Principal Investigator

Prof. Ju-Sheng Zheng

Author 04
By Ju-Sheng · August 20, 2024

Ju-Sheng earned his Ph.D. in Nutrition from Zhejiang University in Hangzhou, China (2009–2014). During his doctoral studies, he completed a one-year training program (2012) in the Nutrition and Genomics Lab at the Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, USA. From 2015 to 2018, he served as a postdoctoral researcher at the MRC Epidemiology Unit at the University of Cambridge, UK, where he was awarded the prestigious Marie Skłodowska-Curie Individual Fellowship by the European Commission. In 2018, Ju-Sheng joined Westlake University as a Principal Investigator and Assistant Professor, and was promoted to a tenured Associate Professor in 2024.

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Research Interests

    1) Precision nutrition
    • Personalized Dietary Interventions: Implementing novel study designs (e.g., n-of-1 clinical trials) and wearable devices (e.g., continuous glucose monitors) to investigate personalized nutritional responses in Chinese populations.
    • Multi-Omics Mechanisms: Leveraging high-throughput multi-omics technologies alongside large-scale human cohort data to uncover the mechanistic links between nutrition, metabolic diseases, and aging-related phenotypes.
    2) Computational Medicine and Molecular Epidemiology
    • Biomarker Discovery & Disease Etiology: Applying advanced computational methods to multi-omics datasets (including nutritional biomarkers, genomics, metabolomics, microbiomics, and proteomics) across diverse cohorts to study the etiology of aging-related diseases and identify novel clinical biomarkers.
    • Maternal & Metabolic Health: Utilizing precision birth cohorts and wearable tracking to determine the optimal balance of diet, physical activity, and lifestyle factors for the prevention of metabolic dysfunctions and adverse pregnancy outcomes.
    3) Bioinformatics and the Functional Gut Microbiome
    • Microbiome-Disease Axis: Using cutting-edge bioinformatics to analyze human cohorts, investigating how gut microbiome composition, structure, and function relate to diabetes and aging-related chronic diseases. These findings are validated and explored mechanistically by integrating human data with animal models.
    • Pioneering Mycobiome Research: Developing high-throughput culturomics technologies and analytical pipelines specifically for the gut mycobiome. A key objective is establishing a new human gut fungal genome database to explore the functional role of gut fungi and diet-mycobiome interactions.


Representative Publications

  • 1. Zhang K, Chen J, Yan Y, Wang H, Miao Z, Gou W, Xiao C, Shi R, Jia X, Du W, Huang Y, Guo T, Su C*, Fu Y*, Chen YM*, Zheng JS*. Longitudinal multimorbidity trajectories shape personalized glycaemic patterns. Nature Metabolism. 2026. doi: 10.1038/s42255-026-01512-0.


  • 2. Shuai M, Xu F, Pan XF, Zhang Z, Zhou D, Zhang K, Wang L, Pu Y, Lin C, Wang X, Liang X, Gou W, Shen L, Zhou W, Yu EYW, Wu P, Ye Y-X, Wang Y, Yuan J, Miao Z, Xu X, Fu Y, Wahlqvist ML, Hu W, Zheng Y*, Chen YM*, Zhu Y*, Pan A*, Zheng JS*. Host genetic regulation of gut mycobiome offers insights into the gut–brain interaction. Vita. 2026. https://doi.org/10.15302/vita.2026.04.0024.


  • 3. Liang S, Sun Y, Miao Z, Li BY, Xing Z, Xie Y, Cai E, Li S, Liu P, Yang M, Shuai M, Gou W, Jiang W, Wang Y, Gao H, Zhang K, Yu J, Cai X, Wang X, Zhu Y*, Chen YM*, Zheng JS*, Guo T*. Large-scale metaproteomics of human gut microbiota reveals microbial functions in metabolic diseases and aging. Cell Metabolism. 2026;38(5):995-1011.e6.


  • 4. Miao Z, Hu S, Wu P, Lai Y, Chen LT, Huang M, Zhang K, He H, Xu F, Li F, Yuan J, Hu Y, Liu G, Huang K, Shuai M, Ye M, Liang X, Xiao C, Gou W, Shi R, Wang X, Jiang Z, Shi MQ, Wu YY, Wang XH, Lu S, Fu Y, Hu W, Qiu X*, Pan A*, Pan XF*, Zheng JS*. Maternal gut microbiome during early pregnancy predicts preterm birth. Cell Host & Microbe. 2025, 33(9):1623-1639.e8.


  • 5. Tang J, Yue L, Xu Y, Xu F, Cai X, Fu Y, Miao Z, Gou W, Hu W, Xue Z, Deng K, Shen L, Jiang Z, Shuai M, Liang X, Xiao C, Xie Y, Guo T*, Chen YM*, Zheng JS*. Longitudinal serum proteome mapping reveals biomarkers for healthy ageing and related cardiometabolic diseases. Nature Metabolism 2025, 7(1):166-181.


