Dataset Information

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots.

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.

SUBMITTER: Snowden SG

PROVIDER: S-EPMC7381462 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots.

Snowden Stuart G SG Korosi Aniko A de Rooij Susanne R SR Koulman Albert A

Metabolomics : Official journal of the Metabolomic Society 20200724 8

<h4>Introduction</h4>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.<h4>Objectives</h4>To ...[more]

PMID: 32710150

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Project description:Background: DNA methylation reflects health-related environmental exposures and genetic risk, providing insights into aetiological mechanisms and potentially predicting disease onset, progression and treatment response. An increasingly recognised need for large-scale, longitudinally-profiled samples collected world-wide has made the development of efficient and straightforward sample collection and storage procedures a pressing issue. An alternative to the low-temperature storage of EDTA tubes of venous blood samples, which are frequently the source of the DNA used in such studies, is to collect and store at room temperature blood samples using purpose built filter paper, such as Whatman FTA® cards. Our goal was to determine whether DNA stored in this manner can be used to generate DNA methylation profiles comparable to those generated using blood samples frozen in EDTA tubes. Methods: DNA methylation profiles were obtained from matched EDTA tube and Whatman FTA® card whole-blood samples from 62 Generation Scotland: Scottish Family Health Study participants using the Infinium HumanMethylation450 BeadChip. Multiple quality control procedures were implemented, the relationship between the two sample types assessed, and epigenome-wide association studies (EWASs) performed for smoking status, age and the interaction between these variables and sample storage method. Results: Dried blood spot (DBS) DNA methylation profiles were of good quality and DNA methylation profiles from matched DBS and EDTA tube samples were highly correlated (mean r = 0.991) and could distinguish between participants. EWASs replicated established associations for smoking and age, with no evidence for moderation by storage method. Conclusions: Our results support the use of Whatman FTA® cards for collecting and storing blood samples for DNA methylation profiling. This approach is likely to be particularly beneficial for large-scale studies and those carried out in areas where freezer access is limited. Furthermore, our results will inform consideration of the use of newborn heel prick DBSs for research use.

Dataset Information

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots.

Introduction

Objectives

Methods

Results

Conclusion

Publications

Combining lipidomics and machine learning to measure clinical lipids in dried blood spots.

Similar Datasets

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