Prevalence of metabolic syndrome and its ...

General Hospital Psychiatry 45 (2017) 62–69

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Prevalence of metabolic syndrome and its components among patients with various psychiatric diagnoses and treatments: A cross-sectional study Fahad D. Alosaimi a,⁎, Mohammed Abalhassan b, Bandar Alhaddad c, Nasser Alzain d, Ebtihaj Fallata e, Abdulhadi Alhabbad f, Mohammed Z. Alassiry g a

Department of Psychiatry, King Saud University, Riyadh, Saudi Arabia Department of Medicine, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia c King Fahad National Guard Hospital, Riyadh, Saudi Arabia d Al-Amal Complex for Mental Health, Dammam, Saudi Arabia e Mental Health Hospital, Jeddah, Saudi Arabia f Prince Mohammed Medical City, Aljouf, Saudi Arabia g Mental Health Hospital, Abha, Saudi Arabia b

a r t i c l e

i n f o

Article history: Received 11 September 2016 Revised 13 December 2016 Accepted 16 December 2016 Available online xxxx Keywords: Psychotropic drugs Mental disorders Metabolic syndrome X Prevalence Saudi Arabia

a b s t r a c t Objectives: To evaluate the prevalence and clinical correlates of metabolic syndrome (MetS) and its components among patients with psychiatric disorders. Methods: A cross-sectional study was conducted among adult patients with psychiatric disorders at major hospitals in Saudi Arabia. After measurements were recorded for all MetS components, demographic and clinical information was obtained mainly by reviewing the patients' medical charts. Results: The prevalence rate of MetS among the 992 study participants was 41.2%, high triglycerides was 32.8%, large waist circumference was 42.2%, high blood pressure was 42.5%, high fasting blood sugar was 47.8%, and low HDL cholesterol was 52.5%. Participants with MetS were more likely to be older, illiterate, divorced or widowed, have a higher number of children, older age of onset of psychiatric illness, longer duration of psychiatric disease, no previous psychiatric hospitalization, and have a history of diabetes and hypertension. After adjusting for significant demographic and clinical characteristics, none of the psychiatric diagnoses and treatments was independently associated with MetS, except the use of mirtazapine and venlafaxine. Conclusions: The prevalence of MetS and its components among patients with psychiatric disorders is alarming irrespective of their diagnoses. Thus, metabolic screening especially among high risk groups is critical. © 2016 Elsevier Inc. All rights reserved.

1. Introduction Patients with serious mental illness (SMI), such as schizophrenia, schizoaffective disorder, bipolar disorder, and major depressive disorder, have higher rates of undiagnosed and untreated medical illnesses compared to the general population, which are mostly due to modifiable lifestyle risk factors [1]. These individuals typically die 10 to 30 years younger than the general population, which represents 2–3 folds higher mortality [2–4]. N60% of this excess mortality is due to

⁎ Corresponding author at: Psychiatry Department #55, King Khalid University Hospital, King Saud University, P.O. Box 7805, Riyadh 11472, Saudi Arabia. E-mail addresses: [email protected] (F.D. Alosaimi), [email protected] (M. Abalhassan), [email protected] (B. Alhaddad), [email protected] (N. Alzain), [email protected] (E. Fallata), [email protected] (A. Alhabbad), [email protected] (M.Z. Alassiry).

http://dx.doi.org/10.1016/j.genhosppsych.2016.12.007 0163-8343/© 2016 Elsevier Inc. All rights reserved.

physical illness, cardiovascular disease, stroke and respiratory tumors [4,5]. This mortality gap has widened in recent decades, even in countries with good health care systems [6–8]. The poor physical health outcomes in people with SMI are mainly attributed to disparities in health care access, utilization, and health care provision [9–12]. Internationally, the prevalence of metabolic syndrome (MetS) and its components such as dyslipidemia, obesity, hypertension and hyperglycemia among patients with SMI is higher than the general population [13,14]. The overall prevalence of MetS among patients with SMI ranges between 25% and 50%, with a relative risk of up to 2.0 compared to the general population [13–15]. The prevalence of MetS rate was estimated at 32.5% to 36.8% among patients with schizophrenia [14,16,17], 37.3% among patients with bipolar disorders [18], and 30.5% to 31.3% among patients with depressive disorders [14,19]. The prevalence of MetS and its components in the general population of the Gulf Cooperative Council countries, including Saudi Arabia

F.D. Alosaimi et al. / General Hospital Psychiatry 45 (2017) 62–69

(SA), is 10–15% higher than most developed countries with an overall prevalence of approximately 40% [20,21]. However, to the best of our knowledge, the prevalence of MetS in patients with psychiatric (mental) disorders has not yet been studied in SA. This study aimed to evaluate the prevalence of MetS and its components among patients with psychiatric disorders in SA and to evaluate their association with various psychiatric diagnoses and psychotropic medications.

