University of Groningen
An inflamed mood Yang, Chenghao
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10.33612/diss.98153713
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Yang, C. (2019). An inflamed mood: studies on the role of inflammation in the pathophysiology and treatment outcome of major depressive disorder. University of Groningen.
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An inflamed mood
Studies on the role of inflammation in the pathophysiology and
treatment outcome of major depressive disorder
The studies described in this thesis were performed at Tianjin Anding Hospital (Tianjin, China), Tianjin Huanhu Hospital (Tianjin, China). This research has been supported by grants from Tianjin Healthy Bureau, Tianjin, China (No.13KG118) and University Medical Center of Groningen (UMCG), Groningen, the Netherlands.
Financial support for the publication of this thesis by University of Groningen, Graduate School of Science, and Research School Behavioral and Congnitive Neurosciences is gratefully acknowledged.
Illustraition cover:
Lay-out: Chenghao Yang
Printing: Ridderprint || www.ridderprint.nl Cover design: Daiyue Liu, Yuhao Wang
ISBN (printed): 978-94-034-1974-9
ISBN (electronic version): 978-94-034-1973-2
Copyright © C. Yang, 2019
All rights reserved. No part of this book may be reproduced in any manner or by any means without permission.
An inflamed mood
Studies on the role of inflammation in the
pathophysiology and treatment outcome of major
depressive disorder
PhD thesis
to obtain the degree of PhD at the University of Groningen
on the authority of the
Rector Magnificus Prof. C. Wijmenga and in accordance with
the decision by the College of Deans. This thesis will be defended in public on Wednesday 9 October 2019 at 11.00 hours
by
Chenghao Yang born on 31 December 1982
Supervisor
Prof. R.A. Schoevers
Co-supervisor
Dr. F.J. Bosker
Assessment Committee
Prof. J. Spijker Prof. U.L.M. Eisel Prof. H. Snieder
Table of Contents
Chapter 1 ……….. Chapter 2 ……….. Chapter 3 ……….. Chapter 4 ……….. Chapter 5 ……….. Chapter 6 ……….. Chapter 7 ……….. Nederlandse samenvatting ... Words of thanks ... List of publications ...Research Institute SHARE ... 132 130 125 111 93 75 59 37 15 3 134
Chapter 1
Major depressive disorder
ajor depressive disorder (MDD) is the second leading cause of disability globally because of its high prevalence, and its impact in terms of functioning, physical health and longevity [1]. In clinical practice, the unclear etiology and diverse presentations of MDD generate substantial obstacles for an accurate diagnosis and effective treatment [2]. Clearly, both aspects will benefit from the elucidation of pathophysiological processes involved and the identification of biological markers for MDD. Given the many pathophysiological processes involved in MDD, such as dysfunctions of neurotransmitter systems, neurotrophic factors and the inflammatory system [3] it is understandable that studies based on descriptive diagnostic classifications yielded mixed results and were difficult to replicate [2]. This has seriously hindered research into the underlying mechanisms of depression [4]. It is thus necessary to develop a better diagnostic classification system, for instance based on biological markers as well as clinical presentation, in order to improve the homogeneity of patient groups with the same diagnosis [5].
Subtypes of MDD
MDD is a highly heterogeneous syndrome with a wide range of symptoms. According to the DSM-IV classification, MDD can have 227 symptom combinations [6]. Furthermore, the newly published DSM-5 still defines MDD as a clinical syndrome rather than a disease, in which the diagnostic criteria of MDD are based on descriptive classification and phenomenological principles, with the core symptoms of depressed mood and/or lack of interest or pleasure [7].
Historically, subtyping of MDD has been based on symptom presentation, severity, onset characteristics and course of illness, with the aim to advance both clinical management and scientific research. Well-established clinical subtypes are based on cross-sectional symptom features and include melancholic, psychotic, atypical and anxious depression. Other subtyping approaches have also been proposed, based on the onset of illness, course of disease, and severity etc. [8]. However, subtyping is still far from perfect given the considerable overlap of MDD characteristics.
A well-designed subtyping strategy should meet the following criteria: first, the subtype aids the choice of treatment strategy; second, the subtype helps to predict treatment response and prognosis; or third, the subtype depicts specific genetic and/or neurobiological characteristics [8]. Several studies have shown that MDD subtypes display different inflammatory marker profiles [9, 10]. Moreover, treatment resistance may be associated with levels of inflammatory markers [11, 12]. Accordingly subtyping of MDD on the basis of inflammatory markers could be a promising strategy.
Links between inflammation and depression
The pathophysiology of MDD is far from clear. In this respect several hypotheses have been proposed, such as the monoamine hypothesis, neurogenesis/neuroplasticity hypothesis, stress hypothesis, immune-inflammation hypothesis and glutamate hypothesis. It is important to note that these proposed pathophysiological mechanisms do not act separately but coexist and
interact with each other in a complex manner, which may partly explain the divergent symptom profiles in MDD.
An involvement of the inflammatory system in depression has already been reported decades ago [13, 14], since then accumulating evidence has been presented for its involvement in the etiology and pathophysiology of MDD. At an early stage the interest in inflammatory processes was limited to the fields of immunology and infectious diseases. The brain was considered to be an immune privileged organ which was largely unaffected by peripheral immune changes because of the existence of the blood-brain barrier (BBB). As a result, the effects of peripheral immune changes on brain function status were generally ignored except for those sporadic cases when the BBB was seriously damaged. More recently it was recognized that MDD is associated with a hyperactive inflammatory response system but also with peripherally elevated concentrations of pro-inflammatory mediators such as tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-1β, and C-reactive protein (CRP) [15-17]. Furthermore, some studies also demonstrated that the levels of certain peripheral inflammatory markers were associated with the outcome of antidepressant treatment [18, 19]. Supportive evidence also came from studies of single nucleotide polymorphisms (SNPs), showing a role of gene variants in the etiology of depression [20, 21]. For example, people carrying the functional genetic variant rs1800795, a SNP in the IL-6 promoter region, tended to have higher blood levels of IL-6, but also a higher incidence of depression than non-carriers [22]. Genomic studies can aid in the identification of biological pathways involved in the pathophysiology of MDD, but as yet results have been mixed even in genome-wide association studies (GWAS) with a huge sample size [21, 23].
