Daily Sepsis Research Analysis

Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (previously subphenotyped hypolipoprotein phenotype [HYPO]) or higher lipoprotein levels and lower mortality (previously subphenotyped normolipoprotein phenotype [NORMO]) were described. We developed a simplified clinical algorithm for bedside subphenotype recognition. Methods: We analyzed data from four prospective studies (internal dataset), focusing on HYPO and NORMO subphenotypes. A 1,000-tree random forest classifier and logistic regression models were built, using clinical features to predict subphenotypes. Performance was evaluated by comparing predictions to actual subphenotypes derived from a machine learning model. The model was applied to an external dataset of 281 patients from three French studies. Results: The internal cohort consisted of 386 patients (median age, 63 years; 46% female). Four clinical features (hepatic SOFA, cardiovascular SOFA, low [low-density lipoprotein cholesterol {LDL-C}] and high-density lipoprotein cholesterol [high-density lipoprotein cholesterol {HDL-C}]) predicted HYPO versus NORMO subphenotypes with an area under the receiver operating characteristic curve of 0.86, a sensitivity of 0.771, and a specificity of 0.779. In the internal dataset, 28-day mortality for HYPO versus NORMO patients was 26% versus 15%, and in the external cohort, 30% versus 10%. HYPO internal versus external dataset LDL-C levels were similar ( P = 0.99), but HDL-C ( P = 0.02) levels were different. Median NORMO internal versus external dataset LDL-C ( P = 0.99) and HDL-C ( P = 0.12) levels were similar. HYPO patients had lower LDL-C, HDL-C and total cholesterol than NORMO patients in both internal and external datasets. Conclusions: Our simplified clinical data algorithm may allow for bedside recognition of septic patients displaying lipid dysregulation subphenotypes. External validation is needed to verify these results.

Background: This study aimed to investigate the predictive value of histone H3 lysine 18 lactylation (H3K18la) for the early identification and prognosis of sepsis-related acute respiratory distress syndrome (ARDS). Methods: This prospective observational study included patients with sepsis admitted to the intensive care unit (ICU) between March 2023 and September 2024. The patients were divided into two groups: the sepsis with ARDS group and the sepsis without ARDS group. Clinical data were collected within 24 h of ICU admission. Bronchoalveolar lavage fluid (BALF) samples were obtained on day 1 for all participants, and a second BALF sample was collected on day 3 from patients requiring continued mechanical ventilation. Results: In total, 91 sepsis patients were enrolled in the study: 36 with ARDS and 55 without ARDS. H3K18la levels in BALF were significantly higher in the sepsis-related ARDS group than in the non-ARDS group and the control group ( P < 0.05). Elevated H3K18la levels were positively correlated with inflammatory markers (lactate, IL-6, and TNF-α), Acute Physiology and Chronic Health Evaluation II scores, and Sequential Organ Failure Assessment scores ( P < 0.01). Logistic regression analysis revealed that H3K18la was an independent predictor of ARDS development ( P < 0.05), and ROC curve analysis revealed that H3K18la had high diagnostic accuracy (AUC = 0.804). Combining H3K18la with the Sequential Organ Failure Assessment score further improved diagnostic performance (AUC = 0.830, sensitivity = 88.9%, specificity = 67.3%). Furthermore, H3K18la levels significantly increased on day 3 in the mortality group. Conclusion: H3K18la is a promising biomarker for the early identification and prognostic prediction of sepsis-related ARDS.

Objective: Sepsis represents a leading cause of global mortality, defined by a dysregulated inflammatory response. This study aims to investigate the potential causal associations between circulating inflammatory proteins and sepsis risk using a two-sample Mendelian randomization (MR) approach. Methods: Publicly available summary statistics from genomewide association studies (GWAS) were used in this study. Genetic instruments for circulating inflammatory protein were derived from a GWAS meta-analysis of 11 cohorts encompassing 14,824 European participants. The relationship between genetically predicted protein levels and sepsis-related outcomes was evaluated using aggregated data from the UK Biobank-a multicenter prospective cohort study comprising over 500,000 European participants. Analyses were stratified by age, 28-day mortality, and ICU admission. Multiple MR methods, including inverse-variance weighted (IVW), MR-Egger, and weighted median, were applied to ensure the robustness of our findings. Results: The MR analysis identified significant causal associations between inflammatory proteins and sepsis outcomes. Genetically predicted elevated levels of β-NGF are associated with a reduced risk of sepsis (odds ratio [OR] 0.77, 95% confidence interval [CI] = 0.60-0.99; P = 0.039). Among sepsis patients aged below 75 years, the risk was reduced by 30% (OR, 0.70; 95% CI = 0.52-0.93; P = 0.013). Genetically predicted increases in TRAIL (OR, 1.11; 95% CI = 1.02-1.20; P = 0.020) and VEGF-A (OR, 1.18; 95% CI = 1.02-1.37; P = 0.031) were positively associated with sepsis incidence, while genetically predicted levels of CST5 (OR, 0.81; 95% CI = 0.69-0.94; P = 0.006) and MCP-1 (OR, 0.64; 95% CI = 0.45-0.92; P = 0.015) were inversely associated with sepsis-induced mortality. Conclusion: This study provides evidence from a Mendelian randomization framework supporting the causal role for specific circulating inflammatory proteins (e.g., β-NGF, VEGF-A, and TRAIL) in influencing sepsis risk and mortality. These findings underscore the potential for therapeutic interventions targeting these proteins to mitigate sepsis risk and improve patient outcomes, along with further investigation into the underlying mechanisms and clinical implications.