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Meat and fish consumption, genetic risk and risk of severe metabolic-associated fatty liver disease: a prospective cohort of 487,875 individuals

Nutrition Journal volume 24, Article number: 65 (2025) Cite this article

Abstract

Background

Diet, specifically meat consumption, has been implicated as a modifiable risk factor in the development of metabolic-associated fatty liver disease (MAFLD). This study aimed to investigate the associations between various types of meat intake and the risk of severe MAFLD and to examine whether genetic risk influences these associations.

Methods

This research utilized data from the UK Biobank, which initially enrolled over 500,000 participants between 2006 and 2010, of whom 487,875 were eligible for our analyses. Meat intake, including unprocessed red meat, processed meat, poultry, and fish, was evaluated through a validated touchscreen questionnaire. Cox proportional hazards models were used to analyze the relationship between meat consumption and severe MAFLD risk, adjusting for potential confounders. Genetic risk scores (GRS) were calculated using five MAFLD-associated SNPs, allowing for analyses of gene-diet interactions.

Results

During a follow-up period totaling 6,036,554 person-years (mean duration: 12.1 years), 5,731 new cases of severe MAFLD were identified. High intakes of total meat, processed meat, unprocessed red meat and poultry were associated with increased MAFLD risk, with adjusted hazard ratios (HR) of 1.76 (95% CI: 1.33–2.33), 1.19 (1.02–1.40), 1.34 (1.17–1.53), and 1.21 (0.98–1.49), respectively, for the highest versus lowest intake categories. In contrast, oily fish intake showed a protective association (HR: 0.72; 95% CI: 0.53–0.97). No significant interaction was observed between meat intake and GRS for any meat subtype, suggesting that the associations were independent of genetic predisposition.

Conclusions

High consumption of red and processed meat was associated with an increased risk of severe MAFLD, while oily fish intake showed an inverse association with the risk of MAFLD. These effects were consistent across genetic risk levels for MAFLD. Our findings reinforce dietary recommendations to limit red and processed meat and encourage oily fish intake for MAFLD prevention, irrespective of individual genetic risk.

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Introduction

Metabolic-associated fatty liver disease (MAFLD) has emerged as the most prevalent chronic liver disease globally, affecting an estimated 25% of the adult population [1]. MAFLD can advance to cirrhosis and hepatocellular carcinoma, representing a leading cause of liver-related mortality worldwide. Due to the increasing prevalence of metabolic risk factors and the aging population globally, the burden of advanced stages of MAFLD and its associated economic impact are anticipated to increase substantially [2,3,4].

The pathogenesis of MAFLD is understood to be influenced by the interplay between genetic predispositions and lifestyle factors [5]. Dietary composition plays a crucial role, with excessive intake of refined carbohydrates, saturated fats, and ultra-processed foods promoting hepatic fat accumulation and inflammation [6]. In the absence of established pharmacological treatments for MAFLD, it is essential to identify modifiable risk factors that may aid in prevention and potentially slow disease progression [7]. Dietary habits, particularly meat consumption (including total meat, processed meat, unprocessed red meat, poultry, and fish), have been identified as modifiable risk factors associated with MAFLD development [8,9,10]. High consumption of red and processed meats has been linked to insulin resistance and an increased risk of MAFLD [11, 12]. However, some studies indicate that, after adjusting for multiple confounders, neither unprocessed nor processed red meat shows a direct association with MAFLD risk [13,14,15]. Similarly, other studies have found no detrimental association between processed meat and MAFLD risk [16, 17]. With regard to fish consumption, its relationship with MAFLD risk at the population level remains inconclusive; while some prospective studies suggest a protective role [16, 18], others report no significant association [19,20,21]. Evidence on poultry consumption and MAFLD risk is also limited and yields inconsistent results [15, 22]. Notably, most available evidence is based on cross-sectional studies with small sample sizes, highlighting the need for further large-scale, prospective research to elucidate the long-term effects of meat consumption on MAFLD development.

