It’s often argued that many of medicine’s greatest discoveries have come about as the result of self-experimentation (not to mention many more by accident!). A controversial practice, the ethical issues surrounding self-experimentation are so unpalatable that most scientists devise strict experimental safeguards to ensure that they act only as impartial observers. There are however, some rare occasions when volunteers are far easier to recruit from inside the lab than from outside of it. One such example, is the fascinating case of Dr Michael Snyder, who published his personal integromein 2012 – a data profile that combined comprehensive information about health drawn from a range of so-called “omics” technologies.
As the principle subject in the study, Snyder had his genome sequenced, along with sampling his RNA, protein, metabolic profile and antibodies 20 times over a 14-month period. The team dubbed the end result an “integrative personal omics profile,” or iPOP; merging biological profiles together to create a comprehensive picture of Snyder’s health. The experiment took a dramatic turn when Snyder’s colleagues discovered he had abnormally elevated blood glucose levels seemingly caused by a recent viral infection. Despite being previously unaware of any family history of diabetes, the results of the team’s analysis revealed that Snyder was genetically predisposed to type 2 diabetes. Following infection, Snyder was diagnosed with diabetes whilst the experiment was still running. Unusually for a trial study, Snyder reacted to the interim results by altering his diet, managing to slow his diabetes progression and treating his illness with lifestyle interventions rather than medications. Whilst the study drew as much criticism as a praise, it could be considered as a proof-of-principle study for a new approach towards healthcare – precision medicine.
Precision medicine is not an entirely new concept, also sometimes referred to as personalized medicine, it proposes patient-focused treatment methods that take into account a person’s individual differences in lifestyle and genetics. Until recently, most treatments of disease have taken a “shotgun” approach. Despite being a relatively straightforward concept, clinicians have found precision medicine difficult to implement in practice owing to numerous barriers, such as the poor availability of effective technologies in primary care blood testing and an absence of accurate population statistics.
For a number of conditions, clinicians have had to select from a meager handful of treatment options available, often leaving them with little choice but to prescribe the same drug to a wide range of (potentially very different) patients. The results are invariably mixed, with some patients showing positive response to treatment, whilst others presenting little change in their condition. Precision medicine offers a more efficient approach - an opportunity to utilize and integrate new technological tools in order to predict diseases before they develop.
The advent of omics, new research fields which aim to map out complex biochemical interactions, has provided researchers with new ways of monitoring health and disease. Coined by Hans Winkler, genomics (combining “gene” and “chromosome”) became the first area of omics interest following the proposal of the Human Genome Project (HGP). The race to map all 3.3 billion base pairs (bp) of human DNA, resulted in the development of a range of techniques that have been further refined today. High-throughput DNA sequencing, bioinformatics tools and sequence assembly techniques are all now commonplace in genomics research, whilst next generation sequencing methods offering even greater advances. HGP also led to the establishment of a number of research areas within genomics: functional genomics (which investigates how genes are transcribed and translated into proteins) and structural genomics (mapping out the structure of every protein produced by a genome).
mRNA strands are transcribed chemical copies of DNA used as instructional messages in protein assembly. It’s only natural that after decoding human DNA, scientists would turn their attention to RNA and its role in disease. The RNA produced in cells varies across cell types and cycle stages, with cells displaying significant differences when in a diseased state. This makes studying RNA transcripts, the transcriptome, particularly useful when trying to understand disease progression. Whilst transcriptomic studies have generated promising results, it appears that many RNA transcripts produced don’t end up being translated into proteins. This has inevitably led to researchers focusing primarily on proteins themselves, creating a new field of research called proteomics. Studies can go beyond simply identifying new proteins, measuring levels of protein abundance, chemical modifications and even specific interactions between proteins (interactomics).
Whilst the initial focus on omics revolved around genomics, an increasing number of researchers are recognizing the need to understand the effects of lifestyle factors on health. The burgeoning field of epigenomics for example, takes into account lifestyle induced changes that may contribute towards disease. Genomics though, mostly provides us with a “static” picture of inherited health information; once sequenced, there is little new to discover from repeating the analysis. Investigating lipids (lipidomics), carbohydrates (glycomics) and other metabolites (molecules produced by biochemical reactions) however, offer us new insights into the dynamic changes that occur in the body in response to lifestyle factors.
