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Prevalence and Predictors of Metabolic Syndrome Among Patients Attending an Outpatient Clozapine Clinic in Australia
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Prevalence and Predictors of Metabolic Syndrome Among Patients Attending an Outpatient Clozapine Clinic in Australia Scott Brunero, Scott Lamont, and Greg Fairbrother Objective: This study aimed to determine the prevalence and predictors of metabolic syndrome in an outpatient clozapine clinic in Australia. Methods: Metabolic syndrome is a cluster of some of the most dangerous cardiovascular risk factors, and its high prevalence in people with mental illness has been demonstrated. Patients attending a clozapine clinic were screened for the following: age, gender, ethnicity, waist circumference, blood pressure, high-density lipoprotein level, low-density lipoprotein level, blood sugar levels, total cholesterol level, triglycerides level, weight, body mass index, insulin resistance level, length of time on clozapine, clozapine dose, smoking status, family history of diabetes and cardiovascular disease, and personal history of polycystic ovarian syndrome. All the variables that were found to be significantly associated with metabolic syndrome were entered into a multivariate logistic regression analysis. Results: Seventy-three patients were screened for metabolic syndrome using the International Diabetes Federation’s (2007) definition. Forty-five (61.6%) patients met the criteria for the syndrome. Increased blood sugar level, high diastolic blood pressure, older age, increased waist circumference, raised triglycerides level, and higher body mass index emerged as significant predictors of metabolic syndrome in the sample. Conclusions: This study adds further support for the systematic screening for metabolic syndrome in patients receiving clozapine. The need for intervention programs which screen for and address the modifiable risk factors of metabolic syndrome is discussed. Crown Copyright © 2009 Published by Elsevier Inc. All rights reserved.
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HE HIGH PREVALENCE of metabolic syndrome (MS) in schizophrenia has been increasingly recognized in the mental health literature in recent years (Procyshyn et al., 2007; Henderson et al., 2000; Lambert, Velakoulis, & Pantelis, 2003; Saha, Chant, & Mcgrath, 2007). The MS is a cluster of the most dangerous known cardiac risk factors (diabetes and prediabetes, abdominal obesity, high cholesterol level, and high blood pressure (BP); International Diabetes Federation, 2007). The existence of an increased mortality risk among people with mental illness due to physical health factors has been demonstrated by several authors (Lawrence, Holman, Jablensky, &
Hobbs, 2003; Babidge, Buhrich, & Butler, 2001; Ruschena et al., 1998). Poor diet, lack of exercise, negative symptoms, stress, smoking, and abnormFrom the Liaison Mental Health Nursing, Prince of Wales Hospital, Sydney, Australia; Mental Health Service, Prince of Wales Hospital, Sydney, Australia; Nursing Education & Research Unit, Prince of Wales Hospital, Sydney, Australia. Corresponding Author: Scott Brunero, DipApSc, BAHsc, MA, Prince of Wales Hospital, EBB, NERU, rm 7 High st Randwick 2031 NSW, Australia. E-mail addresses: [email protected], [email protected] Crown Copyright © 2009 Published by Elsevier Inc. All rights reserved. 0883-9417/1801-0005$34.00/0 doi:10.1016/j.apnu.2008.06.007
Archives of Psychiatric Nursing, Vol. 23, No. 3 (June), 2009: pp 261–268
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alities in the hypothalamic–pituitary–adrenal axis have all been proposed as precursors to the link (Ryan, Collins, & Thakore, 2003; Dixon et al., 2000; Holmberg & Kane, 1999). Several attempts at describing MS have been made. The most recent description was published by the IDF (2007). Previous descriptions such as those put forward by the North American National Cholesterol Education Program (NCEP; 1985) and the World Health Organization (1999) “were never intended to provide exact diagnostic criteria for identifying individuals with metabolic syndrome in clinical practice” (Rationale for the New Worldwide Definition of Metabolic Syndrome, 2007, p. 1). The IDF now provides a diagnostic tool suitable for worldwide use, which addresses the needs of researchers and clinicians as it does not rely upon measurements only found in research settings. The IDF diagnostic criteria include waist circumference ≥94 cm for Europoid men and ≥80 cm for Europoid women, with ethnicity-based values, plus any two of the following: raised triglyceride (TG) level (≥150 mg/dl or 1.7 mmol/L) or specific treatment of this abnormality, reduced high-density lipoprotein (HDL) cholesterol (b40 mg/dl or 1.03 mmol/L in men and b50 mg/dl or 1.29 mmol/L in women) or specific treatment of this abnormality, raised systolic BP (≥130) or diastolic BP (≥85 mm Hg) or treatment of previously diagnosed hypertension, and raised fasting plasma glucose (FPG) level (≥100 mg/dl or 5.6 mmol/L) or previously diagnosed type 2 diabetes. General population rates of MS have been estimated to be between 20% and 25% for the age group of 40- to 49-yearolds, 33% for the age group of 50- to 59-year-olds, and 40%–45% for the age group of 60- to 69-yearolds (Ruano, Zollner, & Goeth, 2005). In a general population study of rural Australians, the prevalence of MS using The NCEP criteria was 27.1% in men and 28.3% in women (Ruano et al., 2005). Mcevoy et al. (2005), in a study of 689 patients with schizophrenia, identified prevalence rates of 40.9% and 42.7% using the NCEP definition and the American Heart Association definitions of MS, respectively. In a North American study of 125 patients with bipolar disorder, 32% were diagnosed with MS using the NCEP criteria (Yumru et al., 2007). In a Finnish study (Suvisaari et al., 2007) of 8,028 patients, the authors obtained a prevalence rate (National Cholesterol Education Program, 1985) of 36.2% for people with schizophrenia,
