Search

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Background: Acute Kidney Injury (AKI) is a very frequent condition, occurring in about one in three patients admitted to an intensive care unit (ICU). AKI is a syndrome defined as a sudden decrease in glomerular filtration rate. However, this unified definition does not reflect the various mechanisms involved in AKI pathophysiology, each with its own characteristics and sensitivity to therapy. In this study, we aimed at developing an innovative machine learning based method able to subphenotype AKI according to its pattern of risk factors., Methods: We adopted a three-step pipeline of analyses. First, we looked for factors associated with AKI using a generalized additive model. Second, we calculated the importance of each identified AKI related factor in the estimated AKI risk to find the main risk factor for AKI, at the single patient level. Lastly, we clusterized AKI patients according to their profile of risk factors and compared the clinical characteristics and outcome of every cluster. We applied this method to a cohort of severe COVID-19 patients hospitalized in the ICU of the Geneva University Hospitals., Results: Among the 248 patients analyzed, we found 7 factors associated with AKI development. Using the individual expression of these factors, we identified three groups of AKI patients, based on the use of Lopinavir/Ritonavir, baseline eGFR, use of dexamethasone and AKI severity. The three clusters expressed distinct characteristics in terms of AKI severity and recovery, metabolic patterns and hospital mortality., Conclusion: We propose here a new method to phenotype AKI patients according to their most important individual risk factors for AKI development. When applied to an ICU cohort of COVID-19 patients, we were able to differentiate three groups of patients. Each expressed specific AKI characteristics and outcomes, which probably reflect a distinct pathophysiology., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Legouis, Criton, Assouline, Le Terrier, Sgardello, Pugin, Marchi and Sangla.)

Acute and chronic kidney disease are responsible for large healthcare costs worldwide. During injury, kidney metabolism undergoes profound modifications in order to adapt to oxygen and nutrient shortage. Several studies highlighted recently the importance of these metabolic adaptations in acute as well as in chronic phases of renal disease, with a potential deleterious effect on fibrosis progression. Until recently, glucose metabolism in the kidney has been poorly studied, even though the kidney has the capacity to use and produce glucose, depending on the segment of the nephron. During physiology, renal proximal tubular cells use the beta-oxidation of fatty acid to generate large amounts of energy, and can also produce glucose through gluconeogenesis. In acute kidney injury, proximal tubular cells metabolism undergo a metabolic shift, shifting away from beta-oxidation of fatty acids and gluconeogenesis toward glycolysis. In chronic kidney disease, the loss of fatty acid oxidation is also well-described, and data about glucose metabolism are emerging. We here review the modifications of proximal tubular cells glucose metabolism during acute and chronic kidney disease and their potential consequences, as well as the potential therapeutic implications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Faivre, Verissimo, Auwerx, Legouis and de Seigneux.)