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<p>Diabetic kidney disease (DKD) is a leading cause of end-stage kidney disease worldwide. Conventional diagnostic tools lack sufficient sensitivity, necessitating novel biomarkers and reliable analytical platforms to improve DKD management. In this study, a serum tryptophan (TRP) metabolomics platform based on liquid chromatography–tandem mass spectrometry (LC-MS/MS) was developed. After analytical optimization and validation, this platform was applied to a hospital-based case-control study (<i>n</i> = 200) for DKD. Associations between TRP metabolites and DKD risk were investigated via multivariable logistic regression. Metabolites demonstrating independent associations underwent diagnostic evaluation using receiver operating characteristic analysis with bootstrap internal validation. This LC-MS/MS platform enabled rapid quantification for TRP and 18 related metabolites within 6 min with low limits of detection, satisfactory recoveries, and acceptable matrix effects, meeting stringent analytical criteria suitable for clinical application. Seven TRP metabolites were significantly associated with DKD after covariate adjustment: 3-hydroxykynurenine (3-HK), 5-hydroxyindole acetic acid, indole-3-acetamide, and indole exhibited positive associations, while 3-hydroxyanthranilic acid, 5-hydroxytryptophan, and xanthurenic acid (XA) showed inverse associations. Individual metabolites demonstrated promising diagnostic performance (AUCs 0.762–0.896, sensitivities 65–80%, specificities 63–91%), except indole (AUC 0.683, sensitivity 51%, specificity 85%). Critically, the biomarker panel combining 3-HK and XA achieved exceptional diagnostic accuracy (AUC 0.970, sensitivity 92%, specificity 98%) for DKD. Bootstrap internal validation confirmed the stability of these findings. This study provided an LC-MS/MS-based platform for serum TRP metabolomics and identified a biomarker panel with exceptional diagnostic accuracy for DKD, offering significant potential as a clinically feasible tool for improving the diagnosis and management of DKD.</p> Graphical Abstract <p></p>

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Rapid LC-MS/MS-based quantitative serum tryptophan metabolomics platform identifies and monitors novel biomarkers for diabetic kidney disease

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Lingshuai Zeng,
  • Juan Bai,
  • Xinrong Zou,
  • Yi Zheng,
  • Guangzhong Liu,
  • Rui Lin,
  • Yu Zhao,
  • Xue Xue,
  • Xiaoqin Wang

摘要

Diabetic kidney disease (DKD) is a leading cause of end-stage kidney disease worldwide. Conventional diagnostic tools lack sufficient sensitivity, necessitating novel biomarkers and reliable analytical platforms to improve DKD management. In this study, a serum tryptophan (TRP) metabolomics platform based on liquid chromatography–tandem mass spectrometry (LC-MS/MS) was developed. After analytical optimization and validation, this platform was applied to a hospital-based case-control study (n = 200) for DKD. Associations between TRP metabolites and DKD risk were investigated via multivariable logistic regression. Metabolites demonstrating independent associations underwent diagnostic evaluation using receiver operating characteristic analysis with bootstrap internal validation. This LC-MS/MS platform enabled rapid quantification for TRP and 18 related metabolites within 6 min with low limits of detection, satisfactory recoveries, and acceptable matrix effects, meeting stringent analytical criteria suitable for clinical application. Seven TRP metabolites were significantly associated with DKD after covariate adjustment: 3-hydroxykynurenine (3-HK), 5-hydroxyindole acetic acid, indole-3-acetamide, and indole exhibited positive associations, while 3-hydroxyanthranilic acid, 5-hydroxytryptophan, and xanthurenic acid (XA) showed inverse associations. Individual metabolites demonstrated promising diagnostic performance (AUCs 0.762–0.896, sensitivities 65–80%, specificities 63–91%), except indole (AUC 0.683, sensitivity 51%, specificity 85%). Critically, the biomarker panel combining 3-HK and XA achieved exceptional diagnostic accuracy (AUC 0.970, sensitivity 92%, specificity 98%) for DKD. Bootstrap internal validation confirmed the stability of these findings. This study provided an LC-MS/MS-based platform for serum TRP metabolomics and identified a biomarker panel with exceptional diagnostic accuracy for DKD, offering significant potential as a clinically feasible tool for improving the diagnosis and management of DKD.

Graphical Abstract