Early Intervention in Bipolar Disorder
Publication: American Journal of Psychiatry
Abstract
Bipolar disorder is a recurrent disorder that affects more than 1% of the world population and usually has its onset during youth. Its chronic course is associated with high rates of morbidity and mortality, making bipolar disorder one of the main causes of disability among young and working-age people. The implementation of early intervention strategies may help to change the outcome of the illness and avert potentially irreversible harm to patients with bipolar disorder, as early phases may be more responsive to treatment and may need less aggressive therapies. Early intervention in bipolar disorder is gaining momentum. Current evidence emerging from longitudinal studies indicates that parental early-onset bipolar disorder is the most consistent risk factor for bipolar disorder. Longitudinal studies also indicate that a full-blown manic episode is often preceded by a variety of prodromal symptoms, particularly subsyndromal manic symptoms, therefore supporting the existence of an at-risk state in bipolar disorder that could be targeted through early intervention. There are also identifiable risk factors that influence the course of bipolar disorder, some of them potentially modifiable. Valid biomarkers or diagnosis tools to help clinicians identify individuals at high risk of conversion to bipolar disorder are still lacking, although there are some promising early results. Pending more solid evidence on the best treatment strategy in early phases of bipolar disorder, physicians should carefully weigh the risks and benefits of each intervention. Further studies will provide the evidence needed to finish shaping the concept of early intervention.
AJP AT 175 Remembering Our Past As We Envision Our Future
April 1925: Interpretations of Manic-Depressive Phases
Earl Bond and G.E. Partridge reviewed a number of patients with manic-depressive illness in search of a unifying endo-psychic conflict. They concluded that understanding either phase of illness was “elusive” and “tantalizing beyond reach.” (Am J Psychiatry 1925: 81: 643–662)
William J. Mayo (1861–1939) stated that “the aim of medicine is to prevent disease and prolong life; the ideal of medicine is to eliminate the need of a physician” (1). Hence, physicians have been trying for almost a century to find early interventions that would prevent the onset of diseases, or at least change their course. Big steps have been made in several fields of medicine, such as cardiology and oncology. When it comes to psychiatry, although there is ground for optimism, there is still a long way to go (2).
Difficulties concerning primary prevention and intervention in psychiatry arise mainly from the absence of a clear etiology. Consequently, psychiatry has focused more on tertiary prevention, that is, in the use of therapies aiming to minimize the consequences of clinically established disease rather than to prevent its occurrence (3). However, considering the high prevalence of mental illnesses, their significant contribution to global disease burden among young people, and their considerable impact on public health, the implementation of early interventions in psychiatry should be considered a major priority.
To achieve this goal, and since early intervention focuses on known risk factors and early signs of the illness, there is a growing interest in understanding the early course of psychiatric conditions. For bipolar disorder, until recently most information regarding early manifestations came from retrospective and cross-sectional studies, which have a high risk of recall bias and do not allow assessment of temporality. Still, current evidence suggests that bipolar disorder has a progressive nature (4–6), therefore supporting the existence of milder phases of the condition prior to the classic presentation of the illness. This progressive nature makes bipolar disorder an ideal candidate for early intervention strategies, especially considering that 50%−70% of people with bipolar disorder usually start to manifest mood symptoms before age 21 (7–12). This highlights the need for early interventions to prevent or at least delay the onset of the full syndromal illness during childhood, which is crucial to avoid impacts on normal developmental tasks and psychosocial or neurobiological deterioration (13) and to prevent future complications, such as the development of psychiatric comorbidities, impaired functioning, or premature death by suicide (14).
Noting that The American Journal of Psychiatry is commemorating its 175th year of publication, we see early intervention in bipolar disorder as one of the cutting-edge topics in psychiatry. Although there are limited data based on this concept arising from the area of psychoses, we believe that ongoing and forthcoming research in this field is going to have a long-lasting impact on the field as mental health care increasingly turns its focus to prevention (15). In fact, more than 20 years ago, The American Journal of Psychiatry published one of the first articles discussing the role of prodromes and precursors in major depression (16); 10 years later the journal published the first paper proposing early intervention to prevent substance abuse in first-episode bipolar disorder (17) and a landmark trial indicating that first-episode psychosis could be treated with lower dosages of antipsychotics than are used in multiple-episode psychoses (18). Hence, in this review we will focus on the results obtained in longitudinal studies assessing variables considered as predictors of conversion to bipolar disorder or of illness course, conducted in offspring at high familial risk for bipolar disorder, community cohorts, and pediatric populations with diagnoses of bipolar disorder. Finally, the available psychological and pharmacological intervention data in the early stages of bipolar disorder will be covered, as well as the point of view of the authors on future directions of the research on the issue.