Cytokines play an essential role in brain development, and also serve to maintain healthy brain function by sustaining neuronal survival, neurogenesis, and synaptic plasticity [24]. Thus, dysregulation of cytokines would interfere brain functioning and even its structure. For instance, peripheral inflammatory markers were found to be significantly increased in patients with MDD and capable of entering the brain and to interact with well-known pathophysiological processes involved in depression, including neurotransmitter synthesis and metabolism, neuroendocrine function, and neuroplasticity. In fact, hyperactivity of inflammatory pathways within the brain is considered to reduce neurotrophic factors, to disrupt the glutamate release/re-uptake balance, and to enhance oxidative stress, leading to excitatory neurotoxicity followed by neuronal and glial cell damage, which is consistent with the neuropathology of MDD [25, 26]. In addition, psychosocial stress can also interact with the inflammatory system through activation of the hypothalamic-pituitary adrenal (HPA)-axis and sympathetic system [27, 28] (see Figure.1 Bio-connections between the stress, inflammatory cytokines and brain). Conversely depression can facilitate the inflammatory response by the interaction between the HPA axis and cytokines, in which dysregulated HPA axis functioning, a key characteristic of depression, decreases the inhibitory feedback on the production of cytokines [29, 30]. In this respect, it is also important to note that in children experiencing multiple depressive episodes significantly increased CRP levels were measured during the depressive state [31].
Figure.1 Bio-connections between the stress, inflammatory cytokines, and the brain. SNS, sympathetic nervous system; BBB,
blood brain barrier; HPA, hypothalamic-pituitary-adrenal axis
In the end, it is noteworthy that depression is only inflammatory driven in a subgroup of
patients, depending on the individual physiological condition including
sympathetic/parasympathetic system, HPA axis activity, hippocampal volume, and personality [32].
Treatment resistant depression
More than 30% of the depressive patients do not respond satisfactorily to subsequent regular antidepressant treatments, and can be classified as having treatment resistant depression (TRD). TRD is responsible for the largest clinical, personal, and economic burden within MDD [33, 34]. Considerable attention has been paid to treatment strategies and the etiology and underlying pathophysiology of TRD, but progress is slow and as yet far from satisfactory. One of the reasons may be a lack of general consensus on the definition of TRD, although most investigators would define TRD by an insufficient response to at least two different antidepressant treatments. As a result, sample composition and outcomes may vary according to the definitions used.
Both categorical and dimensional approaches have been used to characterize TRD, but both have their shortcomings. The categorical approach emphasizes the number of unsuccessful antidepressant treatments but ignores other forms of treatment such as psychotherapy and physical treatments such as modified electroconvulsive treatment (MECT) and important factors such as family history, personality characteristics and comorbid anxiety [35]. Most dimensional approaches include these variables and stage TRD by rating scores, although they can vary considerably from each other, such as the Massachusetts General Hospital Staging Model (MGH-s), Antidepressant Treatment History Form (ATHF), European staging model (ESM), Thase and Rush staging model (TRSM), and Maudsley Staging Model (MSM) [35]. The dimensional approach is sometimes applied in scientific research but not in clinical practice because of its lack of operability. Furthermore, both
approaches only judge the effective response at the endpoint and do not take into account how long the “effective response” could last, which is an important factor with treatment outcome as well as prognosis. A more comprehensive and exclusive definition of TRD is required for both clinical practice and scientific research.
Exploration of new strategies for MDD/TRD treatment
The development of antidepressant medication started in the 1950s with the discovery that tricyclics such as imipramine and monoamine oxidase inhibitors, such as isoniazid, had beneficial effects in the treatment of depression [36, 37]. Thereafter, considerable efforts have been made to develop new antidepressant drugs and strategies, mainly focusing on serotonin and norepinephrine as a key neurotransmitters involved in mood disorders. Nowadays, the common treatments for MDD include psychotherapy, physical treatments, and pharmaceutical treatments, in the form of monotherapy or combined treatment. However, more than 30% of depressed patients develop TRD even after stepwise guideline-based treatments [33, 34]. Given the heavy burden caused by TRD for the individual and society, developing effective treatment strategies is strongly needed. There is growing interest in drugs targeting the inflammatory system, as well as drugs that influence glutamate systems, opioid systems, dopamine systems, and cholinergic systems, which all interact with the inflammatory system and its response [38-41].
Glutamate system
The relation of the glutamate system with cognitive function has prompted investigators to investigate the glutamate receptor antagonist ketamine in this respect. Although it is not fully understood how the ketamine exerts its robust and rapid-onset antidepressant effects, substantial studies reported that the subject’s depressive symptoms were significantly improved after 2 hours of ketamine administration and lasted for 1 week [42, 43]. In one study, TRD patients were divided into four groups receiving placebo or S-ketamine (28, 56, and 84 mg, respectively) for 8 weeks [44]. As turned out, the antidepressant effect of S-ketamine was dose-dependent, and in contrast with current antidepressant its onset of action was rapid. Further observation revealed that S-ketamine administration effectively reduced symptoms of depression also in patients formerly subjected to placebo.
S-ketamine has been marketed in the America for antidepressant treatment [45], despite a risk for hallucinogenic and psychotic effects. The emergence of ketamine as an effective and rapid acting antidepressant drug has boosted researchers’ interest in glutamatergic agents. Other antidepressant compounds targeting the glutamate system are currently under investigation [46-48].
