|Gartner Hype Cycle (taken from TechHui.com)|
As a researcher myself, I also am not insensitive to this sensation. Even though (or because) I have contributed to some large genome sequencing projects (in chicken and cow) and have worked closely with the 1000Genomes team while at the Wellcome Trust Sanger Institute, I feel a growing frustration with genetics and genomics. It's all very interesting to build genomes, find associations of genes with disease, etc, but what can I as an individual do with this information? Yes, our research helps us understand the core of biology, and we do help (parents of) people with rare diseases diagnose those diseases or find the gene that causes the particular congenital condition. But how can this information help the vast majority of the population in their day-to-day life?
My frustration with what genetics can tell me
Under the umbrella of GenomesUnzipped, I had about half a million SNPs genotyped by 23andme (for data: see here). Based on that data and scientific literature, for example, they state that I have a higher chance of getting venous thromboembolism (VTE). Almost 18% of European descent with my genotype will develop VTE before they're 80, compared to 12.3% of the general European male population. The heritability of VTE, by the way, is about 55%. So what does this tell me? I should eat healthy, not smoke, and do more exercise. Still: my genotype can not tell me actually when I will get this, not even just before the event.
The issue is that our genomes are just the blueprints for who we are; they're not us. For that, we need to look at other omes: our transcriptomes, but most of all our proteomes and metabolomes, and our environment. Whatever our genotype is, it has no effect whatsoever unless through how it affects the constitution of the enzymes and other molecules in our cells: proteins could for example not work, or be present in non-optimal amounts.
When you go to a doctor in the hope to get rid of frequent headaches, what does the doctor base his diagnosis on? Those symptoms? More often than not: your memory. "I think I had those headaches last Friday and Monday, and if I recall correctly the one on Monday was a bit worse than the other one". Doctor: "is it always after eating something specific?". You: "I can't remember". Wouldn't it be great if in such cases your GP could diagnose your disease based on actual data?
But then comes the next step: taking drugs. The amount of a particular drug (and actually the drug to be taken itself) is based on population-wide averages of efficiency and occurrence of side effects of that drug. It's not based on how you react to that particular drug.
Enter personalized medicine, and more specifically: P4Medicine (predictive, preventive, personalized and participatory). I'd like to look at this from a little broader perspective, as quantified health: data-driven health.
Since quite a while I've been following the Quantified Self movement (see e.g. quantifiedself.com). The aim of this movement is to improve self knowledge through self tracking: collecting data about yourself to identify trends (e.g. weight loss) and correlations (e.g. linking migraine episodes to triggers). This knowledge can then be used to change someone's behaviour, to predict the onset of disease or episodes thereof, or to prevent it altogether. What if you're smartphone could give you a message on Friday afternoon to get into the dark and drink more fluids otherwise you'd get a migraine episode on that particular Saturday? The big thing here is that any decision would be personalized, and appropriate for that individual, rather than for the majority of the population that that individual belongs to (e.g. male caucasians).
Conceptually, the type of things that we can track can be tracked either externally (i.e. using a fitbit, tracking apps on a smartphone, continuous ECG monitoring) or internally (i.e. using biosensors to follow molecular markers). Working in the omics field, I'm obviously very interested in the latter: what molecules can we easily track in the body that can predict disease? Even as a boy, I fantasized that we could track anything happening in our bodies and use that to stay healthy. D-dimers are a nice example here. These are protein fragments that are produced when a blood clot degrades. Detecting d-dimers has a high sensitivity and negative predictive value for thrombosis (remember that I have a higher genetic predisposition for VTE), but unfortunately a low specificity. This means that if you have a blood clot forming (that could get dislodged) you definitely will show d-dimers; but having d-dimers in your blood does not necessarily mean that you're forming clots. Current practice, where a blood sample is taken when the doctor orders one because there is suspicion of blood clotting, however, has only little value. With age, one starts seeing this molecule in the individual's blood. But if we would monitor this molecule longitudinally (hypothetically: every day), this background/noise would become irrelevant. Hence: quantifying self at the molecular level.
If you're interesed, you should definitely check out the research on the Integrated Personal Omics Profile (iPOP, or "Snyderome") by Chen et al (doi: 10.1016/j.cell.2012.02.009), where Michael Snyder's transcriptome, proteome and metabolome were sampled 20 times during a 14-month period. Another astonishing story is that of Eric Alm's year-long gut microbiome tracking (check the video!). Some interesting conclusions based on his (and one of his student's) data! For his student, traveling only changed his gut microbiome transiently. Getting salmonella, however, resulted in a permanent change in Alm's microbiome constitution. Finally, check out these articles on Larry Smarr: "Is health tracking the next big thing?", and "The Measured Man".
I can also definitely recommend the book Experimental Man by David Ewing Duncan. It shows you what's possible, and most of all what's not (yet) possible.
I've also created a "Quantified Health" paper.li a while ago, which helps me pick up interesting news.
To fully implement quantified health, there are still several challenges. As these are basically n-is-one studies, the statistics and data mining will be different. What's even more important is how do we return results and trends to the end user? It will be very important to display the data/results within context rather than just reporting p-values and "above thresholds". On the molecular side, we also need some good use cases. These should
- have clear "events" that we can track (something happened or didn't happen),
- have useful (combinations of) biomarkers (so that we don't need to do a full-blown discovery phase),
- be in a population that is happy to provide the samples (e.g. daily blood sample),
- have assays to follow those biomarkers
From what I'm finding at the moment, it's the first two conditions that are the hardest to meet. Assays can often be developed using antibodies and/or aptamers (really cool technology, BTW), and for a proof-of-principle it should be possible to start with diabetes patients who have to sample their blood periodically anyway. The use of saliva or urine samples would be nice, but unfortunately most biomarkers will be in the blood...
We're at the brink of truly personalizing medicine and health.