Aim: Metabolic syndrome (MetS) is highly prevalent in patients with bipolar disorder (BD). Little research has evaluated the risk profile of MetS and cardiovascular disease in different gender and age groups in these patients. Our aim is to evaluate the prevalence of MetS in Italian patients with BD stratified by gender and age, and to determine the correlates of MetS.
Methods: Subjects with BD were included and stratified by sex and age according to the following age groups: <30; 30–39; 40–49; 50–59; ≥60 years. Socio-demographic and clinical characteristics, lifestyle information, and comorbidity for cardiovascular diseases and diabetes were collected. MetS was diagnosed according to National Cholesterol Education Program Adult Treatment Panel III modified criteria.
Results: MetS was evaluated in 200 patients, with a prevalence of 26.5%. Men had higher rates of hypertension and hypertriglyceridemia, women had more abdominal obesity. Women had a peak of prevalence in the ≥60 years group, while men displayed high rates even in the young age groups. In young patients, MetS was associated with Cluster B personality disorders and less physical exercise.
Conclusion: Our paper highlights the importance of evaluating MetS even in young patients with bipolar disorder, especially males. The strong association with lack of physical exercise suggests that the implementation of healthy behaviors might be relevant in order to prevent MetS and future adverse cardiovascular outcomes.
METABOLIC SYNDROME (METS) is a constellation of metabolic abnormalities that includes abnormal glucose metabolism (type II diabetes, impaired glucose tolerance, or altered fasting glycemia), central obesity, atherogenic dyslipidemia, reduced HDL cholesterol, and hypertension. Metabolic syndrome is associated with an increased prevalence of cardiovascular disease,1,2 type II diabetes3,4 and stroke.5
The prevalence of MetS in the US general population is 32%,6 while European studies have found lower rates between 8% and 17%.7–10
General population studies have found that the risk of MetS is roughly equal in men and women, with mild differences: US and Asian studies have reported a higher risk in women than men,6,11 while European studies have found prevalence figures slightly higher for men.8,12 While gender differences are not univocal, all studies agree with the fact that MetS increases with age.12 In the US, Ford and colleagues found a linear increase of MetS with age, without any gender differences.6 In Europe, Hu and colleagues similarly found an increased prevalence of MetS with age; men reached the peak in the 60–69-year age group, while in women rates linearly increased through 89-year age group.8 In Italy, the prevalence of MetS increases dramatically with age, from about 3% among people in their 20s to over 25% among people older than 70 years.9
Patients with bipolar disorder (BD) are more frequently obese and affected by the MetS than the general population. US studies found that these patients display remarkably high rates of MetS of 36–49%,13–15 while studies performed in European countries have reported lower rates than the US studies, ranging from 18.5% in Belgium16 to 25.3% in Italy.17 A recent review estimated the global prevalence ratios for MetS between patients with bipolar disorder and the general population to be around 1.6.18
Since the high prevalence of MetS in patients with bipolar disorder it would be clinically relevant to understand whether the risk profile is the same of the general population according to gender and age, in order to detect high-risk groups that deserve to be treated.
In the present paper we report on the prevalence of MetS in Italian patients with bipolar disorder stratified by gender and age, and investigate the socio-demographic and clinical correlates of MetS.
The study had a naturalistic design and involved patients consecutively admitted to the Psychiatric Inpatient Unit and to the Mood and Anxiety Disorders Outpatient Unit of the University of Turin (Italy), from April 2006 to May 2009.
All patients with a diagnosis of BD type I, II, not otherwise specified, or cyclothymia (DSM-IV) were asked to participate. The aims and study procedures were thoroughly explained to potential participants and they had to give their written consent before participation.
Exclusion criteria included age ≤18, pregnancy or postpartum, and refusal to consent participating in the study. All subjects were of Caucasian Italian origin.
Assessments and procedures
All diagnoses were confirmed by means of the Structured Clinical Interview for DSM Axis I Disorders (SCID-I).19 At study entry, general socio-demographic information was collected for each subject.