In recent years, large-scale genome-wide association studies (GWAS) have identified genetic loci associated with MAFLD risk, including variants in genes such as PNPLA3, TM6SF2, and GCKR [23]. Using these genetic variants, researchers can construct a polygenic risk score to quantify genetic susceptibility, which facilitates the study of gene-diet interactions [24]. Dietary factors, including meat consumption, are hypothesized to interact with genetic predispositions to influence MAFLD development [25]. However, evidence on these interactions remains limited. Investigating how different types of meat consumption interact with genetic risk to impact MAFLD incidence may provide insights that inform precision nutrition approaches.

The study had two primary objectives: first, to examine the association between various types of meat consumption and the incidence of severe MAFLD, and second, to evaluate the interactions between meat intake and genetic risk in the development of MAFLD.

Methods

Population

The UK Biobank is a large-scale prospective cohort study that recruited over 500,000 participants aged 37 to 73 years at baseline from diverse regions across the United Kingdom. From 2006 to 2010, participants attended one of 22 assessment centers, where they underwent comprehensive physical measurements, provided biological samples, and completed detailed touchscreen questionnaires. Ethical approval for the UK Biobank study was granted by the North West Multi-centre Research Ethics Committee [26].

For this analysis, the UK Biobank dataset included 502,406 participants. We excluded individuals who subsequently withdrew, those diagnosed with MAFLD or other liver diseases prior to study entry, participants with a history of substance abuse or alcoholism at baseline, and those lacking data on meat intake. A total of 484,875 participants remained eligible for the analysis of associations between meat consumption and MAFLD incidence. For the gene-diet interaction analysis, we further restricted the sample to individuals of white British descent with available genetic data, yielding a final sample of 364,619 participants for this analysis (Fig. 1).

Fig. 1
figure 1

Flow of participants in current UK biobank study

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Meat intake and covariates

The intake of various types of meat, including unprocessed red meat, processed meat, oily fish, non-oily fish, beef, lamb, pork, and poultry, was assessed through a touchscreen-based short food frequency questionnaire (FFQ) covering the past 12 months. Meat intake frequencies were categorized as never, < 1 time/week, 1 time/week, 2–4 times/week, 5–6 times/week, or ≥ 7 times/week. Total meat consumption was calculated by summing the intake frequencies of unprocessed red meat (beef, lamb, and pork), unprocessed poultry, processed meat (bacon, ham, sausages, meat pies, kebabs, burgers, and chicken nuggets), oily fish, and non-oily fish. The short FFQ has been validated against 24-hour dietary recalls. Diet quality was assessed using the Alternate Mediterranean Diet (AMED) score, which ranged from 0 to 9, with higher scores indicating better diet quality, as previously described [27]. Healthy diet score includes 10 foods predictive of cardiometabolic disease risk, emphasizing higher intake of vegetables, fruits, fish, dairy, whole grains, and vegetable oils and lower intake of refined grains, processed meats, unprocessed red meats, and sugar-sweetened beverages [28]. Each dietary component was scored from 0 (unhealthiest) to 10 (healthiest) points, with intermediate values scored proportionally. The total diet quality score was the sum of all the diet component scores and ranged from 0 to 100, with a higher score representing a higher overall diet quality. The UK Biobank also utilized the Oxford WebQ, a web-based 24-hour recall questionnaire administered on five occasions, to collect dietary data between 2009 and 2012. The average dietary intake was calculated using all available assessments to represent long-term dietary intake.

Potential confounding factors were collected via touchscreen questionnaire, including age, sex, ethnicity, weight, height, income, education level, smoking and drinking habits, physical activity, Townsend deprivation index [29], and medical history. The metabolic equivalent of task (MET) was calculated using the short form of the International Physical Activity Questionnaire [30].

Genetic risk score for MAFLD

Genotyping considerations, quality control, and genetic imputation details have been described previously [26]. We selected five single nucleotide polymorphisms (SNPs)—rs738409, rs58542926, rs641738, rs1260326, and rs72613567 (Table S1)—associated with MAFLD risk, based on prior MAFLD cohort analyses. The genetic risk score (GRS) was calculated using these SNPs, with corresponding β coefficients applied in the following formula: GRS = (β₁ × SNP₁ + β₂ × SNP₂ +. + βi × SNPi) × (i / sum of the β coefficients) [31], where β represents the coefficient for each SNP, and i indicates the number of risk alleles for each SNP. A higher GRS reflects greater genetic susceptibility to MAFLD.

