Lipidomics: potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease

Pharmacol Ther. 2014 Jul;143(1):12-23. doi: 10.1016/j.pharmthera.2014.02.001. Epub 2014 Feb 6.


Lipidomics has developed rapidly over the past decade to the point where clinical application may soon be possible. Developments including high throughput technologies enable the simultaneous quantification of several hundred lipid species, thereby providing a global assessment of lipid metabolism. Given the key role of lipids in the pathophysiology of diabetes and cardiovascular disease, lipidomics has the potential to: i) Significantly improve prediction of future disease risk, ii) Inform on mechanisms of disease pathogenesis, iii) Identify patient groups responsive to particular therapies and iv) More closely monitor response to therapy. Lipidomic analyses of both whole plasma and lipoprotein subfractions are integral to the current initiative to understand the relationships between lipoprotein composition and function and how these are affected by disease and treatment. This approach will not only aid in appropriate targeting of existing lipid lowering therapies such as statins and fibrates, but will be important in unravelling the controversies surrounding HDL-based therapies which have failed in clinical trials to date. The ultimate utility of lipidomics to clinical practice will depend firstly on the ability of risk prediction models incorporating lipidomic parameters to significantly improve upon conventional clinical risk markers in predicting future disease risk. Secondly, for widespread application, lipidomic-based measurements must be practical and accessible through standard pathology laboratories. This review will cover developments in lipidomics including methodology, bioinformatics/statistics, insights into disease pathophysiology, the effect of therapeutic interventions, the role of large clinical outcome trials in validating lipidomic approaches to patient management and potential applications in clinical practice.

Keywords: Bioinformatics; Cardiovascular disease; Diabetes; Lipid metabolism; Risk assessment; Therapeutic monitoring.

Publication types

  • Review

MeSH terms

  • Animals
  • Cardiovascular Diseases / etiology*
  • Cardiovascular Diseases / metabolism
  • Cardiovascular Diseases / therapy
  • Chromatography, High Pressure Liquid / methods
  • Computational Biology
  • Data Interpretation, Statistical
  • Diabetes Mellitus, Type 2 / etiology*
  • Diabetes Mellitus, Type 2 / metabolism
  • Diabetes Mellitus, Type 2 / therapy
  • Humans
  • Lipid Metabolism*
  • Principal Component Analysis
  • Risk
  • Risk Assessment