Associations between intake of different types of vegetables and metabolic dysfunction-associated fatty liver disease: a population-based study

Abstract

Background

Metabolic dysfunction-associated fatty liver disease (MAFLD) presently poses a threat to approximately 24% of the global population. The consumption of healthy diets rich in an abundant assortment of vegetables has been scientifically validated to mitigate the progression of MAFLD. However, it remains uncertain whether all categories of vegetables confer benefits for MAFLD. The objective of this study is to investigate the impact of different types of vegetables on MAFLD, aiming to provide a scientific basis for developing more appropriate dietary recommendations for individuals at high risk of MAFLD.

Methods

We investigated the associations between various types of vegetable consumption and the risk of MAFLD, utilizing data sourced from the National Health and Nutrition Examination Survey (NHANES) 2017–2018 cycle. Employing multiple logistic regression and subgroup analyses, we estimated odds ratios (ORs) and 95% confidence intervals (CIs).

Results

Our analysis encompassed a total of 3162 participants. Remarkably, heightened intake of dark green vegetables demonstrated an innovative association with reduced odds of MAFLD (OR = 0.54; 95% CI: 0.36–0.81; p-value = 0.01), while other kinds of vegetable shown no significant association with MAFLD in the full adjusted model (all p-vale > 0.05). In the subgroup analysis, a prominent inverse correlation between the consumption of dark green vegetables and MAFLD was discerned among female and non-Hispanic white people with higher educational attainment.

Conclusions

Our study conclusively demonstrates that a heightened intake of dark green vegetables is linked to diminished odds of MAFLD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-21331-4.

Introduction

Metabolic dysfunction-associated fatty liver disease (MAFLD) exhibits a close association with metabolic syndrome, insulin resistance, and obesity [1]. Criteria for a diagnosis of MAFLD are based on the evidence of hepatic steatosis along with one of the three following criteria: overweight or obesity, type 2 diabetes mellitus, or presence of at least two metabolic risk abnormalities regardless of alcohol consumption or other attendant liver diseases [2]. The global prevalence of MAFLD is currently estimated to be 24% [3]. Moreover, MAFLD can progress to more severe hepatic conditions, such as liver cirrhosis, and even hepatocellular carcinoma [4], without approved pharmacotherapy, thereby posing a great threat to human health [1, 2, 5, 6].

Obesity is the most common risk factor for MAFLD [7], and more than half of adult patients diagnosed with MAFLD are classified as obese [3]. Consequently, high-fat diets (HFD) are frequently employed to induce animal models of MAFLD [8]. Previous studies conducted on HFD-induced animal models of MAFLD have identified reduced mitochondrial quality and increased production of growth differentiation factor 15 as potential contributors to the development of MAFLD [9–11]. In vitro models, such as primary hepatocytes, hSKP-HPC, iPSC-derived hepatocyte-like cells, HepaRG, and others, are commonly exposed to substances such as glucose, insulin, free fatty acids, TNF-α, IL-1β, or TGF-β to simulate the development of specific MAFLD subtypes [12]. In these in vitro models, inflammation and fibrosis are recognized as crucial mechanisms for inducing MAFLD models [13, 14].

Multiple studies have proved that early-stage MAFLD can be effectively controlled through a healthy diet intervention [15]. Zelber-Sagi S et.al conducted a study revealing that patients with MAFLD tend to consume higher amount of red and processed meat but fewer vegetables, fruits, and cereals [16]. Meantime, the decreased consumption of vegetables which are rich in vitamin A, C, E, and carotenoids may contribute to the progression of MAFLD [17–20]. The Mediterranean Diet (MD) and dietary approach to stop hypertension (DASH) diet, characterized by a high proportion of vegetables, legumes, and fruits have been verified to have good effects on patients with MAFLD [21, 22]. However, it is important to note that not all kinds of vegetables contribute equally to human health. Dark green vegetables are known to be abundant sources of nutrients, such as carotenoid and α-tocopherol, which could restrain oxidative stress. In contrast, several studies have demonstrated that a higher intake of starchy vegetables, like potatoes, not only increases the risk of hypertension but is associated with greater weight gain [23, 24]. A meta-analysis incorporating data from 28 cohort studies concludes that higher intakes of most kinds of vegetables except starchy vegetables are associated with lower mortality [25]. However, the heterogeneous effects of different vegetable types on MAFLD still require further investigation.

