Evidence-Based Diabetes Management May 2015
Moving to a Population-Based Approach to Find Links Between Diabetes, Heart Disease
Connections between diabetes and heart disease are well known, but figuring out exact-ly which patients with diabetes will develop conditions such as con-gestive heart failure or cardiovascular disease is less straightforward. Can tools such as biomarkers or imaging help physicians? And how can moving away from “disease-based” to a “population-based” approach to identifying at-risk diabetics, especially women, play a role?
Speakers at the symposium, “Imaging the High Risk Diabetic Patient,” represented a collaboration among several professional associations and the American College of Cardiology (ACC). It was held March 14, 2015, during the 64th Annual Scientific Sessions in San Diego, California.
Matthew J. Budoff, MD, of the Univer-sity of California at Los Angeles, started the discussion by looking at how ge-netics is broadening medicine’s understanding of the connections between diabetes and heart disease. He offered the caveat that diabetes risk is influenced by a mix of genetics, environmental factors, nutrition, and whether a person is obese. The vast majority 92% of patients have insulin resistance, and many develop hypertension. The role that inflammation plays, inflicting damage at both the microvascular and macrovascular level, is key: according to Budoff, it appears that inflammation sets in motion the process that leads to cardiovascular disease.
Budoff discussed markers related to inflammation. The C-reactive protein, which is found in blood plasma and rises in response to inflammation, is a relatively weak marker, yet it is still associated with diabetes. Plasma levels tend to drop in patients with diabetes, also in response to inflammation.
Finding good markers is important, Budoff said, because “There is a rising tide of cardiovascular disease in diabe-tes,” that is hard to explain, even with the rise in diabetes itself.
Robert S. Rosenson, MD, of Mount Si-nai Hospital in New York City, noted the weaknesses of current risk assessment tools: most are based on information reported by patients themselves, and have been developed across a diverse set of populations worldwide. It’s widely recognized that assessment tools that work with 1 population are not transferable to another population with a different ethnic background, diet, or culture.
Studies of the components of metabolic syndrome are pointing toward potentially valid ways to connect risk scores between different conditions. There’s been debate about glycated hemoglobin (A1C) and cardiovascular disease (CVD), and those relationships are quite striking, Rosenson said. While evaluating several CVD risk prediction models, he cautioned that it’s not a simple matter to apply them to a population with diabetes.
For example, a genetic-based tool for coronary artery disease (CAD) did not prove to be a dependable prognostic tool when applied to a diabetes population. New tools are needed that can take existing models and apply them across populations using known biomarkers, while taking ethnic differences into account, he said.
W. Guy Weigold III, MD, of MedStar Washington Hospital Center, said that while there is clearly a relationship be-tween diabetes and heart disease, “a one-size-fits-all paradigm is not accurate.” And this is particularly true when using imaging to test for calcium in persons with diabetes. For example, a calcium score would seem a straightforward biomarker. But while imaging can help, it’s not a simple process, Weigold said. He reviewed several studies of this phenomenon, including a 2003 study by Wong, et al.1 “Patients who had diabetes with metabolic syndrome have some degree of coronary calcium, but some of these patients don’t have any calcium,” he said. “Right off the bat, it’s not necessarily the case that diabetes always equals coronary artery disease.”
Diabetics also accumulate calcium differently, Weigold pointed out; their maximum calcium scores tend to reach a peak, while nondiabetics with heart disease have scores that tend to keep increasing gradually. Calcium scores can be used to stratify diabetics for treatment, but some diabetics still have higher mortality, even with calcium scores of zero.
Prem Soman, MD, of the University of Pittsburgh, found that routine nuclear imaging of diabetics for silent ischemia yielded very little benefit, while Vera Rigolin, MD, of Northwestern University, found that stress echocardiography provided excellent prognostic information, especially given how rapidly conditions can change for persons with diabetes.
Throughout the session, presenters took note of the different indicators and outcomes for men and women. Regina Druz, MD, of iVisitMD in Long Island, New York, said that ensuring better care for women will require a different way of identifying patients who are at risk of heart disease. The process has traditionally been screening, taking history, taking a patients’ baseline, and hoping for early detection and treatment.
But today’s model of population health, Druz said, calls for physicians to deploy a risk stratification model that identifies groups of patients who need care before cardiac events occur. Among persons with diabetes, that may involve biomarkers or calcium tests but also an analysis of environmental and nutrition factors that may affect certain groups of patients. “This is risk-guided treatment,” she said. “We are moving from the disease-centric model to the patient-centric model.”
New indicators will emerge for iden-tifying patients, because even the 2013 guidelines from ACC and the American Heart Association don’t offer cardiolo-gists thorough information on risk assessment, Druz said, adding, “Everything boils down to identification of global CAD risk.” As physicians learn more about the effects of genetics and other factors that propel diabetes to trigger heart disease, “this is a frame-work that will grow with us into the future.”
Wong ND, Sciammarella MG, Polk D, et al. The metabolic syndrome, diabetes, and subclinical atherosclerosis assessed by coronary calcium. J Am Col Cardiol. 2003;41:1547.