- Email: [email protected]
Physical health in individuals with psychiatric disorders in Austria
Journal of Affective Disorders 257 (2019) 38–44
Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Physical health in individuals with psychiatric disorders in Austria a,b
a,b
b,⁎
T
a
Bernd Reininghaus , Karin Riedrich , Nina Dalkner , Laura Antonia Lehner , Alexandra Riegera,b, Carlo Hamma,b, Matthias Dorna, Leopold Gradauera, Alois Hufnagla, Markus Mayr-Mauharta, Günther Minibergera, Andrea Schachnera, Katharina Waggera, Armin Birnerb, Martina Platzerb, Frederike Fellendorfb, Robert Queissnerb, Susanne Bengesserb, Eva Reininghausb a b
Therapiezentrum Justuspark, Bad Hall, 4540, Austria Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Austria
A R T I C LE I N FO
A B S T R A C T
Keywords: Somatic comorbidities Cardiovascular diseases Obesity Diabetes mellitus Thyroid dysfunction Bipolar disorder Mental illness Metabolism Psychosomatic Gender
Introduction: The association between severe psychiatric disorders and metabolic syndrome is well documented and goes along with a reduced life expectancy. The prevalence of medical comorbidities in individuals suffering from psychiatric disorders in Austria has not yet been examined; aim of this study was to analyze the prevalence of comorbid somatic disorder in individuals diagnosed with psychiatric disorders in Austria. Methods: Patients (n = 600) with a life-time diagnosis of mood and anxiety disorders undergoing a six-week course of intensive treatment in a psychiatric rehabilitation center were recruited. Prevalent somatic and psychological conditions, anamnestic data, medical history, blood samples, clinical and psychological tests as well as medication were examined to determine somatic and psychiatric diagnoses. Results: Metabolic disorders were highly prevalent especially in individuals diagnosed with affective disorders, respectively in bipolar disorder. Furthermore, obesity and thyroid dysfunction were found in about 40% of individuals diagnosed with bipolar disorder in the present study. Significant gender differences were found in CVD and hypertension with higher prevalence in men, while thyroid dysfunction occurred more often in women also compared to the general female population. Conclusions: Characterizing somatic comorbidity in individuals with psychiatric disorders can stimulate research to better understand possible shared etiologic factors and has public health implications for improving models of care. This could have a positive effect on the course of mental disorders, and additionally improve social integration and life expectancy. Knowledge about sex differences should be used to further improve individualized treatment of individuals with psychiatric disorders.
1. Introduction Individuals suffering from severe psychiatric conditions are often found to have somatic health problems (De Hert, et al., 2009; Fleischhacker et al., 2008; Laursen et al., 2009; Surtees et al., 2008). Somatic comorbidities occur to a much greater extent than expected in individuals with mental disorders and when compared with the general population. Somatic comorbidities are known to worsen the course of psychiatric diseases as well as burden and quality of life dramatically (McIntyre et al., 2006, 2007). In general, suffering from mental disorders is related to lower life expectancy. For example, suffering from schizophrenia reduces life expectancy by 20–30%, with 60% of deaths being due to physical illness (Brown et al., 2009; Saha et al., 2007).
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From a more detailed view on somatic comorbidities, cardiovascular disease (CVD) is particularly prevalent (Brown et al., 2009; Laursen et al., 2009; Osborn et al., 2007; Surtees et al., 2008). The main causes of CVD are include physical and behavioral factors (i.e., lack of physical activity), as well as socio-psychological conditions (e.g. negative affect, chronic stress, and personality traits) and the lack of personal relationships and social networks. Metabolic illness and depression share similar features as they are both recognized to be chronic low-grade pro-inflammatory states (Soczynska et al., 2011). Beside inflammation, another reason for the strong interaction of physical and mental disorders is the intake of psychopharmacological medication which can result in raised appetite and weight gain leading to overweight/obesity, diabetes mellitus (DM)
Corresponding author at: Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Auenbruggerplatz 31, 8036 Graz, Austria. E-mail address: [email protected] (N. Dalkner).
https://doi.org/10.1016/j.jad.2019.06.045 Received 30 December 2018; Received in revised form 18 June 2019; Accepted 30 June 2019 Available online 02 July 2019 0165-0327/ © 2019 Published by Elsevier B.V.
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and hypertension (De Hert, et al., 2009). Furthermore, individuals diagnosed with psychiatric conditions are more likely to engage in a poor lifestyle, involving a lack of regular physical activity, poor nutrition, substance abuse, and high rates of smoking (De Hert, et al., 2009). As such, these lifestyle outcomes can increase the risk for obesity and the development of metabolic syndrome. Individuals diagnosed with psychiatric disorders often have a lower socioeconomic status and a lower level of education (De Hert, et al., 2011a,b), which may result in poor nutrition less motivation for regular physical activity. In addition, substance abuse and smoking are often used as coping strategies or selfmedication (Breslau et al., 2004; Spencer et al., 2002). Moreover, stigmatization of individuals diagnosed with psychiatric disorders in the medical health care system increases the amount of (untreated) somatic comorbidities (De Hert, et al., 2011a,b). Of note, individuals diagnosed with psychiatric disorders are more stigmatized and discriminated than people with medical illness (ICG Integrated Consulting Group, 2012). Beside the consequences on quality of life and life expectancy also direct and indirect cost factors associated with psychiatric disorder and associated somatic comorbidities are worth to be mentioned. According to the expectations of the World Economic Forum (2017), the estimated costs of treating psychiatric disorders will more than double by the year 2030 and will exceed the costs of non-infectious diseases, cancer or diabetes combined (World economic forum, 2017). Worldwide, individuals diagnosed with psychiatric disorders contribute a four-fold increases in direct and indirect costs as compared to individuals without psychiatric diagnoses (Altamura et al., 2011). Most of the costs associated with psychiatric disorders are the result of psychiatric treatment, increased health service utilization, and impacts on the workforce (Fleishman, 2003; McIntyre and Konarski, 2004; McIntyre et al., 2006; Wancata et al., 2007). As such, treating and preventing medical conditions is not only cost effective but may also save disability provisions later on (World Health Organisation, 2017) and reduce stigmatization. In affective uni- and bipolar disorders, mortality studies indicate that chronic, stress-sensitive, medical conditions, such as obesity, DM and CVD are the most common causes of mortality (Kupfer, 2005; McIntyre et al., 2007). Despite these findings, the prevalence of somatic comorbidities in individuals with diagnosed with psychiatrc disorders in Austria, particularly across the sexes, remains unclear. The aim of the current study was to analyze the prevalence of somatic comorbidities in individuals diagnosed with psychiatric disorders undergoing a six-week course of intensive treatment in a psychiatric rehabilitation center in Austria. Given that there aresex differences in the prevalence of somatic comorbidities, the secondary goal of the study was to explore sex-specific differences. Furthermore, differences between psychiatric diagnoses in the prevalence of somatic comorbidities were explored. Lastly, we compared the prevalence of somatic comorbidities to individuals in the general Austrian population.
