Title: Aggregate index of systemic inflammation tied to increased fatty liver disease risk:
insights from NHANES data
Version: 2 Date: 07 Apr 2025
Reviewer's report:
The study explores the relationship between the Aggregate Index of Systemic Inflammation
(AISI) and fatty liver disease (FLD), aiming to develop a predictive nomogram that could
support early identification of individuals at risk. Given the increasing global burden of FLD and
the emerging role of systemic inflammation in its pathogenesis, this research offers clinically
relevant insights. The methodology is generally sound and the model demonstrates good
discriminative performance; however, certain aspects of the design, analysis, and interpretation
warrant further consideration to enhance the robustness and applicability of the findings.
1. The introduction mentions AISI, but it would be clearer if the full term 'Aggregate Index of
Systemic Inflammation (AISI)' is written out the first time it is introduced. This will help ensure
that readers unfamiliar with the abbreviation understand the term.
2. The description of the data source and population selection is generally clear and reflects
a well-structured use of the NHANES database. However, several aspects could benefit
from clarification to enhance methodological transparency. Specifically, the 'Data sources and
population selection' section would be strengthened by briefly stating how FLD was defined
or diagnosed to identify both the FLD and non-FLD groups. Even if detailed later, a concise
summary here would help contextualize the population selection from the outset.
3. The exclusion of over 80% of the original sample (20,853 of 24,814 participants)
significantly reduces the final analytical sample size. This could affect the representativeness
and generalizability of your findings. I recommend acknowledging this as a limitation and
considering whether imputation methods or sensitivity analyses could have been used to address
missing data.
4. State explicitly which statistical test was used to assess the association between AISI and FLD
(e.g., logistic regression), and whether models were adjusted for confounders.
5. Was the inclusion of variables in the univariate analysis guided by clinical relevance or based
on statistical significance? It’s important to clarify the criteria for selecting variables for further
analysis in the multivariate model.
6. Multiple regression helps adjust for confounders and provides a more accurate estimate of
the relationships between predictors and the outcome. How were the variables for the multiple
logistic regression model selected? Was backward or forward selection used, or were variables
selected based on clinical reasoning? A brief explanation of this process would add transparency
to the model construction.
7. Consider including a model fit assessment, such as the Hosmer-Lemeshow test or checking
the Akaike Information Criterion (AIC), to ensure the goodness-of-fit of the logistic regression
model.
8. Some of the factors identified by the logistic regression (high BMI, age, sex, hypertension,
diabetes) are already included in widely used FLD assessment scores. However, the study
also finds that individuals with elevated AISI have a higher prevalence of FLD. This raises
the question of whether the inclusion of AISI adds any additional predictive value beyond
established indices. A comparative analysis with current clinical scores, considering the AISI
index, would help strengthen the case for its utility in improving FLD risk assessment and
prediction.
9. Further contextualization of the nomogram would be helpful. Could the authors briefly explain
how the nomogram is intended to be used in clinical practice? What scores or ranges would
indicate increased risk? Additionally, please clarify whether the nomogram has been validated
in an independent cohort, or whether future external validation is planned.
10. The authors might want to compare their nomogram to existing clinical models or guidelines
for FLD assessment. This could provide context and demonstrate how the proposed nomogram
stands out or improves upon current practices. A discussion of how this nomogram compares in
terms of accuracy, ease of use, and clinical relevance would highlight its potential advantages.
11. Is there a specific correlation between AISI and other markers of liver inflammation? They
mention that AISI is elevated in FLD, but could they analyze whether its increase is related to
inflammatory biomarkers such as TNF-#, IL-6 or CRP. Was a trend in FLD severity observed?
That is, do patients with more advanced FLD have higher AISI?
12. How does AISI compare with other inflammatory indices in FLD? It could be discussed
whether AISI is a better predictor compared to NLR (neutrophil-to-lymphocyte ratio) or PLR
(platelet-to-lymphocyte ratio), which have also been studied in liver disease.
13. Given that neutrophil infiltration and the roles of lymphocytes and monocytes are mentioned
in liver inflammation, could the authors elaborate on whether AISI levels specifically reflect
these cellular mechanisms? Is the increase in AISI associated with the progression of FLD, and
does it act as a causal factor or merely a biomarker of inflammation?
14. Could the authors elaborate on the novelty of the relationship between AISI and FLD in
the context of existing literature? Are there any studies that have looked at similar systemic
inflammation indices in relation to FLD that could offer a basis for comparison?
15. In terms of clinical application, how could AISI be integrated into current screening practices
for FLD? Could the authors discuss any potential clinical thresholds or cutoffs for AISI that
might be indicative of increased FLD risk?
Title: Aggregate index of systemic inflammation tied to increased fatty liver disease risk:
insights from NHANES data
Version: 2 Date: 07 Apr 2025
Reviewer's report:
The study explores the relationship between the Aggregate Index of Systemic Inflammation
(AISI) and fatty liver disease (FLD), aiming to develop a predictive nomogram that could
support early identification of individuals at risk. Given the increasing global burden of FLD and
the emerging role of systemic inflammation in its pathogenesis, this research offers clinically
relevant insights. The methodology is generally sound and the model demonstrates good
discriminative performance; however, certain aspects of the design, analysis, and interpretation
warrant further consideration to enhance the robustness and applicability of the findings.
1. The introduction mentions AISI, but it would be clearer if the full term 'Aggregate Index of
Systemic Inflammation (AISI)' is written out the first time it is introduced. This will help ensure
that readers unfamiliar with the abbreviation understand the term.
2. The description of the data source and population selection is generally clear and reflects
a well-structured use of the NHANES database. However, several aspects could benefit
from clarification to enhance methodological transparency. Specifically, the 'Data sources and
population selection' section would be strengthened by briefly stating how FLD was defined
or diagnosed to identify both the FLD and non-FLD groups. Even if detailed later, a concise
summary here would help contextualize the population selection from the outset.
3. The exclusion of over 80% of the original sample (20,853 of 24,814 participants)
significantly reduces the final analytical sample size. This could affect the representativeness
and generalizability of your findings. I recommend acknowledging this as a limitation and
considering whether imputation methods or sensitivity analyses could have been used to address
missing data.
4. State explicitly which statistical test was used to assess the association between AISI and FLD
(e.g., logistic regression), and whether models were adjusted for confounders.
5. Was the inclusion of variables in the univariate analysis guided by clinical relevance or based
on statistical significance? It’s important to clarify the criteria for selecting variables for further
analysis in the multivariate model.
6. Multiple regression helps adjust for confounders and provides a more accurate estimate of
the relationships between predictors and the outcome. How were the variables for the multiple
logistic regression model selected? Was backward or forward selection used, or were variables
selected based on clinical reasoning? A brief explanation of this process would add transparency
to the model construction.
7. Consider including a model fit assessment, such as the Hosmer-Lemeshow test or checking
the Akaike Information Criterion (AIC), to ensure the goodness-of-fit of the logistic regression
model.
8. Some of the factors identified by the logistic regression (high BMI, age, sex, hypertension,
diabetes) are already included in widely used FLD assessment scores. However, the study
also finds that individuals with elevated AISI have a higher prevalence of FLD. This raises
the question of whether the inclusion of AISI adds any additional predictive value beyond
established indices. A comparative analysis with current clinical scores, considering the AISI
index, would help strengthen the case for its utility in improving FLD risk assessment and
prediction.
9. Further contextualization of the nomogram would be helpful. Could the authors briefly explain
how the nomogram is intended to be used in clinical practice? What scores or ranges would
indicate increased risk? Additionally, please clarify whether the nomogram has been validated
in an independent cohort, or whether future external validation is planned.
10. The authors might want to compare their nomogram to existing clinical models or guidelines
for FLD assessment. This could provide context and demonstrate how the proposed nomogram
stands out or improves upon current practices. A discussion of how this nomogram compares in
terms of accuracy, ease of use, and clinical relevance would highlight its potential advantages.
11. Is there a specific correlation between AISI and other markers of liver inflammation? They
mention that AISI is elevated in FLD, but could they analyze whether its increase is related to
inflammatory biomarkers such as TNF-#, IL-6 or CRP. Was a trend in FLD severity observed?
That is, do patients with more advanced FLD have higher AISI?
12. How does AISI compare with other inflammatory indices in FLD? It could be discussed
whether AISI is a better predictor compared to NLR (neutrophil-to-lymphocyte ratio) or PLR
(platelet-to-lymphocyte ratio), which have also been studied in liver disease.
13. Given that neutrophil infiltration and the roles of lymphocytes and monocytes are mentioned
in liver inflammation, could the authors elaborate on whether AISI levels specifically reflect
these cellular mechanisms? Is the increase in AISI associated with the progression of FLD, and
does it act as a causal factor or merely a biomarker of inflammation?
14. Could the authors elaborate on the novelty of the relationship between AISI and FLD in
the context of existing literature? Are there any studies that have looked at similar systemic
inflammation indices in relation to FLD that could offer a basis for comparison?
15. In terms of clinical application, how could AISI be integrated into current screening practices
for FLD? Could the authors discuss any potential clinical thresholds or cutoffs for AISI that
might be indicative of increased FLD risk?