The study subjects were stratified by sex and age according to the following age groups: <30; 30–39; 40–49; 50–59; ≥60 years.
Clinical characteristics such as age at onset, duration of illness, number of previous manic/depressive episodes, history of suicide attempt, Axis I and II comorbidity were ascertained either from clinical charts and by direct questioning the study participants.
Use of medications at the time of interview was assessed. We specifically looked at the use of mood stabilizers (MS) and atypical antipsychotics (AA) such as lithium, valproate, carbamazepine, lamotrigine, olanzapine, risperidone, quetiapine and aripiprazole.
Lifestyles were also investigated. Information about exposure to cigarette smoking, duration of alcohol consumption, and physical activity were obtained by directly interviewing the patients. A score was assigned to the intensity of physical exercise: absent, mild (<4 h/week), moderate (4 h/week with a tolerance of ±30 min) and intense (>4 h/week, regular).20
Comorbidity and family history for diabetes or cardiovascular diseases, and current treatments for hypertension, diabetes, or dyslipidemia were assessed by looking at medical reports, and by direct interview of the patients.
At index visit, weight, height, waist circumference, and blood pressure were measured. Weight was measured undressed and fasting, height was measured barefoot. Patients with a body mass index (BMI) ≥30 were categorized as obese according to the WHO classification.21,22 Waist circumference, measuring central adiposity, was taken at midway between the inferior margin of the ribs and the superior border of the iliac crest, at minimal respiration. Two blood pressure measurements were obtained by using a mercury sphygmomanometer: the first with the subject in a lying position, the second with the subject in a seated position at least 2 minutes after the first measurement. The mean blood pressure of the two measurements was used. The attending physician in the hospital setting performed all procedures.
A blood draw for routine blood exam was performed at hospital admission for inpatients, as a part of the clinical management routine. For outpatients, results of previous blood examinations were considered valid if the last blood sample was drawn within 2 months before entry in the study, otherwise patients were scheduled for a blood test within a week from the study visit. At the time when blood was drawn, patients had fasted for the previous 10 h; patients who were not fasting were rescheduled. Blood exams included glucose, total cholesterol, triglycerides, LDL and HDL-C. Blood samples were drawn in our clinic and examined in the ‘Baldi e Riberi’ laboratory of analysis, San Giovanni Battista Hospital, Turin, Italy.
MetS was diagnosed according to the National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III-modified criteria:23
(i) abdominal obesity: waist circumference ≥102 cm in men and ≥88 cm in women;
(ii) hypertriglyceridemia: ≥150 mg/dL or on lipid-lowering medication;
(iii) low HDL-C: <40 mg/dL in men and <50 mg/dL in women;
(iv) high blood pressure: systolic pressure ≥130 mmHg and/or diastolic pressure ≥85 mmHg or on antihypertensive medication;
(v) high fasting glucose: ≥100 mg/dL or on glucose-lowering medication.
Subjects characteristics were summarized as mean and SD for continuous variables and frequency and percentage for categorical variables.
We calculated prevalence of MetS for the included sample, for men and women, and for gender-stratified 10-year age groups. Prevalence of MetS estimated by the 2005 ATP III definitions was presented as age- and gender-stratified graphs.
We examined demographic and clinical correlates of MetS by way of chi-square in the case of categorical variables and independent samples t-tests in the case of continuous variables.
All data were analysed using SPSS version 14.0 (SPSS Inc., Chicago, IL, USA).
Of 229 patients with bipolar disorder consecutively admitted to our unit, 11 refused to consent, seven were below 18 years of age, and five were pregnant at the time of interview. Thus, 206 patients with bipolar disorder were recruited in the study. Only 200 patients had complete clinical and laboratory data, therefore the following calculations were performed on this sample.
The mean age of the sample was 50.9 ± 15.5 years, 60% of the patients were females. The majority of the sample (63.5%) had bipolar II disorder; the mean duration of illness was 19.4 ± 13.8 years. Patients were on a mean of 2.9 ± 1.2 medications for bipolar disorder; 89.9% were receiving at least one mood stabilizer, 42.9% were receiving at least one antipsychotic, and 61.1% were treated with at least an antidepressant. The mean BMI was 26.4 ± 4.9 kg/m2. All socio-demographic and clinical characteristics of the sample are displayed in Table 1.
