Building on the foundation of genomics, proteomics offers an unprecedented view of health status and risk of future disease that integrates environmental, social and behavioural determinants with genetic predisposition. Life science industries are also set to benefit from the technology’s capacity to reveal novel insights on molecular mechanisms of disease, potential drug targets and early warning of adverse reactions to new therapies.  

For health systems to be sustainable far greater emphasis will be needed on disease prevention. Recent therapeutic developments (P4 medicine)1 raise the possibility of pre-emptive treatment before a disease is clinically expressed. But realising that potential advance will require an accurate assessment of risk of developing a disease (or the complications of an existing condition). Much store has been placed on genomics and in discrete fields the potential is clear2. However, the majority of risk reflects the admixture of environmental, social and behavioural determinants of health.

To devise a composite risk score from these myriad sources is an approach being pursued with vigour and integral, for example, to the work of Health Data Research – UK. However, the challenges to this approach should not be underestimated, ranging from data quality, linkage, curation, and data security and public trust alongside digital immaturity and limited interoperability across care systems. Again, exemplars reveal the potential if these issues can be addressed but, in the meantime, can we exploit other means to arrive at a composite risk score?

Recent evidence suggests that the proteome might provide a solution. There are about 19,000 human genes coding for approximately 30,000 proteins3. Of these, up to 2,200 proteins enter the blood stream by purposeful secretion to orchestrate biological processes in health or in disease, including hormones, cytokines, chemokines, adipokines and growth factors. Ways have been devised to measure these proteins with great sensitivity and specificity4 and demonstrate that their expression is influenced not only by genetics5 but social, behavioural and environmental determinants. In effect the human body is acting as the ‘biological integrator’ of these various inputs bypassing the need for extraneous data linkage.

The power of this approach was recently reported in Nature Medicine, providing what was termed a ‘liquid health check’6. The capacity to accurately assess current health status or risk of future disease was recorded for eleven conditions, as judged against ‘truth standards’ informed by conventional best in class diagnostic procedures. The initial eleven are just the start. Extending the range of tests is a function of training an algorithm against new truth standards using existing protein levels and approximately 150 tests are in the pipeline.

Promising though this technology is we will still need to address where it sits in the diagnostic pathway and the relative value and cost utility of the approach. As with all screening processes its potential will only be fulfilled if there are effective and affordable interventions that can be applied. These are likely to embrace social, environmental and behavioural interventions and indeed applying proteomics to large data sets in which these characteristics along with biological data are known will provide new insights into these determinants and help inform public health policy more broadly.

In the field of medical science and the search for innovative therapy it is worth reflecting on the fact that all known drugs act via proteins (e.g. binding to a protein target, changing a protein structure, changing its production or degradation, changing its activity). It is likely therefore that the proteome is a rich source of insight for novel drug development as well as assessment of current and future health status. Furthermore, by tracking the blood protein signature on exposure to a novel therapy under trial conditions may provide early warning of potential adverse effects7 before these are clinically apparent.

We are entering exciting times when the potential for precision prevention is close to being realised. Coupled with evidence-based population level strategies the development of powerful proteomic technologies brings the prospect of a truly health-sustaining health and care system is one step closer.

John Tooke 

Competing interest: Academic Health Solutions is acting as an adviser to SomaLogic.


1. Hood, L. Flores, M. (2012) A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. N Biotechnol. 29(6):613-24

2. Craig, JE et al. (2020) Multitrait analysis of glaucoma identifies new risk loci and enables polygenic prediction of disease susceptibility and progression. Nat Genet. doi: 10.1038/s41588-019-0556-y. (Epub ahead of print)

3. Ezkurdia, I. Ayers, D. Bertino, J et al. (2010) Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum Mol Genet 23(22): 5866-78

4. Gold, L. Ayers, D. Bertino, J et al. (2010) Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoSOne 2010; 5(12): e15004.

5. Sun, BB. Maranville, JC. Peters, JE et al. (2018) Genomic atlas of the human plasma proteome. Nature 558(7708): 73-9.

6. Williams, SA et al. (2019) Plasma protein patterns as comprehensive indicators of health.Nat Med.25(12):1851-1857. doi: 10.1038/s41591-019-0665-2.

7. Williams, SA. Murthy, AC. DeLisle, RK et al. (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.