The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Non-HDL-cholesterol shows improved accuracy for cardiovascular risk score classification compared to direct or calculated LDL cholesterol in a dyslipidemic population
Open accessAbstract
BACKGROUND: Our objective was to evaluate the accuracy of cardiovascular disease (CVD) risk score classification by direct LDL cholesterol (dLDL-C), calculated LDL cholesterol (cLDL-C), and non–HDL cholesterol (non–HDL-C) compared to classification by reference measurement procedures (RMPs) performed at the CDC. METHODS: We examined 175 individuals, including 138 with CVD or conditions that may affect LDL-C measurement. dLDL-C measurements were performed using Denka, Kyowa, Sekisui, Serotec, Sysmex, UMA, and Wako reagents. cLDL-C was calculated by the Friedewald equation, using each manufacturer's direct HDL-C assay measurements, and total cholesterol and triglyceride measurements by Roche and Siemens (Advia) assays, respectively. RESULTS: For participants with triglycerides <2.26 mmol/L (<200 mg/dL), the overall misclassification rate for the CVD risk score ranged from 5% to 17% for cLDL-C methods and 8% to 26% for dLDL-C methods when compared to the RMP. Only Wako dLDL-C had fewer misclassifications than its corresponding cLDL-C method (8% vs 17%; P < 0.05). Non–HDL-C assays misclassified fewer patients than dLDL-C for 4 of 8 methods (P < 0.05). For participants with triglycerides ≥2.26 mmol/L (≥200 mg/dL) and <4.52 mmol/L (<400 mg/dL), dLDL-C methods, in general, performed better than cLDL-C methods, and non–HDL-C methods showed better correspondence to the RMP for CVD risk score than either dLDL-C or cLDL-C methods. CONCLUSIONS: Except for hypertriglyceridemic individuals, 7 of 8 dLDL-C methods failed to show improved CVD risk score classification over the corresponding cLDL-C methods. Non–HDL-C showed overall the best concordance with the RMP for CVD risk score classification of both normal and hypertriglyceridemic individuals.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As a result, under most circumstances, they provide very similar information about ASCVD risk [45,105-108].”
From 2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk (2019) · cites this paper as [107]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Despite this limitation, non-HDLC yields more accurate cardiovascular risk classification than either Friedewald cLDLC or dLDLC, and also more consistent risk scores using different manufacturers' assays for dHDLC in the calculation [58].”
From Quantifying atherogenic lipoproteins for lipid-lowering strategies: Consensus-based recommendations from EAS and EFLM (2020) · cites this paper as [58]
In the citing paper
“In general, LDL-C, non-HDL-C, and ApoB concentrations are very highly correlated.”
From 2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk (2019) · cites this paper as [107]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Friedewald, W. T., Levy, R. I., & Fredrickson, D. S. (1972) · Clinical Chemistry · doi:10.1093/clinchem/18.6.499 · Cited by 32,423
Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,944
Expert Panel on Detection, E. (2001) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.285.19.2486 · Cited by 26,011
Major lipids, apolipoproteins, and risk of vascular disease
The Emerging Risk Factors Collaboration* (2009) · JAMA · doi:10.1001/jama.2009.1619 · Cited by 2,731
Castelli, W. P., Doyle, J. T., Gordon, T., Hames, C. G., Hjortland, M. C., Hulley, S. B., Kagan, A., & Zukel, W. J. (1977) · Circulation · doi:10.1161/01.cir.55.5.767 · Cited by 1,492
Current databases on biological variation: pros, cons and progress
C. Ricós, V. A. (1999) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.1080/00365519950185229 · Cited by 987
Ridker, P. M., Rifai, N., Cook, N. R., Bradwin, G., & Buring, J. E. (2005) · JAMA · doi:10.1001/jama.294.3.326 · Cited by 744
Lipoprotein Management in Patients With Cardiometabolic Risk
Brunzell, J. D., Davidson, M. H., Furberg, C. D., Goldberg, R. B., Howard, B. V., Stein, J. H., & Witztum, J. L. (2008) · Diabetes Care · doi:10.2337/dc08-9018 · Cited by 716
Non–High-Density Lipoprotein Cholesterol Level as a Predictor of Cardiovascular Disease Mortality
Cui, Y. A., Blumenthal, R. S., Flaws, J. A., Whiteman, M. K., LANGENBERG, P. W., Bachorik, P. S., & Bush, T. L. (2001) · Archives of Internal Medicine · doi:10.1001/archinte.161.11.1413 · Cited by 657
Ingelsson, E., Schaefer, E. J., Contois, J. H., McNamara, J. R., Sullivan, L., Keyes, M. J., Pencina, M. J., Schoonmaker, C., Wilson, P. W. F., D’Agostino, R. B., et al. (2007) · JAMA · doi:10.1001/jama.298.7.776 · Cited by 608
Lipoproteins, Cardiovascular Disease, and Death
Gordon, T. (1981) · Archives of Internal Medicine · doi:10.1001/archinte.1981.00340090024008 · Cited by 565
Emerging Risk Factors for Atherosclerotic Vascular Disease
Hackam, D. G., & Anand, S. S. (2003) · JAMA · doi:10.1001/jama.290.7.932 · Cited by 555
Apolipoprotein A-I as a Marker of Angiographically Assessed Coronary-Artery Disease
Maciejko, J. J., Holmes, D. R., Kottke, B. A., Zinsmeister, A. R., Dinh, D. M., & Mao, S. J. T. (1983) · New England Journal of Medicine · doi:10.1056/nejm198308183090701 · Cited by 537
Atherogenic Lipoprotein Particles in Atherosclerosis
Carmena, R., Duriez, P., & Fruchart, J.-C. (2004) · Circulation · doi:10.1161/01.cir.0000131511.50734.44 · Cited by 494
Postprandial plasma lipoprotein changes in human subjects of different ages.
