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. 2022 Sep 2;25(1):150–160. doi: 10.1177/10998004221124273

A Pilot Study of the Gut Microbiota Associated With Depressive Symptoms and Sleep Disturbance Among Chinese and Korean Immigrants in the United States

Chloe Hope 1,#, Natalie Shen 2,#, Wenhui Zhang 1, Hye In Noh 1, Vicki S Hertzberg 1, Sangmi Kim 1, Jinbing Bai 1,3,#
PMCID: PMC10068504  PMID: 36056574

Abstract

Context

Depression is prevalent among Asian Americans (AsA) during the COVID-19 pandemic, and depression often leads to sleep disturbance in this population. The gut microbiota (GM) plays a critical role in mental health and sleep quality, and the composition of the GM is largely unknown among AsA.

Objectives

Examine associations of the GM with depressive symptoms and sleep disturbance among Chinese and Korean American immigrants.

Methods

Depressive symptoms (PROMIS Short Form-Depression) and sleep quality (Pittsburgh Sleep Quality Index [PSQI]) were collected via surveys. PROMIS measure T-score > 55 indicates positive depressive symptoms, and a total PSQI score > 5 indicates sleep disturbance. 16S rRNA V3-V4 gene regions were sequenced from fecal specimens to measure GM. Permutational multivariate analysis of variance and linear discriminant analysis effect size were applied to examine associations of the GM with symptoms.

Results

Among 20 participants, 55% (n = 11) reported depressive symptoms and 35% (n = 7) reported sleep disturbance. A higher α-diversity was marginally associated with lower depressive symptoms: Chao1 (r = −0.39, p = 0.09) and Shannon index (r = −0.41, p = 0.08); β-diversity distinguished participants between categories of depressive symptoms (weighted UniFrac, p=0.04) or sleep disturbance (Jaccard, p=0.05). Those with depressive symptoms showed a higher abundance of Actinobacteria, while those without depressive symptoms had a higher abundance of Bacteroidetes. No significant taxa were identified for sleep disturbance.

Conclusions

Gut microbial diversity showed promising associations with depressive symptoms and sleep disturbance among Chinese and Korean immigrants. Specific taxa were identified as associated with depressive symptoms. Future studies with a larger sample size are warranted to confirm our findings.

Keywords: gut microbiota, depression, sleep, symptoms, 16S rRNA, COVID-19, Asian American

Introduction

Asian Americans (AsA) are the fastest-growing racial or ethnic group in the United States (US) (Pew Research Center, 2019). Chinese (23%) and Korean (9%) immigrants represent the largest subethnic groups within the AsA population. The increasing size of AsA in Georgia and the US demonstrates the need to understand the overall mental health status of this population. Depression is the most frequently diagnosed mental health disorder in AsA. The prevalence of depression in this population ranges from 2.6% to 71.0% (Kim et al., 2015), with a lifetime prevalence of 9.1% (Hong et al., 2014). Cultural stigma regarding mental health care and limited access to mental health services may decrease the likelihood that AsA will seek treatment for their mental health issues (Nagayama et al., 2012). This results in significant under-reporting of depression-like symptoms, which may be exacerbated in AsA due to certain stressful life events, such as immigration and acculturation. Acculturation is a unique stressor experienced by many AsA, and it is driven by immersion in a new culture, navigating arduous immigration processes, and potentially leaving behind family members.

Depression frequently manifests as sleep disturbance, and sleep disturbance is a primary component in the diagnostic criteria for the clinical diagnosis of depression (Murphy & Peterson, 2015). AsA are more likely to report interrupted sleep patterns than non-Hispanic Whites (33% vs. 28%) (Jackson et al., 2014). According to previous research, AsA immigrants experienced racial discrimination, causing increased anxiety and mental stress, which is often associated with sleep disturbance (Gould et al., 2018; Sangalang et al., 2019). Furthermore, post-migration trauma in Asian immigrants was associated with depressive disorder. Therefore, it is important to evaluate sleep patterns and sleep quality as part of the depression screening process in AsA. Moreover, elucidating the relationship between sleep disturbance and depression serves as a basis for determining potential therapeutic implications in the AsA population, such as the use of relaxation techniques that address both endpoints (Li et al., 2020). The COVID-19 pandemic, and the resulting social isolation it has created, has contributed to adverse mental health sequelae for many populations, particularly for AsA. Current risk for depression and sleep disturbance in this population is complicated by the pandemic and preexisting cultural stigmas regarding mental health care. Coupling pandemic-related stressors (e.g., social isolation) with Anti-Asian rhetoric has further increased the incidence of depression in AsA.

