Metabolic Comorbidity and Physical Health Implications for Bipolar Disorder: An Update
Abstract
Medical comorbidities are overrepresented in individuals with bipolar disorder, including, but not limited to, cardiovascular conditions, autoimmune diseases, cancer, and metabolic disorders. Overweight/obesity, metabolic syndrome, and type 2 diabetes mellitus are highly prevalent in individuals with bipolar disorder. Evidence from epidemiological studies indicates that the aforementioned medical comorbidities are responsible for significant morbidity and early mortality. Results from disparate studies indicate that metabolic comorbidities modify and complicate the clinical presentation of bipolar disorder; metabolic comorbidities are associated with a more chronic and severe course of illness, as well as resistance to pharmacological treatment. Furthermore, co-occurrence of obesity or type 2 diabetes mellitus and bipolar disorder has been suggested to contribute to cognitive dysfunction, a core feature of bipolar disorder and a principal mediator of functional disability, highlighting the clinical relevance of this association. Multiple factors contribute to high frequency of metabolic comorbidities in individuals with bipolar disorder. An overlap of risk factors, including psychosocial stress, adverse socioeconomic conditions, and childhood trauma, between the two conditions has been documented. Inadequate diet and sedentary lifestyle, as well as treatment-emergent adverse effects have also been shown to play a role. Nonetheless, accumulating evidence indicates that the relationship between bipolar disorder and metabolic illnesses is bidirectional. Results from mechanistic studies indicate that interacting physiological systems that mediate metabolism are also involved in pathophysiological processes of bipolar disorder. The high prevalence and substantial impact of metabolic comorbidities on the clinical outcome in bipolar disorder underscore the need for prioritizing the management of metabolic health in this clinical population.
Introduction
Bipolar disorder (BD) is a highly prevalent syndrome, with an estimated global prevalence of approximately 1.5% when narrowly defined (1, 2). Bipolar disorder often pursues a chronic, unremitting course and is associated with substantial morbidity. Bipolar disorder is a leading cause of years lived with disability (YLDs) and disability-adjusted life years (DALY), highlighting BD as a public health priority (3–5). Moreover, it has been amply documented that the mortality rate of individuals with BD is greater than that of the general population. Mortality studies indicate that the excess and premature deaths in BD are largely a consequence of natural causes (e.g., cardiovascular disease, diabetes mellitus) rather than unnatural causes (e.g., suicide) (6–9). For example, a cohort study estimated that individuals with BD lost approximately 9 years of potential year of life compared with the general population (8).
The alarming rate of medical comorbidity in BD has provided the impetus to prioritize research in psychiatry toward the investigation of traditional and emerging risk factors that predispose and portend medical comorbidity. Individuals with BD are differentially affected by a wide range of communicable and noncommunicable medical illnesses (10), which are characterized by higher prevalence and earlier age of onset of medical conditions, including, but not limited to, cardiovascular conditions, hypertension, metabolic imbalances, autoimmunity, and cancer (8, 11–17). Conversely, overweight/obesity has been reported to increase the risk of onset of significant depressive symptoms and manic episodes (18–21). Considering the high impact of obesity and BD on disability and morbidity, the co-occurrence of these conditions is pertinent not only in the clinical ecosystem but also from a public health perspective, given the additive illness-associated burden imparted by concurrent medical disorders (22–24).
Epidemiology and Clinical Impact
Epidemiological studies have reported that the prevalence of obesity and metabolic syndrome is substantially increased in individuals with BD when compared with the general population, by a factor of twofold (24–26). The occurrence of comorbid obesity is increased in later stages of the illness (26–28); nonetheless, individuals in early stages are also significantly affected (29–31). Evidence indicates that one-third of youth (age 6–18) with BD exhibited 2 or more chronic health conditions (29). Women with BD, when compared with men with BD, have higher rates of abdominal obesity; however, overall obesity is more frequent in both sexes when compared with the general population (32).
The presence of comorbid obesity has been linked to a distinct and more complicated clinical presentation of BD. Evidence from clinical studies indicates that obesity predisposes BD patients to a predominantly depressive illness, insofar as the duration of depressive episodes tends to be longer and hospitalizations for depression are more frequent (33). Furthermore, a more severe and chronic course of illness, with higher functional disability, as well as an increased risk of suicide, were reported in adults with BD with comorbid obesity (34, 35). Comorbid anxiety disorders are also reported to be more common in overweight/obese individuals with BD (35). Moreover, obesity has been suggested to negatively impact treatment outcomes in BD. For example, results from a clinical trial indicated that higher BMI is associated with poor response to pharmacological treatment, including lithium and valproate (36).