Cohn, J. S., McNamara, J. R., Cohn, S. D., Ordovás, J. M., & Schaefer, E. J. (1988) · Journal of Lipid Research · doi:10.1016/s0022-2275(20)38520-5 · Cited by 448
Mora, S., Rifai, N., Buring, J. E., & Ridker, P. M. (2008) · Circulation · doi:10.1161/circulationaha.108.777334 · Cited by 445
Nauck, M., Warnick, G. R., & Rifai, N. (2002) · Clinical Chemistry · doi:10.1093/clinchem/48.2.236 · Cited by 442
Self-citationMiller, W. G., Myers, G. L., Sakurabayashi, I., Bachmann, L. M., Caudill, S. P., Dziekonski, A., Edwards, S., Kimberly, M. M., Korzun, W. J., Leary, E. T., et al. (2010) · Clinical Chemistry · doi:10.1373/clinchem.2009.142810 · Cited by 386 · cited 4×
“Fueron utilizados los resultados de rc-LDL, rc-HDL, dcLDL, dc-HDL, TG y CT de un estudio anterior (9).”
InfluentialSelf-citationHandbook of Lipoprotein Testing
Rifai, N., Warnick, G. R., & Dominiczak, M. H. (2001) · Medical Entomology and Zoology · Cited by 384
Self-citationNon-HDL Cholesterol as a Predictor of Cardiovascular Disease in Type 2 Diabetes
Lu, W., Resnick, H. E., Jablonski, K. A., Jones, K. L., Jain, A. K., Howard, W. J., Robbins, D. C., & Howard, B. V. (2003) · Diabetes Care · doi:10.2337/diacare.26.1.16 · Cited by 370
Liu, J., Sempos, C. T., Donahue, R. P., Dorn, J., Trevisan, M., & Grundy, S. M. (2006) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2006.06.032 · Cited by 277
Liu, J., Sempos, C. T., Donahue, R. P., Dorn, J. P., Trevisan, M., & Grundy, S. M. (2005) · Diabetes Care · doi:10.2337/diacare.28.8.1916 · Cited by 237
Jiang, R., Schulze, M. B., Li, T., Rifai, N., Stampfer, M. J., Rimm, E. B., & Hu, F. B. (2004) · Diabetes Care · doi:10.2337/diacare.27.8.1991 · Cited by 234
Handbook of lipoprotein testing
Contois, J. H. (2001) · Clinica Chimica Acta · doi:10.1016/s0009-8981(01)00486-7 · Cited by 223
Mora, S., Rifai, N., Buring, J. E., & Ridker, P. M. (2009) · Clinical Chemistry · doi:10.1373/clinchem.2008.117929 · Cited by 176
Bittner, V., Hardison, R., Kelsey, S. F., Weiner, B. H., Jacobs, A. K., & Sopko, G. (2002) · Circulation · doi:10.1161/01.cir.0000038496.57570.06 · Cited by 168
van der Steeg, W. A., Boekholdt, S. M., Stein, E. A., El-Harchaoui, K., Stroes, E. S. G., Sandhu, M. S., Wareham, N. J., Jukema, J. W., Luben, R., Zwinderman, A. H., et al. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-146-9-200705010-00007 · Cited by 165
Myers, G. L., Kimberly, M. M., Waymack, P. P., Smith, S. J., Cooper, G. R., & Sampson, E. J. (2000) · Clinical Chemistry · doi:10.1093/clinchem/46.11.1762 · Cited by 160
Self-citationCohn, J. S., McNamara, J. R., & Schaefer, E. J. (1988) · Clinical Chemistry · doi:10.1093/clinchem/34.12.2456 · Cited by 127
Performance of Four Homogeneous Direct Methods for LDL-Cholesterol
Miller, W. G., Waymack, P. P., Anderson, F. P., Ethridge, S. F., & Jayne, E. C. (2002) · Clinical Chemistry · doi:10.1093/clinchem/48.3.489 · Cited by 104
Self-citationMiller, M., Ginsberg, H. N., & Schaefer, E. J. (2008) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2007.11.046 · Cited by 102
Kukita, H., Hiwada, K., & Kokubu, T. (1984) · Atherosclerosis · doi:10.1016/0021-9150(84)90173-4 · Cited by 89
Schectman, G., Patsches, M., & Sasse, E. A. (1996) · Clinical Chemistry · doi:10.1093/clinchem/42.5.732 · Cited by 75
Standardization of Measurements for Cholesterol, Triglycerides, and Major Lipoproteins
Warnick, G. R., Kimberly, M. M., Waymack, P. P., Leary, E. T., & Myers, G. L. (2008) · Laboratory Medicine · doi:10.1309/6ul9rhjh1jffu4py · Cited by 72
Self-citationRifai, N., Merrill, J. R., & Holly, R. G. (1990) · Annals of Clinical Biochemistry International Journal of Laboratory Medicine · doi:10.1177/000456329002700512 · Cited by 70
Sahu, S., Chawla, R., & Uppal, B. (2005) · Indian Journal of Clinical Biochemistry · doi:10.1007/bf02867401 · Cited by 58
