Oral Microbiome, Mental Health, and Sleep Outcomes During the COVID-19 Pandemic: An Observational Study in Chinese and Korean American Immigrants

Abstract

COVID-19 is a systemic disease whose effects are not limited to the respiratory system. The oral microbiome (OM)-brain axis is of growing interest in understanding the broader, neuropsychiatric, impacts of the COVID-19 pandemic through a systems biology lens. In this context, mental health and sleep disturbance are often reported by Asian Americans. In a cross-sectional observational study design, we examined the associations of the oral microbiome with mental health among Asian Americans during the COVID-19 pandemic (between November 2020 and April 2021). Participants (n = 20) were adult Chinese and Korean American immigrants in Atlanta, Georgia, and primarily born outside the United States (60%) with a mean age of 34.8 years ±14 (standard deviation). Participants reported depressive symptoms, anxiety, and sleep disturbance, as measured by standard questionnaires. The OM was characterized by 16S rRNA V3–V4 gene using saliva. Depressive symptoms and anxiety were reported by 60% (n = 12) of participants, whereas 35% (n = 7) reported sleep disturbance. The α-diversity was significantly associated with depressive symptoms, and marginally with anxiety. Participants with depressive symptoms and anxiety had enriched Rothia and Scardovia, respectively, whereas those without symptoms had enriched Fusobacterium. Individuals with sleep disturbance had enriched Kingella. In conclusion, this study suggests significant associations of the OM diversity with certain mental health dimensions such as depressive symptoms and anxiety. Specific taxa were associated with these symptoms. The present observations in a modest sample size suggest the possible relevance of the OM-brain axis in studies of mental health during COVID-19.

Introduction

There is growing interest in engaging with the oral microbiome (OM)-brain axis in efforts to unpack the broader impacts of the COVID-19 pandemic, as seen through a systems biology lens. It is well known that the racial minority groups experience overwhelming inequality in access to mental health care resources and interventions promoting mental wellness in the United States (US). This situation has worsened in the course of COVID-19, including through biopolitics (Yetiskin, 2022).

During the pandemic, members of these minorities, such as African Americans and Hispanic adults, reported increased depression, suicidal thoughts/ideation, and increased or newly initiated substance use, compared to their White counterparts (McKnight-Eily et al., 2021). Asian Americans (AsA) are of particular interest as a vulnerable population that experiences a high burden of mental health, but does not seek treatment due to stigma tied to mental health disorders and lack of mental health services that take into account the cultural and political dimensions of health (Hall and Yee, 2012; Kim et al., 2015).

Chinese (23%) and Koreans (9%) represent the largest subgroup of Asians in the United States (Pew Research Center, 2021). AsA have a long history of encountering an underestimation of their depression and anxiety due to the misconception of them being a “model minority,” the stereotype that a certain demographic rarely experiences mental health problems (Shih et al., 2019). Immigrant populations often experience immense upheaval of their lives from migration, resulting in changes of language, lifestyle (e.g., patterns of diet), and cultural identity, undergoing processes of cultural retention or acculturation, as they assimilate to the culture of the majority (Miller and Chandler, 2002). They also face a greater risk of racial discrimination and exclusion, further increasing mental health problems (Banks et al., 2006). The COVID-19 pandemic exacerbated these problems with increased violence, hate crimes, and anti-Asian rhetoric toward the AsA community.

There is a multidimensional network of psychosocial determinants (e.g., chronic stress and racial discrimination) and biological factors that are used to determine the risks of depression and anxiety among Asian immigrants (Bacong and Sohn, 2021; Kim et al., 2015; Yip et al., 2008). Depression is the most frequently diagnosed mental disorder among AsA, with the prevalence of depression ranging from 26.9% to 35.6%, while the lifetime prevalence is 9.1% (Hong et al., 2014; Kim et al., 2015). AsA tend to manifest more prevalent, persistent, and ongoing depressive symptoms in contrast to their White counterparts (Kim et al., 2015). Depression can result in significantly more severe health consequences, such as suicide, which is the leading cause of death among AsA aged 15–24 years old (Centers for Disease Control and Prevention, 2016).

