|Year : 2014 | Volume
| Issue : 2 | Page : 95-99
Metabolic syndrome in a sample of patients with bipolar disorder in Sharkia governorate
Mohamed G. Negm, Mohab M. Fawzi, Rafik R. Abd-El Latif
Psychiatry Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt
|Date of Submission||01-Dec-2013|
|Date of Acceptance||01-Jan-2014|
|Date of Web Publication||11-Jun-2014|
MD Mohamed G. Negm
Psychiatry Department, Faculty of Medicine, Zagazig University, Zagazig
Source of Support: None, Conflict of Interest: None
Metabolic syndrome (MetS) is a group of metabolic abnormalities that include abnormal glucose metabolism, central obesity, dyslipidemia, reduced high-density lipoprotein-cholesterol, and hypertension.
Patients and methods
A random sample consisted of 50 bipolar patients recruited from psychiatric outpatient's clinics of Zagazig University Hospitals, whether or not would be admitted to the inpatients wards. The following scales were administered: Young Mania Rating Scale, Positive and Negative Syndrome Scale, and Zagazig Depression Scale.
Our cross-sectional observation confirms that MetS is highly prevalent in patients with bipolar disorder. We found a prevalence of MetS of 64% among patients with bipolar disorder, which is approximately double (30%) the rate reported in our general population sample.
There is a need for psychoeducational programs including healthier eating habits and lifestyle advice in patients with bipolar disorder. Clinicians should be aware of the metabolic issues when treating these patients.
Keywords: bipolar disorder, metabolic syndrome, Sharkia governorate
|How to cite this article:|
Negm MG, Fawzi MM, Abd-El Latif RR. Metabolic syndrome in a sample of patients with bipolar disorder in Sharkia governorate. Egypt J Psychiatr 2014;35:95-9
|How to cite this URL:|
Negm MG, Fawzi MM, Abd-El Latif RR. Metabolic syndrome in a sample of patients with bipolar disorder in Sharkia governorate. Egypt J Psychiatr [serial online] 2014 [cited 2021 Nov 30];35:95-9. Available from: http://new.ejpsy.eg.net/text.asp?2014/35/2/95/134195
| Introduction|| |
Metabolic syndrome (MetS) is a group of metabolic abnormalities. When grouped together, these conditions are associated with an increased prevalence of cardiovascular disease (Lakka et al., 2002), type 2 diabetes (Resnick et al., 2003), and cerebral stroke (Kurl et al., 2006). Bipolar disorder (BD) has often been associated with unhealthy lifestyles such as excessive caloric and cholesterol intake, cigarette smoking, and physical inactivity (Waxmonsky et al., 2005), all factors known to increase the cardiovascular risk (Ford et al., 2005). Furthermore, patients with BD are exposed to lifelong use of medications such as antipsychotics or mood stabilizers, which have been associated with weight gain, dyslipidemia, and development of diabetes (Guo et al., 2006).
| Aim of the study|| |
The aim of the study was to evaluate the prevalence of MetS in a sample of patients from Sharkia governorate and compare the prevalence of MetS in bipolar patients with that in general population.
| Patients and methods|| |
The present study was conducted at psychiatric inpatient department and psychiatric outpatient's clinics of Zagazig University Hospitals from 1 October 2009 to 30 June 2010.
Two groups of patients were invited to participate in the study.
A random sample consisted of 50 patients recruited from psychiatric outpatient's clinics of Zagazig University Hospitals, whether or not would be admitted to the inpatients wards. To be included in the study, patients had to fulfill the DSM-IV-TR criteria for bipolar I disorder (American Psychiatric Association, 2000); their ages were between 18 and 60 years. The study included patients of both sexes, all socioeconomic classes, and educational states. They did not have history of chronic medical illnesses and were not taking medications except antipsychotics and/or mood stabilizers. Other exclusion criteria included patients who were pregnant, active substance abusers, and those who were judged not to have the capacity to give consent.
An equal number of apparently healthy individuals, matching the recruited patients as much as possible for age and sex, with no history of treatment for any neuropsychiatric disorder were collected. Informed consent was obtained from all participants after full explanation of the study aim and procedures involved.
Materials and methods
Clinical assessments: Both patients and controls were evaluated for the following.
Semistructured interview: Participants were subjected to a semistructured psychiatric interview under supervision of the supervisors using a specially designed interview derived from the Psychiatric Department sheet of Zagazig University to cover the following parameters.
- Sociodemographic data including age, sex, marital state, education, and occupation.
- Personal history including childhood, scholastic, marital, occupational, and sexual history.
- Past history data including history of psychiatric disorder, substance abuse, or medical illness.
- Family history of psychiatric disorders.
