Scientific Papers

Exploring the role of gut microbiota in autoimmune thyroid disorders: a systematic review and meta-analysis


1 Introduction

Hashimoto’s thyroiditis (HT) and Graves’ disease (GD) are the most frequent kinds of autoimmune thyroid disorders (AITDs) (1). Their aetiology may be broken down into three categories: environmental variables, genetic susceptibility, and immune system dysfunction (2). Thyroid autoantigens such thyroglobulin (TG), thyroid peroxidase (TPO), and the thyroid-stimulating hormone receptor are reactive because of immune system dysfunction. Thyroid follicle cells and immune system cells are affected by the resulting inflammatory infiltration and cytokine production (1, 3). Although they both have an autoimmune basis, the consequences of HT and GD on thyroid function are different, leading to different clinical manifestations. In contrast to GD, the most abundant reason of Hyperthyroidism in iodine-sufficient areas, which is associated with heat intolerance, weight loss, anxiety, trembling, irritability, and tachycardia (4), while HT determines hypothyroidism, is linked to fatigue, weakness, weight gain, dry skin (5), anaemia (6), and predisposition to depressive conditions (7). Graves’ ophthalmopathy (GO) is a common consequence of GD that causes symptoms including discomfort, excessive weeping, swelling of the eyelids, and sensitivity to light in 25-50% of patients. Corneal collapse Vision loss, and optic nerve neuropathy do occur, but only in a subset of individuals (8, 9)

The importance of microbiota in autoimmune illnesses has come into the spotlight recently. There is no one best pattern of gut bacteria since everyone has a different mix. Nonetheless, Firmicutes and Bacteroidetes are often the most abundant phyla. Factors such as gender, lifestyle age, pharmacological regimens, level of physical activity, and food all influence the microbiome’s makeup (10, 11).

Dysbiosis has been linked to the development of autoimmune illnesses such rheumatoid arthritis, atopic dermatitis, type 1 diabetes, SLE, inflammatory bowel disease, and autoimmune neurological disorders (12). Patho-mechanism interactions in these disorders may be studied at the level of bacteria and their metabolites. There are a number of proposed patho-mechanisms in the literature for AITDs. First, changes in intestinal bacterial composition may enhance intestinal permeability (13), which is linked to a higher amount of zonulin (14, 15), a protein that regulates the communication between cells. When the tight junctions between enterocytes are loosened, antigens from the microbiota may get through and trigger the immune system via molecular mimicry (16). The architecture of autoantigens and several bacterial antigens in the colon are very similar. This resemblance suggests that antigens produced on orbital fibroblasts and thyroid follicle cells in GO may stimulate plasma cells to produce antibodies (17).Increased autoantibody synthesis by posttranslational protein modification is another consequence of dysbiosis. In addition, it promotes the emergence of AITDs by reshaping the Th1 helper lymphocyte pool into the Th2 subset and activating the Toll-like receptor-4 (18, 19).

Thyroid function is also profoundly influenced by microbiome metabolites. Short-chain fatty acids (SCFAs) have been the primary focus of research because of their ability to improve enterocyte integrity, shield against the invasion of pathogenic microorganisms, affect the immune response, and suppress inflammatory response (20, 21). Furthermore, SCFAs have a pivotal role in regulating the ratio of Th17 cells to Treg cells, which is linked to the onset of autoimmune disorders (17, 22).

In addition, the microbiome has a role in thyroid hormone metabolism, which is part of the gut-thyroid axis. Deiodinases have been found in the human gut in previous research. Animal studies have shown that intestinal bacteria may absorb deconjugated iodothyronine and compete with human albumin for binding thyroid hormones (13, 23). In addition, enterohepatic metabolism of thyroid hormones is reportedly aided by intestinal bacteria (24, 25). In addition, the microbiota affects the absorption of microelements including iodine, copper, iron, selenium, and zinc that are vital to the health of the thyroid gland. Although studies in animals have demonstrated reduced iodine absorption in microbially deficient subjects, no such correlation has been shown in people with small bowel syndrome or following bariatric surgery who are parenterally fed (20, 26, 27). However, in low selenium circumstances, bioavailability decreases due to competitive bacterial absorption of selenium (26, 28, 29). Investigation of the role of the microbiome in thyroid diseases is warranted by the wide range of publications describing interactions between the thyroid gland and the microbiome as shown in Figure 1. The PICO (Population, Intervention, Comparison, and Outcome) question that guided the development of this systematic review was “Is there a difference in the microbiome of those who have autoimmune thyroid diseases?”

