Clinical Chemistry Siemens Point of Care - Urinalysis
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Clinical Chemistry 54: 124-130, 2008. First published November 16, 2007; 10.1373/clinchem.2007.093468
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplement Data
Right arrow All Versions of this Article:
clinchem.2007.093468v1
54/1/124    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 ISI 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 ISI Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Khuseyinova, N.
Right arrow Articles by Koenig, W.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Khuseyinova, N.
Right arrow Articles by Koenig, W.
Related Collections
Right arrow Lipids, Lipoproteins, and Cardiovascular Risk Factors
(Clinical Chemistry. 2008;54:124-130.)
© 2008 American Association for Clinical Chemistry, Inc.

Lipids, Lipoproteins, and Cardiovascular Risk Factors

Variability of Serial Lipoprotein-Associated Phospholipase A2 Measurements in Post–Myocardial Infarction Patients: Results from the AIRGENE Study Center Augsburg

Natalie Khuseyinova1, Sonja Greven2,3, Regina Rückerl3, Gerlinde Trischler1, Hannelore Loewel3, Annette Peters3 and Wolfgang Koenig1,a

1 Department of Internal Medicine II—Cardiology, University of Ulm Medical Center, Ulm, Germany; 2 Ludwig-Maximilians-University Munich, Department of Statistics, Munich, Germany; 3 National Research Center for Environment and Health, Institute of Epidemiology, Neuherberg, Germany.

aAddress correspondence to this author at: Department of Internal Medicine II—Cardiology, University of Ulm Medical Center, Robert-Koch Str. 8, D-89081 Ulm, Germany. Fax 49-731-500-45021; e-mail wolfgang.koenig{at}uniklinik-ulm.de.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: Of the numerous emerging biomarkers for coronary heart disease (CHD), lipoprotein-associated phospholipase A2 (Lp-PLA2), an enzyme involved in lipid metabolism and inflammatory pathways, seems to be a promising candidate. Implementation of Lp-PLA2 measurement into clinical practice, however, requires data on the reliability of such measurements.

Methods: We measured Lp-PLA2 concentrations by ELISA in blood samples drawn from 200 post–myocardial infarction patients (39–76 years) at 6 monthly intervals between May 2003 and February 2004, for a total of 1143 samples. We estimated analytical, within-individual, and between-individual variation, the critical difference, and the intraclass correlation coefficient of reliability (ICC) to assess the reliability of serial Lp-PLA2 measurements.

Results: The mean (SD) plasma Lp-PLA2 concentration for the study participants was 188.7 (41.8) µg/L, with no significant difference between men and women. The analytical CV for Lp-PLA2 was 4.4%, the within-individual biological CV was 15%, and the between-individual CV was 22%. The ICC was 0.66. An important part of the total variation in plasma Lp-PLA2 concentration was explained by the between-individual variation (as a percentage of the total variance, 66.1%), whereas the within-individual variance was 31.3%. The analytical variance was as low as 2.6%.

