Staging Models in Bipolar Disorder
Abstract
Since the initial description of bipolar disorder (BD), longitudinal observations suggested that episodes are more frequent as the disorder progressed. Staging models in BD consider that there is a subset of patients who present a more severe course of illness with a higher number of episodes and a propensity to treatment resistance. There is an emerging body of knowledge suggesting that staging models may provide clinicians with a useful tool to predict the course of illness and organize treatment strategies to individual patients. The aim of the present work is to review the evidence related to the use of staging models in bipolar disorder.
Introduction
Bipolar disorder (BD) affects about 2% of the world’s population, with subthreshold forms affecting another 2% (1). Treatment in BD conventionally focuses on acute stabilization of the mood episode and on maintenance, in which the goals are relapse prevention and reduction of subthreshold symptoms (2). Even with syndromal treatment, about 37% of patients relapse into depression or mania within 1 year and 60% within 2 years (3). The rates of completed suicide in BD patients are 7.8% in men and 4.9% in women (4).
Staging models have become an important contribution to understanding disease progression, functional outcome, and treatment response in medicine (5). Physicians and researchers from different specialties rely on staging models to improve available treatments and to generate novel interventions for medical conditions such as heart failure (6), sepsis (7), chronic obstructive pulmonary disease (8), and cancer (9). Staging models in psychiatry assume that some mental illnesses may progress from an at-risk stage to a treatment-refractory end stage (10). Moreover, staging systems also emphasize that while some patients may have a more benign course of illness, others may present with a higher propensity for episode recurrence and refractoriness (11, 12). Table 1 shows staging models proposed for bipolar disorder.
| Stage | Berk et al., 2007 (73) | Kapczinski et al., 2009 (74) | Post et al., 2010 (75) | Duffy et al., 2010 (76) | Reinares et al., 2013 (61) | Cosci and Fava. 2013 (77) |
|---|---|---|---|---|---|---|
| 0 | At risk, asymptomatic period where a range of risk factors converge | Latent phase: mood and anxiety symptoms and increased risk for subthreshold BD | Well | |||
| 1a | Mild or nonspecific symptoms | Well-established periods of euthymia and absence of overt psychiatric morbidity or impairment in between episodes | Vulnerability | Nonmood psychiatric disorders (ADHD, anxiety and/or sleep disorders) during childhood | Low subsyndromal depressive symptoms, increased inhibitory control, and estimated verbal intelligence associated with good outcome | Mild or nonspecific symptoms/prodromal phase |
| 1b | Wide variety of prodromal patterns | Well intervals | Cyclothymia | |||
| 2 | First episode of either polarity, usually depressive | Rapid cycling and DSM–IV Axis I and III comorbidities along with transient impairment in functioning | Minor mood disorders during childhood and/or adolescence | Acute manifestations of major depression or mania/hypomania | ||
| 3a | First recurrence with subthreshold symptoms | Clinically relevant pattern of cognitive and functional deterioration | Prodrome | Major depressive episodes during adolescence | Residual depressive symptoms with increased episode density, low inhibitory control, and estimated verbal intelligence associated with poor outcome | Residual symptoms with cognitive and functional impairment despite treatment |
| 3b | Recurrences with threshold illness | |||||
| 3c | A subsequent pattern of remission and recurrences | |||||
| 4 | Unremitting or treatment refractory course of BD | Significant cognitive and functional impairment and unable to live autonomously | Illness onset | First episode of mania during late adolescence or early adulthood with or without associated substance abuse | Acute episodes despite treatment | |
| 5 | Episode recurrence | |||||
| 6 | Illness progression | |||||
| 7 | Treatment refractoriness | |||||
| 8 | End-stage BD |
Table 1. Proposed Clinical Models for the Staging System in BD
Accordingly, recent studies suggest that staging may help tailor treatment in a subset of BD patients (13). In view of that, we aim to review the literature related to staging models in BD and their potential clinical use.
Illness Progression and Staging
BD patients may present different courses of illness progression (11). Trauma exposure, increased number of episodes, and comorbidity are commonly associated with unfavorable clinical outcomes, such as reduced interepisode intervals, rapid cycling, and cognitive and functional impairments, as well as augmented rates of hospitalizations and suicide (14).
