Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks

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.

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Publication Details

Journal
Nature Communications
Published
2024-11-21
DOI
https://doi.org/10.1038/s41467-024-54357-0
Citations
39
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
8.89

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article

Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks

Taavi Tillmann, Kristian Nybo, Valtteri Mäkelä, Pasi Soininen et al.
39 citations
Nature Communications
Bioinformatics and Genomic Networks
8.89
article

Metabolomic and genomic prediction of common diseases in 700,217 participants in three national biobanks

Taavi Tillmann, Kristian Nybo, Valtteri Mäkelä, Pasi Soininen, Nurlan Kerimov, Luke Jostins-Dean, Mika Tiainen, Jeffrey C. Barrett, Sini Kerminen, Anastassia Kolde, Markus Perola, Peter Würtz, Heli Julkunen, Krista Fischer, Annamari Lundqvist, Maiju Soikkeli, Veikko V Salomaa, Sara Lundgren, Tuija Jääskeläinen, Jaanika Kronberg, Pekka Jousilahti, Antti J. Kangas, Harri Koskela, Tõnu Esko, Kirsten Schut
article en
39 citations

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.

Nature CommunicationsVol. 15(1)
Finnish Institute for Health and Welfare (FI), University of Tartu (EE)
Eesti Teadusagentuur
Zero hunger
Openalex Percentile: Top 1%
Bioinformatics and Genomic Networks
8.89
DOIOpen Access PDFOpenAlex
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