Opioid system
The opioid system is involved in analgesia and the general regulation of inflammatory responses [49]. It had been found that modulators of the opioid system which were used in the management of pain induced fluctuations and recurrence of depressive symptoms. As a result, the potential of opioid system modulators in antidepressant treatment has attracted researchers’ interests. Buprenorphine, a μ-opioid receptor agonist and a κ-opioid receptor (KOR) antagonist, was shown to possess antidepressant effects [50, 51]; ALKS-5461, a
mixture of buprenorphine and samidorphan, had been approved the positive effects for TRD treatment [52]; and a phase 3b extension study is now under development (ClinicalTrials.gov, Identifier NCT03610048). CERC-501, a short-acting selective antagonist of the KOR receptor, has been shown to augment antidepressant response with TRD treatment in phase I trials [53, 54], and phase II trials are still ongoing (ClinicalTrials.gov, Identifier NCT 01913535).
Cholinergic system
Traditionally, the cholinergic system was recognized to be a regulator of cognition and memory [55]. However, it has also been reported that hyperactivity of cholinergic systems may be involved in the pathological mechanism of depression, while nicotine (N) and muscarinic (M) acetyl-choline receptor modulators are expected to be promising candidates for the treatment of depression [56, 57]. One study illustrated that intravenous infusion of scopolamine rapidly exerted antidepressant effects, similar to S-ketamine, by regulating the m-TOR pathway [58]. In addition, SSRIs when combined with oral scopolamine displayed significantly better antidepressant efficacy than SSRIs alone [59]. In addition, mecamylamine, a nicotinic acetylcholine receptors (nACHRs) antagonist and α4β2 antagonist, was effective in the treatment of TRD [60]. CP-601, 927, a partial agonist of nACHRs may also increase leptin levels and also be effective in the treatment of TRD [61].
Dopamine system
Some researchers have proposed dopamine D3 receptor (D3R) as an important therapeutic
target for TRD treatment [62-64]. For example, buspirone used in the second phase of the
STAR*D study possesses regulating effects on the D3R [65, 66], while Cariprazine, a
dopamine D2/D3 receptor partial agonist, is a new antipsychotic drug recently approved by
FDA for the treatment of schizophrenia and bipolar mania, which was shown D3R-dependent
antidepressant effects [62]. Pramipexole is a D2R/D3R agonist that is effective in treating
Parkinson’s comorbid depression [67] as well as in TRD treatment [68].
Immune system
Meta-analyses have shown that non-steroid anti-inflammatory drugs (NSAIDs) such as celecoxib significantly improved symptoms of MDD without increasing the risk of adverse reactions [69, 70]. Furthermore, the functional tumor necrosis factor antagonist infliximab has been reported to improve depressive symptoms in TRD patients with high baseline levels of inflammatory biomarkers [18]. In addition, a large cohort study has compared the efficacy of SSRI monotherapy with the combination of an SSRI and an anti-inflammatory drug [71]. It appeared that a low-dose acetylsalicylic acid significantly reduced the risk of experiencing a depressive episode [Hazard rate ratio 0.71; 95% confidence interval (0.50; 1.01)], suggesting that combining an SSRI with an immune-modulator can be beneficial for depressive patients.
Inflammation could activate an anxiety-related loop, reduce reward loop conduction, and thus play a role in the development of depression. As a multi-effect pro-inflammatory cytokine, IL-6 holds the properties of being a pro-inflammatory factor or an anti-inflammatory factor [72, 73]. A number of studies have found that the outcome of antidepressant treatment is related to a change in IL-6 levels [74, 75]. IL-6-targeting drugs may be promising for
depression treatment. In addition, studies on omega-3 fatty acids, statins, and intestinal probiotics, which all connect with the immune system, have also demonstrated positive results [76-81]. Considering the heterogeneity of depression and the notion that depression is only inflammatory driven in a subset of depressive patients, anti-inflammatory augmentation strategies should preferably be based on measurements of inflammatory markers such as CRP and IL-6.
Other drug targets
New compounds targeting the neurotrophic system and new non-pharmacological treatments are also expected to benefit TRD patients in the future. On the other hand, newly qualified methods of screening drugs are demanded for the development of innovative drugs. Some researchers have suggested that animal models of TRD need to be improved. For example, it needs to meet four criteria: increased responsiveness to stress; poor response to chronic antidepressant treatments; effective to new antidepressant treatments, like ketamine; consistent with known clinical observations.
Overview of the thesis
This thesis aims to explore the usefulness of inflammatory markers to gain insight in the underlying pathophysiology of MDD/TRD. A second objective is to investigate the efficacy of anti-inflammatory treatment in MDD and more specifically TRD patients with high inflammatory activity.
Chapter 2 reviews the distribution of inflammatory markers including interleukins, tumor
necrosis factor-α, and C-reactive protein, in melancholic and non-melancholic depression, exploring the role of inflammatory markers in these well-known clinical subtypes of MDD.
Chapter 3 reviews the validity of blood inflammatory markers in predicting the outcome of
TRD treatment. In addition, this review also pays attention to the relation between the changes in levels of inflammatory markers and the severity of depressive symptoms.
Chapter 4 is a pilot case-control study exploring the associations of FKBP5 SNPs and
haplotypes with susceptibility and treatment response phenotypes in Han Chinese with MDD.
Chapter 5 investigates the associations of CNR1 SNPs and haplotypes with vulnerability and
treatment response phenotypes in Han Chinese with MDD. This case-control association study was conducted in the same population with chapter 4.
Chapter 6 describes the protocol of a randomized double blind placebo controlled study with
N-acetylcysteine as add-on to regular antidepressant medication in TRD, including the rationale, sample, drugs, primary/secondary outcomes, inclusion/exclusion criteria etc.