Cross-sectional and longitudinal studies assessed the impact of trauma exposure on BD severity. A 24-month follow-up study of 131 bipolar I disorder patients has shown that trauma exposure predicted greater severity of interpersonal chronic stressors (15). In addition, among 587 BD patients, consistent associations between childhood trauma (emotional abuse, sexual abuse, and emotional neglect) and more severe clinical outcomes (suicide attempts, rapid cycling, and an increased number of depressive episodes) have been reported (16). A history of verbal abuse in 634 BD outpatients was related to an earlier age of onset of disease and other poor prognosis characteristics, including rapid cycling, and a deteriorating illness course as reflected in ratings of increasing frequency or severity of mania and depression (17).
Increased number of episodes may also have a predictive effect on the risk of recurrence in BD, since vulnerability and latency to relapse vary directly as a function of number of prior hospital admissions (18). In the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD), patients with multiple previous episodes presented with worse functioning and lower quality of life. In addition, in the STEP-BD study, patients with multiple episodes had more disability, as well as more chronic and severe symptoms (19). Moreover, evidence suggests that younger patients with fewer episodes present a better response to treatment with conventional mood stabilizers and psychoeducation (20). Of note, increasing episode number is linked to a reduction in the likelihood of response to lithium (21) and cognitive behavioral therapy (CBT) (22).
Regarding co-occurring disorders, a systematic review with meta-regression has reported a significant correlation of comorbid substance use disorders and suicide attempt (23). Within this same meta-analysis, substance abuse disorder in the preceding 8-week period predicted greater likelihood of suicide attempt in a 5-year follow-up prospective study with 413 youths with BD (24). Current or past substance use disorders have also been associated with greater risk of switch into manic, mixed, or hypomanic states (25). Specifically, cannabis and alcohol use disorders were associated with higher symptomatology and lower functioning in a longitudinal study of 137 BD patients (26). Moreover, BD patients with attention deficit hyperactivity disorder (ADHD) presented an earlier onset and had a chronic course, irritable mood, and greater clinical and functional impairment, and treatment resistance (27). In a meta-regression analysis, comorbid ADHD was significantly associated with suicide attempts (23). Comorbid PTSD also was associated with poor outcomes, including significantly worse social functioning (28), interepisode depression, and quality of life (29). In addition, there is a body of literature suggesting that predictors of poor prognosis (stressors, increased episode number, and comorbidity) may show sensitization to themselves and cross-sensitization to the others, contributing to greater illness burden and treatment resistance (14).
Neuroprogression
The term neuroprogression is used to describe the biological underpinnings of illness progression in the context of BD (11) and may shed some light on how such predictors interact with the pathophysiology of BD (30). Such a process can further disrupt the brain circuits that are responsible for mood regulation and cognition, increasing the vulnerability to illness (30). It is now becoming apparent that the biochemical foundation of neuroprogression is multifactorial and interactive, not only between pathways, but via stress sensitization from the environment stressors and comorbidities (11). As shown in recent meta-analyses and postmortem studies, the core components of this biochemical process of neuroprogression are abnormal levels of inflammatory cytokines (31–33), markers of oxidative stress (34, 35), and neurotrophins, including brain-derived neurotrophic factor (BDNF) (36, 37). There seems to be a significant reduction in BDNF expression and IL-6 levels along with significantly higher levels of tumor necrosis factor–alpha (TNF–α) as patients progress to more severe stages (38). In the same vein, differential changes in oxidative stress activity levels have also been reported as patients progress to later stages of BD (39). A recent PET scan study showed microglial activation in the right hippocampus of BD patients (40). Moreover, there is evidence for the involvement of epigenetic changes, particularly histone and DNA methylation (41, 42) and acetylation (43) leading to long-acting effects on gene expression, which may contribute to neuroprogression.
Noteworthy is the severity of comorbidity between PTSD and BD, since reduced BDNF function from several contributing sources, including the met variant of the BDNF val66met (rs6265) single-nucleotide polymorphism, trauma-induced epigenetic regulation, and current stress, is associated with core characteristics of both disorders (44). Trauma exposure is also associated with significantly lower BDNF levels in BD patients (45).
