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Cancer Epidemiology, Biomarkers & Prevention
Cancer Epidemiology, Biomarkers & Prevention
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Temporal Variability of Oral Microbiota over 10 Months and the Implications for Future Epidemiologic Studies

Emily Vogtmann, Xing Hua, Liang Zhou, Yunhu Wan, Shalabh Suman, Bin Zhu, Casey L. Dagnall, Amy Hutchinson, Kristine Jones, Belynda D. Hicks, Rashmi Sinha, Jianxin Shi and Christian C. Abnet
Emily Vogtmann
1Metabolic Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
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  • For correspondence: emily.vogtmann@nih.gov
Xing Hua
2Biostatistics Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
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Liang Zhou
1Metabolic Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
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Yunhu Wan
2Biostatistics Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
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Shalabh Suman
3Cancer Genomics Research Laboratory, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
4Leidos Biomedical Research Laboratory, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland.
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Bin Zhu
3Cancer Genomics Research Laboratory, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
4Leidos Biomedical Research Laboratory, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland.
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Casey L. Dagnall
3Cancer Genomics Research Laboratory, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
4Leidos Biomedical Research Laboratory, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland.
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Amy Hutchinson
3Cancer Genomics Research Laboratory, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
4Leidos Biomedical Research Laboratory, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland.
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Kristine Jones
3Cancer Genomics Research Laboratory, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
4Leidos Biomedical Research Laboratory, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland.
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Belynda D. Hicks
3Cancer Genomics Research Laboratory, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
4Leidos Biomedical Research Laboratory, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland.
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Rashmi Sinha
1Metabolic Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
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Jianxin Shi
2Biostatistics Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
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Christian C. Abnet
1Metabolic Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland.
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DOI: 10.1158/1055-9965.EPI-17-1004 Published May 2018
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Abstract

Background: Few studies have prospectively evaluated the association between oral microbiota and health outcomes. Precise estimates of the intrasubject microbial metric stability will allow better study planning. Therefore, we conducted a study to evaluate the temporal variability of oral microbiota.

Methods: Forty individuals provided six oral samples using the OMNIgene ORAL kit and Scope mouthwash oral rinses approximately every two months over 10 months. DNA was extracted using the QIAsymphony and the V4 region of the 16S rRNA gene was amplified and sequenced using the MiSeq. To estimate temporal variation, we calculated intraclass correlation coefficients (ICCs) for a variety of metrics and examined stability after clustering samples into distinct community types using Dirichlet multinomial models (DMMs).

Results: The ICCs for the alpha diversity measures were high, including for number of observed bacterial species [0.74; 95% confidence interval (CI): 0.65–0.82 and 0.79; 95% CI: 0.75–0.94] from OMNIgene ORAL and Scope mouthwash, respectively. The ICCs for the relative abundance of the top four phyla and beta diversity matrices were lower. Three clusters provided the best model fit for the DMM from the OMNIgene ORAL samples, and the probability of remaining in a specific cluster was high (59.5%–80.7%).

Conclusions: The oral microbiota appears to be stable over time for multiple metrics, but some measures, particularly relative abundance, were less stable.

Impact: We used this information to calculate stability-adjusted power calculations that will inform future field study protocols and experimental analytic designs. Cancer Epidemiol Biomarkers Prev; 27(5); 594–600. ©2018 AACR.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).

  • Data availability: The raw sequencing data are available through the NCBI Sequence Read Archive (SRP130106).

  • Received October 27, 2017.
  • Revision received December 22, 2017.
  • Accepted February 9, 2018.
  • Published first February 23, 2018.
  • ©2018 American Association for Cancer Research.
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Cancer Epidemiology Biomarkers & Prevention: 27 (5)
May 2018
Volume 27, Issue 5
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Temporal Variability of Oral Microbiota over 10 Months and the Implications for Future Epidemiologic Studies
Emily Vogtmann, Xing Hua, Liang Zhou, Yunhu Wan, Shalabh Suman, Bin Zhu, Casey L. Dagnall, Amy Hutchinson, Kristine Jones, Belynda D. Hicks, Rashmi Sinha, Jianxin Shi and Christian C. Abnet
Cancer Epidemiol Biomarkers Prev May 1 2018 (27) (5) 594-600; DOI: 10.1158/1055-9965.EPI-17-1004

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Temporal Variability of Oral Microbiota over 10 Months and the Implications for Future Epidemiologic Studies
Emily Vogtmann, Xing Hua, Liang Zhou, Yunhu Wan, Shalabh Suman, Bin Zhu, Casey L. Dagnall, Amy Hutchinson, Kristine Jones, Belynda D. Hicks, Rashmi Sinha, Jianxin Shi and Christian C. Abnet
Cancer Epidemiol Biomarkers Prev May 1 2018 (27) (5) 594-600; DOI: 10.1158/1055-9965.EPI-17-1004
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