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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Bipolar Disord. Author manuscript; available in PMC 2011 August 1.
Published in final edited form as:
PMCID: PMC2941396
NIHMSID: NIHMS223975

Metabolic dysfunction in women with bipolar disorder: the potential influence of family history of type 2 diabetes mellitus

Abstract

Objective

Overweight/obesity, insulin resistance (IR), and other types of metabolic dysfunction are common in patients with bipolar disorder (BD); however, the pathophysiological underpinnings of metabolic dysfunction in BD are not fully understood. Family history of type 2 diabetes mellitus (FamHxDM2), which has been shown to have deleterious effects on metabolic function in the general population, may play a role in the metabolic dysfunction observed in BD.

Methods

Using multivariate analysis of variance, the effects of BD illness and/or FamHxDM2 were examined relative to metabolic biomarkers in 103 women with BD and 36 healthy, age-matched control women.

Results

As a group, women with BD had higher levels of fasting plasma insulin (FPI) and fasting plasma glucose (FPG), higher homeostatic assessment of IR (HOMA-IR) scores, body mass index (BMI), waist circumference (WC), and hip circumference (HC) compared to control women. FamHxDM2 was associated with significantly worse metabolic biomarkers among women with BD but not among healthy control women. Among women with BD, there was a significant main effect of FamHxDM2 on FPI, HOMA-IR, BMI, WC, and HC, even after controlling for type of BD illness, duration of medication exposure, and depression severity. Metabolic biomarkers were not influenced by use of weight-liable psychotropic medication (WLM), even after controlling for type of BD illness, duration of medication exposure, and depression severity.

Conclusions

Women with BD have overall worse metabolic biomarkers than age-matched control women. The use of WLM, duration of medication use, type of BD illness, and depression severity did not appear to be associated with more pronounced metabolic dysfunction. FamHxDM2 may represent a risk factor for the development of IR in women with BD. Further, focused studies of the endocrine profiles of families of BD patients are needed.

Keywords: bipolar disorder, family history of diabetes, insulin resistance, metabolic dysfunction, obesity, women

Over the last decade, metabolic dysfunction [i.e., overweight/obesity, insulin resistance (IR), and dyslipidemia] has become increasingly important in clinical medicine in general and in psychiatry in particular (1, 2). Despite intense scientific focus on this topic, risk factors for developing metabolic dysfunction as well as its pathophysiology among patients with mental illness remain to be elucidated.

IR is recognized as central to the pathophysiology of many metabolic abnormalities and is a known precursor condition for type 2 diabetes mellitus (DM2), obesity, cardiovascular illness (3), and neurocognitive disorders (4). With respect to its prevalence in mood disorders, IR is hypothesized to be (i) an underlying state predisposing toward both mood disorders and metabolic disorders, (ii) a side effect of the psychotropic medications used to treat mood disorders, and/or (iii) a consequence of mood disorders themselves (e.g., lifestyle changes or stress). While our group and others have previously reported high rates of overweight/obesity and surrogate biomarkers of IR in samples of women with bipolar disorder (BD) (59), there are no controlled studies utilizing direct measures of insulin sensitivity and glucose utilization in BD patients. The question of trait versus state vulnerability remains unanswered, as the effects of illness per se on glucose handling in these patients are difficult to disentangle from the effects of medications used to treat the illness.

Metabolic dysfunction has been routinely considered a consequence of overweight/obesity among psychiatric patients (1, 2). Weight gain leading to overweight/obesity has been viewed as a culprit in the development of IR. Weight gain in patients with BD may relate to mood state per se (i.e., altered eating patterns and energy expenditure during depressive states) and/or may be a result of treatment and number of psychotropic agents used in treatment (10, 11). Nevertheless, a significant proportion of patients with BD do not gain weight on psychotropic medications that are associated with weight gain (11). Further, it has been shown in nonpsychiatric populations that IR develops even in lean individuals (12), suggesting other potential (genetic) mechanisms for IR unrelated to weight gain.

