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The association between the dietary index for gut microbiota and metabolic dysfunction-associated fatty liver disease: a cross-sectional study

Diabetology & Metabolic Syndrome volume 17, Article number: 17 (2025) Cite this article

Abstract

Background

The relationship between the gut microbiome and metabolic dysfunction-associated fatty liver disease (MAFLD) has garnered increasing attention. However, the association between the dietary index for gut microbiota (DI-GM), a measure of microbiome diversity, and MAFLD has yet to be fully explored.

Methods

Data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES) were analyzed, including 7243 participants. The association between DI-GM and MAFLD was investigated using weighted logistic regression, restricted cubic spline (RCS), and subgroup analyses.

Results

A notable inverse association was identified between DI-GM and the prevalence of MAFLD, with each 1-point increase in DI-GM corresponding to a 6.1% reduction in MAFLD prevalence (OR = 0.939, 95% CI: 0.901–0.980). Individuals with a DI-GM score of 6 or higher had an adjusted OR of 0.794 (95% CI: 0.665–0.947) compared to those with a DI-GM score of 0–3. RCS analysis further revealed a linear relationship between DI-GM and MAFLD risk. Additionally, subgroup analyses suggested that race may modify the association between DI-GM and MAFLD (P for interaction < 0.05).

Conclusions

DI-GM is inversely associated with MAFLD prevalence, and race appears to be a significant modifier of this relationship.

Introduction

Non-alcoholic fatty liver disease (NAFLD) is a common chronic condition characterized by hepatic fat accumulation of ≥ 5% in the absence of other identifiable causes, such as excessive alcohol intake, viral infections, drug-induced liver injury, or autoimmune diseases [1]. Over the past few decades, the global prevalence of NAFLD has increased at an alarming rate, now affecting over 30% of the global population and reaching as high as 39.1% in the United States [2, 3]. Emerging evidence closely links NAFLD to increased risks of all-cause mortality, liver cancer, chronic kidney disease, and cardiovascular disease, imposing a significant burden on healthcare systems and society [4,5,6,7]. For instance, a meta-analysis reported a 64% higher risk of cardiovascular disease in individuals with NAFLD, and another study found that NAFLD accounted for 14.1% of the etiologies of hepatocellular carcinoma [5, 7]. NAFLD frequently coexists with metabolic disorders such as diabetes, obesity, hypertension, and dyslipidemia, which are also key risk factors for its progression [8,9,10,11]. To address these complexities, an international expert panel proposed renaming NAFLD to metabolic dysfunction-associated fatty liver disease (MAFLD) in 2020, replacing exclusion-based diagnostic criteria with positive criteria requiring hepatic steatosis along with metabolic dysfunction [12]. This redefinition emphasizes the importance of early identification and targeted management of metabolic dysfunction to address MAFLD at its root.

The gut microbiota is a pivotal element affecting the pathogenesis and progression of MAFLD. Through mechanisms such as modulating intestinal barrier function, activating inflammatory responses, and interacting with host genetic factors, gut microbiota contributes significantly to the underlying pathology of MAFLD [13,14,15,16,17]. As a key modifiable factor influencing gut microbiota, diet presents a promising intervention strategy to improve gut health and potentially prevent MAFLD [18, 19].

Studies have shown that enhanced consumption of fermented foods and dietary fiber supports the growth of beneficial microbial communities, helping to prevent and correct gut microbiota dysbiosis [20, 21]. Growing evidence indicates that dietary interventions targeting gut microbiota composition may effectively reduce the risk and progression of MAFLD [22,23,24]. Furthermore, microbiota-targeted dietary interventions have demonstrated positive effects in MAFLD animal models, and related clinical research is gradually expanding [25,26,27,28]. In this context, Kase et al. conducted a systematic review of 106 studies on diet and gut microbiota, identifying 14 dietary components with varying effects on the gut microbiome and subsequently developing the dietary index of gut microbiome (DI-GM) to assess dietary patterns that promote or hinder gut microbiota health [29]. DI-GM holds promise as a standardized tool for assessing gut microbiota status and advancing interdisciplinary research between nutrition and microbiology. However, studies exploring the relationship between DI-GM and MAFLD are limited.

