David J. Galas, Ph.D. is principal scientist for the Pacific Northwest Diabetes Research Institute (PNDRI), a Seattle-based non-profit biomedical research organization founded in 1956 that is helping to lead global research into the disease that is diabetes. Dr. Galas, one of the world’s preeminent research scientists in the diabetes field, was responsible for discovering the gene that partly regulates bone metabolism. His breakthrough research has led to the development of a new medicine that may eliminate osteoporosis as a health problem.
Galas, 69, has had a highly distinguished career that has included service as director of health and environmental research at the U.S. Department of Energy, where he directed the Human Genome Project. These days, in addition to continuing to pursue basic research into diabetes, Dr. Galas serves as chairman of the Fannie and John Hertz Foundation board of directors. He himself was a Hertz Fellow, during the period 1968-1972, and benefited from the beneficence of that foundation, whose mission is “to provide unique financial and fellowship support to the nation’s most remarkable PhD students in the physical, biological, and engineering sciences.”
Recently, Dr. Galas spoke with HCI Editor-in-Chief Mark Hagland about what’s being learned in basic science around diabetes, how that research work is dovetailing with advances in diabetes patient care, and the connections between and among advances in those two areas and parallel advances in clinical informatics, population health management, and analytics. Below are excerpts from that interview.
What has been learned in the last 40 years, in diabetes research?
That’s a really good question. Diabetes is clearly one of the most complex of the diseases that we classify as diseases; and that is partly clearly due to the number of different biological systems it engages—metabolism, energy production, and regulation of so many things that are essential to bodily function. My research is very much focused on the most fundamental things driving both diabetes and other related processes; just trying to understand some of the processes involved is a major issue.
So what’s been learned? We have a lot of information about some of the genetic determinants that can predispose people to diabetes; and we also know a lot about the environmental factors connected to diabetes. We know that obesity and diabetes are connected, though we don’t know how. And we don’t understand why diabetes is increasing in almost all populations, not only in the U.S., but also in Asia and Europe. Type 2 diabetes, but not only type 1, is increasing. And the potential complexity is frustrating. We do know a lot more than we did 20 years ago about some of the fundamental metabolic processes.
But overall, in terms of the most common types of diabetes we see in the population—I’d say we haven’t made a lot of progress on the pure science of this. We’ve learned a lot about how to treat it, how to control glucose, for example; but the fundamental mechanisms are still a mystery. But the human genome project and the changes in technology have really revolutionized the kinds of research we can do around this and other problems; and the explosion of detailed molecular information has been so profound, just in the last five years or so, that it leads one to think that a really complex disease like diabetes, is going to yield to some of our investigations in the next few years.
So it’s very exciting to be in this area right now?
Yes, it’s very exciting. Well, it’s very exciting in all biology right now, because of the revolutionary changes that have been taking place.
What should physicians understand about the pure research, and where it’s taking us right now?
Before I would try to answer that, I would say that one of the great challenges we have with this tremendous explosion of basic capabilities is, how do we impact patients and doctors more rapidly than we have in the past? If you wait until some new drug goes through clinical trials, you’re talking about 10 to 15 years before you have an impact. And there, I think, the computational informatics will have a huge impact. We’re beginning to understand more about the genetic susceptibilities. And that’s very important; it’s a lot of what people talk about when they talk about personalized or genomic medicine. And while that’s really important, that’s just one aspect of where this revolution is going.
And the most important thing to realize, for those of us trying to make a difference, is that all of the types of data that one can keep track of with respect to patients, groups of patients, in healthcare systems, in geographic areas, etc., is that keeping track of medical records and any information we have about the environment, the diet, of individuals, can do a lot to help us understand the various risks that certain populations have, whether or not it’s determined fully by genetics or only influenced a little bit by genetics. So the integration of all this data, and the mining of this into informational relationships, is very important, even before we understand the biological basis of all of this.
So physicians should understand that genetics is very important, but it’s not the answer to all the problems we’re dealing with, by a long shot. Personalized medicine is important, but just being able to bring information to the physician-patient relationship, is very important. And it’s not going to be very long before the kinds of information can be ground out by some really interesting and modern data mining and statistical programs, so that you can say, OK, if you’re this, and you have this record, and you live this way, and you’re this many pounds overweight, and you have this kind of regimen, these are your risks. Doctors will be able to share those kinds of predictive analytics with their patients.
And we’re not really certain how much is environmental and how much is genetic, in the explosion of diabetes right now?
For many, the idea of the increase in diabetes has to do with overweight, obesity, and over-nutrition of various kinds. Now, those will be important; but it can’t explain a lot of the increase in diabetes. It can explain some of it, but certainly not anywhere near all of it.
Might there be some environmental elements, as in the natural environment, at play here?
My favorite idea is that it has to do with a change in the microbial populations in humans, with shifts in the microbiome. We know that those can be causal, in mouse experiments. And we know that diabetes and changes in the microbiome can be causal in humans. We don’t know that changes in the microbiome can be causal in humans; we know it is true in mice. I have a strong suspicion that changes in the microbiome in humans can have a causal effect. And that would be great if that turned out to be true, because at least we’d have something to look very carefully at. We’re learning a lot, but we still don’t understand the balance of the microbiome and the ways in which that shifts things in humans, and how much the history of childhood through adulthood has an effect.
Do you believe that the two streams of information, from population health management, and from pure scientific research, can be complementary?
Absolutely. Just doing really good mining of data from patient records, can provide some absolutely key clues that one can use to try to identify fundamental causes.
I think it will be an interesting time in the next five years, as information will be flowing into physicians and healthcare organizations, from two different points.
Yes, and the shift from paper records to electronic records, and finally, to query-able data, will provide interesting and very powerful information for medical research. And there will be problems with privacy and those things, but things have changed so rapidly. So this is probably the best time ever to do biological research.
Is there anything you’d like to add?
I think that one of the things that we should realize is that the computational and the underlying mathematical approaches, to doing this kind of mining, and finding these clues in both the patient data as well as in research-level data—we need a lot more people to pay attention to developing new ways of doing this kind of thing. So getting computer scientists, mathematicians and other people who are trained, to work with both scientists and physicians, is a major new area that we should emphasize. The value of some of the more abstract and arcane mathematics and computer science will be really important for the future of human medicine. When you analyze all kinds of data from climate and agriculture, or whatever, those may be complex, but there isn’t anything I know that’s more complex than the human body; and that requires new approaches.
