Reviewer’s report
Title: 
The Association Between Sexual Orientation, BMI, Obesity Diagnosis,
and Provider Recommendation for Weight Management
Version: 
1 
Date: 
06 Oct 2021
Reviewer: 
Michèle Dramaix-Wilmet
Reviewer's report:
Thank you for your answers and changes that improve the manuscript, however,
I still have a few comments and questions
Methods
Question 1st review. "Study design": were all patient files complete or were files
excluded because some sociodemographic variables or medical conditions were
missing? 
The answer was: In regard to your second question, not all patient files were
complete and some were excluded due to missing inclusion/exclusion criteria.
New question: are these patients part of the 3,334 excluded patients? 
Question 1st review: is there a reason to choose 3 controls? If there is no
matching, why was a different control group chosen for lesbian and bisexual?
The answer was: For every one patient who identified as lesbian or bisexual, we
analyzed three patients who identified as straight. A different straight group was
used when comparing lesbian to straight and bisexual to straight because there
were different numbers of bisexual and lesbian patients so there needed to be a
larger straight comparison group for the lesbian group.
New comment: I agree to say that taking a ratio 1:1 would lead to samples too
small to detect a difference between the groups and taking more controls was a
good idea. Was a sample size computation performed to decide to take 3
controls? However, for case-control studies, a ratio 1:3 is a good choice. I
understand the argument used to take two control samples but if there is not
matching, a large control sample would probably have also been adequate. And
what about the interpretation of the results if the control groups differ? 
Question 1st review: P7, line 53: why use Fisher exact for all analyses of
association between categorical variables; was not the Chi² valid?
Answer: Fisher's exact test was used because of the study's smaller sample size.
New comment: using Fisher exact test is certainly not a problem but if the
study's size is small, then wasn't it a problem to build multivariable logistic
regression models with such a large number of predictors? And since there were
few statistically significant differences between lesbian and straight or between
bisexual and straight, what was the reason for adjusting for all variables?
New comment: in the statistical methodology it is written: "Patient and medical
visit characteristics were summarized with descriptive statistics and compared
between either lesbian or bisexual patients and straight patients using Fisher's
exact test for categorical variables and Wilcoxon rank-sum test for continuous
variables". However in the footnote of table 1, it is t-test that's mentioned even
for the BMI whose distribution is clearly skewed as specified in the discussion
about linear regression.
Question 1st review: p. 8; line 4: these transformations are not usual for BMI,
how to interpret the regression coefficients? Why not use quantile regression
which easier to interpret?
Answer: We agree that the regression coefficients are difficult to interpret but
since most of the associations are not statistically significant, we thought that the
interpretability of the output was less important than model fit.
New comment: I totally agree with the argument that the goodness of fit of a
model is important as well as that's important that the model assumptions are
met. But since statistical significance depends on the number of subjects, when
possible, I think the meaning of the regression coefficients is important to get an
idea of the strength of the effect of a variable on the outcome and in my opinion,
except for the p-values, Table 3 remains difficult to read and understand for
some readers. A quantile regression would allow the coefficients to be
interpreted as differences between the chosen quantiles (usually medians). 
Results
Question 1st review: Supplementary Table 3: some OR are quite different in the
two samples (lesbian + straight and bisexual + straight); for example, the OR for
hypertension is 0.52 in the 1st sample and 2.16 in the second. How to explain
this difference? The same is true for example in supplementary table 4 (OR=2.13
in 1st sample and 7.82 in 2nd sample) and in supplementary table 5 (OR = 1.6 in
1st sample and 6.27 in 2nd sample) for obesity. However, both samples consists
of a majority of random controls.
New question: I will try to express my question differently by taking the example
of hypertension in table 4. The models were established on the one hand in the
sample 340 subjects including lesbians and their controls and on the other hand
in the sample of 194 subjects including bisexuals and their controls. Each of the
two samples includes 75% of controls. In the multivariable model, hypertension
a potential confounder, is inversely associated with "receiving a diagnostic code
for obesity" with OR=0.52 (p=0.03) in the 1st sample (lesbians + controls) and
directly associated with "receiving a diagnostic code for obesity" in the second
sample (OR = 2.13, NS). Could this difference be due to differences between the
control groups? Hypertension isn't the primary exposure in the analysis but I am
surprised to see that in the 1st sample, hypertensive subjects are less likely to
receive a diagnostic code for obesity whereas in the second sample they are
more likely to receive this diagnostic code.
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Is the study design appropriate to answer the research question (including the use
of appropriate controls), and are the conclusions supported by the evidence
presented?
Yes
Are the methods sufficiently described to allow the study to be repeated?
Yes
Is the use of statistics and treatment of uncertainties appropriate?
Yes
Is the presentation of the work clear?
Yes
Are the images in this manuscript (including electrophoretic gels and blots) free
from apparent manipulation?
Yes