DOBi: Dutch Older Bipolar Patients. Bipolar disorder in late life. (WC2014-066 )

Background

Starting date: 01/01/2017

Bipolar disorder is a complex psychiatric illness, characterized by recurrent cycles of depression and mania, that doesn’t fade with age. While prevalence of bipolar disorder decreases from 1.4% of individuals aged 18-44 to 0.1-0.4% of those aged 65 and over (Brieger P, 2005), the absolute number of bipolar elderly will increase substantially in the coming years due to ageing of the total population. To date, up to 25% of diagnosed bipolar patients is older than 60 (Sajatovic et al., 2005). Most of our knowledge on bipolar disorder in older patients is derived from studies in younger or mixed aged samples, however the limited studies in older patients with bipolar disorder confirm the differences between older and younger bipolar patients. For example, where common psychiatric comorbidities in studies among younger adults with bipolar disorder include substance abuse, anxiety disorders, attention-deficit/hyperactivity disorder, eating disorders and personality disorders (Krishnan, 2005), we found low prevalence rates of psychiatric comorbidities in our cohort, except for lifetime alcohol dependence (24.8%) and abuse (13.9%) (Dols et al., 2014). With aging the symptoms profile and need for care may shift, and in older bipolar patients cognitive and physical symptoms commence (Dols et al., 2014; Schouws et al., 2007).

Aging can be understood as a multidimensional process involving physical, cognitive, psychological and social changes, which can be highly variable between individuals. Determinants of successful aging are both genetic and environmental. How aging and bipolar disorder interact in the context of bipolar disorder is unclear. Although bipolar disorder has been understood classically as a cyclic disease with full recovery between episodes, in the last decade evidence has accumulated supporting progressive features of bipolar disorder (Berk et al., 2010; Rizzo et al., 2014). The pathophysiological changes observed in bipolar disorder (brain structural alterations, cognitive deficits, immunological deregulation) converge to a model of accelerated aging (Rizzo et al., 2014). In this model the burden of bipolar disorder (cyclic mood episodes, drug addiction and other comorbidities) promotes exhaustion of mechanisms that promote homeostasis (allostatic load). The degree of accelerated aging will define the clinical stages of bipolar disorder. Different clinical staging models have been proposed for bipolar disorder (Berk et al., 2007; Kapczinski et al., 2009; McGorry et al., 2007) placing the illness in a continuum progressing from a latent or asymptomatic form (stage 0) to a chronic, unremitting presentation (stage IV). The clinical staging models for bipolar disorder specifically differ regarding emphasis on mood symptomatology and patterns of recurrence, functional disability and cognitive decline. Despite the fact that the illness may progress with age, not all older bipolar patients will have bipolar disorder stage IV. Moreover older bipolar patients may have reached their “definite” stage of disease. The history of older bipolar patients will reveal how recurrent their disease was and their inter-episode functioning can be established, this makes older bipolar patients especially suitable for studying clinical staging models.

The concept of accelerated aging defining the clinical stage of disease can be extrapolated for other psychiatric disorders, like major depression and schizophrenia. Staging psychiatric diseases can have clinical implications, given that early and late stages of the disorder appear to present different biological features and therefore may require different treatment strategies (Gama et al., 2013).

Given that bipolar disorder may be intimately associated with chronic stress response and coping mechanisms over the course of disease, cellular resilence mechanisms are proposed to play a key role (Fries et al., 2012). Immunosenescence refers to the decline of immunological function that occurs with aging. During aging, sustained low-grade inflammatory activity gradually evolves (inflammaging). For successful aging decreased inflammatory activity without compromising an effective acute response to new pathogens is key (Franceschi et al., 2007). Bipolar disorder is associated with a persistent and low-intensity pro-inflammatory state, more intense during mood episodes (Brietzke et al., 2009; Modabbernia et al., 2013). In older adults with bipolar disorder the elevated serum levels of the pro-inflammatory cytokine interleukine-1 (IL-1) was associated with worse cognitive function (Lotrich et al., 2013). The amount of pro-inflammatory cytokines (IL-1 and TNF) may be related to the degree of accelerated aging. We propose to link IL-1 and TNF alpha serum levels to various aspects, e.g. cognitive and social functioning, physical health and course of disease (number of mood episodes, age at onset).

In older bipolar patients the stage of their disease will be more profound, accelerated aging will have had its effect on their physical health and immune status. On the other hand we need to recognize the “survivors”, the patients that we diagnosed with bipolar disorder for decades and yet are still functioning well, in these patients protective mechanisms will have acted to prevent accelerated aging.

 

Our cohort of older bipolar patients offer a unique opportunity to study this concept of accelerated aging defining the clinical stage of disease, we will include and follow-up patients aged 50 and over with early and late onset of disease, thereby including early and late stage patients. In 2012 we included patients aged 60 and older, now we propose to include patients aged 50 and older in order to not predominantly include survivors. We will examined thoughtfully their cognition, social functioning, psychiatric comorbidity, physical health, quality of life, family history, medication history and combine these finding into a staging model. In addition we can link peripheral biomarkers to clinical characteristics. Including patients within the current treatment setting will enable participation of patients in all stages of disease. In addition it will be very well feasible to collect clinical data interviewing their psychiatric nurse and checking medical records, thereby providing reliable and “real world” data.