Conclusions: Between-individual variation in Lp-PLA2 concentration was substantially greater than within-individual variation. In general, our data demonstrate considerable stability and good reproducibility of serial Lp-PLA2 measurements, results that compared favorably with those for the more commonly measured lipid markers.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
A systemic inflammatory response represents an important feature in atherothrombogenesis (1); therefore, measurement of plasma or serum concentrations of circulating inflammatory biomarkers could aid in identifying individuals at high risk for coronary heart disease (CHD).1 A number of prospective studies of initially healthy individuals and patients with manifest atherosclerosis have convincingly demonstrated strong and independent associations between even slightly increased concentrations of various systemic markers of inflammation and a number of cardiovascular endpoints (2). One of the various emerging biomarkers, lipoprotein-associated phospholipase A2 (Lp-PLA2), an enzyme that circulates primarily bound to LDL, seems to be a promising candidate, the measurement of which might improve our ability to identify patients at high risk. Indeed, a growing body of evidence from large prospective population-based and clinical studies suggests that Lp-PLA2 is a promising and clinically relevant marker for CHD and probably also for stroke [for recent reviews, see (3)(4)]. Because of its ability to generate potent proatherogenic and proinflammatory compounds such as lysophosphatidylcholine and oxidized free fatty acids via hydrolysis of oxidized LDL, Lp-PLA2 may be directly involved in the development of atherosclerotic plaques (3). In contrast to other emerging biomarkers related to inflammation, Lp-PLA2 is not an acute-phase reactant and thus is unaffected by systemic inflammatory processes (e.g., osteoarthritis and chronic obstructive pulmonary disease) (5). Lp-PLA2 may therefore represent a more specific marker of vascular wall inflammation. That Lp-PLA2 is only minimally associated with systemic inflammatory and hemostatic markers (6) may reflect its independent impact on CHD. Moreover, considering that selective inhibitors of Lp-PLA2 are under evaluation in clinical trials (7), this enzyme may represent an attractive and novel therapeutic target for the treatment of atherosclerosis. Before the widespread use of Lp-PLA2 testing in clinical practice can be recommended, however, several important requirements should be fulfilled (e.g., determination of population-based reference values) (8). Recently, such cutoff points have been proposed, with values for men and women of between 230 µg/L and 250 µg/L and with desirable Lp-PLA2 concentrations of ≤235 µg/L (9). Furthermore, rigorous standardization of assays is necessary to ensure adequate reproducibility of Lp-PLA2 measurements, and the imprecision of such assays should be low enough to enable a reliable and accurate assessment of future disease risk with only 1 or 2 samples. Data on the biological variation in Lp-PLA2 concentration are also of considerable relevance, because they may be important for correctly classifying individuals over time.

Although variability data are available for several commonly measured biochemical analytes, such as lipid markers (10) and C-reactive protein (11)(12)(13), data on the analytical and biological variation in Lp-PLA2 measurements are still lacking. Therefore, we investigated analytical imprecision and intra- and interindividual biological variation in serial Lp-PLA2 measurements made over a 6-month period for a cohort of post–myocardial infarction (post-MI) patients.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
study design and population
The multicenter longitudinal AIRGENE (Air Pollution and Inflammatory Response in Myocardial Infarction Survivors: Gene-Environment Interaction in a High Risk Group) study has investigated the role of air pollution in eliciting inflammation in MI survivors in 6 European cities characterized by different climates, air quality, and lifestyles. The participating centers are Athens, Greece; Augsburg, Germany; Barcelona, Spain; Helsinki, Finland; Rome, Italy; and Stockholm, Sweden. The design and general protocol of the AIRGENE study have recently been described in detail (14).

The present analysis is restricted to study participants from Augsburg, Germany, and includes 200 post-MI patients, ages 39–76 years, from whom approximately 6 blood samples were collected at monthly intervals between May 2003 and March 2004, for a total of 1143 samples. To be included in the study, all study participants had to fulfill the following inclusion criteria: (a) survival after an MI for 3 months to 6 years before study entry (corresponding to an MI occurring between 1997 and 2003) and (b) an age between 35 and 80 years. Post-MI patients were identified through the MONICA/KORA [Monitoring of Trends and Determinants in Cardiovascular Disease/Kooperative Gesundheitsforschung in der Region Augsburg (Cooperative Health Research in the Region of Augsburg)] coronary-event registry, and a major nonfatal MI was diagnosed according to European Society of Cardiology and American College of Cardiology criteria (15)(16). The primary exclusion criteria were an MI and/or interventional procedure <3 months before the beginning of the study, an extended period of absence from the study area planned during the study period, or only 1 or no valid blood sample available for the patient. In addition, none of the study participants were allowed to have any of the following disorders associated with an acute-phase reaction: febrile acute infection, acute state of a chronic infection, or an inflammatory disease; underlying hematologic or malignant diseases; severe liver and renal disorders; surgery; or a major dental procedure during the 3 days preceding the clinical visit. The study was approved by the local authorities. Written informed consent was obtained from all patients at the first clinical visit after a detailed description of the study protocol was presented.

data collection
The health status of each patient was assessed at the first visit, and all study participants underwent a standardized interview carried out by a specially trained team of interviewers. Participants were asked about their medical history, including specific questions related to the history of CHD and other comorbidities. Data regarding current medications, sociodemographic characteristics, and lifestyle habits were also recorded. Finally, all study participants underwent a medical examination, including measurements of blood pressure and body mass index and a resting 12-lead electrocardiogram.

laboratory methods
Venous blood was drawn under standardized conditions at the clinical visits, which were usually scheduled both for the same time of day to minimize the effects of circadian variation and on the same day of the week to minimize preanalytical variation. Samples were cooled down and stored at 4 °C until further processing, which was within 4 h of blood withdrawal. To obtain plasma samples, we centrifuged EDTA-treated blood at 2500g for 20 min and immediately aliquoted and froze the centrifuged samples at –80 °C until further analysis. No samples were inadvertently thawed during storage.

