https://orcid.org/0000-0002-9837-964X
Figures
Abstract
The human microbiome plays a pivotal role in influencing various physiological processes and maintaining overall well-being, including the gastric system. Current diagnostic tests for gastric diseases often involve invasive procedures, sampling limitations, and medication effects, leading to potential diagnostic errors and discomfort to patients. Considering the connection between oral and gastric microbiomes, this cross-sectional study aimed to assess the diagnostic potential of the oral bacterial profile in patients undergoing upper digestive endoscopy. Oral samples from 266 participants across two Brazilian sites (Belterra and Sao Paulo) were sequenced and subjected to bioinformatic analysis to identify microbiome composition across endoscopy outcome groups, exploring alpha and beta-diversity, richness, and differential abundance and prevalence. Prevotella, Haemophilus, Fusobacterium, Neisseria, and Streptococcus were the most abundant genera observed. No significant associations were found between alpha diversity profiles and endoscopy outcomes; beta diversity analyses similarly showed no correlations. Overall, the study did not establish the oral microbiome as a reliable marker for gastric health, underscoring the necessity for broader studies in the development of non-invasive diagnostic tests.
Citation: Martins FP, Andrade-Silva J, Teixeira BL, Ferrari A, Christoff AP, Cruz GNF, et al. (2024) Oral microbiome test as an alternative diagnostic tool for gastric alterations: A prospective, bicentric cross-sectional study. PLoS ONE 19(12): e0314660. https://doi.org/10.1371/journal.pone.0314660
Editor: Baochuan Lin, Defense Threat Reduction Agency, UNITED STATES OF AMERICA
Received: January 12, 2024; Accepted: November 13, 2024; Published: December 2, 2024
Copyright: © 2024 Martins et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All sequence data have been deposited in the NCBI BioProject under the accession number PRJNA604445. All data and code are available from GitHub database, link: https://github.com/biomehub/microbiome-and-gastric-alterations.
Funding: The funders BiomeHub and Albert Einstein Israelite Hospital (HIAE) provided support in form of salaries for the authors and were responsible for the project experimental design approval. HIAE have an additional role in defining sample collection points, but the analysis, the decision to publish and the manuscript preparation was independently performed by the researchers. The specific roles of each author are included in the ‘author contributions’ section.
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Bianca Luise Teixeira, Ana Paula Christoff, Giuliano Netto Flores and Luiz Felipe Valter de Oliveira are currently fulltime employees of BiomehHub Biotechnologies. All other authors declare no conflict of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Introduction
Various regions of the body harbor distinct microbial populations, each contributing to specific functions and interactions. The balance of bacterial composition, metabolic activities, and distribution within the gut plays a crucial role in determining overall health and susceptibility to illness. The relationship between oral and gastric microbiota is intricate, dynamic, and interconnected, with a complex interplay between these microbial communities that can influence oral and gastric health [1].
The global prevalence of upper digestive system disease cases was 780.59 million in 2019 [2], representing about 10% of the world’s population; changes in the structure and function of the stomach can be caused by the presence of microorganisms or the imbalance of the local microbiota. Helicobacter pylori, a gram-negative pathogen, is the predominant microorganism associated with gastric infections, with approximately half of the world’s population being colonized [3]. H. pylori significantly increases the risk of chronic gastritis, ulcers, and various forms of cancer, including adenocarcinoma, as established by World Health Organization’s International Agency for Research on Cancer (IARC). Other microbes than H. pylori also play a role in the development of gastric cancer, such as Peptostreptococcus, Desulfovibrio, and Fusobacterium [4,5]. Changes in the gastric microbial structure could be seen in different precancerous stages, from superficial gastritis to atrophic gastritis, and gastric intraepithelial neoplasia to gastric cancer [6]. In that sense, the gastric microbiota might play different roles in carcinogenesis.
