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Pivotal Dominant Bacteria Ratio and Metabolites Related to Healthy Body Index Revealed by Intestinal Microbiome and Metabolomics

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Abstract

Various body indexes, especially body fat percentage (BFP), are widely used as effective indicators to measure our health. BFP is used in medicine to assess obesity, which is a body fat mass disorder accompanied with changes of the gut microbiota. However, the relationship between BFP and the gut microbiota has not been studied so far. To address this problem, we examined how gut microbiota and metabolome associated with body indices in healthy people. Microbial and metabolomics data based on 16S rDNA sequencing and LC–MS were obtained from stool samples of 20 healthy adults. Bioinformatics analysis was performed to explore the correlations between the body indices and gut microbial characteristics. Significantly different microbes were further validated via qPCR. Differential characteristics were filtered by building machine learning models to predict body status. Our data showed that abundance of Prevotella and the Prevotella/Bacteroides (P/B) ratio in the gut were markedly higher in high-BFP individuals than in low-BFP individuals. Microbial and metabolomics data consistently suggested significant differences in fatty acid metabolism in stool samples from the two groups. The P/B ratio and fatty acids are discriminative for people with different index levels by cross validation tests with machine learning models. These results suggest using Prevotella and fecal fatty acids as predictors may offer an alternative method for evaluating health status or weight loss.

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Availability of data and material

Raw reads of the 16S rRNA sequences have been deposited in the NCBI SRA database under accession number PRJNA388136.

Abbreviations

BFP:

Body fat percentage

LC–MS:

Liquid chromatograph mass spectrometer

P/B:

Prevotella/Bacteroides

BMI:

Body mass index

ICW:

Including intracellular water

ECW:

Extracellular water

SMM:

Skeletal muscle mass

BFM:

Body fat mass

OD:

Obesity degree

BMC:

Bone mineral content

BMR:

Basal metabolic rate

CTAB/SDS:

Cetyltrimethyl Ammonium Bromide/Sodium Dodecyl Sulfonate

OUT:

Operational taxonomic unit

RDP:

Ribosomal database project

LEfSe:

Linear discriminant analysis effect size

LDA:

Linear discriminant analysis

qPCR:

Real-time quantitative PCR

References

  1. Qin J, Li R, Raes J et al (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464:59–65. https://doi.org/10.1038/nature08821

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Turnbaugh PJ, Ley RE, Hamady M et al (2007) The human microbiome project. Nature 449:804–810. https://doi.org/10.1038/nature06244

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Faith JJ, Guruge JL, Charbonneau M et al (2013) The long-term stability of the human gut microbiota. Science 341:1237439. https://doi.org/10.1126/science.1237439

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Deehan EC, Walter J (2016) The fiber gap and the disappearing gut microbiome: implications for human nutrition. Trends Endocrinol Metab 27:239–242. https://doi.org/10.1016/j.tem.2016.03.001

    Article  CAS  PubMed  Google Scholar 

  5. Heiman ML, Greenway FL (2016) A healthy gastrointestinal microbiome is dependent on dietary diversity. Mol Metab 5:317–320. https://doi.org/10.1016/j.molmet.2016.02.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sun L, Ma L, Ma Y et al (2018) Insights into the role of gut microbiota in obesity: pathogenesis, mechanisms, and therapeutic perspectives. Protein Cell 9:397–403. https://doi.org/10.1007/s13238-018-0546-3

    Article  PubMed  PubMed Central  Google Scholar 

  7. Segata N, Izard J, Waldron L et al (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12:R60. https://doi.org/10.1186/gb-2011-12-6-r60

    Article  PubMed  PubMed Central  Google Scholar 

  8. Salminen S, Isolauri E (2006) Intestinal colonization, microbiota, and probiotics. J Pediatrics 149:S115–S120. https://doi.org/10.1016/j.jpeds.2006.06.062

