Vistas and hazards of the foggy Omic Road
I am a PGP director and a participant, and in the latter role I received a recent email that urged me to “READ THIS!” It came from a family member and was triggered by the second segment in a recent series on personal genomics featured on National Public Radio’s Morning Edition (in a previous post Madeleine Ball highlighted the first segment featuring the PGP and George Church). The email-triggering segment featured two scientists whose genomes have been sequenced: James Watson, co-discoverer of the structure of DNA, and Mike Snyder, Director of the Center of Genomics and Personalized Medicine at Stanford University. Both Watson and Snyder presented generally supportive views of the process, but Snyder’s story provides more important lessons about the ups and downs of biomedical self discovery.
Beginning with the upside of Professor Snyder’s story, the publication featuring his genome sequence and other “omic” data provides strong evidence that at least some genomic predictions are both accurate and actionable (1). He learned he has an elevated risk of basal cell carcinoma, hypertriglyceridemia, and Type 2 diabetes (T2D). Upon learning of these risks he began to be monitored for these conditions. Consistent with the genomic prediction he did have high triglycerides, and the problem was successfully medicated.
The most interesting biomedical subtext of Snyder’s story began about a year after the prediction of elevated risk of T2D (due to variants in 3 genes). During the first year his blood glucose and proportion of glycated hemoglobin (HbA1c) were normal (HbA1c is a glycated form of hemoglobin, a protein in red blood cells that aids in oxygen transport; glycation is non-enzymatic attachment of glucose to the protein, which is a measure of persistently high blood glucose levels). Immediately following a viral infection, his blood glucose rose rapidly and in less than a month he had full-blown T2D as measured by high blood glucose (above 126 mg/dL), and about a month later as measured by HbA1c. His levels remained high for about two months after which he changed his diet and began to exercise more, and after six months his glucose and HbA1c levels returned back into the normal range. Overall, his fasting glucose level exceeded the threshold value for a clinical diabetes diagnosis for about 4 months.
But the story doesn’t end there, and this is why I received the exclamatory email. Even though Professor Snyder’s triglycerides and glucose are under control, insurance complications arose from the initial T2D diagnosis. According to NPR:
“After sequencing revealed his high risk for diabetes, his wife tried to increase his life insurance. But because of that high risk, the price shot through the roof. ‘So the bottom line is my life insurance … essentially became prohibitively expensive,’ Snyder says. Federal law bans health insurance companies and employers from penalizing people based on genetic information, but the law doesn’t apply to life insurance or long-term care insurance—leaving people like Snyder vulnerable to discrimination.”
Since there was some uncertainty about this NPR report, I asked Professor Snyder for his input and he emailed me the following clarifications:
- The genomic prediction of T2D triggered frequent glucose tests, which were ordered by his physician, so the results became part of his medical record;
- additional life insurance was sought by his wife after the tests showed high glucose and HbA1c levels;
- the rate of his existing group life-insurance policy did not increase, only the rate for additional insurance.
The NPR story is correct that GINA (the Genetic Information Nondiscrimination Act of 2008) does not protect consumers against genetic discrimination when they purchase life or long-term care insurance. However, the story appears to mischaracterize the causal chain of events in this case: Snyder’s prohibitively high life insurance rate did not result from the discovery of risk alleles in his genome; it resulted from an old-fashioned medical diagnosis of diabetes by his physician based on clinical tests that showed high levels of diabetes-specific biomarkers (2). And despite his insurance problem, Professor Snyder believes genomic self discovery is more positive than negative and that his genome sequence helped him deal with his diabetes in a timely fashion.
It is fairly obvious why Snyder and his family would be happy with the present biomedical outcome but here is something that isn’t quite as obvious: his frequent testing helped him to chart a data-guided course to recovery, but it also made detection of his transiently elevated glucose much more likely than, say, annual testing. A reasonable estimate is that it made it about twice as likely since he had elevated glucose for about four months and sub-threshold levels for the remaining 8 months of the year. So, in addition to potential benefits, there might be a downside to extremely frequent, elective testing.
Professor Snyder’s story reminds us that the builders and first travelers on the road to data-driven healthcare occasionally experience remarkable and previously unknown vistas of self determination, but they also face uncertainty and risk. Many risks can be avoided or managed, but science is often a murky business and news reports on these difficult topics can be misleading, further obscuring the way forward. Our loved ones’ anxieties are heightened by the occasional fog of confusion—triggering emails or discussions of concern, or even alarm. But now that we see the relevant facts in this case—and the red flag posted clearly over one hazard in the road—we also can see ahead more clearly as we push on toward the greater goals of this unprecedented collective experiment.
References and Footnotes
1) Personal omics profiling reveals dynamic molecular and medical phenotypes. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Chen R, Miriami E, Karczewski KJ, Hariharan M, Dewey FE, Cheng Y, Clark MJ, Im H, Habegger L, Balasubramanian S, O’Huallachain M, Dudley JT, Hillenmeyer S, Haraksingh R, Sharon D, Euskirchen G, Lacroute P, Bettinger K, Boyle AP, Kasowski M, Grubert F, Seki S, Garcia M, Whirl-Carrillo M, Gallardo M, Blasco MA, Greenberg PL, Snyder P, Klein TE, Altman RB, Butte AJ, Ashley EA, Gerstein M, Nadeau KC, Tang H, Snyder M. Cell. 2012 Mar 16;148(6):1293-307. PMID: 22424236
2) It is important for PGP participants to understand that PGP-generated data and reports are not equivalent to a clinical test and, according to the PGP consent form “are never intended to substitute in any way for professional medical advice, diagnosis or treatment. You may not use any PGP-generated report or any other PGP-supplied data or results for any medical or clinical purpose until you have confirmed the relevant sequence, data, interpretations and/or findings with a licensed healthcare professional.” Nevertheless, just because these data and reports are not clinical diagnoses doesn’t guarantee that they can’t or won’t be used to make decisions that might adversely affect you.