Clinical Chemistry
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Clinical Chemistry 52: 2049-2053, 2006. First published September 21, 2006; 10.1373/clinchem.2006.070094
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow 070094.Supplemental Data
Right arrow All Versions of this Article:
clinchem.2006.070094v1
52/11/2049    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (8)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Okazaki, M.
Right arrow Articles by Tomoike, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Okazaki, M.
Right arrow Articles by Tomoike, H.
(Clinical Chemistry. 2006;52:2049-2053.)
© 2006 American Association for Clinical Chemistry, Inc.

Lipids, Lipoproteins, and Cardiovascular Risk Factors

Component Analysis of HPLC Profiles of Unique Lipoprotein Subclass Cholesterols for Detection of Coronary Artery Disease

Mitsuyo Okazaki1,a, Shinichi Usui2, Akio Fukui3, Isao Kubota3 and Hitonobu Tomoike4

1 Laboratory of Chemistry, College of Liberal Arts and Sciences, Tokyo Medical and Dental University, Ichikawa, Japan.
2 Faculty of Health Sciences, Okayama University Medical School, Okayama, Japan.
3 Department of Cardiology, Pulmonology and Nephrology, Course of Internal Medicine and Therapeutics, Yamagata University School of Medicine, Yamagata, Japan.
4 National Cardiovascular Center, Osaka, Japan.

aAddress correspondence to this author at: Laboratory of Chemistry, College of Liberal Arts and Sciences, Tokyo Medical and Dental University, 2-8-30, Kohnodai, Ichikawa-shi, Chiba 272-0827, Japan. e-mail okazaki.las{at}tmd.ac.jp.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: Patients with coronary artery disease (CAD) are known to have several lipoprotein abnormalities. We examined plasma cholesterol concentrations of major lipoproteins and their subclasses, using a gel permeation HPLC, to establish an association between a lipoprotein subclass pattern and the presence of CAD.

Methods: We performed a simple and fully automated HPLC, followed by mathematical treatment on chromatograms, for measuring cholesterol concentrations of major lipoproteins and their subclasses in 62 male patients (45 with CAD and 17 controls without CAD) who underwent cardiac catheterization.

Results: For major lipoprotein classes, the patient group had a significantly (P <0.05) higher LDL-cholesterol (LDL-C) and lower HDL-cholesterol (HDL-C), but no difference in VLDL-cholesterol (VLDL-C) concentrations. For lipoprotein subclasses, the patient group had a significantly higher small VLDL-C (mean particle diameter of 31.3 nm, P <0.001), small LDL-C (23.0 nm, P <0.05), and very small LDL-C (16.7–20.7 nm, P <0.001), but a significantly lower large HDL-C (12.1 nm, P <0.001) concentrations. Combined variables of "small VLDL-C + small LDL-C + very small LDL-C – large HDL-C" differentiated the patient from the control group more clearly than single-subclass measurements or calculated traditional lipid markers.

Conclusions: These results suggest the usefulness of multiple and simultaneous subclass analysis of proatherogenic and antiatherogenic lipoproteins and indicate that HPLC and its component analysis can be used for easy detection and evaluation of abnormal distribution of lipoprotein subclasses associated with CAD.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Patients with coronary artery disease (CAD)1 have several lipoprotein abnormalities, such as increased triglyceride (TG) and LDL-cholesterol (LDL-C), and decreased HDL-cholesterol (HDL-C) (1)(2). In epidemiological studies, calculated non–HDL-C concentration [the difference of total cholesterol (TC) and HDL-C] was demonstrated to be a stronger predictor of cardiovascular events than plasma TC alone (3)(4). Partially catabolized TG-rich lipoproteins such as intermediate-density lipoproteins and remnant-like particles (RLP) are considered to be highly atherogenic (5). The atherogenic lipoprotein phenotype has been defined as the presence of a predominance of small, dense LDL particles and high TG and low HDL-C concentrations (6)(7). Prospective studies have reported the small LDL phenotype to be an important predictor of subsequent CAD (8)(9). Therefore, detailed analysis of both major lipoproteins and their subclasses, including remnant lipoproteins, might be required for more effective assessment of CAD risk status. Many techniques have been used for lipoprotein subclass analysis; analytical ultracentrifugation (10), sequential separation at various densities (11), rate zonal ultracentrifugation (12), and density-gradient ultracentrifugation (13). All these techniques are laborious and not available in routine clinical laboratories.

