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ASD-PCR assay described here can be easily adapted for the identification of other drug-resistant mutations in M. tuberculosis. It can also serve as a simple and efficient tool for general single-nucleotide polymorphism analysis.
This work was supported by the Biotechnology Engagement Program Grant 2225 and International Science & Technology Center Grant 1847. We appreciate the excellent support and technical assistance of Dr. A. Onasenko. We thank Dr. Kathy DeRiemer, Stanford Center for Tuberculosis Research, for critical reading of the manuscript. References 1. Tiruviluamala P, Reichman LB. Tuberculosis [Review]. Annu Rev Public Health 2002;23:403–26. 2. Raviglione MC, Snider DE, Kochi A. Global epidemiology of tuberculosis. Morbidity and mortality of a worldwide epidemic. JAMA 1995;273:220 – 6. 3. Ramaswamy S, Musser JM. Molecular genetic basis of antimicrobial agent resistance in Mycobacterium tuberculosis: 1998 update [Review]. Tuber Lung Dis 1998;79:3–29. 4. Mitchison DA. The action of antituberculosis drugs in short-course chemotherapy. Tubercle 1985;66:219 –25. 5. Telenti A, Imboden P, Marchesi F, Lowrie D, Cole S, Colston MJ, et al. Detection of rifampicin-resistance mutations in Mycobacterium tuberculosis. Lancet 1993;341:647–50. 6. Heym B, Alzari PM, Honore M, Cole ST. Missense mutations in the catalase-peroxidase gene, katG, are associated with isoniazid resistance in Mycobacterium tuberculosis. Mol Microbiol 1995;15:235– 45. 7. Huang MM, Arnheim N, Goodman MF. Extension of base mispairs by Taq DNA polymerase: implications for single nucleotide discrimination in PCR. Nucleic Acids Res 1992;20:4567–73. 8. Bifani P, Moghazeh S, Shopsin B, Driscoll J, Ravikovitch A, Kreiswirth BN. Molecular characterization of Mycobacterium tuberculosis H37Rv/Ra variants: distinguishing the mycobacterial laboratory strain. J Clin Microbiol 2000;38:3200 – 4. 9. Del Portillo P, Thomas MC, Martinez E, Maranon C, Valladarez B, Patarroyo M E et al. Multiprimer PCR system for differential identification of mycobacteria in clinical samples. J Clin Microbiol 1996;34:324 – 8. 10. Vera-Cabrera L, Howard ST, Laszlo A, Johnson WM. Analysis of genetic polymorphism in the phospholipase region of Mycobacterium tuberculosis. J Clin Microbiol 1997;35:1190 –5. 11. Bhanu NV, van Soolingen D, van Embden JD, Seth P. Two Mycobacterium fortuitum strains isolated from pulmonary tuberculosis patients in Delhi harbour IS6110 homologue. Diagn Microbiol Infect Dis 2004;48:107–10. 12. van Soolingen D, Hermans PW, de Haas PE, Soll DR, van Embden JD. Occurrence and stability of insertion sequences in Mycobacterium tuberculosis complex strains: evaluation of an insertion sequence-dependent DNA polymorphism as a tool in the epidemiology of tuberculosis. J Clin Microbiol 1991;29:2578 – 86. 13. Shemyakin IG, Stepanshina VN, Ivanov IY. Characterization of M. tuberculosis strains isolated from Russian prisoners [Abstract]. Int J Tuberc Lung Dis 2001;5:S237– 8. 14. Mokrousov I, Otten T, Vyshnevskiy B, Narvskaya O. Allele-specific rpoB PCR assays for detection of rifampin-resistant Mycobacterium tuberculosis in sputum smears. Antimicrob Agents Chemother 2003;47:2231–5. 15. Marttila HJ, Soini H, Eerola E, Vyshnevskaya E, Vyshnevskiy BI, Otten TF, et al. A Ser315Thr substitution in KatG is predominant in genetically heterogeneous multidrug-resistant Mycobacterium tuberculosis isolates originating from the St. Petersburg area in Russia. Antimicrob Agents Chemother 1998;42:2443–5. 16. Viljanen MK, Vyshnevskiy BI, Otten TF, Vyshnevskaya E, Marjamaki M, Soini H, et al. Survey of drug-resistant tuberculosis in northwestern Russia from 1984 through 1994. Eur J Clin Microbiol Infect Dis 1998;17:177– 83. 17. Victor TC, Lee H, Cho SN, Jordaan AM, van der Spuy G, van Helden PD, et al. Molecular detection of early appearance of drug resistance during Mycobacterium tuberculosis infection. Clin Chem Lab Med 2002;40:876 – 81. DOI: 10.1373/clinchem.2004.037309