The human gut hosts an estimated 1014 microorganisms (Thursby & Juge, 2017), which can influence depressive symptoms and sleep quality through proinflammatory cytokines, metabolites (e.g., short-chain fatty acids [SCFAs]), and the monoamine neurotransmission system (e.g., brain-derived neurotrophic factor) (Peirce & Alviña, 2019). Emerging evidence indicates that the gut microbiota (GM) plays a critical role in human mental health and sleep disturbance via the microbiota-gut-brain (MGB) axis (Bai et al., 2020; Margolis et al., 2021; Song & Bai, 2021), a bidirectional communication network between the gut and the brain.

As a transient community of microbes, the GM is heavily influenced by external stimuli, including individual changes in diet, lifestyle, stress, and geographic environment (Bai, Hu, & Bruner, 2019; Vangay et al., 2018). Changes in these external stimuli may contribute to an increase in risk factors for depression among immigrants. The GM composition can also be determined by sociodemographic (e.g., income and health insurance) and immigration-related factors (e.g., years of US residence and diet acculturation) (Kaplan et al., 2019). Literature has shown that immigration to the US or Canada was associated with a significant loss in gut microbial diversity and function, which could predispose AsA to mental health disorders (Copeland et al., 2021; Vangay et al., 2018). Thus, the GM, secondary to immigration and environmental factors, provides an opportunity to ascertain biological mechanisms of depressive symptoms and sleep disturbance in this population.

Due to the bidirectional communication pathways between the gut and the brain (Margolis et al., 2021), the MGB axis supports that changes in the GM throughout the immigration process (e.g., pre- and post-immigration) may contribute to adverse mental health and sleep problems for AsA. Understanding the MGB axis link provides adaptations in screening modalities and precision care in diagnosing and treating depression and sleep disturbance. However, relevant translational evidence from human clinical studies is minimal, and even fewer studies have investigated the role of the GM in depressive symptoms and sleep disturbance among AsA. Therefore, this pilot study was designed to investigate associations of the GM with depressive symptoms and sleep disturbance for AsA during the COVID-19 pandemic.

Materials and Methods

Study Design

This pilot study adopted a cross-sectional observational study design to understand associations of the GM with depressive symptoms and sleep quality. As reported in our parent study protocol, this study was guided by the MGB axis model, with a hypothesis that GM diversity and abundance were associated with mental health and sleep disturbance among AsA (Kim et al., 2021). In the parent study, we recruited 37 participants, and 20 subjects who submitted the GM data were analyzed in this pilot study. The GM data collection was optional in the protocol.

Settings and Sample

This study recruited Chinese and Korean Americans, either foreign-born or US-born, and recruitment took place between November 2020 and April 2021. Eligible participants included those who were: (1) 18 years or older; (2) self-identified as Chinese or Korean Americans; (3) living in metro Atlanta, Georgia, US; and (4) able to read and write English, Chinese, or Korean. Pregnant women, those on antibiotics within the past month, and those clinically diagnosed with depression or sleep disorder were excluded from this study. Pregnancy and antibiotic usage could potentially impact mental health and GM composition, and this study focused on healthy people with symptoms rather than patients with preexisting diagnosed mental health disorders to minimize GM complexities. People with chronic gastrointestinal conditions, such as irritable bowel syndrome and chronic constipation, were excluded.

Variables and Measurements

Depressive symptoms

The study used the Patient-Reported Outcomes Measurement Information System (PROMIS) Short Form–Depression to measure depressive symptoms. This 6-item scale has high reliability and precision comparable to the original 28-item scale (Taple et al., 2019). These items assess depressive symptoms over the past 7 days with response options from 1 (never) to 5 (always). Once collected, the PROMIS measures generate a T-score with a mean of 50 (standard deviation [SD] = 10) based on a reference sample (Irwin et al., 2010). A higher total score indicates more depressive symptoms. T-score is categorized as without depressive symptoms (≤ 55) versus with depressive symptoms (> 55), including mild (from 55 to 60) and moderate/severe (> 60) symptoms. The PROMIS Scale–Depression has high internal consistency (Cronbach’s α = 0.97) and a high test-retest reliability (intraclass correlation coefficient [ICC] = 0.88) (Hitchon et al., 2020). The PROMIS Short Form-Depression had a Cronbach’s α of 0.94 in this study.

