by Kirk Beebe

Precision medicine is at the heart of healthcare providers’ goals to improve health, diagnoses and treatment for their patients. The key to advancing precision medicine lies in first creating a blueprint of human health. This will require collecting and analyzing massive amounts of data to build an expansive toolbox of recommendations about wellness and treatment for individuals.

Many organizations, including Metabolon, have embraced this challenge as interest in precision medicine extends beyond the President’s Precision Medicine Initiative. Several partnerships aim to unlock understanding of health and disease so they can develop better treatments. Examples include Amgen’s acquisition of deCODE Genetics, 23andMe’s agreements with pharmaceutical companies like Genentech and Pfizer, and Geisinger Health System’s collaboration with Regeneron. 

Then there’s the personal health and wellness side of the equation, where Metabolon is collaborating with Craig Venter’s Human Longevity, Inc. and Lee Hood’s newly launched Arivale. These organizations aim to understand the blueprint for health and aging to favorably alter these states. Even Apple is in the game with their ResearchKit for iPhones and its potential to enable personal health and precision medicine. 

A Data Conundrum 
While genomics is undeniably important in creating the precision medicine blueprint, most of these initiatives recognize the need to include other types of data, in addition to medical records and standard clinical assessment. This is because the last decade of genomics research has revealed higher than anticipated individual genetic variation. In addition, most traits of interest involve a combination of many genes1,2; and the majority of mutations reside in non-coding regions of the genome, where we have a very poor understanding of the function3. It cannot be ignored that massive amounts of data must be managed before consensus and actionable data can be effectively mined, as illustrated by a recent whole genome sequencing initiative published in JAMA5,6.

Add to the above the fact that elusive influences such as the microbiome4 and epigenetics are clearly important, and it is evident why many genomics investigators seek additional data types in their efforts to contend with this overwhelmingly complex picture.

The Missing Link in Precision Medicine 
Metabolomics is becoming a core element in defining the blueprint of human health. The reason for its inclusion is simple. Metabolites are central to the health state, and they reflect the role of factors such as genetics and external influences like diet and lifestyle. An individual’s metabolic state offers an intimate assessment of their state of health as it is at the time a sample is provided. Recall, many metabolites such as glucose and cholesterol are already the staples in clinical assessment today.

Metabolic pathways are more completely understood than almost any other aspect of human biology. They have been integral for mapping many complex physiological processes like muscle and cancer metabolism. Metabolomics measures all of the metabolites within these pathways. This is a critical reason why a growing number of large precision medicine and next-generation sequencing (NGS) initiatives have adopted metabolomics as a cornerstone of their programs to link genetics and metabolic profiles to phenotypes or health states.

Metabolomics alleviates some of the challenges of genomics by identifying genes that are effectively “active” and then creating a functional connection between the gene and the health state. This has been demonstrated for a variety of traits in fairly healthy populations7,8and profoundly illustrated in more severe genetic states9,10. PNAS recently published a study conducted by Metabolon and Baylor College of Medicine, which combined the functional measurement of metabolomics with whole exome sequencing (WES) of individual subjects11. Metabolon’s technology successfully spotted underlying health issues that were previously undetected or not highlighted in the genetic data, illustrating that information derived from metabolomics was more precise than genetic information.

There is growing appreciation that complex illnesses such as diabetes, cancer, cardiovascular and neurological diseases are caused by a combination of genetic and non-genetic factors. Clinicians must take into account the impact of these factors to make an informed diagnosis. Metabolomics reflects the influences of genes, diet, lifestyle, environment and xenobiotics to aid in understanding gene function and how diseases originate. It also provides the biomarkers for health assessment and customized therapy.

NGS & Large Cohort Studies Have Tapped into Metabolomics 
Large cohort studies will fill the precision medicine toolbox with more information, leading to far more options for health care and precision medicine. Not surprisingly, metabolomics is routinely being used to augment and highlight important genomic data in these health initiatives. But more broadly, metabolomic profiling is an ideal way to phenotype individuals and establish a beachhead from which to integrate genomics and other types of data. This framework ultimately provides a gateway for deriving the blueprint of individual health. The basis of achieving better individual health by any means, including precision medicine, is to map this blueprint completely and accurately.

Metabolomics has emerged as a powerful technology for precision medicine by dissecting underlying disease processes. This may set the stage for new ways to diagnose, monitor and provide guidance for treatment. Metabolon is participating in many of these efforts to map human health, while also capitalizing on the biomarkers and signatures already derived to assess individual health right now. Whether used for routine health assessment or in conjunction with genetic sequencing, metabolomics must play a vital role in precision medicine. 


  1. Cooper, D.N., Krawczak, M., Polychronakos, C., Tyler-Smith, C. & Kehrer-Sawatzki, H. Where genotype is not predictive of
  2.  phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease. Hum Genetics 132, 1077-1130 (2013).
  3. Reed, L.K. et al. Systems Genomics of Metabolic Phenotypes in Wild-Type Drosophila melanogaster. Genetics 197, 781-793 (2014). 
  4. Topol, E.J. Individualized medicine from prewomb to tomb. Cell 157, 241-253 (2014). 
  5. Beebe, K., Sampey, B., Watkins, S.M., Milburn, M. & Eckhart, A.D. Understanding the apothecaries within: the necessity of a systematic approach for defining the chemical output of the human microbiome. Clin Transl Sci 7, 74-81 (2014).
  6. Feero, W.G. Clinical application of whole-genome sequencing: proceed with care. JAMA 311, 1017-1019 (2014).
  7. Dewey, F.E. et al. Clinical interpretation and implications of whole-genome sequencing. JAMA 311, 1035-1045 (2014). 
  8. Shin, S.Y. et al. An atlas of genetic influences on human blood metabolites. Nature Genetics 46, 543-550 (2014). 
  9. Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54-60 (2011). 
  10. Miller, M.J. et al. Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism. Journal of Inherited Metabolic Disease (2015). 
  11. Atwal, P.S. et al. Aromatic l-amino acid decarboxylase deficiency diagnosed by clinical metabolomic profiling of plasma. Molecular Genetics and Metabolism 115, 91-94 (2015). 
  12. Guo, L. et al. Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. PNAS 2015. 


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