Estimate of Biological Variation of Laboratory Analytes Based on the Third National Health and Nutrition Examination Survey, David A. Lacher,* Jeffery P. Hughes, and Margaret D. Carroll (Division of Health and Nutritional Examination Surveys, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD; * address correspondence to this author at: National Center for Health Statistics, 3311 Toledo Rd, Room 4215, Hyattsville, MD 20782; fax 301-458-4028, e-mail
[email protected]) Laboratory analytes for individuals are subject to several sources of variation, including biological variation, preanalytical variation (specimen collection), analytical variation (bias and imprecision), and postanalytical variation (reporting of results). Biological variation consists of within-person (WP) and between-person (BP) variation. These components of biological variation are used to set analytical quality specifications for bias and imprecision, evaluate serial changes in individual analytes, and assess the clinical utility of population-based reference intervals. Desirable quality specifications for imprecision (I), bias (B), and total error have been related to the WP CV (CVw) and the BP CV (CVg) of laboratory analytes (1–3 ). Imprecision should be ideally less than one half of the CVW, and bias should be ⬍0.25[(CVw)2 ⫹ (CVg)2]1/2. The quality specification for total error is to be less than kI ⫹ B, where k ⫽ 1.65 at ␣ ⫽ 0.05. The total CV (CVt) can be estimated assuming that the CVs of all sources are measured at the same analyte mean and that pre- and postanalytical sources of variation are negligible. The CVt ⫽ [(CVa)2 ⫹ (CVw)2]1/2, where the analytical CV (CVa) equals the laboratory method imprecision (CVi) if there is no bias present. Estimates of CVw and CVg for laboratory analytes were derived from the Third National Health and Nutrition Examination Survey (NHANES III) conducted from 1988 to 1994 (4, 5 ). NHANES III was a cross-sectional survey that collected data on the civilian noninstitutionalized US population through questionnaires and medical examinations, including laboratory analytes. NHANES III used a stratified, multistage probability design to collect a nationally representative sample. The laboratory methods including imprecision (CVi) for NHANES III have been described (6 ). The BP and WP means, SDs, and CVs for 42 general biochemical, nutritional, immunologic, environmental, and hematologic analytes are listed in Table 1. The BP and WP variations were estimated on 24 978 and 2426 sample persons, respectively. The WP sample, ⬃10% of the sample persons, was recruited for a second analyte measurement. The WP sample was not selected randomly, but with the goal for obtaining approximately equal proportions of males and females with one half between 20 and 39 years of age and one half over 40 years of age. When possible, the second examinations were scheduled at the same time of day as the first examinations. Compared with the BP sample, the WP sample was older (mean age, 42.9 vs 30.8 years), had more non-Hispanic whites (42.2%
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Table 1. BP, WP, and method CV and index of individuality for laboratory analytes from NHANES III. BP
WP
Analyte (units)a
n
Mean
SD
CVg, %
n
Mean
SD
CVw, %
Index of individualityb