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41.4% for other nonaffective psychosis, 25% for affective psychosis, and 30.1% for people without psychotic disorders. Straker et al. (2005) found that 29.2% of patients met NCEP criteria for MS in a population of newly admitted patients to a psychiatric inpatient unit in North America. Most studies of the prevalence rates of MS in people with mental illness have shown increased rates in comparison with those of the general population. Thakore (2004), in a literature review of metabolic abnormalities in first-episode schizophrenia, reported that the most predictive studies did not account for likely mediating factors such as previous medication usage, lifestyle, and age. Medication type and dose have been associated with differences in MS prevalence rates among patients with psychiatric illness. L'Italien, Casey, Kan, Carson, and Marcus (2007) found that in patients treated with olanzapine (n = 373) versus patients treated with aripriprazole (n = 380), prevalence rates of 41.6% and 27.9% were shown, respectively. Correll, Frederickson, Kane, and Manu (2007), in a study of patients receiving antipsychotic polypharmacy in the United States, reported an increased rate of MS (NCEP) in patients receiving antipsychotic polypharmacy (50%) versus patients undergoing monotherapy (34.3%). The multivariate analysis indicated that body mass index (BMI), older age, bipolar disorder diagnosis, and schizophrenia diagnosis were also predictors of the syndrome. In a Swedish study of 269 patients, MS prevalence (NCEP) was 34.6% and highest within the age group of 40- to 49-year-olds (43%). Patients who were prescribed clozapine had the highest prevalence rate of 48.5% (Mackin, Watkinson, & Young, 2005). Clozapine has demonstrated its efficacy in the management of treatment-resistant schizophrenia (Volvaka et al., 2005; Kane, Honigfeld, Singer, & Meltzer, 1988) but is also associated with deleterious effects on a range of metabolic parameters, such as weight gain, hypertriglyceridemia, increased total cholesterol level, and hypertension (Gentile, 2006; Henderson et al., 2005). Although these side effects add to the morbidity of patients with schizophrenia, a recent study by Procyshyn et al. (2007) of patients receiving clozapine reported that increases in serum lipids, independent of weight gain, were also associated with significant clinical symptom improvement. Lamberti et al. (2006) studied 93 patients attending a clozapine
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clinic in the United States, with results showing the prevalence of MS in the study group to be 53.8% (NCEP definition). Goethe, Szarek, Caley, and Woolley (2007), in a study of 1,691 psychiatric inpatients in North America, found that 69.3% had at least one correlate of MS. In this study, the odds of a patient having one or more MS correlates were eight times greater among patients receiving clozapine compared with those receiving other antipsychotics. The mounting indications of an association between clozapine prescription and high MS risk provide further challenges to psychiatric prescribers who seek to balance risk and benefit in their decision to commence or continue clozapine for treatment-resistant schizophrenia. In a survey of 300 randomly selected psychiatrists in the United States in 2003, 43%–48% were willing to risk metabolic side effects in lieu of the therapeutic benefits of the atypical antipsychotics (Kohen, 2005). Understanding the prevalence and predictors of MS among patients taking clozapine may enhance prescribers' abilities to make more considered risk–benefit decisions. Nasrallah (2006) suggests that as clinicians refine their practice around the use of the atypical antipsychotics, more research, resources, and knowledge will be required to enable clinicians to fully understand the risk– benefit considerations they face when prescribing atypical antipsychotics. STUDY AIM
The purpose of this study is to determine the prevalence and significant independent predictors of MS in a sample of outpatients taking clozapine in Sydney, Australia. METHOD
Patients attending an outpatient clozapine clinic with a primary diagnosis of schizophrenia using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria (American Psychiatric Association, 1994) were approached and asked to consent to be screened for MS using the IDF (2007) definition from October 2006 to October 2007. The clozapine clinic was chosen because of the relative ease it afforded in accessing a patient sample and the known high risk of MS within this sample (Lamberti et al., 2006). Demographic information was collected from a patient survey and via the medical record.
Blood pathology collected included FPG and a full lipid profile. All the analyses of samples were completed by an independent community pathology service, and results were faxed to the clozapine clinic. Variables relating to family history of diabetes and cardiovascular disease were identified. Gestational diabetes, polycystic ovarian syndrome, and smoking status variables were also collected. Ethnicity-based values were used in assessing waist circumference (IDF, 2007). Insulin resistance was assessed indirectly by the TG/HDL ratio marker, with a ratio of N3.5 being significant (Mclaughlin et al., 2005). All variables found to be significantly associated with the presence of MS following univariate statistical analyses were entered into a multivariate logistic regression analysis, which sought to obtain the best predictive model for the presence of MS. SPSS Version 15.0 (Chicago, IL) was used to conduct the analysis. Ethics approval was granted by the South Eastern Sydney Illawarra Area Health Service Human Research Ethics Committee (Northern Section). RESULTS
One hundred seventy-eight patients presented to the clinic during a predefined study period. The study enrolment procedure required a dedicated mental health nurse. The limited availability of the mental health nurse enabled 103 (58%) of these patients to be screened. Thirty patients returned non-fasting blood screens and were excluded from the study at enrolment. This left a final sample of 73 patients who returned fasting blood pathology and completed all the demographic and medical history information sought. Forty-five (61.6%) patients met the IDF criteria for MS. Using the NCEP criteria, the prevalence rate of MS was found to be 57.5% (n = 42). Of the patients that did not meet the full IDF criteria, 17.8% (n = 13) met one criterion of the syndrome, 13.7% (n = 10) met two criteria, and 6.8% (n = 5) met none of the criteria of the syndrome. Men were more likely than women to have MS (73.3% vs. 26.7%, χ2 = 6.8, P = .009). Table 1 reports the prevalence of the IDF criteria for MS by gender and for the sample in total. Table 2 reports results reflecting the other known risk factors for metabolic abnormalities. For both women and men, increased weight, BMI, and TG/HDL ratio were significantly higher in the MS group. For men and women combined, older
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Table 1. Metabolic Syndrome (IDF) Risk Factors by Gender and Total Women (n = 28) Variable
Waist Systolic Diastolic HDL FPG TG
MS (n = 12), M (SD)
113.3 113.7 84.5 1.2 5.8 2.7
(12.8) (11.6) (7.2) (0.2) (1.3) (1)
Men (n = 45)
Not MS (n = 16), M (SD)
89.6 104.1 73.1 1.3 5 1.3
ANOVA, F (P) ⁎
(13) (10.5) (9.5) (0.3) (0.5) (1)
23.1 4.6 12.0 13.9 5.2 11.5
MS (n = 33), M (SD)
(.001) (.04) (.002) (.001) (.03) (.002)
114.7 122.3 83.9 1.2 6.2 3.5
(18.8) (15.6) (9.7) (0.3) (1.7) (2.7)
Not MS (n = 12), M (SD)
98.4 114.7 79.7 1.6 5.4 1.6
(13.4) (10.8) (7.5) (0.3) (1.2) (0.6)
Total (n = 73) ANOVA, F (P)
7.6 2.7 1.9 1.1 2.2 7.8
(.009) (.11) (.17) (.31) (.15) (.008)
MS (n = 45), M (SD)
114.4 119.9 84.1 1.2 6.1 3.3
(17.2) (15.1) (9) (0.3) (1.6) (2.3)
Not MS (n = 28), M (SD)
93.4 108.4 75.9 1.5 5.2 1.3
(13.7) (11.6) (9.1) (0.3) (0.8) (0.9)
ANOVA, F (P)
29.5 11.7 13.9 11.7 8.0 17.8
(.001) (.001) (.001) (.001) (.006) (.001)
NOTE. ANOVA = one-way analysis of variance. ⁎
P value less than .05 is considered significant.
