighting the importance of the behavioral medicine pathway of "diet–emotion–liver". A healthy dietary pattern, such as a high HEI-2015 score, may alleviate depressive symptoms and alleviate NAFLD by improving gut microbiota composition, increasing the production of neuroactive substances, such as short chain fatty acids (SCFAs), and regulating central nervous system inflammation and neurotransmitter balance[56, 57]. This means that for NAFLD high-risk populations with depressive symptoms, simple nutrition education may have limited effectiveness, and psychological intervention must be integrated to achieve more effective depressive symptoms and treatment of NAFLD.

This clear distinction between psychophysiology and direct metabolic regulation emphasizes a dual pathway model: although metabolic parameters directly affect liver fat accumulation, some lifestyle components partially work by improving mental health, which in turn alleviates NAFLD. Our finding suggests that in the future management of NAFLD, mental health should be used as a predictive biomarker for therapeutic efficacy. Specifically, improving sleep quality and promoting physical activity may have dual benefits by alleviating depressive symptoms, thereby interrupting the key pathway for NAFLD development. The significant heterogeneity observed between gender, age, and socioeconomic class highlights the need for personalized preventive measures, prioritizing depression screening and tailored health guidance for women, the elderly, and socio economically disadvantaged groups. In terms of non-pharmacological interventions, cognitive behavioral therapy (CBT) and mindfulness-based stress reduction (MBSR) are used to improve mental health. In terms of drug intervention, for NAFLD patients with comorbid depression, the selection of antidepressants should consider their metabolic effects.

In summary, a comprehensive model that includes early psychological assessment, lifestyle medical intervention (targeting diet, exercise, sleep), and cautious medication management when necessary is expected to simultaneously improve cardiovascular metabolic health and mental health, providing a new paradigm for precise prevention and treatment of NAFLD.

Advantages and disadvantages

This study is the first time to integrate multidimensional analysis of health behavior, health factor, and depressive symptoms within the LE8 framework, providing a comprehensive strategy for NAFLD prevention. Bootstrap method (5000 repeated samples) to calculate the confidence interval of mediation effects, controlling for a large number of confounding factors, quantifying the proportion of mediation, breaking through simple association analysis, and enhancing the robustness of causal inference. The multivariate adjustment model based on a large sample size (n = 8908) effectively controlled for confounding bias. Although the term metabolic associated fatty liver disease (MAFLD) has gradually gained recognition, this study still adheres to the definition of NAFLD based on FLI ≥ 60. This choice avoids controversy, because MAFLD diagnostic criteria include metabolic abnormalities (such as obesity and diabetes), which overlap with the metabolic components in LE8.

This study emphasizes the interaction between mental health and metabolic pathways, but due to the inability to determine causal relationships in cross-sectional designs, causal relationships need to be validated through prospective studies. PHQ-9 has been validated and widely used as a depression screening tool in multiple large-scale epidemiological studies, but PHQ-9, physical activity, and other factors mostly come from questionnaire surveys and are self-reported, which may introduce call bias and social expectation bias. Although the FLI provides a practical tool for screening NAFLD in large-scale populations, the critical value of FLI ≥ 60 may misclassify some critical cases compared to imaging or histological methods, especially in individuals whose metabolic abnormalities have not been fully captured. Although this study adjusted for key covariates, residual confounding of unmeasured factors such as detailed dietary patterns, alcohol consumption gradients, psychosocial stressors, antidepressant use, sleep apnea, and gut microbiota status may persist. Due to the exploratory nature of these analyses, this study did not formally correct for multiple comparisons. This study focuses on the "LE8 → Depression → NAFLD" pathway. Although the possibility of bidirectional association has been noted, potential reverse causal relationships (such as NAFLD exacerbating depressive symptoms and depression → LE8 → NAFLD) have not been explored. Due to limitations in cross-sectional study design and statistical validity, this study cannot establish a temporal relationship and subgroup validation has not yet been conducted on all mediation pathways.

In the future, further research will be conducted within this theoretical framework, and it is recommended to use longitudinal data and cross lagged models to explore the pathways of NAFLD. At the same time, imaging confirmed fatty degeneration, independent metabolic assessment, and comprehensive lifestyle assessment will be combined to further address existing limitations and provide stronger support for research conclusions.

Conclusion

This large-scale study suggests that adhering to Life's Essential 8 is closely protective associated with NAFLD, with health factor having a more substantial effect than health behavior. Most notably, we have identified depressive symptoms as a key mediating pathway through which some LE8 components have protective effects in NAFLD. These findings support a dual pathway model. Although metabolic parameters directly regulate liver fat accumulation, certain lifestyle components largely play a role in improving mental health. We have discovered a new pathway: health behavior—reducing depressive symptoms—lowering the risk of NAFLD and provides a new "physiological and psychological" comprehensive intervention paradigm for the prevention and management of NAFLD. In addition, our identification of vulnerable groups (women, older adults, higher socioeconomic status) emphasizes the importance of personalized strategies that address both metabolic and mental health issues, particularly in managing addictive behaviors, such as smoking. Future research should validate these causal relationships through prospective design and explore the efficacy of combining targeted interventions (such as cognitive–behavioral therapy) with traditional lifestyle changes in high-risk populations.

Abbreviations

LE8

Life's essential 8

NAFLD

Non-alcoholic fatty liver disease

NHANES

National health and nutrition examination survey

CVH

Cardiovascular health

CVD

Cardiovascular disease

RCS

Restricted cubic spline

BMI

Body Mass Index

PIR

Poverty-to-income ratio

OR

Odds ratio

CI

Confidence interval

PHQ-9

Patient health questionnaire-9

FLI

Fatty liver index

ALT

Alanine aminotransferase

AST

Aspartate aminotransferase

HDL

High-density lipoprotein

LDL

Low-density lipoprotein

TG

Triglycerides

WC

Waist circumference

HPA

Hypothalamic–pituitary–adrenal

IL-6

Interleukin-6

TNF-α

Tumor necrosis factor-alpha

CRP

C-reactive protein

DNL

De novo lipogenesis

PINK1

PTEN-induced kinase 1

MAFLD

Metabolic-associated fatty liver disease

NASH

Non-alcoholic steatohepatitis

CBT

Cognitive behavioral therapy

MBSR

Aindfulness-based stress reduction

Author contributions

SL participated in the formal analysis, visualization, writing-original draft, project administration, writing- review & editing and funding. XFL acquisition of data or analysis, interpretation of data and image compilation. SXZ and LYC participated in the data verification, methodology of the paper. ZLW and XRL participated in acquisition of data. CXJ coordinated the research conception, design, followed the research progress and grasped the research direction. All authors agree to be accountable for all aspects of the work to ensure that the questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding

This study was supported by the Research Initiation Fund of Longgang District Maternity and Child Healthcare Hospital of Shenzhen City (project number: Y2024014) and Shenzhen Longgang District Medical Health Science and Technology Project, project number: LGWJ2023-(55).

Data availability

The dataset supporting the conclusions of this article is available in the NHANES repository https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. All authors had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Declarations

Ethics approval and consent to participate

The National Center for Health Statistics Research Ethics Review Board examined and approved the NHANES-authorized studies involving human subjects. Each participant provided informed consent (https://www.cdc.gov/nchs/nhanes/about/erb.html? and https://www.cdc.gov/nchs/nhanes/irba98.htm.)

Consent for publication

We declare that we have no financial or personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Life's Essential 8 and Non-Alcoholic Fatty Liver Disease: Unmasking Depressive Symptoms’ Mediating Role”.

Competing interests

The authors declare no competing interests.