Sleep quality

The Pittsburgh Sleep Quality Index (PSQI) is a 10-item scale containing 19 self-rated questions that assess sleep quality over the past month (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). This measure records both objective (e.g., how often participants wake up during the night) and subjective (e.g., how rested they typically feel after a night of sleep) sleep quality metrics. These 19 questions are combined to form seven domains (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction), each of which ranges from 0 (no difficulty at all) to 3 (severe difficulty) points. The sum of the seven domain scores creates a global PSQI score ranging from 0 to 21, with higher scores indicating worse sleep quality. For our study, sleep disturbance was defined as PSQI total score > 5. The PSQI showed high test-retest reliability and homogeneity (Cronbach’s α = 0.85) for primary insomnia patients (Backhaus et al., 2002), as well as good convergent and divergent validity among Filipino domestic workers for sleep quality (Cronbach’s α = 0.70) (Xiong et al., 2020). In our parent study with 37 AsA, the Cronbach’s α coefficient was 0.62 for the total scale and 0.7 if the daytime dysfunction domain was excluded.

Gut microbiota

Fecal specimens were collected for analyzing the GM. Following the Human Microbiome Project protocol (Methé et al., 2012), home-based fecal specimens were collected with the provided collection kit, including one biohazard bag with three small fecal collection tubes (Fisher Scientific Co. LLC., Pittsburgh, PA, US). Following collection, the sample tubes were placed into the provided biohazard bag and then placed into a padded freezer bag with ice packs. The freezer bag containing the fecal samples was immediately placed into a freezer until it was either dropped off in our laboratory or shipped via FedEx, within an approximate 24-hour period. Once the samples were delivered to the laboratory, they were stored in −80°C freezers until DNA extraction.

Demographic and clinical variables

One demographic short form was utilized to collect participants’ sociodemographic and health characteristics. Sociodemographic variables included age, gender, self-identified race, marital status, living arrangement, nativity (foreign-born or US-born), year of US residence (for foreign-born), and household income. Health variables include height, weight, body mass index (calculated), disease history, use of probiotics, and use of mental health services.

Study Procedure

Due to the pandemic, study participants were recruited using online platforms such as social media sites (e.g., WeChat and Facebook), ResearchMatch™, and Chinese and Korean websites. All eligible participants provided verbal consent to be included in this study. Following the consent, the study team emailed participants the REDCap online survey link where they completed the online surveys and provided their contact information (name, mailing address, phone number, and email address) for shipping the microbiome data collection kits, which included pictorial and written instructions in English, Chinese, or Korean (Bai et al., 2022). Our research staff also sent weekly reminders prompting participants to return their stool samples to our laboratory.

DNA Extraction and Sequencing for the Gut Microbiota

The gut microbial DNA was extracted from fecal specimens using the PowerSoil isolation kit (Mo Bio Laboratories, Carlsbad, CA, US). The 16S rRNA V3-V4 gene regions (Bukin et al., 2019) were subsequently sequenced. 16S rRNA amplicons were generated using KAPA HiFi HotStart ReadyMix (KAPA Biosystems, KK2600) with primers specific to 16S V3-V4 region of bacteria 341F-805R. The Polymerase Chain Reaction (PCR) 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 via 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. During the sequencing process, microbial community standard controls (2 positive controls, 1 Zymo positive control, 4 negative controls, and 2 NTC negative controls) were used to assess bias and errors in sequencing workflows. This process was conducted at the Emory Integrated Genomics Core (EIGC). The DNA sequence reads were then demultiplexed and paired together to create paired end reads for bioinformatic analysis, which was implemented at the Emory Integrated Computational Core.

Bioinformatics and Statistical Analysis

Descriptive statistics, including mean (SD), median (interquartile range [IQR]), and frequency (%), were conducted for demographic, clinical, and outcome variables (depressive symptoms and sleep quality). Spearman’s correlation and Chi-Square test were adopted to examine the association between depressive symptoms and sleep quality.

