Neuroimmune system may be involved in the pathological process of bipolar disorder (BD), but the essential association is not fully understood. Accumulating evidence has shown that BD involves the activation of immune cells and the release of inflammatory substances in the central nerve system (CNS). Meanwhile, neuroimmune responses also interact with other hypothesis of the etiology of BD that are widely recognized, such as neurotransmitter systems, neuroendocrine systems, neurotrophic factors, and oxidative stress. Simultaneously, related genes and immune changes in peripheral blood vary with it. Overall, neuroimmunity may play an important role in the pathogenesis of BD, and the inflammatory cytokines, especially interleukin-6 and tumor necrosis factor-alpha, have potential value for the clinical diagnosis and prognosis of BD, as well as predicting the therapeutic effects of drugs. Large-scale studies are needed to extend the evidence on neuroimmunity in BD, and to examine its clinical value for applications such as early prediction and treatment.

Classic hypothalamic-pituitary-end-organ feedback loops – the hypothalamic-pituitary-adrenal axis (HPAA), hypothalamic-pituitary-thyroidal axis (HPTA), and hypothalamic-pituitary-gonadal axis (HPGA) – are associated with the neuroendocrine and immune systems in major depressive disorder (MDD). Female patients with MDD present with evident neuroendocrine and immunological changes. Glucocorticoid, thyroid hormone, and reproductive steroid levels fluctuate with menstrual cycles, which might lead to glucocorticoid receptor resistance, impairment of triiodothyronine conversion, and sex hormone secretion disorders. In this review, we summarize the independent and interactive functions of these three axes in female MDD patients. The similar molecular structure of steroids implies an interrelationship between the hypothalamic-pituitary-end-organ axes and the competitive inhibitory effects at the receptor level, especially when considering the HPAA and HPGA.

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.
Biomarkers/metabolism* ; Bipolar Disorder/metabolism* ; Depressive Disorder, Major/diagnosis* ; Early Diagnosis ; Humans ; Oxidative Stress