Association of the Planetary Health Diet 2.0 with risk of metabolic dysfunction-associated steatotic liver disease

Xinyu Xu; Anna Stubbendorff; Yan Borné; Emily Sonestedt; Tao Huang; Shunming Zhang

doi:10.59717/j.xinn-nutri.2026.100041

The Innovation Nutrition > 2026 Vol. 1 > No. 3 > 100041

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Association of the Planetary Health Diet 2.0 with risk of metabolic dysfunction-associated steatotic liver disease

1.
School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
2.
Nutritional Epidemiology, Department of Clinical Sciences Malmö, Lund University, Malmö 21428, Sweden
3.
Department of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing 100191, China

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Corresponding authors: huangtao@bjmu.edu.cn (T.H.); zhangshunming@xjtu.edu.cn (S.Z.)
Received Date: July 28, 2026

Accepted Date: October 18, 2026

Published Online: October 22, 2026

The Innovation Nutrition 1(3), https://doi.org/10.59717/j.xinn-nutri.2026.100041 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Planetary Health Diet 2.0 is a sustainable dietary framework designed to promote human and planetary health.
  
  This diet emphasizes plant-based foods while limiting animal-source foods, saturated fat, sugar, and sodium.
  
  Adherence to this diet is related to lower risk of metabolic dysfunction-associated steatotic liver disease.
  
  Magnetic resonance imaging results support the hepatoprotective link of the Planetary Health Diet 2.0.

ABSTRACT

ABSTRACT

The second EAT-Lancet Commission in 2025 updated the Planetary Health Diet (PHD 2.0) to support the transition toward healthy, sustainable, and just food systems. However, little is known regarding its association with metabolic dysfunction-associated steatotic liver disease (MASLD). This prospective cohort study analyzed data from 107,940 participants who completed at least two 24-hour dietary assessments within the UK Biobank. Adherence to the PHD 2.0 was quantified using the EAT-Lancet index 2.0 (range: 0-45 points) and modified Planetary Health Diet Index (range: 0-150 points). Incident MASLD was identified through electronic health records. In addition, proton density fat fraction (n = 16,770) and iron-corrected T1 (n = 14,015) were assessed using magnetic resonance imaging. Associations were analyzed using Cox proportional hazards models and logistic models. Over a median follow-up of 10.5 years, 775 developed MASLD. The fully adjusted hazard ratios (95% confidence intervals) for incident MASLD comparing the highest with the lowest quartiles were 0.66 (0.53, 0.82) for the EAT-Lancet index 2.0 and 0.61 (0.49, 0.77) for the modified Planetary Health Diet Index, respectively (P for trend < 0.0001). Moreover, both diet indices showed inverse associations with hepatic steatosis defined by proton density fat fraction (odds ratios [95% confidence intervals]: 0.56 [0.50, 0.63] and 0.59 [0.53, 0.66] for comparing the extreme quartiles) and elevated iron-corrected T1 (odds ratios [95% confidence intervals]: 0.63 [0.49, 0.80] and 0.61 [0.48, 0.78]). In conclusion, greater adherence to the PHD 2.0 is associated with a decreased risk of MASLD as well as favorable magnetic resonance imaging biomarkers.
- Planetary Health Diet /
- EAT-Lancet diet /
- hepatic steatosis /
- metabolic dysfunction-associated steatotic liver disease /
- non-alcoholic fatty liver disease