More recently, the role of metabolic comorbidities as moderators of cognitive function has been increasingly recognized (37–45). Cognition has been considered a core dimension of psychopathology in BD (46–50). Cognitive dysfunction has been consistently demonstrated as a core dimension of psychopathology in BD across a number of studies that reported small to moderate overall effect sizes; nonetheless, approximately 25%–50% of the patients exhibited pronounced deficits (48–50). Cognitive deficit in mood disorders is noted to be a principal mediator of psychosocial impairment and disability, independent of concurrent mood symptoms (51–53), and disproportionately accounts for the overall illness-associated costs (54).
Multiple metabolic abnormalities are independently associated with poor cognitive function. Studies in both nonclinical and clinical populations have shown that impaired glucose metabolism and insulin resistance (38, 39, 41–43, 45), visceral adiposity (37, 43), dyslipidemia (40, 45), and high blood pressure (40, 44) are related to impaired executive function. Overweight/obesity, metabolic syndrome, and type 2 diabetes mellitus have all been consistently shown to negatively impact several cognitive domains (55–58). The relevance of the effects of metabolic diseases in neurocognitive function is further underscored by evidence indicating that weight loss in overweight/obese individuals significantly improves cognitive performance (59, 60).
Neurocognitive dysfunction is more severe in overweight/obese individuals when compared with normal weight patients with a mood disorder as evidenced by poor performance in tests measuring attention and psychomotor processing speed, independent of mood symptom severity (61, 62). The role of cognition as moderator of mood disorders can be conceptualized in two nonmutually exclusive ways. Metabolic and mood disorders are independently associated with poor cognitive performance; therefore, the co-occurrence of these conditions may have a synergistic detrimental effect (61–63), consequently leading to greater cognitive impairment. Cognitive dysfunction has been contemplated as a vulnerability factor for mood disorders (64, 65). Cognitive factors, particularly those related to cognitive control, regulation of motivation and reward, as well as the cognitive response to stress, have also been reported to be prominently involved in the development of obesity (66–68). Taken together, neurocognition could be conceptualized as a factor with broader modulatory effect, concurrently regulating the risk for both conditions and moderating the relationship between the conditions.
A meta-analysis of cross-sectional and cohort studies documented a bidirectional association between mood disorders and metabolic syndrome (69). Individuals with BD have been shown to have increased waist circumference, higher proportion of visceral adiposity, raised levels of serum lipids, and higher incidence of hypertension, and consequently, to meet criteria for metabolic syndrome more frequently (16, 70–74). Similar to comorbid obesity, metabolic syndrome has been associated with a more severe disease course and higher risk of suicide in BD patients (15, 70).
A bidirectional relationship between mood disorders and diabetes mellitus has also been proposed. The rate of mood disorders is increased in type 2 diabetes mellitus and vice versa independent of the presence of overweight or obesity (75, 76). The risk of developing type 2 diabetes mellitus is 3 times higher in individuals with BD (13). Furthermore, studies have shown increased morbidity in BD patients with comorbid type 2 diabetes mellitus (77, 78), insofar as individuals with BD and type 2 diabetes mellitus were more likely to have a chronic course of illness, rapid mood cycling, and worse functional impairment, when compared with nondiabetic subjects (77). Insulin resistance (IR), a precursor of type 2 diabetes mellitus and a key marker of metabolic dysfunction and IR prevalence, is also significantly elevated in individuals with BD (79).
Risk Factors
Metabolic disorders and BD share several environmental risk factors. Traditionally, chronic psychosocial stress is accepted as one of the most significant triggers of mood episodes and has been consistently associated with weight gain and subsequent development of obesity (80–82). Evidence from epidemiological studies demonstrates a high prevalence of adverse socioeconomic situations, including poverty, social isolation, lack of support, and low education, in both obese and BD patients (22, 83, 84). Nonetheless, one of the more compelling convergent causative factors is childhood trauma. A history of physical, emotional, or sexual abuse is well established as one of the most impactful environmental risk factors for BD (85–87). More recently, accumulating evidence indicates that early adversity in the form of traumatic experiences has a significant impact on metabolic health; early adversity has been shown to increase the risk of obesity, type 2 diabetes mellitus, and metabolic syndrome in adulthood (88–91).
