Key words
Hashimoto’s disease - Graves’ disease - helper T cell 17 - regulatory T cell - meta-analysis
AITD: Autoimmune thyroid disease
GD: Graves’ disease
HT: Hashimoto’s disease
Th17: Helper T cell 17
Treg: Regulatory T cells
FoxP3: Forkhead box P3
TPOAb: Thyroid peroxidase antibodies
TGAb: Thyroglobulin antibodies
NOS: Newcastle-Ottawa scale
SMD: Standardized mean difference
CI: Confidence interval
OR: Odds ratio
Introduction
Autoimmune thyroid disease (AITD) including Graves’ disease (GD) and
Hashimoto’s disease (HT) [1]
[2] is a classic organ-specific autoimmune
thyroiditis. Its pathogenesis is complex and unclear and may be related to the
living environment, genetic background, and imbalance of immune homeostasis [3]. It is clinically characterized by
infiltration of thyroid tissue via different lymphocytes and reactivity to
thyroid-associated autoantigens [4]. The
lymphocytes that infiltrate the thyroid are T and B lymphocytes. B lymphocytes
primarily produce antibodies and cytokines that promote the development of
inflammation, and T lymphocytes induce other immune cells’ activity by
releasing cytokines. These cytokines help to suppress or modulate the immune
response [1].
Recent studies have found that helper T cell 17 (Th17) or regulatory T cells (Treg)
play an essential role in developing autoimmune diseases [1]. Th17 cells are members of the
CD4+T cell lineage. It is generally believed that Th17 cells
are developmentally and functionally distinct from Th1 and Th2 cells in that they
produce and secrete cytokines such as IL-17 that promote progressive inflammation
[5]. Studies have shown that IL-17 and its
receptor (IL-17R) can participate in the activation of the NF-κB signaling
pathway, which is an important pathway in the human body and is involved in the
transmission of tissue damage, stress, cell differentiation, and apoptosis [6]. Esfahanian et al. found elevated serum
IL-17 levels in HT patients, which suggested a potential role in the pathogenesis
of
the disease [7]. Safdari et al. revealed that
the expressions of T-bet and GATA3, which were specific transcription factors for
Th1 and Th2, respectively, were significantly higher than those in the control
group. In addition, elevated RORα and declined forkhead box P3 (FoxP3),
which reflect the expression of Th17 and Treg, respectively, were observed, with
significant difference [8]. Among severe
graves ophthalmopathy patients, moderate graves ophthalmopathy patients, and GD
patients, the proportion of IFN-γ producing Th1 cells and IL-17A producing
Th17 cells were significantly increased. After steroid pulse therapy, Th1 and Th17
cell were reduced in severe graves ophthalmopathy patients, which indicated that the
status of Th1 and Th17 cells were associated with the severity of the disease [9].
Tregs could inhibit autoimmune response and regulate the immune system by secreting
cytokines such as IL-10 and TGF-β [10]. And the expression of FoxP3 is essential for the development of Tregs
and its role in maintaining autoimmune response and self-tolerance [11]. FoxP3 gene mutation and/or
deletion could lead to the loss of regulatory function of Treg, which results in the
overactivation of T cells and the occurrence of autoimmune response [12]. Among the FoxP3 polymorphisms, the
presence of the FoxP3 rs3761549 “T” allele appears to increase in HT
and GD patients. And an increase in CT heterozygous carrier rates has been reported
in female GD patients in the Polish population. The association of CT heterozygotes
with the pathogenesis of HT and GD can be predicted based on the decrease in FoxP3
transcriptional efficiency, which is likely to impair the regulatory function of
Tregs, leading to uncontrolled clonal and amplification of activated T cells in the
thyroid environment [13].
The mechanism of Treg dysfunction in AITD and other autoimmune diseases remains to
be
determined. However, Treg lymphocytes have the potential to transform into
pro-inflammatory cells (mainly Th17 and Th1 lymphocytes), which may further the
continuation of autoimmune processes. Studies have also shown that the frequency of
transformation of FoxP3 to IL-17 or FoxP3 to IFN-γ lymphocytes was increased
in psoriasis and type 1 diabetes patients, respectively, and the differentiation of
Treg cells into Th17 or Th1 lymphocytes is enhanced [14]. Considering that Th17 cells share a differentiation pathway with
FoxP3 Treg, dysregulation of Th17/Treg homeostasis and changes in its
associated factors may contribute to autoimmune diseases. Therefore, this review
aims to investigate the changes in immune indicators associated with AITD via a
meta-analysis of published case-control studies on newly diagnosed AITD. Hopefully,
it will provide new directions for studying the mechanisms of AITD.