Table 2. Other Known Risk Factors for Cardiometabolic Disorders by Gender and Total Women (n = 28) Variable
43 95.8 35.7 2.41 3 5.3 3008 291 43
(11.8) (18.8) (6.2) (1.3) (0.6) (1.1) (2171.5) (126.2) (11.8)
Not MS (n = 16), M (SD)
37 72.4 26.3 0.9 2.9 5.1 2219 250 37
(9.5) (17.8) (5.6) (0.81) (0.7) (1) (1840) (138.8) (9.5)
Men (n = 45) ANOVA, F (P) ⁎
2.9 11.4 17.8 16.6 0.50 0.14 1.1 0.64 2.9
(.1) (.002) (.001) (.001) (.49) (.71) (.31) (.43) (.1)
MS (n = 33), M (SD)
40 (9.9) 105 (24.8) 32.7 (5.8) 3.65 (3.7) 2.8 (0.9) 5.3 (1) 2673 (1551.2) 353 (164.5) 40 (9.9)
NOTE. ANOVA = one-way analysis of variance; LDL = low-density lipoproteins. ⁎ P value less than .05 is considered significant. † Triglyceride level divided by high-density lipoprotein level (insulin resistance).
Not MS (n = 12), M (SD)
32 88 28.3 1.15 3.2 5.5 188 268 32
(6.3) (16.5) (5.9) (0.86) (1.1) (0.8) (1773.6) (104.5) (6.3)
Total (n = 73) ANOVA, F (P)
6.9 5.0 4.7 5.2 1.3 0.31 2.6 2.8 6.9
(.12) (.03) (.04) (.03) (.27) (.58) (.12) (.10) (.12)
MS (n = 45), M (SD)
42 103.7 33.5 3.32 2.9 5.3 2828.4 337.2 42
(10.3) (23.9) (6) (3.3) (0.8) (1) (1741.5) (156.3) (10.3)
Not MS (n = 28), M (SD)
35 82.4 27.1 1 3 5.3 2077.2 258.5 35
(8.4) (21.5) (5.7) (0.82) (0.9) (0.9) (1786.5) (123.2) (8.4)
ANOVA, F (P)
7.7 20.3 19.5 13.2 0.27 0.009 3.2 5.1 7.7
(.007) (.001) (.001) (.001) (.61) (.93) (.08) (.03) (.007)
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Age Weight BMI TG/HDL ratio † LDL (n = 62) Total cholesterol Clozapine, days Clozapine dose, mg Age
MS (n = 12), M (SD)
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Table 3. Logistic Regression (Forward Stepwise, Conditional) Predictor variable
β
P
OR
95%CI
Increased blood sugar level High diastolic pressure Older age Larger waist circumference Increased TG level Higher body mass index
−1.71 −0.336 −0.158 −0.605 −3.166 1.078
.036 .016 .050 .022 .006 .035
0.181 0.714 0.854 0.546 0.042 2.94
0.037 0.543 0.729 0.326 0.004 1.077
NOTE. Variables rejected by the analysis were systolic, HDL, age, gender, weight, TG/HDL ratio, and clozapine dose. CI = confidence interval; OR = odds ratio.
age, weight, BMI, TG/HDL ratio, and clozapine dose were significantly associated with MS. Increased length of time on clozapine showed a trend toward an association with the presence of MS (P = .08). There was no significant difference between MS and non-MS subgroups in relation to family history of diabetes (n = 15) and cardiovascular disease (n = 17) within gender groupings or overall. There was a trend toward an association among men in relation to smoking and MS presence (P = .07) but no difference for women and for the sample in total. No patients reported a history of gestational diabetes or having a baby over 4 kg, and there were two reports of a history of polycystic ovarian syndrome. Table 3 reports the logistic regression analysis. All significant variables were entered into a forward stepwise logistic regression model. The emerging predictive model of MS retained increased blood sugar level, increased diastolic BP, older age, increased waist circumference, raised TG level, and higher BMI. Eighteen (25%) patients were prescribed more than one antipsychotic. The mean daily clozapine dose was not statistically significantly different for patients whose clozapine dose was augmented (330 mg) versus patients taking clozapine alone (299 mg). Patients were augmented with the following antipsychotics: amisulpiride (n = 10, dose range = 100–400 mg), risperidone (n = 2, dose range = 2–6 mg), haloperidol (n = 3, dose range = 3–10 mg), quetiapine (n = 1, 200 mg), and aripriprazole (n = 3, dose range = 25–30 mg). Analysis of individual antipsychotics that were augmented was not undertaken as the samples sizes were too small for meaningful analysis. DISCUSSION
Using the contemporary definition of MS (International Diabetes Federation, 2007), the prevalence
rate found in this sample (61.6%) is slightly higher than other rates reported in the literature (Lamberti et al., 2006). The MS prevalence rate reported from this study and as reported in the literature suggests a strong case for the systematic monitoring for MS among patients receiving clozapine. The welldocumented side effect of agranulocytosis occurs with 1%–2% of patients receiving clozapine (Alphs & Anand, 1999) and has attracted a rigorous and mandatory monitoring program. The lifetime risk of suicide in schizophrenia is estimated between 4.9% (Palmer, Pankratz, & Bostwick, 2005) and 10% (Miles, 1977) and in Australia has attracted national attention with the publication of the Australian National Suicide Prevention Strategy (1999). Although the risk of death associated with physical health comorbidities has been shown to be significantly higher than the risk of suicide among patients with a severe mental illness (Colton & Manderscheid, 2006), a similar approach has not been taken to the mandatory assessment, screening, and intervention for MS-related and other physical morbidities by national and state departments of health. Increased clozapine dose was associated with the presence of MS in our study, as was increased length of time on clozapine. However, neither of these variables were retained as independently predictive of MS following multivariate analysis. Notably, family history of cardiovascular disease and diabetes were not found to be significantly associated with MS. Apart from systolic BP, the regression analysis identified the remaining defining characteristics of MS: increased blood sugar level, high diastolic pressure, older age, larger waist circumference, increased TG level, and higher BMI. The increased risk of metabolic abnormalities associated with clozapine must also be balanced with the benefits. In the study of Procyshyn et al. (2007) of 65 patients, an increase in serum TG level was associated with improvements in both positive and negative symptoms. The decision to prescribe clozapine is perhaps even more challenging today as the increased risk of lipid abnormalities directly correlates with clozapine's increasing benefits. Adequate treatment of a person's mental state is critical in the ability of the patient to manage any physical health comorbidity as patients need to acquire knowledge and skills to engage themselves in treatment programs for MS (Holt, 2006).
