School of Medicine Publications and Presentations
Document Type
Article
Publication Date
2-20-2024
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.
Recommended Citation
Dakic, A., Wu, J., Wang, T., Huynh, K., Mellett, N., Duong, T., ... & Meikle, P. J. (2024). Imputation of plasma lipid species to facilitate integration of lipidomic datasets. Nature Communications, 15(1), 1540. https://doi.org/10.1038/s41467-024-45838-3
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Nature Communications
DOI
10.1038/s41467-024-45838-3
Academic Level
faculty
Mentor/PI Department
Office of Human Genetics
Comments
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.