    Article  CAS  Google Scholar 

  9. Backhed F, Ding H, Wang T et al (2004) The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A 101:15718–15723. https://doi.org/10.1073/pnas.0407076101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ley RE, Backhed F, Turnbaugh P et al (2005) Obesity alters gut microbial ecology. Proc Natl Acad Sci USA 102:11070–11075. https://doi.org/10.1073/pnas.0504978102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Conterno L, Fava F, Viola R et al (2011) Obesity and the gut microbiota: does up-regulating colonic fermentation protect against obesity and metabolic disease? Genes Nutr 6:241–260. https://doi.org/10.1007/s12263-011-0230-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Backhed F, Manchester JK, Semenkovich CF et al (2007) Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc Natl Acad Sci USA 104:979–984. https://doi.org/10.1073/pnas.0605374104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yun Y, Kim HN, Kim SE et al (2017) Comparative analysis of gut microbiota associated with body mass index in a large Korean cohort. BMC Microbiol 17:151. https://doi.org/10.1186/s12866-017-1052-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Geiss HC, Parhofer KG, Schwandt P (2001) Parameters of childhood obesity and their relationship to cardiovascular risk factors in healthy prepubescent children. Int J Obes Relat Metab Disord 25:830–837. https://doi.org/10.1038/sj.ijo.0801594

    Article  CAS  PubMed  Google Scholar 

  15. Evagelidou EN, Giapros VI, Challa AS et al (2007) Serum adiponectin levels, insulin resistance, and lipid profile in children born small for gestational age are affected by the severity of growth retardation at birth. Eur J Endocrinol 156:271–277. https://doi.org/10.1530/eje.1.02337

    Article  CAS  PubMed  Google Scholar 

  16. Gallagher D, Heymsfield SB, Heo M et al (2000) Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 72:694–701

    Article  CAS  PubMed  Google Scholar 

  17. Deurenberg P, Yap M, van Staveren WA (1998) Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord 22:1164–1171

    Article  CAS  PubMed  Google Scholar 

  18. DiBaise JK, Frank DN, Mathur R (2012) Impact of the gut microbiota on the development of obesity: current concepts. Am J Gastroenterol Suppl 1:22–27. https://doi.org/10.1038/ajgsup.2012.5

    Article  CAS  Google Scholar 

  19. Ogawa H, Fujitani K, Tsujinaka T et al (2011) In Body 720 as a new method of evaluating visceral obesity. Hepatogastroenterology 58:42–44

    PubMed  Google Scholar 

  20. Zhao X, Zhang Z, Hu B et al (2018) Response of gut microbiota to metabolite changes induced by endurance exercise. Front Microbiol 9:765. https://doi.org/10.3389/fmicb.2018.00765

    Article  PubMed  PubMed Central  Google Scholar 

  21. Knight R, Vrbanac A, Taylor BC et al (2018) Best practices for analysing microbiomes. Nat Rev Microbiol 16:410–422. https://doi.org/10.1038/s41579-018-0029-9

    Article  CAS  PubMed  Google Scholar 

  22. Tanja M, Steven LS (2011) FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27:7

    Article  Google Scholar 

  23. Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336. https://doi.org/10.1038/nmeth.f.303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cole JR, Wang Q, Fish JA et al (2014) Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 42:D633-642. https://doi.org/10.1093/nar/gkt1244

    Article  CAS  PubMed  Google Scholar 

  25. Parks DH, Tyson GW, Hugenholtz P et al (2014) STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30:3123–3124. https://doi.org/10.1093/bioinformatics/btu494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Langille MG, Zaneveld J, Caporaso JG et al (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31:814–821. https://doi.org/10.1038/nbt.2676

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Huang HJ, Zhang AY, Cao HC et al (2013) Metabolomic analyses of faeces reveals malabsorption in cirrhotic patients. Dig Liver Dis 45:677–682. https://doi.org/10.1016/j.dld.2013.01.001

    Article  CAS  PubMed  Google Scholar 

  28. Stoll ML, Kumar R, Lefkowitz EJ et al (2016) Fecal metabolomics in pediatric spondyloarthritis implicate decreased metabolic diversity and altered tryptophan metabolism as pathogenic factors. Genes Immun 17:400–405. https://doi.org/10.1038/gene.2016.38

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Yu M, Jia H, Zhou C et al (2017) Variations in gut microbiota and fecal metabolic phenotype associated with depression by 16S rRNA gene sequencing and LC/MS-based metabolomics. J Pharm Biomed Anal 138:231–239. https://doi.org/10.1016/j.jpba.2017.02.008

    Article  CAS  PubMed  Google Scholar 

  30. Melnik AV, da Silva RR, Hyde ER et al (2017) Coupling targeted and untargeted mass spectrometry for metabolome-microbiome-wide association studies of human fecal samples. Anal Chem 89:7549–7559. https://doi.org/10.1021/acs.analchem.7b01381

