Probing the oral-brain connection: Oral microbiome patterns in a large community cohort with anxiety, depression, and trauma symptoms, and periodontal outcomes

doi:10.21203/rs.3.rs-4328261/v1

Download PDF

Article

Probing the oral-brain connection: Oral microbiome patterns in a large community cohort with anxiety, depression, and trauma symptoms, and periodontal outcomes

https://doi.org/10.21203/rs.3.rs-4328261/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 05 Oct, 2024

Read the published version in Translational Psychiatry →

You are reading this latest preprint version

The role of the oral microbiome in mental health has recently been appreciated within the proposed oral-brain axis. This study examined the structure and composition of the salivary microbiome in a large-scale population-based cohort of individuals reporting mental health symptoms (n = 306) compared to mentally healthy controls (n = 164) using 16S rRNA sequencing. Mental health symptoms were evaluated using validated questionnaires and included depression, anxiety, and posttraumatic stress disorder, with accompanying periodontal outcomes. Participants also indicated current or previous diagnoses of anxiety, depression, periodontitis, and gingivitis. Mental and periodontal health variables influenced the overall composition of the oral microbiome. PTSD symptoms correlated with reduced Haemophilus sputorum and elevated Prevotella histicola levels. Anxiety disorder diagnosis was associated with decreased Neisseria elongate and increased Oribacterium asaccharolyticum. P. histicola abundance was also positively associated with depressive scores and negatively associated with psychological quality of life. A higher abundance of Shuttleworthia and a lower abundance of Capnocytophaga were evident in those who reported a clinical periodontitis diagnosis. Functional prediction analysis revealed a potential role for tryptophan metabolism/degradation in the oral-brain axis, which was confirmed by lower plasma serotonin levels across symptomatic groups. Higher Eggerthia and lower Haemophilus parainfluenzae abundance were associated with reported clinical periodontitis diagnosis and psychotherapeutic efficacy. This study sheds light on the intricate interplay between oral microbiota, periodontal outcomes, and mental health, emphasizing the need for further exploration of the oral-brain axis to pave the way for novel therapeutic interventions and predicting therapeutic response.

Biological sciences/Genetics/Genomics/Comparative genomics

Health sciences/Diseases/Psychiatric disorders/Depression

Mental health disorders place a heavy burden on patients, families, societies, and global economies. In 2019, an estimated 418 million disability-adjusted life years (DALYs) could be attributable to mental disorders [1]. In 2017, depression was the leading cause of disability globally, with an estimated 322 million people living with depression [2], whilst about 260 million people suffered from anxiety disorders, and many suffered with additional comorbidities [3]. Mental disorders were the leading cause of the health-related burden of disease, and to worsen the situation, the COVID-19 pandemic has left in its wake a steep rise in the global prevalence of anxiety and depressive disorders [4].

Several factors, including economic insecurity, work-related stress, collective trauma, inequality, modern lifestyles, global events, and environmental factors, have likely contributed to the increased prevalence of mental health disorders [5]. Modern lifestyles, characterized by high stress levels, processed diets, excessive sanitation practices, and antibiotic use, alongside environmental changes like increased pollutants, climate change, and urbanization, have shifted human microbiota towards an industrialized state [6]. These microbiota alterations, coupled with the loss of specific functional attributes, may lead to suboptimal disease-promoting microbial communities, worsening compromised mental health [6].

The burden of mental health disorders is compounded by treatment limitations such as non-adherence, treatment resistance, and relapse [7–9], highlighting the necessity for innovative treatment modalities. The human holobiont, comprising the human host and its symbiotic microorganisms, plays a crucial role in health and disease [10, 11, 12]. While much attention has been given to the gut-brain axis, emerging evidence suggests that the oral microbiota, a less explored niche, may also influence the central nervous system and behavior [13, 14].

The oral cavity hosts a diverse array of bacteria, and dysregulation can lead to disease. Periodontitis, a chronic bacterial infection affecting nearly half the global population [15], triggers systemic inflammation through pro-inflammatory cytokine release and invasion by periodontal keystone pathogens like Porphyromonas gingivalis (P. gingivalis) [16]. Periodontitis not only contributes to chronic inflammatory conditions like atherosclerosis, diabetes, and cardiovascular diseases [17] [18] [19] but also shows associations with psychiatric disorders [20], suggesting involvement in the oral-brain axis. A longitudinal study spanning 10 years found a higher incidence of subsequent depression in individuals with periodontitis compared to those without [21], indicating a potential causal relationship between periodontitis and major depression. Additionally, recent research has identified specific bacterial taxa implicated in periodontal disease as well as anxiety, depressive disorders, and trauma-related disorders [22].

Pathogenic periodontal bacteria can impact the CNS through various pathways, both directly and indirectly [22]. Direct routes include bloodstream transmission or areas with compromised blood-brain barrier (BBB) integrity [23]. Indirectly, they induce pro-inflammatory cytokine production, activating endothelial cells expressing tumor necrosis factor (TNF)-α and interleukin-1 (IL-1) β receptors, which signal perivascular macrophages, leading to neuroinflammation [24]. Keystone pathogens widen intercellular spaces in periodontal pockets, causing epithelial rupture and a "leaky mouth" [25], facilitating lipopolysaccharide (LPS) access to circulation, activating the immune system and the hypothalamic pituitary adrenal (HPA) axis, influencing CNS function [26, 24]. Other entry points include circumventricular organs, the choroid plexus [23, 27], and olfactory/trigeminal nerves [28]. Brain-resident microglia can be influenced by periodontal bacteria via leptomeninges [26]. Periodontal pathogens also affect gut microbial composition/function directly via enteral or indirectly via hematogenous transmission [29].

Clinical data on the oral microbiome's connection to mental health disorders are limited. A study utilizing genetic association analysis and Mendelian randomization to assess links between salivary-tongue dorsum microbiome interactions and anxiety/depression, found significant associations and causal effects [30], especially, Eggerthia was linked to anxiety and depression across multiple databases. Another study in adolescents (n = 66) noted a differential abundance of Actinomyces, Spirochaetaceae, Fusobacterium, and Treponema in those with anxiety and depression symptoms [31]. Wingfield et al. compared oral microbial composition in depressed young adults (n = 40), discovering 21 bacterial taxa with differing levels compared to controls, including increased Neisseria spp. and Prevotella nigrescens [32]. To unravel the oral-brain axis's role in anxiety and depression, larger studies across diverse age groups are needed, gathering both microbiome and mechanistic data.

Clinical data linking the oral microbiome to mental health disorders are limited. A study employing genetic association analysis and Mendelian randomization found significant associations and causal effects between salivary-tongue dorsum microbiome interactions and anxiety/depression, with Eggerthia notably linked to both conditions across multiple databases [30]. Another study involving adolescents (n = 66) observed differing abundances of Actinomyces, Spirochaetaceae, Fusobacterium, and Treponema in individuals with symptoms of anxiety and depression [31]. Wingfield et al. compared the oral microbial composition in depressed young adults (n = 40), identifying 21 bacterial taxa with varying levels compared to controls, including increased Neisseria spp. and Prevotella nigrescens [32]. Larger studies encompassing diverse age groups and gathering microbiome and mechanistic data are needed to elucidate the role of the oral-brain axis in anxiety and depression.