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Although attempts at determining prevalence rates of MS need to continue among patients, the associates of the syndrome are all known to be modifiable risk factors. Several recent publications provide frameworks for the management of metabolic considerations in the context of antipsychotic medication prescription (Newcomer, 2007a, 2007b; Sernyak, 2007; Berk et al., 2007). Attempts have been made at modifying the components of MS among clozapine patients, predominantly with diet and exercise interventions (Wu, Wang, Bai, Huang, & Lee, 2007), pharmacological approaches (Henderson et al., 2005), and increase of patient's knowledge of MS (Brunero, Lamont, Myrtle, & Fairbrother, 2008). These studies are, however, limited in size, and further controlled trials need to be conducted to establish their efficacy. Clozapine has been reported to improve the positive and negative symptoms of schizophrenia, but more recently in a study by Lewis et al. (2006), measures of quality of life did not show improvement among patients receiving clozapine in comparison with those of patients receiving atypical antipsychotics. Improvement in positive and negative symptoms forms but one axis of a complex patient health outcome matrix in schizophrenia. Patients reported that issues such as quality of life, therapeutic relationship, and care and treatment satisfaction are just as important as symptom improvement (McCabe, Saidi, & Priebe, 2007). A key concern for many clinicians is that increasing patient knowledge of the relationship between MS and their prescribed antipsychotic may effect an increase in antipsychotic medication nonadherence. Discontinuation rates of antipsychotics have been attributed to side effects such as weight gain (Thomas, 2007); however, the ethical and medicolegal issues encountered as a consequence of not fully informing the patient of these side effects are likely to be detrimental to individual clinicians (Wirshing, Wirshing, Nystrom, & Buc, 2004). There is debate over who is responsible for monitoring MS. Points of view differ as to whether responsibility should lie with primary or secondary care services (Kohen, 2005). A survey of psychiatrists in the United Kingdom (Kohen, 2005) found that 56% of respondents agreed that primary care services should take full responsibility for physical health monitoring. Although there needs to be a coordinated approach, it is our view that primary responsibility should rest with the treating mental
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health team. As the course and treatment of mental illness may increase the risk of physical health abnormalities, it would make sense that the treating mental health team takes responsibility for coordinating this aspect of care. Localized service protocols for systematic screening for MS, sharing and interpretation of results, and provision of treatment should be led by mental health services with the collaboration of primary care general practitioners and specialist medical services (Alphs & Anand, 1999; Usher, Foster, & Park, 2006). STUDY LIMITATIONS
The study is principally limited by its size. Some of the marginal cross-sectional relationships identified are likely results of this limitation and remain difficult to interpret as a result. The ability to quantify and measure accurately all the associated traditional risks of MS such as family history of diabetes, ethnicity, diet, and exercise levels is limited. It is beyond the scope of this study to attribute a causal relationship between the variables measured and the risk of MS. Holt (2006) argue that there has been an erroneous tendency to lay blame for all the physical abnormalities seen in patients with mental illness to the antipsychotics. Holt et al. describe the first reports of increased risk of diabetes in people with mental illness being observed by Sir Henry Maudsley in 1879, long before the advent of the antipsychotics. Further controlled studies and rigorous accounting for all the known risk factors for MS which include schizophrenia itself need to continue. CONCLUSION
This study adds further support to the argument for systematic screening for MS among patients with mental health problems. The predictors identified by multivariate analysis are all risk factors that are modifiable in this population. More aggressive interventions are required to reduce morbidity and mortality for this patient population. Greater awareness of MS risk among patients receiving clozapine and within the mental health profession would appear warranted. REFERENCES Alphs, L., & Anand, R. (1999). Clozapine: The commitment to patient safety. Journal of Clinical Psychiatry, 60, 39−42. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders. (4th ed.). Washington, DC.
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Lambert, T., Velakoulis, D., & Pantelis, C. (2003). Medical co morbidity in schizophrenia. Medical Journal of Australia, 178, s67−s70. Lamberti, J., Olson, D., Crilly, J., Olivares, T., Williams, G., Tu, X, et al. (2006). Prevalence of the metabolic syndrome among patients receiving clozapine. American Journal of Psychiatry, 163, 1273−1275. Lawrence, D., Holman, D., Jablensky, A., & Hobbs, M. (2003). Death rate from ischaemic heart disease in Western Australian psychiatric patients 1980–1998. British Journal of Psychiatry, 182, 31−36. Lewis, S., Barnes, T., Davies, L., Murray, R., Dunn, G., Hayhurst, K., et al. (2006). Randomised controlled trial of effect of prescription of clozapine versus other secondgeneration antipsychotic drugs in resistant schizophrenia. Schizophrenia Bulletin, 32, 715−723. Mackin, P., Watkinson, H., & Young, A. (2005). Prevalence of obesity, glucose homeostasis disorders and metabolic syndrome in psychiatric patients taking typical or atypical antipsychotic drugs: A cross-sectional study. Diabetlogia, 48, 215−221. McCabe, R., Saidi, M., & Priebe, S. (2007). Patient-reported outcomes in schizophrenia. British Journal of Psychiatry, 191, s21−s28. Mcevoy, J., Meyer, J., Goff, D., Nasrallah, H., Davis, S., Sullivan, L., et al. (2005). Prevalence of the metabolic syndrome in patients with schizophrenia: Baseline results from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial and comparison with national estimates from NHANES III. Schizophrenia Research, 80, 19−32. Mclaughlin, T., Reaven, G., Abbasi, F., Lamendola, C., Saad, M, Waters, D., et al. (2005). Is there a simple way to identify insulin-resistant individuals at increased risk of cardiovascular disease? American Journal of Cardiology www.AJConline.org. Miles, C. (1977). Conditions predisposing to suicide: A review. Journal of Nervous and Mental Disorders, 164, 231−245. National Suicide Prevention Strategy. (1999). Department of Health and Ageing. http://www.aodgp.gov.au/internet/ wcms/publishing.nsf/Content/mental-suicide-overview. Accessed 15th February 2008. National Cholesterol Education Program. (1985). National Cholesterol Education Program (NCEP). http://www. nhlbi.nih.gov/about/ncep/. Accessed 17th June 2008. Nasrallah, H. (2006). Metabolic findings from the CATIE trial and their relation to tolerability. CNS Spectrums, 11, 32−39. Newcomer, J. (2007). Antipsychotic medications: Metabolic and cardiovascular risk. Journal of Clinical Psychiatry, 68, 8−13. Newcomer, J. (2007). Metabolic considerations in the use of antipsychotic medication: A review of recent evidence. Journal of Clinical Psychiatry, 68, 20−27. Palmer, B., Pankratz, S., & Bostwick, J. (2005). The lifetime risk of suicide in schizophrenia. Archives of General Psychiatry, 62, 247−253. Procyshyn, R., Wasan, K., Thornton, A., Barr, A., Chen, E., Pomarol-Clotet, E., et al. (2007). Changes in serum lipids, independent of weight, are associated with changes in symptoms during long term clozapine
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treatment. Journal of Psychiatry Neuroscience, 32, 331−338. Rationale for the New Worldwide Definition of Metabolic Syndrome (2007). Available at: http://www.idf.org/ webdata/docs/Metabolic_syndrome_rationale.pdf. Accessed 14th February 2008. Ruano, G., Zollner, S., & Goeth, J. (2005). Drug-induced metabolic syndrome (DMS) in psychiatry: A diagnostic need uniquely suited to pharmacogenomics. In M. Linder, R. Valdes Jr, & S. Wong (Eds.), Clinical pharmacogenomics. AACC Press, American Association of Clinical Chemistry. Ruschena, D., Mullen, P., Burgess, P., Cordner, S., Barry-Walsh, J., Drummer, O., et al. (1998). Sudden death in psychiatric patients. British Journal of Psychiatry, 173, 270−271. Ryan, M., Collins, P., & Thakore, J. (2003). Impaired fasting glucose tolerance in first episode, drug naive patients with schizophrenia. American Journal of Psychiatry, 160, 284−289. Saha, S., Chant, D., & Mcgrath, J. (2007). A systematic review of mortality in schizophrenia. Archives of General Psychiatry, 64, 1123−1131. Sernyak, M. (2007). Implementation of monitoring and management guidelines for second generation antipsychotics. Journal of Clinical Psychiatry, 68, 14−18. Straker, D., Correll, C., Kramer-Ginsberg, E., Abdulhamid, N., Koshy, F., Rubens, E., et al. (2005). Cost effective screening for the metabolic syndrome in patients treated with second generation antipsychotic medications. American Journal of Psychiatry, 162, 1217−1221. Suvisaari, J., Saarni, S., Perala, J., Suvisaari, J., Harkanen, T., Lonnqvist, J., et al. (2007). Metabolic syndrome among persons with schizophrenia and other psychotic disorders in a general population survey. Journal of Clinical Psychiatry, 68, 1045−1055.
