Abstract
Aims/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. Graphical Abstract: (Figure presented.)
© The Author(s) 2024.
| Original language | English |
|---|---|
| Pages (from-to) | 837-849 |
| Journal | Diabetologia |
| Volume | 67 |
| Issue number | 5 |
| Online published | 27 Feb 2024 |
| DOIs | |
| Publication status | Published - May 2024 |
Funding
This work was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region (CU R4012-18), Research Grants Council Theme-based Research Scheme (T12-402/13N), the Focused Innovation Scheme, the University Grants Matching Scheme, and a Croucher Foundation Senior Medical Research Fellowship. The metabolomic studies in the DCS cohort were performed within the framework of the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI) Metabolomics Consortium funded by BBMRI-NL, a research infrastructure financed by the Dutch Research Council, NWO (grant numbers 184021.007 and 184033.111). It was also funded by ZonMW Priority Medicines Elderly (grant number 113102006) and the Parelsnoer Initiative (PSI). PSI is part of and is funded by the Dutch Federation of University Medical Centres and, from 2007 to 2011, received initial funding from the Dutch government. The funding sources did not have any role in the design, interpretation of the study or the decision to publish the results.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Research Keywords
- Cardiovascular disease
- Diabetic kidney disease
- Metabolomics
- NMR spectroscopy
- Prognostic biomarker
- Risk stratification
- Severely increased albuminuria
- Type 2 diabetes
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
RGC Funding Information
- RGC-funded
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