The ultimate negative outcome of a longstanding IR occurs when insulin-resistant individuals cannot secrete the amount of insulin needed to overcome the IR, and DM2 ensues (13). Familial transmission of DM2 has been widely reported (14), and some studies have shown family history of DM2 to be a risk factor for IR/DM2 (15), especially among women (16). A recent U.S. population study found that family history of DM2 was associated with risk for metabolic syndrome (17). However, it is unclear whether such history may underlie metabolic dysfunction among patients with mental illness receiving psychotropic medications, as only a handful of studies have examined metabolic function in case-controlled design in patients with affective disorders.

The overall aims of the present study were to evaluate metabolic and reproductive function in women with BD in comparison to age-matched healthy controls. Reproductive data are described elsewhere [Rasgon NL, Kenna HA, Reynolds MF et al. Reproductive function in women with bipolar disorder and controls (under review)]. The current manuscript is focused only on the metabolic function assessment. Metabolic biomarkers included fasting plasma insulin (FPI), fasting plasma glucose (FPG), the homeostatic assessment of IR (HOMA-IR), body mass index (BMI), waist circumference (WC), and hip (HC) circumference, which were analyzed for group differences (BD versus controls). Within the women with BD, metabolic biomarkers were tested for differences by use of weight-liable psychotropic medications (WLM). Lastly, differences between groups with respect to the metabolic biomarkers were examined in relation to presence of DM2 in at least one first-degree female relative (FamHxDM2), which is a factor related to metabolic dysfunction in the general population (17).

Methods

The study was approved in its entirety by the Stanford University Administrative Panel on Human Subjects. Subjects provided written and verbal informed consent prior to participation. Women with BD were recruited from the Center for Neuroscience in Women’s Health and the Bipolar Disorders Clinic in the Department of Psychiatry and Behavioral Sciences at Stanford University Medical Center (Stanford, CA, USA), as well as from the community using flyers, advertisements in local newspapers, and on the registry of federally supported clinical trials (http://clinicaltrials.gov/). Control subjects were also recruited from the community using flyers and advertisements in local newspapers. Control subjects with history of psychiatric illness or history of ever having received psychotropic medication were excluded from the study. Other exclusion criteria included illicit drug use in the past six months, uncontrolled medical conditions, peri- or postmenopause [as indicated by follicle stimulating hormone (FSH) levels ≥ 40 mIU/mL], hormonal contraceptive use within the past three months, current pregnancy, breastfeeding, plans to get pregnant, endocrine disease (i.e., diabetes, hypothyroidism), or a mood disorder secondary to general medical condition. Women with BD receiving psychotropic medication were required to have stable medications for at least three months prior to baseline evaluation.

As part of the larger study, all subjects were asked to complete three months of consecutive monthly ovulation tracking. Enrollment numbers for the study called for a 2:1 ratio of women with BD to healthy control subjects based on preliminary reproductive endocrine findings (18, 19). A high rate of dropout was projected for women with BD; thus, a much larger number of women with BD underwent baseline evaluation (including the assessment of metabolic function, as reported herein). A summary of the recruitment flow is summarized in Figure 1.

Fig. 1
Summary of the recruitment flow for women with bipolar disorder (BD) and healthy control women.

Intake evaluation and questionnaires

All study evaluations took place at the Center for Neuroscience in Women’s Health within the Department of Psychiatry and Behavioral Sciences at Stanford University Medical Center and at the Clinical Trials Research Unit at Stanford Hospital. After providing verbal and written informed consent, all subjects underwent the Structured Clinical Interview for DSM-IV (SCID) to establish psychiatric diagnosis in patients with BD and to rule out psychopathology in control subjects. Detailed information was collected from all BD patients regarding current and previous psychotropic treatment. Where available, these data were cross-checked with clinic charts to ensure validity. A trained clinical interviewer rated each subject on the Montgomery-Åsberg Depression Rating Scale (MADRS) (20) and the Young Mania Rating Scale (YMRS) (21) to assess the severity of current depressive and manic symptoms. Data on personal medical history were collected from all subjects, as were data on psychiatric disorders and major medical disorders. As the present analyses were a part of a larger study on both metabolic and reproductive function in women with BD and healthy control women, questionnaires were administered to ascertain endocrine function, including DM2, in first-degree female relatives (reproductive endocrine data are presented elsewhere).

