Achieving Optimal Risk Factor Control in the Right Patient Population for the Prevention of Cardiovascular Disease in Diabetes

The increase in prevalence of diabetes, driven by both a fall in diabetes-related death and a rise in diabetes incidence, is associated with increased costs and constitutes a burden on the healthcare system. As such, current efforts in diabetes management are directed towards mitigating complications of diabetes including micro and macrovascular disease. Cardiovascular disease (CVD) remains the leading cause of mortality and morbidity among persons with diabetes mellitus, and therefore, optimal risk factor control is endorsed by current guidelines for primary prevention of CVD.1 In practice, simultaneous management of multiple CVD risk factors is difficult to achieve, and CVD risk factor control in persons with diabetes mellitus remains less than optimal as several studies in US cohorts have shown.2-5 Both individual and composite risk factor control have proven benefit for reduction of CVD events; however, published data from ethnically diverse US prospective studies is currently lacking.6-9

In this context, a recent article by Wong et al. published in Diabetes Care provides epidemiologic evidence regarding the benefit of individual and composite risk factor control (blood pressure [BP] <130/80 mmHg, low-density lipoprotein cholesterol [LDL-C] <100 mg/dL, and glycated hemoglobin [HbA1c] <7%) for CVD risk reduction in diabetes. Utilizing data from three cohorts: the Atherosclerosis Risk in Communities Study (ARIC), the Multi-Ethnic Study of Atherosclerosis (MESA), and the Jackson Heart Study (JHS), 2,018 asymptomatic adults with diabetes mellitus (43% male, 55% African American) were followed over a mean of 11 years for incident coronary heart disease (CHD) and CVD.

Not surprisingly, a small proportion of individuals with diabetes were at target levels for BP (42%), LDL-C (32%), and HbA1c (42%). For any one, two, or all three factors, 41%, 26%, and 7% were at target levels, respectively. Individuals who were at target levels of BP, LDL-C and HbA1c had a 17%, 33%, and 37% lower adjusted CVD risks and 17%, 41%, and 36% adjusted lower CHD risk, respectively (p < 0.05 for all except for BP and CHD risk). Control of one, two, or all three risk factors at target levels (vs. none) was associated with progressively lower adjusted risks of CVD events (36%, 52%, and 62%, respectively), and progressively lower adjusted risks of CHD events (41%, 56%, and 60%, respectively) (all p < 0.05). In general, findings were similar when stratified by sex, race/ethnicity, and duration of diabetes mellitus (non-significant interaction analysis).

These findings argue in favor of optimal control of multiple CVD risk factors. Current efforts, involving patient education and use of mobile health technologies, may help achieve this goal by improving quality of care and increasing medication adherence.10,11 However, sole reliance on traditional CVD risk factors as treatment targets and for risk prediction may not be sufficient for prevention of CVD events because:

  1. Even with "optimal" management of risk factors, patients with diabetes still experience more CVD events than those without diabetes. This residual CVD risk might result from yet other risk determinants, such as lipoprotein(a), that contribute to CVD in diabetes.12,13
  2. The criteria for "optimal risk factors" have changed over the last decade.14 For example, at the time of the ARIC study the guidelines for intensity of treatment were less stringent compared to when more modern cohorts such as MESA and JHS began enrollment.15-17
  3. Single measurement of traditional risk factors might not be predictive of long-term risk, as risk factor status can change following lifestyle intervention and pharmacological therapy. Other risk factors can also emerge during the course of follow-up. Therefore, while single measurements of traditional risk factors help assess patients' adherence to management plans in the short term, they may not be accurate for prediction of long-term outcomes.
  4. Comprehensive control of risk factors in patients with diabetes is difficult to achieve due to noncompliance, financial difficulties, and patient education. Furthermore, it is unclear whether treatment intensity should be the same for all risk factors.6,18 It would therefore be reasonable to titrate treatment intensity to level of risk such that aggressive risk factor control would only be recommended to high-risk patients who are expected to derive greatest benefit from strict control.

Given these limitations, additional sources of information can be considered for CVD risk stratification in diabetes. Clinical characteristics of diabetes such as age of onset, duration, and severity of diabetes may provide additional prognostic information regarding CVD risk. Concomitant metabolic syndrome, non-alcoholic fatty liver disease, and erectile dysfunction may be considered novel risk factors among persons with diabetes mellitus.19 Among subclinical disease risk markers, coronary artery calcium (CAC), a marker of subclinical coronary heart disease, appears to provide superior discrimination and risk reclassification.20-22 Higher CAC scores indicate a greater burden of atherosclerosis and a propensity to develop CVD events compared to diabetic counterparts with little or no CAC.20

In conclusion, it is important to achieve optimal control of traditional CVD risk factors in all persons with diabetes mellitus. However, in a resource-limited healthcare system, it is reasonable for the clinician to consider ancillary sources of information to identify high-risk patients that would derive greatest benefit from preventive measures. Information from novel risk factors as well as clinical characteristics of diabetes, and importantly subclinical CVD imaging can be synthesized by clinicians in order to comprehensively assess patients' risk status and administer preventive treatment accordingly.


