Title: Urinary phytoestrogens and the risk of uterine leiomyomata in US women
Version: 0 Date: 11 Feb 2023
Reviewer's report:
Urinary phytoestrogens and the risk of uterine leiomyomata in US women
Thanks for giving me a chance to review this study. It is a difficult and sensitive issue. There
is no evidence in this manuscript to causally link the metabolites of urinary phytoestrogen
measurements with uterine leiomyomata (UL). This is an association-based study, and causal
relationships cannot be established. As presented, the data are overinterpreted. Please clarify this
point throughout the document. It is unclear how the author selected confounders and cleaned the
data. I strongly recommend further analyses, including different statistical methods (Bayesian
kernel machine regression (BKMR) and gqcomp (Quantile G-computation)). English need to
improved. Please see the details below.
Introduction
Line 61: Could you please explain which covariates are involved?
Line 64: Could you please describe current studies in detail?Cohort, case-control, or cross-
sectional study? Which population and sample size? Which types of phytoestrogens and
chemicals?
The rationale for the study is unclear, as the introduction is very confusing, comprising several
pieces of apparently unlinked information. A more integrated appraisal of the relevant literature
would be appropriate to provide the context for the study.
Hypotheses and aims are poorly described.
Ln 67: Because the study is a secondary data analysis for NHANES, the authors only used data
to analyze. The author did not design it. Please revise
Ln 70–71: delete the section. If findings are supportive of these statements, move them to
recommendations.
Methods
The criteria for selecting participants is not clear. Why do the authors only select women aged
20–54?
Urinary phytoestrogen measurement is poorly described. There is no information on the
detection limit (LOD) or how many samples have values lower than the LOD. If these samples
were less than LOD, the authors included or excluded these values. It is unclear how the authors
resolve this issue.
Line 130, It made me confused. It means that the criteria and cleaning data process were not
carefully prepared. Missing data can reduce the statistical power of a study and produce biased
estimates, leading to invalid conclusions. Which data is missing? Please explain and clean the
data before analysis.
The criteria for selecting confounders in this study are unclear. Although the author used
univariate logistic regression to select the covariate in Table S3, the authors randomly selected
several confounders, such as age, race, marital status, poverty-to-income ratio, drinking status,
body mass index, waist circumference, menopausal status, ovary removal status, and use of
female hormones, while many important confounders were not considered, including family
history of UL, hormone use, drug use, etc. A clear rationale for the selection of covariates would
be very helpful for the readers, and eventually, a DAG would help to illustrate the hypothetical
model.
The weighted quantile sum (WQS) regression did not describe the method well. There was no
equation. The cutoff values of WQS were not mentioned. These authors should reference the
articles below to update the method section.
DOI: 10.1007/s11356-022-21208-8
Although the authors used advanced statistics (WQS was the primary method) to identify the
relationship between chemicals and UL, WQS has limitations (potential distortion due to high
correlation and multicollinearity, limitations in assessing joint effects of chemicals with diverse
effect directions and interactions).In this study, no results were provided about the correlation or
multicollinearity. Therefore, I strongly recommend authors use more methods to compare with
the current methods and select the optimal chemicals for the final analysis, such as Bayesian
kernel machine regression (BKMR) and gqcomp (quantile g-computation).
Results
Line 159, Please replace the term "correlation" with "association" or "relationship." In this study,
the authors did not assess the correlation.
In the WQS model, no cutoff levels were established, so it is unclear whether the chemicals
meet the threshold or not.
 Please revise the method section after considering my comments in the methods.
Discussion
The manuscript must be improved by paying more attention to the way the findings are described
and discussed, particularly the latter: The manuscript could benefit from a clearer exposition
and a more focused discussion; additionally, the discussion in its current form leaves me
underwhelmed, as it fails to interpret the data in the context of what is known in the field:
it sounds somewhat redundant, as it largely summarizes data already presented in the Results
without placing them in the proper scientific context.
The discussion does not provide an objective interpretation of the data, especially in terms
of an appropriate referral to relevant previous work as well as a delineation of strengths and
limitations, especially molecular mechanisms related to chemicals and UL.