Evidence map›Paper›PMID 40323603›Full record

ArticleJAMA network open2025

Oral Microbiome Profile of the US Population.

Anil K Chaturvedi, Emily Vogtmann, Jianxin Shi, Yukiko Yano, Martin J Blaser, Nicholas A Bokulich, J Gregory Caporaso, Maura L Gillison, Barry I Graubard, Xing Hua and 9 more

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

  1. Article
  2. Journal of bacteriology · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. [Age-Related Changes in the Oral Microbiota].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026
    Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Anil K ChaturvediDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Emily VogtmannDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Jianxin ShiDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Yukiko YanoDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Martin J BlaserCenter for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey.
Nicholas A BokulichDepartment of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
J Gregory CaporasoCenter for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff.
Maura L GillisonDepartment of Thoracic and Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, Texas.
Barry I GraubardDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Xing HuaDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Autumn G HullingsDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Lisa KahleInformation Management Services, Calverton, Maryland.
Rob KnightCenter for Microbiome Innovation, University of California, San Diego, La Jolla.
Shilan LiBloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
Jody McLeanNational Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland.
Vaishnavi PurandareDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Yunhu WanDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
Neal D FreedmanDivision of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland.
Christian C AbnetDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.

Funding

Advanced Development of Informatics Technologies for Cancer Research and Management (U24 Clinical Trial Optional)U24CA248454 · NCI · NORTHERN ARIZONA UNIVERSITY · PI CAPORASO, JAMES GREGORY · 2020 to 2024
$3.7M
PREVALENCE AND DETERMINANTS OF ORAL HPV INFECTION IN THE US POPULATIONR01DE023175 · NIDCR · OHIO STATE UNIVERSITY · PI SYMER, DAVID ERIC · 2012 to 2015
$3.4M
NCI NIH HHS U24 CA248454NIDCR NIH HHS R01 DE023175
6 · The paper itself

Abstract

Importance: The oral microbiome likely plays key roles in human health. Yet, population-representative characterizations are lacking. Objective: To characterize the composition, diversity, and correlates of the oral microbiome in US adults. Design, Setting, and Participants: This cross-sectional study analyzed data from the population-representative National Health and Nutrition Examination Survey (NHANES) from 2009 to 2012. Microbiome data were made publicly available in 2024. NHANES participants were aged 18 to 69 years and provided oral rinse samples in 1 of 2 consecutive NHANES cycles (2009-2010 and 2011-2012). Exposures: Demographic, socioeconomic, behavioral, anthropometric, metabolic, and clinical characteristics. Main Outcomes and Measures: Oral microbiome measures, characterized through 16S ribosomal RNA gene sequencing, included α diversity (observed amplicon sequence variants [ASVs], Faith phylogenetic diversity, Shannon-Weiner Index, and Simpson Index); β diversity (unweighted UniFrac, weighted UniFrac, and Bray-Curtis dissimilarity); and prevalence and relative abundance at phylum level through genus level. Analyses accounted for the NHANES complex sample design. Results: This study included 8237 US adults aged 18 to 69 years, representing 202 314 000 individuals (102 813 000 men [50.8%]; mean [SD] age, 42.3 [14.4] years; 9.3% self-reported as Mexican American, 12.1% as non-Hispanic Black, 64.7% as non-Hispanic White, 5.9% as other Hispanic, and 8.1% as other non-Hispanic individuals). The oral microbiome encompassed 37 bacterial phyla, 99 classes, 212 orders, 446 families, and 1219 genera. Five phyla (Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria, and Fusobacteria) and 6 genera (Veillonella, Streptococcus, Prevotella 7, Rothia, Actinomyces, and Gemella) were present in nearly all US adults (weighted prevalence, >99%). These genera were the most abundant, accounting for 65.7% of total abundance. Observed ASVs showed a quadratic pattern with age (peak at 30 years), were similar by sex, significantly lower among non-Hispanic White individuals, and increased with greater body mass index (BMI), alcohol use, and periodontal disease severity. All covariates together accounted for a modest proportion of oral microbiome variability as measured by β diversity: R2 = 8.7% (95% CI, 8.4%-9.1%) for unweighted UniFrac, R2 = 7.2% (95% CI, 6.6%-7.7%) for weighted UniFrac, and R2 = 6.3% (95% CI, 3.1%-6.7%) for Bray-Curtis matrices. By contrast, relative abundance of a few genera explained a high percentage of variability in β diversity for weighted UniFrac: Aggregatibacter (R2 = 22.4%; 95% CI, 22.1%-22.8%), Lactococcus (R2 = 21.6%; 95% CI, 20.9%-22.3%), and Haemophilus (R2 = 18.4%; 95% CI, 18.1%-18.8%). Prevalence and relative abundance of numerous genera were associated with age, race and ethnicity, smoking, BMI categories, alcohol use, and periodontal disease severity. Conclusions and Relevance: This cross-sectional study of the oral microbiome in US adults showed that a few genera were universally present and a different set of genera explained a high percentage of oral microbiome diversity across the population. This comprehensive characterization provides a contemporary reference standard for future studies.

Indexed as

MicrobiotaMouthAdolescentAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedNutrition SurveysRNA, Ribosomal, 16SUnited StatesYoung AdultRNA, Ribosomal, 16S

Identifiers

PMID40323603
PMCPMC12053784

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.