Emerging evidence highlights the possible roles of inadequate diet and lack of physical exercise, the two mainstays of weight gain, in the onset of BD illness (92–95). Epidemiological data indicates that individuals with BD, on average, have an excessive caloric intake and high glycemic load (94, 95). Moreover, reduced intake of polyunsaturated fatty acids, including, but not limited to, eicosapentaenoic acid and docosahexaenoic acid, has been reported among BD patients (96, 97). Nonetheless, there are contradictory reports to suggest that BD patients consume fewer total calories from carbohydrates and fats when compared with healthy controls (98). Evidence from studies using subjective and objective measurements suggests that individuals with BD were significantly less active and more sedentary than the general population (95, 99).
Treatment-emergent adverse events are also known to contribute to the ever-increasing rates of obesity and the metabolic syndrome. Notwithstanding the well-documented detrimental metabolic effects of atypical antipsychotics (e.g., risperidone, olanzapine, quetiapine), these therapeutic drugs have been increasingly used in the treatment of BD (100). Lithium and valproic acid have also been associated with clinically significant weight gain, as have several of the second-generation antipsychotics (101, 102). Among the most commonly prescribed medications for the treatment of BD, only lamotrigine, aripiprazole, ziprasidone, asenapine, and lurasidone are considered to be “metabolically neutral”; therefore, it is highly likely that most individuals with BD were exposed, at one point or another, to a pharmacological therapy that could potentially promote weight gain and/or metabolic abnormalities.
Despite the importance of the foregoing contributing factors to metabolic abnormalities associated with BD, preliminary evidence demonstrates that these factors alone may not explain the obesity-BD association in its entirety. For example, a study reported an overweight/obesity prevalence of 40.8% among drug-naive BD patients, significantly higher than the comparison group (30). Another study evaluating subjects in the acute phase of treatment found no significant differences in metabolic abnormalities between subjects with and without a history of psychotropic medication (31). A recent study using a large and heterogeneous sample found that although antidepressants, mood stabilizers, and antipsychotics were associated with low HDL and high triglyceride levels, they were not associated with hypertension, hyperlipidemia, or diabetes (103).
Obesity and metabolic abnormalities have been documented as risk factors for the development of mood changes and depression. Diabetes and markers of glucose metabolism, such as fasting glucose and glycated hemoglobin, were associated with a higher risk for the development of depressive symptoms in follow-up studies (104, 105). Similar results were found with obesity, particularly when accompanied by other cardiovascular risk factors (e.g., blood pressure, lipids levels) (106). A recent study linked obesity in childhood and adolescence to a higher risk of depression in adulthood (107), while a separate cohort study reported that a pre-existing diagnosis of obesity is associated with an adolescent onset diagnosis of BD (108). Moreover, obesity and BD share several genetic risk factors, as well as abnormalities in biological systems (e.g., hypothalamic-pituitary-adrenal axis, inflammation). These results indicate that interacting physiological systems that mediate metabolism and manifest phenotypically as metabolic disturbance (e.g., obesity) may underlie etiopathological and psychopathological characteristics of BD.
Management
The high prevalence and impact of metabolic comorbidities in BD provide the impetus to prioritize the management of metabolic health in clinical practice. Clinicians are encouraged to screen and systematically monitor for comorbid conditions in all individuals with BD. Accurate diagnosis and appropriate treatment of metabolic comorbidities are imperative to ensure optimal health outcome in BD patients (109, 110). Dietary intake and physical exercise, as described previously, are relevant factors that should be routine targets of inquiry and recommendations in the clinical practice. Despite the well-known difficulties of adherence to diet and exercise recommendations experienced by most clinicians, recent clinical trials have reported successful results. A behavioral weight-loss intervention for individuals with serious mental illness, including BD, characterized by tailored weight-management sessions and exercise sessions, was shown to significantly reduce weight in overweight and obese patients (111). Moreover, evidence from pharmacological and nonpharmacological weight-loss interventions in major depressive disorder have reported positive effects of weight reduction in mood symptoms (112–115). Nonetheless, there is an unmet need for the development of empirically based therapeutic strategies specifically focused on the management of weight and lifestyle.
