-->PONE-D-25-50146-->-->Association between Diabetic Kidney Disease and Urinary excretion of post-translationally modified fetuin-A fragments in patients with type 2 diabetes.-->-->PLOS ONE

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“Conflict of Interest

Hiroyuki Ito received lecture fees from Novo Nordisk Pharma Ltd., Eli Lilly Japan KK, Sanofi KK, Astellas Pharma, Kowa Company, Ltd., Taisho Pharmaceutical Co., Ltd., Sumitomo Pharma Co., Ltd., Boehringer Ingelheim, Daiichi Sankyo Company, Novartis Pharma KK, Takeda Pharmaceutical Company Ltd., MSD KK, Terumo Corporation, Mochida Pharmaceuticals, Teijin Pharma, Kissei Pharmaceuticals, Mitsubishi Tanabe Pharma Corporation, Sanwa Kagaku Kenkyusho, AstraZeneca KK, Kyowa Kirin Co. Ltd., Otsuka Pharmaceutical Co., Ltd., Bayer Yakuhin, Ltd., EA Pharma Co., Ltd., Ono Pharmaceutical Co., Ltd., and Viatris Inc., and received consulting fees from Becton, Dickinson and Company. Suzuko Matsumoto received lecture fees from Eli Lilly Japan KK, Novo Nordisk Pharma Ltd., Astellas Pharma, Kyowa Kirin Co., Ltd., and AstraZeneca KK. Hideyuki Inoue received lecture fees from Novartis Pharma KK, AstraZeneca KK, and Mochida Pharmaceuticals. Shinichi Antoku received lecture fees from Kyowa Kirin Co., Ltd., Sanofi KK, Taisho Pharmaceutical Co., Ltd., Daiichi Sankyo Company, Novo Nordisk Pharma Ltd., Novartis Pharma KK, AstraZeneca KK, and Otsuka Pharmaceutical Co., Ltd. Toshiko Mori received lecture fees from Novartis Pharma KK. Chizuko Yukawa has no conflict of interest.”

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Additional Editor Comments:

Summary

This cross-sectional study (n=219) explores whether urinary post-translationally modified fetuin-A fragments (uPTM-FetA) measured by a commercial ELISA are associated with KDIGO diabetic kidney disease (DKD) risk categories among Japanese outpatients with type 2 diabetes. The authors find higher prevalence of “high” uPTM-FetA (≥7.53 ng/mgCr, threshold imported from prior work) across higher DKD risk strata and an independent association with DKD categories 2–4 in multivariable models that exclude uL-FABP. Ethics approval and a UMIN registration are reported; analyses include rank-based tests and logistic regression with VIF<2 (no notable multicollinearity); KDIGO risk definitions and the 7.53 ng/mgCr cut-point are described in Methods.

Major comments

1. Cross-sectional design and causal language

The manuscript occasionally implies early detection/predictive potential. Given the design, please avoid inference about prediction or temporal precedence and reframe conclusions strictly as associations. Consider moving predictive language to “Future directions” and explicitly add a STROBE-consistent “Limitations” paragraph.

2. Choice and transportability of the uPTM-FetA cutoff (7.53 ng/mgCr)

The dichotomization threshold is imported from external cohorts. Provide a justification for transportability to this Japanese clinic population: distribution plots, within-sample ROC analysis vs KDIGO categories, and sensitivity analyses using uPTM-FetA as (a) a continuous predictor (with restricted cubic splines) and (b) alternative data-driven thresholds. Right now, heavy reliance on a single external cut-point risks misclassification and loss of information.

3. Model specification and potential mediator/collinearity handling

You present two multivariable models: one with uL-FABP and one excluding it, after noting VIF<2 for all variables. Please clarify the a priori causal framework: Is uL-FABP a competing biomarker, a mediator, or simply a correlated marker of tubular injury? If it’s a mediator, Model 1 may be “over-adjusted”; if not, exclusion in Model 2 risks omitted-variable bias. Pre-register or at least justify this strategy and report both sets fully (including calibrated probabilities and overall fit). Consider providing a DAG to clarify adjustment choices.

4. Outcome definition and risk-category amalgamation

The primary outcome collapses KDIGO categories 2–4 vs 1. Because these categories integrate both albuminuria and eGFR (with multiple pathways to “high risk”), the biology differs across 2, 3, and 4. Please (i) report separate models for each category vs 1, (ii) add trend tests across ordered risk categories, and (iii) provide partial effects plots for interpretability.

5. Confounding by medication and disease severity

Several drug classes (e.g., SGLT2i, RAAS blockade, urate-lowering agents) and comorbidities differ across groups and associate with DKD severity (see Tables; many differences are reported). At minimum, adjust your primary model for classes plausibly linked to tubular biomarkers and DKD severity, provide a medication-adjusted sensitivity analysis, and consider propensity-score or inverse probability weighting as robustness checks.

6. Measurement and pre-analytical detail for the ELISA

Add essential assay characteristics: lot numbers, intra/inter-assay CVs, LOD/LOQ, sample handling (first-morning vs random spot is stated; please add storage temp, freeze-thaw cycles), creatinine assays used for normalization, and whether batch effects were controlled (e.g., randomization of plates, inclusion of pooled QC). These are critical for biomarker papers and currently under-described.

7. Internal validity: multiple testing and model stability

Numerous univariate comparisons are presented. Please specify how you addressed multiplicity (e.g., false discovery rate) or emphasize that p-values are exploratory. For the logistic models, provide optimism-corrected performance (bootstrap) and calibration (e.g., calibration slope/intercept), even though the outcome is a categorical risk state.

