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Articles |
1
Department of Medicine, Divisions of Cardiovascular Medicine and
2
Preventive and Behavioral Medicine, University of Massachusetts Medical School, 55 Lake Ave. North, Worcester, MA 01655.
3
Department of Epidemiology and Biostatistics, University
of South Carolina, School of Public Health, Columbia, SC 29208.
4
Department of Laboratory Medicine, Children’s Hospital,
Harvard Medical School, 300 Longwood Ave., Boston, MA 02115.
5
Division of Preventive Medicine, Harvard Medical School,
Brigham and Women’s Hospital, 900 Commonwealth Ave. East, Boston, MA
02215.
6
Department of Biostatistics and Epidemiology, School of
Public Health and Health Sciences, University of Massachusetts, Arnold
House Box 30430, Amherst, MA 01003.
a Author for correspondence. Fax 508-856-4571, email
ira.ockene{at}umassmed.edu.
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
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Methods: One hundred thirteen individuals were scheduled to have five measurements each of hs-CRP and total cholesterol carried out at quarterly intervals over a 1-year period. Variations of hs-CRP and total cholesterol were characterized, and classification accuracy was described and compared for both.
Results: The relative variation was comparable for hs-CRP and total cholesterol. When classified by quartile, 63% of first and second hs-CRP measurements were in agreement; for total cholesterol it was 60%. Ninety percent of hs-CRP measurements were within one quartile of each other. This relationship was not altered by the use of log-transformed hs-CRP data.
Conclusion: hs-CRP has a degree of measurement stability that is similar to that of total cholesterol.
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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hs-CRP has been noted to add to the prediction of first myocardial infarction when combined with blood lipid measures (8). Given these findings, hs-CRP has been suggested as a potential risk factor for CHD (9)(10), and investigators have begun to seek therapies that may lower hs-CRP concentrations and, presumably, CHD risk (9)(11)(12).
CRP differs from other markers for CHD risk obtained from blood (e.g.,
lipoproteins) because it is an acute phase reactant (13).
Increased hs-CRP concentrations reflect the presence and intensity of
inflammation and, in response to injury or acute infection, can rapidly
increase as much as 1000-fold from basal concentrations, declining to
baseline over a period of 7–12 days (13). Studies of the
distribution of hs-CRP that have excluded individuals with acutely
increased values have found median values of 
1–2 mg/L with the
upper end of the reference interval (i.e., 97.5th percentile)
between 3.2 and 5.5 mg/L (14)(15). Higher hs-CRP
concentrations have been noted in smokers (16), individuals
with osteoarthritis (17), and in the obese (18).
For measurement of hs-CRP to have clinical utility as a CHD risk
factor, the biologic variability of hs-CRP must be quantified and be
low enough to enable reliable risk stratification with one or two blood
samples. In a recent study of 214 postinfarction patients, the
correlation coefficient between baseline hs-CRP concentrations
(obtained at least 6 months post infarction) and values obtained 5
years later was 0.60 (P <0.001), a degree of correlation
that compares favorably to that for total cholesterol
(r = 0.37) (19). Most other longitudinal
studies of hs-CRP variability have been conducted in small (n <30)
homogeneous cohorts over relatively short periods of follow-up (6
months) (14)(20) and were limited by lack of
gender- and age-specific comparisons. Moreover, the reliability of risk
classification using serial hs-CRP measurements has not been formally
examined. The purpose of the present investigation was to quantify the
biologic variability of hs-CRP and to examine the risk classification
accuracy of serial hs-CRP samples within the reference interval in a
large heterogeneous cohort of healthy adults who were each followed for
a 1-year period.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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SEASON participants were recruited from the Fallon Healthcare System, a health maintenance organization serving the central Massachusetts area. Additional minority participants were recruited from the greater Worcester area. Individuals were eligible if they were residents of Worcester County, 20–70 years of age, had telephone service, and were not taking cholesterol-lowering medication. Recruitment was carried out between December 1994 and February 1997, and follow-up was completed in March 1998. The Institutional Review Boards of the Fallon Healthcare System and the University of Massachusetts Medical School approved all participant recruitment and data collection procedures. Each participant read and signed an approved informed consent.