For analytic purposes, psychiatric medications were classified into weight-liable mood stabilizers/anticonvulsants (WL-MS: valproic acid, lithium, carbamazepine), non-weight-liable mood stabilizers/anticonvulsants (NWL-MS: lamotrigine, oxcarbazepine, topiramate, gabapentin), weight-liable atypical antipsychotic medication (WL-AAP: olanzapine, quetiapine, risperidone), non-weight-liable atypical antipsychotic medication (NWL-AAP: aripiprazole, ziprasidone), and antidepressant monotherapy (fluoxetine, bupropion, escitalopram, venlafaxine, sertraline, citalopram). Table 1 represents the frequency of all psychotropic medications received by women with BD in this study.

Table 1
Psychotropic medications in women with bipolar disorder (BD)

Assessment of metabolic function

All subjects were assessed for their height and weight to calculate BMI (in kg/m2) as well as measured for WC and HC (in inches). BMI data were classified according to standard methodology, with normal weight defined as 18.5–24.9 kg/m2, overweight defined as 25–29.9 kg/m2, and obesity defined as > 30 kg/m2. Morning fasting venous blood was collected for the measurement of FPI and FPG. Glucose was measured using the automated Hexokinase immunoassay reference method, while insulin was measured using the automated chemiluminescent enzyme immunometric assay (Siemens Healthcare Diagnostics, Inc., Deerfield, IL, USA). HOMA-IR is highly correlated (r~0.60) with direct measurement of insulin-mediated glucose (22, 23), and therefore was utilized as a surrogate biomarker of IR, as calculated by the standard formula of FPG (mM/L) × FPI (µU/ml)/22.5 (24).

Statistical analysis

Statistical analyses were performed using SPSS software version 18.0 (SPSS Inc., Chicago, IL, USA). All statistical tests were two-tailed and conducted at the 0.05 significance level. Statistical trends ≤ 0.10 were also noted. Metabolic biomarkers were log-transformed to control for skewed distribution. Multivariate analysis of variance (MANOVA) was used to test for differences in the metabolic biomarkers between women with BD illness and/or FamHxDM2, wherein all metabolic dependent variables were entered together, and BD illness and FamHxDM2 were entered as independent variables with both main effects and their interaction examined. Univariate analysis of variance (ANOVA) was conducted to test for differences between women with and without FamHxDM2 in each group (BD or control, separately). Given the extensive clinical interest in the potential metabolic effects of specific types of psychotropic medications used in BD illness, further univariate ANOVA models on the subgroup of women with BD tested for differences by use of WLM, as well as use of AAP, as these are of primary interest in the clinical community. Lastly, exploratory MANOVA on the subgroup of women with BD with and without FamHxDM2 included type of BD illness, duration of medication exposure, and depression severity (MADRS score) as covariates.

Results

Demographic and clinical characteristics for all women with BD and healthy controls relative to their FamHxDM2 status are presented in Table 1. The rates of FamHxDM2 were virtually equal among women with BD and control women (31.1% and 30.6%, respectively; see Table 1). Approximately three-quarters of patients with BD were euthymic or experiencing at most mild mood disturbance at the time of evaluation of their metabolic function. Clinically significant depressive symptoms (MADRS score ≥ 20) were observed in 17.8% of all women with BD. Clinically significant hypomanic symptoms (YMRS score between 12 and 19) were observed in 7.7% of women with BD, and clinically significant manic symptoms (YMRS score > 20) were observed in 1.1% of women with BD. Among women with BD, approximately 50% were receiving MS monotherapy, 14% AAP monotherapy, 17% MS+AAP combination therapy, and 6% antidepressant monotherapy, while 14% were medication-free for at least six months (see Table 1 for details). No significant differences were found between groups with respect to age, parity, years of educational attainment, or marital status (Table 1). As expected in a clinical study comparing patients with BD and control subjects, women with BD as a whole scored significantly higher on both the MADRS and YMRS [F(1,135) = 36.880, p < 0.001 and F(1,135) = 22.461, p < 0.001, respectively]. Among women with BD, no differences were observed between those with and without FamHxDM2 with respect to MADRS or YMRS scores, type of BD illness, age at first affective episode, age at BD diagnosis, type of psychotropic medication received, or duration of medication exposure (Tables 1 and and22).