For the GM data, 16S rRNA V3-V4 sequences were analyzed to obtain microbial diversity (α- and β-diversity), taxonomic composition, and abundance analysis. Quantitative Insight into Microbial Ecology 2 (QIIME 2) default parameters were utilized to determine amplicon sequence variants (ASVs), and the Divisive Amplicon Denoising Algorithm 2 (dada2) software 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 [a quantitative measure of community richness], Chao1 [a nonparametric method to estimate the number of species in a community], Faith’s phylogenetic diversity [a qualitative measure of community richness that incorporates phylogenetic relationships between the features], and Pielou’s evenness [a measure of community evenness]) and β-diversity metrics of bacterial community dissimilarities (Jaccard [a qualitative measure of community dissimilarity] and weighted UniFrac [a quantitative measure of community dissimilarity that incorporates phylogenetic relationships between the features] distances) were obtained to determine the GM diversity within (α-diversity) and between (β-diversity) fecal samples. Spearman correlations (continuous variables) and Kruskal-Wallis pairwise test (categorical variables) were conducted to determine associations between microbial α- or β-diversity indices and outcome variables (depressive symptoms and sleep quality). Principal coordinates analysis (PCoA) was used to visualize β-diversity patterns, which also elucidated their associations with depressive symptoms and sleep quality. Permutational multivariate analysis of variance (PERMANOVA) tested whether β-diversity differed between participants with positive depressive symptoms (T-score > 55) or sleep disturbance (PSQI > 5) versus those no symptoms.

Taxonomies were then assigned by the pre-trained classifier using the Silva database. Top taxa at phylum and genus levels were presented by 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]). Lastly, the linear discriminant analysis (LDA) effect size (LEfSe) (Segata et al., 2011) was used to characterize the taxa differences between different levels of outcome variables. LEfSe uses the nonparametric Kruskal-Wallis sum-rank test. All analyses were conducted using QIIME 2 (Bai, Jhaney, & Wells, 2019; Bolyen et al., 2019) and R 4.1.3, with a significance level of p < 0.05.

Ethical Consideration

The study was approved by our institutional review board (IRB #: STUDY00000935). Participants were allowed to opt-out of the study at any point in the data collection process.

Results

Characteristics of Participants

Participant (n = 20) demographics include: female (n = 15, 75%) versus male (n = 5, 25%); Chinese (n = 11, 55%) versus Korean (n = 9, 45%); foreign-born (n = 12, 60%) versus native born (n = 8, 40%). Most of our participants (65%) received college degrees and had higher household incomes (≥ $50,000). Only 4 (20%) participants reported use of probiotics; 2 (10%) participants had diabetes; and 2 (10%) had cardiovascular disease. Table S1 (see supplemental material) describes demographic details and health history of the participants.

Depressive Symptoms and Sleep Quality

Mean scores were 55 (SD = 9.6) and 5.5 (SD = 2.5) for depressive symptoms and sleep quality, respectively (Table S2, see the supplemental material). In this study, 55% (n = 11, T-score > 55) of participants recorded having depressive symptoms and 35% (n = 7, PSQI total score >5) recorded sleep disturbance. Specific domains of PSQI were reported in Table S2 (see supplemental material), showing our participants with more difficulties in overall sleep quality (n = 17, 100%), sleep disturbance (n = 16, 94.1%), day dysfunction due to sleepiness (n = 13, 76.5%), and sleep latency (n = 11, 64.7%). Our participants had less difficulty in requiring medication to sleep (n = 1, 5.9%), sleep efficiency (n = 2, 11.8%), and duration of sleep (n = 3, 17.6%).

A higher level of depressive symptoms was associated with higher risk of sleep disturbance, but this result was not significant (rs = 0.28, p = 0.27). For this specific population, both the use of probiotics and nativity (foreign-born vs. US-born) had no impact on both depressive symptoms and sleep quality.

Descriptions of the Gut Microbiota

Gut microbiota features

Compared to standard control sequences, the GM sequences analyzed by the EIGC had good quality. After reviewing the quality scores using FastQC, dada2 trimmed and truncated parameters (to remove low-quality base pair genes) were set at 30 and 240, respectively. A total of 2396 unique ASVs (named “features” hereafter) were identified with a total of frequency of 4,517,927. Median frequency of features per sample was 213,201 (IQR = 87,123.25) and median of features was 55 (IQR = 266).

