Title: Using new indices to predict Metabolism dysfunction-associated fatty liver disease
(MAFLD): Analysis of the National Health and Nutrition Examination Survey database.
Version: 0 Date: 09 Jan 2024
Reviewer's report:
This is a cross-sectional study to develop a prediction model to predict metabolism dysfunction-
associated fatty liver disease (MAFLD). The authors evaluated quite a large population size and
validated their results both internally and externally.
However, some issues should be further assessed by the authors.
1. Given the design of the study is cross-sectional in nature, it is critical to emphasize that
accurate predictions cannot be guaranteed by cross-sectional design. Typically, the development
of prediction models should be based on a longitudinal cohort study. If the authors insist on
building the prediction model within the cross-sectional design, I recommend that authors follow
the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or
Diagnosis statement (https://doi.org/10.7326/M14-0698 ).
2. Some issues in the Statistical Analysis part should be addressed.
• 2.1. In P6, lines 113-114 the authors described that “nonnormally distributed data were tested
using Wilcoxon rank sum tests”. However, there are no nonnormally distributed variables
reported correspondingly in Table 1.
• 2.2. As the rare disease assumption is violated in this research (the prevalence of MAFLD is
31%), the estimations of associations between derived indices and MAFLD would be biased by
using odds ratios (ORs) (https://doi.org/10.3389/fvets.2017.00193).
• 2.3. In P7, lines 116-117, the authors mentioned “the data were displayed as odds ratios
(ORs) and 95% confidence intervals (CIs) for unadjusted, partially adjusted, and fully adjusted
confounders”. The descriptions are not clear. In the Result, the authors presented the ORs
generated by Models 1-3. However,  related descriptions for Models 1-3 were not provided in
Methods. It should be listed detailed potential confounding factors in Models 1-3 in Methods.
Again, why did Model 3 not adjust for the confounding factors of age, gender, race, and income
poverty ratio based on Model 2?
• 2.4. No details were provided in the statistical analysis regarding the stratification (stratified
by age, sex, diabetes, and obesity) analyses.
• 2.5. The imputation strategies of missing data imputation were not presented.
3. Some critical problems in Results need to be solved.
• 3.1 Table 1 only presented the descriptive statistics of training sets. This is incomplete as two
validation datasets are important as well.
• 3.2 It should be clarified which dataset did authors used to calculate ORs in Table 2, as the
authors mentioned three datasets in the text. Why the ORs were not calculated for both training
and two validation datasets?
• 3.3 The AUCs were only provided within two validation datasets in Table 3 and Table 4. Why
the AUCs generated by the training dataset were not listed?
• 3.4. Results should be precisely and consistently demonstrated. Contents of AUCs (95% CI),
and cut-off values overlapped in Table 3, Table 4, and Figure 2. Redundant content should be
removed. Moreover, the AUC values of UHR presented in Table 3 (0.723) and in Figure 2A
(0.728) are different.
• 3.5. Fig.3 shows the internal validation ROC curve of the subgroup. However, the notes of
Fig.3 are not correct. The sample size of internal validation in Fig.3 should be 3796 rather than
12654 in the whole dataset (training dataset and internal validation dataset).
• 3.6 Fig.4 indicated the second validation's ROC curve for the subgroup. The notes are exactly
the same as in Fig.3, with the wrong sample size. The second validation dataset (NHANES
2017-2018) should have 2299 participants rather than 12654 of the whole dataset (NHANES
2007-2016).
• 3.7 The number of MAFLD cases and non- MAFLD individuals should be listed in the text
of the manuscript or Fig. 3 and Fig.4.
4. Some typos or error formats should be noted.
• 4.1. The abbreviations should be avoided in the title “Using new indices to predict MAFLD:
Analysis of the NHANES database”.
• 4.2. In P4, line 82, the USFLI was not given the full spell for the first time appeared in the
manuscript.
• 4.3. In P4, line 83, “have 2 of the following items” should be corrected as “have two of the
following items”.
• 4.4. The font type and font sizes are not consistent in Fig.1.
• 4.5 The descriptions of continuous variables in Statistical Analysis (Mean (SE)) are not
consistent with in Table 1 (Mean (SD)).