Chapter 7 is a general discussion on this thesis, including the summary of main findings,
methodological considerations, which talks about the strengths and limitations of all studies, the relationship between inflammatory dysregulation and MDD/TRD, and the
inflammatory treatment in MDD/TRD. Lastly, the clinical implications of our findings, and directions for future research will be discussed.
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Chapter 2
Interleukin, tumor necrosis factor-α and c-reactive
protein profiles in melancholic and non-melancholic
depression: A systematic review
Chenghao Yang, Kim M. Tiemessen, Fokko J. Bosker, Klaas J. Wardenaar, Jie Lie and Robert A. Schoevers
Abstract
Objective: The current diagnostic criteria for major depressive disorder (MDD) do not allow prediction of prognosis and therapeutic response. A possible strategy to improve this situation is the identification of depression subtypes on the bases of biomarkers reflecting underlying pathological processes such as neuro-inflammation.
Methods: The PubMed/Medline database was searched until Apr 25th, 2017. In the initial search 1018 articles were retrieved, which were subsequently screened and only selected when the inclusion and exclusion criteria were fulfilled.
Results: Eight eligible studies were found. Overall, serum interleukin-6 and 1β values were increased in the melancholic MDD subtype compared to controls and the non-melancholic MDD subtype. C-reactive protein was increased in non-melancholic MDD in 2 out of 4 studies, while there was no difference for tumor necrosis factor-α and interleukin-2 and 10.
Conclusion: Given the paucity of eligible studies the tentative conclusion must be drawn that peripheral inflammation markers have limited added value thus far to distinguish between melancholic and non-melancholic depression. To allow for a more definitive conclusion, further research is warranted using a broader panel of inflammatory markers in MDD subtypes, preferably based on a general consensus regarding diagnostic criteria and subtype definitions.
1. Introduction
epression is one of the most prevailing illnesses in the world, with more than 300 million people falling under this category [1]. Major depressive disorder (MDD) has been estimated to account for a total of 63.2 million disability adjusted life years (DALYs) worldwide [2], making it a high cost burden for the society. MDD is a syndrome with a broad spectrum of varying symptoms. On the basis of symptom profiles the diagnostic statistical manual (DSM) has classified MDD into several clinical subtypes. However, field trials for DSM-5 mood disorders diagnoses have shown that 6-month test-retest reliability was poor to fair (kappa 0.20–0.39) for MDD [3] and even poor using the DSM-IV criteria [4]. So, attempts to improve the reliability of these diagnoses are called for. Moreover, such classification appeared to have little predictive power with respect to prognosis and treatment outcome [3-5]. Still, this is what the field has been working with for many years despite many trials to improve it. To the best of our knowledge, no such data is available regarding the subtypes of MDD, but as subtypes are mostly based on symptoms that are also assessed in MDD diagnosis they will probably be in the same range. A more fruitful approach could be a classification of MDD and it subtypes on the basis of underlying pathological processes. Arguably, this will provide a more rational and suitable basis for improving antidepressant treatment.
Several major hypotheses of pathophysiological processes involved in MDD have been raised in the past, including dysfunctions of the monoamine system, the
immune-inflammatory system, the hypothalamic-pituitary-adrenal (HPA) axis and
neurogenesis/neuroplasticity related processes. Previously we have proposed a theoretical model linking clinical presentations of depression to these pathophysiological processes [6]. The present review is focused on the immune-inflammation hypothesis. It postulates that monocytes, T-lymphocytes and cytokines are involved in the pathogenesis of MDD [7, 8]. According to this theory pro-inflammatory cytokines such as tumor necrosis factor alpha (TNF-α), interferon-gamma (IFN-γ) and interleukin-1 (IL-1) play a key role in the control of neuro-endocrine and behavioral characteristics of MDD. Growing evidence suggests indeed that the pathophysiology of depression is associated with dysregulated inflammatory processes and cytokine imbalance [9-15]. Following this line of thought, research into a possible relation of peripheral inflammatory markers with subtypes of MDD might help to pave the way for a more physiologically oriented approach to diagnosis, prognosis and treatment outcome [16]. In addition, it could contribute to developing preventative measures and adjuvant pharmacological treatment strategies [17].
A challenging problem with biomarker research is the heterogeneous character of MDD [5]. Currently, most biomarker research involves patients with divergent symptom profiles. As a consequence, the results may be mixed and possibly delude one another. The biological dysregulations found in patients with MDD have indeed varied across studies [18, 19]. This variability could be due to differences in sample size and composition (such as age and ethnicity) or to methodological differences, but it might also be attributable to the heterogeneity of MDD [20]. It is thus important to identify biological correlates of MDD subtypes, which may also enable the identification of patients “at-risk” for MDD, for instance those with silent chronic inflammation, to enable preventative measures to be taken. Yet
D
attempts to predict antidepressant treatment response in the STAR*D and iSPOT-D trials [5, 21] on the basis of subtypes such as melancholic depression, atypical depression and anxious depression appeared far from successful. Moreover, both trials reported a considerable overlap between these subtypes while 25-33% of the patients could not be categorized through any of them. Given the generally poorer prognosis with the anxious form of depression [22], it can also be argued that this is not a subtype but a comorbid disorder with two distinct biological correlates. In terms of clinical subtypes, the only distinction that has remained over time is between melancholic and atypical depression. These subtypes have a different clinical presentation and may also differ in course and treatment outcome [23-25]. It is important to note here that atypical depression falls under non-melancholic depression The DSM classifications categorize atypical depression by means of specific symptoms, and it often has a chronic course [26, 27], which contrasts with what is often concerned as the typical melancholic form of depression. Both subtypes of depression are relatively common amongst patients diagnosed with MDD, with 15 to 30% of patients displaying atypical features [28, 29] and 25 to 30% displaying melancholic features [29]. Several studies have suggested that melancholic and atypical depression also differ in biological characteristics, which is promising as these two subtypes have remained relatively stable and distinct from one another over time [20, 30, 31]. The biomarkers investigated in this review include interleukin-2 (IL-2), interleukin-6 (IL-6), interleukin-10 (IL-10), interleukin-1 beta (IL-1β), TNF-α, and C-reactive protein (CRP/hsCRP), and are all important players in the human immune system (see table 1). The aim of this systematic review is to investigate whether these peripheral markers provide relevant information regarding inflammatory processes in the melancholic and non-melancholic forms of depression.