Definition of severe MAFLD

In this study, severe MAFLD was defined as hospitalization or death due to MAFLD or non-alcoholic steatohepatitis (NASH), based on linked hospitalization and mortality databases. Hospitalization data, including dates and diagnoses, were obtained from hospital episode statistics, covering participants in England and Wales until September 30, 2021, and in Scotland until September 24, 2021. Severe MAFLD was identified using the International Classification of Diseases, 10th Revision (ICD-10) codes K76.0 (fatty liver, not elsewhere classified) and K75.8 (NASH, other specified inflammatory liver diseases) [32]. The follow-up period was calculated from the date of recruitment to the earliest of the following events: first diagnosis of severe MAFLD, death, loss to follow-up, or the end of the study on November 12, 2021.

Statistical analysis

Continuous variables are presented as mean ± standard deviation, and categorical variables are presented as percentages. To examine the association between meat consumption (assessed by short FFQ) and the risk of severe MAFLD, we used Cox proportional hazards models, categorizing meat intake into groups: never, 0–3 times/week, 3–5 times/week, 5–7 times/week, or ≥ 7 times/week (C1 to C5), with the lowest category as the reference group. The proportional hazards assumption was validated using Schoenfeld residuals, and results were expressed as hazard ratios (HR) with 95% confidence intervals (CI). Sequential models were adjusted for confounders identified in previous studies: Model 1 included adjustments for age (continuous) and sex (men or women); Model 2 further adjusted for ethnicity (white, Asian, black, mixed, or other ethnic group), assessment center location, BMI (in kg/m2; <18.5, 18.5 to 25, 25 to 30, ≥ 30, or missing), education level (college or university degree, vocational qualifications, optional national exams at ages 17–18 years, national exams at age 16 years, others, or missing), household income (<£18,000, £18,000-£30,999, £31,000-£51,999, £52,000-£100,000, >£100,000, or missing), smoking status (never, former, current, or missing), alcohol consumption (never or special occasions only, 1 to 3 times/month, 1 or 2 times/week, 3 or 4 times/week, or daily/almost daily), physical activity (quartiles), and the Townsend deprivation index (quartiles); Model 3 additionally controlled for intake of other meat types (unprocessed red meat, processed meat, unprocessed poultry, oily fish, and non-oily fish; categorical), vegetables (0 to 1 times/day, 1 to 3 times/day, more than 3 times/day), fruits (0 to 2 times/day, 2 to 4 times/day, more than 4 times/day), and total energy intake (quartiles); Model 4 included adjustments in Model 2 as well as additional controls for other meat types (5 categories), total energy intake (quartiles), and AMED score (total score minus the component for meat; quartiles). Missing data were managed by creating a missing indicator category where applicable. Additionally, to enhance the accuracy of dietary assessment and complement the analysis based on frequency, we incorporated food weight, which provides a more precise quantification of intake and reduces potential misclassification. Using variables from 24-hour dietary recalls reflecting mean intake in grams per day (g/d), we further examined the association between meat consumption and the risk of MAFLD based on food weight (n = 207,465).

To assess the interaction between meat intake and MAFLD GRS, we included a multiplicative interaction term in the Cox models. Further categorical analyses were conducted to determine whether HRs for a 1-standard deviation increase in meat consumption varied across tertiles of GRS. Subgroup analyses stratified by baseline characteristics were performed to evaluate the impact of covariate variations on associations. Sensitivity analyses included additional adjustments for lipid-lowering medication use and replacement of the AMED score with a healthy diet score, and exclusion of incident MAFLD cases diagnosed within the first five years of follow-up to minimize reverse causality.

All analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC, USA), and a p-value of less than 0.05 was considered statistically significant.

