Title: Serum Creatinine as a Risk Factor for Endometriosis: Insights from Cross-Sectional
Study, Mendelian Randomization Analysis, and Diagnostic Model Study
Version: 5 Date: 01 May 2025
Reviewer's report:
The manuscript investigates the association between serum creatinine (SCR) and endometriosis
(EM) using cross-sectional, Mendelian randomization (MR), and diagnostic model analyses.
The study addresses an important gap in non-invasive diagnostic biomarkers for EM and
provides multi-dimensional evidence. However, several methodological and interpretative
issues need clarification to strengthen the validity and impact of the findings.
Major Comments
1. The NHANES dataset represents a U.S. population, while the Chinese cohort is from a
single tertiary hospital. This raises concerns about the generalizability of findings to broader
populations, especially given potential genetic, environmental, and healthcare disparities. Please
discuss efforts to validate results in more diverse cohorts or justify the external validity of the
conclusions.
2. SCR is a marker of renal function. While adjustments were made for confounders, residual
confounding due to undiagnosed kidney dysfunction (e.g., subclinical renal impairment) remains
a concern. Including glomerular filtration rate (GFR) or other renal biomarkers (e.g., cystatin
C) in sensitivity analyses would strengthen the argument that SCR’s association with EM is
independent of kidney health.
3. The MR analysis relies on SNPs associated with SCR. While pleiotropy was addressed via
MR-Egger and MR-PRESSO, the reverse MR analysis (EM # SCR) used only two SNPs, which
likely lacks power. Please clarify how the limited SNP count in reverse MR impacts causal
inference. Additionally, justify the exclusion of SNPs linked to kidney-related traits (e.g., GFR)
to ensure no horizontal pleiotropy.
4. The reported odds ratios (ORs) for SCR (e.g., OR=1.08 per 10 µmol/L increase) are
statistically significant but modest. Discuss whether such small effect sizes have meaningful
clinical implications for EM diagnosis or risk stratification. Could SCR be integrated into a panel
of biomarkers to improve diagnostic utility?
5. The AUC values (~0.72) suggest moderate predictive accuracy. Compare these results to
existing diagnostic models for EM (e.g., imaging-based or multi-omics approaches) and discuss
whether SCR adds incremental value. Additionally, clarify how overfitting was mitigated (e.g.,
regularization parameters, cross-validation details).
Minor Comments
1. Table 1 lists "Menstrual pattern" as a variable, but its definition (e.g., regular vs. irregular
cycles) is unclear. Provide  definitions for key variables (e.g., "low birth weight," "menstrual
pattern") to enhance reproducibility.
2. Abbreviations (e.g., IVW, AUC) should be defined upon first use in each section.
Conclusion
The study provides novel insights into SCR as a potential biomarker for EM, supported by cross-
sectional, MR, and diagnostic model evidence. Addressing the above concerns—particularly
regarding confounding, MR validity, and clinical relevance—would significantly enhance the
manuscript’s impact. With revisions, this work could contribute meaningfully to the search for
non-invasive diagnostic tools in EM.