In addition, 10.2% of AsA experience anxiety disorder (Hong et al., 2014). In correlation with depression and anxiety disorder, sleep disturbance is one of the most prominent symptoms, with AsA more likely to report short sleep durations compared to Whites (Cox and Olatunji, 2016; Jackson et al., 2014). Likewise, chronic stress and racial microaggressions are associated with changes in sleep patterns, poorer sleep quality, and shorter sleep duration (Blendon et al., 2014; Ong et al., 2017). Thus, the prevalence of depression, anxiety, and subsequent sleep disturbances experienced by AsA is defined by distinct, modulatory psychosocial and biological factors that have the potential to be unique targets for intervention (Cashion et al., 2016).

The OM comprises the largest and most diverse microbiome in the human body, nourishing symbiotic and pathogenic microorganisms, such as bacteria, fungi, viruses, and protozoa (Dewhirst et al., 2010). The oral cavity is a complex habitat, in which bacteria can colonize multiple niches, including the teeth, gingival sulcus, tongue, and cheeks, as well as the hard and soft palates. As contiguous extensions of the oral cavity, the tonsils, pharynx, esophagus, Eustachian tube, middle ear, trachea, lungs, nasal passages, and sinuses are all defined as components of the OM. These diverse structures and tissues are colonized by distinct microbial communities (Dewhirst et al., 2010).

Current literature reports that ∼96% of the total oral bacteria consist of Firmicutes, Actinobacteria, Proteobacteria, Fusobacteria, Bacteroidetes, Spirochaetes, and smaller proportions of other phyla, including Chlamydiae, Chloroflexi, Spirochaetes, SR1, Synergistetes, Saccharibacteria (TM7), and Gracilibacteria (GN02) (Deo and Deshmukh, 2019; Verma et al., 2018). Thus far, changes in the OM have been observed in a multitude of diseases, such as diabetes, bacteremia, endocarditis, cancer, autoimmune disease, and preterm births (Verma et al., 2018). It is crucial to understand how fluctuations in the OM can influence human health, alter metabolism, and impact immune functions.

Until now, mental health disorders are linked to the OM by increased proinflammatory communication and cortisol as detected in the saliva (Simpson et al., 2020). Recent literature focusing on biological mechanisms to predict depression, anxiety, and sleep quality has discovered a number of biomarkers, including cytokines and inflammatory markers, oxidative stress markers, endocrine markers, and genetic and epigenetic factors (Hacimusalar and Eşel, 2018).

With the emergence of evidence linking mental health and sleep quality to the microbiome-gut-brain axis, the OM might be sensitive to an individual's changes in diet, lifestyle, stress, and geographic environment, all of which represent significant risk factors for depression and anxiety among immigrants (Rhee et al., 2009). Studies on the gut microbiome (GM) have elucidated correlations between decreased microbial diversity and function, and migration from non-Western nations to the United States or Canada. These changes may predispose Asian immigrants to a higher risk of mental disorders (Copeland et al., 2021; Vangay et al., 2018).

Currently, there is inadequate research attention on biological mechanisms of mental health and sleep disturbances in Asian immigrant populations. The purpose of this cross-sectional observational study was to explore the associations of OM with depressive symptoms, anxiety, and sleep disturbance among Chinese and Korean immigrants. By investigating the species present in the oral cavity in relation to mental health among the AsA population, we aimed to inform future targeted prevention, early detection, and individualized treatment of depression, anxiety, and sleep disturbance, as seen through a lens of the OM.

Materials and Methods

Ethical consideration

The study was approved by the authors' institutional review board (IRB no.: STUDY00000935) and a written informed consent was obtained from all participants. Participants were allowed to opt out of the study at any point during the data collection process.

Study design

We utilized a cross-sectional observational study design. This study was adapted from our earlier parent study to observe associations of GM and OM with mental health status of AsA participants during the COVID-19 pandemic (Hope et al., 2022; Kim et al., 2021). Of the 37 participants from the parent study, 20 of them provided saliva for the OM analysis.

Participants

Participants were eligible if they (1) were 18 years of age or older; (2) self-identified as Chinese or Korean immigrants; (3) were living in Atlanta, Georgia; and (4) were able to read and write English, simplified traditional Chinese, or Korean.