Lp-PLA2 concentrations in the plasma were measured with a commercial Lp-PLA2 ELISA (second-generation PLACTM test; diaDexus) (17). The interassay CV was 5.6% at an Lp-PLA2 concentration of 239.1 µg/L and 6.27% at 469.0 µg/L. Total cholesterol (TC) was measured by routine enzymatic methods [cholesterol oxidase-peroxidase method (CHOD-PAP); Boehringer Mannheim]. HDL cholesterol was measured in the supernatant after apolipoprotein B–containing lipoproteins were precipitated with phosphotungstic acid and MgCl2 (Boehringer Mannheim). All analyses were run in a blinded fashion.

statistical analysis
The distributions of baseline demographic features, clinical characteristics, and several biochemical markers in the study population are presented as the mean (SD) or as percentages. Residuals were checked for normality. Following concepts described by Fleiss (18), Bland and Altman (19), and Fraser and Harris (20) for the analysis of biological variation, we computed estimates for the 3 variance components (VCs) [VCa (analytical), VCb (between subjects), and VCw (within subjects)], assuming nested normal random-effects models and using the SAS MIXED procedure (restricted maximum-likelihood method, RANDOM statement; SAS Institute). These components of variation (i.e., VCa, VCb, and VCw) were then transformed into the corresponding CVs (CVa, CVb, and CVw, respectively) by calculating the square root of the respective variance component estimate, dividing by the overall mean, and expressing the quotient as a percentage. We evaluated within-subject variation by calculating the critical difference (also called the reference change value or the 95% repeatability coefficient) according to Bland and Altman (19), as follows: 1.96 x (2)1/2 x (VCw + VCa)1/2. We characterized the repeatability of measurements for comparing subjects or groups of subjects by calculating the intraclass correlation coefficient of reliability (ICC; also known as the reliability coefficient): VCb/(VCb + VCw +VCa) (21). In addition, we assessed the imprecision of measurements (analytical variation, or the error of the measurement process itself) by calculating the critical difference as 1.96 x (2)1/2 x VCa1/2 and the ICC as (VCb + VCw)/(VCb + VCw + VCa). We also calculated an "index of individuality" (IoI), which was evaluated as the ratio: (CVw + CVa)/CVb, with reference values ≤0.6 indicating little utility of the reference values for detecting unusual individual results and values ≥1.4 indicating the usefulness of reference values in many settings (20)(22). The number of measurements required to achieve a reliability of 75% was computed with the Spearman-Brown prediction formula (18). Finally, we also checked for outlying observation variances and subject variances with the Cochran test and checked outlying mean patient values with the Reed criterion (20). The Cochran test was computed with the outliers package in R, version 2.4.0. All other statistical analyses were performed with the SAS software package, version 9.1 for Windows (SAS Institute).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
In this study, we included 200 MI survivors [mean age, 62.0 (9.0) years] who had provided at least 2 valid repeated blood samples. Table 1 summarizes the baseline demographic and clinical characteristics of the study population, as well as the mean values of several laboratory variables measured at the baseline visit. In this predominantly male population (82%), the mean time since the last MI was 2.1 (0.9) years, and 12.5% of the study participants had experienced recurrent MI. Furthermore, 51% of the MI survivors were defined as hypertensive, and about 90% of the study participants reported statin intake.


View this table:
[in this window]
[in a new window]

  		Table 1. Demographic and laboratory characteristics of study participants.1

Blood samples were collected during a mean follow-up period of 7.3 months. The mean number of samples collected per individual was 5.7, and at least 6 blood samples were drawn from 179 study participants (89.5%). We drew a total of 1143 blood samples for Lp-PLA2 measurement over approximately monthly intervals (mean, 47 days). We measured 13% of the samples in duplicate, however, so the overall number of Lp-PLA2 measurements was 1291. The concentrations of Lp-PLA2 in the plasma ranged from 55.1 µg/L to 417.0 µg/L, with a mean value (SD) over all measurements of 188.7 (41.8) µg/L. Women had slightly lower concentrations than men [(180.4 (33.6) µg/L vs 190.5 (43.2) µg/L]; however, this difference was not statistically significant (P = 0.21). After adjustment for age, sex, and statin intake, the mean Lp-PLA2 plasma concentration for all study participants was 216.4 (40.1) µg/L [209.2 (31.6) µg/L for women and 220.5 (41.8) µg/L for men] (no statin intake, average age 61.9).

