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Using classification and regression tree modeling to investigate appetite hormones and proinflammatory cytokines as biomarkers to differentiate bipolar I depression from major depressive disorder

Published online by Cambridge University Press:  10 February 2021

Kai-Lin Huang
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Department of Psychiatry, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
Mu-Hong Chen*
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Department of Psychiatry, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
Ju-Wei Hsu
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Department of Psychiatry, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
Shih-Jen Tsai
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Department of Psychiatry, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
Ya-Mei Bai*
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan Department of Psychiatry, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
*Author for correspondence: Mu-Hong Chen, MD, PhD Email:
*Ya-Mei Bai, MD, PhD Email:



Altered immunity and metabolic profiles have been compared between bipolar depression (BD) and major depressive disorder (MDD). This study aimed at developing a composite predictor of appetite hormones and proinflammatory cytokines to differentiate BD from MDD.


This cross-sectional study enrolled patients with BD and those with MDD aged 20 to 59 years and displaying depressive episodes. Clinical characteristics (age, sex, body mass index, and depression severity), cytokines (C-reactive protein, interleukin [IL]-2, IL-6, tumor necrosis factor [TNF]-α, P-selectin, and monocyte chemoattractant protein), and appetite hormones (leptin, adiponectin, ghrelin, and insulin) were assessed as potential predictors using a classification and regression tree (CRT) model for differentiating BD from MDD.


The predicted probability of a composite predictor of ghrelin and TNF-α was significantly greater (for BD: area under curve = 0.877; for MDD: area under curve = 0.914) than that of any one marker (all P > .05) to distinguish BD from MDD. The most powerful predictors for diagnosing BD were high ghrelin and TNF-α levels, whereas those for MDD were low ghrelin and TNF-α levels.


A composite predictor of ghrelin and TNF-α driven by CRT could assist in the differential diagnosis of BD from MDD with high specificity. Further clinical studies are warranted to validate our results and to explore underlying mechanisms.

Original Research
© The Author(s), 2021. Published by Cambridge University Press

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