2. Methods 2.1. Setting The current study was conducted among patients seeking psychiatric advice at major hospitals in the five main regions of SA. We aimed to choose the largest mental hospital in each of the five major regions of SA, however because of logistic difficulties, not all target hospitals were able to be included. To compensate for that, two hospitals were included from the central region. The hospitals included were King Khalid University Hospital in Riyadh and Zulfi General Hospital (central region), Jeddah Mental Health Hospital (western region), Al-Amal Complex for Mental Health–Dammam (eastern region), Aljouf Mental Health Hospital (northern region), and Abha Mental Health Hospital (southern region). King Khalid University Hospital is a university-affiliated government hospital, whereas the other hospitals are governmentfunded hospitals under the authority of the Ministry of Health. All hospitals included in the study provide free psychiatric inpatient and outpatient healthcare services.

2.2. Study design A cross-sectional observational study was carried out between July 2012 and June 2014. The study obtained all required ethical approvals from the Institutional Review Board at the Faculty of Medicine at King Saud University in Riyadh, as well as administrative approvals from the respective hospitals.

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2.5. Diagnosis of MetS A diagnosis of MetS was based on the criteria described by the joint statement of the International Diabetes Federation Task Force on Epidemiology and Prevention and the American Heart Association, along with other organizations [22,23]. The criteria included three or more of the following five risk factors: waist circumference ≥ 102 cm (40 in.) for men or ≥88 cm (35 in.) for women, fasting blood glucose ≥ 5.6 mmol/l (100 mg/dl) or use of anti-diabetic medications, triglyceride levels ≥ 1.7 mmol/l (150 mg/dl) or use of lipid medications, HDL cholesterol b 1.03 mmol/l (40 mg/dl) in males or b1.29 mmol/l (50 mg/dl) in females or use of lipid medications, and a systolic BP ≥ 130 mmHg or diastolic BP ≥ 85 mmHg or use of antihypertensive medications. 2.6. Classification of psychiatric diagnoses The psychiatric consultants in charge made psychiatric diagnoses of their patients using the DSM-IV-TR criteria. For the analysis, these diagnoses were classified under 8 categories [24,25]. Primary psychotic disorders included schizophrenia, schizoaffective disorder, delusional disorder and brief psychotic disorder. Primary bipolar disorders included bipolar disorders type I and II. Primary depressive disorders included major depressive disorder and dysthymic disorder. Primary anxiety disorders included generalized anxiety disorder, obsessive-compulsive disorder, social anxiety disorder, specific phobia, panic disorder, posttraumatic stress disorder, and acute stress disorder. Personality disorders included personality disorders not otherwise specified (mixed personality disorder), paranoid personality disorder, antisocial personality disorder, and borderline personality disorder. Secondary psychiatric disorders included psychotic disorder due to another medical condition, depression due to another medical condition, dementia, substance use disorder, and substance-induced depressive disorder. Other disorders included undifferentiated somatoform disorder, conversion disorder, mental retardation, attention deficit hyperactivity disorder, dissociative disorder, primary insomnia, adjustment disorder, enuresis disorder, trichotillomania, and anorexia nervosa. Multiple disorders included two or more diagnoses of psychiatric illnesses from previously mentioned classes.

2.3. Population 2.7. Classification of psychotropic medications Consecutive male and female patients seeking psychiatric help at the included hospitals during the study period were asked to join the study. Those who signed the informed consent were included, irrespective of the type of psychiatric diagnosis, the duration of psychiatric disease, or recent use of psychotropic medications. Patients whose records and interview indicated the absence of psychiatric disease or a lack of one or more components of MetS were excluded. Of the patients who were considered for the study, 1205 had a psychiatric diagnosis. Among 1205 patients, 209 were missing at least one of the MetS components, mainly HDL cholesterol and triglycerides findings. Therefore, 996 patients were included in the current analysis.

2.4. Data collection A mini-interview form was developed that included socio-demographic characteristics, medical history, current psychiatric diagnoses, duration of psychiatric disease, and recent use of psychotropic medications. The information was primarily obtained by reviewing the patient chart. Unclear or missing information was confirmed by interviewing the patient and/or the patient's family. Trained psychiatric staff were responsible for reviewing the charts and conducting interviews with the patients and their families. Fasting blood samples and measurements of waist circumference and blood pressure were obtained for the metabolic analysis.