The National Health and Nutrition Examination Survey (NHANES), conducted by the U.S. Centers for Disease Control and Prevention since the 1960s, is a landmark epidemiological program designed to assess the health and nutritional status of the U.S. population. Compared to other databases, NHANES stands out for its nationally representative sample, comprehensive and multi-dimensional data, rich clinical and laboratory information, capacity for long-term trend analysis, and high-quality, publicly accessible datasets, making it an indispensable resource for scientific research. This cross-sectional study aims to address the current knowledge gap by investigating the relationship between DI-GM and MAFLD using data from the 2017–2020 NHANES. Through this analysis, the study seeks to provide scientific evidence that supports early intervention strategies for MAFLD in adult populations.

Method

Study population

This study analyzed data from the 2017–2020 NHANES, including 15,560 participants. NHANES employs a multi-cycle, cross-sectional design and advanced sampling techniques to recruit a nationally representative cohort. The National Center for Health Statistics Research Ethics Review Board approved the NHANES protocols for the corresponding survey years: Continuation of Protocol #2011-17 (effective through October 26, 2017) and Protocol #2018-01 (effective beginning October 26, 2017) for NHANES 2017–2018, and Protocol #2018-01 for NHANES 2019–2020. Detailed information on NHANES ethical review and consent processes is available at https://www.cdc.gov/nchs/nhanes/irba98.htm. All participants provided informed written consent before participating in the survey. Since this study uses secondary, publicly available, and deidentified NHANES data that do not meet the criteria for human participants research, no additional ethical approval was required from the Ethics Committee of Beijing Hospital. Following rigorous screening procedures (as detailed in Fig. 1), the final analysis included data from 7,243 U.S. adults aged 20 years and older.

Fig. 1
figure 1

Selection of participants in the study

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Ascertainments of MAFLD

The 2020 consensus guidelines form the basis for defining MAFLD diagnostic criteria. MAFLD is characterized by hepatic steatosis accompanied by at least one of the following: type 2 diabetes, overweight or obesity, or metabolic dysfunction [12].

In the 2017–2020 NHANES survey, hepatic steatosis and fibrosis were evaluated using vibration-controlled transient elastography via the FibroScan model 502 V2 Touch (Echosens, Waltham, MA). Hepatic steatosis was quantified with the controlled attenuation parameter (CAP), where a CAP score of ≥ 285 dB/m was used to define steatosis, providing a sensitivity of 0.80 and specificity of 0.77 [30].

Calculation of DI-GM

This study used the scoring system developed by Kase et al. to calculate the DI-GM, based on 14 foods or nutrients [29]. The DI-GM was calculated using the 24-hour dietary recall data from the 2017–2020 NHANES. Beneficial foods received a score of 1 if intake met or exceeded the sex-specific median, otherwise 0. Unfavorable foods received a score of 0 if intake met or exceeded the sex-specific median (or above 40% energy from fat), otherwise 1. The total DI-GM score spanned a range of 0 to 14, with beneficial foods contributing 0–10 points and unfavorable foods 0–4 points. Participants were divided into four groups based on quartiles of total scores: 0–3, 4, 5, and 6 or higher. The components and scoring criteria of the DI-GM are detailed in Supplementary Table 1.

Covariates

Covariates included sociodemographic, socioeconomic, and health behavioral characteristics. Sociodemographic and socioeconomic characteristics included age (continuous), gender (male, and female), race (Non-Hispanic White, Non-Hispanic Black, Mexican American, and Other Race), marital status (married/living with partner, never married, and widowed/divorced/separated), education level (less than high school graduate, high school graduate or GED, and some college or above), and poverty income ratio (classified as low income (< 1.30), middle income (1.30–3.49), and high income (≥ 3.50)). Health behaviors included smoking status (never, ex-smoker, and current-smoker), alcohol intake (no-drinking, and drinking). The NHANES dataset includes comprehensive information on demographics, diet, examinations, laboratory results, and questionnaires, along with detailed tools, methods, usage guidelines, and FAQs, as outlined in the NHANES manuals and reports.

Statistical analysis

This study excluded samples with missing information on MAFLD or dietary recall data. Samples with missing covariate values exceeding 20% were not included in the analysis. For covariates with less than 20% missing data, the “mice” package in R was employed to perform multiple imputations. Participant demographic and clinical characteristics were categorized by MAFLD status. The relationships between categorical variables, presented as frequencies and weighted percentages, were assessed using the Rao-Scott χ² test. Continuous variables were reported as weighted means and their corresponding standard errors (SE). The Wilcoxon rank-sum test for complex survey samples was employed to evaluate differences in continuous variables.