treated as part of the psychiatric rehabilitation program. This study had been approved by the local ethics committee (Medical University of Linz, Austria) in compliance with the current revision of the Declaration of Helsinki, ICH guideline for Good Clinical Practice and current regulations (EK-number: E-24–14). According to the study protocol, all individuals were informed about the study at the time of admission and gave written informed consent prior to their participation in the study. Individuals diagnosed with schizophreniform disorders were excluded from the study according to the protocol. All included participants had a life-time history of affective symptomatology. Psychiatric diagnoses were performed by three psychiatric specialists according to the ICD-10 diagnostic criteria (Dilling and Freyberger, 2010). To determine somatic and psychological conditions, anamneses, somatic and psychological diagnoses, we assessed current medication, anthropometric data (weight, height, waist-to-hip-ratio), and administered psychometric measures including the Beck's Depression Inventory (BDI-II) (Hautzinger et al., 1994), the Hamilton Depression Scale (HAM-D) (HAMILTON, 1960), Manic Self report Scale (MSS) (Krüger et al., 1997), Symptom Checklist (SCL-R-90) (Derogatis and Unger, 2010). Single pathological blood parameters (e.g. pathological fasting blood glucose, systolic or diastolic values, lipid levels), not leading to a diagnosis of a specific disease were not included in the current analyses. Individuals were divided according to their psychiatric diagnosis into two main ICD-10 groups including (1) affective disorders (F3x), and (2) anxiety, dissociative, stress-related, somatoform and other nonpsychotic mental disorders (F4x). A third small group was created to include other diagnoses (n = 11, F.1x, F.6x, F.9x, Z73), which were not further analyzed due to their heterogeneity. In the two main groups, we further analyzed subgroups of bipolar disorder (F.31), as well as adjustment disorder (F.43). The bipolar disorder group was analyzed separately because of the known neurobiological differences to unipolar depression. Individuals diagnosed with adjustment disorder were analyzed separately since it was the biggest group in the F4x diagnoses, with 118 of 211 cases (55.9%) and distinguished itself from the other F4x diagnoses, which, in the present cohort, were mainly related to anxiety symptoms. We assessed six somatic conditions, which were identified as the most common metabolism-related disorder in our cohort including CVD (including hypertension), hypertension, lipid disorders, DM, obesity (body mass index (BMI) ≥ 30 kg/m2), and thyroid dysfunction. As about 75% of individuals with CVD suffer from hypertension, we conducted an additional analysis with this subgroup. For prevalence data on the general Austrian population, we searched the “Statistik Austria” database, as well as any additional cohort studies for all somatic comorbidities of interest. If data was not available from the Austrian population, we included data from German health care databases and cohort studies.
2. Materials and methods
2.1. Statistics
The study was conducted at an Austrian psychiatric rehabilitation center and included 600 individuals undergoing a six-week course of intensive treatment between April 2015 and April 2016. The rehabilitation center is semi-publicly funded by the National Health Insurance for all public employees. The psychiatric rehabilitation program included medical, psychiatric, psychological and psychotherapeutic treatments, as well as occupational therapy, physiotherapy and diet counseling. The main target population for the psychiatric rehabilitation program consisted of Austrian individuals with persisting mood symptoms, long periods of unemployment or the imminent premature retirement due to mental illness. Furthermore, individuals who had previously completed acute psychiatric care, who were (1) not sufficiently stabilized, (2) required prolonged psychiatric treatment to facilitate recovery, and (3) had a positive rehabilitation prognosis were
Sex differences in age, body mass index and waist-to-hip-ratio were assessed using independent t-tests. Sex differences in the prevalence of smoking, CVD, hypertension, DM, obesity and thyroid disorders were calculated with χ2-test. Error probabilities below .05 were accepted to denote statistical significance. 3. Results 3.1. General data The present investigation included 600 patients (54.7% female) with a mean age of 52.9 (SD = 7.7) years and a BMI of 26.9 (SD = 5.0). Of all individuals included, 23.5% were public servants, 23.5% were teachers, 7.7% were employees with the police service, while 4.3% had 39
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obesity and overweight for both male and female participants. Moreover, the education level seems to be higher in the study cohort.
Table 1 Descriptive Data of the study cohort.
Age [years] Body Mass Index Waist-to-hip-ratio Smokers Intake of psychopharmacological drugs SSRIs Atypical antipsychotics Lithium
Whole cohort n = 600 Mean (SD)
Females (n = 328)
Males (n = 272)
Mean (SD)
Mean (SD)
52.9 (7.7)* 26.9 (5.0)⁎⁎ 0.91 (0.09)⁎⁎ % (n) 29.7 (165)* 80.7 (484)
53.5 (7.6) 26.4 (5.4) 0.85 (0.09) % (n) 26.1 (77) 80.5 (264)
52.1 (7.9) 27.5 (4.4) 0.97 (0.07) % (n) 32.4 (88) 80.9 (220)
39.6 (237) 13.7 (82) 2.3 (14)
41.9 (137) 12.5 (41) 2.1 (7)
36.8 (100) 15.4 (42) 2.6 (7)
3.2. Somatic diseases – differences in mental disorders Table 3 displays the prevalence of somatic disorders across psychiatric diagnoses. In our cohort, the prevalence of CVD was high in all four diagnostic groups, ranging from 37.1% in bipolar disorder to 48.4% in F4x. diagnoses. Hypertension was relative equally distributed throughout all psychiatric diagnoses. Highest rates of hypertension were found in F4x. diagnoses, with the exception of adjustment disorders (F43.), which had the lowest rates of the whole cohort. Generally, lipid disorders showed a high prevalence in all diagnostic groups with the highest rates among individuals diagnosed with bipolar disorder (65.7%). For DM, a high prevalence was observed in individuals diagnosed with affective disorders (10.8%). However, of note, individuals diagnosed with bipolar disorder showed high prevalence of obesity (40%) and thyroid disorders (40%). In summary, individuals diagnosed with bipolar disorder seemed to show the highest rates of somatic comorbidities in our cohort.
Note: SD = Standard Deviation, n = Number of Participants, SSRIs = Selective Serotonin Reuptake Inhibitors. ⁎ Significant differences between women and men (p < .05). ⁎⁎ Significant differences between women and men (p < .01).
their working place in trade/economy as well as 4.3% worked in technology areas. Notably, 57.4% of individuals had more than one Fdiagnosis, while 22.5% had three or more F-diagnoses. In terms of somatic comorbidities, lipid disorders were the most common with 58.5%, followed by CVD (45.6%) and hypertension (33.0%). More than 53% of individuals had more than one, while 32% had three or more somatic comorbidities. Almost half of individuals were prescribed SSRIs, a smaller group took atypical antipsychotics and 2.3% took lithium as a medication at the time of the study. Overall, 60 individuals (10%) had no medication at the time of testing. Females were significantly older (t598 = 2.30, p = .022), had a significantly lower BMI (t591.9= −2.75, p = .006) and waist-to-hipratio (t383.6= −14.30, p < .001) and showed a significantly lower prevalence of smokers (χ21 = 397, p = .046) compared to males in the present cohort (for further descriptive details of the study cohort see Table 1). Table 2 showcases the differences between the study cohort and the general population in Austria. The study cohort shows higher rates in
3.3. Sex differences and prevalence in comparison with the general population The prevalence of thyroid disorders was significantly higher in females, while CVD and hypertension were significantly more frequent in males. However, males and females showed similar rates of obesity, lipid disorders, and DM (see Table 4). We compared our cohort to data from Austrian or German general population of a similar age range (see Table 4 and Fig. 1). However, due to missing data, we were not able to analyze statistical differences in the prevalence of somatic disorder. Nevertheless, comparing the prevalence of somatic comorbidities in our cohort with the general population, the prevalence of CVD and lipid disorders appeared to be much higher in our cohort across both sexes. Of note, thyroid disorders were more frequent in females compared to the counterparts in the general population. 4. Discussion Despite the known association between psychiatric and somatic disorders, as well as their effect on life expectancy and quality, the data on the prevalence of somatic disorders in psychiatric populations remains sparse. The aim of the current study was to analyze the prevalence of somatic comorbidities in individuals diagnosed with psychiatric disorders in a psychiatric rehabilitation center in Austria. As a secondary aim, we explored sex-specific differences across psychiatric diagnoses, as well as how prevalence of somatic disorders compares to general Austrian and German populations. In our sample of 600 individuals, metabolic disorders showed a higher prevalence compared to the data from the general population of a comparable age. Of note, we observed a high prevalence of individuals diagnosed with affective disorders, particularly bipolar disorder. There is strong evidence to suggest that metabolic disturbances in affective disorders are linked to chronic low-grade pro-inflammatory states (Soczynska et al., 2011). Furthermore, in our cohort, obesity and thyroid dysfunction were found in about 40% of individuals diagnosed with bipolar disorder. There is strong evidence in the literature that greater risk of obesity in bipolar disorder is likely due to illness-influenced behaviors, mood-induced physiological changes, and pharmacotherapy (Shah et al., 2006; Liu et al., 2013). Weight gain and increased visceral fat can often be caused by antipsychotics (i.e. Olanzapine) (Gilles et al., 2010). Regarding thyreoid dysfunction, the literature suggests that autoimmune thyreoiditis could be an endophenotype for bipolar disorder (Vonk et al., 2007). Moreover, lithium treatment is well known to affect thyreoid function in several ways
Table 2 Demographic data of the study cohort and the general population in Austria.
