Title: Development and Validation of a Nomogram-Based Risk Prediction Model forNon-
Alcoholic Fatty Liver Disease (NAFLD): A Logistic Regression Analysis in a Physical
Examination Population
Version: 1 Date: 11 May 2025
Reviewer's report:
This manuscript presents the development of a logistic regression-based nomogram for
predicting the risk of non-alcoholic fatty liver disease (NAFLD) in individuals undergoing
routine physical examinations. Drawing on a large sample of 6,461 individuals from a medical
center in South China, the authors selected 12 variables from an initial list of 21 clinical
indicators through univariate and multivariate analysis, subsequently building a predictive
model. The model was trained and internally validated using a 7:3 split of the dataset, achieving
excellent discrimination as demonstrated by ROC curves with AUC values of 0.909 and 0.905
for the training and validation cohorts, respectively. The nomogram was further evaluated
by calibration curves and decision curve analysis (DCA), showing good agreement between
predicted and observed probabilities, and suggesting clinical benefit across a wide range of
threshold probabilities. The authors argue that their model could serve as a low-cost, efficient
screening tool for early detection of NAFLD, particularly in resource-limited environments.
Despite the strengths in statistical execution and model performance, the manuscript has several
significant shortcomings that limit its suitability for immediate publication. One of the most
critical limitations lies in the absence of external validation. While the internal metrics for
performance are impressive, a model’s clinical utility and generalizability cannot be assessed
adequately without testing it across different populations, clinical settings, or geographical
areas. The entire study is derived from a single-center dataset based in South China, which
raises concerns about ethnic, regional, and healthcare system-related biases. Given the known
variability in NAFLD prevalence and clinical markers across populations, an external validation
cohort is essential to confirm the robustness and transferability of the model. This omission
significantly weakens the practical implications of the findings.
Another concern relates to the clinical applicability of the model. While the authors aim to build
a screening tool based on “routine” physical examination parameters, several included variables
—such as FT4, AST, ALT, AFP, and TyG—are not necessarily part of universal screening
protocols, especially in non-specialist or low-resource settings. The authors do not address
whether these variables are routinely collected in the target population, nor do they justify their
inclusion from a cost-effectiveness or logistical perspective. This raises questions about whether
the proposed model can truly be integrated into standard practice without requiring changes
to current protocols or added testing burdens. Furthermore, although the model demonstrates
strong statistical calibration and discrimination, the authors do not provide a practical guide
for its use. There is no discussion of how clinicians are expected to interpret the total score
from the nomogram, what thresholds should trigger clinical action, or whether the model should
supplement or replace existing diagnostic strategies.
Interpretation of the model’s predictors also raises concerns. Several variables are reported as
being negatively associated with NAFLD (e.g., FT4, AST, Cr, and AFP), which is somewhat
counterintuitive or insufficiently explained. Although the discussion briefly touches on possible
mechanisms, such as a protective role of HDL-C and the renal function marker creatinine, the
justifications are speculative and at times unconvincing. For example, the inverse association
of AFP with NAFLD is presented without sufficient supporting literature, and the potential
confounding effect of hepatic functional status on AFP levels is not considered. Similarly, the
negative correlation of AST with NAFLD is attributed to regional ethnic variation, but this
explanation is vague and unsupported by referenced data. These interpretations require deeper
critical appraisal, more nuanced discussion, and citation of high-quality studies for credibility.
Moreover, the manuscript is hindered by significant issues in its methodological reporting.
The eligibility criteria and selection process are redundantly stated in multiple parts of the
methods section, leading to unnecessary repetition and confusion. The criteria themselves also
appear inconsistent between different parts of the manuscript, particularly regarding exclusion
of patients with severe organ dysfunction. Clear, concise, and non-repetitive descriptions of
patient selection are essential for replicability and transparency. The authors should consolidate
and clarify these sections.
In terms of scientific writing and presentation, the manuscript requires substantial improvement.
Numerous language issues interfere with readability, including grammatical errors, awkward
sentence structures, and inconsistent terminology. For instance, the term “BIM” appears
throughout instead of the correct abbreviation “BMI,” and phrases such as “we develop a
prediction that included…” exhibit poor tense agreement. Several paragraphs in the discussion
drift into general pathophysiological speculation rather than focusing on data interpretation
or practical implications. Additionally, the flow of arguments in the discussion is sometimes
incoherent, with excessive references to Chinese studies without integrating global literature
or established guidelines from major associations such as AASLD or EASL. International
contextualization of findings would enhance the relevance of the study and strengthen its
contribution to the global understanding of NAFLD.
The authors briefly mention limitations, but this section is too superficial to be considered
adequate. The study is limited to a retrospective design, a single center, and lacks prospective
validation. Moreover, while the decision curve analysis suggests broad clinical utility, the
net benefit in real-world practice is not discussed or quantified. The authors should also
acknowledge that NAFLD was diagnosed via ultrasound, which, while non-invasive, may miss
early or subtle cases of hepatic steatosis and is highly operator-dependent. These diagnostic
limitations could introduce misclassification bias that affects model accuracy.
From a referencing and formatting standpoint, inconsistencies are present in citation style and
the source quality of references varies. Several claims in the discussion are supported by regional
or local studies, with limited inclusion of meta-analyses or high-impact journals. The reference
list should be reviewed and strengthened by incorporating authoritative and up-to-date sources.
Additionally, figures and tables are useful but could benefit from better integration into the
manuscript text. For example, Figure 2 (the nomogram) is not accompanied by a worked example
or explanation of how clinicians should interpret its scoring system. This omission limits its
utility as a clinical tool.
In conclusion, while the manuscript addresses an important public health issue and offers
a well-performing model based on an extensive dataset, the lack of external validation,
ambiguous clinical applicability, interpretive overreach, and suboptimal manuscript quality
render it unsuitable for publication in its current form. I recommend major revisions, with an
emphasis on external validation, clinical utility clarification, language refinement, and more
rigorous discussion of findings. The authors must also consider how their model fits into
current diagnostic pathways for NAFLD and whether it provides additive value. Without these
improvements, the utility and scientific merit of the study remain limited.
Title: Development and Validation of a Nomogram-Based Risk Prediction Model forNon-
Alcoholic Fatty Liver Disease (NAFLD): A Logistic Regression Analysis in a Physical
Examination Population
Version: 1 Date: 11 May 2025
Reviewer's report:
This manuscript presents the development of a logistic regression-based nomogram for
predicting the risk of non-alcoholic fatty liver disease (NAFLD) in individuals undergoing
routine physical examinations. Drawing on a large sample of 6,461 individuals from a medical
center in South China, the authors selected 12 variables from an initial list of 21 clinical
indicators through univariate and multivariate analysis, subsequently building a predictive
model. The model was trained and internally validated using a 7:3 split of the dataset, achieving
excellent discrimination as demonstrated by ROC curves with AUC values of 0.909 and 0.905
for the training and validation cohorts, respectively. The nomogram was further evaluated
by calibration curves and decision curve analysis (DCA), showing good agreement between
predicted and observed probabilities, and suggesting clinical benefit across a wide range of
threshold probabilities. The authors argue that their model could serve as a low-cost, efficient
screening tool for early detection of NAFLD, particularly in resource-limited environments.
Despite the strengths in statistical execution and model performance, the manuscript has several
significant shortcomings that limit its suitability for immediate publication. One of the most
critical limitations lies in the absence of external validation. While the internal metrics for
performance are impressive, a model’s clinical utility and generalizability cannot be assessed
adequately without testing it across different populations, clinical settings, or geographical
areas. The entire study is derived from a single-center dataset based in South China, which
raises concerns about ethnic, regional, and healthcare system-related biases. Given the known
variability in NAFLD prevalence and clinical markers across populations, an external validation
cohort is essential to confirm the robustness and transferability of the model. This omission
significantly weakens the practical implications of the findings.
Another concern relates to the clinical applicability of the model. While the authors aim to build
a screening tool based on “routine” physical examination parameters, several included variables
—such as FT4, AST, ALT, AFP, and TyG—are not necessarily part of universal screening
protocols, especially in non-specialist or low-resource settings. The authors do not address
whether these variables are routinely collected in the target population, nor do they justify their
inclusion from a cost-effectiveness or logistical perspective. This raises questions about whether
the proposed model can truly be integrated into standard practice without requiring changes
to current protocols or added testing burdens. Furthermore, although the model demonstrates
strong statistical calibration and discrimination, the authors do not provide a practical guide
for its use. There is no discussion of how clinicians are expected to interpret the total score
from the nomogram, what thresholds should trigger clinical action, or whether the model should
supplement or replace existing diagnostic strategies.
Interpretation of the model’s predictors also raises concerns. Several variables are reported as
being negatively associated with NAFLD (e.g., FT4, AST, Cr, and AFP), which is somewhat
counterintuitive or insufficiently explained. Although the discussion briefly touches on possible
mechanisms, such as a protective role of HDL-C and the renal function marker creatinine, the
justifications are speculative and at times unconvincing. For example, the inverse association
of AFP with NAFLD is presented without sufficient supporting literature, and the potential
confounding effect of hepatic functional status on AFP levels is not considered. Similarly, the
negative correlation of AST with NAFLD is attributed to regional ethnic variation, but this
explanation is vague and unsupported by referenced data. These interpretations require deeper
critical appraisal, more nuanced discussion, and citation of high-quality studies for credibility.
Moreover, the manuscript is hindered by significant issues in its methodological reporting.
The eligibility criteria and selection process are redundantly stated in multiple parts of the
methods section, leading to unnecessary repetition and confusion. The criteria themselves also
appear inconsistent between different parts of the manuscript, particularly regarding exclusion
of patients with severe organ dysfunction. Clear, concise, and non-repetitive descriptions of
patient selection are essential for replicability and transparency. The authors should consolidate
and clarify these sections.
In terms of scientific writing and presentation, the manuscript requires substantial improvement.
Numerous language issues interfere with readability, including grammatical errors, awkward
sentence structures, and inconsistent terminology. For instance, the term “BIM” appears
throughout instead of the correct abbreviation “BMI,” and phrases such as “we develop a
prediction that included…” exhibit poor tense agreement. Several paragraphs in the discussion
drift into general pathophysiological speculation rather than focusing on data interpretation
or practical implications. Additionally, the flow of arguments in the discussion is sometimes
incoherent, with excessive references to Chinese studies without integrating global literature
or established guidelines from major associations such as AASLD or EASL. International
contextualization of findings would enhance the relevance of the study and strengthen its
contribution to the global understanding of NAFLD.
The authors briefly mention limitations, but this section is too superficial to be considered
adequate. The study is limited to a retrospective design, a single center, and lacks prospective
validation. Moreover, while the decision curve analysis suggests broad clinical utility, the
net benefit in real-world practice is not discussed or quantified. The authors should also
acknowledge that NAFLD was diagnosed via ultrasound, which, while non-invasive, may miss
early or subtle cases of hepatic steatosis and is highly operator-dependent. These diagnostic
limitations could introduce misclassification bias that affects model accuracy.
From a referencing and formatting standpoint, inconsistencies are present in citation style and
the source quality of references varies. Several claims in the discussion are supported by regional
or local studies, with limited inclusion of meta-analyses or high-impact journals. The reference
list should be reviewed and strengthened by incorporating authoritative and up-to-date sources.
Additionally, figures and tables are useful but could benefit from better integration into the
manuscript text. For example, Figure 2 (the nomogram) is not accompanied by a worked example
or explanation of how clinicians should interpret its scoring system. This omission limits its
utility as a clinical tool.
In conclusion, while the manuscript addresses an important public health issue and offers
a well-performing model based on an extensive dataset, the lack of external validation,
ambiguous clinical applicability, interpretive overreach, and suboptimal manuscript quality
render it unsuitable for publication in its current form. I recommend major revisions, with an
emphasis on external validation, clinical utility clarification, language refinement, and more
rigorous discussion of findings. The authors must also consider how their model fits into
current diagnostic pathways for NAFLD and whether it provides additive value. Without these
improvements, the utility and scientific merit of the study remain limited.
Please review this paper.
user
Title: Development and Validation of a Nomogram-Based Risk Prediction Model forNon-
Alcoholic Fatty Liver Disease (NAFLD): A Logistic Regression Analysis in a Physical
Examination Population
Version: 1 Date: 11 May 2025
Reviewer's report:
This manuscript presents the development of a logistic regression-based nomogram for
predicting the risk of non-alcoholic fatty liver disease (NAFLD) in individuals undergoing
routine physical examinations. Drawing on a large sample of 6,461 individuals from a medical
center in South China, the authors selected 12 variables from an initial list of 21 clinical
indicators through univariate and multivariate analysis, subsequently building a predictive
model. The model was trained and internally validated using a 7:3 split of the dataset, achieving
excellent discrimination as demonstrated by ROC curves with AUC values of 0.909 and 0.905
for the training and validation cohorts, respectively. The nomogram was further evaluated
by calibration curves and decision curve analysis (DCA), showing good agreement between
predicted and observed probabilities, and suggesting clinical benefit across a wide range of
threshold probabilities. The authors argue that their model could serve as a low-cost, efficient
screening tool for early detection of NAFLD, particularly in resource-limited environments.
Despite the strengths in statistical execution and model performance, the manuscript has several
significant shortcomings that limit its suitability for immediate publication. One of the most
critical limitations lies in the absence of external validation. While the internal metrics for
performance are impressive, a model’s clinical utility and generalizability cannot be assessed
adequately without testing it across different populations, clinical settings, or geographical
areas. The entire study is derived from a single-center dataset based in South China, which
raises concerns about ethnic, regional, and healthcare system-related biases. Given the known
variability in NAFLD prevalence and clinical markers across populations, an external validation
cohort is essential to confirm the robustness and transferability of the model. This omission
significantly weakens the practical implications of the findings.