This study aimed to contribute to the limited oral microbiome data currently available, and to explore the oral-brain axis, by investigating the salivary microbiome in individuals with symptoms of anxiety, depression, and posttraumatic stress disorder (PTSD) with periodontal outcomes.

Study participant evaluation and enrollment

This cohort comprised two Spanish study populations from PsicoBioma (n = 186, March 2021 - Jan 2022) and TRIAD (n = 284, Nov 2021 - Dec 2022), both based on population-based microbiome projects. PsicoBioma recruited participants from Spain, while TRIAD recruited from Madrid, Barcelona, Vitoria, and Oviedo municipalities (allowing blood collection). Both cohorts provided saliva samples and completed similar online questionnaires. TRIAD also provided blood samples for plasma analysis and completed a more comprehensive periodontal health questionnaire. The research adhered to The Code of Ethics of the World Medical Association (Declaration of Helsinki) for human experiments, and data processing followed Spanish Organic Law 3/2018 on Personal Data Protection and Digital Rights Guarantee (BOE 16673 of 6 Dec 2018) and its 17th Additional Provision. Approval was obtained from the Ethics Committees of Hospital Clínico San Carlos (Madrid), Medical Research Ethics Committee of Asturias, Basque Medicine Research Ethics Committee, and Drug Research Ethics Committee of Hospital de la Santa Creu i Sant Pau (PSQ-19-2 C.I. 196/474-E). All research participants provided online, written informed consent.

The study recruited healthy controls, participants with a current/previous diagnosis of anxiety, depressive, or trauma-related disorders, or individuals who were experiencing these symptoms. Spanish residents, 18 years or older, who were proficient in reading and understanding Spanish were included. Individuals who used antibiotics within the previous six months, and those diagnosed with any other major psychiatric disorders including psychotic disorders, personality disorders, or neurodegenerative disorders, were excluded.

Demographic and clinical data

Demographic, health, and clinical data were collected using a secure online questionnaire. Psychological evaluations relied on standardized self-report questionnaires validated for the Spanish population; this study focused on symptoms rather than formal diagnoses. However, participants also indicated on the questionnaire whether they had a previous/current clinical diagnosis of anxiety or depression (“diagnosis” henceforth refers to clinical diagnoses, and “symptoms” refers to self-report questionnaire data). Depressive symptoms were assessed using the Centre for Epidemiologic Studies Depression (CESD) scale; state and trait anxiety symptoms were evaluated using the state-trait anxiety inventory (STAI). The posttraumatic stress disorder (PTSD) Checklist for the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5, PCL-5), and the Childhood Trauma Questionnaire-Short Form (CTQ-SF) evaluated trauma exposure. Additionally, quality of life was measured using the World Health Organization Quality Of Life Questionnaire (WHOQOL).

Psychiatric symptoms were determined based on the following criteria:

Depressive symptoms: CESD scores of 16–24 indicated mild and 25–55 severe depressive symptoms [33]. PTSD symptoms: PCL-5 score > 33 + more than 3 symptom clusters [34]. State anxiety symptoms: STAI-S scores > 41; trait anxiety symptoms: STAI-T scores > 45; CTQ-SF [35] total score was used to evaluate the severity of childhood maltreatment.

The TRIAD cohort completed a periodontal health questionnaire [36] (validated for the Spanish population [37]) to predict severe periodontitis, according to Montero et al., 2020 [37]. Specifically, severe periodontitis was defined according to three criteria: (i) the Centers for Disease Control/American Academy of Periodontology (CDC/AAP) case definition [38], henceforth referred to as SeverePerioCDCAAP; (ii) the presence of ≥ 50% of teeth with clinical attachment level (CAL) ≥ 5 mm, henceforth referred to as TeethCAL5; (iii) the presence of ≥ 25% of teeth with probing pocket depth (PPD) ≥ 6 mm, henceforth referred to as TeethPPD6. In addition, participants also indicated whether they had a previous/current clinical diagnosis of periodontitis and/or gingivitis (“diagnosis” refers to a self-reported clinical diagnosis, and “predicted severe periodontitis” refers to self-reported questionnaire data).

Blood collection and processing

Whole blood (10ml) was collected using BD Vacutainer® EDTA tubes. Blood was centrifuged at 1800 rpm for 10 minutes at room temperature, and the resulting supernatant (plasma) was transferred into clean 1.5ml Eppendorf tubes for storage at -80°C for later use.

Kynurenine, tryptophan, and serotonin quantification in plasma

Plasma levels of kynurenine (KYN), tryptophan (TRP), and serotonin (5-HT) were measured using High-performance liquid chromatography (HPLC). TRP and 5-HT were detected fluorometrically at excitation/emission wavelengths of 270/360 nm and 290/398 nm, respectively (Waters 2475, Multi fluorescence Detector; Waters, Milford, MA, USA). The ratios of KYN or 5-HT to TRP concentrations were calculated and used as a measure of TRP degradation (see Supplementary Materials for details).

Bacterial DNA extraction, 16S rRNA gene sequencing and analysis

Participants self-collected saliva samples in DNA/RNA Shield Safe Collect Saliva Collection tubes (Zymo Research, Irvine, California, USA), from which microbial DNA was extracted (ZymoBIOMICS DNA Miniprep Kit, Zymo Research). Bacterial 16S rRNA gene V3-4 amplicons were generated, using previously described primers [39] and sequenced (2 x 300bp paired-end) (Centre de Regulació Genòmica, Barcelona, Spain), on the Illumina NextSeq2000 platform (see Supplementary Material for details).

Quality control of FASTQ sequencing files was performed using fastqc and multiqc. Raw sequence reads were [41], de-replicated and de-noised to combine identical reads into amplicon sequence variants (ASVs) and construct consensus quality profiles for each combined set of sequences (dada2 version 3.11 [40]). Following chimeras removal, a consensus paired-end reads file was generated for feature construction and downstream analysis. Taxonomic binning of classified sequences was built using a local copy of the Ribosomal Database Project (RDP) Classifier (Train Set 19 [42]), and normalized data were produced from the relative abundance of taxa present in each sample. A feature table of 54 817 unique ASVs with an average read length of 391 nucleotides in 470 samples was consequently constructed (following pre-processing, the minimum number of reads per sample was 18 868, and the average number of reads per sample was 95 763).

Statistical analyses

Data was analyzed using bioinformatics and statistical analysis packages in R [41], including the packages dada2 (version 3.18, [40]), vegan (version 2.6.4, [43]), phyloseq (version 1.46.0, [44]), ggplot2 (version 3.4.4, [45]), CoDaSeq (version 0.99.7, [46, 47]. For clinical and demographic data, continuous variables were summarized as means (M) and standard deviations (SD) if normally distributed or as medians and interquartile ranges (IQRs) if non-normally distributed. To assess differences in the metadata variables between symptomatic and control groups, Student's t-tests and Wilcoxon rank sum tests were used to assess differences between normally and non-normally distributed data (normality tested using Shapiro-Wilk Normality Test), respectively. Categorical data were summarized as counts (n) and percentages, and χ² or Fisher exact tests were used to assess differences between groups, where appropriate. Significance was defined as p ≤ 0.05.

