Title: Machine Learning-Based Plasma Metabolomics for Improved Cirrhosis Risk
Stratification
Version: 2 Date: 30 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the study defines the starting point as CLD and the endpoint as cirrhosis, it would be
helpful to clarify whether the study distinguishes between different stages of liver disease (e.g.,
fibrosis vs cirrhosis) or if the term "CLD" includes both early-stage liver fibrosis and advanced
cirrhosis. This distinction is important for the study’s target population and could affect the
interpretation of results.
-The manuscript should provide more information on how the 168 metabolites were selected
from the initial 249 biomarkers. Were they pre-selected based on prior knowledge of their
potential relevance to liver disease, or were they selected through statistical methods? This
should be clarified in the Methods section.
-While the study adjusts for important demographic and clinical factors (age, sex, liver
enzymes, etc.), other potential confounders such as alcohol consumption, medication use,
and diet are only briefly mentioned in the predictor extraction section. Since these factors
can significantly influence both the metabolome and cirrhosis progression, the authors should
consider elaborating on how these factors were accounted for or provide additional sensitivity
analyses to assess their impact on the results.
-The study excludes participants using lipid-lowering medications, which is a reasonable step,
but it might be worth discussing the potential impact of excluding this group. For instance,
could their exclusion bias the findings, or could the model be adjusted for the effects of such
medications?
-While the manuscript effectively identifies several metabolites associated with cirrhosis risk,
the biological mechanisms underlying these associations are not explored in detail. It would be
beneficial to provide some insight into the metabolic pathways these metabolites belong to, and
how they could influence liver function or fibrosis. For example, how do the altered levels of
free fatty acids, ketone bodies, or amino acids relate to liver fibrosis or cirrhosis progression?
-A more detailed discussion of the metabolites in the context of known liver disease biology
would help readers understand why these metabolites are predictive and may offer insights into
new therapeutic targets.
-The authors correctly note the limitations related to the UK Biobank's "healthy volunteer"
bias, particularly the overrepresentation of older and healthier individuals. While this is a valid
concern, the authors should expand on this point by discussing how this might affect the results.
Specifically, could the metabolomics model perform differently in more diverse or younger
populations? Would the results differ in patients with more severe liver disease or those with co-
morbidities like diabetes or cardiovascular disease? Exploring these issues could provide further
context for the applicability of the model in real-world settings.
To strengthen the case for external validation, the authors could outline the next steps for
validating the model in diverse populations, perhaps including data from other cohorts with
varied ethnicities, comorbidities, and disease etiologies.
-The discussion mentions the strength of the 1H-NMR metabolomics platform but could provide
more detail on the platform's limitations. For instance, while 1H-NMR is robust, it may
not capture the full complexity of the metabolome compared to other techniques like mass
spectrometry. The authors could briefly compare these methods in terms of their sensitivity,
specificity, and the types of metabolites they are most suited to detecting.
Additionally, while the authors mention the rich data generated by the metabolomics platform,
they do not discuss the specific metabolites that were most predictive. ------Highlighting key
metabolites or metabolic pathways that drive the predictive power of the model could add more
substance to the discussion. For example, which specific metabolites were most important in the
final model, and why might these be of particular interest in cirrhosis risk?
Title: Machine Learning-Based Plasma Metabolomics for Improved Cirrhosis Risk
Stratification
Version: 2 Date: 30 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the study defines the starting point as CLD and the endpoint as cirrhosis, it would be
helpful to clarify whether the study distinguishes between different stages of liver disease (e.g.,
fibrosis vs cirrhosis) or if the term "CLD" includes both early-stage liver fibrosis and advanced
cirrhosis. This distinction is important for the study’s target population and could affect the
interpretation of results.
-The manuscript should provide more information on how the 168 metabolites were selected
from the initial 249 biomarkers. Were they pre-selected based on prior knowledge of their
potential relevance to liver disease, or were they selected through statistical methods? This
should be clarified in the Methods section.
-While the study adjusts for important demographic and clinical factors (age, sex, liver
enzymes, etc.), other potential confounders such as alcohol consumption, medication use,
and diet are only briefly mentioned in the predictor extraction section. Since these factors
can significantly influence both the metabolome and cirrhosis progression, the authors should
consider elaborating on how these factors were accounted for or provide additional sensitivity
analyses to assess their impact on the results.
-The study excludes participants using lipid-lowering medications, which is a reasonable step,
but it might be worth discussing the potential impact of excluding this group. For instance,
could their exclusion bias the findings, or could the model be adjusted for the effects of such
medications?
-While the manuscript effectively identifies several metabolites associated with cirrhosis risk,
the biological mechanisms underlying these associations are not explored in detail. It would be
beneficial to provide some insight into the metabolic pathways these metabolites belong to, and
how they could influence liver function or fibrosis. For example, how do the altered levels of
free fatty acids, ketone bodies, or amino acids relate to liver fibrosis or cirrhosis progression?
