Review for 10.1186/s12876-025-03832-z (2025)

Title: The best Anthropometric indices to predict colon cancer
Version: 1 Date: 02 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-The introduction does a good job of outlining the background and the various anthropometric
indices, but it would benefit from a clearer and more explicit research hypothesis. For example,
you might state, "This study aims to compare the effectiveness of traditional and newer
anthropometric indices in predicting colon cancer risk."
-While the study considers age, sex, race, education, and income as covariates, it could be
valuable to mention how these covariates will be handled in the statistical modeling. Are they
included in a multivariable logistic regression model to adjust for potential confounding factors?
This is important for ensuring that the observed relationships between anthropometric indices
and colon cancer risk are not confounded by other factors.
-In addition to the covariates mentioned (age, sex, race, education level, income), other factors
like physical activity, dietary habits, family history of colon cancer, smoking, and alcohol
consumption could influence the results. It would be beneficial to note whether these factors
were considered and how they might be accounted for in the analysis.
-The study mentions using multiple imputations to handle missing covariate data, which is
a good approach. However, it would be helpful to describe the extent of missing data and
why multiple imputation is considered the best approach in this case. Additionally, the results
could be impacted by the assumptions made during imputation, and clarifying this would add
transparency.
-The study relies on self-reported data from the NHANES database regarding colon cancer
diagnosis ("Have you ever had colon cancer?"). This approach can introduce reporting bias,
as individuals may misremember or underreport their diagnosis. It would be good to mention
whether any validation of self-reported cancer diagnoses has been done in prior NHANES
studies and whether this could influence the study’s findings.
-Although NHANES is a representative sample of the U.S. population, it may not fully represent
individuals outside of the U.S. or those with certain sociodemographic characteristics. It would
be useful to discuss the external validity and generalizability of the findings to populations
outside of the U.S.
-ROC curves and AUC help compare the performance of different indices, but the study
could benefit from a deeper discussion of the clinical relevance of the results. For instance,
even if an index has a higher AUC, it may not be clinically significant unless it offers a
meaningful improvement in predictive accuracy over existing indices (such as BMI and waist
circumference). Additionally, the study should provide specific cut-off values or thresholds for
the indices if possible.
-The manuscript outlines a comprehensive approach to the analysis, but the discussion section
would benefit from a deeper exploration of the potential implications of the findings. For
example, if one index (say, ABSI) turns out to be more predictive than others, what are the
practical implications? Would it be feasible to implement this index in clinical practice? What
limitations or challenges exist in applying the results to the broader population?
-Although you mention that ABSI, ConI, and WWI performed better than other indices, it would
be helpful to elaborate on the statistical analysis a bit more. For example, what were the AUC
values for each index? Were there any significant differences between the three top-performing
indices? More details on the statistical significance of these findings would strengthen the
conclusion.
-The self-reported data on colon cancer diagnosis is a potential limitation that deserves further
emphasis. Self-reporting of medical conditions can lead to misclassification or recall bias. While
you briefly mention this issue, it would strengthen your argument to discuss how this could
specifically impact the results and what steps could be taken to minimize such biases in future
studies (e.g., using medical records for verification).