Multiple invasive and non-invasive diagnostic tests are available to detect pathogenic microorganisms that can potentially influence human health. The selection of a suitable test depends on factors such as test availability, the patient’s clinical condition, and the diagnostic accuracy observed in different clinical scenarios. Invasive procedures, like upper digestive endoscopy, are commonly employed to diagnose H. pylori infection. Additional invasive tests can be performed on the mucosal tissue by obtaining a gastric biopsy, including urease testing, histology, culture, and molecular methods [7]. However, since the distribution of infection within the gastric mucosa is not uniform, there is a possibility of diagnostic errors due to sampling limitations. Furthermore, certain medications like proton pump inhibitors and antibiotics can decrease the sensitivity of those tests [8–10].
Considering the vital link between the oral and gastric microbiome, previous studies have shown that changes in the richness, evenness, and/or number of bacterial species inside the oral cavity could be a diagnostic biomarker for chronic gastritis [11] and gastric cancer [12,13].
Therefore, developing a non-invasive method that evaluates the oral microbiome from a buccal sample seems important for gastric disease screening and diagnosis. In this study, we aimed to evaluate the oral bacterial profile in patients referred for upper digestive endoscopy to assess its diagnostic potential related to gastric alterations.
Methods
Ethics statement
The study was approved by the Research Ethics Committee of Hospital Israelita Albert Einstein (approval numbers 2.392.780 and 4.333.608) and was conducted according to the principles expressed in the Declaration of Helsinki. All subjects provided written informed consent.
Study design and setting
A prospective cross-sectional survey was carried out at two centers in Brazil, located in Belterra (Cohort 1) and São Paulo (Hospital Israelita Albert Einstein—Cohort 2) from November to December 2017, and from March to September 2021, respectively. The primary outcome was a composite of gastric alterations as detected in endoscopy, including erosive esophagitis and/or gastroduodenal peptic disease. The study is reported according to both the STROBE statement for cross-sectional investigations and the STORMS statement for human microbiome studies.
Participants
Participants with an indication of upper digestive endoscopy to detect gastric alterations were recruited consecutively based on predefined inclusion and exclusion criteria. The study inclusion criteria were age > 18 years old, referral to upper digestive endoscopy for detection of gastric alterations, even when the participants were using proton pump inhibitors and/or antibiotics. Participants who had eaten or brushed their teeth in the 4 hours before the sample collection were excluded. Also, we excluded participants with esophagus, stomach, or duodenum surgery history since this factor can change the endoscopic findings regardless of the presence of microorganisms. Those who did not sign the informed consent form were not included.
Sociodemographic, lifestyle, and clinical variables
After consent, all eligible participants answered a specific questionnaire used for sociodemographic, lifestyle, and clinical data collection. Sociodemographic variables included age, sex, and education; lifestyle habits data included smoking and alcohol use. Clinical variables consisted of symptoms, use of proton pump inhibitors, use of antibiotics in the last 30 days, and the upper digestive endoscopy results.
Sample collection, DNA extraction, and 16S rRNA amplicon sequencing
For oral microbiome analysis, sampling was performed using sterile nylon flocked swabs (Copan Inc., Italy or Puritan, USA) and stored in a stabilizing solution (BiomeHub, Brazil) for transport at room temperature. Samples were collected with a swab from the posterior region of the oral cavity (which includes teeth (molars and premolars), plaque regions, oral mucosa, and dorsal tongue). This approach covers multiple oral sites and comprehensively represents the oral microbiota, reducing potential bias from specific areas. The swabs were transported to the laboratory facilities at room temperature and processed within a maximum of 30 days after sample collection. Bacterial DNA from the samples was obtained using the DNeasy Qiaamp DNA Blood Mini Kit (QIAGEN) according to the manufacturer’s instructions.
