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. 2025 Jun 23;13(6):e70446.
doi: 10.1002/fsn3.70446. eCollection 2025 Jun.

Multi-Omics Analysis Reveals Causal Relationships and Potential Mediators Between Dietary Preferences and Risk of NAFLD

Affiliations

Multi-Omics Analysis Reveals Causal Relationships and Potential Mediators Between Dietary Preferences and Risk of NAFLD

Qingan Fu et al. Food Sci Nutr. .

Abstract

Non-alcoholic fatty liver disease (NAFLD) is a prevalent condition closely associated with obesity and metabolic syndrome, with its global incidence on the rise. This study aims to explore the causal relationship between dietary preferences and NAFLD risk using multi-omics analysis, and to comprehensively explore possible mediating factors and their underlying mechanisms. We analyzed data from genome-wide association studies (GWAS) to assess the potential genetic links between various dietary preferences and NAFLD. A two-step Mendelian randomization (MR) analysis was conducted to evaluate whether dietary preferences affect NAFLD risk by regulating inflammatory factors. Further, co-localization analysis was used to identify gene loci driving the causal relationships between dietary preferences and NAFLD risk. Finally, clinical cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) and bioinformatics analysis were used to validate the findings.MR analysis revealed that a preference for a low-calorie diet significantly reduces NAFLD risk by modulating DNER. Co-localization analysis identified the FTO gene variant rs28429148 as a key driver of the causal relationship between soft cheese and fruit juice preferences, with soft cheese increasing and fruit juice reducing NAFLD risk. These findings were further validated by clinical cross-sectional and bioinformatics analysis. This study, for the first time, comprehensively elucidates the causal relationship between dietary preferences and NAFLD risk from a multi-omics perspective and identifies FTO and DNER as potential therapeutic targets. These findings provide new insights into the importance of personalized dietary interventions in the prevention of NAFLD and informs clinical treatment.

Keywords: FTO gene; Mendelian randomization; dietary preferences; inflammatory factors; non‐alcoholic fatty liver disease.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Design and flowchart of this study. DNER = delta and notch‐like epidermal growth factor‐related receptor, FPED = Nutritional Dietary Research Database, GWAS = Genome‐Wide Association Study, MR = Mendelian randomization, MR‐PRESSO = Mendelian Randomization Pleiotropy RESidual Sum and Outlier, NAFLD = non‐alcoholic fatty liver disease, NHANES = National Health and Nutrition Examination Survey, SNP = single nucleotide polymorphism, UK = United Kingdom.
FIGURE 2
FIGURE 2
Distribution of p‐values for the causal relationship between 187 dietary preferences and NAFLD from the two‐sample Mendelian randomization analysis. The dashed line represents the critical value for the suggestive significance level, set at p = 0.05.
FIGURE 3
FIGURE 3
Forest plot of significant causal effects of dietary preferences on NAFLD risk as assessed by the inverse variance weighted approach. CI = confidence interval, FDR = false discovery rate, OR = odds ratio, SNP = single nucleotide polymorphism.
FIGURE 4
FIGURE 4
LocusCompare plots mark the loci driving causality in the results of co‐localization analyses of FDR‐corrected strong‐positive causality and distinguish false‐positive genes. (A, B) Distribution of SNPs—log10(p) in GWAS for soft cheese preference, juice preference, and NAFLD, respectively, is depicted, where rs28429148 is considered the mutant locus driving causality.
FIGURE 5
FIGURE 5
(A) Demonstrates the specific locations where the FTO and DNER genes, respectively, are present on human chromosomes. (B) Shows the expression content and expression trend of FTO and DNER genes in the human transcriptome dataset GSE260666.
FIGURE 6
FIGURE 6
Enrichment analysis of the specific mechanism of action of FTO genes in NAFLD. (A) The results of GO enrichment analysis of the FTO gene. (B) Expression up‐regulation pathway results of GSEA enrichment analysis of the FTO gene. (C) Graph of pathway results of down‐regulated expression in GSEA enrichment analysis of FTO genes.
FIGURE 7
FIGURE 7
Expression and distribution of FTO and DNER genes in the NAFLD single‐cell dataset GSE202379. (A) Cellular subpopulation maps of tissues after downscaling in GSE202379. (B) Expression and distribution plots of FTO in NAFLD patient tissues in different cellular subpopulations. (C) Expression and distribution plots of FTO and DNER genes in cellular subpopulations in tissues from healthy controls and NAFLD samples at different stages of the disease.

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References

    1. Balbuena, E. , Cheng J., and Eroglu A.. 2022. “Carotenoids in Orange Carrots Mitigate Non‐Alcoholic Fatty Liver Disease Progression.” Frontiers in Nutrition 9: 987103. - PMC - PubMed
    1. Barrett, T. , Wilhite S. E., Ledoux P., et al. 2013. “NCBI GEO: Archive for Functional Genomics Data Sets—Update.” Nucleic Acids Research 41: D991–D995. - PMC - PubMed
    1. Bowden, J. , Davey Smith G., and Burgess S.. 2015. “Mendelian Randomization With Invalid Instruments: Effect Estimation and Bias Detection Through Egger Regression.” International Journal of Epidemiology 44, no. 2: 512–525. - PMC - PubMed
    1. Bowden, J. , Davey Smith G., Haycock P. C., and Burgess S.. 2016. “Consistent Estimation in Mendelian Randomization With Some Invalid Instruments Using a Weighted Median Estimator.” Genetic Epidemiology 40, no. 4: 304–314. - PMC - PubMed
    1. Burgess, S. , Butterworth A., and Thompson S. G.. 2013. “Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data.” Genetic Epidemiology 37, no. 7: 658–665. - PMC - PubMed

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