To determine the correlations between intakes of diverse vegetables and MAFLD, we take advantage of the data from the National Health and Nutrition Examination Survey (NHANES) 2017–2018, aiming to investigate the impact of different types of vegetables on MAFLD, and to provide a scientific basis for developing more appropriate dietary recommendations for individuals at high risk of MAFLD.

Materials & methods

Study population

We enrolled participants from the NHANES database (2017–2018 cycle). NHANES is a multistage, continuous, and cross-sectional survey conducted by the National Center for Health Statistics (NCHS), covering demographic, dietary, questionnaire, laboratory, examination, and limited access data. The NHANES data are publicly available at https://www.cdc.gov/nchs/nhanes/index.htm. Among 9254 participants in 2017–2018 cycle of NHANES, 4746 participants had a specific diagnosis of MAFLD or not based on the diagnostic criteria followed. And these 4746 participants had an explicit intake of diverse vegetables. In addition, those participants who had missing data in age, sex, ethnicity, education, hypertension, hyperlipidemia, diabetes mellitus (DM), body mass index (BMI), smoking, alcohol consumption, alanine aminotransaminase (ALT), and aspartate aminotransferase (AST) were excluded. Finally, a total of 3162 participants were included in the analysis (Supplementary Figure S1). The program has been approved by the National Center for Health Statistics Ethics Review Board. Written informed consent was obtained from each participant in the program, so specific written consent was not required for further analysis.

MAFLD assessment

The criteria for a diagnosis of Metabolic dysfunction-associated fatty liver disease (MAFLD) were below [2]:

Hepatic steatosis in adults (detected either by imaging techniques, blood biomarkers/scores or by liver histology) in addition to one of the three following criteria following:

1. Overweight or obesity (defined as BMI ≥ 25 kg/m² in Caucasians or BMI ≥ 23 kg/m.² in Asians)

2. Type 2 diabetes mellitus

3. Lean/normal weight (defined as BMI < 25 kg/m² in Caucasians or BMI < 23 kg/m² in Asians) presence of at least two metabolic risk abnormalities below:

   1. Waist circumference ≥ 102/88 cm in Caucasians men and women or ≥ 90/80 cm in Asian men and women
   2. Blood pressure ≥ 130/85 mmHg or specific drug treatment
   3. Plasma triglycerides ≥ 150 mg/dL (≥ 1.70 mmol/L) or specific drug treatment
   4. Plasma high-density lipoprotein (HDL)-cholesterol < 40 mg/dL (< 1.0 mmol/L) for men and < 50 mg/dL (< 1.3 mmol/L) for women or specific drug treatment
   5. Prediabetes (i.e., fasting glucose levels 100 to 125 mg/dL [5.6 to 6.9 mmol/L], or 2-h post-load glucose levels 140 to 199 mg/dL [7.8 to 11.0 mmol/L] or hemoglobin A1c (HbA1c) 5.7% to 6.4% [39 to 47 mmol/mol])
   6. Homeostasis model assessment of insulin resistance score ≥ 2.5
   7. Plasma high-sensitivity C-reactive protein level > 2 mg/L

Intake of different types of vegetable assessment

NHANES (2017–2018 cycle) investigated two 24-h dietary recalls, collected in-person and by phone. In both interviews, each food item and its quantity were recorded. A set of measuring guides, including glasses, bowls, mugs, spoons, measuring cups and spoons, drink boxes and bottles, beanbags, a ruler, and thickness sticks were made available to the participants during in-person interviews to report the quantity of foods and beverages. Upon completion of the in-person interview, participants were provided with measuring guides and a food model booklet, which contained two-dimensional drawings of the measuring guides, to use for reporting food quantities during the phone interview. Phone interviews were conducted 3–10 days after the in-person interview.

