Title: Association between Lifetime Endogenous Estrogen Exposure and Body Composition
Metrics in Postmenopausal Women: Findings from the Tehran Lipid and Glucose Study
Version: 2 Date: 16 Jul 2024
Reviewer:  Erand Llanaj
Reviewer's report:
Authors investigate trends in macronutrient intake correlations among different familial pairs
(parent-offspring, siblings, and spouses) over a 9-year period using data from the Tehran Lipid
and Glucose Study (TLGS). The study aims to showcase the dynamic nature of familial dietary
correlations and the influence of family environment.
Minor comments:
Lines 28-32: Combine these lines into one concise sentence. For example, "Research has shown
that nutritional traits among family members, influenced by genetics and family environment,
are significant during early life."
Lines 33-39: Streamline and focus on key findings.
Lines 56-61: Similar to above, make it more concise.
Lines 40-44: Condense the information to highlight key points.
Lines 49-55: Clarify and tighten the focus on this point. The sentence looks incomplete (“…
of how familial resemblance in macronutrient…”). Makes some sense, but the sentence can be
improved for better readability.
You cannot infer genetics as you did not provide any genetic data. Stay on familial or family
ecosystem correlation.
Lines 75-81: Present the criteria for selection more concisely.
Lines 82-84: Reviewer Comments:
Major concern for potential selection bias: Selecting participants from the third survey who had
complete dietary and familial data in the subsequent survey (survey 4) introduces a potential
selection bias. This bias arises because the subset of participants with complete data may
differ systematically from those without, potentially skewing the study's results. This bias can
compromise the generalizability of the findings, making them less applicable to the broader
population in Tehran. It is essential to ensure that the study results are reflective of the entire
cohort to provide meaningful insights. For this reason, the authors can choose to either: (1)
estimate and include missing data, allowing a more representative analysis, or (2) perform
sensitivity analyses to assess the robustness of the findings under various assumptions about the
missing data or apply longitudinal weighting to account for differential follow-up and attrition
rates, enhancing the representativeness of the analysis. By using one of these strategies, the
study can provide more reliable and generalizable conclusions regarding the 3-year changes
in macronutrient intake correlations. This issue comes up later in the results when authors
describe that over the different follow-up periods (3, 6, and 9 years), there might be varying
rates of participant dropout. Those who remain in the study might have different characteristics
compared to those who leave, affecting the study's findings. Changes observed in macronutrient
intake correlations might be influenced by the characteristics of the remaining participants,
rather than reflecting true population-level changes.
Line 64-66: Clarify the representativeness of the District No. 13 sample to Tehran’s overall
population. Discuss potential demographic biases and how they may impact your results/
conclusions.
Line 75-77: Provide a detailed rationale for selecting participants with complete dietary data
initially measured.
Line 78-81: Describe efforts to minimize selection bias, such as random sampling or
stratification.
Lines 90-95: This section looks like it belongs in Statistical Analyses.
Lines 104-107: Mention how local food variations not covered by USDA FCT were addressed
in the Iranian FCT.
Lines 112-119: The analysis might not adequately control for confounding variables such as
physical activity, socio-economic status, or other lifestyle factors that can influence both dietary
intake and BMI. The correlations observed might be confounded by these unaccounted variables,
leading to biased estimates of the relationship between dietary intake and health outcomes.
Mention this in the limitations or rerun everything. Also, provide more detail on the statistical
models used for longitudinal analysis. Explain how age adjustment and normalization were
performed. Include information on how S.A.G.E. software parameters were set for estimating
ICCs and address potential biases. Justify the choice of a P-value < 0.05 and discuss multiple
testing corrections. The p-value provided will probably indicate statistical significance for
various comparisons. However, multiple comparisons increase the risk of Type I error, and
consider applying a correction for multiple comparisons (e.g., Bonferroni correction) to control
the family-wise error rate. For example, if you decide to use Bonferroni correction for 6
comparisons, the significance level would be adjusted to 0.05/6 = 0.0083. And state if post-
hoc tests (e.g., Tukey's HSD) were performed following ANOVA to identify specific pair
differences. In its present form, it is unclear if post-hoc tests were conducted after ANOVA to
maintain the error rate.
Major point: please remove the description in the results text that says that over x year period age
increased: of course it increased! Also, in the results (e.g., lines 124-144) authors state that over
the 9-year period BMI, carbohydrate intake, protein intake, and fiber intake increased, but energy
intake, fat intake, MUFA, PUFA, SFA, and cholesterol intake decreased. This simultaneous
observation is scientifically inconsistent because carbohydrates, protein, and fiber all contribute
to overall energy intake. If these macronutrients are increasing, the total energy intake should
logically increase or at least remain stable, assuming that the intake of other energy sources is not
decreasing disproportionately. However, the text mentions that energy intake decreases, which
contradicts the increase in energy-contributing macronutrients like carbohydrates and proteins.
Without additional details indicating a significant decrease in other energy-dense nutrients not
mentioned (beyond fats, which are mentioned), this inconsistency suggests a potential issue in
data collection, analyses, reporting, or interpretation. Also, inconsistent significant differences
across various surveys can be difficult to interpret and might suggest underlying issues in the
data quality or the methods used for analysis.
Major general points for results: The study focuses on correlations within families (parent-
offspring, sibling pairs). Familial aggregation of dietary habits can be influenced by shared
environment and genetic factors. It might be challenging to disentangle the effects of
shared environment from genetic influences, potentially overestimating the strength of dietary
correlations within families. Also, the description of changes in dietary intake and correlations
over different periods and across different family pairs is complex and sometimes appears
inconsistent (e.g., increases and decreases in intake are not always clearly contextualized). This
complexity can lead to misinterpretation of results. A more concise and consistent reporting
format would help clarify the findings.
Major point: It is logical that age and BMI would increase over time since the same participants
are followed longitudinally, and aging naturally leads to these increases. Decreasing trends in
fat, MUFA, PUFA, SFA, cholesterol, and caffeine intake might be logical if there has been a shift
in dietary guidelines or public health initiatives encouraging lower intake of fats and cholesterol
over time. Authors should clarify this in the discussion perhaps.
Major point: Also, changes in macronutrient correlations over time could reflect different dietary
habits between fathers and sons versus mothers and sons, which is plausible. The observed
changes, including increased correlations for certain macronutrients and decreased correlations
for others, align with the idea that familial dietary habits can vary widely. The trends observed
in brother-brother and sister-sister pairs might suggest that siblings might have similar dietary
habits, which is consistent with shared household environments and genetic factors. No?
Lines 128-130: Integrate this information with the previous summary to avoid repetition.
Lines 135-144: Condense the findings for better readability.
Lines 151-157, 176-190, and 217-228: These sections can be more condensed and focused.
Lines 146-149: Justify the significance threshold and provide more detail.
General Comment: Revising and detailing statistical analyses will help ensure reproducibility.
The study provides valuable insights into the dynamic nature of familial dietary correlations over
time, but addressing the major and minor issues identified above will significantly strengthen
the manuscript. The findings have important implications for public health interventions aimed
at improving dietary habits within families and preventing diet-related chronic diseases.