Gut microbiota and its relation to inflammation in patients with bipolar depression: a cross-sectional study

doi:10.21203/rs.3.rs-2723530/v1

Download PDF

Research Article

Gut microbiota and its relation to inflammation in patients with bipolar depression: a cross-sectional study

https://doi.org/10.21203/rs.3.rs-2723530/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 19 May, 2023

Read the published version in Annals of General Psychiatry →

You are reading this latest preprint version

Background

To explore the gut microbiota characteristics in depressed patients with bipolar disorder (BD) as well as the connection between the gut microbiota and inflammatory markers.

Methods

Totally 72 depressed BD patients and 16 healthy controls (HCs) were enrolled in the study. Blood and feces samples were taken from each subject. With the help of 16S-ribosomal RNA gene sequencing, the characteristics of the gut microbiota in each participant were examined. Correlation analysis was then utilized to assess the relationship between the gut microbiota and clinical parameters.

Results

We found the taxonomic composition of the gut microbiota, but not its diversity, was significantly different in BD patients compared to HCs. We found the abundance of Bacilli, Lactobacillales and genus Veillonella were higher in BD patients than in HCs, while genus Dorea was more abundant in HCs. Additionally, correlation analysis showed that the bacterial genera’ abundance in BD patients was strongly correlated with the severity of depression and inflammatory markers.

Conclusions

According to these results, the gut microbiota characteristics were changed in depressed BD patients, which may have been associated with the severity of depression and the inflammatory pathways.

Bipolar disorder

depression

gut microbiota

gene sequencing

inflammation

Bipolar disorder (BD) is a severe mood disorder that results in significant deficits in social functioning and is characterized by recurring manic/hypomanic or depressive episodes ^[1]. Recent developments in the sciences of genetics ^[2], epigenetics ^[3], metabolomics ^[4], and radiomics ^[5] have started to reveal the complexity of BD. But there are still no clear indicators for the pathophysiology of BD, making clinical diagnosis difficult. Therefore, there is an urgent need to further explain the pathophysiology and biomarkers of BD.

Inflammatory mechanisms have been linked by prior research to the pathophysiology of BD. Serum and plasma inflammatory factors changes were observed in BD patients, such as IL-6, IL-8, TNF-α, interferon (INF)-γ and C-reactive protein (CRP) ^{[6, 7]}. Additionally, earlier research revealed that BD patients have lower levels of cytotoxic T lymphocytes (CD3 + CD8+) ^{[8, 9]}. Increased production of inflammatory cytokines linked to brain plasticity, the hypothalamic-pituitary-adrenal (HPA) axis, and neurotransmitter metabolism may cause the start of BD ^[10]. It is yet unknown how these elements interact, though.

The microbiota-gut-brain (MGB) axis, a system that mediates bidirectional modulation between the brain and the gut microbiota through neuroanatomical, neuroimmune, and neuroendocrine pathways, is currently receiving increased attention in relation to mental illnesses ^{[11, 12]}. The development of mood disorders may be influenced by ongoing low-grade immune activation seen in the gut mucosal barrier ^[13]. Probiotic and prebiotic supplements may help to reduce inflammatory reactions and encourage the restoration of intestinal barrier function ^[13]. For instance, recent research has shown that giving probiotics to mice for five weeks reduces depressive-like behavior and TNF and IL-6 levels ^[14]. Maternal-separated rats with Bifidobacteria-treated exhibited a lower degree of anxiety in the swimming and climbing tests ^[15]. Notably, germ-free mice showed increased depressive-like behaviors after receiving fecal transplantation from depressed patients ^[16]. The brain-gut-microbiota axis has so far provided preliminary evidence from ongoing studies that the gut microbiota plays a significant role in BD ^[17]. Given that the gut microbiota has been linked to immune function, gut dysbiosis may be a significant component in BD patients' immunological failure ^[18].

Therefore, this preliminary study aimed to investigate the gut microbiota characteristics in depressed BD patients and healthy controls (HCs), and its relation to inflammation and clinical parameters.