The semistructured interview contained a complete psychiatric sheet, which allowed each patient to receive psychiatric diagnosis by its end during which the DSM-IV-TR (American psychiatric association, 2000) diagnosis of BD was confirmed.
Psychometric procedures: The following scales were administered.
Young mania rating scale (Young et al., 1978)
The Young Mania Rating Scale (YMRS) is an 11-items clinician-administered scale and one of the most frequently utilized rating scales to assess manic symptoms. It is based on the patient's subjective report of his or her clinical condition over the previous 48 h. The YMRS follows the style of the Hamilton Rating Scale for Depression, with each item given a severity rating. There are four items that are graded on a 0-8 scale (irritability, speech, thought content, and disruptive/aggressive behavior), whereas the remaining seven items are graded on a 0-4 scale. These four items are given twice the weight of the others to compensate for poor cooperation from severely ill patients.
The scale is generally performed by a clinician and takes 15-30 min to complete.
Positive and negative syndrome scale (Kay et al., 1987)
The Positive and Negative Syndrome Scale (PANSS) was developed in the late 1980s to assess the clinical symptoms of schizophrenia (Kay et al., 1986, 1987).
The PANSS includes 30 items on three subscales: seven items covering positive symptoms (e.g. delusions and hallucinations), seven items covering negative symptoms (e.g. social withdrawal, flat affect, and lack of motivation), and 16 items covering general psychopathology (e.g. anxiety, depression). The PANSS was conceived as an operationalized instrument that provides a balanced representation of positive and negative symptoms as well as mood and anxiety symptoms.
Psychometric issues (reliability and validity): The PANSS has high inter-rater reliabilities (0.80). The split-half reliability of the general psychopathology subscale is 0.80. The scale has also demonstrated excellent criterion-related validity and construct validity (Kay et al., 1988).
Zagazig depression scale (Fawzi et al., 1982)
This is a self-rating instrument for measuring the severity of depression. Presented in the form of 52 questions written in Arabic, the answer for each question is either 'yes' or 'no' and the questions cover 17 items involving the symptoms and signs of depression which are as follows:
- Depressive mood;
- Guilty feelings;
- Suicidal tendency;
- Early insomnia;
- Interrupted sleep;
- Late insomnia;
- Work and interests;
- Psychic tension;
- Somatic tension;
- Gastrointestinal symptoms;
- General somatic symptoms;
- Loss of insight; and
- Loss of weight.
- Standardized medical history taking and thorough physical examination were carried out.
- Waist circumference (measuring the central adiposity) was measured at the high point of the iliac crest and at the level of the umbilicus, at minimal respiration.
- Blood pressure measurement was obtained using a mercury sphygmomanometer, with the participant in a seated position.
- BMI was calculated.
It is a statistical measure of the weight of a person scaled according to the height, sometimes referred to as BMI.
BMI is defined as the individual's body weight divided by the square of their height. The formula universally used in medicine produce a unit of measure of kg/m 2 . BMI may be accurately calculated using the formula below.
Patients with a BMI equal to or more than 30 kg/m 2 were categorized as obese according to the WHO classification (World Health Organization, 1997; James, 2001).
Laboratory investigations : Blood was drawn for routine blood examination upon hospital admission from patients as part of the clinical management routine. When blood was drawn, patients were fasting for the previous 12 h; complete clot formation had taken place before centrifugation (Clinical and Laboratory Standards Institute, 2004).
Blood examination included assessment of the following: serum lipids [total cholesterol, triglycerides, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol (HDL-C)] and blood glucose level (fasting and 2-h postprandial). The International Diabetes Federation guidelines were used for the diagnosis of the MetS (International Diabetes Federation, 2006).
Data were collected, revised, verified, and then edited on a PC. The data collected were analyzed using the Statistical Program for Social Sciences 11.0.1 software (SPSS for Windows, 2001) by a professional statistician. The tests performed were Mean, SD, the T test for independent samples, Pearson's correlation coefficient (r), and the χ2 -test. P value was used to indicate the level of significance.
| Results|| |
[Table 1] shows that there is statistically significant difference between the group of patients and controls with respect to MetS and its parameters (MetS, waist circumference, BMI, reduced HDL-C, triglycerides, and systolic blood pressure) were more in patients than in the control group and the difference was statistically highly significant.
[Table 2] shows that the MetS is more in patients receiving olanzapine and risperidone (atypical antipsychotics) than that receiving haloperidol (typical antipsychotics) and the difference was statistically significant.
|Table 2: The antipsychotics in patients with and without metabolic syndrome|
Click here to view
[Table 3] shows that the MetS is more in patients receiving valproate and lithium than that receiving other mood stabilizers and the difference was statistically significant.
|Table 3: The mood stabilizers in patients with and without metabolic syndrome|
Click here to view
| Discussion|| |
Our cross-sectional observation confirms that the MetS is highly prevalent in patients with BD.