Structural neuroimaging studies have reported an association between the number of episodes and the enlargement of the lateral ventricles (46, 47) as well as decreases in cerebellar vermal volume (48). While changes during the first episode of mania seem to be related specifically to white matter pathology (49, 50), progressive loss of gray matter volume in prefrontal areas (51, 52) has been reported in late-stage BD (53, 54). For instance, in contrast to findings in schizophrenia, which have shown that hippocampal volume loss and ventricular dilatation may occur prior to the first episode, the literature suggests that in BD, gross brain structure is relatively preserved during its early phases (53). BD patients have shown hippocampal subfield volume reductions in cornu ammonis (CA) subfields CA2/3, CA4/dentate gyrus, subiculum, and right CA1 (55). Moreover, lithium treatment has been shown to prevent such reduction (56, 57).
Functional Outcomes
Psychosocial functioning describes a person’s ability to function socially and occupationally and to live independently (58). Patients with BD may suffer from functional impairment, even when euthymic, and may experience serious dysfunction in distinct life domains, such as work productivity, social activities, and autonomy (59). A 24-month follow-up study found that almost 100% of first-episode manic patients with BD had syndromal recovery within 2 years, but only one-third achieved functional recovery (60). Therefore, functional and symptomatic recoveries are not always associated and may need different therapeutic approaches.
The ability to function in daily-life activities has also been proposed as an important correlate of staging and illness progression in BD (61, 62) and may provide clinicians with a proxy of staging (63). By applying latent class analysis in a sample of 106 remitted adults with BD, a recent study identified two subtypes of patients presenting “good” and “poor” functional outcome (61). Episode density, level of residual depressive symptoms, estimated verbal intelligence, and inhibitory control emerged as the most significant predictors of such subtype membership (61). An interesting point of this study is that functional outcome was not predicted by illness duration, since the two groups were comparable in age, age of onset, and illness duration (61). Prior studies have also supported number of hospitalizations and comorbidity as determinants of poor functional outcome (26, 64, 65).
BD patients may suffer from marked cognitive impairment even when euthymic (66, 67), which could also limit long-term psychosocial functioning (68, 69). Beyond estimated verbal intelligence and inhibitory control, executive and memory dysfunctions tend to show greater impairment in daily-life activities (70, 71). In comparisons of neurocognitive performance between patients with low and high functioning scores, patients with poor functioning had significantly more severe impairment in memory tasks, inhibitory control, and working memory (72).
Functional staging in BD may provide a practical model to evaluate the progressive course of illness, as well as guide therapy to improve patient’s quality of life, the ultimate goal of treatment. In this regard, a strong linear association was found between functioning assessment short test scores and the clinical stages described by Kapczinski (73), suggesting a progressive functional decline from stage I through stage IV of BD (63). Although significant differences in functional status were found between patients in all stages, only patients in severe stages (III and IV) were more impaired than healthy subjects (63).
Concluding Remarks
It has been widely accepted that earlier interventions in BD are likely to be associated with a better response to treatment and lower rates of both functional and cognitive impairment (63, 73). Conversely, interventions for late-stage BD patients usually require more complex treatment regimens (73, 74). While treatments such as lithium monotherapy, CBT, and psychoeducation seem to be better suited to prevent further recurrences in early-stage patients, complex pharmacological strategies along with functional and cognitive remediation may be considered optimal to alleviate functional changes and prevent further deterioration in late-stage patients.
Clinical staging as a means to guide and tailor treatments according to patient’s needs may provide an important tool for clinicians working with BD patients. However, the potential value of staging systems with regard to their specificity and validity remains to be clarified by longitudinal studies. In the meantime, targeting treatment for each BD patient from a staging perspective may provide clinicians with a heuristic model as to how to approach BD patients according to their individual needs.
