Quantitative, Targeted Analysis of Gut Microbiota Derived Metabolites Provides Novel Biomarkers of Early Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Abstract

Diabetic kidney disease (DKD) is one of the most debilitating complications of type 2 diabetes mellitus (T2DM), as it progresses silently to end-stage renal disease (ESRD). The discovery of novel biomarkers of early DKD becomes acute, as its incidence is reaching catastrophic proportions. Our study aimed to quantify previously identified metabolites from serum and urine through untargeted ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques, such as the following: arginine, dimethylarginine, hippuric acid, indoxyl sulfate, p-cresyl sulfate, L-acetylcarnitine, butenoylcarnitine and sorbitol. The study concept was based on the targeted analysis of selected metabolites, using the serum and urine of 20 healthy subjects and 90 T2DM patients with DKD in different stages (normoalbuminuria—uACR < 30 mg/g; microalbuminuria—uACR 30–300 mg/g; macroalbuminuria—uACR > 300 mg/g). The quantitative evaluation of metabolites was performed with pure standards, followed by the validation methods such as the limit of detection (LOD) and the limit of quantification (LOQ). The following metabolites from this study resulted as possible biomarkers of early DKD: in serum—arginine, dimethylarginine, hippuric acid, indoxyl sulfate, butenoylcarnitine and sorbitol and in urine—p-cresyl sulfate.

Metabolites Potentially Derived from Gut Microbiota Associated with Podocyte, Proximal Tubule, and Renal and Cerebrovascular Endothelial Damage in Early Diabetic Kidney Disease in T2DM Patients

“…The study was designed to be a pilot and cross-sectional study. It consisted of the enrollment, from the general physician’s office, of 90 T2DM patients (P group) and of 20 healthy individuals (C group) [ 12 , 13 ]. UACR was the parameter by which the pathological group (P) was divided into 3 subgroups (P1—normal to mildly increased albuminuria/normoalbuminuria, P2—moderately increased albuminuria/microalbuminuria, and P3—severely increased albuminuria/macroalbuminuria), based on the KDIGO and ADA Consensus [ 14 ].The patients included had been suffering from T2DM for at least 5 years, had no clinical symptoms or history of cerebrovascular disease, and their HbA1c did not exceed 10%.…”

“…At that point, by observing the dynamics of peak intensities of acylcarnitine and carbohydrate metabolites in the normoalbuminuric category, it was decided to also include the metabolites derived from these pathways for quantification. Information about stock solutions and the validation methods can be found in detail in our second study [ 13 ].…”

“…In the first two studies, we examined and discussed the dynamics of metabolic pathways and their resulting metabolites, the correlation between serum and urine findings, and their influence on the physiopathological mechanisms of early DKD [ 12 , 13 ].…”

“…Going further, in a second study, we have managed to point out an implication of gut-derived metabolites belonging to these pathways in early DKD, by quantitative analysis, independent of albuminuria levels. We have also explored additional metabolites related to mitochondrial dysfunction belonging to the acylcarnitine pathway [ 13 ].…”

Gut microbiota microbial metabolites in diabetic nephropathy patients: far to go

“…Two oligopeptides, Asn-Met-Cys-Ser and Asn-Cys-Pro-Pro, were correlated with the severity of proteinuria, with AUC values of 0.8857 and 0.9963, respectively, making them potential biomarkers for differentiating the severity of DN ( Peng et al., 2022 ). Through UHPLC-QTOF-ESI-MS analysis of serum and urine from 90 DN patients, arginine (AUC=0.500), L-acetylcarnitine (AUC=0.600), hippuric acid (AUC=0.700), indoxyl sulfate (AUC=0.600), butenoyl carnitine (AUC=0.600), and sorbitol (AUC=0.500) in serum, and p-cresylsulfate (AUC=0.800) in urine may serve as biomarkers for early DN ( Balint et al., 2023 ). In the rat diabetic model constructed by Kikuchi et al.…”