Why population health studies must apply lipidomics
Population health studies frequently rely on omics technologies but biomarker identification studies benefit only if the data are of high quality and reliably reproducible.
Watch this episode
This session of “The Lipidomics Webinar” aired on December 2, 2021 and you can watch the recording here!
In the past century, population health studies have proven a powerful approach to reveal connections between diseases and their risk factors in epidemiology. We have now moved into an era, in which population health studies frequently rely on omics technologies (such as genomics, transcriptomics, metabolomics, and lipidomics) to identify disease risk factors at the molecular level.
Omics technologies provide unprecedented phenotypic details by accumulating vast amounts of data. While these are potentially of enormous value, biomarker identification studies benefit only if the data are of high quality and reliably reproducible. Fortunately, shotgun lipidomics fulfills these requirements and can therefore successfully be applied in biomarker identification studies covering indications such as obesity, diabetes, cardiovascular, and neurodegenerative diseases. Most importantly, human plasma lipidomics seems to reflect the body metabolism and has the potential to fill a hole in clinical diagnostics that lacks methods to measure metabolism.
Besides revealing promising potential multiparametric diagnostic and prognostic markers, large population health studies have proven that lipidomics is a powerful approach to shed light on disease mechanisms or relationships between genotype and phenotype in studies run over several years and involving tens of thousands of participants.
Here is what you can learn
• Power of lipidomics in human biomarker studies
• Lipidomics combined with other omics technologies (multi-omics)
• Contribution to research on obesity, diabetes, CVD, neurodegeneration etc.
• Translational value of lipidomics biomarkers & challenges in diagnostics
Dr. Christian Klose
Chief Technology Officer (CTO) of Lipotype