1 : Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication. Arch Gen Psychiatry 2007; 64:543–552Crossref, Google Scholar
2 : Treatment of bipolar disorder. Lancet 2013; 381:1672–1682Crossref, Google Scholar
3 : Relapse and impairment in bipolar disorder. Am J Psychiatry 1995; 152:1635–1640Crossref, Google Scholar
4 : Absolute risk of suicide after first hospital contact in mental disorder. Arch Gen Psychiatry 2011; 68:1058–1064Crossref, Google Scholar
5 : Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat Rev Drug Discov 2007; 6:287–293Crossref, Google Scholar
6 : Heart failure: TNM-like classification. J Am Coll Cardiol 2014; 63:1959–1960Crossref, Google Scholar
7 : Proof of principle: the predisposition, infection, response, organ failure sepsis staging system. Crit Care Med 2011; 39:322–327Crossref, Google Scholar
8 : The potential value of biomarkers in diagnosis and staging of COPD and exacerbations. Semin Respir Crit Care Med 2010; 31:267–275Crossref, Google Scholar
9 : Novel approaches of chemoradiotherapy in unresectable stage IIIA and stage IIIB non-small cell lung cancer. Oncologist 2012; 17:682–693Crossref, Google Scholar
10 : Clinical staging of psychiatric disorders: a heuristic framework for choosing earlier, safer and more effective interventions. Aust N Z J Psychiatry 2006; 40:616–622Crossref, Google Scholar
11 : Pathways underlying neuroprogression in bipolar disorder: focus on inflammation, oxidative stress and neurotrophic factors. Neurosci Biobehav Rev 2011; 35:804–817Crossref, Google Scholar
12 : The potential use of biomarkers as an adjunctive tool for staging bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2009; 33:1366–1371Crossref, Google Scholar
13 : Does stage of illness impact treatment response in bipolar disorder? Empirical treatment data and their implication for the staging model and early intervention. Bipolar Disord 2011; 13:87–98Crossref, Google Scholar
14 : Bipolar disorder and substance misuse: pathological and therapeutic implications of their comorbidity and cross-sensitisation. Br J Psychiatry 2013; 202:172–176Crossref, Google Scholar
15 : Chronic stressors and trauma: prospective influences on the course of bipolar disorder. Psychol Med 2013; 43:2583–2592Crossref, Google Scholar
16 : Childhood trauma is associated with severe clinical characteristics of bipolar disorders. J Clin Psychiatry 2013; 74:991–998Crossref, Google Scholar
17 : Verbal abuse, like physical and sexual abuse, in childhood is associated with an earlier onset and more difficult course of bipolar disorder. Bipolar Disord (Epub ahead of print Oct 13, 2014; doi:
18 : Predictive effects of previous episodes on the risk of recurrence in depressive and bipolar disorders. Curr Psychiatry Rep 2005; 7:413–420Crossref, Google Scholar
19 : Cumulative morbidity and prognostic staging of illness in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Aust N Z J Psychiatry 2012; 46:1058–1067Crossref, Google Scholar
20 ;
21 : Differential effect of number of previous episodes of affective disorder on response to lithium or divalproex in acute mania. Am J Psychiatry 1999; 156:1264–1266Google Scholar
22 : Cognitive-behavioural therapy for severe and recurrent bipolar disorders: randomised controlled trial. Br J Psychiatry 2006; 188:313–320Crossref, Google Scholar
23 : Suicidal ideation and suicide attempts in children and adolescents with bipolar disorder: a systematic review of prevalence and incidence rates, correlates, and targeted interventions. Bipolar Disord 2013; 15:507–523Crossref, Google Scholar
24 : Predictors of prospectively examined suicide attempts among youth with bipolar disorder. Arch Gen Psychiatry 2012; 69:1113–1122Crossref, Google Scholar
25 ;
26 : Characterizing the longitudinal patterns of substance use among individuals diagnosed with serious mental illness after psychiatric hospitalization. Addiction 2013; 108:1259–1269Crossref, Google Scholar
27 : Developmental pathways for different subtypes of early-onset bipolarity in youths. J Clin Psychiatry 2012; 73:1335–1341Crossref, Google Scholar
