Do you know how many different hats you wear in a day? Think about all the different roles you may take on during the course of the day – parent, significant other, boss, employee, cook, cleaner, accountant, household zookeeper, negotiator, etc. The number of roles can seem endless, but they reflect just how “dynamic” you can be and have to be. Like you, life is, indeed, dynamic. Including our health, especially how it changes over time. The question is what data will help – static or dynamic data – in managing your health?
What would static data be? Well, your genetic code that you were born with could count as static for the most part. For those of us growing up in the post-Human-Genome-Project world, it has been drummed into our heads – hyped even – that our genes define us. By examining our DNA (the body’s equivalent to the 0’s and 1’s used to create software, such as genes), we can see what our health future has in store. But what if you are dealt a dreadful set of gene cards? Fortunately, we have learned enough now to know that you are not out of luck: We now know that genes do not dictate fate in the vast majority of cases.
Also, sequencing your genome (all the DNA stuff you inherit) may not accurately describe your complete genetic portrait. You may in fact have more than one genome residing in your body for a variety of reasons which, in addition to just the regular errors associated with DNA sequencing at scale, could compromise the accuracy of any conclusions drawn.
However, even if you had your complete and error-free genetic report in hand, interpreting what it means with regards to health is still uncertain. Turns out that a single mutation (i.e., variation) rarely means you will get “x” disease or condition. In fact, research has shown that variants found in 11,544 genes to be associated with at least one of 518 traits (Watanabe et al., 2019). So, thousands of genes may have influence on a single trait.
Now, let’s examine dynamic data. A wonderful example would be proteomic (all your proteins) data. Proteins are the products of your genes. Yet, how much or little a protein exists can be influenced by so many factors and change throughout the day, etc.
At SomaLogic, we have worked for 20 years to develop the technology to monitor the rise and fall of protein levels and understand how those reflect a tremendous amount of detail about your multi-tasking body and your health. In fact, these fluctuations have generated so much information that hundreds of papers have been written, with many more yet to appear. From the bounty, we see just how protein information might help with arthritis, alert patients about an impending early demise or a non-ideal surgery outcome (Fong et al., 2019), foretell possible failure of a clinical trial (Williams et al., 2018), provide a better alert than traditional “gold standards” regarding a cardiovascular event (Ganz et al., 2016), etc. Protein information could even tell how your body responds to a diet (Thrush et al., 2018) or how exercise is affecting it (Santos-Parker, Santos-Parker, McQueen, Martens, & Seals, 2018).
Let’s get back to our question. What data are as dynamic as you? The answer lies in your proteins. Knowing your protein changes and how to optimize them could help you more effectively meet all the demands of your many different roles.
Fong, T. G., Chan, N. Y., Dillon, S. T., Zhou, W., Tripp, B., Ngo, L. H., . . . Libermann, T. A. (2019). Identification of Plasma Proteome Signatures Associated With Surgery Using SOMAscan. Ann Surg. doi:10.1097/SLA.0000000000003283
Ganz, P., Heidecker, B., Hveem, K., Jonasson, C., Kato, S., Segal, M. R., . . . Williams, S. A. (2016). Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients With Stable Coronary Heart Disease. JAMA, 315(23), 2532-2541. doi:10.1001/jama.2016.5951
Santos-Parker, J. R., Santos-Parker, K. S., McQueen, M. B., Martens, C. R., & Seals, D. R. (2018). Habitual Aerobic Exercise and Circulating Proteomic Patterns in Healthy Adults: Relation to Indicators of Healthspan. J Appl Physiol (1985). doi:10.1152/japplphysiol.00458.2018
Thrush, A. B., Antoun, G., Nikpay, M., Patten, D. A., DeVlugt, C., Mauger, J. F., . . . Harper, M. E. (2018). Diet-resistant obesity is characterized by a distinct plasma proteomic signature and impaired muscle fiber metabolism. Int J Obes (Lond), 42(3), 353-362. doi:10.1038/ijo.2017.286
Watanabe, K., Stringer, S., Frei, O., Umicevic Mirkov, M., de Leeuw, C., Polderman, T. J. C., . . . Posthuma, D. (2019). A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. doi:10.1038/s41588-019-0481-0
Williams, S. A., Murthy, A. C., DeLisle, R. K., Hyde, C., Malarstig, A., Ostroff, R., . . . Ganz, P. (2018). Improving Assessment of Drug Safety Through Proteomics: Early Detection and Mechanistic Characterization of the Unforeseen Harmful Effects of Torcetrapib. Circulation, 137(10), 999-1010. doi:10.1161/CIRCULATIONAHA.117.028213