Table 1. Overview of interleukins, TNF-α and CRP: functions, receptors, and targets. Cytokine Cytokine Receptor Cytokine Source Cytokine Targets Cytokine Main Function Cytokine Disease Association
IL-1α Has two sites of binding to IL1RI and IL1R-AcP
Macrophages, epithelial cells, many others Macrophages, thymocytes, CNS, others Inflammatory; promotes activation, costimulation, and secretion of cytokines and other acute-phase proteins; pyrogenic; kills a limited number of tumor cells types
↑ = inflammatory bone resorption; gout; promotes Th17 response; interaction with TNF-α involving in insulin resistance
IL-1β IL1RI and IL1R-AcP Macrophages, many others Macrophages, thymocytes, CNS, others Inflammatory; contributes pain sensitivity; promotes activation, costimulation, and secretion of cytokines and other acute-phase proteins; pyrogenic
↑ = inflammatory bone resorption; gout; promotes Th17 response
IL-2 IL2Rα, IL2Rb, and IL2Rγ T cells T, B, NK cells, and macrophages
Proliferation; enhancement of cytotoxicity, IFNγ secretion, and antibody production
↓ = lymphoproliferative disease and susceptibility to autoimmune disease; reduced Treg development. ↑ = reduced Th17 development.
IL-6 IL6Rα and gp130
Macrophages, T cells, fibroblasts, and others Wide variety of cells: B cells, T cells, thymocytes, myeloid cells, osteoclasts Inflammatory and costimulatory action; induces proliferation and differentiation; synergizes with TGFb to drive Th17
↓ = deficient innate immunity and acute- phase responses, lymphopenia
IL-10 IL10R1 and IL10R2
Differentiated T helper cells, Tregs, B cells, dendritic cells, others Macrophages, T cells, dendritic cells, B cells Immune suppression; decreases antigen presentation and MHC class II expression of dendritic cells; down- regulates pathogenic Th1, Th2, and Th17 responses
↓ = immune pathology due to uncontrolled inflammation. ↑ = inhibits sterile immunity to some pathogens. TNF-α TNFR1 (CD120a) and TNFR2 (CD120b) Macrophages, CD4+ lymphocytes, NK cells, neutrophils, mast cells, eosinophils, and neurons Most tissues in the body (TNFR1) and cells of the immune system (TNFR2). Regulation of immune cells; inducing fever, cell apoptosis, cachexia, and inflammation; inhibition of oncogenesis and viral replication; responding to sepsis via IL1 & IL6 producing cells.
↑= promotes the inflammatory response. This causes many of the clinical problems associated with autoimmune disorders.
Also induces fever, cell death, and shock-like symptoms.
↓= induces cachexia.
CRP/hsCRP
Phosphocholine on the surface of dead or dying cells and some bacteria; FcgammaRI , FcgammaRIIa, and FcgammaRIIb Synthesized by the liver in response to biomarkers released by macrophages and adipocytes Damaged cells, dead cells, complement system, bacteria Activates the complement system, promoting phagocytosis of dead or dying cells (or bacteria) by
macrophages.
↑ = Increasing phagocytosis and release of cytokines; binding to damaged membranes; increasing clearance of apoptotic cells; masking autoantigens from the immune system or enhancing their clearance.[32]
Taken and adapted from http://www.sinobiological.com/What-are-Interleukins-a-6072.html, with added information IL-1α was only included in this overview to show the many similarities with IL-1β with respect to source, receptors, targets and main functions, but this cytokine was not specifically assessed in any of the papers.
2. Materials and Methods
The database used was Pubmed (Medline). The search string included the following terms: (((((((((("Biological Markers"[Mesh]) OR "C-Reactive Protein"[Mesh]) ) OR "Interferon-gamma"[Mesh])) OR "Interleukins"[Mesh])) OR "Tumor Necrosis Factor-alpha"[Mesh]) ) OR (biomarker*[tw] OR "inflammatory marker*"[tw] OR c-reactive protein[tw] OR CRP[tw] OR high-sensitive CRP[tw] OR hsCRP[tw] OR interferon gamma[tw] OR interleukin[tw] OR tumor necrosis factor[tw])) AND ("Depressive Disorder, Major"[Mesh] OR atypical depress*[tw] OR melanchol*[tw]).
The PubMed search was performed on Apr 25th 2017, and yielded 1018 articles (8
studies in non-human species were excluded; see figure 1 below). The titles and abstracts of the articles were scanned to see if they met the inclusion criteria. If there were any doubts whether an article should be included or not, the whole text was read. Previous review studies, including meta-analyses, were not used for this review, but their reference list was scanned for articles that might have been missed by the PubMed search. Articles that primarily focused on somatic diseases (such as cardiovascular disease, cancer or autoimmune disease) with co-morbid depression were also excluded from the study. An exception to this exclusion criterion was made for depression with co-morbid anxiety disorder as these very often co-occur [33]. It is important to note that only studies reporting baseline serum values of biomarkers were taken into consideration, thus excluding challenge studies to assess the cytokine production capacity. Some antidepressants can alter the immune response [34, 35]. Yet we have also included studies wherein part of the patients was treated with antidepressants, as long as co-variate analyses indicated that antidepressant treatment did not appreciably influence the outcome. Finally, it was required that studies included both melancholic and non-melancholic subtypes in relation to biomarker levels. References in all included studies were screened for cross references of eligible studies possibly missed by the PubMed search. The articles were then evaluated whether useful information was provided regarding inflammatory processes in the two subtypes. It is also important to note that a study not making a distinction between IL-1α and IL-1β has been excluded in this respect [36]. Finally, on the basis of the reported sample size, mean value and standard deviation Forest plots were constructed showing the Hedges’g effect sizes for the markers. Given the small sample size of some of the studies (n<20) we have used the Hedges’g formula instead of the simpler one from Cohen.