The Simpson index was used to evaluate α-diversity [48]. Taxa were agglomerated to genus level, assigning species-level where possible. Data was transformed to relative abundance out of 100 to account for differences in total depth per sample. Variance filtering was performed (genefilter function, version 1.84.0), which removed taxa with the lowest 40% variance. Abundance matrices were centred log-ratio (clr)-transformed, using the minimum proportional abundance detected for each taxon for the imputation of zeros. The ordination of community variation was visualized using multidimensional scaling (MDS) of genus-level Aitchison distances. The capscale function (vegan package) [43] was used to determine the contribution of metadata variables to microbiome community variation.

The ASV table was filtered to retain taxa observed in at least 15% of participants. Associations between taxonomic abundance and metadata variables were analyzed using a linear modeling approach (fw_glm function, Tjazi package) [49]), whilst adjusting for covariates including age, Body mass index (BMI), smoking status, and cholesterol medication use. We performed false discovery rate (FDR) correction using the Benjamini-Hochberg procedure and significance was defined as q ≤ 0.1.

We utilized PICRUSt2 [50] to predict the central nervous system (CNS)-related functional potential of oral taxa, by focusing on gut-brain modules (GBMs) [51]. Associations between GBMs and mental health outcomes were tested using the same linear modeling approach as previously described, with significance set at q ≤ 0.1.

Clinical and demographic characteristics

Clinical and demographic characteristics of the depressive (n = 148), state (n = 256) and trait anxiety (n = 281), and PTSD (n = 73) symptomatic cohorts (according to criteria described in the Methods and Materials), and healthy controls (no significant mental health symptoms) (n = 164) are described in Tables 1–4. In our cohort of 470 individuals, 306 presented with at least one or a combination of the aforementioned symptoms. Females constituted 72% of our cohort, and the median age was 40 years. Comorbidity of these psychiatric symptoms was common; of the 232 individuals who had both state and trait anxiety symptoms, 133 of them also had depressive symptoms (57.3%), 67 had comorbid PTSD symptoms (28.9%), and 52 (22.4%) had PTSD and depressive symptoms. Of the 148 individuals with depressive symptoms, 144 (97.3%) also had trait anxiety symptoms, and 54 (36.5%) had symptoms of PTSD.

A total of 34 (7%) individuals had a current clinical diagnosis of periodontitis, and 30 (11%) had a gingivitis diagnosis. The self-reported periodontal health questionnaire administered to most of the TRIAD cohort (n = 196) (unavailable for the PsicoBioma cohort) showed 81 individuals (41%) had probable severe periodontitis using TeethPPD6 criteria, 93 (47%) had it based on the TeethCAL5 criteria, and 106 (54%) had it based on the Centers for Disease Control (CDC)/American Academy of Periodontology (AAP) criteria (SeverePerioCDCAAP).

Periodontal and mental health variables influence oral microbiome community composition

The Simpson alpha diversity index showed no significant differences between the mental health symptomatic groups and controls. However, among individuals who completed the self-reported periodontal health questionnaire (n = 196, 48%), those with receding gums had a lower Simpson alpha diversity index compared to those without (Wilcoxon rank-sum tests, p = 0.005, r = 0.17, n = 284). Several variables influenced the overall oral microbial composition (β-diversity), with smoking status eliciting the largest effect, followed by age, living environment (city, town, or rural setting), and alveolar bone loss. The most significant subset (R² ≥ 0.002 and q ≤ 0.1) of variables is illustrated in Fig. 1 (Supplementary Table 1 contains the full set of significant variables).

Oral taxa associated with trauma, mental health outcomes, and psychological quality of life

Our symptomatic cohort reported significantly higher levels of childhood trauma compared to controls. Individuals who reported high levels of emotional neglect had significantly lower abundance of Streptococcus mutans (GLM q = 0.07, β = -0.6, n = 470) (Fig. 2a). Individuals with PTSD symptoms (n = 73) had significantly lower relative abundance of Haemophilus sputorum (H. sputorum) (GLM, q = 0.09, β = -1.2, n = 237) and higher levels of Prevotella histicola (P. histicola) (GLM, q = 0.09, β = 1.6, n = 237), compared to controls (n = 164) (Fig. 2b). Those with a current anxiety disorder diagnosis (n = 134) harbored significantly lower levels of Neisseria (GLM, q = 0.001, β = -0.96, n = 470), specifically Neisseria elongate (N. elongate) (GLM, q = 0.09, β = -0.94, n = 470) and significantly higher levels of Oribacterium asaccharolyticum (O. asaccharolyticum) (GLM q = 0.03, β = 0.53, n = 470) compared to those without a current diagnosis (n = 336) (Fig. 2c). Interestingly, higher relative abundance of P. histicola was also evident in individuals with higher CESD depressive scores (GLM q = 0.05, β = 0.04, n = 470) (Fig. 2d) and those with poor psychological quality of life scores (GLM q = 0.08, β = -0.2, n = 470) (Fig. 2e). Individuals with higher CESD scores also had a higher relative abundance of Lancefieldella (GLM q = 0.05, β = 0.01, n = 470) and O. asaccharolyticum (GLM q = 0.05, β = 0.02, n = 470), however, the effect sizes were relatively small (Fig. 2f).

Oral microbiome signatures related to periodontal health

Several oral taxa were associated with periodontal health variables (Fig. 3, Supplementary Table 2). The abundance of Shuttleworthia was higher in participants with a self-reported periodontitis diagnosis and those with predicted severe periodontitis based on the TeethCAL5 criteria. The abundance of Capnocytophaga was lower in participants with a self-reported clinical periodontitis diagnosis and those with predicted severe periodontitis based on the TeethPPD6 criteria. Several common taxa were differentially abundant in those with a self-reported clinical periodontitis diagnosis and those who reported loose teeth (a symptom of periodontitis), including a higher relative abundance of Tannerella forsythia, Metaprevotella, Fretibacterium fastidiosum, and lower relative abundance of Prevotellaceae and Haemophilus parainfluenzae. Three taxa had a higher abundance in participants with a self-reported clinical gingivitis diagnosis, Parvimonas, Gallibacter, and Eggerthia; Eggerthia was the only common taxon between periodontitis and gingivitis diagnosis, whose abundance was altered similarly for both diagnoses.

Functional potential of the oral microbiome: possible implications for mental health outcomes

Functional prediction revealed lower tryptophan metabolism/degradation in individuals with PTSD symptoms (GLM, q = 0.02, β = -0.8, n = 470), those who experienced higher levels of childhood trauma (GLM, q = 0.09, β = -0.01), and those with lower quality of life (specifically personal relationships) (GLM, q = 0.06, β = 0.06). Interestingly, lower metabolism/degradation of tryptophan was also predicted in individuals with predicted severe periodontitis based on the TeethCAL5 criteria (n = 93) and TeethPPD6 criteria (n = 81) (GLM, q = 0.07, β = -0.52, n = 196 and GLM, q = 0.02, β = -0.6, n = 196, respectively). Figure 4 illustrates the full set of predicted gut-brain modules (GBMs) linked to severe periodontitis, mental health, childhood trauma, and quality of life.