  • 6. Jiang Z, He L, Li D, Zhuo L, Chen L, Shi RQ, Luo J, Feng Y, Liang Y, Li D, Congmei X, Fu Y, Chen YM*, Zheng JS*, Tao L*. Human gut microbial aromatic amino acid and related metabolites prevent obesity through intestinal immune control. Nature Metabolism. 2025, 7(4):808-822.


  • 7. Cheng C, Xu F, Pan XF, Wang C, Fan J, Yang Y, Liu Y, Sun L, Liu X, Xu Y, Zhou Y, Xiao C, Gou W, Miao Z, Yuan J, Shen L, Fu Y, Sun X, Chen Y*, Pan A*, Zhou D*, Zheng JS*. Genetic mapping of serum metabolome to chronic diseases among Han Chinese. Cell Genomics 2025, 5(2):100743.


  • 8. Fu Y, Gou W, Wu P, Lai Y, Liang X, Zhang K, Shuai M, Tang J, Miao Z, Chen J, Yuan J, Zhao B, Yang Y, Liu X, Hu Y, Pan A*, Pan XF*, Zheng JS*. Landscape of the gut mycobiome dynamics during pregnancy and its relationship with host metabolism and pregnancy health. Gut 2024, 73(8):1302-1312.


  • 9. Shuai M, Fu Y, Zhong HL, Gou W, Jiang Z, Liang Y, Miao Z, Xu JJ, Huynh T, Wahlqvist ML*, Chen YM*, Zheng JS*. Mapping the human gut mycobiome in middle-aged and elderly adults: multiomics insights and implications for host metabolic health. Gut 2022, 71(9):1812-1820.


  • 10. Xu F, Yu EY, Cai X, Yue L, Jing LP, Liang X, Fu Y, Miao Z, Yang M, Shuai M, Gou W, Xiao C, Xue Z, Xie Y, Li S, Lu S, Shi M, Wang X, Hu W, Langenberg C, Yang J, Chen YM*, Guo T*, Zheng JS*. Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility. Nature Communications 2023,14(1):896.


  • 11. Jiang Z, Zhuo LB, He Y, Fu Y, Shen L, Xu F, Gou W, Miao Z, Shuai M, Liang Y, Xiao C, Liang X, Tian Y, Wang J, Tang J, Deng K, Zhou H*, Chen YM*, Zheng JS*. The gut microbiota-bile acid axis links the positive association between chronic insomnia and cardiometabolic diseases. Nature Communications 2022, 13(1):3002.


  • 12. Gou W, Ling CW, He Y, Jiang Z, Fu Y, Xu F, Miao Z, Sun TY, Lin JS, Zhu HL, Zhou H, Chen YM*, Zheng JS*. Interpretable machine learning framework reveals robust gut microbiome features associated with type 2 diabetes. Diabetes Care 2021, 44(2):358-366.


  • 13. Miao Z, Lin JS, Mao Y, Chen GD, Zeng FF, Dong HL, Jiang ZL, Wang JL, Xiao CM, Shuai M, Gou W, Fu Y, Imamura F, Chen YM*, Zheng JS*. Erythrocyte n-6 polyunsaturated fatty acids, gut microbiota and incident type 2 diabetes: a prospective cohort study. Diabetes Care 2020, 43(10):2435-2443.


  • 14. Zheng JS*, Sharp SJ, Imamura F,  Rajiv C, Thomas GE, Marinka S, Sluijs I, van der Schouw YT, Agudo A, Aune D, Barricarte A, Boeing H, Chirlaque MD, Dorronsoro M, Freisling H, Fatoui DE, Franks PW, Fagherazzi G, Grioni S, Gunter MJ, Kyrø C, Katzke V, Kühn T, Khaw KT, Laouali N, Masala G, Nilsson PM, Overvad K, Panico S, Papier K, Quirós JR, Rolandsson O, Redondo-Sánchez D, Ricceri F, Schulze MB, Spijkerman AMW, Tjønneland A, Tong TYN, Tumino R, Weiderpass E, Danesh J, Butterworth AS, Riboli E, Forouhi NG*, Wareham NJ. Association of plasma biomarkers of fruit and vegetable intake with incident type 2 diabetes: The EPIC-InterAct case-cohort study in eight European countries. BMJ 2020;370:m2194.


  • Full list of publications:
  • PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=ju-sheng+zheng+or+jusheng+zheng
  • Google Scholar: https://scholar.google.co.uk/citations?user=XHcSy5MAAAAJ&hl=en
  • Lab website: http://zheng.lab.westlake.edu.cn/

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