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Research Paper
Open Access
INTERACTION OF THE GUT MICROBIOTA AND BRAIN FUNCTIONAL CONNECTIVITY IN LATE-LIFE
DEPRESSION
Chia-Fen Tsai, Chia-Hsien Chuang, Pei-Chi Tu, Wan-Chen Chang, Yen-Po Wang,
Pei-Yi Liu, Po-Shan Wu, Chung-Yen Lin and Ching-Liang Lu
J Psychiatry Neurosci September 19, 2024 49 (5) E289-E300; DOI:
https://i646f69o6f7267z.oszar.com/10.1503/jpn.240050
Chia-Fen Tsai
From the Institute of Brain Science (Wang, Liu, Wu, Lu), Faculty of Medicine
(Tsai, Wang, Lu), Institute of Philosophy of Mind and Cognition (Tu), Department
of Biomedical Engineering (Chang), the National Yang Ming Chiao Tung University,
Taipei, Taiwan; the Endoscopy Center for Diagnosis and Treatment (Wang, Liu,
Lu), Department of Medicine (Wang, Lu), Division of Gastroenterology, Department
of Psychiatry (Tu, Chang), Department of Medical Research (Tu, Chang),
Department of Dietetics & Nutrition (Wu), Taipei Veterans General Hospital,
Taipei, Taiwan; the Institute of Information Science (Chuang, Lin), Academia
Sinica, Taiwan; Yours Clinic (Tsai), Taipei, Taiwan
MD, PhD
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Chia-Hsien Chuang
From the Institute of Brain Science (Wang, Liu, Wu, Lu), Faculty of Medicine
(Tsai, Wang, Lu), Institute of Philosophy of Mind and Cognition (Tu), Department
of Biomedical Engineering (Chang), the National Yang Ming Chiao Tung University,
Taipei, Taiwan; the Endoscopy Center for Diagnosis and Treatment (Wang, Liu,
Lu), Department of Medicine (Wang, Lu), Division of Gastroenterology, Department
of Psychiatry (Tu, Chang), Department of Medical Research (Tu, Chang),
Department of Dietetics & Nutrition (Wu), Taipei Veterans General Hospital,
Taipei, Taiwan; the Institute of Information Science (Chuang, Lin), Academia
Sinica, Taiwan; Yours Clinic (Tsai), Taipei, Taiwan
MS
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Pei-Chi Tu
From the Institute of Brain Science (Wang, Liu, Wu, Lu), Faculty of Medicine
(Tsai, Wang, Lu), Institute of Philosophy of Mind and Cognition (Tu), Department
of Biomedical Engineering (Chang), the National Yang Ming Chiao Tung University,
Taipei, Taiwan; the Endoscopy Center for Diagnosis and Treatment (Wang, Liu,
Lu), Department of Medicine (Wang, Lu), Division of Gastroenterology, Department
of Psychiatry (Tu, Chang), Department of Medical Research (Tu, Chang),
Department of Dietetics & Nutrition (Wu), Taipei Veterans General Hospital,
Taipei, Taiwan; the Institute of Information Science (Chuang, Lin), Academia
Sinica, Taiwan; Yours Clinic (Tsai), Taipei, Taiwan
MD, PhD
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Wan-Chen Chang
From the Institute of Brain Science (Wang, Liu, Wu, Lu), Faculty of Medicine
(Tsai, Wang, Lu), Institute of Philosophy of Mind and Cognition (Tu), Department
of Biomedical Engineering (Chang), the National Yang Ming Chiao Tung University,
Taipei, Taiwan; the Endoscopy Center for Diagnosis and Treatment (Wang, Liu,
Lu), Department of Medicine (Wang, Lu), Division of Gastroenterology, Department
of Psychiatry (Tu, Chang), Department of Medical Research (Tu, Chang),
Department of Dietetics & Nutrition (Wu), Taipei Veterans General Hospital,
Taipei, Taiwan; the Institute of Information Science (Chuang, Lin), Academia
Sinica, Taiwan; Yours Clinic (Tsai), Taipei, Taiwan
MS
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Yen-Po Wang
From the Institute of Brain Science (Wang, Liu, Wu, Lu), Faculty of Medicine
(Tsai, Wang, Lu), Institute of Philosophy of Mind and Cognition (Tu), Department
of Biomedical Engineering (Chang), the National Yang Ming Chiao Tung University,
Taipei, Taiwan; the Endoscopy Center for Diagnosis and Treatment (Wang, Liu,
Lu), Department of Medicine (Wang, Lu), Division of Gastroenterology, Department
of Psychiatry (Tu, Chang), Department of Medical Research (Tu, Chang),
Department of Dietetics & Nutrition (Wu), Taipei Veterans General Hospital,
Taipei, Taiwan; the Institute of Information Science (Chuang, Lin), Academia
Sinica, Taiwan; Yours Clinic (Tsai), Taipei, Taiwan
MD
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Pei-Yi Liu
From the Institute of Brain Science (Wang, Liu, Wu, Lu), Faculty of Medicine
(Tsai, Wang, Lu), Institute of Philosophy of Mind and Cognition (Tu), Department
of Biomedical Engineering (Chang), the National Yang Ming Chiao Tung University,
Taipei, Taiwan; the Endoscopy Center for Diagnosis and Treatment (Wang, Liu,
Lu), Department of Medicine (Wang, Lu), Division of Gastroenterology, Department
of Psychiatry (Tu, Chang), Department of Medical Research (Tu, Chang),
Department of Dietetics & Nutrition (Wu), Taipei Veterans General Hospital,
Taipei, Taiwan; the Institute of Information Science (Chuang, Lin), Academia
Sinica, Taiwan; Yours Clinic (Tsai), Taipei, Taiwan
PhD
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Po-Shan Wu
From the Institute of Brain Science (Wang, Liu, Wu, Lu), Faculty of Medicine