At baseline and in each of four subsequent quarters of follow-up (at 90-day intervals), individuals came to the clinic to provide blood samples, have their body mass measured, and to return a series of self-administered questionnaires. Physical activity, diet, and light exposure data were collected using three 24-h recall interviews of each of the five data collection points.
clinical measurements
Demographic data (e.g., age, gender, marital status, education,
employment) were collected by self-administered questionnaire at study
baseline. Anthropometric data, including body mass (kg), height (m),
and waist and hip circumferences (cm) were measured during clinic
visits by the SEASON staff.
Information regarding infections was collected by self-report of the
number of cold or flu episodes in the previous 90 days. The mean (SD)
annual number of upper-respiratory tract infections was 1.4
(1.5) events per year. The occurrence of upper-respiratory tract
infections peaked in the winter months (40%) and was lowest in the
summer (10%). These data closely resemble annual and seasonal
incidence of upper-respiratory tract infections reported in other
investigations using more intensive self-report methods
(22)(23).
blood measurements
Fasting (>12 h) venous blood samples were collected into
EDTA-containing tubes between 0700 and 0900. Blood plasma was harvested
by low-speed centrifugation at 4 °C, aliquoted into individual
tubes, and quickly frozen to -70 °C. On a regular basis, plasma
samples were packed in dry ice and shipped for analysis via courier
service to the Centers for Disease Control and Prevention standardized
laboratory for lipid testing at the University of Massachusetts at
Lowell (24). Total cholesterol and triglycerides were
measured in plasma by enzymatic methods using a Beckman System 700
automated analyzer (25)(26).
HDL-cholesterol was measured in the resulting supernatant after
heparin manganese precipitation of apolipoprotein-B-containing
proteins (27). LDL-cholesterol values were calculated by the
Friedewald equation using total cholesterol, triglycerides, and
HDL-cholesterol in individuals with triglyceride values <4.52 mmol/L
(400 mg/dL) (28).
hs-CRP methodology
hs-CRP concentrations were determined at Children’s Hospital in
Boston using latex-enhanced immunonephelometric assays on a BN II
analyzer (Dade Behring) as described previously
(29). The assay has a detection limit of 0.15 mg/L
and day-to-day imprecision (CV) of 5% for concentrations of
0.35 and 0.5 mg/L.
statistical methodology
Variance components and intraclass correlations were estimated
using a random effects analysis of variance model using the loneway
procedure in Stata, Ver. 6.0 (30). Categorical variables
were compared by Fisher exact tests. Unweighted 
statistics were
computed for comparison of classification of the first and second
measurements of cholesterol and hs-CRP (31).
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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lists the participant characteristics. The values in Table 1
are based on averaged values over all quarterly measurements.
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The gender difference in hs-CRP was significant based on a rank-sum test (P = 0.04). This difference was entirely explained by differences in body mass index (BMI), and after controlling for BMI, there was no longer a significant difference between males and females in mean hs-CRP. Gender differences in hs-CRP have not been previously described (14)(32), but the relationship between BMI and hs-CRP has been noted by others (32). Overall concentrations of hs-CRP were comparable to values noted previously in the literature, as obtained by similar methodologies (3)(33).
Further analyses were carried out using pooled gender data, both because of the lack of independent significance for this variable and to maintain comparability with total cholesterol, which despite important gender differences does not have gender-specific guidelines (34).
Values of hs-CRP >10 mg/L were relatively rare in both men (4.8% of 292 h-CRP measurements) and women (2.8% of 214 measurements). The gender difference was not statistically significant (P = 0.36). Eighty percent of the 20 values >10 mg/L were associated with a report of an episode of cold or flu within the prior 90 days, vs only 30.2% for those <10 mg/L. In subsequent analyses, values >10 mg/L were removed under the assumption that they represented acute illness or inflammation. After hs-CRP values >10 mg/L were excluded, the mean (SD) hs-CRP value was 2.05 (1.8) mg/L based on an mean (SD) of 4.4 (1.8) measurements per subject.