Percentage of whole [%] Body mass indexa Normal weight [%] Overweight [%] Obesity [%] Education levelb Apprenticeship [%] A-levels [%] University [%] Employmentc Part time [%] Full time [%] Smokingd [%]
Females in sample (n = 328)
Females in Austria
Males in sample (n = 272)
Males in Austria
55
51
45
49
45.5 30.8 23.1
55.6 26.5 15.9
31.6 43.9 24.5
34.6 45.4 19.7
12.2 23.8 18.9
26.7 14.9 14.9
30.9 21.7 16.5
42.0 14.4 14.5
27.4 72.6 26.1
47 53 26.9
5.85 94.14 32.4
11 89 29.8
a Statistik Austria, Gesundheitsbefragung 2014, Austrian population 45–60 years. b Statistik Austria, Bildungsstandregister 2016, Austrian population 15–60 years. c Bundesministerium für Bildung und Frauen. Frauen und Männer in Österreich Gender Index 2015, Austrian population ≥15 years. d STATISTIK AUSTRIA, Gesundheitsbefragung 2014., Austrian population 45–60 years.
40
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Table 3 Somatic diseases – prevalence in mental disorders. Diagnosis n = 600a
CVD
Affective disorders (F3x) n = 415 [% (n)] Bipolar disorder n = 35 [% (n)] Anxiety, stress-related, somatoform disorders (F4x), n = 93 [% (n)] Adjustment disorders n = 118 [% (n)]
47.0 37.1 48.4 41.4
(189) (13) (45) (48)
Hyper-tension
Lipid disorders
DM
Obesity
Thyroid disorders
34.3 31.4 35.5 26.7
57.8 65.7 60.6 56.8
10.6 (43) 11.8 (4) 6.5 (6) 5.2 (6)
23.5 (97) 40 (14) 22.1 (21) 22.6 (26)
23.0 (93) 40 (14) 22.1 (21) 26.7 (31)
(139) (11) (33) (31)
(237) (23) (57) (67)
a Number of individuals does not sum up to 600 because most of the patients had more than one psychiatric diagnose n = number of participants, CVD = cardiovascular disease, DM = diabetes mellitus.
inflammatory mechanisms, which can increase the vulnerability for somatic and psychiatric problems in males. In our cohort, females diagnosed with psychiatric disorders showed a four-times higher rate of thyroid dysfunction as compared to male, with and without psychiatric diagnoses, as well as females without psychiatric diagnoses. However, the latter cannot be explained by medication alone, since females were prescribed less medication linked to altering the thyroid metabolism (i.e., lithium, atypical antipsychotics) as compared to males. Therefore, further research is required to investigate this result. Additionally, a strong association between mental health and socioeconomic status has been described in the literature. In general, individuals with low socioeconomic status have poorer mental health than persons with high socio-economic status. Lifestyle factors, as physical activity, alcohol consumption and smoking are known to influence socio-economic inequalities (Kautzky-Willer et al., 2012). Furthermore, socioeconomically disadvantaged children and adolescents were two to three times more likely to develop mental health problems (Reiss, 2013).
(Bocchetta and Loviselli, 2006). In addition, we observed that the prevalence of CVD in our cohort was four- to eight- times higher than in the general Austrian population. Specifically, the mean prevalences of DM and obesity were higher than in the general population, particularly as compared to individuals diagnosed with affective disorders. Significant sex differences were found in CVD and hypertension with males showing a higher prevalence, while thyroid dysfunction occurred more often in females. Higher rates of heart problems as physical comorbidity in men were also found by the Australian Institute of health and welfare (AIHW) in 2007 (AIHW, 2007). The interest in sex as a moderating factor of vulnerability for somatic disorders comes, in part, from epidemiological data that reveals sex differences in the prevalence of many disorders that are exacerbated by stress (Valentino and Bangasser, 2016). For example, studies suggest greater HPA axis dysregulation in females with stress-related psychiatric disorders. Moreover, preclinical data demonstrates sex differences in several cellular and molecular mechanisms. As such, sex differences may be linked to increased endocrine, emotional, or arousal responses to stress in females (Valentino and Bangasser, 2016). On a clinical level, however, biological sex differences might be reversed and influenced by different coping strategies, as well as adaption to stress and social roles. Our findings show that the prevalence of somatic comorbidity in individuals diagnosed with disorders is alarmingly high, with evidence of sex differences. Increased rates of CVD and hypertension in males may partly be explained by weight differences across our two defined diagnostic groups. This can also be found in the literature (Lee et al., 2003). Furthermore, oestrogen and progesterone may influence oxidative and antioxidative parameters via influence of DNA repair, activation of antioxidative defence (Bengesser et al., 2016) and act as protective factors for cardiovascular outcomes in females. In addition, we observed the rate of smoking significantly higher in males. As such, the combination of smoking, obesity, CVD, and hypertension may influence
4.1. Clinical relevance Somatic comorbidities are one main reason for the reduced life expectancy of individuals with psychiatric disorders. Our findings underline the well-documented existence of somatic comorbidities amongst individuals diagnosed with psychiatric disorder. Despite this evidence, there is no systematic procedure in most health systems all over the world that aims to prevent and monitor the incidence of somatic disorders in psychiatric populations. In addition, individuals diagnosed with psychiatric conditions have less access to general healthcare systems, which can result in lower exposure to CVD risk screening and prevention (De Hert, et al., 2009). The increased healthcare needs of individuals with psychiatric diseases are often
Table 4 Sex differences.
CVD Hypertension Lipid disorders DM Obesity Thyroid disorders
Whole cohort (n = 600) Females% (n) Males% (n)
Statistical difference (between females and males)
General Population (Austria/Germany) Females% Males%
40.9 (130) 28.3 (91) 54.8 (178) 8.1 (26) 23.1 (75) 36.3 (116)
χ2 = 5.06, p = .025* χ2 = 5.79, p = .016* χ2 = 3.23, p = .07 χ2 = 1.35, p = .25 χ2 = 0.17, p = .68 χ2 = 51.53, p < .001⁎⁎
6.4–8.9a 21.2b 32.1d 4.0b,c 15.9b 11.2e
50.2 37.6 62.1 10.9 24.5 10.6
(133) (100) (167) (29) (66) (28)
Note: n = number of participants. CVD = cardiovascular disease. DM = diabetes mellitus. ⁎ Significant differences between women and men (p < .05). ⁎⁎ Significant differences between women and men (p < .01). a 40–79 year old German individuals (DEGS1 and BGS98) (Gosswald et al., 2013). b Austrian individuals 45 to 60 years old (Statistik Austria, 2015). c German individuals 50–59 years (Heidemann et al., 2013). d 18–79 year old German individuals (Scheidt-Nave et al., 2013). e 50–80 year old German individuals (Seck et al., 1997). 41
12.1–12.3a 25.8b 36.6d 5.9–7.3b,c 19.7b 8.0e
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Fig. 1. Sex specific prevalence in comparison with the general population.
