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Profiling gut microbiome dynamics in subacute thyroiditis: Implications for pathogenesis, diagnosis, and treatment

  • Jiayun Li , Jiasheng Ju , Qian Xu , Xiang Han and Haibing Ju EMAIL logo
Published/Copyright: October 15, 2025
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Open Medicine
From the journal Open Medicine Volume 20 Issue 1

Abstract

Aim

The aim of this study is to characterize gut microbiome alterations in newly diagnosed subacute thyroiditis (SAT) patients, and identify potential microbial signatures associated with SAT and treatment response.

Methods

Fecal samples collected from 20 newly diagnosed SAT patients and 20 healthy controls were analyzed using 16S ribosomal RNA gene sequencing. Bioinformatics analysis was performed to assess alpha and beta diversity, taxonomic composition, and differential abundance of gut microbiota between the groups. Correlations between gut microbiome and clinical parameters were also investigated.

Results

Newly diagnosed SAT patients exhibited significant alterations in gut microbiota composition. There was increased abundance of Escherichia-Shigella, Bifidobacterium, Akkermansia, Veillonella, and Streptococcus, while the abundance of Bacteroidetes, Faecalibacterium, Prevotella, Roseburia, and Ruminococcus were significantly decreased. Prednisolone treatment partially normalized the gut microbiota, with Lactobacillus, Lactobacillaceae, Lactobacillus reuteri, and Prevotella emerging as key biomarkers in post-treatment SAT. Significant correlations were found between specific gut microbiome and clinical markers.

Conclusion

SAT is associated with distinct gut microbiome alterations, partially reversible with treatment, which suggest a potential role for the gut microbiome in SAT pathogenesis and treatment response.

Keywords: subacute thyroiditis; gut microbiota; 16S ribosomal RNA; prednisone

1 Introduction

The gut microbiome, a complex and diverse microbial ecosystem, is often referred to as the “second genome” due to its significant impact on human physiology. These microorganisms play pivotal roles in various physiological processes, including nutrient metabolism, immune modulation, endocrine system homeostasis, and neurodevelopment [1]. The thyroid gland’s embryonic origin in the primitive foregut [2], a shared developmental origin with the gastrointestinal system, suggests a potential relationship between thyroid disorders and gut microbiome dysbiosis. This developmental link underscores the importance of investigating the intricate relationship between these two systems to elucidate the role of gut microbiome in thyroid pathophysiology.

Advancements in sequencing technology have revolutionized the study of the gut microbiota by enabling the identification and quantification of bacterial populations at an unprecedented level of detail. This has unlocked novel insights into the intricate interplay between gut microbiome and human disease conditions. In 2017, Lerner et al. [3] provided compelling evidence for a potential pathogenic role of the gut microbiome in primary hypothyroidism, introducing the concept of the “thyroid-gut axis” [4] and highlighting the intricate relationship between these two systems. Subsequent studies have confirmed associations between autoimmune thyroid diseases [5,6,7,8], such as Graves’ disease (GD) and Hashimoto’s thyroiditis (HT), and dysbiosis in the gut microbiome.

Subacute thyroiditis (SAT) is the most prevalent self-limiting form of thyroiditis, characterized by thyroidal pain, systemic inflammation, and thyrotoxicosis. However, its precise etiology and pathogenesis remain incompletely understood [9], and the potential contribution of the gut microbiome to this condition has not been fully elucidated. The aim of this study is to address this knowledge gap by profiling the gut microbiota composition in SAT patients compared to healthy controls. Additionally, we sought to investigate dynamic changes in the gut microbiome before and after prednisolone treatment, and to assess potential correlations between these alterations and relevant clinical parameters. By identifying distinct microbial signatures associated with SAT, this research aims to provide insights into the underlying pathophysiology of the disease and potentially pave the way for the development of novel diagnostic biomarkers, personalized therapeutic interventions, and improved prognostication for SAT patients.

2 Materials and methods

2.1 Participant selection and enrollment

Inclusion criteria for the SAT cohort included a diagnosis of SAT according to the “Chinese Guidelines for the Diagnosis and Treatment of Thyroid Diseases: Thyroiditis”, age between 25 and 40 years, Han ethnicity, body mass index (BMI) between 18 and 28 kg/m2, and no history of other thyroid, autoimmune, endocrine, or metabolic disorders. Additionally, individuals with no recent history of diarrhea or antibiotic/probiotic use within 1 month prior to stool sample collection were included.

Exclusion criteria were a history of using antibiotics, hormones (excluding thyroid hormone replacement), laxatives, herbal medicines, or microbial preparations within 3 months prior to enrollment. Individuals with hypertension, diabetes mellitus, dyslipidemia, or a BMI outside the normal range were also excluded. Further exclusion criteria included a history of malignancy, gastrointestinal disease, or gastrointestinal surgery, as well as pregnancy, lactation, smoking, or alcohol consumption. Finally, individuals with other chronic inflammatory conditions were not eligible for participation.