Weighted logistic regression models were applied to evaluate the association between DI-GM (as both a continuous and categorical variable) and the risk of MAFLD, with results expressed as odds ratios (OR) and 95% confidence intervals (CI). Restricted cubic splines (RCS) were employed to investigate potential nonlinear relationships between DI-GM and MAFLD. The Akaike Information Criterion guided the location and number of knots in RCS knots to balance model fit and overfitting [31]. Subgroup analyses were conducted to identify factors influencing the relationship between DI-GM and MAFLD. The analysis involved stratifying the final analytical sample by age (< 65, and ≥ 65 years), gender (male and female), race (Non-Hispanic White, Non-Hispanic Black, Mexican American, and Other Race), marital status (married/living with partner, never married, and widowed/divorced/separated), education level (less than high school graduate, high school graduate or GED, and some college or above), poverty income ratio (classified as low income (< 1.30), middle income (1.30–3.49), and high income (≥ 3.50)), smoking status (never, ex-smoker, and current-smoker), alcohol intake (no-drinking, and drinking), respectively. A multiplicative interaction term among the subgroups, DI-GM, and MAFLD was fitted into the model to assess for potential interaction effects.

All analyses were performed using R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria < http://www.R-project.org < ). Differences with P < 0.05 indicated statistical significance.

Results

Baseline characteristics of participants

Table 1 summarizes the weighted characteristics of the study population, grouped by MAFLD status. A total of 7243 participants were included in the analysis, of whom 3665(50.96%) were female, and 4104 (54.91%) had MAFLD. The mean (SE) age was 48.22(0.58) years, with an average (SE) poverty income ratio (PIR) of 3.12(0.05) and a mean (SE) DI-GM score of 4.59(0.05). Compared to participants without MAFLD, those with MAFLD were more likely to be older, male, of Mexican American or other racial backgrounds, married, have lower educational attainment, be former smokers (all P < 0.05). Participants with MAFLD exhibited lower DI-GM compared to those without MAFLD, with fewer participants reaching DI-GM levels of 6 or higher. However, these differences were not statistically significant (all P > 0.05).

Table 1 Characteristics of the NHANES 2017–2020 participants
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Associations between DI-GM and MAFLD

The associations between DI-GM and MAFLD were shown in Table 2. In Model 1, adjusted only for age, each 1-point increase in DI-GM corresponded to a 5.9% decrease in MAFLD prevalence (OR = 0.941, 95% CI: 0.903–0.981). Model 2, additionally adjusted for gender and race, showed that this inverse relationship persisted as significant (OR = 0.940, 95% CI: 0.904–0.977). In Model 3, further adjustments were made for marital status, education level, and poverty income ratio (PIR), with the association persisting (OR = 0.940, 95% CI: 0.902–0.979). In Model 4, additional adjustments for smoking status and alcohol intake retained the significant inverse relationship between DI-GM and MAFLD (OR = 0.939, 95% CI: 0.901–0.980).

Table 2 Association between DI-GM and MAFLD of the NHANES 2017–2020 participants
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For categorical analysis, DI-GM levels were divided into four groups: 0–3, 4, 5, and 6 or higher, with 0–3 serving as the reference group. In Model 1, after adjusting for age, DI-GM of 6 or higher were significantly correlated with a decreased risk of MAFLD (OR = 0.799, 95% CI: 0.673–0.948, P for trend = 0.004). Model 2, with additional adjustment for gender and race, confirmed this significant association (OR = 0.795, 95% CI: 0.677–0.934, P for trend = 0.002). In Model 3, further adjusted for marital status, education level, and PIR, the significant inverse association persisted (OR = 0.795, 95% CI: 0.670–0.942, P for trend = 0.004). Model 4, which additionally adjusted for smoking status and alcohol intake, also sustained the significant inverse relationship between DI-GM of 6 or higher and reduced MAFLD risk (OR = 0.794, 95% CI: 0.665–0.947, P for trend = 0.004).

Dose-response analysis of DI-GM with MAFLD

The dose-response relationship between DI-GM and MAFLD risk was assessed using a multivariable-adjusted RCS analysis (Fig. 2). With the P-value for nonlinearity exceeding 0.05 (P for nonlinearity = 0.279), the nonlinear association between DI-GM and MAFLD risk was not statistically significant. Instead, the analysis demonstrated a clear linear relationship, showing that MAFLD risk decreases as DI-GM levels rise (P for overall < 0.001).