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Thakore, J. (2004). Metabolic disturbance in first-episode schizophrenia. British Journal of Psychiatry, 184, s76−s79. Thomas, P. (2007). The stable patient with schizophrenia—from antipsychotic effectiveness to adherence. European Neuropsychopharmacology, 17, s115−s122. Usher, K., Foster, K., & Park, T. (2006). The metabolic syndrome and schizophrenia: The latest evidence and nursing guidelines for management. Journal of Psychiatric and Mental Health Nursing, 13, 730−734. Volvaka, J., Nolan, K., Kline, L., Czobor, P., Citrome, L., Sheitman, B., et al. (2005). Efficacy of clozapine, olanzapine, risperidone, and haloperidol in schizophrenia and schizoaffective disorder assessed with nurses observation scale for inpatient evaluation. Schizophrenia Research, 76, 127−129. World Health Organization. (1999). In A. Alwan, & H. King (Eds.), Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and classification of diabetes mellitus (pp. 1−59). Geneva: World Health Organization, Department of Non communicable Disease Surveillance. Wirshing, D., Wirshing, W., Nystrom, M., & Buc, P. (2004). Medicolegal considerations in the treatment of psychosis with second generation antipsychotics. Psychiatric Times, 21. Wu, M., Wang, C., Bai, Y., Huang, C., & Lee, S. (2007). Outcomes of obese, clozapine-treated inpatients with schizophrenia placed on a six month diet and physical activity program. Psychiatric Services, 58, 544−550. Yumru, M., Savas, H., Kurt, E., Kaya, M., Selek, S., Savas, E., et al. (2007). Atypical antipsychotics related metabolic syndrome in bipolar patients. Journal of Affective Disorders, 98, 247−252.
Prevalence and Predictors of Metabolic Syndrome Among Patients Attending an Outpatient Clozapine Clinic in Australia Scott Brunero, Scott Lamont, and Greg Fairbrother Objective: This study aimed to determine the prevalence and predictors of metabolic syndrome in an outpatient clozapine clinic in Australia. Methods: Metabolic syndrome is a cluster of some of the most dangerous cardiovascular risk factors, and its high prevalence in people with mental illness has been demonstrated. Patients attending a clozapine clinic were screened for the following: age, gender, ethnicity, waist circumference, blood pressure, high-density lipoprotein level, low-density lipoprotein level, blood sugar levels, total cholesterol level, triglycerides level, weight, body mass index, insulin resistance level, length of time on clozapine, clozapine dose, smoking status, family history of diabetes and cardiovascular disease, and personal history of polycystic ovarian syndrome. All the variables that were found to be significantly associated with metabolic syndrome were entered into a multivariate logistic regression analysis. Results: Seventy-three patients were screened for metabolic syndrome using the International Diabetes Federation’s (2007) definition. Forty-five (61.6%) patients met the criteria for the syndrome. Increased blood sugar level, high diastolic blood pressure, older age, increased waist circumference, raised triglycerides level, and higher body mass index emerged as significant predictors of metabolic syndrome in the sample. Conclusions: This study adds further support for the systematic screening for metabolic syndrome in patients receiving clozapine. The need for intervention programs which screen for and address the modifiable risk factors of metabolic syndrome is discussed. Crown Copyright © 2009 Published by Elsevier Inc. All rights reserved.
