by Brad

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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