Study is important step in assessing utility of abnormal blood glucose testing in ASCVD primary prevention
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Adding hemoglobin A1C (HbA1c) to
conventional cardiovascular risk assessment had only a modest effect on
post-test, 10-year atherosclerotic cardiovascular disease (ASCVD) risk,
researchers reported.
Using data from the 2010-2011 National Health and Nutrition Examination Surveys (NHANES), Mark J. Pletcher, MD, MPH, of the University of California San Francisco, and colleagues devised a risk regression model that allowed them to incorporate HbA1C into the 2013 American College of Cardiology (ACC)/American Heart Association (AHA) 10-year ASCVD risk calculator.
They found that having an HbA1C of less than 5.7% (normal level) was found to reduce post ASCVD-test risk by 0.4% to 2.0% points, while having an HbA1C of 6.5% (the threshold for diabetes mellitus) or greater increased post-test risk by 1.0% to 2.5% points, they wrote in Circulation: Cardiovascular Quality and Outcomes.
"Hemoglobin A1C testing is inexpensive and has few direct adverse effects, so it is possible that even small changes in risk prediction may be valuable enough to warrant measurement," they stated.
But in an accompanying editorial, Khurram Nasir, MD, expressed doubt that adding HbA1C to conventional cardiovascular (CVD) risk assessment would meaningfully inform patient management or improve outcomes. Nasir is the director of the Center for Healthcare Advancement & Outcomes for the healthcare network Baptist Health South Florida in Miami.
He noted that among patients with a pretest ASCVD risk of 7.5%, which indicates elevated 10-year risk warranting statin treatment, a normal HbA1C would lower the estimated post-test 10-year risk by just over 1%.
"Whether this difference is meaningful and would affect the decision to avoid statins is debatable, as the risk still remains above threshold suggested by guidelines for considering moderate intensity statins," he wrote.
In another accompanying editorial, Harlan M. Krumholz, MD, of Yale University School of Medicine, pointed out that the Institute of Medicine "defines biomarkers as indicators of normal biological processes, pathogenic processes, or pharmacological responses to an intervention, but even as biomarkers can reflect the influence of an intervention, changes in their levels may not be indicative of changes in risk."
The 2013 AHA/ACC ASCVD model incorporates a clinical diagnosis of type 2 diabetes, but not specific glycemic level, in the risk calculator.
Although HbA1C is associated with increased risk of cardiovascular events, its use for prediction of CVD events in combination with conventional risk factors remains controversial, the researchers noted.
In an effort to better understand the impact of HBA1C on CVD risk prediction, the authors devised regression models that incorporated the diabetes biomarker into the ASCVD model. They identified 2,000 individuals, ages 40-79, from NHANES who did not have pre-existing diabetes or CVD.
The regression model was used to predict HbA1C distribution based on individual patient characteristics, and the researchers calculated post-test 10-year ASCVD risk by incorporating actual versus predicted HbA1C.
Age, sex, race/ethnicity, and traditional cardiovascular risk factors were significant predictors of HbA1C in the model, with the expected HbA1C distribution being significantly higher in non-Hispanic black, non-Hispanic Asian, and Hispanic individuals versus non-Hispanic whites and others.
"Our analysis is an intermediate step toward the larger goal of evaluating the clinical utility of HbA1C ," the researchers wrote, adding that adequately designed, cost-effectiveness studies will be needed to determine if HbA1C has clinical value in CVD risk assessment.
The cross-sectional nature of the NHANES data used in the analysis was cited by the researchers as a potential study limitation.
"Although the costs of HbA1C testing are low and potential consequences of testing seem benign, the net comparative effectiveness and efficiency (cost-effectiveness) of this approach for guiding HbA1C testing has not been proven," the researchers wrote. "Future randomized, controlled trials of an integrated screening and targeted prevention strategy or careful modeling of expected benefits, harms, and costs are necessary to fully assess the potential implications of this strategy."
In his editorial, Nasir suggested that the focus on biomarkers to improve CVD risk prediction may be misplaced.
"Given the plethora of biomarkers that have been associated with cardiovascular outcomes, why did the current risk prediction model still end up with 6 basic risk factors first established almost a half a century ago?," he asked, adding that none of the candidate biomarkers have been proven to have a meaningful impact on patient risk assessment.
"If we truly wish to accelerate our efforts in personalizing patient-centric risk prediction models, our persistent resolve with biomarkers in this pursuit is perplexing, especially when historically this approach has consistently not yielded high dividends in CVD risk reclassification," he wrote.
Nasir noted that directly identifying disease progression, such as using noncontrast CT to identify coronary artery calcium (CAC), may better inform clinicians about patient risk.
"Strikingly, within primary prevention settings, no other test offers better prognostication, discrimination and reclassification for CVD risk," he wrote. "Compared with limited performance with surrogate biomarkers, CAC testing provides net reclassification improvements in nearly two-thirds of individuals with intermediate risk level to significantly influence treatment decisions."
Using data from the 2010-2011 National Health and Nutrition Examination Surveys (NHANES), Mark J. Pletcher, MD, MPH, of the University of California San Francisco, and colleagues devised a risk regression model that allowed them to incorporate HbA1C into the 2013 American College of Cardiology (ACC)/American Heart Association (AHA) 10-year ASCVD risk calculator.
They found that having an HbA1C of less than 5.7% (normal level) was found to reduce post ASCVD-test risk by 0.4% to 2.0% points, while having an HbA1C of 6.5% (the threshold for diabetes mellitus) or greater increased post-test risk by 1.0% to 2.5% points, they wrote in Circulation: Cardiovascular Quality and Outcomes.