T
HE HIGH PREVALENCE of metabolic syndrome (MS) in schizophrenia has been increasingly recognized in the mental health literature in recent years (Procyshyn et al., 2007; Henderson et al., 2000; Lambert, Velakoulis, & Pantelis, 2003; Saha, Chant, & Mcgrath, 2007). The MS is a cluster of the most dangerous known cardiac risk factors (diabetes and prediabetes, abdominal obesity, high cholesterol level, and high blood pressure (BP); International Diabetes Federation, 2007). The existence of an increased mortality risk among people with mental illness due to physical health factors has been demonstrated by several authors (Lawrence, Holman, Jablensky, &
Hobbs, 2003; Babidge, Buhrich, & Butler, 2001; Ruschena et al., 1998). Poor diet, lack of exercise, negative symptoms, stress, smoking, and abnormFrom the Liaison Mental Health Nursing, Prince of Wales Hospital, Sydney, Australia; Mental Health Service, Prince of Wales Hospital, Sydney, Australia; Nursing Education & Research Unit, Prince of Wales Hospital, Sydney, Australia. Corresponding Author: Scott Brunero, DipApSc, BAHsc, MA, Prince of Wales Hospital, EBB, NERU, rm 7 High st Randwick 2031 NSW, Australia. E-mail addresses: [email protected], [email protected] Crown Copyright © 2009 Published by Elsevier Inc. All rights reserved. 0883-9417/1801-0005$34.00/0 doi:10.1016/j.apnu.2008.06.007
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alities in the hypothalamic–pituitary–adrenal axis have all been proposed as precursors to the link (Ryan, Collins, & Thakore, 2003; Dixon et al., 2000; Holmberg & Kane, 1999). Several attempts at describing MS have been made. The most recent description was published by the IDF (2007). Previous descriptions such as those put forward by the North American National Cholesterol Education Program (NCEP; 1985) and the World Health Organization (1999) “were never intended to provide exact diagnostic criteria for identifying individuals with metabolic syndrome in clinical practice” (Rationale for the New Worldwide Definition of Metabolic Syndrome, 2007, p. 1). The IDF now provides a diagnostic tool suitable for worldwide use, which addresses the needs of researchers and clinicians as it does not rely upon measurements only found in research settings. The IDF diagnostic criteria include waist circumference ≥94 cm for Europoid men and ≥80 cm for Europoid women, with ethnicity-based values, plus any two of the following: raised triglyceride (TG) level (≥150 mg/dl or 1.7 mmol/L) or specific treatment of this abnormality, reduced high-density lipoprotein (HDL) cholesterol (b40 mg/dl or 1.03 mmol/L in men and b50 mg/dl or 1.29 mmol/L in women) or specific treatment of this abnormality, raised systolic BP (≥130) or diastolic BP (≥85 mm Hg) or treatment of previously diagnosed hypertension, and raised fasting plasma glucose (FPG) level (≥100 mg/dl or 5.6 mmol/L) or previously diagnosed type 2 diabetes. General population rates of MS have been estimated to be between 20% and 25% for the age group of 40- to 49-yearolds, 33% for the age group of 50- to 59-year-olds, and 40%–45% for the age group of 60- to 69-yearolds (Ruano, Zollner, & Goeth, 2005). In a general population study of rural Australians, the prevalence of MS using The NCEP criteria was 27.1% in men and 28.3% in women (Ruano et al., 2005). Mcevoy et al. (2005), in a study of 689 patients with schizophrenia, identified prevalence rates of 40.9% and 42.7% using the NCEP definition and the American Heart Association definitions of MS, respectively. In a North American study of 125 patients with bipolar disorder, 32% were diagnosed with MS using the NCEP criteria (Yumru et al., 2007). In a Finnish study (Suvisaari et al., 2007) of 8,028 patients, the authors obtained a prevalence rate (National Cholesterol Education Program, 1985) of 36.2% for people with schizophrenia,
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41.4% for other nonaffective psychosis, 25% for affective psychosis, and 30.1% for people without psychotic disorders. Straker et al. (2005) found that 29.2% of patients met NCEP criteria for MS in a population of newly admitted patients to a psychiatric inpatient unit in North America. Most studies of the prevalence rates of MS in people with mental illness have shown increased rates in comparison with those of the general population. Thakore (2004), in a literature review of metabolic abnormalities in first-episode schizophrenia, reported that the most predictive studies did not account for likely mediating factors such as previous medication usage, lifestyle, and age. Medication type and dose have been associated with differences in MS prevalence rates among patients with psychiatric illness. L'Italien, Casey, Kan, Carson, and Marcus (2007) found that in patients treated with olanzapine (n = 373) versus patients treated with aripriprazole (n = 380), prevalence rates of 41.6% and 27.9% were shown, respectively. Correll, Frederickson, Kane, and Manu (2007), in a study of patients receiving antipsychotic polypharmacy in the United States, reported an increased rate of MS (NCEP) in patients receiving antipsychotic polypharmacy (50%) versus patients undergoing monotherapy (34.3%). The multivariate analysis indicated that body mass index (BMI), older age, bipolar disorder diagnosis, and schizophrenia diagnosis were also predictors of the syndrome. In a Swedish study of 269 patients, MS prevalence (NCEP) was 34.6% and highest within the age group of 40- to 49-year-olds (43%). Patients who were prescribed clozapine had the highest prevalence rate of 48.5% (Mackin, Watkinson, & Young, 2005). Clozapine has demonstrated its efficacy in the management of treatment-resistant schizophrenia (Volvaka et al., 2005; Kane, Honigfeld, Singer, & Meltzer, 1988) but is also associated with deleterious effects on a range of metabolic parameters, such as weight gain, hypertriglyceridemia, increased total cholesterol level, and hypertension (Gentile, 2006; Henderson et al., 2005). Although these side effects add to the morbidity of patients with schizophrenia, a recent study by Procyshyn et al. (2007) of patients receiving clozapine reported that increases in serum lipids, independent of weight gain, were also associated with significant clinical symptom improvement. Lamberti et al. (2006) studied 93 patients attending a clozapine
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clinic in the United States, with results showing the prevalence of MS in the study group to be 53.8% (NCEP definition). Goethe, Szarek, Caley, and Woolley (2007), in a study of 1,691 psychiatric inpatients in North America, found that 69.3% had at least one correlate of MS. In this study, the odds of a patient having one or more MS correlates were eight times greater among patients receiving clozapine compared with those receiving other antipsychotics. The mounting indications of an association between clozapine prescription and high MS risk provide further challenges to psychiatric prescribers who seek to balance risk and benefit in their decision to commence or continue clozapine for treatment-resistant schizophrenia. In a survey of 300 randomly selected psychiatrists in the United States in 2003, 43%–48% were willing to risk metabolic side effects in lieu of the therapeutic benefits of the atypical antipsychotics (Kohen, 2005). Understanding the prevalence and predictors of MS among patients taking clozapine may enhance prescribers' abilities to make more considered risk–benefit decisions. Nasrallah (2006) suggests that as clinicians refine their practice around the use of the atypical antipsychotics, more research, resources, and knowledge will be required to enable clinicians to fully understand the risk– benefit considerations they face when prescribing atypical antipsychotics. STUDY AIM
The purpose of this study is to determine the prevalence and significant independent predictors of MS in a sample of outpatients taking clozapine in Sydney, Australia. METHOD
Patients attending an outpatient clozapine clinic with a primary diagnosis of schizophrenia using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria (American Psychiatric Association, 1994) were approached and asked to consent to be screened for MS using the IDF (2007) definition from October 2006 to October 2007. The clozapine clinic was chosen because of the relative ease it afforded in accessing a patient sample and the known high risk of MS within this sample (Lamberti et al., 2006). Demographic information was collected from a patient survey and via the medical record.
