Biomarker screen makes case for MODY genetic testing

Anne Paxton

February 2020—Cost-effectiveness analysis of health care diagnosis and treatment, unfortunately connoting quotas and spartan budgets, may not have the best reputation among the general public. “A lot of people approach studies of cost-effectiveness with suspicion,” says Matthew S. GoodSmith, a medical student and researcher at the University of Chicago Pritzker School of Medicine. “When you say ‘cost-effectiveness,’ people think ‘death panels.’”

A new study using computer simulation models to demonstrate the cost-effectiveness of genetic testing for maturity-onset diabetes of the young (MODY) in a subpopulation of patients could help turn that impression around while making progress in treatment of this inherited disease. Surprisingly, it turns out that for MODY, not only is genetic testing of a patient cost-effective, but cascade testing of the patient’s relatives is cost-effective as well.

In the recently published study, “The Impact of Biomarker Screening and Cascade Genetic Testing on the Cost-Effectiveness of MODY Genetic Testing,” GoodSmith and colleagues at the University of Chicago report that a combined strategy of biomarker screening and genetic testing for MODY in the U.S. pediatric diabetes population is cost-saving compared with usual care, increasing average quality of life and decreasing costs by $191 per simulated patient over 30 years. The addition of cascade genetic testing boosts cost savings even further, to $735. “Widespread implementation of this strategy could improve the lives of patients with MODY while saving the health system money,” the authors conclude (GoodSmith MS, et al. Diabetes Care. 2019;​42[12]:2247–2255).

The MODY study is a strong example of the promise of personalized medicine, says coauthor Elbert S. Huang, MD, MPH, professor of medicine, director of the Center for Chronic Disease Research and Policy, and associate director of the Chicago Center for Diabetes Translation Research, University of Chicago. “It shows that routine testing for genetic disorders can be tailored based on a simple clinical screen and it can improve the health of people while also not increasing costs to the health system.”

Although only about 0.8 to 2.5 percent of the diabetes population has MODY, the extent of misdiagnosis of MODY and the expense, discomfort, and potential harm of incorrect therapies make better testing strategies important. But genetic testing for MODY has often been delayed because of insurers’ reluctance to cover it, GoodSmith notes. “At the moment, genetic testing for MODY is often denied by payers. The goal of testing the cost-effectiveness of alternative strategies for genetic testing is to identify the strategies that benefit patients and make sense financially for payers and society.”

Payers may have had good reason to be wary of MODY genetic testing. As researchers at the same center found in the first cost-effectiveness analysis of MODY genetic testing conducted in the U.S., which compared Sanger sequencing for mutations in HNF1A, HNF4A, and GCK to no genetic testing, it would be “prohibitively expensive” to implement such genetic testing across the entire study population of (in this case) young adults with type two diabetes. The incremental cost-effectiveness ratio—that is, dollars per quality-adjusted life year—was $205,000, the authors found (Naylor RN, et al. Diabetes Care. 2014;37[1]:202–209). In other words, “If you just start genetic testing in the 95 percent of people who have type two diabetes, without any kind of clinical screening or attempt to narrow the testing population to people likely to have the genetic disorder, it turns out not to be cost-effective,” Dr. Huang says.

In this new study, the coauthors believed that appropriately winnowing down the screening population might justify the cost of genetic testing. They used the fact that the vast majority of MODY patients are negative for islet autoantibodies and positive for C-peptide to select biomarker screening of GAD65 and IA-2 autoantibodies and plasma fasting C-peptide. These tests were applied to the entire testing arm cohort of the study to identify patients with a high probability of having MODY. The biomarker screening restricted genetic testing to 23.9 percent of the total U.S. pediatric population with diabetes. These patients were modeled to undergo simultaneous genetic testing for heterozygous mutations in GCK, HNF1A, and HNF4A at a set cost of $3,723.96 per individual, including an additional outpatient visit. About five percent of the patients who received the genetic tests were MODY positive.

Computer simulations are a powerful means of drawing actionable conclusions about diagnosis and treatment. “In the case of chronic conditions like diabetes, coronary artery disease, or hypertension,” Dr. Huang says, “we almost always rely on forecasting models or simulation models of complications. You’re making changes in treatment, and because of the long-term nature of those diseases, diabetes in particular—between diagnosis and the emergence of complications is a decade—typically to see the effects of therapy requires a simulation model.” Otherwise, he says, “For many of us, the trials would outlive us. We wouldn’t have any answers to make decisions today.”

Cost-effectiveness analysis in diabetes goes back to the late 1990s, when some of the first questions around the economic value of diabetes treatment emerged, Dr. Huang says. More recently, studies employing simulation models of distinct forms of diabetes to forecast the clinical and economic consequences of testing strategies have explored the cost-effectiveness of new technologies used particularly in type one diabetes, such as continuous glucose monitoring and insulin pumps. “In comparison to studies of new drugs and technologies, health economic research on genetic diagnosis of diabetes is still a relatively new field,” Dr. Huang says. The University of Chicago’s Center for Chronic Disease Research and Policy is one of the only groups that has repeatedly revisited the idea of genetic testing.

In Europe, cost-effectiveness analyses are more common. “That’s one of the ways that the U.K. government decides what it pays for,” GoodSmith says. “There, cost-effectiveness is much more entrenched in their decision-making process.”

The process does require assumptions to be made about future costs. “There is always the risk—and this has definitely happened—that costs of therapies will change in ways we didn’t expect,” GoodSmith says. “But if you need to make a decision today, we use the best information we have right now with what we know about the natural history of diseases. These models provide evidence to help answer a question now.”

“The traditional cutoff for cost-effectiveness and the one used in most studies is $50,000 per quality-adjusted life year,” he adds. “That’s kind of a convention that’s developed. Some studies say it might be closer to $70,000 or $80,000 per quality-adjusted life year, in terms of what payers would be willing to pay for.”

A testing strategy’s cost-effectiveness can be improved, however, by narrowing the population to be tested. The first U.S. cost-effectiveness study’s finding of an incremental cost-effectiveness ratio of $205,000 was far above the threshold, GoodSmith says. “But in their sensitivity analysis in that paper, they showed that if the MODY prevalence was increased among the testing population to six percent, that would make the testing intervention cost-effective.” A U.K. study from 2016 (Shepherd M, et al. Diabetes Care. 2016;39[11]:1879–1888) did accomplish that, using biomarker screening in six pediatric clinics to help narrow the genetic testing pool.

For the latest research, GoodSmith says, “we decided to conduct the study in a pediatric population where the prevalence of type one diabetes is higher. The average cost for a type one diabetes patient is generally higher due to increased insulin requirements and earlier development of many complications. With this in mind, we thought we could perhaps find a cost-effective strategy in genetic testing.”

MODY is an inherited disorder, a monogenic diabetes, causing symptoms that develop gradually. But MODY can be confused with type one or type two diabetes. That was one reason the researchers chose to focus on MODY, GoodSmith says. “It’s frequently misdiagnosed, and there are easy treatment changes you can make for the patient to quickly change their outcomes.”