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Figure 1 Possible connections between changes in gut microbiota and the development of autoimmune thyroid illness.

2 Methodology

Tis meta-analysis is reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline (30), with its protocol registered in PROSPERO CRD42023432455.

2.1 Search strategy and data extraction

A systematic literature search for relevant studies published in English was conducted in the databases Scopus, PubMed, and Web of Science from inception up to December 2022. These are some examples of queries looked up for: (ophthalmopathy OR3orbitopathy) OR (Graves OR thyroid) AND (thyroiditis OR disease] AND (Hashimoto OR Graves) AND (microbiota OR microbiome). Studies published after the year 2015 were included in the analysis. Two researchers independently reviewed the findings’ titles, abstracts, and entire texts. PICO criteria was used to determine which studies would be included in this systematic review as shown in Table 1. In Figure 2, a comprehensive search process flowchart is shown.

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Table 1 Evaluation of the study’s overall quality, with special attention paid to the most serious sources of bias (risk category: green = low, yellow = uncertain, red = high; quality category: green = good, yellow = mediocre, red = bad).

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Figure 2 PRISMA Diagram.

2.2 Inclusion and exclusion criteria

The PICO parameters for this study are as follows. The inclusion criteria are patients with autoimmune thyroid diseases (AITD), including Graves’ Disease (GD) and Hashimoto Thyroiditis (HT), aged 18-65 years, of both genders. The exclusion criteria are individuals with additional autoimmune disorders. Intervention and comparison are not applicable, and the outcomes are changes in the quantity, richness, and diversity of the microbiota and changes in the microbiota’s composition without a clear indication of its diversity. The study design includes cohort, case-control, and cross-sectional studies, while conference reports, expert opinions, literature reviews, letters to the editor, case reports, and studies published before 2015 or not published in English are excluded.

2.3 Quality assessment

The “Study Quality Assessment Tool” developed by the National Heart, Lung, and Blood Institute at the National Institutes of Health was used to evaluate the potential for bias in each of the included studies. Both investigators filled out this form, and any discrepancies were discussed and settled. Supplementary Table 1 shows a summary of the quality ratings given to the different studies. Each possible risk criteria was assigned a point value from 1 (low) to 0.5 (uncertain) to 0 (high), and these values were added together to provide a summary of the critical evaluation. Twelve research (75%) were deemed to be of “good” quality (80% overall score), whereas four studies (25% total score) were deemed to be of “intermediate” quality. The Oxford Centre for Evidence-Based Medicine’s diagnostic evidence categorization system was used to evaluate the quality of the available research. All the studies that were considered for inclusion provided either moderate or strong evidence (levels 3 and 4 on this 5-point scale).

2.4 Data synthesis and statistical analysis

Using Comprehensive Meta-analysis Software (CMA version 4) we plotted the meta-analysis findings as forest plots. The GD and HT subsets were used in the meta-analysis. The comparative richness at the phylum and genus levels was computed as a continuous variable, and the pooled standardized mean differences for diversity indices were determined.

3 Results

16 papers were included in this evaluation after the studies were screened using the exclusion and inclusion criteria. As a result, data from approximately 750 human individuals with diagnosed AITDs) and 488 controls were obtained in five different countries. Figure 1 displays the study’ thorough selection process. Information on the publication year, study location, study contestants, AITD identification, exclusion and inclusion criteria, thyroid parameters measured, and supplementary drugs was gathered from each research that satisfied the inclusion criteria for this systematic review. Supplementary Table 1 lists all of the specifics, including the materials used in the lab, the microbiological procedures used, the changes in microbiota composition, and the variations in richness and diversity indices. Except for one study, which employed 16S rDNA gene sequencing, every other research looked at stool samples and studied it using 16S rRNA gene sequencing. Figures 36 show the plotted pooled standardized mean differences in the richness (ACE and Chao1) and diversity (Simpson and Shannon) indices for GD and HT. Every value was lower in GD patients than in control groups, although ACE was exceptionally low. The ACE and Chao1 indices, however, had substantially higher mean values in HT patients than in healthy controls (p-values of 0.0059 and 0.08, respectively). Table 2 shows significant variations in relative abundance (at the genus and phylum levels) across the included studies (which provided p-values for evaluations).

