Title: The Role of the Advanced Lung Cancer Inflammation Index (ALI) in the Risk of
Liver Fibrosis and Mortality Among US Adult MAFLD Patients: A Cross-Sectional Study of
NHANES 1999–2018
Version: 2 Date: 06 Jan 2025
Reviewer's report:
The paper titled "The Role of the Advanced Lung Cancer Inflammation Index (ALI) in the Risk
of Liver Fibrosis and Mortality Among US Adult MAFLD Patients: A Cross-Sectional Study
of NHANES 1999–2018" provides an insightful analysis of ALI's utility in a specific patient
population. However, there are areas where further refinement could enhance the clarity, depth,
and impact of the findings. Here is a critique with specific suggestions for revision:
1. Clarify the Study's Cross-sectional Nature: Emphasize the limitations inherent to the cross-
sectional design, particularly in drawing causal inferences between ALI and outcomes like
liver fibrosis and mortality. The discussion could benefit from a deeper analysis of how these
limitations might affect the interpretation of the data and conclusions drawn.
2. Enhance Methodological Details: Provide more details on the statistical methods, especially
the handling of missing data and the rationale behind the choice of covariates in the regression
models. Clarify whether there were any checks for multicollinearity among the covariates, given
the large number adjusted for.
3. Strengthen the Results Section:
o Non-linear Associations: The discussion on the non-linear associations identified via RCS and
threshold effect analysis could be expanded. Include a clearer explanation of why these non-
linearities might exist and their potential biological implications.
o Stratified Analysis: The stratified analysis showing stronger associations in subgroups such
as those with central obesity and prediabetes is intriguing. Discuss possible mechanisms or
literature supporting these findings to give the reader a better understanding of why these
subgroups are affected differently.
o Details on Mortality Outcomes: The finding that ALI is inversely associated with all-cause
and CVD mortality but not with cancer mortality warrants a deeper discussion. Speculate on
potential biological or clinical reasons why these differences might exist, backed by existing
research.
4. Discussion of Confounding Factors: While many covariates are adjusted for, discuss potential
residual confounding. For example, factors like medication use, detailed dietary patterns, or
genetic predispositions could influence both ALI and the study outcomes but were not accounted
for.
5. Interpretation of ALI Cutoffs: The clinical applicability of the identified ALI cutoffs (e.g.,
5.68 for liver fibrosis) could be better justified. Discuss how these cutoffs compare with other
studies and their potential utility in clinical settings.
6. Broader Literature Context: Integrate more comparative analysis with other biomarkers or
indices used in MAFLD assessment. This would help position the study within the broader field
and highlight ALI's unique contributions or limitations.
7. Address Sample Representativeness: Discuss the representativeness of the NHANES sample
for the MAFLD population and how this might affect the generalizability of the findings.
8. Update and Expand References: Ensure that all references are up-to-date and consider
including recent studies that may have similar findings or relevant conclusions.
9. Enhance Conclusion: Strengthen the conclusion by more explicitly linking the study findings
to potential clinical applications and future research directions. Highlight how these findings
could influence current clinical practices or guidelines in managing MAFLD patients.
Title: The Role of the Advanced Lung Cancer Inflammation Index (ALI) in the Risk of
Liver Fibrosis and Mortality Among US Adult MAFLD Patients: A Cross-Sectional Study of
NHANES 1999–2018
Version: 2 Date: 06 Jan 2025
Reviewer's report:
The paper titled "The Role of the Advanced Lung Cancer Inflammation Index (ALI) in the Risk
of Liver Fibrosis and Mortality Among US Adult MAFLD Patients: A Cross-Sectional Study
of NHANES 1999–2018" provides an insightful analysis of ALI's utility in a specific patient
population. However, there are areas where further refinement could enhance the clarity, depth,
and impact of the findings. Here is a critique with specific suggestions for revision:
1. Clarify the Study's Cross-sectional Nature: Emphasize the limitations inherent to the cross-
sectional design, particularly in drawing causal inferences between ALI and outcomes like
liver fibrosis and mortality. The discussion could benefit from a deeper analysis of how these
limitations might affect the interpretation of the data and conclusions drawn.
2. Enhance Methodological Details: Provide more details on the statistical methods, especially
the handling of missing data and the rationale behind the choice of covariates in the regression
models. Clarify whether there were any checks for multicollinearity among the covariates, given
the large number adjusted for.