We found a prevalence of MetS of 64% among patients with BD, which is approximately double (30%) the rate reported in our general population sample.
These results are in agreement with the findings of John et al. (2009) who reported on the prevalence of MetS among Australian individuals with BD; the reported rate of MetS was 67%, approximately double the rate reported in the general population.
In addition, closer to our results, the prevalence of MetS in Turkish patients with BD was 79% higher than that in the general Turkish adult population (Sanisoglu et al., 2006).
In contrast to our study, various studies conducted on bipolar samples reported prevalence rates from 21 to 22% in European patients (Birkenaes et al., 2007; Garcia-Portilla et al., 2008).
When analyzing the prevalence of the single components of MetS in our population, we found that abdominal obesity is the most common metabolic abnormality followed by low HDL, hypertriglyceridemia, and hypertension.
Our patients with bipolar disorder display a very similar pattern of metabolic abnormalities compared with that seen in Spanish patients (Garcia-Portilla et al., 2008), with the exception of hypertension, which was more prevalent in our sample and was similar to the US rates (Cardenas et al., 2008).
This observation gives strength to the hypothesis that the different nutritional patterns seen in our area could play a role in determining a higher risk for MetS and, specifically, abdominal obesity in these patients.
Sociodemographic characteristics such as sex and education in our study were not associated with MetS. These findings are in agreement with a recent study conducted on a Norwegian sample that did not find any difference regarding sex and education in patients versus controls, despite the metabolic disturbances that were more represented in the patient group (Birkenaes et al., 2006).
Although sex difference was not associated with MetS, female patients showed a higher prevalence of MetS than male patients but the difference was not statistically significant.
Only Yumru et al. (2007) have analyzed the influence of sex on MetS (but not its components), failing to find a statistically significant association.
The YMRS scores were numerically higher in patients with MetS than in those without MetS. However, perhaps because of the small sample size, the differences in the values did not achieve statistical significance. Similar results were detected by Dale et al. (2007).
The PANSS scores were numerically higher in patients with MetS than in those without MetS. However, perhaps because of the small sample size, the differences in the values did not achieve statistical significance.
Peralta and Cuesta (1994) found that the PANSS scores obtained by the patients with MetS were higher than those obtained by the patients without MetS, including the total PANSS scores and by both positive and negative symptoms subscales. Differences were particularly noticeable in the total score and the negative subscale and it was statistically significant.
The Zagazig Depression Scale scores also were numerically higher in patients with MetS than in those without MetS, but the differences in the values did not achieve statistical significance.
Concerning the relationships between medications and MetS, we found that the MetS is more in patients receiving valproate and lithium than in those receiving other mood stabilizers and in patients receiving second-generation antipsychotics than in those receiving first-generation antipsychotics.
Studies on lithium have failed to demonstrate such an association (Yumru et al., 2007), whereas second-generation antipsychotics have been associated with high prevalence of MetS (Mackin et al., 2005; Yumru et al., 2007).
This study had some limitations. First, the cross-sectional design reflects only a specific moment in time. However, this study provides a good idea of the scope of MetS and its components that clinicians need to monitor when treating these patients.
However, although this sample may not be representative of the Egyptian patients with BD, the number of patients enrolled was sufficient to merit taking these results into consideration.
There is a need for psychoeducational programs including healthier eating habits and lifestyle advice in patients with BD.
Effective programs aimed at early detection and adequate treatment and also that emphasizes lifestyle modification to reduce weight in overweight/obese patients can positively influence many components of MetS.
Clinicians should be aware of the metabolic issues and appropriately monitor patients with BD for components of MetS as part of the standard of care when treating these patients.
| Acknowledgements|| |
Conflicts of interest
There are no conflicts of interest.