Method CVi, %
Alanine aminotransferasec (U/L) Albumin (g/L) Alkaline phosphatasec (U/L) Apolipoprotein A (g/L) Apolipoprotein B (g/L) Aspartate aminotransferasec (U/L) -Carotenec (mol/L) -Cryptoxanthinc (mol/L) Bicarbonate (mmol/L) Bilirubin, totalc (mol/L) C peptidec (nmol/L) Calcium, ionizedc (mmol/L) Calcium, totalc (mmol/L) Chloride (mmol/L) Cholesterol, totalc (mmol/L) Creatininec (mol/L) Creatinine, urinec (mmol/L) Fibrinogen, plasmac (g/L) Folatec (nmol/L) ␥-Glutamyl transferasec (U/L) Glucose, plasmac (mmol/L) Glycohemoglobin, bloodc (%) HDL-cholesterolc (mmol/L) Homocysteinec (mol/L) Ironc (mol/L) Total iron-binding capacityc (mol/L) Insulin, plasmac (pmol/L) Lactate dehydrogenasec (U/L) Lutein/Zeaxanthinc (mol/L) Lycopenec (mol/L) Phosphorusc (mmol/L) Potassiumc (mmol/L) Protein, total (g/L) Seleniumc (nmol/L) Sodiumc (mmol/L) Triglyceridesc (mmol/L) Urea nitrogenc (mmol/L) Uric acidc (mmol/L) Varicella antibody Vitamin A (mol/L) Vitamin B12c (pmol/L) Vitamin Ec (mol/L)
15 535 16 118 15 281 10 128 10 168 15 614 20 002 20 046 16 116 15 902 13 549 14 593 16 135 16 118 20 690 16 026 19 676 7681 20 568 11 784 15 341 19 846 20 534 7253 23 582 22 933 12 960 16 024 20 192 20 435 16 103 16 098 16 118 15 998 16 065 20 271 15 904 16 112 18 607 20 455 10 330 20 197
15.3 42.2 84.1 1.41 0.98 20.0 0.32 0.16 28.1 10.15 0.68 1.24 2.32 104.5 5.01 92.70 11.55 3.02 15.27 22.0 5.07 5.16 1.31 9.00 15.9 64.6 56.62 157.80 0.36 0.44 1.14 4.06 73.2 1.58 141.3 1.37 4.92 0.31 12.71 1.91 370.72 23.97
7.7 3.7 28.0 0.25 0.27 5.8 0.22 0.10 3.7 4.46 0.45 0.04 0.11 3.2 1.12 17.36 7.08 0.77 9.81 13.2 0.63 0.49 0.37 3.29 6.6 10.0 31.66 34.09 0.16 0.21 0.18 0.31 4.5 0.21 2.3 0.78 1.58 0.08 5.48 0.59 154.32 8.42
50.2 8.9 33.4 17.8 27.6 29.1 67.4 58.8 13.3 43.9 65.7 3.6 4.7 3.1 22.3 18.7 61.3 25.6 64.3 59.8 12.5 9.6 28.3 36.6 41.6 15.5 55.9 21.6 46.0 47.4 15.8 7.7 6.2 13.2 1.6 56.8 32.1 27.1 43.2 30.7 41.6 35.1
1976 2076 1996 1060 1073 1993 2202 2225 2076 2038 1218 1182 1416 2076 2299 2062 1626 1148 2231 1554 1950 2195 2266 851 2303 2213 1177 2064 2233 2253 2076 2071 2076 2016 2064 2219 2054 2075 1575 2261 1128 2219
15.1 41.2 87.8 1.44 1.04 20.4 0.32 0.18 27.8 9.57 0.76 1.24 2.31 104.5 5.21 93.11 11.89 3.12 14.18 24.6 5.14 5.34 1.31 9.15 15.1 64.3 66.36 164.27 0.38 0.41 1.15 4.08 73.6 1.54 141.1 1.49 4.95 0.32 11.93 1.92 377.94 24.89
3.6 1.8 6.9 0.12 0.10 3.2 0.08 0.04 3.1 2.37 0.22 0.03 0.08 2.3 0.43 6.38 5.11 0.52 3.24 4.0 0.43 0.18 0.16 1.74 4.4 4.4 18.80 16.25 0.07 0.11 0.11 0.22 2.6 0.11 1.8 0.43 0.91 0.03 1.82 0.19 55.68 2.90
23.7 2.8 4.4 7.0 9.5 15.1 24.2 20.5 11.0 24.6 28.4 2.4 3.3 1.9 8.2 6.8 43.0 16.2 22.6 16.2 8.3 1.5 12.4 18.0 29.0 6.9 25.2 7.9 17.4 26.1 9.2 5.4 3.5 5.1 1.3 28.8 18.0 9.0 13.7 9.5 13.4 11.3
0.47 0.31 0.13 0.39 0.34 0.52 0.36 0.35 0.83 0.56 0.43 0.67 0.70 0.61 0.37 0.36 0.70 0.63 0.35 0.27 0.66 0.16 0.44 0.49 0.70 0.45 0.45 0.37 0.38 0.55 0.58 0.70 0.56 0.39 0.81 0.51 0.56 0.33 0.32 0.31 0.32 0.32
3.2 3.4 6.5 4.8 2.7 3.4 7.4 7.7 2.4 3.0 7.2 1.4 2.2 1.0 2.3 1.0 2.2 3.9 3.6 1.7 1.7 3.1 2.5 6.0 3.2 3.3 13.0 6.0 9.1 8.5 2.0 0.5 0.9 4.8 0.7 4.7 3.7 0.7 6.7 2.5 6.2 2.9
a
Specimen type is serum unless noted. Index of individuality is the ratio of CVw to CVg. c Outliers were deleted by the Tukey method. b
vs 34.7%), and had fewer Mexican Americans (25.4% vs 30.6%). There was no statistically significant difference in gender proportions between the BP and WP samples. The CVg values were estimated using a weighted, complex sample design by Taylor series linearization (7 ). The BP standard deviation was calculated as (SE2 ⫹ SDsrs2)1/2, where SE is the standard error of the mean obtained with the complex design and SDsrs2 is the square