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Figure 3 Graves’ disease-specific pooled standardized mean differences in ACE and Chao1.

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Figure 4 Hashimoto’s thyroiditis-specific pooled standardized mean differences in ACE and Chao1.

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Figure 5 Differences in the pooled standard deviations of the Simpson and Shannon indices in Graves’ disease.

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Figure 6 Differences in the pooled standard deviations of the Simpson and Shannon indices in Hashimoto’s thyroiditis.

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Table 2 Relative abundance of significantly altered microbiota in autoimmune thyroid disease patients.

4 Discussion

4.1 Alpha-diversity of the microbiota in people with AITD

Specific alpha-diversity indices are used to describe the variety of bacteria in the microbiome and the number of microbial species. Richness can be measured using indices like Chao1 and ACE, whereas diversity can be measured using indices like Simpson and Shannon (41).

Patients with GD had low levels of richness indices than healthy people in all trials included in this meta-analysis. Three studies (32, 38, 44) found statistically significant differences. Six research (34, 38, 39, 42, 44, 45) revealed considerably lower scores on the Shannon index, whereas three studies (38, 42, 45) reported significantly lower scores on the Simpson index, and one study reported significantly higher scores (44). One research showed drastically different findings, with GD patients showing higher values for all alpha-diversity indices (41). Despite these findings, the meta-analysis demonstrated a significant downward trend in all indicators in GD patients as compared to healthy controls. Hypothesized link between reduced diversity and inflammation caused by altered host immune function (44). A decline in microbial diversity has been linked to weakened ecosystem function, leaving it more vulnerable to harmful influences from the outside (47). Several clinical problems, including obesity, inflammatory bowel disease, colon cancer, diabetes, and polycystic ovarian syndrome have been linked to this condition (4850). Interestingly, Chen et al. (42) found that methimazole therapy dramatically increased microbiome diversity after 3-5 months.

Richness indices were observed to be greater in HT participants than in healthy people in two investigations (31, 33). The Simpson and Shannon indexes were both found to have similar results in these analyses. Two studies (37, 43) found that the Shannon index was lesser in HT participants compared to healthy persons. Both richness indices and the Shannon index were shown to rise in HT patients by the current meta-analysis. This may be because hypothyroid individuals have prolonged gastrointestinal transit times due to intestinal dysmotility (51), making them more susceptible to bacterial overgrowth. An increase in protein breakdown and a reduction in polyphenol conversion, epithelial turnover, and mucus production (18) are just some of the negative impacts that have been linked to a more diverse microbiota.

The majority of the included studies also revealed Good’s coverage indices of more than 99%, indicating that the present sequencing depth correctly captured the state of fecal samples for gut microbiota. It’s also important to remember that factors like food, eating habits, and origin may have a major impact on the variety of microbes living in your digestive tract (52, 53). However, only five studies (34, 41, 42, 45), and (43) eliminated strict vegetarians because they did not account for their diet. Vegetarians, according to Losasso et al. (54), have much higher Chao1 indices of richness than omnivores. Other investigations (55, 56) could not support these dietary differences between omnivores and vegans or vegetarians. Cigarette smoking, a major risk factor for both GD and GO (57), was also a possible confounding factor since it has been shown to alter the gut microbiota (57, 58).

4.2 Microbiota abundance in patients with GD and HT

The gut microbiome has been under scrutiny in recent years for its potential involvement in diversity’s emergence and maintenance. Consistent with the findings of the alpha-diversity study, there were substantial differences in the phylum and genus makeup of the intestinal microflora between GD participants and healthy group. The human gut microbiota is dominated by the Firmicutes and Bacteroidetes phyla, which together account for almost 90% of the whole population. The ratio of Firmicutes to Bacteroidetes (F/B) is an important indicator of gut dysbiosis and has been linked to a wide range of clinical states (59, 60). The F/B ratio was consistently lower in GD participants compared to healthy participants, indicating a role for this index in the development of GD in the majority of studies that measured it. In patients with GO, a similar correlation was seen (34, 45). However, Yang et al. (35) found that the fraction of Firmicutes was considerably greater in GD patients compared to controls, whereas the number of Bacteroidetes was significantly lower. Obesity is often associated with an elevated F/B ratio (48). Due to the increased basal metabolic rate and the influence of thyroid hormones on the composition and function of the gut flora, weight changes may occur (44). This meta-analysis found that compared to healthy individuals, AITD patients had a greater abundance of Bacteroidetes but no difference in the abundance of Firmicutes.

