Keywords
premature birth - hypothyroidism - thyroxine - hypothyroxinemia - obstetrics
Introduction
Multiple factors contribute to the etiology of premature births. Premature births
are now considered to be the clinical endpoint of various pathophysiological cascades,
of a complexity that
has to date made it impossible to provide causal therapy. Despite intense national
and global efforts, it has not been possible to reduce the rate of premature births.
Premature birth remains
a medical challenge [1].
The prevalence of premature birth in Germany is 8.6% [2]
[3]
[4]
[5], one of the highest within the European Union [6]. 70% are etiologically classified as spontaneous premature births [5]
[7]
[8]
[9], while 30% have medical causes and are iatrogenically induced. Over the past few
decades, the prevalence of premature births in Germany has remained stable overall;
however, the number of premature births occurring before the 28th GW has increased
by 65% [4].
Thyroid function disorders during pregnancy, such as manifest hypothyroidism or manifest
hyperthyroidism, are established risk factors for the occurrence of premature birth
[10]. Epidemiologically, thyroid disease is one of the most common diseases in women
of childbearing age [11]. Due to its high prevalence and relatively few symptoms, thyroid disease, which
is associated with decreased hormone production, is of particular relevance before
and
during pregnancy. From an epidemiological point of view, in addition to manifest and
latent hypothyroidism, isolated hypothyroxinemia is also considered a risk factor
for premature birth [10]
[12]. This is defined in terms of laboratory testing as a combination of decreased free
thyroxine level with normal TSH concentration in the expectant mother [13]
[14].
To date, there is no consensus among the professional endocrinological associations
on whether latent hypothyroidism or hypothyroxinemia during pregnancy requires treatment.
The American
Endocrine Society leaves the decision of whether to treat expectant mothers with L-thyroxine
largely to the attending gynecologists [14], while the American Thyroid Association opposes treatment in the absence of thyroid
antibodies and natural conception [15].
The European Thyroid Association calls for further studies to be able to more accurately
assess the effects of thyroid hormone deficiency on the health of the unborn baby.
Due to the
potential risk to fetal brain development, treatment with L-thyroxine is still recommended,
despite the lack of interventional studies [16].
The guidelines of the German Association of Scientific Medical Societies (AWMF) of
October 2022 on prevention and treatment of premature birth do not mention thyroid
dysfunction as a risk
factor requiring treatment [5]
[7]
[8].
The aim of this study is to clarify the extent to which the rate of premature births
is influenced by regular monitoring of the maternal free thyroxine level and pregnancy-adapted
L-thyroxine
replacement therapy before and during pregnancy in the case of existing or newly diagnosed
latent and manifest hypothyroidism or hypothyroxinemia.
Patients and Methods
In this retrospective cohort study, we analyzed a total of 1440 anonymized survey
questionnaires completed by women with a thyroid function disorder. The raw data were
made available as an
Excel file by Forschung Beratung Evaluation GmbH (FBE) in Berlin. This data is based
on a survey questionnaire that queries a number of known risk factors for premature
birth as well as
perinatal data. In addition to biometric and demographic data, such as body mass index,
maternal age, nationality, level of education, and parity, socioeconomic data, a family
history, and a
patient-reported history for the last 12 months prior to conception were also collected.
The questionnaire was completed by the pregnant women and their physicians and returned
to the FBE for evaluation with their written consent.
Study group and control group
A total of 3489 survey questionnaires were provided from various medical practices.
Of these, 500 women had taken L-thyroxine before and during pregnancy, as documented
in their medical
records. These 500 questionnaires were used as a control group. The criteria for the
provision of L-thyroxine replacement therapy in the control group are not known, because
the survey did
not include any questions regarding these.
The study group consisted of 940 women from the same medical practice who had also
completed the FBE questionnaire. These pregnant women had been taking L-thyroxine
because of latent and
manifest hypothyroidism or hypothyroxinemia. The study group was divided into group
A, consisting of 360 pregnant women who had already been taking L-thyroxine prior
to conception, and group
B, consisting of 580 pregnant women who only took L-thyroxine after conception.
Inclusion and exclusion criteria
Only single pregnancies were included in the study. The study groups had a maximum
gestational age of 12 + 0 GW. There was no information on gestational age in the control
group. Underlying
diseases, such as thrombophilia, hypertension, diabetes mellitus, etc. were not counted
as exclusion criteria.
