Kidney Week

Abstract: FR-PO1160

Metabolomics Uncover a Lipid-Driven Signature of Albuminuria-Independent CKD

Session Information

• CKD: Screening, Diagnosis, Serum and Urine Biomarkers, and Scoring Indices
  November 07, 2025 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

• 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

• Kishi, Seiji, Kawasaki Ika Daigaku, Kurashiki, Okayama Prefecture, Japan

Seiji Kishi, MD, PhD

• Raita, Yoshihiko, Okinawa Kenritsu Chubu Byoin, Uruma, Japan

Yoshihiko Raita, MD, MPH

• Nagasu, Hajime, Kawasaki Ika Daigaku, Kurashiki, Okayama Prefecture, Japan

Hajime Nagasu, MD, PhD

• Sugawara, Yuka, Tokyo Daigaku, Bunkyo, Tokyo, Japan

Yuka Sugawara, MD, PhD

• Hirakawa, Yosuke, Tokyo Daigaku, Bunkyo, Tokyo, Japan

Yosuke Hirakawa, MD, PhD

• Goto, Tadahiro, Yokohama Shiritsu Daigaku, Yokohama, Kanagawa Prefecture, Japan

Tadahiro Goto, MD, PhD, MPH

• Koshiba, Seizo, Tohoku Daigaku, Sendai, Miyagi Prefecture, Japan

Seizo Koshiba, PhD

• Nangaku, Masaomi, Tokyo Daigaku, Bunkyo, Tokyo, Japan

Masaomi Nangaku, MD, PhD

• Kashihara, Naoki, Kawasaki Ika Daigaku, Kurashiki, Okayama Prefecture, Japan

Naoki Kashihara, MD, PhD

Group or Team Name

• J-Kidney-Biobank Collaborations.

Background

eGFR and UACR often fail to flag early renal decline, leaving silent CKD undetected. We tested whether untargeted plasma metabolomics can identify biochemical signatures that classify CKD regardless of albuminuria status.

Methods

LC-MS quantified 251 plasma metabolites in 532 adults with CKD (70±11yr; 76 % men) enrolled in the multicenter J-Kidney Biobank. Participants were stratified as albuminuria-negative (n=90) or albuminuria-positive (n=442). Data were log-transformed, imputed with missForest, and analysed with sparse partial least-squares discriminant analysis (sPLS-DA) using five-fold cross-validation. Discriminatory performance was summarised by the area under the receiver-operating-characteristic curve (AUC). Analyses were repeated after adjusting metabolite intensities for baseline eGFR. Metabolites differing between groups were assessed by logistic regression with Benjamini–Hochberg false-discovery-rate (FDR) correction. Metabolite-set enrichment analysis (MSEA) identified enriched pathways.

Results

sPLS-DA identified discriminatory metabolites (e.g., choline, trigonelline, p-cresol sulfate, symmetric dimethyl-arginine, creatinine, and multiple phosphatidylcholines) and yielded an AUC of 0.87 (95 % CI 0.82–0.91). After eGFR adjustment, discrimination remained strong (AUC 0.77, 0.72–0.82), demonstrating information beyond glomerular filtration. Eighteen metabolites retained independent associations with albuminuria status after FDR correction (< 0.05). MSEA highlighted phospholipid and phosphatidylcholine biosynthesis as the most enriched pathways (> 10-fold, FDR<1×10-⁴; taurine–hypotaurine metabolism correlated positively with log-transformed albuminuria). These findings were consistent across sensitivity analyses, indicating a robust albuminuria-independent metabolic signature.

Conclusion

A lipid- and amino-acid–centerd plasma signature reliably distinguishes albuminuria-negative from -positive CKD and remains informative after accounting for eGFR. These biomarkers refine risk stratification, and spotlight phospholipid metabolism as a potential therapeutic target. Prospective validation in longitudinal and ethnically diverse cohorts is warranted.

Funding

• Government Support – Non-U.S.

Digital Object Identifier (DOI)

• doi: 10.1681/ASN.2025s6t3hp06