Title: Weight-adjusted-waist index: an innovative indicator of breast cancer hazard
Version: 0 Date: 30 Aug 2024
Reviewer's report:
The authors performed a cross-sectional assessment of the correlation between weight-adjusted-
waist index (WWI) and breast cancer (BC) in a cohort of adult American women. The manuscript
claimed a positive correlation between WWI and BC. Below are a few questions and comments.
Major comments
Comment 1:
In line 31: ‘WWI and BC laid out an overwhelming positive rapport’; in line 169-170: ‘A
substantial positive correlation between WWI ad BC was observed in the unadjusted model’; in
line 174-175: ‘After correcting for all confounders in model 4, the positive correlation remained
statistically significant’; in line 216: ‘a positive correlation between WWI and BC’; in line
219-220: ‘Subgroup analysis and interaction tests verified the strength of a positive correlation
across a range of statistical scenarios’. All above statements suggest a positive correlation
between WWI and BC. However, in line 195-200, the authors reported ‘After controlling for
age ……, the RCS analysis demonstrated a substantial inverted U-shaped correlation between
WWI and BC. The inflection point of the curve illustrates the turning point of the relationship
between WWI and BC.’ Based on this finding, it is problematic to perform the above analysis and
interpretations assuming a linear or no linear but one direction relationship between WWI and
BC. Please modify or explain the rational of concluding a positive relationship between WWI
and BC even though the inflection point of the curve were clearly demonstrated in this study.
Comment 2:
Cross-sectional analysis was conducted in this study to assess a correlation by using
observational cohort data. This methodology and study design don’t support a causal relationship
assessment and thus challenge the application of causal mediation analysis. In addition,
published literatures suggest that less physical activity can cause overweight or obesity, but there
is no strong evidence to back up the reverse causal relationship. Further, in line 183-184, the
authors suggested that ‘physical activity, ……, did not have a notable impact on this favorable
correlation’. Please justify the application of causal mediation analysis of physical activity given
above questions.
Minor comments
Comment 3:
In line 98: ‘The medical Conditions Questionnaire was the source of BC’s self-reported
diagnoses. The survey-based diagnostic of BC can lead to reporting bias which can weak the
validity of the detected correlation. A registry-based confirmation of BC can reduce the bias and
add weight to the correlation assessment.
Comment 4:
In line 113: ‘Individuals were split into two categories by age: <70 years and #70 years’. Table
1 showed that only 14.92% of total adults in this group and Table 2 showed a heavy skewed
distribution from 3.72% to 28.39% of samples within this group stratified by weight-adjusted
waist index subgrouping. Please explain the rational of the cut of 70 years for age groups in this
study considering both biological and analysis power perspective.
Comment 5:
In line 125-126: ‘Self-reported physical activity was categorized as either vigorous/moderate, or
nonphysical recreation’. In Table 1, physical activity was defined as binary (Yes or no). Given
the fact that the relationship between physical activity and clinical outcomes are complex, please
explain the rational of this simple categorization of physical activity variable.
Comment 6:
In line 139-143: ‘A total of four models were constructed: ……’. It is reasonable for a
preliminary analysis to perform this correlation analysis based on multiple models under the
variation of confounding variables. However, consider the similarity of these models, it is
unnecessary and tedious to report all the results from these 4 models. In actual, a fully adjusted
model is the most reliable model given the assumption met and confounding bias adjustment. It
is fine to also include the results from model 1 if you want to show the impact of confounding
variables with a comparison of the assessment. However, the results from model 2 & 3 can be
removed given no or limit additional information were given.
Comment 7:
Cross-sectional analysis design adopted in this study ignored the variation of survival durations
between the baseline primary exposure and the uncensored participants with confirmation of
final clinical outcome. This is one of the limitations for the static analysis. A survival analysis
in future can account for the uneven distribution of time durations if data is available.