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REFERENCES

[1]	Tilg H., Petta S., Stefan N., et al. (2026). Metabolic dysfunction-associated steatotic liver disease in adults: A review. JAMA 335:163−174. DOI:10.1001/jama.2025.19615 View in Article CrossRef Google Scholar
[2]	Targher G., Byrne C. D. and Tilg H. (2024). MASLD: A systemic metabolic disorder with cardiovascular and malignant complications. Gut 73:691−702. DOI:10.1136/gutjnl-2023-330595 View in Article CrossRef Google Scholar
[3]	Li W., Alazawi W. and Loomba R. (2026). Current and emerging therapeutic landscape for metabolic dysfunction-associated steatohepatitis. Lancet Gastroenterol. Hepatol. 11:150−162. DOI:10.1016/S2468-1253(25)00260-2 View in Article CrossRef Google Scholar
[4]	Zeng X. F., Varady K. A., Wang X. D., et al. (2024). The role of dietary modification in the prevention and management of metabolic dysfunction-associated fatty liver disease: An international multidisciplinary expert consensus. Metabolism 161:156028. DOI:10.1016/j.metabol.2024.156028 View in Article CrossRef Google Scholar
[5]	Tilman D. and Clark M. (2014). Global diets link environmental sustainability and human health. Nature 515:518−522. DOI:10.1038/nature13959 View in Article CrossRef Google Scholar
[6]	Willett W., Rockstrom J., Loken B., et al. (2019). Food in the Anthropocene: The EAT-Lancet commission on healthy diets from sustainable food systems. Lancet 393:447−492. DOI:10.1016/S0140-6736(18)31788-4 View in Article CrossRef Google Scholar
[7]	Zhang S., Yan Y., Zeng X. F., et al. (2025). Associations of the EAT-Lancet reference diet with metabolic dysfunction-associated steatotic liver disease and its severity: A multicohort study. Hepatology 81:1583−1594. DOI:10.1097/HEP.0000000000001039 View in Article CrossRef Google Scholar
[8]	Zhang S., Zeng X. F., Borne Y., et al. (2025). Adherence to the EAT-Lancet reference diet and risk of type 2 diabetes, metabolic dysfunction-associated steatotic liver disease, and their co-occurrence. Food Funct. 16:6773−6785. DOI:10.1039/d4fo05852f View in Article CrossRef Google Scholar
[9]	Stubbendorff A., Janzi S., Jukkola J., et al. (2025). Mini-review of the EAT-Lancet planetary health diet and its role in cardiometabolic disease prevention. Metabolism 172:156373. DOI:10.1016/j.metabol.2025.156373 View in Article CrossRef Google Scholar
[10]	Rockstrom J., Thilsted S. H., Willett W. C., et al. (2025). The EAT-Lancet Commission on healthy, sustainable, and just food systems. Lancet 406:1625−1700. DOI:10.1016/S0140-6736(25)01201-2 View in Article CrossRef Google Scholar
[11]	Shojaei-Zarghani S., Safarpour A. R., Fattahi M. R., et al. (2022). Sodium in relation with nonalcoholic fatty liver disease: A systematic review and meta-analysis of observational studies. Food Sci. Nutr. 10:1579−1591. DOI:10.1002/fsn3.2781 View in Article CrossRef Google Scholar
[12]	Zhang S., Huo Z., Borne Y., et al. (2025). Adding salt to foods and risk of metabolic dysfunction-associated steatotic liver disease and other chronic liver diseases. Eur. J. Nutr. 64:224. DOI:10.1007/s00394-025-03745-3 View in Article CrossRef Google Scholar
[13]	Stubbendorff A., Ericson U., Hallstrom E., et al. (2026). EAT-Lancet index 2.0: A dietary index measuring adherence to the Planetary Health Diet 2.0. Lancet Planet Health 10:101469. DOI:10.1016/j.lanplh.2026.101469. View in Article Google Scholar
[14]	Bui L. P., Pham T. T., Wang F., et al. (2024). Planetary Health Diet Index and risk of total and cause-specific mortality in three prospective cohorts. Am. J. Clin. Nutr. 120:80−91. DOI:10.1016/j.ajcnut.2024.03.019 View in Article CrossRef Google Scholar
[15]	Ollier W., Sprosen T. and Peakman T. (2005). UK Biobank: From concept to reality. Pharmacogenomics 6:639−646. DOI:10.2217/14622416.6.6.639 View in Article CrossRef Google Scholar
[16]	Xu X., Dong Y., Hu J., et al. (2026). Planetary health diet index and risk of irritable bowel syndrome: A prospective cohort study. Eur. J. Nutr. 65:199. DOI:10.1007/s00394-026-04055-y View in Article CrossRef Google Scholar
[17]	Vandenbroucke J. P., von Elm E., Altman D. G., et al. (2014). Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration. Int J Surg 12:1500−1524. DOI:10.1016/j.ijsu.2014.07.014 View in Article CrossRef Google Scholar
[18]	Lachat C., Hawwash D., Ocke M. C., et al. (2016). Strengthening the Reporting of Observational Studies in Epidemiology-Nutritional Epidemiology (STROBE-nut): An Extension of the STROBE Statement. PLoS Med. 13:e1002036. DOI:10.1371/journal.pmed.1002036 View in Article CrossRef Google Scholar