Metabolic dysfunction in BD may also be relevant in the context of prevention. A population-based study comprising 800,000 subjects reported that individuals with untreated type 2 diabetes mellitus had a 2.6-fold increase in the risk of developing BD. However, individuals receiving a combination of sulfonylurea and metformin had a significantly reduced incidence of BD, suggesting that treating diabetes may prevent the onset of BD (116). As the field of psychiatry moves toward a model centered on preemptive and preventive strategies, metabolic-based approaches have been considered to be particularly promising, insofar as they involve nonpharmacological interventions (e.g., diet and physical exercise), which may be safely and ethically applied to a wide array of at-risk individuals.
Limitations
Notwithstanding the accumulating body of evidence linking obesity, metabolic abnormalities, and BD, there are important methodological limitations that should be considered. A significant number of studies failed to find an association between these conditions or could not support the idea that this association incurred in differences in phenomenology, trajectory, or treatment response (117–119). This variability in results is meaningful and could be explained by some factors. First, most of the aforementioned studies did not consider age and gender in the analyses. As it is known that weight and mood disorders, as well as other important clinical characteristics, are distributed differently between age groups or gender (32), this omission could have biased some of the results. Second, BD is known to be phenotypically and pathogenically heterogeneous. Genetic and psychometric studies have reported that the symptomatic criteria for BD do not reflect a single underlying factor (120), and, as a result, there is a noteworthy interindividual variability in risk/resilience factors, phenomenology, trajectory, and treatment response. Overall, evidence indicates that the same is true for the incidence and impact of medical comorbidities, and, therefore, it is likely that only a subset of individuals with BD are more vulnerable to the effects of medical comorbidities described in this review.
1 : Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry 2005; 62:593–602; Erratum in Arch Gen Psychiatry. 2005 Jul;62(7):768Crossref, Google Scholar
2 : Lifetime prevalence of psychotic and bipolar I disorders in a general population. Arch Gen Psychiatry 2007; 64:19–28Crossref, Google Scholar
3 Mathers, C.D., A.D. Lopez, and C.J.L. Murray, The Burden of Disease and Mortality by Condition: Data, Methods, and Results for 2001. 2006Google Scholar
4 : Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380:2163–2196Crossref, Google Scholar
5 : Estimating remission from untreated major depression: a systematic review and meta-analysis. Psychol Med 2013; 43:1569–1585Crossref, Google Scholar
6 : Comorbid somatic illnesses in patients with severe mental disorders: clinical, policy, and research challenges. J Clin Psychiatry 2008; 69:514–519Crossref, Google Scholar
7 : The metabolic syndrome, depression, and cardiovascular disease: interrelated conditions that share pathophysiologic mechanisms. Med Clin North Am 2006; 90:573–591Crossref, Google Scholar
8 : Comorbidities and mortality in bipolar disorder: a Swedish national cohort study. JAMA Psychiatry 2013; 70:931–939Crossref, Google Scholar
9 : Lifetime manic spectrum episodes and all-cause mortality: 26-year follow-up of the NIMH Epidemiologic Catchment Area Study. J Affect Disord 2013; 151:337–342Crossref, Google Scholar
10 : Prevalence and burden of general medical conditions among adults with bipolar I disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions. J Clin Psychiatry 2009; 70:1407–1415Crossref, Google Scholar
11 : Excess mortality in bipolar and unipolar disorder in Sweden. Arch Gen Psychiatry 2001; 58:844–850Crossref, Google Scholar
12 : A high prevalence of organ-specific autoimmunity in patients with bipolar disorder. Biol Psychiatry 2004; 56:476–482Crossref, Google Scholar
13 : Bipolar disorder and diabetes mellitus: epidemiology, etiology, and treatment implications. Ann Clin Psychiatry 2005; 17:83–93Crossref, Google Scholar
14 : Medical comorbidity in bipolar disorder: implications for functional outcomes and health service utilization. Psychiatr Serv 2006; 57:1140–1144Link, Google Scholar