"Hemoglobin A1C testing is inexpensive and has few direct adverse effects, so it is possible that even small changes in risk prediction may be valuable enough to warrant measurement," they stated.
But in an accompanying editorial, Khurram Nasir, MD, expressed doubt that adding HbA1C to conventional cardiovascular (CVD) risk assessment would meaningfully inform patient management or improve outcomes. Nasir is the director of the Center for Healthcare Advancement & Outcomes for the healthcare network Baptist Health South Florida in Miami.
He noted that among patients with a pretest ASCVD risk of 7.5%, which indicates elevated 10-year risk warranting statin treatment, a normal HbA1C would lower the estimated post-test 10-year risk by just over 1%.
"Whether this difference is meaningful and would affect the decision to avoid statins is debatable, as the risk still remains above threshold suggested by guidelines for considering moderate intensity statins," he wrote.
In another accompanying editorial, Harlan M. Krumholz, MD, of Yale University School of Medicine, pointed out that the Institute of Medicine "defines biomarkers as indicators of normal biological processes, pathogenic processes, or pharmacological responses to an intervention, but even as biomarkers can reflect the influence of an intervention, changes in their levels may not be indicative of changes in risk."
The 2013 AHA/ACC ASCVD model incorporates a clinical diagnosis of type 2 diabetes, but not specific glycemic level, in the risk calculator.
Although HbA1C is associated with increased risk of cardiovascular events, its use for prediction of CVD events in combination with conventional risk factors remains controversial, the researchers noted.
In an effort to better understand the impact of HBA1C on CVD risk prediction, the authors devised regression models that incorporated the diabetes biomarker into the ASCVD model. They identified 2,000 individuals, ages 40-79, from NHANES who did not have pre-existing diabetes or CVD.
The regression model was used to predict HbA1C distribution based on individual patient characteristics, and the researchers calculated post-test 10-year ASCVD risk by incorporating actual versus predicted HbA1C.
Age, sex, race/ethnicity, and traditional cardiovascular risk factors were significant predictors of HbA1C in the model, with the expected HbA1C distribution being significantly higher in non-Hispanic black, non-Hispanic Asian, and Hispanic individuals versus non-Hispanic whites and others.
"Our analysis is an intermediate step toward the larger goal of evaluating the clinical utility of HbA1C ," the researchers wrote, adding that adequately designed, cost-effectiveness studies will be needed to determine if HbA1C has clinical value in CVD risk assessment.
The cross-sectional nature of the NHANES data used in the analysis was cited by the researchers as a potential study limitation.
"Although the costs of HbA1C testing are low and potential consequences of testing seem benign, the net comparative effectiveness and efficiency (cost-effectiveness) of this approach for guiding HbA1C testing has not been proven," the researchers wrote. "Future randomized, controlled trials of an integrated screening and targeted prevention strategy or careful modeling of expected benefits, harms, and costs are necessary to fully assess the potential implications of this strategy."
In his editorial, Nasir suggested that the focus on biomarkers to improve CVD risk prediction may be misplaced.
"Given the plethora of biomarkers that have been associated with cardiovascular outcomes, why did the current risk prediction model still end up with 6 basic risk factors first established almost a half a century ago?," he asked, adding that none of the candidate biomarkers have been proven to have a meaningful impact on patient risk assessment.
"If we truly wish to accelerate our efforts in personalizing patient-centric risk prediction models, our persistent resolve with biomarkers in this pursuit is perplexing, especially when historically this approach has consistently not yielded high dividends in CVD risk reclassification," he wrote.
Nasir noted that directly identifying disease progression, such as using noncontrast CT to identify coronary artery calcium (CAC), may better inform clinicians about patient risk.
"Strikingly, within primary prevention settings, no other test offers better prognostication, discrimination and reclassification for CVD risk," he wrote. "Compared with limited performance with surrogate biomarkers, CAC testing provides net reclassification improvements in nearly two-thirds of individuals with intermediate risk level to significantly influence treatment decisions."
The study was funded by the NIH and the National Cancer Institute.
Pletcher and co-authors disclosed no relevant relationships with industry. One co-author disclosed serving as a member of the U.S. Preventive Services Task Force.
Nasir disclosed relevant relationships with Quest Diagnostic and Regeneron.
Krumholz disclosed support from the National Heart, Lung, and Blood Institute through his institution. He disclosed relevant relationships with Medtronic, Johnson & Johnson, and United Healthcare.
Pletcher and co-authors disclosed no relevant relationships with industry. One co-author disclosed serving as a member of the U.S. Preventive Services Task Force.
Nasir disclosed relevant relationships with Quest Diagnostic and Regeneron.
Krumholz disclosed support from the National Heart, Lung, and Blood Institute through his institution. He disclosed relevant relationships with Medtronic, Johnson & Johnson, and United Healthcare.
last updated
Primary Source
Circulation: Cardiovascular Quality and Outcomes
Source Reference: Jarmul JA, et al "Interpreting hemoglobin A1C in combination with conventional risk factors for prediction of cardiovascular risk" Circ: Cardio Qual Outcomes 2015; DOI: 10.1161/CIRCOUTCOMES.115.001639.Secondary Source
Circulation: Cardiovascular Quality and Outcomes
Source Reference: Nasir K "Overhauling cardiovascular risk prediction in primary prevention difficult journey worth the destination" Circ: Cardio Qual Outcomes 2015; DOI: 10.1161/CIRCOUTCOMES.115.002207.Additional Source
Circulation: Cardiovascular Quality and Outcomes
Source Reference: Krumholz HM "Biomarkers, Risk Factors, and risk clarifying the controversy about surrogate end points and clinical outcomes" Circ: Cardio Qual Outcomes 2015; DOI: 10.1161/CIRCOUTCOMES.115.002245.
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