Objectives: The authors sought to identify causal factors that explain the selective benefit of prehospital administration of thawed plasma (TP) in traumatic brain injury (TBI) patients using mediation analysis of a multiomic database. Background: The Prehospital Air Medical Plasma (PAMPer) Trial showed that patients with TBI and a pronounced systemic response to injury [defined as endotype 2 (E2)], have a survival benefit from prehospital administration of TP. An interrogation of high dimensional proteomics, lipidomics and metabolomics previously demonstrated unique patterns in circulating biomarkers in patients receiving prehospital TP, suggesting that a deeper analysis could reveal causal features specific to TBI patients. Methods: A novel proteomic database (SomaLogic Inc., aptamer-based assay, 7K platform) was generated using admission blood samples from a subset of patients (n=149) from the PAMPer Trial. This proteomic dataset was combined with previously reported metabolomic and lipidomic datasets from these same patients. A 2-step analysis was performed to identify factors that promote survival in E2-TBI patients who had received early TP. First, features were selected using both linear and multivariate-latent-factor regression analyses. Then, the selected features were entered into the causal mediation analysis. Results: Causal mediation analysis of observable features identified 16 proteins and 41 lipids with a high proportion of mediated effect (>50%) to explain the survival benefit of early TP in E2-TBI patients. The multivariate latent-factor regression analyses also uncovered 5 latent clusters of features with a proportion effect >30%, many in common with the observable features. Among the observable and latent features were protease inhibitors known to inhibit activated protein C and block fibrinolysis (SERPINA5 and CPB2), a clotting factor (factor XI), as well as proteins involved in lipid transport and metabolism (APOE3 and sPLA(2)-XIIA). Conclusions: These findings suggest that severely injured patients with TBI process exogenous plasma differently than those without TBI. The beneficial effects of early TP in E2-TBI patients may be the result of improved blood clotting and the effect of brain protective factors independent of coagulation.

Severe injury is known to cause a systemic cytokine storm that is associated with adverse outcomes. However, a comprehensive assessment of the time-dependent changes in circulating levels of a broad spectrum of protein immune mediators and soluble immune mediator receptors in severely injured trauma patients remains uncharacterized. To address this knowledge gap, we defined the temporal and outcome-based patterns of 184 known immune mediators and soluble cytokine receptors in the circulation of severely injured patients. Proteomics (aptamer-based assay, SomaLogic, Inc) was performed on plasma samples drawn at 0, 24, and 72 hours (h) from time of admission from 150 trauma patients, a representative subset from the Prehospital Plasma during Air Medical Transport in Trauma Patients at Risk for Hemorrhagic Shock (PAMPer) trial. Patients were categorized into outcome groups including Early Non-Survivors (died within 72 h; ENS; n=38), Non-Resolvers (died after 72 h or required ≥7 days of intensive care; NR; n=78), and Resolvers (survivors that required < 7 days of intensive care; R; n=34), with low Injury Severity Score (ISS) patients from the Tranexamic Acid During Prehospital Transport in Patients at Risk for Hemorrhage After Injury (STAAMP) trial as controls. The major findings include an extensive release of immune mediators and cytokine receptors at time 0h that is more pronounced in ENS and NR patients. There was a selective subset of mediators elevated at 24 and 72 h to a greater degree in NR patients, including multiple cytokines and chemokines not previously described in trauma patients. These findings were validated in a quantitative fashion using mesoscale discovery immunoassays (MSD) from an external validation cohort (VC) of samples from 58 trauma patients matched for R and NR status. This comprehensive longitudinal description of immune mediator patterns associated with trauma outcomes provides a new level of characterization of the immune response that follows severe injury.

@ShimenaL; @BrachHerzig; @JunruWu1; @SultanAbdelH; @SciosciaPio; @JBonarotiMD; @macky_neal; @jishnu1729; @Jjasonsperrymd; @TheBilliarLab; @trbilliar; #universityofpittsburghUsing comprehensive plasma proteomics, we identified that biomarkers for liver, pulmonary, neurologic, and particularly cardiac injury remained elevated in patients with complex clinical courses after severe injury.

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.