MedElevator
Войти в исследования Diabetes research and clinical practice · 2026
Urine peptides enable non-invasive differential diagnosis of nodular diabetic nephropathy.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kondyli Margarita, Siwy Justyna, Jaimes Campos Mayra Alejandra, Amann Kerstin, Beige Joachim, Keller Felix, Mischak Harald, Frantzi Maria, Wendt Ralph, Rossing Peter, Rupprecht Harald, Catanese Lorenzo, Jankowski Vera
Аннотация
AIMS: Risk stratification in diabetic kidney disease is challenging. Identifying nodular diabetic nephropathy (NDN), the most specific histological feature of diabetic nephropathy (DN), associated with poor prognosis, currently requires kidney biopsy. We aimed to identify urinary peptide biomarkers distinguishing NDN from other chronic kidney disease etiologies in diabetes (nNDN).
METHODS: In this case-control study, urinary peptide profiles from biopsy-proven DN patients (43 NDN, 38 nNDN) were analyzed using capillary electrophoresis-mass spectrometry. Real patient data were combined with synthetic datasets generated using a Gaussian copula approach and divided into discovery and validation cohorts. Differential peptides were identified by Wilcoxon testing with Benjamini-Hochberg correction, and combined into a support vector machine model.
RESULTS: In the discovery cohort, 207 peptides were significantly altered. NDN was characterized by reduced collagen fragments and increased fibrinogen, apolipoprotein A-IV, and α1-antitrypsin peptides. The 99-peptide classifier (NDN99) discriminated NDN from nNDN with an AUC of 0.87 (combined validation) and 0.79 (real-patient validation). NDN99 predicted major adverse kidney events (MAKE; death, kidney failure, or 40% eGFR loss), and outperformed histology (HR = 2.9, p = 0.0006 vs HR = 1.9, p = 0.04).
CONCLUSIONS: Urinary peptide signatures enable non-invasive discrimination of NDN and predict MAKE, potentially complementing kidney biopsy for subtype identification and risk stratification in DN.
METHODS: In this case-control study, urinary peptide profiles from biopsy-proven DN patients (43 NDN, 38 nNDN) were analyzed using capillary electrophoresis-mass spectrometry. Real patient data were combined with synthetic datasets generated using a Gaussian copula approach and divided into discovery and validation cohorts. Differential peptides were identified by Wilcoxon testing with Benjamini-Hochberg correction, and combined into a support vector machine model.
RESULTS: In the discovery cohort, 207 peptides were significantly altered. NDN was characterized by reduced collagen fragments and increased fibrinogen, apolipoprotein A-IV, and α1-antitrypsin peptides. The 99-peptide classifier (NDN99) discriminated NDN from nNDN with an AUC of 0.87 (combined validation) and 0.79 (real-patient validation). NDN99 predicted major adverse kidney events (MAKE; death, kidney failure, or 40% eGFR loss), and outperformed histology (HR = 2.9, p = 0.0006 vs HR = 1.9, p = 0.04).
CONCLUSIONS: Urinary peptide signatures enable non-invasive discrimination of NDN and predict MAKE, potentially complementing kidney biopsy for subtype identification and risk stratification in DN.