How Low to Go With Glucose, Cholesterol, and Blood Pressure in Primary Prevention of CVD

Kimberly N. Hong, MD, MHSA; Valentin Fuster, MD; Robert S. Rosenson, MD; Clive Rosendorff, MD, PHD; Deepak L. Bhatt, MD, MPH


J Am Coll Cardiol. 2017;70(17):2171-21-85. 

In This Article

Current Guidelines

Prevention of CVD through glucose lowering in persons with DM and blood pressure in persons with HTN are target based. Specifically, in known diabetic patients, the target hemoglobin A1c (HgA1c) is 7%, with a lower threshold of 6.5% accepted when patient-specific characteristics, including length of disease and known CAD, are considered.[15] In HTN, Eighth Joint National Committee recommendations suggested initiation of treatment at 140/90 mm Hg and have raised the target blood pressures for high-risk patients, including those with DM, cerebrovascular disease, chronic kidney disease, and CAD, from 130/80 to 140/90 mm Hg. There was also a change in blood pressure goal in individuals age >60 years to 150/90 mm Hg.[16] This new upper threshold for treatment in this age group is particularly controversial, and the American Heart Association (AHA) and the American College of Cardiology (ACC) are expected to publish updated HTN guidelines soon.[17]

The 2013 AHA/ACC cholesterol guidelines departed significantly from the paradigm of treating to a target low-density lipoprotein cholesterol (LDL-C) and instead shifted treatment toward differentiating between primary versus secondary CVD, and the overall risk of developing CVD. Specifically, 4 separate groups were defined based on presence or absence of CVD, LDL-C levels ≥190 mg/dl, DM, and atherosclerotic cardiovascular disease (ASCVD) risk as determined by pooled risk equations, which are the basis for the AHA/ACC ASCVD risk calculator (Figure 1). In those who do not meet these criteria, other risk factors including genetic hyperlipidemias, family history, elevated high-sensitivity C-reactive protein (CRP), coronary artery calcium score, ankle-brachial index <0.9, and elevated life time risk for ASCVD can be used to refine treatment decisions.[18,19]

Figure 1.

2013 ACC/AHA Statin Therapy Recommendations
This figure depicts a flowchart of ACC/AHA's recommended algorithm for the initiation of statins. The first clinical variable affecting initiation of statin therapy is the presence of CVD. In the absence of CVD, LDL cholesterol, diabetes, and ASCVD risk make up the criteria for starting statins for the primary prevention of CVD. Adapted with permission from Stone et al.(18). ACC = American College of Cardiology; AHA = American Heart Association; ASCVD = atherosclerotic cardiovascular disease; CVD = cardiovascular disease; LDL = low density lipoprotein.

Increasingly, clinical trials and guidelines are using CVD risk to guide treatment strategies.[20,21] The AHA/ACC recommends using the pooled cohort ASCVD risk calculator. Assessment of risk is challenging because of limitations of contemporary data, including a delay in the acquisition of real-time clinical event data, and unexplained correlations resulting from unknown confounders or interactions with unmeasured variables. The most significant concern regarding this calculator is the overestimation of risk that has been attributed to the use of older National Heart, Lung, and Blood Institute data. Older data may not reflect current demographic changes, advances in disease modifying therapies, or population-based lifestyle changes that include trends in smoking and eating habits.[19,21–23] One validation study that used a multiethnic cohort identified in 2008 and followed through 2013 overestimated ASCVD risk at 5 years in all risk groups.[24] Another study that enrolled individuals from 2008 to 2009 found that ASCVD risk was overestimated by 167% in the total cohort.[25] A separate study by Muntner et al.[26] compared observed with estimated rates of CVD using the ASCVD risk in individuals enrolled between 2003 and 2007. Although this study also showed an overestimation of risk in the overall cohort, the size of the overestimation decreased when higher-risk patients were excluded or when outcomes data were augmented with Medicare claims data.[26]