Table 2
Demographic and clinical characteristics of women with bipolar disorder (BD) and healthy control women with (+) and without (−) presence of type 2 diabetes mellitus in at least one first-degree female relative (FamHxDM2)

MANOVA that included both group (BD versus control) and FamHxDM2 as independent variables and the log-transformed metabolic biomarkers as dependent variables showed a significant main effect of group, but not FamHxDM2, on all metabolic biomarkers. Specifically, women with BD had significantly higher values compared to control women for FPI [F(1,135) = 4.963, p = 0.028], FPG [F(1,135) = 4.283, p = 0.041], HOMA-IR [F(1,135) = 5.669, p = 0.019], BMI [F(1,135) = 10.296, p = 0.002], WC [F(1,135) = 15.662, p < 0.001], and HC [F(1,135) = 12.955, p < 0.001]. Group means and standard deviations are displayed in Table 3. A significant interaction effect between group and FamHxDM2 was observed for all metabolic biomarkers [FPI: F(1,135) = 5.329, p = 0.023; HOMA-IR: F(1,135) = 4.810, p = 0.030; BMI: F(1,135) = 7.895, p = 0.006; WC: F(1,135) = 8.406, p = 0.005; HC: F(1,135) = 5.514, p = 0.021] except for FPG [F(1,135) = 3.451, p = NS] (Fig. 2).

Fig. 2
Multivariate analysis of variance results showed significant main effects for all metabolic biomarkers [FPI: F(1,135) = 4.134, p = 0.044; FPG: F(1,135) = 4.134, p = 0.044; HOMA-IR: F(1,135) = 6.293, p = 0.014; BMI: F(1,135) = 9.892, p = 0.002; WC: F(1,135) ...
Table 3
Metabolic biomarkers in women with bipolar disorder (BD) and control women with (+) and without (−) presence of type 2 diabetes mellitus in at least one first-degree female relative (FamHxDM2)

Univariate ANOVA conducted on each subgroup (BD or control) for comparison of those with and without FamHxDM2 showed significantly worse metabolic biomarkers among the women with BD and FamHxDM2. Healthy control women with FamHxDM2 showed no differences in FPI, FPG, or HOMA-IR, trend-level lower BMI, and significantly lower WC (Table 3).

Univariate ANOVA tests on the subgroup of women with BD showed no differences in metabolic biomarkers by use of WLM or use of AAP (Table 4). Exploratory MANOVA conducted on the subgroup of women with BD continued to show a significant main effect of FamHxDM2 on FPI [F(1,101) = 6.619, p = 0.014], HOMA-IR [F(1,101) = 6.805, p = 0.013], BMI [F(1,101) = 7.463, p = 0.009], and WC [F(1,101) = 6.804, p = 0.013] upon inclusion of type of BD illness, duration of medication exposure, and MADRS score as covariates.

Table 4
Summary of data on differences within women with bipolar disorder (BD) by type of medication received

Discussion

While metabolic dysfunction in patients with major mental illnesses has been a salient topic for almost a decade, few studies have assessed disease-specific metabolic profiles in a controlled design, and even less attention had been paid to gender-specific metabolic markers in psychiatric patients. The majority of studies have focused on the metabolic consequences of treatment with AAP and mood stabilizers in patients with schizophrenia and schizoaffective disorder (25).

Excessive weight is seen as an important culprit in metabolic dysfunction. It is estimated that 55% of Americans are overweight, with roughly half of this subpopulation considered obese (26). The Centers for Disease Control and Prevention estimate that about 10% of the general U.S. adult population has DM2. Individuals with BD have a greater prevalence of obesity (11, 27) and DM2 (28) compared with the general population, with rates as much as threefold higher, and women with BD have a greater risk for obesity than female healthy controls (27). Pharmacotherapy of patients with BD includes the use of MS, AAP, antidepressants, and benzodiazepines. Some of these medications alter endocrine and metabolic function (29) and have been implicated in weight gain and IR (30), although not all patients receiving these medications develop weight gain and metabolic dysfunction. Alternatively, IR often develops even in normal-weight individuals without mental illness (12).

Several overlapping factors have been proposed to explain high rates of overweight/obesity and metabolic dysfunction in patients with BD. Most frequently cited are: (i) the effects of specific psychotropic medications used in the management of BD on weight and insulin metabolism (31), (ii) the effects of the BD illness itself on food intake and energy expenditure (32), and (iii) neuroendocrine dysregulation in BD (e.g., hypercortisolemia) leading to an increase in fat deposition and hyperinsulinemia (33).

