Among all nephropathies, diabetic kidney disease (DKD) is the most common cause of kidney impairment advancement to end-stage renal disease (ESRD). Although DKD has no cure, the disease is commonly managed by strict control of blood glucose and blood pressure, and in most of these cases, kidney function often deteriorates, resulting in dialysis, kidney replacement therapy, and high mortality. The difficulties in finding a cure for DKD are mainly due to a poor understanding of the underpinning complex cellular mechanisms that could be identified as druggable targets for the treatment of this disease. The review is thus aimed at giving insight into the interconnection between chronic hyperglycaemia and cellular mechanistic perturbations of nephropathy in diabetes. A comprehensive literature review of observational studies on DKD published within the past ten years, with 57 percent published within the past three years was carried out. The article search focused on original research studies and reviews published in English. The articles were explored using Google Scholar, Medline, Web of Science, and PubMed databases based on keywords, titles, and abstracts related to the topic. This article provides a detailed relationship between hyperglycaemia, oxidative stress, and various cellular mechanisms that underlie the onset and progression of the disease. Moreover, it also shows how these mechanisms affect organelle dysfunction, resulting in fibrosis and podocyte impairment. The advances in understanding the complexity of DKD mechanisms discussed in this review will expedite opportunities to develop new interventions for treating the disease.

Background/Objectives: Metabolic syndrome (MetS) is a heterogeneous cluster of cardiometabolic risk factors whose underlying molecular heterogeneity remains incompletely understood. Methods: Targeted metabolomics (388 metabolites) and untargeted proteomics (842 proteins) were analyzed in the KORA FF4 study (n = 1921). Multivariable regression, LASSO, and Multi-Omics Factor Analysis (MOFA) were applied to identify MetS-associated signatures. As a secondary analysis, soft-clustering archetypal analysis based on 22 clinical variables was performed within the MetS subgroup (n = 582) to identify extreme clinical subphenotypes. Results: A total of 117 metabolites and 70 proteins were associated with MetS, including five metabolites and eight proteins shared across all MetS traits. Key metabolomic features comprised diacylglycerols, triglyceride score, hexoses, and branched-chain amino acids. Prominent proteins included APOC4, APOE, APOB, APOD, APOF, adiponectin, LBP, and SHBG, reflecting alterations in lipid metabolism, inflammation, and metabolic regulation. MOFA identified a latent factor strongly linked to MetS, integrating signatures of lipid metabolism, insulin resistance, and inflammation. Archetypal analysis identified extreme MetS profiles and associated with metabolites and proteins shared across all Mets. Conclusions: Integrated multi-omics analysis revealed MetS-associated molecular signatures characterized primarily by dysregulated lipid metabolism, insulin resistance, and chronic low-grade inflammation. Secondary archetypal analysis highlighted clinical heterogeneity at the extreme boundaries of MetS while supporting a shared molecular foundation. These findings highlight the value of combining clinical, metabolomics and proteomics to improve characterization of MetS heterogeneity and inform future precision risk stratification.