Title: Correlation between hs-CRP-Triglyceride Glucose Index and NAFLD and Liver Fibrosis
Version: 1 Date: 17 Mar 2025
Reviewer's report:
Dear Authors,
Thank you to the authors for their work on this important topic. The study uses clear methods and
appropriate statistical analyses to examine the results and outcomes. Overall, the paper provides
useful findings, but some revisions are needed to address minor issues.
1. Write the full form of abbreviated words the first time they appear. For example, “TyG” in
the abstract and “CTI” in the introduction.
2. Since NHANES is cross-sectional, using the word “developing” might incorrectly suggest
a causal relationship or imply prognostic factors. For example, in the abstract, the sentence
“Increased CTI levels are independently correlated with an increased risk of developing liver
fibrosis and NAFLD” could be better phrased as: “Higher CTI levels are directly linked to
significant liver fibrosis and the presence of NAFLD.” To draw conclusions about “developing”
NAFLD, the authors should use a Cox proportional hazards regression model and report Hazard
Ratios for CTI, which isn’t possible to apply to the NHANES dataset. Instead, the authors should
clarify that CTI and TyG were calculated in a cross-sectional context, meaning they reflect a
snapshot of participants’ lives. To use “developing,” these scores should be checked before the
disease occurs and evaluated in a prospective study. If CTI and TyG are used as diagnostic
biomarkers for NAFLD, the incidence of the disease should be discussed. Please review the
abstract conclusion and lines 174–175.
3. The authors defined significant fibrosis as F2 or LSM # 8.2 kPa. I recommend adding results
for advanced fibrosis (F3 or LSM # 9.7 kPa) alongside the current findings.
4. The authors did not exclude participants taking specific medications. Medications can
suppress inflammation and lower CRP, TG, and FPG levels. Additionally, conditions like
autoimmune diseases, CKD, cancers, menstrual cycles, pregnancy, and aging can affect CRP
levels. The study does not mention adjusting for or excluding these factors. The authors should
also state that the cause of fibrosis in participants may not be solely due to NAFLD.
5. Please check for statistical differences in “BMI, ALT, CTI, GGT, age, diabetes, uric acid,
hypertension, AST, race, creatinine, gender, albumin, TC, and ALP” between fibrotic and non-
fibrotic subjects.
6. Did the authors include insulin use in the Boruta algorithm? Also, clarify whether the model’s
performance was validated using an independent dataset or cross-validation.
7. In line 153, the use of “incidence” does not align with the cross-sectional nature of the study.
8. In lines 219–221, the authors emphasize the role of CTI in insulin resistance and cancers.
However, there is no data on IR markers or cancer in this study. It would be better to discuss
IR and inflammation in relation to NAFLD, the risk of liver cancer due to NAFLD/fibrosis, and
then connect these to CTI, IR, inflammation, NAFLD/fibrosis, and cancer.
9. In Table 1, please confirm that the number of participants (n) is correct for each quartile
(currently listed as n=872 for all).
Title: Correlation between hs-CRP-Triglyceride Glucose Index and NAFLD and Liver Fibrosis
Version: 1 Date: 17 Mar 2025
Reviewer's report:
Dear Authors,
Thank you to the authors for their work on this important topic. The study uses clear methods and
appropriate statistical analyses to examine the results and outcomes. Overall, the paper provides
useful findings, but some revisions are needed to address minor issues.
1. Write the full form of abbreviated words the first time they appear. For example, “TyG” in
the abstract and “CTI” in the introduction.
2. Since NHANES is cross-sectional, using the word “developing” might incorrectly suggest
a causal relationship or imply prognostic factors. For example, in the abstract, the sentence
“Increased CTI levels are independently correlated with an increased risk of developing liver
fibrosis and NAFLD” could be better phrased as: “Higher CTI levels are directly linked to
significant liver fibrosis and the presence of NAFLD.” To draw conclusions about “developing”
NAFLD, the authors should use a Cox proportional hazards regression model and report Hazard
Ratios for CTI, which isn’t possible to apply to the NHANES dataset. Instead, the authors should
clarify that CTI and TyG were calculated in a cross-sectional context, meaning they reflect a
snapshot of participants’ lives. To use “developing,” these scores should be checked before the
disease occurs and evaluated in a prospective study. If CTI and TyG are used as diagnostic
biomarkers for NAFLD, the incidence of the disease should be discussed. Please review the
abstract conclusion and lines 174–175.