Excluded participants were (1) pregnant women; (2) those on antibiotics; (3) those clinically diagnosed with depressive or sleep disorders; and (4) those with chronic gastrointestinal conditions (e.g., irritable bowel syndrome). Pregnancy and antibiotic usage within the last month can potentially affect mental health, sleep patterns, and OM composition (Dagli et al., 2016). In addition, this study focused on Chinese and Korean immigrants who are not formally diagnosed with depression or sleep disorders, but at high risk of such and could benefit from the study's future interventions.

Study procedure

Participants were recruited between November 2020 and April 2021 using online platforms, such as Research Match. All eligible participants were verbally consented. Once consented, the study team emailed them the REDCap survey link. Participants completed the online survey, and then provided their contact information (e.g., mailing address) to ship the microbiome collection kits (Bai et al., 2022).

Variables and measures

Demographic and health-related variables

One demographic short form was utilized to collect participants' demographic and health-related characteristics. Demographic variables included age, gender, self-identified race, marital status, living arrangement, nativity (born outside the United States vs. U.S. born), year of U.S. residence (only for those born outside the United States), and household income. Health-related variables included height, weight, disease history, and use of probiotics.

Depressive symptoms

The Patient-Reported Outcomes Measurement Information System Short Form (PROMISSF)–Depression uses a scale consisting of six items that assess different depressive symptoms over the past 7 days (Taple et al., 2019). We used this system of measurement with response options from 1 (never) to 5 (always), which were converted into a T-score with a mean of 50 (standard deviation [SD] = 10) based on a reference sample (Irwin et al., 2010). The T-score was categorized as depressive symptoms with scores above 55 and no depressive symptoms with scores less than or equal to 55. There was a Cronbach's α of 0.94 for PROMIS SF-Depression.

Anxiety

The study used the PROMIS SF–Anxiety to measure anxiety. This scale is composed of eight items, which in terms of reliability and accuracy are comparable to the full item bank (Taple et al., 2019). The eight items assessed anxiety symptoms over the past 7 days. The response options gauged the participants' anxiety from 1 (never) to 5 (almost always) (Irwin et al., 2010). Their responses were transformed into a T-score for analysis, in which higher scores represent greater levels of anxiety. The PROMIS Scale-Anxiety has a high internal consistency and a Cronbach's α of 0.93.

Sleep

The Pittsburgh Sleep Quality Index (PSQI), a 10-item scale with 19 questions, gauged the participant's sleep quality over the past month (Buysse et al., 1989). This scale includes both numeric and qualitative sleep quality metrics. The 19 questions make up 7 domains (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction), which were evaluated from 0 (no difficulty at all) to 3 (severe difficulty).

The sum of the seven domains was calculated, producing the global PSQI score, which ranged from 0 to 21. Higher scores represented decreased sleep quality. For this study, sleep disturbance was defined as a PSQI total score >5. The PSQI showed high test-retest reliability (Cronbach's α = 0.85) for primary insomnia patients, as well as good validity among frontline health care workers for sleep quality (Backhaus et al., 2002). In the parent study, the Cronbach's α coefficient was 0.62 for the total scale and 0.7, excluding the daytime dysfunction domain (Hope et al., 2022).

Oral microbiome

Saliva was collected for analysis of the OM. Following the Human Microbiome Project protocol (Methé et al., 2012), participants were asked to refrain from eating, drinking, smoking, or chewing gum 30 min before saliva collection. Following the provided collection instructions, participants let the saliva collect in their mouth for at least 1 min, and then allowed the drool to fall into the OMNIgene^® ORAL collection kit (DNA Genotek, Ottawa, Canada). This process was repeated a few times to collect the required volume of 1 mL.

Once the saliva reached the marked fill line, participants followed the user instructions by mixing the stabilizing fluid with the saliva, capping the collection tube, and inverting several times to mix the contents. Participants then followed packaging instructions, placing the sample tubes in the provided biohazard and padded freezer bags to return within a 24-h window through FedEx shipping. Upon receipt of the sample, the saliva sample was immediately transported to the laboratory and stored in −80°C freezers until DNA extraction.