Lipoprotein particle size analysis is usually performed with nondenaturing gradient gel electrophoresis (8)(9). Alternative techniques include a Lipoprint LDL system with a nongradient (3%) polyacrylamide gel electrophoretic method (14) and nuclear magnetic resonance (15)(16)(17). In addition, Hirano et al. (18) reported a new technique for small LDL-C measurement that combines selective precipitation and a homogeneous LDL-C assay. Techniques for remnant lipoprotein measurement include a homogeneous assay developed by Miyauchi et al. (19) and an immunoseparation method by Nakajima et al. (20). Detailed HDL subclass analysis has been performed with 2-dimentional gel electrophoresis (21)(22).

HPLC with gel permeation columns is an alternative method for classifying and quantifying lipoproteins on the basis of differences of particle size (23)(24)(25). Component analysis of cholesterol profiles after HPLC can provide useful information about almost all of the above-mentioned atherogenic lipoprotein subclasses (26)(27).

In the current study, we applied simple and fully automated HPLC, followed by mathematical treatment analysis of chromatograms, to identify lipoprotein subclass patterns associated with the presence of CAD in men who underwent cardiac catheterization.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
study participants
The 62 study patients, [mean (SD) age 62 (9) years, range 41–76 years], were selected from 609 consecutive male patients who underwent cardiac catheterization in Yamagata University Hospital. We excluded patients who had diabetes mellitus, renal or liver disease, or were receiving any lipid-lowering medication. All participants gave their informed consent before entering the study. The study was carried out according to guidelines of the institutional review board.

The presence of CAD was assessed by coronary angiography. Narrowing of the luminal diameter of the coronary artery by ≥75% was considered significant. Coronary vasospasm was induced by a provocation test with ergonovine (2–20 µg), and only total or subtotal occlusion was considered positive. According to these assessments, the CAD patient group (n = 45) comprised 21 patients who were myocardial infarction (MI) survivors (acute MI, 3; MI within 1 month, 11; previous MI, 7), 22 patients with angina (effort angina, 7; unstable angina, 4; vasospastic angina, 11), and 2 patients with silent myocardial ischemia with considerable coronary stenosis. The non-CAD group (n = 17) comprised patients without substantial coronary stenosis (atypical chest pain, 4; cardiomyopathy, 6; aortic valve disease, 4; electrocardiogram abnormality, 3).

After patients had fasted overnight, venous blood was drawn and placed in tubes containing disodium EDTA (1 g/L). Plasma samples were kept in a refrigerator and analyzed within 7 days after blood collection.

hplc method
Plasma lipoproteins were analyzed by HPLC, as previously described (24)(27)(28). We defined 3 VLDL subclasses, 4 LDL subclasses, and 5 HDL subclasses according to lipoprotein particle size (diameter) from 20 component peaks [see Table 1 . in the Data Supplement that accompanies the online version of this article at http://www.clinchem.org/content/vol52/issue11] determined on the basis of mean profiles of healthy and hyperlipidemic persons (27).


View this table:
[in this window]
[in a new window]
  		Table 1. Clinical characteristics, lipid and major lipoprotein concentrations in studied participants.1

other clinical and lipid parameter analysis
We used commercial enzymatic reagent sets (Kyowa Medex) to measure plasma TG. Fasting blood sugar was measured by the enzymatic method (Arkray Inc.). We used medical records and questionnaires to obtain data on age, weight, height, smoking history, family history, hypertension, previous MI, angina pectoris, diabetes mellitus, and medication use. Body mass index was calculated from weight and height as weight (kg) per [height (m)]2.

statistical analysis
Data were analyzed with SPSS (Version 10.0, SPSS Inc.). Continuous measures are expressed as mean (SD), and judged by Student t-test. Dichotomous variables were tested by means of {chi}2 analysis. Area under the curve (AUC) of ROC curve was calculated to describe the power of variables to distinguish the CAD patients from the control individuals. A value of P <0.05 was considered to be statistically different.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Clinical characteristics, lipid and major lipoprotein concentrations in 62 men in this study are shown in Table 1Up . The patient group had significantly (P <0.05) higher LDL-C and lower HDL-C, but there was no difference in VLDL-C concentrations.

