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Analytical Considerations in the Clinical Laboratory Assessment of Metals

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Abstract

The presence of metals in the environment is ubiquitous and humans are constantly being exposed to them. As such, a general concern exists about potential health consequences that result from the exposure to metals. The continued efforts of environmental scientists to measure metals in clinical specimens are important for defining the extent of human exposure to these chemicals. Laboratory methods to measure the concentration of metals in human blood or urine are available, and they can be used to assess the extent of human exposure to these chemicals. However, several considerations should be reviewed when requesting a laboratory measurement of metals because some factors can affect the test result or its interpretation. These considerations are discussed in this article and include pre-analytical, analytical, and post-analytical factors. Clinicians with this knowledge will be able to request these laboratory tests for their patients with enhanced confidence.

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References

  1. Centers for Disease Control and Prevention (1991) Acute and chronic poisoning from residential exposures to elemental mercury—Michigan, 1989–1990. MMWR Morb Mortal Wkly Rep 40:393–395

    Google Scholar 

  2. Dooyema CA, Neri A, Lo YC, Durant J, Dargan PI, Swarthout T et al (2012) Outbreak of fatal childhood lead poisoning related to artisanal gold mining in northwestern Nigeria, 2010. Environ Health Perspect 120:601–607

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Gensheimer KF, Rea V, Mills DA, Montagna CP, Simone K (2010) Arsenic poisoning caused by intentional contamination of coffee at a church gathering—an epidemiological approach to a forensic investigation. J Forensic Sci 55:1116–1119

    Article  PubMed  Google Scholar 

  4. National Research Council (2006) Human biomonitoring for environmental chemicals. National Academy Press, Washington, D.C.

  5. Centers for Disease Control and Prevention (2009) Fourth national report on human exposure to environmental chemicals. U.S. Department of Health and Human Services, Atlanta, GA. http://www.cdc.gov/exposurereport/pdf/FourthReport.pdf. Accessed 09 Sept 2013

  6. Centers for Disease Control and Prevention (1994) Blood lead levels—United States, 1988–1991. MMWR Morb Mortal Wkly Rep 43:545–547

    Google Scholar 

  7. Centers for Disease Control and Prevention (2013) Blood lead levels in children aged 1–5 years—United States, 1999–2010. MMWR Morb Mortal Wkly Rep 62:245–248

    Google Scholar 

  8. Clinical and Laboratory Standards Institute C (2008) Defining, establishing, and verifying reference intervals in the clinical laboratory; approved guideline. CLSI, Wayne

    Google Scholar 

  9. National Research Council (2001) Arsenic in drinking water: 2001 update, Washington, D.C. accessed on pp 143–147

  10. Agency for Toxic Substances and Disease Registry (2001) Hair analysis panel discussion exploring the state of the science. ATSDR, Atlanta, GA http://www.atsdr.cdc.gov/HAC/hair_analysis/table.html. Accessed 09 Sept 2013

  11. Clarkson TW, Vyas JB, Ballatori N (2007) Mechanisms of mercury disposition in the body. Am J Ind Med 50:757–764

    Article  CAS  PubMed  Google Scholar 

  12. Norseth T, Clarkson TW (1970) Studies on the biotransformation of 203Hg-labeled methyl mercury chloride in rats. Arch Environ Health 21:717–727

    Article  CAS  PubMed  Google Scholar 

  13. Rowland IR (1988) Interactions of the gut microflora and the host in toxicology. Toxicol Pathol 16:147–153

    Article  CAS  PubMed  Google Scholar 

  14. Martin MD, McCann T, Naleway C, Woods JS, Leroux BG, Bollen AM (1996) The validity of spot urine samples for low-level occupational mercury exposure assessment and relationship to porphyrin and creatinine excretion rates. J Pharmacol Exp Ther 277:239–244

    CAS  PubMed  Google Scholar 

  15. Centers for Disease Control and Prevention (2013) Urine multi-element ICP-DRC-MS, DLS Method. CLIA method Division of Laboratory Sciences Centers for Disease Control and Prevention Atlanta GA

  16. Caldwell KL, Jones RL, Verdon CP, Jarrett JM, Caudill SP, Osterloh JD (2009) Levels of urinary total and speciated arsenic in the US population: National Health and Nutrition Examination Survey 2003–2004. J Expo Sci Environ Epidemiol 19:59–68

    Article  CAS  PubMed  Google Scholar 

  17. Centers for Disease Control and Prevention (2013) Triple spike isotope dilution-gas chromatography-inductively coupled plasma-dynamic reaction cell-mass spectrometry, DLS Method. CLIA method Division of Laboratory Sciences Centers for Disease Control and Prevention Atlanta GA

  18. Taylor J (1987) Quality assurance of chemical measurements. Lewis Publishers, Boac Raton

    Google Scholar 

  19. Jain RB, Caudill SP, Wang RY, Monsell E (2008) Evaluation of maximum likelihood procedures to estimate left censored observations. Anal Chem 80:1124–1132

    Article  CAS  PubMed  Google Scholar 

  20. Grasbeck R (2004) The evolution of the reference value concept. Clin Chem Lab Med 42:692–697

    Article  PubMed  Google Scholar 

  21. Solberg HE (1983) The theory of reference values Part 5. Statistical treatment of collected reference values. Determination of reference limits. J Clin Chem Clin Biochem 21:749–760

    CAS  PubMed  Google Scholar 

  22. Centers for Disease Control and Prevention. National Center for Health Statistics. National Health and Nutrition Examination Survey. http://www.cdc.gov/nchs/nhanes.htm. Accessed 09 Sept 2013

  23. Centers for Disease Control and Prevention. National Center for Environmental Health. National Biomonitoring Program. http://www.cdc.gov/biomonitoring/. Accessed 09 Sept 2013