(Tsai, Wang, Lu), Institute of Philosophy of Mind and Cognition (Tu), Department
of Biomedical Engineering (Chang), the National Yang Ming Chiao Tung University,
Taipei, Taiwan; the Endoscopy Center for Diagnosis and Treatment (Wang, Liu,
Lu), Department of Medicine (Wang, Lu), Division of Gastroenterology, Department
of Psychiatry (Tu, Chang), Department of Medical Research (Tu, Chang),
Department of Dietetics & Nutrition (Wu), Taipei Veterans General Hospital,
Taipei, Taiwan; the Institute of Information Science (Chuang, Lin), Academia
Sinica, Taiwan; Yours Clinic (Tsai), Taipei, Taiwan
BS
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Chung-Yen Lin
From the Institute of Brain Science (Wang, Liu, Wu, Lu), Faculty of Medicine
(Tsai, Wang, Lu), Institute of Philosophy of Mind and Cognition (Tu), Department
of Biomedical Engineering (Chang), the National Yang Ming Chiao Tung University,
Taipei, Taiwan; the Endoscopy Center for Diagnosis and Treatment (Wang, Liu,
Lu), Department of Medicine (Wang, Lu), Division of Gastroenterology, Department
of Psychiatry (Tu, Chang), Department of Medical Research (Tu, Chang),
Department of Dietetics & Nutrition (Wu), Taipei Veterans General Hospital,
Taipei, Taiwan; the Institute of Information Science (Chuang, Lin), Academia
Sinica, Taiwan; Yours Clinic (Tsai), Taipei, Taiwan
PhD
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Ching-Liang Lu
From the Institute of Brain Science (Wang, Liu, Wu, Lu), Faculty of Medicine
(Tsai, Wang, Lu), Institute of Philosophy of Mind and Cognition (Tu), Department
of Biomedical Engineering (Chang), the National Yang Ming Chiao Tung University,
Taipei, Taiwan; the Endoscopy Center for Diagnosis and Treatment (Wang, Liu,
Lu), Department of Medicine (Wang, Lu), Division of Gastroenterology, Department
of Psychiatry (Tu, Chang), Department of Medical Research (Tu, Chang),
Department of Dietetics & Nutrition (Wu), Taipei Veterans General Hospital,
Taipei, Taiwan; the Institute of Information Science (Chuang, Lin), Academia
Sinica, Taiwan; Yours Clinic (Tsai), Taipei, Taiwan
MD
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ABSTRACT
Background: Increasing evidence suggests an important role of the gut microbiome
in the pathogenesis of mental disorders, including depression, along the
microbiota–gut–brain axis. We sought to explore the interactions between gut
microbe composition and neural circuits in late-life depression (LLD).
Methods: We performed fecal 16S ribosomal RNA (rRNA) sequencing and
resting-state functional magnetic resonance imaging in a case–control cohort of
older adults with LLD and healthy controls to characterize the association
between gut microbiota and brain functional connectivity (FC). We used the
Hamilton Depression Rating Scale (HAMD) to assess depressive symptoms.
Results: We included 32 adults with LLD and 16 healthy controls. At the genus
level, the relative abundance of Enterobacter, Akkermansiaceae, Hemophilus,
Burkholderia, and Rothia was significantly higher among patients with LDD than
controls. Reduced FC within mood regulation circuits was mainly found in the
frontal cortex (e.g., the right superior and inferior frontal gyrus, right
lateral occipital cortex, left middle frontal gyrus, and left caudate) among
patients with MDD. Group-characterized gut microbes among controls and patients
showed opposite correlations with seed-based FC, which may account for the
aberrant emotion regulation among patients with LDD. The abundance of
Enterobacter (dominant genus among patients with LLD) was positively correlated
with both HAMD scores (r = 0.49, p = 0.0004) and group-characterized FC (r =
−0.37, p < 0.05), while Odoribacter (dominant genus among controls) was
negatively correlated with both HAMD scores (r = −0.30, p = 0.04) and
group-characterized FC.
Limitations: The study’s cross-sectional design and small sample size limit
causal inferences; larger longitudinal studies are required for detailed
subgroup analyses.
Conclusion: We identified significant correlations between LDD-characterized gut
microbes and brain FC, as well as depression severity, which may contribute to
the pathophysiology of depression development among patients with LLD. Specific
microbes were linked to altered brain connectivity, suggesting potential targets
for treating LLD.
INTRODUCTION
Depression is a highly prevalent psychiatric disorder associated with emotional
distress, poor quality of life, disability, increased risk of suicide, cognitive
decline, and death among older adults worldwide.1 Decreased efficacy and
increased adverse events following pharmacological treatments are usually
observed among patients of advanced age, which can be owing to medical
comorbidities and drug–drug interactions.2 Therefore, it is important to explore
potential neuropathological mechanisms in late-life depression (LLD) and to find
novel adjuvant therapeutic interventions.