Participants

In the study, drug-naive BD patients with depressive episodes from the Psychiatry Department of the First Affiliated Hospital of Zhejiang University were enrolled in the study. As HCs, healthy volunteers from the neighborhood were enlisted. All participants have signed the written informed consent for this study. All depressed BD patients' clinical data was gathered by a psychiatrist. The diagnosis was obtained using the Mini International Neuropsychiatric Examination (M.I.N.I.) ^[19], a structured psychiatric interview in accordance with DSM-IV-TR criteria. BD individuals with any additional psychiatric or severe physical conditions were disqualified. Additionally, all individuals were prohibited from using probiotics, prebiotics, or antibiotics within 4 weeks. All female contestants who were pregnant or nursing were disqualified.

Clinical data collection

Demographic and clinical data of all participants, including sex, age, body mass index (BMI), and onset age, duration of illness, disease type, and family history, were collected. The Montgomery-Asberg Depression Rating Scale (MADRS) ^[20] was used to evaluate patients' depressive symptoms clinically. The Young Mania Rating Scale (YMRS) was used to define the severity of mania ^[21].

Plasma cytokines and T lymphocytes level determination

Venous blood samples (1 mL) were collected from all patients between 8:30 a.m. and 10:30 a.m. after visiting the hospital. The lymphocyte subsets were identified using 50uL blood samples, and the remaining blood was separated within 15 minutes, with the plasma being stored at -80°C. The plasma levels of cytokines (IL-2, IL-4, IL-6, IL-10, IL-17A, and TNF-α) were determined by a BD cytometric bead array (CBA) kit, the human Th1/Th2/Th17 kit (BD Biosciences, CA, USA), according to the manufacturer's instructions. Briefly, the CBA assay contained six beads coated with capture antibodies, and each bead had different fluorescence intensities. The cytokine capture beads were mixed with test samples, antibodies on the cytokine capture beads combined with the corresponding antigen or protein in the samples, and then incubated with the phycoerythrin-conjugated detection antibodies to form sandwich complexes. Sample data were obtained and analyzed using FACS Calibur™ flow cytometer (BD Biosciences, CA, USA) and BD CBA Software (BD Biosciences, CA, USA). Each plasma cytokine was given its own standard curve. The minimum limit of detection for all the cytokines was 0.1 pg/ml. The CRP level was measured by enzyme-linked immunosorbent assay (ELISA) using the Human CRP ELISA kit (R&D Systems, Minnesota, USA) following the manufacturer’s instructions. The following antibodies were used for flow cytometry analysis of lymphocyte subsets (CD3, CD4, CD8, and natural killer [NK] cells): Pcy5-conjugated anti-CD3, FITC-conjugated anti-CD4, P-phycoerythrin-conjugated anti-CD8, and PE-conjugated anti-CD16/CD56 (BD Biosciences, CA, USA) (Beckman Coulter, CA, USA). Blood samples (50 µL) were added to a tube containing 10 µL of each antibody, mixed in the dark and incubated at room temperature for 20 minutes. Then red blood cells were lysed and the cells were fixed. Data on a minimum of 20,000 lymphocytes was counted and analyzed using the BD FACS Calibur™ flow cytometer (BD Biosciences, CA, USA) and BD CellQuest Pro software (BD Biosciences, CA, USA).

Fecal sample collection and gene sequencing

Fecal samples from all subjects were collected and divided into 0.2g each, then stored at -80°C within half an hour after collection. DNA was extracted using the PSP® Spin Stool DNA Plus kit following the manufacturer’s instructions. The degree of DNA degradation and potential contamination was estimated using agarose gel electrophoresis. The extracted DNA was diluted to 10ng/µL for microbial analysis after being quantified with a Qubit® Fluorometer.