Taxonomy

Based on the Silva 132 database with a 97% identity threshold, a Naive Bayes classifier was trained to map our sequences to taxonomy, leading to 15 phyla and 249 genera in our samples. The top 5 phyla of our samples were Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria, and Verrucomicrobia; at the genus level, the top 5 taxa were Lachnospiraceae (family), Bifidobacterium, Blautia, Tyzzerella, and Anaerostipes (Figure S1, see supplemental material). At the phylum level, participants with less depressive symptoms had a higher abundance of Bacteroidetes and lower abundance of Actinobacteria (Figure S1(a)), while those with less sleep disturbance seemed to have a higher abundance of Bacteroidetes and lower abundance of Actinobacteria and Firmicutes (Figure S1(c)). At the genus level, those with less depressive symptoms seemed to have a higher abundance of Bacteroides and a lower abundance of Bifidobacterium (Figure S1(b)), while those with less sleep disturbance had a higher abundance of Faecalibacterium and a lower abundance of Bifidobacterium (Figure S1(d)).

Associations of the Gut Microbiota with Depressive Symptoms and Sleep Disturbance

α-diversity

A lower gut microbial α-diversity was marginally associated with a higher level of depressive symptoms (Chao1 [r = −0.39, p = 0.09] and Shannon index [r = −0.41, p = 0.08]) (Table 1). There were no significant associations between the α-diversity and sleep disturbance (all metrics with p > 0.05).

Table 1.

Diversity analyses of the gut microbiota by depressive symptoms and sleep quality.

Depressive Symptoms (Yes vs. No) (n = 20) Depressive Symptoms (No vs. Mild vs. Moderate/Severe) (n = 20) Sleep Disturbance (Yes vs. No) (n = 17)
α-diversity
 Shannon index Spearman’s correlationa r = –0.41, p = 0.08 r = −0.33, p = 0.19
KW pairwise testb H = 1.21, p = 0.27 H = 2.17, p = 0.34 H = 0.46, p = 0.79
 Chao1 Spearman’s correlation r = –0.39, p = 0.09 r = −0.28, p = 0.27
K-W pairwise test H = 0.32, p = 0.57 H = 3.06, p = 0.22 H = 0.88, p = 0.64
 Faith’s_PD Spearman’s correlation r = −0.31, p = 0.18 r = −0.26, p = 0.32
K-W pairwise test H = 0.76, p = 0.38 H = 1.91, p = 0.38 H = 1.56, p = 0.46
 Pielou’s evenness Spearman’s correlation r = –0.38, p = 0.10 r = −0.36, p = 0.16
K-W pairwise test H = 2.43, p = 0.12 H = 2.54, p = 0.28 H = 0.66, p = 0.72
β-diversity
 Jaccard distance PERMANOVA pseudo-F = 0.91, p = 0.87 Pseudo-F = 0.91, p = 0.87 Pseudo-F = 1.18, p = 0.05
 Weighted UniFrac distance PERMANOVA Pseudo-F = 2.50, p = 0.04 Pseudo-F = 1.91, p = 0.07 Pseudo-F = 1.98, p = 0.08
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Note. Faith’s_PD, Faith’s phylogenetic diversity; K-W, Kruskal-Wallis; PERMANOVA, permutational multivariate analysis of variance.

afor continuous variables.

bfor categorical variables.

Bold fonts indicated significant findings (p < 0.05); Bold and italicized fonts indicated findings with a trend of significance (p < 0.10).

β-diversity

Based on the PCoA and PERMANOVA, Jaccard distance showed similarities between participants with and without depressive symptoms (pseudo-F = 0.91, p = 0.87, Table 1 and Figure 1 (a)), but weighted UniFrac distance showed dissimilarities between participants with and without depressive symptoms (pseudo-F = 2.50, p = 0.04, Table 1 and Figure 1 (b)). Marginally significant differences were found between those with sleep disturbance versus without sleep disturbance using Jaccard (pseudo-F = 1.18, p = 0.05, Table 1 and Figure 1 (c)) and weighted UniFrac (pseudo-F = 1.98, p = 0.08, Table 1 and Figure 1 (d)) distances.

Figure 1.

Figure 1.

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Dissimilarities of the Gut Microbiome (β-diversity) for Depressive Symptoms and Sleep Disturbance. A (Yes vs. No) and B (No vs. Mild vs. Moderate/Severe) for weighted UniFrac distance in depressive symptoms. C (Yes vs. No) for Jaccard distance and D (Yes vs. No) for weighted UniFrac distance for sleep disturbance. Each bubble presents one subject, and the scale of the bubble presents the real values total depressive symptoms or sleep quality score.