Fig. 2
figure 2

Association between DI-GM and MAFLD using a restricted cubic spline model. Multivariable adjusted odds ratios (solid line) with 95% confidence interval (shaded area) for the association of DI-GM with MAFLD disease. Adjusted for age (continuous), gender (male, and female), race (Non-Hispanic White, Non-Hispanic Black, Mexican American, and Other Race), marital status (married/living with partner, never married, and widowed/divorced/separated), education level (less than high school graduate, high school graduate or GED, and some college or above), PIR (continuous), smoking status (never, ex-smoker, and current-smoker), alcohol intake (no-drinking, and drinking). Abbreviations: DI-GM, dietary index for gut microbiota; MAFLD, metabolic dysfunction-associated fatty liver disease; PIR, Poverty Income Ratio

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Subgroup analyses

Subgroup analyses were conducted to examine whether any factors modified the relationship between DI-GM (as both a continuous and categorical variable) and MAFLD (Tables 3 and 4). After adjusting for confounders, no significant interactions were found across subgroups stratified by age, gender, marital status, education level, PIR, smoking status, or alcohol intake (P for interaction > 0.05). Conversely, race emerged as a potential moderating factor in the relationship between DI-GM (as continuous variable) and MAFLD (P for interaction < 0.05).

Table 3 Associations between DI-GM and MAFLD of the NHANES 2017–2020 participants, stratified by selected factors
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Table 4 Associations between DI-GM levels and MAFLD of the NHANES 2017–2020 participants, stratified by selected factors
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Discussion

With the rising prevalence of obesity and type 2 diabetes, fatty liver disease has become a leading cause of chronic liver disease worldwide [2, 3]. In 1986, Jurgen Ludwig introduced the term NAFLD, defining it as hepatic steatosis involving at least 5% of hepatocytes in the absence of other causes of liver damage [32]. However, NAFLD is widely regarded as an inappropriate and inaccurate term. Its exclusion-based diagnostic criteria and the stigma associated with the terminology have been heavily criticized [33]. Moreover, numerous studies have demonstrated that the progression of NAFLD is closely linked to systemic metabolic dysfunction [8,9,10,11]. To address these limitations, an international expert panel proposed renaming NAFLD to MAFLD in 2020, emphasizing the pivotal role of metabolic dysfunction in disease progression [12]. In 2023, the concept was further refined with the introduction of metabolic dysfunction-associated steatotic liver disease (MASLD), which optimized the definition of metabolic abnormalities, reflecting an evolving understanding of fatty liver diseases [34]. The disease spectrum of MASLD encompasses simple hepatic steatosis, metabolic dysfunction-associated steatohepatitis (MASH), MASH-related fibrosis, cirrhosis, and even hepatocellular carcinoma. These terminological updates not only revised diagnostic criteria but also redefined target populations [35]. Despite these advancements, the differences in clinical outcomes between MAFLD and MASLD remain unclear, and debates surrounding their nomenclature persist, warranting further investigation. This study adopts MAFLD as its primary research focus.

This study was the first to demonstrate a robust inverse relationship between higher DI-GM, specifically within the DI-GM ≥ 6 group, and the prevalence of MAFLD. RCS further confirmed a linear relationship between DI-GM and MAFLD prevalence. Moreover, race emerged as a potential moderating factor in this association (P for interaction < 0.05).

Increasing evidence indicates a strong link between alterations in gut microbiota composition and the onset of MAFLD. Compared with healthy individuals, patients with MAFLD show significantly reduced gut microbiota diversity and distinct shifts in microbial composition [16, 36,37,38]. Specifically, the abundance of Gram-negative bacteria is markedly increased, while the prevalence of short-chain fatty acids (SCFAs)-producing bacteria is diminished, which was also observed in animal models [36, 37]. Additionally, MAFLD patients exhibit notable changes in the fungal microbiota, with an increased prevalence of Candida albicans and Mucor [38]. Studies further reveal that distinct gut microbiota profiles are associated with specific stages of MAFLD, closely aligning with disease progression [39,40,41,42]. For instance, elevated levels of Prevotella have been linked to greater severity of hepatic fibrosis [40].

