Title: Non-linear Relationship Between the Hepatic Steatosis Index and Glycemic Progression:
Evidence from a Large-Scale Retrospective Cohort Study in a Chinese Population
Version: 0 Date: 02 Apr 2025
Reviewer's report:
The manuscript presents a large-scale retrospective cohort study investigating the association
between the hepatic steatosis index (HSI) and the risk of developing prediabetes in a Chinese
population. This study is well-structured and addresses an important public health issue.
However, several methodological and presentation issues need to be addressed to strengthen
the manuscript. Below, I provide detailed comments on major and minor issues that should be
resolved before the manuscript can be considered for publication:
1. How did the authors ensure that the data from the original study adequately met the needs of
the new hypothesis? Were there any limitations in the original dataset that could have impacted
the validity of the findings? A discussion of these aspects would enhance the credibility of the
study. There is no punctuation at the end of a sentence on Line 111.
2. The exclusion of approximately 90% of the initial cohort raises concerns about potential
selection bias. Could the authors clarify whether this exclusion introduces bias and discuss how
it might impact the generalizability of the findings? Furthermore, participants with a follow-up
duration of less than two years were excluded. Why was the two-year threshold chosen, and how
might this exclusion affect the study outcomes?
3. The diagnosis of prediabetes was based solely on fasting plasma glucose (FPG). The absence
of oral glucose tolerance test (OGTT) or HbA1c measurements may lead to an underestimation
of prediabetes cases, particularly in individuals with impaired glucose tolerance. The authors
should provide a detailed discussion of how this limitation might affect the study's findings and
whether it introduces potential biases.
4. For male participants, the HSI formula is presented as HSI = (ALT/AST) × 8 + BMI, whereas
for female participants, it is HSI = 2 + (ALT/AST) × 8 + BMI. The authors should explain
the rationale behind the addition of the constant "2" for females. A brief explanation of the
rationale behind the HSI formula and its gender-specific components would enhance readers'
understanding.
5. The use of multiple imputation methods to handle missing data is appropriate; however, the
manuscript lacks sufficient detail on the imputation process. Specifically, the authors should
provide information on:
- The number of imputations performed,
- The variables included in the imputation model, and
- The assumption that data were missing at random (MAR).
The MAR assumption should be explicitly stated and justified, as violations of this assumption
could lead to biased results. Additionally, the authors should consider performing a sensitivity
analysis to assess the impact of missing data on the results. For example, comparing results
obtained from multiple imputation with those from complete case analysis could help evaluate
the robustness of the findings.
6. The exclusion of TC due to collinearity is noted. However, the manuscript does not specify
which diagnostics were used to identify collinearity. Providing this information would clarify
the methodological approach.
7. The proportional hazards assumption was tested by including an interaction term between HSI
and the logarithm of follow-up time. Could the authors specify the threshold used to determine
a violation of this assumption?
8. Lines 182–183 should include citations justifying the thresholds for TC used to exclude
participants in the sensitivity analysis. This will provide a stronger rationale for the
methodological approach.
9. The authors present cumulative incidence rates for prediabetes across HSI quartiles but do
not provide confidence intervals (CI) for these rates. Including CI would give a more complete
picture of the variability in the data and enhance the reliability of the findings.
10. The nonlinear association with inflection points at HSI values of 32.88 for females and 34.05
for males is a novel finding. The authors should discuss potential reasons why the association
becomes non-significant cant or weaker beyond these thresholds. This discussion could provide
valuable insights into the underlying mechanisms.
11. While the discussion section is comprehensive, it would benefit from a more focused
exploration of the clinical implications of the findings. Specifically, the authors should discuss
how the results could inform clinical practice, such as the potential use of HSI as a biomarker
for prediabetes risk stratification.
12. In addition to the limitations already mentioned, the study should explicitly address potential
biases associated with the use of secondary data. For example, the lack of information on lifestyle
factors or dietary patterns, which might influence both HSI and prediabetes risk, should be
acknowledged and discussed.