In the present study, women with BD exhibited worse metabolic biomarkers than age-matched controls, which is consistent with our previous findings (59). The observed preponderance of overweight/obesity and IR among women with BD in comparison with controls is not surprising. What is surprising, however, is that current findings do not support the commonly suggested association between the use of WLM and metabolic dysfunction. Metabolic dysfunction may be a part of an illness per se in patients with BD, as well as a result of its treatment (5, 18). The lack of association between type of BD, type of medications, and metabolic biomarkers in the present study lends further support to this postulate. Further, unlike Fagiolini et al. (10, 11), who suggested that patients with BD type I are heavier and have worse treatment outcome as a result of obesity, differences in BMI or other surrogate biomarkers of metabolic function between women with BD type I or II were not found in the present study. It could be the case that in the delivery of clinical care, careful attention to weight and metabolic status in selection of antipsychotic medications, and early interventions to mitigate the adverse weight and metabolic effects of WL antipsychotics contributed to our inability to demonstrate a relationship between increased metabolic dysfunction and use of WLM.

In the present sample, 30% of women with BD were obese, which is lower than what our group observed in the earlier study [45% obese (5)] and the rates observed in other studies (30). By contrast, only about 11% of control women were obese. Obesity itself may be a part of the bipolar clinical endophenotype, as the 14% of women with BD in the present sample who were medication-free exhibited high BMI and HOMA-IR, similar to those women treated with psychotropic medications (data not shown). These findings extend our previously published observation in an unrelated small sample of untreated women with BD (6), suggesting that obesity in BD might be a consequence of changes in diet and activity levels related to mood states. However, it is not certain whether the risk for obesity is due to the psychotropic medications used to treat BD, to the illness itself, or some combination thereof (34). The data from Fagiolini et al. (10) suggest that repeated depressive episodes or courses of pharmacotherapies may increase the risk of weight gain; thus, as most patients in referenced studies had been diagnosed and receiving treatment for a substantial amount of time already (an average of five years), changes in weight may have occurred earlier in the development of BD or in the treatment regimen. Plateaus in weight gain after six months to two years have also been suggested (10, 35).

Considering the potentially grave long-term sequelae of obesity and IR, identification of individuals at risk for developing IR is important. IR is a crucial component in the pathophysiology of obesity, DM2, and cardiovascular disease. IR may initially be manifested by glucose intolerance for years prior to the onset of overt DM2, as the pancreas is able to compensate by increasing secretion of insulin in order to maintain normal glucose levels. Over time, the degree of IR increases as insulin secretion by pancreatic cells is reduced, resulting in overt DM2. However, the temporal relationship between IR and DM2 in the disease process remains unclear (15).

Individuals with FamHxDM2 are at greater risk of developing DM2 than people without such history (36). While familial transmission of DM2 has been well established, it is not known whether having a first-degree relative with DM2 may have a differential role in persons with mental illness who carry an additional risk for metabolic dysfunction by virtue of the mental illness and its treatment. Although not a primary aim of the study, our findings suggest for the first time that FamHxDM2 in women with BD is associated with worse metabolic profiles in women with BD, but not in controls. FamHxDM2 was equally represented in women with BD and controls (about one-third of each subject group had FamHxDM2), but women with BD with FamHxDM2 had significantly more pronounced dysregulation of metabolic function than women with BD without FamHxDM2. In contrast, age-matched female controls did not have a significant association between FamHxDM2 and metabolic function. No other demographic variables, such as years of education or marital status, differed between groups and therefore cannot explain differences in metabolic biomarkers within women with BD or between groups. These results suggest that a subpopulation of women with BD may develop more pronounced metabolic dysfunction with weight gain than others and that genetic vulnerability to DM2 may be a risk factor for such dysfunction, rather than medication and/or illness-related behavior per se.

