Editorial [Hot Topic:Translating Clinical Evidence into the Practice of Diabetes Care (Guest Editor: Richard W. Grant Contents)]

Hand-in-hand with obesity, the type 2 diabetes epidemic has become a major health problem in both developed and developing countries, with the estimated number of cases increasing from 170 million to 366 million worldwide by 2030 [1,2]. In the US, diabetes is the 6th leading cause of death and in some race/ethnic groups the predicted chance of developing diabetes among children born today is 40% [3, 4]. Proportionate with its impact on society, diabetes has also been the focus of years of both basic and clinical research. In particular, some of the most groundbreaking and important randomized controlled trials have been conducted among patients with diabetes. From the venerable UKPDS and DCCT studies to a plethora of cardiology trials, the evidence base for managing diabetes is considerable [5-10]. And the drug development process continues to yield new therapies from novel drug classes designed to treat the underlying pathophysiology of diabetes and associated metabolic derangements [11,12]. In the United States, an estimated 5.6% of total health expenditures are spent on biomedical research, more than any other country [13]. However, less than 0.1% of this effort is devoted to health services research. Measured another way, less than 1 cent is spent on evaluating health care delivery for every dollar spent on health care. Given this relative lack of funding for research specifically aimed at translating and implementing clinical advances into more effective clinical care, it should come as no surprise that most people with diabetes remain poorly controlled. In the U.S., fewer than 40% of patients with diabetes have HbA1c levels below goal and fewer than 5% meet all evidence-based goals of care [14-16]. Although future and ongoing scientific research developments hold tremendous promise for patients with diabetes, the fact remains that more effective use of existing therapies will have a much greater impact on diabetes control [17]. For example, as described by Woolf and Johnson in their seminal analysis “The Break-Even Point: When Medical Advances are less Important than Improving the Fidelity with which they are Delivered”, the next generation of cholesterol-lowering drugs would have to be three times more potent than today's statins to deliver the same population health benefits that would occur if everyone who currently needed a statin was prescribed one and took it regularly [18]. The barrier between clinical evidence and clinical practice has been described as a “quality chasm” by the Institute of Medicine [19]. Overcoming this barrier between evidence and practice is the goal of translation research. In this special issue of Current Diabetes Reviews, leading translational researchers review the current evidence for changing current diabetes care. This issue begins with a review of the Chronic Care Model as a conceptual framework for understanding the role of clinical care systems, clinicians, and patients in the process of diabetes management. The next four papers review specific elements within this framework: 1) Interventions to support patient self-management 2) Interventions using Health Information Technology, 3) The role of patient/provider communities linked via electronic personal health records, and 4) The transition between in-patient and outpatient care. The remainder of the issue focuses on the unique barriers faced by specific patient groups. Professor Arlene Brown addresses the role of race/ethnic disparities in diabetes care with an emphasis on patients, clinicians, health systems, and communities. Additional articles review the role of depression in diabetes, the link between neuroendocrine activity and diabetes, and how to apply the current evidence base to elderly patients with diabetes. This last topic introduces the key concept that translating evidence into practice requires a sophisticated understanding of the risks and benefits of therapy and discourages the notion of “one-size-fits-all” algorithmic medicine for this complex disease.