Cholesterol concentrations in each lipoprotein subclass are summarized in Fig. 1 . The patient group had significant increases in small VLDL-C (P <0.001), small LDL-C (P <0.05), and very small LDL-C (P <0.001), but a significant decrease in large HDL-C (P <0.001).


Figure 1
View larger version (19K):
[in this window]
[in a new window]
  		Figure 1. Comparison of cholesterol concentrations in lipoprotein subclasses between the CAD patients (n = 45) and the control participants (n = 17).

The values are shown as mean (SD). Closed and open columns represent the CAD patient and the control groups, respectively.

To more clearly distinguish between the 2 groups, several derived variables were calculated from each subclass. Three subclasses (small VLDL, small LDL, and very small LDL), which were significantly increased in the patient group, were combined and expressed as Vs + Ls. Ls represented the sum of small LDL and very small LDL. The difference or the ratio of increased subclasses (Vs + Ls) and a decreased subclass (large HDL) was expressed as Vs + Ls – Hs, and (Vs + Ls)/Hs or Ls/Hs, respectively.

In Table 2 , these derived variables by HPLC and the calculated traditional risk markers, non-HDL-C, TC/HDL-C, and LDL-C/HDL-C ratios, were compared between the patient and the control groups. All derived HPLC variables were significantly higher in the patient group, and Vs + Ls – Hs showed the highest significant differences (P <0.001) in the 2 groups. All the traditional risk markers, non-HDL-C, TC/HDL-C, and LDL-C/HDL-C ratios, were significantly higher in the patient group.


View this table:
[in this window]
[in a new window]
  		Table 2. Comparison of derived HPLC and traditional variables between the patients with CAD and the control participants without CAD.

On ROC analysis, AUCs for the derived HPLC variables and the calculated traditional risk markers were estimated to describe the power of the variables to distinguish between the 2 groups. AUCs and the range of 95% confidence intervals are presented in Table 2Up . From ROC analysis, the derived HPLC variable Vs + Ls – Hs produced the greatest AUC, indicating that this variable was a more powerful discriminator than any other derived variable, including traditional risk markers.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
In this study, we compared for the first time lipoprotein cholesterol profiles between CAD patients and controls and propose a unique lipoprotein subclass pattern for identifying the patients at increased risk for CAD. HPLC has been used for decades in lipoprotein research applications and as a routine method (24)(25)(27), but the present study is a first report for clinical and diagnostic testing of lipoprotein subclass analysis by HPLC in CAD patients. Analytical precision of HPLC was previously demonstrated to be acceptable in the determination of 3 VLDL, 4 LDL, and 5 HDL subclasses, with CV values of 1%–4% (n = 5), results that were comparable to those obtained with major lipoprotein quantification (27)(28). Our HPLC and the traditional methods [Friedewald equation for LDL-C (29) and the precipitation method for HDL-C (30)] were in agreement for LDL-C and HDL-C values (r >0.97), as described previously (27).

As presented in Tables 1Up and 2Up , significantly higher LDL-C and lower HDL-C concentrations (P <0.05) were observed in the patient group, and these significant differences were more clearly differentiated by computed values of TC/HDL-C or LDL-C/HDL-C (P <0.01). Non-HDL-C has been recently recognized as one of the calculated risk markers for CAD (3)(4) but was less significant (P <0.05) in this study (Table 2Up .).