Table 2 demonstrates the calculated values for the measures of Lp-PLA2 variation. Biological variation (VCb and CVb), intraindividual variation (VCw and CVw), analytical variation (VCa and CVa), the critical difference, the ICC and the number of measurements needed for a given ICC, and the IoI were estimated to assess the reliability of serial Lp-PLA2 measurements. In this study, the CVb was 22% (VCb, 1744.9), the Lp-PLA2 CVw was 15% (VCw, 826.7), and the CVa was 4.4% (VCa, 69.1). An important part of the total variance in plasma Lp-PLA2 concentration was explained by between-subject variation, which was 66.1% of the total variance, whereas the within-subject variance was 31.3% of the total variance. Analytical variance constituted 2.6% of the total variance. In addition, the within-subject reproducibility of Lp-PLA2 measurements (ICC, 0.66) indicates a good correlation of the serial measurements from the same individual during follow-up. The within-subject critical difference was 83.0 µg/L, indicating that, for post-MI patients, 2 Lp-PLA2 measurements for the same patient at different times should be within 83 µg/L of each other 95% of the time. For the assessment of the measurement process, it is apparent that the ICC is large (0.97), i.e., very close to the maximum of 1. Furthermore, on the basis of our estimates, the value for the critical difference for analytical variation demonstrates that 2 Lp-PLA2 measurements for the same patient at the same time would be within 23.0 µg/L of each other 95% of the time. Thus, these results suggest that the measurement error in the Lp-PLA2 assay is very low. We also calculated the necessary number of measurements for the present analysis and found that 2 serial Lp-PLA2 measurements are required to achieve an ICC of 0.75. Finally, our calculation of IoI, which describes the relationship between within-subject variation and between-subject variation, yielded a value of 0.89.


View this table:
[in this window]
[in a new window]

  		Table 2. Medium-term variability components for Lp-PLA2.

In this study, we also applied 3 levels of outlier tests [described in (20)] and found that there were no outlying subject means according to the Reed criterion. The results of the Cochran test indicated 2 outlying observation variances and 2 outlying subject variances for Lp-PLA2 concentration. Therefore, to investigate the sensitivity of our results, we repeated all analyses without these observations/subjects. (The results of such analyses are available as a Data Supplement that accompanies the online version of this article at http://www.clinchem.org/content/vol54/issue1.) In general, the exclusion of these values essentially did not change our primary results.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
We investigated medium-term variation (over a 6-month period) in serial measurements of Lp-PLA2 concentration in 200 post-MI patients who participated in the AIRGENE study in Augsburg, Germany, and from whom 6 blood samples were taken at monthly intervals. In general, our data demonstrate considerable stability in and good reproducibility of the serial Lp-PLA2 measurements.

The measurement error of the assay was extremely small (a low CVa of 4.4% and a high ICC of 0.97). Indeed, only 2.6% of the total variation in Lp-PLA2 concentration was ascribable to test imprecision. The CVa for the present analysis compares well with the CVas for commonly measured lipid variables (1.0%–1.9% for TC, 2.3% for apolipoprotein A-I, and 3.4% for triglycerides) (10). Furthermore, a desirable goal for analytical imprecision (23), which is usually set at less than half the average within-subject variation (CVa <0.50 CVw—i.e., <7.6% in our case), was also met in this study; however, optimum performance, which is defined as a CVa <0.25 CVw (i.e., <3.8% in our case) was not achieved here, although this preferable CVa value was very close to the actual CVa value of 4.4% calculated in the present analysis. Nonetheless, our data in general suggest that there is little measurement error in the analytical process.