The V3-V4 variable region in the 16S rRNA gene was selected, and the primers were 341F 5′-barcode-CCTACGGGNGGCWGCAG-3′ and 785R 5′-GACTACHVGGGTATCTAATCC-3′ for PCR assay. 2µl PCR products were taken for 2% agarose gel electrophoresis detection after the first round PCR amplification, then purified using AMPure XP Beads. In the second round PCR amplification, the target fragments were cut and recovered, and the library was purified and then checked for library quality. The high-throughput sequencing was performed using an Illumina MiSeq platform according to the manufacturer’s instructions.

Clusters randomly selected sequences from all samples into operational taxonomic units (OTU) using the "Usearch -cluster_otus" function with default parameters. OTU profiles were constructed by aligning randomly picked sequences with a representative OTU sequence as a reference using the "Usearch -usearch_global" function with a 97% cutoff. The relative abundance of OTU was used to calculate the alpha diversity and beta diversity, and to analyze the microbiota with statistically significant differences in relative richness between the two groups.

Statistics analysis

Clinical data analysis was conducted using the SPSS 20.0 statistical software (IBM, IL, USA). Measurement data was expressed as "mean ± standard deviation (x̅ ± S)". The alpha diversity was calculated using the Wilcoxon rank sum test. The difference in gut microbiota between BD patients and HCs was compared using linear discriminant analysis (LDA), with an LDA score (log10) = 2 serving as the crucial value. The correlations between gut microbiota and clinical indicators in BD patients were analyzed using Pearson/Spearman correlation. P < 0.05 was used to indicate a statistical difference.

Clinical characteristics

Finally, 72 depressed BD patients and 16 HCs were recruited in the study. Compared to HCs, there was no discernible difference in the sex and BMI values in BD patients. The two groups did differ in terms of age, with HCs being more likely to be older (P < 0.05). Table 1 displays certain attributes.

Table 1

Demographic and Clinical Details of Recruited Subjects
Demographic and Clinical Indexes		BD		HCs		P
Demographic and Clinical Indexes		N = 72	%	N = 16	%	P
Sex	Female	33	47.14	9	56.25	0.59
Sex	Male	37	52.86	7	43.75	0.59
Age (year, mean ± SD)		24.16 ± 9.26		42.75 ± 11.22		< 0.001
BMI (kg/m², mean ± SD)		21.39 ± 9.32		21.89 ± 12.29		0.60
MARDS Scores (mean ± SD)		25.93 ± 10.18		0		< 0.001
YMRS Scores (mean ± SD)		0.45 ± 0.80		0.44 ± 0.73		0.81
Onset age (year, mean ± SD)		19.29 ± 7.58		-		-
Duration of illness (year, mean ± SD)		4.92 ± 5.63		-		-
Bipolar diagnosis	I	13	18.06
	II	45	62.50
	Other	14	19.44
Family history	Yes	16	22.22
	No	44	61.11
	Unknown	12	16.67
Abbreviations: BD, Bipolar disorder; HCs, Healthy controls; BMI, Body Mass Index; MARDS, Montgomery-Asberg Depression Rating Scale; YMRS, Young Mania Rating Scale.

Inflammatory profile changes in BD patients

Regarding to inflammatory profiles, significant differences were found in serum IL-6, TNF-α and CRP levels between BD patients and HCs. While no significant difference was found in IL-2, IL-4, IL-10, IL-17A and CD3, CD4, CD8, NK levels. Details are shown in Table 2.