Food intake was averaged from the two dietary recalls and subsequently converted using the Food Patterns Equivalents Database (FPED). The FPED converts foods and beverages into 37 USDA Food Pattern components, including the number of cup equivalents of vegetables. Different types of vegetable intake include all dietary sources, regardless of form, processing, or other ingredients [26]. In this study, cup equivalents of vegetables were examined separately.

Definition of each kind of vegetables was based on the US Department of Agriculture Food and Nutrition Database for Dietary Studies (Supplementary Table S1). Vegetables were divided into legumes and total vegetables including dark green vegetables (e.g., raw and cooked broccoli, spinach, romaine, and collards), red–orange vegetables, starchy vegetables, and other vegetables (e.g., raw avocado, radicchio, cabbage, cactus). Red orange vegetables were subdivided into red tomato (e.g., boiled, fried, stewed tomatoes and tomato products) and red other vegetables (e.g., raw carrots, raw and cooked pumpkin, carrots, squash). Starchy vegetables were subdivided into starchy potatoes (e.g., raw and cooked white potatoes and potato chips, French fries) and starchy other vegetables (e.g., raw and cooked immature lima beans, corn, peas).

Assessment of covariates

Covariates were selected based on prior studies and clinical judgment regarding their potential association with MAFLD. After evaluating potential collinearity, we included the following covariates: demographic variables (age, sex, race/ethnicity, education, and income-to-poverty ratios), behavioral variables (smoking, alcohol consumption, energy intake, and Healthy Eating Index 2020), as well as laboratory variables and comorbidities (body mass index, alanine aminotransferase, aspartate transaminase, hypertension, hyperlipidemia, and diabetes mellitus). Please refer to the Supplementary Table S2 for specific definitions. Further details of these measurements were documented in the NHANES Laboratory Medical Technologists Procedures Manual.

Statistical analysis

In this study, all analyses incorporated appropriate sampling weights, stratification, and clustering of the complex sampling design in the present study to ensure nationally representative estimates [27]. Multiple logistic regression models were performed to estimate odds ratios (ORs) and 95% confidential interval (CIs) associated from vegetable intake and MAFLD with the first group as reference. Three multivariable models for MAFLD were conducted. Model 1 was adjusted for no covariates. Model 2 was adjusted for age, gender and race/ ethnicity, education level. Model 3 was further adjusted for smoking, alcohol consumption, PIR, HEI2020, energy intake, BMI, ALT, AST, hypertension, hyperlipidemia, and DM. Subgroup analyses were performed to evaluate the relationship between dark green vegetable intake and MAFLD in different subgroups, including age, sex, ethnicity, education level, PIR, BMI, smoking, alcohol consumption, hypertension, hyperlipidemia, and DM. All analysis was performed by R software (4.1.0). All probability values were two-sided, and a p-value < 0.05 was considered statistically significant.

Results

Participants’ characteristics

Our study included a total of 3162 participants who were subjected to final analysis. Among them, 1427 participants did not have MAFLD while 1735 participants were diagnosed with MAFLD. The basic characteristics of participants were presented in Table 1. As shown in Table 1, participants with MAFLD were inclined to be older, male, never smoking, and non-Hispanic White. Additionally, participants with MAFLD exhibited a higher energy intake, higher prevalence of hypertension and hyperlipidemia, along with elevated BMI, and ALT. Conversely, their intake of dark green vegetables and HEI2020 was comparatively lower.

Table 1.