Title: Using new indices to predict Metabolism dysfunction-associated fatty liver disease
(MAFLD): Analysis of the National Health and Nutrition Examination Survey database.
Version: 0 Date: 09 Jan 2024
Reviewer's report:
This is a cross-sectional study to develop a prediction model to predict metabolism dysfunction-
associated fatty liver disease (MAFLD). The authors evaluated quite a large population size and
validated their results both internally and externally.
However, some issues should be further assessed by the authors.
1. Given the design of the study is cross-sectional in nature, it is critical to emphasize that
accurate predictions cannot be guaranteed by cross-sectional design. Typically, the development
of prediction models should be based on a longitudinal cohort study. If the authors insist on
building the prediction model within the cross-sectional design, I recommend that authors follow
the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or
Diagnosis statement (https://doi.org/10.7326/M14-0698 ).
2. Some issues in the Statistical Analysis part should be addressed.
• 2.1. In P6, lines 113-114 the authors described that “nonnormally distributed data were tested
using Wilcoxon rank sum tests”. However, there are no nonnormally distributed variables
reported correspondingly in Table 1.
• 2.2. As the rare disease assumption is violated in this research (the prevalence of MAFLD is
31%), the estimations of associations between derived indices and MAFLD would be biased by
using odds ratios (ORs) (https://doi.org/10.3389/fvets.2017.00193).
• 2.3. In P7, lines 116-117, the authors mentioned “the data were displayed as odds ratios
(ORs) and 95% confidence intervals (CIs) for unadjusted, partially adjusted, and fully adjusted
confounders”. The descriptions are not clear. In the Result, the authors presented the ORs
generated by Models 1-3. However,  related descriptions for Models 1-3 were not provided in
Methods. It should be listed detailed potential confounding factors in Models 1-3 in Methods.
Again, why did Model 3 not adjust for the confounding factors of age, gender, race, and income
poverty ratio based on Model 2?
• 2.4. No details were provided in the statistical analysis regarding the stratification (stratified
by age, sex, diabetes, and obesity) analyses.
• 2.5. The imputation strategies of missing data imputation were not presented.
3. Some critical problems in Results need to be solved.
• 3.1 Table 1 only presented the descriptive statistics of training sets. This is incomplete as two
validation datasets are important as well.
• 3.2 It should be clarified which dataset did authors used to calculate ORs in Table 2, as the
authors mentioned three datasets in the text. Why the ORs were not calculated for both training
and two validation datasets?
• 3.3 The AUCs were only provided within two validation datasets in Table 3 and Table 4. Why
the AUCs generated by the training dataset were not listed?
• 3.4. Results should be precisely and consistently demonstrated. Contents of AUCs (95% CI),
and cut-off values overlapped in Table 3, Table 4, and Figure 2. Redundant content should be
removed. Moreover, the AUC values of UHR presented in Table 3 (0.723) and in Figure 2A
(0.728) are different.
• 3.5. Fig.3 shows the internal validation ROC curve of the subgroup. However, the notes of
Fig.3 are not correct. The sample size of internal validation in Fig.3 should be 3796 rather than
12654 in the whole dataset (training dataset and internal validation dataset).
• 3.6 Fig.4 indicated the second validation's ROC curve for the subgroup. The notes are exactly
the same as in Fig.3, with the wrong sample size. The second validation dataset (NHANES
2017-2018) should have 2299 participants rather than 12654 of the whole dataset (NHANES
2007-2016).
• 3.7 The number of MAFLD cases and non- MAFLD individuals should be listed in the text
of the manuscript or Fig. 3 and Fig.4.
4. Some typos or error formats should be noted.
• 4.1. The abbreviations should be avoided in the title “Using new indices to predict MAFLD:
Analysis of the NHANES database”.
• 4.2. In P4, line 82, the USFLI was not given the full spell for the first time appeared in the
manuscript.
• 4.3. In P4, line 83, “have 2 of the following items” should be corrected as “have two of the
following items”.
• 4.4. The font type and font sizes are not consistent in Fig.1.
• 4.5 The descriptions of continuous variables in Statistical Analysis (Mean (SE)) are not
consistent with in Table 1 (Mean (SD)).