[19]	Greenwood D. C., Hardie L. J., Frost G. S., et al. (2019). Validation of the Oxford WebQ online 24-hour dietary questionnaire using biomarkers. Am. J. Epidemiol. 188:1858−1867. DOI:10.1093/aje/kwz165 View in Article CrossRef Google Scholar
[20]	Perez-Cornago A., Pollard Z., Young H., et al. (2021). Description of the updated nutrition calculation of the Oxford WebQ questionnaire and comparison with the previous version among 207,144 participants in UK Biobank. Eur. J. Nutr. 60:4019−4030. DOI:10.1007/s00394-021-02558-4 View in Article CrossRef Google Scholar
[21]	Piernas C., Perez-Cornago A., Gao M., et al. (2021). Describing a new food group classification system for UK Biobank: Analysis of food groups and sources of macro- and micronutrients in 208,200 participants. Eur. J. Nutr. 60:2879−2890. DOI:10.1007/s00394-021-02535-x View in Article CrossRef Google Scholar
[22]	Hagstrom H., Adams L. A., Allen A. M., et al. (2021). Administrative coding in electronic health care record-based research of NAFLD: An expert panel consensus statement. Hepatology 74:474−482. DOI:10.1002/hep.31726 View in Article CrossRef Google Scholar
[23]	Wilman H. R., Kelly M., Garratt S., et al. (2017). Characterisation of liver fat in the UK Biobank cohort. PLoS One 12:e0172921. DOI:10.1371/journal.pone.0172921 View in Article CrossRef Google Scholar
[24]	Jackson E., Dennis A., Alkhouri N., et al. (2025). Cardiac and liver impairment on multiorgan MRI and risk of major adverse cardiovascular and liver events. Nat. Med. 31:2289−2296. DOI:10.1038/s41591-025-03654-2 View in Article CrossRef Google Scholar
[25]	Caussy C., Alquiraish M. H., Nguyen P., et al. (2018). Optimal threshold of controlled attenuation parameter with MRI-PDFF as the gold standard for the detection of hepatic steatosis. Hepatology 67:1348−1359. DOI:10.1002/hep.29639 View in Article CrossRef Google Scholar
[26]	Craig C. L., Marshall A. L., Sjostrom M., et al. (2003). International physical activity questionnaire: 12-country reliability and validity. MED SCI SPORT EXER 35:1381−1395. DOI:10.1249/01.MSS.0000078924.61453.FB View in Article CrossRef Google Scholar
[27]	Schisterman E. F., Cole S. R. and Platt R. W. (2009). Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology 20:488−495. DOI:10.1097/EDE.0b013e3181a819a1 View in Article CrossRef Google Scholar
[28]	Desquilbet L. and Mariotti F. (2010). Dose-response analyses using restricted cubic spline functions in public health research. Stat. Med. 29:1037−1057. DOI:10.1002/sim.3841 View in Article CrossRef Google Scholar
[29]	Mathur M. B., Ding P., Riddell C. A., et al. (2018). Website and R package for computing E-values. Epidemiology 29:e45−e47. DOI:10.1097/EDE.0000000000000864 View in Article CrossRef Google Scholar
[30]	VanderWeele T. J. and Ding P. (2017). Sensitivity analysis in observational research: Introducing the E-value. Ann. Intern. Med 167:268−274. DOI:10.7326/M16-2607 View in Article CrossRef Google Scholar
[31]	Uno H., Cai T., Pencina M. J., et al. (2011). On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat. Med. 30:1105−1117. DOI:10.1002/sim.4154 View in Article CrossRef Google Scholar
[32]	Harrell F. E., Jr., Lee K. L. and Mark D. B. (1996). Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.Stat. Med. 15:361−387. DOI:3.0.CO;2-4">10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4 View in Article CrossRef Google Scholar
[33]	Pencina M. J., D'Agostino R. B., Sr. and Steyerberg E. W. (2011). Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat. Med. 30:11−21. DOI:10.1002/sim.4085 View in Article CrossRef Google Scholar
[34]	Wu S. Y., Bo Y. C., Li Z. Y., et al. (2025). EAT-Lancet diet and risk of metabolic dysfunction-associated steatotic liver disease and other liver chronic diseases: A large prospective cohort study in the UK Biobank. Front. Nutr. 12:1589424. DOI:10.3389/fnut.2025.1589424 View in Article CrossRef Google Scholar
[35]	Wu H., Wei J., Wang S., et al. (2024). Dietary pattern modifies the risk of MASLD through metabolomic signature. JHEP Rep 6:101133. DOI:10.1016/j.jhepr.2024.101133 View in Article CrossRef Google Scholar
[36]	Qiu X., Shen S., Jiang N., et al. (2025). Adherence to the planetary health diet index and metabolic dysfunction-associated steatotic liver disease: A cross-sectional study. Front. Nutr. 12:1534604. DOI:10.3389/fnut.2025.1534604 View in Article CrossRef Google Scholar