Despite the small analytical variation in the method for Lp-PLA2 assessment, our data further indicated that intraindividual variation is not negligible (CVw, 15%). In agreement with our findings are the preliminary data from a small study on short-term Lp-PLA2 variation that included data for 43 nonfasting healthy adults who had serum or plasma drawn at least 7 times over a 4-week period, for a total of 364 pairs of blood samples (5). In this study, Wolfert et al. demonstrated 14.9% imprecision in repeated Lp-PLA2 measurements from the same individual, and this estimate was nearly identical with the 15% CVw estimate in our study. A comparison of the intraindividual variation in Lp-PLA2 concentration with the intraindividual variation reported in various studies of commonly measured lipid variables showed that the CVw for Lp-PLA2 was slightly higher than for LDL cholesterol (6.5%, 7.4%, and 8.3% across different studies) (5)(24) and TC (5.8%, 6.0%, 8.8%, 9.3%) (10)(11)(13)(24) but was similar to or even better than for triglycerides (CVws of 15%, 21%, and 23%) (5)(10)(24).

In addition, we measured the size of a change that would indicate a statistically significant difference between 2 measurements from the same patient at different times and found the critical difference between 2 measurements to be 83 µg/L. The Lp-PLA2 ICC value of 0.66 further demonstrates the acceptable within-individual reproducibility of serial Lp-PLA2 measurements. To achieve a reliability of 0.75, however, we recommend 2 serial measurements of Lp-PLA2. A similar number of measurements was found to be needed for TC (12), and the National Cholesterol Education Program guidelines thus recommend collecting 2 samples a week apart before deciding on a therapeutic strategy (25).

The estimated CVb for Lp-PLA2 concentration was 22%, significantly greater than the CVw value (15%); however, the CVb value in general compared favorably with values reported for the more commonly measured lipid variables. For example, several studies have reported CVb estimates of 11% (10), 15% (24), and 19%(11) for TC, and 28% for triglycerides (10). In the study of short-term Lp-PLA2 variation by Wolfert et al. (5), between-individual variation for Lp-PLA2 was 33%, compared with the 24% and 50% values for LDL cholesterol and triglycerides, respectively. Therefore, the CVb value for Lp-PLA2 in our study was even lower than that reported by Wolfert et al., despite the fact that different populations (post MI-patients vs healthy individuals) have been investigated and the possibility that biological variation might be higher in pathologic states than in the healthy state.

We also were interested in the utility of conventional reference values for Lp-PLA2 in clinical practice (e.g., for monitoring, diagnosis, and screening) and calculated the IoI as a measure of how individuals vary with respect to the population distribution. Conventional population-based reference values are of utility only when within-individual variation exceeds between-variation variation. In our study, however, the IoI for Lp-PLA2 was 0.89, which is quite low, thereby demonstrating the marked individuality and limited utility of conventional reference values. Furthermore, a low IoI has several clinical implications. It may indicate that despite the possibility of detecting a highly unusual analyte concentration for a given individual, such values nonetheless would lie within the conventional population-based reference interval, thereby limiting the interpretation of even small changes from the set-point of this person. In such cases, it may be more important to accumulate data to evaluate the individual’s own homeostatic set-point, which we recommend to be used instead of a population-based reference interval (20). We note, however, that for most of the emerging cardiovascular biomarkers, within-individual biological variation is less than between-individual variation, e.g., IoIs not greater than 0.83 (10)(12)(13).

Our study has several limitations that merit consideration. First, only post-MI patients were included; therefore, extrapolation to healthy individuals may not be justified, because the biological variation in cases of pathologic conditions may be significantly higher than in apparently healthy individuals. The only study to date that has assessed variation in Lp-PLA2 measurements in healthy individuals (5) reported similar CVs. Moreover, only a small number of female participants (approximately 20%) were recruited in the present study, and the participants were mainly middle-aged individuals. Thus, sex- and age-specific comparisons of Lp-PLA2 variation represent an issue that could not have been addressed in this study. In addition, we assessed only variation in serial Lp-PLA2 measurements over a medium-term period (6 months). From an epidemiologic point of view, however, long-term variation may be even more important than short- or medium-term variation. Therefore, further studies with larger sample sizes are necessary to assess the long-term variation in Lp-PLA2 measurements. Finally, we note that 90% of our study participants were receiving statin therapy, which lowered Lp-PLA2 concentrations. This factor might have produced lower Lp-PLA2 values and less variation in this biomarker in our population than in a population not exposed to statin treatment.