Title: Non-linear Relationship Between the Hepatic Steatosis Index and Glycemic Progression:
Evidence from a Large-Scale Retrospective Cohort Study in a Chinese Population
Version: 0 Date: 02 Apr 2025
Reviewer's report:
The manuscript presents a large-scale retrospective cohort study investigating the association
between the hepatic steatosis index (HSI) and the risk of developing prediabetes in a Chinese
population. This study is well-structured and addresses an important public health issue.
However, several methodological and presentation issues need to be addressed to strengthen
the manuscript. Below, I provide detailed comments on major and minor issues that should be
resolved before the manuscript can be considered for publication:
1. How did the authors ensure that the data from the original study adequately met the needs of
the new hypothesis? Were there any limitations in the original dataset that could have impacted
the validity of the findings? A discussion of these aspects would enhance the credibility of the
study. There is no punctuation at the end of a sentence on Line 111.
2. The exclusion of approximately 90% of the initial cohort raises concerns about potential
selection bias. Could the authors clarify whether this exclusion introduces bias and discuss how
it might impact the generalizability of the findings? Furthermore, participants with a follow-up
duration of less than two years were excluded. Why was the two-year threshold chosen, and how
might this exclusion affect the study outcomes?
3. The diagnosis of prediabetes was based solely on fasting plasma glucose (FPG). The absence
of oral glucose tolerance test (OGTT) or HbA1c measurements may lead to an underestimation
of prediabetes cases, particularly in individuals with impaired glucose tolerance. The authors
should provide a detailed discussion of how this limitation might affect the study's findings and
whether it introduces potential biases.
4. For male participants, the HSI formula is presented as HSI = (ALT/AST) × 8 + BMI, whereas
for female participants, it is HSI = 2 + (ALT/AST) × 8 + BMI. The authors should explain
the rationale behind the addition of the constant "2" for females. A brief explanation of the
rationale behind the HSI formula and its gender-specific components would enhance readers'
understanding.
5. The use of multiple imputation methods to handle missing data is appropriate; however, the
manuscript lacks sufficient detail on the imputation process. Specifically, the authors should
provide information on:
- The number of imputations performed,
- The variables included in the imputation model, and
- The assumption that data were missing at random (MAR).
The MAR assumption should be explicitly stated and justified, as violations of this assumption
could lead to biased results. Additionally, the authors should consider performing a sensitivity
analysis to assess the impact of missing data on the results. For example, comparing results
obtained from multiple imputation with those from complete case analysis could help evaluate
the robustness of the findings.
6. The exclusion of TC due to collinearity is noted. However, the manuscript does not specify
which diagnostics were used to identify collinearity. Providing this information would clarify
the methodological approach.
7. The proportional hazards assumption was tested by including an interaction term between HSI
and the logarithm of follow-up time. Could the authors specify the threshold used to determine
a violation of this assumption?
8. Lines 182–183 should include citations justifying the thresholds for TC used to exclude
participants in the sensitivity analysis. This will provide a stronger rationale for the
methodological approach.
9. The authors present cumulative incidence rates for prediabetes across HSI quartiles but do
not provide confidence intervals (CI) for these rates. Including CI would give a more complete
picture of the variability in the data and enhance the reliability of the findings.
10. The nonlinear association with inflection points at HSI values of 32.88 for females and 34.05
for males is a novel finding. The authors should discuss potential reasons why the association
becomes non-significant cant or weaker beyond these thresholds. This discussion could provide
valuable insights into the underlying mechanisms.
11. While the discussion section is comprehensive, it would benefit from a more focused
exploration of the clinical implications of the findings. Specifically, the authors should discuss
how the results could inform clinical practice, such as the potential use of HSI as a biomarker
for prediabetes risk stratification.
12. In addition to the limitations already mentioned, the study should explicitly address potential
biases associated with the use of secondary data. For example, the lack of information on lifestyle
factors or dietary patterns, which might influence both HSI and prediabetes risk, should be
acknowledged and discussed.