In characterizing the variation of hs-CRP, the scale used is an
important factor. Fig. 1
illustrates the distribution of hs-CRP and, for comparison,
that of total cholesterol (all subjects represented by their mean
values for all measurements). The data for total cholesterol are
relatively symmetric and can be modeled appropriately by a gaussian
distribution. The distribution of hs-CRP was highly skewed even with
the higher values removed. Fig. 1C
shows the log distribution (hs-CRP),
which is much more symmetric and less skewed. A Kolmogorov–Smirnov
test indicated that there was no statistical evidence of a deviation
from normality for total cholesterol (P = 0.78) or for
log hs-CRP (P = 0.20), but the test rejected normality
for hs-CRP (P <0.001). Variation estimates were made in
both scales for comparative purposes.
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The within-subject data for total cholesterol and hs-CRP, rank-ordered
by mean values, are illustrated in Fig. 2
. The first hs-CRP plot (Fig. 2
, middle panel) is in the
measured scale, the second plot (Fig. 2
, bottom panel) is in a log
scale.
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Variation is usually separated into analytic, within-individual (biologic), and between-individual variation. Analytic variation is the variation attributable to measurement error (a sample is analyzed multiple times and variation is estimated). Individual, or biologic, variation is the variation within an individual accounting for or adjusting for analytic variation. Multiple measures within a subject are used to estimate variation, and analytic variation is subtracted out. Between-individual variation is the variation in average response between individuals. With these data, we can estimate within- and between-individual variation where within-individual variation is a combination of analytic and biologic variation.
Variation can be expressed by dividing overall variation into between-
and within-subject variation and describing the percentage of overall
variation attributable to each component. If we let
b2 be the
between-subject variance and
w2 be the within-subject
variance, then
b2/(b2+
w2) is the intraclass correlation and
100 x intraclass correlation is the percentage of variation
explained by between-subject variation. The remaining variation is the
within-subject variation, which is the combined biologic and analytic
variation. This is an appropriate comparison if we have a
representative sample of individuals.
A random-effects analysis of variance estimated the between-subject
standard deviation for total cholesterol to be 0.946 mmol/L (36.6
mg/dL) and the within-subject standard deviation to be 0.447 mmol/L
(17.3 mg/dL); for hs-CRP, the between-subject standard deviation was
1.66 mg/L and the within-subject standard deviation was 1.19 mg/L. The
estimated intraclass correlation for total cholesterol was 0.82 (82%
of variation explained by between-subject variation and 18% by
within-subject variation); for hs-CRP, the estimated intraclass
correlation was 0.66. Table 2
lists the variance components for each of the measures.
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classification accuracy
To determine classification accuracy, values of hs-CRP were
divided into four groups: <0.50, 0.50–0.99, 1.00–1.99, and 
2.0
mg/L (2).
The agreement between the first and second measurements in all 113
individuals is shown in Fig. 3
, which for comparison provides equivalent data for total
cholesterol divided into quartiles. Overall, for hs-CRP, 71 of 113
(62.8%) of the results were in agreement. The statistic was
estimated to be 0.479 (95% confidence interval, 0.39–0.60).
When we removed measurement pairs where a value was >10 mg/L, there
was a small increase in overall agreement (63.5%), with a
statistic of 0.511 (95% confidence interval, 0.40–0.62). When we
examined the classification accuracy across the 12-month follow-up
period (excluding pairs with values >10 mg/L), correct classification
was observed for 237 of 374 pairs (63.4%). For total cholesterol,
agreement between the first and second measurements was 59.3% with a
value of 0.456 (95% confidence interval, 0.35–0.56). Thus,
variability of repeat hs-CRP measurements was comparable to that of
total cholesterol.
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In those individuals having at least four hs-CRP measurements, a similar analysis was carried out comparing the average of two randomly selected measures with the average of two other randomly selected measures. The use of four measurements in this manner increased overall agreement to 68%.
We also carried out a similar analysis classifying cholesterol into three groups using the classification methodology of the National Cholesterol Education Program (<200, 200–240, and >240 mg/dL) (34). Use of this scheme improved overall agreement between the first and second measurements to 71.7%, but the improvement was related to the smaller number of groups (three vs four).
