麻豆传媒

Expert Critique

FROM THE ASCO Reading Room

Darius A. Schneider MD, PhD Diabetes and Endocrine Associates Scripps Memorial Campus

In type 2 diabetes an insufficient beta-cell response to the progressive insulin resistance that typically accompanies advancing age, inactivity, and weight gain leads to cardiovascular disease, renal failure, and blindness.

At a genomic level, patients with type 2 diabetes are highly diverse, accounting for marked interindividual differences in predisposition, course, and response to specific treatments: a defect in the gene SLC3OA8 alters the binding of zinc to insulin; a defect in genes regulating the Golgi apparatus leads to impaired packing and transport of insulin to the cell membrane; other gene variations interfere with the release of insulin from the beta cells. Some people with diabetes never get complications, despite less-than-ideal glycemic control. As long as the mechanisms underlying these differences remain obscure, medical decisions are based on population averages: Metformin will have no effect in 20% of patients of European ancestry taking it, because of a variation in the organic cation transporter 1 (OCT1), yet, it is the most prescribed drug for type 2 diabetes worldwide.

Genetic information needs only be determined once per individual, and its increasing cost-effectiveness allows us to define type 2 diabetes subtypes much better than ever before according to their susceptibility to complications, or their predicted response to medication. Combining genetic mapping, transcriptomics, proteomics, and metabolomics with clinical data to compute clinical risk scores will allow us, for the first time, to accurately match the right patient with the right medication at the right dose. Personalized care is precision medicine at its best, and key to tackle chronic disease in an efficient and socioeconomically affordable way.

Full Critique

Is there more than one kind of type 2 diabetes?

That's precisely the question precision medicine seeks to answer.

"Type 2 diabetes is a grab-all term," said Jose Florez, MD, PhD, chief of the diabetes unit at Massachusetts General Hospital in Boston and investigator at the Center for Genomic Medicine.

"We see heterogeneity in our practice; we know people with type 2 diabetes have different disease progression, complications, and responses to therapy," he observed.

While specific patients present diagnostic challenges, diabetes treatment is based on population averages. "Everybody with type 2 diabetes gets metformin upfront unless they have renal failure or liver failure, but some people don't respond well or have side effects," he noted. "It's not one-size-fits-all."

The diabetes field needs to change the way the cancer field has, he said.

"We need a much more tailored approach to how we treat people with diabetes, depending on the type of physiology they exhibit," he stated. "The unmet need for this is clear."

Identifying Subtypes

Precision medicine looks at variances in individuals to determine clinical and molecular signatures, then stratifies patients into disease subtypes. An important goal of the U.S. is to improve how well clinicians can predict how individual patients will respond to treatment.

The first step is deciding how to classify patients with type 2 diabetes, Florez said. The next is to determine whether a subtype of patients responds to certain classes of diabetes drugs over others.

Florez and colleagues offered one approach to stratifying patients at the meeting. They analyzed genome-wide association studies (GWAS) and identified four clusters of common type 2 diabetes genetic variants that represented distinct biological pathways that led to the disease: an obesity cluster, an insulin resistant cluster, a beta-cell cluster, and a lipid cluster.

Leif Groop, MD, PhD, and colleagues at the Lund University Diabetes Center in Sweden presented a different at recent meetings. His team has studied thousands of people with diabetes, looking for markers to explain the disease course in individual patients by analyzing DNA, tracking prescriptions, monitoring HbA1c levels, and evaluating other variables.

They identified five clusters of diabetes patients, each with significantly different characteristics and risk of diabetic complications. One cluster had an increased risk of diabetic kidney disease, for example. Another had the highest risk of retinopathy.

Mark McCarthy, MD, professor of diabetic medicine at the University of Oxford, U.K., proposed a more dynamic picture of diabetes patients. Because genetic and non-genetic factors that affect type 2 diabetes risks are common, shared, and overlapping, he created what he calls the model.

McCarthy's analogy is to a painter who mixes a series of primary colors to achieve an unlimited spectrum of saturation and hues. In his palette model, base colors are the traits and processes that contribute directly to the development of type 2 diabetes.

"Instead of focusing on trying to segment individuals with type 2 diabetes into rigid categories like type 2A, type 2B, etc., the palette view is that risk of type 2 diabetes -- and to some extent, the way in which that diabetes presents and develops -- is best viewed as the summation of individual variation for relevant intermediate traits such as obesity, beta-cell function, and insulin resistance," McCarthy explained.

By characterizing where an individual sits along these axes through a combination of genetics, lifestyle factors, physical examinations, and biomarkers, clinicians could better quantify risk, predict disease progression, and offer optimal treatment, he noted.

The Future of Diabetes

Precision medicine can produce better patient outcomes, which is why the work to differentiate type 2 diabetes patients is particularly exciting, noted Paul Franks, PhD, MPhil, MSc, of the Lund University Diabetes Center and a professor at Harvard School of Public Health in Boston.

"Current diagnostic standards focus on glycemia, which is the outcome of complex and heterogeneous processes," he said. "But despite this common denominator, these underlying processes may require quite different therapeutic strategies."

With precision medicine, clinicians may be able to identify treatments that are best for each patient with less trial-and-error, he added.

And while precision medicine's focus on individual treatment might alarm some public health professionals, Donna Arnett, PhD, MSPH, of the University of Kentucky in Lexington, thinks it actually will .

"We know that social and behavioral risk factors determine the majority of risk for diabetes," she said. "Social determinants are potent predictors of diet quality, dietary intake, and physical activity. These are strong predictors of obesity, and subsequently, diabetes."

"But when we find the common and rare genetic variants that predispose to diabetes, we could screen for these early and target individuals at greater risk with more intensive lifestyle management," she noted.

"It is also possible that with newer 'omic' approaches -- transcriptomics, proteomics, and others -- we could identify how genetic variants impact risk and design novel treatment targets."