Title: Gender difference in the association between serum uric acid and metabolic dysfunction-
associated steatotic liver disease in patients with newly diagnosed type 2 diabetes
Version: 3 Date: 10 Apr 2025
Reviewer's report:
advantage
1. Stratified analysis and multivariate adjustment: Stratified by gender and BMI, fully
considering the heterogeneity between different subgroups, revealing the interaction between
gender and BMI on the SUA-MASLD relationship. The logistic regression model adjusted the
potential confounding factors such as age, blood pressure, blood lipids, liver enzymes, and renal
function, and enhanced the robustness of the results. The impact of confounding factors on
associations was clearly demonstrated using unadjusted models (Model 1) and stepwise adjusted
models (Model 2, Model 3).
Comprehensive suggestion:
1. Conduct prospective cohort studies in the future: clarify the causal relationship between SUA
and MASLD, and explore the dynamic changes of gender differences.
2. The next step is to incorporate more confounding factors, such as waist circumference, insulin
resistance indicators, sex hormone levels, lifestyle, etc., to reduce residual confounding.
3. Multi center collaboration: Enhance sample representativeness and improve the generalization
of results.
Title: Gender difference in the association between serum uric acid and metabolic dysfunction-
associated steatotic liver disease in patients with newly diagnosed type 2 diabetes
Version: 3 Date: 10 Apr 2025
Reviewer's report:
advantage
1. Stratified analysis and multivariate adjustment: Stratified by gender and BMI, fully
considering the heterogeneity between different subgroups, revealing the interaction between
gender and BMI on the SUA-MASLD relationship. The logistic regression model adjusted the
potential confounding factors such as age, blood pressure, blood lipids, liver enzymes, and renal
function, and enhanced the robustness of the results. The impact of confounding factors on
associations was clearly demonstrated using unadjusted models (Model 1) and stepwise adjusted
models (Model 2, Model 3).
Comprehensive suggestion:
1. Conduct prospective cohort studies in the future: clarify the causal relationship between SUA
and MASLD, and explore the dynamic changes of gender differences.
2. The next step is to incorporate more confounding factors, such as waist circumference, insulin
resistance indicators, sex hormone levels, lifestyle, etc., to reduce residual confounding.
3. Multi center collaboration: Enhance sample representativeness and improve the generalization
of results.
Please review this paper.
user
Title: Gender difference in the association between serum uric acid and metabolic dysfunction-
associated steatotic liver disease in patients with newly diagnosed type 2 diabetes
Version: 3 Date: 10 Apr 2025
Reviewer's report:
advantage
1. Stratified analysis and multivariate adjustment: Stratified by gender and BMI, fully
considering the heterogeneity between different subgroups, revealing the interaction between
gender and BMI on the SUA-MASLD relationship. The logistic regression model adjusted the
potential confounding factors such as age, blood pressure, blood lipids, liver enzymes, and renal
function, and enhanced the robustness of the results. The impact of confounding factors on
associations was clearly demonstrated using unadjusted models (Model 1) and stepwise adjusted
models (Model 2, Model 3).
Comprehensive suggestion:
1. Conduct prospective cohort studies in the future: clarify the causal relationship between SUA
and MASLD, and explore the dynamic changes of gender differences.
2. The next step is to incorporate more confounding factors, such as waist circumference, insulin
resistance indicators, sex hormone levels, lifestyle, etc., to reduce residual confounding.
3. Multi center collaboration: Enhance sample representativeness and improve the generalization
of results.
assistant