Intra-body dynamics of d-serine reflects the origin of kidney diseases

Okushima, Hiroki; Iwata, Yukimasa; Hesaka, Atsushi; Sugimori, Eri; Ikeda, Tatsuhiko; Nakane, Maiko; Mita, Masashi; Hayashi, Terumasa; Isaka, Yoshitaka; Kimura, Tomonori

doi:10.1007/s10157-021-02052-5

Cited by 18 publications

(13 citation statements)

References 23 publications

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“…For the development of new therapies, it is necessary to investigate the characteristics of the patient group showing treatment resistance more broadly. For example, it is expected that performing whole-exosome analysis of miR-NAs [25] or further profiling of kidney biomarkers such as d-amino acids [26][27][28], long-term undetected enantiomers of amino acids, will not only lead to the discovery of useful novel biomarkers, but also elucidate the mechanism of treatment resistance.…”

Section: Discussionmentioning

confidence: 99%

Deep learning analysis of clinical course of primary nephrotic syndrome: Japan Nephrotic Syndrome Cohort Study (JNSCS)

et al. 2022

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Background Prognosis of nephrotic syndrome has been evaluated based on pathological diagnosis, whereas its clinical course is monitored using objective items and the treatment strategy is largely the same. We examined whether the entire natural history of nephrotic syndrome could be evaluated using objective common clinical items. Methods Machine learning clustering was performed on 205 cases from the Japan Nephrotic Syndrome Cohort Study, whose clinical parameters, serum creatinine, serum albumin, dipstick hematuria, and proteinuria were traceable after kidney biopsy at 5 measured points up to 2 years. The clinical patterns of time-series data were learned using long short-term memory (LSTM)-encoder–decoder architecture, an unsupervised machine learning classifier. Clinical clusters were defined as Gaussian mixture distributions in a two-dimensional scatter plot based on the highest log-likelihood. Results Time-series data of nephrotic syndrome were classified into four clusters. Patients in the fourth cluster showed the increase in serum creatinine in the later part of the follow-up period. Patients in both the third and fourth clusters were initially high in both hematuria and proteinuria, whereas a lack of decline in the urinary protein level preceded the worsening of kidney function in fourth cluster. The original diseases of fourth cluster included all the disease studied in this cohort. Conclusions Four kinds of clinical courses were identified in nephrotic syndrome. This classified clinical course may help objectively grasp the actual condition or treatment resistance of individual patients with nephrotic syndrome.

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Section: Discussionmentioning

confidence: 99%

Deep learning analysis of clinical course of primary nephrotic syndrome: Japan Nephrotic Syndrome Cohort Study (JNSCS)

et al. 2022

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“…The sample size calculation was performed on the basis of a previous study (15). Suppose the plasma level of d- serine was 2.8, 2.3, 2.8, and 1.5 μ M for patients with IgAN, MCD, and DN and normal participants, respectively; therefore, the number needed for a one-way ANOVA F test for group effect was 11 per group.…”

Section: Methodsmentioning

confidence: 99%

“…Studies based on 2D-HPLC revealed that D-serine reflects kidney function and disease activity 9,10 . A previous study also revealed that the levels of D-serine in blood and urine are also useful in the diagnosis of the origin of kidney diseases 15 .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Background: The diagnosis of diabetic nephropathy (DN), the major cause of end-stage kidney disease, requires kidney biopsy. D-Serine, present only in trace amounts in humans, is a biomarker for kidney diseases and shows its 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: Patients with biopsy-proven DN, primary glomerulonephritis (minimal change disease and IgA nephropathy), and participants without kidney disease, were enrolled. A total of 388 participants was included in this study, and levels of D-serine in blood and urine were measured using two-dimensional high-performance liquid chromatography, and urinary fractional excretion (FE) of D-serine was calculated. Utilizing data from 259 participants, we developed prediction models for detecting DN by logistic regression analyses, and the models were validated in 129 participants. Results: The blood level of D-serine above 2.34 uM demonstrated a high specificity of 83.3% (95% CI, 69.8-92.5%) for excluding participants without kidney diseases. In participants with the blood level of D-serine above 2.34 uM, the threshold of 46.6% in FE of D-serine provided an optimal threshold for the detection of DN [AUC, 0.85 (0.76-0.95); sensitivity, 78.8% (61.1-91.0%); specificity, 83.3% (CI, 67.2-93.6%). This plasma-high and FE-high profile of D-serine in combination with clinical factors (age, sex, estimated glomerular filtration rate, and albuminuria) correctly predicted DN with a sensitivity of 91.3% (95% confidence interval, 72.0-98.9%) and a specificity of 79.3% (63.3-80.0%), and outperformed the model based on clinical factors alone in the validation dataset (p < 0.015). 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 for the origins of kidney diseases.

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“…Moreover, D-Ser has been recently highlighted as a promising biomarker for evaluating kidney functions [1,[13][14][15]. In chronic kidney disease, D-Ser, D-Asn, and D-Pro levels are increased compared with healthy people, being strongly correlated with kidney function (glomerular filtration rate), while D-Asp and D-Pro may indicate the copresence of diabetes [16].…”

Section: Introductionmentioning

confidence: 99%

Indirect Enantioseparations: Recent Advances in Chiral Metabolomics for Biomedical Research

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Pralea

Moldovan

et al. 2022

IJMS

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Chiral metabolomics is starting to become a well-defined research field, powered by the recent advances in separation techniques. This review aimed to cover the most relevant advances in indirect enantioseparations of endogenous metabolites that were published over the last 10 years, including improvements and development of new chiral derivatizing agents, along with advances in separation methodologies. Moreover, special emphasis is put on exciting advances in separation techniques combined with mass spectrometry, such as chiral discrimination by ion-mobility mass spectrometry together with untargeted strategies for profiling of chiral metabolites in complex matrices. These advances signify a leap in chiral metabolomics technologies that will surely offer a solid base to better understand the specific roles of enantiomeric metabolites in systems biology.

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Intra-body dynamics of d-serine reflects the origin of kidney diseases

Cited by 18 publications

References 23 publications

Deep learning analysis of clinical course of primary nephrotic syndrome: Japan Nephrotic Syndrome Cohort Study (JNSCS)

Deep learning analysis of clinical course of primary nephrotic syndrome: Japan Nephrotic Syndrome Cohort Study (JNSCS)

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

Indirect Enantioseparations: Recent Advances in Chiral Metabolomics for Biomedical Research

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