of the standard deviation assuming a simple random sample. The WP variations were estimated from a nonrandom, unweighted sample with a mean (SD) of 17 (8) days between two analyte measurements. The CVw was calculated as [(CVt)2 ⫺ (CVa)2]1/2. The distributions of several analytes were nongaussian, and extreme outliers were excluded to obtain an approximately gaussian distribution with more stable estimates of variation. Outliers
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were eliminated by use of Tukey’s method, which defines outliers as three interquartile ranges below the 25th percentile or above the 75th percentile (8 ). Statistical analyses were carried out with SAS for Windows software (SAS Institute) and SUDAAN software (Research Triangle Institute). The CVw and CVg exceeded the CVi for laboratory analytes (Table 1). For most laboratory analytes, the mean BP and WP analyte values were similar despite some demographic differences between the two groups. The analytical quality specifications for imprecision and bias can be judged by use of the CVw and CVg. For example, the total cholesterol imprecision should be less than one half of CVw (8.2%), or 4.1%. The method imprecision was 2.3%. The bias for total cholesterol should be ⬍0.25[(CVw)2 ⫹ (CVg)2]1/2, or 0.25[(0.079)2 ⫹ (0.226)2]1/2, or ⬃6.0%. The quality specification for total error is estimated as B ⫹ 1.65(I), or 6.0% ⫹ 1.65(4.1%), or 12.8%. This compares with the National Cholesterol Education Program (NCEP) performance criteria of ⬍3% for imprecision and bias and 9% for total error (9 ). Serum sodium had the lowest CVg (1.6%) and lowest CVw (1.3%). This reflects the narrow homeostatic range for sodium that the body maintains. High CVw and CVg values were seen for several analytes. High CV values could result from natural population or individual variations, diurnal variations, disease, outlying analyte values, and relatively lower analyte values. The ratio of CVw to CVg, also known as the index of individuality, is important in determining the use of population-based reference (normal) intervals in detecting changes of disease status in individuals (10, 11 ). When the index of individuality is low (⬍0.6), the individual results stay within a narrow range compared with the population reference interval. Hence, a low index suggests the utility of evaluating serial changes in analyte values in an individual, and population-based reference intervals would be of limited use. A high index (⬎1.4) suggests that the reference interval is appropriate. The index of individuality ranged from 0.13 for serum alkaline phosphatase to 0.83 for serum bicarbonate (Table 1). In this study, BP and WP estimates of CV were obtained for some selective environmental and nutritional analytes. NHANES III provides a better estimate of CVg than do other short-term local studies because the NHANES III sample was nationally representative (1, 12, 13 ). The CVw estimate was limited by the nonrandom, self-selected design and reflected a mean of 17 days between two measurements. The estimate of CVw could be improved by use of a stratified, multistage probability design over different time periods. Differences for CVw and CVg among subpopulations (gender, age, race, and ethnicity) can be further investigated by use of NHANES III data. References 1. Ricos C, Alvarez V, Cava F, Garcia-Lario JV, Hernandez A, Jimenez CV, et al. Current databases on biological variation: pros, cons and progress. Scand J Clin Lab Invest 1999;59:491–500. 2. Fraser CG. Biological variation: from principles to practice. Washington DC: AACC Press, 2001:151pp.
