Kidney disease affects approximately ~850 million people globally, with millions dependent on dialysis as life-sustaining renal replacement therapy. While dialysis corrects solute accumulation and fluid overload, it incompletely replicates normal renal physiology and introduces its own biological stressors, perpetuating a cycle of systemic dysfunction that extends well beyond the kidney. Central to this dysfunction is the kidney–gut–immune–nutrition (KGIN) axis: a bidirectional network linking renal clearance, intestinal microbial homeostasis, immune regulation, and nutritional metabolism. In end-stage renal disease, each component of this axis is simultaneously disrupted and becomes a driver of further deterioration. Uremic toxin accumulation remodels the gut microbiome toward a dysbiotic state, characterized by depletion of short-chain fatty acid-producing organisms and expansion of pathobionts. The resulting compromise of intestinal barrier integrity permits systemic translocation of lipopolysaccharide and other microbial products, sustaining chronic low-grade inflammation and a paradoxical state of immune dysfunction marked by heightened inflammatory signaling alongside impaired pathogen defense. These perturbations converge on nutritional status, driving protein-energy wasting through catabolic cytokine signaling, anorexia, and suppression of anabolic pathways, with sarcopenia reported in several dialysis cohorts to progress at two to three fold faster than age related muscle loss. Hemodialysis and peritoneal dialysis produce distinct patterns of axis disruption: hemodialysis is characterized by intermittent toxin peaks, bioincompatible membrane-driven complement activation, and more severe dysbiosis, whereas peritoneal dialysis introduces continuous peritoneal glucose exposure and a unique local immune microenvironment, with generally slower decline in residual kidney function. These modality-specific differences translate into divergent cardiovascular, infectious, and nutritional risk profiles that are incompletely explained by traditional clinical variables. Emerging multi-omics approaches, including metagenomics, metabolomics, and high-throughput immune profiling, are beginning to characterize axis disruption with unprecedented resolution, enabling in-depth characterization of host-microbiome-immune identification of patient subgroups with distinct mechanistic signatures that cannot be captured by single domain analysis alone. To bridge this analytical capability with clinical practice, we propose the Microbiome–Immune–Nutrition (MIN) Index, a conceptual, hypothesis generating composite biomarker framework designed to synthesize data across all three axis domains and stratify patients by the severity of disruption to guide targeted, personalized interventions, which remain to be demonstrated through prospective validation studies. Therapeutic strategies targeting the axis, including dietary fiber and plant-based approaches, synbiotics, fecal microbiota transplantation, advanced dialysis modalities, and omega-3 and polyphenol supplementation, show mechanistic promise but require larger, better-designed trials with hard clinical endpoints before routine clinical adoption. Standardization of microbiome research methodology, investment in longitudinal multi-omics cohorts, and development of international collaborative consortia are research priorities that will be essential to translate mechanistic insights into improvements in survival and quality of life for patients on maintenance dialysis.

Renal fibrosis is an important pathogenic characteristic in the course of chronic kidney disease, driven by intricate interactions between mitochondrial failure and epigenetic control. The kidney is highly energy dependent and hence particularly vulnerable to mitochondrial disturbances such as decreased oxidative phosphorylation, mitochondrial reactive oxygen species (ROS) overproduction, reduced ATP production and mitochondrial DNA damage. Such changes induce oxidative stress and inflammatory reactions leading to tubular damage and fibrotic remodeling. Epigenetic processes such as DNA methylation, histone modifications and non-coding RNAs influence gene-expression programmes linked to inflammation, death, epithelial plasticity and extracellular-matrix deposition. Indeed, recent research indicates that there is reciprocal crosstalk between mitochondrial metabolism and epigenetic regulation, with metabolites such as NAD+, acetyl-CoA, α-ketoglutarate, succinate, fumarate and lactate, as well as ROS signaling, as mediators of this crosstalk. This interaction may also enhance inflammatory and fibrotic systems that drive chronic kidney disease progression. This mito-epigenetic axis has emerged as a therapeutic target. Potential strategies to mitigate renal fibrosis include SIRT1/PGC-1α signaling, AMPK activation, NRF2 antioxidant responses, epigenetic regulators (e.g., HDACs and DNMTs), and mitochondria-targeted antioxidants. Hence, this review analyses evidence according to illness, renal cell type, immunological state and translational maturity rather than considering mitochondrial and epigenetic alterations as uniform whole-kidney processes.

Background: Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed because of a lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated comorbidities. Methodology: The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites of diabetes (DB) and diabetes-related comorbidity (DC) along with the healthy control (HC) subjects. A combined and pairwise analysis was performed, between the serum samples of HC (n=50), and DB (n=38), and DC (n=35) individuals to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t-test, AUROC analysis to examine the statistical significance of the identified metabolites. Results: The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline and threonine). The metabolites were further used to identify the perturbed metabolic pathway and correlation of metabolites in DC patients. Conclusion: The 1H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its comorbidities on their onset or progression by determining the altered levels of the metabolites in serum.