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Although the stability of hs-CRP measurements is similar to that of total cholesterol, considerable within-subject variability does exist in both, and a single test will have a wide confidence interval (36). Because an analysis using four measurements per individual yielded only a modest increase in classification agreement (68% vs 63% with two measurements), the use of two sequential measures is appropriate for clinical use.
We are not recommending the use of log-transformed data for the clinical use of hs-CRP. A log-transformed number has a fixed relationship to the original number from which it is derived and so is not intrinsically of greater value. The use of log-transformed data does produce interquartile distances that are equal on a measurement scale, but in a clinical setting this is not important because the guideline cutpoints are defined for the clinician, and log-transformed data are not used for other measurements that are equally skewed (e.g., serum triglycerides).
Because hs-CRP is an acute phase reactant, it could be argued that the lowest of several measurements should be used as the predictive value, as opposed to the mean. Although this approach has biologic as well as clinical appeal, insufficient data exist at the present time to make this recommendation.
In conclusion, we believe the current data have several important clinical implications. (a) In a recent study directly comparing the magnitude of predictive value of 12 putative risk factors, including lipoprotein(a), homocysteine, and a full lipid panel, hs-CRP was the single strongest marker of risk for future myocardial infarction and stroke (3), data that underscore the critical role of inflammation in atherothrombosis. (b) Several studies have now shown that hs-CRP concentrations predict coronary risk even in the absence of hyperlipidemia (2)(3)(8), an important issue because one-half of all cardiovascular events occur among individuals with cholesterol concentrations not defined as increased by National Cholesterol Education Program criteria (37). (c) Because statin therapy appears to lower hs-CRP in a cholesterol-independent fashion (19), monitoring the inflammatory response has been suggested as a method to improve utilization of statin therapy, particularly in the primary prevention of vascular events. Thus, the observation in the current data that hs-CRP has a measurement stability similar to that of total cholesterol provides further evidence of the potential clinical utility of hs-CRP screening as a novel tool for vascular risk prediction (38).
Supported by NHLBI Grant R01-HL52745 (to Dr. Ockene).
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Footnotes |
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References |
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V M Pinto-Plata, H Mullerova, J F Toso, M Feudjo-Tepie, J B Soriano, R S Vessey, and B R Celli C-reactive protein in patients with COPD, control smokers and non-smokers Thorax, January 1, 2006; 61(1): 23 - 28. [Abstract] [Full Text] [PDF]
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P. W. F. Wilson, B.-H. Nam, M. Pencina, R. B. D'Agostino Sr, E. J. Benjamin, and C. J. O'Donnell C-Reactive Protein and Risk of Cardiovascular Disease in Men and Women From the Framingham Heart Study Arch Intern Med, November 28, 2005; 165(21): 2473 - 2478. [Abstract] [Full Text] [PDF]
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 A. M Carter Inflammation, thrombosis and acute coronary syndromes Diabetes and Vascular Disease Research, October 1, 2005; 2(3): 113 - 121. [Abstract] [PDF]
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A. Viardot, P. Huber, J. J. Puder, H. Zulewski, U. Keller, and B. Muller Reproducibility of Nighttime Salivary Cortisol and Its Use in the Diagnosis of Hypercortisolism Compared with Urinary Free Cortisol and Overnight Dexamethasone Suppression Test J. Clin. Endocrinol. Metab., October 1, 2005; 90(10): 5730 - 5736. [Abstract] [Full Text] [PDF]