-A more detailed discussion of the metabolites in the context of known liver disease biology
would help readers understand why these metabolites are predictive and may offer insights into
new therapeutic targets.
-The authors correctly note the limitations related to the UK Biobank's "healthy volunteer"
bias, particularly the overrepresentation of older and healthier individuals. While this is a valid
concern, the authors should expand on this point by discussing how this might affect the results.
Specifically, could the metabolomics model perform differently in more diverse or younger
populations? Would the results differ in patients with more severe liver disease or those with co-
morbidities like diabetes or cardiovascular disease? Exploring these issues could provide further
context for the applicability of the model in real-world settings.
To strengthen the case for external validation, the authors could outline the next steps for
validating the model in diverse populations, perhaps including data from other cohorts with
varied ethnicities, comorbidities, and disease etiologies.
-The discussion mentions the strength of the 1H-NMR metabolomics platform but could provide
more detail on the platform's limitations. For instance, while 1H-NMR is robust, it may
not capture the full complexity of the metabolome compared to other techniques like mass
spectrometry. The authors could briefly compare these methods in terms of their sensitivity,
specificity, and the types of metabolites they are most suited to detecting.
Additionally, while the authors mention the rich data generated by the metabolomics platform,
they do not discuss the specific metabolites that were most predictive. ------Highlighting key
metabolites or metabolic pathways that drive the predictive power of the model could add more
substance to the discussion. For example, which specific metabolites were most important in the
final model, and why might these be of particular interest in cirrhosis risk?
Please review this paper.
user
Title: Machine Learning-Based Plasma Metabolomics for Improved Cirrhosis Risk
Stratification
Version: 2 Date: 30 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the study defines the starting point as CLD and the endpoint as cirrhosis, it would be
helpful to clarify whether the study distinguishes between different stages of liver disease (e.g.,
fibrosis vs cirrhosis) or if the term "CLD" includes both early-stage liver fibrosis and advanced
cirrhosis. This distinction is important for the study’s target population and could affect the
interpretation of results.
-The manuscript should provide more information on how the 168 metabolites were selected
from the initial 249 biomarkers. Were they pre-selected based on prior knowledge of their
potential relevance to liver disease, or were they selected through statistical methods? This
should be clarified in the Methods section.
-While the study adjusts for important demographic and clinical factors (age, sex, liver
enzymes, etc.), other potential confounders such as alcohol consumption, medication use,
and diet are only briefly mentioned in the predictor extraction section. Since these factors
can significantly influence both the metabolome and cirrhosis progression, the authors should
consider elaborating on how these factors were accounted for or provide additional sensitivity
analyses to assess their impact on the results.
-The study excludes participants using lipid-lowering medications, which is a reasonable step,
but it might be worth discussing the potential impact of excluding this group. For instance,
could their exclusion bias the findings, or could the model be adjusted for the effects of such
medications?
-While the manuscript effectively identifies several metabolites associated with cirrhosis risk,
the biological mechanisms underlying these associations are not explored in detail. It would be
beneficial to provide some insight into the metabolic pathways these metabolites belong to, and
how they could influence liver function or fibrosis. For example, how do the altered levels of
free fatty acids, ketone bodies, or amino acids relate to liver fibrosis or cirrhosis progression?
-A more detailed discussion of the metabolites in the context of known liver disease biology
would help readers understand why these metabolites are predictive and may offer insights into
new therapeutic targets.
-The authors correctly note the limitations related to the UK Biobank's "healthy volunteer"
bias, particularly the overrepresentation of older and healthier individuals. While this is a valid
concern, the authors should expand on this point by discussing how this might affect the results.
Specifically, could the metabolomics model perform differently in more diverse or younger
populations? Would the results differ in patients with more severe liver disease or those with co-
morbidities like diabetes or cardiovascular disease? Exploring these issues could provide further
context for the applicability of the model in real-world settings.
To strengthen the case for external validation, the authors could outline the next steps for
validating the model in diverse populations, perhaps including data from other cohorts with
varied ethnicities, comorbidities, and disease etiologies.
-The discussion mentions the strength of the 1H-NMR metabolomics platform but could provide
more detail on the platform's limitations. For instance, while 1H-NMR is robust, it may
not capture the full complexity of the metabolome compared to other techniques like mass
spectrometry. The authors could briefly compare these methods in terms of their sensitivity,
specificity, and the types of metabolites they are most suited to detecting.
Additionally, while the authors mention the rich data generated by the metabolomics platform,
they do not discuss the specific metabolites that were most predictive. ------Highlighting key
metabolites or metabolic pathways that drive the predictive power of the model could add more
substance to the discussion. For example, which specific metabolites were most important in the
final model, and why might these be of particular interest in cirrhosis risk?
assistant