Materials and Methods
This systematic review and meta-analysis is registered at the International
Prospective Register of Systematic Reviews (Number CRD42022353625).
Search strategy
Information retrieval conducted through Pubmed, Web of Science, Cochrane Library,
Scopus, China National Knowledge Infrastructure, and Wanfang data. The data were
searched on case-control studies of patients with newly diagnosed AITD to
compare Th17 and Treg levels and alterations in FoxP3mRNA and FoxP3
(rs3761548, rs3761549) in peripheral blood of patients with
newly diagnosed HT, GD, and healthy subjects, to provide clinical evidence for
inferring the etiology of these patients from inception to August 15, 2022. The
search keywords were “Autoimmune thyroid disease”,
“Thyroiditis”, “Th17”, “Type 17 Helper
cell”, “Treg”, “T lymphocytes,
regulatory”, and “FoxP3”. In addition, we meticulously
searched the references of the articles initially included in the systematic
search in order not to leave any relevant article behind and to provide
comprehensive coverage of Th17, Treg levels, and alterations in FoxP3mRNA and
FoxP3 (rs3761548, rs3761549) in peripheral blood of patients with
newly diagnosed AITD.
Inclusion and exclusion criteria
Two researchers evaluated the titles and abstracts of the studies from the
initial search as per the inclusion and exclusion criteria, respectively. If two
researchers disagreed about inclusion in the study, a third researcher would
make determinations on whether to include the disputed study on the basis of the
opinions of the first two. The inclusion criteria were (1) only a retrospective
case-control study was investigated; (2) the research group was patients with
newly diagnosed AITD (including HT and GD patients) without any drug
intervention, and the healthy population served as the control group; and (3)
Th17, Treg, FoxP3mRNA, and FoxP3 (rs3761548, rs3761549) were
recorded in the study in patients and healthy populations. Exclusion criteria
(the study would be excluded if any of the following conditions existed): (1)
AITD patients of research included those who had performed any pharmacological
intervention; (2) the full text of the study was not available, or the required
data could not be extracted from the full text; (3) the same trial was
repeatedly published; (4) the reported data were incomplete and not available
through any credible source; and (5) the study design was significantly flawed,
or the results were reported with significant bias.
Data extraction and quality assessment
Data extraction was implemented by two researchers using a pre-designed data
extraction table, respectively. A third researcher checked and solved the
problem of the discordance of extracted data. The pre-designed data extraction
table included study title, first author of the article, year of publication and
journal of publication, type and number of newly diagnosed AITD patients
included in the study, subgroup status, age, thyroid peroxidase antibodies
(TPOAb), thyroglobulin antibodies (TGAb), outcome indicators [levels of Th17,
Treg, FoxP3mRNA and FoxP3 (rs3761548, rs3761549)], inclusion and
exclusion criteria, outcome indicators of measurements (methods of measurements,
markers of Tregs and type of cells for measurement), study design-related
indicators.
The Newcastle-Ottawa Scale (NOS) for observational case-control studies was used
for quality assessment by two independent researchers [15]. If there was no consensus between the
two researchers, a third researcher would join in the discussion forum focusing
on the quality score of the disputed study and make a final determination. The
assessment items were [1] the
appropriateness of the selection of newly diagnosed AITD patients and controls:
(1); for example, the strongly positive immunological test of newly diagnosed
AITD patients and healthy population and the source of study object selection;
(2) the comparability of cases and controls; and (3) the appropriateness of
exposure determination. Observational studies were rated as high quality if they
scored 6–9, moderate-quality if they scored 4 or 5, and poor quality if
they scored 3 or lower.
Statistical analysis
We used Stata 15.1 software to process the extracted literature data [16]
[17]. Because the gender composition, the age, TPOAb levels, and TGAb
levels of patients with newly diagnosed AITD included in studies were very far
apart, it did not have good clinical consistency. Therefore, we could combine
the results using a random-effects model to compare the differences in Th17,
Treg, FoxP3mRNA, and FoxP3 (rs3761548, rs3761549) in peripheral
blood between patients with newly diagnosed AITD and the healthy population. In
this study, the Q-test (chi-square test) and I2 statistics were
combined to evaluate the heterogeneity or homogeneity of the studies. A
p-value of the Q-test greater than 0.05 was considered homogeneity.