Twenty newly diagnosed, treatment-naïve SAT patients (8 men, 12 women) with a mean age of 45.37 ± 10.53 years and a mean BMI of 24.17 ± 2.23 kg/m², presenting to the Endocrinology Department of the 920th Hospital of the Joint Logistics Support Force of the People’s Liberation Army of China between June 2022 and March 2023, were enrolled as the experimental group, and were treated with oral prednisone (0.5–1 mg/kg/day) for 6–8 weeks. Concurrently, 20 healthy individuals (9 men, 11 women) with a mean age of 48.24 ± 11.33 years and a mean BMI of 24.45 ± 2.56 kg/m², undergoing routine physical examinations during the same period, were recruited as the control group.

2.2 Sample collection and processing

Stool samples were collected from a total of 60 participants, comprising 20 SAT patients before treatment, 20 SAT patients after 12 weeks of treatment, and 20 healthy control subjects. All samples were immediately stored at −80°C. Blood samples were also obtained from SAT patients before and after treatment for subsequent analysis.

2.3 DNA extraction, amplification, and library preparation

Microbial genomic DNA was extracted from all stool samples using a modified cetyltrimethylammonium bromide protocol optimized for fecal material. DNA purity and integrity were assessed using spectrophotometry and agarose gel electrophoresis. The extracted metagenomic DNA served as the template for multiplex PCR and high-fidelity low-cycle PCR amplification. Amplicons were purified using AmPure XT beads (Beckman Coulter, CA) following the manufacturer’s instructions. Purified amplicons were quantified, normalized, and pooled in equimolar ratios to create the final sequencing library.

2.4 Sequencing and data processing

The DNA libraries were assessed for quality and quantity using an Agilent 2100 Bioanalyzer and Illumina library quantification kit. Libraries with a concentration of 2 nM or higher were considered suitable for sequencing. Paired-end sequencing (2 × 250 base pairs) was performed on a NovaSeq 6000 platform. Raw sequencing data underwent quality filtering, adapter trimming, and chimera removal using established pipelines.

2.5 Bioinformatics analysis

Amplicon Sequence Variants (ASVs) were inferred using Qiime dada2 denoise-paired and served as the operational taxonomic units for downstream analysis. Taxonomic classification and annotation of ASVs were performed against the SILVA 16S rRNA gene database. Alpha diversity and beta diversity metrics were calculated to assess microbial community richness, evenness, and composition. Differential abundance analysis was conducted to identify taxa significantly associated with SAT status and treatment response.

2.6 Statistical analysis

Statistical analyses and visualization were performed using R (Version 4.2.1), Origin (Version 2021b), and SPSS (Version 26.0). Spearman correlation analysis was used to assess the relationship between gut microbiome composition and clinical indicators. For normally distributed data, results were presented as mean value ± standard deviation (SD). Independent t-tests were used for between-group comparisons (SAT vs. control), paired t-tests for within-group comparisons before and after treatment, and one-way analysis of variance followed by Tukey’s honestly significant difference test for multiple group comparisons. Non-normally distributed data were presented as median and interquartile range and compared using the Mann-Whitney U test for two groups and the Kruskal-Wallis test for multiple groups. Mann-Whitney U p-values were adjusted for multiple comparisons via Benjamini-Hochberg false discovery rate (FDR) correction control. Significance was defined as q-value <0.05.

  1. Ethical approval: The study protocol was approved by the Medical Ethics Committee of the 920th Hospital of the Joint Logistics Support Force of the People’s Liberation Army of China. This study was conducted in accordance with the declaration of Helsinki and approved by the Ethics Committee of the 920th Hospital of the Joint Logistics Support Force of the Chinese People’s Liberation Army (No. 2022-128-01).

  2. Informed consent: All participants provided written informed consent before enrollment.

3 Results

3.1 Prednisone normalizes thyroid function and suppresses inflammation in SAT patients

After treatment with oral prednisone (0.5–1 mg/kg/day) for 6–8 weeks, significant reductions (P < 0.05) were observed in serum concentrations of tri-iodothyronine (T3), thyroxine (T4), free tri-iodothyronine (FT3), free thyroxine (FT4), thyroglobulin (Tg), thyroid peroxidase antibody (TPO), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), white blood cell count (WBC), neutrophil (NEU) count, ferritin (FT), and fibrinogen (FIB), while thyroid-stimulating hormone (TSH) levels increased significantly (P < 0.05) (Table 1). These results indicate that prednisone effectively reduces both thyroid hormone production and the inflammatory response in SAT patients.