Microbial DNA extraction

The PowerSoil isolation kit (Mo Bio Laboratories, Carlsbad, CA, United States) was utilized to extract oral microbial DNA from the saliva samples. We sequenced 16S rRNA V3–V4 gene regions (Bukin et al., 2019). Using KAPA HiFi HotStart ReadyMix (KAPA Biosystems; KK2600) with primers distinct to 16S V3–V4 regions of bacteria 341F–805R, we generated the 16S rRNA amplicons. The polymerase chain reaction product purification was performed using AMPure XP beads (Beckman; A63880), and indices were attached using the Nextera XT Index kit (Illumina; FC-131-1001). Final 16S library pools were quantitated using qPCR (Kapa Biosystems; catalog KK4824) and sequenced on an Illumina miSeq using miSeq v3 600 cycle chemistry (Illumina; catalog MS-102-3003) at a loading density of 8 p.m. with 20% PhiX, at PE300 reads.

To assess bias and errors in sequencing workflows, microbial community standard controls [two positive controls, one Zymo-positive control, four negative controls, and two (no template control) NTC-negative controls] were used during the sequencing process, conducted by the Emory Integrated Genomics Core. Then to create paired end reads for analysis, the obtained DNA sequence reads were demultiplexed and paired together.

Bioinformatics and statistical analysis

As a means of describing the data for the demographic and clinical variables, we employed descriptive statistics, finding the mean (SD), median (interquartile range [IQR]), and frequency (%). 16S rRNA sequences were analyzed to obtain microbial α- and β-diversity, taxonomic composition, and abundance analysis. Quantitative Insight into Microbial Ecology 2 (QIIME 2) was utilized to determine amplicon sequence variants, and the Divisive Amplicon Denoising Algorithm 2 (DADA2) package was applied to correct amplicon errors and filter the sequence quality (Callahan et al., 2016). The α-diversity metrics of richness and evenness (e.g., Shannon, Chao1, Faith's phylogenetic diversity, and Pielou's evenness) and β-diversity metrics of bacterial community dissimilarities (Jaccard distance and unweighted UniFrac distance) were obtained to determine the OM diversity.

Spearman correlations and Kruskal–Wallis pairwise tests were conducted to determine associations between α- or β-diversity indices and outcomes (depressive symptoms, anxiety, and sleep disturbance). Principal coordinate analysis (PCoA) was used to visualize β-diversity patterns, which also elucidated their associations with anxiety, depression, and sleep disturbances. Permutational multivariate analysis of variance (PERMANOVA) tested whether β-diversity differed between participants with positive anxiety (T-score >55), depressive symptoms (T-score >55), or sleep disturbance (PSQI >5) versus those with no symptoms.

Taxonomies were then assigned by the pretrained classifier using the Human Oral Microbiome Database (HOMD). Top taxa at phylum and genus levels were presented by anxiety (Yes [T-score >55] vs. No [T-score ≤55]), depressive symptoms (Yes [T-score >55] vs. No [T-score ≤55]), and sleep disturbance (Yes [total PSQI score >5] vs. No [total PSQI score ≤5]). The linear discriminant analysis effect size (LEfSe) distinguished taxa differences between different levels of outcome variables (Segata et al., 2011), using the nonparametric Kruskal–Wallis sum-rank test. QIIME 2 (Bai et al., 2019; Bolyen et al., 2019) and R 4.1.3, with a significance level of p < 0.05, were used for all analyses.

Results

Characteristics of participants

Participant (n = 20) demographics include the following: female (n = 15, 75%); Chinese (n = 11, 55%) or Korean (n = 9, 45%); and born outside the United States (n = 12, 60%). Only 4 (20%) participants reported probiotic usage, whereas 2 (10%) participants had diabetes and 2 (10%) had cardiovascular disease. Table 1 describes demographics of the participants.

Table 1.

Demographic Status and Health History of the Study Participants

Variables	n (%)	Mean (SD)
Age in years, mean (SD)		34.80 (14)
Gender
Male	5 (25)
Female	15 (75)
Ethnicity
Chinese	11 (55)
Korean	9 (45)
Marital status
Married	13 (65)
Single	7 (35)
BMI		24 (3.9)
Normal	13 (65)
Overweight	6 (30)
Obesity	1 (5)
Employment status
Employed	12 (60)
Unemployed	4 (20)
Unpaid, at-home worker	4 (20)
Highest education status
High school	2 (10)
Some college	5 (25)
Bachelor's degree or higher	13 (65)
Annual household income, $
<50,000	7 (35)
≥50,000	13 (65)
Place of birth
Born outside the US	12 (60.00)
U.S. born	8 (40.00)
Use of probiotics, yes	4 (20)
Diagnosis of diseases, yes
Diabetes melitus	2 (10)
Cardiovascular disease	2 (10)

BMI category is defined based on CDC guideline: underweight (BMI <18.5), normal (18.5 ≤ BMI <25), overweight (25.0 ≤ BMI <30), and obesity (BMI ≥30).

