MD Conference Express ADA 2011 - (Page 7)

Metabolic diseases often have the unified underlying pathophysiological process of adiposopathy or “sick fat.” This may help explain why the distributions of BMI for diabetes mellitus, hypertension, and dyslipidemia are generally similar. The obesity epidemic contributes to the increasing prevalence of high blood sugar, high blood pressure, and dyslipidemia [Bays HE. Am J Med 2009]. SHIELD respondents at high-risk or and those with T2DM (>46%) received recommendations to change their lifestyle habits (increase exercise and change eating habits) compared with 29% of low-risk respondents (p<0.0001). Although T2DM and high-risk respondents reported attitudes and knowledge that were conducive to good health, the majority did not translate these positive traits into healthy behavior with respect to diet, exercise, and weight loss [Green AJ et al. Int J Clin Pract 2007]. Clinical and Health Care Policy Implications James R. Gavin, III, MD, PhD, Emory University School of Medicine, Atlanta, Georgia, USA, discussed the clinical and health care policy implications for management of diabetes and individuals who are at risk, based on findings from the SHIELD study. Largely because of the pathogenic potential of excess adipose tissue, an increase in body fat is generally associated with heightened risk of metabolic diseases, such as T2DM [Bays HE et al. Int J Clin Pract 2007]. SHIELD outcomes pointed to a need to identify gaps in the treatment of obesity; increase advice to low-risk individuals on therapeutic lifestyle changes; and provide practical, patient-friendly self-management tools for those with T2DM. Dr. Gavin stressed the need for informed and empowered patients—those with the motivation, skills, and confidence to effectively make decisions about and manage their health. He cited a need for more education and outreach to promote active self-management with accessible, easyto-use tools. He also called for enhanced peer support/ interaction and communication between patients and health care providers (eg, through cell phones, web portals, and wellness coaches). Because the majority of SHIELD respondents was aware of healthy behaviors but failed to engage in them, Dr. Gavin recommended more support for diabetes educators and health care providers to assist them in teaching patients how to make healthy choices using public policy as a tool (eg, pay for performance). He suggested that data on attitudes/behaviors from SHIELD be used in developing the approach and content of group visits and creating effective surrogates for health care visits, since increased frequency of contact translates into better adherence and outcomes. Diabetes and prediabetes are common in the US [Nathan DM et al. Diabetes Care 2007] (Figure 2)— factors with implications for risk assessment include: • • • • • Use of SHIELD data to more accurately identify withingroup predictors of risk progression Linking of available prevention tools to indications of increased risk for progression Use of patient-measured parameters to derive risk predictors of sufficient rigor Enhanced linking of attitudes and behaviors to risk in patient-centered ways Strengthening of current screening tools Figure 2. Diabetes and Prediabetes in the US. Total US Population Aged ≥20 Years Pre- Diabetes and Diabetes Aged ≥20 Years 7 million 18.8 million 203.9 million Normal glucose tolerance 104.8 million 79 million Undiagnosed Diabetes Diagnosed Diabetes (90% to 95% T2DM) IFG/IGT/Pre-Diabetes (Up to 70% will develop T2DM IFG=impaired fasting glucose; IGT=impaired glucose tolerance. US Centers for Disease Control and Prevention. http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf. US Census Bureau. http://www.census.gov/. Nathan DM et al. Diabetes Care 2007. The SHIELD study indicated a need to consider cardiometabolic risk and factors beyond type T2DM in prediction models. These include demographics (eg, prescription drug coverage, geographic region, ethnicity); diabetes symptoms (eg, irritability, “high sugar,” gestational diabetes); lifestyle (eg, exercise, health, and smoking status); and medical conditions (eg, COPD, circulation problems, arthritis, asthma). Clinical and health care implications range from actions that are taken by primary care physicians before patients develop T2DM to assessment of how SHIELD outcomes can be used to improve screening beyond the disease. Consideration needs to be given to what health policies should be implemented to reduce the epidemic—eg, outreach efforts, use of social media; what role endocrinologists and cardiologists should play in identifying at-risk individuals; and how we can better leverage new insights into public attitudes and behaviors. 7 Peer-Reviewed Highlights from the American Diabetes Association 71st Annual Scientific Sessions  http://www.mdconferencexpress.com http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf http://www.census.gov/

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MD Conference Express ADA 2011

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