Table 1

Comparison of clinical and laboratory parameters in SAT patients before and after treatment

Control value Before treatment After treatment z-value p-value
T3 (nmol/L) 1.01–2.96 3.25 (2.79, 4.47) 1.92 (1.56, 2.07) −3.36 0.001*
T4 (nmol/L) 55.4–161.25 204.80 (158.55, 237.03) 112.90 (92.78, 129.63) −3.31 0.001*
TSH (nmol/L) 0.38–4.34 0.01 (0.01, 0.08) 2.00 (0.69, 2.87) −3.36 0.001*
FT3 (nmol/L) 2.77–6.31 14.30 (9.77, 26.84) 4.87 (4.79, 6.33) −3.52 0.000*
FT4 (nmol/L) 10.44–24.38 38.49 (29.45, 46.10) 15.38 (13.83, 19.04) −3.516 0.000*
Tg (nmol/L) 0–20 20.59 (15.32, 26.88) 16.83 (8.72, 18.65) −2.068 0.039*
TgAb (IU/mL) 0–2.1 1.90 (0.79, 3.70) 2.06 (1.10, 8.02) −1.112 0.266
TPO (IU/mL) 0–2.1 4.18 (2.79, 8.29) 1.92 (0.94, 2.74) −3.103 0.002*
CRP (mg/L) 0.0–8.2 36.80 (20.35, 105.30) 3.84 (2.56, 7.98) −3.413 0.001*
ESR (mm/H) Male <20 100.00 (50.75, 117.00) 4.00 (3.25, 19.50) −3.465 0.001*
Female <34
WBC (109/L) 3.50–9.50 9.92 (5.10, 18.56) 6.64 (5.91, 7.58) −2.414 0.006*
Neu (109/L) 1.80–6.30 5.49 (3.98, 6.91) 3.71 (3.39, 4.70) −2.603 0.015*
Lym (109/L) 1.10–3.20 1.72 (1.28, 2.12) 1.94 (1.74, 2.30) −1.655 0.098
UA (μmol/L) Male: 208–428 318.00 (264.25, 412.50) 284.50 (248.00, 341.00) −1.034 0.301
Female: 155–357
FT (ng/mL) 18.2–341.2 5.91 (4.36, 7.41) 2.43 (1.97, 3.24) −3.206 0.001*
FIB (g/L) 150–350 425.60 (389.15, 599.500) 155.60 (51.02, 236.90) −2.844 0.004*
Glu (mmol/L) 3.89–6.11 5.24 (4.76, 7.27) 5.12 (4.61, 7.26) −0.259 0.796
TG (mmol/L) 0–1.7 1.38 (0.80, 1.86) 1.84 (1.11, 3.46) −1.448 0.148
TC (mmol/L) 0–5.2 4.01 (3.13, 4.72) 4.09 (2.98, 5.45) −0.233 0.816

This table presents the mean values and SD of various clinical and laboratory parameters in SAT patients before and after prednisone treatment. T3: Triiodothyronine; T4: Thyroxine; TSH: Thyroid stimulating hormone; FT3: Free triiothyronine; FT4: Free thyroxine; Tg: Thyroglobulin; TgAb: Antithyroglobulin antibody; TPO: Antithyroid peroxidase antibody; CRP: C-reactive protein; ESR: Blood sedimentation; WBC: White blood cell; NEU: Neutrophils; Lym: Lymphocytes; UA: Uric acid; FT: Ferritin; FIB: Fibrinogen; Glu: Glucose; TG: Triglycerides; TC: Cholesterol. *Indicates p < 0.05.

3.2 Gut microbiota diversity analysis

3.2.1 Alpha diversity

Newly diagnosed SAT patients exhibited a significantly higher Chao1 index, indicating increased microbial richness, compared to the healthy control group. The Shannon index, indicative of microbial diversity, and Pielou’s evenness index, measuring the uniformity of species distribution, were significantly lower in SAT patients, suggesting reduced diversity and evenness in their gut microbiota (Table 2). This indicates that while SAT patients harbor a greater number of microbial species, their gut microbiota is less diverse and more unevenly distributed. Although not statistically significant (p > 0.05), a trend toward decreasing richness and increasing diversity and evenness was observed following prednisone treatment, suggesting a potential shift in microbial composition toward a healthier profile.

Table 2

Alpha diversity indices of gut microbiota in SAT patients before and after treatment, and healthy controls

Healthy control Before treatment After treatment q 1-value Q 2-value q 3-value
Chao1 290.48 ± 71.22 299.48 ± 73.01 268.81 ± 75.20 0.34 0.65 0.64
Shannon 5.44 ± 0.68 4.57 ± 1.17 4.96 ± 0.92 0.48 0.09 0.34
Pielou 0.62 ± 0.12 0.57 ± 0.11 0.63 ± 0.07 0.11 0.05 0.26

Data are presented as mean values ± SD. q 1: q-value comparing pre-treatment SAT patients with post-treatment SAT patients; q 2: q-value comparing pre-treatment SAT patients with healthy controls; q 3: q-value comparing post-treatment SAT patients with healthy controls. FDR q-value < 0.05.

3.2.2 Beta diversity

Unweighted unique fraction metric (UniFrac) analysis, a phylogenetic distance metric, revealed distinct clustering of gut microbiota profiles between SAT patients (both pre- and post-treatment) and healthy controls, as visualized along both principal coordinate analysis (PCoA) axes 1 and 2. This separation indicates significant differences in the overall microbial community composition between the groups. Notably, samples within each group clustered tightly together, suggesting greater homogeneity in species composition within groups compared to between them (Figure 1a). Analysis of similarities (ANOSIM) revealed statistically significant group dissimilarities (R = 0.633, p = 0.001), indicating higher variation between groups compared to within groups (Figure 1b).