Taxa abundance

LEfSe was conducted to identify differences in taxa abundance for depressive symptoms (Yes vs. No), level of depressive symptoms (No vs. Mild vs. Moderate/severe), or sleep disturbance (Yes vs. No). Results (Figure 2 (a)-(c)) showed that those with depressive symptoms had an enriched abundance of Actinobacteria (phylum), whereas those without depressive symptoms had higher abundances of Bacteroidetes (phylum). We further analyzed the abundance of taxa based on levels of depressive symptoms (No vs. Mild vs. Moderate/severe). Figure S2 (see supplemental material) showed that those with moderate/severe depressive symptoms had enriched abundance of Lactobacillales (order), Bacteroides (genus), Bacteroidaceae (family) and Actinobacteria (phylum), whereas those with mild depressive symptoms had higher abundance of Prevotellaceae (family), and those without depressive symptoms had higher abundances of Bacteroidetes (phylum), Bacteroidia (class), Bacteroidales (order), Rikenellaceae (family), and genera Alistipes and Holdemania. No significant taxa were identified to distinguish sleep disturbance.

Figure 2.

Figure 2.

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Taxa Abundance by Depressive Symptoms (Yes vs. No). A visualizes differential taxa ranked by effect size; B presents relative taxa on phylogenetic trees; C shows examples of plots for taxa statistically significant differences between those with significant depressive symptoms versus those without.

Discussion

Depression is a common mental health problem in the US. Depression can be debilitating and even lead to suicide, making it an urgent public health issue. 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). With the onset of the COVID-19 pandemic, AsA have experienced increased racial discrimination, including verbal harassment and physical assault, which have been attributed to an increasing anti-Asian sentiment in the US (Horse et al., 2021). Moreover, it has been noted that AsA face increased mental health stressors originating from acculturative stress and language barriers (Jang & Chiriboga, 2010). A combination of these stressors can pose a serious threat to mental health as demonstrated by an 18.7% increase in depressive symptoms among AsA during the COVID-19 pandemic versus pre-pandemic (Lozano et al., 2022).

Novel findings pertaining to this study suggest that the MGB axis plays a potential role in the regulation of depressive symptoms and sleep disturbance (Naseribafrouei et al., 2014; Smith et al., 2019). Specifically, the GM affects the central nervous system through various bidirectional biological pathways either directly through anatomical connections (e.g., vagal nerves) or indirectly via microbial-derived metabolites (e.g., SCFAs) and entero-and neuroendocrine peptides (Sonali et al., 2022). The bidirectional nature of the MGB axis has been widely implicated in animal models. Specifically, depressive symptoms were found in rodents after receiving a fecal transplant from major depressive disorder (MDD) patients. Further tests showed rodents that experienced stress and depression demonstrated a decreased microbial diversity and a lower abundance of beneficial gut microbes (Winter et al., 2018). These reductions in the GM diversity and relative abundance of healthy bacteria (e.g., Lactobacillus and Bifidobacteria) are thought to be associated with mental disorders (Järbrink-Sehgal & Andreasson, 2020). Our data indicated that increased abundance of Lactobacillales at the order level was associated with moderate/severe depressive symptoms. In contrast, Bravo et al. demonstrated Lactobacillus rhamnosus - fed mice showed decreased stress-induced elevations in corticosterone, anxiety, and depression-related symptoms (Bravo et al., 2011). The inconsistencies regarding Lactobacilli abundance across depression categories are likely due to our small sample size or the use of 16S sequencing, which has limitations to identify the GM beyond the genus level. Future studies are needed to investigate the role of Lactobacillus in the MGB axis and its downstream neuro-modulatory effects on mood, particularly evaluating the anti-depressive mechanisms associated with increased abundance of Lactobacillus.

When comparing our findings to pertinent literature, we discovered several consistent results. At the phylum level, levels of Actinobacteria were increased in animal models of depression (Yang et al., 2017). Consistent with this result, we found that participants with depressive symptoms had a greater abundance of Actinobacteria, whereas those without depressive symptoms demonstrated higher abundances of Bacteroidetes. Although the implications of Actinobacteria in the pathophysiology of depression are not fully understood, these collective findings suggest that increased levels of Actinobacteria in the GM are associated with depression. Our results were also consistent across the genus level in that fewer or less severe depressive symptoms were associated with a higher abundance of Bacteroidetes. As such, this presents evidence that perturbations in the GM, involving Actinobacteria, plays a role in the pathophysiology of mood disorders (i.e., depression) via the MGB axis.

