by Brad
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| Patterns in Nature by Melina Meza |
A couple months ago I attended a conference in Seattle sponsored by US HUPO (Human Proteome Organization) called “From Genes to Function.” Afterward, while I was mulling over the talks, I was reminded of some earlier posts I’ve written where I discussed studies that were “under powered,” that is, they didn’t have enough subjects and controls to come to any firm, or statistically validated, conclusions. These included the papers that looked at reduced IL-6 levels (an inflammatory cytokine) or the effect on telomere length (the ends of DNA sequence that correlate with age) in practioners of yoga (see Science, Aging and Yoga). In another post, I discussed the issue of the “N of one (N = 1),” a reference to anecdotal cases or one’s own experience. I have often felt that we should not be overly tough on the lack of data on some aspects of our behavior before embarking on changes, as the bar for scientific proof is often simply too high—and the likelihood of someone carrying out the rigorous study needed to obtain a higher level of confidence is not likely to happen any time soon.
So, it was interesting to be reminded of two important trends taking place in the health sciences these days: the “N of one” and the “N of all.” Both of these concepts came up in the talk by Leroy Hood, CEO of the Institute of Systems Biology, a major leader in the field of integrated “omics” technologies in medicine, i.e., genomics, proteomics, etc. His point was that with new technologies becoming cheaper, we could envision a situation in the very near future of routine genomic sequencing and other data intensive methods to follow one’s own health (the N of one) as well as this data being integrated with thousands of patients for a virtual cloud of data (the N of all). The N of one concept was reiterated in a fascinating talk from Michael Snyder of Stanford in which he discussed a project where such efforts are being carried out longitudinally to monitor precise changes in one’s microbiome and other parameters. This idea stemmed in part from his own self-diagnosis of type 2 diabetes from information he was collecting on himself (talk about personalized medicine!). It is now expanding into larger, but still relatively small cohort of 50 pre-diabetics, to identify genes and other factors that contribute to type 2 diabetes. This project is already underway and will likely be expanded to include several thousand subjects if successful. Such technology innovations would allow you to have vast information about your own DNA, blood and microbiome status from which to make more informed health decisions.
Obviously the N of all concept is related to the N of one, but refers to an even larger amount of medical data that is now becoming available to researchers, as these data from thousands, if not millions, of people become available. Access to such a trove of information would allow scientists to identify genes for rare diseases, as well as to better pinpoint behaviors that are linked to diabetes or Alzheimer’s, for example. These data would also allow individuals to make better-informed decisions about what drugs they should or should not be taking, as well as what behavioral modifications would be best for their personalized health. As this idea really gets traction, maybe we will be able to better assess how the practice of yoga affects our health (and contributes to healthy aging), both from a personalized view (N of 1) as well as that gleaned from thousands of people (N of all). Until then, using your subjective experience along with input from your yoga teachers and colleagues to evaluate which yoga practices are beneficial to you is a perfectly valid way to make decisions about what and how to practice.
One last thought: This scenario may seem like a dystopian future to many of you, as there are clear issues of privacy and how much information we really want to know about ourselves or others to know about us. Not surprisingly, these issues are being hotly debated among scientists, medical ethicists, and policy makers—and we should be participating in this discussion as well. There are already some pretty serious guidelines in place to insure privacy and remove personal identifiers from much of this data, but things are changing rapidly. The explosion of medical and behavioral information about ourselves and others will almost certainly happen as the technology become cheaper and more widely available. It will be very interesting to see how this plays out—and whether the rewards of personalized medicine will lead us to better ways to manage our health.
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