Both individual psychotropic medications and pharmacological groups were recorded for the analysis. These included antipsychotics (low potency first generation, high potency first generation, and second generation), antidepressants (selective serotonin reuptake inhibitors [SSRIs], tricyclics, and others), mood stabilizers, and anti-anxiety medications. 2.8. Statistical analysis The data was presented as frequencies and percentages for categorical data and as the mean and standard deviation (SD) for continuous data. Individual psychiatric diagnoses and psychotropic medications were categorized into standard groups. Significant differences in the socio-demographic and clinical characteristics between patients with and without MetS were tested using a chi-square test or Fisher's exact test (as appropriate) for categorical data and Student's t-test or MannWhitney test (as appropriate) for continuous data. Similarly, the associations of MetS and its components with different psychiatric diagnoses (yes and no) and the use of various psychotropic medications (yes or no) were tested using a chi-square test or Fisher's exact test (as appropriate). Independent associations of MetS with various psychiatric diagnoses and psychotropic medications were evaluated using three multivariate logistic regression models using the stepwise backward elimination method. All models had MetS as outcome (dependent)

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variable and were adjusted for demographic and clinical characteristics that were significantly different between patients with and without MetS (Table 1) including age, gender, marital status, number of children, education level, age at disease onset, duration of psychiatric disease and previous hospitalizations. We did not add any components of MetS as the outcome was defined based on them and to avoid collinearity of the model. To detect the independent impact of various

psychiatric diagnoses and psychotropic medications, additional adjustment variables were added; model (1) psychiatric diagnoses but not psychotropic medications, model (2) psychotropic medications but not psychiatric diagnoses, model (3) both psychiatric diagnoses and psychotropic medications. All P-values were two-tailed. A P-value b0.05 was considered significant. SPSS software (release 20.0, Armonk, NY: IBM Corp) was used for all statistical analyses.

Table 1 Socio-demographic and clinical characteristics by metabolic syndrome status (N = 996). Metabolic syndrome

Age (years) Mean ± SD b40 40–60 N60 Gender Male Female Marital status Married Single Divorced Widowed Number of children Mean ± SD None 1–3 children N3 children Educational level Illiterate Secondary or less University/others Obesity indicators BMI Waist circumference (cm) Blood pressure Systolic (mmHg) Diastolic (mmHg) Laboratory measures Fasting blood sugar (mmol/l) HDL cholesterol (mmol/l) Triglycerides (mmol/l) Type of care received In patient Out patient Number of psychiatric diagnoses One Two or more Age at disease onset Mean ± SD b25 25–50 N50 Duration of psychiatric disease (years) Mean ± SD ≤1 2 to 5 6 to 10 N10 Previous hospitalization No Yes Number of previous hospitalizations Medical history Diabetes mellitus Hypertension Hyperlipidemia Medication use Diabetes mellitus Hypertension Hyperlipidemia

Overall

No

Yes

P-value

38.2 ± 13.2 591 (59.6%) 350 (35.3%) 51 (5.1%)

34.4 ± 10.9 418 (70.7%) 159 (45.4%) 8 (15.7%)

43.7 ± 14.2 173 (29.3%) 191 (54.6%) 43 (84.3%)

b0.001 b0.001

533 (53.5%) 463 (46.5%)

318 (59.7%) 268 (57.9%)

215 (40.3%) 195 (42.1%)

0.569

428 (45.0%) 425 (44.6%) 89 (9.3%) 10 (1.1%)

215 (50.2%) 302 (71.1%) 42 (47.2%) 3 (30.0%)

213 (49.8%) 123 (28.9%) 47 (52.8%) 7 (70.0%)

b0.001

3.5 ± 3.1 115 (21.9%) 187 (35.6%) 224 (42.6%)

3.0 ± 2.8 59 (51.3%) 112 (59.9%) 89 (39.7%)

4.0 ± 3.3 56 (48.7%) 75 (40.1%) 135 (60.3%)

b0.001 b0.001

202 (20.8%) 646 (66.7%) 121 (12.5%)

87 (43.1%) 399 (61.8%) 83 (68.6%)

115 (56.9%) 247 (38.2%) 38 (31.4%)

b0.001

28.6 ± 7.7 92.4 ± 18.8

26.3 ± 6.9 87.1 ± 17.6

31.9 ± 7.4 100.1 ± 17.7

b0.001 b0.001

122.7 ± 14.0 78.7 ± 9.9

118.7 ± 12.2 76.1 ± 9.3

128.6 ± 14.4 82.4 ± 9.5

b0.001 b0.001

6.16 ± 2.48 1.22 ± 0.59 1.48 ± 1.01

5.39 ± 1.90 1.35 ± 0.69 1.17 ± 0.81

7.26 ± 2.79 1.04 ± 0.35 1.94 ± 1.10

b0.001 b0.001 b0.001

329 (33.0%) 667 (67.0%)

206 (62.6%) 380 (57.0%)

123 (37.4%) 287 (43.0%)

0.089

898 (90.2%) 98 (9.8%)

528 (58.8%) 58 (59.2%)

370 (41.2%) 40 (40.8%)

0.941

28.7 ± 12.1 398 (40.8%) 528 (54.1%) 50 (5.1%)

