Early Diagnosis of Bipolar Disorder Coming Soon: Application of an Oxidative Stress Injury Biomarker (BIOS) Model
Zhiang Niu1 • Xiaohui Wu1 • Yuncheng Zhu1,2 • Lu Yang1 • Yifan Shi1 • Yun Wang1 • Hong Qiu3 • Wenjie Gu3 • Yina Wu1 • Xiangyun Long4 • Zheng Lu4 • Shaohua Hu5 • Zhijian Yao6 • Haichen Yang7 • Tiebang Liu7 • Yong Xia8 • Zhiyu Chen8 • Jun Chen1 • Yiru Fang1,9,101 Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
2 Division of Mood Disorders, Shanghai Hongkou Mental Health Center, Shanghai 200083, China
3 Information and Statistics Department, Shanghai Mental Health Center, Shanghai 200030, China
4 Department of Psychiatry, Tongji Hospital, Tongji University School of Medicine, Shanghai 200333, China
5 Department of Psychiatry, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
6 Nanjing Brain Hospital, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
7 Shenzhen Mental Health Center, Shenzhen 518003, China
8 Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou Seventh People’s Hospital, Hangzhou 310013, China
9 CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China 10 Shanghai Key Laboratory of Psychotic Disorders, Shanghai 201108, China
Abstract
Early distinction of bipolar disorder (BD) from major depressive disorder (MDD) is difficult since no tools are available to estimate the risk of BD. In this study, we aimed to develop and validate a model of oxidative stress injury for predicting BD. Data were collected from 1252 BD and 1359 MDD patients, including 64 MDD patients identified as converting to BD from 2009 through 2018. 30 variables from a randomly-selected subsample of 1827 (70%) patients were used to develop the model, including age, sex, oxidative stress markers (uric acid, bilirubin, albumin, and prealbumin), sex hormones, cytokines, thyroid and liver function, and glycolipid metabolism. Univariate analyses and the Least Absolute Shrinkage and Selection Operator were applied for data dimension reduction and variable selection. Multivariable logistic regression was used to construct a model for predicting bipolar disorder by oxidative stress biomarkers (BIOS) on a nomogram. Internal validation was assessed in the remaining 784 patients (30%), and independent external validation was done with data from 3797 matched patients from five other hospitals in China. 10 predictors, mainly oxidative stress markers, were shown on the nomogram. The BIOS model showed good discrimination in the training sample, with an AUC of 75.1% (95% CI: 72.9%–77.3%), sensitivity of 0.66, and specificity of 0.73. The discrimination was good both in internal validation (AUC 72.1%, 68.6%–75.6%) and external validation (AUC 65.7%, 63.9%–67.5%). In this study, we developed a nomogram centered on oxidative stress injury, which could help in the individualized prediction of BD. For better real-world practice, a set of measurements, especially on oxidative stress markers, should be emphasized using big data in psychiatry.
Keywords
Early recognition; Bipolar disorder; Big data;Oxidative stress; BIOS model
