ReviewInternational journal of molecular sciences2026
The Need for Omics Studies in Chronic Kidney Disease of Unknown Etiology (CKDu): A Narrative Review and Perspective.
Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors.
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Abstract
Chronic Kidney Disease of Unknown Etiology (CKDu) is an ongoing global health concern, particularly affecting agricultural communities in equatorial regions. Unlike traditional chronic kidney disease (CKD), CKDu occurs without common risk factors such as diabetes, hypertension, or kidney stones. Its etiology remains poorly understood, with environmental exposures, occupational hazards, and genetic susceptibility proposed as contributing factors. Omic technologies including genomics, transcriptomics, proteomics, metabolomics, and exposomics offer promising avenues to elucidate CKDu pathogenesis by enabling comprehensive molecular profiling and identification of biomarkers. Recent genomic studies have explored single nucleotide polymorphisms (SNPs) linked to kidney injury susceptibility, while transcriptomic analyses have identified differential expression of genes involved in oxidative stress and tubular injury pathways. Proteomic investigations have revealed candidate urinary biomarkers such as heat shock proteins and inflammatory mediators, and metabolomic profiling has highlighted alterations in amino acid and energy metabolism in affected individuals. Exposomic approaches are beginning to characterize cumulative chemical exposures, including pesticides and heavy metals, in endemic regions. This narrative review synthesizes current evidence on the application of omics approaches in CKDu research, highlights knowledge gaps, and proposes future directions for integrating multi-omics studies with machine learning and artificial intelligence approaches. Advancing omics-based investigations may provide critical insights into disease mechanisms, improve diagnostic precision, and inform targeted interventions for vulnerable populations.
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