Laboratory tests
TSH and free thyroxine (fT4) levels were measured regularly for dose adjustment purposes
in all women in the study groups as part of prenatal care. The aim was to maintain
the free
thyroxine level in the euthyroid hyperthyroxinemic range (high-normal area of reference
range) within the pregnancy adapted reference range. There are no data on this for
the control
group.
The Ethics Committee of Baden-Württemberg did not consider an ethics committee vote
to be necessary because the survey questionnaires were pseudonymized.
Statistical analyses
Statistical analyses were performed using the statistical software SAS, release 9.4
(SAS Institute Inc., Cary, North Carolina, USA). Absolute and relative frequencies
are reported for
qualitative factors; for quantitative variables, the mean and standard deviation are
reported. The ages were divided into six groups; these age groups were considered
to be an ordinal scaled
variable. The following tests were used to compare the two groups: the Chi2 test for nominally scaled factors, Fisher’s exact test (if the conditions of the
Chi2 test
were not met), the Cochran-Armitage trend test for ordinal scaling, the t-test for
comparing two mean values for approximately normally distributed data, and the Mann
and Whitney U test for
quantitative, non-normally distributed data. For comparisons between three groups,
the Chi2 test (for nominal scaling) or the Kruskal-Wallis test (for quantitative, non-normally
distributed data) was used. A test result with a p-value of < 0.05 was considered
statistically significant; a result with a p-value between 0.05 and 0.10 was considered
weakly
significant. All factors for which a statistical association with the target variable
“premature birth” was demonstrated in univariate tests were evaluated simultaneously
by means of a
multiple logistic regression analysis after assessing their medical significance and
the quality and completeness of the data. The “Backward Selection” method was used:
First, logistic
regression was performed using all existing parameters. Then, in a second step, the
parameter with the highest p-value was eliminated from the statistical model. This
step was repeated until
only statistically significant parameters remained in the model. For each parameter,
the odds ratio (OR) was calculated as an approximation to the relative risk.
Results
A direct comparison of the premature birth rates in the three groups shows that the
premature birth rate in group B, at 6.0%, is almost twice that in group A, at 3.1%
(p = 0.0396); the
premature birth rates in both group A and group B were significantly lower than the
premature birth rate in the control group, which was 10.4% (p < 0.0001 or p = 0.0086)
([Table 1]). Since the variables age distribution and parity status are closely associated
with the target variable “premature birth”, these were compared with each other. On
average, the women in group B are slightly younger than the women in group A and in
the control group. The difference is statistically significant (Kruskal-Wallis test
p < 0.0001) ([Table 1]).