25.9 ± 9.5 281 (70.6%) 286 (54.2%) 11 (22.0%)

32.7 ± 14.3 117 (29.4%) 242 (45.8%) 39 (78.0%)

b0.001 b0.001

9.4 ± 9.2 152 (15.6%) 282 (29.0%) 226 (23.2%) 314 (32.2%)

8.4 ± 8.3 112 (73.7%) 175 (62.1%) 120 (53.1%) 171 (54.5%)

11.0 ± 10.2 40 (26.3%) 107 (37.9%) 106 (46.9%) 143 (45.5%)

b0.001 0.001

546 (54.8%) 450 (45.2%) 3.7 ± 4.3

305 (55.9%) 281 (62.4%) 3.5 ± 4.0

241 (44.1%) 169 (37.6%) 4.0 ± 4.8

0.036

99 (9.9%) 85 (8.5%) 11 (1.1%)

28 (28.3%) 19 (22.4%) 5 (45.5%)

71 (71.7%) 66 (77.6%) 6 (54.5%)

b0.001 b0.001 0.365

71 (7.1%) 40 (4.0%) 18 (1.8%)

18 (25.4%) 13 (32.5%) 1 (5.6%)

53 (74.6%) 27 (67.5%) 17 (94.4%)

b0.001 0.001 b0.001

0.346


3. Results A total of 996 patients (329 inpatients and 667 outpatients) were included in the current analysis. As shown in Table 1, the average age was 38.2 ± 13.2 years, with the majority (59.6%) of patients below the age of 40 years. Approximately 53.5% of the patients were males and 46.5% were females. Around 45% of the patients were married and the average number of children was 3.5 ± 3.1. Most patients (80.9%) were living in urban communities. The majority (66.7%) of the patients had less than secondary education, in addition, 20.8% of patients were illiterate. A greater number of the patients (70.4%) were unemployed while 17.1% were working in governmental occupations. Most patients lived in low-income families with 61.3% of them had a family income of 6000 SR or less per month. The overall prevalence of MetS was 41.2%. Additionally, the prevalence of the five individual MetS components (in descending order) is as follows: a reduced HDL cholesterol or use of lipid medications (52.5%), high fasting blood sugar or use of anti-diabetic medications (47.8%), high blood pressure or use of antihypertensive medications (42.5%), a large waist circumference (42.2%), and high triglycerides or use of lipid medications (32.8%). Compared with those who did not have MetS, patients with MetS were more likely to be older (P b 0.001), currently or previously married (b 0.001), and illiterate (P b 0.001). Patients with MetS were also more likely to have a higher number of children (P b 0.001). However, there were no differences in gender, residency, work type, or family income between the groups. As expected, all the MetS components were significantly higher in patients who had MetS (P b 0.001 for all comparisons). Compared with patients who did not have MetS, patients with MetS were more likely to be diagnosed at an older age (P b 0.001), have a longer duration of psychiatric disease (P = 0.001), have a history of diabetes and hypertension (P b 0.001 for each), and use medications for diabetes (P b 0.001), hypertension (P = 0.001), and hyperlipidemia (P b 0.001). MetS patients were also less likely to have previous psychiatric hospitalizations (P = 0.036) (as shown in Table 1). The associations of MetS and its individual components with various psychiatric diagnoses are shown in Table 2. For the prevalence of MetS by psychiatric diagnosis, MetS was less frequently seen among patients with primary psychotic disorders (marginally significant in patients

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with schizophrenia; 37.4% Vs. 43.5%, P = 0.055and non-significant in patients with schizoaffective disorders 30.4% Vs. 41.4%, P = 290). On the other hand, MetS was more frequently seen among patients with primary depressive disorders (47.5%) and primary anxiety disorders (46.4%) but the difference was not statistically significant. Large waist circumference was more frequently seen among patients with primary bipolar, depressive and anxiety disorders but less frequently seen among patients with primary psychotic disorders and secondary psychiatric disorders. High blood pressure and fasting blood glucose were both more frequently seen among patients with secondary psychiatric disorders and multiple disorders, whereas reduced HDL cholesterol levels were less frequently seen among patients with primary psychotic disorders. The associations of MetS and its individual components with various psychotropic medications are shown in Table 3. MetS was more frequently seen among patients who were taking tricyclics or other antidepressant medications (mainly mirtazapine and venlafaxine), but it was less frequently seen among those who were taking mood stabilizers. Apart from olanzapine, which was negatively associated with MetS (31.1% Vs. 44.7%, P b 0.001), antipsychotics in general, including clozapine, were not associated with MetS. Large waist circumference was more frequently seen among patients who were taking anti-depressant medications (especially SSRIs and tricyclics) but less frequently among those who were taking second-generation antipsychotic medications or antipsychotic medications in general. High blood pressure, high fasting blood glucose levels, and high triglycerides were more frequently seen among patients who were taking mirtazapine and venlafaxine antidepressant medications. However, high fasting blood glucose and triglyceride levels were less frequently seen among patients who were taking SSRI antidepressant medications and mood stabilizers, respectively. Reduced HDL cholesterol was more frequently seen among those who were taking antianxiety medications. Table 4 shows the multivariate logistic regression analysis of the associations of MetS with various psychiatric diagnoses and/or psychotropic medications. For the model included psychiatric diagnoses but not psychotropic medications, none of the diagnoses were independently associated with MetS, after adjusting for significant demographic and clinical characteristics (not related to MetS) including age, gender, marital status, number of children, education level, age at disease onset,