Baseline characteristics of participants

Characteristics	Total (n = 3162)	MAFLD		p-value
		NO (n = 1427)	YES (n = 1735)
Age, years	45.44±0.71	40.87±0.74	49.59±0.77	<0.01
Sex, n (%)				0.01
Male	1533 (48.48%)	629 (44.07%)	904 (53.22%)
Female	1629 (51.52%)	798 (55.98%)	831 (46.78%)
Ethnicity, n (%)				<0.01
Mexican American	415 (13.12%)	127 (6.14%)	288 (11.40%)
Non-Hispanic Black	771 (24.38%)	400 (12.86%)	371 (10.15%)
Non-Hispanic White	1106 (34.98%)	497 (65.21%)	609 (60.55%)
Other Hispanic	287 (9.08%)	120 (6.41%)	167 (6.98%)
Other Race	583 (18.44%)	283 (9.39%)	300 (10.92%)
Education, n (%)				0.09
Less than high school	171 (5.41%)	53 (1.49%)	118 (3.03%)
High school	1086 (34.35%)	511 (33.35)	575 (33.39%)
More than high school	1905 (60.25%)	863 (65.16%)	1042 (63.58%)
Hypertension, n (%)				<0.01
Yes	1312 (41.49%)	403 (20.28%)	909 (50.41%)
No	1850 (58.51%)	1024 (79.72%)	826 (49.59%)
Hyperlipidemia, n (%)				<0.01
Yes	2046 (64.71%)	705 (47.96%)	1341 (77.03%)
No	1116 (35.29%)	722 (52.04%)	394 (22.97%)
DM, n (%)				<0.01
No	2380 (75.27%)	1257 (92.43%)	1123 (69.21%)
IFG	226 (7.15%)	69 (3.87%)	157 (9.20%)
DM	556 (17.58%)	101 (3.70%)	455 (21.59%)
BMI, n (%)				<0.01
≥29.9 kg/m2	1353 (42.79%)	268 (18.98%)	1085 (66.08%)
≥24.9, <29.9 kg/m2	975 (30.83%)	437 (28.50%)	538 (29.10%)
<24.9 kg/m2	834 (26.38%)	722 (52.52%)	112 (4.82%)
Smoke, n (%)				0.03
Never	1925 (60.88%)	919 (65.71%)	1006 (59.46%)
Former	677 (21.41%)	236 (18.62%)	441 (24.95%)
Current	560 (17.71%)	272 (15.67%)	288 (15.59%)
Alcohol, n (%)				0.23
Never	418 (13.22%)	199 (10.04%)	219 (8.70%)
Mild	1418 (44.85%)	613 (43.04%)	805 (46.43%)
Moderate	648 (20.49%)	319 (24.65%)	329 (20.59%)
Heavy	678 (21.44%)	296 (22.26%)	382 (24.27%)
ALT (IU/L)	24.04±0.58	19.88±1.00	27.79±0.74	<0.01
AST (IU/L)	23.19±0.52	22.41±1.06	23.90±0.66	0.31
HEI2020	50.19±0.81	51.64±0.97	48.85±0.81	<0.01
Energy intake	2197.18±27.50	2127.46±43.36	2260.48±35.45	0.03
PIR	3.20±0.07	3.22±0.11	3.17±0.07	0.66
Total vegetables intake (cup)	1.52±0.06	1.54±0.06	1.50±0.07	0.49
Dark green vegetables intake (cup)	0.17±0.01	0.21±0.02	0.13±0.01	<0.01
Red orange vegetables intake (cup)	0.36±0.02	0.38±0.03	0.35±0.02	0.21
Red tomato intake (cup)	0.27±0.01	0.27±0.02	0.26±0.01	0.52
Red other vegetables intake (cup)	0.10±0.01	0.11±0.01	0.09±0.01	0.14
Starchy vegetables intake (cup)	0.44±0.02	0.41±0.02	0.46±0.03	0.19
Starchy potato intake (cup)	0.37±0.02	0.36±0.02	0.39±0.02	0.36
Starchy other vegetables intake (cup)	0.06±0.01	0.05±0.00	0.08±0.01	0.05
Other vegetables intake (cup)	0.56±0.03	0.55±0.03	0.56±0.03	0.62
Legumes intake (cup)	0.12±0.01	0.12±0.01	0.11±0.01	0.31

Abbreviation: DM Diabetes mellitus, IFG Impaired fasting glucose, BMI Body mass index, ALT Alanine aminotransaminase, AST Aspartate aminotransferase, HEI2020 Healthy eating index 2020, PIR Income-to-poverty ratio

The associations between different kinds of vegetables intake and the occurrence of MAFLD