In the subclass analysis by HPLC (Fig. 1Up ), small VLDL-C, small LDL-C, very small LDL-C, and large HDL-C were found to be significantly different between the 2 groups. Increased small VLDL-C in the CAD group might represent an increase in remnant lipoproteins, although neither intermediate-density lipoprotein-C nor RLP-C was measured in this study. Our previous study showed that RLP fractions isolated by an immunoaffinity separation were very heterogeneous (31), but the particle size of RLP from Type III hyperlipidemia corresponded mainly to small VLDL (peak 7). In another previous study, all VLDL subclasses were positively correlated with visceral fat area, and small VLDL remained considerable after adjustment for serum TG concentration (27). In conjunction with these previous studies, the increase of small VLDL-C in CAD patients also supports the concept that smaller, partially catabolized triglyceride-rich lipoprotein (VLDL remnants) and/or a part of intermediate-density lipoprotein are atherogenic.

Although HDL subclasses are also heterogeneous and their atherogenic properties differ between subclasses (32), many investigators suggest that measuring HDL subclasses may provide additional information about risk for the development of CAD. In this study, AUC for large HDL was larger than total HDL-C (results not shown), indicating the potential usefulness of HDL subclass analysis.

Increased small LDL-C and very small LDL-C in the CAD patients were consistent with atherogenic profiles of increasing remnant lipoproteins as well as small, dense LDL reported by Cohn et al. (33)(34), but their ability to differentiate between the CAD patients and the controls was not as strong when compared with small VLDL-C, judged by AUC (results not shown).

To more clearly differentiate between the 2 groups, several derived variables were calculated from each subclass, as shown in Table 2Up . AUC for all derived HPLC variables except for Ls (small LDL + very small LDL) was larger than that for traditional risk marker, and Vs + Ls – Hs produced the largest AUC. These observations indicate that the new parameter, Vs + Ls – Hs, might be useful for total interpretation of both proatherogenic and antiatherogenic lipoproteins and provide additional clinical information to evaluate the risk status for CAD.

In conclusion, component analysis after HPLC provided the cholesterol concentrations of major lipoproteins and their subclasses within 16 min with a small volume of plasma or serum (<10 µL). Our results support the general concept of the usefulness of lipoprotein subclass analysis for diagnostic testing. Larger clinical trials are needed to establish the diagnostic significance of our proposed parameter, Vs + Ls – Hs, for identifying the patients at increased risk for CAD.


   Acknowledgments
 
We gratefully acknowledge Kyowa Medex, Japan for providing enzyme reagents for the cholesterol measurement by HPLC. We greatly thank Skylight Biotech Inc. (Akita, Japan) for technical assistances in this study.