Plasma measures

Analyses to confirm lower TRP metabolism/degradation revealed lower plasma levels of 5-HT and the ratio of 5-HT/TRP in individuals with depressive symptoms (Wilcoxon rank-sum test, p < 0.01, mean difference (MD) = 0.8, and p < 0.01, MD = 0.9, n = 282 respectively), state anxiety symptoms (p < 0.01, MD = 0.6, and p < 0.01, MD = 0.5, n = 282 respectively), trait anxiety symptoms (p < 0.01, MD = 0.6, and p < 0.01, MD = 0.6, n = 282 respectively), and PTSD symptoms (p < 0.01, MD = 1.0, and p < 0.01, MD = 0.9, n = 282 respectively) compared to healthy controls (Fig. 5).

Oral microbes and therapeutic response

Interestingly, two taxa associated with a current self-reported clinical diagnosis of periodontitis and/or gingivitis were also associated with self-reported efficacy of psychotherapy, namely Eggerthia and Haemophilus parainfluenza. Eggerthia was present at a higher relative abundance in those with a current self-reported clinical diagnosis of periodontitis and/or gingivitis and in individuals with poor self-reported psychotherapeutic efficacy (GLM q = 0.12, r = -0.62, n = 322), whereas the abundance of H. parainfluenza was lower in these individuals (GLM q = 0.12, r = 0.60, n = 322), although the association did not reach the threshold for statistical significance of q ≤ 0.1.

This study represents one of the largest oral microbiome investigations in mental health, to date. The composition of the overall oral microbiome was significantly impacted by several mental health variables (including clinical diagnoses of anxiety disorders or depression), as well as periodontal symptoms, predicted severe periodontitis. and self-reported clinical diagnoses of gingivitis and/or periodontitis. These findings correlate with previous research highlighting the significant effects of mental [32] and periodontal [52, 53] health on the oral microbiome beta diversity. Various additional factors shaped the oral microbiome composition, aligning with earlier research emphasizing the impact of factors such as smoking [54], BMI, [55], age [52], arthritis [56], gout [57], and geographic location [58] on the oral microbiome.

Although none of the mental health variables or self-reported periodontal outcomes influenced Simpson’s diversity, lower diversity was evident among individuals with self-reported receding gums. Earlier studies also failed to detected differences in alpha diversity between individuals with depression and anxiety compared to controls [31], and between periodontitis patients and controls [53].

Several taxa were associated with mental health, trauma, and well-being. P. histicola is of particular interest, with a higher relative abundance in individuals with PTSD symptoms, those with higher CESD scores, and those with poorer interpersonal quality of life. Prevotella is the second most common bacteria dominating the oral cavity and this diverse genus includes several species. P. histicola is a facultative oral pathogen, which can cause pathologies such as caries and periodontitis [59, 60]. Previously, lower levels of Prevotella were noted in the oropharyngeal microbiota in schizophrenia and mania cohorts compared to controls [61], and a negative association was observed between the abundance of Prevotella and distress [62]. Importantly, Prevotella is a genus strongly associated with waking samples, and the majority of our samples were collected close to waking, which could explain the discrepancies between the findings. Furthermore, the majority of studies report on genus level, whereas our finding is for the species P. histicola, and research has shown that species from the same genus could fulfill vastly different roles [63, 64], therefore future studies should aim for higher resolution of taxonomic classification to disentangle taxonomic functions and disease associations.

A lower relative abundance of N. elongata in individuals with a clinical diagnosis of anxiety disorders echoes similar observations in patients with schizophrenia and mania [61]. Conversely, previous research in young adults linked depression with increased levels of Neisseria spp [32]. Discrepancies in these findings may be attributed to differences in sample size and age of the population, as well as the resolution of microbial analysis (species vs. genus). Neisseria species, integral members of the oropharyngeal flora [65], play crucial roles in maintaining oral [66] and cardiovascular health [66, 67]. Their presence correlates with good oral health, attributed to their aerobic, nitrite-reducing capabilities essential for gum health [66–68]. Moreover, Neisseria-dominated oral microbiomes exhibit a reduced likelihood of hosting the cariogenic pathogen S. mutans [69]. Notably, we detected a higher abundance of S. mutans in individuals reporting childhood emotional neglect, a known risk factor for mental health disorders. These findings underscore the intricate interplay between oral microbial composition, mental health outcomes, and early life adversity. The involvement of cross-feeding and interactions among microbial taxa adds complexity to understanding comorbidity and risk factors in mental health conditions.

Individuals with an anxiety disorder diagnosis and higher CESD scores harbored a higher abundance of O. asaccharolyticum. This correlates with increased abundance reported in elderly people receiving treatment for anxiety, depression, and insomnia [70]. Furthermore, an increased abundance of the Odoribacter genus was also noted in the gut microbiomes of patients with depression [71], a preclinical model of depression [72], and individuals with periodontal disease [73]. Elevated levels of the Odoribacter genus were also detected in the oral microbiome of periodontitis patients [74]. These findings underscore the potential interconnectivity between oral and gut microbiomes and taxa implicated in both oral and periodontal health, with implications for the oral-gut-brain axis. Furthermore, in a randomized, double-blind placebo-controlled trial, synbiotics reduced both systemic inflammation and systemic lupus erythematosus disease activity, whilst simultaneously also depleting O. asaccharolyticum from the microbiome [75], suggesting pathogenicity and a potential therapeutic target to facilitate anti-inflammatory effects, which warrant further investigation.

Participants with PTSD symptoms had a lower abundance of H. sputorum, correlating with findings in young adults with depression [32]. Haemophilus is a nitrate-reducing genus, and therefore, higher levels are associated with good oral health. This taxon is also depleted in the oral and gut microbiomes of individuals with rheumatoid arthritis (RA), which also correlated with higher levels of serum autoantibodies [76], suggesting a potential involvement in autoimmunity and inflammation. Interestingly, PTSD is associated with RA, with female PTSD patients having a 76% higher risk of developing RA [77]. Levels of Haemophilus in the oral and gut microbiomes could therefore be involved in this comorbidity, possibly via its immunomodulatory effects.

The abundance of several taxa was associated with periodontal outcomes; Shuttleworthia and Capnocytophaga were associated with a self-reported clinical diagnosis of periodontitis and predicted severe periodontitis. Previous studies reported similar findings in periodontitis [74, 78], and gingivitis patients [79]. Shuttleworthia and Capnocytophaga should therefore be investigated as potential non-invasive, salivary microbiome markers of periodontitis.

Eggerthia was more prevalent in those with a periodontitis/gingivitis diagnosis, correlating with previous reports in periodontitis [80], and gingivitis patients [81]. These findings suggest that salivary levels of Eggerthia should be investigated as an early, non-invasive indicator of periodontal health problems. We however did not detect differences in the relative abundance of certain keystone periodontitis-associated taxa, including Porphyromonas gingivalis, which could be attributed to analyzing saliva samples and not periodontal pocket samples [82].

