Title: Worldwide burden and cross-regional health inequalities of high BMI-attributable
colorectal cancer by gender from 1990 to 2021, with predictions through 2041
Version: 0 Date: 30 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the methodological section is largely clear, the description of the ARIMA model lacks
some detail, especially in terms of parameter selection (p and q). More specific explanations
on how these parameters were determined would help readers understand the prediction
model better. Additionally, further clarification on the model validation process and potential
limitations of the ARIMA model would be beneficial.
-The ARIMA model assumes that the time series is stationary, which may not always be the case
with healthcare data that could have external influencing factors (e.g., healthcare improvements,
changing lifestyle factors). This assumption may limit the accuracy of long-term predictions,
and acknowledging this potential limitation could strengthen the paper’s validity.
-While the study mentions gender and age subgroup analysis, the manuscript could provide
a more in-depth discussion of how the findings differ across these subgroups. Gender and
age disparities in CRC risk related to overweight are important, but the manuscript doesn’t
sufficiently delve into this aspect. Further discussion and analysis on the gender-specific risk
factors and the impact of overweight on different age groups would enhance the understanding
of these disparities.
-The study focuses on overweight but does not distinguish between overweight (BMI 25–29.9
kg/m²) and obesity (BMI #30 kg/m²). While overweight may be a significant risk factor for
CRC, obesity is generally a stronger predictor. It would strengthen the paper to explore whether
these two categories have differing impacts on CRC burden and whether obesity specifically
contributes more significantly to regional differences.
-The manuscript does not sufficiently address the limitations of the GBD data, particularly
related to potential biases in reporting or data completeness. Data from low-middle or low SDI
regions may be less reliable due to poorer healthcare infrastructure or reporting systems, which
could affect the accuracy of the estimates in these areas. Acknowledging and discussing these
limitations would provide a more balanced view of the findings.
-Although the study analyzes regional differences, the discussion could benefit from further
exploration of the socio-cultural, economic, and healthcare system factors contributing to
these disparities. Understanding why certain regions have higher CRC burdens attributable to
overweight could help contextualize the data and inform targeted interventions.
-While the regional disparities in CRC burden attributable to overweight are identified, the
discussion could benefit from a deeper exploration of the underlying mechanisms contributing to
these differences. For example, more specific information on the role of healthcare infrastructure
or cultural dietary patterns in high-SDI versus low-SDI regions could offer more nuanced
insights into why certain regions have higher or accelerating rates of overweight-attributable
CRC.
-While the discussion emphasizes the need for public health interventions, it does not delve
deeply into specific prevention strategies. More concrete suggestions on how to combat
overweight-related CRC through targeted prevention programs (e.g., anti-obesity campaigns,
improved access to screening, dietary interventions) would make the conclusions more
actionable. Additionally, the role of policy changes (such as taxes on sugary foods, urban
planning to promote physical activity) could be better explored.
-The discussion touches on obesity as a key factor but does not fully address the potential
confounding factors that could influence CRC risk. For instance, smoking, alcohol consumption,
and other lifestyle factors, which are often interrelated with obesity, could have contributed to
the findings. While the focus on overweight is clear, a broader discussion of lifestyle factors
influencing CRC would provide a more holistic view of the issue.
-The prediction through 2036, while valuable, could be expanded further. The discussion
could benefit from addressing potential scenarios beyond this period, considering factors
such as healthcare advancements, the impact of global obesity prevention campaigns, or the
influence of climate change on dietary patterns and physical activity levels. Acknowledging
these uncertainties would strengthen the robustness of the predictions.
-The mention of potential data quality issues in low-SDI regions is a good point, but the
discussion could benefit from exploring how these data gaps might impact the generalizability
of the findings. For example, how might the underreporting in certain regions bias the global
estimates of CRC burden attributable to overweight? Discussing the implications of these gaps
on the overall conclusions would enhance the transparency of the study.