Results

Baseline characteristics of study participants

In this study, total meat intake was classified into five categories based on weekly consumption frequency: 0 times/week, 0.1-3 times/week, 3.1–4.9 times/week, 5.0-6.9 times/week, and ≥ 7 times/week. Baseline characteristics were examined across these groups, revealing variations in demographic, socioeconomic, and health-related factors (Table 1). Participants with higher total meat intake were more likely to be male, older, and have a higher BMI. This group was predominantly White, had higher household incomes, was more likely to be former smokers, and reported more frequent alcohol consumption. Additionally, individuals with higher meat intake tended to have lower levels of education and reported greater consumption of unprocessed red meat, processed meat, and poultry.

Table 1 Baseline characteristics of participants by categories of total meat intake in the UK biobank cohort
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Relationship between meat consumption and severe MAFLD risk

Over a follow-up period totaling 6,036,554 person-years, with an average duration of 12.1 years per participant, 5,731 new cases of severe MAFLD were identified. The analysis demonstrated a consistent and significant association between frequent consumption of total meat, processed meat, and unprocessed red meat and an increased risk of severe MAFLD. This association was observed in both age- and sex-adjusted models (Model 1) and in multivariable-adjusted models (Model 2, which controlled for demographic and lifestyle factors; Model 3, which additionally adjusted for intake of other food types), as shown in Table 2. In the final model, which further adjusted for total dietary energy intake and the AMED score (Model 4), the association remained unchanged. Specifically, participants in the highest category of total meat intake exhibited a 76% higher risk of MAFLD compared to those in the lowest category (HR: 1.76; 95% CI: 1.33–2.33; P for trend < 0.001). Processed meat consumption was associated with a 19% increased risk (HR: 1.19; 95% CI: 1.02–1.40; P for trend = 0.003), and unprocessed red meat intake was associated with a 34% increased risk (HR: 1.34; 95% CI: 1.17–1.53; P for trend < 0.001) when comparing the highest and lowest consumption categories. Unprocessed poultry consumption was also associated with a higher MAFLD risk (HR for highest vs. lowest category: 1.21; 95% CI: 0.98–1.49; P for trend < 0.001). In contrast, oily fish intake showed an inverse association with severe MAFLD risk (P for trend = 0.033), with a multivariable-adjusted (Model 4) HR of 0.72 (95% CI: 0.53–0.97) for the highest versus lowest intake category. No association was observed for non-oily fish consumption. Additionally, we also assessed the association between meat intake and MAFLD risk based on food intake in “g/d” rather than frequency and showed essentially similar results (Table S2). Total meat and processed meat consumption were positively associated with MAFLD risk, whereas the inverse association between fish intake and MAFLD remained but did not reach statistical significance.

Table 2 Associations between meat intake and MAFLD risk
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Interactions of different types of meat with MAFLD genetic risk

Table 3 presents the interactions between meat intake and the GRS for MAFLD. The analysis revealed a positive association between total meat intake and MAFLD risk, with a significant β of 0.074 (SE: 0.018, P < 0.001). Both unprocessed red meat and unprocessed poultry showed similar associations with MAFLD risk (β = 0.067 and 0.058, respectively, both P < 0.001). However, no significant interaction was found between meat intake and GRS for any meat subtype (all P interaction > 0.05).

Table 3 Interactions of meat intake with GRS for MAFLD
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In a categorical analysis, we further evaluated the HR of MAFLD associated with different meat types, stratified by tertiles of genetic risk (low, normal, and high risk). Total meat, unprocessed red meat, and unprocessed poultry intake were consistently associated with a higher MAFLD risk across tertiles of genetic risk (Fig. 2). Conversely, oily fish intake was inversely associated with MAFLD risk among those with normal or high genetic risk, with HRs not significantly different across tertiles of GRS. Overall, no significant interactions between subtypes of meat and tertiles of GRS for MAFLD were detected (all P > 0.05). These findings suggest that the relationship between meat intake and MAFLD is largely independent of genetic predisposition.

