Discriminating factors of body composition characteristics for academic performance in nursing college students: a cross-sectional study.

BACKGROUND: Poor body composition may affect health status, and better body composition is often associated with better academic performance. Nursing students face heavy academic and practical pressures, and the relationship between body composition and academic performance in this group is not fully understood. METHODS: This cross-sectional observational study used de-identified student data from a university of technology in southern Taiwan to analyze the correlation between body composition characteristics and academic performance using regression models. RESULTS: A total of 275 nursing college students were divided into four groups according to academic performance. The group with the lowest academic performance had a lower percentage of body fat (P < 0.05) but a higher percentage of muscle mass (P < 0.05) than the other three groups. Academic performance was positively correlated with percentage of body fat (R = 0.16, P < 0.01) and body age (R = 0.41, P < 0.01), but was negatively correlated with percentage of muscle mass (R = - 0.16, P < 0.01). Percentage of body fat, visceral fat area, and body age were significant discriminators of academic performance (P < 0.05). CONCLUSIONS: The relationship between academic performance and body composition among nursing college students is not straightforward. Contrary to our initial hypothesis, students with higher academic performance tended to have a higher percentage of body fat and a lower percentage of muscle mass. Percentage of body fat, visceral fat area, and body age were significant discriminators of academic performance, indicating that body composition should be considered an important factor in nursing education and practice.

DNA methylation signature aberration as potential biomarkers in treatment-resistant schizophrenia: Constructing a methylation risk score using a machine learning method.

Treatment-resistant schizophrenia (TRS) is defined as a non-response to at least two trials of antipsychotic medication with an adequate dose and duration. We aimed to evaluate the discriminant abilities of DNA methylation probes and methylation risk score between treatment-resistant schizophrenia and non-treatment-resistant schizophrenia. This study recruited 96 schizophrenia patients (TRS and non-TRS) and 56 healthy controls (HC). Participants were divided into a discovery set and a validation set. In the discovery set, we conducted genome-wide methylation analysis (human MethylationEPIC 850K BeadChip) on the subject's blood DNA and discriminated significant methylation signatures, then verified these methylation signatures in the validation set. Based on genome-wide scans of TRS versus non-TRS, thirteen differentially methylated probes were identified at FDR <0.05 and >20% differences in DNA methylation ß-values. Next, we selected six probes within gene coding regions (LOC404266, LOXL2, CERK, CHMP7, and SLC17A9) to conduct verification in the validation set using quantitative methylation-specific PCR (qMSP). These six methylation probes showed satisfactory discrimination between TRS patients and non-TRS patients, with an AUC ranging from 0.83 to 0.92, accuracy ranging from 77.8% to 87.3%, sensitivity ranging from 80% to 90%, and specificity ranging from 65.6% to 85%. This methylation risk score model showed satisfactory discrimination between TRS patients and non-TRS patients, with an accuracy of 88.3%. These findings support that methylation signatures may be used as an indicator of TRS vulnerability and provide a model for the clinical use of methylation to identify TRS.

DNA methylation signature of psychological resilience in young adults: Constructing a methylation risk score using a machine learning method.

Resilience is a process associated with the ability to recover from stress and adversity. We aimed to explore the resilience-associated DNA methylation signatures and evaluate the abilities of methylation risk scores to discriminate low resilience (LR) individuals. The study recruited 78 young adults and used Connor-Davidson Resilience Scale (CD-RISC) to divide them into low and high resilience groups. We randomly allocated all participants of two groups to the discovery and validation sets. We used the blood DNA of the subjects to conduct a genome-wide methylation scan and identify the significant methylation differences of CpG Sites in the discovery set. Moreover, the classification accuracy of the DNA methylation probes was confirmed in the validation set by real-time quantitative methylation-specific polymerase chain reaction. In the genome-wide methylation profiling between LR and HR individuals, seventeen significantly differentially methylated probes were detected. In the validation set, nine DNA methylation signatures within gene coding regions were selected for verification. Finally, three methylation probes [cg18565204 (AARS), cg17682313 (FBXW7), and cg07167608 (LINC01107)] were included in the final model of the methylation risk score for LR versus HR. These methylation risk score models of low resilience demonstrated satisfactory discrimination by logistic regression and support vector machine, with an AUC of 0.81 and 0.93, accuracy of 72.3% and 87.1%, sensitivity of 75%, and 87.5%, and specificity of 70% and 80%. Our findings suggest that methylation signatures can be utilized to identify individuals with LR and establish risk score models that may contribute to the field of psychology.