this into account, our results reflect clinical reality. In line with this, clinical reality often means polypharmacy. In this study participants had various combinations of one up to three different categories of psychopharmacological treatments. Thus, a major limitation of this study is undoubtedly that the heterogeneity and different combinations in the individual therapy as well as moderate sample size rendered a consideration of medication unfeasible. A further limitation is the source of prevalence of different somatic diseases in Austria as well as in Germany. While for some disorders we were able to extract prevalence information from Statistics Austria, others we could only obtain from position papers or were derived from cohorts, which may not be representative of the Austrian population. Nevertheless, we obtained data from official statistics on the prevalence of disorder in Austria or Germany, which are the most reliable sources available. In this context we have to state that data on the prevalence of thyroid disorders were the most difficult to find as the exact prevalence of thyroid disorders in general cohorts might not be available at official databases. In general, we decided to include only studies about known diagnoses from the general population and not studies that estimated the prevalence, including unknown and just supposed cases. Furthermore, in cohort studies on the prevalence of CVD, it was not clear if hypertension was included in the prevalence data of the cohort of the general population. A further limitation points to the fact that our cohort was recruited in a psychiatric rehabilitation center, were mainly government employees (teachers, policemen, postman, etc.) were treated which may not reflect of socioeconomic strata within the Austrian population. Ideally, a matched cohort from the general Austrian population with similar socioeconomic characteristics, similar characteristics in occupation level, or similar risk factors for psychiatric and somatic disease would have been needed to compare the data of our study, a setting which is probably impossible to achieve. Due to the aim of the psychiatric rehabilitation program being rapid re-integration into the workforce, most of the individuals included in this study had sufficient access to healthcare services that may not be entirely representative of all socioeconomic states in Austria. Furthermore, given that our cohort consisted of a large proportion of government employees, we may postulate that the rates of somatic disorders may be more prevalent in unemployed individuals who are also liable for higher rates of psychiatric disorders. In addition, the generalizability of findings from our cohort may be limited, given that
coupled with a disparity in access and provision of general somatic care (Fleischhacker et al., 2008). There is a lack of consensus as to which health care professionals should be responsible for the prevention and management of comorbid somatic disorders. In this context, clinicians should take pre-existing metabolic abnormalities into consideration before initiating treatment with psychopharmacological medication that may have potential metabolic side effects or result in polypharmacy. Furthermore, somatic parameters must be monitored on a regular basis in individuals diagnosed with psychiatric disorder to prevent and detect metabolic comorbidities. The suboptimal integration of general somatic and psychiatric care services lead to inferior provision of medical health care. Therefore, more interdisciplinary care may improve the understanding and identification of serious health conditions, which may be treated or prevented (De Hert, et al., 2009). Amongst individuals diagnosed with psychiatric disorders, this problem may be compounded by a persistent lack of information concerning the prevalence of somatic disorders, which we have tried to address in our study, as well as the rate of mortality. Therefore, metabolic dysfunction in individuals diagnosed with psychiatric disorders should be closely examined to improve treatment and lower the rate of mortality associated with psychiatric disorders. Furthermore, effective prevention programs and therapy of somatic comorbidities in the psychiatric population are necessary to positively affect the course of psychiatric disorders, as well as on social integration and life expectancy. As such, guidelines for the monitoring and treatment of somatic comorbidities must be implemented, as well as integrated into clinical practice. While some conferences have tried to develop such guidelines (De Hert, et al., 2009; Fleischhacker et al., 2008), they remain to be implemented into clinical practice in Austria and around the world. 4.2. Limitation In the examined cohort, we observed a high rate of individuals that were diagnosed with more than one psychiatric disorder; therefore, conclusions can only be drawn for individuals in the general psychiatric population. Further studies could focus on somatic comorbidities in individuals diagnosed with a single psychiatric disorder to avoid heterogeneity. Nevertheless, comorbidities amongst psychiatric disorders are very common, especially affective and anxiety disorders. Taking 42
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Markus Mayr-Mauhart: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Günther Miniberger: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Andrea Schachner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Katharina Wagger: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Armin Birner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Martina Platzer: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Frederike Fellendorf: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Robert Queissner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Susanne Bengesser: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Eva Reininghaus: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing.
all participants were treatment-seeking and obtained from a single clinic. Of note, our cohort has a relatively high number of older individuals, with a mean of 52.3 years. Therefore, further research should include also younger individuals with psychiatric disorders to explore whether these findings are age-dependent. In sum, causality cannot be inferred from our findings regarding the relationship between psychiatric and somatic disorders. Optimally, future longitudinal studies will be conducted which include younger, physically healthy adults to provide further insight into the associations between psychiatric and somatic disorders, as well as their etiologies. 5. Conclusion Our analyzed cohort of individuals diagnosed with psychiatric disorders showed a high prevalence of somatic comorbidities. While we cannot draw causal conclusions, the bi-directional relationship between psychiatric and somatic disorders is well documented. Therefore, comorbid psychiatric and somatic disorders should be closely monitored to lower the high mortality rates associated with psychiatric disorders. To do so, changes in public health policy and further medical training are required to treatment of somatic disorders in the psychiatric population. Furthermore, effective prevention and therapeutic programs for somatic comorbidities in individuals diagnosed with psychiatric disorders are necessary. Guidelines for the monitoring and treatment of somatic comorbidities must be implemented and brought to a clinical and usable model. Moreover, knowledge about sex differences should be used to further improve individualized treatment of individuals with psychiatric disorders. Characterizing the nature of comorbidity between psychiatric and cardiometabolic conditions can stimulate research to better understand their shared etiologies, as well as to promote public health changes to improve clinical care, social integration and individual life expectancy.
Acknowledgements The authors thank all participants of the study. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.06.045.
Conflicts of interest
References
None of the authors have a conflict of interest.
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Role of the funding source There has been no significant financial support for this work that could have influenced its outcome. CRediT authorship contribution statement Bernd Reininghaus: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Karin Riedrich: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Nina Dalkner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Laura Antonia Lehner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Alexandra Rieger: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Carlo Hamm: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Matthias Dorn: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Leopold Gradauer: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Alois Hufnagl: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. 43
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Osborn, D.P., Levy, G., Nazareth, I., Petersen, I., Islam, A., King, M.B., 2007. Relative risk of cardiovascular and cancer mortality in people with severe mental illness from the United Kingdom's general practice research database. Arch. Gen. Psychiatry 64, 242–249. Reiss, F., 2013. Socioeconomic inequalities and mental health problems in children and adolescents: a systematic review. Soc. Sci. Med. 90, 24–31. Saha, S., Chant, D., McGrath, J., 2007. A systematic review of mortality in schizophrenia: is the differential mortality gap worsening over time? Arch. Gen. Psychiatry 64, 1123–1131. Scheidt-Nave, C., Du, Y., Knopf, H., Schienkiewitz, A., Ziese, T., Nowossadeck, E., Gosswald, A., Busch, M.A., 2013. Prevalence of dyslipidemia among adults in Germany: results of the German health interview and examination survey for adults (DEGS 1). Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 56, 661–667. Seck, T., Scheidt-Nave, C., Ziegler, R., Pfeilschifter, J., 1997. Prevalence of thyroid gland dysfunctions in 50- to 80-year-old patients. An epidemiologic cross-sectional study in a southwestern community. Med. Klin. (Munich). 92, 642–646. Shah, A., Shen, N., El-Mallakh, R.S., 2006. Weight gain occurs after onset of bipolar illness in overweight bipolar patients. Ann. Clin. Psychiatry 18 (4), 239–241. Soczynska, J.K., Kennedy, S.H., Woldeyohannes, H.O., Liauw, S.S., Alsuwaidan, M., Yim, C.Y., McIntyre, R.S., 2011. Mood disorders and obesity: understanding inflammation as a pathophysiological nexus. Neuromolecular Med. 13, 93–116. Spencer, C., Castle, D., Michie, P.T., 2002. Motivations that maintain substance use among individuals with psychotic disorders. Schizophr. Bull. 28, 233–247. Statistik Austria, 2015. Österreichische Gesundheitsbefragung 2014 Hauptergebnisse des Austrian Health Interview Survey (ATHIS) und methodische Dokumentation. Wien. Surtees, P.G., Wainwright, N.W., Luben, R.N., Wareham, N.J., Bingham, S.A., Khaw, K.T., 2008. Depression and ischemic heart disease mortality: evidence from the EPICNorfolk United Kingdom prospective cohort study. Am. J. Psychiatry 165, 515–523. Valentino, R.J., Bangasser, D.A., 2016. Sex-biased cellular signaling: molecular basis for sex differences in neuropsychiatric diseases. Dialogues Clin. Neurosci. 18 (4), 385–393. Vonk, R., van der Schot, A.C., Kahn, R.S., Nolen, W.A., Drexhage, H.A., 2007. Is autoimmune thyroiditis part of the genetic vulnerability (or an Endophenotype) for bipolar Disorder? Biol. Psychiatry 62, 135–140. Wancata, J., Sobocki, P., Katschnig, H., Cost of Disorders of the Brain in Europe Study Group, 2007. Cost of disorders of the brain in Austria in the year 2004. Wien. Klin. Wochenschr. 119, 91–98. World economic forum, 2017. Human-Centric Health: Behaviour Change and the Prevention of Non- Communicable Diseases. White paper. 2017. World Health Organisation, 2017. Adressing Comorbidity Between Mental Disorders and Major Noncommunicable Diseases. ISBN:9789289052535.
