TY - JOUR
T1 - Identification of novel urinary biomarkers for predicting renal prognosis in patients with type 2 diabetes by glycan profiling in a multicenter prospective cohort study
T2 - U-CARE study 1
AU - Mise, Koki
AU - Imamura, Mariko
AU - Yamaguchi, Satoshi
AU - Teshigawara, Sanae
AU - Tone, Atsuhito
AU - Uchida, Haruhito A.
AU - Eguchi, Jun
AU - Nakatsuka, Atsuko
AU - Ogawa, Daisuke
AU - Yoshida, Michihiro
AU - Yamada, Masao
AU - Shikata, Kenichi
AU - Wada, Jun
N1 - Funding Information:
Acknowledgments. The authors thank Dr. Kazuyuki Hida (National Hospital Organization Okayama Medical Center), Dr. Tatsuaki Nakato (Okayama Saiseikai General Hospital), Dr. Takashi Matsuoka (Kurashiki Central Hospital), Dr. Ikki Shimizu(TheSakakibaraHeartInstituteofOkayama), Dr. Tomokazu Nunoue (Tsuyama Chuo Hospital), Dr. Katsuhiro Miyashita (Japanese Red Cross Okayama Hospital), Dr. Shinichiro Ando (Okayama City General Medical Center), and Dr. Akiho Seki (Okayama Health Foundation) for providing data and for patient treatments. The authors also thank Drs. Ryo Kodera and Satoshi Miyamoto (Okayama University Hospital), Drs. Akihiro Katayama, Chigusa Higuchi, Hidemi Takeuchi, Yusuke Shibata, Ichiro Nojima, Yuzuki Kano, Yuriko Yamamura, Nozomu Otaka, Chieko Kawakita, Keigo Hayashi, and Yasuhiro Onishi (Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama) for collecting data. The authors greatly appreciate Dr. Hirofumi Makino (Okayama University) for critical suggestions in the design of the current investigation. Funding. This work was partly supported by the Ministry of Health, Labour and Welfare in Japan (Health Labor Sciences Research Grant 201413003), the Japan Agency for Medical Research and Development (AMED) (grant 17ek0210095h0001), Novo Nordisk Pharma Ltd. (Junior Scientist Development Grant [2016–2017]) from the Okinaka Memorial Institute for Medical Research (a grant in 2017), and The Yukiko Ishibashi Foundation (a grant in 2016). Duality of Interest. M.Ya. was a former employee of GP BioSciences Co., Ltd., and is currently an employee of GlycoTechnica, Ltd. No other potential conflicts of interest relevant to this article were reported. Author Contributions. K.M. contributed to designing the research, analyzing and interpreting data, measuring urinary glycan levels, collecting and summarizing clinical data, and writing the manuscript. M.I. contributed to collecting, summarizing, and assessing clinical data. S.Y. contributed to measuring urinary glycan levels and collecting and summarizing clinical data. S.T., A.T., H.A.U., J.E., A.N., and D.O., contributed to managing patients and assessing data. M.Yo. contributed to interpreting data and performing statistical analyses. M.Ya. contributed to measuring urinary glycan levels; interpreting data, especially urinary glycan data; and writing the manuscript. K.S. contributed to managing patients and assessing data. J.W. was responsible for the study design, supervised data collection and data analysis, and contributed to drafting and editing the manuscript. J.W. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Prior Presentation. Part of this work was presented at the American Society of Nephrology Scientific Oral Sessions, New Orleans, LA, 31 October–5 November 2017, and the International Society of Nephrology Frontiers Meetings, Tokyo, Japan, 22–25 February 2018.
Publisher Copyright:
© 2018 by the American Diabetes Association.
PY - 2018/8/1
Y1 - 2018/8/1
N2 - OBJECTIVE: Because quantifying glycans with complex structures is technically challenging, little is known about the association of glycosylation profiles with the renal prognosis in diabetic kidney disease (DKD). RESEARCH DESIGN AND METHODS: In 675 patients with type 2 diabetes, we assessed the baseline urinary glycan signals binding to 45 lectins with different specificities. The end point was a decrease of estimated glomerular filtration rate (eGFR) by ≥30% from baseline or dialysis for end-stage renal disease. RESULTS: During a median follow-up of 4.0 years, 63 patients reached the end point. Cox proportional hazards analysis revealed that urinary levels of glycans binding to six lectins were significantly associated with the outcome after adjustment for known indicators of DKD, although these urinary glycans, except that for DBA, were highly correlated with baseline albuminuria and eGFR. Hazard ratios for these lectins were (+1 SD for the glycan index) as follows: SNA (recognizing glycan Siaa2-6Gal/GalNAc), 1.42 (95% CI 1.14-1.76); RCA120 (Galb4GlcNAc), 1.28 (1.01-1.64); DBA (GalNAca3GalNAc), 0.80 (0.64-0.997); ABA (Galb3GalNAc), 1.29 (1.02-1.64); Jacalin (Galb3GalNAc), 1.30 (1.02-1.67); and ACA (Galb3GalNAc), 1.32 (1.04-1.67). Adding these glycan indexes to a model containing known indicators of progression improved prediction of the outcome (net reclassification improvement increased by 0.51 [0.22-0.80], relative integrated discrimination improvement increased by 0.18 [0.01-0.35], and the Akaike information criterion decreased from 296 to 287). CONCLUSIONS: The urinary glycan profile identified in this study may be useful for predicting renal prognosis in patients with type 2 diabetes. Additional investigation of glycosylation changes and urinary glycan excretion in DKD is needed.
AB - OBJECTIVE: Because quantifying glycans with complex structures is technically challenging, little is known about the association of glycosylation profiles with the renal prognosis in diabetic kidney disease (DKD). RESEARCH DESIGN AND METHODS: In 675 patients with type 2 diabetes, we assessed the baseline urinary glycan signals binding to 45 lectins with different specificities. The end point was a decrease of estimated glomerular filtration rate (eGFR) by ≥30% from baseline or dialysis for end-stage renal disease. RESULTS: During a median follow-up of 4.0 years, 63 patients reached the end point. Cox proportional hazards analysis revealed that urinary levels of glycans binding to six lectins were significantly associated with the outcome after adjustment for known indicators of DKD, although these urinary glycans, except that for DBA, were highly correlated with baseline albuminuria and eGFR. Hazard ratios for these lectins were (+1 SD for the glycan index) as follows: SNA (recognizing glycan Siaa2-6Gal/GalNAc), 1.42 (95% CI 1.14-1.76); RCA120 (Galb4GlcNAc), 1.28 (1.01-1.64); DBA (GalNAca3GalNAc), 0.80 (0.64-0.997); ABA (Galb3GalNAc), 1.29 (1.02-1.64); Jacalin (Galb3GalNAc), 1.30 (1.02-1.67); and ACA (Galb3GalNAc), 1.32 (1.04-1.67). Adding these glycan indexes to a model containing known indicators of progression improved prediction of the outcome (net reclassification improvement increased by 0.51 [0.22-0.80], relative integrated discrimination improvement increased by 0.18 [0.01-0.35], and the Akaike information criterion decreased from 296 to 287). CONCLUSIONS: The urinary glycan profile identified in this study may be useful for predicting renal prognosis in patients with type 2 diabetes. Additional investigation of glycosylation changes and urinary glycan excretion in DKD is needed.
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U2 - 10.2337/dc18-0030
DO - 10.2337/dc18-0030
M3 - Article
C2 - 29930140
AN - SCOPUS:85053544001
SN - 1935-5548
VL - 41
SP - 1765
EP - 1775
JO - Diabetes Care
JF - Diabetes Care
IS - 8
ER -
