Quantifying importance of major risk factors for coronary heart disease
Open accessAbstract
Background: To optimize preventive strategies for coronary heart disease (CHD), it is essential to understand and appropriately quantify the contribution of its key risk factors. Our objective was to compare the associations of key modifiable CHD risk factors—specifically lipids, systolic blood pressure (SBP), diabetes mellitus, and smoking—with incident CHD events based on their prognostic performance, attributable risk fractions, and treatment benefits, overall and by age. Methods: Pooled participant-level data from 4 observational cohort studies sponsored by the National Heart, Lung, and Blood Institute were used to create a cohort of 22 626 individuals aged 45 to 84 years who were initially free of cardiovascular disease. Individuals were followed for 10 years from baseline evaluation for incident CHD. Proportional hazards regression was used to estimate metrics of prognostic model performance (likelihood ratio, C index, net reclassification, discrimination slope), hazard ratios, and population attributable fractions for SBP, non–high-density lipoprotein cholesterol (non–HDL-C), diabetes mellitus, and smoking. Expected absolute risk reductions for antihypertensive and lipid-lowering treatment were assessed. Results: Age, sex, and race capture 63% to 80% of the prognostic performance of cardiovascular risk models. In contrast, adding either SBP, non–HDL-C, diabetes mellitus, or smoking to a model with other risk factors increases the C index by only 0.004 to 0.013. However, primordial prevention could have a substantial effect as demonstrated by population attributable fractions of 28% for SBP≥130 mm Hg and 17% for non–HDL-C≥130 mg/dL. Similarly, lowering the SBP of all individuals to <130 mm Hg or lowering low-density lipoprotein cholesterol by 30% would be expected to lower a baseline 10-year CHD risk of 10.7% to 7.0 and 8.0, respectively (absolute risk reductions: 3.7% and 2.7%, respectively). Prognostic performance decreases with age (C indices for age groups 45–54, 55–64, 65–74, 75–84 are 0.75, 0.72, 0.66, and 0.62, respectively), whereas absolute risk reductions increase (SBP: 1.1%, 2.3%, 5.4%, 10.3%, respectively; non–HDL-C: 1.1%, 2.0%, 3.7%, 5.9%, respectively). Conclusions: Although individual modifiable CHD risk factors contribute only modestly to prognostic performance, our models indicate that eliminating or controlling these individual factors would lead to substantial reductions in total population CHD events. Metrics used to judge importance of risk factors should be tailored to the research objectives.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Standard statistical measures that quantify the added prognostic value of new biomarkers in risk prediction models were not designed to compare highly correlated and biologically tightly related variables such as LDL-C, non-HDL-C, or apoB.
“Furthermore, standard statistical measures that quantify the added prognostic value of new biomarkers in risk prediction models were not designed to compare highly correlated and biologically tightly related variables such as LDL-C, non-HDL-C, or apoB.14”
From Discordance among apoB, non-high-density lipoprotein cholesterol, and triglycerides: implications for cardiovascular prevention (2024) · cites this paper as [14]
The Discussion (page unavailable) says that added-last prognostic measures are well suited to new biomarkers but can mask the importance of established, treatable risk factors. It does not say these measures were not designed to compare highly correlated lipid variables, or assess that limitation for LDL-C, non-HDL-C, and apoB. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Stone, N. J., Robinson, J. G., Lichtenstein, A. H., Bairey Merz, C. N., Blum, C. B., Eckel, R. H., Goldberg, A. C., Gordon, D., Levy, D., Lloyd-Jones, D. M., et al. (2014) · Circulation · doi:10.1161/01.cir.0000437738.63853.7a · Cited by 7,081 · see citing works
Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493 · see citing works
Self-citation2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk
Goff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., et al. (2014) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2013.11.005 · Cited by 4,945 · see citing works
Self-citationGoff, D. C., Lloyd-Jones, D. M., Bennett, G., Coady, S., D’Agostino, R. B., Gibbons, R., Greenland, P., Lackland, D. T., Levy, D., O’Donnell, C. J., et al. (2014) · Circulation · doi:10.1161/01.cir.0000437741.48606.98 · Cited by 3,697 · see citing works
Self-citationPencina, M. J., & D’Agostino, R. B. (2004) · Statistics in Medicine · doi:10.1002/sim.1802 · Cited by 1,442 · see citing works
Self-citationMultiple Biomarkers for the Prediction of First Major Cardiovascular Events and Death
Wang, T. J., Gona, P. N., Larson, M. G., Tofler, G. H., Levy, D. A., Newton‐Cheh, C., Jacques, P. F., Rifai, N., Selhub, J., Robins, S. J., et al. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa055373 · Cited by 1,337 · see citing works
Self-citationWhelton, P. K., Carey, R. M., Aronow, W. S., Casey, D. E., Collins, K. J., Dennison Himmelfarb, C., DePalma, S. M., Gidding, S., Jamerson, K. A., Jones, D. W., et al. (2018) · Circulation · doi:10.1161/cir.0000000000000596 · Cited by 1,127 · see citing works
Tobin, M. D., Sheehan, N. A., Scurrah, K. J., & Burton, P. R. (2005) · Statistics in Medicine · doi:10.1002/sim.2165 · Cited by 695 · see citing works
Evidence for a Gene Influencing Blood Pressure on Chromosome 17
Levy, D. A., DeStefano, A. L., Larson, M. G., O’Donnell, C. J., Lifton, R. P., Gavras, H. P., Cupples, L. A., & Myers, R. H. (2000) · Hypertension · doi:10.1161/01.hyp.36.4.477 · Cited by 594 · see citing works
Hyperlipidemia in Early Adulthood Increases Long-Term Risk of Coronary Heart Disease
Navar‐Boggan, A. M., Peterson, E. D., D’Agostino, R. B., Neely, B., Sniderman, A. D., & Pencina, M. J. (2015) · Circulation · doi:10.1161/circulationaha.114.012477 · Cited by 408 · see citing works
Self-citationDetermining When to Add Nonstatin Therapy
Robinson, J. G., Huijgen, R., Ray, K. K., Persons, J. E., Kastelein, J. J. P., & Pencina, M. J. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.09.928 · Cited by 128 · see citing works
Self-citationLaaksonen, M. A., Knekt, P., Rissanen, H. A., Härkänen, T., Virtala, E. T., Marniemi, J., Aromaa, A., Heliövaara, M., & Reunanen, A. (2009) · European Journal of Epidemiology · doi:10.1007/s10654-009-9405-0 · Cited by 93 · see citing works
Net reclassification index at event rate: properties and relationships
Pencina, M. J., Steyerberg, E. W., & D’Agostino, R. B. (2016) · Statistics in Medicine · doi:10.1002/sim.7041 · Cited by 91 · see citing works
Self-citationDrugs for Primary Prevention of Atherosclerotic Cardiovascular Disease
Karmali, K. N., Lloyd‐Jones, D. M., Berendsen, M. A., Goff, D. C., Sanghavi, D. M., Brown, N. C., Korenovska, L., & Huffman, M. D. (2016) · JAMA Cardiology · doi:10.1001/jamacardio.2016.0218 · Cited by 88 · see citing works
Thanassoulis, G., Williams, K., Altobelli, K. K., Pencina, M. J., Cannon, C. P., & Sniderman, A. D. (2016) · Circulation · doi:10.1161/circulationaha.115.018383 · Cited by 85 · see citing works
Self-citationAllen, N. B., Zhao, L., Liu, L., Daviglus, M., Liu, K., Fries, J., Shih, Y.-C. T., Garside, D., Vu, T.-H., Stamler, J., et al. (2017) · Circulation · doi:10.1161/circulationaha.116.026252 · Cited by 81 · see citing works
Giffen, C. A., Carroll, L. E., Adams, J. T., Brennan, S. P., Coady, S., & Wagner, E. L. (2015) · Biopreservation and Biobanking · doi:10.1089/bio.2014.0050 · Cited by 79 · see citing works
Laaksonen, M. A., Knekt, P., Härkänen, T., Virtala, E. T., & Oja, H. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwp457 · Cited by 41 · see citing works
Assessing the incremental role of novel and emerging risk factors.