Table 2

Serum Inflammatory Factors in BD patients and HCs
Inflammatory factors	BD	HCs	P
Inflammatory factors	N = 72	N = 16	P
IL-2 (pg/ml, mean ± SD)	0.41 ± 0.25	0.74 ± 0.52	0.29
IL-4 (pg/ml, mean ± SD)	0.15 ± 0.17	0.18 ± 0.82	0.69
IL6 (pg/ml, mean ± SD)	17.72 ± 42.97	0.43 ± 0.38	0. 007
IL-10 pg/ml, (mean ± SD)	0.94 ± 1.47	0.95 ± 1.40	0.99
IL-17A (pg/ml, mean ± SD)	3.13 ± 9.39	0.99 ± 2.86	0.31
TNFα (pg/ml, mean ± SD)	1.89 ± 4.34	0.47 ± 0.44	0.045
CRP (mg/l, mean ± SD)	1.77 ± 2.87	0.32 ± 0.38	0.025
CD3 (%, mean ± SD)	65.86 ± 12.66	62.60 ± 12.73	0.34
CD4 (%, mean ± SD)	33.50 ± 9.33	33.05 ± 9.66	0.82
CD8 (%, mean ± SD)	27.82 ± 9.69	26.56 ± 9.17	0.62
NK (%, mean ± SD)	14.59 ± 6.82	17.91 ± 9.50	0.11
Abbreviations: BD, Bipolar disorder; HCs, Healthy controls; CRP, C-reactive protein; INF, interferon; NK, natural killer

Changes of gut microbiota diversity in BD patients

In the study, the changes in gut microbiota diversity were assessed by the α-diversity and the β-diversity indices. Several α-diversity indexes, including Shannon, Simpson, inverse Simpson [invSimpson], Obs, Chao and Incidence-based Coverage Estimators [ICE]) revealed no significant difference in gut microbiota α-diversity between BD patients and HCs (Fig. 1-i). The gut microbiota β-diversity was used to analyze the similarities and differences of gut microbiota in depressed BD patients and HCs. Principal coordinate analysis (PCoA) was performed to compare the phylogenetic similarity distance of microbial communities. There was no significant difference between depressed BD patients and HCs (Fig. 1-ii).

Changes of gut microbiota taxonomic composition in BD patients

According to the taxonomic information of bacteria, the bacterial species were carried out at the five levels of phylum, class, order, family and genus. Respectively, 97.6% and 91.03% of all reads could be assigned to family and genus levels. Lefse analysis showed that compared with HCs, the abundance of Bacilli, Lactobacillales and Veillonella was increased, while the genus Dorea was more abundant in HCs (P < 0.05, LDA score > 2) (Fig. 2).

Associations of gut microbiota with clinical parameters

We assessed the relationship between gut microbiota and clinical parameters. The severity of depression and inflammatory markers were closely associated with the abundance rates of bacterial genera in depressed BD patients (Fig. 3). MADRS scores were positively correlated with Lachnospiracea_incertae_sedis abundance, but negatively correlated with Faecalibacterium, Pseudomonas and Fusobacterium abundance (P < 0.05; Fig. 4). Serum levels of IL-6 were positively correlated with Enterobacter, Pseudomonas and Leuconostoc abundance, but negatively associated with Cloacibacillus abundance (P < 0.05). TNF-α levels correlated positively with Parabacteroides, Clostridium IV and Bilophila abundance but negatively with Prevotella abundance (P < 0.05). Furthermore, Serum CRP levels were positively correlated with Prevotella abundance, but negatively associated with Butyricicoccus, Lachnospiraceae incertae sedis and Dorea abundance (P < 0.05; Fig. 5).

Our study used high-throughput sequencing technology to explore the gut microbiota characteristics in depressed BD patients and the relationship between gut microbiota and inflammatory markers. The findings indicated that the taxonomic composition of the gut microbiota, but not its diversity, was significantly different in BD patients compared to HCs. Additionally, the abundance of several genera was correlated with inflammatory factors and depressive severity.

We found that there was a higher abundance of Dorea in healthy individuals, while a greater abundance of Veillonella was found in BD patients. This finding appears to be in line with previous studies. Blautia, Fecalibacterium, and Dorea were found to be related to cognition function and could alleviate the inflammatory reaction in cirrhosis patients ^[22]. Veillonella levels have been found to be higher in patients with irritable bowel syndrome ^[23], and the lipopolysaccharide (LPS) produced by Veillonella could stimulate the release of TNF-α, IL-1, IL-6 and IL-10 within Toll-like receptor (TLR) pathways ^[24]. As a result, Veillonella might participate in the brain-gut axis through inflammatory mechanisms.