The progression of MAFLD is influenced by gut microbiota through multiple mechanisms [16, 43,44,45,46,47,48,49,50,51]. First, dysbiosis disrupts the intestinal barrier, enabling harmful substances to infiltrate the circulation and activate hepatic immune responses, promoting lipid accumulation and liver fibrosis [43]. Second, alterations in bile acid metabolism due to microbial imbalance impair hepatic lipid metabolism, resulting in lipid accumulation and hepatocellular injury [44, 45]. Third, the microbial breakdown of choline into trimethylamine reduces choline bioavailability, inducing hepatic steatosis [46, 47]. Fourth, reduced production of SCFAs compromises intestinal barrier function and lipid metabolism, further contributing to hepatic steatosis [48, 49]. Fifth, excessive microbial production of alcohol exacerbates hepatic fat accumulation, advancing MAFLD progression [50, 51]. Finally, interactions between the host genetics, epigenetics, and gut microbiome also play a role. Changes in gut microbiota may induce epigenetic alterations, while genetic variations associated with MAFLD can influence microbial composition, accelerating disease progression [16].

Evidence indicates that the composition of the gut microbiota is shaped by multiple factors, including host age, genetic background, immune function, dietary habits, medication use, lifestyle, and environmental exposures [52]. Among these, diet stands out as a key modifiable factor with significant potential to improve gut health [7]. To evaluate dietary patterns and their effects on health, researchers have developed several dietary indices. Widely used indices include the Healthy Eating Index (HEI), the Mediterranean Diet Score (MDS), and the Dietary Approaches to Stop Hypertension (DASH) [53]. These indices are valuable tools for assessing overall dietary quality, but they do not specifically address associations between diet and the gut microbiota. Furthermore, their correlations with metrics of gut microbiota diversity and richness have shown inconsistencies [54,55,56]. A more targeted tool is the Sulfate-Metabolizing Diet Score, which identifies dietary components associated with the enrichment of sulfate-metabolizing bacteria [57, 58]. Studies have linked this dietary pattern to the proliferation of these bacteria, whose overgrowth is associated with colorectal cancer [58]. However, the scope of this index is narrow, as it focuses solely on the associations with specific sulfate-metabolizing bacteria.

In contrast, the Dietary Index for Gut Microbiota (DI-GM) offers a more comprehensive framework for analyzing the relationship between dietary patterns and gut microbiota health. Developed by Kase and colleagues through a systematic review of 106 studies, DI-GM incorporates 14 dietary components identified for their effects on gut microbiota diversity, production of SCFAs, and specific bacterial populations [29]. Compared with other dietary indices, DI-GM uniquely emphasizes the impact of individual foods on the gut microbiota, providing a robust scientific basis and greater precision. Its broad scope encompasses diversity indices, production of SCFAs production, and phylum-level changes, making it applicable to a wide range of studies beyond single bacterial groups. Moreover, although DI-GM was specifically designed to assess dietary quality in relation to gut microbiota health, it also reflects overall dietary health. Its correlations with HEI-2015 and MDS further highlight its dual applicability and scientific relevance [29].

The observed association between higher DI-GM and a reduced prevalence of MAFLD further highlights the potential of diet as a modifiable factor in promoting gut health. For instance, fermented dairy products, as beneficial elements of the DI-GM, may significantly contribute to the prevention and control of MAFLD. Research has shown that, even under a high-sugar, high-fat diet, mice consuming yogurt exhibit substantial improvements in liver health, particularly in terms of reduced hepatic steatosis and decreased liver inflammation [59]. Long-term yogurt consumption has been found to increase gut microbiome diversity and significantly enhance the proliferation of advantageous bacteria, such as Bifidobacterium, which is favorably connected with yogurt consumption [60]. Furthermore, multiple studies have confirmed that Bifidobacterium may alleviate hepatic steatosis and inflammation in mouse models of metabolic syndrome, providing protective effects against MAFLD [61, 62]. Further research incorporating gut microbiome data is essential to comprehensively assess the practical applicability of DI-GM.