   Footnotes
 
1 Nonstandard abbreviations: CAD, coronary artery disease; TG, triglyceride; LDL-C, LDL-cholesterol; HDL-C, HDL-cholesterol; TC, total cholesterol; RLP, remnant-like particle; MI, myocardial infarction; AUC, area under the curve.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Kannel WB, Castelli WP, Gordon T, McNamara PM. Serum cholesterol, lipoproteins and risk of coronary heart diseases. The Framingham study. Ann Intern Med 1971;74:1-12.[Abstract/Free Full Text]
  2. Wilson PW, Abbott RD, Castelli WP. High density lipoprotein cholesterol and mortality. The Framingham Heart Study. Arteriosclerosis 1988;8:737-741.[Abstract/Free Full Text]
  3. Grundy SM. Low-density lipoprotein, non-high-density lipoprotein, and apolipoprotein B as targets of lipid-lowering therapy. Circulation 2002;106:2526-2529.[Free Full Text]
  4. Frost PH, Havel RJ. Rationale for use of non-high-density lipoprotein cholesterol rather than low-density lipoprotein cholesterol as a tool for lipoprotein cholesterol screening and assessment of risk and therapy. Am J Cardiol 1998;81:26B-31B.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  5. Zilversmit DB. Atherogenic nature of triglycerides, postprandial lipidemia, and triglyceride-rich remnant lipoproteins. Clin Chem 1995;41:153-158.[Abstract/Free Full Text]
  6. Austin MA, King MC, Vranizan KM, Krauss RM. Atherogenic lipoprotein phenotype: a proposed genetic marker for coronary heart disease risk. Circulation 1990;82:495-506.[Abstract/Free Full Text]
  7. Campos H, Genest JJ, Jr, Blijlevens E, McNamara JR, Jenner JL, Ordovas JM, et al. Low density lipoprotein particle size and coronary artery disease. Arterioscler Thromb 1992;12:187-195.[Abstract/Free Full Text]
  8. Lamarche B, Tchernof A, Moorjani S, Cantin B, Dagenais GR, Lupien PJ, et al. Small, dense low-density lipoprotein particles as a predictor of the risk of ischemic heart disease in men. Circulation 1997;95:69-75.[Abstract/Free Full Text]
  9. St-Pierre AC, Cantin B, Dagenais GR, Mauriege P, Bernard PM, Despres JP, et al. Low-density lipoprotein subfractions and the long-term risk of ischemic heart disease in men: 13-year follow-up data from the Quebec Cardiovascular Study. Arterioscler Thromb Vasc Biol 2005;25:553-559.[Abstract/Free Full Text]
  10. Krauss RM, Lindgren FT, Ray RM. Interrelationship among subgroups of serum lipoproteins in normal human subjects. Clin Chim Acta 1980;104:275-290.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  11. Havel RJ, Eder HA, Bragdon J. The distribution and chemical composition of ultracentrifugally separated lipoproteins in human serum. J Clin Invest 1955;34:1345-1353.[Web of Science][Medline] [Order article via Infotrieve]
  12. Patsch JR, Patsch W. Zonal ultracentrifugation. Method Enzymol 1986;129:3-26.[Medline] [Order article via Infotrieve]
  13. Shen MM, Krauss RM, Lindgren FT, Forte TM. Heterogeneity of serum low density lipoproteins in normal human subjects. J Lipid Res 1981;22:236-244.[Abstract]
  14. Hoefner DM, Hodel SD, O’Brien JF, Branum EL, Sun D, Meissner I, et al. Development of a rapid, quantitative method for LDL subfractionation with use of the Quantimetrix Lipoprint LDL System. Clin Chem 2001;47:266-274.[Abstract/Free Full Text]
  15. Otvos JD, Jeyarajah EJ, Bennett DW, Krauss RM. Development of a proton nuclear magnetic resonance spectroscopic method for determining plasma lipoprotein concentrations and subspecies distributions from a single, rapid measurement. Clin Chem 1992;38:1632-1638.[Abstract/Free Full Text]
  16. Kuller L, Arnold A, Tracy R, Otvos J, Burke G, Psaty B, et al. Nuclear magnetic resonance spectroscopy of lipoproteins and risk of CHD in the Cardiovascular Health Study. Arterioscler Thromb Vasc Biol 2002;22:1175-1180.[Abstract/Free Full Text]
  17. Freedman DS, Otvos JD, Jeyarajah EJ, Shalaurova I, Cupples LA, Parise H, et al. Sex and age differences in lipoprotein subclasses measured by nuclear magnetic resonance spectroscopy: The Framingham Study. Clin Chem 2004;50:1189-1200.[Abstract/Free Full Text]
  18. Hirano T, Ito Y, Saegusa H, Yoshino G. A novel and simple method for quantification of small, dense LDL. J Lipid Res 2003;44:2193-2201.[Abstract/Free Full Text]
