Total sleep time and kynurenine metabolism associated with mood symptom severity in bipolar disorder

Mukherjee, Dahlia; Krishnamurthy, Venkatesh; Millett, Christopher; Reider, Aubrey; Can, Adem; Groër, Maureen; Fuchs, Dietmar; Postolache, Teodor T.; Saunders, Erika F.H.

doi:10.1111/bdi.12529

Cited by 42 publications

(36 citation statements)

References 52 publications

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“…In this sense, our data are in line with the literature, where meta-analyses have shown significant alterations in interleukins in BD patients compared to HC, including IL-4, IL-6 and IL-10 (Modabbernia et al, 2013;Munkholm et al, 2013). Moreover, these alterations in patients with BD have been linked to severity of symptoms (Mukherjee et al, 2018), mood episodes (Kapczinski et al, 2010), staging (Kauer-Sant'Anna et al, 2009) and medication use (Lim et al, 2016). Regarding MDD, a meta-analysis has shown that IL-1, IL-6 and TNF-α levels in peripheral blood are increased in patients compared to HC (Dowlati et al, 2010), while another meta-analysis has described alterations in IL-10 levels while findings in IL-4 and IL17-A levels are still heterogeneous (Köhler et al, 2017).…”

Section: Discussionsupporting

confidence: 92%

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach

Wollenhaupt-Aguiar

Librenza-Garcia

Bristot

et al. 2019

Aust N Z J Psychiatry

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Objective: This study used machine learning techniques combined with peripheral biomarker measurements to build signatures to help differentiating (1) patients with bipolar depression from patients with unipolar depression, and (2) patients with bipolar depression or unipolar depression from healthy controls. Methods: We assessed serum levels of interleukin-2, interleukin-4, interleukin-6, interleukin-10, tumor necrosis factor-α, interferon-γ, interleukin-17A, brain-derived neurotrophic factor, lipid peroxidation and oxidative protein damage in 54 outpatients with bipolar depression, 54 outpatients with unipolar depression and 54 healthy controls, matched by sex and age. Depressive symptoms were assessed using the Hamilton Depression Rating Scale. Variable selection was performed with recursive feature elimination with a linear support vector machine kernel, and the leave-one-out cross-validation method was used to test and validate our model. Results: Bipolar vs unipolar depression classification achieved an area under the receiver operating characteristics (ROC) curve (AUC) of 0.69, with 0.62 sensitivity and 0.66 specificity using three selected biomarkers (interleukin-4, thiobarbituric acid reactive substances and interleukin-10). For the comparison of bipolar depression vs healthy controls, the model retained five variables (interleukin-6, interleukin-4, thiobarbituric acid reactive substances, carbonyl and interleukin-17A), with an AUC of 0.70, 0.62 sensitivity and 0.7 specificity. Finally, unipolar depression vs healthy controls comparison retained seven variables (interleukin-6, Carbonyl, brain-derived neurotrophic factor, interleukin-10, interleukin-17A, interleukin-4 and tumor necrosis factor-α), with an AUC of 0.74, a sensitivity of 0.68 and 0.70 specificity. Conclusion: Our findings show the potential of machine learning models to aid in clinical practice, leading to more objective assessment. Future studies will examine the possibility of combining peripheral blood biomarker data with other biological data to develop more accurate signatures.

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Section: Discussionsupporting

confidence: 92%

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach

Wollenhaupt-Aguiar

Librenza-Garcia

Bristot

et al. 2019

Aust N Z J Psychiatry

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“…Replicated evidence has demonstrated sleep dysfunction to be associated with increased levels of pro-inflammatory cytokines with a bidirectional causal association identified [ 106 , 107 ]. As such, interest has grown in immune dysfunction as a potential nexus sub-serving the bidirectional interaction between sleep dysfunction and BD [ 108 , 109 ].…”

Section: Pathophysiology Of the Inflammatory-mood Pathwaymentioning

confidence: 99%

Bipolar Disorder and Immune Dysfunction: Epidemiological Findings, Proposed Pathophysiology and Clinical Implications

2017

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Bipolar disorder (BD) is strongly associated with immune dysfunction. Replicated epidemiological studies have demonstrated that BD has high rates of inflammatory medical comorbidities, including autoimmune disorders, chronic infections, cardiovascular disease and metabolic disorders. Cytokine studies have demonstrated that BD is associated with chronic low-grade inflammation with further increases in pro-inflammatory cytokine levels during mood episodes. Several mechanisms have been identified to explain the bidirectional relationship between BD and immune dysfunction. Key mechanisms include cytokine-induced monoamine changes, increased oxidative stress, pathological microglial over-activation, hypothalamic-pituitary-adrenal (HPA) axis over-activation, alterations of the microbiome-gut-brain axis and sleep-related immune changes. The inflammatory-mood pathway presents several potential novel targets in the treatment of BD. Several proof-of-concept clinical trials have shown a positive effect of anti-inflammatory agents in the treatment of BD; however, further research is needed to determine the clinical utility of these treatments. Immune dysfunction is likely to only play a role in a subset of BD patients and as such, future clinical trials should also strive to identify which specific group(s) of BD patients may benefit from anti-inflammatory treatments.

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“…Aberrant sleep is associated with increased levels of proinflammatory cytokines with a bidirectional causal association identified (154, 155). As such, interest has grown in immune dysfunction as a potential link that underwent two-way interaction between sleep dysfunction and BD (156, 157). Both postmortem and in vivo studies showed that microglial activation is involved in the neurobiology of BD (158, 159).…”

Section: Sleep Astroglia and Bipolar Disordermentioning

confidence: 99%

Sleep Disturbance in Bipolar Disorder: Neuroglia and Circadian Rhythms

et al. 2019

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The worldwide prevalence of sleep disorders is approximately 50%, with an even higher occurrence in a psychiatric population. Bipolar disorder (BD) is a severe mental illness characterized by shifts in mood and activity. The BD syndrome also involves heterogeneous symptomatology, including cognitive dysfunctions and impairments of the autonomic nervous system. Sleep abnormalities are frequently associated with BD and are often a good predictor of a mood swing. Preservation of stable sleep–wake cycles is therefore a key to the maintenance of stability in BD, indicating the crucial role of circadian rhythms in this syndrome. The symptom most widespread in BD is insomnia, followed by excessive daytime sleepiness, nightmares, difficulty falling asleep or maintaining sleep, poor sleep quality, sleep talking, sleep walking, and obstructive sleep apnea. Alterations in the structure or duration of sleep are reported in all phases of BD. Understanding the role of neuroglia in BD and in various aspects of sleep is in nascent state. Contributions of the different types of glial cells to BD and sleep abnormalities are discussed in this paper.

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Total sleep time and kynurenine metabolism associated with mood symptom severity in bipolar disorder

Abstract: Inflammation, sleep, and mood are closely intertwined. Future research into the effect of inflammation on sleep in BD may lead to clinical markers of outcome.

Cited by 42 publications

References 52 publications

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach

Bipolar Disorder and Immune Dysfunction: Epidemiological Findings, Proposed Pathophysiology and Clinical Implications

Sleep Disturbance in Bipolar Disorder: Neuroglia and Circadian Rhythms

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