Combining lipidomics and machine learning to measure clinical lipids in dried blood spots
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ORIGINAL ARTICLE
Combining lipidomics and machine learning to measure clinical lipids in dried blood spots Stuart G. Snowden1 · Aniko Korosi2 · Susanne R. de Rooij3,4 · Albert Koulman1 Received: 9 March 2020 / Accepted: 11 July 2020 © The Author(s) 2020
Abstract Introduction Blood-based sample collection is a challenge, and dried blood spots (DBS) represent an attractive alternative. However, for DBSs to be an alternative to venous blood it is important that these samples are able to deliver comparable associations with clinical outcomes. To explore this we looked to see if lipid profile data could be used to predict the concentration of triglyceride, HDL, LDL and total cholesterol in DBSs using markers identified in plasma. Objectives To determine if DBSs can be used as an alternative to venous blood in both research and clinical settings, and to determine if machine learning could predict ‘clinical lipid’ concentration from lipid profile data. Methods Lipid profiles were generated from plasma (n = 777) and DBS (n = 835) samples. Random forest was applied to identify and validate panels of lipid markers in plasma, which were translated into the DBS cohort to provide robust measures of the four ‘clinical lipids’. Results In plasma samples panels of lipid markers were identified that could predict the concentration of the ‘clinical lipids’ with correlations between estimated and measured triglyceride, HDL, LDL and total cholesterol of 0.920, 0.743, 0.580 and 0.424 respectively. When translated into DBS samples, correlations of 0.836, 0.591, 0.561 and 0.569 were achieved for triglyceride, HDL, LDL and total cholesterol. Conclusion DBSs represent an alternative to venous blood, however further work is required to improve the combined lipidomics and machine learning approach to develop it for use in health monitoring. Keywords Lipidomics · Total cholesterol · Triglyceride · HDL · LDL
1 Introduction Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11306-020-01703-0) contains supplementary material, which is available to authorized users. * Albert Koulman [email protected] 1
Core Metabolomics and Lipidomics Laboratory, Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Level 4 Pathology, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
2
Centre for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
3
Department of Clinical Epidemiology, Amsterdam University Medical Centre, Biostatistics & Bio informaticslocation AMC, Amsterdam, The Netherlands
4
Department of Public Health, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
Traditionally, in high-income countries there is significant infrastructure to facilitate preclinical population health monitoring and research that is not present in low income countries leaving a significant unmet need for research and health monitoring (Kreuter et al. 2016; Johanne
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