The variation in the number of the genera was substantially greater between GD patients and healthy controls at lower taxonomic levels. Two investigations (41, 44) found that GD patients had a considerably greater abundance of Bacteroides than the healthy controls. Bacteroides was shown to be considerably less common in the intestinal microbiota of GD patients compared to healthy controls, according to research published by Shi et al. (45). The INDIGO European collaboration found many disease-associated taxa, notably reduced Bacteroides, in a mouse model of GD/GO (61). Antibiotics, probiotics, and the transfer of human fecal material all influenced the gut microbiota of the GD/GO mouse model, leading to the development and modification of the illness (62). The same multinational team of researchers subsequently validated that GD and GO patients had a lesser richness of Bacteroides and a greater richness of Actinobacteria than healthy controls. Bacteroides produces leaky gut syndrome (LGS), characterized by tight junctions, reduction in mucin production, and intestinal permeability, by fermenting glucose and lactate to SCFAs other than butyrate, such as succinate, acetate, and propionate. In addition, this causes a disturbance in gut homeostasis, which may play a role in the etiology and worsening of autoimmune illnesses (63). Additionally, five studies (32, 39, 4144) found a significantly increased prevalence of Prevotella in GD patients. In chronic inflammatory illnesses, Prevotella mediate mucosal inflammation, leading to the systemic dissemination of inflammatory mediators and bacterial products. The majority of species in this genus activate Toll-like receptor 2, which in turn induces Th17-polarizing cytokine production (IL-1, IL-6, and IL-23) and neutrophil recruitment (IL-17 synthesis) and so facilitates neutrophil recruitment (64). Prevotella may potentially influence the effectiveness of GD medications, as shown by Yan et al. (39). Similarly, Prevotella was found in greater numbers in GO patients (34).

Furthermore, it was shown that the phylum Actinobacteria was more prevalent in the afflicted than in the healthy, particularly for two genera. Bifidobacterium species may be protective or progressive in autoimmune disorders, making their involvement in immunopathogenesis unclear (65). Autoimmune illnesses are linked to Th17 polarization, which is induced by bacteria like Bifidobacterium bifidum (66). Collinsella overgrowth is also linked to increased interleukin-17 production and altered intestinal mucosal permeability (67).

However, compared to patients with GD, the comparative richness of the gut microflora in HT participants was equal to that of healthy participants, demonstrating an opposite pattern of changes. The Firmicutes genus Blautia illustrates the concept of inverse interdependence between GD and HT. Beneficial anti-inflammatory effects may be mediated by these commensal bacteria (68). Blautia abundance was also observed to be inversely associated to visceral fat deposition in both sexes (69).

In addition to diversity, the type of food also affects the comparative richness of the gut microflora. Previous research has linked the Mediterranean diet to a decrease in Streptococcus and a rise in Bifidobacterium, Prevotella, and Roseburia, all of which degrade dietary fiber (56, 70). Lower levels of Lactobacillus and Faecalibacterium were associated with a Western diet heavy in fast food (70, 71).

4.3 Thyroid functional parameters and correlations with changes in microbiota

Examining the connections between immunological and functional changes in microbiota composition and thyroid functioning parameters might provide light on the involvement of microflora in the prognosis of AITDs. Significant associations were identified for TPOAb, one of the thyroid functioning measures examined. Changes in the composition of the microbiota were also linked to elevations or decreases in TSH and TRAb. A small subset of bacteria were found to have any association with TGAb. However, determining the precise orientations of these relationships is challenging. Bacteroidetes linked inversely with TSH and favorably with TPOAb and TRAb at the phylum level (38, 41, 43). However, the connections between Proteobacteria and Synergistetes were very dissimilar (30, 42). In the phylum Firmicutes in particular, there was a large variation in findings at the genus level. TSH was inversely connected with Veillonella and Streptococcus, and positively correlated with TRAb and TPOAb, with the latter genera also correlating with TGAb (33, 38). Furthermore, Bifidobacterium demonstrated the similar results (46). In contrast, Bacteroides and Faecalibacterium strains demonstrated contradictory associations (33, 36, 38, 41, 42, 44).