| References|| |
|1.||American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 4th ed., text revision (DSM-IV-TR). Washington, DC: APA; 2000. |
|2.||Birkenaes AB, Sogaard AJ, Engh JA (2006). Socio-demographic characteristics and cardiovascular risk factor in patients with severe mental disorders compared with the general population. J Clin Psychiatry 67:425-433. |
|3.||Birkenaes AB, Opjordsmoen S, Brunborg C (2007). The level of cardiovascular risk factors in bipolar disorder equals that of schizophrenia: a comparative study. J Clin Psychiatry 68:917-923. |
|4.||Cardenas J, Frye MA, Marusak SL (2008). Modal subcomponents of metabolic syndrome in patients with bipolar disorder. J Affect Disord 106:91-97. |
|5.||Clinical and Laboratory Standards Institute (2004): NCCLS procedures for the handling and processing of blood specimens; Approved guideline-third edition NCCLS 940 West Valley Road Suite 1400 Wayne, PA 19087 USA PHONE 610.688.0100 FAX 610.688.0700 E-MAIL: [email protected] WEBSITE: www.nccls.org ISBN 1-56238-555-0.USA. |
|6.||Dale A, D'Mello Supriya, Narang and Gina Agredano (2007): Prevalence and Consequences of Metabolic Syndrome in Bipolar Disorder: Psychiatric Times. Vol. 24 No. 1, 01 Jan 2007. |
|7.||Fawzi M, El-Maghraby Z, El-Amin H, Sahloul M. The Zagazig Depression Scale Manual. Cairo: El-Nahda El-Massriya; 1982. |
|8.||Ford ES, Kohl HW, Mokdad AH, Ajani UA (2005). Sedentary behavior, physical activity, and the metabolic syndrome among U.S. adults. Obes Res 13:608-614. |
|9.||Garcia-Portilla MP, Saiz PA, Benabarre A, Sierra P, Perez J, Rodriguez A, et al (2008). The prevalence of metabolic syndrome in patients with bipolar disorder. J Affect Disord 106:197-201. |
|10.||Guo JJ, Keck PE, Corey-Lisle PK (2006). Risk of diabetes mellitus associated with atypical antipsychotic use among patients with bipolar disorder: a retrospective, population-based, case-control study. J Clin Psychiatry 67:1055-1061. |
|11.||International Diabetes Federation (2006): The IDF consensus worldwide definition of the metabolic syndrome, International Diabetes Federation Avenue Emile De Mot 19, B-1000 Brussels, Belgium by fax at +32-2-5385114 or by e-mail at [email protected] |
|12.||James WP (2001). The dietary challenge for the European Union. Public Health Nutr 4:341-351. |
|13.||John AP, Koloth R, Dragovic M, Lim SC (2009). Prevalence of metabolic syndrome among Australians with severe mental illness. Med J Aust 190:176-179. |
|14.||Kay SR, Opler LA, Fiszbein A (1986). Significance of positive and negative syndromes in chronic schizophrenia. Br J Psychiatry 149:439-448. |
|15.||Kay SR, Fiszbein A, Opler LA (1987). The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophr Bull 13:261-276. |
|16.||Kay SR, Opler LA, Lindenmayer JP (1988): Reliability and validity of the positive and negative syndrome scale for schizophrenics. Psychiatry Res; 23:99-110 |
|17.||Kurl S, Laukkanen JA, Niskanen L (2006). Metabolic syndrome and the risk of stroke in middle-aged men. Stroke 37:806-811. |
|18.||Lakka HM, Laaksonen DE, Lakka TA (2002). The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 288:2709-2716. |
|19.||Mackin P, Watkinson HM, Young AH (2005). Prevalence of obesity, glucose homeostasis disorders and metabolic syndrome in psychiatric patients taking typical or atypical antipsychotic drugs: a cross-sectional study. Diabetologia 48:215-221. |
|20.||Peralta V, Cuesta M (1994). Validation of positive and negative symptom scale (PANSS) in a sample of Spanish schizophrenic patients. Actas Luso Esp Neurol Psiquiatr Cienc Afines 22:171-177. |
|21.||Resnick HE, Jones K, Ruotolo G (2003). Insulin resistance, the metabolic syndrome, and risk of incident cardiovascular disease in nondiabetic American Indians: the Strong Heart Study. Diabetes Care 26:861-867. |
|22.||Sanisoglu SY, Oktenli C, Hasimi A (2006). Prevalence of metabolic syndrome-related disorders in a large adult population in Turkey. BMC Public Health 6:92. |
|23.||SPSS for Windows. Statistical Package for the Social Sciences, version 11.0.1, 2001. Chicago: SPSS Inc.; 2001. |
|24.||Waxmonsky JA, Thomas MR, Miklowitz DJ (2005). Prevalence and correlates of tobacco use in bipolar disorder: data from the first 2000 participants in the Systematic Treatment Enhancement Program. Gen Hosp Psych 27:321-328. |
|25.||World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation on obesity. Geneva: World Health Organization; 1997. |
|26.||Young RC, Biggs JT, Ziegler VE, Meyer DA (1978). A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry 133:429-435. |
|27.||Yumru M, Savas HA, Kurt E (2007). Atypical antipsychotics related metabolic syndrome in bipolar patients. J Affect Disord 98:247-252. |
[Table 1], [Table 2], [Table 3]