Prediction of adolescent subjective well being: A machine learning approach

Background Subjective well-being(SWB),also known as happiness,plays an important role in evaluating both mental and physical health.Adolescents deserve specific attention because they are under a great variety of stresses and are at risk for mental disorders during adulthood.Aim The present paper aims to predict undergraduate students1 SWB by machine learning method.Methods Gradient Boosting Classifier which was an innovative yet validated machine learning approach was used to analyse data from 10518 Chinese adolescents.The online survey included 298 factors such as depression and personality.Quality control procedure was used to minimise biases due to online survey reports.We applied feature selection to achieve the balance between optimal prediction and result interpretation.Results The top 20 happiness risks and protective factors were finally brought into the predicting model.Approximately 90%individuals'SWB can be predicted correctly,and the sensitivity and specificity were about 92%and 90%,respectively.Conclusions This result identifies at-risk individuals according to new characteristics and established the foundation for adolescent prevention strategies.

Association between SLC17A7 gene polymorphisms and venlafaxine for major depressive disorder in a Chinese Han population:a prospective pharmacogenetic case-control study

Objective: Venlafaxine is a common antidepressant and its therapeutic effect varies among people with different genetic backgrounds. The aim of this study was to investigate whether single nucleotide polymorphisms (SNPs) in theSLC17A7 gene are associated with the treatment outcome of venlafaxine in a Chinese Han population with major depressive disorder.Methods: This prospective pharmacogenetic case-control study that involved genotyping of four SNPs ofSLC17A7 was conducted on 175 major depressive disorder patients of Chinese Han origin, aged 18 to 65 years, participated in the study from April 2005 to September 2006. Comparisons of allele and genotype frequencies of all SNPs were performed between the responder/remission group and the nonresponder/nonremission group. This study was approved by the Institutional Ethics Committee of Sichuan University (approval No. 20151112-265).Results: The allele and genotype frequencies of the four candidate SNPs inSCL17A7 showed no significant difference between responders and nonresponders. Meanwhile, no significant difference was detected in the four investigatedSLC17A7 SNPs between patients who did and did not exhibit remission. Although one of the investigatedSLC17A7 variants (rs1578944) demonstrated a significant association (P=0.022) with a response to venlafaxine after 6 weeks of treatment in the survival analysis, the association was unclear after a Bonferroni multiple comparisons test was conducted.Conclusion: No significant association exists between the four candidate SNPs (rs1043558, rs1320301, rs1578944, and rs74174284) inSLC17A7 and venlafaxine treatment in the Chinese Han population.

Single nucleotide polymorphism rs11191454 in arsenite methyltransferase is associated with flow in Chinese students:a genetic study on flow experience

Flow has been widely studied in the field of positive psychology.However,little is known regarding its biological mechanism.This study aimed to ascertain flow-related gene loci.We investigated the association between flow and five single nucleotide polymorphisms associated with common mental disorders among a sample of 870 healthy 1 st year students of Jining Medical University,Shandong Province,China.This study was approved by the Ethics Committee of Jining Medical University(approval number:JNMC-2016-KY-001)on June 1,2016.rs11191454 demonstrated significant statistical association with flow after adjusting for age and gender(P=0.004).The allele carriers achieved higher scores in all 4 dimensions of flow:merging of action and awareness,challenge-skill balance,sense of control,and clear goals.This biological research article indicates that rs11191454 in the arsenite methyltransferase(AS3MT)gene might be associated with flow in a Chinese Han population,and that might result from altered arsenic metabolism.