Figure 1 (a) Principal coordinate analysis (PCoA) of unweighted UniFrac distances in gut microbiota. This plot illustrates the distinct clustering of gut microbial communities among the three groups: Pre-treatment SAT patients (A1), post-treatment SAT patients (A2), and healthy control group (Control); (b) ANOSIM results for gut microbiota composition. This diagram displays the results of the ANOSIM analysis (R = 0.633, p = 0.001), quantifying the dissimilarity between microbial communities in pre-treatment SAT (A1), post-treatment SAT (A2), and healthy controls (Control).
Figure 1

(a) Principal coordinate analysis (PCoA) of unweighted UniFrac distances in gut microbiota. This plot illustrates the distinct clustering of gut microbial communities among the three groups: Pre-treatment SAT patients (A1), post-treatment SAT patients (A2), and healthy control group (Control); (b) ANOSIM results for gut microbiota composition. This diagram displays the results of the ANOSIM analysis (R = 0.633, p = 0.001), quantifying the dissimilarity between microbial communities in pre-treatment SAT (A1), post-treatment SAT (A2), and healthy controls (Control).

3.3 Phylum-level composition and differential abundance analysis of gut microbiota

The gut microbiota composition in SAT patients significantly differed from that of healthy controls, particularly in the relative abundance of key phyla. Relative abundance analysis of the 30 most abundant bacterial taxa, classified at the phylum level, revealed that Firmicutes, Bacteroidota, Proteobacteria, Actinobacteriota, and Verrucomicrobiota were the predominant phyla, constituting over 98% of the total gut microbiota in both healthy controls and SAT patients. Compared to healthy controls, newly diagnosed SAT patients exhibited a significant decrease in the relative abundance of Firmicutes and Verrucomicrobiota (p < 0.05). This depletion of Firmicutes, typically associated with maintaining gut homeostasis, and Verrucomicrobiota, known for anti-inflammatory properties, could contribute to the dysregulated immune response and inflammation seen in SAT. Following prednisone treatment, a significant increase in Verrucomicrobiota was observed (p < 0.05), suggesting a potential restoration of beneficial microbes that could contribute to the resolution of inflammation (Table 3).

Table 3

Relative abundance of gut microbiota at the phylum level in SAT patients before and after treatment, and healthy controls

Healthy control Before treatment After treatment t 1-value t 2-value q 1-value q 2-value
Firmicutes 63.33 ± 4.11 49.09 ± 6.37 58.66 ± 7.62 −2.45 1.21 0.04* 0.27
Bacteroidota 22.33 ± 5.00 13.10 ± 5.12 14.35 ± 5.30 −1.50 0.15 0.18 0.88
Proteobacteria 8.49 ± 4.12 19.58 ± 8.66 15.72 ± 4.18 1.10 −0.48 0.30 0.65
Actinobacteriota 4.65 ± 1.22 10.41 ± 4.39 10.34 ± 3.85 1.27 −0.10 0.25 0.99
Verrucomicrobiota 0.23 ± 0.22 0.07 ± 0.01 0.10 ± 0.01 −1.72 4.31 0.004* 0.03*

Data are presented as mean values ± SD. Statistical comparisons were performed using t-tests, with * indicating significant differences (q < 0.05). t 1 and q 1 represent the t-value and q-value comparing pre-treatment SAT patients with healthy controls, while t 2 and q 2 represent the t-value and q-value comparing post-treatment SAT patients with pre-treatment SAT patients.

3.4 Genus-level composition and differential abundance analysis of gut microbiota

Differential abundance analysis, using the Mann-Whitney U test, identified significant shifts in the relative abundance of specific bacterial genera in SAT patients compared to healthy controls. Newly diagnosed SAT patients exhibited significant enrichments in Escherichia-Shigella, Bifidobacterium, Akkermansia, Lactobacillus, and Veillonella (q < 0.05), while Prevotella, Faecalibacterium, Streptococcus, Enterococcus, and Clostridium were significantly depleted (q < 0.05). Following SAT treatment, a reversal of some of these trends was observed, with significant increases in Streptococcus, Enterococcus, Ruminococcus, Faecalibacterium, Rothia, and Clostridium and concomitant decreases in Escherichia-Shigella, Bacteroides, Lactobacillus, Akkermansia, and Veillonella (q < 0.05) (Table 4). These findings highlight the dynamic nature of the gut microbiota in SAT patients and its potential responsiveness to prednisone treatment.