    Article  CAS  PubMed  Google Scholar 

  31. Frank E, Hall M, Trigg L et al (2004) Data mining in bioinformatics using weka. Bioinformatics 20:2479–2481. https://doi.org/10.1093/bioinformatics/bth261

    Article  CAS  PubMed  Google Scholar 

  32. Turnbaugh PJ, Ley RE, Mahowald MA et al (2006) An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444:1027–1031. https://doi.org/10.1038/nature05414

    Article  PubMed  Google Scholar 

  33. Castaner O, Goday A, Park YM et al (2018) The gut microbiome profile in obesity: a systematic review. Int J Endocrinol 2018:4095789. https://doi.org/10.1155/2018/4095789

    Article  PubMed  PubMed Central  Google Scholar 

  34. Arumugam M, Raes J, Pelletier E et al (2011) Enterotypes of the human gut microbiome. Nature 473:174–180. https://doi.org/10.1038/nature09944

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wu GD, Chen J, Hoffmann C et al (2011) Linking long-term dietary patterns with gut microbial enterotypes. Science 334:105–108. https://doi.org/10.1126/science.1208344

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Garza JL, Dugan AG, Faghri PD et al (2015) Demographic, health-related, and work-related factors associated with body mass index and body fat percentage among workers at six connecticut manufacturing companies across different age groups: a cohort study. BMC Obes 2:43. https://doi.org/10.1186/s40608-015-0073-1

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lozupone CA, Stombaugh JI, Gordon JI et al (2012) Diversity, stability and resilience of the human gut microbiota. Nature 489:220–230. https://doi.org/10.1038/nature11550

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ray K (2012) Gut microbiota: married to our gut microbiota. Nat Rev Gastroenterol Hepatol 9:555. https://doi.org/10.1038/nrgastro.2012.165

    Article  PubMed  Google Scholar 

  39. Singhvi N, Gupta V, Gaur M et al (2020) Interplay of human gut microbiome in health and wellness. Indian J Microbiol 60:26–36. https://doi.org/10.1007/s12088-019-00825-x

    Article  CAS  PubMed  Google Scholar 

  40. Boulange CL, Neves AL, Chilloux J et al (2016) Impact of the gut microbiota on inflammation, obesity, and metabolic disease. Genome Med 8:42. https://doi.org/10.1186/s13073-016-0303-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ley RE, Turnbaugh PJ, Klein S et al (2006) Microbial ecology: human gut microbes associated with obesity. Nature 444:1022–1023. https://doi.org/10.1038/4441022a

    Article  CAS  PubMed  Google Scholar 

  42. Tilg H, Kaser A (2011) Gut microbiome, obesity, and metabolic dysfunction. J Clin Invest 121:2126–2132. https://doi.org/10.1172/JCI58109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Clarke SF, Murphy EF, Nilaweera K et al (2012) The gut microbiota and its relationship to diet and obesity: new insights. Gut Microbes 3:186–202. https://doi.org/10.4161/gmic.20168

    Article  PubMed  PubMed Central  Google Scholar 

  44. Shah HN, Collins DM (1990) Prevotella, a new genus to include Bacteroides melaninogenicus and related species formerly classified in the genus Bacteroides. Int J Syst Bacteriol 40:205–208. https://doi.org/10.1099/00207713-40-2-205

    Article  CAS  PubMed  Google Scholar 

  45. Ley RE (2016) Gut microbiota in 2015: Prevotella in the gut: choose carefully. Nat Rev Gastroenterol Hepatol 13:69–70. https://doi.org/10.1038/nrgastro.2016.4

    Article  CAS  PubMed  Google Scholar 

  46. Morotomi M, Nagai F, Sakon H et al (2009) Paraprevotella clara gen. nov., sp. nov. and Paraprevotella xylaniphila sp. nov., members of the family “Prevotellaceae” isolated from human faeces. Int J Syst Evol Microbiol 59:1895–1900. https://doi.org/10.1099/ijs.0.008169-0

    Article  PubMed  Google Scholar 

  47. Rosenberg E (2014) The family prevotellaceae 825–827. https://doi.org/10.1007/978-3-642-38954-2_131

  48. Zhang H, DiBaise JK, Zuccolo A et al (2009) Human gut microbiota in obesity and after gastric bypass. Proc Natl Acad Sci U S A 106:2365–2370. https://doi.org/10.1073/pnas.0812600106