BMI, body mass index; CDC, Centers for Disease Control and Prevention; IQR, interquartile range; SD, standard deviation; US, United States.

Depressive symptoms, anxiety, and sleep disturbance

In this study, 55% (n = 11, T-score >55) of participants recorded having depressive symptoms, 55% (n = 11, T-score >55) recorded anxiety, and 35% (n = 7, PSQI total score >5) recorded sleep disturbance. As noted in our previous work (Hope et al., 2022), participants who experienced more difficulties with overall sleep quality (n = 17, 100%) reported trouble in specific domains: sleep disturbance (n = 16, 94.1%), day dysfunction due to sleepiness (n = 13, 76.5%), and sleep latency (n = 11, 64.7%).

OM status

The OM sequences analyzed showed dissimilarities from standard control sequences (positive controls, negative controls, Zymo control, and NTC controls) and the GM, indicating good quality of our data (Fig. 1). Based on the HOMD database, a Naive Bayes classifier was trained to map our sequences to taxonomy, leading to 12 phyla and 123 genera in our samples. The top five phyla were Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria; at the genus level, the top five taxa were Streptococcus, Neisseria, Prevotella, Rothia, and Haemophilus (Fig. 2). Figure 1A–C describes the OM taxa based on levels of depressive symptoms (A), anxiety (B), and sleep disturbance (C) at the phylum level; Figure 2D–F describes the OM taxa based on levels of depressive symptoms (D), anxiety (E), and sleep disturbance (F) at the genus level.

FIG. 1.

Differences of the OM from controls and the gut microbiome. OM, oral microbiome.

FIG. 2.

Oral microbial taxa based on depressive symptoms, anxiety, and sleep disturbance. (A–C) Taxa at the phylum level; (D–F) taxa at the genus level. Blue dash line means the sample separation by depressive symptoms (Yes vs. No), anxiety (Yes vs. No), and sleep disturbance (PSQI >5 vs. PSQI ≤5). PSQI, Pittsburgh Sleep Quality Index.

OM associated with depressive symptoms, anxiety, and sleep disturbance

α-diversity

A lower α-diversity of the OM was marginally associated with a higher level of anxiety (Pielou's evenness [r = −0.44, p = 0.05]) (Table 2). Meanwhile, greater species richness was associated with increased depressive symptoms (Chao1 [r = 0.58, p = 0.007] and observed features [r = 0.54, p = 0.01]) (Table 2). There was no significant correlation between the α-diversity and sleep disturbance (all metrics with p > 0.05).

Table 2.

Associations of the Oral Microbial Diversity with Depressive Symptoms, Anxiety, and Sleep Disturbance

Metric	Analysis method	Anxiety (Yes vs. No)	Depressive symptoms (Yes vs. No)	Sleep disturbance (Yes vs. No)
α-diversity
Shannon index	Spearmana	r = −0.07, p = 0.78	r = −0.27, p = 0.24	r = −0.10, p = 0.71
	Kruskal–Wallisb	H = 0.21, p = 0.64	H = 0.48, p = 0.49	H = 0.04, p = 0.85
Chao1 index	Spearmana	r = 0.23, p = 0.34	r = 0.58, p = 0.01	r = −0.07, p = 0.79
	Kruskal–Wallisb	H = 2.63, p = 0.105	H = 2.38, p = 0.12	H = 0.01, p = 0.92
Faith's phylogenetic diversity	Spearmana	r = 0.32, p = 0.18	r = 0.47, p = 0.04	r = −0.16, p = 0.53
	Kruskal–Wallisb	H = 0.60, p = 0.44	H = 0.29, p = 0.59	H = 0.01, p = 0.92
Pielou's_e	Spearmana	r = −0.44, p = 0.05	r = −0.07, p = 0.78	r = −0.02, p = 0.95
	Kruskal–Wallisb	H = 1.72, p = 0.19	H = 0.01, p = 0.94	H = 0.15, p = 0.70
Observed OTUs	Spearmana	r = 0.20, p = 0.39	r = 0.54, p = 0.01	r = −0.16, p = 0.54
	Kruskal–Wallisb	H = 2.15, p = 0.14	H = 1.93, p = 0.16	H = 0.04, p = 0.85
β-diversity
Jaccard distance	PERMANOVA	F = 1.02, p = 0.38	F = 1.10, p = 0.07	F = 0.88, p = 0.95
Unweighted UniFrac distance	PERMANOVA	F = 0.81, p = 0.73	F = 1.42, p = 0.06	F = 0.83, p = 0.74