Title: Worldwide burden and cross-regional health inequalities of high BMI-attributable
colorectal cancer by gender from 1990 to 2021, with predictions through 2041
Version: 0 Date: 30 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the methodological section is largely clear, the description of the ARIMA model lacks
some detail, especially in terms of parameter selection (p and q). More specific explanations
on how these parameters were determined would help readers understand the prediction
model better. Additionally, further clarification on the model validation process and potential
limitations of the ARIMA model would be beneficial.
-The ARIMA model assumes that the time series is stationary, which may not always be the case
with healthcare data that could have external influencing factors (e.g., healthcare improvements,
changing lifestyle factors). This assumption may limit the accuracy of long-term predictions,
and acknowledging this potential limitation could strengthen the paper’s validity.
-While the study mentions gender and age subgroup analysis, the manuscript could provide
a more in-depth discussion of how the findings differ across these subgroups. Gender and
age disparities in CRC risk related to overweight are important, but the manuscript doesn’t
sufficiently delve into this aspect. Further discussion and analysis on the gender-specific risk
factors and the impact of overweight on different age groups would enhance the understanding
of these disparities.
-The study focuses on overweight but does not distinguish between overweight (BMI 25–29.9
kg/m²) and obesity (BMI #30 kg/m²). While overweight may be a significant risk factor for
CRC, obesity is generally a stronger predictor. It would strengthen the paper to explore whether
these two categories have differing impacts on CRC burden and whether obesity specifically
contributes more significantly to regional differences.
-The manuscript does not sufficiently address the limitations of the GBD data, particularly
related to potential biases in reporting or data completeness. Data from low-middle or low SDI
regions may be less reliable due to poorer healthcare infrastructure or reporting systems, which
could affect the accuracy of the estimates in these areas. Acknowledging and discussing these
limitations would provide a more balanced view of the findings.
-Although the study analyzes regional differences, the discussion could benefit from further
exploration of the socio-cultural, economic, and healthcare system factors contributing to
these disparities. Understanding why certain regions have higher CRC burdens attributable to
overweight could help contextualize the data and inform targeted interventions.
-While the regional disparities in CRC burden attributable to overweight are identified, the
discussion could benefit from a deeper exploration of the underlying mechanisms contributing to
these differences. For example, more specific information on the role of healthcare infrastructure
or cultural dietary patterns in high-SDI versus low-SDI regions could offer more nuanced
insights into why certain regions have higher or accelerating rates of overweight-attributable
CRC.
-While the discussion emphasizes the need for public health interventions, it does not delve
deeply into specific prevention strategies. More concrete suggestions on how to combat
overweight-related CRC through targeted prevention programs (e.g., anti-obesity campaigns,
improved access to screening, dietary interventions) would make the conclusions more
actionable. Additionally, the role of policy changes (such as taxes on sugary foods, urban
planning to promote physical activity) could be better explored.
-The discussion touches on obesity as a key factor but does not fully address the potential
confounding factors that could influence CRC risk. For instance, smoking, alcohol consumption,
and other lifestyle factors, which are often interrelated with obesity, could have contributed to
the findings. While the focus on overweight is clear, a broader discussion of lifestyle factors
influencing CRC would provide a more holistic view of the issue.
-The prediction through 2036, while valuable, could be expanded further. The discussion
could benefit from addressing potential scenarios beyond this period, considering factors
such as healthcare advancements, the impact of global obesity prevention campaigns, or the
influence of climate change on dietary patterns and physical activity levels. Acknowledging
these uncertainties would strengthen the robustness of the predictions.
-The mention of potential data quality issues in low-SDI regions is a good point, but the
discussion could benefit from exploring how these data gaps might impact the generalizability
of the findings. For example, how might the underreporting in certain regions bias the global
estimates of CRC burden attributable to overweight? Discussing the implications of these gaps
on the overall conclusions would enhance the transparency of the study.