Please review this paper.
user
Title: Non-linear Relationship Between the Hepatic Steatosis Index and Glycemic Progression:
Evidence from a Large-Scale Retrospective Cohort Study in a Chinese Population
Version: 0 Date: 02 Apr 2025
Reviewer's report:
The manuscript presents a large-scale retrospective cohort study investigating the association
between the hepatic steatosis index (HSI) and the risk of developing prediabetes in a Chinese
population. This study is well-structured and addresses an important public health issue.
However, several methodological and presentation issues need to be addressed to strengthen
the manuscript. Below, I provide detailed comments on major and minor issues that should be
resolved before the manuscript can be considered for publication:
1. How did the authors ensure that the data from the original study adequately met the needs of
the new hypothesis? Were there any limitations in the original dataset that could have impacted
the validity of the findings? A discussion of these aspects would enhance the credibility of the
study. There is no punctuation at the end of a sentence on Line 111.
2. The exclusion of approximately 90% of the initial cohort raises concerns about potential
selection bias. Could the authors clarify whether this exclusion introduces bias and discuss how
it might impact the generalizability of the findings? Furthermore, participants with a follow-up
duration of less than two years were excluded. Why was the two-year threshold chosen, and how
might this exclusion affect the study outcomes?
3. The diagnosis of prediabetes was based solely on fasting plasma glucose (FPG). The absence
of oral glucose tolerance test (OGTT) or HbA1c measurements may lead to an underestimation
of prediabetes cases, particularly in individuals with impaired glucose tolerance. The authors
should provide a detailed discussion of how this limitation might affect the study's findings and
whether it introduces potential biases.
4. For male participants, the HSI formula is presented as HSI = (ALT/AST) × 8 + BMI, whereas
for female participants, it is HSI = 2 + (ALT/AST) × 8 + BMI. The authors should explain
the rationale behind the addition of the constant "2" for females. A brief explanation of the
rationale behind the HSI formula and its gender-specific components would enhance readers'
understanding.
5. The use of multiple imputation methods to handle missing data is appropriate; however, the
manuscript lacks sufficient detail on the imputation process. Specifically, the authors should
provide information on:
- The number of imputations performed,
- The variables included in the imputation model, and
- The assumption that data were missing at random (MAR).
The MAR assumption should be explicitly stated and justified, as violations of this assumption
could lead to biased results. Additionally, the authors should consider performing a sensitivity
analysis to assess the impact of missing data on the results. For example, comparing results
obtained from multiple imputation with those from complete case analysis could help evaluate
the robustness of the findings.
6. The exclusion of TC due to collinearity is noted. However, the manuscript does not specify
which diagnostics were used to identify collinearity. Providing this information would clarify
the methodological approach.
7. The proportional hazards assumption was tested by including an interaction term between HSI
and the logarithm of follow-up time. Could the authors specify the threshold used to determine
a violation of this assumption?
8. Lines 182–183 should include citations justifying the thresholds for TC used to exclude
participants in the sensitivity analysis. This will provide a stronger rationale for the
methodological approach.
9. The authors present cumulative incidence rates for prediabetes across HSI quartiles but do
not provide confidence intervals (CI) for these rates. Including CI would give a more complete
picture of the variability in the data and enhance the reliability of the findings.
10. The nonlinear association with inflection points at HSI values of 32.88 for females and 34.05
for males is a novel finding. The authors should discuss potential reasons why the association
becomes non-significant cant or weaker beyond these thresholds. This discussion could provide
valuable insights into the underlying mechanisms.
11. While the discussion section is comprehensive, it would benefit from a more focused
exploration of the clinical implications of the findings. Specifically, the authors should discuss
how the results could inform clinical practice, such as the potential use of HSI as a biomarker
for prediabetes risk stratification.
12. In addition to the limitations already mentioned, the study should explicitly address potential
biases associated with the use of secondary data. For example, the lack of information on lifestyle
factors or dietary patterns, which might influence both HSI and prediabetes risk, should be
acknowledged and discussed.
assistant