While no shared oral taxa were associated with both mental and periodontal health, we found a common functional pathway: metabolism/degradation of TRP. This pathway was diminished in individuals with PTSD symptoms, those who experienced childhood trauma, those with poor interpersonal quality of life, and those with predicted periodontitis. Decreased degradation of TRP through the 5-HT pathway could result in lower 5-HT levels and higher TRP levels. Our data revealed reduced plasma levels of 5-HT and 5-HT/TRP ratios in all symptomatic groups compared to healthy controls. Decreased 5-HT/TRP ratios may result from lower 5-HT levels and increased TRP levels. Directly measuring 5-HT levels in plasma can be challenging due to factors like its short half-life and difficulties in accurately measuring relatively low 5-HT plasma levels [83]. Therefore, the 5-HT/TRP ratio allows for an indirect assessment of serotonin synthesis capacity, which may indicate alterations in serotonin function in the CNS [84, 85].

Decreased levels of 5-HT align with previous research in depression [86] and PTSD [87]. Serotonin-mediated neurotransmission is implicated in anxiety disorders, although its relationship is complex due to the diversity of anxiety disorders [88]. Serotonin is crucial for CNS development and function [89, 90], yet it also affects oral health. Psychotropic drugs like selective serotonin reuptake inhibitors (SSRIs) can reduce salivary flow rate and cause xerostomia (dry mouth) [91], affecting oral cleansing and tooth decay prevention [92]. While our study didn't find statistically significant differences in 5-HT levels in those with clinical diagnosis or predicted severe periodontitis, altered serotonin levels could influence oral health.

Although serotonin is vital for mental health, most (~ 90%) of its production occurs in the gastrointestinal tract [93], influencing various physiological processes beyond the CNS, including colonic motility [95]. While our findings suggest potential oral microbiota involvement in TRP metabolism and systemic serotonin levels, systemic levels don't directly reflect CNS levels due to the blood-brain barrier (BBB). Psychotropic medications, like SSRIs, may have affected CNS serotonin levels in this cohort. Nevertheless, our study underscores the importance of the serotonergic system in mental and oral health, suggesting avenues for further research into oral microbiota, TRP metabolism, and serotonin production interplay. Understanding these relationships could lead to novel therapeutic approaches for mental health disorders associated with serotonin dysregulation.

Identifying treatment response markers is crucial to lighten the burden of disease and enhance treatment efficacy. Although oral taxa were not associated with psychoactive medication use, a higher relative abundance of Eggerthia and a lower abundance of H. parainfluenza was evident in individuals with a self-reported periodontitis diagnosis and those reporting poor psychotherapeutic efficacy, hinting at a potential effect of oral health on treatment efficacy. The psychotherapeutic efficacy association narrowly missed statistical significance, and warrants further investigation. Data on oral microbiome and treatment response associations are limited, however, research suggests a causal effect of elevated levels of Eggerthia on anxiety and depression [30]; links between lower levels of H. parainfluenza and generalized anxiety disorder [96], and higher levels of this taxon in individuals with periodontitis + IBS [97]. These taxa are good candidates to explore in future longitudinal treatment outcome studies, especially in patients with periodontal health problems.

Limitations of this study include the absence of clinical assessments of anxiety, depression, PTSD, periodontitis, and gingivitis. Instead, validated questionnaires were used to assess symptoms, in addition, participants reported current diagnoses of any of these disorders/conditions. Mental health disorders are complex, with varying symptom presentations even among individuals diagnosed with the same disorder. Understanding these disorders in this context is crucial. Additionally, diagnoses and treatment strategies are informed by symptoms rather than rigid diagnostic criteria, with associations with biological markers often correlating more strongly with symptoms [98]. Further oral microbiome studies with well-defined clinical samples are warranted to compare findings to self-reported symptom cohorts.

Different oral niches harbor distinct microbiomes. This study investigated self-collected saliva samples as a proxy for the oral microbiome. Samples from the periodontal pocket would be ideal for studying the microbiome related to periodontitis. However, the aim of this study was mental health outcomes whilst considering self-reported periodontal health outcomes. Furthermore, studies have shown that saliva samples were the most stable within-subjects (temporal) as well as between-subjects [99]. Lastly, our cross-sectional study can only report on microbial associations with disease; future longitudinal studies are needed to infer causality between the oral microbiome and mental health symptoms.

This study reveals a compelling connection between the composition of oral microbiota, mental health conditions, early life experiences, as well as periodontal outcomes. We highlighted taxa and functional pathways implicated in both mental and oral health, which expands our current knowledge of the newly described oral-brain axis, which encompasses a complex interplay between microbial composition, systemic neuromodulators, and outcomes in mental and oral health. Understanding these relationships offers promising avenues for integrated approaches to promote oral and psychological resilience, emphasizing the importance of considering both oral and mental health within a holistic framework of care.

Acknowledgments

This research was supported by a 2018 NARSAD Young Investigator Grant from the Brain and Behaviour Research Foundation (grant number: 27050), Una4Career grant (European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 847635), a Knowledge Generation Grant from the Ministry of Science and Innovation (Spain) (PID2021-126468OA-I00), and Ministry of Health (Spain) research grant (PNSD 2022I033).

The authors would like to thank the following groups and individuals: The Genomics Unit at the CRG for assistance with library preparation and 16S rRNA sequencing; the UCM Occupational Medicine Service, María Suárez, Alexandra Becedas López, and Miriam Jubero

for assistance with blood collection, Dr. JH Müller for statistical assistance., and the participants of this project.

The data that support the findings of this study are available from the corresponding author, [S Malan-Müller], upon reasonable request. Data will be deposited in the National Microbiome Data Collaborative after acceptance of the manuscript.

Conflict of interest

The authors report there are no competing interests to declare.