Fleishman, M., 2003. Economic grand rounds: psychopharmacosocioeconomics and the global burden of disease. Psychiatr. Serv. 54, 142–144. Gilles, M., Hentschel, F., Paslakis, G., Glahn, V., Lederbogen, F., Deuschle, M., 2010. Visceral and subcutaneous fat in patients treated with olanzapine: a case series. Clin. Neuropharmacol. 33 (5), 248–249. Gosswald, A., Schienkiewitz, A., Nowossadeck, E., Busch, M.A., 2013. Prevalence of myocardial infarction and coronary heart disease in adults aged 40-79 years in Germany: results of the German health interview and examination survey for adults (DEGS1). Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 56, 650–655. Hamilton, M., 1960. A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23, 56–62. Hautzinger, M., Bailer, M., Worall, H., Keller, F., 1994. Beck-Depressions-Inventar (BDI). Bearbeitung der Deutschen Ausgabe. Huber. Heidemann, C., Du, Y., Schubert, I., Rathmann, W., Scheidt-Nave, C., 2013. Prevalence and temporal trend of known diabetes mellitus: results of the German health interview and examination survey for adults (DEGS1). Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 56, 668–677. ICG Integrated Consulting Group, 2012. Study “seelische gesundheit in Österreich. Kautzky-Willer, A., Dorner, T., Jensby, A., Rieder, A., 2012. Women show a closer association between educational level and hypertension or diabetes mellitus than males: a secondary analysis from the Austrian HIS. BMC Public Health 12, 392. Krüger, S., Bräunig, P., Shugar, G., 1997. Manie-Selbstbeurteilungsskala. Deutsche Bearbeitung Des Self-Report Manic Inventory (SRMI). Göttingen Beltz Test GmbH. Kupfer, D.J., 2005. The increasing medical burden in bipolar disorder. JAMA 293, 2528–2530. Laursen, T.M., Munk-Olsen, T., Agerbo, E., Gasse, C., Mortensen, P.B., 2009. Somatic hospital contacts, invasive cardiac procedures, and mortality from heart disease in patients with severe mental disorder. Arch. Gen. Psychiatry 66, 713–720. Lee, S., Colditz, G.A., Berkman, L.F., Kawachi, I., 2003. Caregiving and risk of coronary heart disease in U.S. women: a prospective study. Am. J. Prev. Med. 24, 113–119. Liu, C.S., Carvalho, A.F., Mansur, R.B., McIntyre, R.S., 2013. Obesity and bipolar disorder: synergistic neurotoxic effects? Adv. Ther. 30 (11), 987–1006. McIntyre, R.S., Konarski, J.Z., 2004. Bipolar disorder: a national health concern. CNS Spectr. 9, 6–15. McIntyre, R.S., Konarski, J.Z., Soczynska, J.K., Wilkins, K., Panjwani, G., Bouffard, B., Bottas, A., Kennedy, S.H., 2006. Medical comorbidity in bipolar disorder: implications for functional outcomes and health service utilization. Psychiatr. Serv. 57, 1140–1144. McIntyre, R.S., Soczynska, J.K., Beyer, J.L., Woldeyohannes, H.O., Law, C.W., Miranda, A., Konarski, J.Z., Kennedy, S.H., 2007. Medical comorbidity in bipolar disorder: reprioritizing unmet needs. Curr. Opin. Psychiatry 20, 406–416.
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Contents lists available at ScienceDirect
Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Physical health in individuals with psychiatric disorders in Austria a,b
a,b
b,⁎
T
a
Bernd Reininghaus , Karin Riedrich , Nina Dalkner , Laura Antonia Lehner , Alexandra Riegera,b, Carlo Hamma,b, Matthias Dorna, Leopold Gradauera, Alois Hufnagla, Markus Mayr-Mauharta, Günther Minibergera, Andrea Schachnera, Katharina Waggera, Armin Birnerb, Martina Platzerb, Frederike Fellendorfb, Robert Queissnerb, Susanne Bengesserb, Eva Reininghausb a b
Therapiezentrum Justuspark, Bad Hall, 4540, Austria Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Austria
A R T I C LE I N FO
A B S T R A C T
Keywords: Somatic comorbidities Cardiovascular diseases Obesity Diabetes mellitus Thyroid dysfunction Bipolar disorder Mental illness Metabolism Psychosomatic Gender
Introduction: The association between severe psychiatric disorders and metabolic syndrome is well documented and goes along with a reduced life expectancy. The prevalence of medical comorbidities in individuals suffering from psychiatric disorders in Austria has not yet been examined; aim of this study was to analyze the prevalence of comorbid somatic disorder in individuals diagnosed with psychiatric disorders in Austria. Methods: Patients (n = 600) with a life-time diagnosis of mood and anxiety disorders undergoing a six-week course of intensive treatment in a psychiatric rehabilitation center were recruited. Prevalent somatic and psychological conditions, anamnestic data, medical history, blood samples, clinical and psychological tests as well as medication were examined to determine somatic and psychiatric diagnoses. Results: Metabolic disorders were highly prevalent especially in individuals diagnosed with affective disorders, respectively in bipolar disorder. Furthermore, obesity and thyroid dysfunction were found in about 40% of individuals diagnosed with bipolar disorder in the present study. Significant gender differences were found in CVD and hypertension with higher prevalence in men, while thyroid dysfunction occurred more often in women also compared to the general female population. Conclusions: Characterizing somatic comorbidity in individuals with psychiatric disorders can stimulate research to better understand possible shared etiologic factors and has public health implications for improving models of care. This could have a positive effect on the course of mental disorders, and additionally improve social integration and life expectancy. Knowledge about sex differences should be used to further improve individualized treatment of individuals with psychiatric disorders.
1. Introduction Individuals suffering from severe psychiatric conditions are often found to have somatic health problems (De Hert, et al., 2009; Fleischhacker et al., 2008; Laursen et al., 2009; Surtees et al., 2008). Somatic comorbidities occur to a much greater extent than expected in individuals with mental disorders and when compared with the general population. Somatic comorbidities are known to worsen the course of psychiatric diseases as well as burden and quality of life dramatically (McIntyre et al., 2006, 2007). In general, suffering from mental disorders is related to lower life expectancy. For example, suffering from schizophrenia reduces life expectancy by 20–30%, with 60% of deaths being due to physical illness (Brown et al., 2009; Saha et al., 2007).
⁎
From a more detailed view on somatic comorbidities, cardiovascular disease (CVD) is particularly prevalent (Brown et al., 2009; Laursen et al., 2009; Osborn et al., 2007; Surtees et al., 2008). The main causes of CVD are include physical and behavioral factors (i.e., lack of physical activity), as well as socio-psychological conditions (e.g. negative affect, chronic stress, and personality traits) and the lack of personal relationships and social networks. Metabolic illness and depression share similar features as they are both recognized to be chronic low-grade pro-inflammatory states (Soczynska et al., 2011). Beside inflammation, another reason for the strong interaction of physical and mental disorders is the intake of psychopharmacological medication which can result in raised appetite and weight gain leading to overweight/obesity, diabetes mellitus (DM)
Corresponding author at: Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Auenbruggerplatz 31, 8036 Graz, Austria. E-mail address: [email protected] (N. Dalkner).
https://doi.org/10.1016/j.jad.2019.06.045 Received 30 December 2018; Received in revised form 18 June 2019; Accepted 30 June 2019 Available online 02 July 2019 0165-0327/ © 2019 Published by Elsevier B.V.