Table 4

Relative abundance of gut microbiota at the genus level in SAT patients before and after treatment, and healthy controls

Healthy control Before treatment After treatment t 1-value t 2-value q 1-value q 2-value
Bacteroidota 17.94 ± 2.60 10.18 ± 1.40 11.79 ± 1.80 −10.66 0.401 0.000* 0.207
Faecalibacterium 14.32 ± 1.19 6.86 ± 1.29 6.98 ± 1.50 −9.58 0.21 0.000* 0.84
Escherichia- Shigella 7.31 ± 1.74 15.47 ± 1.46 9.67 ± 1.99 15.14 −5.23 0.000* 0.001*
Bifidobacterium 3.39 ± 0.49 9.55 ± 0.45 8.9 ± 0.62 29.54 −1.848 0.000* 0.107
Lachnospiraceae 3.53 ± 1.08 4.58 ± 1.7 4.47 ± 1.19 0.487 −0.086 0.641 0.934
Agathobacter 7.72 ± 1.26 8.42 ± 1.15 2.80 ± 0.77 0.16 −13.76 0.877 0.000*
Streptococcus 1.53 ± 0.08 0.42 ± 0.07 2.16 ± 0.36 −8.009 4.607 0.000* 0.002*
Ruminococcus gnavus 1.36 ± 0.77 5.98 ± 1.01 3.64 ± 1.93 2.97 −2.3 0.039* 0.048*
Subdoligranulum 1.59 ± 0.16 1.05 ± 0.15 0.98 ± 0.17 −2.277 −0.225 0.057 0.828
Dialister 2.92 ± 0.23 0.11 ± 0.003 2.76 ± 0.005 −2.264 2.305 0.038* 0.023*
Megamonas 2.83 ± 0.64 0.04 ± 0.002 0.04 ± 0.022 −2.69 0.17 0.034* 0.87
Prevotella 1.83 ± 0.79 0.37 ± 0.09 0.13 ± 0.02 −4.64 −0.665 0.002* 0.527
Roseburia 17.94 ± 2.60 0.94 ± 0.10 1.07 ± 0.16 −2.918 0.708 0.022* 0.502
Ruminococcus 14.32 ± 1.19 0.61 ± 0.023 5.16 ± 1.09 −4.63 9.76 0.002* 0.000*
Coprococcus 7.31 ± 1.74 0.445 ± 0.016 3.99 ± 0.62 −2.96 15.67 0.021* 0.000*
Akkermansia muciniphila 3.39 ± 0.49 5.82 ± 1.15 0.10 ± 0.001 6.87 −7.601 0.000* 0.000*
Lactobacillus reuteri 3.53 ± 1.08 0.53 ± 0.03 2.37 ± 0.15 −1.35 5.89 0.216 0.001*
Veillonella 7.72 ± 1.26 1.95 ± 0.40 0.20 ± 0.03 −3.91 −14.30 0.006* 0.000*
Lachnoclostridium 0.15 ± 0.004 0.044 ± 0.006 0.21 ± 0.01 −0.73 4.73 0.753 0.000*

Data are presented as mean value ± SD. Statistical comparisons were performed using t-tests, with * indicating significant differences (q < 0.05). t 1 and q 1 represent the t-value and q-value comparing pre-treatment SAT patients with healthy controls, while t2 and q 2 represent the t-value and q-value comparing post-treatment SAT patients with pre-treatment SAT patients.

3.5 Identification of microbial biomarkers

Linear discriminant analysis (LDA) effect size (LEfSe) analysis revealed distinct microbial signatures associated with SAT and its treatment response. Nineteen taxa were found to be enriched in healthy controls, while eight taxa were specifically enriched in SAT patients, with Prevotella and Akkermansia emerging as key discriminative biomarkers (Figure 2a). Interestingly, the microbial signature shifted following prednisone treatment. Twenty-one taxa were identified as biomarkers in the pre-treatment group, whereas only seven were identified in the post-treatment group, including Ruminococcus, Rothia, and Prevotella (Figure 2b). These findings underscore the distinct microbial signatures associated with SAT and its treatment response, highlighting the potential of these taxa as biomarkers for monitoring therapeutic efficacy and elucidating the dynamic shifts in gut microbiota composition associated with thyroid disorders.

Figure 2 (a) Histogram of LEfSe scores for differentially abundant taxa in pre-treatment SAT patients and healthy controls. This histogram displays the distribution of LDA scores for bacterial taxa identified as significantly differentially abundant between pre-treatment SAT patients (A1) and healthy controls (Control); (b) histogram of LEfSe scores for differentially abundant taxa in post-treatment and pre-treatment SAT patients. This histogram displays the distribution of LDA scores for bacterial taxa identified as significantly differentially abundant between post-treatment SAT patients (A2) and pre-treatment SAT patients (A1). The LDA score quantifies the magnitude and direction of the effect (positive for enrichment in A2, negative for enrichment in A1).
Figure 2

(a) Histogram of LEfSe scores for differentially abundant taxa in pre-treatment SAT patients and healthy controls. This histogram displays the distribution of LDA scores for bacterial taxa identified as significantly differentially abundant between pre-treatment SAT patients (A1) and healthy controls (Control); (b) histogram of LEfSe scores for differentially abundant taxa in post-treatment and pre-treatment SAT patients. This histogram displays the distribution of LDA scores for bacterial taxa identified as significantly differentially abundant between post-treatment SAT patients (A2) and pre-treatment SAT patients (A1). The LDA score quantifies the magnitude and direction of the effect (positive for enrichment in A2, negative for enrichment in A1).