Otherwise, heterogeneity was considered. The I2-values of less than
50% and more than 50% suggested a low and high heterogeneity
among studies, respectively [18]. The
standardized mean difference (SMD) and odds ratios (OR), and their 95%
confidence interval (95%CI) were calculated for continuous variables and
dichotomous variables, respectively to compare whether the factor is associated
with the etiology of AITD. If the number of studies was≥5,
Egger’s test and funnel plot were used to assess the publication bias of
the results. Duval and Tweedie’s trim and fill test was used to evaluate
the sensitivity of the results [19]
[20]. Exact p-values are presented, unless
p<0.001. The size of the test was α=0.05.
Results
Literature search, study characteristics, and quality assessment
A search of six database systems yielded 4018 articles, and four articles were
obtained after a manual search of references for initial inclusion in the
literature. After 1466 duplicate retrieved articles were removed, the titles and
abstracts of the accepted articles were screened again, and 2524 articles were
excluded for not meeting the inclusion criteria (not related to autoimmune
thyroid disease n=823; review or in vitro/animal studies or
letter or editorial or conference paper n=436; not related to
case-control study focusing on the comparison between newly diagnosed AITD
patients and healthy population n=1203; not related to adopted
indicators n=62). Six out of the 32 articles subsequently participating
in the full-text assessment were excluded due to repeated population, no data
available or could not be translated into valuable data. At last, 26 studies
were included for quantitative analysis in this systematic review ([Fig. 1]). A total of 1242 newly diagnosed
HT patients and 1,302 newly diagnosed GD patients, and 1815 healthy individuals
were included for quantitative analysis. The primary characteristics of the
included 26 case-control studies are shown in [Table 1]
[10]
[12]
[13]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43].
Fig. 1 Study selection flowchart of Th17 and Treg levels in
peripheral blood of newly diagnosed patients with autoimmune thyroid
disease: a systematic review and meta-analysis.
Table 1 Baseline characteristics of included studies for
meta-analysis.
|
First author, year [Ref]
|
AITD type
|
Research group
|
Control group
|
Indicators
|
|
No. of cases (male)
|
Age
|
TPOAb
|
TGAb
|
No. of cases (male)
|
Age
|
TPOAb
|
TGAb
|
|
Song JZ, 2009 [21]
|
HT
|
30 (4)
|
45.6±11.1
|
423.3±167.2
|
1587.9±1210.4
|
20 (2)
|
45.7±12.1
|
15.2±10.4
|
36.3±26.9
|
Th17
|
|
Jin X, 2018 [22]
|
HT
|
40 (7)
|
28.7±6.2
|
268.2±106.3
|
769.5±165.6
|
40 (6)
|
28.3±6.2
|
14.8±8.4
|
50.4±21.6
|
Th17, Treg, Th17/Treg, Treg Foxp3
|
|
Li XJ, 2016 [23]
|
GD
|
50 (12)
|
38.3±4.7
|
–
|
–
|
50 (9)
|
39.4±5.1
|
–
|
–
|
Th17
|
|
HT
|
50 (11)
|
37.6±4.2
|
–
|
–
|
–
|
–
|
–
|
–
|
|
Gao ST, 2011 [24]
|
GD
|
20 (5)
|
41.6±12.9
|
151.0±143.0
|
117.0±146.0
|
20 (8)
|
35.6±10.2
|
Negative
|
Negative
|
Foxp3
|
|
HT
|
20 (7)
|
35.8±12.8
|
565.0±275.0
|
667.0±780.0
|
–
|
–
|
–
|
–
|
|
Xue HB, 2012 [25]
|
HT
|
40 (6)
|
28.4±8.3
|
263.4±160.9
