Title: Association between blood heavy metals exposure with uterine fibroids among American
women: A cross-sectional analysis from NHANES data
Version: 1 Date: 15 Jul 2024
Reviewer's report:
This manuscript evaluated the associations between three heavy metals in the blood with uterine
fibroids among US women using the NHANES data.  With this cross-sectional design, bias or
even reverse causation may need to be considered, especially when those with fibroids tend to
have heavy menstrual bleeding, which creates an additional route of excreting heavy metals via
menstrual blood, so the strength of associations seen could have been stronger if this factor is
accounted for. There are also several places where revisions and improvements can be made:
Abstract: In addition the the p-values, actual effect estimates and 95% CIs showing the
magnitude of associations need to be presented throughout the abstract text whenever
mentioning a result.
Language editing is needed with more rigorous reflection of scientific findings. There are
several places in the main text where  findings/conclusions/implications are exaggerated  or have
wording not typically used in a scientific paper.
Introduction:
Since this is a US-based study, I recommend showing the prevalence of fibroids in the US.
"black women" Please check conventions when describing race and ethnicity as a social
construct in the US. Usually, the term "non-Hispanic Black" is used.
Methods:
Need to spell out NHANES when first mentioning it.
As a covariate, race was aggregated to white, Black, and other. Please justify why this is
aggregated. NHANES should have enough sample size to look at non-aggregated racial and
ethnic categories in its originally reported form.
Subgroup analysis was mentioned, but no details on what statistical models were used for
calculating p-for-interactions.
Results:
The word "uterine" is repeated twice in the 2nd line of the first paragraph.
Again, mean values (or other measures of magnitude) needs to be shown in addition to just p-
values.
2nd paragraph: Sometimes the authors refer to correlations and sometimes as associations.
Please be consistent.  Based on the methods used, the ORs should be interpreted as associations.
Also unclear how the p-for-trend was calculated as no mentioning of the data imputation methods
used.
For the ORs with 95% CIs including the value of 1, I wouldn't interpret them as an association
based on statistical significance. You may highlight how the point OR is in the positive direction,
but I would not call is a positive association.
"Notably, it lowers the risk of uterine fibroids among the poorly educated." This feels
counterintuitive. I wonder if any type of selection bias or depletion of susceptibles may be
playing a role here. Please use a DAG (directed acyclic graph) to further define your causal
question and how confounders/selection bias may be at play.
In addition to interactions between each metal and the covariate, what about metal*metal
interactions. In recent years, many models have been developed to look at chemicals or metals as
a mixture to understand their overall health impacts, such as the BKMR approach which allows
for flexible modeling of non-linearity and chemical-chemical interactions.  This is especially
important when heavy metals can co-occur in the environment (and the authors have not checked
the correlation of the 3 metals concentrations in this data). The authors should attempt to use
these methods. Some examples are shown here:
https://bookdown.org/andreabellavia/mixtures/preface.html
https://www.sciencedirect.com/science/article/pii/S0160412021003159
https://pubmed.ncbi.nlm.nih.gov/38853304/
https://pubmed.ncbi.nlm.nih.gov/34166661/
Figure 4:  CIs very wide for the high concentrations (likely driven by small n with high
concentrations), so estimates may not be as stable. This should be discussed when discussing
the inflection points chosen just based on this graph.