Our study found that the abundance of Faecalibacterium, Pseudomonas and Fusobacterium was negatively correlated with the severity of depression. Previous studies have reported consistent conclusions that the negative association between Faecalibacterium abundance and depression severity in depressive and BD patients ^{[25, 26]}. Faecalibacterium was found to produce a protein which was involved in anti-inflammatory activities ^[27]. Notably, as a member of the Firmicutes phylum, Faecalibacterium was found to be important for the biosynthesis of the microbial product butyrate, which may be beneficial for immune accommodation, gut barrier regulation, gut metabolism, and energy modulation in the gut ^{[28, 29]}. Furthermore, butyrate in the central nervous system could influence the function of the hippocampus and facilitate upregulation of BDNF, which may have an antidepressant-like effect in animal models ^{[30, 31]}. Fusobacterium, which belongs to the Fusobacteria phylum, was also identified as one of the potential butyrate producers, which could modulate the gut microbiota and even modify anti-inflammatory, tumor-fighting, and metabolic pathways ^{[32, 33]}. Hence, low abundance of Faecalibacterium, Pseudomonas and Fusobacterium in depressed BD patients may have implications for the neurobiology of BD.

BD is closely associated with immune dysfunction, and a higher level of pro-inflammatory factors has been found in BD patients ^[34]. One study examined gut microbiota and its relationship to inflammation in BD patients and discovered that changes in gut microbiota in BD patients could be a factor contributing to immune alterations ^[35]. In the current study, serum IL-6 levels were found to be positively correlated with Enterobacter, Pseudomonas and Leuconostoc abundance, but negatively correlated with Cloacibacillus abundance in BD patients. Enterobacter belongs to the Enterobacteriaceae family and is enriched in aged individuals ^[36]. Compared with healthy controls, it has been previously reported that the abundance of Enterobacteriaceae and Alistipes was increased, and Faecalibacterium abundance was decreased in patients with major depressive disorder ^[26]. Our findings are the first to report the association between Cloacibacillus, an amino-acid-fermenting bacterium ^[37], and IL-6 in BD patients. In addition, TNF-α was found to be positively correlated with the abundance of Parabacteroides, Bilophila, and Clostridium IV, but negatively correlated with the abundance of Prevotella. When compared to non-stressed control mice, the abundance of Parabacteroides was found to be increased in stressor-exposed mice, as were the circulating levels of TNF-α and TNF-γ ^[38]. However, the relationship between the bacteria and these cytokine levels was not investigated in this study ^[38]. In contrast, another study showed that oral administration of Parabacteroides distasonis could reduce the production of TNF-α ^[39]. In healthy individuals, Bilophila was negatively correlated with LPS-induced production of TNF-α ^[40]. This is inconsistent with our findings in BD patients. Acute stress could reduce Prevotella abundance and result in an inflammatory response ^[41]. In our study, serum CRP concentration was positively correlated with Prevotella abundance, but negatively correlated with Butyricicoccus, Lachnospiraceae incertae sedis and Dorea abundance in BD patients. Similar results have been reported in obese adolescents where the abundance of Prevotella was positively associated with serum levels of triglycerides (TG) and high-sensitive CRP (hs-CRP) ^[42]. As a butyrate-producing bacteria, Butyricicoccus was positively correlated with anti-inflammatory cytokines such as IL-4, IL-10, and IL-11 ^[43]. The negative association between CRP and Butyricicoccus abundance may reflect an imbalance between pro- and anti- inflammatory reactions.

Several limitations of the study need to be pointed out. The influence of diet and region was not evaluated in this study. Although no significant difference was found in terms of gender and BMI between BD patients and controls, the diet and age of participants were not well controlled. Finally, the sample size in HCs was relatively small.

This study suggests that there are significant alterations in the taxonomic compositions of the gut microbiota in depressed BD patients, which may be related to inflammatory pathways and depression severity. The study provides preliminary evidence that the gut microbiota and inflammatory processes may have an impact on the neurobiology of BD.