Our study also has several strengths of note. We collected all samples under extremely standardized conditions at each clinical visit to avoid preanalytical variation in Lp-PLA2 concentrations. All samples were collected in the fasting state and mostly at the same time (98%) of the same weekday (95%) to minimize the impact of circadian and day-to-day variation.

In conclusion, the between-individual variation in Lp-PLA2 concentration in blood samples taken repeatedly from post-MI patients over a 6-month period was substantially larger than within-individual variation, and the ICC value of 0.66 indicated the good reproducibility of serial measurements. In general, the values for measures of biological Lp-PLA2 variation compared favorably with those of several "traditional" lipid variables; however, data on both seasonal and circadian variation in Lp-PLA2 concentration and long-term variation are still lacking, and this paucity of data thus represents opportunities for further research.


   Acknowledgments
 
Grant/funding Support: The AIRGENE study was funded as part of the European Union’s 5th Framework Programme, key action number 4: "Environment and Health", contract number QLRT-2002-02236. In addition, the study was supported by an unrestricted grant from diaDexus, Inc. (South San Francisco, USA). N.K. was supported by the German Research Council (DFG: Graduiertenkolleg GRK 1041, "Molekulare Diabetologie und Endokrinologie in der Medizin").

Financial Disclosures: W.K. has received honoraria for lectures from diaDexus and GlaxoSmithKline.

Acknowledgments: We thank Helmut Küchenhoff (Ludwig-Maximilians-University Munich) for statistical advice and Jens Baumert for critical reading of the manuscript. N.K. and S.G. contributed equally to this study.