Figure 1: Flowchart of the methodology 3. Results
In the 8 studies eligible for analysis, 6307 persons were included. In total, 5455 controls were compared to 852 MDD patients. Most studies used the symptom-based DSM-IV criteria to diagnose MDD and to define the subtypes, although a few used alternative methods such as the sign-based CORE measure, which assesses psychomotor and neuroendocrine disturbances instead of symptoms. Other assessments included the Diagnostic Interview for Genetic Studies (DIGS) combined with the General Health Questionnaire-12 items (GHQ-12), the Composite International Diagnostic Interview-version 2.1 (CIDI-2.1) or Latent Class Analysis (LCA). A summary of the results can be found in Table 2, while the statistically significant findings are summarized in the text below. Only 6 out of 8 studies were suitable to construct Forest plots, depicting the Hedges’g effect sizes. These are shown in figure 2 together with the number of patients, mean values and standard deviations.
Figure 2: Forest plots depicting the differences between controls, melancholic and atypical (non-melancholic) patients for 6
out 8 studies, including number of patients, mean values and standard deviations. Notably, the Hedges’g effect sizes of IL-1 from the study by Maes et al. (38) are also included, but since it was not specified whether it concerned IL-1α or IL-1β these data were not used in the final evaluation.
IL-2 in melancholic and non-melancholic depression
Spanemberg et al. found no statistically significant difference between melancholic and non-melancholic groups for IL-2 (p>0.05) [37]. Overall, there is no tendency for IL-2 to be increased in patients suffering from non-melancholic MDD (including atypical depression).
IL-6 in melancholic and non-melancholic depression
Dunjic-Kostic et al. found a tendency for increased IL-6 levels in melancholic patients [38]. The serum concentration of IL-6 was found to be higher in the melancholic subtype compared to the atypical subtype, although Fisher’s least significant difference (LSD) showed no difference in IL-6 between the two groups [38]. The only statistically significant difference was between melancholic depression and controls (p<0.05) [38].
Karlovic et al. did not find statistically significant differences in IL-6 concentrations between the melancholic group and the atypical group when multinomial logistic regression was used (p>0.003) [39]. However, levels were significantly higher in the melancholic group when compared to controls (p<0.003) [39]. Atypical IL-6 levels versus controls were not significant (p>0.003) [39].
Lamers et al. found that IL-6 levels were elevated in atypical depression when compared to melancholic depression and to healthy controls [30]. However, when multivariate models were run with adjustment for BMI, the differences between the groups were no longer significant [30]. The atypical and melancholic subtypes were identified based on a LCA rather than on DSM criteria and only those classified as having severe MDD were studied.
Spanemberg et al. found similar data, and also reported no differences in IL-6 concentrations between melancholic and non-melancholic subtypes identified using the CORE measure (p>0.05) [37]. MDD IL-6 levels were significantly higher than controls (p<0.05), levels in melancholic depression were significantly higher than controls (p<0.05), and non-melancholic versus controls levels were not significantly different (p>0.05) [37].
Glaus et al. found that IL-6 levels did not differ in healthy controls, atypical subtype and melancholic subtype (p>0.05) [40], also after adjustment for comorbid disorders, diabetes, smoking, BMI, selective serotonin reuptake inhibitors (SSRI), mood stabilizers, antipsychotics (p>0.05) [40].
IL-10 in melancholic and non-melancholic depression
Huang et al. did not find differences in serum levels between melancholic patients and those with non-melancholic features for IL-10 when ANCOVA with age and BMI adjustment was used (F = 2.014; d.f. = 1,40; P = 0.165) [41]. The same was found for IL-10 levels in MDD versus controls (p> 0.05) [41], but there was no direct comparison between the subtypes of MDD and controls.
Spanemberg et al., similarly, found no statistically significant difference in IL-10 levels between melancholic and non-melancholic patients (p>0.05) [37]. Using DSM-IV criteria 33 patients were diagnosed as being depressed. Patients were classified as melancholic or non-melancholic using the CORE measure. This evaluates 18 observable features of melancholia on a 4-point scale, and measures its absence or presence. A CORE score of ≥8
was taken as determining melancholia. MDD IL-10 levels versus controls were also not found to be significantly different (p>0.05) [37].
IL-1β in melancholic and non-melancholic depression
Huang et al. found significantly higher serum levels of IL-1β in patients with melancholic features than in those with non-melancholic features after adjusting for age and BMI using analysis of covariance (ANCOVA) (F = 5.703; d.f. = 1,40; P = 0.023) [41]. Not mentioned in this study is how many females and males were included in the separate subtypes of MDD. IL-1β levels in MDD were not significantly different from controls (p>0.05) [41].
Glaus et al. found no statistically significant differences in IL-1β levels among healthy control, atypical subtype and melancholic subtype (p>0.05) [40], both before and after adjustment for comorbid disorders, diabetes, smoking, BMI, SSRI, mood stabilizers, antipsychotics (p>0.05) [40].