3. The authors defined significant fibrosis as F2 or LSM # 8.2 kPa. I recommend adding results
for advanced fibrosis (F3 or LSM # 9.7 kPa) alongside the current findings.
4. The authors did not exclude participants taking specific medications. Medications can
suppress inflammation and lower CRP, TG, and FPG levels. Additionally, conditions like
autoimmune diseases, CKD, cancers, menstrual cycles, pregnancy, and aging can affect CRP
levels. The study does not mention adjusting for or excluding these factors. The authors should
also state that the cause of fibrosis in participants may not be solely due to NAFLD.
5. Please check for statistical differences in “BMI, ALT, CTI, GGT, age, diabetes, uric acid,
hypertension, AST, race, creatinine, gender, albumin, TC, and ALP” between fibrotic and non-
fibrotic subjects.
6. Did the authors include insulin use in the Boruta algorithm? Also, clarify whether the model’s
performance was validated using an independent dataset or cross-validation.
7. In line 153, the use of “incidence” does not align with the cross-sectional nature of the study.
8. In lines 219–221, the authors emphasize the role of CTI in insulin resistance and cancers.
However, there is no data on IR markers or cancer in this study. It would be better to discuss
IR and inflammation in relation to NAFLD, the risk of liver cancer due to NAFLD/fibrosis, and
then connect these to CTI, IR, inflammation, NAFLD/fibrosis, and cancer.
9. In Table 1, please confirm that the number of participants (n) is correct for each quartile
(currently listed as n=872 for all).
Please review this paper.
user
Title: Correlation between hs-CRP-Triglyceride Glucose Index and NAFLD and Liver Fibrosis
Version: 1 Date: 17 Mar 2025
Reviewer's report:
Dear Authors,
Thank you to the authors for their work on this important topic. The study uses clear methods and
appropriate statistical analyses to examine the results and outcomes. Overall, the paper provides
useful findings, but some revisions are needed to address minor issues.
1. Write the full form of abbreviated words the first time they appear. For example, “TyG” in
the abstract and “CTI” in the introduction.
2. Since NHANES is cross-sectional, using the word “developing” might incorrectly suggest
a causal relationship or imply prognostic factors. For example, in the abstract, the sentence
“Increased CTI levels are independently correlated with an increased risk of developing liver
fibrosis and NAFLD” could be better phrased as: “Higher CTI levels are directly linked to
significant liver fibrosis and the presence of NAFLD.” To draw conclusions about “developing”
NAFLD, the authors should use a Cox proportional hazards regression model and report Hazard
Ratios for CTI, which isn’t possible to apply to the NHANES dataset. Instead, the authors should
clarify that CTI and TyG were calculated in a cross-sectional context, meaning they reflect a
snapshot of participants’ lives. To use “developing,” these scores should be checked before the
disease occurs and evaluated in a prospective study. If CTI and TyG are used as diagnostic
biomarkers for NAFLD, the incidence of the disease should be discussed. Please review the
abstract conclusion and lines 174–175.
3. The authors defined significant fibrosis as F2 or LSM # 8.2 kPa. I recommend adding results
for advanced fibrosis (F3 or LSM # 9.7 kPa) alongside the current findings.
4. The authors did not exclude participants taking specific medications. Medications can
suppress inflammation and lower CRP, TG, and FPG levels. Additionally, conditions like
autoimmune diseases, CKD, cancers, menstrual cycles, pregnancy, and aging can affect CRP
levels. The study does not mention adjusting for or excluding these factors. The authors should
also state that the cause of fibrosis in participants may not be solely due to NAFLD.
5. Please check for statistical differences in “BMI, ALT, CTI, GGT, age, diabetes, uric acid,
hypertension, AST, race, creatinine, gender, albumin, TC, and ALP” between fibrotic and non-
fibrotic subjects.
6. Did the authors include insulin use in the Boruta algorithm? Also, clarify whether the model’s
performance was validated using an independent dataset or cross-validation.
7. In line 153, the use of “incidence” does not align with the cross-sectional nature of the study.
8. In lines 219–221, the authors emphasize the role of CTI in insulin resistance and cancers.
However, there is no data on IR markers or cancer in this study. It would be better to discuss
IR and inflammation in relation to NAFLD, the risk of liver cancer due to NAFLD/fibrosis, and
then connect these to CTI, IR, inflammation, NAFLD/fibrosis, and cancer.
9. In Table 1, please confirm that the number of participants (n) is correct for each quartile
(currently listed as n=872 for all).
assistant