Blood pathology collected included FPG and a full lipid profile. All the analyses of samples were completed by an independent community pathology service, and results were faxed to the clozapine clinic. Variables relating to family history of diabetes and cardiovascular disease were identified. Gestational diabetes, polycystic ovarian syndrome, and smoking status variables were also collected. Ethnicity-based values were used in assessing waist circumference (IDF, 2007). Insulin resistance was assessed indirectly by the TG/HDL ratio marker, with a ratio of N3.5 being significant (Mclaughlin et al., 2005). All variables found to be significantly associated with the presence of MS following univariate statistical analyses were entered into a multivariate logistic regression analysis, which sought to obtain the best predictive model for the presence of MS. SPSS Version 15.0 (Chicago, IL) was used to conduct the analysis. Ethics approval was granted by the South Eastern Sydney Illawarra Area Health Service Human Research Ethics Committee (Northern Section). RESULTS
One hundred seventy-eight patients presented to the clinic during a predefined study period. The study enrolment procedure required a dedicated mental health nurse. The limited availability of the mental health nurse enabled 103 (58%) of these patients to be screened. Thirty patients returned non-fasting blood screens and were excluded from the study at enrolment. This left a final sample of 73 patients who returned fasting blood pathology and completed all the demographic and medical history information sought. Forty-five (61.6%) patients met the IDF criteria for MS. Using the NCEP criteria, the prevalence rate of MS was found to be 57.5% (n = 42). Of the patients that did not meet the full IDF criteria, 17.8% (n = 13) met one criterion of the syndrome, 13.7% (n = 10) met two criteria, and 6.8% (n = 5) met none of the criteria of the syndrome. Men were more likely than women to have MS (73.3% vs. 26.7%, χ2 = 6.8, P = .009). Table 1 reports the prevalence of the IDF criteria for MS by gender and for the sample in total. Table 2 reports results reflecting the other known risk factors for metabolic abnormalities. For both women and men, increased weight, BMI, and TG/HDL ratio were significantly higher in the MS group. For men and women combined, older
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Table 1. Metabolic Syndrome (IDF) Risk Factors by Gender and Total Women (n = 28) Variable
Waist Systolic Diastolic HDL FPG TG
MS (n = 12), M (SD)
113.3 113.7 84.5 1.2 5.8 2.7
(12.8) (11.6) (7.2) (0.2) (1.3) (1)
Men (n = 45)
Not MS (n = 16), M (SD)
89.6 104.1 73.1 1.3 5 1.3
ANOVA, F (P) ⁎
(13) (10.5) (9.5) (0.3) (0.5) (1)
23.1 4.6 12.0 13.9 5.2 11.5
MS (n = 33), M (SD)
(.001) (.04) (.002) (.001) (.03) (.002)
114.7 122.3 83.9 1.2 6.2 3.5
(18.8) (15.6) (9.7) (0.3) (1.7) (2.7)
Not MS (n = 12), M (SD)
98.4 114.7 79.7 1.6 5.4 1.6
(13.4) (10.8) (7.5) (0.3) (1.2) (0.6)
Total (n = 73) ANOVA, F (P)
7.6 2.7 1.9 1.1 2.2 7.8
(.009) (.11) (.17) (.31) (.15) (.008)
MS (n = 45), M (SD)
114.4 119.9 84.1 1.2 6.1 3.3
(17.2) (15.1) (9) (0.3) (1.6) (2.3)
Not MS (n = 28), M (SD)
93.4 108.4 75.9 1.5 5.2 1.3
(13.7) (11.6) (9.1) (0.3) (0.8) (0.9)
ANOVA, F (P)
29.5 11.7 13.9 11.7 8.0 17.8
(.001) (.001) (.001) (.001) (.006) (.001)
NOTE. ANOVA = one-way analysis of variance. ⁎
P value less than .05 is considered significant.
Table 2. Other Known Risk Factors for Cardiometabolic Disorders by Gender and Total Women (n = 28) Variable
43 95.8 35.7 2.41 3 5.3 3008 291 43
(11.8) (18.8) (6.2) (1.3) (0.6) (1.1) (2171.5) (126.2) (11.8)
Not MS (n = 16), M (SD)
37 72.4 26.3 0.9 2.9 5.1 2219 250 37
(9.5) (17.8) (5.6) (0.81) (0.7) (1) (1840) (138.8) (9.5)
Men (n = 45) ANOVA, F (P) ⁎
2.9 11.4 17.8 16.6 0.50 0.14 1.1 0.64 2.9
(.1) (.002) (.001) (.001) (.49) (.71) (.31) (.43) (.1)
MS (n = 33), M (SD)
40 (9.9) 105 (24.8) 32.7 (5.8) 3.65 (3.7) 2.8 (0.9) 5.3 (1) 2673 (1551.2) 353 (164.5) 40 (9.9)
NOTE. ANOVA = one-way analysis of variance; LDL = low-density lipoproteins. ⁎ P value less than .05 is considered significant. † Triglyceride level divided by high-density lipoprotein level (insulin resistance).
Not MS (n = 12), M (SD)
32 88 28.3 1.15 3.2 5.5 188 268 32
(6.3) (16.5) (5.9) (0.86) (1.1) (0.8) (1773.6) (104.5) (6.3)
Total (n = 73) ANOVA, F (P)
6.9 5.0 4.7 5.2 1.3 0.31 2.6 2.8 6.9
(.12) (.03) (.04) (.03) (.27) (.58) (.12) (.10) (.12)
MS (n = 45), M (SD)
42 103.7 33.5 3.32 2.9 5.3 2828.4 337.2 42
(10.3) (23.9) (6) (3.3) (0.8) (1) (1741.5) (156.3) (10.3)
Not MS (n = 28), M (SD)
35 82.4 27.1 1 3 5.3 2077.2 258.5 35
(8.4) (21.5) (5.7) (0.82) (0.9) (0.9) (1786.5) (123.2) (8.4)
ANOVA, F (P)
7.7 20.3 19.5 13.2 0.27 0.009 3.2 5.1 7.7
(.007) (.001) (.001) (.001) (.61) (.93) (.08) (.03) (.007)
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Age Weight BMI TG/HDL ratio † LDL (n = 62) Total cholesterol Clozapine, days Clozapine dose, mg Age
MS (n = 12), M (SD)
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Table 3. Logistic Regression (Forward Stepwise, Conditional) Predictor variable
β
P
OR
95%CI
Increased blood sugar level High diastolic pressure Older age Larger waist circumference Increased TG level Higher body mass index
−1.71 −0.336 −0.158 −0.605 −3.166 1.078
.036 .016 .050 .022 .006 .035
0.181 0.714 0.854 0.546 0.042 2.94
0.037 0.543 0.729 0.326 0.004 1.077
NOTE. Variables rejected by the analysis were systolic, HDL, age, gender, weight, TG/HDL ratio, and clozapine dose. CI = confidence interval; OR = odds ratio.