Please review this paper.
user
Title: Worldwide burden and cross-regional health inequalities of high BMI-attributable
colorectal cancer by gender from 1990 to 2021, with predictions through 2041
Version: 0 Date: 30 Mar 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the methodological section is largely clear, the description of the ARIMA model lacks
some detail, especially in terms of parameter selection (p and q). More specific explanations
on how these parameters were determined would help readers understand the prediction
model better. Additionally, further clarification on the model validation process and potential
limitations of the ARIMA model would be beneficial.
-The ARIMA model assumes that the time series is stationary, which may not always be the case
with healthcare data that could have external influencing factors (e.g., healthcare improvements,
changing lifestyle factors). This assumption may limit the accuracy of long-term predictions,
and acknowledging this potential limitation could strengthen the paper’s validity.
-While the study mentions gender and age subgroup analysis, the manuscript could provide
a more in-depth discussion of how the findings differ across these subgroups. Gender and
age disparities in CRC risk related to overweight are important, but the manuscript doesn’t
sufficiently delve into this aspect. Further discussion and analysis on the gender-specific risk
factors and the impact of overweight on different age groups would enhance the understanding
of these disparities.
-The study focuses on overweight but does not distinguish between overweight (BMI 25–29.9
kg/m²) and obesity (BMI #30 kg/m²). While overweight may be a significant risk factor for
CRC, obesity is generally a stronger predictor. It would strengthen the paper to explore whether
these two categories have differing impacts on CRC burden and whether obesity specifically
contributes more significantly to regional differences.
-The manuscript does not sufficiently address the limitations of the GBD data, particularly
related to potential biases in reporting or data completeness. Data from low-middle or low SDI
regions may be less reliable due to poorer healthcare infrastructure or reporting systems, which
could affect the accuracy of the estimates in these areas. Acknowledging and discussing these
limitations would provide a more balanced view of the findings.
-Although the study analyzes regional differences, the discussion could benefit from further
exploration of the socio-cultural, economic, and healthcare system factors contributing to
these disparities. Understanding why certain regions have higher CRC burdens attributable to
overweight could help contextualize the data and inform targeted interventions.
-While the regional disparities in CRC burden attributable to overweight are identified, the
discussion could benefit from a deeper exploration of the underlying mechanisms contributing to
these differences. For example, more specific information on the role of healthcare infrastructure
or cultural dietary patterns in high-SDI versus low-SDI regions could offer more nuanced
insights into why certain regions have higher or accelerating rates of overweight-attributable
CRC.
-While the discussion emphasizes the need for public health interventions, it does not delve
deeply into specific prevention strategies. More concrete suggestions on how to combat
overweight-related CRC through targeted prevention programs (e.g., anti-obesity campaigns,
improved access to screening, dietary interventions) would make the conclusions more
actionable. Additionally, the role of policy changes (such as taxes on sugary foods, urban
planning to promote physical activity) could be better explored.
-The discussion touches on obesity as a key factor but does not fully address the potential
confounding factors that could influence CRC risk. For instance, smoking, alcohol consumption,
and other lifestyle factors, which are often interrelated with obesity, could have contributed to
the findings. While the focus on overweight is clear, a broader discussion of lifestyle factors
influencing CRC would provide a more holistic view of the issue.
-The prediction through 2036, while valuable, could be expanded further. The discussion
could benefit from addressing potential scenarios beyond this period, considering factors
such as healthcare advancements, the impact of global obesity prevention campaigns, or the
influence of climate change on dietary patterns and physical activity levels. Acknowledging
these uncertainties would strengthen the robustness of the predictions.
-The mention of potential data quality issues in low-SDI regions is a good point, but the
discussion could benefit from exploring how these data gaps might impact the generalizability
of the findings. For example, how might the underreporting in certain regions bias the global
estimates of CRC burden attributable to overweight? Discussing the implications of these gaps
on the overall conclusions would enhance the transparency of the study.
assistant