|
808.5±721.9
|
31 (4)
|
31.1±6.4
|
15.1±9.6
|
49.9±33.3
|
Th17, Treg, Foxp3
|
|
Zheng LT, 2012 [26]
|
GD
|
30 (10)
|
38.2±3.2
|
–
|
–
|
20 (7)
|
35.1±1.3
|
–
|
–
|
Treg, Foxp3
|
|
HT
|
20 (6)
|
39.1±2.8
|
–
|
–
|
–
|
–
|
–
|
–
|
|
Chen Q, 2012 [27]
|
GD
|
59 (19)
|
31 (22–43)
|
202.2±164.5
|
99.7±53.3
|
55 (22)
|
32 (16–59)
|
1.8±1.0
|
2.2±1.0
|
Treg, Foxp3
|
|
HT
|
63 (11)
|
36 (26–61)
|
465.3±282.9
|
233.4±184.2
|
–
|
–
|
–
|
–
|
|
Zhao JY, 2012 [28]
|
GD
|
30 (13)
|
38.0±11.3
|
143.5±177.4
|
270.6±272.3
|
20 (11)
|
40.6±11.5
|
16.3±12.4
|
55.4±48.9
|
Th17
|
|
HT
|
30 (11)
|
40.2±13.5
|
342.6±237.3
|
1146.3±1126.3
|
–
|
–
|
–
|
–
|
|
Huang GY, 2017 [29]
|
GD
|
40 (17)
|
40.6±9.3
|
152.2±52.3
|
169.4±46.4
|
20 (7)
|
39.8±9.1
|
Negative
|
Negative
|
Treg, Foxp3
|
|
HT
|
40 (16)
|
41.3±10.2
|
521.3±102.3
|
643.6±102.3
|
–
|
–
|
–
|
–
|
|
Hu Y, 2019 [30]
|
HT
|
42 (5)
|
40.3±13.2
|
238.6±167.7
|
545.8±432.2
|
20 (5)
|
38.9±8.1
|
13.0±11.2
|
32.2±20.6
|
Treg
|
|
Mao C, 2011 [31]
|
GD
|
77 (12)
|
41.1±12.7
|
90.6±65.3
|
26.1±12.2
|
74 (14)
|
40.7±10.5
|
3.5±2.1
|
0.5±0.3
|
Treg
|
|
Qin J, 2017 [32]
|
GD
|
20 (7)
|
36.7±11.0
|
261.0 (10.0–1000.0)
|
20.0 (20.0–793.0)
|
20 (7)
|
32.0±6.1
|
10.0 (10.0–16.1)
|
20.0 (20.0–69.6)
|
Th17, Treg
|
|
Xue HB, 2015 [33]
|
HT
|
40 (5)
|
28.9±8.3
|
200.0 (148.4–299.9)
|
489.8 (300.7–774.7)
|
30 (4)
|
30.9±6.4
|
14.3 (7.5–24.0)
|
51.0 (19.8–84.3)
|
Th17
|
|
Şıklar Z, 2016 [34]
|
HT
|
32 (4)
|
13.2±3.7
|
–
|
–
|
24 (6)
|
12.8±3.4
|
–
|
–
|
Treg, Foxp3
|
|
Yang X, 2018 [35]
|
HT
|
30 (5)
|
43.3±0.9
|
472.5±74.0
|
278.9±67.6
|
30 (7)
|
44.9±0.9
|
4.1±0.2
|
4.1±2.6
|
Treg, Foxp3
|
|
Klatka M, 2014 [36]
|
GD
|
60 (12)
|
14.1±1.9
|
–
|
–
|
20 (5)
|
14.4±2.2
|
–
|
–
|
Treg
|
|
Rydzewska M, 2018 [37]
|
GD
|
145 (36)
|
16.5±2.0
|
331.9±58.1
|
347.4±86.7
|
161 (86)
|
16.3±3.1
|
66.5±52.7
|
91.6±30.5
|
rs3761548, rs3761549
|
|
HT
|
87 (13)
|
15.2±2.2
|
329.9±92.9
|
620.9±240.3
|
–
|
–
|
–
|
–
|
|
Fathima N, 2019 [13]
|
GD
|
80 (6)
|
33.9±14.7
|
–
|
–
|
285 (30)
|
32.1±12.6
|
–
|
–
|
rs3761548, rs3761549
|
|
HT
|
275 (17)
|
33.9±11.9
|
–
|
–
|
–
|
–
|
–
|
–
|
|
Kalantar K, 2019 [12]
|
HT
|
129 (0)
|
38.1±12.8
|
–
|
–
|
127
|
44.4±2.2
|
–
|
–
|
rs3761548, rs3761549
|
|
Inoue N, 2010 [38]
|
GD
|
65 (8)
|
35.2±14.7
|
–
|
–
|
71 (10)
|
44.1±12.2
|
–
|
–
|
rs3761548, rs3761549
|
|
HT
|
38 (5)
|
37.3±11.1
|
–
|
–
|
–
|
–
|
–
|
–
|
|
Zheng L, 2015 [39]
|
GD
|
308 (91)
|
39.7±13.8
|
–
|
–
|
306 (107)
|
41.6±10.1
|
–
|
–
|
rs3761548, rs3761549
|
|
Safdari V, 2017 [40]
|
HT
|
40 (0)
|
38.6±10.6
|
–
|
–
|
40 (0)
|
36.1±11.2
|
–
|
–
|
Foxp3
|
|
Li C, 2016 [10]
|
GD
|
16 (4)
|
39.3±11.8
|
1607.2±1494.3
|
28.4±19.4
|
12 (3)
|
36.9±6.3
|
7.8±2.0
|
26.2±12.9
|
Th17, Treg, Foxp3
|
|
HT
|
15 (3)
|
38.0±11.0
|
2070.5±1345.2
|
215.3±656.3
|
–
|
–
|
–
|
–
|
|
Xue HB, 2015 [41]
|
HT
|
48 (6)
|
29.2±8.6
|
216.2±78.3
|
619.5±221.4
|
32 (4)
|
31.2±6.3
|
13.4±7.8
|
46.0±31.2
|
Th17, Treg, Foxp3
|
|
Tan Y, 2019 [42]
|
GD
|
28 (7)
|
–
|
102.2±73.6
|
19.3±11.2
|
24 (8)
|
–
|
1.0±0.2
|
0.1±0.1
|
Foxp3
|
|
Ren X, 2022 [43]
|
GD
|
26 (8)
|
37.7±11.8
|
971.8±564.4
|
250.5±193.1
|
20 (7)
|
39.3±14.4
|
37.1±9.8
|
24.9±9.2