Recent studies have outlined the important role of the microbiota through the
gut–brain axis, bidirectional biochemical signalling that takes place between
the gastrointestinal tract and the central nervous system via regulation of the
hypothalamic–pituitary–adrenal system (HPA axis), which modulates vagal nerve
activity, neurotransmitters, and immune-related pathways.3,4
Furthermore, interest in elucidating interactions between certain bacterial
strains and brain function has surged. A recent animal study showed that
specific gut bacteria (e.g., Enterococcus faecalis) may prohibit activation of
the HPA axis and interfere with social activity among mice, supporting the idea
that gut microbiota can affect social behaviours through distinct neuronal
circuits to modulate stress responses in the brain.5 Among patients with
schizophrenia, regional homogeneity, measured by resting-state functional
magnetic resonance imaging (fMRI), was positively associated with the
α-diversity of the gut microbiota and negatively associated with the abundance
of Roseburia.6 Another report showed close associations between the relative
abundance of Lactobacilli, regional homogeneity values in the left fusiform
gyrus, and depression scores among patients with chronic insomnia.7 Psychiatric
disorders such as depression are increasingly recognized as conditions
characterized by disruptions in brain connectivity.8 Consistent evidence has
indicated impaired resting-state functional connectivity (FC) within the default
mode network (DMN) among people with major depressive disorder (MDD).9 In
late-life depression (LLD), Alexopoulos and colleagues9 observed heightened
resting-state FC within the DMN. First-episode and treatment-naïve patients with
MDD have shown increased FC in the anterior medial cortex and decreased FC in
the posterior medial cortex.10 When using the precuneus as a single seed,
drug-naive patients with MDD exhibited hyperconnectivity between the dorsal
anterior subregion and brain regions involved in executive control,
sensorimotor, and attention functions.11 In addition, using cerebellar seeds,
people with MDD demonstrated reduced dynamic cerebellar–cerebral FC involving
cerebellar subregions.12 Investigating the relationships between gut microbial
composition and intrinsic brain connectivity may further improve understanding
of mental diseases.
In the current study, we hypothesized that between-group differences in gut
microbial composition might be associated with between-group differences in
brain FC and clinical symptoms among older patients with depression. Thus, we
aimed to assess the differences in gut microbiota and brain FC among patients
with LLD using seed-based analysis via resting-state fMRI.
METHODS
PARTICIPANTS
To perform this case–control study, we enrolled patients with LLD who were older
than 55 years and fulfilled the MDD diagnostic criteria according to the
Diagnostic and Statistical Manual of Mental Disorders, 4th Edition.13 We
recruited patients from the outpatient clinic of a tertiary medical centre in
northern Taiwan. We used poster advertisements to recruit controls. For both
groups, we excluded people with other major neuropsychiatric disorders,
including neurocognitive disorders, schizophrenia, and bipolar disorder; those
with major physical diseases, such as malignant cancer or a history of organic
gastrointestinal disease, including liver cirrhosis, fatty liver disease, peptic
ulcer, or inflammatory bowel disease; those with any active bacterial, fungal,
or viral infection; those who took antibiotics, prebiotics, or probiotics within
90 days before enrolment; and those with a history of receiving gastrointestinal
tract surgery, appendectomy, or cholecystectomy in the preceding year.
Board-certified psychiatrists used the Mini-International Neuropsychiatric
Interview to exclude anyone with psychiatric illness in the control group.14 The
Montreal Cognitive Assessment was used to exclude any individuals with
neurocognitive impairment in both groups.15
MEASUREMENTS OF SOCIODEMOGRAPHIC DATA, CLINICAL SYMPTOMS, AND DIET HISTORY
We collected sociodemographic data (e.g., age, sex, education), anthropometric
data (e.g., weight, height), and history of diabetes mellitus at baseline. We
calculated body mass index (BMI), defined as the weight (kg) divided by squared
height (m). We assessed depressive symptoms according to the 17-item Hamilton
Depression Rating Scale (HAMD),16 which is a widely used measurement of the
severity of depressive symptoms, rated by semi-structured interviews. We
assessed dietary patterns using a validated semi-quantitative simplified food
frequency questionnaire.17
MICROBIOME DATA SEQUENCING AND PREPROCESSING
For both groups, we requested that each participant collect the stool from their
first bowel movement of the day in the morning and freeze the stool sample
immediately. Stool specimens were stored in an RNA stabilizing reagent
(RNALater) at −80°C until further analysis. As previously described, we
extracted, amplified, and sequenced bacterial DNA from the stool specimens.18
The 16S ribosomal RNA (rRNA) gene sequence libraries were produced using the
V3–V4 (341F (CCTACGGGNGGCWGCAG)/805R (GACTACHVGGGTATCTAATCC)) primer region and
sequenced on an Illumina MiSeq sequencer.19 Cases and controls were mixed within
each platform to reduce sequencing bias between groups caused by batch effects.