Table 1
Comparison of the study groups with the control group.
|
Variables
|
Test
|
Group A
(n = 360)
|
Group B
(n = 580)
|
Control group
(n = 500)
|
Group A p-value
|
Group B p-value
|
|
∅ = Mean; FH = Family history; Compl. = Complications; SD = Standard Deviation
|
|
Total premature births
|
|
3.06%
|
6.03%
|
10.40%
|
p < 0.0001
|
p = 0.0086
|
|
Premature births in primiparae
|
|
3.47% (n = 144)
|
8.04% (n = 286)
|
10.30% (n = 398)
|
p = 0.0117
|
p = 0.3169
|
|
Premature births in multiparae
|
|
2.78% (n = 216)
|
4.08% (n = 294)
|
10.78% (n = 102)
|
p = 0.0031
|
p = 0.0126
|
|
Age cohort (in years)
|
U-test
|
|
|
|
p = 0.1145
|
p < 0.0001
|
|
18–24
|
|
5.87%
|
0.17%
|
2.20%
|
|
|
|
25–29
|
|
26.82%
|
12.59%
|
23.20%
|
|
|
|
30–34
|
|
39.66%
|
30.00%
|
45.60%
|
|
|
|
35–39
|
|
21.79%
|
37.93%
|
26.40%
|
|
|
|
40–44
|
|
5.59%
|
15.52%
|
2.60%
|
|
|
|
≥ 45
|
|
0.28%
|
3.79%
|
0.00%
|
|
|
|
BMI
|
t-test
|
∅ 25.13 (SD 4.84)
|
∅ 24.75 (SD 4.87)
|
∅ 24.34 (SD 5.10)
|
p = 0.0237
|
p = 0.1740
|
|
German citizenship
|
Chi2
|
85.75%
|
81.31%
|
96.77%
|
p < 0.0001
|
p < 0.0001
|
|
Education (in years)
|
U-test
|
∅ 10.4 (SD 1.6)
|
∅ 10.3 (SD 1.7)
|
∅ 11.2 (SD 1.0)
|
p < 0.0001
|
p < 0.0001
|
|
Smokers
|
Chi2
|
19.44%
|
28.32%
|
16.60%
|
p = 0.2819
|
p < 0.0001
|
|
Participation in sports/exercise
|
Chi2
|
48.61%
|
49.91%
|
46.68%
|
p = 0.5763
|
p = 0.2900
|
|
Fertility treatment/IVF
|
Chi2
|
8.06%
|
5.21%
|
36.90%
|
p < 0.0001
|
p < 0.0001
|
|
Self-assessed health (good – moderate – poor)
|
Trend test
|
94.44% – 4.44% – 1.11%
|
95.34% – 4.14% – 0.52%
|
71.94% – 25.05% – 3.01%
|
p < 0.0001
|
p < 0.0001
|
|
Hypertension
|
Chi2
|
2.55%
|
1.92%
|
6.00%
|
p = 0.0175
|
p = 0.0005
|
|
Diabetes
|
Fisher
|
1.99%
|
0.17%
|
1.00%
|
p = 0.2497
|
p = 0.1027
|
|
Eating disorders
|
Fisher
|
1.14%
|
1.05%
|
0.80%
|
p = 0.7239
|
p = 0.7568
|
|
Addiction problems
|
Fisher
|
0.28%
|
0.70%
|
0.00%
|
p = 0.4131
|
p = 0.1277
|
|
Migraine
|
Chi2
|
12.22%
|
10.10%
|
19.00%
|
p = 0.0081
|
p < 0.0001
|
|
Vag. infection in the past 12 months (none – one – several)
|
Trend test
|
47.88% – 15.31% – 36.81%
|
43.18% – 22.52% – 34.30%
|
72.62% – 18.55% – 8.82%
|
p < 0.0001
|
p < 0.0001
|
|
Hospital treatment
|
Chi2
|
17.32%
|
11.09%
|
19.32%
|
p = 0.4578
|
p = 0.0002
|
|
Family stress
|
Chi2
|
90.20%
|
88.33%
|
34.07%
|
p < 0.0001
|
p < 0.0001
|
|
Occupation
|
Chi2
|
70.00%
|
71.68%
|
93.17%
|
p < 0.0001
|
p < 0.0001
|
|
Workload
|
Chi2
|
35.71%
|
42.22%
|
25.00%
|
p = 0.0030
|
p < 0.0001
|
|
S.p. gyn. surgery (none – one – several)
|
Trend test
|
54.24% – 32.77% – 12.99%
|
70.69% – 21.49% – 7.82%
|
64.16% – 22.75% – 13.09%
|
p = 0.0509
|
p = 0.0049
|
|
Premature births in FH
|
Chi2
|
15.00%
|
14.17%
|
8.80%
|
p = 0.0061
|
p = 0.0081
|
|
Diabetes in FH
|
Chi2
|
20.99%
|
17.52%
|
35.80%
|
p < 0.0001
|
p < 0.0001
|
|
Number of children
|
U-test
|
∅ 1.1 (SD 0.7)
|
∅ 1.3 (SD 1.1)
|
∅ 0.7 (SD 0.8)
|
p < 0.0001
|
p < 0.0001
|
|
S.p. induced abortion (none – one – several)
|
Trend test
|
2.06% – 4.53% – 93.42%
|
2.69% – 9.58% – 87.72%
|
1.61% – 16.13% – 82.26%
|
p = 0.0049
|
p = 0.2641