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and hypertension (De Hert, et al., 2009). Furthermore, individuals diagnosed with psychiatric conditions are more likely to engage in a poor lifestyle, involving a lack of regular physical activity, poor nutrition, substance abuse, and high rates of smoking (De Hert, et al., 2009). As such, these lifestyle outcomes can increase the risk for obesity and the development of metabolic syndrome. Individuals diagnosed with psychiatric disorders often have a lower socioeconomic status and a lower level of education (De Hert, et al., 2011a,b), which may result in poor nutrition less motivation for regular physical activity. In addition, substance abuse and smoking are often used as coping strategies or selfmedication (Breslau et al., 2004; Spencer et al., 2002). Moreover, stigmatization of individuals diagnosed with psychiatric disorders in the medical health care system increases the amount of (untreated) somatic comorbidities (De Hert, et al., 2011a,b). Of note, individuals diagnosed with psychiatric disorders are more stigmatized and discriminated than people with medical illness (ICG Integrated Consulting Group, 2012). Beside the consequences on quality of life and life expectancy also direct and indirect cost factors associated with psychiatric disorder and associated somatic comorbidities are worth to be mentioned. According to the expectations of the World Economic Forum (2017), the estimated costs of treating psychiatric disorders will more than double by the year 2030 and will exceed the costs of non-infectious diseases, cancer or diabetes combined (World economic forum, 2017). Worldwide, individuals diagnosed with psychiatric disorders contribute a four-fold increases in direct and indirect costs as compared to individuals without psychiatric diagnoses (Altamura et al., 2011). Most of the costs associated with psychiatric disorders are the result of psychiatric treatment, increased health service utilization, and impacts on the workforce (Fleishman, 2003; McIntyre and Konarski, 2004; McIntyre et al., 2006; Wancata et al., 2007). As such, treating and preventing medical conditions is not only cost effective but may also save disability provisions later on (World Health Organisation, 2017) and reduce stigmatization. In affective uni- and bipolar disorders, mortality studies indicate that chronic, stress-sensitive, medical conditions, such as obesity, DM and CVD are the most common causes of mortality (Kupfer, 2005; McIntyre et al., 2007). Despite these findings, the prevalence of somatic comorbidities in individuals with diagnosed with psychiatrc disorders in Austria, particularly across the sexes, remains unclear. The aim of the current study was to analyze the prevalence of somatic comorbidities in individuals diagnosed with psychiatric disorders undergoing a six-week course of intensive treatment in a psychiatric rehabilitation center in Austria. Given that there aresex differences in the prevalence of somatic comorbidities, the secondary goal of the study was to explore sex-specific differences. Furthermore, differences between psychiatric diagnoses in the prevalence of somatic comorbidities were explored. Lastly, we compared the prevalence of somatic comorbidities to individuals in the general Austrian population.
treated as part of the psychiatric rehabilitation program. This study had been approved by the local ethics committee (Medical University of Linz, Austria) in compliance with the current revision of the Declaration of Helsinki, ICH guideline for Good Clinical Practice and current regulations (EK-number: E-24–14). According to the study protocol, all individuals were informed about the study at the time of admission and gave written informed consent prior to their participation in the study. Individuals diagnosed with schizophreniform disorders were excluded from the study according to the protocol. All included participants had a life-time history of affective symptomatology. Psychiatric diagnoses were performed by three psychiatric specialists according to the ICD-10 diagnostic criteria (Dilling and Freyberger, 2010). To determine somatic and psychological conditions, anamneses, somatic and psychological diagnoses, we assessed current medication, anthropometric data (weight, height, waist-to-hip-ratio), and administered psychometric measures including the Beck's Depression Inventory (BDI-II) (Hautzinger et al., 1994), the Hamilton Depression Scale (HAM-D) (HAMILTON, 1960), Manic Self report Scale (MSS) (Krüger et al., 1997), Symptom Checklist (SCL-R-90) (Derogatis and Unger, 2010). Single pathological blood parameters (e.g. pathological fasting blood glucose, systolic or diastolic values, lipid levels), not leading to a diagnosis of a specific disease were not included in the current analyses. Individuals were divided according to their psychiatric diagnosis into two main ICD-10 groups including (1) affective disorders (F3x), and (2) anxiety, dissociative, stress-related, somatoform and other nonpsychotic mental disorders (F4x). A third small group was created to include other diagnoses (n = 11, F.1x, F.6x, F.9x, Z73), which were not further analyzed due to their heterogeneity. In the two main groups, we further analyzed subgroups of bipolar disorder (F.31), as well as adjustment disorder (F.43). The bipolar disorder group was analyzed separately because of the known neurobiological differences to unipolar depression. Individuals diagnosed with adjustment disorder were analyzed separately since it was the biggest group in the F4x diagnoses, with 118 of 211 cases (55.9%) and distinguished itself from the other F4x diagnoses, which, in the present cohort, were mainly related to anxiety symptoms. We assessed six somatic conditions, which were identified as the most common metabolism-related disorder in our cohort including CVD (including hypertension), hypertension, lipid disorders, DM, obesity (body mass index (BMI) ≥ 30 kg/m2), and thyroid dysfunction. As about 75% of individuals with CVD suffer from hypertension, we conducted an additional analysis with this subgroup. For prevalence data on the general Austrian population, we searched the “Statistik Austria” database, as well as any additional cohort studies for all somatic comorbidities of interest. If data was not available from the Austrian population, we included data from German health care databases and cohort studies.
2. Materials and methods
2.1. Statistics
The study was conducted at an Austrian psychiatric rehabilitation center and included 600 individuals undergoing a six-week course of intensive treatment between April 2015 and April 2016. The rehabilitation center is semi-publicly funded by the National Health Insurance for all public employees. The psychiatric rehabilitation program included medical, psychiatric, psychological and psychotherapeutic treatments, as well as occupational therapy, physiotherapy and diet counseling. The main target population for the psychiatric rehabilitation program consisted of Austrian individuals with persisting mood symptoms, long periods of unemployment or the imminent premature retirement due to mental illness. Furthermore, individuals who had previously completed acute psychiatric care, who were (1) not sufficiently stabilized, (2) required prolonged psychiatric treatment to facilitate recovery, and (3) had a positive rehabilitation prognosis were
Sex differences in age, body mass index and waist-to-hip-ratio were assessed using independent t-tests. Sex differences in the prevalence of smoking, CVD, hypertension, DM, obesity and thyroid disorders were calculated with χ2-test. Error probabilities below .05 were accepted to denote statistical significance. 3. Results 3.1. General data The present investigation included 600 patients (54.7% female) with a mean age of 52.9 (SD = 7.7) years and a BMI of 26.9 (SD = 5.0). Of all individuals included, 23.5% were public servants, 23.5% were teachers, 7.7% were employees with the police service, while 4.3% had 39
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obesity and overweight for both male and female participants. Moreover, the education level seems to be higher in the study cohort.
Table 1 Descriptive Data of the study cohort.
Age [years] Body Mass Index Waist-to-hip-ratio Smokers Intake of psychopharmacological drugs SSRIs Atypical antipsychotics Lithium
Whole cohort n = 600 Mean (SD)
Females (n = 328)
Males (n = 272)
Mean (SD)
Mean (SD)
52.9 (7.7)* 26.9 (5.0)⁎⁎ 0.91 (0.09)⁎⁎ % (n) 29.7 (165)* 80.7 (484)
53.5 (7.6) 26.4 (5.4) 0.85 (0.09) % (n) 26.1 (77) 80.5 (264)
52.1 (7.9) 27.5 (4.4) 0.97 (0.07) % (n) 32.4 (88) 80.9 (220)
39.6 (237) 13.7 (82) 2.3 (14)
41.9 (137) 12.5 (41) 2.1 (7)
36.8 (100) 15.4 (42) 2.6 (7)
3.2. Somatic diseases – differences in mental disorders Table 3 displays the prevalence of somatic disorders across psychiatric diagnoses. In our cohort, the prevalence of CVD was high in all four diagnostic groups, ranging from 37.1% in bipolar disorder to 48.4% in F4x. diagnoses. Hypertension was relative equally distributed throughout all psychiatric diagnoses. Highest rates of hypertension were found in F4x. diagnoses, with the exception of adjustment disorders (F43.), which had the lowest rates of the whole cohort. Generally, lipid disorders showed a high prevalence in all diagnostic groups with the highest rates among individuals diagnosed with bipolar disorder (65.7%). For DM, a high prevalence was observed in individuals diagnosed with affective disorders (10.8%). However, of note, individuals diagnosed with bipolar disorder showed high prevalence of obesity (40%) and thyroid disorders (40%). In summary, individuals diagnosed with bipolar disorder seemed to show the highest rates of somatic comorbidities in our cohort.
