Cited by 164Lipoproteins and Cardiovascular HealthDiabetes, Cardiovascular Risks, and LipoproteinsCholesterol and Lipid Metabolism

Apolipoprotein B improves risk assessment of future coronary heart disease in the Framingham Heart Study beyond LDL-C and non-HDL-C

Pencina, Michael J, D’Agostino, Ralph B, Zdrojewski, Tomasz, Williams, Ken, Thanassoulis, George, Furberg, Curt D, Peterson, Eric D, Vasan, Ramachandran S +1 more·2015·European Journal of Preventive Cardiology

Abstract

AIMS: Analyses using conventional statistical methodologies have yielded conflicting results as to whether low-density lipoprotein cholesterol (LDL-C) or non-high-density lipoprotein cholesterol (non-HDL-C) or apolipoprotein B (apoB) is the best marker of the apoB-associated risk of coronary heart disease. The aim of this study was to determine the additional value of apoB beyond LDL-C or non-HDL-C as a predictor of coronary heart disease. METHODS AND RESULTS: For each patient from the Framingham Offspring Cohort aged 40-75 years (n = 2966), we calculated the extent to which the observed apoB differed from the expected apoB based on their LDL-C or non-HDL-C. We added this difference to a Cox model predicting new onset coronary heart disease over a maximum of 20 years adjusting for standard risk factors plus LDL-C or non-HDL. The difference between observed and expected apoB over LDL-C or non-HDL-C was highly prognostic of future coronary heart disease events: adjusted hazard ratios 1.26 (95% confidence interval: 1.15, 1.37) and 1.20 (1.11, 1.29), respectively, for each standard deviation increase beyond expected apoB levels. When this difference between observed and expected apoB was added to standard coronary heart disease prediction models including LDL-C or non-HDL-C, prediction improved significantly (likelihood ratio test p-values <0.0001) and discrimination c-statistics increased from 0.72 to 0.73. The corresponding relative integrated discrimination improvements were 11% and 8%, respectively. CONCLUSIONS: apoB improves risk assessment of future coronary heart disease events over and beyond LDL-C or non-HDL-C, which is consistent with coronary risk being more closely related to the number of atherogenic apoB particles than to the mass of cholesterol within them.

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Crossref · 10.1177/2047487315569411

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Authors
Pencina, M. J., D’Agostino, R. B., Zdrojewski, T., Williams, K., Thanassoulis, G., Furberg, C. D., Peterson, E. D., Vasan, R. S., & Sniderman, A. D.
Affiliations
  • Duke University (US)
  • Boston University (US)
  • Gdańsk Medical University (PL)
  • KenAnCo Biostatistics, San Antonio, USA
  • McGill University Health Centre (CA)
  • Wake Forest University Health Sciences (US)
  • Duke Medical Center (US)
  • Framingham Heart Study (US)
  • Royal Victoria Hospital (CA)
Corresponding author
Pencina, Michael J
Venue
European Journal of Preventive Cardiology
Published
2015-01-29
Volume
22
Issue
10
Pages
1321-1327
ISSN
  • 2047-4873 (ISSN-L)
  • 2047-4881
OpenAlex
W1984970871
Cited by
164
References
27
Citations by year
2026: 19 · 2025: 12 · 2024: 14 · 2023: 17 · 2022: 16 · 2021: 13 · 2020: 9 · 2019: 16 · 2018: 17 · 2017: 11 · 2016: 13 · 2015: 6
FWCI
15.83
Type
journal article
Language
en
Open access
closed
Locations
  • European Journal of Preventive Cardiology (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (n01hc25195-41-0-1)
  • National Heart, Lung, and Blood Institute (N01‐HC25195)
Topics
  • Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Surgery › Cholesterol and Lipid Metabolism
Keywords
apolipoprotein B, coronary heart disease risk prediction, LDL cholesterol, non-HDL cholesterol, Framingham Offspring cohort, cardiovascular risk assessment
MeSH terms
Adult, Aged, Cholesterol, Coronary Disease, Decision Support Techniques, Female, Humans, Cholesterol, LDL, Male, Massachusetts, Middle Aged, Predictive Value of Tests, Risk Factors, Time Factors, Biomarkers, Multivariate Analysis, Proportional Hazards Models, Risk Assessment, Disease-Free Survival, Dyslipidemias, Kaplan-Meier Estimate, Apolipoprotein B-100

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