(1) Background: The association between metabolic obesity phenotypes and incident lung cancer (LC) remains unclear. (2) Methods: Based on the combination of baseline BMI categories and metabolic health status, participants were categorized into eight groups: metabolically healthy underweight (MHUW), metabolically unhealthy underweight (MUUW), metabolically healthy normal (MHN), metabolically unhealthy normal (MUN), metabolically healthy overweight (MHOW), metabolically unhealthy overweight (MUOW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). The Cox proportional hazards model and Mendelian randomization (MR) were applied to assess the association between metabolic obesity phenotypes with LC risk. (3) Results: During a median follow-up of 9.1 years, 3654 incident LC patients were confirmed among 450,482 individuals. Compared with participants with MHN, those with MUUW had higher rates of incident LC (hazard ratio (HR) = 3.24, 95% confidence interval (CI) = 1.33–7.87, p = 0.009). MHO and MHOW individuals had a 24% and 18% lower risk of developing LC, respectively (MHO: HR = 0.76, 95% CI = 0.61–0.95, p = 0.02; MHO: HR = 0.82, 95% CI = 0.70–0.96, p = 0.02). No genetic association of metabolic obesity phenotypes and LC risk was observed in MR analysis. (4) Conclusions: In this prospective cohort study, individuals with MHOW and MHO phenotypes were at a lower risk and MUUW were at a higher risk of LC. However, MR failed to reveal any evidence that metabolic obesity phenotypes would be associated with a higher risk of LC.

Intestinal barrier injury and hyperglycemia are common in patients with sepsis. Bacteria translocation and systemic inflammatory response caused by intestinal barrier injury play a significant role in sepsis occurrence and deterioration, while hyperglycemia is linked to adverse outcomes in sepsis. Previous studies have shown that hyperglycemia is an independent risk factor for intestinal barrier injury. Concurrently, increasing evidence has indicated that some anti-hyperglycemic agents not only improve intestinal barrier function but are also beneficial in managing sepsis-induced organ dysfunction. Therefore, we assume that these agents can block or reduce the severity of sepsis by improving intestinal barrier function. Accordingly, we explicated the connection between sepsis, intestinal barrier, and hyperglycemia, overviewed the evidence on improving intestinal barrier function and alleviating sepsis-induced organ dysfunction by anti-hyperglycemic agents (eg, metformin, peroxisome proliferators activated receptor-γ agonists, berberine, and curcumin), and summarized some common characteristics of these agents to provide a new perspective in the adjuvant treatment of sepsis.

Purpose: To compare the dietary inflammatory index (DII), triglyceride glucose index (TyG), waist triglyceride index (WTI), and body mass index (BMI) in predicting the survival of chronic kidney disease (CKD).Methodology: Inclusion of 23,099 participants from the NHANES database who met specific criteria. Baseline was established using quartiles of DII index. The relationship between DII index, WTI index, TyG index, and BMI index with mortality rate in CKD patients was evaluated using Kaplan-Meier curves. Univariate and multivariate COX regression risk models were used to study the relationship between DII index, WTI index, and TyG index with mortality risk in CKD patients. Stratification of eGFR by age and gender was conducted to investigate the association between DII index, WTI index, and TyG index with mortality risk in CKD patients. Restricted cubic spline analysis was used to study the correlation between DII index, WTI index, and TyG index with mortality risk in CKD patients.Results: The incidence of CKD increased with the increase of DII index, WTI index and TyG index. After multivariable adjustment, the fourth quartile of DII index, TyG index and WTI index showed the highest risk for CKD [DII: hazard ratio (HR) 1.36, 95% confidential interval (CI) (1.23–1.51); TyG: HR 1.21; 95% CI (1.07–1.37); WTI: HR 1.29; 95% CI (1.13–1.46)]. There was no difference in the risk of developing CKD between the obese group (BMI ≥24 kg/m2) and the normal weight group (P>0.05).Conclusion: This study has identified a significant association between elevated DII index, WTI index, and TyG index with the risk of CKD. Furthermore, the DII index demonstrated superior prognostic capability in predicting CKD compared to other indicators.

Objective: To investigate the association between driving pressure (ΔP) and 90-day mortality in patients following lung transplantation (LTx) in patients who developed primary graft dysfunction (PGD). Methods: This prospective, observational study involved consecutive patients who, following LTx, were admitted to our intensive care unit (ICU) from January 2022 to January 2023. Patients were separated into two groups according to ΔP at time of admission (i.e., low, ≤15 cmH₂O or high, >15 cmH₂O). Postoperative outcomes were compared between groups. Results: In total, 104 patients were involved in the study, and of these, 69 were included in the low ΔP group and 35 in the high ΔP group. Kaplan-Meier analysis of 90-day mortality showed a statistically significant difference between groups with survival better in the low ΔP group compared with the high ΔP group. According to Cox proportional regression model, the variables independently associated with 90-day mortality were ΔP and pneumonia. Significantly more patients in the high ΔP group than the low ΔP group had PGD grade 3 (PGD3), pneumonia, required tracheostomy, and had prolonged postoperative extracorporeal membrane oxygenation (ECMO) time, postoperative ventilator time, and ICU stay. Conclusions: Driving pressure appears to have the ability to predict PGD3 and 90-day mortality of patients following LTx. Further studies are required to confirm our results.

Background.: Primary graft dysfunction (PGD) develops within 72 h after lung transplantation (Lung Tx) and greatly influences patients’ prognosis. This study aimed to establish an accurate machine learning (ML) model for predicting grade 3 PGD (PGD3) after Lung Tx. Methods.: This retrospective study incorporated 802 patients receiving Lung Tx between July 2018 and October 2023 (640 in the derivation cohort and 162 in the external validation cohort), and 640 patients were randomly assigned to training and internal validation cohorts in a 7:3 ratio. Independent risk factors for PGD3 were determined by integrating the univariate logistic regression and least absolute shrinkage and selection operator regression analyses. Subsequently, 9 ML models were used to construct prediction models for PGD3 based on selected variables. Their prediction performances were further evaluated. Besides, model stratification performance was assessed with 3 posttransplant metrics. Finally, the SHapley Additive exPlanations algorithm was used to understand the predictive importance of selected variables. Results.: We identified 9 independent clinical risk factors as selected variables. Among 9 ML models, the random forest (RF) model displayed optimal performance (area under the curve [AUC] = 0.9415, sensitivity [Se] = 0.8972, specificity [Sp] = 0.8795 in the training cohort; AUC = 0.7975, Se = 0.7520, Sp = 0.7313 in the internal validation cohort; and AUC = 0.8214, Se = 0.8235, Sp = 0.6667 in the external validation cohort). Further assessments on calibration and clinical usefulness indicated the promising applicability of the RF model in PGD3 prediction. Meanwhile, the RF model also performed best in terms of risk stratification for postoperative support (extracorporeal membrane oxygenation time: P < 0.001, mechanical ventilation time: P = 0.006, intensive care unit time: P < 0.001). Conclusions.: The RF model had the optimal performance in PGD3 prediction and postoperative risk stratification for patients after Lung Tx.