  1. Fox CS, Golden SH, Anderson C, et al. Update on prevention of cardiovascular disease in adults with type 2 diabetes mellitus in light of recent evidence: a scientific statement from the American Heart Association and the American Diabetes Association. Diabetes Care 2015;38:1777-803.
  2. Farkouh ME, Boden WE, Bittner V, et al. Risk factor control for coronary artery disease secondary prevention in large randomized trials. J Am Coll Cardiol 2013;61:1607-15.
  3. Saydah SH, Fradkin J, Cowie CC. Poor control of risk factors for vascular disease among adults with previously diagnosed diabetes. JAMA 2004;291:335-42.
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  7. Adler AI, Stratton IM, Neil HA, et al. Association of systolic blood pressure with macrovascular and microvascular complications of type 2 diabetes (UKPDS 36): prospective observational study. BMJ 2000;321:412-9.
  8. Colhoun HM, Betteridge DJ, Durrington PN, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet 2004;364:685-96.
  9. Gaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorail intervention on mortality in type 2 diabetes. N Engl J Med 2008;358:580-91.
  10. Fonarow GC, Gawlinski A, Moughrabi S, Tillisch JH. Improved treatment of coronary heart disease by implementaiton of a Cardiac Hospitalization Atherosclerosis Management Program (CHAMP). Am J Cardiol 2001;87:819-22.
  11. Yusuf S. Two decades of progress in preventing vascular disease. Lancet 2002;360:2-3.
  12. Sharif S, van der Graaf Y, Nathoe HM, et al. HDL cholesterol as a residual risk factor for vascular events and all-cause mortality in patients with type 2 diabetes. Diabetes Care 2016. [Epub ahead of print]
  13. Tsimikas S, Hall JL. Lipoprotein(a) as a potential causal genetic risk factor of cardiovascular disease: a rationale for increased efforts to understand its pathophysiology and develop targeted therapies. J Am Coll Cardiol 2012;60:716-21.
  14. SPRINT Research Group, Wright JT, Williamson JD, et al. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med 2015;373:2103-16.
  15. Chambless LE, Heiss G, Folsom AR, et al. Association of coronary heart disease incidence with carotid arterial wall thickness and major risk factors: the Atherosclerosis Risk in Communities (ARIC) Study, 1987-1993. Am J Epidemiol 1997;146:483-94.
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  17. Jones DW, Chambless LE, Folsom AR, et al. Risk factors for coronary heart disease in African Americans: the atherosclerosis risk in communities study, 1987-1997. Arch Intern Med 2002;162:2565-71.
  18. Elam MB, Lovato LC, Ginsberg HN. The effect of combined statin/fibrate therapy on cardiovascular disease is influenced by sex and dyslipidemia: ACCORDION-Lipid long-term follow-up. Circulation 2015;132:A15997.
  19. Kianoush S, Al Rifai M, Whelton SP, et al. Stratifying cardiovascular risk in diabetes: the role of diabetes-related clinical characteristics and imaging. J Diabetes Complications 2016. [Epub ahead of print]
  20. Wong ND, Zhao Y, Patel R, et al. Cardiovascular risk factor targets and cardiovascular disease event risk in diabetes: a pooling project of the Atherosclerosis Risk in Communities Study, Multi-Ethnic Study of Atherosclerosis, and Jackson Heart Study. Diabetes Care 2016;39:668-76.
  21. Yeboah J, Young R, McClelland RL, et al. Utility of nontraditional risk markers in atheroslcerotic cardiovascular disease risk assessment. J Am Coll Cardiol 2016;67:139-47.
  22. Blaha MJ, Cainzos-Achirica M, Greenland P, et al. Role of coronary artery calcium score of zero and other negative risk markers for cardiovascular disease: the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 2016;133:849-58.

Clinical Topics: Diabetes and Cardiometabolic Disease, Dyslipidemia, Prevention, Atherosclerotic Disease (CAD/PAD), Advanced Lipid Testing, Lipid Metabolism, Nonstatins

Keywords: Atherosclerosis, Blood Pressure, Cholesterol, LDL, Coronary Artery Disease, Diabetes Mellitus, Erectile Dysfunction, Glycated Hemoglobin A, Life Style, Lipoprotein(a), Medication Adherence, Metabolic Syndrome, Primary Prevention, Prospective Studies, Risk Factors, Risk Reduction Behavior, Telemedicine

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