Review for 10.1186/s12876-025-04024-5 (2025)

Title: A Preliminary Exploration of a Predictive Model and Nomogram for the Efficacy of
Compound Digestive Enzyme Therapy Based on Serum (PG#, PG#, VIP, and PRDX1) in
Patients with Functional Dyspepsia
Version: 0 Date: 05 Apr 2025
Reviewer's report:
This study constructs a nomogram prediction model to assess the efficacy of compound digestive
enzyme therapy in functional dyspepsia (FD) patients using serum biomarkers (PG#, PG#,
VIP, PRDX1) and clinical/laboratory factors. The model demonstrated good discrimination
(AUC ~0.83 in training/validation sets) and calibration, with decision curve analysis (DCA)
suggesting clinical utility. Key risk factors included elevated PG#, VIP, PRDX1, and reduced
PG#. However, limitations include single-center data, lack of external validation, short follow-
up, and unaddressed confounders (e.g., genetics, microbiome).
Major Comments
1. External Validation
- The model was validated internally but not externally. Generalizability is uncertain due to
homogeneity in the study population (single center). Multi-center collaboration is critical for
broader applicability.
2. Clinical Relevance of Biomarkers
- While PG#, PG#, VIP, and PRDX1 were statistically significant, their biological plausibility
in FD pathophysiology needs deeper discussion. For example, how do elevated VIP levels
mechanistically reduce enzyme efficacy?
3. Sample Size and Overfitting
- The training set (n=174) and validation set (n=75) are modest. Overfitting risks are mitigated
via bootstrapping, but the model’s stability in larger cohorts remains untested.
4. Long-Term Outcomes:
- The 4-week follow-up limits insights into sustained treatment effects. Long-term data would
strengthen clinical relevance.
5. Unmeasured Confounders
- Factors like gut microbiota, genetic polymorphisms, and psychosocial stressors (common in
FD) were not considered, potentially biasing results.
Minor Comments
1. In Table 2, "Valid (n=139)" and "Invalid (n=35)" are ambiguous. Replace with "Effective"
and "Ineffective" for consistency with the text.
2. Ensure uploaded figures (nomograms, ROC curves) match manuscript references (e.g., Fig.
1 description mentions x1-x7 variables, but image content lists "x1-x7" without clear labels).
3. Statistical Reporting
- Report confidence intervals (CIs) alongside p-values (e.g., Table 3: ORs lack 95% CIs).
- Clarify whether multiple testing corrections were applied during variable selection.
4. Formatting Issues
- Table 1: Units for "White blood cell count" are incomplete (×10/L # ×10#/L).
- Figure 1 legend: Abbreviations (e.g., "WBC") should be defined in full.
5. Discussion Flow
- The discussion section is lengthy and repetitive. Condense mechanistic explanations (e.g., PG#/
PG# roles) and focus on novel insights.
Limitations
1. Single-Center Design
- Regional biases (e.g., dietary habits, healthcare access) may limit generalizability.
2. Short Follow-Up
- No data on relapse or long-term efficacy post-treatment.
3. Lack of Biomarker Cutoffs
- Clinically actionable thresholds for PG#, PG#, VIP, or PRDX1 are not defined, limiting
practical utility.
4. Retrospective Data Collection:
- Pre-treatment symptom severity scores (e.g., FD Symptom Index) were not standardized,
risking outcome misclassification.
5. Ethnic Homogeneity
- All participants were Chinese; FD pathophysiology and biomarker profiles may differ across
ethnicities.