Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank
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AbstractAims/hypothesis
The 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.
Methods
From 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.
Results
At 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/interpretation
Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.
The 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.
Methods
From 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.
Results
At 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/interpretation
Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.
All Author(s) ListJin Q, Lau ESH, Luk AO, Tam CHT, Ozaki R, Lim CKP, Wu H, Chow EYK, Kong APS, Lee HM, Fan B, Ng ACW, Jiang G, Lee KF, Siu SC, Hui G, Tsang CC, Lau KP, Leung JY, Tsang MW, Cheung EYN, Kam G, Lau IT, Li JK, Yeung VTF, Lau E, Lo S, Fung S, Cheng YL, Chow CC, Yu W, Tsui SKW, Tomlinson B, Huang Y, Lan HY, Szeto CC, So WY, Jenkins AJ, Fung E, Muilwijk M, Blom MT, 't Hart LM, Chan JCN, Ma RCW, Hong Kong Diabetes Biobank Study Group
Journal nameDiabetologia
Year2024
Month5
Volume Number67
Issue Number5
PublisherSpringer Science and Business Media Deutschland GmbH
Pages837 - 849
ISSN0012-186X
eISSN1432-0428
LanguagesEnglish-United Kingdom