Finally, other endocrine mechanisms can contribute to obesity and IR in BD. The dysfunction in the hypothalamic-pituitary-adrenal axis may lead to elevated levels of leptin and increased adipose tissue, which could in turn result in obesity (33). Individuals with mood disorders are at particular risk for abdominal obesity (9, 37). In our sample, WC (a surrogate marker for abdominal obesity) positively correlated with IR in both women with BD and female controls, with the highest correlation seen in women with BD and FamHxDM2. In addition to total body weight, the presence of centrally deposited adipose tissue independently contributes to the morbidity associated with excess body fat (38). Body fat distribution has also been related to fasting and stimulated levels of glucose and insulin, respectively, and to increased rates of diabetes mellitus (39). Body fat distribution is a more robust predictor of incident coronary heart disease when compared with total body fat (40), and indeed, patients with BD are at increased risk of comorbid cardiovascular disorders (25).

There are noteworthy limitations of the current findings. Although the present study was controlled, sample size limitations (specifically, for the smaller control group) may have contributed to the lack of differences between groups for some variables. At the same time, the present study included a fairly large sample of women with BD (n = 103). The reason for the unequal numbers of women with BD and control women is that the original proposal was designed with a 2:1 ratio of women with BD to healthy controls for three-month ovulation tracking data as part of the overall metabolic and reproductive aims of the study. Given that a high rate of dropout was projected for women with BD, a much larger number of women with BD underwent baseline evaluation (including the assessment of metabolic function, as reported herein). As it was not an aim, exercise and dietary habits were not assessed; therefore, their contribution to IR could not be quantified. As in almost all studies of BD, a significant portion of BD patients were receiving more than one psychotropic medication. While clustering of these drugs was attempted according to known weight-gain liability, the absence of uniform treatment represents a limitation of the results. Small cell sizes prevented analysis of the effects of AAP dosage upon metabolic parameters. At the same time, this study is a reflection of real-life treatment of BD, as no single treatment approach provides adequate outcomes for many of the diverse patients with this illness. Our findings may be potentially limited to women. Although gender differences in rates of metabolic dysfunction in patients with mood disorders are known (41), it is unclear whether men with BD would have similar results, as this was part of a larger study of endocrine function conducted only in women with BD. Finally, as a constraint of being a part of the larger endocrine study in women with BD, self-reported FamHxDM2 was only ascertained among female first-degree relatives, and it would be informative to assess whether having male relatives, or more than one relative, with DM2 would have a differential effect on biomarkers of IR in BD patients. Further, since FamHxDM2 was per self-report, we cannot rule out recall bias. We suggest consideration of the present results as noteworthy pilot findings. Further studies specifically designed to assess endocrinological profiles of first-degree relatives of patients with BD are necessary.

In summary, women with BD had worse metabolic biomarkers compared to age-matched control women. The use of WLM was not associated with metabolic dysfunction. The presence of FamHxDM2 in women with BD was associated with the worst metabolic profiles compared to women with BD without FamHxDM2 and to control women, even after controlling for clinically important variables, such as type of BD illness, duration of medication exposure, and depression severity. Given the present findings, it might be that the risk for having metabolic dysfunction among women with BD is mediated by genetic trait vulnerability to DM2. It is not clear, however, whether this is a gender-specific risk, as only data on female first-degree relatives were collected in the present study. Further elucidation of the impact of FamHxDM2 on developing metabolic consequences in both men and women with BD may have significant clinical implications in identifying persons at risk and optimizing psychotropic interventions.

Acknowledgements

This study was funded by a grant from the National Institute on Mental Health (R01 MH66033) and supported in part by grant M01 RR-00070 from the National Center for Research Resources, National Institutes of Health. NLR has full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

NLR has received grant/research support and/or has been a consultant and/or received lecture honoraria from Abbott Laboratories, Bayer HealthCare, Bristol-Myers Squibb, Forest Laboratories, GlaxoSmithKline, Pfizer, and Wyeth-Ayerst. PW has received grant/research support and/or has been a consultant and/or received lecture honoraria from Abbott Laboratories, AstraZeneca, Bristol-Myers Squibb, Cephalon, Eli Lilly & Co., GlaxoSmithKline, Janssen, Jazz Pharmaceuticals, Novartis, Organon, Otsuka, Pfizer, Repligen, Solvay, Valeant Pharmaceuticals, and Vanda Pharmaceuticals. TAK has received grant/research support and/or has been a consultant and/or received lecture honoraria from Abbott Laboratories, AstraZeneca, Bristol-Myers Squibb, Cephalon, Eli Lilly & Co., GlaxoSmithKline, Janssen, Jazz Pharmaceuticals, Johnson & Johnson, Novartis, Organon, Otsuka, Pfizer, Repligen, Solvay, Valeant Pharmaceuticals, and Vanda Pharmaceuticals. HAK, MFR-M, PGS, MV, and WM do not have any financial disclosures to report.