|
|
S.p. miscarriage (none – one – several)
|
Trend test
|
13.20% – 32.80% – 54.00%
|
4.79% – 20.36% – 74.85%
|
14.97% – 38.50% – 46.52%
|
p = 0.1812
|
p < 0.0001
|
|
S.p. premature birth (none – one – several)
|
Trend test
|
0.82% – 6.15% – 93.03%
|
0.93% – 6.48% – 92.59%
|
3.92% – 21.57% – 74.51%
|
p < 0.0001
|
p < 0.0001
|
|
Compl. last pregnancy
|
Chi2
|
18.44%
|
14.20%
|
46.60%
|
p < 0.0001
|
p < 0.0001
|
|
Fertility treatment
|
Chi2
|
15.00%
|
7.41%
|
55.49%
|
p < 0.0001
|
p < 0.0001
|
|
Caesarean section
|
Chi2
|
17.66%
|
18.40%
|
36.44%
|
p < 0.0001
|
p < 0.0001
|
|
Primiparae/Multiparae
|
Chi2
|
40.00%/60.00%
|
49.31%/50.69%
|
79.60%/20.40%
|
p < 0.0001
|
p < 0.0001
|
|
Premature labor
|
Chi2
|
0.28%
|
2.24%
|
4.60%
|
p = 0.0001
|
p = 0.0313
|
|
Cervical insufficiency
|
Chi2
|
0.28%
|
0.69%
|
4.60%
|
p = 0.0001
|
p < 0.0001
|
|
Premature rupture of membranes
|
Chi2
|
0.56%
|
1.55%
|
9.00%
|
p < 0.0001
|
p < 0.0001
|
|
Child female/male
|
Chi2
|
48.33%/51.67%
|
43.57%/56.43%
|
47.33%/52.67%
|
p = 0.7785
|
p = 0.2374
|
The three groups also differed significantly in terms of parity status (p < 0.0001)
([Fig. 1]). The proportion of primiparae was significantly higher in the control group than
in the study groups A and B (79.6% versus 40.0% and 49.3%, respectively). Since
primiparity is epidemiologically associated with a higher risk of premature birth
[17], the rates of premature birth in groups A and B were broken down according to parity
status and compared to the corresponding rate in the control group ([Table 1]).
Fig. 1
Parity status in the study groups and the control group n = 1440.
The premature birth rates for the primiparae (3.5%) and multiparae (2.8%) in group
A were significantly lower than those in the control group (10.3% and 10.8% respectively,
with p = 0.0117
and p = 0.0031). In group B, only the multiparae were found to differ significantly
from the control group (4.1% versus 10.8%; p = 0.0126), while there was no significant
difference in the
primiparae (p = 0.3169). In addition, the premature birth rate among primiparae in
group A compared to group B is strikingly lower (p = 0.0669), while there was no significant
difference for
multiparae (p = 0.4304).
Evaluation of risk factors
Group A differs from the control group due to a lower proportion of women having undergone
fertility treatment/IVF (8.06% versus 36.90%; p < 0.0001), fewer women with hypertension
(2.6%
versus 6.0%; p = 0.0175), more vaginal infections in the past 12 months (52.1% versus
27.4%; p < 0.0001), more multiparae (60% versus 20.4%; p < 0.0001), correspondingly
fewer
primiparae (40.0% versus 79.6%; p < 0.0001) and a smaller proportion of fertility
treatments (15.0% versus 55.5%; p < 0.0001 ([Table 1]).
In order to determine the statistically significant variables associated with the
target variable “premature birth”, the data from group A and the control group were
combined, as both
groups had already been taking L-thyroxine prior to conception ([Table 2]).
Table 2
Associations of risk factors with premature birth, n = 860 (group A and control group).