Table 1. Socio-demographic and clinical characteristics of the sample (n = 200)
BD, bipolar disorder; BMI, body mass index.
Females, n (%)
Age (years), mean ± SD
50.9 ± 15.5
Education (years), mean ± SD
12.0 ± 4.1
Occupational status, n (%)
Bipolar type I, n (%)
Age of onset (years), mean ± SD
31.5 ± 13.2
Duration of illness (years), mean ± SD
19.4 ± 13.8
At least one suicide attempt, n (%)
Number of drugs for BD, mean ± SD
2.9 ± 1.2
Antidepressants, n (%)
Mood stabilizers, n (%)
Antipsychotics, n (%)
BMI, mean ± SD
26.4 ± 4.9
Metabolic syndrome was present in the 26.5% of the sample, and was slightly more prevalent in men (32.5%) than in women (22.5%), although this difference was not statistically significant.
Men had higher prevalence of hypertension (67.5% vs 43.3%; χ2 = 11.254, d.f. = 1, P = 0.001) and hypertriglyceridemia (48.8% vs 24.4%; χ2 = 12.641, d.f. = 1, P < 0.001) than women. Conversely, the prevalence of abdominal obesity was higher in women than men (53.3% vs 37.5%; χ2 = 4.831, d.f. = 1, P = 0.028). Similar prevalences of low HDL-C levels (25.7% vs 33.9%; P = n.s.) and hyperglycemia (6.2% vs 13.4%; P = n.s.) were observed in men and women (Table 2).
Table 2. Prevalence of metabolic syndrome and its individual components by gender
When we stratified the sample in five age groups, we found that MetS linearly increased with age, ranging from 9.1% in subjects <30 years to 41.8% in those ≥60 years. When we compared the age-stratified prevalence of MetS in males and females, we found that, while MetS in females was mostly represented in the older age groups, with a statistically significant difference between groups (χ2 = 2.131, d.f. = 4, P < 0.001), males had high rates of MetS even in the younger age groups, with no significant difference between groups (χ2 = 3.419, d.f. = 4, P = 0.490). In particular, rates of MetS were higher in 30–39-years men than women (41.7% vs 0%; χ2 = 6.771, d.f. = 1, P = 0.009), and in the 40–49-years men than women (35.7% vs 7.1%; χ2 = 5.486, d.f. = 1, P = 0.019) (Fig. 1).
Since the unexpectedly high prevalence of MetS in 30- and 40-year-old males, we looked at the correlates of MetS in this group of young patients, considered as a whole. We found that young patients with the MetS showed a higher rate of cluster B personality disorders (21.4% vs 4%; χ2 = 5.700, d.f. = 1, P = 0.017), and a lower rate of performing any physical exercise (28.6% vs 60%; χ2 = 4.709, d.f. = 1, P = 0.030) than those without (Table 3). MetS was also strongly associated with diabetes and cardiovascular disease: 28.6% of patients these young with MetS had either a diagnosis of cardiovascular disease or diabetes, versus 5.3% of those without MetS (χ2 = 7.788, d.f. = 1, P = 0.005).