Table 2 Associations of metabolic syndrome and its individual components with the psychiatric diagnoses included in the patient sample (N = 996). Metabolic syndrome Primary psychotic disorders Primary bipolar disorders Primary depressive disorders Primary anxiety disorders Personality disorders Secondary psychiatric disorders Multiple disorders Other disorders

No

256 (44.2%)

Large waist circumference

Raised blood pressure

Raised fasting blood sugar

Reduced HDL cholesterol

Raised triglycerides

0.021⁎ 270 (46.6%)

0.001⁎

244 (42.1%)

0.805

282 (48.7%)

0.496

321 (55.4%)

0.029⁎ 190 (32.8%)

0.990

Yes 154 (36.9%) No 350 (41.2%)

0.985

150 (36.0%) 346 (40.7%)

0.024⁎

179 (42.9%) 372 (43.8%)

0.046⁎

194 (46.5%) 411 (48.4%)

0.392

202 (48.4%) 439 (51.6%)

0.188

137 (32.9%) 285 (33.5%)

0.258

Yes 60 (41.1%) No 334 (40.0%)

0.076

74 (50.7%) 337 (40.3%)

0.007⁎

51 (34.9%) 365 (43.7%)

0.082

65 (44.5%) 391 (46.8%)

0.140

84 (57.5%) 434 (51.9%)

0.389

42 (28.8%) 264 (31.6%)

0.054

Yes 76 (47.5%) No 358 (40.5%)

0.230

83 (51.9%) 362 (41.0%)

0.029⁎

58 (36.3%) 374 (42.3%)

0.771

85 (53.1%) 431 (48.8%)

0.087

89 (55.6%) 462 (52.3%)

0.660

63 (39.4%) 286 (32.4%)

0.366

Yes 52 (46.4%) No 406 (41.3%)

0.443

58 (51.8%) 416 (42.3%)

0.402

49 (43.8%) 418 (42.5%)

0.769

45 (40.2%) 471 (47.9%)

0.498

61 (54.5%) 516 (52.5%)

0.923

41 (36.6%) 323 (32.9%)

1.000

Yes 4 (30.8%) No 391 (40.7%)

0.149

4 (30.8%) 415 (43.2%)

5 (38.5%) b0.001⁎ 395 (41.1%)

5 (38.5%) b0.001⁎ 451 (47.0%)

7 (53.8%) 0.008⁎ 507 (52.8%)

0.324

4 (30.8%) 314 (32.7%)

0.669

25 (69.4%) 432 (46.7%) 44 (62.9%) 463 (48.5%) 13 (31.0%)

16 (44.4%) 0.009⁎ 484 (52.3%) 39 (55.7%) 0.026⁎ 498 (52.2%) 25 (59.5%)

Yes No Yes No Yes

19 (52.8%) 377 (40.7%) 33 (47.1%) 398 (41.7%) 12 (28.6%)

0.292 0.090

5 (13.9%) 393 (42.4%) 27 (38.6%) 401 (42.0%) 19 (45.2%)

0.527 0.681

28 (77.8%) 382 (41.3%) 41 (58.6%) 411 (43.1%) 12 (28.6%)

0.005⁎ 0.063

⁎ Indicates a significant difference (P b 0.05) between presence and lack of psychiatric diagnoses.

0.578 0.352

13 (36.1%) 311 (33.6%) 16 (22.9%) 316 (33.1%) 11 (26.2%)

0.065 0.349

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Table 3 Associations of metabolic syndrome and its individual components with the psychotropic medications used within the patient sample (N = 996). Metabolic syndrome Any antipsychotic First generation, low potency First generation, high potency Second generation Any antidepressant SSRI antidepressants Tricyclic antidepressants

Mood Stabilizers Antianxiety

No Yes No Yes No Yes No Yes No Yes No Yes No Yes No

93 (44.1%) 284 (40.3%) 351 (40.9%) 26 (45.6%) 319 (41.5%) 58 (39.5%) 128 (44.4%) 249 (39.7%) 210 (38.8%) 167 (44.7%) 270 (41.3%) 107 (41.0%) 344 (40.1%) 33 (56.9%) 308 (39.2%)

