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Microbial Genomics logo Microbial Genomics

Volume 11, Issue 4

Consensus approach to differential abundance analysis detects few differences in the oral microbiome of pregnant women due to pre-existing type 2 diabetes mellitus Open Access

Abstract

Oral microbiome dysbiosis has been proposed as a potential contributing factor to rising rates of diabetes in pregnancy, with oral health previously associated with an increased risk of numerous chronic diseases and complications in pregnancy, including gestational diabetes mellitus (GDM). However, whilst most studies examining the relationship between GDM and the oral microbiome identify significant differences, these differences are highly variable between studies. Additionally, no previous research has examined the oral microbiome of women with pre-existing type 2 diabetes mellitus (T2DM), which has greater risks of complications to both mother and baby. In this study, we compared the oral microbiome of 11 pregnant women with pre-existing T2DM with 28 pregnant normoglycaemic controls. We used shotgun metagenomic sequencing to examine buccal swab and saliva rinse samples at two time points between 26 and 38 weeks of gestation. To reduce variation caused by the choice of differential abundance analysis tool, we employed a consensus approach to identify differential taxa and pathways due to diabetes status. Differences were identified at the late time point only. In swab samples, there was increased , , SGB2479, SGB2492 and SGB9447 abundance in T2DM as well as increased Shannon diversity and richness. In rinse samples, there was an increased abundance of , , and . In contrast to studies of the oral microbiome in T2DM or GDM that use a single differential abundance analysis tool, our consensus approach identified few differences between pregnant women with and without T2DM.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): diabetes mellitus , differential abundance analysis and oral microbiome
Funding
This study was supported by the:
  • Australian Government Research Training Program
    • Principal Award Recipient: SophieMeghan Leech
  • Diabetes Australia Research Program
    • Principal Award Recipient: HelenL Barrett
© 2025 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2025-04-15
2026-03-09

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References

  1. Willis JR, Gabaldón T. The human oral microbiome in health and disease: from sequences to ecosystems. Microorganisms 2020; 8:308 [View Article] [PubMed]
     [Google Scholar]
  2. Daalderop LA, Wieland BV, Tomsin K, Reyes L, Kramer BW et al. Periodontal disease and pregnancy outcomes: overview of systematic reviews. JDR Clin Trans Res 2018; 3:10–27 [View Article] [PubMed]
     [Google Scholar]
  3. Gil-Montoya JA, Rivero-Blanco T, Leon-Rios X, Exposito-Ruiz M, Pérez-Castillo I et al. Oral and general health conditions involved in periodontal status during pregnancy: a prospective cohort study. Arch Gynecol Obstet 2023; 308:1765–1773 [View Article] [PubMed]
     [Google Scholar]
  4. La X, Jiang H, Chen A, Zheng H, Shen L et al. Profile of the oral microbiota from preconception to the third trimester of pregnancy and its association with oral hygiene practices. J Oral Microbiol 2022; 14:2053389 [View Article] [PubMed]
     [Google Scholar]
  5. Sparvoli LG, Cortez RV, Daher S, Padilha M, Sun SY et al. Women’s multisite microbial modulation during pregnancy. Microb Pathog 2020; 147:104230 [View Article] [PubMed]
     [Google Scholar]
  6. Jang H, Patoine A, Wu TT, Castillo DA, Xiao J. Oral microflora and pregnancy: a systematic review and meta-analysis. Sci Rep 2021; 11:16870 [View Article] [PubMed]
     [Google Scholar]
  7. Crusell MKW, Brink LR, Nielsen T, Allin KH, Hansen T et al. Gestational diabetes and the human salivary microbiota: a longitudinal study during pregnancy and postpartum. BMC Pregnancy Childbirth 2020; 20:69 [View Article] [PubMed]