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 H. Sun, T. Koike, T. Ichikawa, K. Hatakeyama, M. Shiomi, B. Zhang, S. Kitajima, M. Morimoto, T. Watanabe, Y. Asada, et al. C-Reactive Protein in Atherosclerotic Lesions: Its Origin and Pathophysiological Significance Am. J. Pathol., October 1, 2005; 167(4): 1139 - 1148. [Abstract] [Full Text] [PDF]
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L. W. Cho, V. Jayagopal, E. S. Kilpatrick, and S. L. Atkin The Biological Variation of C-Reactive Protein in Polycystic Ovarian Syndrome Clin. Chem., October 1, 2005; 51(10): 1905 - 1907. [Full Text] [PDF]
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I. E. Widmer, J. J. Puder, C. Konig, H. Pargger, H. R. Zerkowski, J. Girard, and B. Muller Cortisol Response in Relation to the Severity of Stress and Illness J. Clin. Endocrinol. Metab., August 1, 2005; 90(8): 4579 - 4586. [Abstract] [Full Text] [PDF]
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S. Devaraj, G. O'Keefe, and I. Jialal Defining the Proinflammatory Phenotype Using High Sensitive C-Reactive Protein Levels as the Biomarker J. Clin. Endocrinol. Metab., August 1, 2005; 90(8): 4549 - 4554. [Abstract] [Full Text] [PDF]
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C. A. Blum, B. Muller, P. Huber, M. Kraenzlin, C. Schindler, C. De Geyter, U. Keller, and J. J. Puder Low-Grade Inflammation and Estimates of Insulin Resistance during the Menstrual Cycle in Lean and Overweight Women J. Clin. Endocrinol. Metab., June 1, 2005; 90(6): 3230 - 3235. [Abstract] [Full Text] [PDF]
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M. Di Napoli, M. Schwaninger, R. Cappelli, E. Ceccarelli, G. Di Gianfilippo, C. Donati, H. C.A. Emsley, S. Forconi, S. J. Hopkins, L. Masotti, et al. Evaluation of C-Reactive Protein Measurement for Assessing the Risk and Prognosis in Ischemic Stroke: A Statement for Health Care Professionals From the CRP Pooling Project Members Stroke, June 1, 2005; 36(6): 1316 - 1329. [Abstract] [Full Text] [PDF]
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 M. G. Dik, C. Jonker, C. E. Hack, J. H. Smit, H. C. Comijs, and P. Eikelenboom Serum inflammatory proteins and cognitive decline in older persons Neurology, April 26, 2005; 64(8): 1371 - 1377. [Abstract] [Full Text] [PDF]
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J. Genius, T. Dong-Si, A. P. Grau, and C. Lichy Postacute C-Reactive Protein Levels Are Elevated in Cervical Artery Dissection Stroke, April 1, 2005; 36(4): e42 - e44. [Abstract] [Full Text] [PDF]
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S. M. Zhang, J. E. Buring, I-M. Lee, N. R. Cook, and P. M. Ridker C-Reactive Protein Levels Are Not Associated with Increased Risk for Colorectal Cancer in Women Ann Intern Med, March 15, 2005; 142(6): 425 - 432. [Abstract] [Full Text] [PDF]
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P. Bogaty, J. M. Brophy, L. Boyer, S. Simard, L. Joseph, F. Bertrand, and G. R. Dagenais Fluctuating Inflammatory Markers in Patients With Stable Ischemic Heart Disease Arch Intern Med, January 24, 2005; 165(2): 221 - 226. [Abstract] [Full Text] [PDF]
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G. L. Myers, N. Rifai, R. P. Tracy, W. L. Roberts, R. W. Alexander, L. M. Biasucci, J. D. Catravas, T. G. Cole, G. R. Cooper, B. V. Khan, et al. CDC/AHA Workshop on Markers of Inflammation and Cardiovascular Disease: Application to Clinical and Public Health Practice: Report From the Laboratory Science Discussion Group Circulation, December 21, 2004; 110(25): e545 - e549. [Full Text] [PDF]
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R. Rej Clinical Chemistry through Clinical Chemistry: A Journal Timeline Clin. Chem., December 1, 2004; 50(12): 2415 - 2458. [Abstract] [Full Text] [PDF]
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 K. Sliwa, A. Woodiwiss, E. Libhaber, F. Zhanje, C. Libhaber, R. Motara, and R. Essop C-reactive protein predicts response to pentoxifylline in patients with idiopathic dilated cardiomyopathy Eur J Heart Fail, October 1, 2004; 6(6): 731 - 734. [Full Text] [PDF]
