IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0126680.html
   My bibliography  Save this article

Benchmark Dose for Urinary Cadmium based on a Marker of Renal Dysfunction: A Meta-Analysis

Author

Listed:
  • Hae Dong Woo
  • Weihsueh A Chiu
  • Seongil Jo
  • Jeongseon Kim

Abstract

Background: Low doses of cadmium can cause adverse health effects. Benchmark dose (BMD) and the one-sided 95% lower confidence limit of BMD (BMDL) to derive points of departure for urinary cadmium exposure have been estimated in several previous studies, but the methods to derive BMD and the estimated BMDs differ. Objectives: We aimed to find the associated factors that affect BMD calculation in the general population, and to estimate the summary BMD for urinary cadmium using reported BMDs. Methods: A meta-regression was performed and the pooled BMD/BMDL was estimated using studies reporting a BMD and BMDL, weighted by sample size, that were calculated from individual data based on markers of renal dysfunction. Results: BMDs were highly heterogeneous across studies. Meta-regression analysis showed that a significant predictor of BMD was the cut-off point which denotes an abnormal level. Using the 95th percentile as a cut off, BMD5/BMDL5 estimates for 5% benchmark responses (BMR) of β2-microglobulinuria (β2-MG) estimated was 6.18/4.88 μg/g creatinine in conventional quantal analysis and 3.56/3.13 μg/g creatinine in the hybrid approach, and BMD5/BMDL5 estimates for 5% BMR of N-acetyl-β-d-glucosaminidase (NAG) was 10.31/7.61 μg/g creatinine in quantal analysis and 3.21/2.24 g/g creatinine in the hybrid approach. However, the meta-regression showed that BMD and BMDL were significantly associated with the cut-off point, but BMD calculation method did not significantly affect the results. The urinary cadmium BMDL5 of β2-MG was 1.9 μg/g creatinine in the lowest cut-off point group. Conclusion: The BMD was significantly associated with the cut-off point defining the abnormal level of renal dysfunction markers.