0.334

101 (47.9%) 277 (39.3%) 0.485 352 (41.0%) 26 (45.6%) 0.639 310 (40.4%) 68 (46.3%) 0.177 139 (48.3%) 239 (38.1%) 0.078 202 (37.3%) 176 (47.1%) 0.936 249 (38.1%) 129 (49.4%) 0.012⁎ 344 (40.1%) 34 (58.6%) 0.002⁎ 327 (41.6%)

Yes No Yes No Yes

69 (53.5%) 289 (43.7%) 88 (34.8%) 351 (41.1%) 26 (42.6%)

51 (39.5%) 0.015⁎ 271 (40.9%) 107 (42.3%) 0.815 347 (40.6%) 31 (50.8%)

Raised blood pressure 0.027⁎ 89 (42.2%) 307 (43.6%) 0.496 370 (43.1%) 26 (45.6%) 0.184 338 (44.0%) 58 (39.5%) 0.004⁎ 116 (40.3%) 280 (44.7%) 0.003⁎ 234 (43.3%) 162 (43.3%) 0.002⁎ 291 (44.5%) 105 (40.2%) 0.006⁎ 370 (43.2%) 26 (44.8%) 0.658 329 (41.9%)

0.709 0.118

67 (51.9%) 290 (43.8%) 106 (41.9%) 374 (43.8%) 22 (36.1%)

Raised fasting blood sugar 0.713

93 (44.1%) 351 (49.9%) 0.713 414 (48.3%) 30 (52.6%) 0.307 375 (48.8%) 69 (46.9%) 0.214 131 (45.5%) 313 (49.9%) 0.985 267 (49.4%) 177 (47.3%) 0.240 332 (50.8%) 112 (42.9%) 0.806 414 (48.3%) 30 (51.7%) 0.032⁎ 365 (46.4%)

0.602 0.239

Reduced HDL cholesterol

Raised triglycerides

0.140

118 (55.9%) 363 (51.6%) 0.522 451 (52.6%) 30 (52.6%) 0.675 405 (52.7%) 76 (51.7%) 0.213 161 (55.9%) 320 (51.0%) 0.546 279 (51.6%) 202 (54.0%) 0.032⁎ 346 (52.9%) 135 (51.7%) 0.614 445 (51.9%) 36 (62.1%) 0.002⁎ 408 (51.9%)

79 (61.2%) 326 (49.2%) 118 (46.6%) 411 (48.1%) 33 (54.1%)

0.481 0.367

0.266 0.992 0.818 0.171 0.468 0.747 0.134 0.324

73 (56.6%) 352 (53.2%) 129 (51.0%) 441 (51.6%) 40 (65.6%)

68 (32.2%) 231 (32.8%) 275 (32.1%) 24 (42.1%) 248 (32.3%) 51 (34.7%) 101 (35.1%) 198 (31.6%) 171 (31.6%) 128 (34.2%) 220 (33.6%) 79 (30.3%) 277 (32.3%) 22 (37.9%) 244 (31.0%)

55 (42.6%) 232 (35.0%) 67 (26.5%) 0.035⁎ 278 (32.6%) 21 (34.4%) 0.554

0.874 0.117 0.569 0.296 0.407 0.326 0.378 0.009⁎

0.014⁎ 0.763

⁎ Indicates a significant difference (P b 0.05) between the use and non-use of psychotropic medications.

Table 4 Multivariate logistic regression analysis of metabolic syndrome, psychiatric diagnoses and psychotropic medications within the patient sample (N = 917). 95% confidence interval Odds ratio

Lower

Upper

P-value

1.07 1.06 0.75

1.05 1.04 0.56

1.08 1.08 1.02

b0.001 b0.001 0.063

1.06 1.07 0.69 0.70 1.81 1.72

1.05 1.05 0.49 0.47 0.95 1.06

1.08 1.09 0.96 1.03 3.42 2.81

b0.001 b0.001 0.028 0.070 0.070 0.030

1.07 1.07 0.71

1.05 1.05 0.52

1.08 1.09 0.97

b0.001 b0.001 0.031

a

Model (1): Psychiatric diagnoses (N = 917) Age at disease onset Duration of psychiatric disease (years) Previous hospitalization Model (2): Psychotropic medications (N = 846)a Age at disease onset Duration of psychiatric disease (years) Previous hospitalization Any antidepressant Tricyclic antidepressants Other antidepressants (Mirtazapine and Venlafaxine) Model (3):Psychiatric diagnoses and medications (N = 846)a Age at disease onset Duration of psychiatric disease (years) Previous hospitalization

R-square 0.173

0.182

0.172

a All models were adjusted for the following demographic and clinical characteristics; age, gender, marital status, number of children, education level, age at disease onset, duration of psychiatric disease, previous hospitalizations. Additional adjustment variables included; psychiatric diagnoses but not psychotropic medications in model (1), psychotropic medications but not psychiatric diagnoses in model (2), and both psychiatric diagnoses and psychotropic medications in model (3).


