Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition

Abstract In this paper, the stability of the plasma metabolome at −20 °C for up to 30 days was evaluated using liquid chromatography-high resolution mass spectrometric metabolomics analysis. To follow the time-series deterioration of the plasma metabolome, the use of an elastic net regularized regre...

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Autores principales:	Gerard Bryan Gonzales, Sarah De Saeger
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2455e61f54b64371bac586a5ccdbc5cc
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spelling	oai:doaj.org-article:2455e61f54b64371bac586a5ccdbc5cc2021-12-02T15:07:45ZElastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition10.1038/s41598-018-21851-72045-2322https://doaj.org/article/2455e61f54b64371bac586a5ccdbc5cc2018-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-21851-7https://doaj.org/toc/2045-2322Abstract In this paper, the stability of the plasma metabolome at −20 °C for up to 30 days was evaluated using liquid chromatography-high resolution mass spectrometric metabolomics analysis. To follow the time-series deterioration of the plasma metabolome, the use of an elastic net regularized regression model for the prediction of storage time at −20 °C based on the plasma metabolomic profile, and the selection and ranking of metabolites with high temporal changes was demonstrated using the glmnet package in R. Out of 1229 (positive mode) and 1483 (negative mode) metabolite features, the elastic net model extracted 32 metabolites of interest in both positive and negative modes. L-gamma-glutamyl-L-(iso)leucine (tentative identification) was found to have the highest time-dependent change and significantly increased proportionally to the storage time of plasma at −20 °C (R2 = 0.6378 [positive mode], R2 = 0.7893 [negative mode], p-value < 0.00001). Based on the temporal profiles of the extracted metabolites by the model, results show only minimal deterioration of the plasma metabolome at −20 °C up to 1 month. However, majority of the changes appeared at around 12–15 days of storage. This allows scientists to better plan logistics and storage strategies for samples obtained from low-resource settings, where −80 °C storage is not guaranteed.Gerard Bryan GonzalesSarah De SaegerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gerard Bryan Gonzales Sarah De Saeger Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition
description	Abstract In this paper, the stability of the plasma metabolome at −20 °C for up to 30 days was evaluated using liquid chromatography-high resolution mass spectrometric metabolomics analysis. To follow the time-series deterioration of the plasma metabolome, the use of an elastic net regularized regression model for the prediction of storage time at −20 °C based on the plasma metabolomic profile, and the selection and ranking of metabolites with high temporal changes was demonstrated using the glmnet package in R. Out of 1229 (positive mode) and 1483 (negative mode) metabolite features, the elastic net model extracted 32 metabolites of interest in both positive and negative modes. L-gamma-glutamyl-L-(iso)leucine (tentative identification) was found to have the highest time-dependent change and significantly increased proportionally to the storage time of plasma at −20 °C (R2 = 0.6378 [positive mode], R2 = 0.7893 [negative mode], p-value < 0.00001). Based on the temporal profiles of the extracted metabolites by the model, results show only minimal deterioration of the plasma metabolome at −20 °C up to 1 month. However, majority of the changes appeared at around 12–15 days of storage. This allows scientists to better plan logistics and storage strategies for samples obtained from low-resource settings, where −80 °C storage is not guaranteed.
format	article
author	Gerard Bryan Gonzales Sarah De Saeger
author_facet	Gerard Bryan Gonzales Sarah De Saeger
author_sort	Gerard Bryan Gonzales
title	Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition
title_short	Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition
title_full	Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition
title_fullStr	Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition
title_full_unstemmed	Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition
title_sort	elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/2455e61f54b64371bac586a5ccdbc5cc
work_keys_str_mv	AT gerardbryangonzales elasticnetregularizedregressionfortimeseriesanalysisofplasmametabolomestabilityundersuboptimalfreezingcondition AT sarahdesaeger elasticnetregularizedregressionfortimeseriesanalysisofplasmametabolomestabilityundersuboptimalfreezingcondition
_version_	1718388372130496512

Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition

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