TNF-α in melancholic and non-melancholic depression
Dunjic-Kostic et al. used Fisher’s least significant difference (LSD) and found no significant difference in TNF-α levels between patients with melancholic MDD and patients with atypical MDD, although levels were slightly higher in the melancholic group [38]. There was a significant difference in TNF-α only between the atypical group and controls (p<0.05) [38].
Karlovic et al. found no statistically significant difference for TNF-α across
melancholic and atypical MDD groups [39]. No significant difference was found with control groups either [39].
Huang et al. found no difference in TNF-α levels across melancholic and non-melancholic groups (p> 0.05) [41]. Analysis of covariance (ANCOVA) was used to perform data analysis, with adjustments for age and BMI for mean group differences. TNF-α levels in MDD versus controls were not significantly different (p> 0.05) [41]. MDD had significantly higher TNF-α levels than controls after correcting for BMI (p< 0.05). Melancholic versus non-melancholic groups showed no significant difference in TNF-α levels even after age and BMI were corrected for (p>0.05) [41].
Lamers et al., on the other hand, found TNF-α levels in the atypical depression group to be significantly higher than in the melancholic group when adjusted for age, sex, educational level and smoking (p<0.05) [30]. When multivariate models were used to correct for BMI, it still remained significant (P=0.01). The atypical and melancholic subtypes were identified based on a LCA rather than on DSM criteria, and only those classified as having severe MDD were studied [30]. Differences in TNF-α levels between the melancholic groups and controls were not significant (p> 0.05). Atypical TNF-α levels were significantly higher than controls (p<0.05), also after correction for BMI [30].
Maes et al. found serum TNF-α levels to be higher in melancholic patients than in non-melancholic patients [36]. TNF-α levels were significantly higher in MDD when compared to controls (p<0.05) [36]. There was some disparity in the subtyping, where normal MDD was used (which could be non-melancholic or typical). TNF-α levels in melancholic patients were significantly higher than normal MDD levels (P<0.05) [36].
Spanemberg et al. found no significant difference in TNF-α levels between the melancholic and non-melancholic groups (p>0.05) [37]. No significant difference was found for TNF-α levels in MDD versus controls (p>0.05) [37].
Glaus et al. found that TNF- α levels did not differ in healthy control, atypical subtype and melancholic subtype (p>0.05) [40], also after adjustment for comorbid disorders, diabetes, smoking, BMI, SSRI, mood stabilizers, antipsychotics (p>0.05) [40].
CRP in melancholic and non-melancholic depression
Karlovic et al. found no statistically significant difference in serum CRP concentrations between melancholic and atypical MDD groups [39]. No difference was found between the atypical versus control groups (p>0.003) or in the melancholic versus atypical group (p>0.03) [39].
In contrast, Lamers et al. found CRP levels to be higher in atypical depression than in melancholic depression (p<0.05) [30]. After adjustment for age, sex, educational level and smoking, atypical depression still retained higher CRP levels, although between-group differences were no longer significant when multivariate models with adjustment for BMI were used [30]. The atypical and melancholic subtypes were identified based on a LCA rather than on DSM criteria and only those classified as having severe MDD were studied. ANOVA was used to compare the subtypes, and effect sizes (Cohen’s d) were also calculated. Melancholic versus controls were not significant for CRP levels (p>0.05) [30]. CRP levels in the atypical group were significantly higher than in controls (p<0.05) [30].
Hickman et al. found that CRP levels in atypical depression were significantly elevated when compared to non-atypical depression and to healthy controls (p<0.05) [42]. Multiple linear regressions were used to examine the association between serum CRP levels and the subtypes of MDD (demographics-adjusted, confounder-adjusted and fully-adjusted). Logistic regression was also performed. After adjusting for potential cofounders, BMI, and smoking, CRP levels remained significantly higher in atypical MDD than in either non-atypical MDD or healthy controls (p<0.05) [42].
Glaus et al. found that hsCRP levels were decreased in the melancholic subtype compared to healthy controls (p<0.05), but not after adjusting for dyslipidemia, diabetes, BMI, hypertension (p>0.05) [40]. There was no difference between the atypical subtype and healthy controls. No data was provided for the comparison between melancholic and atypical subtype [40]. A summary of the most important findings can be found in table 2 below.