Other antidepressants (Mirtazapine and Venlafaxine)

Large waist circumference


duration of psychiatric disease, and previous hospitalization. For the model included psychotropic medications but not psychiatric diagnoses, only mirtazapine and venlafaxine were independently associated with MetS (OR = 1.72, 95% confidence 1.06–2.81, P = 0.030),after adjusting for significant demographic and clinical characteristics (not related to MetS)·However, in model containing both psychiatric diagnoses and psychotropic medications, an older age at the disease onset, longer duration of psychiatric disease and lack of previous hospitalizations were independently associated with MetS. 4. Discussion 4.1. Overall prevalence of MetS To the best of our knowledge, this is the first study that addresses MetS and its components among patients with psychiatric disorders in the five main regions of SA. People with SMI have a higher risk of developing MetS [13,14]. The present study showed that the prevalence of MetS among patients with psychiatric disorders in SA was 41.2%. This percentage is higher than reported in several populations around the world, which range from 25 to 35% [13–15]. The prevalence of MetS in SA was not studied in psychiatric patients and ranged between 20 and 44% in the general population [20,21,26–28]. The higher prevalence of MetS in the general population in SA has been linked to the socioeconomic development with a dominant lifestyle characterized by poor physical activity and unhealthy diet together with higher rates of obesity and diabetes [20,29]. Therefore, a study from the nearby United Arab Emirates, whose population share a similar lifestyle, reported 48.1% prevalence of MetS among inpatients with SMI [30]. 4.2. Socio-demographic characteristics The variability of MetS among psychiatric patients by socio-demographic characteristics has been studied extensively. The current study showed a steady increase in the prevalence of MetS by age; ranging from 29.3% among those aged b 40 years to 84.3% among those aged N60 years. The positive association between age and MetS has been consistently reported in psychiatric patients worldwide [16,18] and in the general population of SA [20,28]. For example, a nationwide sample of N4500 Saudis aged 15–64 years estimated the prevalence of MetS at 33.0% among those aged b45 years and 51.5% among those aged N55 years. The higher prevalence of MetS in older age can be explained by the higher prevalence of its components such as hyperglycemia and dyslipidemia in older age [26]. Similar to reports among psychiatric patients worldwide, we did not detect any gender difference in the prevalence of MetS among our patients [16,19,31]. In SA, there are mixed findings in the gender-specific data of MetS in the general population, with some reported higher prevalence in females [20,21] and others in males [28,29]. 4.3. Clinical characteristics As expected, MetS in our patients was associated with diabetes and hypertension and use of their medications. It has been shown that patients with SMI have significantly increased risk of the components of MetS [14]. Additionally, it has been shown that the population-attributable fraction for the metabolic syndrome, irrespective of psychiatric disease, is about 6–7% for all-cause mortality, 12–17% for cardiovascular disease, and 30–52% for diabetes [32–34]. Similarly, hypertension is likely to contribute to the increased risk of cardiovascular disease seen in individuals with MetS [35]. In our study, we found that the duration of psychiatric disorders was significantly associated with MetS. The results showed that 46.9% of patients with MetS had disease duration between 6 and 10 years. Two meta-analyses found that the duration of psychiatric disorders was the strongest influence of MetS [16,18]. The duration of psychiatric