Additionally, taxonomic abundance associated with varying degrees of depressive symptoms was analyzed. At the phylum level, those with moderate/severe depressive symptoms exhibited enriched abundance of Actinobacteria, whereas those without depressive symptoms had higher abundances of Bacteroidetes. Interestingly, Naseribafrouei et al. (2014) demonstrated decreased levels of Bacteroidetes in patients presenting with mild to severe MDD compared to healthy individuals. These findings consistently support the idea that increased abundance of Bacteroidetes may serve as a protective factor against the pathophysiological development of depression via the MGB axis. Further study of the gut microbial taxa contributing to depressive symptoms should be conducted in a large AsA cohort.

When considering microbiome diversity, our findings suggest a marginally higher α-diversity of the GM in those with lower depressive symptoms. This is consistent with a similar study which observed a lower bacterial diversity, using the Shannon index, in patients with major depressive disorders compared to controls (Huang et al., 2018). Additionally, our study’s β-diversity measures of the GM by weighted UniFrac distance between those with and without depressive symptoms were significant, consistently with a prior study observed significant differences in weighted and unweighted UniFrac distances, as well as the Bray-Curtis dissimilarity indices (Barandouzi et al., 2020). Our consistent findings seemed to indicate that interventions targeting at increasing the GM diversity may help relieve depressive symptoms among AsA.

Stress and anxiety, compounded by acculturative stress, certainly contribute to the underlying mechanisms that drive racial and ethnic disparities in sleep disturbance among AsA (Cunningham et al., 2016). This study found 35% of participants reported sleep disturbance. There was no significant difference found between foreign-born and US-born AsA, likely due to our small sample size and the less diverse collection of socioeconomic profiles (e.g., more resources to cope with unique stressors). Interestingly, there appears to be a bidirectional relationship between GM and sleep. For example, jet lag in human participants was associated with increases in Firmicutes relative abundance (Thaiss et al., 2014). Moreover, Escherichia coli and Enterococcus are two common GM that produce 5-HT, a precursor to serotonin that is involved in rapid eye movement (REM) sleep (Rieder et al., 2017). Additionally, it is thought that symptoms of sleep disturbance may present as an early manifestation of an underlying depressive disorder (Jang et al., 2011). However, further analysis showed that the association between depressive symptoms and sleep disturbance was not significant. These findings are not widely generalizable due to the small sample size and should be interpreted cautiously. Further work is necessary to confirm these findings, and it is critical to understand the relationship between depressive symptoms and sleep disturbance because this can create complexity in treatment.

Our data for sleep disturbance demonstrate that those with less sleep disturbance seem to have a higher abundance of Bacteroidetes and a lower abundance of Actinobacteria and Firmicutes at the phylum level, but without significant findings. Literature showed increases in Bacteroidetes and Firmicutes were positively correlated with sleep efficiency (Smith et al., 2019), which can be explained as secondary metabolites produced by Bacteroidetes and Firmicutes having implications in sleep optimization. Interestingly, terpene was a dominant secondary metabolite in the metabolomic analysis of Bacteroidetes species (Nkosi et al., 2022). Terpene was also detected in the metabolomic profile of Firmicutes species. Monoterpenes (i.e., α- and β-pinene), a group of naturally occurring compounds, have demonstrated anxiolytic and sleep effects (Woo, 2020). Moreover, these compounds have been noted in several classes of GM-derived metabolites (Yamada et al., 2015). Future studies are needed to investigate the effects of GM-derived metabolites on sleep health.

At the genus level, those with less sleep disturbance seemed to show a lower abundance of Bifidobacterium and a higher abundance of Faecalibacterium. Nevertheless, no significant taxa were identified to distinguish sleep disturbance in this study, potentially due to small sample size. However, one study showed a significant difference between gut microbial diversity and normal, short, and long sleeper categories in humans. Specifically, short sleepers exhibited significantly lower GM α-diversity, whereas long sleepers were associated with greater GM α-diversity (Fei et al., 2021). Future studies would be beneficial for exploring how such shifts in gut microbial diversity contribute to alterations in sleep patterns as well as investigating the underlying biomechanisms that drive these changes in sleep quality.

