Mendelian Randomization Study on Serum Metabolites and Diabetic Nephropathy Risk: Identifying Potential Biomarkers...

In this study, the causation between serum metabolites and the risk of Diabetic Nephropathy (DN) was investigated by means of a Mendelian Randomization (MR) analysis.

Our data on diabetic nephropathy were obtained from the IEU OpenGWAS Project database, while serum metabolite data originated came from the GWAS summary statistics by Chen et al. The Inverse Variance Weighted (IVW) method was the main analysis approach, with Weighted Median (WME) and MREgger regression serving as supplementary approaches to construing the causalities between serum metabolites and the DN risk. In addition to the MR-Egger regression intercept, Cochran's Q test was utilized for sensitivity analysis, with P values used as the metric to assess the results.

In total, 14 SNPs regarding serum metabolites were chosen as Instrumental Variables (IVs). The IVW results indicated that levels of Behenoylcarnitine (C22), Arachidoylcarnitine (C20), and the ratio of 5-methylthioadenosine (MTA) to phosphate exerted a positive causal effect on the DN risk. Conversely, levels of 5-hydroxylysine, Butyrylglycine, 1-stearoyl-glycerophosphocholine (18:0), Isobutyrylglycine, 1-stearoyl-2- oleoyl-GPE (18:0/18:1), N2,N5-diacetylornithine, 2-butenoylglycine, 3-hydroxybutyroylglycine, N-acetylisoputreanine, the ratio of Arginine to Ornithine, and the ratio of Aspartate to Mannose exerted a negative impact of causality on the DN risk. By identifying these serum metabolites, high-risk patients can be recognized in the early stages of diabetic nephropathy, enabling preventive measures or delaying its progression. These findings also provide a solid foundation for further research into the underlying etiology of diabetic nephropathy.

The translation of serum metabolites into clinical applications for DN aims to utilize changes in serum metabolites as biomarkers for early diagnosis, thereby monitoring the progression of DN and providing a foundation for personalized treatment. For instance, the development of serum metabolite diagnostic kits could be used for early detection and prevention of DN. Changes in metabolites can help identify different stages of DN.