Firstly, we conducted a univariate logistic regression analysis to explore the relationship between MAFLD and different kinds of vegetables intake. Initially, vegetables were categorized into legumes and total vegetables excluding legumes. However, no significant associations were observed between MAFLD and total vegetable intake (OR = 0.81; 95% CI: 0.53–1.24; p-value = 0.31) or legumes intake (OR = 1.05; 95% CI: 0.85–1.31; p-value = 0.17). Subsequently, total vegetables were further divided into dark green vegetables, red orange vegetables, starchy vegetables, and other vegetables. Among these subcategories, intake of dark green vegetables (OR = 0.46; 95% CI: 0.33–0.66; p-value < 0.001) was negatively associated with MAFLD. Conversely, no significant correlations were found between MAFLD and consumption of red orange vegetables (OR = 0.80; 95% CI: 0.57–1.13; p-value = 0.19), starchy vegetables (OR = 1.22; 95% CI: 0.91–1.64; p-value = 0.17), or other vegetables (OR = 0.96; 95% CI: 0.86–1.08; p-value = 0.49). Specifically examining representative vegetables from the red orange and starchy categories, the results indicated that intake of red tomato (OR = 0.89; 95% CI: 0.26–1.31; p-value = 0.52), red other vegetables (OR = 0.58; 95% CI: 0.26–1.29; p-value = 0.16), and starchy potato (OR = 1.13; 95% CI: 0.86–1.50; p-value = 0.35) did not exhibit significant associations with MAFLD. However, a higher intake of starchy other vegetables (OR = 2.36; 95% CI: 1.13–4.96; p-value = 0.03) was significantly associated with increased odds of MAFLD (Table 2).

Table 2.

Univariate logistic regression analysis of the association between different kinds of vegetables intake and MAFLD

Characteristics	OR (95% CI)	p-value
Total vegetables intake	0.81 (0.53, 1.24)	0.31
Dark green vegetables intake	0.46 (0.33, 0.66)	< 0.001
Red orange vegetables intake	0.80 (0.57, 1.13)	0.19
Red tomato intake	0.89 (0.26, 1.31)	0.52
Red other vegetables intake	0.58 (0.26, 1.29)	0.16
Starchy vegetables intake	1.22 (0.91, 1.64)	0.17
Starchy potato intake	1.13 (0.86, 1.50)	0.35
Starchy other vegetables intake	2.36 (1.13, 4.96)	0.03
Other vegetables intake	0.96 (0.86, 1.08)	0.49
Legumes intake	1.05 (0.85, 1.31)	0.17

Multivariate logistic regression models were subsequently employed to verify the associations between MAFLD and various types of vegetable intake. After adjusting for covariates, it was surprising to observe that dark green vegetable intake remained negatively associated with MAFLD in both model 2 (OR = 0.46 95%CI: 0.29–0.71; p = 0.01) and model 3 (OR = 0.54 95%CI: 0.36–0.81; p-value = 0.01). Nevertheless, no significant connections were observed between the consumption of other types of vegetables and MAFLD (Table 3). RCS curve showed a negative linear relationship between dark green vegetable intake and MAFLD (Figure S2).

Table 3.

Association between different kinds of vegetables intake and MAFLD

Characteristics	Model 1* OR (95% CI) p-value	Model 2* OR (95% CI) p-value	Model 3* OR (95% CI) p-value
Total vegetables intake	0.81 (0.53, 1.24)	0.90 (0.77, 1.05)	1.01 (0.86, 1.19)
	0.31	0.16	0.90
Dark green vegetables intake	0.46 (0.33, 0.66)	0.46 (0.29, 0.71)	0.54 (0.36, 0.81)
	< 0.01	0.01	0.01
Red orange vegetables intake	0.80 (0.57, 1.13)	0.68 (0.46, 1.02)	0.76 (0.49, 1.17)
	0.19	0.06	0.19
Starchy vegetables intake	1.22 (0.91, 1.64)	1.16 (0.80, 1.68)	1.22 (0.93, 1.60)
	0.17	0.37	0.15
Legumes intake	1.05 (0.85, 1.31)	0.63 (0.37, 1.06)	0.83 (0.46, 1.50)
	0.61	0.07	0.83