     [Google Scholar]
  8. Xu Y, Zhang M, Zhang J, Sun Z, Ran L et al. Differential intestinal and oral microbiota features associated with gestational diabetes and maternal inflammation. Am J Physiol Endocrinol Metab 2020; 319:E247–E253 [View Article] [PubMed]
     [Google Scholar]
  9. Yao H, Xu D, Zhu Z, Wang G. Gestational diabetes mellitus increases the detection rate and the number of oral bacteria in pregnant women. Medicine 2019; 98:e14903 [View Article] [PubMed]
     [Google Scholar]
  10. Li X, Zheng J, Ma X, Zhang B, Zhang J et al. The oral microbiome of pregnant women facilitates gestational diabetes discrimination. J Genet Genomics 2021; 48:32–39 [View Article] [PubMed]
     [Google Scholar]
  11. Corrêa JD, Faria GA, Fernandes LL. The oral microbiota and gestational diabetes mellitus. Front Clin Diabetes Healthc 2023; 4:1120920 [View Article] [PubMed]
     [Google Scholar]
  12. Sabharwal A, Ganley K, Miecznikowski JC, Haase EM, Barnes V et al. The salivary microbiome of diabetic and non-diabetic adults with periodontal disease. J Periodontol 2019; 90:26–34 [View Article] [PubMed]
     [Google Scholar]
  13. Shaalan A, Lee S, Feart C, Garcia-Esquinas E, Gomez-Cabrero D et al. Alterations in the oral microbiome associated with diabetes, overweight, and dietary components. Front Nutr 2022; 9:914715 [View Article] [PubMed]
     [Google Scholar]
  14. Yang Y, Liu S, Wang Y, Wang Z, Ding W et al. Changes of saliva microbiota in the onset and after the treatment of diabetes in patients with periodontitis. Aging 2020; 12:13090–13114 [View Article]
     [Google Scholar]
  15. Saeb ATM, Al-Rubeaan KA, Aldosary K, Udaya Raja GK, Mani B et al. Relative reduction of biological and phylogenetic diversity of the oral microbiota of diabetes and pre-diabetes patients. Microb Pathog 2019; 128:215–229 [View Article] [PubMed]
     [Google Scholar]
  16. Vieira Lima CP, Grisi DC, Guimarães MDCM, Salles LP, Kruly P de C et al. Enrichment of sulphate-reducers and depletion of butyrate-producers may be hyperglycaemia signatures in the diabetic oral microbiome. J Oral Microbiol 2022; 14:2082727 [View Article] [PubMed]
     [Google Scholar]
  17. Casarin RCV, Barbagallo A, Meulman T, Santos VR, Sallum EA et al. Subgingival biodiversity in subjects with uncontrolled type‐2 diabetes and chronic periodontitis. J Periodontal Res 2013; 48:30–36 [View Article]
     [Google Scholar]
  18. Long J, Cai Q, Steinwandel M, Hargreaves MK, Bordenstein SR et al. Association of oral microbiome with type 2 diabetes risk. J Periodontal Res 2017; 52:636–643 [View Article]
     [Google Scholar]
  19. Feig DS. Epidemiology and therapeutic strategies for women with preexisting diabetes in pregnancy: How far have we come? The 2021 Norbert Freinkel Award Lecture. Diabetes Care 2022; 45:2484–2491 [View Article] [PubMed]
     [Google Scholar]
  20. Ye W, Luo C, Huang J, Li C, Liu Z et al. Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis. BMJ 2022; 377:e067946 [View Article] [PubMed]
     [Google Scholar]
  21. Murray SR, Reynolds RM. Short‐ and long‐term outcomes of gestational diabetes and its treatment on fetal development. Prenatal Diagnosis 2020; 40:1085–1091 [View Article]
     [Google Scholar]
  22. Ruan X, Luo J, Zhang P, Howell K. The salivary microbiome shows a high prevalence of core bacterial members yet variability across human populations. NPJ Biofilms Microbiomes 2022; 8:85 [View Article] [PubMed]
     [Google Scholar]
  23. Mark Welch JL, Ramírez-Puebla ST, Borisy GG. Oral microbiome geography: micron-scale habitat and niche. Cell Host Microbe 2020; 28:160–168 [View Article] [PubMed]
     [Google Scholar]
  24. Seifi S, Feizi F, Moazzezi Z, Mehdizadeh M, Zamani B. Evaluation of oral mucosal epithelium in diabetic male patients by exfoliative cytology method. J Diabetes Metab Disord 2014; 13:77 [View Article] [PubMed]