3. Strengthen the Results Section:
o Non-linear Associations: The discussion on the non-linear associations identified via RCS and
threshold effect analysis could be expanded. Include a clearer explanation of why these non-
linearities might exist and their potential biological implications.
o Stratified Analysis: The stratified analysis showing stronger associations in subgroups such
as those with central obesity and prediabetes is intriguing. Discuss possible mechanisms or
literature supporting these findings to give the reader a better understanding of why these
subgroups are affected differently.
o Details on Mortality Outcomes: The finding that ALI is inversely associated with all-cause
and CVD mortality but not with cancer mortality warrants a deeper discussion. Speculate on
potential biological or clinical reasons why these differences might exist, backed by existing
research.
4. Discussion of Confounding Factors: While many covariates are adjusted for, discuss potential
residual confounding. For example, factors like medication use, detailed dietary patterns, or
genetic predispositions could influence both ALI and the study outcomes but were not accounted
for.
5. Interpretation of ALI Cutoffs: The clinical applicability of the identified ALI cutoffs (e.g.,
5.68 for liver fibrosis) could be better justified. Discuss how these cutoffs compare with other
studies and their potential utility in clinical settings.
6. Broader Literature Context: Integrate more comparative analysis with other biomarkers or
indices used in MAFLD assessment. This would help position the study within the broader field
and highlight ALI's unique contributions or limitations.
7. Address Sample Representativeness: Discuss the representativeness of the NHANES sample
for the MAFLD population and how this might affect the generalizability of the findings.
8. Update and Expand References: Ensure that all references are up-to-date and consider
including recent studies that may have similar findings or relevant conclusions.
9. Enhance Conclusion: Strengthen the conclusion by more explicitly linking the study findings
to potential clinical applications and future research directions. Highlight how these findings
could influence current clinical practices or guidelines in managing MAFLD patients.
Please review this paper.
user
Title: The Role of the Advanced Lung Cancer Inflammation Index (ALI) in the Risk of
Liver Fibrosis and Mortality Among US Adult MAFLD Patients: A Cross-Sectional Study of
NHANES 1999–2018
Version: 2 Date: 06 Jan 2025
Reviewer's report:
The paper titled "The Role of the Advanced Lung Cancer Inflammation Index (ALI) in the Risk
of Liver Fibrosis and Mortality Among US Adult MAFLD Patients: A Cross-Sectional Study
of NHANES 1999–2018" provides an insightful analysis of ALI's utility in a specific patient
population. However, there are areas where further refinement could enhance the clarity, depth,
and impact of the findings. Here is a critique with specific suggestions for revision:
1. Clarify the Study's Cross-sectional Nature: Emphasize the limitations inherent to the cross-
sectional design, particularly in drawing causal inferences between ALI and outcomes like
liver fibrosis and mortality. The discussion could benefit from a deeper analysis of how these
limitations might affect the interpretation of the data and conclusions drawn.
2. Enhance Methodological Details: Provide more details on the statistical methods, especially
the handling of missing data and the rationale behind the choice of covariates in the regression
models. Clarify whether there were any checks for multicollinearity among the covariates, given
the large number adjusted for.
3. Strengthen the Results Section:
o Non-linear Associations: The discussion on the non-linear associations identified via RCS and
threshold effect analysis could be expanded. Include a clearer explanation of why these non-
linearities might exist and their potential biological implications.
o Stratified Analysis: The stratified analysis showing stronger associations in subgroups such
as those with central obesity and prediabetes is intriguing. Discuss possible mechanisms or
literature supporting these findings to give the reader a better understanding of why these
subgroups are affected differently.
o Details on Mortality Outcomes: The finding that ALI is inversely associated with all-cause
and CVD mortality but not with cancer mortality warrants a deeper discussion. Speculate on
potential biological or clinical reasons why these differences might exist, backed by existing
research.
4. Discussion of Confounding Factors: While many covariates are adjusted for, discuss potential
residual confounding. For example, factors like medication use, detailed dietary patterns, or
genetic predispositions could influence both ALI and the study outcomes but were not accounted
for.
5. Interpretation of ALI Cutoffs: The clinical applicability of the identified ALI cutoffs (e.g.,
5.68 for liver fibrosis) could be better justified. Discuss how these cutoffs compare with other
studies and their potential utility in clinical settings.
6. Broader Literature Context: Integrate more comparative analysis with other biomarkers or
indices used in MAFLD assessment. This would help position the study within the broader field
and highlight ALI's unique contributions or limitations.
7. Address Sample Representativeness: Discuss the representativeness of the NHANES sample
for the MAFLD population and how this might affect the generalizability of the findings.
8. Update and Expand References: Ensure that all references are up-to-date and consider
including recent studies that may have similar findings or relevant conclusions.
9. Enhance Conclusion: Strengthen the conclusion by more explicitly linking the study findings
to potential clinical applications and future research directions. Highlight how these findings
could influence current clinical practices or guidelines in managing MAFLD patients.
assistant