Personal Baselines: A Longitudinal Big Data Approach for Preventive Precision Health

We are entering a new era of data-driven health monitoring. In addition to conventional approaches, we can now determine genome sequences, collect data about thousands of molecules (RNA, protein, metabolites, lipids), perform advanced imaging, and continuously monitor physiology.

Importantly, we can follow people over time, during periods of disease and health. In today’s Nature Medicine article, my scientific colleagues and I describe the results of a research project called Integrative Personal Omics Profiling (iPOP) that illustrates the value of using advanced technologies to carefully follow 109 people for about three years (many for four or more years) and how this can be applied to manage health. This study uncovered 49 clinically significant health findings — plus 17 more if hypertension is included. Some of these findings were very consequential — early detection of lymphoma, two precancerous conditions, and two serious heart conditions. There were a variety of disease risks identified (e.g. for cancer, cardiovascular disease) and early signs of disease (e.g. diabetes) that were actionable.

The ability to focus on early intervention and prevention represents significant cost savings in healthcare and, more importantly, better individual health outcomes.

One of the core beliefs of this approach is that tracking individual changes over time is better than examining population averages alone in identifying clinically actionable, early health interventions. This study confirms that belief. Population health data inherently looks at averages and may miss early signs of disease progression in an otherwise asymptomatic individual.

Existing medical knowledge is biased and there is a need to de-conflate the measurement of our biology from the analysis of our health. Tracking a well-defined set of biomarkers longitudinally offers the clinical advantage of detection at the earliest stages of disease where an intervention may be more likely to succeed in reducing long term morbidity and mortality.

Developing the Physical of the Future

While presenting the iPOP project over the years and all over the world, many people have asked how they can get access to these technologies to follow their own health. Consequently, Jeff Kaditz, Garry Choy, and I have spun off a derivative of iPOP called a Quantitative exam (or “Q” for short) which is offered by Q under an institutional review board (IRB) approved protocol. The Q protocol brings together many of the health-related features of iPOP and adds whole body MRI (magnetic resonance imaging). We began piloting the Q protocol in 2017 and have since expanded to include select partners, and interest is high.

The Q protocol has already shown promise that goes beyond iPOP by generating additional data around known and actionable set of biomarkers that includes non-invasive, whole-body, comprehensive imaging data.
This has allowed the team to not just track personalized, longitudinal changes…

…but to track these changes at depth across specific biomarkers.

By utilizing a multiomics approach similar to iPOP but further incorporating biometrics and non-invasive radiomics, to date Q has found information for at least one previously unknown health-related condition in 97% of member visits; this information is valuable for ongoing preventative monitoring. It is also further evidence that comparing against population averages does not really reveal much about the line between health and sickness. Given the healthy population served, the large majority of members did not have follow-up care required. However, in 21% of those first visits, the Q protocol uncovered clinically significant findings that were both high risk for affecting mortality and at an early stage informing clinical decision for early intervention or additional diagnostic evaluation. The ability to focus on early intervention and prevention represents significant cost savings in healthcare and, more importantly, better individual health outcomes. To quote one Q member: “Nobody has ever told me so much about me.”

We are optimistic that longitudinal deep profiling, accompanied by powerful data integration and analysis, will ultimately help improve healthcare. Individuals and their healthcare providers will obtain a clearer picture of disease risk and evolving health status. As one Q referring physician and member recently shared, “Q does a great job leveraging advanced biomedical science and technology to assist primary care providers to provide more informed, precise, proactive care plans to their individual patients. Q brings the ‘possible’ in the future of medicine to the actual care delivery in the clinic, now.”

We share this excitement and believe that future emphasis may be increasingly focused on keeping people healthy through predicting disease risk and catching disease early to avoid adverse health outcomes. We hope that precision medicine will become an integral part of healthcare and available for all.

by Michael Snyder, Ph.D.
Stanford W. Ascherman Professor and Chair, Department of Genetics Director, Center for Genomics and Personalized Medicine