Table 3. Socio-demographic and clinical correlates of patients <50-years old with MetS
MetS (n = 14)
No MetS (n = 75)
Age (years), mean ± SD
36.9 ± 7.0
36.6 ± 9.6
Education (years), mean ± SD
12.6 ± 2.7
12.4 ± 3.4
Bipolar disorder, type, n (%)
Index episode, n (%)
Age of onset (years), mean ± SD
21.9 ± 5.8
25.2 ± 8.9
Duration of illness (years), mean ± SD
14.9 ± 6.5
11.4 ± 8.3
Number of manic episodes, mean ± SD
2.8 ± 2.5
3.1 ± 2.5
Number of depressive episodes, mean ± SD
4.3 ± 2.9
3.7 ± 2.6
At least one lifetime suicide attempt, n (%)
Axis I lifetime comorbidity, n (%)
Axis II comorbidity, n (%)
Number of medications for BD, mean ± SD
2.7 ± 1.2
2.8 ± 1.0
Psychiatric family history, n (%)
CV family history, n (%)
Tobacco smoke, n (%)
Alcohol consumption, n (%)
Physical activity, n (%)
CV/diabetes comorbidity, n (%)
No associations between the presence of MetS and use of mood stabilizers or atypical antipsychotics in young patients could be found (Table 4).
Table 4. Use of medication and MetS status in patients <50 years-old with MetS
MetS (n = 14)
No MetS (n = 75)
Number of drugs, mean ± SD
2.7 ± 1.2
2.8 ± 1.0
Lithium, n (%)
Valproate, n (%)
Carbamazepine, n (%)
Lamotrigine, n (%)
Olanzapine, n (%)
Risperidone, n (%)
Quetiapine, n (%)
Aripiprazole, n (%)
This study looks at the prevalence of MetS in a representative sample of Italian patients with bipolar disorder.
We previously reported that MetS was as high as 25% in our patients with bipolar disorder.17 In the present study, performed on a bigger therefore more representative sample, we report a prevalence of MetS of 26.5%, which confirms our previous findings. This prevalence is definitely higher than the prevalence of MetS reported in the Italian general population; studies conducted in northern and central Italy employing NCEP ATP-III criteria have reported highly homogeneous rates ranging from 16% to 17.8%.9,24 Of note, although MetS increases with age, our prevalence of MetS is higher than the general population even though our patients are younger than those enrolled in the two population studies. Thus, our results further confirm that subjects with bipolar disorder are at higher risk for MetS than the general population.
Although it did not reach statistical significance, it appears that males with bipolar disorder have a higher risk for developing MetS, with prevalence around 32% in men versus 22% in women. A slightly higher prevalence of MetS in males than in females has also been reported in the Italian general population: three studies reported a prevalence of MetS around 18% in men, and 15% in women.9,20,24
When analyzing the prevalence of the single components of MetS in our population, we found that hypertension is the most common metabolic abnormality, followed by abdominal obesity, hypertriglyceridemia, low HDL-C levels, and hyperglycemia. Our data show a substantial heterogeneity in the distribution of each trait of the metabolic syndrome: for instance, large waist circumference was more common in women, while hypertension and elevated triglyceride levels were more frequent in men. Scott and colleagues have highlighted that the prevalence of obesity is nearly double in women suffering from depression or anxiety disorders than in men,25 and several clinical studies on patients with bipolar disorder have reported that women display significantly higher rates of abdominal obesity than men.13,16,26,27 However, despite the higher prevalence of obesity in women, rates of MetS in our study are slightly higher in men; the same findings have been reported in the general population of several European countries, where obesity was more common in women but MetS was more frequent in men due to the higher prevalence of hypertension and dyslipidemia.8
When we stratified the sample according to different age groups, we found a different age distribution of MetS between sexes. Females with bipolar disorder of age <50 years displayed a very low prevalence of MetS, not different from the general population, with a rise after that age. Conversely, in males with bipolar disorder MetS did not linearly increase with age, with 42% of patients in their thirties and 36% in their forties displaying the MetS. In the Italian general population, the prevalence of MetS in men and women is similar, with rates around 3% in people 30–39-year-olds and 10% in people 40–49-year-olds.9 Therefore the prevalence of MetS is extraordinarily high in young men with bipolar disorders.
Epidemiological studies have found a slightly different age distribution in relation to gender, with MetS in females increasing linearly with age and males displaying higher rates of MetS at earlier age.3,8,28 However, the excess prevalence of MetS seen in our young male bipolar patients is far above the slight increase seen in the epidemiological studies, and suggests that bipolar disorder might convey a specific risk of metabolic abnormalities in this subgroup of patients. The only study that has assessed the distribution of MetS according to age in patients with bipolar disorder also reported that the patients had a significantly higher prevalence of MetS than the reference control population in the 30–39 and the 40–49 years subgroups.27 Nevertheless, the authors only reported a generally heightened risk for MetS in bipolar patients compared with the general population, while we found that this is true especially for males.