IMAGE ACQUISITION
We acquired images using a 3.0 T GE Discovery MR750 whole-body high-speed MRI
device. Head stabilization was achieved with cushioning, and all participants
wore earplugs (29 dB rating) to attenuate the noise. A high-resolution
structural image was acquired in the axial plane using a fast spoiled
gradient-echo (FSPGR) sequence (BRAVO) on GE equipment with a repetition time of
12.23 ms, echo time of 5.18 ms, inversion time of 450 ms, flip angle of 12°, and
an isotropic 1 mm voxel (field of view 256 × 256). The resting-state functional
images were collected using a gradient echo T2*-weighted sequence (repetition
time 2500 ms, echo time 30 ms, flip angle 90°). We acquired and interleaved 47
contiguous horizontal slices parallel to the intercommissural plane (voxel size
3.5 × 3.5 × 3.5 mm). These slices covered the cerebellum of each participant.
During the functional scans, the participants were instructed to remain awake
with their eyes open; each scan lasted 8 minutes and 24 seconds across 200 time
points.
SAMPLE SIZE CALCULATION
We calculated our sample size using G*Power (version 3.1.9.2).20 The estimated
parameters used to reject the null hypothesis included the population means of
the LLD and control groups being equal with a probability (power) of 0.9; the
type I error probability associated with this test’s null hypothesis was 0.01.
Based on the previous study,21 we set the allocation ration at 2 and the dropout
rate at 20%. We determined that we required 32 patients with LDD and 16
controls.
DEMOGRAPHIC AND CLINICAL DATA ANALYSIS
We used χ2 tests for comparisons of categorical variables and Student t tests
for comparisons of continuous variables based on 2-tailed alternatives.
Significance was defined as a 2-tailed p value of less than 0.05 for all
variables. There are no cases with missing data. We used SPSS version 17 and SAS
version 9.1 to perform data processing and statistical analyses.
RESTING-STATE FUNCTIONAL CONNECTIVITY ANALYSIS
All FC preprocessing was performed using the Data Processing Assistant for
resting-state fMRI (http://www.restfmri.net), which is based on Statistical
Parametric Mapping
(http://i777777o66696co696f6eo75636co6163o756bz.oszar.com/spm) and the
resting-state fMRI Data Analysis Toolkit (http://www.restfmri.net). The
functional scans received slice-timing correction and motion correction and were
registered with the Montreal Neurological Institute (MNI152) atlas. Additional
preprocessing steps, which have been described in previous reports,22 used to
prepare the data for FC analysis included spatial smoothing using a Gaussian
kernel (6 mm full width at half-maximum), temporal filtering (0.009 Hz < f <
0.08 Hz), and removal of spurious or nonspecific sources of variance by
regression of 6 head motion parameters and autoregressive models of motion (6
head motion parameters, 6 head motion parameters 1 time point before, and the 12
corresponding squared items, using the Friston 24-parameter model), the mean
whole-brain signal, the mean signal within the lateral ventricles, and the mean
signal within a white-matter mask. We conducted these analyses with and without
global signal regression (GSR).23 The regressors used in the method of scrubbing
within regression were also included to minimize the effect of head motion on
the FC measurement. We simultaneously computed the regression of each signal; we
retained the residual time course for the correlation analysis.
Six seed regions were created in the bilateral prefrontal regions for FC
analysis and included the dorsal anterior cingulate cortex (dACC), the
dorsolateral prefrontal cortex (dlPFC), and the medial prefrontal cortex (mPFC).
The seeds were defined as structures with a 4-mm radius around the coordinates,
according to previous fMRI studies of the salience network, executive control
network, and DMN.24 We identified the FC map of each region of interest in each
participant by correlating the low-frequency fMRI fluctuations with the seeds.
We applied the Fisher r-to-z transformation to convert correlation maps into z
maps.22 We compared the z-transformed maps of patients and controls via
independent-sample t tests using age, sex, and education as the covariates of no
interest. We used an uncorrected threshold of p less than 0.001 for the initial
voxel-wise comparisons. To correct for multiple comparisons, we performed a
Monte Carlo simulation (10 000 times) using AlphaSim via Analysis of Functional
NeuroImages. Only the clusters with a significance threshold of p less than 0.05
at the cluster level were defined as regions of interest; then, we used the
Spearman rank-order correlation to measure the degree of association between
these regions and the microbiota that characterized patients with LDD.
BIOINFORMATICS ANALYSIS OF GUT MICROBIOTA
We followed a standardized microbiome analysis pipeline that included
preprocessing, quality control, taxonomic classification, and determination of
microbiome diversity. We preprocessed raw fastq files in QIIME 2 after
polymerase chain reaction (PCR) amplification and sequencing on an Illumina
platform. Pair-end sequence primer adaptors were trimmed using cutadapt (via
q2-cutadapt).25 To identify amplicon sequence variants, sequences were quality
filtered and denoised using the DADA2 algorithm (via q2-dada2).26 Amplicon
sequence variants were aligned to construct a phylogenetic tree using mafft and
fasttree2 (via q2-phylogeny).27 We calculated α- (Faith’s phylogenetic
diversity) and β- (unweighted UniFrac distance)28 diversity metrics, and a
principal coordinate analysis from rarefied samples using q2-diversity. We used
R for the statistical analysis and drawing of the figure illustrating
α-diversity metrics using the ggplot2 and ggpubr packages.29 We conducted the
taxonomic classification with a naïve Bayes taxonomy classifier (via
q2-feature-classifier classify-sklearn) through the reference sequence from 6
databases (MetaSquare, Silva, Greengenes, RDP, HOMD, and EzBioCloud).30 We also
added the latest published novel species sequences to MetaSquare without
redundancy. Based on MetaSquare, we had high resolution in microbial taxonomy
and few unclassified sequences.