  19. Miyauchi K, Kayahara N, Mori H, Sugiuchi H, Irie T. Development of homogeneous assay for measuring remnant-like particle cholesterol in serum [abstract]. Clin Chem 2005;51:A129.
  20. Nakajima K, Saito T, Tamura A, Suzuki M, Nakano T, Adachi M, et al. Cholesterol in remnant-like lipoproteins in human serum using monoclonal anti apolipoprotein B-100 and anti apolipoprotein A-I immunoaffinity mixed gels. Clin Chim Acta 1993;223:53-71.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  21. Ishida BY, Frolich J, Fielding CJ. Prebeta-migrating high density lipoprotein: quantitation in normal and hyperlipidemic plasma by solid phase radioimmunoassay following electrophoretic transfer. J Lipid Res 1987;28:778-786.[Abstract]
  22. Asztalos BF, Lefevre M, Foster TA, Tulley R, Windhauser M, Wong L, et al. . Arterioscler Thromb Vasc Biol 1997;17:1885-1893.[Abstract/Free Full Text]
  23. Hara I, Okazaki M. High-performance liquid chromatography of serum lipoproteins. Methods Enzymol 1986;129:57-78.[Medline] [Order article via Infotrieve]
  24. Okazaki M, Usui S, Hosaki S.. Analysis of plasma lipoproteins by gel permeation chromatography. Rifai N Warnick GR Dominiczak MH eds. Handbook of Lipoprotein Testing 2000:647-669 AACC Press Washington DC. .
  25. Usui S, Hara Y, Hosaki S, Okazaki M. A new on-line dual enzymatic method for simultaneous quantification of cholesterol and triglycerides in lipoproteins by HPLC. J Lipid Res 2002;43:805-814.[Abstract/Free Full Text]
  26. Kazama H, Usui S, Okazaki M, Hosoi T, Ito H, Orimo H. Effects of bezafibrate and pravastatin on remnant-like lipoprotein particles and lipoprotein subclasses in type 2 diabetes. Diabetes Res Clin Pract 2003;59:181-189.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  27. Okazaki M, Usui S, Ishigami M, Sakai N, Nakamura T, Matsuzawa Y, Yamashita S. Identification of unique lipoprotein subclasses for visceral obesity by component analysis of cholesterol profile in high-performance liquid chromatography. Arterioscler Thromb Vasc Biol 2005;25:578-584.[Abstract/Free Full Text]
  28. Usui S, Nakamura M, Jitsukata K, Nara M, Hosaki S, Okazaki M. Assessment of between-instrument variations in a HPLC method for serum lipoproteins and its traceability to reference methods for total cholesterol and HDL-cholesterol. Clin Chem 2000;46:63-72.[Abstract/Free Full Text]
  29. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972;18:499-502.[Abstract]
  30. Okazaki M, Sasamoto K, Muramatsu T, Hosaki S. Evaluation of precipitation and direct methods for HDL-cholesterol assay by HPLC. Clin Chem 1997;43:1885-1890.[Abstract/Free Full Text]
  31. Okazaki M, Usui S, Tada N, Nakano T, Nakajima K. The relation between RLP-triglyceride to RLP-cholesterol ratio in RLP-cholesterol profiles by HPLC. Clin Chim Acta 2000;296:135-149.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  32. Silverman DI, Ginsburg GS, Pasternak RC. High-density lipoprotein subfractions. Am J Med 1993;94:636-645.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  33. Cohn JS, Marcoux C, Davignon J. Detection, quantification, and characterization of potentially atherogenic triglyceride-rich remnant lipoproteins. Arterioscler Thromb Vasc Biol 1999;19:2474-2486.[Abstract/Free Full Text]
  34. Twickler TB, Dallinga-Thie GM, Cohn JS, Chapman MJ. Elevated remnant-like particle cholesterol concentration: a characteristic feature of the atherogenic lipoprotein phenotype. Circulation 2004;109:1918-1925.[Free Full Text]



The following articles in journals at HighWire Press have cited this article:


Home page
 J. Lipid Res.Home page
D. Masuda, K.-i. Hirano, H. Oku, J. C. Sandoval, R. Kawase, M. Yuasa-Kawase, Y. Yamashita, M. Takada, K. Tsubakio-Yamamoto, Y. Tochino, et al.
Chylomicron remnants are increased in the postprandial state in CD36 deficiency
J. Lipid Res., May 1, 2009; 50(5): 999 - 1011.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow 070094.Supplemental Data
Right arrow All Versions of this Article:
clinchem.2006.070094v1
52/11/2049    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (8)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Okazaki, M.
Right arrow Articles by Tomoike, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Okazaki, M.
Right arrow Articles by Tomoike, H.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS