Review for 10.1186/s12876-025-04115-3 (2025)

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: 07 May 2025
Reviewer's report:
This manuscript presents a predictive model for NAFLD based on routine clinical data from
a large health examination cohort in Guangxi, China. Using multivariate logistic regression,
the authors identified 12 independent predictors and developed a nomogram that demonstrated
strong internal validity. The study addresses a critical gap by proposing a non-invasive, cost-
effective screening method that could aid early detection and intervention for NAFLD.
The study is well-conceived and methodologically rigorous. However, there are several critical
improvements required ranging from clarity in presentation and statistical reporting to adherence
to scientific writing conventions (e.g., symbol usage, referencing style, and figure legends). The
following detailed comments are intended to help the authors strengthen the manuscript for
publication.
Detailed Comments
1. Title and Abstract
• The title is descriptive and informative. However, consider adding “nomogram” to the title, as
the use of this visual tool is a key feature of the study.
• The abstract is comprehensive but overly dense. It would benefit from:
• Separating methods and results more clearly.
• Including numerical values for sensitivity and specificity with confidence intervals.
• Clarifying abbreviations (e.g., TyG, UA, Cr) upon first use.
• Removing the use of symbols such as “##” to denote equal contribution. Use superscripted
numerical or alphabetical symbols instead (e.g., * or †) per standard editorial practice. Emojis
or informal notation are inappropriate in scientific writing.
2. Introduction
• The introduction provides relevant background and justifies the need for a predictive model.
• However, the literature review could be improved by:
• Citing existing models (e.g., Fatty Liver Index, NAFLD Liver Fat Score) for comparison.
• Highlighting specific limitations in previous models to better establish novelty.
• Clarifying that the study is focused on a general physical examination population, which is
important for generalizability.
3. Methods
Population & Sampling:
• The inclusion/exclusion criteria are comprehensive, but the repetition of exclusion criteria in
multiple places should be cleaned up for clarity.
• Specify how NAFLD was diagnosed with ultrasound (e.g., based on what criteria or radiologist
confirmation?).
Statistical Analysis:
• Appropriate use of univariate and multivariate logistic regression.
• Include more information on model selection (e.g., backward/forward stepwise? AIC/BIC?).
• Consider adding internal cross-validation or bootstrapping to further assess model stability.
• For transformation of variables (e.g., TyG), briefly state why a log transformation was used.
4. Results
Table Presentation (Table 1 & 2):
• Well-organized, but revise titles to be more descriptive (e.g., “Clinical Characteristics of
NAFLD and Non-NAFLD Groups”).
• Present odds ratios (ORs) with 95% confidence intervals to convey precision.
• Highlight statistically significant variables using bold font or asterisk (*), with a footnote
explanation.
Predictive Model:
• The inclusion of 12 predictors is justified statistically and clinically.
• The nomogram (Figure 2) is intuitive but lacks a detailed caption. Provide interpretation
guidance for clinicians (e.g., “Sum the points to estimate NAFLD risk on the bottom axis”).
Model Performance:
• ROC curves (AUC = 0.909, 0.905) show excellent discrimination.
• Calibration plots (p = 0.989, 0.263) indicate a good fit.
• DCA demonstrates high net clinical benefit.
• Consider reporting Net Reclassification Index (NRI) or Integrated Discrimination
Improvement (IDI) if available, to supplement evaluation.
5. Discussion
• The discussion appropriately interprets key findings and contextualizes them with prior
literature.
• Strengths include identifying novel protective factors (HDL-C, FT4, Cr) and discussing their
plausible physiological mechanisms.
• However, the discussion is overly lengthy and redundant in parts. Suggestions:
• Consolidate paragraphs discussing metabolic syndrome and reduce overlap.
• Avoid overly speculative claims without referencing (e.g., AST level variation by ethnicity).
• Add a brief comparison with existing NAFLD prediction models and show where this model
excels (e.g., use of FT4, TyG, Cr).
Limitations Section:
• The limitations are acknowledged, but should be better structured:
• Diagnosis based on ultrasound alone, which lacks sensitivity.
• No external validation—this restricts the generalizability.
• Only internal validation was performed.
• Ethnic homogeneity of the population (mostly Zhuang ethnicity) should be noted.
6. Figures and Tables
• Figure 2 (Nomogram): Useful but needs a legend explaining how to use it for NAFLD risk
estimation.
• Figure 3-5: Add exact AUC values, sample sizes, and statistical test results to the figure
captions.
• Table 1 & 2: Improve readability by ensuring consistent decimal places and aligning numerical
values.
7. Language and Formatting
• The manuscript should undergo thorough language editing to address:
• Grammatical errors (e.g., “The model gives a small scale score…” # “The model assigns a
point score…”).
• Sentence structure (e.g., avoid long, run-on sentences).
• Avoid informal language and ensure consistent terminology.
8. References
• The reference list is largely up-to-date and relevant.
• Ensure uniform formatting (e.g., spacing, journal names, DOI formatting).
• Verify that all references mentioned in the text are cited accurately (e.g., for cited Chinese
studies, ensure English translation and standard citation format).
Conclusion and Recommendation
This manuscript makes a valuable contribution by providing a clinically applicable, well-
validated model for NAFLD risk prediction using routine parameters. The inclusion of TyG,
FT4, and Cr is noteworthy, and the model performance is good.