Race significantly influences gut microbiota, with effects that extend beyond geographical location to encompass specific lifestyle and cultural practices [63,64,65]. Studies in Amsterdam, for example, have demonstrated distinct gut microbial characteristics across different ethnic groups, with Dutch individuals exhibiting the highest microbial diversity and South Asian Surinamese individuals showing the lowest diversity [63]. Furthermore, research in Singapore has indicated that racial differences in gut microbiome composition emerge by three months of age in infants [64]. In our study, race appeared to play a moderating role in the link between DI-GM and MAFLD risk (P for interaction < 0.05), suggesting that the protective effects of DI-GM may vary across racial groups. Incorporating racial variations into gut microbiome studies and prevention strategies for MAFLD may provide a more tailored approach, enhancing the effectiveness of health interventions.

This study has several notable strengths. First, it is the first to demonstrate a significant relationship between DI-GM and MAFLD after controlling for confounding factors, with higher DI-GM linked to a gradual reduction in MAFLD risk. Second, the study establishes a linear exposure-response relationship between DI-GM and MAFLD risk, indicating a consistent protective effect of DI-GM against the development of MAFLD. This finding suggests that dietary modifications to increase DI-GM could aid in reducing MAFLD risk. Lastly, subgroup analysis identified race as a potential moderating factor in the relationship between DI-GM and MAFLD, indicating that the protective effect of DI-GM may vary across racial groups (P for interaction < 0.05). This highlights the need to consider racial differences when designing dietary interventions for the precise prevention of MAFLD.

Nonetheless, this study has certain limitations. First, the cross-sectional design of NHANES limits the ability to infer causality, underscoring the need for future studies that incorporate more comprehensive prospective cohort designs. Second, the construction of DI-GM relied on intake data from 14 specific foods; any missing data on these foods led to the exclusion of participants, potentially introducing selection bias. Moreover, although adjustments were made for multiple potential confounders, residual confounding and unmeasured factors (such as genetic influences) cannot be entirely ruled out. Finally, as this study focuses exclusively on the relationship between DI-GM and MAFLD, the findings may not be directly applicable to the newly defined MASLD and its broader disease spectrum. Thus, further longitudinal epidemiological and experimental research is required to corroborate our findings.

Conclusion

This study demonstrated a significant negative association between the DI-GM, reflecting diet quality related to gut microbiota diversity, and the prevalence of MAFLD. Subgroup analysis revealed that race serves as a key moderating factor in this relationship, indicating that the association varies across different racial groups (P for interaction < 0.05). Thus, this study provides scientific evidence for preventing MAFLD through dietary modifications and highlights the importance of considering racial differences when developing intervention strategies for more precise MAFLD prevention.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

DI-GM:

dietary index for gut microbiota

NAFLD:

non-alcoholic fatty liver disease

MAFLD:

metabolic dysfunction-associated fatty liver disease

MASLD:

metabolic dysfunction-associated steatotic liver disease

MASH:

metabolic dysfunction-associated steatohepatitis

NHANES:

National Health and Nutrition Examination Survey

CI:

Confidence interval

OR:

Odd Ratio

PIR:

poverty income ratio

SCFAs:

short-chain fatty acids

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Acknowledgements

We appreciate all the efforts made by the staff of National Health and Nutrition Examination Survey (NHANES) in collecting and presenting data.

Funding

This work was supported by National High Level Hospital Clinical Research Funding (Grant no. BJ-2023-083).

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Author notes

  1. Yangyang Zheng and Jinhui Hou contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, Department of Hepato-bilio-pancreatic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China

    Yangyang Zheng, Jinhui Hou, Shiqi Guo & Jinghai Song

  2. Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China

    Yangyang Zheng, Shiqi Guo & Jinghai Song

  3. Department of General Surgery, Beijing Hospital, NO. 1 Da Hua Road, Dong Dan, Beijing, 100730, P.R. China

    Jinghai Song

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  1. Yangyang Zheng
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Contributions

Jinghai Song: Conceptualization, Writing-Review, Editing and Supervision; Yangyang Zheng, and Jinhui Hou: Methodology, Statistical analysis, and Writing of Draft; Shiqi Guo: Supervision, and Validation.All authors read and approved the final manuscript.

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Correspondence to Jinghai Song.

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This study used anonymous data from the National Health and Nutrition Examination Survey and complied with the ethical guidelines and regulations of the Declaration of Helsinki. The study was approved by the National Center for Health Statistics Ethics Review Board, and all participants provided written informed consent before the study.

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Zheng, Y., Hou, J., Guo, S. et al. The association between the dietary index for gut microbiota and metabolic dysfunction-associated fatty liver disease: a cross-sectional study. Diabetol Metab Syndr 17, 17 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01589-9

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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