Cross-platform metabolomics imputation using importance-weighted autoencoders
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Journal Article
Abstract
Metabolomics data are often generated through different platforms and quantification methods which makes their synthesis and large-scale replication challenging. This study developed an ensemble of importance-weighted autoencoders to perform cross-platform metabolomics imputation between two metabolomics platforms, Metabolon and National
Phenome Centre (NPC) at Imperial College, using 979 samples from the Airwave Health Monitoring Study.
The generated samples were highly correlated with real values across all metabolites (µρ = 0.61 (0.55-0.67)). The well-imputed subset contained 199 metabolites (22%), capturing ≥55% variance (R² ≥ 0.55) with minimal uncertainty (R² variance ≤ 0.025), including 43
metabolites unique to Metabolon.
The concordance of associations in 2,971 validation samples between real and imputed metabolites with two clinical outcomes, body mass index (BMI) and C-reactive protein (CRP), were highly correlated (ρBMI = 0.93; ρCRP = 0.89) with minimal mean difference (BMI µΔ = 0.005 (0.04); CRP µΔ = 0.005 (0.04)). Similar concordance occurred with equivalent UK Biobank (BMI µΔ = -0.007 (0.05); CRP µΔ = 0.01 (0.04)) and NPC (BMI µΔ = -0.013 (0.04); CRP µΔ = -0.019 (0.04)) metabolites.
This methodological innovation offers a scalable and accurate method for cross-platform imputation, enabling the aggregation of metabolomics data from different epidemiological studies for replication and meta-analyses.
Phenome Centre (NPC) at Imperial College, using 979 samples from the Airwave Health Monitoring Study.
The generated samples were highly correlated with real values across all metabolites (µρ = 0.61 (0.55-0.67)). The well-imputed subset contained 199 metabolites (22%), capturing ≥55% variance (R² ≥ 0.55) with minimal uncertainty (R² variance ≤ 0.025), including 43
metabolites unique to Metabolon.
The concordance of associations in 2,971 validation samples between real and imputed metabolites with two clinical outcomes, body mass index (BMI) and C-reactive protein (CRP), were highly correlated (ρBMI = 0.93; ρCRP = 0.89) with minimal mean difference (BMI µΔ = 0.005 (0.04); CRP µΔ = 0.005 (0.04)). Similar concordance occurred with equivalent UK Biobank (BMI µΔ = -0.007 (0.05); CRP µΔ = 0.01 (0.04)) and NPC (BMI µΔ = -0.013 (0.04); CRP µΔ = -0.019 (0.04)) metabolites.
This methodological innovation offers a scalable and accurate method for cross-platform imputation, enabling the aggregation of metabolomics data from different epidemiological studies for replication and meta-analyses.
Date Issued
2026-02-11
Date Acceptance
2025-12-29
Citation
npj Systems Biology and Applications, 2026, 12
ISSN
2056-7189
Publisher
Nature Portfolio
Journal / Book Title
npj Systems Biology and Applications
Volume
12
Copyright Statement
©TheAuthor(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Publication Status
Published
Article Number
23
Date Publish Online
2026-01-10