Background: Metabolic syndrome (MetS) is a prevalent multifactorial disorder that can increase the risk of developing diabetes, cardiovascular diseases, and cancer. We aimed to compare different machine learning classification methods in predicting metabolic syndrome status as well as identifying influential genetic or environmental risk factors. Methods: This candidate gene study was conducted on 4756 eligible participants from the Tehran Cardio-metabolic Genetic study (TCGS). We compared predictive models using logistic regression (LR), Random Forest (RF), decision tree (DT), support vector machines (SVM), and discriminant analyses. Demographic and clinical features, as well as variables regarding common GCKR gene polymorphisms, were included in the models. We used a 10-repeated tenfold cross-validation to evaluate model performance. Results: 50.6% of participants had MetS. MetS was significantly associated with age, gender, schooling years, BMI, physical activity, rs780094, and rs780093 (P < 0.05) as indicated by LR. RF showed the best performance overall (AUC-ROC = 0.804, AUC-PR = 0.776, and Accuracy = 0.743) and indicated BMI, physical activity, and age to be the most influential model features. According to the DT, a person with BMI < 24 and physical activity < 8.8 possesses a 4% chance for MetS. In contrast, a person with BMI ≥ 25, physical activity < 2.7, and age ≥ 33, has 77% probability of suffering from MetS. Conclusion: Our findings indicated that, on average, machine learning models outperformed conventional statistical approaches for patient classification. These well-performing models may be used to develop future support systems that use a variety of data sources to identify persons at high risk of getting MetS.

Early blood product resuscitation is often essential for optimal trauma care. However, the effects of different products on the underlying trauma-induced coagulopathy and immune dysfunction are not well described. Here, we use high-dimensional analysis and causal modeling in a longitudinal study to explore the circulating proteomic response to plasma as a distinct component versus low-titer O whole blood (LTOWB), which contains plasma. We highlight the differential impacts of plasma and LTOWB on immune mediator levels and the distinct capacity of plasma to modulate coagulation by elevating fibrinogen and factor XIII and reducing platelet factor 4. A higher proportion of plasma in prehospital resuscitation is associated with improved admission time coagulation parameters in patients with severe shock and elevated brain injury markers and reduced post-admission transfusion volumes in those suffering from traumatic brain injury (TBI) and blunt injury. While LTOWB offers broad hemostatic benefits, our findings demonstrate specific advantages of plasma and support individualized transfusion strategies.

Severe injury accompanied by hemorrhagic shock triggers an early release of cell constituents into the circulation, referred to as the systemic storm. The systemic storm drives the systemic inflammatory response and is associated with increased mortality. The role of programmed cell death (PCD) in the systemic storm was investigated in mice that underwent hemorrhagic shock with tissue trauma (HS/T) followed by crystalloid resuscitation. Wild-type (WT) mice were treated with inhibitors, including z-VAD, necrostatin-1, ferrostatin-1, or disulfiram (DSF), to block the different forms of PCD. Gasdermin D (GSDMD)–dependent PCD was further targeted using Gsdmd ^−/− mice. Untargeted metabolomics and proteomics were used as the primary end point to gain a comprehensive view of the composition changes in the circulation. Although all inhibitor strategies partially reversed the systemic storm, the largest reversal occurred in Gsdmd ^−/− mice and WT mice treated with DSF, a nonspecific inhibitor of GSDMD pore formation. Weighted correlation network analysis and machine learning identified a subset of omics features increased in the circulation in a GSDMD-dependent manner. Linear regression analysis using a GSDMD-specific omics score (GSOS) in a published multiomic dataset from patients with trauma showed correlation of the GSOS with outcomes and inflammation. Female sex emerged as the variable with the strongest relationship to the GSOS in injured humans, a finding confirmed in mice. Last, DSF treatment or GSDMD deletion led to improvement in blood pressure recovery and increased survival in both sexes. These protective effects of DSF were observed when administered after HS/T, underscoring its potential as a therapeutic intervention.

Background/Objectives: Liver injury is common after abdominal trauma. However, the established biomarkers of liver injury, such as alanine aminotransferase (ALT) and aspartate aminotransferase (AST), lack accuracy. This study investigates whether specific liver-related microRNAs (miRNAs) are released into the circulation in trauma patients with liver injury and whether they can indicate liver damage in the early phase after major trauma. Methods: A retrospective analysis of prospectively collected data and blood samples from 26 trauma patients was conducted. The levels of miRNA-21-5p, -122-5p, -191-5p, -192-3p, and -212-3p were measured in patients with computed tomography-confirmed liver trauma (LT group, n = 12) and polytrauma patients without liver trauma (PT group, n = 14) upon emergency room (ER) admission, and 24 and 48 h after trauma. Additionally, liver-type fatty acid binding protein (L-FABP) was measured, as it has recently been discussed in the context of abdominal trauma. Results: Only miRNA-122-5p showed a significant increase in the LT group compared to the PT group, but only at the 48 h time point (p = 0.032). Conversely, L-FABP (p = 0.018) and ALT (p = 0.05) were significantly elevated in the LT group compared to the PT group at the time of ER admission. There was a moderate correlation between miRNA-122-5p and AIS_Abdomen (p = 0.056) and transfused red blood cell concentrates (p = 0.055). L-FABP correlated strongly with the ALT levels (p = 0.0009) and the length of stay in the ICU (p = 0.0086). Conclusions: In this study, the liver-specific miRNA-122-5p did not effectively indicate liver injury in the early acute post-traumatic phase. Future research with a large sample size should investigate whether other miRNAs can more accurately predict liver injury and the extent of hepatocellular injury, particularly in the acute post-traumatic phase.