3.6 Correlation analysis between gut microbiota and clinical parameters in SAT patients

Spearman correlation analysis revealed significant associations between the relative abundance of specific gut microbial taxa and clinical parameters in SAT patients, suggesting a potential interplay between gut microbiota and thyroid function and inflammation. Ruminococcus abundance was negatively correlated with FT4 and ESR, and positively correlated with Tg. Streptococcus abundance exhibited negative correlations with CRP, total T4, FIB, and TPO. Actinomyces abundance was negatively correlated with Tg and thyroglobulin antibody (TgAb). Romboutsia abundance exhibited negative correlations with CRP and total cholesterol (TC). Enterobacter abundance was negatively correlated with WBC, T3, T4, FT3, TPO, and FIB, and positively correlated with FT. Bacteroides abundance was negatively correlated with FIB, FT3, and T3, and positively correlated with TSH and Tg. Lactobacillus abundance was positively correlated with lymphocytes (Lym) and Tg, while Enterococcus abundance was negatively correlated with ESR. Additionally, the abundance of Bifidobacterium, Roseburia, Bacteroides, and Faecalibacterium demonstrated positive correlations with TgAb and TPO (Figure 3).

Figure 3 Heatmap of spearman correlation analysis between gut microbiota and clinical parameters in SAT patients. This heatmap illustrates the strength and direction of correlations between the relative abundance of specific gut microbial taxa (rows) and clinical parameters (columns) in SAT patients. Positive correlations are represented by warm colors (red), negative correlations by cool colors (blue), and non-significant correlations by white. The intensity of the color reflects the magnitude of the correlation coefficient. *P < 0.05, **P < 0.01.
Figure 3

Heatmap of spearman correlation analysis between gut microbiota and clinical parameters in SAT patients. This heatmap illustrates the strength and direction of correlations between the relative abundance of specific gut microbial taxa (rows) and clinical parameters (columns) in SAT patients. Positive correlations are represented by warm colors (red), negative correlations by cool colors (blue), and non-significant correlations by white. The intensity of the color reflects the magnitude of the correlation coefficient. *P < 0.05, **P < 0.01.

These findings reveal potential associations between specific gut microbial taxa and clinical parameters in SAT patients, suggesting their potential relevance as biomarkers in the assessment and management of thyroid-related conditions.

4 Discussion

Our study investigated the gut microbiome of patients with SAT before and after prednisone treatment, revealing a distinct microbial signature associated with the disease. Notably, SAT patients exhibited reduced microbial diversity and enrichment of specific taxa such as Prevotella and Akkermansia, correlating with altered thyroid function and elevated inflammatory markers. This suggests a potential role for gut dysbiosis in SAT pathogenesis. Importantly, prednisone treatment not only ameliorated the clinical manifestations of SAT but also shifted the gut microbiome toward a healthier state, as evidenced by the increased abundance of beneficial bacteria such as Verrucomicrobiota. These findings underscore the intricate interaction between the gut microbiome and thyroid health, highlighting potential avenues for novel diagnostic and therapeutic strategies in SAT.

Consistent with previous studies, our findings indicate that SAT is associated with elevated levels of inflammatory markers, including FT, FIB, ESR and CRP. FT is an acute-phase reactant that increases in response to systemic inflammation. Previous studies in patients with SAT have reported significant decreases in FT, ESR, and CRP levels following treatment, with a positive correlation observed between FT and both ESR and CRP [10]. FIB, another acute-phase protein, is also elevated in SAT and decreases as the condition improves [11]. Our study further substantiates these findings by demonstrating that newly diagnosed SAT patients exhibit elevated levels of T3, T4, FT3, FT4, Tg, TPO, CRP, ESR, WBC, NEU count, FT, and FIB. Following prednisone treatment, these biomarkers significantly decreased, consistent with the expected resolution of inflammation. This observation underscores the potential of FT and FIB, in addition to ESR and CRP, as inflammatory biomarkers that reflect the severity of SAT and enable the monitoring of disease activity and response to treatment.

While research on the gut microbiome in SAT is limited, studies on other thyroid disorders such as GD and HT provide context for interpreting our findings. In GD, studies consistently report enrichment of genera such as Bifidobacterium and Lactobacillus, with concomitant depletion of Blautia and Ruminococcus [5,6]. In contrast, research in HT reveals increased abundance of Blautia, Roseburia, Ruminococcus, Dialister, Sporobacter, and Haemophilus, alongside reductions in Bifidobacterium and Prevotella [7,8]. Our study findings demonstrate that newly diagnosed SAT patients exhibit a distinct gut microbiota profile characterized by significant increases in the abundance of genera such as Escherichia-Shigella, Bifidobacterium, Akkermansia, Veillonella, and Enterococcus, accompanied by notable decreases in genera such as Bacteroides, Faecalibacterium, Streptococcus, Veillonella, and Ruminococcus. Following prednisone treatment, this profile shifts, with significant increased abundance in Streptococcus, Enterococcus, Ruminococcus, Faecalibacterium, Roseburia, and Lactobacillus, and decreased abundance in Escherichia-Shigella, Bacteroides, Veillonella, Akkermansia, and Enterococcus.

