Title: Evaluating the Role of Circadian Syndrome in Predicting MASLD Risk: Evidence from
a Large Cross-Sectional Study
Version: 0 Date: 20 Apr 2025
Reviewer's report:
This study addresses an important and understudied area. However, a more cautious tone in
the interpretation and conclusions would be appropriate given the cross-sectional nature of the
study design.
Introduction:
The introduction is overly long. Consider condensing the background to maintain reader focus
—some sections (e.g., those discussing circadian rhythm regulation) are content-rich but could
be tightened to avoid redundancy.
Methods – Study Population (Line 100):
The exclusion of 419 participants due to missing covariate data represents a substantial
proportion of the initial sample. This should be flagged clearly as a limitation. Moreover, the
characteristics of excluded participants should be reported—are they significantly different from
those included in the analysis? This could impact the generalizability of the findings, even after
applying survey weights.
Covariates:
I recommend including the overall Healthy Eating Index (HEI) as a covariate to account for
overall diet quality, which may confound the relationship between circadian syndrome and
MASLD.
Additionally, consider presenting the criteria for assessing Circadian Syndrome in a dedicated
table to improve clarity and accessibility.
Results and Interpretation:
The statement referring to “CircS as a predictive marker for MASLD risk” is too strong for
a cross-sectional study. NHANES data do not support predictive claims. A more appropriate
phrasing would be “potential marker” or “associated factor.”
Avoid overinterpretation of the AUC values. While AUCs of 0.732 and 0.804 are respectable,
they are not “excellent” and should be interpreted more conservatively.
Title: Evaluating the Role of Circadian Syndrome in Predicting MASLD Risk: Evidence from
a Large Cross-Sectional Study
Version: 0 Date: 20 Apr 2025
Reviewer's report:
This study addresses an important and understudied area. However, a more cautious tone in
the interpretation and conclusions would be appropriate given the cross-sectional nature of the
study design.
Introduction:
The introduction is overly long. Consider condensing the background to maintain reader focus
—some sections (e.g., those discussing circadian rhythm regulation) are content-rich but could
be tightened to avoid redundancy.
Methods – Study Population (Line 100):
The exclusion of 419 participants due to missing covariate data represents a substantial
proportion of the initial sample. This should be flagged clearly as a limitation. Moreover, the
characteristics of excluded participants should be reported—are they significantly different from
those included in the analysis? This could impact the generalizability of the findings, even after
applying survey weights.
Covariates:
I recommend including the overall Healthy Eating Index (HEI) as a covariate to account for
overall diet quality, which may confound the relationship between circadian syndrome and
MASLD.
Additionally, consider presenting the criteria for assessing Circadian Syndrome in a dedicated
table to improve clarity and accessibility.
Results and Interpretation:
The statement referring to “CircS as a predictive marker for MASLD risk” is too strong for
a cross-sectional study. NHANES data do not support predictive claims. A more appropriate
phrasing would be “potential marker” or “associated factor.”
Avoid overinterpretation of the AUC values. While AUCs of 0.732 and 0.804 are respectable,
they are not “excellent” and should be interpreted more conservatively.
Please review this paper.
user
Title: Evaluating the Role of Circadian Syndrome in Predicting MASLD Risk: Evidence from
a Large Cross-Sectional Study
Version: 0 Date: 20 Apr 2025
Reviewer's report:
This study addresses an important and understudied area. However, a more cautious tone in
the interpretation and conclusions would be appropriate given the cross-sectional nature of the
study design.
Introduction:
The introduction is overly long. Consider condensing the background to maintain reader focus
—some sections (e.g., those discussing circadian rhythm regulation) are content-rich but could
be tightened to avoid redundancy.
Methods – Study Population (Line 100):
The exclusion of 419 participants due to missing covariate data represents a substantial
proportion of the initial sample. This should be flagged clearly as a limitation. Moreover, the
characteristics of excluded participants should be reported—are they significantly different from
those included in the analysis? This could impact the generalizability of the findings, even after
applying survey weights.
Covariates:
I recommend including the overall Healthy Eating Index (HEI) as a covariate to account for
overall diet quality, which may confound the relationship between circadian syndrome and
MASLD.
Additionally, consider presenting the criteria for assessing Circadian Syndrome in a dedicated
table to improve clarity and accessibility.
Results and Interpretation:
The statement referring to “CircS as a predictive marker for MASLD risk” is too strong for
a cross-sectional study. NHANES data do not support predictive claims. A more appropriate
phrasing would be “potential marker” or “associated factor.”
Avoid overinterpretation of the AUC values. While AUCs of 0.732 and 0.804 are respectable,
they are not “excellent” and should be interpreted more conservatively.
assistant