|
Th17
|
AITD: Autoimmune thyroid disease; TPOAb: Thyroid peroxidase antibodies;
TGAb: Thyroglobulin antibodies; NOS: Newcastle-Ottawa scale; HT:
Hashimoto’s disease; GD: Graves’ disease; Th17: Helper T
cell 17; Treg=regulatory T cells; Foxp3=Forkhead box
P3.
The NOS quality assessment scale scored 22 included studies between 5 and 9, with
appropriate selection of cases and controls, and reliable study metric measures.
So, the overall quality of studies was considered to be medium to high. The
scoring results are displayed in [Table
2]. Notably, before study initiation, most studies excluded patients
with conditions such as (1) comorbid cardiovascular and cerebrovascular systemic
disease; (2) medication before examination and a history of related illness; and
(3) pregnant and lactating women. The studies thus did not have significant
missing data that would have seriously compromised test efficacy but a limited
extrapolation of the findings. In summary, the overall quality of the studies
included in this meta-analysis was assessed as good quality, and the research
results were reliable.
Table 2 Quality assessment of Newcastle-Ottawa Scale for
case-control studies.
|
Study [Ref]
|
Selection
|
Comparability of cases and controls
|
Exposure
|
Scores
|
|
Adequate definition of cases
|
Representativeness of the cases
|
Selection of Controls
|
Definition of Controls
|
Ascertainment of exposure
|
Same method of ascertainment for cases and controls
|
Non-response rate
|
|
Song JZ, 2009 [21]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
|
Jin X, 2018 [22]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
|
Li XJ, 2016 [23]
|
1
|
1
|
0
|
1
|
1
|
1
|
1
|
0
|
6
|
|
Gao ST, 2011 [24]
|
1
|
1
|
0
|
1
|
1
|
1
|
1
|
1
|
7
|
|
Xue HB, 2012 [25]
|
1
|
1
|
1
|
1
|
2
|
1
|
1
|
1
|
9
|
|
Zheng LT, 2012 [26]
|
1
|
1
|
0
|
1
|
0
|
1
|
1
|
0
|
5
|
|
Chen Q, 2012 [27]
|
1
|
1
|
0
|
1
|
1
|
1
|
1
|
0
|
6
|
|
Zhao JY, 2012 [28]
|
1
|
1
|
0
|
1
|
0
|
1
|
1
|
0
|
5
|
|
Huang GY, 2017 [29]
|
1
|
1
|
0
|
1
|
0
|
1
|
1
|
0
|
5
|
|
Hu Y, 2019 [30]
|
1
|
1
|
0
|
1
|
1
|
1
|
1
|
1
|
7
|
|
Mao C, 2011 [31]
|
1
|
1
|
0
|
1
|
0
|
1
|
1
|
0
|
5
|
|
Qin J, 2017 [32]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
|
Xue H, 2015 [33]
|
1
|
1
|
0
|
1
|
1
|
1
|
1
|
0
|
7
|
|
Şıklar Z, 2016 [34]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
|
Yang X, 2018 [35]
|
1
|
1
|
1
|
1
|
2
|
1
|
1
|
1
|
9
|
|
Klatka M, 2014 [36]
|
1
|
1
|
0
|
1
|
1
|
1
|
1
|
0
|
6
|
|
Rydzewska M, 2018 [37]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
|
Fathima N, 2019 [13]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
|
Kalantar K, 2019 [12]
|
1
|
1
|
0
|
1
|
0
|
1
|
1
|
0
|
5
|
|
Inoue N, 2010 [38]
|
1
|
1
|
1
|
1
|
2
|
1
|
1
|
1
|
9
|
|
Zheng L, 2015 [39]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
|
Safdari V, 2017 [40]
|
1
|
1
|
0
|
1
|
1
|
1
|
1
|
0
|
6
|
|
Li C, 2016 [10]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
|
Xue HB, 2015 [41]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
|
Tan Y, 2019 [42]
|
1
|
1
|
0
|
1
|
1
|
1
|
1
|
0
|
6
|
|
Ren X, 2022 [43]
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
1