8. Interpretation versus uL-FABP

The statement that uPTM-FetA “rises earlier than uL-FABP” is inferred from cross-sectional proportions (e.g., no high uL-FABP in lower DKD risk groups) rather than longitudinal change. Please temper this claim and reframe as “uPTM-FetA was more frequently elevated than uL-FABP in lower KDIGO risk states in this cross-sectional snapshot,” or demonstrate incremental discrimination (e.g., likelihood-ratio test for nested models, partial AUC) within this design.

9. COI/Data availability inconsistencies

The submission packet contains both a templated “NO authors have competing interests” statement and, elsewhere, detailed financial relationships; this must be reconciled to PLOS standards. Similarly, the Data Availability statement asserts that “All relevant data are within the manuscript and its Supporting Information files” — ensure the S1 dataset actually contains row-level, de-identified data and a codebook that reproduce all tables/figures.

Specific/section-by-section notes

Title/Abstract

• Clear scope. In the Conclusions, remove predictive framing (“early detection”) or qualify it as hypothesis-generating given the design.

• Include the study dates and setting in the abstract Methods for clarity (Edogawa Hospital; 2023-11-16 to 2024-02-26) to align with STROBE.

Introduction

• Nicely motivated. Consider briefly differentiating mechanistic pathways (glomerular vs tubular injury) to frame why uPTM-FetA might capture injury distinct from albuminuria and uL-FABP, and cite the KDIGO risk grid’s composite nature to justify stratified analyses.

Methods

• KDIGO risk categories and biomarker cut-points are well defined. Please correct small typos (“Mesurements”), and add analytic decisions: handling of outliers, missingness, transformations (log-uPTM-FetA, log-uACR), and model diagnostics.

• Ethics and registration are appropriately documented (committee, number/date, UMIN ID) — good

• Explicitly state the eGFR formula (Japanese Society of Nephrology) and lab methods; you currently reference the formula but not its equation or creatinine assay traceability

Results

• Provide distributions (histograms or density plots) for uPTM-FetA and uL-FABP, stratified by KDIGO categories, plus medians [IQR] alongside means ± SD where skew is evident.

• Some table entries appear internally inconsistent or potentially mislabeled (e.g., units/layout for uPCR and the header “N† Number estimated”). Please standardize units and footnotes, and ensure alignment between text and tables.

• Where you state “no patients” had high uL-FABP in lower categories, explicitly report denominators; consider confidence intervals for proportions to show uncertainty.

Statistics

• Add a primary model specification (variables, transformation, and the clinical/biological rationale) before looking at univariate p-values to reduce data-driven selection.

• Report effect sizes for uPTM-FetA as continuous (per SD increase, per doubling if log-scaled) in addition to the binary threshold.

• Add tests for linear trend across ordered KDIGO categories and a sensitivity analysis excluding extreme albuminuria (A3) or low eGFR (G4–G5), to probe whether associations persist in earlier disease.

Discussion

• Strengths: First evaluation in a Japanese cohort; practical clinic setting.

• Limitations are acknowledged, but please explicitly add: single-center design; potential selection bias of outpatients; residual confounding (therapy intensity is a proxy for severity); single spot-urine measurement for both uACR and uPTM-FetA (intra-individual variability) — which you note for uACR and should also mention for uPTM-FetA.

• Avoid implying superiority of uPTM-FetA over albuminuria without direct head-to-head discrimination/ reclassification metrics in the same modeling framework.

Transparency & compliance

• Ethics/Registration: Adequately reported with approval number/date and UMIN ID.

• COI: Resolve the conflicting statements and present a single, accurate Competing Interests section consistent with PLOS policy (you already list specific companies and fees).

• Data availability: Ensure S1 contains the full de-identified dataset with a variable dictionary sufficient to reproduce every figure/table (as claimed).

Minor comments (editorial/format)

• Fix typographical errors (e.g., “Mesurements”) and ensure uniform units/abbreviation definitions at first mention.

• Clarify whether “first-morning” or “random spot” urine was used (you note “morning on the day of visit” — specify fasting status and timing) and whether samples were processed identically.

• Add the exact SPSS version (you have “version 29.0”), and provide the code or detailed steps sufficient for replication (or upload analysis scripts as supplement).

• Figures: consider adding confidence intervals to proportions in Figs 2–4 and include denominators on bars; supply accessible color palettes and ensure vector formats.

• Ensure consistent reporting of decimals and SI units across all tables.

Recommendation

Major revision. The study addresses an important clinical gap and is suitable for PLOS ONE’s scope if framed as an associative analysis. With clarified modeling, stronger assay/reporting details, resolved COI/data-availability issues, and tempered causal claims, the manuscript would be substantially strengthened.

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<p>Association of urinary post-translationally modified fetuin-A fragments with diabetic kidney disease risk stratification in Japanese patients with type 2 diabetes

PONE-D-25-50146R1

Dear Dr. Ito,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewer #1: All comments have been addressed

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Reviewer #1: Yes

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Reviewer #1: I Don't Know

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Reviewer #1: Yes

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Reviewer #1: Authors have made changes while revising the manuscript. However, if a professional biostatistician certificate is also attached that robust review of all the statistics related data done would be more appropriate.

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Reviewer #1: Yes: Jamshed Akhtar

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