Biomarker Development

Biomarker discovery is one of the most important goals from metabolomic profiling. Often combined with a form of proteomics and clinical data, metabolomic biomarkers are common in today’s clinical analysis. Cholesterol, Triglycerides, Amino Acids, Glucose to name a few are widely used by medical community to assess and diagnose for health and disease. While diseases such as cancer, chronic kidney disease, multiple sclerosis, cardiovascular disease, Parkinson’s disease, Alzheimer’s and more are more complex due to the vary nature of these diseases and the biodiversity of human species. Biomarkers for these diseases and more continuing to be investigated and developed with the most success when focused on specific clinical phenotypes. HMT continues to contribute to these studies and biomarker development continues to grow and validate through large studies.

 

 

The size of a human study range from small pilot studies of 10 to 20 individuals to medium sized discovery studies of 50 to over 100 individuals to validation and population studies of over a few hundred individuals. HMT has the knowledge and capabilities to contribute along this biomarker pipeline from discovery to validation. For small pilot studies of under 100 individuals, we have a reference library of over 900 metabolites that can serve as a virtual healthy reference for human plasma that contains over 200 quantified metabolites. Using this reference against small studies allows us to draw more accurate assumptions about differences between a small (under 100) healthy control group and a small disease group. For larger studies, a larger control group is needed to really understand the biodiversity in human populations. As the size of the human cohort increases, so does the potential to observe subtypes of individuals in both the healthy and disease cohorts. Since biology among humans can be unique in terms of genetics and microbiome, there are different profiles of healthy individuals and different profiles for patient populations. Contributions from BMI and lifestyle habits can also contribute significantly to population diversity. While there is a small subset of metabolites with standard deviations under 10 or 20%, the majority of metabolites have standard deviations over 20% due to genetics, lifestyle, exposure, microbial and environmental influences. At HMT we examine human plasma data and correlate patient and control samples according to specific markers, not just for quality, but for lifestyle and biodiversity so that biomarkers for disease can be easier observed for validation studies.