Identification of hub genes in calcific aortic valve disease.

Calcific aortic valve disease (CAVD) is a heart valve disorder characterized primarily by calcification of the aortic valve, resulting in stiffness and dysfunction of the valve. CAVD is prevalent among aging populations and is linked to factors such as hypertension, dyslipidemia, tobacco use, and genetic predisposition, and can result in becoming a growing economic and health burden. Once aortic valve calcification occurs, it will inevitably progress to aortic stenosis. At present, there are no medications available that have demonstrated effectiveness in managing or delaying the progression of the disease. In this study, we mined four publicly available microarray datasets (GSE12644 GSE51472, GSE77287, GSE233819) associated with CAVD from the GEO database with the aim of identifying hub genes associated with the occurrence of CAVD and searching for possible biological targets for the early prevention and diagnosis of CAVD. This study provides preliminary evidence for therapeutic and preventive targets for CAVD and may provide a solid foundation for subsequent biological studies.

Nutritional indices at admission are associated with mortality rates of patients in the intensive care unit.

BACKGROUND: Malnutrition is a common occurrence in critically ill patients, and has been related to poor prognosis in various diseases. Here, we assess the prognostic value of malnutrition using nutritional indices in intensive care units (ICU) patients. METHODS: We retrieved information on 2060 patients from the Medical Information Mart for Intensive Care III, and randomized the patients into training and validation cohorts, at a ratio of 7:3. We estimated their nutritional indices using prognostic nutritional index (PNI), geriatric nutritional risk index (GNRI), and controlling nutritional status (CONUT) score. Both multivariate regression analysis and the Kaplan-Meier (KM) survival curve were used to examine the prognostic role of nutritional indices in ICU mortality. Then we evaluated the additional predictive significance of each nutritional index beyond the baseline model including conventional risk factors. RESULTS: Multivariate regression analysis revealed that PNI, GNRI, and CONUT were independent predictors of in-hospital and 1-year mortality (all P < 0.001). KM curves showed higher 1-year mortality rates in having nutritional risk patients (PNI ≤ 38 or GNRI ≤ 98 or CONUT ≥ 2). Moreover, subgroup analyses revealed a significant association between each nutritional index and 1-year mortality in patients with different comorbidities. We also observed a pronounced additional impact on the predictive value of 1-year mortality when PNI, GNRI, and CONUT were separately added to the baseline model. The additional role of each nutritional index was further verified in the validation cohort. CONCLUSIONS: Our results revealed that the nutritional indices at admission are significantly correlated with increased mortality rates in ICU adult patients.