     [Google Scholar]
  25. Nearing JT, Douglas GM, Hayes MG, MacDonald J, Desai DK et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun 2022; 13:342 [View Article] [PubMed]
     [Google Scholar]
  26. Yang L, Chen J. A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions. Microbiome 2022; 10:130 [View Article] [PubMed]
     [Google Scholar]
  27. Wirbel J, Essex M, Forslund SK, Zeller G. A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies. Genome Biol 2024; 25:247 [View Article] [PubMed]
     [Google Scholar]
  28. Victoria CC. Dietary questionnaires. Cancer Council Victoria; 2025 https://www.cancervic.org.au/research/epidemiology/nutritional_assessment_services accessed 30 February 2025
  29. FastQC: a quality control tool for high throughput sequence data Babraham bioinformatics 2010. n.d https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
  30. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016; 32:3047–3048 [View Article] [PubMed]
     [Google Scholar]
  31. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
     [Google Scholar]
  32. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–359 [View Article] [PubMed]
     [Google Scholar]
  33. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J et al. The sequence alignment/map format and SAMtools. Bioinformatics 2009; 25:2078–2079 [View Article] [PubMed]
     [Google Scholar]
  34. Blanco-Miguez A, Beghini F, Cumbo F, McIver LJ, Thompson KN et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species with MetaPhlAn 4. Bioinformatics2022 [View Article]
     [Google Scholar]
  35. Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. elife 2021; 10:e65088 [View Article] [PubMed]
     [Google Scholar]
  36. Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 2018; 6:226 [View Article] [PubMed]
     [Google Scholar]
  37. Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 2017; 13:e1005752 [View Article] [PubMed]
     [Google Scholar]
  38. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013; 8:e61217 [View Article] [PubMed]
     [Google Scholar]
  39. Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci 2003; 14:927–930 [View Article]
     [Google Scholar]
  40. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw 2015; 67:48 [View Article]
     [Google Scholar]
  41. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest package: tests in linear mixed effects models. J Stat Soft 2017; 82:26 [View Article]
     [Google Scholar]
  42. Lin H, Peddada SD. Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nat Methods 2024; 21:83–91 [View Article] [PubMed]
     [Google Scholar]
  43. Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 2021; 17:e1009442 [View Article] [PubMed]
     [Google Scholar]
  44. Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One 2013; 8:e67019 [View Article] [PubMed]
     [Google Scholar]
  45. Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR et al. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2014; 2:15 [View Article] [PubMed]
     [Google Scholar]
  46. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15:550 [View Article] [PubMed]
     [Google Scholar]
  47. Zhou H, He K, Chen J, Zhang X. LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome Biol 2022; 23:95 [View Article] [PubMed]
     [Google Scholar]
  48. Yang LC, Chen J. Performing differential abundance analysis using ZicoSeq; 2024 https://cran.r-project.org/web/packages/GUniFrac/vignettes/ZicoSeq.html accessed 13 November 2024
  49. McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol 2014; 10:e1003531 [View Article] [PubMed]
     [Google Scholar]
  50. DiGiulio DB, Callahan BJ, McMurdie PJ, Costello EK, Lyell DJ et al. Temporal and spatial variation of the human microbiota during pregnancy. Proc Natl Acad Sci USA 2015; 112:11060–11065 [View Article]