Arias D, Saxena S, Verguet S (2022). Quantifying the global burden of mental disorders and their economic value. eClinicalMedicine. doi: 10.1016/j.eclinm.2022.101675.
Friedrich MJ (2017). Depression Is the Leading Cause of Disability Around the World. JAMA, 317:1517.
Depression and Other Common Mental Disorders. https://www.who.int/publications-detail-redirect/depression-global-health-estimates. Accessed 23 Feb 2024.
Santomauro DF, Herrera AMM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. (2021). Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet, 398:1700–1712.
Hidaka BH (2012). Depression as a disease of modernity: explanations for increasing prevalence. J Affect Disord, 140:205–214.
Sonnenburg JL, Sonnenburg ED (2019). Vulnerability of the industrialized microbiota. Science, 366:eaaw9255.
Johnston KM, Powell LC, Anderson IM, Szabo S, Cline S (2019). The burden of treatment-resistant depression: A systematic review of the economic and quality of life literature. Journal of Affective Disorders, 242:195–210.
Taylor S, Abramowitz JS, McKay D (2012). Non-adherence and non-response in the treatment of anxiety disorders. Journal of Anxiety Disorders, 26:583–589.
Cooper C, Bebbington P, King M, Brugha T, Meltzer H, Bhugra D, et al. (2007). Why people do not take their psychotropic drugs as prescribed: results of the 2000 National Psychiatric Morbidity Survey. Acta Psychiatrica Scandinavica, 116:47–53.
Salvucci E (2016). Microbiome, holobiont and the net of life. Critical Reviews in Microbiology, 42:485–494.
Shreiner AB, Kao JY, Young VB (2015). The gut microbiome in health and in disease. Curr Opin Gastroenterol, 31:69–75.
Bercik P, Collins SM, Verdu EF (2012). Microbes and the gut-brain axis. Neurogastroenterology & Motility, 24:405–413.
Martín-Hernández D, Caso JR, Bris ÁG, Maus SR, Madrigal JLM, García-Bueno B, et al. (2016). Bacterial translocation affects intracellular neuroinflammatory pathways in a depression-like model in rats. Neuropharmacology, 103:122–133.
Leira Y, Domínguez C, Seoane J, Seoane-Romero J, Pías-Peleteiro JM, Takkouche B, et al. (2017). Is Periodontal Disease Associated with Alzheimer’s Disease? A Systematic Review with Meta-Analysis. Neuroepidemiology, 48:21–31.
GBD 2017 Oral Disorders Collaborators, Bernabe E, Marcenes W, Hernandez CR, Bailey J, Abreu LG, et al. (2020). Global, Regional, and National Levels and Trends in Burden of Oral Conditions from 1990 to 2017: A Systematic Analysis for the Global Burden of Disease 2017 Study. J Dent Res, 99:362–373.
Hajishengallis G (2015). Periodontitis: from microbial immune subversion to systemic inflammation. Nature Reviews Immunology, 15:30–44.
Velsko IM, Chukkapalli SS, Rivera MF, Lee J-Y, Chen H, Zheng D, et al. (2014). Active Invasion of Oral and Aortic Tissues by Porphyromonas gingivalis in Mice Causally Links Periodontitis and Atherosclerosis. PLoS One. doi: 10.1371/journal.pone.0097811.
Sanz M, Ceriello A, Buysschaert M, Chapple I, Demmer RT, Graziani F, et al. (2018). Scientific evidence on the links between periodontal diseases and diabetes: Consensus report and guidelines of the joint workshop on periodontal diseases and diabetes by the International diabetes Federation and the European Federation of Periodontology. Diabetes Res Clin Pract, 137:231–241.
Sanz M, Marco Del Castillo A, Jepsen S, Gonzalez-Juanatey JR, D’Aiuto F, Bouchard P, et al. (2020). Periodontitis and cardiovascular diseases: Consensus report. J Clin Periodontol, 47:268–288.
Hashioka S, Inoue K, Miyaoka T, Hayashida M, Wake R, Oh-Nishi A, et al. (2019). The Possible Causal Link of Periodontitis to Neuropsychiatric Disorders: More Than Psychosocial Mechanisms. Int J Mol Sci. doi: 10.3390/ijms20153723.
Hsu C-C, Hsu Y-C, Chen H-J, Lin C-C, Chang K-H, Lee C-Y, et al. (2015). Association of Periodontitis and Subsequent Depression: A Nationwide Population-Based Study. Medicine (Baltimore), 94:e2347.
Martínez M, Postolache TT, García-Bueno B, Leza JC, Figuero E, Lowry CA, et al. (2022). The Role of the Oral Microbiota Related to Periodontal Diseases in Anxiety, Mood and Trauma- and Stress-Related Disorders. Frontiers in Psychiatry 12:.
Solár P, Zamani A, Kubíčková L, Dubový P, Joukal M (2020). Choroid plexus and the blood–cerebrospinal fluid barrier in disease. Fluids and Barriers of the CNS, 17:35.
Martínez M, Martín‐Hernández D, Virto L, MacDowell KS, Montero E, González‐Bris Á, et al. Periodontal diseases and depression: A pre-clinical in vivo study. Journal of Clinical Periodontology. doi: https://doi.org/10.1111/jcpe.13420.
Chapple ILC, Mealey BL, Van Dyke TE, Bartold PM, Dommisch H, Eickholz P, et al. (2018). Periodontal health and gingival diseases and conditions on an intact and a reduced periodontium: Consensus report of workgroup 1 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. J Periodontol, 89 Suppl 1:S74–S84.
Liu Y, Wu Z, Zhang X, Ni J, Yu W, Zhou Y, et al. (2013). Leptomeningeal cells transduce peripheral macrophages inflammatory signal to microglia in reponse to Porphyromonas gingivalis LPS. Mediators Inflamm, 2013:407562.
Kamer AR, Dasanayake AP, Craig RG, Glodzik-Sobanska L, Bry M, de Leon MJ (2008). Alzheimer’s disease and peripheral infections: the possible contribution from periodontal infections, model and hypothesis. J Alzheimers Dis, 13:437–449.
Yu X-C, Yang J-J, Jin B-H, Xu H-L, Zhang H-Y, Xiao J, et al. (2017). A strategy for bypassing the blood-brain barrier: Facial intradermal brain-targeted delivery via the trigeminal nerve. Journal of Controlled Release, 258:22–33.
Kitamoto S, Nagao-Kitamoto H, Hein R, Schmidt TM, Kamada N (2020). The Bacterial Connection between the Oral Cavity and the Gut Diseases. J Dent Res, 99:1021–1029.
Li C, Wen Y, Cheng S, Zhang H, Meng P, Zhang F (2022). A genetic association study reveals the relationship between the oral microbiome and anxiety and depression symptoms. Front Psychiatry. doi: 10.3389/fpsyt.2022.960756.
Simpson CA, Adler C, du Plessis MR, Landau ER, Dashper SG, Reynolds EC, et al. (2020). Oral microbiome composition, but not diversity, is associated with adolescent anxiety and depression symptoms. Physiol Behav, 226:113126.
Wingfield B, Lapsley C, McDowell A, Miliotis G, McLafferty M, O’Neill SM, et al. (2021). Variations in the oral microbiome are associated with depression in young adults. Sci Rep, 11:15009.
Vilagut G, Forero CG, Barbaglia G, Alonso J (2016). Screening for Depression in the General Population with the Center for Epidemiologic Studies Depression (CES-D): A Systematic Review with Meta-Analysis. PLoS One, 11:e0155431.
Blevins CA, Weathers FW, Davis MT, Witte TK, Domino JL (2015). The Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): Development and Initial Psychometric Evaluation. Journal of Traumatic Stress, 28:489–498.
Bernstein DP, Fink L, Handelsman L, Foote J, Lovejoy M, Wenzel K, et al. (1994). Initial reliability and validity of a new retrospective measure of child abuse and neglect. Am J Psychiatry, 151:1132–1136.
Eke PI, Dye BA, Wei L, Slade GD, Thornton-Evans GO, Beck JD, et al. (2013). Self-reported measures for surveillance of periodontitis. J Dent Res, 92:1041–1047.
Montero E, La Rosa M, Montanya E, Calle-Pascual AL, Genco RJ, Sanz M, et al. (2020). Validation of self-reported measures of periodontitis in a Spanish Population. Journal of Periodontal Research, 55:400–409.
Eke PI, Page RC, Wei L, Thornton-Evans G, Genco RJ (2012). Update of the case definitions for population-based surveillance of periodontitis. J Periodontol, 83:1449–1454.
Willis JR, González-Torres P, Pittis AA, Bejarano LA, Cozzuto L, Andreu-Somavilla N, et al. (2018). Citizen science charts two major “stomatotypes” in the oral microbiome of adolescents and reveals links with habits and drinking water composition. Microbiome, 6:218.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13:581–583.
R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria; 2020.
Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. (2014). Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res, 42:D633–D642.
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin P, O’Hara B, et al. (2015). Vegan: Community Ecology Package. R Package Version 22-1, 2:1–2.
McMurdie PJ, Holmes S (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE, 8:e61217.
Wickham, Hadley ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York; 2016.
Gloor GB, Reid G (2016). Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can J Microbiol, 62:692–703.
Gloor GB, Wu JR, Pawlowsky-Glahn V, Egozcue JJ (2016). It’s all relative: analyzing microbiome data as compositions. Annals of Epidemiology, 26:322–329.
Haegeman B, Hamelin J, Moriarty J, Neal P, Dushoff J, Weitz JS (2013). Robust estimation of microbial diversity in theory and in practice. ISME J, 7:1092–1101.
Bastiaanssen TFS, Quinn TP, Loughman A (2023). Bugs as features (part 1): concepts and foundations for the compositional data analysis of the microbiome–gut–brain axis. Nat Mental Health, 1:930–938.
Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. (2020). PICRUSt2 for prediction of metagenome functions. Nat Biotechnol, 38:685–688.
Valles-Colomer M, Falony G, Darzi Y, Tigchelaar EF, Wang J, Tito RY, et al. (2019). The neuroactive potential of the human gut microbiota in quality of life and depression. Nature Microbiology, 4:623–632.
Schwartz JL, Peña N, Kawar N, Zhang A, Callahan N, Robles SJ, et al. (2021). Old age and other factors associated with salivary microbiome variation. BMC Oral Health, 21:490.
Kim Y-T, Jeong J, Mun S, Yun K, Han K, Jeong S-N (2022). Comparison of the oral microbial composition between healthy individuals and periodontitis patients in different oral sampling sites using 16S metagenome profiling. J Periodontal Implant Sci, 52:394–410.
Wu J, Peters BA, Dominianni C, Zhang Y, Pei Z, Yang L, et al. (2016). Cigarette smoking and the oral microbiome in a large study of American adults. The ISME Journal, 10:2435–2446.
Wu Y, Chi X, Zhang Q, Chen F, Deng X (2018). Characterization of the salivary microbiome in people with obesity. PeerJ, 6:e4458.
Chen B, Zhao Y, Li S, Yang L, Wang H, Wang T, et al. (2018). Variations in oral microbiome profiles in rheumatoid arthritis and osteoarthritis with potential biomarkers for arthritis screening. Sci Rep, 8:17126.
Chu Y, Sun S, Huang Y, Gao Q, Xie X, Wang P, et al. (2021). Metagenomic analysis revealed the potential role of gut microbiome in gout. npj Biofilms Microbiomes, 7:1–13.
Ma G, Qiao Y, Shi H, Zhou J, Li Y (2022). Comparison of the Oral Microbiota Structure among People from the Same Ethnic Group Living in Different Environments. Biomed Res Int, 2022:6544497.
Alauzet C, Marchandin H, Lozniewski A (2010). New insights into Prevotella diversity and medical microbiology. Future Microbiology, 5:1695–1718.
Takeshita T, Kageyama S, Furuta M, Tsuboi H, Takeuchi K, Shibata Y, et al. (2016). Bacterial diversity in saliva and oral health-related conditions: the Hisayama Study. Sci Rep, 6:22164.
Yolken R, Prandovszky E, Severance EG, Hatfield G, Dickerson F (2021). The oropharyngeal microbiome is altered in individuals with schizophrenia and mania. Schizophrenia Research, 234:51–57.
Kohn JN, Kosciolek T, Marotz C, Aleti G, Guay-Ross RN, Hong S-H, et al. (2020). Differing salivary microbiome diversity, community and diurnal rhythmicity in association with affective state and peripheral inflammation in adults. Brain Behav Immun, 87:591–602.
Schloissnig S, Arumugam M, Sunagawa S, Mitreva M, Tap J, Zhu A, et al. (2013). Genomic variation landscape of the human gut microbiome. Nature, 493:45–50.
Liao H, Ji Y, Sun Y (2023). High-resolution strain-level microbiome composition analysis from short reads. Microbiome, 11:183.
Liu G, Tang CM, Exley RM (2015). Non-pathogenic Neisseria: members of an abundant, multi-habitat, diverse genus. Microbiology, 161:1297–1312.
Rosier BT, Takahashi N, Zaura E, Krom BP, MartÍnez-Espinosa RM, van Breda SGJ, et al. (2022). The Importance of Nitrate Reduction for Oral Health. J Dent Res, 101:887–897.
Rosier BT, Buetas E, Moya-Gonzalvez EM, Artacho A, Mira A (2020). Nitrate as a potential prebiotic for the oral microbiome. Sci Rep, 10:12895.
Burne RA, Marquis RE (2000). Alkali production by oral bacteria and protection against dental caries. FEMS Microbiology Letters, 193:1–6.
The 5 most abundant and common oral bacteria and what they mean for yo. In: Bristle. https://www.bristlehealth.com/blogs/oral-microbiome/the-5-most-abundant-and-common-oral-bacteria-and-what-they-mean-for-your-health. Accessed 22 Mar 2024.
Pesantes N, Barberá A, Pérez-Rocher B, Artacho A, Vargas SL, Moya A, et al. (2023). Influence of mental health medication on microbiota in the elderly population in the Valencian region. Front Microbiol, 14:1094071.
Liu P, Gao M, Liu Z, Zhang Y, Tu H, Lei L, et al. (2021). Gut Microbiome Composition Linked to Inflammatory Factors and Cognitive Functions in First-Episode, Drug-Naive Major Depressive Disorder Patients. Front Neurosci, 15:800764.
Zhang M, Li A, Yang Q, Li J, Wang L, Liu X, et al. (2021). Beneficial Effect of Alkaloids From Sophora alopecuroides L. on CUMS-Induced Depression Model Mice via Modulating Gut Microbiota. Front Cell Infect Microbiol. doi: 10.3389/fcimb.2021.665159.
Lourenςo TGB, Spencer SJ, Alm EJ, Colombo APV (2018). Defining the gut microbiota in individuals with periodontal diseases: an exploratory study. J Oral Microbiol, 10:1487741.
Liu S, Xie G, Chen M, He Y, Yu W, Chen X, et al. (2023). Oral microbial dysbiosis in patients with periodontitis and chronic obstructive pulmonary disease. Front Cell Infect Microbiol, 13:1121399.
Widhani A, Djauzi S, Suyatna FD, Dewi BE (2022). Changes in Gut Microbiota and Systemic Inflammation after Synbiotic Supplementation in Patients with Systemic Lupus Erythematosus: A Randomized, Double-Blind, Placebo-Controlled Trial. Cells, 11:3419.
Zhang X, Zhang D, Jia H, Feng Q, Wang D, Liang D, et al. (2015). The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat Med, 21:895–905.
Lee YC, Agnew-Blais J, Malspeis S, Keyes K, Costenbader K, Kubzansky LD, et al. (2016). Posttraumatic Stress Disorder and Risk for Incident Rheumatoid Arthritis. Arthritis Care Res (Hoboken), 68:292–298.
Holdeman LV, Moore WEC, Cato EP, Burmeister JA, Palcanis KG, Ranney RR (1985). Distribution of Capnocytophaga in periodontal microfloras. Journal of Periodontal Research, 20:475–483.
Idate U, Bhat K, Kulkarni R, Kumbar V, Pattar G (2018). Identification of Capnocytophaga species from oral cavity of healthy individuals and patients with chronic periodontitis using phenotypic tests. JCRI, 5:173–177.
Yu X-L, Chan Y, Zhuang L, Lai H-C, Lang NP, Keung Leung W, et al. (2019). Intra-oral single-site comparisons of periodontal and peri-implant microbiota in health and disease. Clinical Oral Implants Research, 30:760–776.
Deng K, Ouyang XY, Chu Y, Zhang Q (2017). Subgingival Microbiome of Gingivitis in Chinese Undergraduates. Chin J Dent Res, 20:145–152.
Haffajee AD, Socransky SS (1992). Effect of sampling strategy on the false-negative rate for detection of selected subgingival species. Oral Microbiol Immunol, 7:57–59.
Boadle-Biber MC (1993). Regulation of serotonin synthesis. Progress in Biophysics and Molecular Biology, 60:1–15.
Russo S, Kema IP, Bosker F, Haavik J, Korf J (2009). Tryptophan as an evolutionarily conserved signal to brain serotonin: molecular evidence and psychiatric implications. World J Biol Psychiatry, 10:258–268.
Fernstrom JD, Wurtman RJ (1971). Brain serotonin content: physiological dependence on plasma tryptophan levels. Science, 173:149–152.
Trujillo-Hernández PE, Sáenz-Galindo A, Saucedo-Cárdenas O, Villarreal-Reyna M de LÁ, Salinas-Santander MA, Carrillo-Cervantes AL, et al. (2021). Depressive Symptoms are Associated with low Serotonin Levels in Plasma but are not 5-HTTLPR Genotype Dependent in Older Adults. Span J Psychol, 24:e28.
Ogłodek EA (2022). Changes in the Serum Concentration Levels of Serotonin, Tryptophan and Cortisol among Stress-Resilient and Stress-Susceptible Individuals after Experiencing Traumatic Stress. Int J Environ Res Public Health, 19:16517.
Stein DJ, Stahl S (2000). Serotonin and anxiety: current models. Int Clin Psychopharmacol, 15 Suppl 2:S1-6.
Lin S-H, Lee L-T, Yang YK (2014). Serotonin and Mental Disorders: A Concise Review on Molecular Neuroimaging Evidence. Clin Psychopharmacol Neurosci, 12:196–202.
Stanley M, Mann JJ (1983). Increased serotonin-2 binding sites in frontal cortex of suicide victims. Lancet, 1:214–216.
Daly C (2016). Oral and dental effects of antidepressants. Aust Prescr, 39:84.
Hopcraft MS, Tan C (2010). Xerostomia: an update for clinicians. Aust Dent J, 55:238–244; quiz 353.
Gershon MD (2013). 5-Hydroxytryptamine (serotonin) in the gastrointestinal tract. Curr Opin Endocrinol Diabetes Obes, 20:14–21.
Berger M, Gray JA, Roth BL (2009). The expanded biology of serotonin. Annu Rev Med, 60:355–366.
Kendig DM, Grider JR (2015). Serotonin and Colonic Motility. Neurogastroenterol Motil, 27:899–905.
Guo X, Lin F, Yang F, Chen J, Cai W, Zou T (2022). Gut microbiome characteristics of comorbid generalized anxiety disorder and functional gastrointestinal disease: Correlation with alexithymia and personality traits. Front Psychiatry. doi: 10.3389/fpsyt.2022.946808.
Sohn J, Li L, Zhang L, Genco RJ, Falkner KL, Tettelin H, et al. (2023). Periodontal disease is associated with increased gut colonization of pathogenic Haemophilus parainfluenzae in patients with Crohn’s disease. Cell Rep, 42:112120.
Marshall M (2020). The hidden links between mental disorders. Nature, 581:19–21.
Pandey D, Szczesniak M, Maclean J, Yim HCH, Zhang F, Graham P, et al. (2022). Dysbiosis in Head and Neck Cancer: Determining Optimal Sampling Site for Oral Microbiome Collection. Pathogens, 11:1550.