Lifestyle effects are reflected by changes in the metabolites produced by biochemical reactions (a person’s metabolism), the field studying these changes is called metabolomics. By measuring concentrations of different metabolites, we can observe the effects of lifestyle factors such as diet and exercise on the health state of individuals. Often these measurements are combined into metabolic profiles; snapshots of the different metabolites present at any given time. Blood is commonly used as a sample for metabolomics studies, as many of the products of cellular reactions are transported in the bloodstream, providing an overview of systemic metabolic activity. Technologies such as Nuclear Magnetic Resonance spectroscopy and Mass Spectroscopy are commonly used to generate metabolic profiles.
Metabolomics can be used to diagnose and predict the risk of developing certain illnesses, with chronic disease prediction being a crucial area where repeating metabolic profile analyses could provide more accurate prediction. Metabolic profile changes can be identified and used to diagnose the early stages of disease progression as chronic diseases often create distinct changes in a person’s metabolism. These changes are similar to how lifestyle factors can influence a person’s metabolic profile, except that the differences are a feature of the disease itself and are typically repeated in other sufferers. The metabolites that indicate disease risk (also called biomarkers) can be monitored through regular metabolic profiling (e.g. routine blood tests), allowing clinicians to oversee a timeline of each patient’s health and detect any early signs of disease.
It’s extremely helpful to know if you’re in the very early stages of developing a chronic disease, for example in the case of cardiovascular diseases (CVD). If a person discovers that they are not only at risk of developing CVD but also in the early stages of its progression, they can make a decision with their physician on the best strategy to take to stop their condition from worsening. This could include lifestyle changes, such as switching diet or incorporating more exercise into their weekly routine. For the majority of patients, the opportunity to play a role in maintaining their health is welcomed. Metabolomics offer patients and clinicians the ability to evaluate the effectiveness of treatments and intervention strategies. If an individual with an elevated risk for heart disease changes their diet, a broad analysis of metabolic biomarkers makes it possible to track and assess whether this decision results in genuine health benefits.
Receiving responsive and accurate feedback from metabolomics data is revolutionary, but it’s also just the beginning. The future of medicine lies in combing genetic information with metabolomics to provide us with a powerful tool which we can use to holistically assess health and disease. Combining multiple omics technologies can provide researchers and clinicians with the ability to predict disease and other systemic changes to an unprecedented level of accuracy. If the case of Dr Snyder and his iPOP tells us anything, it’s that the concept of precision medicine is already here today and can be integrated into healthcare.
Some of the applications of combined omics data have frankly been out of this world! NASA’s ongoing “Twins Study” has utilized a range of omics methods to measure the DNA, RNA, proteins, metabolites and even microbial DNA from two genetically identical astronauts. One of the most ambitious observational studies ever conducted, the aim of the project is to establish the full effects of space travel on human physiology by comparing Scott Kelly (who spent 340 days on the International Space Station) with his (control study) brother, Mark Kelly, who remained on Earth. Whilst the full results will be published in 2018, early findings suggest that gene expression “skyrockets” as soon as the human body goes into space.
Ultimately, omics technologies provide us with incredible possibilities for the future. By moving away from the “one-size-fits-all” approach, towards a new era of precision medicine that provides individually tailored and feedback-responsive treatments, we can reduce healthcare costs and dramatically improve global health.
Some have argued that Omics technologies may hold the key to our future, providing health data that fuels accurate and early prediction of chronic diseases. Nightingale’s metabolomics platform is an example of an omics technology that’s already available today. Affordable, fast and easy to integrate into existing healthcare systems, Nightingale shows that precision medicine isn’t a far-off future concept but is here now and is already starting to transform how we predict chronic diseases.
Extra reading: Ready to dive deeper in omics? Discover more about metabolomics with our in-depth guide.
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