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disorders is often a proxy of duration of exposure to psychotropic medication and is related to patient age, both of which can positively influence MetS [16]. Timely interventions at an early stage might help to reduce the incidence, severity and complications of MetS. 4.4. Psychiatric diagnoses characteristics We did not observe the previously reported higher MetS rates in patients with schizophrenia and bipolar disorder [13] nor the higher rates in patients with schizoaffective disorder compared to others psychoses [17]. According to a recent systematic review and meta-analysis, there were no significant differences in the prevalence of MetS among studies that directly compared patients with schizophrenia and bipolar disorder, or among those that directly compared patients with bipolar disorder and major depressive disorder [14,36]. The lack of differences in prevalence of MetS between patients with various psychiatric disorders is believed to be due to sharing similar risky health behaviors such as poor physical activity, unhealthy diet, and smoking [36]. In our study, the prevalence of MetS among patients with schizophrenia and bipolar disorders were 37.4% and 41.1%, respectively, which were relatively close to the rates reported worldwide in meta-analyses; 32.5% to 36.8% among patients with schizophrenia [14,16,17]and 37.3% among patients with bipolar disorders [18]. However, the prevalence of MetS among depressive patients was much higher than that reported in several reports worldwide (47.5% Vs. 30.5% to 31.3%) [14,19]. The higher risk of MetS among depressive patients compared with the general population can be explained by the higher risk of hyperglycemia, hypertriglyceridemia, and the use of antipsychotics [19]. Interestingly, the higher risk of MetS among patients with depression and the lower risk of MetS among patients with schizophrenia observed in univariate analysis in the current study disappeared after adjusting for demographic and clinical characteristics as well as medication use. Therefore, none of the psychiatric diagnoses in the study was independently associated with MetS. 4.5. Psychotropic medication characteristics Only the use of antidepressant medications, especially mirtazapine and venlafaxine, were independently associated with MetS. Mirtazapine has been associated with clinically significant increases in weight, serum triglycerides and LDL-C levels [37]. Venlafaxine has been also associated with increases in LDL-C and HDL-C levels, systolic/diastolic blood pressure and sustained diastolic hypertension and weight gain [37,38]. Unlike our findings, patients using antipsychotics in general, especially clozapine and olanzapine (except Vs. clozapine), have been consistently found to have a higher MetS risk compared to antipsychoticnaïve participants [14]. In contrast, the MetS risk was significantly lower with aripiprazole than other antipsychotics (except amisulpride) [14]. In the CATIE Schizophrenia Trial, the data indicated that within 3 months of exposure to olanzapine, there was a significant increase in the proportion of subjects who met the MetS criteria [39]. The prevalence of MetS increased for olanzapine (from 34.8% to 43.9%) but decreased for ziprasidone (from 37.7% to 29.9%). Moreover, a continued deterioration of metabolic parameters has been reported during longterm clozapine exposure [40]. Therefore, routine metabolic screening and multidisciplinary management of metabolic conditions are needed in these patients. Moreover, the risks of antipsychotics should be considered when making treatment choices [14]. There are several possible reasons for the counterintuitive finding of the lack of association between MetS and antipsychotics in our study. First, it is doubtful that the use of antipsychotics in our patients was long enough to produce its known detrimental metabolic effects, which is typically months. For the purpose of this study, regular medication was defined as any medication taken regularly for more than a week. Additionally, antipsychotic medications were used in 94.3% of

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inpatients and 65.7% of outpatients; these high figures suggest that antipsychotic medications were used temporarily as augmenting agents in small doses and poly-pharmacy in many cases. Furthermore, one parameter that our study did not address was the compliance with medications. It has been shown that patients taking antipsychotics are less compliant to their medications than people using antidepressants [41]. In addition, patients with schizophrenia typically have a lower socioeconomic status and poorer insight, which are factors that could contribute to the lower adherence and higher metabolic derangement with antidepressants compared to antipsychotics. Moreover, race was reported as an important moderator of metabolic risk during atypical antipsychotic therapy [42,43]. Also, the higher background of MetS in the general population in SA may have overridden or affected the risk factors associated with MetS in our psychiatric patients [20]. Finally, clinicians might have inclined to prescribe antipsychotics to participants with better metabolic profiles, and where possible avoid prescribing antipsychotics to participants who already have metabolic disorders such as obesity, hyperglycemia, and/or dyslipidemia. 4.6. Limitations Even though our study is the largest, multicenter, national study that has investigated MetS among patients with psychiatric disorders from the five main regions of SA, there are some limitations. First, because of the use of convenience sampling, our results should be generalized to all psychiatric patients in SA with caution. Another limitation, the cross-sectional design in the current study does not allow for causal inferences. Finally, although we collected very comprehensive medication data, we did not have a tool to verify the patient compliance. 4.7. Conclusions The prevalence of MetS and its components among patients with psychiatric disorders is alarming irrespective of their diagnoses. The current findings may emphasize the critical need for metabolic screening and management especially among older patients with longstanding psychiatric disease, and those on specific antidepressant medications. Further prospective studies are needed to confirm our findings and evaluate various preventive and management programs, to address metabolic disorders in the population with psychiatric disorders. Conflict of interest None. Acknowledgments This project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (MED3130). Furthermore, the authors would like to express their gratitude to Dr. Aiman El-Saed for his assistance in data analysis and Ms. Fatima Jama for her assistance in data entry. References [1] De Hert M, Correll CU, Bobes J, Cetkovich-Bakmas M, Cohen D, Asai I, et al. Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care. World Psychiatry 2011;10:52–77. [2] Holt R, Peveler R. Diabetes and cardiovascular risk in severe mental illness: a missed opportunity and challenge for the future. Pract Diabetes Int 2010;27:79–84ii. [3] Nordentoft M, Wahlbeck K, Hallgren J, Westman J, Osby U, Alinaghizadeh H, et al. Excess mortality, causes of death and life expectancy in 270,770 patients with recent onset of mental disorders in Denmark, Finland and Sweden. PLoS One 2013; 8:e55176.

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