^* Model 1: no covariates were adjusted. Model 2: age, gender, race/ethnicity, and education level were adjusted. Model 3: age, sex, ethnicity, education level, smoking, alcohol consumption, PIR, BMI, ALT, AST, HEI2020, energy intake, hypertension, hyperlipidemia, and DM were adjusted

Abbreviation: DM Diabetes mellitus, BMI Body mass index, ALT Alanine aminotransaminase, AST Aspartate aminotransferase, HEI2020 Healthy eating index 2020, PIR income-to-poverty ratio

Subgroup analysis

From our analyses above, we made an innovative discovery that the intake of dark green vegetables serves as a protective factor against MAFLD. To further investigate this association, we conducted a subgroup analysis to examine the correlation between dark green vegetable intake and MAFLD in different subgroups. Subgroup analysis revealed no significant differential association between dark green vegetable intake and the odds of MAFLD across age groups, educational levels, PIR grade, smoking status, alcohol drinking, hypertension, hyperlipidemia, and DM. However, it was noteworthy that female, non-Hispanic white people with higher education seemed to make a greater contribution to the negative association between dark green vegetables intake and MAFLD (Fig. 1).

Fig. 1.

Subgroup analysis of the association between dark green vegetables intake and MAFLD. Abbreviation: DM, diabetes mellitus; IFG, impaired fasting glucose; BMI, body mass index; PIR, income-to-poverty ratio

Discussion

In this study, we explored the correlations between the consumption of various types of vegetables and MAFLD. Notably, we innovatively found that dark green vegetable intake was associated with a reduced prevalence of MAFLD, while other kinds of vegetables shown no significant association with MAFLD. These results highlight the importance of considering the specific types of vegetables in dietary interventions for the prevention and management of MAFLD.

Representatives of dark green vegetables are spinach, broccoli, romaine, etc. And common starchy vegetables are potatoes, peas, beans, etc. Previous research has also evaluated the association between the intake of different types of vegetables and MAFLD. For instance, a cross-sectional study conducted in China, involving 26,891 adults who participated in health examinations from 2013 to 2017, found an inverse association between the consumption of green leafy vegetables and the prevalence of MAFLD [28], aligning with our findings. Another study reported a similar inverse association between total and raw spinach intake with the odds of MAFLD [29]. But a study from Japan, with a smaller sample size of 2444 participants, concluded that the significant association between green and yellow vegetables and MAFLD was only observed in females [30]. These variations in results may be attributed to differences in study designs and the characteristics of participants enrolled.

The observed associations in our analysis can be explained from two aspects. Firstly, dark green vegetables are known to be abundant sources of vitamins, micronutrients, minerals, and dietary fiber which have been associated with a lower risk of chronic liver disease [31–34]. For instance, carotenoid and α-tocopherol, well-known antioxidant nutrients found in dark green vegetables, could decrease the origination of ROS and restrain oxidative stress [35]. Oxidative stress like lipid peroxidation contributes to liver cell injury and the progression of chronic liver disease. Additionally, an animal study reported that mice fed with HFD developed MAFLD, but this progression was attenuated by nitrate which was plentiful in dark green vegetables and can increase AMPK activity [36]. Another animal study revealed that dietary nitrate could stimulate the nitrate-nitrite-NO pathway, leading to the activation of AMP-activated protein and a reduction in liver steatosis [31]. Furthermore, dark green vegetables were also rich in magnesium which may be associated with lower risks of liver steatosis and prediabetes [32]. These potential mechanisms discussed above might contribute to the observed negative association between dark green vegetable intake and MAFLD. However, further experimental studies are necessary to validate and confirm our results.