3. Fraser CG, Harris EK. Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci 1989;27:409 –37. 4. National Center for Health Statistics. Plan and operation of the third National Health and Nutrition Examination Survey, 1988 –94. Vital Health Stat 1994;1(32):415pp. 5. US Department of Health and Human Services. Third National Health and Nutrition Examination Survey (NHANES III), 1988 –94. NHANES III second examination file documentation. Series 11, No. 3A. Hyattsville, MD: National Center for Health Statistics, 1999. http://www.cdc.gov/nchs/about/major/ nhanes/nh3data.htm#NHANES%20III%20Series%2011,%20No.%203a (accessed September 2004). 6. National Center for Health Statistics. Laboratory procedures used for the Third National Health and Nutrition Examination Survey (NHANES III), 1988 – 94. http://www.cdc.gov/nchs/data/nhanes/nhanes3/cdrom/nchs/manuals/ labman.pdf (accessed September 2004). 7. Wolter KM. Introduction to variance estimation. New York: Springer-Verlag, 1985:221– 47. 8. Tukey JW. Exploratory data analysis. Reading, MA: Addison-Wesley Publishing Co., 1977:44. 9. Current status of blood cholesterol measurement in clinical laboratories in the United States: a report from the laboratory standardization panel of the National Cholesterol Education Program. Clin Chem 1988;34:193–201. 10. Fraser CG. Inherent biological variation and reference values. Clin Chem Lab Med 2004;42:758 – 64. 11. Harris EK. Effects of intra- and interindividual variation on the appropriate use of normal intervals. Clin Chem 1974;20:1535– 42. 12. Fraser CG. Biological variation in clinical chemistry, an update: collated data, 1988 –1991. Arch Pathol Lab Med 1992;116:916 –23. 13. Ricos C, Garcia-Lario JV, Alvarez V, Caval F, Domenech M, Hernandez A, et al. Biological variation database, and quality specifications for imprecision, bias and total error (desirable and minimum). The 2004 update. http:// www.westgard.com/guest26.htm (accessed September 2004). Previously published online at DOI: 10.1373/clinchem.2004.039354
Breast Cancer Progression and Host Polymorphisms in the Chemokine System: Role of the Macrophage Chemoattractant Protein-1 (MCP-1) ⴚ2518 G Allele, Giorgio Ghilardi,1* Maria Luisa Biondi,2 Anna La Torre,2 Lodovica Battaglioli,2 and Roberto Scorza1 (1 Dipartimento MCO, Clinica Chirurgica Generale, Universita` degli Studi di Milano–Polo S. Paolo, Milan, Italy; 2 Laboratorio di Chimica Clinica e Microbiologia, Ospedale S. Paolo–Polo Universitario, Milan, Italy; * address correspondence to this author at: Dipartimento MCO, Clinica Chirurgica Generale, Universita` degli Studi di Milano–Polo S. Paolo, Via A. Di Rudinı`, 8, I-20142 Milan, Italy; e-mail
[email protected]) Interaction between tumor cells and stroma is essential for tumor growth. Tumor cells stimulate the formation of stromal tissue, which excretes a variety of growth factors, cytokines, and proteases. Tumor-associated macrophages (TAMs) are one of the major components of tumor stromal tissue and are capable of eliciting diverse aspects of tumor growth as either a positive or negative regulator (1 ). In breast carcinoma, large numbers of infiltrating T cells and TAMs are often observed. The leukocyte infiltrate is found within the tumor stromal areas as well as in the epithelial areas that constitute the tumor mass (2–13 ). Recent reports suggest that the inflammatory reaction at the breast tumor site affects tumor growth and progression. Whereas lymphocytes have been shown to have divergent effects on development of breast cancer (2, 10 –