Bold fonts indicate significant findings (p < 0.05) or findings with a trend of significance (p < 0.10).

^a Spearman's correlation for continuous variables.

^b Kruskal–Wallis test for categorical variables.

OTU, operational taxonomic unit; PERMANOVA, permutational multivariate analysis of variance.

β-diversity

Based on the PCoA and PERMANOVA, Jaccard distance and unweighted UniFrac distance showed marginal differences between participants with and without depressive symptoms (Jaccard distance [F = 1.10, p = 0.07] and unweighted UniFrac distance [F = 1.42, p = 0.06]) (Table 2 and Fig. 3). There was no significant dissimilarity between participants with or without anxiety, as well as none between participants with or without sleep disturbances (all metrics with p > 0.05).

FIG. 3.

The β-diversity of the OM by depressive symptoms (Yes vs. No). (A) The dissimilarity using Jaccard distance and (B) the dissimilarity using unweighted UniFrac distance.

Abundance analysis

The LEfSe was conducted to identify differences in taxa abundance for depressive symptoms (Yes vs. No), anxiety (Yes vs. No), and sleep disturbance (Yes vs. No) (Fig. 4). Several taxa differentiated participants with and without symptoms. For example, participants with anxiety had an enriched relative abundance of Bifidobacteriales (order), Bifidobacteriaceae (family), and genera Scardovia, Mitsuokella, and Shuttleworthia. In contrast, participants without anxiety had a higher abundance of Fusobacteriaceae (family) and genus Fusobacterium (Fig. 4A). Participants with depressive symptoms had an enriched abundance of Actinobacteria (phylum), Actionmycetales (order), Micrococcaceae (family), and genera Rothia and Scardovia.

FIG. 4.

Taxa abundance of the OM by anxiety (A), depressive symptoms (B), and sleep disturbance (C).

Those without depressive symptoms had higher abundances of Fusobacteria (phylum), Fusobacteriales (order), Fusobacteriaceae (family), Porphyromonadaceae (family), and genera Fusobacterium and Porphyromonas (Fig. 4B). Those who experienced sleep disturbance had higher abundances of Pseudomonadales (order), Moraxellaceae (family), and genus Kingella, while participants without sleep disturbances had a higher abundance of Cardiobacteriales (order), Cardiobacteriaceae (family), and genus Cardiobacterium (Fig. 4C).

Discussion

Emerging evidence reports a correlation between GM and mood disorders based on the GM-brain axis. Due to a distinct overlap in microbial taxa existing between the GM and the OM, our results identified specific oral microbial taxa associated with these symptoms. However, the generalizability of our findings should be interpreted cautiously as this was an exploratory pilot study with a small sample size due to the specifics of our target population, the AsA community.

In this study, 55.5% of our participants reported feelings of anxiety, which is higher than measures noted in past research (Lee and Waters, 2021). The COVID-19 pandemic presented a particular set of circumstances as AsA experienced increased racial discrimination, including verbal harassment, shunning, and physical assault, which were a result of rising anti-Asian sentiment in the United States (Horse et al., 2021). The increased harassment during the pandemic (both real and threatened) likely exacerbated the mental health risk experienced by this community. In this study, 55% of our participants reported depressive symptoms, which is significantly higher than most reports from previous literature (Hong et al., 2014; Kim et al., 2015). Like changes in anxiety levels, the onset of the COVID-19 pandemic presented a resurgence of challenges to the AsA community.