Collectively, our results provide credence to the hypothesis that microbiota modifications are strongly linked to the onset and progression of AITDs by confirming substantial associations between various gut bacteria and thyroid measures. The oral bacteria Veillonella and Streptococcus cause periodontitis and dental cavities (72). These two families have been shown to have metabolic crosstalk that induces cytokine production by dendritic cells, potentially disrupting thyroid autoimmunity (73). And since their amino acid sequences are similar to TG and TPO, strains of Lactobacillus and Bifidobacterium may preferentially interact with autoantibodies, setting off AITDs via molecular simulation pathways (74).

However, low levels of Faecalibacterium are associated with an increased risk of developing inflammatory bowel disease and colon cancer in the gut (75), despite the fact that this bacterium is often regarded as beneficial during autoimmune processes. In addition, it’s linked to a drop in antibodies that stimulate the thyroid gland (36). Similarly, a decrease in Phascolarctobacterium levels has been linked to changes in SCFA synthesis and, by extension, a disruption of immunological homeostasis that makes the host more vulnerable to metabolic and gastrointestinal illnesses (76). The ratio of Th17 to Treg cells, which controls the production and release of autoantibodies in people with autoimmune disorders, may be influenced by the phylum-level abundance of Synergistetes (77).

Taking antithyroid medications (ATD) may also alter the bacteria that reside in your intestines. Only in vitro investigations involving 40 different bacterial strains have documented any effects at all from ATD, and those effects were considered to be rather small (78).

Intestinal microbial community composition may also be influenced by gender and sex hormone levels (79). Women are more likely to have and struggle to treat subclinical thyroid problems (80). Small intestine bacterial overgrowth (SIBO) has been linked to the development of subclinical hypothyroidism (SCH). TPOAb positivity was found to be more common in SIBO-positive participants than in SIBO-negative individuals, as reported by Wang et al. (81). Differences in intestinal microflora composition and metabolic function were also seen between TPOAb-positive and TPOAb-negative individuals in a separate investigation of pregnant women with SCH (82).

A similar meta-analysis was conducted in 2021 by Gong et al. and showed a significant correlation between the dynamics of gut microbiota and occurrence of Autoimmune Thyroid Disease. However, research has grown exponentially to determine this relationship since 2021, and a new meta-analysis was warranted to coalesce these findings (83).

5 Limitations

Particularly, this systematic evaluation is constrained by the shortcomings of the papers that were included. These studies sometimes had limited sample numbers, with people of different ages or sexes serving as controls. Most of the studies were done in Asia, but we also included one from Europe, one from Africa, and one from South America. Due to a lack of comprehensive data on diversity indices and relative abundance of the chosen phyla and genera, not all research could be included in the meta-analysis (and what data was available was only shown in inaccurately scaled diagrams). Despite the increasing availability of data from other, separate groups, only one research has provided an unambiguous description of GO patients. Although modifications in the gut microbiota were determined in all these investigations, the oral microbiome was not studied, which might be of relevance in future research.

6 Conclusion

This meta-analysis found that people with both GD and HT had significantly different gut microbiota in terms of diversity and composition. Patients with HT had greater diversity indices than healthy participants, whereas those with GD had lower values. Patients with GD also had a greater relative abundance of Bacteroidetes and Actinobacteria. TPOAb levels are often associated with changes in the diversity of microbiota. Additional research is needed to verify these results.

Author contributions

DA, NA, US and RS: were responsible for the conception or design of the work, drafting the work, giving final approval, and ensuring accuracy and integrity. SF, MF, FR and RW: were responsible for interpreting the data for the work, drafting the work, giving final approval, and ensuring accuracy and integrity. SG, KA, MA and DT: were responsible for interpreting the data, drafting the work, giving final approval, and ensuring accuracy and integrity. HS AD, MH and AT were responsible for interpreting the data, drafting the work, giving final approval, and ensuring accuracy and integrity. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1238146/full#supplementary-material

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