Amplicon sequencing library preparation for bacteria was performed using the V3/V4 16S rRNA gene primers 341F (CCTACGGGRSGCAGCAG) [14] and 806R (GGACTACHVGGGTWTCTAAT) [15] in a two-step equivolumetric PCR protocol [16]. The first PCR was performed with V3/V4 universal primers containing a partial Illumina adaptor, based on TruSeq structure (Illumina, USA), allowing the second PCR with indexing sequences. The PCR reactions were carried out in triplicates using Platinum Taq (Invitrogen, USA) with the conditions: 95°C for 5 min, 25 cycles of 95°C for 45s, 55°C for 30s and 72°C for 45s, and a final extension of 72°C for 2 min for PCR 1. In PCR 2, the conditions were 95°C for 5 min, 10 cycles of 95°C for 45s, 66°C for 30s and 72°C for 45s, and a final extension of 72°C for 2 min. The final PCR reactions were cleaned up using AMPureXP beads (Beckman Coulter, USA) and an equivalent volume of each sample was added in the sequencing pool. Negative control reactions were included to assess possible PCR reagent contaminations. The DNA concentration of the libraries pool was estimated with Picogreen dsDNA assays (Invitrogen, Waltham, MA, USA) and then diluted for accurate qPCR quantification using the Collibri Library Quantification kit (Invitrogen, USA). The sequencing pool was adjusted to a final concentration of 11 pM and sequenced in a MiSeq system (Illumina, USA), using the standard Illumina primers provided by the manufacturer kit. Single-end 300 cycle runs were performed using a V2 × 300 sequencing kit (Illumina, USA) with average sample coverages set to 45,000 reads per sample in all sequencing runs.
Bioinformatics analysis
The read sequences were analyzed using a bioinformatics pipeline previously described [16–18] (BiomeHub, Brazil-hospital_microbiome_rrna16s: v1). Illumina FASTQ files had the primers trimmed and their accumulated error evaluated [16]. Reads were analyzed with the Deblur package [19] to remove possible erroneous reads and then identical read sequences were grouped into oligotypes (clusters with 100% identity, ASVs amplicon sequencing variants). Next, VSEARCH[20] was used to remove chimeric amplicons. An additional filter was implemented to remove oligotypes below the frequency cutoff of 0.2% in the final sample counts. We also implemented a negative control filter since oral microbiomes generally are low biomass samples [16]. In each processing batch, we used negative controls (reagent blanks) for the DNA extraction and PCR reactions. If any oligotype is recovered in the negative control results, they are checked against the samples and automatically removed from the results only if their abundance (in number of reads) are no greater than two times their respective counts in the sample. The remaining oligotypes in the samples are used for taxonomic assignment with the BLAST tool [21] against a reference genomic database (encoderef16s_rev6_190325). This reference database was constructed with complete and draft bacterial genomes, which were obtained from NCBI, focused on clinically relevant bacteria. It is composed of 11,750 sequences, including 1,843 different bacterial taxonomies.
Taxonomy was assigned to each oligotype using a lowest common ancestor (LCA) algorithm. If more than one reference can be assigned to the same oligotype with equivalent similarity and coverage metrics, the taxonomic assignment algorithm leads the taxonomy to the lowest level of possible unambiguous resolution (genus, family, order, class, phylum, or kingdom), according to the similarity thresholds [22].
Statistical analysis
All statistical analyses were carried out using R (v. 4.0.2). Baseline characteristics were summarized as median and inter-quartiles range or as absolute frequency and percentages, stratified by the primary outcome. Alpha- and beta-diversity analyses employed the Shannon index and the Bray-Curtis dissimilarity, respectively. Beta-diversity was presented as Principal Coordinate Analysis with PERMANOVA marginal tests using the adonis2 function from the vegan package (v. 2.6.4) [23]. We also assessed richness as a secondary alpha-diversity outcome. Differential abundance (DA) analysis employed four methods to reach consensus: corncob (v. 0.2.0), DESeq2 (v. 1.36.0), limma+voom (3.52.0), and linDA/MicrobiomeStat (v. 1.1) [24–27]. We also added a fifth method to test differential prevalence, i.e., the likelihood of binary detection of each taxon across study groups (or “presence/absence analysis”). Differential prevalence (DP) was assessed using Firth’s Bias-Reduced Logistic Regression as implemented in the logistf R package (v 1.24.1) [28]. False-discovery rate (FDR) was controlled at 10% for the DA/DP analyses using the Benjamini-Hochberg procedure [29]. FDR correction was performed within each DA/DP tool for each taxonomic rank (including amplicon sequencing variant, species, genus, family, and phylum). DA/DP analyses employed (penalized) likelihood ratio, F, or Wald tests as appropriate to test the significance of the endoscopy outcome as a whole as well as for the post-hoc tests across endoscopy outcome groups. Since a consensus approach is currently recommended, we considered taxa to be differentially abundant/prevalent if detected by at least two independent methods [30].
