Serum integrative omics reveals the landscape of human diabetic kidney disease

Objective: Diabetic kidney disease (DKD) is the most common microvascular complication of type 2 diabetes mellitus (2-DM). Currently, urine and kidney biopsy specimens are the major clinical resources for DKD diagnosis. Our study proposes to evaluate the diagnostic value of blood in monitoring the o...

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Autores principales:	Shijia Liu, Yuan Gui, Mark S. Wang, Lu Zhang, Tingting Xu, Yuchen Pan, Ke Zhang, Ying Yu, Liangxiang Xiao, Yi Qiao, Christopher Bonin, Geneva Hargis, Tao Huan, Yanbao Yu, Jianling Tao, Rong Zhang, Donald L. Kreutzer, Yanjiao Zhou, Xiao-Jun Tian, Yanlin Wang, Haiyan Fu, Xiaofei An, Silvia Liu, Dong Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Diabetic kidney disease Type 2 diabetes mellitus Serum Proteomics Metabolomics Machine learning Internal medicine RC31-1245
Acceso en línea:	https://doaj.org/article/5c92b2d350eb4b5a838c01e88d8ffde6
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spelling	oai:doaj.org-article:5c92b2d350eb4b5a838c01e88d8ffde62021-11-22T04:24:51ZSerum integrative omics reveals the landscape of human diabetic kidney disease2212-877810.1016/j.molmet.2021.101367https://doaj.org/article/5c92b2d350eb4b5a838c01e88d8ffde62021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2212877821002143https://doaj.org/toc/2212-8778Objective: Diabetic kidney disease (DKD) is the most common microvascular complication of type 2 diabetes mellitus (2-DM). Currently, urine and kidney biopsy specimens are the major clinical resources for DKD diagnosis. Our study proposes to evaluate the diagnostic value of blood in monitoring the onset of DKD and distinguishing its status in the clinic. Methods: This study recruited 1,513 participants including healthy adults and patients diagnosed with 2-DM, early-stage DKD (DKD-E), and advanced-stage DKD (DKD-A) from 4 independent medical centers. One discovery and four testing cohorts were established. Sera were collected and subjected to training proteomics and large-scale metabolomics. Results: Deep profiling of serum proteomes and metabolomes revealed several insights. First, the training proteomics revealed that the combination of α2-macroglobulin, cathepsin D, and CD324 could serve as a surrogate protein biomarker for monitoring DKD progression. Second, metabolomics demonstrated that galactose metabolism and glycerolipid metabolism are the major disturbed metabolic pathways in DKD, and serum metabolite glycerol-3-galactoside could be used as an independent marker to predict DKD. Third, integrating proteomics and metabolomics increased the diagnostic and predictive stability and accuracy for distinguishing DKD status. Conclusions: Serum integrative omics provide stable and accurate biomarkers for early warning and diagnosis of DKD. Our study provides a rich and open-access data resource for optimizing DKD management.Shijia LiuYuan GuiMark S. WangLu ZhangTingting XuYuchen PanKe ZhangYing YuLiangxiang XiaoYi QiaoChristopher BoninGeneva HargisTao HuanYanbao YuJianling TaoRong ZhangDonald L. KreutzerYanjiao ZhouXiao-Jun TianYanlin WangHaiyan FuXiaofei AnSilvia LiuDong ZhouElsevierarticleDiabetic kidney diseaseType 2 diabetes mellitusSerumProteomicsMetabolomicsMachine learningInternal medicineRC31-1245ENMolecular Metabolism, Vol 54, Iss , Pp 101367- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Diabetic kidney disease Type 2 diabetes mellitus Serum Proteomics Metabolomics Machine learning Internal medicine RC31-1245
spellingShingle	Diabetic kidney disease Type 2 diabetes mellitus Serum Proteomics Metabolomics Machine learning Internal medicine RC31-1245 Shijia Liu Yuan Gui Mark S. Wang Lu Zhang Tingting Xu Yuchen Pan Ke Zhang Ying Yu Liangxiang Xiao Yi Qiao Christopher Bonin Geneva Hargis Tao Huan Yanbao Yu Jianling Tao Rong Zhang Donald L. Kreutzer Yanjiao Zhou Xiao-Jun Tian Yanlin Wang Haiyan Fu Xiaofei An Silvia Liu Dong Zhou Serum integrative omics reveals the landscape of human diabetic kidney disease
description	Objective: Diabetic kidney disease (DKD) is the most common microvascular complication of type 2 diabetes mellitus (2-DM). Currently, urine and kidney biopsy specimens are the major clinical resources for DKD diagnosis. Our study proposes to evaluate the diagnostic value of blood in monitoring the onset of DKD and distinguishing its status in the clinic. Methods: This study recruited 1,513 participants including healthy adults and patients diagnosed with 2-DM, early-stage DKD (DKD-E), and advanced-stage DKD (DKD-A) from 4 independent medical centers. One discovery and four testing cohorts were established. Sera were collected and subjected to training proteomics and large-scale metabolomics. Results: Deep profiling of serum proteomes and metabolomes revealed several insights. First, the training proteomics revealed that the combination of α2-macroglobulin, cathepsin D, and CD324 could serve as a surrogate protein biomarker for monitoring DKD progression. Second, metabolomics demonstrated that galactose metabolism and glycerolipid metabolism are the major disturbed metabolic pathways in DKD, and serum metabolite glycerol-3-galactoside could be used as an independent marker to predict DKD. Third, integrating proteomics and metabolomics increased the diagnostic and predictive stability and accuracy for distinguishing DKD status. Conclusions: Serum integrative omics provide stable and accurate biomarkers for early warning and diagnosis of DKD. Our study provides a rich and open-access data resource for optimizing DKD management.
format	article
author	Shijia Liu Yuan Gui Mark S. Wang Lu Zhang Tingting Xu Yuchen Pan Ke Zhang Ying Yu Liangxiang Xiao Yi Qiao Christopher Bonin Geneva Hargis Tao Huan Yanbao Yu Jianling Tao Rong Zhang Donald L. Kreutzer Yanjiao Zhou Xiao-Jun Tian Yanlin Wang Haiyan Fu Xiaofei An Silvia Liu Dong Zhou
author_facet	Shijia Liu Yuan Gui Mark S. Wang Lu Zhang Tingting Xu Yuchen Pan Ke Zhang Ying Yu Liangxiang Xiao Yi Qiao Christopher Bonin Geneva Hargis Tao Huan Yanbao Yu Jianling Tao Rong Zhang Donald L. Kreutzer Yanjiao Zhou Xiao-Jun Tian Yanlin Wang Haiyan Fu Xiaofei An Silvia Liu Dong Zhou
author_sort	Shijia Liu
title	Serum integrative omics reveals the landscape of human diabetic kidney disease
title_short	Serum integrative omics reveals the landscape of human diabetic kidney disease
title_full	Serum integrative omics reveals the landscape of human diabetic kidney disease
title_fullStr	Serum integrative omics reveals the landscape of human diabetic kidney disease
title_full_unstemmed	Serum integrative omics reveals the landscape of human diabetic kidney disease
title_sort	serum integrative omics reveals the landscape of human diabetic kidney disease
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/5c92b2d350eb4b5a838c01e88d8ffde6
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Serum integrative omics reveals the landscape of human diabetic kidney disease

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