Biochemical risk markers and 10-year incidence of atherosclerotic cardiovascular disease: independent predictors, improvement in pooled cohort equation, and risk reclassification

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Abstract

Background

The recommendation for statins in primary atherosclerotic cardiovascular disease (ASCVD) prevention begins with risk estimation using the pooled cohort equation (PCE). However, treatment decisions may still remain uncertain after PCE-based assessment. We therefore developed a simple biomarker score that could supplement decision making.

Methods

Using the prospectively collected database of the Multi-Ethnic Study of Atherosclerosis, we identified biochemical risk markers that independently predicted 10-year risk of ASCVD and developed an aggregate biomarker score based on them. Thereafter, we assessed for incremental benefit of these markers over the PCE using C-statistic, net reclassification index (NRI), and integrated discrimination index (IDI).

Results

A total of 5,303 adults free of ASCVD at baseline were included in this study. Five biochemical risk markers—high-sensitivity C-reactive protein, homocysteine, albuminuria, N-terminal prohormone of brain natriuretic peptide, and troponin T—that predicted 10-year risk of ASCVD were combined into an aggregate biomarker score (CHAN2T3), which demonstrated a graded increase in the rate of incident ASCVD from 2.1% among participants with score of 0 to 25% among participants with score of 5. In addition, a biomarker score of ≥2 was associated with improvement in the C-statistic of the PCE (0.748 vs 0.734, P = .02), integrated discrimination index (P < .001), category-free NRI of 45% (95% CI, 31%-57%), and net categorical NRI of 5.4% in the full cohort. Lastly, a biomarker score of ≥4 resulted in 6% net reclassification across ASCVD risk cut point of 7.5% among nondiabetic individuals with LDL-C <190 mg/dL.

Conclusions

A novel CHAN2T3 biomarker score could supplement risk-based discussion for ASCVD prevention, especially when treatment decision is uncertain. Further validation in other cohorts is however warranted

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