Our published data have indicated that the omega-3 polyunsaturated fatty acid eicosapentaenoic acid (EPA) provides beneficial effects by attenuating neuronal damage induced by interleukin-1β (IL-1β), and up-regulation of the expression of brain-derived neurotrophic factor (BDNF) represents a crucial part in the neuroprotective effect of EPA. However, the mechanisms of how EPA regulates BDNF expression remains incompletely understood. The present study investigated the role of Akt/CREB signaling in the effect of EPA on BDNF expression and its neuroprotective effect. The present results showed that IL-1β reduced hippocampal neuronal viability and that EPA showed a concentration-dependent neuroprotective effect, but the neuroprotective effects of EPA were abolished by inhibition of Akt using KRX-0401, an inhibitor of Akt. Treatment of hippocampal neurons with EPA also ameliorated the decrease in Akt and CREB phosphorylation induced by IL-1β and BDNF down-regulation mediated by IL-1β. However, inhibition of Akt reversed the effect of EPA on levels of p-Akt, p-CREB, and BDNF. Our data indicate that EPA elicited neuroprotection toward IL-1β-induced cell damage and BDNF decrease and that its effects potentially occurred via the Akt/CREB signaling pathway.

The ratio of fibrinogen to albumin (FAR) is considered a new inflammatory biomarker and a predictor of cardiovascular disease risk. However, its prognostic value for patients with chronic heart failure (CHF) with different ejection fractions (EFs) remains unclear. A total of 916 hospitalized patients with CHF from January 2017 to October 2021 in the First Affiliated Hospital of Kunming Medical University were included in the study. Death occurred in 417 (45.5%) patients out of 916 patients during a median follow-up time of 750 days. Among these patients, 381 patients suffered from HFrEF (LVEF <40%) and 535 patients suffered from HFpEF or HFmrEF (HFpEF plus HFmrEF, LVEF ≥ 40%). Patients were categorized into high-level FAR (FAR-H) and low-level FAR (FAR-L) groups based on the optimal cut-off value of FAR (9.06) obtained from receiver operating characteristic (ROC) curve analysis. Upon analysing the Kaplan - Meier plots, the incidence of death was significantly higher in all patients with FAR-H and patients in both HF subgroups (p < 0.001). The multivariate Cox proportional hazard analyses indicated that the FAR was an independent predictor of all-cause mortality, regardless of heart failure subtype. (HR 1.115, 95% CI 1.089-1.142, p < 0.001; HFpEF plus HFmrEF, HR 1.109, 95% CI 1.074-1.146, p < 0.0001; HFrEF, HR 1.138, 95% CI 1.094-1.183, p < 0.0001) The optimal cut-off value of FAR in predicting all-cause mortality was 9.06 with an area under the curve value of 0.720 (95% CI: 0.687-0.753, p < 0.001), a sensitivity of 68.8% and a specificity of 65.6%. After adjusting for the traditional indicators (LVEF, Lg BNP, etc.), the new model with the FAR had better prediction ability in patients with CHF. Elevated FAR is an independent predictor of death in CHF and is not related to the HF subtype.

Background: This study was undertaken to explore the predictive value of the advanced lung cancer inflammation index (ALI) combined with the geriatric nutritional risk index (GNRI) for all-cause mortality in patients with heart failure (HF). Methods and results: We enrolled 1123 patients with HF admitted to our cardiology department from January 2017 to October 2021. Patients were divided into four groups, according to the median ALI and GNRI. From the analysis of the relationship between the ALI and GNRI, we concluded that there was a mild positive linear correlation (r = 0.348, p < 0.001) and no interaction (p = 0.140) between the ALI and GNRI. Kaplan‒Meier analysis showed that the cumulative incidence of all-cause mortality in patients with HF was highest in Group 1 (log-rank χ² 126.244, p < 0.001). Multivariate Cox proportional hazards analysis revealed that ALI and GNRI were independent predictors of all-cause mortality in HF patients (ALI: HR 0.407, 95% CI 0.296–0.560, p < 0.001; GNRI: HR 0.967, 95% CI 0.954–0.980, p < 0.001). The area under the curve (AUC) for ALI combined with GNRI was 0.711 (p < 0.001), according to the time-dependent ROC curve. Conclusion: ALI and GNRI were independent predictors of all-cause mortality in HF patients. Patients with HF had the highest risk of all-cause mortality when the ALI was < 24.60 and the GNRI was < 94.41. ALI combined with the GNRI has good predictive value for the prognosis of HF patients.

