NEJM -- Genomewide Association Studies and Human Disease
Courtesy of a company called Proventys, I came across this curve below, which I found helpful in thinking about what personalized medicine could really mean, and what are the levers available for delivering it. Of disclosure, Jeff and I know the Chief Medical Officer - Surya Singh - who is also a hospitalist at the Brigham.
As you see from this chart, genetic studies can help determine both baseline and preclinical risk. However, I think we can continue to do more in developing out clinical risk models - that tiny little last bullet under dynamic testing. As we digitize more medical records, and migrate to increasingly standardized medical vocabulary, I can imagine a not so distant future where with powerful computing and robust clinical risk modeling, a physician can better understand and act upon a patient's "preclinical progression" or predisposition profile. This week's NEJM articles suggest unfortunately, that we may be further from understanding baseline risk than we originally thought, but that doesn't mean that personalized medicine is dead in the water as the New York Times suggests.
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From Christine Lee -
ReplyDeleteI looked at your website. I'm not a blogger, so I can't post onto it. However, I did want to comment on your post in reference to the NEJM papers earlier this month.
I am skeptical of GWA studies unveiling snps that will be useful in risk prediction (unless models include a combination of snps, clinical features, and their interactions, but even then...).
Nevertheless, I find great value in GWA studies. I believe that they will direct the scientific community in researching new mechanisms of disease.
Future clinical application by screening for snps in patients could then be the creation of "tailored" medicine (ie. By knowing mechanism of disease for a person for a given snp, physicians may be able to select drugs that would work more effectively for given variants and avoiding those that don't).
We are and will soon be even more overwhelmed with the amount of data generated by these GWA studies. I encourage critical analysis of these studies and recommend reading Tom Pearson's article on how to analyze GWA studies published in JAMA 2008.
Looks like you're enjoying your work. Glad to see it.