|
Variable
|
Category
|
Proportion of premature births
|
Test
|
p-value
|
|
∅ = Mean; FH = Family history; Compl. = Complications; SD = Standard Deviation
|
|
Age cohort (in years)
|
|
|
U-test
|
p = 0.8649
|
|
18–24
|
3.13%
|
|
|
|
25–29
|
6.13%
|
|
|
|
30–34
|
9.73%
|
|
|
|
35–39
|
5.71%
|
|
|
|
40–44
|
3.03%
|
|
|
|
≥ 45
|
0
|
|
|
|
BMI
|
|
∅ 23.15 (SD 4.35)
|
t-test
|
p = 0.0131
|
|
Nationality
|
German – other
|
7.62% – 4.48%
|
Fisher
|
p = 0.4677
|
|
Smoking
|
Yes – no
|
6.54% – 7.50%
|
Chi2
|
p = 0.6793
|
|
Sports activity
|
Yes – no
|
7.37% – 7.33%
|
Chi2
|
p = 0.9832
|
|
Fertility treatment/IVF
|
Yes – no
|
10.38% – 6.37%
|
Chi2
|
p = 0.0524
|
|
Self-assessed health
|
good – moderate – poor
|
6.72% – 8.51% – 21.05%
|
Trend test
|
p = 0.0493
|
|
Hypertension
|
Yes – no
|
23.08% – 6.63%
|
Fisher
|
p = 0.0013
|
|
Diabetes
|
Yes – no
|
0% – 7.50%
|
Fisher
|
p = 1.0000
|
|
Eating disorders
|
Yes – no
|
0% – 7.46%
|
Fisher
|
p = 1.0000
|
|
Addiction problems
|
Yes – no
|
0% – 7.40%
|
Fisher
|
p = 1.0000
|
|
Migraine
|
Yes – no
|
8.70% – 7.14%
|
Chi2
|
p = 0.5234
|
|
Vag. infection in the past 12 months
|
None – one – several
|
8.12% – 10.85% – 1.97%
|
Trend test
|
p = 0.0404
|
|
Hospital treatment in the past 12 months
|
Yes – no
|
12.66% – 6.17%
|
Chi2
|
p = 0.0048
|
|
Family stress
|
Yes – no
|
5.91% – 9.39%
|
Chi2
|
p = 0.0544
|
|
Occupation
|
Yes – no
|
8.10% – 3.52%
|
Chi2
|
p = 0.0560
|
|
Workload
|
Yes – no
|
8.00% – 8.23%
|
Chi2
|
p = 0.9191
|
|
S.p. gyn. surgery
|
None – one – several
|
7.54% – 7.21% – 6.54%
|
Trend test
|
p = 0.7209
|
|
Premature births in FH
|
Yes – no
|
7.61% – 7.55%
|
Chi2
|
p = 0.9853
|
|
Diabetes in FH
|
Yes – no
|
6.88% – 7.80%
|
Chi2
|
p = 0.6478
|
|
S.p. induced abortion
|
None – one – several
|
5.00% – 9.76% – 12.50%
|
Trend test
|
p = 0.1305
|
|
S.p. miscarriage
|
None – one – several
|
4.50% – 6.49% – 6.56%
|
Trend test
|
p = 0.4042
|
|
S.p. premature birth
|
None – one – several
|
1.65% – 29.73% – 16.67%
|
Trend test
|
p < 0.0001
|
|
Compl. last pregnancy
|
Yes – no
|
5.38% – 4.72%
|
Fisher
|
p = 0.7829
|
|
Fertility treatment
|
Yes – no
|
8.00% – 5.48%
|
Chi2
|
p = 0.2782
|
|
Delivery by caesarean section
|
Yes – no
|
13.85% – 4.70%
|
Chi2
|
p < 0.0001
|
|
Primiparae
|
Yes – no
|
8.49% – 5.35%
|
Chi2
|
p = 0.0879
|
|
Premature labor
|
Yes – no
|
33.33% – 6.58%
|
Fisher
|
p = 0.0002
|
|
Cervical insufficiency
|
Yes – no
|
37.50% – 6.46%
|
Fisher
|
p < 0.0001
|
|
Premature rupture of membranes
|
Yes – no
|
38.30% – 5.54%
|
Fisher
|
p < 0.0001
|
|
Gender of premature birth
|
female – male
|
6.43% – 7.98%
|
Chi2
|
p = 0.3923
|
BMI, poor self-assessed state of health, hypertension, group assignment (group A versus
control group), vaginal infections in the past 12 months, hospital treatment, s.p.
premature births,
delivery by caesarean section, premature labor, cervical insufficiency, and premature
rupture of membranes are statistically significant. Weakly significant variables are
fertility
treatment/IVF, occupation, family stress in the past 12 months, and primiparity.
Variables that are highly subjective in their assessment and difficult to quantify
and operationalize, such as family stress, occupation, and self-assessed state of
health, were not taken
into account despite their significance or weak significance in the univariate analysis.
Moreover, variables that are inextricably linked to the pathophysiology of premature
birth, such as
hospital admissions, type of delivery, premature labor, cervical insufficiency, and
premature rupture of membranes were also not taken into account.