Given the particularly high prevalence of MetS in young patients (<50-year-olds) with bipolar disorder, we investigated the clinical correlates in these at-risk patients, in order to find any associated clinical feature that would contribute to explain this association.
Since metabolic abnormalities in bipolar patients have generally been attributed to the use of psychotropic drugs such as atypical antipsychotics and anticonvulsants,15,29–31 we looked at the use of such medications in these patients. However, we did not find any significant association between the presence of MetS and the use of mood stabilizers and atypical antipsychotics. This result is most probably due to the naturalistic nature of our study, in which we did not randomize the patients to treatment; as a consequence, overweight patients may have been prescribed medications less likely to induce weight gain and MetS, thus producing a ‘confounding by indication’ bias. Another explanation is the young age of patients in which we were looking at clinical correlates of MetS; these patients may have been exposed to medications for a time too short to develop metabolic disturbances.
Looking at other clinical characteristics associated with having MetS in patients <50-year-olds, we found that the absence of physical exercise was strongly associated with increased risk of MetS. This finding, together with the lack of correlation with prescribed medications, suggests that in these young patients unhealthy lifestyles play a big role in leading to weight gain and metabolic syndrome. In our center, we have recently observed that even drug-naïve patients with bipolar disorders are more frequently overweight than either the general population or patients with anxiety disorders. In that study overweight was associated with a higher number of depressive episodes, which might have carried unhealthy behaviors such as engaging a high sucrose diet or not performing any physical exercise.32 Physical exercise promotes fat oxidation, which is believed to reduce weight and improve insulin sensitivity.33,34 Since patients with bipolar disorder are less able to utilize fat at rest due to reduced fat oxidation,35 it may be possible that the absence of physical exercise is particularly relevant in increasing the risk for weight gain and eventually metabolic syndrome in these patients. This hypothesis deserves further investigation.
Most relevant, our data show that 29% of young patients with the metabolic syndrome were affected by cardiovascular disease or diabetes at the time of interview, versus the 5% of those without the metabolic syndrome. The high relative risk of cardiovascular disease in young patients with bipolar disorder and MetS should convince clinicians working in hospital settings in making the assessment of MetS a priority even in the young patient with bipolar disorder and not only in the old.
A limitation of the study is the lack of a control group from the general population. However, the subjects in our study are similar to the individuals included in other studies on the Italian general population for mean age, gender distribution, and most of all ethnicity; therefore the rate of MetS observed in our patients can be indirectly but reliably compared to the very homogeneous rates obtained in those studies.9,23 Another limitation may be related to the clinical nature of our study; only patients that decided to come to our clinic entered the study, therefore our sample may not be representative of all patients with bipolar disorder. However, our 26.5% prevalence rate is in line with those reported in other European studies estimating the prevalence of MetS in bipolar disorder,26,27,36 highlighting a comparably high risk for the broad population of patients with bipolar disorder.
In conclusion, Italian patients with bipolar disorder are at high risk of metabolic syndrome. Weight and waist circumference assessment and blood examinations should be routinely performed in this patient population, whose general medical issues are frequently neglected. Furthermore, the extremely high prevalence of MetS in the young men with bipolar disorder and the high prevalence of cardiovascular morbidity in these patients with MetS are alarming, and stress the need to perform a systematic screening for metabolic abnormalities in all patients with BD, regardless of age. Since the lack of physical exercise is strongly associated with MetS in these young patients, the implementation of programs aimed at increasing physical activity in the young patient with bipolar disorder is paramount in order to prevent future adverse cardiovascular outcomes.
We would like to acknowledge the staff of the Mood and Anxiety Disorders Unit of the University of Turin, which helped to collect all patients' data.