Introduction d-Serine, present only in trace amounts in humans, is now recognized as a biomarker of chronic kidney disease (CKD). CKD is heterogeneous in its original kidney diseases, whose diagnoses require kidney biopsy. In this study, we examined whether the intra-body dynamics of d-serine, indexed by its blood and urinary levels, reflects the origin of kidney diseases. Methods Patients with six kinds of kidney disease undergoing kidney biopsy were enrolled in a single center. Levels of d- and l-serine were measured using two-dimensional high-performance liquid chromatography. The associations between the origin of kidney diseases and the intra-body dynamics of d-serine were examined using multivariate cluster analyses. Results Unlike the non-CKD profile, patients with CKD showed broadly-distributed profiles of intra-body dynamics of d-serine. The plasma level of d-serine plays a key role in the detection of kidney diseases, whereas a combination of plasma and urinary levels of d-serine distinguished the origin of CKD, especially lupus nephritis. Conclusion Intra-body dynamics of d-serine have the potential to predict the origin of kidney diseases. Monitoring of d-serine may guide specific treatments for the origin of kidney diseases.

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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.