Imputation of plasma lipid species to facilitate integration of lipidomic datasets

Aleksandar Dakic, Jingqin Wu, Tingting Wang, Kevin Huynh, Natalie Mellett, Thy Duong, Habtamu B. Beyene, Dianna J. Magliano, Jonathan E. Shaw, Melinda J. Carrington, Michael Inouye, Jean Y. Yang, Gemma A. Figtree, Joanne E. Curran, John Blangero, John Simes, Corey Giles () and Peter J. Meikle ()
Additional contact information
Aleksandar Dakic: Baker Heart and Diabetes Institute
Jingqin Wu: Baker Heart and Diabetes Institute
Tingting Wang: Baker Heart and Diabetes Institute
Kevin Huynh: Baker Heart and Diabetes Institute
Natalie Mellett: Baker Heart and Diabetes Institute
Thy Duong: Baker Heart and Diabetes Institute
Habtamu B. Beyene: Baker Heart and Diabetes Institute
Dianna J. Magliano: Baker Heart and Diabetes Institute
Jonathan E. Shaw: Baker Heart and Diabetes Institute
Melinda J. Carrington: Baker Heart and Diabetes Institute
Michael Inouye: Baker Heart and Diabetes Institute
Jean Y. Yang: The University of Sydney
Gemma A. Figtree: The University of Sydney
Joanne E. Curran: School of Medicine at University of Texas Rio Grande Valley
John Blangero: School of Medicine at University of Texas Rio Grande Valley
John Simes: University of Sydney
Corey Giles: Baker Heart and Diabetes Institute
Peter J. Meikle: Baker Heart and Diabetes Institute

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.

Date: 2024
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DOI: 10.1038/s41467-024-45838-3

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