15 : Bipolar disorder and the metabolic syndrome: causal factors, psychiatric outcomes and economic burden. CNS Drugs 2008; 22:655–669Crossref, Google Scholar
16 : Bipolar disorder and metabolic syndrome: a systematic review. Rev Bras Psiquiatr 2013; 35:88–93Crossref, Google Scholar
17 : Immune dysregulation and autoimmunity in bipolar disorder: Synthesis of the evidence and its clinical application. Aust N Z J Psychiatry 2013; 47:1136–1151Crossref, Google Scholar
18 : Associations of obesity with psychiatric disorders and suicidal behaviors in a nationally representative sample. J Psychosom Res 2009; 66:277–285Crossref, Google Scholar
19 : Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 2010; 67:220–229Crossref, Google Scholar
20 : Association of depressive disorders, depression characteristics and antidepressant medication with inflammation. Transl Psychiatr 2012; 2:e79Crossref, Google Scholar
21 : Does obesity predict bipolarity in major depressive patients? J Affect Disord 2014; 155:118–122Crossref, Google Scholar
22 : Epidemiologic evidence for the relation between socioeconomic status and depression, obesity, and diabetes. J Psychosom Res 2002; 53:891–895Crossref, Google Scholar
23 : Changes in cardiovascular disease burden associated with psychopathology in Australian adults 2004-2008. Gen Hosp Psychiatry 2012; 34:345–351Crossref, Google Scholar
24 : General medical burden in bipolar disorder: findings from the LiTMUS comparative effectiveness trial. Acta Psychiatr Scand 2014; 129:24–34Crossref, Google Scholar
25 : Metabolic syndrome in bipolar disorder: a cross-sectional assessment of a Health Management Organization database. Bipolar Disord 2008; 10:607–616Crossref, Google Scholar
26 : Bipolar disorder and metabolic syndrome: an international perspective. J Affect Disord 2010; 126:366–387Crossref, Google Scholar
27 : Overweight and obesity in patients with bipolar disorder or schizophrenia compared with a non-psychiatric sample. Prog Neuropsychopharmacol Biol Psychiatry 2012; 37:169–175Crossref, Google Scholar
28 : Diagnosing and treating comorbid (complicated) bipolar disorder. J Clin Psychiatry 2004; 65(Suppl 15):35–44Google Scholar
29 : Medical comorbidity among youth diagnosed with bipolar disorder in the United States. J Clin Psychiatry 2009; 70:1461–1466Crossref, Google Scholar
30 : Prevalence and correlates of overweight in drug-naïve patients with bipolar disorder. J Affect Disord 2008; 110:149–155Crossref, Google Scholar
31 : Correlates of metabolic abnormalities in bipolar I disorder at initiation of acute phase treatment. Psychiatry Investig 2009; 6:78–84Crossref, Google Scholar
32 : Sex differences in rates of obesity in bipolar disorder: postulated mechanisms. Bipolar Disord 2014; 16:83–92Crossref, Google Scholar
33 : The burden of obesity among adults with bipolar disorder in the United States. Bipolar Disord 2011; 13:387–395Crossref, Google Scholar
34 : Bipolar disorder and suicide: research synthesis and clinical translation. Curr Psychiatry Rep 2008; 10:66–72Crossref, Google Scholar
35 : Can body mass index help predict outcome in patients with bipolar disorder? Bipolar Disord 2009; 11:650–656Crossref, Google Scholar
36 : Medical comorbidity in bipolar disorder: relationship between illnesses of the endocrine/metabolic system and treatment outcome. Bipolar Disord 2010; 12:404–413Crossref, Google Scholar
37 : Metabolic and endocrine correlates of cognitive function in healthy young women. Obesity (Silver Spring) 2013; 21:1343–1349Crossref, Google Scholar
38 : Impaired glucose regulation is associated with poorer performance on the Stroop Task. Physiol Behav 2013; 122:113–119Crossref, Google Scholar
39 : Fasting plasma insulin and the default mode network in women at risk for Alzheimer’s disease. Neurobiol Aging 2013; 34:641–649Crossref, Google Scholar
40 : Biological correlates of adult cognition: midlife in the United States (MIDUS). Neurobiol Aging 2014; 35:387–394Crossref, Google Scholar
41 : Executive functions and information processing in patients with type 2 diabetes in comparison to pre-diabetic patients. J Diabetes Metab Disord 2014; 13:27Crossref, Google Scholar
