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Gut microbiome meta-analysis reveals dysbiosis is independent of body mass index in predicting risk of obesity-associated CRC
  1. K Leigh Greathouse1,2,
  2. James Robert White3,
  3. R Noah Padgett4,
  4. Brittany G Perrotta2,
  5. Gregory D Jenkins5,6,
  6. Nicholas Chia5,6,7,
  7. Jun Chen7
  1. 1 Nutrition Sciences Division, Robbins College of Health and Human Science, Baylor University, Waco, Texas, USA
  2. 2 Department of Biology, Baylor University, Waco, Texas, USA
  3. 3 Resphera Biosciences, Baltimore, Maryland, USA
  4. 4 Department of Educational Psychology, Baylor University, Waco, Texas, USA
  5. 5 Department of Surgery, Mayo Clinic, Rochester, New York, USA
  6. 6 Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, New York, USA
  7. 7 Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, New York, USA
  1. Correspondence to Dr K Leigh Greathouse; Leigh_Greathouse{at}baylor.edu

Abstract

Objective Obesity is a risk factor for colorectal cancer (CRC), accounting for more than 14% of CRC incidence. Microbial dysbiosis and chronic inflammation are common characteristics in both obesity and CRC. Human and murine studies, together, demonstrate the significant impact of the microbiome in governing energy metabolism and CRC development; yet, little is understood about the contribution of the microbiome to development of obesity-associated CRC as compared to individuals who are not obese.

Design In this study, we conducted a meta-analysis using five publicly available stool and tissue-based 16S rRNA and whole genome sequencing (WGS) data sets of CRC microbiome studies. High-resolution analysis was employed for 16S rRNA data, which allowed us to achieve species-level information to compare with WGS. 

Results Characterisation of the confounders between studies, 16S rRNA variable region and sequencing method did not reveal any significant effect on alpha diversity in CRC prediction. Both 16S rRNA and WGS were equally variable in their ability to predict CRC. Results from diversity analysis confirmed lower diversity in obese individuals without CRC; however, no universal differences were found in diversity between obese and non-obese individuals with CRC. When examining taxonomic differences, the probability of being classified as CRC did not change significantly in obese individuals for all taxa tested. However, random forest classification was able to distinguish CRC and non-CRC stool when body mass index was added to the model.

Conclusion Overall, microbial dysbiosis was not a significant factor in explaining the higher risk of colon cancer among individuals with obesity.

  • colonic bacteria
  • colorectal cancer
  • obesity

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors KLG conceived of the study, analysis plan, analysed data, interpreted results and participated in writing and review. JW downloaded and processed all sequencing data. JC, GDJ and RNP conducted statistical analyses. BGP processed data. JRW, KLG, JC, GDJ, RNP, BGP and NC provided technical and data interpretation assistance and manuscript review. All authors read and approved the final manuscript.

  • Funding This work was supported by the Baylor University Summer Sabbatical Grant (PI–summer salary support). NC was supported by the NCI award R01CA179243.

  • Competing interests JRW is a significant shareholder in the company Resphera Biosciences LLC.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement All data are publicaly availably and all processed data are available at https://github.com/GreathouseLab/CRC_BMI_meta_analysis

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