Primary analyses were either marginal or adjusted by center. Sensitivity analyses included further covariate adjustment (proton pump inhibitors use, smoking status, alcohol status, and presence of symptoms) as well as stratification by center. Missing covariate data were handled with multivariate imputation by chained equations using the mice package (v. 3.15.0) [31]. Since the frequency of missing data was less than 1% for all but one covariate (presence of symptoms, 8%), sensitivity analyses were conducted with averaged imputed data as well as with complete case data. Further multiplicity adjustment was also performed to assess the robustness of potentially significant results, given the multiple tools and taxonomic ranks used for the primary analyses. Data and code are available at github: https://github.com/biomehub/microbiome-and-gastric-alterations.
Results
Characteristics of the participants
A total of 266 samples were prospectively collected. The oral microbiome of 73 individuals in Cohort 1, and 193 individuals in Cohort 2, was characterized. The sociodemographic, lifestyle habits, and clinical findings of the participants, including age, gender, education level, smoking, alcohol use, gastrointestinal symptoms, use of proton pump inhibitors, and upper gastrointestinal endoscopy outcomes are presented in Table 1.
The mean age was 56.54 years in Cohort 1 and 49 years in Cohort 2. There was no difference regarding gender distribution, smoking, and alcohol use in both cohorts. However, there was a remarkable disparity in education level, mainly due to the lower income for participants in Cohort 1. Considering the prevalence of symptoms and the use of proton pump inhibitors, Cohort 1 had fewer symptomatic patients.
Description of the oral microbiome composition
The overall composition of the oral microbiome as assessed by 16S amplicon sequencing is depicted in Fig 1. The relative abundance for all genera presents in at least 5% of all samples (14 of 266) ranked by overall average abundance is shown in Fig 1A. The five most abundant classified genera included Prevotella, Haemophilus, Fusobacterium, Neisseria, and Streptococcus. The average abundance of unclassified sequences at the genus level was 13.4% (95% CI, 12.3% to 14.6%).
Heat map of relative abundance of genera present in at least 5% of all samples (A). Box plot of the twenty most abundant families (B) and phyla (C) by center.
The five most abundant families correspond closely to the most abundant genera (Fig 1B). Unclassified sequences were much less abundant on average at the family level: 3.2% (95% CI, 2.6% to 3.8%). Abundance distribution was similar between centers except for the Streptococcaceae and Micrococcaceae families. At the phylum level, the most abundant taxa were Bacteroidetes, Proteobacteria, Fusobacteria, Firmicutes, and Actinobacteria. Center 2 showed substantially higher abundance of Firmicutes and Actinobacteria as compared to Center 1 (Wilcoxon rank sum test p < 10−16 for both), while Center 1 showed slightly higher Bacteroidetes (Wilcoxon rank sum test p < 10−6). Other differences were small. Additionaly, we searched for the specific presence of 16S rRNA from H. pylori in oral samples as the direct causative agent. However, it was only detected in three samples. Aiming to increase the sensitivity for H.pylori detection, 40 cycles, qPCR-sybr green reactions were performed in oral samples for its specific resistance/virulence genes: vacA, cagE, cagA, tsaA, and ureA, but the results were inconclusive (S1 Table).
Diversity analysis
We assessed richness and Shannon indexes as primary metrics of alpha diversity and the results are shown in Fig 2. We investigated whether the alpha diversity differed between patients with or without any endoscopic alteration (Fig 2A) as well as across endoscopy subgroups (Fig 2B). We did not find any association between alpha diversity profiles and endoscopy outcomes. We observed a slightly higher alpha diversity in Center 2 than in Center 1 (Fig 2C). Similar results were obtained with covariate-adjusted analyses as well as analyses stratified by center (S1 Fig).
Box plots of Richness and Shannon index across patients with or without any endoscopic alteration (A), across endoscopy outcomes subgroups (B), and between centers (C). GPD: Gastroduodenal peptic disease. EE: Erosive esophagitis.
