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Optimizing the lipidomics workflow for clinical studies--practical considerations.
Tuulia Hyötyläinen1, Matej Orešič
1Steno Diabetes Center, Niels Steensens Vej 2, 2820, Gentofte, Denmark, tuhy@steno.dk.
Analytical and Bioanalytical Chemistry
|April 10, 2015
Summary
Clinical lipidomics offers disease prediction but requires careful study design. This review details the lipidomics workflow and confounding factors for accurate blood lipid metabolite measurements.
Area of Science:
- Clinical lipidomics
- Metabolomics
- Biomarker discovery
Background:
- Lipidomics is a growing field in clinical research for disease prediction and detection.
- Accurate lipid measurements are sensitive to analytical, physiological, and environmental factors, posing challenges for clinical applications.
Purpose of the Study:
- To critically discuss the complete clinical lipidomics workflow.
- To review confounding factors influencing blood lipid metabolite measurements.
Main Methods:
- Discussion of subject selection, sample types, and preprocessing.
- Review of analytical and data processing methods in lipidomics.
- Examination of confounding factors like age, gender, fasting, and handling procedures.
Main Results:
- Identification of key challenges in clinical lipidomics due to high sensitivity to various factors.
- Highlighting the importance of accounting for confounding variables in study design.
- Comprehensive overview of the lipidomics workflow from sample to data analysis.
Conclusions:
- Standardized protocols and consideration of confounding factors are crucial for reliable clinical lipidomics.
- Robust study design is essential to harness the full potential of lipidomics for disease prediction.
- Further research is needed to refine methodologies and minimize variability in lipid metabolite analysis.
