Dietary pattern modifies the risk of MASLD through metabolomic signature
Hanzhang Wu,
Jiahe Wei,
Shuai Wang,
Liangkai Chen,
Jihui Zhang,
Ningjian Wang,
Xiao Tan
Affiliations
Hanzhang Wu
Department of Big Data in Health Science, Zhejiang University School of Public Health, Hangzhou, China. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
Jiahe Wei
Department of Big Data in Health Science, Zhejiang University School of Public Health, Hangzhou, China. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
Shuai Wang
Department of Big Data in Health Science, Zhejiang University School of Public Health, Hangzhou, China. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
Liangkai Chen
Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
Jihui Zhang
Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
Ningjian Wang
Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Corresponding authors. Addresses: Department of Big Data in Health Science, Zhejiang University School of Public Health and Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. (X. Tan), or Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (N. Wang).
Xiao Tan
Department of Big Data in Health Science, Zhejiang University School of Public Health, Hangzhou, China. Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China; Department of Medical Sciences, Uppsala University, Uppsala, Sweden; Corresponding authors. Addresses: Department of Big Data in Health Science, Zhejiang University School of Public Health and Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. (X. Tan), or Institute and Department of Endocrinology and Metabolism, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. (N. Wang).
Background & Aims: The EAT-Lancet Commission in 2019 advocated a plant-centric diet for health and environmental benefits, but its relation to metabolic dysfunction-associated steatotic liver disease (MASLD) is unclear. We aimed to discover the metabolite profile linked to the EAT-Lancet diet and its association with MASLD risk, considering genetic predisposition. Methods: We analyzed data from 105,752 UK Biobank participants with detailed dietary and metabolomic information. We constructed an EAT-Lancet diet index and derived a corresponding metabolomic signature through elastic net regression. A weighted polygenic risk score for MASLD was computed from associated risk variants. The Cox proportional hazards model was employed to estimate hazard ratios (HRs) and 95% CIs for the risk of MASLD (defined as hospital admission or death). Results: During a median follow-up period of 11.6 years, 1,138 cases of MASLD were documented. Participants in the highest group for the EAT-Lancet diet index had a multivariable HR of 0.79 (95% CI 0.66–0.95) for MASLD compared to the lowest group. The diet's impact was unaffected by genetic predisposition to MASLD (p = 0.42). Moreover, a robust correlation was found between the metabolomic signature and the EAT-Lancet diet index (Pearson r = 0.29; p <0.0001). Participants in the highest group for the metabolomic signature had a multivariable HR of 0.46 (95% CI 0.37–0.58) for MASLD, in comparison to those in the lowest group. Conclusions: Higher intake of the EAT-Lancet diet and its associated metabolite signature are both linked to a reduced risk of MASLD, independently of traditional risk factors. Impact and implications:: Our analysis leveraging the UK Biobank study showed higher adherence to the EAT-Lancet diet was associated with a reduced risk of metabolic dysfunction-associated steatotic liver disease (MASLD). We identified a unique metabolite signature comprising 81 metabolites associated with the EAT-Lancet diet, potentially underlying the diet's protective mechanism against MASLD. These findings suggest the EAT-Lancet diet may offer substantial protective benefits against MASLD.
