Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning.

Background: Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity population in China, and to explore the predictive ability of body composition indices to distinguish MU by generating machine learning algorithms. Methods: A cross-sectional study was conducted and the subjects who came to the hospital to receive a health examination were enrolled. Body composition was assessed using bioelectrical impedance analyser. A model generator with a gradient-boosting tree algorithm (LightGBM) combined with the SHapley Additive exPlanations method was adapted to train and interpret the model. Receiver-operating characteristic curves were used to analyze the predictive value. Results: We found the significant difference in body composition parameters between the metabolic healthy normal weight (MHNW), metabolic healthy obesity (MHO), metabolic unhealthy normal weight (MUNW) and metabolic unhealthy obesity (MUO) individuals, especially among the MHNW, MUNW and MUO phenotype. MHNW phenotype had significantly lower whole fat mass (FM), trunk FM and trunk free fat mass (FFM), and had significantly lower visceral fat areas compared to MUNW and MUO phenotype, respectively. The bioimpedance phase angle, waist-hip ratio (WHR) and free fat mass index (FFMI) were found to be remarkably lower in MHNW than in MUNW and MUO groups, and lower in MHO than in MUO group. For predictive analysis, the LightGBM-based model identified 32 status-predicting features for MUNW with MHNW group as the reference, MUO with MHO as the reference and MUO with MHNW as the reference, achieved high discriminative power, with area under the curve (AUC) values of 0.842 [0.658, 1.000] for MUNW vs. MHNW, 0.746 [0.599, 0.893] for MUO vs. MHO and 0.968 [0.968, 1.000] for MUO and MHNW, respectively. A 2-variable model was developed for more practical clinical applications. WHR > 0.92 and FFMI > 18.5 kg/m2 predict the increased risk of MU. Conclusion: Body composition measurement and validation of this model could be a valuable approach for the early management and prevention of MU, whether in obese or normal population.

Expression of lncRNA APF in Peripheral Blood of Patients with Acute Myocardial Infarction Caused by Coronary Heart Disease and its Clinical Significance.

Coronary heart disease (CHD) is the leading cause of death from cardiovascular disease. This study investigated the expression and clinical significance of long noncoding RNA (lncRNA) autophagy promoting factor (APF) in peripheral blood of patients with acute myocardial infarction (AMI) caused by CHD. Patients with angina pectoris (AP) (n = 80) and AMI (n = 96) and other patients (n = 60) with precordial discomfort but no CHD were included. The serum levels of lncRNA APF, MIAT, MALAT1, H19, CHAST, CDR1AS, miR-188-3p, and cardiac troponin I (cTnI) /creatine kinase (CK) /creatine kinase isozymes (CK-MB) were detected using reverse transcription-quantitative polymerase chain reaction or enzyme-linked immunosorbent assay. Patients with AMI were divided into high/low expression groups based on the median level of APF, and the clinical baseline indicators of patients with AMI were compared. The correlation between lncRNA APF and cTnI/CK/CK-MB/miR-188-3p was analyzed using Pearson analysis, and the clinical value of lncRNA APF was evaluated using the receiver operating characteristic curve. The levels of lncRNA APF, MIAT, MALAT1, H19, CHAST, and CDR1AS in patients with AMI were increased, whereas there were no differences in patients with AP. The APF levels in patients with AMI were higher than MIAT, MALAT1, and CHAST, whereas there were no differences between APF and H19 and CDR1AS. In patients with AMI, the high level of lncRNA APF was correlated with the history of smoking/drinking. Moreover, lncRNA APF was positively correlated with cTnI/CK/CK-MB levels and negatively correlated with miR-188-3p. LncRNA APF has high diagnostic efficacy for AMI. Overall, lncRNA APF is highly expressed in patients with AMI caused by CHD and has high diagnostic efficacy for AMI.