ArticleMetabolomics : Official journal of the Metabolomic Society2020
Combining lipidomics and machine learning to measure clinical lipids in dried blood spots.
Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Comparative analysis of postprandial changes in the lipidome of capillary plasma and dry blood spots of young women.European journal of nutrition · 2026Article
- Method Comparison of Dried Blood Spot and Plasma Apolipoprotein B Measurement on a Semi-Automated Analyser: Toward Accessible Lipid Profiling in Resource-Limited Settings.Analytical science advances · 2026Article
- Translational lipidomics reveals BMP and its precursor LPG as biomarkers for CLN5 Batten disease.bioRxiv : the preprint server for biology · 2026Article
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
- Targeted and untargeted metabolomics and lipidomics in dried blood microsampling: Recent applications and perspectives.Analytical science advances · 2024Review
- Novel Metabolomic Approach for Identifying Pathology-Specific Biomarkers in Rare Diseases: A Case Study in Oculopharyngeal Muscular Dystrophy (OPMD).Metabolites · 2023Article
- Dried blood spots in clinical lipidomics: optimization and recent findings.Analytical and bioanalytical chemistry · 2022Review
- Non-Lethal Blood Sampling of Fish in the lab and Field With Methods for Dried Blood Plasma Spot Omic Analyses.Frontiers in genetics · 2022Article
- Untargeted metabolomics of newborn dried blood spots reveals sex-specific associations with pediatric acute myeloid leukemia.Leukemia research · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
introductionBlood-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.
objectivesTo 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.
methodsLipid 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'.
resultsIn 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.
conclusionDBSs 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.