Compounding social factors and the racial discrimination they faced have been revealed as having a greater than twofold increase on depressive symptoms, from about 9% prepandemic to 21% during its height (Lozano et al., 2022). Anxiety and depression are frequently linked to disturbances in sleep quality and can lead to neuropsychological impairment, substance abuse, and suicidal ideation (Airaksinen et al., 2005; Fernandez-Mendoza et al., 2010; Gualtieri et al., 2006). In our study, 35% of participants reported decreased sleep quality, significantly higher than other reports on sleep outcomes due solely on acculturative stress (Lee et al., 2022).

Studying the OM represents a significant opportunity to investigate whether shifts in oral microbial composition may directly contribute to the etiology of mental disorders. Two recent studies reported a differential abundance of specific bacterial taxa, including Spirochaetaceae, Actinomyces, Treponema, Fusobacterium, and Leptotrichia spp with depression- and anxiety-like symptoms in adolescents (Simpson et al., 2020; Wingfield et al., 2021).

In addition, evidence suggests that microbiome compositional fluctuations may be influenced by host circadian biology. Although poorly understood, psychological stress may impact circadian regulated neuroendocrine and immunological factors that play a role in the oral microenvironment, in turn impacting systemic health (Kohn et al., 2020; Nobs et al., 2019). Previous research has noted changes in the OM with age, but more specifically to shifts in sleep patterns and thus circadian rhythmicity (Alvaro et al., 2013). Short-chain fatty acids and other microbiome-derived metabolites fluctuate in a circadian manner, controlling the host's metabolic clock in intestinal epithelial cells, the liver, and other surrounding tissues (Tahara et al., 2018).

Consistent with our results, the two most common phyla in healthy individuals are Bacteroides and Firmicutes (Magne et al., 2020). Anxiety, depression, and other mental health disorders cause changes in the OM through several behavioral, immunological, and neuroendocrine pathways that alter saliva composition (Simpson et al., 2020). Our study identified that participants experiencing depressive symptoms had increased abundance of Actinobacteria (phylum) and Actionmycetales (order).

This is consistent with previous findings that reported a positive correlation between cortisol (basal cortisol levels are associated with depression and anxiety) and taxa Actinobacteria (phylum), Gracilibacteria (phylum), and Actinomycetales (order) (Simpson et al., 2020). Likewise, C-reactive protein, a marker for the systemic inflammatory response that precedes anxiety and depression, was positively associated with Proteobacteria (phylum) and Neisseriales (order) (Simpson et al., 2020). Both Proteobacteria and Neisseria were highly represented in our samples, suggesting an urgent need to further examine the role of the OM in depressive symptoms, anxiety, and sleep disturbance among the AsA community.

Alterations in the OM most often suggest local infections such as dental caries and periodontal disease; however, these in turn may also indicate a higher risk for other health conditions such as cancer, cardiovascular and irritable bowel disease, and rheumatoid arthritis (Gao et al., 2018; Simpson et al., 2020). Streptococcus mutans is well known for its strong association with dental caries, but a new study provided evidence of a significant relationship between severe early childhood caries and Scardovia wiggsiae, an acid-tolerant pathogenic species (Kressirer et al., 2017). Both Streptococcus and Scardovia appeared in relative abundance in our samples, the latter exhibiting a distinct correlation with participants experiencing anxiety-like symptoms.

Similarly, researchers identified that other species related to the progression of carious lesions, including Rothia dentocariosa and Scardovia inopinata, a genetically close relative to Scardovia wiggsiae, were in the caries-active group (Chalmers et al., 2015; Jiang et al., 2014; Ledezma-Rasillo et al., 2010). Our results demonstrated an enriched abundance of both in participants with depressive symptoms. Furthermore, Heamophilus parainfluanzae is a common commensal found throughout the oral cavity, which not only has antiproliferative effects against cancerous cells but can also behave as an opportunistic pathogen, which is a plausible explanation for the enriched abundance our study revealed (Utter et al., 2020; Wingfield et al., 2021). In general, a greater abundance of Streptococcus, Neisseria, Prevotella, Rothia, and Haemophilus was observed in our samples, indicating these taxa as consistent pathogens in the diseased, dysbiotic state of the OM.