|
8
|
Indicators
Comparison between newly diagnosed AITD patients and healthy
people
Th17
Eight studies compared the differences in Th17 levels of peripheral blood
mononuclear cells (PBMC) between newly diagnosed HT patients and the
healthy population. Five studies reported differences in Th17 levels of
PBMC between newly diagnosed GD patients and the healthy population. The
meta-analysis results showed that Th17 levels in PBMC were higher in
both HT patients and GD patients than in the healthy population, and the
differences were statistically significant (HT: SMD=2.35,
95% CI: 1.98, 2.72; GD: SMD=1.61, 95% CI: 1.23,
1.98; [Fig. 2a]). Obviously, the
results of the index of Th17 levels in PBMC suggested high heterogeneity
in HT and medium heterogeneity in GD (HT:
I2=60.3%, p=0.014; GD:
I2=37.7%, p=0.170; [Fig. 2a]). However, the
significance of this result needs to be further discussed.
Fig. 2 Comparison of newly diagnosed AITD patients and
healthy people: A: Th17; B: Treg.
Treg
The meta-analysis results reported in ten studies showed that Treg levels
marked by
CD4+CD25+FoxP3+and
measured by flow cytometric were lower in patients with newly diagnosed
HT than in the healthy population (Supplemental Table 1S). The
difference was statistically significant (HT: SMD=–2.04,
95% CI: –2.67, –1.42; [Fig. 2b]). Moreover, the same
phenomenon was observed in newly diagnosed GD patients (GD:
SMD=–1.35, 95% CI: –2.11, –0.58;
[Fig. 2b]). It was also
noteworthy that the between-study heterogeneity statistic I2
both suggested that high heterogeneity was observed for this indicator
on HT disease and GD disease.
FoxP3 mRNA
Eleven studies reported FoxP3 mRNA levels in PBMC of newly diagnosed HT
patients, and six studies reported FoxP3 mRNA levels in PBMC of newly
diagnosed GD patients (Supplemental Table 1S). The meta-analysis results
showed that FoxP3 mRNA levels in PBMC of both HT and GD patients were
lower than those in the healthy population, with statistically
significant differences (HT: SMD=–2.58, 95% CI:
–3.12, –2.05; GD: SMD=–2.13, 95%
CI: –2.56, –1.70; [Fig.
3]).
Fig. 3 Comparison of newly diagnosed AITD patients and
healthy people. Foxp3 mRNA.
The single nucleotide polymorphisms in the FoxP3 promoter region would
likely affect FoxP3 expression, of which rs3761548 and
rs3761549 were analyzed for comparison. The results are shown
in [Fig. 4]. When the genomes of
newly diagnosed HT patients and newly diagnosed GD patients were
compared with the control group by PCR amplification, the genotypes
“CC,” “CA,” and “AA” of
rs3761548 were not significantly different from those of the
control group (p>0.05). However, when rs3761549 of HT and
GD patients were compared with the controls, the results showed a
statistically significant difference between HT patients and the
controls in comparing genotype “CT” (OR=1.66,
95% CI: 1.18, 2.34; [Fig.
4c]). While comparing the remaining two genotypes, the results
showed no statistically significant differences. In addition, in the
comparison between GD patients and the controls, there were no
statistically significant differences in the levels of genotypes
“CC,” “CT,” and “TT”
(p>0.05).