ANALYSIS TO DETERMINE DEPRESSION-ASSOCIATED GUT FLORA AND CORRELATIONS WITH
BRAIN FUNCTIONAL CONNECTIVITY
We used linear discriminant analysis effect size (LEfSe)31 to examine which
microbial taxa most contributed to differences between patients and controls. We
defined microbial flora with linear discriminant analysis scores (log10) greater
than 2 as significantly different in abundance between groups. We further
performed Spearman correlation analysis between differentially abundant microbes
or α-diversity and variations in brain FC. To complement the correlation
analysis results, we applied a generalized linear model for regression analysis.
This model examined variations in brain FC using standardized predictors
including differentially abundant microbes and covariates such as antidepressant
drug usage, sex, and dietary intake (vegetables and fruits). We assessed model
fit using adjusted R2 values. We further evaluated significant associations
between variations in brain FC and microbes identified in both correlation and
regression analyses. Finally, these significant associations were visualized in
a correlation heatmap plot.
ETHICS APPROVAL
All participants provided written informed consent before participating in the
study. The study was approved by the ethics review committee of the Taipei
Veterans General Hospital (no. 2017-01-005B), in accordance with the Declaration
of Helsinki.
RESULTS
DEMOGRAPHIC AND CLINICAL DATA
We enrolled 32 patients with LLD and 16 controls (Table 1). The mean age of the
participants in this study was 64.79 (standard deviation [SD] 7.89) years (range
55–85 yr). Age, sex, years of education, history of diabetes, and cognitive
performance were similar between groups. Healthy controls reported higher
calorie intake than patients (1722.19 [SD 601.59] v. 1316.85 [SD 612.48], p =
0.04); however, there were no significant differences in dietary components —
including consumption of carbohydrates, protein, fat, and fibre — or BMI between
groups. Patients with LLD had higher HAMD scores than controls (12.97 [SD 7.16]
v. 1.44 [SD 1.75], p < 0.001). Half of the patients took either selective
serotonin reuptake inhibitors or serotonin–norepinephrine reuptake inhibitors,
and the other half took other antidepressants, such as agomelatine, bupropion,
mirtazapine, or trazodone.
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Table 1
Participant demographic and clinical data
BACTERIAL TAXONOMIC CHARACTERIZATION OF THE GUT MICROBIOTA IN LATE-LIFE
DEPRESSION
Patients with LLD had significantly lower α-diversity (p = 0.04) and β-diversity
(p = 0.009) than the control group (Appendix 1, Figure 1a and 1b, available at
www.jpn.ca/lookup/doi/10.1503/jpn.240050/tab-related-content). Compared with the
controls, taxa with higher relative abundance in the LLD group were assigned to
Patescibacteria at the phylum level; Alphaproteobacteria, Saccharimonadia, and
Verrucomicrobiae at the class level; Verrucomicrobiales, Pasterurellales,
Micrococcales, and Saccharimonadales at the order level; Erysipelotrichaceae,
Akkermansiaceae, Atopobiaceae, Pasteurellaceae, Burkholderiaceae, and
Micrococcaceae at the family level; and Akkermansia, Enterobacter, Haemophilus,
Burkholderia, and Rothia at the genus level. The relative abundance of
Alistipes, Lachnospiraceae ND3007, Eubacterium hallii, UCG-002, Eubacterium
ruminantium, Barnesiella, UCG-005, Eubacterium ventriosum, Dialister, UCG-003,
Lachnospiraceae UCG-001, Odoribacter, Monoglobus, Ruminococcus gauvreauii,
Selenomonas, Christensenellaceae R7, Eubacterium xylanophilum,
Sanguibacteroides, and Family XIII UCG-001 was significantly higher in the
control group than in the LLD group at the genus level (Figure 1).
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Figure 1
Abundance of bacterial taxa at the genus level among patients with late-life
depression (LLD) and healthy controls, as determined with the linear
discriminant analysis (LDA) effect size method. Taxa enriched in the LLD group
are indicated with a positive LDA score (red), and taxa enriched in the control
group have a negative score (blue). Only taxa meeting the LDA significance
threshold (> 2) are shown. See Appendix 1, Supplementary Table 1 for accessible
version.
FUNCTIONAL CONNECTIVITY ANALYSIS
Seed-based FC analysis showed robust network differences between the LLD and
control groups (Table 2 and Figure 2). The resting FC in the left dACC seed with
the right middle temporal gyrus and left frontal pole was significantly higher
in the LLD group than the control group. Significantly higher FC in the right
dACC seed with the left temporal occipital fusiform cortex and lower FC in the
right dACC seed with the right superior frontal gyrus were noted in the LLD
group compared with the control group. We identified significantly enhanced FC
between the left dlPFC and left frontal orbital cortex, as well as lower FC
between the left dlPFC and right lateral occipital cortex, in the LLD group.
Significantly lower FC between the right dlPFC and the left middle frontal gyrus
and between the right inferior frontal gyrus and left caudate were noted in the
LLD group compared with the control group. The resting-state FC of the left mPFC
seed with the left caudate was significantly higher in the LLD group than the
control group (all p < 0.05, corrected). Regardless of whether the analysis
included GSR or not, the results consistently showed similar outcomes (Appendix
1, Figure 2).