28 : Trauma exposure and posttraumatic stress disorder among primary care patients with bipolar spectrum disorder. Bipolar Disord 2008; 10:503–510Crossref, Google Scholar
29 : Effects of trauma on bipolar disorder: the mediational role of interpersonal difficulties and alcohol dependence. Bipolar Disord 2008; 10:293–302Crossref, Google Scholar
30 : Allostatic load in bipolar disorder: implications for pathophysiology and treatment. Neurosci Biobehav Rev 2008; 32:675–692Crossref, Google Scholar
31 : Cytokine alterations in bipolar disorder: a meta-analysis of 30 studies. Biol Psychiatry 2013; 74:15–25Crossref, Google Scholar
32 : Increased excitotoxicity and neuroinflammatory markers in postmortem frontal cortex from bipolar disorder patients. Mol Psychiatry 2010; 15:384–392Crossref, Google Scholar
33 : Cytokines in bipolar disorder vs. healthy control subjects: a systematic review and meta-analysis. J Psychiatr Res 2013; 47:1119–1133Crossref, Google Scholar
34 : An updated meta-analysis of oxidative stress markers in bipolar disorder. Psychiatry Res 2014; 218:61–68Crossref, Google Scholar
35 : Mitochondrial complex I activity and oxidative damage to mitochondrial proteins in the prefrontal cortex of patients with bipolar disorder. Arch Gen Psychiatry 2010; 67:360–368Crossref, Google Scholar
36 : Serum brain-derived neurotrophic factor is decreased in bipolar disorder during depressive and manic episodes. Neurosci Lett 2006; 398:215–219Crossref, Google Scholar
37 : Decreased peripheral brain-derived neurotrophic factor levels are a biomarker of disease activity in major psychiatric disorders: a comparative meta-analysis. Mol Psychiatry 2014; 19:750–751Crossref, Google Scholar
38 : Brain-derived neurotrophic factor and inflammatory markers in patients with early- vs. late-stage bipolar disorder. Int J Neuropsychopharmacol 2009; 12:447–458Crossref, Google Scholar
39 : 3-Nitrotyrosine and glutathione antioxidant system in patients in the early and late stages of bipolar disorder. J Psychiatry Neurosci 2009; 34:263–271Google Scholar
40 : Neuroinflammation in bipolar disorder—A [(11)C]-(R)-PK11195 positron emission tomography study. Brain Behav Immun 2014; 40:219–225Crossref, Google Scholar
41 : Aberrant DNA methylation associated with bipolar disorder identified from discordant monozygotic twins. Mol Psychiatry 2008; 13:429–441Crossref, Google Scholar
42 : Enrichment of cis-regulatory gene expression SNPs and methylation quantitative trait loci among bipolar disorder susceptibility variants. Mol Psychiatry 2013; 18:340–346Crossref, Google Scholar
43 : Valproate alters dopamine signaling in association with induction of Par-4 protein expression. PLoS ONE 2012; 7:e45618Crossref, Google Scholar
44 : BDNF function as a potential mediator of bipolar disorder and post-traumatic stress disorder comorbidity. Mol Psychiatry 2012; 17:22–35Crossref, Google Scholar
45 : Traumatic life events in bipolar disorder: impact on BDNF levels and psychopathology. Bipolar Disord 2007; 9(Suppl 1):128–135Crossref, Google Scholar
46 : Ventricular and periventricular structural volumes in first- versus multiple-episode bipolar disorder. Am J Psychiatry 2002; 159:1841–1847Crossref, Google Scholar
47 : MRI study of posterior fossa structures and brain ventricles in bipolar patients. J Psychiatr Res 2001; 35:313–322Crossref, Google Scholar
48 : MRI analysis of the cerebellum in bipolar disorder: a pilot study. Neuropsychopharmacology 1999; 21:63–68Crossref, Google Scholar
49 : Gray matter, white matter, brain, and intracranial volumes in first-episode bipolar disorder: a meta-analysis of magnetic resonance imaging studies. Bipolar Disord 2009; 11:807–814Crossref, Google Scholar
50 : Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder. Biol Psychiatry 2010; 67:1097–1105Crossref, Google Scholar
51 : Preliminary evidence for progressive prefrontal abnormalities in adolescents and young adults with bipolar disorder. J Int Neuropsychol Soc 2009; 15:476–481Crossref, Google Scholar