We also performed Principal Coordinate Analysis to investigate associations between the endoscopy outcomes and the overall oral microbiome profiles regarding beta diversity (Bray-Curtis). Once again, no association with the endoscopy outcomes was detected (Fig 3A and 3B). The second and third coordinates showed clear association with the study populations (Belterra and Sao Paulo), explaining around 6% of the total variance (Fig 3C). These results were consistent with adjusted analyses as well as with analyses stratified by center (S2 Fig).
Principal Coordinate Analysis (PCoA) derived from Bray-Curtis dissimilarities exploring associations between patients with or without any endoscopic alteration (A), across endoscopy outcomes subgroups (B), and between centers (C). GPD: Gastroduodenal peptic disease. EE: Erosive esophagitis.
Differential abundance analysis
The association between bacterial abundance and endoscopy outcomes was assessed using a consensus approach based on four differential abundance (DA) methods as well as one differential prevalence (DP) method (see Methods). This consensus approach was chosen as DA is known to generate inconsistent results[30]. DP analysis was employed to assess variation in presence/absence of taxa across the studied groups. At the same time, the four DA tools were chosen to cover a broad range of statistical approaches. The results are shown in Fig 4.
GPD: Gastroduodenal peptic disease. EE: Erosive esophagitis.
Confirming the diversity analysis, we observed no major differences in the oral microbiome composition across the endoscopy outcomes: while some taxa were detected by a single statistical method, no taxa were consistently detected by all methods. An increase in the abundance/prevalence of the phylum Tenericutes was detected by four methods (all but DESeq2) in the GPD+EE group as compared to controls, even after taking into account the additional multiplicity due to using multiple DA/DP tools (Fig 5). However, this result is not to be overinterpreted as the GPD+EE group contains only 19 participants, all from the Cohort 2. Moreover, the statistical significance disappears fromall methods once the multiplicity adjustment accounts for the multiple taxonomic rankings tested. Given the apparently weak signal and extensive data exploration, any hope of clinical importance of this difference requires careful validation of external data.
Box plots of the relative abundance of Tenericutes between patients with or without (“normal”) endoscopic alteration, by center. GPD: Gastroduodenal peptic disease. EE: Erosive esophagitis.
Discussion
Belterra and Sao Paulo, two Brazilian cities, exhibit marked differences. According to the Brazilian Institute of Geography and Statistics (IBGE), Belterra has a population of 18,099, whereas Sao Paulo is home to 11,451,245 people. Belterra’s expansive territory results in a low population density of 4.11 individuals per square kilometer, in contrast to Sao Paulo’s 7,527.76. The Human Development Index (HDI) reveals significant disparities, with Belterra at 0.588 and Sao Paulo at 0.805, indicating variations in development. Economically, Belterra’s Gross Domestic Product (GDP) per capita is R$10,460.11, significantly less than Sao Paulo’s R$60,750.09 [32,33]. These differences in population, geography, HDI, and GDP per capita contribute to unique socio-economic structures, which, in turn, influence the experiences and opportunities available to residents of these two cities.
Despite being conducted among the European population, the study performed by Paliova and colleagues (2019) [34] highlights a significant connection between a higher Human Development Index (HDI) and increased years of schooling. This correlation could explain the disparities in educational levels between the two cohorts evaluated in this study.
A low Human Development Index (HDI) and low education levels are usually connected to poorer health status [35,36]. For instance, communities or regions with lower HDI scores typically face challenges in establishing and maintaining healthcare infrastructure, reducing access to medical facilities, professionals, and essential treatments. Consequently, these can result in inadequate healthcare outcomes and an overall lower quality of life for the population. In this study, we observed that in Cohort 1 (Belterra), only 16% of participants with symptoms use proton pump inhibitors, while in Cohort 2 (São Paulo), 32.6% of participants with symptoms use these medications. This discrepancy may be attributed to limited access to healthcare services and to lower educational levels in Belterra’s population, potentially resulting in inadequate healthcare treatment.
Despite sociodemographic differences between the two cohorts, this study’s genera, families, and phyla profiles aligned with anticipated expectations. For instance, the higher presence of genera such as Prevotella, Haemophilus, Fusobacterium, Neisseria, and Streptococcus was already observed in previous studies [37,38].