Incidentally, our data suggests that the GM β-diversity distinguishes participants with sleep disturbance from those without sleep disturbance, with marginal significance in Jaccard and weighted UniFrac distances. Similarly, a previous study found different weighted UniFrac distances between children with low and high total sleep time compared to average sleep times (Wang et al., 2022). These findings suggest a relationship between perturbed sleep patterns and gut microbial diversity. Prospective studies involving larger samples are needed to confirm these findings and to weigh the therapeutic implications in optimizing gut microbial composition against sleep disturbance.

Several limitations must be noted in this study. The convenience sampling and small sample size of this study limited the generalizability of our results. Our participants were recruited from metro Atlanta, Georgia, which may not be fully representative of other areas, as the GM is sensitive to living environment and geographic locations (Yatsunenko et al., 2012). A larger and more diverse AsA cohort (e.g., education level and socioeconomic status) should be recruited to confirm our results. Second, this pilot study did not control major confounders, such as acculturative stress, participants’ access to quality foods, and participants’ knowledge of nutrition, all of which can affect the GM profile. Multiple tests were not corrected considering the pilot study feature of this study. Future studies need to consider recruitment of a representative and large sample size so that these confounders and multiple test issues can be addressed. Third, due to the variable and multifactorial nature of the GM composition, there may be bias in our study as we did not control other potential confounders, such as diet and nativity. Finally, as a cross-sectional study, we cannot determine causality or temporality between the GM compositions and health indicators we used. A longitudinal study design is encouraged to further identify the GM contributing to pathologic mechanisms of mental health and sleep disorders.

In conclusion, promising associations between the gut microbial diversity and depressive symptoms (α-diversity) and sleep quality (β-diversity) were found among Chinese and Korean Americans. Specific taxa were identified and associated with depressive symptoms. Larger studies that control for major confounders (e.g., diet and acculturative stress) and investigate differences in the GM by nativity, subethnicity, and sex, are needed to confirm our findings, as well as to further elucidate novel associations between the GM and symptoms in AsA.

Supplemental Material

Supplemental Material - A Pilot Study of the Gut Microbiota Associated With Depressive Symptoms and Sleep Disturbance Among Chinese and Korean Immigrants in the United States
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Supplemental A Pilot Study of the Gut Microbiota Associated With Depressive Symptoms and Sleep Disturbance Among Chinese and Korean Immigrants in the United States by Chloe Hope, MNc, BS, Natalie Shen, MPH, BS, Wenhui Zhang, PhD, MS, RN, Hye In Noh, BSN , Vicki S. Hertzberg, PhD, FASA, PStat, Sangmi Kim, PhD, MPH, RN, and Jinbing Bai, PhD, MSN, RN, FAAN in Biological Research For Nursing.

Author contributions: Bai, J contributed to conception and design contributed to acquisition critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy Hope, C contributed to interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy Shen, N contributed to analysis and interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy Kim, S contributed to conception and design contributed to interpretation critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy Zhang, W contributed to conception and design contributed to analysis critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy Hertzberg, V contributed to design contributed to interpretation critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy Noh, H.I. contributed to acquisition critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Institute of Health/National Institute of Nursing Research (1K99NR017897-01, 4R00NR017897-03, PI: Bai); the Office of the Senior Vice President for Research at Emory University Bidirectional Global Health Disparities Research Pilot Grant (PI: Bai and Kim).

Supplemental Material: Supplemental material for this article is available online.

ORCID iD

Jinbing Bai, MNc, BS https://orcid.org/0000-0001-6726-5714

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Supplementary Materials

Supplemental Material - A Pilot Study of the Gut Microbiota Associated With Depressive Symptoms and Sleep Disturbance Among Chinese and Korean Immigrants in the United States
Click here for additional data file. (496.2KB, pdf)

Supplemental A Pilot Study of the Gut Microbiota Associated With Depressive Symptoms and Sleep Disturbance Among Chinese and Korean Immigrants in the United States by Chloe Hope, MNc, BS, Natalie Shen, MPH, BS, Wenhui Zhang, PhD, MS, RN, Hye In Noh, BSN , Vicki S. Hertzberg, PhD, FASA, PStat, Sangmi Kim, PhD, MPH, RN, and Jinbing Bai, PhD, MSN, RN, FAAN in Biological Research For Nursing.

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