Background: The goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that adequately explained 3-year all-cause mortality in patients with chronic heart failure. Methods: The data in this paper were selected from patients with chronic heart failure who were hospitalized at the First Affiliated Hospital of Kunming Medical University, from 2017 to 2019 with cardiac function class III-IV. The dataset was explored using six different machine learning models, including logistic regression, naive Bayes, random forest classifier, extreme gradient boost, K-nearest neighbor, and decision tree. Finally, interpretable methods based on machine learning, such as SHAP value, permutation importance, and partial dependence plots, were used to estimate the 3-year all-cause mortality risk and produce individual interpretations of the model's conclusions. Result: In this paper, random forest was identified as the optimal aools lgorithm for this dataset. We also incorporated relevant machine learning interpretable tand techniques to improve disease prognosis, including permutation importance, PDP plots and SHAP values for analysis. From this study, we can see that the number of hospitalizations, age, glomerular filtration rate, BNP, NYHA cardiac function classification, lymphocyte absolute value, serum albumin, hemoglobin, total cholesterol, pulmonary artery systolic pressure and so on were important for providing an optimal risk assessment and were important predictive factors of chronic heart failure. Conclusion: The machine learning-based cardiovascular risk models could be used to accurately assess and stratify the 3-year risk of all-cause mortality among CHF patients. Machine learning in combination with permutation importance, PDP plots, and the SHAP value could offer a clear explanation of individual risk prediction and give doctors an intuitive knowledge of the functions of important model components.

Background: The serum C-reactive protein-to-albumin ratio (CAR) has been identified as an adverse prognostic indicator in a variety of diseases. Nevertheless, there have been not been any studies reporting a relationship between CAR and the prognosis of chronic heart failure (CHF). This study was designed to evaluate the association between CAR and all-cause mortality in CHF patients with different ejection fractions. Methods: A total of 1221 heart failure (HF) patients were enrolled at the First Affiliated Hospital of Kunming Medical University due to acute exacerbation of chronic HF from January 2017 to October 2021. The main outcome was all-cause mortality. After collecting baseline characteristics and laboratory results from all patients, we classified all participants into four groups based on CAR quartile (G1–G4). Kaplan-Meier survival curves and multivariate Cox proportional hazard models were employed to investigate the association between CAR and all-cause mortality in the patients. Furthermore, receiver operating characteristic (ROC) curves were constructed for CARs, and the area under the curve (AUC) was calculated. Results: After excluding ineligible patients, we ultimately included 1196 patients with CHF. The mean age was 66.38 ± 12.521 years, and 62% were male. According to the Kaplan‒Meier analysis, with different ejection fractions, the risk of all-cause mortality was always highest for G4 (CAR >63.27) and lowest for G1 (CAR ≤7.67). Cox multivariate regression analyses indicated that the CAR was an independent predictor of all-cause mortality in all HF patients and in patients with different HF subtypes. According to the ROC curves, the AUC for the CAR was 0.732 (p < 0.001), with a sensitivity of 66.2% and the specificity of 72.7%. CAR had a greater predictive value for all-cause mortality than did C-reactive protein (CRP). Conclusions: An elevated serum CAR was independently associated with an increased risk of all-cause death, regardless of heart failure subtype.

Background: The prognostic nutritional index (PNI) and serum chloride level are related to adverse outcomes in patients with heart failure. However, little is known about the relationship between the PNI and serum chloride level in predicting the poor prognosis of patients with acute decompensated heart failure (ADHF).Methods and results: We reviewed 1221 consecutive patients with ADHF admitted to the First Affiliated Hospital of Kunming Medical University from January 2017 to October 2021. After excluding patients with in hospital death, missing follow-up data, missing chloride data, missing lymphocyte (LYM) count data, or missing serum albumin data, 805 patients were included. PNI was calculated using the formula: serum albumin (ALB) (g/L) + 5 × LYM count (10^9/L). Patients were divided into 4 groups according to the quartiles of the PNI, and the highest PNI quartile (PNI Q4: PNI ≥ 47.3) was set as the reference group. The patients in the lowest PNI quartile (PNI Q1: PNI < 40.8) had the lowest cumulative survival rate, and mortality risk decreased progressively through the quartiles (log-rankχ²142.283,P < 0.0001). Patients with ADHF were divided into 8 groups by quartiles of PNI and median levels of serum chloride. After adjustment, the hazard ratio (HR) for all-cause mortality in ADHF patients in Group 1 was 8.7 times higher than that in the reference Group 8. Furthermore, the addition of serum chloride level and PNI quartile to the Cox model increased the area under the Receiver operating characteristic (ROC) curve by 0.05, and the area under the ROC curve of the new model was higher than that of the original model with traditional risk factors.Conclusions: Both the lowest PNI quartiles and low chloride level indicate a higher risk of all-cause death in patients with ADHF.