Although the oral microbiota has been explored as a diagnostic method for various cancers, including oral squamous cell carcinoma[39,40], pancreatic cancer [41], and lung cancer [42,43], the current research does not provide sufficient evidence to establish the oral microbiome as a biomarker for gastric diseases, as no significant correlation between microbial patterns and gastric alterations was observed.
A noteworthy finding in the current study was the identification of the Tenericutes phylum in individuals diagnosed with erosive esophagitis combined with gastroduodenal peptic disease (GPD+EE). Intriguingly, a study conducted in 2014 detected Tenericutes in small quantities in salivary samples from patients with oral squamous cell carcinoma, while no presence of Tenericutes was observed in corresponding control samples. This finding suggests the potential of Tenericutes as a biomarker for oral cancer [44]. However, despite this connection, the presence of Tenericutes in our present study does not supply robust enough evidence to label it as a biomarker for gastric disease. An important limitation of this analysis lies in the small sample size, involving only 19 subjects with GPD+EE from a single cohort, restricting its representativeness for broader generalization.
The most significant difference observed in this study was the oral microbiome profile between the two cohorts. Although the primary aim of this study was to evaluate a broad diagnostic test for gastric alterations, the heterogeneity of the study population also represented a significant limitation. Variations in oral health status[45,46], comorbidities[45], and ongoing antibiotic treatments [47] are potential confounding factors that could influence the oral microbiome profile and, consequently, the results observed.
This observation underscores the need for a more extensive study to address the influence of population stratification, especially when aiming to develop a diagnostic test with widespread applicability. Without such studies, it may be necessary to identify disease markers specific to each population or their distinctive microbiome features. Among the two populations analyzed in the present study, there was no indication that the oral microbiome can be a valuable tool for directly detecting H. Pylori or generating diagnostic markers for evaluating gastric health. So, it may be worthwhile to consider an analysis refinement by categorizing populations into subgroups based on factors like disease grade or other specific disease criteria.
Conclusion
While identifying Tenericutes in erosive esophagitis with gastroduodenal peptic disease was intriguing, the data generated by this study did not establish the oral microbiome composition as a reliable marker for gastric health. Moreover, the significant differences in oral microbiome profiles highlight the need for broader studies in developing non-invasive diagnostic tests for gastric diseases.
Supporting information
S1 Fig. Alpha diversity analysis stratified by center.
Top panels show Shannon diversity index for center 1 (A) and center 2 (B). Bottom panels show richness for center 1 (C) and center 2 (D).
https://doi.org/10.1371/journal.pone.0314660.s001
(TIF)
S2 Fig. Beta diversity analysis stratified by center.
Top panels show Principal Coordinate Analysis (PCoA) for center 1 (A), while bottom panel shows PCoA for center 2 (B). All PCoAs were based on Bray-Curtis dissimilarities.
https://doi.org/10.1371/journal.pone.0314660.s002
(TIF)
S1 Table. H.pylori detection sensitivity.
qPCR-sybr green reactions were performed in oral samples for specific resistance/virulence genes: vacA, cagE, cagA, tsaA and ureA.