Yu, H., Ginsburg, G. S., Harris, N., & Rifai, N. (1997) · The American Journal of Cardiology · doi:10.1016/s0002-9149(97)00668-1 · Cited by 26
Lund, S. S., Petersen, M., Frandsen, M., Smidt, U., Parving, H. ‐., Vaag, A. A., & Jensen, T. (2008) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.1080/00365510801995736 · Cited by 13
D’Agostino (1998) · doi:10.1002/(sici)1097-0258(19981015)17:19
Research works printed but not linked: 1
- Nauck, 2000, Measurement of high-density-lipoprotein cholesterol, 227
Cited by
Sorted by citations
Mach, F., Baigent, C., Catapano, A. L., Koskinas, K. C., Casula, M., Badimon, L., Chapman, M. J., De Backer, G. G., Delgado, V., Ference, B. A., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehz455 · Cited by 8,961
Mach, F., Baigent, C., Catapano, A. L., Koskinas, K. C., Casula, M., Badimon, L., Chapman, M. J., De Backer, G. G., Delgado, V., Ference, B. A., et al. (2019) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2019.08.014 · Cited by 4,854
Sampson, M. L., Ling, C., Sun, Q., Harb, R., Ashmaig, M., Warnick, R., Sethi, A. A., Fleming, J. K., Otvos, J. D., Meeusen, J. W., et al. (2020) · JAMA Cardiology · doi:10.1001/jamacardio.2020.0013 · Cited by 593
Self-citationLanglois, M. R., Chapman, M. J., Cobbaert, C., Mora, S., Remaley, A. T., Ros, E., Watts, G. F., Borén, J., Baum, H., Bruckert, E., et al. (2018) · Clinical Chemistry · doi:10.1373/clinchem.2018.287037 · Cited by 260
Self-citationNordestgaard, B. G., Langlois, M. R., Langsted, A., Chapman, M. J., Aakre, K. M., Baum, H., Borén, J., Bruckert, E., Catapano, A., Cobbaert, C., et al. (2020) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2019.12.005 · Cited by 246
Self-citation
Metadata
- Authors
- van Deventer, H. E., Miller, W. G., Myers, G. L., Sakurabayashi, I., Bachmann, L. M., Caudill, S. P., Dziekonski, A., Edwards, S., Kimberly, M. M., Korzun, W. J., Leary, E. T., Nakajima, K., Nakamura, M., Shamburek, R. D., Vetrovec, G. W., Warnick, G. R., & Remaley, A. T.
- Affiliations
- National Institutes of Health, Bethesda, MD
- Virginia Commonwealth University (US)
- Centers for Disease Control and Prevention (US)
- Jichi Medical University (JP)
- Virginia Commonwealth University, Richmond, VA
- Pacific Biomarkers (United States) (US)
- Otsuka (Japan) (JP)
- Osaka Medical Center for Health Science and Promotion, Osaka, Japan
- National Institutes of Health (US)
- Health Diagnostics Laboratory, Richmond, VA
- Corresponding author
- Remaley, Alan T
- ORCID
- Venue
- Clinical Chemistry
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-03-01
- Volume
- 57
- Issue
- 3
- Pages
- 490-501
- ISSN
- 0009-9147 (ISSN-L)
- 1530-8561
- PMC
- PMC4684263
- OpenAlex
- W2158071129
- Cited by
- 112
- References
- 39
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 6 · 2023: 10 · 2022: 5 · 2021: 3 · 2020: 9 · 2019: 8 · 2018: 12 · 2017: 10 · 2016: 6 · 2015: 6 · 2014: 9 · 2013: 6 · 2012: 9
- FWCI
- 9.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Clinical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Genzyme
- Pointe Scientific
- Pacific Biometrics Research Foundation provided
- Virginia Commonwealth University
- National Institutes of Health
- Warren Grant Magnuson Clinical Center
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Health Sciences › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- non-HDL cholesterol, LDL cholesterol, cardiovascular risk score, dyslipidemia, Friedewald formula, hypertriglyceridemia, assay comparison, reference measurement procedure, misclassification rate
- MeSH terms
- Blood Chemical Analysis, Cardiovascular Diseases, Data Interpretation, Statistical, Fasting, Humans, Cholesterol, HDL, Cholesterol, LDL, Risk Factors, Sensitivity and Specificity, Triglycerides, Ultracentrifugation, Reproducibility of Results, Case-Control Studies, Risk Assessment, Dyslipidemias
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