Please review this paper.
user
Title: Aggregate index of systemic inflammation tied to increased fatty liver disease risk:
insights from NHANES data
Version: 2 Date: 07 Apr 2025
Reviewer's report:
The study explores the relationship between the Aggregate Index of Systemic Inflammation
(AISI) and fatty liver disease (FLD), aiming to develop a predictive nomogram that could
support early identification of individuals at risk. Given the increasing global burden of FLD and
the emerging role of systemic inflammation in its pathogenesis, this research offers clinically
relevant insights. The methodology is generally sound and the model demonstrates good
discriminative performance; however, certain aspects of the design, analysis, and interpretation
warrant further consideration to enhance the robustness and applicability of the findings.
1. The introduction mentions AISI, but it would be clearer if the full term 'Aggregate Index of
Systemic Inflammation (AISI)' is written out the first time it is introduced. This will help ensure
that readers unfamiliar with the abbreviation understand the term.
2. The description of the data source and population selection is generally clear and reflects
a well-structured use of the NHANES database. However, several aspects could benefit
from clarification to enhance methodological transparency. Specifically, the 'Data sources and
population selection' section would be strengthened by briefly stating how FLD was defined
or diagnosed to identify both the FLD and non-FLD groups. Even if detailed later, a concise
summary here would help contextualize the population selection from the outset.
3. The exclusion of over 80% of the original sample (20,853 of 24,814 participants)
significantly reduces the final analytical sample size. This could affect the representativeness
and generalizability of your findings. I recommend acknowledging this as a limitation and
considering whether imputation methods or sensitivity analyses could have been used to address
missing data.
4. State explicitly which statistical test was used to assess the association between AISI and FLD
(e.g., logistic regression), and whether models were adjusted for confounders.
5. Was the inclusion of variables in the univariate analysis guided by clinical relevance or based
on statistical significance? It’s important to clarify the criteria for selecting variables for further
analysis in the multivariate model.
6. Multiple regression helps adjust for confounders and provides a more accurate estimate of
the relationships between predictors and the outcome. How were the variables for the multiple
logistic regression model selected? Was backward or forward selection used, or were variables
selected based on clinical reasoning? A brief explanation of this process would add transparency
to the model construction.
7. Consider including a model fit assessment, such as the Hosmer-Lemeshow test or checking
the Akaike Information Criterion (AIC), to ensure the goodness-of-fit of the logistic regression
model.
8. Some of the factors identified by the logistic regression (high BMI, age, sex, hypertension,
diabetes) are already included in widely used FLD assessment scores. However, the study
also finds that individuals with elevated AISI have a higher prevalence of FLD. This raises
the question of whether the inclusion of AISI adds any additional predictive value beyond
established indices. A comparative analysis with current clinical scores, considering the AISI
index, would help strengthen the case for its utility in improving FLD risk assessment and
prediction.
9. Further contextualization of the nomogram would be helpful. Could the authors briefly explain
how the nomogram is intended to be used in clinical practice? What scores or ranges would
indicate increased risk? Additionally, please clarify whether the nomogram has been validated
in an independent cohort, or whether future external validation is planned.
10. The authors might want to compare their nomogram to existing clinical models or guidelines
for FLD assessment. This could provide context and demonstrate how the proposed nomogram
stands out or improves upon current practices. A discussion of how this nomogram compares in
terms of accuracy, ease of use, and clinical relevance would highlight its potential advantages.
11. Is there a specific correlation between AISI and other markers of liver inflammation? They
mention that AISI is elevated in FLD, but could they analyze whether its increase is related to
inflammatory biomarkers such as TNF-#, IL-6 or CRP. Was a trend in FLD severity observed?
That is, do patients with more advanced FLD have higher AISI?
12. How does AISI compare with other inflammatory indices in FLD? It could be discussed
whether AISI is a better predictor compared to NLR (neutrophil-to-lymphocyte ratio) or PLR
(platelet-to-lymphocyte ratio), which have also been studied in liver disease.
13. Given that neutrophil infiltration and the roles of lymphocytes and monocytes are mentioned
in liver inflammation, could the authors elaborate on whether AISI levels specifically reflect
these cellular mechanisms? Is the increase in AISI associated with the progression of FLD, and
does it act as a causal factor or merely a biomarker of inflammation?
14. Could the authors elaborate on the novelty of the relationship between AISI and FLD in
the context of existing literature? Are there any studies that have looked at similar systemic
inflammation indices in relation to FLD that could offer a basis for comparison?
15. In terms of clinical application, how could AISI be integrated into current screening practices
for FLD? Could the authors discuss any potential clinical thresholds or cutoffs for AISI that
might be indicative of increased FLD risk?
assistant