Ta b le 2 . S u m m a ry o f re su lts Au th o r (r ef. n r) N (M a le/Fe m a le) a Ag e in y ea rs (m ea n ± s.d ) Cla ss ifi ca ti o n o f M DD (sub ty p e co m p o sitio n ) S u b ty p e d efi n iti o n Cy to k in es m e a su r ed Re su lts o f stud y sho w n a s co n ce n tr a tio n o f cy to k in e s (m ea n ± s.d ) As sa y Ad ju st m e n t fo r co n fo u n d e rs Diffe re n ce in r es u lts a fte r a d ju st m e n t # S p an em b erg et al, [ 3 7 ] T o tal 8 7 (7 4 .1 % fe m ale) C: 5 4 (7 4 .1 % fe m ale) M : 1 3 (7 6 .9 % fe m ale) NM: 2 0 (9 0 .0 % fe m ale) C: 4 7 .4 ± 9 .9 7 M : 5 2 .8 ± 1 0 .7 NM: 4 8 .4 ± 7 .7 DSM -IV t o d iag n o se M DD ; CORE to id en ti fy su b ty p es (M v s NM ) M : th e CORE sc o re o f p ati en t w it h M DD ≥ 8 ; NM: ap art fro m th o se w it h m elan ch o li c d ep re ss io n IL -2 , IL -6 , IL -1 0 , T NF -α IL -2 : NM=M=C C: 0 .2 5 ± 0 .0 8 p g /m l ＆ M : 0 .2 4 ± 0 .0 7 p g /m l ＆ NM: 0 .2 7 ± 0 .1 2 p g /m l ＆ IL -6: M DD > C; M > C; NM=C C: 0 .8 8 ± 0 .6 9 p g /m l ＆ M : 1 .4 5 ± 2 .5 5 p g /m l ＆ NM: 1 .2 8 ± 1 .1 0 p g /m l ＆ IL -1 0 : M DD = C; M = NM C: 0 .3 7 ± 0 .2 6 p g /m l ＆ M : 0 .3 4 ± 0 .2 5 p g /m l ＆ NM: 0 .2 8 ± 0 .5 2 p g /m l ＆ T NF -α : M DD = C; NM=M C: 0 .9 8 ± 0 .3 0 p g /m l ＆ M : 1 .0 2 ± 0 .3 5 p g /m l ＆ NM: 0 .9 7 ± 0 .2 9 p g /m l ＆ C y to m etr y No N/A Du n ji c-Ko stic et al, [3 8 ] T o tal 8 6 C: 3 9 (1 7 /2 2 ) M : 2 9 (1 3 /1 6 ) A D: 1 8 (8 /1 0 ) C: 4 9 .9 0 ± 4 .9 9 M : 5 0 .2 8 ± 7 .4 1 A D: 5 2 .2 6 ± 7 .2 9 DSM -IV (M v s A D) M : m elan ch o li c fe atu re s sp ec if ier o f DSM -IV A D: at y p ica l fe atu re s sp ec if ier o f DSM -IV IL -6 , T NF -α IL -6 : M > C; A D= C; A D = M C: 5 .2 1 ± 2 .7 0 p g /m l M : 1 2 .5 4 ± 1 4 .6 8 p g /m l A D: 7 .0 6 ± 5 .0 8 p g /m l TN F -α : M = A D; A D < C; M = C C: 9 .0 8 ± 6 .8 7 p g /m l M : 7 .4 6 ± 5 .8 5 p g /m l A D: 5 .6 7 ± 2 .9 1 p g /m l EL IS A Ye s: Ag e, g en d er, BM I, sm o k in g No
Ka rlo v ic et al, [3 9 ] T o tal 7 3 C: 1 8 (8 /1 0 ) M : 3 2 (2 2 /1 0 o r 2 0 /1 2 * * ) A D: 2 3 (1 5 /8 ) C: 4 5 .0 ± 9 .5 M : 4 8 .6 ± 8 .1 A D: 5 0 .9 ± 8 .3 DSM -IV (M v s A D) M : m elan ch o li c fe atu re s sp ec if ier o f DSM -IV A D: at y p ica l fe atu re s sp ec if ier o f DSM -IV IL -6 , T NF -α , CRP IL -6 : M > C; A D= C; AD = M C: 1 .7 5 ± 1 .1 p g /L M : 3 .1 1 ± 1 .6 p g /L A D: 2 .4 5 ± 1 .2 p g /L T NF -α : A D= M = C C: 5 .4 0 ± 1 .5 p g /L M : 6 .9 1 ± 2 .9 p g /L A D: 5 .8 6 ± 1 .8 p g /L CRP : M > C; A D= C; M = A D C: 0 .9 ± 0 .8 m g /L M : 3 .2 2 ± 3 .0 m g /L A D: 2 .2 3 ± 2 .0 m g /L EL IS A & imm u n o tu rb id im etri c ass a y Ye s; Ag e, g en d er, e m p lo y m en t sta tu s, ed u ca ti o n , p lac e o f li v in g , m arriag e sta tu s, sm o k in g No # M ae s e t al , [3 6 ] T o tal 5 7 C: 2 0 M DD : 3 7 M : 1 2 NM: 2 5 S ex ra ti o o n ly fo r co n tro ls an d t o tal M DD p ati en ts: C: 2 6 (1 1 /1 5 ) M DD : 8 5 (3 6 /4 9 ) C: 4 2 .1 ± 1 2 .8 M DD : 4 2 .0 ± 1 1 .0 DSM -IV (M v s NM ) M : m elan ch o li c fe atu re s sp ec if ier o f DSM -IV NM: a p art fro m th o se w it h m ela n ch o li c d ep re ss io n T NF -α T NF -α : M DD > C; M > NM C: 7 .4 2 ± 1 .3 4 p g /m l M DD : 1 1 .9 4 ± 4 .4 3 p g /m l M : 1 4 .7 6 ± 4 .2 4 p g /m l NM: 1 0 .5 9 ± 3 .9 1 EL IS A No N/A Hu an g e t al, [4 1 ] T o tal 8 2 C: 4 0 (1 5 /2 5 ) M DD : 4 2 (1 2 /3 0 ) M : 2 5 NM: 1 7 C: 3 1 ,4 ± 3 .9 M : 4 0 .2 ± 8 .0 N M : 3 4 .7 ± 7 .6 DSM -IV (M v s NM ) M : m elan ch o li c fe atu re s sp ec if ier o f DSM -IV NM: a p art fro m th o se w it h m elan ch o li c d ep re ss io n IL -1 0 , IL -1β, T NF -α IL -1 0 : M DD = C; NM=M C: 1 2 .2 ± 5 .9 p g /m l M : 2 4 .5 ± 3 0 .0 p g /m l NM: 2 4 .9 ± 2 1 .2 p g /m l IL -1 β: M DD = C; M > NM C: 1 2 .4 ± 1 5 .1 p g /m l M : 5 .8 ± 8 .6 p g /m l NM: 1 .9 ± 3 .7 p g /m l T NF -α : M DD = C; NM=M C: 0 .6 ± 3 .7 p g /m l M : 1 .1 ± 2 .6 p g /m l EL IS A Ye s: ag e, BM I N/A : o n ly ad ju ste d re su lt s av ail ab le (se e co n ce n tratio n o f c y to k in es co lu m n ). Un ad ju ste d re su lt s a re n o t av ail ab le ex ce p t fo r
