title: Comparative study on serum levels of macro and trace elements in schizophrenia based on supervised learning methods.
abstract: The etiology and pathophysiology of schizophrenia (SCZ) remain obscure. This study explored the associations between SCZ risk and serum levels of 39 macro and trace elements (MTE). A 1:1 matched case-control study was conducted among 114 schizophrenia patients and 114 healthy controls matched by age, sex and region. Blood samples were collected to determine the concentrations of 39 MTE by ICP-AES and ICP-MS. Both supervised learning methods and classical statistical testing were used to uncover the difference of MTE levels between cases and controls. The best prediction accuracies were 99.21% achieved by support vector machines in the original feature space (without dimensionality reduction), and 98.82% achieved by Naive Bayes with dimensionality reduction. More than half of MTE were found to be significantly different between SCZ patients and the controls. The presented investigation showed that there existed remarkable differences in concentrations of MTE between SCZ patients and healthy controls. The results of this study might be useful to diagnosis and prognosis of SCZ; they also indicated other promising applications in pharmacy and nutrition. However, the results should be interpreted with caution due to limited sample size and the lack of potential confounding factors, such as alcohol, smoking, body mass index (BMI), use of antipsychotics and dietary intakes. In the future the application of the analyses will be useful in designs that have larger sample sizes.
authors.affiliation: The Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University, Beijing 100871, China. Electronic address: lintong@pku.edu.cn.
authors.forename: Tong
authors.initials: T
authors.lastname: Lin
authors.affiliation: Civil Aviation Medicine Center, Civil Aviation Administration of China, Beijing 100123, China. Electronic address: ltbing@yeah.net.
authors.forename: Tiebing
authors.initials: T
authors.lastname: Liu
authors.affiliation: The Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University, Beijing 100871, China. Electronic address: xklyc@pku.edu.cn.
authors.forename: Yucheng
authors.initials: Y
authors.lastname: Lin
authors.affiliation: Center of Medical & Health Analysis, School of Public Health, Peking University, Beijing 100191, China. Electronic address: mike30103@aliyun.com.
authors.forename: Lailai
authors.initials: L
authors.lastname: Yan
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington 47405, USA. Electronic address: zc3@indiana.edu.
authors.forename: Zhongxue
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Center of Medical & Health Analysis, School of Public Health, Peking University, Beijing 100191, China. Electronic address: wjy@bjmu.edu.cn.
authors.forename: Jingyu
authors.initials: J
authors.lastname: Wang
mesh_terms: D000328:Adult; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008297:Male; D012559:Schizophrenia / Q000097:blood*; D000069553:Supervised Machine Learning; D014131:Trace Elements / Q000097:blood*
chemical_list: D014131:Trace Elements
keywords: Macro elements; Schizophrenia; Supervised learning methods; Trace elements