References

1. Sachs GS. Strategies for improving treatment of bipolar disorder: integration of measurement and management. Acta Psychiatr Scand Suppl. 2004;(422):7–17. [PubMed]
2. Newcomer J. Metabolic syndrome and mental illness. Am J Manag Care. 2007;13:S170–S177. [PubMed]
3. Reaven GM. Insulin resistance: the link between obesity and cardiovascular disease. Endocrinol Metab Clin North Am. 2008;37:581–601. [PubMed]
4. Yaffe K. Metabolic syndrome and cognitive decline. Curr Alzheimer Res. 2007;4:123–126. [PubMed]
5. Rasgon NL, Altshuler LL, Fairbanks L, et al. Reproductive function and risk for PCOS in women treated for bipolar disorder. Bipolar Disord. 2005;7:246–259. [PubMed]
6. Stemmle PG, Kenna HA, Wang PW, Hill SJ, Ketter TA, Rasgon NL. Insulin resistance and hyperlipidemia in women with bipolar disorder. J Psychiatr Res. 2009;43:341–343. [PubMed]
7. Sicras A, Rejas J, Navarro R, Serrat J, Blanca M. Metabolic syndrome in bipolar disorder: a cross-sectional assessment of a Health Management Organization database. Bipolar Disord. 2008;10:607–616. [PubMed]
8. Cardenas J, Frye MA, Marusak SL, et al. Modal subcomponents of metabolic syndrome in patients with bipolar disorder. J Affect Disord. 2008;106:91–97. [PubMed]
9. Garcia-Portilla M, Saiz P, Benabarre A, et al. The prevalence of metabolic syndrome in patients with bipolar disorder. J Affect Disord. 2008;106:197–201. [PubMed]
10. Fagiolini A, Frank E, Houck P, et al. Prevalence of obesity and weight change during treatment in patients with bipolar I disorder. J Clin Psychiatry. 2002;63:528–533. [PubMed]
11. Fagiolini A, Kupfer D, Houck P, Novick D, Frank E. Obesity as a correlate of outcome in patients with bipolar I disorder. Am J Psychiatry. 2003;160:112–117. [PubMed]
12. McLaughlin T, Allison G, Abbasi F, Lamendola C, Reaven G. Prevalence of insulin resistance and associated cardiovascular disease risk factors among normal weight, overweight, and obese individuals. Metabolism. 2004;53:495–499. [PubMed]
13. Gerich J. Contributions of insulin-resistance and insulin-secretory defects to the pathogenesis of type 2 diabetes mellitus. Mayo Clin Proc. 2003;78:447–456. [PubMed]
14. Scollan-Koliopoulos M, O'Connell KA, Walker EA. The first diabetes educator is the family: using illness representation to recognize a multigenerational legacy of diabetes. Clin Nurse Spec. 2005;19:302–307. [PubMed]
15. Goldfine A, Bouche C, Parker R, et al. Insulin resistance is a poor predictor of type 2 diabetes in individuals with no family history of diabetes. Proc Nat Acad Sci. 2003;100:2724–2729. [PubMed]
16. Balkau B, Lange C, Fezeu L, et al. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR) Diabetes Care. 2008;31:2056–2061. [PMC free article] [PubMed]
17. Ghosh A, Liu T, Khoury MJ, Valdez R. Family history of diabetes and prevalence of the metabolic syndrome in U.S. adults without diabetes: 6-Year results from the National Health and Nutrition Examination Survey (1999–2004) Public Health Genomics. 2009 DOI: 10.1159/000262330. [PubMed]
18. Rasgon N, Altshuler L, Gudeman D, et al. Medication status and polycystic ovary syndrome in women with bipolar disorder: a preliminary report. J Clin Psychiatry. 2000;61:173–178. [PubMed]
19. Rasgon N, Bauer M, Glenn T, Elman S, Whybrow PC. Menstrual cycle related mood changes in women with bipolar disorder. Bipolar Disord. 2003;5:48–52. [PubMed]
20. Montgomery S, Åsberg M. A new depression scale designed to be sensitive to change. Br J Psychiatry. 1979;134:382–389. [PubMed]
