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Deep phenotyping of health–disease continuum in the Human Phenotype Project

Reicher, Lee
; 
Shilo, Smadar
; 
Godneva, Anastasia
; 
Lutsker, Guy
; 
Zahavi, Liron
; 
Shoer, Saar
; 
Krongauz, David
; 
Rein, Michal
; 
Kohn, Sarah
; 
Segev, Tomer
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Author
Reicher, Lee
Shilo, Smadar
Godneva, Anastasia
Lutsker, Guy
Zahavi, Liron
Shoer, Saar
Krongauz, David
Rein, Michal
Kohn, Sarah
Segev, Tomer
Schlesinger, Yishay
Barak, Daniel
Levine, Zachary
Keshet, Ayya
Shaulitch, Rotem
Lotan-Pompan, Maya
Elkan, Matan
Talmor-Barkan, Yeela
Aviv, Yaron
Dadiani, Maya
Tsodyks, Yonatan
Gal-Yam, Einav Nili
Leibovitzh, Haim
Werner, Lael
Tzadok, Roie
Maharshak, Nitsan
Koga, Shin
Glick-Gorman, Yulia
Stossel, Chani
Raitses-Gurevich, Maria
Golan, Talia
Dhir, Raja
Reisner, Yotam
Weinberger, Adina
Rossman, Hagai
Song, Le
Xing, Eric P.
Segal, Eran
Supervisor
Department
Computational Biology
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Type
Journal article
Date
2025
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Language
English
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Abstract
The Human Phenotype Project (HPP) is a large-scale deep-phenotype prospective cohort. To date, approximately 28,000 participants have enrolled, with more than 13,000 completing their initial visit. The project is aimed at identifying novel molecular signatures with diagnostic, prognostic and therapeutic value, and at developing artificial intelligence (AI)-based predictive models for disease onset and progression. The HPP includes longitudinal profiling encompassing medical history, lifestyle and nutrition, anthropometrics, blood tests, continuous glucose and sleep monitoring, imaging and multi-omics data, including genetics, transcriptomics, microbiome (gut, vaginal and oral), metabolomics and immune profiling. Analysis of these data highlights the variation of phenotypes with age and ethnicity and unravels molecular signatures of disease by comparison with matched healthy controls. Leveraging extensive dietary and lifestyle data, we identify associations between lifestyle factors and health outcomes. Finally, we present a multi-modal foundation AI model, trained using self-supervised learning on diet and continuous-glucose-monitoring data, that outperforms existing methods in predicting disease onset. This framework can be extended to integrate other modalities and act as a personalized digital twin. In summary, we present a deeply phenotyped cohort that serves as a platform for advancing biomarker discovery, enabling the development of multi-modal AI models and personalized medicine approaches.
Citation
L. Reicher et al., “Deep phenotyping of health-disease continuum in the Human Phenotype Project.,” Nat Med, pp. 1–13, Jul. 2025, doi: 10.1038/S41591-025-03790-9
Source
Nature Medicine
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Publisher
Springer Nature
DOI
10.1038/s41591-025-03790-9
Additional links
https://www.nature.com/articles/s41591-025-03790-9
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