BD: Bipolar disorder; CRP: C-reactive protein; HPA: hypothalamic-pituitary-adrenal; MGB: microbiota-gut-brain; HCs: healthy controls; MINI: Mini International Neuropsychiatric Interview; MADRS: Montgomery-Asberg Depression Rating Scale; BMI: body mass index; YMRS: Young Manic Rating Scale; CBA: cytometric bead array; ELISA: enzyme-linked immunosorbent assay; NK: natural killer; invSimpson: inverse Simpson; ICE: Incidence-based Coverage Estimators; PCoA : Principal coordinate analysis; LPS: lipopolysaccharide; TLR: Toll-like receptor; LDA: Linear discriminant analysis; Lefse: LDA effect size; hs-CRP: high-sensitive CRP.

Ethics approval and consent to participate

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. This study was approved by the Institutional Review Board of the First Affiliated Hospital, School of Medicine of Zhejiang University (reference number #2017-397).

Consent for publication

The participants give their consent to publication of their data.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The Authors declare that there is no conflict of interest.

Funding

This study was supported by the Program from the Health and Family Planning Commission of Zhejiang Province (No. 2021KY232 to Tingting Huang) and the Natural Science Foundation of Zhejiang Province (No. LTGY23H090012 to Tingting Huang).

Authors' contributions

XJ and HSH designed the project; HTT wrote the manuscript; HTT managed lab work and the data analyses. SYS, DCX and ZQX performed the experiments.

Acknowledgements

We sincerely acknowledge all the BD patients and healthy volunteers who participated in the study.

