Growth Differentiation Factor-15 Orchestrates Inflammation-Related Diseases via Macrophage Polarization.

Macrophage polarization is a critical determinant of disease progression and regression. Studies on macrophage plasticity and polarization can provide a theoretical basis for the tactics of diagnosis and treatment for macrophage-related diseases. These include inflammation-related diseases, such as sepsis, tumors, and metabolic disorders. Growth differentiation factor-15 (GDF-15) or macrophage inhibitory cytokine-1, a 25 kDa secreted homodimeric protein, is a member of the transforming growth factor-ß (TGF-ß) superfamily that is released in response to external stressors. GDF-15 regulates biological effects such as tumor occurrence, inflammatory response, tissue damage, angiogenesis, and bone metabolism. It has been shown to exert anti-inflammatory and pro-inflammatory effects in inflammation-related diseases. Moreover, inflammatory stimuli can induce GDF-15 expression in immune and parenchymal cells. GDF-15 exhibits a feedback inhibitory effect by inhibiting tumor necrosis factor-α secretion during the macrophage activation anaphase, suggesting that there may be a close association between the two. GDF-15 directly induces CD14+ monocytes to produce the M2-like macrophage phenotype, inhibits monocyte-derived macrophage for M1-like polarization, and induces monocyte-derived Mφ for M2-like polarization. This review summarizes the macrophage polarization mechanism of GDF-15 under the conditions of sepsis, colon cancer, atherosclerosis, and obesity. An improved understanding of the role and molecular mechanisms of action of GDF-15 could greatly elucidate the mechanism of disease occurrence and development and provide new ideas for targeted disease prevention and treatment. An advanced understanding of the function and molecular mechanisms of action of GDF-15 may be helpful in the assessment of its potential value as a therapeutic and diagnostic target.

Development of early prediction model of in-hospital cardiac arrest based on laboratory parameters.

BACKGROUND: In-hospital cardiac arrest (IHCA) is an acute disease with a high fatality rate that burdens individuals, society, and the economy. This study aimed to develop a machine learning (ML) model using routine laboratory parameters to predict the risk of IHCA in rescue-treated patients. METHODS: This retrospective cohort study examined all rescue-treated patients hospitalized at the First Medical Center of the PLA General Hospital in Beijing, China, from January 2016 to December 2020. Five machine learning algorithms, including support vector machine, random forest, extra trees classifier (ETC), decision tree, and logistic regression algorithms, were trained to develop models for predicting IHCA. We included blood counts, biochemical markers, and coagulation markers in the model development. We validated model performance using fivefold cross-validation and used the SHapley Additive exPlanation (SHAP) for model interpretation. RESULTS: A total of 11,308 participants were included in the study, of which 7779 patients remained. Among these patients, 1796 (23.09%) cases of IHCA occurred. Among five machine learning models for predicting IHCA, the ETC algorithm exhibited better performance, with an AUC of 0.920, compared with the other four machine learning models in the fivefold cross-validation. The SHAP showed that the top ten factors accounting for cardiac arrest in rescue-treated patients are prothrombin activity, platelets, hemoglobin, N-terminal pro-brain natriuretic peptide, neutrophils, prothrombin time, serum albumin, sodium, activated partial thromboplastin time, and potassium. CONCLUSIONS: We developed a reliable machine learning-derived model that integrates readily available laboratory parameters to predict IHCA in patients treated with rescue therapy.

A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts.

BACKGROUND: High throughput gene expression profiling is a valuable tool in providing insight into the molecular mechanism of human diseases. Hypoxia- and lactate metabolism-related genes (HLMRGs) are fundamentally dysregulated in sepsis and have great predictive potential. Therefore, we attempted to build an HLMRG signature to predict the prognosis of patients with sepsis. METHODS: Three publicly available transcriptomic profiles of peripheral blood mononuclear cells from patients with sepsis (GSE65682, E-MTAB-4421 and E-MTAB-4451, total n = 850) were included in this study. An HLMRG signature was created by employing Cox regression and least absolute shrinkage and selection operator estimation. The CIBERSORT method was used to analyze the abundances of 22 immune cell subtypes based on transcriptomic data. Metascape was used to investigate pathways related to the HLMRG signature. RESULTS: We developed a prognostic signature based on five HLMRGs (ERO1L, SIAH2, TGFA, TGFBI, and THBS1). This classifier successfully discriminated patients with disparate 28-day mortality in the discovery cohort (GSE65682, n = 479), and consistent results were observed in the validation cohort (E-MTAB-4421 plus E-MTAB-4451, n = 371). Estimation of immune infiltration revealed significant associations between the risk score and a subset of immune cells. Enrichment analysis revealed that pathways related to antimicrobial immune responses, leukocyte activation, and cell adhesion and migration were significantly associated with the HLMRG signature. CONCLUSIONS: Identification of a prognostic signature suggests the critical role of hypoxia and lactate metabolism in the pathophysiology of sepsis. The HLMRG signature can be used as an efficient tool for the risk stratification of patients with sepsis.