Cook, N. R. (2010) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-010-0084-x · Cited by 39 · see citing works
Návar, A. M., Peterson, E. D., WOJDYLA, D. M., Sanchez, R. J., Sniderman, A. D., D’Agostino, R. B., & Pencina, M. J. (2016) · JAMA · doi:10.1001/jama.2016.13614 · Cited by 34 · see citing works
Self-citationAssessing Cardiovascular Risk to Guide Hypertension Diagnosis and Treatment
Návar, A. M., Pencina, M. J., & Peterson, E. D. (2016) · JAMA Cardiology · doi:10.1001/jamacardio.2016.2861 · Cited by 32 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [11]Harrell FE Jr Regression modeling strategies 2001 Springer
Cited by
Sorted by citations
Ross, R., Neeland, I. J., Yamashita, S., Shai, I., Seidell, J., Magni, P., Santos, R. D., Arsenault, B., Cuevas, A., Hu, F. B., et al. (2020) · Nature Reviews Endocrinology · doi:10.1038/s41574-019-0310-7 · Cited by 1,966 · see citing works
Khan, S. S., Coresh, J., Pencina, M. J., Ndumele, C. E., Rangaswami, J., Chow, S. L., Palaniappan, L. P., Sperling, L. S., Virani, S. S., Ho, J. E., et al. (2023) · Circulation · doi:10.1161/cir.0000000000001191 · Cited by 797 · see citing works
Self-citationApolipoprotein B Particles and Cardiovascular Disease: A Narrative Review
Sniderman, A. D., Thanassoulis, G., Glavinovic, T., Navar, A. M., Pencina, M., Catapano, A., & Ference, B. A. (2019) · JAMA Cardiology · doi:10.1001/jamacardio.2019.3780 · Cited by 593 · see citing works
Self-citationWelsh, C., Celis-Morales, C. A., Brown, R., Mackay, D. F., Lewsey, J., Mark, P. B., Gray, S. R., Ferguson, L. D., Anderson, J. J., Lyall, D. M., et al. (2019) · Circulation · doi:10.1161/circulationaha.119.041149 · Cited by 191 · see citing works
Sniderman, A. D., Dufresne, L., Pencina, K. M., Bilgic, S., Thanassoulis, G., & Pencina, M. J. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae258 · Cited by 117 · see citing works
“14 Finally, when recommended by guidelines and consensus groups, measurement of apoB has been prioritized or restricted to patients with hypertriglycer-idaemia, the presumption being cholesterol-depleted apoB particles are only common in such individuals.”
BackgroundSelf-citationLong-Term Effectiveness of Liraglutide for Weight Management and Glycemic Control in Type 2 Diabetes
Mirabelli, M., Chiefari, E., Caroleo, P., Arcidiacono, B., Corigliano, D. M., Giuliano, S., Brunetti, F. S., Tanyolaç, S., Foti, D. P., Puccio, L., et al. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17010207 · Cited by 61 · see citing works
Grancieri, M., Verediano, T. A., Sant'Ana, C. T., de Assis, A., Toledo, R. L., de Mejia, E. G., & Martino, H. S. D. (2022) · PharmaNutrition · doi:10.1016/j.phanu.2022.100298 · Cited by 37 · see citing works
- Authors
- Pencina, M. J., Navar, A. M., Wojdyla, D., Sanchez, R. J., Khan, I., Elassal, J., D’Agostino, R. B., Peterson, E. D., & Sniderman, A. D.
- Affiliations
- Duke University (US)
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC (M.J.P., A.M.N., D.W., E.D.P.).
- Regeneron (United States) (US)
- Sanofi (United States) (US)
- Boston University (US)
- McGill University Health Centre (CA)
- Corresponding author
- Pencina, Michael J.
- ORCID
- Venue
- Circulation
- Published
- 2019-03-26
- Volume
- 139
- Issue
- 13
- Pages
- 1603-1611
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- PMC
- PMC6433489
- OpenAlex
- W2905401804
- Cited by
- 247
- References
- 21
- Citations by year
- 2026: 28 · 2025: 42 · 2024: 42 · 2023: 41 · 2022: 35 · 2021: 23 · 2020: 21 · 2019: 14
- FWCI
- 16.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Circulation (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Sanofi
- Regeneron Pharmaceuticals
- Javna Agencija za Raziskovalno Dejavnost RS
- National Heart, Lung, and Blood Institute (K01HL133416)
- Duke Clinical Research Institute
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Keywords
- coronary artery disease, cardiovascular risk factors, systolic blood pressure, non-HDL cholesterol, diabetes mellitus, smoking, population attributable fraction, absolute risk reduction, cardiovascular risk prediction models, primordial prevention, antihypertensive therapy, lipid-lowering therapy, cardiovascular disease prevention
- MeSH terms
- Aged, Aged, 80 and over, Blood Pressure, Coronary Disease, Diabetes Mellitus, Female, Humans, Cholesterol, HDL, Cholesterol, LDL, Male, Middle Aged, Risk Factors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