Due to numerous missing values the variables “pregnancies after fertility treatment”
and “s.p. premature birth” were not included in the multiple analysis and were considered
separately.
Since fertility therapy/IVF is a significant risk factor [18], this variable was taken into account in the multiple regression analysis.
The multiple logistic regression analysis for group A was performed using the following
variables: group assignment (group A versus control group), BMI, hypertension, vaginal
infections in
the past 12 months, and fertility treatment/IVF ([Table 3]).
Table 3
Multiple logistic regression analysis of group A and control group (n = 860).
|
Risk factor
|
p-value
|
Odds Ratio
|
|
Group assignment
|
p = 0.0005
|
OR = 0.29
|
|
Hypertension
|
p = 0.0002
|
OR = 5.21
|
|
BMI in kg/m2
|
p = 0.0061
|
OR = 0.91
|
|
Vaginal infections in the past 12 months
|
p = 0.5504
|
|
|
Fertility treatment/IVF
|
p = 0.4990
|
|
A higher risk of premature birth can be found for the variables group assignment (group
A versus control group; OR = 0.289; p = 0.0005), hypertension (OR = 5.214; p = 0.0002),
and BMI in
kg/m2 (OR = 0.91; p = 0.0061). Vaginal infections in the past 12 months and previous fertility
treatments/IVF are not significant.
Although L-thyroxine was only taken after conception in group B, the analysis was
performed in the same way as for group A, as a comparable control group was not available.
Statistically
significant variables for the target variable “premature birth” included poor self-assessed
state of health, hypertension, hospital admissions, occupation, previous miscarriages,
s.p.
premature births, delivery by caesarean section, primiparity, premature labor, cervical
insufficiency, and premature rupture of membranes. Weakly significant variables were
a lower BMI,
longer school education, fertility treatment/IVF, and induced abortion in the medical
history.
Occupation, previous induced abortion, miscarriages, s.p. premature birth, hospital
stay, type of delivery, premature labor, cervical insufficiency, premature rupture
of membranes, and
self-assessed state of health were not taken into account for the same reasons as
in group A.
The multiple logistic regression analysis for group B was performed with the following
variables: group assignment (group B versus control group), hypertension, and parity,
as well as BMI
despite its weak significance, given that BMI is a recognized risk factor ([Table 4]).
Table 4
Multiple logistic regression group B and control group (n = 1080).
|
Risk factor
|
p-value
|
Odds Ratio
|
|
Group assignment
|
p = 0.0437
|
OR = 0.62
|
|
Hypertension
|
p = 0.0221
|
OR = 4.70
|
|
BMI in kg/m2
|
p = 0.0002
|
OR = 0.94
|
|
Parity
|
p = 0.1564
|
|
A higher risk of premature birth is found for the variables group assignment (group
B versus control group; OR = 0.623; p = 0.0437), hypertension (OR = 4.699; p = 0.0002),
and BMI
(OR = 0.940; p = 0.0221). Parity status was not significant.
Supplementary analysis for missing data
The variables vaginal infections, fertility treatment, and s.p. premature birth were
missing a large number of data points. To analyze the extent to which these missing
data points affect
the result, we performed a simulation calculation.
Initially, all missing data points for vaginal infection were replaced with a “yes”
and then with a “no”. In either constellation, there was no effect on the rate of
premature births. For
the “fertility treatment” variable, 62% of the data are missing in the control group,
while in group A, the data are complete. Since the questionnaire in the control group
was predominantly
filled in by the participants themselves, it is reasonable to propose that the missing
data should be replaced with a “no”. As a result, the group difference would no longer
be significant.
In case the answer to the missing data was “yes”, the difference would be significant.
This does not change the outcome, as fertility treatment is not statistically associated
with the rate
of premature births ([Table 2]).
A previous premature birth (s.p. premature birth) is a significant risk factor for
another premature birth [19]. This risk factor was associated particularly frequently with the risk factors “self-assessed
state of health”, “stress”, and “high blood pressure”. Based on this
association, logistic regression was used to estimate the probability for “s.p. premature
birth”. If the probability was greater than 50%, the parameter was assumed to be “yes”;
otherwise it
was assumed to be “no”.