Fig. 2
figure 2

Hazard ratios for MAFLD for 1 standard deviation increment in meat intake across genetic risk categories. The multivariable model was adjusted for age and sex, ethnicity (White, Asian, Black, mixed, or other ethnic group), centers (22 categories), BMI (in kg/m2; <18.5, 18.5 to 25, 25 to 30, 30 to 35, ≥ 35, or missing), education (college or university degree, vocational qualifications, optional national exams at ages 17–18 years, national exams at age 16 years, others, or missing), Townsend deprivation index (quartiles), household income (<£18,000, £18,000-£30,999, £31,000-£51,999, £52,000-£100,000, >£100,000, or missing), smoking (never, former, current, or missing), alcohol consumption (never or special occasions only, 1 to 3 times/month, 1 or 2 times/week, 3 or 4 times/week, or daily/almost daily), physical activity (in MET-h/wk; quartiles), other remaining meats (unprocessed red meat, processed meat, unprocessed poultry, oily fish and non-oily fish), total energy intake, and Alternate Mediterranean Diet score (total score minus the meat component)

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Subgroup analysis

Subgroup analyses revealed notable variations in the association between meat intake and the risk of severe MAFLD (Table S3). Specifically, the positive association between total meat intake and severe MAFLD risk was more pronounced among participants under 60 years of age (P for interaction < 0.001) and those without a history of diabetes or hypertension (P for interaction < 0.001). Additionally, the association between unprocessed red meat intake and severe MAFLD was stronger in participants under 60, current smokers, and those without a history of diabetes or hypertension (all P for interaction < 0.001). For processed meat, the risk increase was particularly marked among participants under 60 and those without a history of diabetes or hypertension (all P for interaction < 0.001). Conversely, higher oily fish intake was inversely associated with MAFLD risk across various subgroups, with a more pronounced association observed among older adults and those without diabetes or with hypertension (all P for interaction < 0.001).

Sensitivity analysis

Sensitivity analyses further confirmed the robustness of the associations between meat intake and the risk of severe MAFLD (Table S4). After adjusting for lipid-lowering medication use, total meat, unprocessed red meat, processed meat, and unprocessed poultry remained significantly associated with an increased risk of MAFLD. Adjusting for a healthy diet score (instead of an AMED score) produced similar results, particularly for total meat and unprocessed red meat intake. When excluding MAFLD cases that developed within the first five years of follow-up to minimize reverse causality, the positive associations persisted, reinforcing the strength of these findings. Oily fish intake continued to show an inverse association.

Discussion

In this extensive cohort study involving 487,875 individuals, we observed a significant association between high meat consumption and an increased risk of severe MAFLD. High intake of total meat, processed meat, and unprocessed red meat was linked to a higher risk of severe MAFLD, whereas oily fish consumption was associated with a reduced risk. Importantly, these associations were not modified by the genetic risk of MAFLD.

Previous studies have reported inconsistent associations between unprocessed red meat intake and the incidence of MAFLD [10, 14, 15, 22], possibly due to differences in the definition of red meat, variations in intake standards, limited sample sizes, or insufficient follow-up periods to capture long-term effects [33]. However, our large-scale study (n = 487,875) with an extended follow-up duration (12.1 years) strongly supports a positive link between high red meat intake and severe MAFLD. Unprocessed red meat is rich in saturated fatty acids (SFAs), which have been shown to increase liver lipid storage, affecting energy metabolism and insulin resistance [34]. These changes potentially promote MAFLD development through the regulation of liver gene expression and signaling. High SFA levels initiate the unfolded protein response by saturating the endoplasmic reticulum (ER) membrane, leading to ER stress and elevated levels of lysophosphatidylcholine (LPC) [35]. Mitochondrial dysfunction also plays a crucial role in the effects of high SFA levels on MAFLD [35]. Additionally, inflammation, oxidative stress, ER stress, and increased LPC levels induced by high SFAs can activate the c-Jun N-terminal kinase (JNK) stress pathway, which promotes hepatocyte apoptosis—a key factor in the development of NASH [36].

