OBJECTIVES: To investigate the association of serum albumin level after human albumin infusion with 28-day mortality in critically ill patients with acute kidney injury (AKI) and its impact on 90-day outcomes of the patients. METHODS: We conducted a retrospective cohort study based on the MIMIC IV database (2008-2019), including 5918 AKI patients treated with albumin in the ICU. Based on serum albumin levels within 72 h after albumin infusion, the patients were divided into low (<30 g/L), medium (30-35 g/L), and high albumin (>35 g/L) groups. Restricted cubic spline regression and multivariate logistic regression were used to analyze the association of albumin levels with patient mortality, and the results were verified in a external validation cohort consisting of 110 sepsis-induced AKI patients treated in Nanfang Hospital between 2017 and 2022 using survival analysis and multivariate adjustment. RESULTS: In the MIMIC training cohort, multivariate logistic regression showed no significant differences in 28-day mortality of the patients with different albumin levels (P>0.05). However, restricted cubic spline analysis indicated a non-linear dose-response relationship between albumin levels and 28-day mortality (threshold effect: risk increased when albumin levels >3.6 g/dL). Secondary endpoint analysis revealed that the patients with high albumin levels had a shorter duration of mechanical ventilation (P<0.001) but a longer ICU stay (P<0.001). In the validation cohort, albumin levels ≥30 g/L were significantly associated with a reduced 28-day mortality rate (P<0.05). CONCLUSIONS The association between increased serum albumin levels following albumin infusion and 28-day mortality of critically ill patients with AKI exhibits a cohort dependency and can be influenced by multiple factors including disease type and severity, infusion strategies, and statistical methods.
Humans ; Acute Kidney Injury/therapy* ; Critical Illness/mortality* ; Retrospective Studies ; Serum Albumin/analysis* ; Male ; Female ; Intensive Care Units ; Middle Aged ; Logistic Models ; Aged

Diabetic nephropathy is a common microvascular complication of diabetes mellitus and one of the main causes of death in patients with diabetes mellitus. Ferroptosis is a newly discovered iron-dependent regulated cell death, which may contribute to the pathogenesis and development of diabetic nephropathy. Adenosine monophosphate-activated protein kinase (AMPK)-mediated ferroptosis-related signaling pathways can slow down the progression of diabetic nephropathy, but excessive activation of AMPK signaling pathway may induce cells to undergo autophagic death. Activation of the signaling pathway mediated by nuclear factor-erythroid 2-related factor (Nrf) 2 and heme oxygenase (HO)-1 can inhibit ferroptosis of cells and alleviate diabetic nephropathy. However, the regulatory effect of HO-1 on ferroptosis is bidirectional, and activation of HIF-1α/HO-1 pathway may lead to intracellular iron overload and ultimately promote ferroptosis. Transforming growth factor (TGF)-β1 mediated signaling pathways can accelerate lipid peroxidation by down-regulating the levels of SLC7A11/GSH/GPX4. The ferroptosis-related signaling pathways mediated by exosome lncRNAs/circRNAs/miRNAs are also involved in the pathogenesis and development of diabetic nephropathy. In addition, signaling pathways mediated by stimulator of interferon gene (STING) and the novel ferroptosis promoter acyl-CoA synthetase long-chain family (ACSL) 1 can induce ferroptosis to promote the progression of diabetic nephropathy. In this review, we focus on the roles of ferroptosis in diabetic nephropathy through the signaling pathways mediated by AMPK, Nrf2/HO-1, TGF-β and exosomes, to elaborate the pathogenesis and development of diabetic nephropathy, and the potential therapeutic target for diabetic nephropathy.
Diabetic Nephropathies/etiology* ; Ferroptosis ; Humans ; Signal Transduction ; Heme Oxygenase-1/genetics* ; NF-E2-Related Factor 2/metabolism* ; AMP-Activated Protein Kinases/metabolism* ; Transforming Growth Factor beta1/metabolism* ; MicroRNAs/metabolism* ; RNA, Long Noncoding/genetics* ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Lipid Peroxidation ; Animals