Note: SD = Standard Deviation, n = Number of Participants, SSRIs = Selective Serotonin Reuptake Inhibitors. ⁎ Significant differences between women and men (p < .05). ⁎⁎ Significant differences between women and men (p < .01).
their working place in trade/economy as well as 4.3% worked in technology areas. Notably, 57.4% of individuals had more than one Fdiagnosis, while 22.5% had three or more F-diagnoses. In terms of somatic comorbidities, lipid disorders were the most common with 58.5%, followed by CVD (45.6%) and hypertension (33.0%). More than 53% of individuals had more than one, while 32% had three or more somatic comorbidities. Almost half of individuals were prescribed SSRIs, a smaller group took atypical antipsychotics and 2.3% took lithium as a medication at the time of the study. Overall, 60 individuals (10%) had no medication at the time of testing. Females were significantly older (t598 = 2.30, p = .022), had a significantly lower BMI (t591.9= −2.75, p = .006) and waist-to-hipratio (t383.6= −14.30, p < .001) and showed a significantly lower prevalence of smokers (χ21 = 397, p = .046) compared to males in the present cohort (for further descriptive details of the study cohort see Table 1). Table 2 showcases the differences between the study cohort and the general population in Austria. The study cohort shows higher rates in
3.3. Sex differences and prevalence in comparison with the general population The prevalence of thyroid disorders was significantly higher in females, while CVD and hypertension were significantly more frequent in males. However, males and females showed similar rates of obesity, lipid disorders, and DM (see Table 4). We compared our cohort to data from Austrian or German general population of a similar age range (see Table 4 and Fig. 1). However, due to missing data, we were not able to analyze statistical differences in the prevalence of somatic disorder. Nevertheless, comparing the prevalence of somatic comorbidities in our cohort with the general population, the prevalence of CVD and lipid disorders appeared to be much higher in our cohort across both sexes. Of note, thyroid disorders were more frequent in females compared to the counterparts in the general population. 4. Discussion Despite the known association between psychiatric and somatic disorders, as well as their effect on life expectancy and quality, the data on the prevalence of somatic disorders in psychiatric populations remains sparse. The aim of the current study was to analyze the prevalence of somatic comorbidities in individuals diagnosed with psychiatric disorders in a psychiatric rehabilitation center in Austria. As a secondary aim, we explored sex-specific differences across psychiatric diagnoses, as well as how prevalence of somatic disorders compares to general Austrian and German populations. In our sample of 600 individuals, metabolic disorders showed a higher prevalence compared to the data from the general population of a comparable age. Of note, we observed a high prevalence of individuals diagnosed with affective disorders, particularly bipolar disorder. There is strong evidence to suggest that metabolic disturbances in affective disorders are linked to chronic low-grade pro-inflammatory states (Soczynska et al., 2011). Furthermore, in our cohort, obesity and thyroid dysfunction were found in about 40% of individuals diagnosed with bipolar disorder. There is strong evidence in the literature that greater risk of obesity in bipolar disorder is likely due to illness-influenced behaviors, mood-induced physiological changes, and pharmacotherapy (Shah et al., 2006; Liu et al., 2013). Weight gain and increased visceral fat can often be caused by antipsychotics (i.e. Olanzapine) (Gilles et al., 2010). Regarding thyreoid dysfunction, the literature suggests that autoimmune thyreoiditis could be an endophenotype for bipolar disorder (Vonk et al., 2007). Moreover, lithium treatment is well known to affect thyreoid function in several ways
Table 2 Demographic data of the study cohort and the general population in Austria.
Percentage of whole [%] Body mass indexa Normal weight [%] Overweight [%] Obesity [%] Education levelb Apprenticeship [%] A-levels [%] University [%] Employmentc Part time [%] Full time [%] Smokingd [%]
Females in sample (n = 328)
Females in Austria
Males in sample (n = 272)
Males in Austria
55
51
45
49
45.5 30.8 23.1
55.6 26.5 15.9
31.6 43.9 24.5
34.6 45.4 19.7
12.2 23.8 18.9
26.7 14.9 14.9
30.9 21.7 16.5
42.0 14.4 14.5
27.4 72.6 26.1
47 53 26.9
5.85 94.14 32.4
11 89 29.8
a Statistik Austria, Gesundheitsbefragung 2014, Austrian population 45–60 years. b Statistik Austria, Bildungsstandregister 2016, Austrian population 15–60 years. c Bundesministerium für Bildung und Frauen. Frauen und Männer in Österreich Gender Index 2015, Austrian population ≥15 years. d STATISTIK AUSTRIA, Gesundheitsbefragung 2014., Austrian population 45–60 years.
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Table 3 Somatic diseases – prevalence in mental disorders. Diagnosis n = 600a
CVD
Affective disorders (F3x) n = 415 [% (n)] Bipolar disorder n = 35 [% (n)] Anxiety, stress-related, somatoform disorders (F4x), n = 93 [% (n)] Adjustment disorders n = 118 [% (n)]
47.0 37.1 48.4 41.4
(189) (13) (45) (48)
Hyper-tension
Lipid disorders
DM
Obesity
Thyroid disorders
34.3 31.4 35.5 26.7
57.8 65.7 60.6 56.8
10.6 (43) 11.8 (4) 6.5 (6) 5.2 (6)
23.5 (97) 40 (14) 22.1 (21) 22.6 (26)
23.0 (93) 40 (14) 22.1 (21) 26.7 (31)
(139) (11) (33) (31)
(237) (23) (57) (67)
a Number of individuals does not sum up to 600 because most of the patients had more than one psychiatric diagnose n = number of participants, CVD = cardiovascular disease, DM = diabetes mellitus.
inflammatory mechanisms, which can increase the vulnerability for somatic and psychiatric problems in males. In our cohort, females diagnosed with psychiatric disorders showed a four-times higher rate of thyroid dysfunction as compared to male, with and without psychiatric diagnoses, as well as females without psychiatric diagnoses. However, the latter cannot be explained by medication alone, since females were prescribed less medication linked to altering the thyroid metabolism (i.e., lithium, atypical antipsychotics) as compared to males. Therefore, further research is required to investigate this result. Additionally, a strong association between mental health and socioeconomic status has been described in the literature. In general, individuals with low socioeconomic status have poorer mental health than persons with high socio-economic status. Lifestyle factors, as physical activity, alcohol consumption and smoking are known to influence socio-economic inequalities (Kautzky-Willer et al., 2012). Furthermore, socioeconomically disadvantaged children and adolescents were two to three times more likely to develop mental health problems (Reiss, 2013).
(Bocchetta and Loviselli, 2006). In addition, we observed that the prevalence of CVD in our cohort was four- to eight- times higher than in the general Austrian population. Specifically, the mean prevalences of DM and obesity were higher than in the general population, particularly as compared to individuals diagnosed with affective disorders. Significant sex differences were found in CVD and hypertension with males showing a higher prevalence, while thyroid dysfunction occurred more often in females. Higher rates of heart problems as physical comorbidity in men were also found by the Australian Institute of health and welfare (AIHW) in 2007 (AIHW, 2007). The interest in sex as a moderating factor of vulnerability for somatic disorders comes, in part, from epidemiological data that reveals sex differences in the prevalence of many disorders that are exacerbated by stress (Valentino and Bangasser, 2016). For example, studies suggest greater HPA axis dysregulation in females with stress-related psychiatric disorders. Moreover, preclinical data demonstrates sex differences in several cellular and molecular mechanisms. As such, sex differences may be linked to increased endocrine, emotional, or arousal responses to stress in females (Valentino and Bangasser, 2016). On a clinical level, however, biological sex differences might be reversed and influenced by different coping strategies, as well as adaption to stress and social roles. Our findings show that the prevalence of somatic comorbidity in individuals diagnosed with disorders is alarmingly high, with evidence of sex differences. Increased rates of CVD and hypertension in males may partly be explained by weight differences across our two defined diagnostic groups. This can also be found in the literature (Lee et al., 2003). Furthermore, oestrogen and progesterone may influence oxidative and antioxidative parameters via influence of DNA repair, activation of antioxidative defence (Bengesser et al., 2016) and act as protective factors for cardiovascular outcomes in females. In addition, we observed the rate of smoking significantly higher in males. As such, the combination of smoking, obesity, CVD, and hypertension may influence
4.1. Clinical relevance Somatic comorbidities are one main reason for the reduced life expectancy of individuals with psychiatric disorders. Our findings underline the well-documented existence of somatic comorbidities amongst individuals diagnosed with psychiatric disorder. Despite this evidence, there is no systematic procedure in most health systems all over the world that aims to prevent and monitor the incidence of somatic disorders in psychiatric populations. In addition, individuals diagnosed with psychiatric conditions have less access to general healthcare systems, which can result in lower exposure to CVD risk screening and prevention (De Hert, et al., 2009). The increased healthcare needs of individuals with psychiatric diseases are often
Table 4 Sex differences.
