Transcriptomic and Proteomic Characterization of the Immune Response to Elective Spinal Reconstructive Surgery: Impact of Aging and Comparison with Traumatic Injury Response.

BACKGROUND: Major surgery triggers trauma-like stress responses linked to age, surgery duration, and blood loss, resembling polytrauma. This similarity suggests elective surgery as a surrogate model for studying polytrauma immune responses. We investigated stress responses across age groups and compared them with those of polytrauma patients. STUDY DESIGN: Patients undergoing major spinal reconstruction surgery were divided into older (age >65 years, n = 5) and young (age 18 to 39 years, n = 6) groups. A comparison group consisted of matched trauma patients (n = 8). Blood samples were collected before, during, and after surgery. Bone marrow mononuclear cells and peripheral blood mononuclear cells were analyzed using cellular indexing of transcriptomes and epitopes sequencing or single-cell RNA sequencing. Plasma was subjected to dual-platform proteomic analysis (SomaLogic and O-link). RESULTS: Response to polytrauma was highest within 4 hours. By comparison, the response to surgery was highest at 24 hours. Both insults triggered significant changes in cluster of differentiation 14 monocytes, with increased inflammation and lower major histocompatibility complex-class 2 expression. Older patient's cluster of differentiation 14 monocytes displayed higher inflammation and less major histocompatibility complex-class 2 suppression; a trend was also seen in bone marrow mononuclear cells. Although natural killer cells were markedly activated after polytrauma, they were suppressed after surgery, especially in older patients. In plasma, innate immunity proteins dominated at 24 hours, shifting to adaptive immunity proteins by 6 weeks with heightened inflammation in older patients. Senescence-associated secretory phenotype proteins were higher in older patients at baseline and further elevated during and after surgery. CONCLUSIONS: Although both major surgery and polytrauma initiate immune and stress responses, substantial differences exist in timing and cellular profiles, suggesting major elective surgery is not a suitable surrogate for the polytrauma response. Nonetheless, distinct responses in young vs older patients highlight the utility of elective spinal in studying patient-specific factors affecting outcomes after major elective surgery.

High-dimensional proteomics identifies organ injury patterns associated with outcomes in human trauma.

INTRODUCTION: Severe traumatic injury with shock can lead to direct and indirect organ injury; however, tissue-specific biomarkers are limited in clinical panels. We used proteomic and metabolomic databases to identify organ injury patterns after severe injury in humans. METHODS: Plasma samples (times 0, 24, and 72 hours after arrival to trauma center) from injured patients enrolled in two randomized prehospital trials were subjected to multiplexed proteomics (SomaLogic Inc., Boulder, CO). Patients were categorized by outcome: nonresolvers (died >72 hours or required ≥7 days of critical care), resolvers (survived to 30 days and required <7 days of critical care), and low Injury Severity Score (ISS) controls. Established tissue-specific biomarkers were identified through a literature review and cross-referenced with tissue specificity from the Human Protein Atlas. Untargeted plasma metabolomics (Metabolon Inc., Durham, NC), inflammatory mediators, and endothelial damage markers were correlated with injury biomarkers. Kruskal-Wallis/Mann-Whitney U tests with false discovery rate correction assessed differences in biomarker expression across outcome groups (significance; p < 0.1). RESULTS: Of 142 patients, 78 were nonresolvers (median ISS, 30), 34 were resolvers (median ISS, 22), and 30 were low ISS controls (median ISS, 1). A broad release of tissue-specific damage markers was observed at admission; this was greater in nonresolvers. By 72 hours, nine cardiac, three liver, eight neurologic, and three pulmonary proteins remained significantly elevated in nonresolvers compared with resolvers. Cardiac damage biomarkers showed the greatest elevations at 72 hours in nonresolvers and had significant positive correlations with proinflammatory mediators and endothelial damage markers. Nonresolvers had lower concentrations of fatty acid metabolites compared with resolvers, particularly acyl carnitines and cholines. CONCLUSION: We identified an immediate release of tissue-specific biomarkers with sustained elevation in the liver, pulmonary, neurologic, and especially cardiac injury biomarkers in patients with complex clinical courses after severe injury. The persistent myocardial injury in nonresolvers may be due to a combination of factors including metabolic stress, inflammation, and endotheliopathy.

High Dimensional Multiomics Reveals Unique Characteristics of Early Plasma Administration in Polytrauma Patients With TBI.

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.

Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study.

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.

Dexamethasone Reduces Postoperative Nausea in Pediatric Upper Endoscopy With Deep Sedation: A Randomized Controlled Trial.

OBJECTIVES: We aimed to evaluate the effect of a single dose of preoperative dexamethasone on postoperative nausea and vomiting (PONV), a frequent complication and a major cause of delayed recovery in pediatric upper gastrointestinal endoscopy (UGIE) under sedation. METHODS: In this double-blind randomized controlled study, 98 children aged 2 to 14, with American Society of Anesthesiologists status I to II, and undergoing elective UGIE with deep sedation were included and randomly assigned to 2 groups. Preoperatively, after anesthesia induction with sodium thiopental and maintenance with sevoflurane, patients in the intervention (n = 49) and control (n = 49) groups, respectively received 0.1 mg/kg i.v. dexamethasone and 2 cm i.v. 0.9% saline. Postoperatively, PONV incidence was measured as the primary outcome. RESULTS: PONV incidence was significantly less in dexamethasone group (8.2%) compared to the control group (26.5%) (difference = 18.3%, 95% confidence interval: 3.4%-33%, P = 0.016). For secondary outcomes, between-group differences were not statistically significant: incidence of bronchospasm or laryngospasm (both 4.1%, P = 1); emergence delirium assessed with Pediatric Anesthesia Emergence Delirium scale (5.9 ±â€Š3.4 vs 5.7 ±â€Š3.2, P = 0.751); Modified Aldrete score at 0 minutes (9.4 ±â€Š0.8 vs 9.3 ±â€Š0.9, P = 0.909) and at 5 minutes (9.5 ±â€Š0.7 vs 9.4 ±â€Š0.9, P = 0.527); and recovery time (21.1 ±â€Š6.6 vs 23.4 ±â€Š8.6 minutes, P = 0.130). CONCLUSIONS: A single preoperative dose of i.v. dexamethasone reduces PONV in children undergoing elective UGIE with deep sedation, but has no significant effect on the patient recovery time or the incidence of postoperative bronchospasm or laryngospasm and emergence delirium.