Title: Machine Learning-Based Plasma Metabolomics for Improved Cirrhosis Risk
Stratification
Version: 2 Date: 05 Jan 2025
Reviewer's report:
This UK Biobank-based study utilizes serum metabolomics for cirrhosis classification
improvement. Using 1H-NMR data, the authors developed and validated models integrating
metabolomics with known NIT-based models like APRI and FIB-4 indices.  A key strength
is the large dataset and the machine learning approach, which identifies specific metabolites
associated with cirrhosis risk and shows the merit in diagnostic improvement. Despite several
weaknesses, the study offers a promising optimization.
Concerns:
-The “fasting state” requires more elaboration on the mechanic reasons and potential
improvement in future studies, e.g. elaboration on publications relevant to the impact of fasting
state, etc. Please concisely elaborate on possible mitigations, solutions, evaluations for this
factor-- status of fasting or not-- in later studies, as concerned by hepatologists.
- Elaborate by adding one more sentence of statement for the following method-“All metabolites
were log-normalized and standardized to a mean of 0 and standard deviation of 1."
- "The cohort was then divided into derivation (80%) and validation (20%) cohorts." This
statement needs concise, additional statement explaining why stratified splitting were not
applied in this context.
- "incomplete parameters, such as missing data on age or liver enzymes" and "We further
excluded individuals with incomplete parameters, such as missing data on age or liver
enzymes..." raise concerns and worries. Please clarify concisely.
- "To identify the most predictive metabolites, we applied a Cox-PH model with EN
regularization to the training set, optimizing model discrimination using 10-fold cross-
validation..." Please concisely address the parameters and expand the expected statement.
- Common, obvious understanding and concerns regarding the variable selection bias, healthy
volunteer bias, data imbalance, batch effect correction, multiple comparison correction, etc.
in current context needs to be concisely discussed as well.
- The current presentation in technical details and titles and footnotes of tables and figures
of the promising study still needs to be acknowledged and improved for expected clarity,
transparency, and reproducibility, as compared with relevant reports including Guo et al.
Machine-learning-based plasma metabolomic profiles for predicting long-term complications
of cirrhosis. Hepatology 2024.
- For example, hepatologists are concerned about the self-explaining clarity of Table 1. Please
clarify “Yes”, “incident liver fibrosis”, in Table 1.
- Regarding the core target: “cirrhosis status”, please concisely expand and elaborate the
biomarkers selected for facilitating discrimination, in terms of the biomolecular perspectives,
in relation to the defined cirrhosis.
Title: Machine Learning-Based Plasma Metabolomics for Improved Cirrhosis Risk
Stratification
Version: 2 Date: 05 Jan 2025
Reviewer's report:
This UK Biobank-based study utilizes serum metabolomics for cirrhosis classification
improvement. Using 1H-NMR data, the authors developed and validated models integrating
metabolomics with known NIT-based models like APRI and FIB-4 indices.  A key strength
is the large dataset and the machine learning approach, which identifies specific metabolites
associated with cirrhosis risk and shows the merit in diagnostic improvement. Despite several
weaknesses, the study offers a promising optimization.
Concerns:
-The “fasting state” requires more elaboration on the mechanic reasons and potential
improvement in future studies, e.g. elaboration on publications relevant to the impact of fasting
state, etc. Please concisely elaborate on possible mitigations, solutions, evaluations for this
factor-- status of fasting or not-- in later studies, as concerned by hepatologists.
- Elaborate by adding one more sentence of statement for the following method-“All metabolites
were log-normalized and standardized to a mean of 0 and standard deviation of 1."
- "The cohort was then divided into derivation (80%) and validation (20%) cohorts." This
statement needs concise, additional statement explaining why stratified splitting were not
applied in this context.
- "incomplete parameters, such as missing data on age or liver enzymes" and "We further
excluded individuals with incomplete parameters, such as missing data on age or liver
enzymes..." raise concerns and worries. Please clarify concisely.
- "To identify the most predictive metabolites, we applied a Cox-PH model with EN
regularization to the training set, optimizing model discrimination using 10-fold cross-
validation..." Please concisely address the parameters and expand the expected statement.
- Common, obvious understanding and concerns regarding the variable selection bias, healthy
volunteer bias, data imbalance, batch effect correction, multiple comparison correction, etc.
in current context needs to be concisely discussed as well.
- The current presentation in technical details and titles and footnotes of tables and figures
of the promising study still needs to be acknowledged and improved for expected clarity,
transparency, and reproducibility, as compared with relevant reports including Guo et al.
Machine-learning-based plasma metabolomic profiles for predicting long-term complications
of cirrhosis. Hepatology 2024.
- For example, hepatologists are concerned about the self-explaining clarity of Table 1. Please
clarify “Yes”, “incident liver fibrosis”, in Table 1.
- Regarding the core target: “cirrhosis status”, please concisely expand and elaborate the
biomarkers selected for facilitating discrimination, in terms of the biomolecular perspectives,
in relation to the defined cirrhosis.
Please review this paper.
user
Title: Machine Learning-Based Plasma Metabolomics for Improved Cirrhosis Risk
Stratification
Version: 2 Date: 05 Jan 2025
Reviewer's report:
This UK Biobank-based study utilizes serum metabolomics for cirrhosis classification
improvement. Using 1H-NMR data, the authors developed and validated models integrating
metabolomics with known NIT-based models like APRI and FIB-4 indices.  A key strength
is the large dataset and the machine learning approach, which identifies specific metabolites
associated with cirrhosis risk and shows the merit in diagnostic improvement. Despite several
weaknesses, the study offers a promising optimization.
Concerns:
-The “fasting state” requires more elaboration on the mechanic reasons and potential
improvement in future studies, e.g. elaboration on publications relevant to the impact of fasting
state, etc. Please concisely elaborate on possible mitigations, solutions, evaluations for this
factor-- status of fasting or not-- in later studies, as concerned by hepatologists.
- Elaborate by adding one more sentence of statement for the following method-“All metabolites
were log-normalized and standardized to a mean of 0 and standard deviation of 1."
- "The cohort was then divided into derivation (80%) and validation (20%) cohorts." This
statement needs concise, additional statement explaining why stratified splitting were not
applied in this context.
- "incomplete parameters, such as missing data on age or liver enzymes" and "We further
excluded individuals with incomplete parameters, such as missing data on age or liver
enzymes..." raise concerns and worries. Please clarify concisely.
- "To identify the most predictive metabolites, we applied a Cox-PH model with EN
regularization to the training set, optimizing model discrimination using 10-fold cross-
validation..." Please concisely address the parameters and expand the expected statement.
- Common, obvious understanding and concerns regarding the variable selection bias, healthy
volunteer bias, data imbalance, batch effect correction, multiple comparison correction, etc.
in current context needs to be concisely discussed as well.
- The current presentation in technical details and titles and footnotes of tables and figures
of the promising study still needs to be acknowledged and improved for expected clarity,
transparency, and reproducibility, as compared with relevant reports including Guo et al.
Machine-learning-based plasma metabolomic profiles for predicting long-term complications
of cirrhosis. Hepatology 2024.
- For example, hepatologists are concerned about the self-explaining clarity of Table 1. Please
clarify “Yes”, “incident liver fibrosis”, in Table 1.
- Regarding the core target: “cirrhosis status”, please concisely expand and elaborate the
biomarkers selected for facilitating discrimination, in terms of the biomolecular perspectives,
in relation to the defined cirrhosis.
assistant