     [Google Scholar]
  51. Balan P, Chong YS, Umashankar S, Swarup S, Loke WM et al. Keystone species in pregnancy gingivitis: a snapshot of oral microbiome during pregnancy and postpartum period. Front Microbiol 2018; 9:2360 [View Article] [PubMed]
     [Google Scholar]
  52. Chen B, Wang Z, Wang J, Su X, Yang J et al. The oral microbiome profile and biomarker in Chinese type 2 diabetes mellitus patients. Endocrine 2020; 68:564–572 [View Article] [PubMed]
     [Google Scholar]
  53. Guo X, Dai S, Lou J, Ma X, Hu X et al. Distribution characteristics of oral microbiota and its relationship with intestinal microbiota in patients with type 2 diabetes mellitus. Front Endocrinol 2023; 14: [View Article]
     [Google Scholar]
  54. González-Serrano J, Serrano J, López-Pintor RM, Paredes VM, Casañas E et al. Prevalence of oral mucosal disorders in diabetes mellitus patients compared with a control group. J Diabetes Res 2016; 2016:5048967 [View Article] [PubMed]
     [Google Scholar]
  55. Lu C, Zhao Q, Deng J, Chen K, Jiang X et al. Salivary microbiome profile of diabetes and periodontitis in a Chinese population. Front Cell Infect Microbiol 2022; 12:933833 [View Article] [PubMed]
     [Google Scholar]
  56. Zhu W, Shen W, Wang J, Xu Y, Zhai R et al. Capnocytophaga gingivalis is a potential tumor promotor in oral cancer. Oral Dis 2024; 30:353–362 [View Article] [PubMed]
     [Google Scholar]
  57. Jolivet-Gougeon A, Bonnaure-Mallet M. Screening for prevalence and abundance of Capnocytophaga spp by analyzing NGS data: a scoping review. Oral Dis 2021; 27:1621–1630 [View Article] [PubMed]
     [Google Scholar]
  58. Lyu X, Xu X, Shen S, Qin F. Genetics causal analysis of oral microbiome on type 2 diabetes in East Asian populations: a bidirectional two-sample Mendelian randomized study. Front Endocrinol 2024; 15:1452999 [View Article] [PubMed]
     [Google Scholar]
  59. Ortiz AP, Acosta-Pagán KT, Oramas-Sepúlveda C, Castañeda-Avila MA, Vilanova-Cuevas B et al. Oral microbiota and periodontitis severity among Hispanic adults. Front Cell Infect Microbiol 2022; 12:965159 [View Article] [PubMed]
     [Google Scholar]
  60. Agarwal J, Pandey P, Saxena SK, Kumar S. Comparative analysis of salivary microbiota in diabetic and non-diabetic individuals of North India using metagenomics. J Oral Biol Craniofac Res 2024; 14:22–26 [View Article] [PubMed]
     [Google Scholar]
  61. Sun X, Li M, Xia L, Fang Z, Yu S et al. Alteration of salivary microbiome in periodontitis with or without type-2 diabetes mellitus and metformin treatment. Sci Rep 2020; 10:15363 [View Article] [PubMed]
     [Google Scholar]
  62. Antezack A, Etchecopar‐Etchart D, La Scola B, Monnet‐Corti V. New putative periodontopathogens and periodontal health‐associated species: a systematic review and meta‐analysis. J Periodontal Res 2023; 58:893–906 [View Article]
     [Google Scholar]
  63. Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med 2017; 23:850–858 [View Article] [PubMed]
     [Google Scholar]
  64. Gu M, Wang P, Xiang S, Xu D, Jin C et al. Effects of type 2 diabetes and metformin on salivary microbiota in patients with chronic periodontitis. Microb Pathog 2021; 161:105277 [View Article] [PubMed]
     [Google Scholar]
  65. McGuinness AJ, Stinson LF, Snelson M, Loughman A, Stringer A et al. From hype to hope: considerations in conducting robust microbiome science. Brain Behav Immun 2024; 115:120–130 [View Article] [PubMed]
     [Google Scholar]
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Consensus approach to differential abundance analysis detects few differences in the oral microbiome of pregnant women due to pre-existing type 2 diabetes mellitus
M Gen 11, 001385 (2025); https://doi.org/10.1099/mgen.0.001385
/content/journal/mgen/10.1099/mgen.0.001385
/content/journal/mgen/10.1099/mgen.0.001385
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