42 : The impact of glucose disorders on cognition and brain volumes in the elderly: the Sydney Memory and Ageing Study. Age (Dordr) 2014; 36:977–993Crossref, Google Scholar
43 : Relationship between markers of insulin resistance, markers of adiposity, HbA1c, and cognitive functions in a middle-aged population-based sample: the MONA LISA study. Diabetes Care 2013; 36:1512–1521Crossref, Google Scholar
44 : Abnormal levels of brain metabolites may mediate cognitive impairment in stroke-free patients with cerebrovascular risk factors. Age Ageing 2014; 43:681–686Crossref, Google Scholar
45 Yogi-Morren D, Galioto R, Strandjord SE, Kennedy L, Manroa P, Kirwan JP, Kashyap S, Gunstad J: Duration of type 2 diabetes and very low density lipoprotein levels are associated with cognitive dysfunction in metabolic syndrome. Cardiovasc Psychiatry Neurol, 2014; 2014:656341Google Scholar
46 : Meta-analyses of cognitive functioning in euthymic bipolar patients and their first-degree relatives. Psychol Med 2008; 38:771–785Crossref, Google Scholar
47 : Cognitive endophenotypes of bipolar disorder: a meta-analysis of neuropsychological deficits in euthymic patients and their first-degree relatives. J Affect Disord 2009; 113:1–20Crossref, Google Scholar
48 : Neuropsychological testing of cognitive impairment in euthymic bipolar disorder: an individual patient data meta-analysis. Acta Psychiatr Scand 2013; 128:149–162Crossref, Google Scholar
49 : Meta-analysis of neuropsychological functioning in euthymic bipolar disorder: an update and investigation of moderator variables. Bipolar Disord 2011; 13:334–342Crossref, Google Scholar
50 : A meta-analysis of neuropsychological functioning in first-episode bipolar disorders. J Psychiatr Res 2014; 57:1–11Crossref, Google Scholar
51 : Meta-analysis of the association between cognitive abilities and everyday functioning in bipolar disorder. Bipolar Disord 2012; 14:217–226Crossref, Google Scholar
52 : The relation between mood, cognition and psychosocial functioning in psychiatric disorders. Eur Neuropsychopharmacol 2012; 22(Suppl 3):S499–S504Crossref, Google Scholar
53 : The predictive significance of neurocognitive factors for functional outcome in bipolar disorder. Curr Opin Psychiatry 2013; 26:54–59Crossref, Google Scholar
54 : Cost of illness for bipolar disorder: a systematic review of the economic burden. Bipolar Disord 2014; 16:337–353Crossref, Google Scholar
55 : Elevated body mass index is associated with executive dysfunction in otherwise healthy adults. Compr Psychiatry 2007; 48:57–61Crossref, Google Scholar
56 : Cognitive dysfunction associated with metabolic syndrome. Obes Rev 2007; 8:409–418Crossref, Google Scholar
57 : Longitudinal examination of obesity and cognitive function: results from the Baltimore longitudinal study of aging. Neuroepidemiology 2010; 34:222–229Crossref, Google Scholar
58 : Diabetes and cognitive dysfunction. Lancet 2012; 379:2291–2299Crossref, Google Scholar
59 : Intentional weight loss in overweight and obese individuals and cognitive function: a systematic review and meta-analysis. Obes Rev 2011; 12:968–983Crossref, Google Scholar
60 : Cognitive function after bariatric surgery: evidence for improvement 3 years after surgery. Am J Surg 2014; 207:870–876Crossref, Google Scholar
61 : The effect of overweight/obesity on cognitive function in euthymic individuals with bipolar disorder. Eur Psychiatry 2012; 27:223–228Crossref, Google Scholar
62 : Association of obesity and treated hypertension and diabetes with cognitive ability in bipolar disorder and schizophrenia. Bipolar Disord 2014; 16:422–431Crossref, Google Scholar
63 : Cognitive function in adults with type 2 diabetes and major depression. Arch Clin Neuropsychol 2006; 21:787–796Crossref, Google Scholar
64 : Differentiation in the preonset phases of schizophrenia and mood disorders: evidence in support of a bipolar mania prodrome. Schizophr Bull 2007; 33:703–714Crossref, Google Scholar
65 : Symptoms and signs of the initial prodrome of bipolar disorder: a systematic review. J Affect Disord 2010; 126:1–13Crossref, Google Scholar
66 : Discrete neurochemical coding of distinguishable motivational processes: insights from nucleus accumbens control of feeding. Psychopharmacology (Berl) 2007; 191:439–459Crossref, Google Scholar
67 : Higher body fat percentage is associated with increased cortisol reactivity and impaired cognitive resilience in response to acute emotional stress. Int J Obes (Lond) 2009; 33:157–165Crossref, Google Scholar