https://doi.org/10.1371/journal.pone.0314660.s003
(DOCX)
References
- 1. Acharya C, Sahingur SE, Bajaj JS. Microbiota, cirrhosis, and the emerging oral-gut-liver axis. JCI Insight. 2017;2(19). pmid:28978799
- 2. Wang R, Li Z, Liu S, Zhang D. Global, regional, and national burden of 10 digestive diseases in 204 countries and territories from 1990 to 2019. Front Public Health. 2023;11:1061453. pmid:37056655
- 3. Lee YC, Dore MP, Graham DY. Diagnosis and Treatment of Helicobacter pylori Infection. Annu Rev Med. 2022;73:183–95. pmid:35084993
- 4. Coker OO, Dai Z, Nie Y, Zhao G, Cao L, Nakatsu G, et al. Mucosal microbiome dysbiosis in gastric carcinogenesis. Gut. 2018;67(6):1024–32. pmid:28765474
- 5. Liu S, Dai J, Lan X, Fan B, Dong T, Zhang Y, et al. Intestinal bacteria are potential biomarkers and therapeutic targets for gastric cancer. Microb Pathog. 2021;151:104747. pmid:33484807
- 6. Zhang X, Li C, Cao W, Zhang Z. Alterations of Gastric Microbiota in Gastric Cancer and Precancerous Stages. Front Cell Infect Microbiol. 2021;11:559148. pmid:33747975
- 7. Wang YK, Kuo FC, Liu CJ, Wu MC, Shih HY, Wang SS, et al. Diagnosis of Helicobacter pylori infection: Current options and developments. World J Gastroenterol. 2015;21(40):11221–35. pmid:26523098
- 8. Siavoshi F, Saniee P, Khalili-Samani S, Hosseini F, Malakutikhah F, Mamivand M, et al. Evaluation of methods for H. pylori detection in PPI consumption using culture, rapid urease test and smear examination. Ann Transl Med. 2015;3(1):11. pmid:25705643
- 9. Lee JY, Kim N. Diagnosis of Helicobacter pylori by invasive test: histology. Ann Transl Med. 2015;3(1):10. pmid:25705642
- 10. Alcedo J, Casas D, Gotor J, Lafuente M, Llorente M, Sanz-Segura P, et al. The Validity of the Invasive Tests for Helicobacter Pylori Diagnosis is Unequally Affected by the Consumption of Antibiotics or Pump Inhibitors. Test Performance under Real-World Conditions. J Gastrointestin Liver Dis. 2021;30(2):198–204. pmid:34174054
- 11. Contaldo M, Fusco A, Stiuso P, Lama S, Gravina AG, Itro A, et al. Oral Microbiota and Salivary Levels of Oral Pathogens in Gastro-Intestinal Diseases: Current Knowledge and Exploratory Study. Microorganisms. 2021;9(5). pmid:34069179
- 12. Sun JH, Li XL, Yin J, Li YH, Hou BX, Zhang Z. A screening method for gastric cancer by oral microbiome detection. Oncol Rep. 2018;39(5):2217–24. pmid:29498406
- 13. Park SY, Hwang BO, Lim M, Ok SH, Lee SK, Chun KS, et al. Oral-Gut Microbiome Axis in Gastrointestinal Disease and Cancer. Cancers (Basel). 2021;13(9). pmid:33924899
- 14. Wang Y, Qian PY. Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies. PLoS One. 2009;4(10):e7401. pmid:19816594
- 15. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6(8):1621–4. pmid:22402401
- 16. Cruz GNF, Christoff AP, de Oliveira LFV. Equivolumetric Protocol Generates Library Sizes Proportional to Total Microbial Load in 16S Amplicon Sequencing. Front Microbiol. 2021;12:638231. pmid:33717032
- 17. Christoff AP, Sereia AFR, Cruz GNF, Bastiani DC, Silva VL, Hernandes C, et al. One year cross-sectional study in adult and neonatal intensive care units reveals the bacterial and antimicrobial resistance genes profiles in patients and hospital surfaces. PLoS One. 2020;15(6):e0234127. pmid:32492060
- 18. Sereia AFR, Christoff AP, Cruz GNF, da Cunha PA, da Cruz GCK, Tartari DC, et al. Healthcare-Associated Infections-Related Bacteriome and Antimicrobial Resistance Profiling: Assessing Contamination Hotspots in a Developing Country Public Hospital. Front Microbiol. 2021;12:711471. pmid:34484149
- 19. Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT, Zech Xu Z, et al. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems. 2017;2(2). pmid:28289731
- 20. Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. pmid:27781170
- 21. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10. pmid:2231712
- 22. Yarza P, Yilmaz P, Pruesse E, Glockner FO, Ludwig W, Schleifer KH, et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014;12(9):635–45. pmid:25118885
- 23.
Oksanen J, Simpson G, Blanchet F, Kindt R, Legendre P, Minchin P, et al. vegan: Community Ecology Package. R package version 2.6–5, 2023 [https://github.com/vegandevs/vegan.
- 24. Martin BD, Witten D, Willis AD. Modeling Microbial Abundances and Dysbiosis with Beta-Binomial Regression. Ann Appl Stat. 2020;14(1):94–115. pmid:32983313
- 25. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. pmid:25516281
- 26. Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. pmid:24485249
- 27. Zhou H, He K, Chen J, Zhang X. LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome Biol. 2022;23(1):95. pmid:35421994
- 28. Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002;21(16):2409–19. pmid:12210625
- 29. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B (methodological). 1995;57(1):289–300.