Even after including this variable in the multiple regression analysis for group A
and group B, the result does not change. Again, group assignment (group A or group
B versus control group)
is a significant risk factor, with an OR = 0,30 (p = 0.0007) in group A and an OR = 0.629
(p = 0.0496) in group B. Other risk factors were hypertension (OR = 3.479, p = 0.0090)
and BMI per
unit (OR = 0.897, p = 0.0032), as well as the variable s.p. premature birth (OR = 3.555,
p = 0.0006) ([Table 5], [Table 6]).
Table 5
Extended multiple logistic regression for group A and control group (n = 860).
|
Risk factor
|
p-value
|
Odds Ratio
|
|
Group assignment
|
p = 0.0007
|
OR = 0.30
|
|
Hypertension
|
p = 0.0090
|
OR = 3.48
|
|
BMI in kg/m2
|
p = 0.0032
|
OR = 0.90
|
|
S.p. premature birth
|
p = 0.0006
|
OR = 3.56
|
Table 6
Extended multiple logistic regression for group B and control group (n = 1080).
|
Risk factor
|
p-value
|
Odds Ratio
|
|
Group assignment
|
p = 0.0496
|
OR = 0.63
|
|
Hypertension
|
p = 0.0080
|
OR = 3.30
|
|
BMI in kg/m2
|
p = 0.0196
|
OR = 0.99
|
|
S.p. premature birth
|
p = 0.0021
|
OR = 3.04
|
Discussion
Taking into account hypertension and BMI, as well as s.p. premature birth in the case
of the simulation calculation, a low maternal free thyroxine level prior to conception
seems to be
another risk factor for premature birth. L-thyroxine replacement therapy prior to
conception which increases the maternal free thyroxine level to a value within the
high-normal range, such
that euthyroid hyperthyroxinemia is already present at conception, appears to effectively
reduce the rate of premature births. This is very clearly evident in group A, which
has almost 70%
fewer premature births compared to the control group. In group B, which only received
L-thyroxine replacement therapy after conception, the effect was not quite so pronounced;
however, with
40% fewer premature births compared to the control group, it was still clearly visible.
Despite many epidemiological studies on the correlation between maternal thyroxine
deficiency and the risk of premature birth, there is no consensus on whether systematic
screening before and
during pregnancy and, if necessary, subsequent L-thyroxine replacement is useful in
latent forms of hypothyroidism. A 2013 Cochrane study analyzed four randomized controlled
trials with a
moderate risk of bias. A total of 362 pregnancies were evaluated. This study showed
that L-thyroxine replacement therapy prior to conception in euthyroid women, similar
to group A, led to a
72% reduction in the rate of premature births. Nevertheless, the available data were
considered insufficient, so no general recommendation was made [20]. In another Cochrane study from 2015, this effect could not be confirmed. L-thyroxine
showed no advantages or disadvantages for the outcome of mother and child [21].
A recently published study of pregnant women with thyroid insufficiency before the
9th GW receiving L-thyroxine replacement therapy, similar to group B, was able to
show a 14–29% reduction in
the rate of premature birth before the 32nd GW. Replacement therapy with L-thyroxine
was considered safe from a clinical point of view, and an improvement in the course
of pregnancy was
documented [22].
The Working Group for Obstetrics, Department of Maternal Diseases (AGG) of the German
Society of Gynecology and Obstetrics (DGGG) recommends screening by means of a TSH
test for all pregnant
women with a history of risk factors. According to an established algorithm, L-thyroxine
replacement therapy can be considered in the case of TSH concentrations of 2.5 to
4.0 mU/L and positive
TPO antibodies (TPO-Ab). From a TSH value of 4.0 mU/L and with positive TPO-Ab, L-thyroxine
replacement therapy should be given [23]. The AGG recommendations only apply post-conception, in line with group B, and do
not take into account maternal free thyroxine level, which reflects the secretory
performance of the thyroid gland regardless of the TSH level and TPO-Ab. Free thyroxine
which can cross the placental barrier is considered as a measure of fetal hormone
supply while TSH which
can not cross the placental barrier indicates maternal metabolism [24]. Maternal euthyroidism does not rule out hypothyroxinemia, and thus insufficient
thyroxine supply to the fetus.
