Prognostic nutritional index during hospitalization correlates with adverse outcomes in elderly patients with acute myocardial infarction: a single-center retrospective cohort study.

BACKGROUND AND AIMS: Acute myocardial infarction (AMI) is one of the most prevalent illnesses endangering the elderly's health. The predictive nutritional index (PNI) has been shown in several studies to be a good predictor of nutritional prognosis. In this study, we explored the correlation between PNI during hospitalization and the outcome of elderly AMI patients. METHODS: Elderly AMI patients in the Cardiac Intensive Care Unit of Huadong Hospital from September 2017 to April 2020 were recruited for analysis. The clinical and laboratory examination data of subjects were retrieved. All enrolled patients were monitored following discharge. The primary clinical endpoints encompass major adverse cardiovascular events (MACEs) and Composite endpoint (MACEs and all-cause mortality). Survival analyses were conducted via the Kaplan-Meier and the log-rank analyses, and the Cox, proportional hazards model, was employed for hazard rate (HR) calculation. RESULTS: 307 subjects were recruited for analysis. The optimal PNI threshold is 40.923. Based on the Kaplan-Meier analysis, the elevated PNI group experienced better prognosis (P < 0.001). Cox analysis demonstrated that the PNI group was a stand-alone predictor for elderly AMI patient prognosis (HR = 1.674, 95% CI 1.076-2.604, P = 0.022). Subgroup analysis showed that the HR of the PNI group was the highest in the ST-segment elevation myocardial infarction (STEMI) subgroup (HR = 3.345, 95% CI 1.889-5.923, P = 0.05), but no discernible difference was observed in the non-ST-segment elevation myocardial infarction (NSTEMI) subgroup. CONCLUSION: Based on our analyses, the PNI during hospitalization can accurately predict the prognosis of elderly STEMI patients but not that of elderly NSTEMI patients.

Identification of Biomarkers Associated with Septic Cardiomyopathy Based on Bioinformatics Analyses.

This study intended to identify biomarkers for septic cardiomyopathy (SC). Microarray data GSE79962 including 20 SC samples and 11 normal samples were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between SC and control groups were identified, followed with functional enrichment analyses. In addition, the protein-protein interaction (PPI) network and modules were constructed. Finally, a transcription factors (TFs)-microRNA (miRNA)-target gene network was constructed and the potential drugs targeting key DEGs were searched. There were 119 upregulated and 80 downregulated genes in the SC group compared with the control group. The upregulated DEGs were significantly enriched tumor necrosis factor signaling pathway, Jak-signal transducer and activator of transcription (STAT) signaling pathway, hypoxia-inducible transcription factor-1 signaling pathway, chemokine signaling pathway, and cytokine-cytokine receptor interaction. The downregulated genes involved in biological processes of negative regulation of DNA biosynthetic process, and skeletal muscle cell differentiation. CCL2, STAT3, MYC, and SERPINE1 were hub nodes in the PPI network and modules. miR-29 family and miR-30 family were considered as key miRNAs, and TATA, MEF2, and STAT5B were considered as key TFs. SERPINE1 and MYC were also drug target genes. The identified DEGs and pathways may be implicated in the progression of human SC, which may lead to a better understanding of SC pathogenesis.