Identification of Potential Serum Metabolic Biomarkers of Diabetic Kidney Disease: A Widely Targeted Metabolomics Study

Abstract

Background and Objectives. Diabetic kidney disease is a leading cause of chronic kidney disease and end-stage renal disease across the world. Early identification of DKD is vitally important for the effective prevention and control of it. However, the available indicators are doubtful in the early diagnosis of DKD. This study is aimed at determining novel sensitive and specific biomarkers to distinguish DKD from their counterparts effectively based on the widely targeted metabolomics approach. Materials and Method. This case-control study involved 44 T2DM patients. Among them, 24 participants with DKD were defined as the cases and another 20 without DKD were defined as the controls. The ultraperformance liquid chromatography-electrospray ionization-tandem mass spectrometry system was applied for the assessment of the serum metabolic profiles. Comprehensive analysis of metabolomics characteristics was conducted to detect the candidate metabolic biomarkers and assess their capability and feasibility. Result. A total of 11 differential metabolites, including Hexadecanoic Acid (C16:0), Linolelaidic Acid (C18:2N6T), Linoleic Acid (C18:2N6C), Trans-4-Hydroxy-L-Proline, 6-Aminocaproic Acid, L-Dihydroorotic Acid, 6-Methylmercaptopurine, Piperidine, Azoxystrobin Acid, Lysopc 20:4, and Cuminaldehyde, were determined as the potential biomarkers for the DKD early identification, based on the multivariable generalized linear regression model and receiver operating characteristic analysis. Conclusion. Serum metabolites might act as sensitive and specific biomarkers for DKD early detection. Further longitudinal studies are needed to confirm our findings.

Metabolomics as a tool for the early diagnosis and prognosis of diabetic kidney disease

“…Interestingly, Han et al also reported changes in NEFAs and EFAs concentrations through different stages of DKD, which were hypothesized to be secondary to metabolic adjustment during kidney disease progression at the cost of end‐organ damage 86 . In the case‐control study previously described by Zhang et al, 69 it was reported that elevated concentrations of palmitic acid (C16:0), linolelaidic acid (C18:2N6T), linoleic acid (C18:2N6C), and lysophosphatidylcholine (20:4) in serum were associated with DKD onset. In support to this study, Li et al 89 used GC‐MS to analyze urine obtained from patients with type 2 diabetes with or without DKD and also detected that increased levels of both palmitic acid (C16:0) and stearic acid (C18:0) were related to DKD.…”

“…In fact, indoxyl sulfate, indole-3 acetic acid, and trimethylamine N-oxide are also known uremic toxins derived from protein metabolism by gut microbiota, and its high concentrations are associated with both DKD and cardiovascular disease. 67,68 In a case-control study, Zhang et al 69 analyzed the serum of 44 individuals with type 2 diabetes, 24 of those with DKD, and observed that trans-4-hydroxy-L-proline and 6-aminocaproic acid, a derivate of proline and derivative of lysine respectively, were associated with early DKD. In a nontargeted serum metabolomics study on 56 patients with DKD, Tavares et al 70 reported that norvaline and aspartate were inversely associated with the progress of DKD and risk of mortality.…”

Diabetic Kidney Disease in Childhood and Adolescence: Conventional and Novel Renoprotective Strategies

“…Proteomics and metabolomics have identified potential biomarkers to monitor DKD and its progression and provided information on disease pathophysiologic mechanisms. [79][80][81][82] More research on these 'multi-omics' methods will provide clinicians with the best approach to manage DKD in the future. Finally, bioinformatics have enabled the identification of noncoding RNA as potential biomarkers and therapeutic targets given their important role in DKD progression.⁸3 Investigating the regulatory network of these noncoding RNA will open a new outlook for understanding the molecular mechanisms in DKD and how the transcriptomes can act as novel biomarkers and potential therapeutic targets for DKD.⁸3 CONCLUSIONS DKD evolves from diabetic nephropathy, a critical microvascular complication of T1DM and T2DM.…”

Potential Value of TNF-α (–376 G/A) Polymorphism and Cystatin C (CysC) in the Diagnosis of Sepsis Associated Acute Kidney Injury (S-AK I) and Prediction of Mortality in Critically Ill patients

“…In recent years, numerous biomarkers have been proposed for the diagnosis of AKI, most of which are in development or validation stages (Wettersten and Weiss, 2013;Darshi et al, 2016;Zhang et al, 2020). Nevertheless, it is still unclear, whether the diagnosis of AKI requires a single biomarker approach or multiple biomarkers approaches, taking into consideration the complexity and multifactorial aspects of AKI (Baker, 2005;Abdelsattar et al, 2021).…”