Another concern relates to the clinical applicability of the model. While the authors aim to build
a screening tool based on “routine” physical examination parameters, several included variables
—such as FT4, AST, ALT, AFP, and TyG—are not necessarily part of universal screening
protocols, especially in non-specialist or low-resource settings. The authors do not address
whether these variables are routinely collected in the target population, nor do they justify their
inclusion from a cost-effectiveness or logistical perspective. This raises questions about whether
the proposed model can truly be integrated into standard practice without requiring changes
to current protocols or added testing burdens. Furthermore, although the model demonstrates
strong statistical calibration and discrimination, the authors do not provide a practical guide
for its use. There is no discussion of how clinicians are expected to interpret the total score
from the nomogram, what thresholds should trigger clinical action, or whether the model should
supplement or replace existing diagnostic strategies.
Interpretation of the model’s predictors also raises concerns. Several variables are reported as
being negatively associated with NAFLD (e.g., FT4, AST, Cr, and AFP), which is somewhat
counterintuitive or insufficiently explained. Although the discussion briefly touches on possible
mechanisms, such as a protective role of HDL-C and the renal function marker creatinine, the
justifications are speculative and at times unconvincing. For example, the inverse association
of AFP with NAFLD is presented without sufficient supporting literature, and the potential
confounding effect of hepatic functional status on AFP levels is not considered. Similarly, the
negative correlation of AST with NAFLD is attributed to regional ethnic variation, but this
explanation is vague and unsupported by referenced data. These interpretations require deeper
critical appraisal, more nuanced discussion, and citation of high-quality studies for credibility.
Moreover, the manuscript is hindered by significant issues in its methodological reporting.
The eligibility criteria and selection process are redundantly stated in multiple parts of the
methods section, leading to unnecessary repetition and confusion. The criteria themselves also
appear inconsistent between different parts of the manuscript, particularly regarding exclusion
of patients with severe organ dysfunction. Clear, concise, and non-repetitive descriptions of
patient selection are essential for replicability and transparency. The authors should consolidate
and clarify these sections.
In terms of scientific writing and presentation, the manuscript requires substantial improvement.
Numerous language issues interfere with readability, including grammatical errors, awkward
sentence structures, and inconsistent terminology. For instance, the term “BIM” appears
throughout instead of the correct abbreviation “BMI,” and phrases such as “we develop a
prediction that included…” exhibit poor tense agreement. Several paragraphs in the discussion
drift into general pathophysiological speculation rather than focusing on data interpretation
or practical implications. Additionally, the flow of arguments in the discussion is sometimes
incoherent, with excessive references to Chinese studies without integrating global literature
or established guidelines from major associations such as AASLD or EASL. International
contextualization of findings would enhance the relevance of the study and strengthen its
contribution to the global understanding of NAFLD.
The authors briefly mention limitations, but this section is too superficial to be considered
adequate. The study is limited to a retrospective design, a single center, and lacks prospective
validation. Moreover, while the decision curve analysis suggests broad clinical utility, the
net benefit in real-world practice is not discussed or quantified. The authors should also
acknowledge that NAFLD was diagnosed via ultrasound, which, while non-invasive, may miss
early or subtle cases of hepatic steatosis and is highly operator-dependent. These diagnostic
limitations could introduce misclassification bias that affects model accuracy.
From a referencing and formatting standpoint, inconsistencies are present in citation style and
the source quality of references varies. Several claims in the discussion are supported by regional
or local studies, with limited inclusion of meta-analyses or high-impact journals. The reference
list should be reviewed and strengthened by incorporating authoritative and up-to-date sources.
Additionally, figures and tables are useful but could benefit from better integration into the
manuscript text. For example, Figure 2 (the nomogram) is not accompanied by a worked example
or explanation of how clinicians should interpret its scoring system. This omission limits its
utility as a clinical tool.
In conclusion, while the manuscript addresses an important public health issue and offers
a well-performing model based on an extensive dataset, the lack of external validation,
ambiguous clinical applicability, interpretive overreach, and suboptimal manuscript quality
render it unsuitable for publication in its current form. I recommend major revisions, with an
emphasis on external validation, clinical utility clarification, language refinement, and more
rigorous discussion of findings. The authors must also consider how their model fits into
current diagnostic pathways for NAFLD and whether it provides additive value. Without these
improvements, the utility and scientific merit of the study remain limited.
assistant