age, weight, BMI, TG/HDL ratio, and clozapine dose were significantly associated with MS. Increased length of time on clozapine showed a trend toward an association with the presence of MS (P = .08). There was no significant difference between MS and non-MS subgroups in relation to family history of diabetes (n = 15) and cardiovascular disease (n = 17) within gender groupings or overall. There was a trend toward an association among men in relation to smoking and MS presence (P = .07) but no difference for women and for the sample in total. No patients reported a history of gestational diabetes or having a baby over 4 kg, and there were two reports of a history of polycystic ovarian syndrome. Table 3 reports the logistic regression analysis. All significant variables were entered into a forward stepwise logistic regression model. The emerging predictive model of MS retained increased blood sugar level, increased diastolic BP, older age, increased waist circumference, raised TG level, and higher BMI. Eighteen (25%) patients were prescribed more than one antipsychotic. The mean daily clozapine dose was not statistically significantly different for patients whose clozapine dose was augmented (330 mg) versus patients taking clozapine alone (299 mg). Patients were augmented with the following antipsychotics: amisulpiride (n = 10, dose range = 100–400 mg), risperidone (n = 2, dose range = 2–6 mg), haloperidol (n = 3, dose range = 3–10 mg), quetiapine (n = 1, 200 mg), and aripriprazole (n = 3, dose range = 25–30 mg). Analysis of individual antipsychotics that were augmented was not undertaken as the samples sizes were too small for meaningful analysis. DISCUSSION
Using the contemporary definition of MS (International Diabetes Federation, 2007), the prevalence
rate found in this sample (61.6%) is slightly higher than other rates reported in the literature (Lamberti et al., 2006). The MS prevalence rate reported from this study and as reported in the literature suggests a strong case for the systematic monitoring for MS among patients receiving clozapine. The welldocumented side effect of agranulocytosis occurs with 1%–2% of patients receiving clozapine (Alphs & Anand, 1999) and has attracted a rigorous and mandatory monitoring program. The lifetime risk of suicide in schizophrenia is estimated between 4.9% (Palmer, Pankratz, & Bostwick, 2005) and 10% (Miles, 1977) and in Australia has attracted national attention with the publication of the Australian National Suicide Prevention Strategy (1999). Although the risk of death associated with physical health comorbidities has been shown to be significantly higher than the risk of suicide among patients with a severe mental illness (Colton & Manderscheid, 2006), a similar approach has not been taken to the mandatory assessment, screening, and intervention for MS-related and other physical morbidities by national and state departments of health. Increased clozapine dose was associated with the presence of MS in our study, as was increased length of time on clozapine. However, neither of these variables were retained as independently predictive of MS following multivariate analysis. Notably, family history of cardiovascular disease and diabetes were not found to be significantly associated with MS. Apart from systolic BP, the regression analysis identified the remaining defining characteristics of MS: increased blood sugar level, high diastolic pressure, older age, larger waist circumference, increased TG level, and higher BMI. The increased risk of metabolic abnormalities associated with clozapine must also be balanced with the benefits. In the study of Procyshyn et al. (2007) of 65 patients, an increase in serum TG level was associated with improvements in both positive and negative symptoms. The decision to prescribe clozapine is perhaps even more challenging today as the increased risk of lipid abnormalities directly correlates with clozapine's increasing benefits. Adequate treatment of a person's mental state is critical in the ability of the patient to manage any physical health comorbidity as patients need to acquire knowledge and skills to engage themselves in treatment programs for MS (Holt, 2006).
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Although attempts at determining prevalence rates of MS need to continue among patients, the associates of the syndrome are all known to be modifiable risk factors. Several recent publications provide frameworks for the management of metabolic considerations in the context of antipsychotic medication prescription (Newcomer, 2007a, 2007b; Sernyak, 2007; Berk et al., 2007). Attempts have been made at modifying the components of MS among clozapine patients, predominantly with diet and exercise interventions (Wu, Wang, Bai, Huang, & Lee, 2007), pharmacological approaches (Henderson et al., 2005), and increase of patient's knowledge of MS (Brunero, Lamont, Myrtle, & Fairbrother, 2008). These studies are, however, limited in size, and further controlled trials need to be conducted to establish their efficacy. Clozapine has been reported to improve the positive and negative symptoms of schizophrenia, but more recently in a study by Lewis et al. (2006), measures of quality of life did not show improvement among patients receiving clozapine in comparison with those of patients receiving atypical antipsychotics. Improvement in positive and negative symptoms forms but one axis of a complex patient health outcome matrix in schizophrenia. Patients reported that issues such as quality of life, therapeutic relationship, and care and treatment satisfaction are just as important as symptom improvement (McCabe, Saidi, & Priebe, 2007). A key concern for many clinicians is that increasing patient knowledge of the relationship between MS and their prescribed antipsychotic may effect an increase in antipsychotic medication nonadherence. Discontinuation rates of antipsychotics have been attributed to side effects such as weight gain (Thomas, 2007); however, the ethical and medicolegal issues encountered as a consequence of not fully informing the patient of these side effects are likely to be detrimental to individual clinicians (Wirshing, Wirshing, Nystrom, & Buc, 2004). There is debate over who is responsible for monitoring MS. Points of view differ as to whether responsibility should lie with primary or secondary care services (Kohen, 2005). A survey of psychiatrists in the United Kingdom (Kohen, 2005) found that 56% of respondents agreed that primary care services should take full responsibility for physical health monitoring. Although there needs to be a coordinated approach, it is our view that primary responsibility should rest with the treating mental
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health team. As the course and treatment of mental illness may increase the risk of physical health abnormalities, it would make sense that the treating mental health team takes responsibility for coordinating this aspect of care. Localized service protocols for systematic screening for MS, sharing and interpretation of results, and provision of treatment should be led by mental health services with the collaboration of primary care general practitioners and specialist medical services (Alphs & Anand, 1999; Usher, Foster, & Park, 2006). STUDY LIMITATIONS
The study is principally limited by its size. Some of the marginal cross-sectional relationships identified are likely results of this limitation and remain difficult to interpret as a result. The ability to quantify and measure accurately all the associated traditional risks of MS such as family history of diabetes, ethnicity, diet, and exercise levels is limited. It is beyond the scope of this study to attribute a causal relationship between the variables measured and the risk of MS. Holt (2006) argue that there has been an erroneous tendency to lay blame for all the physical abnormalities seen in patients with mental illness to the antipsychotics. Holt et al. describe the first reports of increased risk of diabetes in people with mental illness being observed by Sir Henry Maudsley in 1879, long before the advent of the antipsychotics. Further controlled studies and rigorous accounting for all the known risk factors for MS which include schizophrenia itself need to continue. CONCLUSION
This study adds further support to the argument for systematic screening for MS among patients with mental health problems. The predictors identified by multivariate analysis are all risk factors that are modifiable in this population. More aggressive interventions are required to reduce morbidity and mortality for this patient population. Greater awareness of MS risk among patients receiving clozapine and within the mental health profession would appear warranted. REFERENCES Alphs, L., & Anand, R. (1999). Clozapine: The commitment to patient safety. Journal of Clinical Psychiatry, 60, 39−42. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders. (4th ed.). Washington, DC.
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