Fig. 4 Comparison of newly diagnosed AITD patients and
healthy people: a: the genotypes “CC,”
“CA,” and “AA” of
rs3761548 in Hashimoto’s disease; b:
the genotypes “CC,” “CA,” and
“AA” of rs3761548 in Graves’
disease; c: the genotypes “CC,”
“CT,” and “TT” of
rs3761549 in Hashimoto’s disease; d:
the genotypes “CC,” “CT,” and
“TT” of rs3761549 in Graves’
disease.
Publication bias assessment and sensitivity analysis
Egger’s test was used to detect publication bias for each
indicator, and the results showed no significant publication bias for
all indexes (p>0.05) ([Table
3]). The funnel plot also reflected the symmetry of indicators
Th17, Treg, and FoxP3, and high heterogeneity among studies Supplemental
(Fig. 1S). The results of
Duval and Tweedie’s trim and fill test showed that the effect
sizes of all quantitative analysis were stable and did not generate
significant change before and after trim and fill, with clear guidance
([Table 3]).
Table 3 Evaluation of publication bias and
sensitivity analysis.
|
Index
|
Egger’s regression
|
Duval and Tweedie’s trim and fill
|
|
Intercept
|
p
|
Original effect size
|
Studies trimmed
|
Adjusted effect size
|
|
HT
|
|
Th17
|
1.715
|
0.621
|
2.35 (1.98, 2.72)
|
2
|
2.23 (1.89, 2.57)
|
|
Treg
|
− 6.944
|
0.145
|
− 2.04 (− 2.67,
− 1.42)
|
0
|
− 2.04 (− 2.67,
− 1.42)
|
|
Foxp3
|
− 5.637
|
0.112
|
− 2.58 (− 3.12,
− 2.05)
|
0
|
− 2.58 (-3.12,
− 2.05)
|
|
GD
|
|
Treg
|
− 2.342
|
0.667
|
− 1.35 (− 2.11,
− 0.58)
|
0
|
− 1.35 (− 2.11,
− 0.58)
|
|
Th17
|
1.339
|
0.650
|
1.61 (1.24, 1.98)
|
0
|
1.61 (1.24, 1.98)
|
|
Foxp3
|
− 2.482
|
0.367
|
− 2.13 (− 2.56,
− 1.71)
|
0
|
− 2.13 (− 2.56,
− 1.71)
|
Abbreviation: HT=Hashimoto’s disease;
GD=Graves’ disease; Th17=helper T cell
17; Treg=regulatory T cells; Foxp3=Forkhead box
P3.
Discussion
AITD is an autoimmune disease that manifests primarily as HT and GD. HT is
characterized by infiltration of lymphocytes in thyroid tissue and destruction of
thyroid follicles, leading to hypothyroidism. On the other hand, GD is characterized
by hyperthyroidism due to excessive thyrotropin receptor-specific stimulating
autoantibodies (TSAb). GD and HT exhibit different clinical features, but they show
similarities in tissue damage, such as lymphocyte infiltration and abnormal cytokine
secretion in vivo [44]. Previous studies
suggested that the imbalance between Th1/Th2 contributed to the development
of AITD. However, recent studies have shown that newly identified subsets of T
lymphocytes, such as Th17 and Treg, and their associated cytokines may be associated
with autoimmune diseases.
It was evident from the results of the meta-analysis that both newly diagnosed HT
and
newly diagnosed GD patients had statistically significant elevated levels of Th17
in
their peripheral blood compared to healthy controls. Even though there was high
heterogeneity in the meta-analysis results for the indicator Th17, it was believed
to be related to the included patients’ essential characteristics, such as
gender differences and mean age. However, the high heterogeneity of the results did
not affect our assertion that HT patients and GD patients had significantly higher
levels of Th17 in their peripheral blood than healthy controls under them. It was
observed from the forest plot ([Fig. 2a])
that the newly diagnosed AITD patients in each study exhibited higher Th17 levels
than the healthy control population, and the differences were all statistically
significant. Moreover, we did not observe substantial publication bias in the
indicator Th17 from Egger’s test and instability in Duval and
Tweedie’s trim and fill test. Therefore, there was no doubt about the
assertion that peripheral blood levels of Th17 were significantly higher in newly
diagnosed AITD patients than in healthy controls. Th17 lymphocytes are essentially
pro-inflammatory and mainly produce cytokines such as IL-17A/F, and IL-21
which could act as a causative agent in many chronic inflammatory and autoimmune
diseases such as various asthma, allergies, and many other diseases [1]. Zake et al. found that IL-17
immunopositivity is observed in thyroid cells and inflammatory infiltrates in
patients with HT and that IL-17-positive thyroid follicles frequently showed
impaired integrity and destruction of follicular cells [45]. In summary, the meta-analysis results
provide clinical evidence that Th17 plays a possible key role in developing
AITD.
