Metabolomic Profile Predicts Development of Microalbuminuria in Individuals with Type 1 Diabetes

Abstract

Elevated urinary albumin excretion (microalbuminuria) is an early marker of diabetic nephropathy, but there is an unmet need for better biomarkers that capture the individuals at risk with higher accuracy and earlier than the current markers do. We performed an untargeted metabolomic study to assess baseline differences between individuals with type 1 diabetes who either developed microalbuminuria or remained normoalbuminuric. A total of 102 individuals progressed to microalbuminuria during a median follow-up of 3.2 years, whereas 98 sex-, age- and body mass index (BMI) matched non-progressors remained normoalbuminuric during a median follow-up of 7.1 years. Metabolomic screening identified 1,242 metabolites, out of which 111 differed significantly between progressors and non-progressors after adjustment for age of diabetes onset, baseline glycosylated hemoglobin A1c (HbA_1c), and albumin excretion rate (AER). The metabolites that predicted development of microalbumiuria included several uremic toxins and carnitine metabolism related molecules. Iterative variable selection indicated erythritol, 3-phenylpropionate, and N-trimethyl-5-aminovalerate as the best set of variables to predict development of microalbuminuria. A metabolomic index based on these metabolites improved the prediction of incident microalbuminuria on top of the clinical variables age of diabetes onset, baseline HbA_1c and AER (ROC_AUC = 0.842 vs 0.797), highlighting their ability to predict early-phase diabetic nephropathy.

Figure 1

The top 30 metabolites in the RF analysis ordered by Gini-index. Progression to microalbuminuria was set as the response variable and all serum metabolites identified by the platform were set as predictors. Yellow = carbohydrate/polyol, green = peptide intermediate, blue = amino acid intermediate, orange = lipid, violet = nucleotide intermediate, light blue = amino acid, purple = steroid, grey = unknown. The odds ratios for individual metabolites adjusted for age of diabetes onset, baseline HbA_1c and AER are shown on the right.

Figure 2

Correlation plot for the top 30 random forest selected metabolites and the three clinical variables age of diabetes onset, baseline HbA_1c and AER. The values [−100, 100] represent correlation coefficients which were multiplied by 100. The clinical variables showed only weak to modest correlations with the top 30 RF selected metabolites (highest between HbA_1c and N-trimethyl-5-aminovalerate, r = 0.37). The strongest correlations between the metabolites can be seen between X-11440 and pregnen-diol disulfate (r = 0.85) and between γ-glutamylglutamate and γ-glutamyllysine (r = 0.71).

Figure 3

(a) When the Mtb.index is added to the most significant clinical factors (age of diabetes onset, baseline HbA_1c and AER), ROC_AUC increases to 0.842 compared to 0.797 for clinical variables only. (b) Survival plot of progression to microalbuminuria of individuals with type 1 diabetes by quartiles of metabolomics index (Mtb.index). Individuals in the two top quartiles (i.e. Mtb.index above median) showed more rapid progresion to the microalbuminuria compared to the patients at the bottom quartiles.