Correlation analyses revealed distinct associations between specific bacterial taxa and clinical parameters in SAT patients, highlighting their potential roles in disease pathogenesis and treatment response. Key biomarkers identified in newly diagnosed SAT patients include Prevotella and Akkermansia, while post-treatment biomarkers comprise Faecalibacterium, Roseburia, and Prevotella. Escherichia-Shigella abundance correlates positively with Tg, CRP, and Lym, but negatively with NEU count. Veillonella abundance correlates positively with CRP, ESR, and the neutrophil-to-lymphocyte ratio (NLR), and negatively with NEU count. Lactobacillus abundance exhibits positive correlations with FT4, total T3, total T4, ESR, NLR, uric acid (UA), and FT. Akkermansia abundance positively correlates with FT4, while Faecalibacterium abundance correlates positively with TC and negatively with FT3. Enterococcus abundance positively correlates with Tg, and Streptococcus abundance with TSH. Prevotella, Veillonella, Faecalibacterium, Enterococcus, and Ruminococcus abundances demonstrate positive correlations with TPO. Roseburia abundance correlates positively with TgAb, glucose (Glu), and TC. Collinsella, Dorea, and Ruminococcus abundances correlate positively with Glu, while Actinomyces, Faecalibacterium, and Bifidobacterium abundances positively correlate with TgAb.

Prevotella, known for its anti-inflammatory properties due to the production of metabolites such as propionic acid [12], has been shown to suppress T helper 17 (Th17) polarization and promote anti-inflammatory regulatory T cells (Treg)/T regulatory type 1 (Tr1) cell differentiation in the gut [13,14]. Consistent with findings in other autoimmune diseases, such as HT [15], type 1 diabetes [16], and multiple sclerosis [17], we observed a decreased relative abundance of Prevotella in SAT patients. This depletion, which was positively correlated with TPO levels, suggests a potential contribution to the inflammatory and autoimmune processes in SAT. Excessive proliferation of Akkermansia can disrupt the intestinal barrier integrity and exacerbate inflammation [18]. Our study revealed a significant enrichment of Akkermansia in SAT patients, coupled with decreased abundances of commensal bacteria such as Faecalibacterium and Roseburia. This dysbiosis may contribute to reduced species diversity, intestinal barrier dysfunction, and ultimately, the progression of SAT.

Ruminococcus, an important gut commensal, ferments polysaccharides into short-chain fatty acids (SCFAs) such as butyrate and acetate [19]. These SCFAs promote intestinal epithelial cell growth, enhance gut barrier function, and modulate immune responses [20]. As a biomarker enriched in the post-treatment SAT group, Ruminococcus may play a crucial role in restoring gut microbial balance and facilitating recovery from SAT. Additionally, Veillonella, by metabolizing lactate into SCFAs, regulates SCFA and lactate dynamics, contributing to the maintenance of normal gut function [21,22,23].

Glucocorticoids (GCs), primary stress hormones with anti-inflammatory and immunosuppressive properties, are known to influence gut microbiota composition. Yun [24] demonstrated that prednisolone treatment in rats increased the relative abundance of Proteobacteria and Bacillus, while decreasing Bacteroides, Prevotella, and Lactobacillus. Similarly, a study by Lauren [25] in red squirrels (Tamiasciurus hudsonicus) revealed a negative correlation between host GC levels and gut microbiota alpha diversity. Elevated GC levels were associated with increased abundance of Enterobacteriaceae, Streptococcus, Enterococcus, Prevotella, Ruminococcus, Clostridium, Treponema, and Megasphaera, and decreased abundance of Yersinia and Salmonella.

In our study, all SAT patients received glucocorticoid therapy, a factor that may have influenced the observed changes in gut microbiota composition. Specifically, we noted decreased microbial richness and increased diversity at the phylum level, characterized by an increase in Firmicutes, Bacteroidetes, and Verrucomicrobia, and a decrease in Actinobacteria and Proteobacteria. At the genus level, we observed a decrease in abundance of Escherichia-Shigella, Akkermansia, and Veillonella, and an increased abundance of Prevotella. To minimize the direct effects of glucocorticoid on the gut microbiota, patients received a low dose of oral prednisone (0.5–1 mg/kg/day) for 6 to 8 weeks, followed by a 4 week washout period before fecal sample collection. Therefore, the changes observed in the post-treatment group likely reflect a combination of the disease itself and the residual effects of glucocorticoid therapy.