68 : Obesity—a neuropsychological disease? Systematic review and neuropsychological model. Prog Neurobiol 2014; 114:84–101Crossref, Google Scholar
69 : Bidirectional association between depression and metabolic syndrome: a systematic review and meta-analysis of epidemiological studies. Diabetes Care 2012; 35:1171–1180Crossref, Google Scholar
70 : Metabolic syndrome in bipolar disorder: findings from the Bipolar Disorder Center for Pennsylvanians. Bipolar Disord 2005; 7:424–430Crossref, Google Scholar
71 : Salivary cortisol, serum lipids, and adiposity in patients with depressive and anxiety disorders. Metabolism 2009; 58:821–827Crossref, Google Scholar
72 : Active depression is associated with regional adiposity in the upper abdomen and the neck. Int J Psychiatry Med 2011; 41:271–280Crossref, Google Scholar
73 : Metabolic syndrome and metabolic abnormalities in bipolar disorder: a meta-analysis of prevalence rates and moderators. Am J Psychiatry 2013; 170:265–274Crossref, Google Scholar
74 : Metabolic syndrome in bipolar disorder: a review with a focus on bipolar depression. J Clin Psychiatry 2014; 75:46–61Crossref, Google Scholar
75 : The prevalence of co-morbid depression in adults with Type 2 diabetes: a systematic review and meta-analysis. Diabet Med 2006; 23:1165–1173Crossref, Google Scholar
76 : The prevalence of co-morbid depression in adults with Type 1 diabetes: systematic literature review. Diabet Med 2006; 23:445–448Crossref, Google Scholar
77 : Clinical features of bipolar disorder with and without comorbid diabetes mellitus. Can J Psychiatry 2003; 48:458–461Crossref, Google Scholar
78 : Treatment patterns and resource use among patients with comorbid diabetes mellitus and major depressive disorder. J Med Econ 2011; 14:440–447Crossref, Google Scholar
79 : Assessment of insulin resistance and metabolic syndrome in drug naive patients of bipolar disorder. Indian J Clin Biochem 2014; 29:51–56Crossref, Google Scholar
80 : Predictors of relapse in bipolar disorder: A review. J Psychiatr Pract 2006; 12:269–282Crossref, Google Scholar
81 : Stress, visceral obesity, and metabolic complications. Ann N Y Acad Sci 2006; 1083:77–110Crossref, Google Scholar
82 : A comparison of life events in patients with unipolar disorder or bipolar disorder and controls. Compr Psychiatry 2010; 51:157–164Crossref, Google Scholar
83 Sassi F, Devaux M, Church J: “Education and Obesity in Four OECD Countries”, in OECD Health Working Paper. 2009, OECD Publishing.Google Scholar
84 : Social inequalities in obesity and overweight in 11 OECD countries. Eur J Public Health 2013; 23:464–469Crossref, Google Scholar
85 : Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: a meta-analysis. Am J Psychiatry 2012; 169:141–151Crossref, Google Scholar
86 : The role of early life stress in adult psychiatric disorders: a systematic review according to childhood trauma subtypes. J Nerv Ment Dis 2013; 201:1007–1020Crossref, Google Scholar
87 : Childhood trauma in bipolar disorder. Aust N Z J Psychiatry 2013; 48:564–570Crossref, Google Scholar
88 : Exposure to early life trauma is associated with adult obesity. Psychiatry Res 2006; 142:31–37Crossref, Google Scholar
89 : Childhood abuse is associated with adiposity in midlife women: possible pathways through trait anger and reproductive hormones. Psychosom Med 2010; 72:215–223Crossref, Google Scholar
90 : Childhood physical abuse is associated with incident metabolic syndrome in mid-life women. Health Psychol 2013; 32:121–127Crossref, Google Scholar
91 : Metabolic consequences of stress during childhood and adolescence. Metabolism 2012; 61:611–619Crossref, Google Scholar
92 : A review of physical activity correlates in patients with bipolar disorder. J Affect Disord 2013; 145:285–291Crossref, Google Scholar
93 : A systematic review and meta-analysis of dietary patterns and depression in community-dwelling adults. Am J Clin Nutr 2014; 99:181–197Crossref, Google Scholar
94 : Diet quality in bipolar disorder in a population-based sample of women. J Affect Disord 2011; 129:332–337Crossref, Google Scholar
95 : Determinants of overweight and obesity in patients with bipolar disorder. J Clin Psychiatry 2001; 62:486–491, quiz 492–493Crossref, Google Scholar