  24. Centers for Disease Control and Prevention (2012) Second national report on biochemical indicators of diet and nutrition in the U.S. population. U.S. Department of Health and Human Services, Atlanta, GA. http://www.cdc.gov/nutritionreport/. Accessed 09 Sept 2013

  25. Gamble MV, Liu X, Slavkovich V, Pilsner JR, Ilievski V, Factor-Litvak P et al (2007) Folic acid supplementation lowers blood arsenic. Am J Clin Nutr 86:1202–1209

    CAS  PubMed Central  PubMed  Google Scholar 

  26. Harris EK (1974) Effects of intra- and interindividual variation on the appropriate use of normal ranges. Clin Chem 20:1535–1542

    CAS  PubMed  Google Scholar 

  27. Gowans EM, Fraser CG (1988) Biological variation of serum and urine creatinine and creatinine clearance: ramifications for interpretation of results and patient care. Ann Clin Biochem 25(Pt 3):259–263

    Article  CAS  PubMed  Google Scholar 

  28. Harris EK, Kanofsky P, Shakarji G, Cotlove E (1970) Biological and analytic components of variation in long-term studies of serum constituents in normal subjects. II. Estimating biological components of variation. Clin Chem 16:1022–1027

    CAS  PubMed  Google Scholar 

  29. Lacher DA, Hughes JP, Carroll MD (2010) Biological variation of laboratory analytes based on the 1999–2002 National Health and Nutrition Examination Survey. Natl Health Stat Report:1–7

  30. Williams GZ, Young DS, Stein MR, Cotlove E (1970) Biological and analytic components of variation in long-term studies of serum constituents in normal subjects. I. Objectives, subject selection, laboratory procedures, and estimation of analytic deviation. Clin Chem 16:1016–1021

    CAS  PubMed  Google Scholar 

  31. Fraser CG, Harris EK (1989) Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci 27:409–437

    Article  CAS  PubMed  Google Scholar 

  32. Harris EK, Yasaka T (1983) On the calculation of a “reference change” for comparing two consecutive measurements. Clin Chem 29:25–30

    CAS  PubMed  Google Scholar 

  33. Centres for Disease Control and Prevention (2004) Blood Lead, Cadmium, and Mercury ICP-DRC-MS, DLS Method. CLIA method Division of Laboratory Sciences Centers for Disease Control and Prevention, Atlanta, GA

  34. Cohen JP, Ruha AM, Curry SC, Biswas K, Westenberger B, Ye W et al. (2013) Plasma and urine dimercaptopronesulfonate concentrations after dermal application of transdermal DMPS (TD-DMPS). J Med Toxicol 9:9–15

    Google Scholar 

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Conflict of Interest

Wang, none; Caldwell, none; and Jones, none.

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Correspondence to Richard Y. Wang.

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The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of Centers for Disease Control and Prevention.

Appendix

Appendix

Index of Individuality

The degree of individuality can be expressed as the index of individuality (II) [31], where II = (CVa2 + CVi2)1/2 / CVg and CVa, CVi, and CVg represent the analytical method, intra-individual, and inter-individual coefficient variations, respectively. The equation for II can be simplified to CVi / CVg when CVa contributes minimally to II.

The II for a laboratory test can vary depending on the analyte. For example, the II for serum iron (0.76) was found to be higher than that for serum creatinine (0.24) in a convenience sample of 853 survey participants aged 16 to 69 years who had two blood measurements that were at least 8 days apart [29]. In another study of 68 adult volunteers who had weekly blood measurements for 10 to 12 weeks, the II for 15 routine serum chemistries varied from 0.38 (total cholesterol in women greater than 30 years old) to 1.38 (serum sodium) when the analytical method coefficient variation was included in the calculation [28, 30].

A reference value based on a laboratory test result with a high degree of individuality (i.e., an index of individuality <0.6) is not sensitive to a change in the test result for a specific person [26, 31]. On the other hand, a reference value for a test result with a low degree of individuality (i.e., an index of individuality >1.4) is more useful than the former test to detect a change in a test result for a specific person. This circumstance occurs because the distribution of values from an individual will cover much of the distribution of values for the reference interval derived from the group of reference individuals. Thus, the intra- and inter-individuality of the laboratory test result needs to be considered when developing or using reference values.

The index of individuality for a laboratory test can be increased by stratifying the population into groups, which will enhance the usefulness of the test to monitor individuals. For example, the II for urinary creatinine (millimoles per day) collected by 24-h sampling at four weekly intervals over a period of 40 weeks for a group of seven men and eight women was 0.46 for the overall group, 1.42 for the women, and 1.83 for the men [27]. Thus, the reference value for urinary creatinine is more useful when it is determined for each sex than for a group containing both sexes.

Reference Change Value

If the reference value for a laboratory test is not sensitive to monitor for change in a specific person, then a clinically based fixed criteria or a reference change value can be used to evaluate for change in the test result for that person [32]. The reference change value is a method used to interpret a difference in measurements in a patient. The current test result is compared with the patient’s past results and not a population-based reference value. The reference change value determines the percent change between two values that is statistically significant based on a Z-score (Z-statistics). The reference change value (RCV%) is calculated as: RCV% = 1.414 × Z × (CVa2 + CVi2)1/2. Z represents the Z-score, which is determined by the desired level of confidence in the estimated value (a bidirectional Z = 1.96 for a 95 % probability or a false-positive rate of 5 %).

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Wang, R.Y., Caldwell, K.L. & Jones, R.L. Analytical Considerations in the Clinical Laboratory Assessment of Metals. J. Med. Toxicol. 10, 232–239 (2014). https://doi.org/10.1007/s13181-014-0381-8

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