   Footnotes
 
1 Nonstandard abbreviations: CHD, coronary heart disease; Lp-PLA2, lipoprotein-associated phospholipase A2; ICC, intraclass correlation coefficient of reliability; AIRGENE, Air Pollution and Inflammatory Response in Myocardial Infarction Survivors: Gene-Environment Interaction in a High Risk Group; MI, myocardial infarction; TC, total cholesterol; VC, variance component; VCa, analytical VC; VCb, between-subject VC; VCw, within-subject VC; and IoI, index of individuality.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Ross R. Atherosclerosis—an inflammatory disease. N Engl J Med 1999;340:115-126.[Free Full Text]
  2. Vasan RS. Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation 2006;113:2335-2362.[Free Full Text]
  3. Caslake MJ, Packard CJ. Lipoprotein-associated phospholipase A2 as a biomarker for coronary disease and stroke. Nat Clin Pract Cardiovasc Med 2005;2:529-535.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  4. Zalewski A, Macphee C. Role of lipoprotein-associated phospholipase A2 in atherosclerosis: biology, epidemiology, and possible therapeutic target. Arterioscler Thromb Vasc Biol 2005;25:923-931.[Abstract/Free Full Text]
  5. Wolfert RL, Kim NW, Selby RG, Sarno MJ, Warnick RG, Sudhir K. Biological variability and specificity of lipoprotein-associated phospholipase A2, a novel marker of cardiovascular risk. Circulation 2004;110(Suppl III):III-309.
  6. Khuseyinova N, Imhof A, Rothenbacher D, Trischler G, Kuelb S, Scharnagl H, et al. Association between Lp-PLA2 and coronary artery disease: focus on its relationship with lipoproteins and markers of inflammation and hemostasis. Atherosclerosis 2005;182:181-188.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  7. Johnson A, Zalewski A, Janmohamed S, Sawyer J, Rolfe T, Staszkiewicz W, et al. Lipoprotein-associated phospholipase A2 activity, an emerging CV risk marker, can be inhibited in atherosclerotic lesions and plasma by novel pharmacologic intervention: the results of a multicenter clinical study. Circulation 2004;110(Suppl III):III-590.
  8. Stampfer MJ, Ridker PM, Dzau VJ. Risk factor criteria. Circulation 2004;109(Suppl 1):IV3-IV5.[Medline] [Order article via Infotrieve]
  9. Lanman B, Wolfert RL, Fleming JK, Jaffe AS, Roberts WL, Warnick GR, McConnell JP. Lipoprotein-associated phospholipase A2: review and recommendation of a clinical cut point for adults. Prev Cardiol 2006;9:138-143.[Medline] [Order article via Infotrieve]
  10. Fraser CG, Cummings ST, Wilkinson SP, Neville RG, Knox JDE, Ho O, MacWalter RS. Biological variability of 26 clinical chemistry analytes in elderly people. Clin Chem 1989;35:783-786.[Abstract/Free Full Text]
  11. Macy EM, Hayes TE, Tracy RP. Variability in the measurement of C-reactive protein in healthy subjects: implications for reference intervals and epidemiological considerations. Clin Chem 1997;43:52-58.[Abstract/Free Full Text]
  12. Ockene IS, Matthews CE, Rifai N, Ridker PM, Reed G, Stanek E. Variability and classification accuracy of serial high-sensitivity C-reactive protein measurements in healthy adults. Clin Chem 2001;47:444-450.[Abstract/Free Full Text]
  13. Sakkinen PA, Macy EM, Callas PW, Cornell ES, Hayes TE, Kuller LH, Tracy RP. Analytical and biological variability in measures of hemostasis, fibrinolysis, and inflammation: assessment and implications for epidemiology. Am J Epidemiol 1999;149:261-267.[Abstract/Free Full Text]
  14. Peters A, Schneider A, Greven S, Bellander T, Forastiere F, Ibald-Mulli A, et al. Air pollution and inflammatory response in myocardial infarction survivors: Gene-environment-interactions in a high risk group. Inhal Toxicol 2007;19(Suppl 1):161-175.[Web of Science][Medline] [Order article via Infotrieve]
  15. Luepker RV, Apple FS, Christenson RH, Crow RS, Fortmann SP, Goff D, et al. Case definitions for acute coronary heart disease in epidemiology and clinical research studies: a statement from the AHA Council on Epidemiology and Prevention; AHA Statistics Committee; World Heart Federation Council on Epidemiology and Prevention; the European Society of Cardiology Working Group on Epidemiology and Prevention; Centers for Disease Control and Prevention; and the National Heart, Lung, and Blood Institute. Circulation 2003;108:2543-2549.[Free Full Text]
  16. Alpert JS, Thygesen K, Antman E, Bassand JP. Myocardial infarction redefined—a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol 2000;36:959-969.[Free Full Text]
  17. Hoogeveen RC, Ballantyne CM. PLACTM test for identification of individuals at increased risk for coronary heart disease. Expert Rev Mol Diagn 2005;5:9-14.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  18. Fleiss JL. The Design and Analysis of Clinical Experiments 1986 John Wiley & Sons New York, NY. .
  19. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135-160.[Abstract/Free Full Text]
  20. Fraser CG, Harris EK. The generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci 1989;27:409-437.[Web of Science][Medline] [Order article via Infotrieve]
  21. Spiegelhalter DJ, Thomas A, Best NG. WinBUGS Version 1.3 User Manual 2000:26 Medical Research Council Biostatistics Unit Cambridge, UK. .
  22. Harris EK. Statistical aspects of reference values in clinical pathology. Prog Clin Pathol 1981;8:45-66.[Medline] [Order article via Infotrieve]
  23. Fraser CG, Hyltoft, Petersen P, Libeer JC, Ricós C. Proposals for setting generally applicable quality goals solely based on biology. Ann Clin Biochem 1997;34(Pt 1):8-12.[Web of Science][Medline] [Order article via Infotrieve]
  24. Ricós C, Alvarez V, Cava F, García-Lario JV, Hernández A, Jiménez CV, et al. Current databases on biological variation: pros, cons and progress. Scand J Clin Lab Invest 1999;59:491-500.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  25. Summary of the second report of the National Cholesterol Educational Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II). JAMA 1993;269:3015-3023.[Abstract/Free Full Text]



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


Home page
 Clin. Chem.Home page
J. P. McConnell and A. S. Jaffe
Variability of Lipoprotein-Associated Phospholipase A2 Measurements
Clin. Chem., May 1, 2008; 54(5): 932 - 933.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplement Data
Right arrow All Versions of this Article:
clinchem.2007.093468v1
54/1/124    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 ISI 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 ISI Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Khuseyinova, N.
Right arrow Articles by Koenig, W.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Khuseyinova, N.
Right arrow Articles by Koenig, W.
Related Collections
Right arrow Lipids, Lipoproteins, and Cardiovascular Risk Factors

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS