Background: Sepsis-associated acute kidney injury (SA-AKI) is a frequent complication in patients with sepsis and is associated with high mortality. Therefore, early recognition of SA-AKI is essential for administering supportive treatment and preventing further damage. This study aimed to identify and validate metabolite biomarkers of SA-AKI to assist in early clinical diagnosis. Methods: Untargeted renal proteomic and metabolomic analyses were performed on the renal tissues of LPS-induced SA-AKI and sepsis mice. Glomerular filtration rate (GFR) monitoring technology was used to evaluate real-time renal function in mice. To elucidate the distinctive characteristics of SA-AKI, a multi-omics Spearman correlation network was constructed integrating core metabolites, proteins, and renal function. Subsequently, metabolomics analysis was used to explore the dynamic changes of core metabolites in the serum of SA-AKI mice at 0, 8, and 24 h. Finally, a clinical cohort (28 patients with SA-AKI vs. 28 patients with sepsis) serum quantitative metabolomic analysis was carried out to build a diagnostic model for SA-AKI via logistic regression (LR). Results: Thirteen differential renal metabolites and 112 differential renal proteins were identified through a multi-omics study of SA-AKI mice. Subsequently, a multi-omics correlation network was constructed to highlight five core metabolites, i.e., 3-hydroxybutyric acid, 3-hydroxymethylglutaric acid, creatine, myristic acid, and inosine, the early changes of which were then observed via serum time series experiments of SA-AKI mice. The levels of 3-hydroxybutyric acid, 3-hydroxymethylglutaric acid, and creatine increased significantly at 24 h, myristic acid increased at 8 h, while inosine decreased at 8 h. Ultimately, based on the identified core metabolites, we recruited 56 patients and constructed a diagnostic model named IC3, using inosine, creatine, and 3-hydroxybutyric acid, to early identify SA-AKI (AUC = 0.90). Conclusions: We proposed a blood metabolite model consisting of inosine, creatine, and 3-hydroxybutyric acid for the early screening of SA-AKI. Future studies will observe the performance of these metabolites in other clinical populations to evaluate their diagnostic role.

Although the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has spread, data on the clinical characteristics of infected patients are limited. In this study, the demographic, clinical characteristics, and laboratory data of 310 SARS-CoV-2 Omicron variant patients treated at Haihe Hospital of Tianjin were collected and analyzed. Information on these patients was compared to 96 patients with the Delta variant of concern (VOC) and 326 patients with the Beta VOC during the previous coronavirus disease 2019 (COVID-19) outbreak in Harbin. Of the 310 patients infected with the Omicron variant, the median age was 35 years. Most patients were clinically classified as mild (57.74%), and the most common symptoms were cough (48.71%), fever (39.35%), and sore throat (38.26%). The results for different vaccination groups in the Omicron group showed that the median of “SARS-CoV-2 specific IgG” after 2 or 3 doses of vaccination was higher than the unvaccinated group (all Ps ​< ​0.05). Older age was associated with a higher proportion of moderate cases and lower asymptomatic and mild cases based on clinical classifications. Compared to the Delta and Beta groups, the median age of the Omicron group was younger. The total number of asymptomatic patients and mild patients in the Omicron virus group was higher than the Delta and Beta groups (60.97% vs. 54.17% vs. 47.55%). This study presented the clinical characteristics of the first group of patients infected with the Omicron variant in Tianjin, China, and compared their clinical features with patients infected by the Delta and Beta variants, which would increase our understanding of the characteristics of SARS-CoV-2 Omicron variant.