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Figure 2
Regions showing significant differences in functional connectivity between
patients with late-life depression and healthy controls. The colour bar shows
the scale of the t values. Seeds were set in the bilateral dorsal anterior
cingulate cortex (dACC), dorsolateral prefrontal cortex (dlPFC), and medial
prefrontal cortex (mPFC). All p < 0.05 (corrected for false discovery rate).
Supporting data are presented in Table 2.
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Table 2
Seed-based functional connectivity analysis of significant differences between
patients with late-life depression (LLD) and healthy controls (HC)
DEPRESSION-RELATED GUT MICROBES AND BRAIN FUNCTIONAL CONNECTIVITY PATTERNS
After identifying the genera of gut microbiota and the characteristic brain FC
among patients with LLD, we further examined the associations between the
relative abundance of gut microbiota and seed-based resting-state brain
networks. We found that group-characterized micro-organisms were significantly
correlated with group-characterized brain networks (Figure 3 and Figure 4, all
correlations p < 0.05). As shown in Figure 4, we further noticed that the
control-dominant gut microbes were positively correlated with all 3 seed-based
FCs that were more enhanced among controls, while the abundance of these
microbes was negatively correlated with all of the FCs that were more enhanced
among patients with LLD. With the same trend direction, the LLD-dominant gut
microbes (except Akkermansia) were positively correlated with the 3 seed-based
FCs that were enhanced among patients with LLD but negatively correlated with
the FCs that were enhanced among controls. These results suggest that
depression-specific gut microbes, either increasing or decreasing, share a
similar direction trend with depression-characterized FCs and may contribute to
the clinical manifestations of LLD. Using a multiple comparison regression model
to adjust for antidepressant drug usage, sex, and dietary patterns, significant
correlations between brain FC and Alistipes, Barnesiella, and Odoribacter
remained significant (Appendix 1, Figure 3). The detailed association between
group-characterized FC and microbial genera is shown in Appendix 1,
Supplementary Table 1. In addition, neither α- nor β-diversity showed a
significant association with FC among participants.
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Figure 3
Group-characteristic gut microbiota correlated with brain functional
connectivity (FC) among patients with late-life depression and controls. The FC
is based on the seeds in the dorsolateral prefrontal cortex (dlPFC), dorsal
anterior cingulate cortex (dACC), and medial prefrontal cortex (mPFC). Gut
microbiota and FC matrices are shown as circles and squares, respectively. The
sizes of circles and squares are proportional to the numbers of associated
microbiota taxa or rain networks, respectively. The connection lines represent
significant correlations (p < 0.05) between gut microbes and FC networks.
Supporting data are presented in Appendix 1, Supplementary Table 2. fMRI =
functional magnetic resonance imaging; FOC = frontal orbital cortex; IFG =
inferior frontal gyrus; LOC = lateral occipital complex; MFG = middle frontal
gyrus; MTG = middle temporal gyrus; SFG = superior frontal gyrus.
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Figure 4
Heat map of Spearman rank correlation coefficients of the relative abundance of
different gut microbes at the genus level with brain functional connectivity
networks and clinical symptoms among patients with late-life depression (LLD)
and healthy controls (HC). The darkness of the colour represents the magnitude
of the correlation. Supporting data are presented in Appendix 1, Supplementary
Table 3. dlPFC = dorsolateral prefrontal cortex; dACC = dorsal anterior
cingulate cortex; FOC = frontal orbital cortex; HAMD = Hamilton Depression
Rating Scale; IFG = inferior frontal gyrus; LOC = lateral occipital cortex; MFG
= middle frontal gyrus; mPFC = medial prefrontal cortex; MTG = middle temporal
gyrus; SFG = superior frontal gyrus.
DEPRESSION-RELATED GUT MICROBES AND CLINICAL DEPRESSION SYMPTOMS
We explored potential relationships between specific gut microbes distinguishing
either group and HAMD scores among all participants. The genera enriched among
patients with LLD, Enterobacter (r = 0.49, p = 0.0004) and Burkholderia (r =
0.50, p = 0.0003), showed positive correlations with HAMD scores, while the
genera enriched among healthy controls, including Sanguibacteroides (r = −0.36,
p = 0.01) and Odoribacter (r = −0.30, p = 0.04), showed negative correlations
with HAMD scores (Figure 4). The significant associations with Enterobacter and
Burkholderia persisted after adjusting for antidepressant drug usage, sex, and
dietary patterns (Appendix 1, Figure 3).
DISCUSSION
By comparing the gut microbes and brain resting-state fMRI results from patients
with LLD and controls, we found that patients were characterized by alterations
in microbial taxa composition, brain FC patterns, and clinical symptoms. Further
analysis showed FC in the emotion regulation network — including the fusiform
gyrus, caudate, orbital frontal cortex, and superior frontal gyrus — was related
to gut microbial dysbiosis among older adults with LLD, highlighting potential
targets along this axis.
