Discussion
Pregnant women in mid-pregnancy period are prone to
ID2. Serum ferritin plays an important role in
reflecting the iron reserves4. Our study showed
significant correlations between thyroid hormones and iron status.
Although there were some correlations between thyroid hormones and iron
status factors, there were not completely consistent in a previous
study. Shan et al. pointed out that ID women had a lower FT4 levels, but
had no significant difference in TSH levels19. We did
not have evidence that directly supported the specific relationship
between thyroid hormones and iron nutrition. However, our results and
those of previous studies indicated that close relationship between
thyroid hormone and iron nutrition. Especially, our results can be
considered as a certain reference due to be based on a large sample
size.
In a previous study that established the trimester-specific reference
interval of thyroid hormones in pregnant women, it was pointed out that
the FT4 RI in the second trimester was 11.70 (8.67, 16.21) pmol / L, and
the TSH RI in the second trimester was 1.64 (0.27, 4.53)
μIU/mL20. The RIs of thyroid hormones in our study
differed from those in previous reports because of differences in
detection reagents, races, detection methods or kits,and individual
differences. Compared with other studies, the reference critical values
mentioned in our study were slighter higher. However, the IRs of each
parameter established in this study had certain reference significance.
In recent years, the number of studies on the relationship between
thyroid and iron nutrition increased. Some papers pointed out that the
high level of TSH and the low level of FT4 were independently associated
with ID in pregnant women21. Other studies also showed
that SF was an important predicter of TSH during the second trimester of
pregnancy4. ID might result in higher TSH and lower
FT4, FT3 in the pregnancy, which affects the thyroid function of
pregnant woman22. Some researchers found that ID
decreases the level of SF, FT3, FT4, and enzyme activity of TPO in
rats23. However, a study indicated no significant
correlation was found between thyroid function and ferritin in pregnant
women from Indonesia24. And a study pointed out that
iron nutrition was positively correlated with FT4 during pregnancy,
except in the third trimester25. In the current study,
we observed that women in ID group who were assessed for SF levels
during the second trimester pregnancy showed lower levels of FT3 and FT4
and a higher level of TSH. Our results were consistent with those of
most previous studies. Therefore, comparison of the findings with other
previous studies could confirm that the iron nutrition status was
positively associated with thyroid hormones during the second trimester
pregnancy. However, it is unclear how iron status interacts with thyroid
functions specifically. Thus, further research is needed.
Although iron status is not the only factor affecting thyroid function,
it has a significant auxiliary effect4. Several
studies indicated that ID influenced thyroid function by the following
mechanism. Some studies pointed out that the decline in FT4
concentrations might be associated with the change of the TPO activity;
TPO is an iron-dependent enzyme that plays an important role in the
production of thyroid hormones; therefore, ID might cause lower FT4
levels via reducing TPO activity; then, the levels of TSH increase in
negative feedback26-28. Briefly, the lower FT4 levels
secondary to ID results in the decrease in FT4 negative feedback
inhibition of TSH levels, thereby increasing TSH
levels16.
When ID develops to some extent, it will result in ID anemia. Thus, as
an anemia index, Hb is also involved in this study to compare the
influence on thyroid function with SF (Hb < 110g/L were found
in 59% in ID group). Interestingly, Spearman correlation analysis
showed that both SF and Hb positively correlated with FT4 and FT3 and
negatively correlated with TSH. Yet canonical correlation analysis
reflected the relationship between SF and FT3. Therefore, we were sure
that iron nutritional status was associated with thyroid hormone, and we
predicted SF and Hb were risk factors that contribute to thyroid
hormones. Furthermore, as the results of canonical correlation
contradicted with the results of regression, whether SF or Hb plays a
key role in affecting the thyroid hormones is uncertain if our data
analysis result is the only basis. Previous experiments suggested that
Hb synthesis occurs prior to TPO when ID occurs29.
Thus, we also speculated that when ID occurs, iron will be
preferentially supplied to the maternal hemoglobin to maintain current
levels, which results in lower SF levels and decreases synthesis and
activity of TPO. Then, thyroxine insufficiency arises, especially the
FT4 and FT3 levels. Lower FT3 and FT4 levels are obtained, thereby
raising TSH levels via negative feedback inhibition.
Canonical correlation analysis is a useful statistical method to analyze
the interrelationships between subsets of multiple dependent and
independent variable quantities30. It could be used to
analyze the complex interactions of date from two subsets of variable
quantities31. In our study, to reduce the influence of
collinearity and highlight the main relationship in two sets, canonical
correction analysis was added. Considering that the units of variables
studied were different, the standardized correlation coefficients were
used. The larger the standardization coefficient was, the greater the
weight reflected in typical variables in its subsets. The fact that both
the standardized and the structure coefficients of SF and FT3 were the
highest in their respective subsets reflected the relationship between
SF and thyroid functions. SF was positively correlated with FT3 and FT4
and negatively correlated with TSH.
In addition, the results of
standardization coefficient and structure coefficient were consistent in
our study.
The elastic network model is one of the most important models on
nonlinear prediction. This model is controlled by two parameters,
namely, Alpha and Lambda32. Being different from ridge
and lasso regression, the elastic network model merges both
regularization skills within the one model; thus, it not merely lessens
the feature coefficients but also installs some of the coefficients to
zero to reduce the dimensionality of the feature space; then, the
potentially meaningful correlation structure also could be preserved32, 33. In our study, none of the included variables
were excluded, and the coefficient of SF is slightly smaller than that
of Hb. The results of elastic network contradicted with the results of
canonical correlation, maybe because both SF and Hb played certain roles
in regulating thyroid function. However, they are all indicators of iron
nutrition. Thus, iron nutritional status is associated with thyroid
hormone levels in the second trimester of pregnancy.
The innovations in our study were pointed out as follows. First of all,
our result was based on larger samples from the same regions, and all of
recruited women were in the second trimester of pregnancy. Throughout
the whole pregnancy, the thyroxin level in serum is fluctuating. For
example, due to the change of human chorionic gonadotropin, the level of
FT4 elevated temporarily and then declined
gradually34. To our knowledge, this is the first study
to compare the influence of SF and Hb on thyroid hormones via canonical
correlation and elastic network regression analysis. Our study had some
limitations. Firstly, our study was based on the results of large
population survey. Thus, there was a lack of experimental for further
demonstrations. However, the change tendency of our results was
consistent with previous studies and experiments. Then, we did not
measure the levels of urine iodine or thyroid antibodies. Thus, we could
not perform a comprehensive evaluation of pregnant women. Nevertheless,
it will be beneficial to perform similar studies in different trimesters
of pregnancy to do further study the relationship between iron
nutritional status and thyroid function.