Research reportMedical comorbidity and health-related quality of life in bipolar disorder across the adult age span
Introduction
Medical conditions tend to be under-recognized in psychiatric clinics (Koranyi, 1979), state mental health systems (Koran et al., 1989) and general psychiatric inpatient units (Hall et al., 1981, D'Ercole et al., 1991). Among those with mixed psychiatric diagnoses, an association has been found between under-diagnosis of medical comorbid illness and increased risk of poorer quality medical care and greater mortality, especially in older patients (Felker and Yazel, 1996, Zubenko et al., 1997a, Zubenko et al., 1997b, Druss et al., 2001). Patients with psychiatric disorders in general and depressive symptoms in particular have an increased risk of poor functional outcome, reduced quality of life, and physical compromise in the presence of medical comorbidity (Booth et al., 1998, Penninx et al., 1998). In primary care settings, mental disorders have been correlated with greater impairment in health-related quality of life (HRQOL) and distinctive patterns of impairment (Spitzer et al., 1995). The combined effect of depressive symptoms and medical conditions has been found to be more than additive in reducing function in those late middle-aged and older (Ormel et al., 1998).
Little is known, however, about the relationship between medical comorbidity, HRQOL, and function at different ages in bipolar disorder (Shulman et al., 1992). Early research demonstrated that individuals with bipolar disorder and medical comorbidity may have a poorer response to psychiatric treatment (Black et al., 1988), and two recent studies have demonstrated an association between obesity and poor functional outcome (Fagiolini et al., 2002, Fagiolini et al., 2003). Importantly, the impact of age per se has not been addressed in prior studies. We are also aware of no data on populations treated in the public sector.
Moreover, prevalence rates of medical illness in bipolar disorder have not been reliably delineated. The few studies of patients with any medical comorbidity in bipolar affective disorder have yielded widely varying rates, between 2.7% and 75%, likely due in part to sample differences and variability in assessment methodology (Ghadirian and Engelsmann, 1985, Black et al., 1988, Winokur et al., 1993, Gierz and Jeste, 1993, Strakowski et al., 1991). Data on prevalence of specific comorbid medical conditions are limited and also show substantial variability (e.g., Lilliker, 1980, Ritchie et al., 1996, Cassidy et al., 1999, Regenold et al., 2002, Fagiolini et al., 2003).
There are several reasons why better understanding of the interaction of age and medical comorbidity in bipolar affective disorder may improve its assessment and treatment. As mentioned above, reduced physical function with age or illness may increase propensity to depression, or vice versa (Broadhead et al., 1990, Musselman et al., 1998, Penninx et al., 1998). Certain medical comorbidities in bipolar disorder may delineate separate subgroups, such as those with cerebrovascular disease (e.g., Krauthammer and Klerman, 1978, Berrios and Bakshi, 1991, Shulman et al., 1992, Young and Klerman, 1992, Fujikawa et al., 1995, Zubenko et al., 1997a, Zubenko et al., 1997b, Young, 1997, Krishnan et al., 1997, Hays et al., 1998). Medications for bipolar disorder may interact with drugs used to treat concurrent medical conditions among older patients (Cozza and Armstrong, 2001). Finally, comorbid medical conditions such as renal or hepatic disease may lead to alteration in the pharmacodynamics, pharmacokinetics, or toxicity of relevant drugs (Bauer, 2003).
We therefore investigated overall prevalence rates of current and lifetime medical illness and their relationship to function in bipolar affective disorder at different adult ages. We used a dataset of 330 well-characterized bipolar inpatients from the Department of Veterans Affairs (VA) Cooperative Study #430 to systematically determine frequencies of medical comorbidity across the adult age span. We then examined the relationship of medical comorbidity to health-related quality of life (HRQOL) at different ages, as assessed by the 36-item Short-Form from the Medical Outcomes Study (SF-36; Stewart et al., 1988). Specifically, we hypothesized:
- 1.
Prevalence of medical comorbidity in bipolar disorder increases with age.
- 2.
Physical and mental HRQOL decrease with age.
- 3.
Decrements in physical and mental HRQOL with age are not significant after controlling for number of current medical comorbidities. That is, decrements in physical and mental HRQOL with age in bipolar disorder are associated with medical comorbidity rather than factors inherent in bipolar illness or age.
Section snippets
Sample
Subjects were recruited as part of VA Cooperative Study #430, “Reducing the Efficacy-Effectiveness Gap in Bipolar Disorder,” an 11-site randomized controlled trial of an easy access treatment program vs. usual VA care (Bauer, 2001, Bauer et al., 2001). This study recruited a sample with a wide age range and minimal inclusion and exclusion criteria in order to represent as closely as possible the population of veterans with bipolar disorder seen in VA clinical practice. Subjects were recruited
Prevalence of medical comorbidity in bipolar affective disorder
Current and lifetime medical disorders were common across all organ systems. Table 2 summarizes diagnoses by organ system, including the most salient diagnoses within each organ system. As summarized in Table 3, current comorbidity was found in 81%. Only 5.9% of subjects were free of lifetime comorbidity of sufficient severity to be recorded in the medical record. Participants had a median of 2 current (quartiles: 1–3) and 4 lifetime (quartiles: 2–6) comorbidities.
Hypothesis 1: medical comorbidity across the life span
As hypothesized, the
Limitations of the study
Subjects who entered the study were predominantly male, self-selected users of VHA healthcare services, sufficiently ill to be hospitalized for an acute affective episode and willing to undergo subsequent treatment randomization. Cell size for the age extremes of ≤30 and ≥70 years of age was relatively small, even in this large sample, so that the estimates may be somewhat unstable. Treatment trial criteria excluded subjects with an MMSE score ≤26. Thus these data derive from a sample
Acknowledgements
This study was supported in part by VA Cooperative Study #430: “Reducing the Efficacy-Effectiveness Gap in Bipolar Disorder.” These data were presented at the International Conference on Bipolar Disorders at Pittsburgh, Pennsylvania on June 13, 2003 and at the American Association of Geriatric Psychiatry in Baltimore, MD on February 21, 2004.
The CSP #430 Study Team includes:
Denise Evans, M.D., Augusta (GA) VAMC;
George Jaskiw, M.D., Cleveland (OH) VAMC;
Janet Tekell, M.D., Steven Eilers, M.D.
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2020, Clinics in Geriatric MedicineCitation Excerpt :Somatic comorbidity is frequent in OABD, with an average of 3 to 4 comorbid medical conditions, including metabolic syndrome (up to 50%), hypertension (45%–69%), diabetes mellitus (18%–31%), cardiovascular disease (9%–49%), respiratory illness (4%–15%), arthritis (16%–21%), endocrine abnormalities (17%–22%),27 as well as atopic diseases such as allergic rhinitis and asthma (6%–20%).27,59 This burden of poor somatic health is greater than in unipolar depressed comparators,60 and is reported to further increase with age in OABD,61 resulting in polypharmacy17 and decreased mean survival of 10 years.62 Somatic comorbidity limits treatment options for BD by drug interactions and altered drug metabolism.
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2018, Psychiatric Clinics of North AmericaDysregulation of glucose metabolism since young adulthood increases the risk of cardiovascular diseases in patients with bipolar disorder
2017, Kaohsiung Journal of Medical SciencesCitation Excerpt :Similarly, obesity has been found to be less severe in Eastern adults with BD [3,16,21]. By contrast, evidence suggests that the prevalence of diabetes mellitus is comparably high worldwide in patients with BD throughout the adult age span [17,20,22]. These findings may first reflect a racial difference in the CVD risk profiles of patients with BD.