Suggested Citation

  • Hae Dong Woo & Weihsueh A Chiu & Seongil Jo & Jeongseon Kim, 2015. "Benchmark Dose for Urinary Cadmium based on a Marker of Renal Dysfunction: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-12, May.
    • Handle: RePEc:plo:pone00:0126680
    DOI: 10.1371/journal.pone.0126680
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0126680
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0126680&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0126680?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kenny S. Crump, 1995. "Calculation of Benchmark Doses from Continuous Data," Risk Analysis, John Wiley & Sons, vol. 15(1), pages 79-89, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Katsuyuki Murata & Esben Budtz‐Jørgensen & Philippe Grandjean, 2002. "Benchmark Dose Calculations for Methylmercury‐Associated Delays on Evoked Potential Latencies in Two Cohorts of Children," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 465-474, June.
    2. Zi-Fan Yu & Paul J. Catalano, 2005. "Quantitative Risk Assessment for Multivariate Continuous Outcomes with Application to Neurotoxicology: The Bivariate Case," Biometrics, The International Biometric Society, vol. 61(3), pages 757-766, September.
    3. Walter W. Piegorsch, 2010. "Translational benchmark risk analysis," Journal of Risk Research, Taylor & Francis Journals, vol. 13(5), pages 653-667, July.
    4. Kenny S. Grump & Tord Kjellström & Annette M. Shipp & Abraham Silvers & Alistair Stewart, 1998. "Influence of Prenatal Mercury Exposure Upon Scholastic and Psychologica Test Performance: Benchmark Analysis of a New Zealand Cohort," Risk Analysis, John Wiley & Sons, vol. 18(6), pages 701-713, December.
    5. Maria A. Sans‐Fuentes & Walter W. Piegorsch, 2021. "Benchmark dose risk analysis with mixed‐factor quantal data in environmental risk assessment," Environmetrics, John Wiley & Sons, Ltd., vol. 32(5), August.
    6. Walter W. Piegorsch & Hui Xiong & Rabi N. Bhattacharya & Lizhen Lin, 2014. "Benchmark Dose Analysis via Nonparametric Regression Modeling," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 135-151, January.
    7. Kristi Kuljus & Dietrich Von Rosen & Salomon Sand & Katarina Victorin, 2006. "Comparing Experimental Designs for Benchmark Dose Calculations for Continuous Endpoints," Risk Analysis, John Wiley & Sons, vol. 26(4), pages 1031-1043, August.
    8. Steven B. Kim & Ralph L. Kodell & Hojin Moon, 2014. "A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging," Risk Analysis, John Wiley & Sons, vol. 34(3), pages 453-464, March.
    9. Walter W. Piegorsch & R. Webster West, 2005. "Benchmark Analysis: Shopping with Proper Confidence," Risk Analysis, John Wiley & Sons, vol. 25(4), pages 913-920, August.
    10. Matthew W. Wheeler & A. John Bailer & Tarah Cole & Robert M. Park & Kan Shao, 2017. "Bayesian Quantile Impairment Threshold Benchmark Dose Estimation for Continuous Endpoints," Risk Analysis, John Wiley & Sons, vol. 37(11), pages 2107-2118, November.
    11. Signe M. Jensen & Felix M. Kluxen & Christian Ritz, 2019. "A Review of Recent Advances in Benchmark Dose Methodology," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2295-2315, October.
    12. Mirjam Moerbeek & Aldert H. Piersma & Wout Slob, 2004. "A Comparison of Three Methods for Calculating Confidence Intervals for the Benchmark Dose," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 31-40, February.
    13. Matteo Goldoni & Maria Vittoria Vettori & Rossella Alinovi & Andrea Caglieri & Sandra Ceccatelli & Antonio Mutti, 2003. "Models of Neurotoxicity: Extrapolation of Benchmark Doses in Vitro," Risk Analysis, John Wiley & Sons, vol. 23(3), pages 505-514, June.
    14. Hoda Izadi & Jean E. Grundy & Ranjan Bose, 2012. "Evaluation of the Benchmark Dose for Point of Departure Determination for a Variety of Chemical Classes in Applied Regulatory Settings," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 830-835, May.
    15. Keiko Kubo & Kazuhiro Nogawa & Teruhiko Kido & Muneko Nishijo & Hideaki Nakagawa & Yasushi Suwazono, 2017. "Estimation of Benchmark Dose of Lifetime Cadmium Intake for Adverse Renal Effects Using Hybrid Approach in Inhabitants of an Environmentally Exposed River Basin in Japan," Risk Analysis, John Wiley & Sons, vol. 37(1), pages 20-26, January.
    16. David W. Gaylor & William Slikker, 2004. "Role of the Standard Deviation in the Estimation of Benchmark Doses with Continuous Data," Risk Analysis, John Wiley & Sons, vol. 24(6), pages 1683-1687, December.
    17. Harvey J. Clewell & Gregory A. Lawrence & Donald B. Calne & Kenny S. Crump, 2003. "Determination of an Occupational Exposure Guideline for Manganese Using the Benchmark Method," Risk Analysis, John Wiley & Sons, vol. 23(5), pages 1031-1046, October.
    18. Chu‐Chih Chen & James J. Chen, 2014. "Benchmark Dose Calculation for Ordered Categorical Responses," Risk Analysis, John Wiley & Sons, vol. 34(8), pages 1435-1447, August.
    19. Jingyu Liu & Walter W. Piegorsch & A. Grant Schissler & Susan L. Cutter, 2018. "Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 803-823, June.
    20. Esben Budtz-Jørgensen & Niels Keiding & Philippe Grandjean, 2001. "Benchmark Dose Calculation from Epidemiological Data," Biometrics, The International Biometric Society, vol. 57(3), pages 698-706, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0126680. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.