52 : Dynamic mapping of cortical development before and after the onset of pediatric bipolar illness. J Child Psychol Psychiatry 2007; 48:852–862Crossref, Google Scholar
53 : Progressive neurostructural changes in adolescent and adult patients with bipolar disorder. Bipolar Disord 2011; 13:396–405Crossref, Google Scholar
54 : Bilateral hippocampal volume increases after long-term lithium treatment in patients with bipolar disorder: a longitudinal MRI study. Psychopharmacology (Berl) 2007; 195:357–367Crossref, Google Scholar
55 : In Vivo Hippocampal Subfield Volumes in Schizophrenia and Bipolar Disorder Biol. Psychiatry, (Epub ahead of print July 3, 2014; doi:
56 : Smaller hippocampal volumes in patients with bipolar disorder are masked by exposure to lithium: a meta-analysis. J Psychiatry Neurosci 2012; 37:333–343Crossref, Google Scholar
57 : Hippocampal volumes in bipolar disorders: opposing effects of illness burden and lithium treatment. Bipolar Disord 2012; 14:261–270Crossref, Google Scholar
58 : What is “functional impairment”? Disentangling disability from clinical significance. World Psychiatry 2009; 8:82–85Crossref, Google Scholar
59 : Functional impairment in patients with remitted bipolar disorder. Psychother Psychosom 2008; 77:390–392Crossref, Google Scholar
60 : Two-year syndromal and functional recovery in 219 cases of first-episode major affective disorder with psychotic features. Am J Psychiatry 2000; 157:220–228Crossref, Google Scholar
61 : Towards a clinical staging for bipolar disorder: defining patient subtypes based on functional outcome. J Affect Disord 2013; 144:65–71Crossref, Google Scholar
62 : Staging bipolar disorder: clinical, biochemical, and functional correlates. Acta Psychiatr Scand 2014; 129:437–444Crossref, Google Scholar
63 : Clinical staging in bipolar disorder: focus on cognition and functioning. J Clin Psychiatry 2014; 75:e450–e456Crossref, Google Scholar
64 : Psychosocial functioning among bipolar youth. J Affect Disord 2009; 114:174–183Crossref, Google Scholar
65 : Disability and its treatment in bipolar disorder patients. Bipolar Disord 2007; 9:183–196Crossref, Google Scholar
66 : Cognitive function across manic or hypomanic, depressed, and euthymic states in bipolar disorder. Am J Psychiatry 2004; 161:262–270Crossref, Google Scholar
67 : Neuropsychological testing of cognitive impairment in euthymic bipolar disorder: an individual patient data meta-analysis. Acta Psychiatr Scand 2013; 128:149–162Crossref, Google Scholar
68 : Twelve-month outcome of adolescents with bipolar disorder following first hospitalization for a manic or mixed episode. Am J Psychiatry 2007; 164:582–590Crossref, Google Scholar
69 : Neurocognitive test performance predicts functional recovery from acute exacerbation leading to hospitalization in bipolar disorder. Bipolar Disord 2007; 9:93–102Crossref, Google Scholar
70 : Neurocognitive and clinical predictors of functional outcome in patients with schizophrenia and bipolar I disorder at one-year follow-up. J Affect Disord 2008; 109:286–299Crossref, Google Scholar
71 : Neurocognitive impairments and their relationship with psychosocial functioning in euthymic bipolar II disorder. J Nerv Ment Dis 2011; 199:459–464Crossref, Google Scholar
72 : Neurocognitive impairment in bipolar patients with and without history of psychosis. J Clin Psychiatry 2008; 69:233–239Crossref, Google Scholar
73. : Setting the stage: from prodrome to treatment resistance in bipolar disorder. Bipolar Disord. 2007; 9:671–8Crossref, Google Scholar
74 : Clinical implications of a staging model for bipolar disorders. Expert Rev Neurother 2009; 9:957–966Crossref, Google Scholar
75. : Mechanisms of illness progression in the recurrent affective disorders. Neurotox. Res. 2010; 18:256–71Crossref, Google Scholar
76. : Early stages in the development of bipolar disorder. J. Affect. Disord. 2010; 121:127–35Crossref, Google Scholar
77. : Staging of mental disorders: systematic review. Psychother. Psychosom. 2013; 82:20–34Crossref, Google Scholar
