Optimizing the lipidomics workflow for clinical studies—practical considerations

• Published in: Analytical and bioanalytical chemistry Vol. 407; no. 17; pp. 4973 - 4993
• Main Authors: Hyotyläinen, Tuulia, Oresic, Matej
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2015; Springer; Springer Nature B.V
• Subjects: Analytical Chemistry; analytical methods; Anticoagulants; Biochemistry; Biomarkers; Biomarkers - analysis; Biomarkers - blood; Biomarkers - metabolism; Blood; blood lipids; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Chromatography; Chromatography, Liquid - methods; Cohort analysis; Data processing; Environmental factors; Fasting; Fatty acids; Food Science; gender; Humans; Laboratory Medicine; Lipid Metabolism; Lipidomics; Lipids; Lipids - analysis; Lipids - blood; Mass spectrometry; Mass Spectrometry - methods; Mathematical analysis; measuring; Medical research; Metabolites; Metabolomics; Metabolomics - methods; Methods; Monitoring/Environmental Analysis; Optimization; Physiology; Plasma; prediction; Preprocessing; Review; Scientific imaging; Systems biology; Systems Biology - methods; Systemsmedicine; Validation Studies as Topic; Workflow; Biomarkers; Metabolomics; Lipidomics; Systems biology; Systems medicine; Mass spectrometry
• ISSN: 1618-2642; 1618-2650; 1618-2650
• DOI: 10.1007/s00216-015-8633-2

Lipidomics is increasingly being used in clinical research, offering new opportunities for disease prediction and detection. One of the key challenges of clinical applications of lipidomics is the high sensitivity of measured lipid levels to many analytical, physiological, and environmental factors, which therefore must be taken into account when designing the studies. Here we critically discuss the complete clinical lipidomics workflow, including selection of the subjects, the sample type, the sample preprocessing conditions, and the analytical method and methods for data processing. We also review the lipidomics applications which investigate the confounding factors such as age, gender, fasting time, and handling procedures for measuring blood lipid metabolites.