Our findings underscore the potential for dietary interventions that prioritize dark green vegetables as a strategy to mitigate the risk of MAFLD. Dark green vegetables, such as spinach, kale, and broccoli, are nutrient-dense and rich in dietary fiber, antioxidants, and bioactive compounds like carotenoids and flavonoids [37, 38]. These components are known to improve insulin sensitivity, reduce oxidative stress, and combat inflammation, all of which are critical in addressing the pathophysiology of MAFLD [1, 39–41]. Consequently, integrating these vegetables into dietary recommendations could serve as a practical, accessible, and sustainable public health strategy to reduce the burden of MAFLD. Furthermore, our findings align with existing dietary guidelines, such as the Mediterranean diet, which emphasizes the consumption of nutrient-dense, plant-based foods. The Mediterranean diet is rich in vegetables, particularly dark green leafy vegetables, and has been associated with improved liver health and reduced risk of metabolic diseases [42]. From a public health perspective, the integration of these dietary strategies into national health campaigns could help mitigate the growing burden of MAFLD. Furthermore, clinical counseling for high-risk patients should include tailored nutrition advice that prioritizes these foods. By embedding these dietary changes into routine care, clinicians can help reduce the incidence of MAFLD and improve overall metabolic health in at-risk populations.

This study explored the association between vegetables intake and MAFLD through a large sample cross-sectional research and adjusted for many variables. Nevertheless, this study has several limitations that should be acknowledged. First, the data we analyzed were in this study were limited to American adults. Dietary cultural differences may affect the prevalence of MAFLD [43, 44]. Different cooking methods may also modify the metabolic impact of vegetable subtypes, potentially yielding varying outcomes regarding their association with MAFLD. Second, given the cross-sectional design of our study, we cannot infer causality between vegetable intake and MAFLD. Third, our reliance on self-reported dietary data introduces the potential for recall bias, which may affect the accuracy of intake estimates. Lastly, while we made efforts to adjust for a wide range of covariates, it is possible that unmeasured variables or residual confounding may have influenced the results. Future studies could address these limitations by incorporating additional variables or employing more sophisticated statistical methods to account for potential confounders and biases. To overcome these limitations and provide more robust evidence, further prospective studies and experimental research are needed to explore the underlying mechanisms.

Conclusion

In our study, we observed heterogeneous effects of different types of vegetables on MAFLD. Specifically, a high intake of dark green vegetables was associated with low odds of MAFLD, which indicated the intake of dark green vegetables might be a potential protective factor for MAFLD.

Abbreviations

ALT

Alanine aminotransaminase

AMP

Adenosine monophosphate

AMPK

Adenosine 5 ‘-monophosphate-activated protein kinase

AST

Aspartate aminotransferase

BMI

Body mass index

CIs

Confidence intervals

CRP

C-reactive protein

DASH

Dietary approach to stop hypertension

DM

Diabetes mellitus

FPED

Food Patterns Equivalents Database

HEI2020

Healthy Eating Index 2020

hSKP-HPC

Human skin-derived precursor hepatocyte-like cell

IFG

Impaired fasting glucose

IL-1β

Interleukin 1 beta

iPSC

Induced pluripotent stem cell

MAFLD

Metabolic dysfunction-associated fatty liver disease

MASLD

Metabolic dysfunction-associated steatotic liver disease

MD

Mediterranean Diet

NHANES

National Health and Nutrition Examination Survey

NCHS

National Center for Health Statistics

ORs

Odds ratios

OGTT

Oral glucose tolerance test

PIR

Income-to-poverty ratio

ROS

Reactive oxygen species

TGF-β

Transforming growth factor beta

TNF-α

Tumor necrosis factor alpha

Authors’ contributions

C.L. and Y.B.L. designed research, analyzed data, and wrote the paper. J.L. and K.X. helped interpreter the results and revise the manuscript. J.L., T.L. J.J.Y and G.Z.Z were responsible for the revision of the manuscript. K.X. ensured that the descriptions were accurate and had primary responsibility for final content. All authors have read and approved the final manuscript.

Funding

This study was supported by National Natural Science Foundation of China (Nos. 82000357), Natural Science Foundation of Hunan Provincial (2022JJ40755 and 2023JJ40914), Changsha Municipal Natural Science Foundation (kq2014254) and Major Project of Natural Science Foundation of Hunan Province (Open Competition) (2021JC0002).

Data availability

The data used in this study are from a public database at https://wwwn.cdc.gov/nchs/nhanes/default.aspx.

Declarations

Ethics approval and consent to participate

The National Center for Health Statistics (NCHS) ethics review board approved the protocols, and all participants were provided informed written consent at enrollment.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.