The canonical correlation analysis between iron nutrition and
thyroid hormone
We performed canonical correction analysis to explore the structure of
correlation by using a method similar to multivariate analysis. The set
of iron nutrition was assigned as X variables (X1 = SF and X2 = Hb).
Meanwhile, the set of thyroid hormones was assigned as Y variables (Y1 =
T3, Y2 = FT3, Y3 = T4, Y4 = FT4, and Y5 = TSH). There were two canonical
functions. However, only the first canonical function was statistically
significant (canonical coefficient = 0.431, P < 0.01). On the
base of standardized canonical coefficients between the canonical
function and all variables (U in iron status and V in thyroid hormones),
the equations could be established as follows:
U1= -0.648X1 - 0.591X2
V1= 0.388Y1 - 0.970Y2 + 0.135Y3 - 0.308Y4 + 0.213Y5
The canonical correlation analysis showed that a significant positive
correlation existed between iron nutrition and thyroid hormone. The
proportion of explaining the variance attributed to a given canonical
were 96.22% in the 1st canonical function
(Table S2 ). U1 and V1 explained
35.6% and 65.1% of iron nutritional status and thyroid hormone
variables, respectively. As shown in the illustration, the structure
coefficients pointed that except the variable of TSH, other variables
had positive correlations with iron nutrition variances
(Fig. 2 andFig. S3).
Moreover, the standardized coefficients of SF and FT3 were the highest
in their respective subsets. The larger the standardized canonical
coefficients are, the greater the weight in their respective typical
variables is. Consistent with the former, the standardized canonical
coefficients of SF and FT3 were the highest in their respective subsets
as well. We next performed elastic network regression due to the fact
that coefficient of SF was almost the same as the Hb in our results.