Title: Biological Age Prediction and NAFLD Risk Assessment: A Machine Learning Model
Based on a Multicenter Population in Nanchang, Jiangxi, China
Version: 2 Date: 25 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the paper mentions splitting the data into training and test sets (80:20), there is limited
detail on how model performance was validated. It would be beneficial to include information
on cross-validation procedures, such as k-fold cross-validation, to assess model stability and
generalizability. Further, including a comparison of model performance across different datasets
(Nanchang vs. NHANES) would strengthen the validity of the findings.
-The imputation method for missing values is based on a chi-square binning method, which
may be suboptimal in model accuracy. The manuscript should provide a more detailed rationale
for choosing this imputation method over other potential methods, such as multiple imputation
by chained equations (MICE) or k-nearest neighbors imputation. This may yield more reliable
results for missing data handling.
-While the paper mentions using common biomarkers across datasets, the specific biomarkers
included and their selection process (beyond the feature selection methods) are not clearly
outlined. The authors should provide more information on the rationale for selecting specific
biomarkers and how these are linked to aging and NAFLD pathophysiology. -Additionally,
a more detailed explanation of the standardization process (e.g., the use of Z-scores) and its
potential impact on the analysis would help understand the model's sensitivity to different data
distributions.
-Although the NC-BA model performs well across multiple datasets (Nanchang and NHANES),
the manuscript does not provide a thorough external validation of the model using an
independent cohort. Validation with a third dataset (e.g., a cohort from a different region or
country) would further strengthen the generalizability of the NC-BA model. This is particularly
important given the study's emphasis on developing a tool that can be applied to diverse
populations.
-How might the choice of biomarkers and features affect the accuracy or applicability of the
model in different populations or disease states?
-What are the limitations of using a machine learning approach (e.g., risk of overfitting or model
interpretability)?
-Are there specific patient subgroups (e.g., elderly, very young, severely ill) for which the model
may not perform well?
-Was the model properly calibrated for the NHANES dataset, or does the decrease in correlation
suggest that adjustments are needed for other population-specific factors?
-Would incorporating additional variables (e.g., genetic data, environmental factors) help
improve model accuracy and calibration across diverse populations?
Title: Biological Age Prediction and NAFLD Risk Assessment: A Machine Learning Model
Based on a Multicenter Population in Nanchang, Jiangxi, China
Version: 2 Date: 25 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the paper mentions splitting the data into training and test sets (80:20), there is limited
detail on how model performance was validated. It would be beneficial to include information
on cross-validation procedures, such as k-fold cross-validation, to assess model stability and
generalizability. Further, including a comparison of model performance across different datasets
(Nanchang vs. NHANES) would strengthen the validity of the findings.
-The imputation method for missing values is based on a chi-square binning method, which
may be suboptimal in model accuracy. The manuscript should provide a more detailed rationale
for choosing this imputation method over other potential methods, such as multiple imputation
by chained equations (MICE) or k-nearest neighbors imputation. This may yield more reliable
results for missing data handling.
-While the paper mentions using common biomarkers across datasets, the specific biomarkers
included and their selection process (beyond the feature selection methods) are not clearly
outlined. The authors should provide more information on the rationale for selecting specific
biomarkers and how these are linked to aging and NAFLD pathophysiology. -Additionally,
a more detailed explanation of the standardization process (e.g., the use of Z-scores) and its
potential impact on the analysis would help understand the model's sensitivity to different data
distributions.
-Although the NC-BA model performs well across multiple datasets (Nanchang and NHANES),
the manuscript does not provide a thorough external validation of the model using an
independent cohort. Validation with a third dataset (e.g., a cohort from a different region or
country) would further strengthen the generalizability of the NC-BA model. This is particularly
important given the study's emphasis on developing a tool that can be applied to diverse
populations.
-How might the choice of biomarkers and features affect the accuracy or applicability of the
model in different populations or disease states?
-What are the limitations of using a machine learning approach (e.g., risk of overfitting or model
interpretability)?
-Are there specific patient subgroups (e.g., elderly, very young, severely ill) for which the model
may not perform well?
-Was the model properly calibrated for the NHANES dataset, or does the decrease in correlation
suggest that adjustments are needed for other population-specific factors?
-Would incorporating additional variables (e.g., genetic data, environmental factors) help
improve model accuracy and calibration across diverse populations?
Please review this paper.
user
Title: Biological Age Prediction and NAFLD Risk Assessment: A Machine Learning Model
Based on a Multicenter Population in Nanchang, Jiangxi, China
Version: 2 Date: 25 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the paper mentions splitting the data into training and test sets (80:20), there is limited
detail on how model performance was validated. It would be beneficial to include information
on cross-validation procedures, such as k-fold cross-validation, to assess model stability and
generalizability. Further, including a comparison of model performance across different datasets
(Nanchang vs. NHANES) would strengthen the validity of the findings.
-The imputation method for missing values is based on a chi-square binning method, which
may be suboptimal in model accuracy. The manuscript should provide a more detailed rationale
for choosing this imputation method over other potential methods, such as multiple imputation
by chained equations (MICE) or k-nearest neighbors imputation. This may yield more reliable
results for missing data handling.
-While the paper mentions using common biomarkers across datasets, the specific biomarkers
included and their selection process (beyond the feature selection methods) are not clearly
outlined. The authors should provide more information on the rationale for selecting specific
biomarkers and how these are linked to aging and NAFLD pathophysiology. -Additionally,
a more detailed explanation of the standardization process (e.g., the use of Z-scores) and its
potential impact on the analysis would help understand the model's sensitivity to different data
distributions.
-Although the NC-BA model performs well across multiple datasets (Nanchang and NHANES),
the manuscript does not provide a thorough external validation of the model using an
independent cohort. Validation with a third dataset (e.g., a cohort from a different region or
country) would further strengthen the generalizability of the NC-BA model. This is particularly
important given the study's emphasis on developing a tool that can be applied to diverse
populations.
-How might the choice of biomarkers and features affect the accuracy or applicability of the
model in different populations or disease states?
-What are the limitations of using a machine learning approach (e.g., risk of overfitting or model
interpretability)?
-Are there specific patient subgroups (e.g., elderly, very young, severely ill) for which the model
may not perform well?
-Was the model properly calibrated for the NHANES dataset, or does the decrease in correlation
suggest that adjustments are needed for other population-specific factors?
-Would incorporating additional variables (e.g., genetic data, environmental factors) help
improve model accuracy and calibration across diverse populations?
assistant