Tables 1-4 are available in the Supplementary Files section.

The authors have declared there is NO conflict of interest to disclose

Table1.xlsx
Table 1: Comparative statistics of continuous and categorical variables for the depressive symptom vs. healthy control cohorts
Table2.xlsx
Table 2: Comparative statistics of continuous and categorical variables for the trait anxiety symptom vs. healthy control cohorts
Table3.xlsx
Table 3: Comparative statistics of continuous and categorical variables for the state anxiety symptom vs. healthy control cohorts
Table4.xlsx
Table 4: Comparative statistics of continuous and categorical variables for the PTSD symptom vs. healthy control cohorts
SupplementaryMaterial.docx
SupplementaryTable1.xlsx

Download PDF

Journal Publication

published 05 Oct, 2024

Read the published version in Translational Psychiatry →

Editorial decision: revise
12 Jul, 2024
Review #1 received at journal
13 Jun, 2024
Review #3 received at journal
12 Jun, 2024
Reviewer #3 agreed at journal
30 May, 2024
Reviewer #2 agreed at journal
30 May, 2024
Reviewer #1 agreed at journal
29 May, 2024
Reviewers invited by journal
16 May, 2024
Submission checks completed at journal
29 Apr, 2024
First submitted to journal
26 Apr, 2024
Unknown event
26 Apr, 2024
Editor assigned by journal
26 Apr, 2024

You are reading this latest preprint version

Probing the oral-brain connection: Oral microbiome patterns in a large community cohort with anxiety, depression, and trauma symptoms, and periodontal outcomes

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Methods

Study participant evaluation and enrollment

Demographic and clinical data

Blood collection and processing

Kynurenine, tryptophan, and serotonin quantification in plasma

Bacterial DNA extraction, 16S rRNA gene sequencing and analysis

Statistical analyses

Results

Clinical and demographic characteristics

Periodontal and mental health variables influence oral microbiome community composition

Oral taxa associated with trauma, mental health outcomes, and psychological quality of life

Oral microbiome signatures related to periodontal health

Functional potential of the oral microbiome: possible implications for mental health outcomes

Plasma measures

Oral microbes and therapeutic response

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1