McIntyre R, Berk M, Brietzke E, Goldstein B, López-Jaramillo C, Kessing L, et al. Bipolar disorders. Lancet. 2020; 396: 1841-1856.
Craddock N, Sklar P. Genetics of bipolar disorder. Lancet .2013, 381: 1654-1662.
Ludwig B, Dwivedi Y. Dissecting bipolar disorder complexity through epigenomic approach. Mol psychiatry. 2016; 21: 1490-1498.
Lan M, McLoughlin G, Griffin J, Tsang T, Huang J, Yuan P, et al. Metabonomic analysis identifies molecular changes associated with the pathophysiology and drug treatment of bipolar disorder. Mol psychiatry. 2009;14: 269-279.
Wang Y, Sun K, Liu Z, Chen G, Jia Y, Zhong S, et al. Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis. Cereb cortex. 2020; 30: 1117-1128.
Drago A, Crisafulli C, Calabrò M, Serretti A. Enrichment pathway analysis. The inflammatory genetic background in Bipolar Disorder. J Affect Disord. 2015; 179: 88-94.
Fernandes B, Steiner J, Molendijk M, Dodd S, Nardin P, Gonçalves C, et al. C-reactive protein concentrations across the mood spectrum in bipolar disorder: a systematic review and meta-analysis. Lancet Psychiatry. 2016; 3: 1147-1156.
Wu W, Zheng YL, Tian LP, Lai JB, Hu CC, Zhang P, et al. Circulating T lymphocyte subsets, cytokines, and immune checkpoint inhibitors in patients with bipolar II or major depression: a preliminary study. Sci Rep. 2017; 7: 40530.
Barbosa I, Rocha N, Assis F, Vieira É, Soares J, Bauer M, et al. Monocyte and lymphocyte activation in bipolar disorder: a new piece in the puzzle of immune dysfunction in mood disorders. Int J Neuropsychopharmacol. 2014; 18: pyu021.
Berk M, Kapczinski F, Andreazza A, Dean O, Giorlando F, Maes M, et al. Pathways underlying neuroprogression in bipolar disorder: focus on inflammation, oxidative stress and neurotrophic factors. Neurosci Biobehav Rev. 2011; 35: 804-817.
Morais L, Schreiber H, Mazmanian S. The gut microbiota-brain axis in behaviour and brain disorders. Nat Rev Microbiol. 2021; 19: 241-255.
Long-Smith C, O'Riordan KJ, Clarke G, Stanton C, Dinan TG, Cryan JF. Microbiota-Gut-Brain Axis: New Therapeutic Opportunities. Annu Rev Pharmacol Toxicol. 2020; 60: 477-502.
Rios A, Maurya P, Pedrini M, Zeni-Graiff M, Asevedo E, Mansur R, et al. Microbiota abnormalities and the therapeutic potential of probiotics in the treatment of mood disorders. Rev Neurosci. 2017; 28: 739-749.
Abildgaard A, Elfving B, Hokland M, Wegener G, Lund S. Probiotic treatment reduces depressive-like behaviour in rats independently of diet. Psychoneuroendocrinology. 2017; 79: 40-48.
Desbonnet L, Garrett L, Clarke G, Kiely B, Cryan J, Dinan T. Effects of the probiotic Bifidobacterium infantis in the maternal separation model of depression. Neuroscience. 2010; 170: 1179-1188.
Zheng P, Zeng B, Zhou C, Liu M, Fang Z, Xu X, et al. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host's metabolism. Mol Psychiatry 2016; 21: 786-796.
Huang T, Lai J, Du Y, Xu Y, Ruan L, Hu S. Current Understanding of Gut Microbiota in Mood Disorders: An Update of Human Studies. Front Genet. 2019; 10: 98.
Borre Y, O'Keeffe G, Clarke G, Stanton C, Dinan T, Cryan J. Microbiota and neurodevelopmental windows: implications for brain disorders. Trends Mol Med. 2014; 20: 509-518.
Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998; 59 Suppl 20: 22-33;quiz 34-57.
Montgomery SA, Asberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979; 134: 382-389.
Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978; 133: 429-435.
Bajaj J, Hylemon P, Ridlon J, Heuman D, Daita K, White M, et al. Colonic mucosal microbiome differs from stool microbiome in cirrhosis and hepatic encephalopathy and is linked to cognition and inflammation. Am J Physiol Gastrointest Liver Physiol. 201; 303: G675-685.
Tana C, Umesaki Y, Imaoka A, Handa T, Kanazawa M, Fukudo S. Altered profiles of intestinal microbiota and organic acids may be the origin of symptoms in irritable bowel syndrome. Neurogastroenterol Motil. 2010; 22: 512-519, e114-515.
Matera G, Muto V, Vinci M, Zicca E, Abdollahi-Roodsaz S, van de Veerdonk F, et al. Receptor recognition of and immune intracellular pathways for Veillonella parvula lipopolysaccharide. Clin Vaccine Immunol. 2009; 16: 1804-1809.
Evans SJ, Bassis CM, Hein R, Assari S, Flowers SA, Kelly MB, et al. The gut microbiome composition associates with bipolar disorder and illness severity. J Psychiatr Res. 2017; 87: 23-29.