Previous studies have shown a significant association between high processed meat intake and an increased risk of severe MAFLD [12,13,14, 33], which aligns with our findings. However, some research has questioned the strength of this relationship [37], possibly due to differences in sample characteristics and assessment methods. Processed meats are high in SFAs and typically contain elevated levels of sodium (up to 400% more than unprocessed meats) and additives such as nitrites, which have been linked to insulin resistance and oxidative stress, contributing to MAFLD development [38, 39]. High intake of processed meats is also associated with hepatic accumulation of advanced glycation end products (AGEs), which are closely related to the progression of NASH through activation of the TGF-β signaling pathway, a critical factor in liver fibrosis [40].

The relationship between fish consumption and MAFLD has also been contentious [16,17,18]. Our study revealed that oily fish, but not non-oily fish, was associated with a lower risk of severe MAFLD. Consistent with our findings, a recent South Korean cohort study reported that oily fish and its fatty acids were protective against MAFLD, particularly in women [16]. Similarly, the Guangzhou Biobank Cohort Study found an inverse association between fatty fish consumption and MAFLD [15]. Oily fish is a significant source of n-3 polyunsaturated fatty acids (PUFA) [41], which have been shown to inhibit the activity of sterol regulatory element-binding protein-1c (SREBP-1c) and reduce the cellular abundance of Max-like protein, thereby suppressing fatty acid synthesis and regulating enzymes such as acetyl-CoA carboxylase and fatty acid synthase involved in fat formation [42, 43]. Additionally, n-3 PUFA can promote insulin receptor activation and IRS-1 tyrosine phosphorylation, leading to the activation of the PI3K-Akt pathway and translocation of glucose transporter 4, which enhances glucose uptake into cells and reduces liver fat accumulation [44]. Furthermore, n-3 PUFA acts as a ligand for peroxisome proliferator-activated receptor-α, activating ACOX and medium-chain acyl-CoA dehydrogenase, thus promoting fatty acid β-oxidation [45]. Due to its anti-oxidative and anti-inflammatory properties, n-3 PUFA shows promise in treating metabolic syndrome, though its effects on oxidative stress have shown variable results [46,47,48]. Overall, n-3 PUFA appears to play a protective role in the progression of severe MAFLD, supporting recommendations for oily fish consumption to help prevent MAFLD.

Limited data are available on the relationship between poultry consumption and MAFLD. A positive association between poultry intake and MAFLD was reported in a nested case-control analysis of 2,974 MAFLD cases and 29,474 matched controls [22]. However, this association was not observed in the Guangzhou Biobank Cohort Study, which included 1,862 participants aged 50 years or older [15]. Our finding of a positive link between unprocessed poultry consumption and severe MAFLD should be interpreted with caution, as the highest category of consumption (≥ 5 times/week) did not show a statistically significant hazard ratio (HR: 1.21; 95% CI: 0.98–1.49) compared with non-consumers. Additional studies are needed to validate our results.

MAFLD is thought to develop from complex interactions between genetic and lifestyle factors, including diet [24]. However, we did not observe significant interactions between different types of meat consumption and the MAFLD GRS, suggesting that the detrimental effects of red and processed meat and the protective effects of oily fish on MAFLD risk are consistent across varying levels of genetic risk. These findings indicate that dietary guidelines recommending reduced consumption of red and processed meats, along with increased oily fish intake, may be beneficial for the general population regardless of genetic predisposition to MAFLD. Our subgroup analyses further indicated that the associations between red or processed meat intake and MAFLD risk were stronger among younger participants and those without a history of diabetes or hypertension, suggesting that healthier individuals might be more susceptible to the harmful effects of red or processed meat. Excluding individuals with known liver disease at baseline may have disproportionately removed older individuals and those with metabolic conditions, resulting in relatively lower risk in healthier subgroups. However, further mechanistic research is needed to clarify this observation.

