Metabolomic Assessment Reveals Alteration in Polyols and Branched Chain Amino Acids Associated With Present and Future Renal Impairment in a Discovery Cohort of 637 Persons With Type 1 Diabetes

Abstract

Background: Improved understanding of the pathophysiology causing diabetic kidney disease (DKD) is imperative. The aim of this study was to uncover associations between serum metabolites and renal outcomes.Methods: Non-targeted serum metabolomics analyses were performed in samples from 637 persons with type 1 diabetes using two-dimensional gas chromatography coupled to time-of-flight mass-spectrometry. Longitudinal data at follow-up (median 5.5 years) on renal events were obtained from national Danish health registries. A composite renal endpoint (n = 123) consisted of estimated glomerular filtration rate (eGFR) decline from baseline (≥30%), progression to end-stage renal disease and all-cause mortality. Metabolites with significant associations (p < 0.05) in any of the cross-sectional analyses with eGFR and albuminuria were analyzed for specific and composite endpoints. Adjustments included traditional cardiovascular risk factors and correction for multiple testing.Results: A data-driven partial correlation analysis revealed a dense fabric of co-regulated metabolites and clinical variables dominated by eGFR. Ribonic acid and myo-inositol were inversely associated with eGFR, positively associated with macroalbuminuria (p < 0.02) and longitudinally associated with higher risk of eGFR decline ≥30% (HR 2.2–2.7, CI [1.3–4.3], p < 0.001). Ribonic acid was associated with a combined renal endpoint (HR 1.8, CI [1.3–2.3], p = 0.001). The hydroxy butyrate 3,4-dihydroxybutanoic acid was cross-sectionally associated with micro- and macroalbuminuria, urinary albumin excretion rate and inversely associated with eGFR (p < 0.04) while branched chain amino acids were associated with eGFR and lower risk of the combined renal endpoint (p < 0.02).Conclusions: Alterations in serum metabolites, particularly polyols and amino acids, were associated with renal endpoints in type 1 diabetes highlighting molecular pathways associated with progression of kidney disease. External validation is needed to further assess their roles and potentials as future therapeutic targets.

Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

“…None of the metabolites measured were significantly associated with urinary albumin excretion. These findings are in line with results from a recent study in type 1 diabetes where metabolites were mainly associated with eGFR and not albuminuria (29).…”

Circulating Metabolites and Lipids Are Associated to Diabetic Retinopathy in Individuals With Type 1 Diabetes

“…The metabolomics analysis is detailed in Tofte et al ( 11 ), and the lipidomics analysis is detailed in Tofte et al ( 12 ). For completeness, the analyses are outlined here as follows: Serum samples, stored at −80°C, were analyzed by two different analytical methods.…”

“…The raw data were preprocessed with MZmine 2 ( 16 ). A complete list of identified metabolites is available in Tofte et al ( 11 ). Finally, the metabolomics and lipidomics data were postprocessed in R software, as described previously ( 11 , 12 ).…”

A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes

“…Serum lipidomic measurements of 669 individuals with T1D from Steno identified cross-sectional associations between phosphatidylcholine and sphingomyelin species and eGFR and albuminuria, out of which 13 lipids were longitudinally associated with eGFR or albuminuria slope [15]. Serum metabolomic measurements in this cohort additionally revealed ribonic acid and myo-inositol to be inversely associated with ≥30% eGFR decline [16]. Also, in a study of 465 individuals with T1D from CACTI, a panel of 4 out of 252 urine peptides identified in a label-free discovery analysis improved prediction of annual eGFR decline ≥3.3% and/or development of albuminuria when added to DKD risk factors (increase in AUC from 0.84 to 0.89) [43].…”

“…The development of “omics” approaches has facilitated biomarker identification in DKD via quantification of low-molecular weight proteins, metabolites, and lipids in blood and urine using refined mass spectrometry techniques [15, 16]. A new multidimensional urinary proteome classifier (CKD273) has identified new peptide markers of interest and demonstrated promise in detecting individuals with diabetes who are at risk for progression of DKD, though studies have primarily focused on individuals with type 2 diabetes (T2D) [17, 18].…”