Likewise, Kingella, which belongs to the family Neisseriaceae, frequently colonizes the mucous membranes of the oropharynx (Bush and Vazquez-Pertejo, 2022). Children suffer the highest rates of colonization and invasive disease from this pathogen, which, based on our results, appeared in high abundance in individuals struggling with sleep difficulties. The link between poor oral health and neurological disorders has been acknowledged in literature, but disentangling the highly interconnected cause from consequence has yet to be done (Wingfield et al., 2021).

The GM-brain axis gives new meaning to the pathophysiology of diseases, in which psychological stress and inflammation are key to microbiome shifts (Dinan and Cryan, 2017). Stress and inflammation play a role in depression, schizophrenia, autism spectrum disorder, epilepsy, and migraines, which can also coexist (Golofast and Vales, 2020; Kotwas et al., 2017; Theoharides et al., 2019). Thus, in patients suffering from migraines, there is higher prevalence of gut diseases, displaying the intersection between the GM, OM, and pathogenesis of neuropsychiatric and neurological diseases (van Hemert et al., 2014).

This study has several limitations, including reliance on convenience sampling; our small sample size limits the generalizability of our results to a broader population. Since our participants were recruited from metro-Atlanta area, Georgia, these findings may not be fully representative of other areas. Second, this study did not control major confounding factors, such as acculturative stress, participants' access to quality foods, and participants' knowledge of nutrition, all of which can affect the OM profile. While age, sex, and diet can modulate the OM composition, it is also important that future research assess the role of human host genetics.

Future directions must consider recruitment of a representative and large sample size and conduct oral clinical examinations, so that these issues can be addressed. Finally, as a cross-sectional study, we cannot determine causality or temporality between the OM compositions and health indicators. A longitudinal study design is encouraged to further identify the OM contributing to pathologic mechanisms of mental health and sleep disturbance. Although with these limitations, this pilot study identified promising roles of specific OM taxa in modulating mental health and sleep disturbance. Our findings echo the likelihood of precision medicine in relation to microbiome (Behrouzi et al., 2019; Zmora et al., 2016), and therefore, OM may provide a novel therapeutic strategy for mental health outcomes.

Conclusions

Promising associations were identified between the oral microbial diversity and anxiety and depressive symptoms among Chinese and Korean Americans. Specific taxa of the OM were identified, associated with depressive symptoms, anxiety, and sleep disturbance. Further research is necessary as we continue to unpack the broader systems biology impacts of the COVID-19 pandemic. For example, larger studies that control for major confounders (e.g., diet and acculturative stress patterns, COVID-19 and vaccination information) and investigate differences in the OM by place of birth, ethnicity, and sex, are needed to confirm our findings, as well as to elucidate novel associations between the OM and AsA. The present observations in a modest sample size suggest the possible relevance of the OM-brain axis in future studies of mental health during COVID-19.

Abbreviations Used

AsA

Asian Americans

BMI

body mass index

CDC

Centers for Disease Control and Prevention

DADA2

Divisive Amplicon Denoising Algorithm 2

GM

gut microbiome

HOMD

Human Oral Microbiome Database

IQR

interquartile range

NTC

no template control

OM

oral microbiome

OTU

operational taxonomic unit

PCoA

principal coordinates analysis

PERMANOVA

permutational multivariate analysis of variance

PSQI

Pittsburgh Sleep Quality Index

QIIME 2

Quantitative Insight into Microbial Ecology 2

SD

standard deviation

US

United States

Authors' Contributions

J.B., S.K., I.Y., and W.Z. contributed to conception and design. B.R., H.I.N., and C.W. contributed to data collection, data management, and drafting the article. J.B. and W.Z. contributed to data curation and data analysis. B.R., H.I.N., and C.W. contributed to editing this article. All authors critically revised the article and gave final approval for publication.

Author Disclosure Statement

The authors declare they have no conflicting financial interests.

Funding Information

Funded by support from the National Institute of Health/National Institute of Nursing Research (4R00NR017897-03, PI: J.B.) and the Office of the Senior Vice President for Research at Emory University Bidirectional Global Health Disparities Research Pilot Grant (PI: J.B. and S.K.).