Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks
Abstract Identifying individuals at high risk of chronic diseases via easily measured biomarkers could enhance efforts to prevent avoidable illness and death. Using ’omic data can stratify risk for many diseases simultaneously from a single measurement that captures multiple molecular predictors of...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54357-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850129089552187392 |
|---|---|
| author | Nightingale Health Biobank Collaborative Group |
| author_facet | Nightingale Health Biobank Collaborative Group |
| author_sort | Nightingale Health Biobank Collaborative Group |
| collection | DOAJ |
| description | Abstract Identifying individuals at high risk of chronic diseases via easily measured biomarkers could enhance efforts to prevent avoidable illness and death. Using ’omic data can stratify risk for many diseases simultaneously from a single measurement that captures multiple molecular predictors of risk. Here we present nuclear magnetic resonance metabolomics in blood samples from 700,217 participants in three national biobanks. We built metabolomic scores that identify high-risk groups for diseases that cause the most morbidity in high-income countries and show consistent cross-biobank replication of the relative risk of disease for these groups. We show that these metabolomic scores are more strongly associated with disease onset than polygenic scores for most of these diseases. In a subset of 18,709 individuals with metabolomic biomarkers measured at two time points we show that people whose scores change have different risk of disease, suggesting that repeat measurements capture changes both to health status and disease risk possibly due to treatment, lifestyle changes or other factors. Lastly, we assessed the incremental predictive value of metabolomic scores over existing clinical risk scores for multiple diseases and found modest improvements in discrimination for several diseases whose clinical utility, while promising, remains to be determined. |
| format | Article |
| id | doaj-art-901a5f1bf5164724aab0d56dff1cbee5 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-901a5f1bf5164724aab0d56dff1cbee52025-08-20T02:33:06ZengNature PortfolioNature Communications2041-17232024-11-0115111410.1038/s41467-024-54357-0Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanksNightingale Health Biobank Collaborative Group0Nightingale HealthAbstract Identifying individuals at high risk of chronic diseases via easily measured biomarkers could enhance efforts to prevent avoidable illness and death. Using ’omic data can stratify risk for many diseases simultaneously from a single measurement that captures multiple molecular predictors of risk. Here we present nuclear magnetic resonance metabolomics in blood samples from 700,217 participants in three national biobanks. We built metabolomic scores that identify high-risk groups for diseases that cause the most morbidity in high-income countries and show consistent cross-biobank replication of the relative risk of disease for these groups. We show that these metabolomic scores are more strongly associated with disease onset than polygenic scores for most of these diseases. In a subset of 18,709 individuals with metabolomic biomarkers measured at two time points we show that people whose scores change have different risk of disease, suggesting that repeat measurements capture changes both to health status and disease risk possibly due to treatment, lifestyle changes or other factors. Lastly, we assessed the incremental predictive value of metabolomic scores over existing clinical risk scores for multiple diseases and found modest improvements in discrimination for several diseases whose clinical utility, while promising, remains to be determined.https://doi.org/10.1038/s41467-024-54357-0 |
| spellingShingle | Nightingale Health Biobank Collaborative Group Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks Nature Communications |
| title | Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks |
| title_full | Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks |
| title_fullStr | Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks |
| title_full_unstemmed | Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks |
| title_short | Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks |
| title_sort | metabolomic and genomic prediction of common diseases in 700 217 participants in three national biobanks |
| url | https://doi.org/10.1038/s41467-024-54357-0 |
| work_keys_str_mv | AT nightingalehealthbiobankcollaborativegroup metabolomicandgenomicpredictionofcommondiseasesin700217participantsinthreenationalbiobanks |