CVD Hypertension Lipid disorders DM Obesity Thyroid disorders
Whole cohort (n = 600) Females% (n) Males% (n)
Statistical difference (between females and males)
General Population (Austria/Germany) Females% Males%
40.9 (130) 28.3 (91) 54.8 (178) 8.1 (26) 23.1 (75) 36.3 (116)
χ2 = 5.06, p = .025* χ2 = 5.79, p = .016* χ2 = 3.23, p = .07 χ2 = 1.35, p = .25 χ2 = 0.17, p = .68 χ2 = 51.53, p < .001⁎⁎
6.4–8.9a 21.2b 32.1d 4.0b,c 15.9b 11.2e
50.2 37.6 62.1 10.9 24.5 10.6
(133) (100) (167) (29) (66) (28)
Note: n = number of participants. CVD = cardiovascular disease. DM = diabetes mellitus. ⁎ Significant differences between women and men (p < .05). ⁎⁎ Significant differences between women and men (p < .01). a 40–79 year old German individuals (DEGS1 and BGS98) (Gosswald et al., 2013). b Austrian individuals 45 to 60 years old (Statistik Austria, 2015). c German individuals 50–59 years (Heidemann et al., 2013). d 18–79 year old German individuals (Scheidt-Nave et al., 2013). e 50–80 year old German individuals (Seck et al., 1997). 41
12.1–12.3a 25.8b 36.6d 5.9–7.3b,c 19.7b 8.0e
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Fig. 1. Sex specific prevalence in comparison with the general population.
this into account, our results reflect clinical reality. In line with this, clinical reality often means polypharmacy. In this study participants had various combinations of one up to three different categories of psychopharmacological treatments. Thus, a major limitation of this study is undoubtedly that the heterogeneity and different combinations in the individual therapy as well as moderate sample size rendered a consideration of medication unfeasible. A further limitation is the source of prevalence of different somatic diseases in Austria as well as in Germany. While for some disorders we were able to extract prevalence information from Statistics Austria, others we could only obtain from position papers or were derived from cohorts, which may not be representative of the Austrian population. Nevertheless, we obtained data from official statistics on the prevalence of disorder in Austria or Germany, which are the most reliable sources available. In this context we have to state that data on the prevalence of thyroid disorders were the most difficult to find as the exact prevalence of thyroid disorders in general cohorts might not be available at official databases. In general, we decided to include only studies about known diagnoses from the general population and not studies that estimated the prevalence, including unknown and just supposed cases. Furthermore, in cohort studies on the prevalence of CVD, it was not clear if hypertension was included in the prevalence data of the cohort of the general population. A further limitation points to the fact that our cohort was recruited in a psychiatric rehabilitation center, were mainly government employees (teachers, policemen, postman, etc.) were treated which may not reflect of socioeconomic strata within the Austrian population. Ideally, a matched cohort from the general Austrian population with similar socioeconomic characteristics, similar characteristics in occupation level, or similar risk factors for psychiatric and somatic disease would have been needed to compare the data of our study, a setting which is probably impossible to achieve. Due to the aim of the psychiatric rehabilitation program being rapid re-integration into the workforce, most of the individuals included in this study had sufficient access to healthcare services that may not be entirely representative of all socioeconomic states in Austria. Furthermore, given that our cohort consisted of a large proportion of government employees, we may postulate that the rates of somatic disorders may be more prevalent in unemployed individuals who are also liable for higher rates of psychiatric disorders. In addition, the generalizability of findings from our cohort may be limited, given that
coupled with a disparity in access and provision of general somatic care (Fleischhacker et al., 2008). There is a lack of consensus as to which health care professionals should be responsible for the prevention and management of comorbid somatic disorders. In this context, clinicians should take pre-existing metabolic abnormalities into consideration before initiating treatment with psychopharmacological medication that may have potential metabolic side effects or result in polypharmacy. Furthermore, somatic parameters must be monitored on a regular basis in individuals diagnosed with psychiatric disorder to prevent and detect metabolic comorbidities. The suboptimal integration of general somatic and psychiatric care services lead to inferior provision of medical health care. Therefore, more interdisciplinary care may improve the understanding and identification of serious health conditions, which may be treated or prevented (De Hert, et al., 2009). Amongst individuals diagnosed with psychiatric disorders, this problem may be compounded by a persistent lack of information concerning the prevalence of somatic disorders, which we have tried to address in our study, as well as the rate of mortality. Therefore, metabolic dysfunction in individuals diagnosed with psychiatric disorders should be closely examined to improve treatment and lower the rate of mortality associated with psychiatric disorders. Furthermore, effective prevention programs and therapy of somatic comorbidities in the psychiatric population are necessary to positively affect the course of psychiatric disorders, as well as on social integration and life expectancy. As such, guidelines for the monitoring and treatment of somatic comorbidities must be implemented, as well as integrated into clinical practice. While some conferences have tried to develop such guidelines (De Hert, et al., 2009; Fleischhacker et al., 2008), they remain to be implemented into clinical practice in Austria and around the world. 4.2. Limitation In the examined cohort, we observed a high rate of individuals that were diagnosed with more than one psychiatric disorder; therefore, conclusions can only be drawn for individuals in the general psychiatric population. Further studies could focus on somatic comorbidities in individuals diagnosed with a single psychiatric disorder to avoid heterogeneity. Nevertheless, comorbidities amongst psychiatric disorders are very common, especially affective and anxiety disorders. Taking 42
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Markus Mayr-Mauhart: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Günther Miniberger: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Andrea Schachner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Katharina Wagger: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Armin Birner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Martina Platzer: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Frederike Fellendorf: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Robert Queissner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Susanne Bengesser: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Eva Reininghaus: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing.
all participants were treatment-seeking and obtained from a single clinic. Of note, our cohort has a relatively high number of older individuals, with a mean of 52.3 years. Therefore, further research should include also younger individuals with psychiatric disorders to explore whether these findings are age-dependent. In sum, causality cannot be inferred from our findings regarding the relationship between psychiatric and somatic disorders. Optimally, future longitudinal studies will be conducted which include younger, physically healthy adults to provide further insight into the associations between psychiatric and somatic disorders, as well as their etiologies. 5. Conclusion Our analyzed cohort of individuals diagnosed with psychiatric disorders showed a high prevalence of somatic comorbidities. While we cannot draw causal conclusions, the bi-directional relationship between psychiatric and somatic disorders is well documented. Therefore, comorbid psychiatric and somatic disorders should be closely monitored to lower the high mortality rates associated with psychiatric disorders. To do so, changes in public health policy and further medical training are required to treatment of somatic disorders in the psychiatric population. Furthermore, effective prevention and therapeutic programs for somatic comorbidities in individuals diagnosed with psychiatric disorders are necessary. Guidelines for the monitoring and treatment of somatic comorbidities must be implemented and brought to a clinical and usable model. Moreover, knowledge about sex differences should be used to further improve individualized treatment of individuals with psychiatric disorders. Characterizing the nature of comorbidity between psychiatric and cardiometabolic conditions can stimulate research to better understand their shared etiologies, as well as to promote public health changes to improve clinical care, social integration and individual life expectancy.
Acknowledgements The authors thank all participants of the study. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.06.045.
Conflicts of interest
References
None of the authors have a conflict of interest.
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Role of the funding source There has been no significant financial support for this work that could have influenced its outcome. CRediT authorship contribution statement Bernd Reininghaus: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Karin Riedrich: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Nina Dalkner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Laura Antonia Lehner: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Alexandra Rieger: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Carlo Hamm: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Matthias Dorn: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Leopold Gradauer: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. Alois Hufnagl: Conceptualization, Investigation, Data curation, Project administration, Formal analysis, Writing - original draft, Writing - review & editing. 43
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