Title: Sex-Specific Adverse Effects of Lipid Accumulation Products and Cardiometabolic
Indices on the Prevalence of Gallstones: Insights from the 2017-2020 National Health and
Nutrition Examination Survey
Version: 0 Date: 18 Mar 2025
Reviewer's report:
This article comprehensively uses weighted logistic regression (adjusting complex sampling
weights), restricted cubic spline (RCS) model (exploring nonlinear relationships), subgroup
analysis, and ROC curve to select reasonable and diversified methods. The dose-response
relationship is verified through the RCS model, and the risk of model fitting and overfitting is
balanced based on AIC node selection. By clearly distinguishing gender effects, it was found
that LAP and CMI are significantly associated with the risk of gallstones in women, while there
is no such association in men. This highlights the gender specificity of metabolic indicators and
is innovative.
Comparing the predictive power of multiple obesity related indicators (such as LAP, CMI, BMI,
etc.) through ROC curves provides reference for the selection of clinical screening tools.
Improvement suggestions
1. The causal relationship between LAP/CMI and gallstones cannot be determined, and the
results can only suggest a correlation. It is necessary to clearly emphasize this limitation in the
discussion and suggest future validation through cohort studies.
2. The diagnosis of gallstones relies on self-report without imaging confirmation, which may
underestimate or overestimate the incidence rate. Please provide a detailed explanation of its
impact on the results in the limitations section.
3. Multiple comparison problem: Failure to mention correction (such as Bonferroni) when
analyzing 7 obesity indicators may increase the risk of Class I errors. Additional explanation is
required or error detection rate (FDR) correction should be used.
4. Model hypothesis validation: Linear hypothesis testing (such as Box Tidwell test) for logistic
regression models that are not described, as well as specific criteria for selecting spline model
nodes (such as whether to validate the sensitivity of 3-5 nodes).
5. Data loss and selection bias: Excluding a large amount of missing data (from 15560 people to
3541 people) may lead to selection bias. Need to supplement the proportion and feature analysis
of missing data, or use multiple imputation method for processing.
6. Limited clinical significance: The AUC of LAP is 0.659, with weak discriminatory ability
(usually # 0.7), and its screening value needs to be interpreted cautiously in combination with
sensitivity (59.3%) and specificity (64.9%).
7. Insufficient mechanism exploration: Only speculating on the effect of estrogen, lacking direct
evidence (such as hormone level data) or citing more mechanism studies.
8. The sample size of some subgroups (such as specific race, diabetes stratification) is small,
which may reduce the test efficiency, leading to the unreliable conclusion of "no interaction".
Need to supplement the effectiveness analysis or cautious wording of the interaction.
9. Formulas and Indicator Definitions
The citation of CMI formula needs to be verified: the original definition is TG/HDL-C × WtHR,
but consistency with Wakabayashi and other original literature needs to be confirmed to avoid
calculation bias.
10. The article mentions Figures 2-4 (such as RCS curves and heat maps), but does not display
the content of the charts, which affects the visualization and reproduction of the results. Suggest
adding key charts or providing detailed descriptions.
Title: Sex-Specific Adverse Effects of Lipid Accumulation Products and Cardiometabolic
Indices on the Prevalence of Gallstones: Insights from the 2017-2020 National Health and
Nutrition Examination Survey
Version: 0 Date: 18 Mar 2025
Reviewer's report:
This article comprehensively uses weighted logistic regression (adjusting complex sampling
weights), restricted cubic spline (RCS) model (exploring nonlinear relationships), subgroup
analysis, and ROC curve to select reasonable and diversified methods. The dose-response
relationship is verified through the RCS model, and the risk of model fitting and overfitting is
balanced based on AIC node selection. By clearly distinguishing gender effects, it was found
that LAP and CMI are significantly associated with the risk of gallstones in women, while there
is no such association in men. This highlights the gender specificity of metabolic indicators and
is innovative.
Comparing the predictive power of multiple obesity related indicators (such as LAP, CMI, BMI,
etc.) through ROC curves provides reference for the selection of clinical screening tools.
Improvement suggestions
1. The causal relationship between LAP/CMI and gallstones cannot be determined, and the
results can only suggest a correlation. It is necessary to clearly emphasize this limitation in the
discussion and suggest future validation through cohort studies.
2. The diagnosis of gallstones relies on self-report without imaging confirmation, which may
underestimate or overestimate the incidence rate. Please provide a detailed explanation of its
impact on the results in the limitations section.
3. Multiple comparison problem: Failure to mention correction (such as Bonferroni) when
analyzing 7 obesity indicators may increase the risk of Class I errors. Additional explanation is
required or error detection rate (FDR) correction should be used.