[37]	Liu Z. Q., Zhou B. G., Fang J. W., et al. (2025). Protective role of the planetary health diet index against metabolic dysfunction-associated steatotic liver disease: Global and individual evidence. Front. Nutr. 12:1673662. DOI:10.3389/fnut.2025.1673662 View in Article CrossRef Google Scholar
[38]	Doustmohammadian A., Esfandyari S., Motamed N., et al. (2026). EAT-Lancet dietary pattern adherence and metabolic dysfunction-associated steatotic liver disease: A cross-sectional analysis of the Amol Cohort Study. J Nutr 156:101433. DOI:10.1016/j.tjnut.2026.101433 View in Article CrossRef Google Scholar
[39]	Kamrani F., Imannezhad M., Etesami E., et al. (2026). Higher adherence to the EAT-Lancet diet is associated with reduced risk of metabolic dysfunction-associated steatotic liver disease: a 5-year middle eastern cohort study. Diabetol Metab Syndr 18:176. DOI:10.1186/s13098-026-02198-w View in Article CrossRef Google Scholar
[40]	Mozaffarian D., Fahimi S., Singh G. M., et al. (2014). Global sodium consumption and death from cardiovascular causes. NEJM 371:624−634. DOI:10.1056/NEJMoa1304127 View in Article CrossRef Google Scholar
[41]	Collaborators G. B. D. D. (2019). Health effects of dietary risks in 195 countries, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 393:1958−1972. DOI:10.1016/S0140-6736(19)30041-8 View in Article CrossRef Google Scholar
[42]	Hu F. B. (2024). Diet strategies for promoting healthy aging and longevity: An epidemiological perspective. J. Intern. Med. 295:508−531. DOI:10.1111/joim.13728 View in Article CrossRef Google Scholar
[43]	Jurek J. M., Zablocka-Slowinska K., Pieczynska J., et al. (2025). Systematic review of the effects of plant-based foods on metabolic outcomes in adults with MASLD and comorbidities such as obesity, metabolic syndrome, and type 2 diabetes. Nutrients 17:3020. DOI:10.3390/nu17183020 View in Article CrossRef Google Scholar
[44]	Lv Y., Rong S., Deng Y., et al. (2023). Plant-based diets, genetic predisposition and risk of non-alcoholic fatty liver disease. BMC MED 21:351. DOI:10.1186/s12916-023-03028-w View in Article CrossRef Google Scholar
[45]	Carli F., Della Pepa G., Sabatini S., et al. (2024). Lipid metabolism in MASLD and MASH: From mechanism to the clinic. JHEP Rep 6:101185. DOI:10.1016/j.jhepr.2024.101185 View in Article CrossRef Google Scholar
[46]	Zhou Y., Chen D., Zhu W., et al. (2025). Causal relationships between iron status and nonalcoholic fatty liver disease: Two-sample, multivariable, and two-step Mendelian randomization. Int. J. Vitam. Nutr. Res. 95:26773. DOI:10.31083/IJVNR26773 View in Article CrossRef Google Scholar
[47]	Lanaspa M. A., Kuwabara M., Andres-Hernando A., et al. (2018). High salt intake causes leptin resistance and obesity in mice by stimulating endogenous fructose production and metabolism. PNAS. 115:3138−3143. DOI:10.1073/pnas.1713837115 View in Article CrossRef Google Scholar
[48]	Che Z., Xue W., Zhao X., et al. (2025). Regulatory role and biomarker potential of gut microbiota metabolites in the progression of metabolic dysfunction-associated steatotic liver disease to hepatocellular carcinoma. CTG 16:e00914. DOI:10.14309/ctg.0000000000000914 View in Article CrossRef Google Scholar
[49]	Bahitham W., Banoun Y., Aljahdali M., et al. (2025). "Trust your gut": Exploring the connection between gut microbiome dysbiosis and the advancement of metabolic associated steatosis liver disease (MASLD)/metabolic associated steatohepatitis (MASH): A systematic review of animal and human studies. Front. Nutr. 12:1637071. DOI:10.3389/fnut.2025.1637071 View in Article CrossRef Google Scholar
[50]	Zhang J., Geng T., Wang Y., et al. (2026). Challenges, advances and future directions in nutritional epidemiology. Innov.Nutri. 1:100009. DOI:10.59717/j.xinn-nutri.2026.100009 View in Article CrossRef Google Scholar

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Xu X., Stubbendorff A., Borné Y., et al. (2026). Association of the Planetary Health Diet 2.0 with risk of metabolic dysfunction-associated steatotic liver disease. The Innovation Nutrition 1:100041. https://doi.org/10.59717/j.xinn-nutri.2026.100041

Xu X., Stubbendorff A., Borné Y., et al. (2026). Association of the Planetary Health Diet 2.0 with risk of metabolic dysfunction-associated steatotic liver disease. The Innovation Nutrition 1:100041. https://doi.org/10.59717/j.xinn-nutri.2026.100041

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Association of the Planetary Health Diet 2.0 with risk of metabolic dysfunction-associated steatotic liver disease

GRAPHICAL ABSTRACT

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ABSTRACT