Biomarker screen makes case for MODY genetic testing

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For HNF1A and HNF4A MODY patients, “switching to sulfonylureas is known to improve glucose control and reduce the number of adverse events,” he explains. “This medication works downstream of the genetic defect to allow for increased insulin release, so it improves HbA1c levels and general diabetes management relative to usual care. For GCK MODY, it’s known that no medication is required, outside of pregnancy. However, many doctors do not routinely think about GCK MODY in their clinical practice, and as a result, a lot of patients are prescribed insulin just because they were repeatedly tested and continued to be hyperglycemic. This unnecessary insulin use leads to negative effects.” Insulin is expensive relative to sulfonylureas, which is one of the older class of therapy, so removing it when it’s not necessary can produce cost savings, he adds. “These are things that make MODY a good candidate for this kind of study.”

The choice of a 30-year time frame was based in part on the pediatric population (age 10 to 20) under study. “It is a concrete period of time that people can grasp, and it adds immediacy to the study versus a lifetime model,” GoodSmith says. “And you can make more of an argument that this is something that will save costs to the health system sooner if you do it on a more contracted timescale than over a lifetime.” However, the researchers also ran lifetime numbers (included in a supplemental table) which reaffirmed the cost-effectiveness of the testing strategy. “It increased cost savings further and increased quality of life further.”

This study is a rare application of precision medicine, GoodSmith says. “In precision medicine, an individual’s genetics, environment, and lifestyle are explored to help develop new treatment strategies. This includes applying a specific genetic testing strategy to individual patients to improve their health. Normally that’s an expensive proposition because you’re applying expensive tests to a small number of people, which is hard to do on a populationwide scale, unless you do something like we did—an innovative, focused testing strategy that identifies the people most likely to benefit.”

He didn’t expect to see the impact of even a relatively conservative use of cascade genetic testing—testing of the patients’ relatives to further narrow the screening population. The MODY study shows that cost savings simply with biomarker screening and genetic testing can be achieved. But with cascade genetic testing, the cost savings increase considerably. “With the biomarker screening and genetic testing, the average cost savings is $191 per patient. That increases to $735 when you add the cascade testing. That’s quite a cost difference, and you also get a change in the quality-of-life benefits. So even though it is difficult to sample the relative and find relatives willing to undergo genetic testing, any one person you find through cascade testing increases cost-effectiveness drastically, because the chance of that one person having MODY is so high,” GoodSmith says.

The potential power of cascade testing has been relatively untapped, he believes. “We have never seen a cost-effectiveness analysis for genetic testing in MODY that included cascade testing, which we thought was surprising because its potential to increase cost-effectiveness is so great. But as you can imagine, at least in the current environment, one of the barriers to cascade testing is this: If insurance providers don’t approve the test to begin with for the proband, how would you expect them to approve the test for their relative?” The researchers still hope, however, that such expanded genetic testing will get a better hold on MODY, GoodSmith says.

The high cost of commercial genetic tests also continues to be a barrier. “We know from our prior work that 73 percent of patients found to have MODY were diagnosed as part of a research study rather than by commercial-based genetic testing. So that kind of shows the barriers to clinical implementation of genetic testing for MODY in the U.S. The findings from our paper provide more evidence that this barrier should be removed,” GoodSmith says.

“When you start one of these studies,” Dr. Huang says, “you don’t really know what side of the ledger some of these results will be on, whether they will be cost-effective or cost-saving, because few people have the genetic condition and you’re spending a lot of money to screen a pretty large population. So it was a bit surprising to find that the therapy saved money.” Despite introducing the cost of genetic testing, “you end up with kind of a neutral economic picture.”

The takeaway from this study and others, Dr. Huang says, is that personalized medicine is a better way of providing care. “What you’re doing is shifting resources around in the population, and it’s probably a better way of allocating resources. But it doesn’t produce dramatic cost savings that would solve the major health care problem of rising health care costs.”

Predicting testing costs, Dr. Huang says, is easier than predicting treatment costs. “Genetic testing costs have been steadily declining, while treatment costs are going in the opposite direction. So if we can generate a lot of value from diagnostic testing, that’s a really smart way to go because it’s something that’s declining in cost.”

Interestingly, the MODY study represents a switch from original cost-effectiveness analyses conducted two decades ago because it addresses quality of life, Dr. Huang points out. “Those earlier studies ignored quality-of-life issues around the daily experience of taking medications and how it affects people. We have done two large-scale studies of patient quality of life. And I think many clinicians would not be surprised at this, but the route of administration of a drug matters a lot to patients. The desire to avoid an injectable drug is powerful, and we’ve quantified what that means to patients. If you can allocate therapies to match what the patient needs and switch from injectables like insulin to oral agents, it is an additional benefit to patients’ quality of life.”

Insurance coverage remains a significant factor, Dr. Huang agrees. “Genetic testing is simply not systematically covered by insurance. I also think that the insurance plans don’t have a clear understanding that these different forms of diabetes even exist. So patients and their doctors who are ordering these tests face many challenges in explaining what the test is for.”

“Figuring out insurance coverage in the era of precision medicine is a bigger, broader problem,” he continues. “This particular paper is talking about young adults, and most of the insurance plans that would be affected by this sort of cost-effectiveness analysis would probably be employer-sponsored insurance, rather than Medicare. In my experience, insurance plans have a lot of difficulty keeping up with changes in medicine, and explaining to somebody on the phone who doesn’t know these genes or doesn’t know what the consequences of testing are is going to be difficult.” Cascade testing will be even more difficult to justify or explain, he adds.

To flesh out the cost-effectiveness questions, it would also be important to address the societal component. “We likely don’t capture all the societal costs. There’s a lot we don’t know about the societal spillover effects of how having genetic information about people might affect their employment or their education attainment, for example.” Whether a person is on insulin can affect whether he or she can have a job such as bus driver or airline pilot, jobs that historically have been barred to people with type one diabetes, Dr. Huang says. “But let’s say you have a genetic form of diabetes that can be managed successfully with oral agents. That could change your outlook completely, above and beyond the health effects of the better regulation of the disease.”

The most exciting implication of this cost-effectiveness study, GoodSmith says, is that it shows the benefits of the focused use of a personalized medicine tool. The implications of the study could go well beyond diabetes, in his view. “If you apply expensive tests in innovative ways, it can lead to cost savings. I imagine there are other diseases with other available biomarkers where this kind of analysis could be applied.”

Anne Paxton is a writer and attorney in Seattle.