We identified significant between-group differences in fecal microbiota and
brain connectivity using 16S rRNA sequencing and resting-state fMRI. These
differences align with previous studies on mood disorders, including
depression,31 bipolar disorder, and anxiety disorders. Reduced FC was mainly
observed in the frontal gyrus of patients with depression, related to emotional
perception and regulation, compared with healthy controls.33 These findings echo
previous reports showing correlations between depression symptoms and FC
involving various brain networks, such as the frontolimbic, temporolimbic, and
limbic striatal circuits, the affective network, the salience network, and the
DMN.34
We analyzed the correlation between depression-characterized gut microbes and
resting-state FC in the dlPFC, dACC, and mPFC. Functional connectivity in the
bilateral dlPFC was positively associated with healthy-enriched micro-organisms
and negatively associated with depression-enriched microbes. Structural and
functional alterations in the dlPFC are linked to resilience,35 with resilient
high-risk adolescent females showing greater FC in the dlPFC than those who
developed depression.36 In contrast, FC in the bilateral dACC and left mPFC
increased mainly in the depression group, showing negative correlations with
healthy-enriched microbes and positive correlations with
depression-characterized microbes. Altered connectivity in the mPFC (a DMN node)
and the dACC (a salience network node) is associated with anhedonia and
rumination in MDD.34 The enhanced FC in the dlPFC among healthy controls,
coupled with enhanced FC in the dACC and mPFC among patients with LLD, seems
compensatory. Functional connectivity from the dlPFC seed was correlated with
healthy-enriched microbiomes, while maladaptive FC patterns from the dACC and
mPFC seeds were linked to depression-characterized microbiomes in emotion
regulatory networks. These findings highlight the importance of disrupted
intrinsic connectivity in the pathophysiology of depression along the
microbiota–gut–brain axis.
We focused on specific regions of interest, revealing that FC in the left
temporal occipital fusiform gyrus (seed in the right dACC) and the left caudate
(seed in the left mPFC) was associated with the most differentiating microbiota
taxa between groups (Enterobacter, Alistipes, Barnesiella, Odoribacter,
Sanguibacteroides). The fusiform gyrus — involved in emotional perception and
expression recognition37 — shows functional abnormalities that are positively
correlated with depression severity.38 Positive correlations between α-diversity
evenness and regional homogeneity indices of the bilateral fusiform gyrus were
demonstrated among patients with schizophrenia.6 Another study found significant
negative associations between the relative abundance of probiotic Lactobacilli
and regional homogeneity values in the left fusiform gyrus among patients with
chronic insomnia.7 The caudate nucleus, part of the cortico–striatal–thalamic
circuits, plays roles in regulation and reward-based decision-making.39 Smaller
caudate volumes have been reported among older adults with depression compared
with healthy controls,40 and both structural and functional abnormalities in the
caudate are consistently implicated in MDD.41 Disrupted brain connectivity in
the caudate and microbiota linkage has been found among patients with irritable
bowel syndrome.42 Probiotic Bifidobacterium longum NCC3001 consumption reduced
depressive symptoms and altered brain activity in frontolimbic regions,
including the caudate, compared with placebo among patients with irritable bowel
syndrome exposed to negative emotional stimuli.43 These brain networks are
likely central to brain–gut communication among patients with LLD. Gut microbes
modulate brain function through endocrine circulation, vagal sensory signalling,
and neurotransmitter signalling;3,4 neural signals alter gut sensorimotor and
secretory functions.44 Our results suggest that gut microbiota affects older
adults with depression through interactions with brain FC related to emotion
recognition.
We found that higher abundance of the genera Enterobacter (enriched among
depressed patients) and Odoribacter (enriched among healthy controls) was
associated with depression-characterized connectivity networks and symptoms of
clinical depression. Enterobacter, a genus within the family Enterobacteriaceae,
has been linked to bipolar disorder45 and depression;32 it has also been linked
to clinical depressive episodes among patients with bipolar disorder.46
Increased abundance of Enterobacteriaceae is associated with nosocomial
infections, central nervous system infections, lipid metabolism, inflammation,47
and a leaky gut,48 contributing to metabolic disturbances and proinflammatory
activities in mood disorders.45 Conversely, a lower abundance of Odoribacter —
linked to diseases like inflammatory bowel disease, nonalcoholic fatty liver
disease, and cystic fibrosis — has been found among patients with depression.49
Odoribacter metabolizes dietary fibre and resistant starch to produce
short-chain fatty acids and sulfonolipids, which regulate inflammation and alter
ceramide pathways.50 Aberrant sphingolipid metabolism, involved in Odoribacter’s
beneficial effects, has been reported among patients with depression.51 The
health-promoting metabolites of Odoribacter, such as butyrate, maintain
epithelial barrier function and gut homeostasis by exerting immunomodulatory
activity in the intestinal mucosa.52 Our study identified 2 specific microbes,
Enterobacter and Odoribacter, associated with altered brain FC involving emotion
regulation and clinical symptoms, suggesting potential targets for the treatment
of LLD. Moreover, recent studies demonstrate significant associations between
gut microbial diversity and inter-network FC involving the executive control,
default mode, and sensorimotor systems. These findings underscore how gut
microbiome profiles influence brain connectivity patterns and relate to
emotions, mood, and human behaviours, including executive function.53–55
Our study found that seed-based FC patterns showed consistent results whether
GSR was applied or not. However, Liu and colleagues56 recently demonstrated that
differences in DMN FC patterns between patients with MDD and controls were
noticeable only when including the global signal (non-GSR), rather than when
using GSR. These findings suggest that changes in DMN connectivity in MDD have a
global rather than local origin.23,56 Given the generalized connection between
gut and brain, a global approach may be more suitable for understanding their
relationship. Although our results showed consistent patterns before and after
GSR analysis, we cannot rule out the possibility of global influences on
resting-state FC among patients with LLD.57
LIMITATIONS
The cross-sectional study design limits causal inferences between gut microbiota
and brain activity. Future longitudinal studies with interventions targeting gut
flora are needed. Although we applied strict exclusion criteria, not all
confounding factors affecting gut microbial composition could be eliminated. The
relatively limited sample size means that our findings are exploratory or
preliminary. Therefore, we were not able to perform subgroup analyses based on
different categories of antidepressants. Larger independent samples are
necessary for reproducibility and detailed subgroup analysis. The varied
medications among patients may have influenced gut microbiota and brain
activities. Previous studies have shown clinical improvement after
antidepressants is associated with changes in brain connectivity. However, lack
of control groups in many studies leaves the extent of alterations in brain
connectivity after antidepressant treatment unclear.58 We found differences in
FC between patients with LLD and healthy controls, which may reflect fundamental
pathophysiology; yet the effect of antidepressants on gut microbiota remains
controversial.32,59 Future research on drug-naïve patients is needed to clarify
these effects. Patients with LLD reported lower calorie intake than controls.