4. Model hypothesis validation: Linear hypothesis testing (such as Box Tidwell test) for logistic
regression models that are not described, as well as specific criteria for selecting spline model
nodes (such as whether to validate the sensitivity of 3-5 nodes).
5. Data loss and selection bias: Excluding a large amount of missing data (from 15560 people to
3541 people) may lead to selection bias. Need to supplement the proportion and feature analysis
of missing data, or use multiple imputation method for processing.
6. Limited clinical significance: The AUC of LAP is 0.659, with weak discriminatory ability
(usually # 0.7), and its screening value needs to be interpreted cautiously in combination with
sensitivity (59.3%) and specificity (64.9%).
7. Insufficient mechanism exploration: Only speculating on the effect of estrogen, lacking direct
evidence (such as hormone level data) or citing more mechanism studies.
8. The sample size of some subgroups (such as specific race, diabetes stratification) is small,
which may reduce the test efficiency, leading to the unreliable conclusion of "no interaction".
Need to supplement the effectiveness analysis or cautious wording of the interaction.
9. Formulas and Indicator Definitions
The citation of CMI formula needs to be verified: the original definition is TG/HDL-C × WtHR,
but consistency with Wakabayashi and other original literature needs to be confirmed to avoid
calculation bias.
10. The article mentions Figures 2-4 (such as RCS curves and heat maps), but does not display
the content of the charts, which affects the visualization and reproduction of the results. Suggest
adding key charts or providing detailed descriptions.
Please review this paper.
user
Title: Sex-Specific Adverse Effects of Lipid Accumulation Products and Cardiometabolic
Indices on the Prevalence of Gallstones: Insights from the 2017-2020 National Health and
Nutrition Examination Survey
Version: 0 Date: 18 Mar 2025
Reviewer's report:
This article comprehensively uses weighted logistic regression (adjusting complex sampling
weights), restricted cubic spline (RCS) model (exploring nonlinear relationships), subgroup
analysis, and ROC curve to select reasonable and diversified methods. The dose-response
relationship is verified through the RCS model, and the risk of model fitting and overfitting is
balanced based on AIC node selection. By clearly distinguishing gender effects, it was found
that LAP and CMI are significantly associated with the risk of gallstones in women, while there
is no such association in men. This highlights the gender specificity of metabolic indicators and
is innovative.
Comparing the predictive power of multiple obesity related indicators (such as LAP, CMI, BMI,
etc.) through ROC curves provides reference for the selection of clinical screening tools.
Improvement suggestions
1. The causal relationship between LAP/CMI and gallstones cannot be determined, and the
results can only suggest a correlation. It is necessary to clearly emphasize this limitation in the
discussion and suggest future validation through cohort studies.
2. The diagnosis of gallstones relies on self-report without imaging confirmation, which may
underestimate or overestimate the incidence rate. Please provide a detailed explanation of its
impact on the results in the limitations section.
3. Multiple comparison problem: Failure to mention correction (such as Bonferroni) when
analyzing 7 obesity indicators may increase the risk of Class I errors. Additional explanation is
required or error detection rate (FDR) correction should be used.
4. Model hypothesis validation: Linear hypothesis testing (such as Box Tidwell test) for logistic
regression models that are not described, as well as specific criteria for selecting spline model
nodes (such as whether to validate the sensitivity of 3-5 nodes).
5. Data loss and selection bias: Excluding a large amount of missing data (from 15560 people to
3541 people) may lead to selection bias. Need to supplement the proportion and feature analysis
of missing data, or use multiple imputation method for processing.
6. Limited clinical significance: The AUC of LAP is 0.659, with weak discriminatory ability
(usually # 0.7), and its screening value needs to be interpreted cautiously in combination with
sensitivity (59.3%) and specificity (64.9%).
7. Insufficient mechanism exploration: Only speculating on the effect of estrogen, lacking direct
evidence (such as hormone level data) or citing more mechanism studies.
8. The sample size of some subgroups (such as specific race, diabetes stratification) is small,
which may reduce the test efficiency, leading to the unreliable conclusion of "no interaction".
Need to supplement the effectiveness analysis or cautious wording of the interaction.
9. Formulas and Indicator Definitions
The citation of CMI formula needs to be verified: the original definition is TG/HDL-C × WtHR,
but consistency with Wakabayashi and other original literature needs to be confirmed to avoid
calculation bias.
10. The article mentions Figures 2-4 (such as RCS curves and heat maps), but does not display
the content of the charts, which affects the visualization and reproduction of the results. Suggest
adding key charts or providing detailed descriptions.
assistant