    Article  PubMed  PubMed Central  Google Scholar 

  49. Zhu L, Baker RD, Baker SS (2015) Gut microbiome and nonalcoholic fatty liver diseases. Pediatr Res 77:245–251. https://doi.org/10.1038/pr.2014.157

    Article  CAS  PubMed  Google Scholar 

  50. Christensen L, Roager HM, Astrup A et al (2018) Microbial enterotypes in personalized nutrition and obesity management. Am J Clin Nutr 108:645–651. https://doi.org/10.1093/ajcn/nqy175

    Article  PubMed  Google Scholar 

  51. Gorvitovskaia A, Holmes SP, Huse SM (2016) Interpreting Prevotella and Bacteroides as biomarkers of diet and lifestyle. Microbiome 4:15. https://doi.org/10.1186/s40168-016-0160-7

    Article  PubMed  PubMed Central  Google Scholar 

  52. Mariat D, Firmesse O, Levenez F et al (2009) The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol 9:123. https://doi.org/10.1186/1471-2180-9-123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hjorth MF, Roager HM, Larsen TM et al (2017) Pre-treatment microbial Prevotella-to-Bacteroides ratio, determines body fat loss success during a 6-month randomized controlled diet intervention. Int J Obes (Lond). https://doi.org/10.1038/ijo.2017.220

    Article  Google Scholar 

  54. Larsen PE, Dai Y (2015) Metabolome of human gut microbiome is predictive of host dysbiosis. Gigascience 4:42. https://doi.org/10.1186/s13742-015-0084-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Canfora EE, Meex RCR, Venema K et al (2019) Gut microbial metabolites in obesity, NAFLD and T2DM. Nat Rev Endocrinol 15:261–273. https://doi.org/10.1038/s41574-019-0156-z

    Article  CAS  PubMed  Google Scholar 

  56. Tremaroli V, Backhed F (2012) Functional interactions between the gut microbiota and host metabolism. Nature 489:242–249. https://doi.org/10.1038/nature11552

    Article  CAS  PubMed  Google Scholar 

  57. Ridlon JM, Kang DJ, Hylemon PB et al (2014) Bile acids and the gut microbiome. Curr Opin Gastroenterol 30:332–338. https://doi.org/10.1097/MOG.0000000000000057

    Article  PubMed  PubMed Central  Google Scholar 

  58. Ramirez-Perez O, Cruz-Ramon V, Chinchilla-Lopez P et al (2017) The role of the gut microbiota in bile acid metabolism. Ann Hepatol 16:s15–s20. https://doi.org/10.5604/01.3001.0010.5494

    Article  CAS  PubMed  Google Scholar 

  59. McCrory MA, Gomez TD, Bernauer EM et al (1995) Evaluation of a new air displacement plethysmograph for measuring human body composition. Med Sci Sports Exerc 27:1686–1691

    Article  CAS  PubMed  Google Scholar 

  60. Conway JM, Norris KH, Bodwell CE (1984) A new approach for the estimation of body composition: infrared interactance. Am J Clin Nutr 40:1123–1130

    Article  CAS  PubMed  Google Scholar 

  61. Mazess RB, Barden HS, Bisek JP et al (1990) Dual-energy x-ray absorptiometry for total-body and regional bone-mineral and soft-tissue composition. Am J Clin Nutr 51:1106–1112

    Article  CAS  PubMed  Google Scholar 

  62. Durnin JV, Womersley J (1974) Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 32:77–97

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The author appreciated Xia Zhao and Bin Hu for their enthusiastic help for participants recruit and sample collection.

Funding

This work was supported by the National Natural Science Foundation of China (grant Nos. 31771468) and the Fundamental Research Foundation of Shenzhen (Nos. JCYJ20190809 -182411369).

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  1. Sichuan EYE Hospital, Aier EYE Hospital Group, No. 153, Tianfu Fourth Street, High-tech Zone, Chengdu, 610047, China

    Lingyun Zou

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Zou, L. Pivotal Dominant Bacteria Ratio and Metabolites Related to Healthy Body Index Revealed by Intestinal Microbiome and Metabolomics. Indian J Microbiol 62, 130–141 (2022). https://doi.org/10.1007/s12088-021-00989-5

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