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M. Lambert, E. E. Delvin, G. Paradis, J. O'Loughlin, J. A. Hanley, and E. Levy C-Reactive Protein and Features of the Metabolic Syndrome in a Population-Based Sample of Children and Adolescents Clin. Chem., October 1, 2004; 50(10): 1762 - 1768. [Abstract] [Full Text] [PDF]
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C. Gillespie, C. Ballew, B. A Bowman, R. Donehoo, and M. K Serdula Intraindividual variation in serum retinol concentrations among participants in the third National Health and Nutrition Examination Survey, 1988-1994 Am. J. Clinical Nutrition, April 1, 2004; 79(4): 625 - 632. [Abstract] [Full Text] [PDF]
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 J. Danesh, J. G. Wheeler, G. M. Hirschfield, S. Eda, G. Eiriksdottir, A. Rumley, G. D.O. Lowe, M. B. Pepys, and V. Gudnason C-Reactive Protein and Other Circulating Markers of Inflammation in the Prediction of Coronary Heart Disease N. Engl. J. Med., April 1, 2004; 350(14): 1387 - 1397. [Abstract] [Full Text] [PDF]
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 G. Tsirpanlis, P. Bagos, D. Ioannou, A. Bleta, I. Marinou, A. Lagouranis, S. Chatzipanagiotou, and C. Nicolaou The variability and accurate assessment of microinflammation in haemodialysis patients Nephrol. Dial. Transplant., January 1, 2004; 19(1): 150 - 157. [Abstract] [Full Text] [PDF]
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A. J. MacGregor, J. R. Gallimore, T. D. Spector, and M. B. Pepys Genetic Effects on Baseline Values of C-Reactive Protein and Serum Amyloid A Protein: A Comparison of Monozygotic and Dizygotic Twins Clin. Chem., January 1, 2004; 50(1): 130 - 134. [Abstract] [Full Text] [PDF]
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 H. D. Sesso, J. E. Buring, N. Rifai, G. J. Blake, J. M. Gaziano, and P. M. Ridker C-Reactive Protein and the Risk of Developing Hypertension JAMA, December 10, 2003; 290(22): 2945 - 2951. [Abstract] [Full Text] [PDF]
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 G. J. Blake and P. M. Ridker C-reactive protein: a surrogate risk marker or mediator of atherothrombosis? Am J Physiol Regulatory Integrative Comp Physiol, November 1, 2003; 285(5): R1250 - R1252. [Full Text] [PDF]
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 W. Koenig, M. Sund, M. Frohlich, H. Lowel, W. L. Hutchinson, and M. B. Pepys Refinement of the Association of Serum C-reactive Protein Concentration and Coronary Heart Disease Risk by Correction for Within-Subject Variation over Time: The MONICA Augsburg Studies, 1984 and 1987 Am. J. Epidemiol., August 15, 2003; 158(4): 357 - 364. [Abstract] [Full Text] [PDF]
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T. B. Ledue and N. Rifai Preanalytic and Analytic Sources of Variations in C-reactive Protein Measurement: Implications for Cardiovascular Disease Risk Assessment Clin. Chem., August 1, 2003; 49(8): 1258 - 1271. [Abstract] [Full Text] [PDF]
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M. F. Carroll and D. S. Schade Timing of Antioxidant Vitamin Ingestion Alters Postprandial Proatherogenic Serum Markers Circulation, July 8, 2003; 108(1): 24 - 31. [Abstract] [Full Text] [PDF]
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 N. Aziz, J. L. Fahey, R. Detels, and A. W. Butch Analytical Performance of a Highly Sensitive C-Reactive Protein-Based Immunoassay and the Effects of Laboratory Variables on Levels of Protein in Blood Clin. Vaccine Immunol., July 1, 2003; 10(4): 652 - 657. [Abstract] [Full Text] [PDF]
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S. Davison and S. R. Davis New Markers for Cardiovascular Disease Risk in Women: Impact of Endogenous Estrogen Status and Exogenous Postmenopausal Hormone Therapy J. Clin. Endocrinol. Metab., June 1, 2003; 88(6): 2470 - 2478. [Abstract] [Full Text] [PDF]