The strengths of our study include its large sample size, long duration of follow-up, and the assessment of gene-diet interactions. However, several limitations should be considered. First, MAFLD diagnosis was based on hospital admission and mortality records, primarily capturing advanced or severe cases, which may have excluded milder instances of the disease. Second, although the FFQ used to assess meat intake was validated, self-reported dietary data are subject to measurement errors. We incorporated 24-hour dietary recall data as a supplementary analysis, which allows for a more precise estimation of meat intake based on food weight. Our findings from 24-hour dietary recall data remained largely consistent with the primary results, although reduced sample size limited statistical power for certain food groups, such as fish. Future studies should integrate comprehensive dietary assessments, including quantitative intake measurements, to corroborate our findings. Third, meat consumption was measured only at baseline, so any dietary changes over time were not captured, which may have led to an underestimation of associations. Fourth, including fish in the “meat” food group presents challenges in interpreting its overall association with MAFLD risk, as meat and fish have divergent health effects. This classification may obscure the distinct roles of different protein sources, with processed and red meats generally increasing risk, while fish consumption tends to offer protective effects. Fifth, despite adjusting for a wide range of covariates, the possibility of unmeasured or residual confounding cannot be completely ruled out. Sixth, our study population consisted only of European men and women, limiting the generalizability of the findings to other ethnic groups. Finally, given the observational design of this study, causal relationships cannot be established.

Conclusions

In conclusion, our study found that higher consumption of total meat, processed meat, and unprocessed red meat was associated with an increased risk of severe MAFLD, while oily fish consumption was associated with a reduced risk. These associations were independent of genetic risk for MAFLD. Our findings provide strong evidence supporting the recommendation to reduce red and processed meat consumption and increase oily fish intake for the primary prevention of MAFLD, regardless of individual genetic risk.

Data availability

The UK Biobank data are available from the UK Biobank on request (www.ukbiobank.ac.uk).

Abbreviations

MAFLD:

metabolic-associated fatty liver disease

GRS:

Genetic risk scores

HR:

hazard ratios

FFQ:

food frequency questionnaire

AMED:

Alternate Mediterranean Diet

MET:

metabolic equivalent of task

SNPs:

single nucleotide polymorphisms

NASH:

non-alcoholic steatohepatitis

CI:

confidence intervals

ER:

endoplasmic reticulum

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Acknowledgements

We are grateful to the participants of the UK Biobank. This research has been conducted using the UK Biobank Resource under Application Number 47365.

Funding

This research was supported by the Natural Science Foundation of Zhejiang Province (grant no. LY23C200007), the National Natural Science Foundation of China (grant no. 32202057), the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (grant no. YESS20220066), and the Ningbo Municipal Health Commission Young Technical Backbone Talents Training Program (grant no. 2024102).

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Author notes

  1. Jianjin Wang and Jianshu Mo are joint first authors and contributed equally to this work.

Authors and Affiliations

  1. Department of Clinical Medicine, Shaoxing University School of Medicine, Zhejiang, 312000, Shaoxing, China

    Jianjin Wang

  2. Department of Nutrition, School of Public Health, Department of Clinical Nutrition, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China

    Xuzhi Wan

  3. Department of Secondary Internal Medicine, Yuyao Hospital of Traditional Chinese Medicine, Yuyao, 315400, Zhejiang, China

    Jianshu Mo

  4. Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310002, Zhejiang, China

    Pan Zhuang

  5. Key Laboratory of Drug Prevention and Control Technology of Zhejiang Province, Zhejiang Police College, Hangzhou, 310053, Zhejiang, China

    Yilei Fan

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Contributions

PZ and YF conceptualized the study, PZ handled the data curation, JW, JM, and XW compiled the data and performed statistical analyses with supervisory input from PZ and YF All authors contributed to the finalization of statistical models and interpretation of findings, JW and JM wrote the first draft of the manuscript, and XW and PZ critically reviewed and edited the manuscript. All authors read and approved the final manuscript, and accept responsibility for the decision to submit for publication.

Corresponding authors

Correspondence to Yilei Fan or Pan Zhuang.

Ethics declarations

Ethics approval and consent to participate

The UK Biobank study was conducted according to the guidelines of the Declaration of Helsinki and approved by the North West Multicentre Research Ethics Committee (REC reference: 21/NW/0157). Informed consent was obtained from all subjects involved in the study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Wang, J., Mo, J., Wan, X. et al. Meat and fish consumption, genetic risk and risk of severe metabolic-associated fatty liver disease: a prospective cohort of 487,875 individuals. Nutr J 24, 65 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12937-025-01134-4

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Keywords

Nutrition Journal

ISSN: 1475-2891

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