MetabHub: an integrative platform for plasma nuclear magnetic resonance metabolomics using mediation analysis, disease prediction, and risk stratification
- 1Department of Clinical Medicine, Nanjing Medical University Tianyuan Honors School, Nanjing Medical University , Nanjing, Jiangsu 211166 ,
- 2Department of Cardiology, First Affiliated Hospital of Nanjing Medical University , Nanjing, Jiangsu 210000 ,
- 3Jiangsu Key Laboratory for Biomedical Electromagnetic Precision Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University , Nanjing, Jiangsu 210000 ,
Abstract
Plasma nuclear magnetic resonance (NMR) metabolomics offers a scalable molecular readout linking genetic variation, environmental exposure, and clinical physiology to future disease risk. Existing resources typically address metabolite–disease associations, metabolite quantitative trait loci, or prediction separately, with limited support for integrated mediation and longitudinal risk stratification. MetabHub (https://www.metabhub.net) is a web-based resource built from individual-level UK Biobank data, comprising 364 795 European participants, 251 plasma NMR traits, 129 clinical measurements, 54 environmental exposures, genome-wide variants, and 57 incident diseases. It maps sex-combined and sex-specific associations across measurements, exposures, genotypes, metabolites, and diseases, yielding 2 950 764 significant associations. Using two-step screening and natural effect models, MetabHub identifies 1 886 224 significant clinical measurement, environmental factor, and genotype mediation pathways. It also provides metabolite-based prediction models for incident disease using penalized logistic regression, XGBoost, LightGBM, and multilayer perceptron, together with an online interface for applying pre-trained models to user-uploaded profiles. Model-derived metabolomic risk scores are further evaluated using Cox regression and survival stratification over ~14 years of follow-up. Global search, interactive visualization, downloadable tables, prediction, and stratification modules support mechanistic discovery and metabolomics-informed precision prevention.
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BibTeX
@article{yuan2026metabhub,
title = {{MetabHub: an integrative platform for plasma nuclear magnetic resonance metabolomics using mediation analysis, disease prediction, and risk stratification}},
author = {Jiahao Yuan and Xiaoxuan Gong and Chunwu Diao and Jiazhi Ren and Yingdan Wang and Aixi Bao and Ziyuan Jin and Xiuzhe Zhang and Chen Cao and Chunjian Li},
journal = {Nucleic Acids Research},
year = {2026},
month = oct,
doi = {10.1093/nar/gkag1066},
url = {https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1066/9004254},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - MetabHub: an integrative platform for plasma nuclear magnetic resonance metabolomics using mediation analysis, disease prediction, and risk stratification
AU - Jiahao Yuan
AU - Xiaoxuan Gong
AU - Chunwu Diao
AU - Jiazhi Ren
AU - Yingdan Wang
AU - Aixi Bao
AU - Ziyuan Jin
AU - Xiuzhe Zhang
AU - Chen Cao
AU - Chunjian Li
PY - 2026
DA - 2026/10/29
JO - Nucleic Acids Research
DO - 10.1093/nar/gkag1066
UR - https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1066/9004254
AB - Plasma nuclear magnetic resonance (NMR) metabolomics offers a scalable molecular readout linking genetic variation, environmental exposure, and clinical physiology to future disease risk. Existing resources typically address metabolite–disease associations, metabolite quantitative trait loci, or prediction separately, with limited support for integrated mediation and longitudinal risk stratification. MetabHub (https://www.metabhub.net) is a web-based resource built from individual-level UK Biobank data, comprising 364 795 European participants, 251 plasma NMR traits, 129 clinical measurements, 54 environmental exposures, genome-wide variants, and 57 incident diseases. It maps sex-combined and sex-specific associations across measurements, exposures, genotypes, metabolites, and diseases, yielding 2 950 764 significant associations. Using two-step screening and natural effect models, MetabHub identifies 1 886 224 significant clinical measurement, environmental factor, and genotype mediation pathways. It also provides metabolite-based prediction models for incident disease using penalized logistic regression, XGBoost, LightGBM, and multilayer perceptron, together with an online interface for applying pre-trained models to user-uploaded profiles. Model-derived metabolomic risk scores are further evaluated using Cox regression and survival stratification over ~14 years of follow-up. Global search, interactive visualization, downloadable tables, prediction, and stratification modules support mechanistic discovery and metabolomics-informed precision prevention.
SN - 0305-1048
SN - 1362-4962
ER -