Jiang H, Ling Z, Zhang Y, Mao H, Ma Z, Yin Y, et al. Altered fecal microbiota composition in patients with major depressive disorder. Brain Behav Immun. 2015; 48: 186-194.
Quévrain E, Maubert M, Michon C, Chain F, Marquant R, Tailhades J, et al. Identification of an anti-inflammatory protein from Faecalibacterium prausnitzii, a commensal bacterium deficient in Crohn's disease. Gut. 2016; 65: 415-425.
Tanca A, Abbondio M, Palomba A, Fraumene C, Manghina V, Cucca F, et al. Potential and active functions in the gut microbiota of a healthy human cohort. Microbiome 2017, 5(1): 79.
Leonel A, Alvarez-Leite J. Butyrate: implications for intestinal function. Curr Opin Clin Nutr Metab Care. 2012; 15: 474-479.
Yamawaki Y, Fuchikami M, Morinobu S, Segawa M, Matsumoto T, Yamawaki S. Antidepressant-like effect of sodium butyrate (HDAC inhibitor) and its molecular mechanism of action in the rat hippocampus. World J Biol Psychiatry. 2012; 13: 458-467.
Wei Y, Melas P, Wegener G, Mathé A, Lavebratt C. Antidepressant-like effect of sodium butyrate is associated with an increase in TET1 and in 5-hydroxymethylation levels in the Bdnf gene. Int J Neuropsychopharmacol. 2014; 18.
Vital M, Howe A, Tiedje J. Revealing the bacterial butyrate synthesis pathways by analyzing (meta)genomic data. mBio. 2014; 5: e00889.
Fernández J, Redondo-Blanco S, Gutiérrez-del-Río I, Miguélez EM, Villar CJ, Lombó F. Colon microbiota fermentation of dietary prebiotics towards short-chain fatty acids and their roles as anti-inflammatory and antitumour agents: A review. J Funct Foods. 2016; 25: 511-522.
Colpo G, Leboyer M, Dantzer R, Trivedi M, Teixeira A. Immune-based strategies for mood disorders: facts and challenges. Expert Rev Neurother. 2018; 18: 139-152.
Painold A, Mörkl S, Kashofer K, Halwachs B, Dalkner N, Bengesser S, et al. A step ahead: Exploring the gut microbiota in inpatients with bipolar disorder during a depressive episode. Bipolar disord. 2019; 21: 40-49.
Crapser J, Ritzel R, Verma R, Venna V, Liu F, Chauhan A, et al. Ischemic stroke induces gut permeability and enhances bacterial translocation leading to sepsis in aged mice. Aging. 2016; 8: 1049-1063.
Looft T, Levine U, Stanton T. Cloacibacillus porcorum sp. nov., a mucin-degrading bacterium from the swine intestinal tract and emended description of the genus Cloacibacillus. Int J Syst Evol Microbiol. 2013; 63: 1960-1966.
Bailey M, Dowd S, Galley J, Hufnagle A, Allen R, Lyte M. Exposure to a social stressor alters the structure of the intestinal microbiota: implications for stressor-induced immunomodulation. Brain Behav Immun .2011, 25: 397-407.
Kverka M, Zakostelska Z, Klimesova K, Sokol D, Hudcovic T, Hrncir T, et al. Oral administration of Parabacteroides distasonis antigens attenuates experimental murine colitis through modulation of immunity and microbiota composition. Clin Exp Immunol. 2011; 163: 250-259.
Schirmer M, Smeekens S, Vlamakis H, Jaeger M, Oosting M, Franzosa E, et al. Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity. Cell. 2016; 167: 1125-1136.e1128.
Maslanik T, Tannura K, Mahaffey L, Loughridge A, Beninson L, Benninson L, et al. Commensal bacteria and MAMPs are necessary for stress-induced increases in IL-1β and IL-18 but not IL-6, IL-10 or MCP-1. PloS one. 2012; 7: e50636.
Hu H, Park S, Jang H, Choi M, Choi M, Park K, et al. Obesity Alters the Microbial Community Profile in Korean Adolescents. PloS one. 2015; 10: e0134333.
Tao J, Duan J, Jiang S, Feng N, Qiu W, Ling Y. Chrysanthemum morifoliumPolysaccharides from Ramat ameliorate colitis rats by modulating the intestinal microbiota community. Oncotarget. 2017; 8: 80790-80803.

No competing interests reported.

Download PDF

Journal Publication

published 19 May, 2023

Read the published version in Annals of General Psychiatry →

Editorial decision: Accepted
13 May, 2023
Reviews received at journal
01 May, 2023
Reviewers agreed at journal
21 Apr, 2023
Reviewers invited by journal
17 Apr, 2023
Submission checks completed at journal
23 Mar, 2023
Editor assigned by journal
23 Mar, 2023
First submitted to journal
22 Mar, 2023

You are reading this latest preprint version

Gut microbiota and its relation to inflammation in patients with bipolar depression: a cross-sectional study

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1