Title: Urinary phytoestrogens and the risk of uterine leiomyomata in US women
Version: 0 Date: 16 Feb 2023
Reviewer's report:
This study conducted a cross-sectional analysis to investigate the associations between urinary
concentrations of phytoestrogen metabolites and a history of uterine leiomyomata among U.S.
female adults. Due to a huge possibility of reverse causation in the study design, which cannot
be fixed by the revision, it is hard to recommend publication of this paper. Although the authors
mentioned the possibility of reverse causation in the limitation paragraph, this statement is a
general statement that can be found in most cross-sectional studies. The risk of reverse causation
in this study is not the same as the risk of reverse causation in the general cross-sectional studies,
because, in this study, the exposure measurement indicates recent exposure to phytoestrogen,
but the outcome measurement incorporates historical diagnosis which is temporally earlier than
the exposure measurement. The results of this analysis do not indicate the causal effects of
phytoestrogen exposure on the incidence of HL. Rather, the results more indicate the causal
effects of historical HL diagnosis on phytoestrogen-related diet intake. Publication of these
results can mislead the readers. Although the authors may clearly state the risk of reverse
causation in the limitation paragraph, still there might be some readers who just take the
OR values without reading the discussion section carefully. Rejection of this manuscript is
recommended, but if the authors want to continue to improve this, please refer to the following
specific comments. Most are about clarification of the details.
1) Line 57: When citing previous studies, please include a brief description of the study
design (e.g., cross-sectional) and how the outcome was measured (e.g., self-report of historical
diagnosis) to provide enough information to readers.
2) Line 105: Please consider using µg/g creatinine rather than ng/g creatinine based on the
magnitude of the values shown in Supplemental Table 1.
3) Line 112: Details should be added. How were the variables measured? For some variables:
were they self-reported, or measured in another way? How were the categorical variables
grouped? How was the cotinine < LOD treated? Were they imputed as zero or imputed with
another method? Please clarify if the cotinine was measured in urine. The race category includes
ethnicity, so race/ethnicity would be a more proper term for the variable.
4) Line 122: WTSB2YR and WTSPH2YR in NHANES are not weight variables for the
interviews, but weight variables for the urinary metabolites measurement.
5) Line 130: How was the multiple interpolation conducted? Details should be explained.
6) Supplemental table 2: In NHANES, PIR values greater than 5 were coded as 5. Therefore, it
is not proper to calculate the mean and S.E. of PIR.
7)   The authors should provide the reason why WQS was chosen among the currently available
mixture approaches. There are supervised approaches such as WQS, BKMR, and quantile
g-comp, and unsupervised approaches such as k-means clustering and item response theory
(recently introduced in Environmental Health Perspectives). Each method has both strong and
weak points. These points should be discussed before choosing one of the methods for the current
study.
8) Line 135-147: Details of WQS should be included. How did the authors divide the data
into training and validation sets? Which quantile was used? Decile? Or percentile? Was
bootstrapping used? These kinds of details can help readers replicate the data analysis.
9) Line 146: “T” in “therefore” should be capitalized.
10) Table 1: Some categories such as “Widowed” and “Separated” in “Marital status” and “Less
than 9th grade” in “Education level” contain a too small number of participants, which means
they are statistically less meaningful. It is recommended to combine those groups with another
group having similar characteristics. In addition, again, we cannot calculate the actual mean and
S.E. of PIR of the participants. Also, (IMPORTANT) provide summary statistics for when the
participants had the first UL diagnosis. This can be calculated using RHQ390 variable (age of
first diagnosis).
11) Table 2: How did the authors select the covariates? Fully describe the selection criteria.
How about hysterectomy? Can the history of hysterectomy be considered a confounding factor
in this relationship? Dietary factors can be also considered as a confounder because diet may
affect both incidence of UL and exposure to phytoestrogens. Total energy intake, Healthy Eating
Index, or fiber intake can be examples of the dietary factors that can be included.
12) Table 3: Because analysis with the single chemicals used tertile variable, and some of them
showed non-monotonous associations (Table 2), it would be more comparable if the WQS index
is categorized into tertiles in the analysis shown in Table 3.
13) Line 190-198: There are plenty of studies using mixture approaches in this field. However,
the authors cited only two specific studies which used mixture approaches to metal exposure.
Citing relevant reviews or method papers is recommended rather than these too specific studies.