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P. M Ridker Clinical Application of C-Reactive Protein for Cardiovascular Disease Detection and Prevention Circulation, January 28, 2003; 107(3): 363 - 369. [Full Text] [PDF]
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T. A. Pearson, G. A. Mensah, R. W. Alexander, J. L. Anderson, R. O. Cannon III, M. Criqui, Y. Y. Fadl, S. P. Fortmann, Y. Hong, G. L. Myers, et al. Markers of Inflammation and Cardiovascular Disease: Application to Clinical and Public Health Practice: A Statement for Healthcare Professionals From the Centers for Disease Control and Prevention and the American Heart Association Circulation, January 28, 2003; 107(3): 499 - 511. [Full Text] [PDF]
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B. Campbell, R. Flatman, T. Badrick, D. Kanowski, I. S. Ockene, C. E. Matthews, N. Rifai, P. M. Ridker, G. Reed, and E. Stanek Problems with High-Sensitivity C-Reactive Protein * The authors of the article discussed in the above letter respond: Clin. Chem., January 1, 2003; 49(1): 201 - 202. [Full Text] [PDF]
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P. M. Ridker, N. Rifai, L. Rose, J. E. Buring, and N. R. Cook Comparison of C-Reactive Protein and Low-Density Lipoprotein Cholesterol Levels in the Prediction of First Cardiovascular Events N. Engl. J. Med., November 14, 2002; 347(20): 1557 - 1565. [Abstract] [Full Text] [PDF]
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A. D. Pradhan, J. E. Manson, J. E. Rossouw, D. S. Siscovick, C. P. Mouton, N. Rifai, R. B. Wallace, R. D. Jackson, M. B. Pettinger, and P. M Ridker Inflammatory Biomarkers, Hormone Replacement Therapy, and Incident Coronary Heart Disease: Prospective Analysis From the Women's Health Initiative Observational Study JAMA, August 28, 2002; 288(8): 980 - 987. [Abstract] [Full Text] [PDF]
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D. L. Bhatt and E. J. Topol Need to Test the Arterial Inflammation Hypothesis Circulation, July 2, 2002; 106(1): 136 - 140. [Full Text] [PDF]
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 W.F. Riesen, H. Engler, M. Risch, W. Korte, and G. Noseda Short-term effects of atorvastatin on C-reactive protein Eur. Heart J., May 2, 2002; 23(10): 794 - 799. [Abstract] [Full Text] [PDF]
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I. Kushner and A. R. Sehgal Is High-Sensitivity C-Reactive Protein an Effective Screening Test for Cardiovascular Risk? Arch Intern Med, April 22, 2002; 162(8): 867 - 869. [Abstract] [Full Text] [PDF]
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 M. A Vickers, F. R Green, C. Terry, B. M Mayosi, C. Julier, M. Lathrop, P. J Ratcliffe, H. C Watkins, and B. Keavney Genotype at a promoter polymorphism of the interleukin-6 gene is associated with baseline levels of plasma C-reactive protein Cardiovasc Res, March 1, 2002; 53(4): 1029 - 1034. [Abstract] [Full Text] [PDF]
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S. Rothkrantz-Kos, M. P.J. Schmitz, O. Bekers, P. P.C.A. Menheere, and M. P. van Dieijen-Visser High-Sensitivity C-Reactive Protein Methods Examined Clin. Chem., February 1, 2002; 48(2): 359 - 362. [Full Text] [PDF]
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 G. J. Blake and P. M. Ridker Novel Clinical Markers of Vascular Wall Inflammation Circ. Res., October 26, 2001; 89(9): 763 - 771. [Abstract] [Full Text] [PDF]
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A. D. Pradhan, J. E. Manson, N. Rifai, J. E. Buring, and P. M. Ridker C-Reactive Protein, Interleukin 6, and Risk of Developing Type 2 Diabetes Mellitus JAMA, July 18, 2001; 286(3): 327 - 334. [Abstract] [Full Text] [PDF]
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P. M. Ridker High-Sensitivity C-Reactive Protein : Potential Adjunct for Global Risk Assessment in the Primary Prevention of Cardiovascular Disease Circulation, April 3, 2001; 103(13): 1813 - 1818. [Abstract] [Full Text] [PDF]
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