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Purpose: This study aimed to characterize the gut microbiota in obese adolescents from Shenzhen (China), and evaluate influence of gender on BMI-related differences in the gut microbiome. Methods: Evaluation of physical examination, blood pressure measurement, serological assay and body composition were conducted in 205 adolescent subjects at Shenzhen. Fecal microbiome composition was profiled via high-throughput sequencing of the V3-V4 regions of the 16S rRNA gene. A Random Forest (RF) classifier model was built to distinguish the BMI categories based on the gut bacterial composition. Results: Fifty-six taxa consisting mainly of Firmicutes were identified that having significant associations with BMI; 2 OTUs belonging to Ruminococcaceae and 1 belonging to Lachnospiraceae had relatively strong positive correlations with body fate rate, waistline and most of serum biochemical properties. Based on the 56 BMI-associated OTUs, the RF model showed a robust classification accuracy (AUC 0.96) for predicting the obese phenotype. Gender-specific differences in the gut microbiome composition was obtained, and a lower relative abundance of Odoribacter genus was particularly found in obese boys. Functional analysis revealed a deficiency in bacterial gene contents related to peroxisome and PPAR signaling pathway in the obese subjects for both genders. Conclusions: This study reveals unique features of gut microbiome in terms of microbial composition and metabolic functions in obese adolescents, and provides a baseline for reference and comparison studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-023-00236-9.
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Existing models for predicting mortality based on traditional Cox proportional hazard approach (CPH) often have low prediction accuracy. This paper aims to develop a clinical risk model with good accuracy for predicting 1-year mortality in cardiac arrhythmias patients using random survival forest (RSF), a robust approach for survival analysis. 10,488 cardiac arrhythmias patients available in the public MIMIC II clinical database were investigated, with 3,452 deaths occurring within 1-year followups. Forty risk factors including demographics and clinical and laboratory information and antiarrhythmic agents were analyzed as potential predictors of all-cause mortality. RSF was adopted to build a comprehensive survival model and a simplified risk model composed of 14 top risk factors. The built comprehensive model achieved a prediction accuracy of 0.81 measured by c-statistic with 10-fold cross validation. The simplified risk model also achieved a good accuracy of 0.799. Both results outperformed traditional CPH (which achieved a c-statistic of 0.733 for the comprehensive model and 0.718 for the simplified model). Moreover, various factors are observed to have nonlinear impact on cardiac arrhythmias prognosis. As a result, RSF based model which took nonlinearity into account significantly outperformed traditional Cox proportional hazard model and has great potential to be a more effective approach for survival analysis.
