Metabolites. 2022;12(11):metabo12111125.

Identification of Serum Metabolites for Predicting Chronic Kidney Disease Progression according to Chronic Kidney Disease Cause.

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제1저자 강은정, 이욱비, 김보라 · 교신저자 오국환, 조주연

PMID 36422264 PMC9696352 DOI 10.3390/metabo12111125 IF 4.5 신장내과

소속 (11)

Department of Internal Medicine, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul 07804, Republic of Korea.
Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul 03080, Republic of Korea.
Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Department of Internal Medicine, National Medical Center, Seoul 04564, Republic of Korea.
Department of Biochemistry, Ewha Womans University College of Medicine, Seoul 07804, Republic of Korea.
Department of Microbiology, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea.
Department of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
Department of Biomedical Sciences, Seoul National University Graduate School, Seoul 03080, Republic of Korea.
Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
Department of Internal Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.

Abstract

Early detection and proper management of chronic kidney disease (CKD) can delay progression to end-stage kidney disease. We applied metabolomics to discover novel biomarkers to predict the risk of deterioration in patients with different causes of CKD. We enrolled non-dialytic diabetic nephropathy (DMN, n = 124), hypertensive nephropathy (HTN, n = 118), and polycystic kidney disease (PKD, n = 124) patients from the KNOW-CKD cohort. Within each disease subgroup, subjects were categorized as progressors (P) or non-progressors (NP) based on the median eGFR slope. P and NP pairs were randomly selected after matching for age, sex, and baseline eGFR. Targeted metabolomics was performed to quantify 188 metabolites in the baseline serum samples. We selected ten progression-related biomarkers for DMN and nine biomarkers each for HTN and PKD. Clinical parameters showed good ability to predict DMN (AUC 0.734); however, this tendency was not evident for HTN (AUC 0.659) or PKD (AUC 0.560). Models constructed with selected metabolites and clinical parameters had better ability to predict CKD progression than clinical parameters only. When selected metabolites were used in combination with clinical indicators, random forest prediction models for CKD progression were constructed with AUCs of 0.826, 0.872, and 0.834 for DMN, HTN, and PKD, respectively. Select novel metabolites identified in this study can help identify high-risk CKD patients who may benefit from more aggressive medical treatment.

Keywords

chronic kidney diseasedisease progressionmetabolomicsserum biomarkers

Figures

**Figure 1** Schematic description of this study. Clinical data and serum samples from three chronic kidney disease cohorts, namely diabetic nephropathy (DMN, n = 124), hypertensive nephropathy (HTN, n = 118), and polycystic kidney disease (PKD, n = 124), were collected. In each disease group, half of the patients with lower eGFR slope were defined as disease-progressors, while the others were defined as non-progressors. Other clinical parameters including age, sex, and baseline eGFR, were confirmed to have no significant differences between progressors and non-progressors. Targeted metabolomics was performed to quantify 188 metabolites in the baseline serum samples of all the cohorts. For each group, disease progression-related metabolite biomarkers were selected based on statistical analysis. Finally, predictive models were generated and validated through 5-fold cross-validation. Images were created with BioRender.com.

**Figure 2** Multivariate statistical analysis and predictive modeling of the selected DMN progression-related biomarkers. (a) Individual box-and-whisker plot (median, 5–95 percentile) of the selected metabolites in DMN progressors (P, red) and non-progressors (NP, blue). (b) PCA of two components of selected metabolites from DMN progressors (P, red) and non-progressors (NP, blue). (c) Heatmap and hierarchical clustering analysis of the selected metabolites from DMN progressors (P, red) and non-progressors (NP, blue). (d). ROC curves for three prediction models of DMN progression: models using clinical parameters only (red) or together with selected metabolite biomarkers (green: by logistic regression; blue: by random forest). Solid line: mean AUC of the 5-fold cross-validation. Shaded area: 95% CI of AUC. * q < 0.05, ** q < 0.01. PC aa: diacyl-phosphatidylcholine; PC ae: acyl-alkyl-phosphatidylcholine.

**Figure 3** Multivariate statistical analysis and predictive modeling of the selected HTN progression-related biomarkers. (a) Individual box-and-whisker plot (median, 5–95 percentile) of the selected metabolites in HTN progressors (P, red) and non-progressors (NP, blue). (b) PCA of two components of selected metabolites from HTN progressors (P, red) and non-progressors (NP, blue). (c) Heatmap and hierarchical clustering analysis of the selected metabolites from HTN progressors (P, red) and non-progressors (NP, blue). (d) ROC curves for three prediction models of HTN progression: models using clinical parameters only (red) or together with selected metabolite biomarkers (green: by logistic regression; blue: by random forest). Solid line: mean AUC of the 5-fold cross-validation. Shaded area: 95% CI of AUC. * q < 0.05, ** q < 0.01, *** q < 0.001. PC aa: diacyl-phosphatidylcholine; PC ae: acyl-alkyl-phosphatidylcholine.

**Figure 4** Multivariate statistical analysis and predictive modeling of the selected PKD progression-related biomarkers. (a) Individual box-and-whisker plot (median, 5–95 percentile) of the selected metabolites in PKD progressors (P, red) and non-progressors (NP, blue). (b) PCA of two components of selected metabolites from PKD progressors (P, red) and non-progressors (NP, blue). (c) Heatmap and hierarchical clustering analysis of the selected metabolites from PKD progressors (P, red) and non-progressors (NP, blue). (d) ROC curves for three prediction models of PKD progression: models using clinical parameters only (red) or together with selected metabolite biomarkers (green: by logistic regression; blue: by random forest). Solid line: mean AUC of the 5-fold cross-validation. Shaded area: 95% CI of AUC. * q < 0.05, ** q < 0.01, *** q < 0.001. PC aa: diacyl-phosphatidylcholine; PC ae: acyl-alkyl-phosphatidylcholine.

**Figure 5** Changes in metabolic patterns of disease progressors compared to non-progressors in the three disease groups. MetaMapp metabolite networks of 67 metabolites altered in DMN (a), HTN (b), and PKD (c), with nodes representing metabolites and edges, indicating chemical relationships. ADMA, asymmetric dimethylarginine; alpha-AAA, L-2-aminoadipic acid; C7-DC, pimelylcarnitine; C12:1, dodecenoylcarnitine; C16:1, hexadecenoylcarnitine; PC aa: diacyl-phosphatidylcholine; PC ae: acyl-alkyl-phosphatidylcholine.

그림 출처: PubMed Central 공개 논문 (PMC9696352)

Conflict of interest statement

None of the authors have conflicts of interest to declare.

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