Identification of Serum Metabolites for Predicting Chronic Kidney Disease Progression according to Chronic Kidney Disease Cause
- 주제(키워드) chronic kidney disease , disease progression , metabolomics , serum biomarkers
- 주제(기타) Biochemistry & Molecular Biology
- 설명문(일반) [Kang, Eunjeong] Ewha Womans Univ, Seoul Hosp, Dept Internal Med, Coll Med, Seoul 07804, South Korea; [Li, Yufei; Kim, Bora; Huh, Ki Young; Cho, Joo-Youn] Seoul Natl Univ, Dept Clin Pharmacol & Therapeut, Coll Med & Hosp, Seoul 03080, South Korea; [Kim, Bora] NCI, NIH, Ctr Canc Res, Lab Metab, Bethesda, MD 20892 USA; [Han, Miyeun] Natl Med Ctr, Dept Internal Med, Seoul 04564, South Korea; [Ahn, Jung-Hyuck; Sung, Hye Youn] Ewha Womans Univ, Dept Biochem, Coll Med, Seoul 07804, South Korea; [Park, Yong Seek; Lee, Seung Eun] Kyung Hee Univ, Sch Med, Dept Microbiol, Seoul 02447, South Korea; [Lee, Sangjun; Park, Sue K.] Seoul Natl Univ, Dept Prevent Med, Coll Med, Seoul 03080, South Korea; [Lee, Sangjun; Park, Sue K.] Seoul Natl Univ, Canc Res Inst, Coll Med, Seoul 03080, South Korea; [Lee, Sangjun; Cho, Joo-Youn] Seoul Natl Univ Grad Sch, Dept Biomed Sci, Seoul 03080, South Korea; [Park, Sue K.] Seoul Natl Univ, Integrated Major Innovat Med Sci, Coll Med, Seoul 03080, South Korea; [Oh, Kook-Hwan] Seoul Natl Univ, Dept Internal Med, Coll Med, Seoul 03080, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 MDPI
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000203899
- 본문언어 영어
- Published As https://doi.org/10.3390/metabo12111125
초록/요약
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.
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