Please review this paper.
user
Title: Using new indices to predict Metabolism dysfunction-associated fatty liver disease
(MAFLD): Analysis of the National Health and Nutrition Examination Survey database.
Version: 0 Date: 09 Jan 2024
Reviewer's report:
This is a cross-sectional study to develop a prediction model to predict metabolism dysfunction-
associated fatty liver disease (MAFLD). The authors evaluated quite a large population size and
validated their results both internally and externally.
However, some issues should be further assessed by the authors.
1. Given the design of the study is cross-sectional in nature, it is critical to emphasize that
accurate predictions cannot be guaranteed by cross-sectional design. Typically, the development
of prediction models should be based on a longitudinal cohort study. If the authors insist on
building the prediction model within the cross-sectional design, I recommend that authors follow
the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or
Diagnosis statement (https://doi.org/10.7326/M14-0698 ).
2. Some issues in the Statistical Analysis part should be addressed.
• 2.1. In P6, lines 113-114 the authors described that “nonnormally distributed data were tested
using Wilcoxon rank sum tests”. However, there are no nonnormally distributed variables
reported correspondingly in Table 1.
• 2.2. As the rare disease assumption is violated in this research (the prevalence of MAFLD is
31%), the estimations of associations between derived indices and MAFLD would be biased by
using odds ratios (ORs) (https://doi.org/10.3389/fvets.2017.00193).
• 2.3. In P7, lines 116-117, the authors mentioned “the data were displayed as odds ratios
(ORs) and 95% confidence intervals (CIs) for unadjusted, partially adjusted, and fully adjusted
confounders”. The descriptions are not clear. In the Result, the authors presented the ORs
generated by Models 1-3. However,  related descriptions for Models 1-3 were not provided in
Methods. It should be listed detailed potential confounding factors in Models 1-3 in Methods.
Again, why did Model 3 not adjust for the confounding factors of age, gender, race, and income
poverty ratio based on Model 2?
• 2.4. No details were provided in the statistical analysis regarding the stratification (stratified
by age, sex, diabetes, and obesity) analyses.
• 2.5. The imputation strategies of missing data imputation were not presented.
3. Some critical problems in Results need to be solved.
• 3.1 Table 1 only presented the descriptive statistics of training sets. This is incomplete as two
validation datasets are important as well.
• 3.2 It should be clarified which dataset did authors used to calculate ORs in Table 2, as the
authors mentioned three datasets in the text. Why the ORs were not calculated for both training
and two validation datasets?
• 3.3 The AUCs were only provided within two validation datasets in Table 3 and Table 4. Why
the AUCs generated by the training dataset were not listed?
• 3.4. Results should be precisely and consistently demonstrated. Contents of AUCs (95% CI),
and cut-off values overlapped in Table 3, Table 4, and Figure 2. Redundant content should be
removed. Moreover, the AUC values of UHR presented in Table 3 (0.723) and in Figure 2A
(0.728) are different.
• 3.5. Fig.3 shows the internal validation ROC curve of the subgroup. However, the notes of
Fig.3 are not correct. The sample size of internal validation in Fig.3 should be 3796 rather than
12654 in the whole dataset (training dataset and internal validation dataset).
• 3.6 Fig.4 indicated the second validation's ROC curve for the subgroup. The notes are exactly
the same as in Fig.3, with the wrong sample size. The second validation dataset (NHANES
2017-2018) should have 2299 participants rather than 12654 of the whole dataset (NHANES
2007-2016).
• 3.7 The number of MAFLD cases and non- MAFLD individuals should be listed in the text
of the manuscript or Fig. 3 and Fig.4.
4. Some typos or error formats should be noted.
• 4.1. The abbreviations should be avoided in the title “Using new indices to predict MAFLD:
Analysis of the NHANES database”.
• 4.2. In P4, line 82, the USFLI was not given the full spell for the first time appeared in the
manuscript.
• 4.3. In P4, line 83, “have 2 of the following items” should be corrected as “have two of the
following items”.
• 4.4. The font type and font sizes are not consistent in Fig.1.
• 4.5 The descriptions of continuous variables in Statistical Analysis (Mean (SE)) are not
consistent with in Table 1 (Mean (SD)).
assistant