Likewise, after considering both forest plots, publication bias, and outcome
stability tests for FoxP3 mRNA and Treg indicators in the meta-analysis results, we
could confirm that FoxP3 mRNA and Treg results in newly diagnosed AITD patients were
consistent, and the patients’ peripheral blood levels were lower than the
healthy control populations. The differences were statistically significant. The
high heterogeneity of the results for both metrics may guide more profound studies
but will not impact the conclusions of this meta-analysis. Th17 cells share a
differentiation pathway with FoxP3 and Treg and transforming growth factor β
is involved in the development of Th17 and Treg and plays a crucial role in
maintaining the subpopulation of T cells involved in the pathogenesis of AITD.
Immune tolerance is generated by stimulating Treg and suppressing cells [1]. Regulatory T cells (Treg) are characterized
by the expression of the transcription factor FoxP3, a Treg-specific marker [46]. Loss-of-function mutations in the FoxP3
gene are associated with immune dysregulation, multiple endocrine disorders,
inflammatory bowel disease, and severe allergies [47]. This study showed that Treg levels and FoxP3 mRNA levels were
reduced in peripheral blood of newly diagnosed AITD patients, indicating a
regulatory role of FoxP3 for Treg. In other words, its expression was not lost in
either physiological or inflammatory responses, providing a theoretical basis for
its role in the pathogenesis of autoimmune thyroid.
In the experiments of Cao et al. rat model, the measurement of Th17 and Treg levels
after successful induction of HT model and bone marrow mesenchymal cell intervention
in vitro showed that the percentage of Th17 decreases and Treg levels increase
significantly after the intervention, further confirming the possible relevance of
Th17/Treg to the development of AITD disorders [48]. Zhang et al. suggested that the
Th17/Treg ratio imbalance may positively correlate with TGAb and TPOAb [49]. Thus, the Th17/Treg ratio
imbalance may be involved in the development of AITD.
FoxP3 promoter region polymorphism can affect the expression of this transcription
factor. Moreover, in this meta-analysis, the association of FoxP3 rs3761548
with rs37615489 gene single nucleotide polymorphism in newly diagnosed GD
patients was evaluated, revealing no significant difference in genotype frequency
with this single patient nucleotide polymorphism, suggesting that this polymorphism
is not associated with GD susceptibility. Kalantar et al. reported that FoxP3
rs3761548 “CC” is significantly correlated with the TPOAb
level of HT and is associated with the disease’s activity [12]. However, it requires further clarification
by a large number of experimental studies. For the present results, the
“CT” of rs37615489 was different in HT patients compared to
the controls, suggesting that this change in genotype frequency may predispose to
HT. However, due to the lack of relevant literature, it is still necessary to find
more relationships between FoxP3 gene polymorphism and AITD to understand better the
immune regulation mechanism, pathogenesis, and prognosis of the disease.
The main limitations of this meta-analysis are as follows: 1. Some of the literature
did not provide data on relevant indicators, or the data provided were not
extractable, and these non-included studies might have a slight impact on the
results; and 2. The literature on some indicators was small, and the sample size was
insufficient. So, the results were unstable. Therefore, the current results still
need a large sample of studies to elucidate further.
Conclusion
In conclusion, the Th17/Treg ratio imbalance in AITD patients may develop
AITD. The monitoring of Th17 and Treg levels may become a critical tool to evaluate
the immune homeostasis of the body to guide clinical diagnosis and treatment,
contributing to the disease assessment. However, more studies are required to
explore the specific pathogenesis of AITD.
Authors’ Contributions
Aizhi Chen, Liang Huang: Critical revision of the manuscript; Aizhi Chen, Liang
Huang, Liqin Zhang: Substantial contribution to the conception and design,
manuscript drafting; Aizhi Chen, Liang Huang, Liqin Zhang: Acquisition, analysis,
and interpretation of the data; Aizhi Chen, Liang Huang, Liqin Zhang: Revising the
manuscript critically for final approval of the version to be published. All authors
have read and approved the final manuscript.
Notice
This article was changed according to the erratum on
July 06, 2023.
Erratum
In the above-mentioned article the authors Aizhi Chen and
Liang Huang contributed equally.
