Article
作者: Chow, Chun Chung ; Wu, Hongjiang ; Lee, Ka Fai ; Fung, Samuel ; Lan, Hui-Yao ; Jenkins, Alicia J ; Hui, Grace ; Tam, Claudia H T ; Li, June K ; Tomlinson, Brian ; Luk, Andrea O ; Chan, Juliana C N ; Kam, Grace ; Lim, Cadmon K P ; Muilwijk, Mirthe ; Chow, Elaine Y K ; Ozaki, Risa ; Jin, Qiao ; Yeung, Vincent T F ; Jiang, Guozhi ; Lo, Stanley ; Lau, Ip Tim ; Lau, Emmy ; Blom, Marieke T ; Tsang, Chiu Chi ; Ma, Ronald C W ; Kong, Alice P S ; Yu, Weichuan ; 't Hart, Leen M ; Leung, Jenny Y ; Cheng, Yuk Lun ; Siu, Shing Chung ; Ng, Alex C W ; Tsui, Stephen K W ; Fan, Baoqi ; So, Wing Yee ; Lau, Kam Piu ; Tsang, Man-Wo ; Szeto, Cheuk Chun ; Huang, Yu ; Lee, Heung Man ; Lau, Eric S H ; Fung, Erik ; Cheung, Elaine Y N
AbstractAims/hypothesisThe aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers.MethodsFrom a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m2) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts.ResultsAt false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts.Conclusions/interpretationAltered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.Graphical Abstract