Literature DB >> 35372995

Identification of Diabetic Nephropathy in Patients Undergoing Kidney Biopsy through Blood and Urinary Profiles of d-Serine.

Yukimasa Iwata1, Hiroki Okushima1, Atsushi Hesaka2,3,4, Masataka Kawamura5, Ryoichi Imamura5, Shiro Takahara6, Masaru Horio7, Youko Tanaka2,3, Tatsuhiko Ikeda8, Maiko Nakane8, Masashi Mita8, Terumasa Hayashi1, Yoshitaka Isaka4, Tomonori Kimura2,3,4.   

Abstract

Background: The diagnosis of diabetic nephropathy (DN), the major cause of ESKD, requires kidney biopsy. d-Serine, present only in trace amounts in humans, is a biomarker for kidney diseases and shows potential to distinguish the origin of kidney diseases, whose diagnoses usually require kidney biopsy. We extended this concept and examined the potential of d-serine in the diagnosis of DN.
Methods: We enrolled patients with biopsy sample-proven DN and primary GN (minimal change disease and IgA nephropathy) and participants without kidney disease. A total of 388 participants were included in this study, and d-serine levels in blood and urine were measured using two-dimensional high-performance liquid chromatography, and urinary fractional excretion (FE) of d-serine was calculated. Using data from 259 participants, we developed prediction models for detecting DN by logistic regression analyses, and the models were validated in 129 participants.
Results: A d-serine blood level of >2.34 μM demonstrated a high specificity of 83% (95% CI, 70% to 93%) for excluding participants without kidney diseases. In participants with a d-serine blood level >2.34 μM, the threshold of 47% in FE of d-serine provided an optimal threshold for the detection of DN (AUC, 0.85 [95% CI, 0.76 to 0.95]; sensitivity, 79% [95% CI, 61% to 91%]; specificity, 83% [95% CI, 67% to 94%]). This plasma-high and FE-high profile of d-serine in combination with clinical factors (age, sex, eGFR, and albuminuria) correctly predicted DN with a sensitivity of 91% (95% CI, 72% to 99%) and a specificity of 79% (95% CI, 63% to 80%), and outperformed the model based on clinical factors alone in the validation dataset (P<0.02). Conclusions: Analysis of d-serine in blood and urinary excretion is useful in identifying DN in patients undergoing kidney biopsy. Profiling of d-serine in patients with kidney diseases supports the suitable treatment through the auxial diagnosis of the origins of kidney diseases.
Copyright © 2021 by the American Society of Nephrology.

Entities:  

Keywords:  biomarker; chiral amino acids; d-serine; diabetes; diabetes and the kidney; diabetic kidney disease; diabetic nephropathy; diagnosis; glomerulonephritis; kidney biopsy

Mesh:

Substances:

Year:  2021        PMID: 35372995      PMCID: PMC8785851          DOI: 10.34067/KID.0004282021

Source DB:  PubMed          Journal:  Kidney360        ISSN: 2641-7650


  26 in total

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Journal:  Biochem J       Date:  1935-07       Impact factor: 3.857

Review 2.  Summary of KDIGO 2012 CKD Guideline: behind the scenes, need for guidance, and a framework for moving forward.

Authors:  Adeera Levin; Paul E Stevens
Journal:  Kidney Int       Date:  2013-11-27       Impact factor: 10.612

3.  Nondiabetic renal disease in noninsulin-dependent diabetics in a south Indian Hospital.

Authors:  G T John; A Date; A Korula; L Jeyaseelan; J C Shastry; C K Jacob
Journal:  Nephron       Date:  1994       Impact factor: 2.847

4.  Renal biopsy in patients with diabetes: a pooled meta-analysis of 48 studies.

Authors:  Marco Fiorentino; Davide Bolignano; Vladimir Tesar; Anna Pisano; Wim Van Biesen; Giovanni Tripepi; Graziella D'Arrigo; Loreto Gesualdo
Journal:  Nephrol Dial Transplant       Date:  2017-01-01       Impact factor: 5.992

5.  Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes.

Authors:  Toshiharu Ninomiya; Vlado Perkovic; Bastiaan E de Galan; Sophia Zoungas; Avinesh Pillai; Meg Jardine; Anushka Patel; Alan Cass; Bruce Neal; Neil Poulter; Carl-Erik Mogensen; Mark Cooper; Michel Marre; Bryan Williams; Pavel Hamet; Giuseppe Mancia; Mark Woodward; Stephen Macmahon; John Chalmers
Journal:  J Am Soc Nephrol       Date:  2009-05-14       Impact factor: 10.121

6.  Serine racemase is predominantly localized in neurons in mouse brain.

Authors:  Kazushi Miya; Ran Inoue; Yoshimi Takata; Manabu Abe; Rie Natsume; Kenji Sakimura; Kazuhisa Hongou; Toshio Miyawaki; Hisashi Mori
Journal:  J Comp Neurol       Date:  2008-10-20       Impact factor: 3.215

7.  Revised equations for estimated GFR from serum creatinine in Japan.

Authors:  Seiichi Matsuo; Enyu Imai; Masaru Horio; Yoshinari Yasuda; Kimio Tomita; Kosaku Nitta; Kunihiro Yamagata; Yasuhiko Tomino; Hitoshi Yokoyama; Akira Hishida
Journal:  Am J Kidney Dis       Date:  2009-04-01       Impact factor: 8.860

8.  Dynamics of D-serine reflected the recovery course of a patient with rapidly progressive glomerulonephritis.

Authors:  Atsushi Hesaka; Keiko Yasuda; Shinsuke Sakai; Hiroaki Yonishi; Tomoko Namba-Hamano; Atsushi Takahashi; Masayuki Mizui; Kenji Hamase; Rakan Matsui; Masashi Mita; Masaru Horio; Yoshitaka Isaka; Tomonori Kimura
Journal:  CEN Case Rep       Date:  2019-07-29

9.  Intra-body dynamics of D-serine reflects the origin of kidney diseases.

Authors:  Hiroki Okushima; Yukimasa Iwata; Atsushi Hesaka; Eri Sugimori; Tatsuhiko Ikeda; Maiko Nakane; Masashi Mita; Terumasa Hayashi; Yoshitaka Isaka; Tomonori Kimura
Journal:  Clin Exp Nephrol       Date:  2021-03-25       Impact factor: 2.801

10.  Chiral amino acid metabolomics for novel biomarker screening in the prognosis of chronic kidney disease.

Authors:  Tomonori Kimura; Kenji Hamase; Yurika Miyoshi; Ryohei Yamamoto; Keiko Yasuda; Masashi Mita; Hiromi Rakugi; Terumasa Hayashi; Yoshitaka Isaka
Journal:  Sci Rep       Date:  2016-05-18       Impact factor: 4.379

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