- 30. Nearing JT, Douglas GM, Hayes MG, MacDonald J, Desai DK, Allward N, et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun. 2022;13(1):342. pmid:35039521
- 31. van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software. 2011;45(3):1–67.
- 32.
IBGE IBdGeE. Belterra: panorama 2022 [https://cidades.ibge.gov.br/brasil/pa/belterra/panorama.
- 33.
IBGE IBdGeE. Sao Paulo: panorama 2022 [https://cidades.ibge.gov.br/brasil/sp/sao-paulo/panorama.
- 34. Paliova I, McNown R, Nulle G. Multiple Dimensions of Human Development Index and Public Social Spending for Sustainable Development. International Monetary Fund. 2019;2019(204):1–42.
- 35. Bayar Y, Gavriletea MD, Pintea MO, Sechel IC. Impact of Environment, Life Expectancy and Real GDP per Capita on Health Expenditures: Evidence from the EU Member States. Int J Environ Res Public Health. 2021;18(24). pmid:34948785
- 36.
Zimmerman E, Woolf SH. Understanding the Relationship Between Education and Health. NAM Perspectives. 2015.
- 37. Zaura E, Keijser BJ, Huse SM, Crielaard W. Defining the healthy "core microbiome" of oral microbial communities. BMC Microbiol. 2009;9:259. pmid:20003481
- 38. Baker JL, Mark Welch JL, Kauffman KM, McLean JS, He X. The oral microbiome: diversity, biogeography and human health. Nat Rev Microbiol. 2023. pmid:37700024
- 39. Mager DL, Haffajee AD, Devlin PM, Norris CM, Posner MR, Goodson JM. The salivary microbiota as a diagnostic indicator of oral cancer: a descriptive, non-randomized study of cancer-free and oral squamous cell carcinoma subjects. J Transl Med. 2005;3:27. pmid:15987522
- 40. Zhu W, Shen W, Wang J, Xu Y, Zhai R, Zhang J, et al. Capnocytophaga gingivalis is a potential tumor promotor in oral cancer. Oral Dis. 2022. pmid:36093607
- 41. Farrell JJ, Zhang L, Zhou H, Chia D, Elashoff D, Akin D, et al. Variations of oral microbiota are associated with pancreatic diseases including pancreatic cancer. Gut. 2012;61(4):582–8. pmid:21994333
- 42. Yan X, Yang M, Liu J, Gao R, Hu J, Li J, et al. Discovery and validation of potential bacterial biomarkers for lung cancer. Am J Cancer Res. 2015;5(10):3111–22. pmid:26693063
- 43. Leng Q, Holden VK, Deepak J, Todd NW, Jiang F. Microbiota Biomarkers for Lung Cancer. Diagnostics (Basel). 2021;11(3). pmid:33673596
- 44. Henrich B, Rumming M, Sczyrba A, Velleuer E, Dietrich R, Gerlach W, et al. Mycoplasma salivarium as a dominant coloniser of Fanconi anaemia associated oral carcinoma. PLoS One. 2014;9(3):e92297. pmid:24642836
- 45. Giordano-Kelhoffer B, Lorca C, March Llanes J, Rabano A, Del Ser T, Serra A, et al. Oral Microbiota, Its Equilibrium and Implications in the Pathophysiology of Human Diseases: A Systematic Review. Biomedicines. 2022;10(8). pmid:36009350
- 46. Sedghi L, DiMassa V, Harrington A, Lynch SV, Kapila YL. The oral microbiome: Role of key organisms and complex networks in oral health and disease. Periodontol 2000. 2021;87(1):107–31. pmid:34463991
- 47. Kopra E, Lahdentausta L, Pietiainen M, Buhlin K, Mantyla P, Horkko S, et al. Systemic Antibiotics Influence Periodontal Parameters and Oral Microbiota, But Not Serological Markers. Front Cell Infect Microbiol. 2021;11:774665. pmid:35004349