Multi-omics integration predicts 17 disease incidences in the UK Biobank
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Importance
Traditional clinical predictors for disease risks have limitations in capturing underlying disease complexity. Multi-omics technologies, such as metabolomics and proteomics, offer deeper molecular perspectives that could enhance risk prediction, but large-scale studies integrating the two omics are scarce.
Objectives
The primary objective is to systematically evaluate whether adding metabolomics and/or proteomics data to traditional clinical predictors improves risk prediction for 17 common incident diseases. A secondary objective is to identify key disease-related omics features.
Data Sources and Participants
Our study incorporated 23,776 UK Biobank participants who had complete baseline omics data for 159 NMR-based metabolites and 2,923 Olink affinity-based proteins.
Main Outcomes and Measures
We evaluated the model prediction of 17 incident diseases by fitting Cox proportional hazard models and obtaining Harrell’s C-index. Feature importance scores were calculated to identify key molecules contributing to each disease risk prediction.
Results
Adding omics data significantly improved risk prediction for all 17 diseases compared to models with clinical predictors alone (p-value < 2E-4). Proteomics-only models generally demonstrated superior predictive performance over metabolomics-only models for 14 of the 17 endpoints. We also identified key proteins, including established biomarkers like KLK3 (PSA) for prostate cancer and CRYBB2 for cataracts.
Conclusion and Relevance
Integration of Olink proteomics, and to a lesser extent Nightingale metabolomics, substantially improves risk prediction for a wide range of common diseases beyond established clinical factors. These findings highlight the clinical utility of proteomics for enhancing individual risk prediction and provide molecular insights into disease mechanisms, which may potentially guide future therapeutic development.
Key Points
Question
Do multi-omics profiles improve disease risk prediction compared to models using only traditional clinical risk factors and what is the best strategy to integrate metabolomics and proteomics in disease prediction?
Findings
In this study, we investigated 17 incident diseases across 23,776 UK Biobank individuals with complete records of both Nightingale metabolomics and Olink proteomics profiles, and found that integrating omics data significantly enhanced disease prediction over traditional approaches, with Olink proteomics consistently providing more predictive power than Nightingale metabolomics for most diseases. We also identified key proteins, including both well-established ones like KLK3 (PSA) for prostate cancer and potential novel ones like PRG3 for skin cancer. We also connected diseases with medication, socioeconomic, demographic, and lifestyle risk factors through these key proteins.
Meaning
Our findings suggest the potential clinical utility of integrating multi-omics in risk prediction and biomedical discoveries. To the best of our knowledge, our study is currently the largest to systematically evaluate contributions of both metabolomics and proteomics profiles to the prediction of various incident clinical endpoints.