5 Conclusion

Our study provides valuable insights into the gut microbiome in SAT. However, this study has several limitations. The cross-sectional design precludes determination of causal relationships between observed gut microbiota alterations and SAT pathogenesis or treatment response. Additionally, the relatively small sample size may limit the generalizability of our findings to larger populations. While we identified correlations between gut microbiota composition and SAT, the underlying mechanisms remain elusive. Future studies with larger cohorts and longitudinal designs are needed to establish causality and to track the dynamic changes in the gut microbiome throughout the course of SAT and treatment, and to explore the potential of modulating gut microbiome as a therapeutic intervention for this condition.

# Jiayun and Jiasheng Ju contributed equally to this work.

Acknowledgments

The authors would like to acknowledge TopEdit (www.topeditsci.com) for their valuable linguistic support during the preparation of this manuscript.

  1. Funding information: This work was supported by the Yunnan Province Xingdian Talent, Medical and Health Talent Project (XDYC-YLWS-2023-0098).

  2. Author contributions: Jiayun Li: data curation, formal analysis, and writing – original draft. Jiasheng Ju: conceptualization, formal analysis, and writing – original draft. Qian Xu: data curation. Xiang Han: data curation. Haibing Ju: conceptualization, methodology, writing – original draft, and writing – review and editing.

  3. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

  4. Data availability statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

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Received: 2024-12-24
Revised: 2025-07-07
Accepted: 2025-08-29
Published Online: 2025-10-15

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  211. Lathyrol affects the expression of AR and PSA and inhibits the malignant behavior of RCC cells
  212. The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges
  213. Bone scintigraphy and positron emission tomography in the early diagnosis of MRONJ
  214. Meta-analysis of clinical efficacy and safety of immunotherapy combined with chemotherapy in non-small cell lung cancer
  215. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
  216. Exploration of mRNA-modifying METTL3 oncogene as momentous prognostic biomarker responsible for colorectal cancer development
  217. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  218. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
  219. The mRNA MOXD1: Link to oxidative stress and prognostic significance in gastric cancer
  220. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part II
  221. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
  222. A prognostic model correlated with fatty acid metabolism in Ewing’s sarcoma based on bioinformatics analysis
  223. Red cell distribution width predicts early kidney injury: A NHANES cross-sectional study
  224. Special Issue Diabetes mellitus: pathophysiology, complications & treatment
  225. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  226. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  227. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
  228. A review of brain research on T2DM-related cognitive dysfunction
  229. Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
  230. Special Issue Innovative Biomarker Discovery and Precision Medicine in Cancer Diagnostics
  231. CircASH1L-mediated tumor progression in triple-negative breast cancer: PI3K/AKT pathway mechanisms
https://doi.org/10.1515/med-2025-1291
Keywords for this article
subacute thyroiditis; gut microbiota; 16S ribosomal RNA; prednisone
Creative Commons
BY 4.0

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  201. Corrigendum
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  203. Corrigendum to “Comparing the therapeutic efficacy of endoscopic minimally invasive surgery and traditional surgery for early-stage breast cancer: A meta-analysis”
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  205. Retraction
  206. Retraction of “miR-654-5p promotes gastric cancer progression via the GPRIN1/NF-κB pathway”
  207. Retraction of: “LncRNA CASC15 inhibition relieves renal fibrosis in diabetic nephropathy through downregulating SP-A by sponging to miR-424”
  208. Retraction of: “SCARA5 inhibits oral squamous cell carcinoma via inactivating the STAT3 and PI3K/AKT signaling pathways”
  209. Special Issue Advancements in oncology: bridging clinical and experimental research - Part II
  210. Unveiling novel biomarkers for platinum chemoresistance in ovarian cancer
  211. Lathyrol affects the expression of AR and PSA and inhibits the malignant behavior of RCC cells
  212. The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges
  213. Bone scintigraphy and positron emission tomography in the early diagnosis of MRONJ
  214. Meta-analysis of clinical efficacy and safety of immunotherapy combined with chemotherapy in non-small cell lung cancer
  215. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
  216. Exploration of mRNA-modifying METTL3 oncogene as momentous prognostic biomarker responsible for colorectal cancer development
  217. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  218. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
  219. The mRNA MOXD1: Link to oxidative stress and prognostic significance in gastric cancer
  220. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part II
  221. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
  222. A prognostic model correlated with fatty acid metabolism in Ewing’s sarcoma based on bioinformatics analysis
  223. Red cell distribution width predicts early kidney injury: A NHANES cross-sectional study
  224. Special Issue Diabetes mellitus: pathophysiology, complications & treatment
  225. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  226. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  227. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
  228. A review of brain research on T2DM-related cognitive dysfunction
  229. Metformin and estrogen modulation in LABC with T2DM: A 36-month randomized trial
  230. Special Issue Innovative Biomarker Discovery and Precision Medicine in Cancer Diagnostics
  231. CircASH1L-mediated tumor progression in triple-negative breast cancer: PI3K/AKT pathway mechanisms
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