5-Hydroxyhexanoic Acid Predicts Early Renal Functional Decline in Type 2 Diabetes Patients with Microalbuminuria

Abstract

Background/Aims: Diabetic nephropathy (DN) is a leading cause of end-stage renal disease. Microalbuminuria (MA) is widely used to predict early progressive renal function decline (ERFD) of DN in type 2 diabetes mellitus (T2D) patients, but the sensitivity and specificity of MA have been questioned. Here, we determined the urine metabolites differences between T2D patients with MA who maintained stable renal function and those who progressed to ERFD in order to identify specific biomarkers of the progression of renal dysfunction. Methods: A total of 102 T2D patients with MA and normal renal function at baseline were followed up for 5–6 years. Of these, 52 patients were selected and classified into two groups according to the later renal function; 25 patients who experienced ERFD were regarded as the progressive group, while 27 patients who maintained stable renal function were considered as the stable group. In the pilot study, untargeted, broad-spectrum urine metabolomics was performed on the urine of 12 subjects from the progressive group (5 patients as “progressors”) and stable group (7 patients as “non-progressors”) to discover candidate markers. We then used a targeted metabolomics analysis to identify the selected markers in the urine of an additional 40 patients (20 from the progressive group as cases, and 20 from the stable group as controls) in the validation study. Results: A total of 318 known metabolites were detected in the pilot study and 6 metabolites with significant difference between progressors and non-progressors were identified. The levels of 4 metabolites, including azelaic acid, adipic acid, 5-hydroxyhexanoic acid, and L-tryptophan decreased significantly, while levels of L-pyroglutamic acid and D-norvaline increased observably in the progressors compared with non-progressors. Furthermore, in the validation study, 6 metabolites were confirmed by quantitative measurements and their concentrations were consistent with the changes in the pilot study. Concentrations of L-pyroglutamic acid and D-norvaline still increased in the cases, but were not statistically significant. Of the 4 metabolites with decreased concentrations among the cases, only 5-hydroxyhexanoic acid remained statistically significant while the other 3 metabolites did not differ between cases and controls. Conclusion: We have identified urine metabolites and shown that 5-hydroxyhexanoic acid can be used as a predictor of progression of ERFD in T2D patients with MA. This finding provides the new perspective that 5-hydroxyhexanoic acid may be useful to identify T2D patients with MA who are at risk of ERFD.

Urinary metabolite profiling identifies biomarkers for risk of progression of diabetic nephropathy in 2,670 individuals with type 1 diabetes

“…Urinary pyroglutamate, that was associated with progression from macroalbuminuria to ESKD, has previously been linked to incident macroalbuminuria in individuals with type 2 diabetes and microalbuminuria. 46 A previous study in type 1 diabetes with 25 individuals linked pyroglutamate to the progression of early kidney disease from normoalbuminuria to microalbuminuria. 47 However, in our study, we did not find any link between pyroglutamate and such early progression.…”

Urine Metabolomics Analysis in Patients With Normoalbuminuric Diabetic Kidney Disease

“…Recently, the metabolomics related to the occurrence and development of DKD has attracted much attention ( Rhee, 2015 ). Although there have been some reports on metabolomics in DKD patients, these studies ( Chen et al, 2018 ; Tang et al, 2019 ) have mostly focused on DKD patients with elevated urinary albumin. However, studies on NADKD patients have been rare.…”

Transcriptome and metabolite analyses provide insights into zigzag-shaped stem formation in tea plants (Camellia sinensis)

“…The effluent was further transported to an ESItriple quadrupole-linear ion trap (QTRAP)-MS. Analyst 1.6.1 software was employed for metabolite identification. Details on data filtering, peak detection, alignment, calculations, and differential metabolite identification were described by Tang et al [64]. Particularly, metabolites with T-test P values < 0.05 and VIP ≥ 1 were considered as differential metabolites and mapped to KEGG metabolic pathways for pathway enrichment analysis (FDR ≤ 0.05) [65].…”