96 : Cross-national comparisons of seafood consumption and rates of bipolar disorders. Am J Psychiatry 2003; 160:2222–2227Crossref, Google Scholar
97 : Dietary intake and plasma metabolomic analysis of polyunsaturated fatty acids in bipolar subjects reveal dysregulation of linoleic acid metabolism. J Psychiatr Res 2014; 57:58–64Crossref, Google Scholar
98 : Metabolic syndrome in bipolar disorder and schizophrenia: dietary and lifestyle factors compared to the general population. Bipolar Disord 2014; 16:277–288Crossref, Google Scholar
99 : Are adults with bipolar disorder active? Objectively measured physical activity and sedentary behavior using accelerometry. J Affect Disord 2014; 152-154:498–504Crossref, Google Scholar
100 : Comparative efficacy and tolerability of 15 antipsychotic drugs in schizophrenia: a multiple-treatments meta-analysis. Lancet 2013; 382:951–962Crossref, Google Scholar
101 : Lithium toxicity profile: a systematic review and meta-analysis. Lancet 2012; 379:721–728Crossref, Google Scholar
102 : Weight change associated with antiepileptic drugs. J Neurol Neurosurg Psychiatry 2013; 84:796–799Crossref, Google Scholar
103 : Medical burden in bipolar disorder: findings from the Clinical and Health Outcomes Initiative in Comparative Effectiveness for Bipolar Disorder study (Bipolar CHOICE). Bipolar Disord 2014 (Epub ahead of print: doi:
104 : Examining a bidirectional association between depressive symptoms and diabetes. JAMA 2008; 299:2751–2759Crossref, Google Scholar
105 : Haemoglobin A1c, fasting glucose and future risk of elevated depressive symptoms over 2 years of follow-up in the English Longitudinal Study of Ageing. Psychol Med 2011; 41:1889–1896Crossref, Google Scholar
106 : Risk of future depression in people who are obese but metabolically healthy: the English longitudinal study of ageing. Mol Psychiatry 2012; 17:940–945Crossref, Google Scholar
107 : Perceived and actual obesity in childhood and adolescence and risk of adult depression. J Epidemiol Community Health 2013; 67:81–86Crossref, Google Scholar
108 : A cohort study of the prevalence and impact of comorbid medical conditions in pediatric bipolar disorder. J Clin Psychiatry 2010; 71:1518–1525Crossref, Google Scholar
109 : The Canadian Network for Mood and Anxiety Treatments (CANMAT) task force recommendations for the management of patients with mood disorders and comorbid metabolic disorders. Ann Clin Psychiatry 2012; 24:69–81Google Scholar
110 : Managing medical and psychiatric comorbidity in individuals with major depressive disorder and bipolar disorder. Ann Clin Psychiatry 2012; 24:163–169Google Scholar
111 : A behavioral weight-loss intervention in persons with serious mental illness. N Engl J Med 2013; 368:1594–1602Crossref, Google Scholar
112 : Association between change in depression and change in weight among women enrolled in weight loss treatment. Gen Hosp Psychiatry 2010; 32:583–589Crossref, Google Scholar
113 : Intentional weight loss and changes in symptoms of depression: a systematic review and meta-analysis. Int J Obes (Lond) 2011; 35:1363–1376Crossref, Google Scholar
114 : A randomized controlled trial of behavioral weight loss treatment versus combined weight loss/depression treatment among women with comorbid obesity and depression. Ann Behav Med 2011; 41:119–130Crossref, Google Scholar
115 : Reliable change in depression during behavioral weight loss treatment among women with major depression. Obesity (Silver Spring) 2013; 21:E211–E218Crossref, Google Scholar
116 : Increased risk of affective disorders in type 2 diabetes is minimized by sulfonylurea and metformin combination: a population-based cohort study. BMC Med 2012; 10:150Crossref, Google Scholar
117 : Isolating the effect of major depression on obesity: role of selection bias. J Ment Health Policy Econ 2011; 14:165–186Google Scholar
118 : Obesity and the three-year longitudinal course of bipolar disorder. Bipolar Disord 2013; 15:284–293Crossref, Google Scholar
119 : Relationship between obesity and depression: characteristics and treatment outcomes with antidepressant medication. Psychosom Med 2013; 75:863–872Crossref, Google Scholar
120 : Evidence for multiple genetic factors underlying DSM-IV criteria for major depression. JAMA Psychiatry 2013; 70:599–607Crossref, Google Scholar