Although previous studies have suggested that caloric restriction can alter the
composition of gut microbiota,60,61 the average intake of our patients was about
1300 kilocalories, minimizing the potential effect of lower calorie intake.
Nonetheless, dietary habits can affect gut bacteria composition and diversity,
complicating the attribution of changes solely to depression. Finally, we
examined associations between brain FC and gut microbial composition only.
Future studies should include metabolomics and metabolic neuroimaging
techniques, such as fluorodeoxyglucose positron emission tomography or magnetic
resonance spectroscopy, to elucidate the mediators and signalling pathways
linking gut microbes and brain FC.
CONCLUSION
By analyzing both gut microbiota and brain FC among patients with LLD, we
demonstrated a close correlation between FC in mood regulation–related brain
networks and alterations in the composition of fecal microbiota, which provides
evidence of the role of the microbiota–gut–brain axis in the pathophysiology of
MDD.
DATA AVAILABILITY
The data that support the findings of this study are available on request from
the corresponding author. The data are not publicly available because of
restrictions from the institutional review board as the information may
compromise the privacy of research participants.
FOOTNOTES
* Competing interests: None declared.
* Contributors: Chia-Fen Tsai, Pei-Chi Tu, Yen-Po Wang, Chung-Yen Lin, and
Ching-Liang Lu contributed to the conception and design of the work. Chia-Fen
Tsai, Chia-Hsien Chuang, Yen-Po Wang, Pei-Yi Liu, Po-Shan Wu, and Ching-Liang
Lu contributed to data acquisition. Chia-Fen Tsai, Pei-Chi Tu, Yen-Po Wang,
Pei-Yi Liu, Po-Shan Wu, Chung-Yen Lin, and Ching-Liang Lu contributed to data
analysis. Chia-Fen Tsai, Pei-Chi Tu, Wan-Chen Chang, Yen-Po Wang, Chung-Yen
Lin, and Ching-Liang Lu contributed to data interpretation. Chia-Fen Tsai,
Chia-Hsien Chuang, and Pei-Chi Tu drafted the manuscript. All of the authors
revised it critically for important intellectual content, gave final approval
of the version to be published, and agreed to be accountable for all aspects
of the work.
* Funding: This study was supported by the Taipei Veterans General Hospital
(no. V105C-201, V107C-153, V109C-136, V110C-119, V113C-146, VTA106-V1-6-1,
VTA107-V1-9-1, VTA109-V1-5-1); and the Ministry of Science and Technology,
Taiwan (MOST 104-2314-B-075-040, MOST 111-2221-E-075-006). The funding
sources did not have any role in processing of our manuscript.
* Received May 9, 2024.
* Revision received July 26, 2024.
* Accepted July 30, 2024.
This is an Open Access article distributed in accordance with the terms of the
Creative Commons Attribution (CC BY-NC-ND 4.0) licence, which permits use,
distribution and reproduction in any medium, provided that the original
publication is properly cited, the use is noncommercial (i.e., research or
educational use), and no modifications or adaptations are made. See:
https://i6372656174697665636f6d6d6f6e73o6f7267z.oszar.com/licenses/by-nc-nd/4.0/
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J Psychiatry Neurosci
Vol. 49, Issue 5
25 Oct 2024
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Interaction of the gut microbiota and brain functional connectivity in late-life
depression
Chia-Fen Tsai, Chia-Hsien Chuang, Pei-Chi Tu, Wan-Chen Chang, Yen-Po Wang,
Pei-Yi Liu, Po-Shan Wu, Chung-Yen Lin, Ching-Liang Lu
J Psychiatry Neurosci Sep 2024, 49 (5) E289-E300; DOI: 10.1503/jpn.240050
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Interaction of the gut microbiota and brain functional connectivity in late-life
depression
Chia-Fen Tsai, Chia-Hsien Chuang, Pei-Chi Tu, Wan-Chen Chang, Yen-Po Wang,
Pei-Yi Liu, Po-Shan Wu, Chung-Yen Lin, Ching-Liang Lu
J Psychiatry Neurosci Sep 2024, 49 (5) E289-E300; DOI: 10.1503/jpn.240050
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