21. Young R, Biggs J, Ziegler V, Meyer D. A rating scale for mania: reliability, validity, and sensitivity. Br J Psychiatry. 1978;133:429–435. [PubMed]
22. Yeni-Komshian H, Carantoni M, Abbasi F, Reaven G. Relationship between several surrogate estimates of insulin resistance and quantification of insulin-mediated glucose disposal in 490 healthy nondiabetic volunteers. Diabetes Care. 2000;23:171–175. [PubMed]
23. Kim S, Abbasi F, Reaven G. Impact of degree of obesity on surrogate estimates of insulin resistance. Diabetes Care. 2004;27:1998–2002. [PubMed]
24. Matthews D, Hosker J, Rudenski A, Naylor B, Treacher D, Turner R. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–419. [PubMed]
25. Newcomer J. Antipsychotic medications; metabolic and cardiovascular risk. J Clin Psychiatry. 2007;68:8–13. [PubMed]
26. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002;288:1723–1727. [PubMed]
27. Elmslie JL, Silverstone JT, Mann JI, Williams SM, Romans SE. Prevalence of overweight and obesity in bipolar patients. J Clin Psychiatry. 2000;61:179–184. [PubMed]
28. Cassidy F, Ahearn E, Carroll B. Elevated frequency of diabetes mellitus in hospitalized manic-depressive patients. Am J Psychiatry. 1999;156:1417–1420. [PubMed]
29. Goodwin F, Jamison K. Manic-Depressive Illness. New York, NY: Oxford University Press; 1990.
30. Keck P, McElroy S. Bipolar disorder, obesity, and pharmacotherapy-associated weight gain. J Clin Psychiatry. 2003;64:1426–1435. [PubMed]
31. Masand P, Culpepper L, Henderson D, et al. Metabolic and endocrine disturbances in psychiatric disorders: a multidisciplinary approach to appropriate atypical antipsychotic utilization. CNS Spectr. 2005;10:1–15. [PubMed]
32. Elmslie JL, Mann JI, Silverstone JT, Williams SM, Romans SE. Determinants of overweight and obesity in patients with bipolar disorder. J Clin Psychiatry. 2001;62:486–491. [PubMed]
33. Taylor V, MacQueen G. Associations between bipolar disorder and metabolic syndrome: A review. J Clin Psychiatry. 2006;67:1034–1041. [PubMed]
34. Maina G, Salvi V, Vitalucci A, D'Ambrosio V, Bogetto F. Prevalence and correlates of overweight in drug-naive patients with bipolar disorder. J Affect Disord. 2008;110:149–155. [PubMed]
35. Haupt D, Newcomer J. Abnormalities in glucose regulation associated with mental illness and treatment. J Psychosom Res. 2002;53:925–933. [PubMed]
36. Centers for Disease Control and Prevention. Washington, DC: U.S. Department of Health and Human Services; 2005. National diabetes fact sheet: general information and national estimates on diabetes in the United States, 2005.
37. Fleet-Michaliszyn SB, Soreca I, Otto AD, et al. A prospective observational study of obesity, body composition, and insulin resistance in 18 women with bipolar disorder and 17 matched control subjects. J Clin Psychiatry. 2008;69:1892–1900. [PMC free article] [PubMed]
38. Shen W, Punyanitya M, Chen J, et al. Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity. 2006;14:727–736. [PMC free article] [PubMed]
39. Mitchell BD, Zaccaro D, Wagenknecht LE, et al. Insulin sensitivity, body fat distribution, and family diabetes history: the IRAS Family Study. Obes Res. 2004;12:831–839. [PubMed]
40. Nicklas BJ, Cesari M, Penninx BW, et al. Abdominal obesity is an independent risk factor for chronic heart failure in older people. J Am Geriatr Soc. 2006;54:413–420. [PubMed]
41. McIntyre RS, Rasgon NL, Kemp DE, et al. Metabolic syndrome and major depressive disorder: co-occurrence and pathophysiologic overlap. Curr Diab Rep. 2009;9:51–59. [PubMed]