Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics.

Hirakawa, Yosuke; Yoshioka, Kentaro; Kojima, Kensuke; Yamashita, Yasuho; Shibahara, Takuma; Wada, Takehiko; Nangaku, Masaomi; Inagi, Reiko

Hirakawa, Yosuke; Yoshioka, Kentaro; Kojima, Kensuke; Yamashita, Yasuho; Shibahara, Takuma; Wada, Takehiko; Nangaku, Masaomi; Inagi, Reiko.

Afiliación

Hirakawa Y; Division of Nephrology and Endocrinology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan.
Yoshioka K; Kyowa Kirin Co., Ltd., Tokyo, Japan.
Kojima K; Division of Chronic Kidney Disease Pathophysiology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan.
Yamashita Y; Kyowa Kirin Co., Ltd., Tokyo, Japan.
Shibahara T; Research and Development Group, Hitachi, Ltd., Tokyo, Japan.
Wada T; Research and Development Group, Hitachi, Ltd., Tokyo, Japan.
Nangaku M; Division of Nephrology, Endocrinology and Metabolism, Tokai University School of Medicine, Isehara, Japan.
Inagi R; Division of Nephrology and Endocrinology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan. mnangaku@m.u-tokyo.ac.jp.

Sci Rep ; 12(1): 16287, 2022 09 29.

Article en En | MEDLINE | ID: mdl-36175470

Asunto(s)

Diabetes Mellitus; Nefropatías Diabéticas; Albúminas; Biomarcadores; Creatinina; Nefropatías Diabéticas/diagnóstico; Humanos; Aprendizaje Automático

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diabetes Mellitus / Nefropatías Diabéticas Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2022 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Reino Unido

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