From a pathophysiological point of view, little research has been done on a possible
correlation between thyroxine deficiency and the risk of premature birth. It is considered
certain that
the maternal thyroid gland is subjected to considerable additional stress as a result
of pregnancy. During pregnancy, thyroid hormone requirements increase by approximately
25–50%. Over 99% of
thyroid hormones are bound to transport proteins in serum and are therefore not metabolically
active. The dominant binding protein is thyroxine-binding globulin (TBG), the concentration
of
which increases two to three-fold from the baseline values depending on the estrogen
level up to the 12th–14th GW. This additional TBG reduces the concentration of free
thyroid hormones by an
average of 10–15%, thus removing them from the metabolism. To maintain maternal euthyroidism,
the synthesis and secretion of thyroid hormones is increased by approximately 30–100%.
TBG synthesis is continuously stimulated in the maternal and fetal liver by placental
estrogen up until delivery. Since the estrogen level increases continuously during
pregnancy, this leads
to maximum continuous stimulation of the maternal thyroid gland, as well as the fetal
thyroid gland. In a healthy, iodine-filled thyroid gland, thyroxine is predominantly
synthesized and
secreted. Physiological maternal euthyroid hyperthyroxinemia develops, and does not
subside until 14 days after delivery [25]
[26]. Women are five times more likely to suffer from latent and manifest hypothyroidism
than men; nevertheless, the role of maternal thyroid function in preventing premature
birth has received little attention to date [5]
[7]
[8].
Other risk factors include BMI and hypertension. This is consistent with a number
of epidemiological studies [27]
[28]
[29]
[30]
[31]. Being significantly overweight or underweight increases the risk of premature birth
compared to being of normal weight or mildly overweight. When BMI and the risk of
premature birth is plotted on a graph, the result is a U-shaped curve in which the
risk of premature birth is at its highest in women who are underweight or extremely
overweight. In the
present study, the risk of premature birth was higher for underweight women than for
overweight or mildly obese women. However, too little weight gain during pregnancy
has been shown to be a
more suitable predictor of premature birth, with a particular increase in risk among
women who have a low BMI prior to conception.
Due to a lack of data, the recognized risk factor s.p. premature birth had to be statistically
estimated. The result confirms a recent epidemiological study, which has shown that
s.p.
premature birth is a significant risk factor [19]. The same applies to hypertension, which has long been known as a risk factor [31].
This study has limitations in terms of its design. In theory, it seems reasonable
to conduct a double-blind randomized controlled trial. This would largely ensure both
structural and
observational uniformity. However, a study design of this kind would not be ethically
acceptable.
Furthermore, in group A, three different degrees of thyroxine deficiency (hypothyroxinemia,
latent hypothyroidism, and manifest hypothyroidism) have been combined into one group.
It can be
postulated that the women with manifest hypothyroidism are likely to have benefited
the most. The extent to which women with hypothyroxinemia or latent hypothyroidism
benefit from L-thyroxine
replacement therapy would need to be clarified in a separate study. About the control
group we only know that the women had hypothyroidism and were therefore treated with
L-thyroxine. It can
be assumed that the control group had significantly more manifest hypothyroidism and
thus a more severe thyroxine deficiency, especially since hypothyroxinemia, as the
mildest variant of
thyroxine deficiency, currently does not receive any attention from internal medicine
or obstetrics. Even if it is not possible to accurately assess from this study which
form of thyroid
insufficiency carries the highest risk of premature birth, it still seems clear that
women with hypothyroidism need to be given much better care prior to conception. This
limitation also
applies to group B.
Other variables that contributed to a structural inequality among the groups were
taken into account in the analysis. In light of this, there is reason to believe that
the results obtained in
this study can be generalized. In order to verify the validity of the results, it
would be desirable to follow this study up with a prospective observational study
comprising a sufficiently
large number of cases.
Conclusion
Inadequately treated hypothyroidism increases the risk of premature birth. Preconceptional
intervention and L-thyroxine dose adjustment appear to most effectively reduce the
rate of premature
births. Compared to the control group, the premature birth rate was 70% lower in study
group A (p < 0.0001) and 42% lower in study group B (p = 0.0086). In the multiple
logistic regression
analyses and in the simulation calculation, in addition to group assignment (A or
B versus control group), hypertension, BMI, and a previous premature birth were identified
as other
independent risk factors. Inadequately treated manifest hypothyroidism is a significant
risk factor for premature birth. Further studies should clarify whether hypothyroxinemia
and latent
hypothyroidism are also risk factors.