Identifying Risk Factors and Prodromal Symptoms as Predictors of Bipolar Disorder Onset and Course
The identification of risk factors or prodromal symptoms defining an at-risk stage has important treatment implications, as early stages are anticipated to be likely to be more responsive to treatment and therefore may need less complex interventions (19, 20). Moreover, psychiatric treatments likely have a more beneficial impact when applied at an earlier stage of the disease (21). A key issue is that the at-risk state in most disorders, including bipolar disorder, is pleomorphic and nonspecific and has the potential to evolve into diverse formed phenotypes or no disorder.
Environmental Risk Factors
Although bipolar disorder has a high genetic loading (22), it is considered a multifactorial disease that is influenced by environmental factors (23), some of which might be used as targets of early intervention strategies since they can be potentially modified (24). Life events have been proposed as triggers of future bipolar disorder (25), but results are controversial. While some studies (26, 27) found a positive association between mean life events and risk of mood disorder, Wals and colleagues (28) found that stressful life events were not related to the onset of mood episodes after adjustment for prior anxious or depressive symptoms. Considering the impact of life events in illness trajectory, lifetime sexual abuse seems to be related to a worse course of bipolar disorder (29–32). Recent public outrage at institutional childhood sexual abuse and campaigns to address this in many countries are an exemplar of a policy approach that may have an impact on a critical risk (33). Antidepressant use in depressed youths also may be a risk factor (34), as antidepressants might induce (hypo)manic symptoms (35).
Substance misuse is a prevalent condition in mood disorders that worsens illness prognosis (36). Moreover, its presence has been related to an increased risk of bipolar disorder at follow-up in patients seeking help for depression, anxiety, or substance use disorder (37). Although some studies have found a lower prevalence of substance use disorder in patients with a first mania episode compared with multiple-episode patients (38–40), this finding suggests that primary prevention of a secondary condition, in this case substance abuse in patients with bipolar disorder, needs to be considered (40). Substance use disorder can be predicted by lifetime alcohol experimentation, lifetime oppositional defiant disorder and panic disorder, family history of substance use disorder, or low family cohesiveness (39); these risk factors show a compounding effect. Presence of mixed features also appears to increase the risk of developing substance use disorder (17). Smoking may be associated with an increased risk of psychiatric disorders from depression to schizophrenia (41). Of concern, even maternal smoking may increase risk in offspring (42, 43).
Biological Risk Factors
Family history of bipolar disorder is one of the more solid risk factors for bipolar disorder (44) and is a primary threshold from universal to indicated prevention strategies. Longitudinal studies conducted in bipolar offspring found that age at onset and mood disorder subtype of the probands influence the heritability and course of bipolar disorder (38, 45, 46). For instance, these studies showed that offspring of early-onset bipolar disorder probands were at an increased risk for any bipolar disorder (45, 46) and that lithium nonresponsiveness in parents was related to a poorer premorbid functioning, a more chronic course, and a higher prevalence of psychotic disorders in their offspring (38).
Neurodevelopmental factors are being studied as potential early markers of specific mental illness. A prebirth cohort study found that child developmental delay assessed with the Denver Developmental Screening Test, which measures fine and gross motor skills, language, and personal–social development, was a predictor of later mania but not of depression or psychosis (47). In the same study, premorbid cognitive ability predicted only psychosis (47). However, there are data indicating that children with the highest academic attainment may be at greatest risk of bipolar disorder, while those with the weakest grades were at moderately increased risk (48) (Table 1).
| Characteristic | Bipolar Disorder Prodromal Stage | Psychosis Prodromal Stage (145, 157) |
|---|---|---|
| Main risk factor | Family history of early-onset bipolar disorder | Family history of psychosis |
| Early symptoms | Subjective sleep disturbances, anxiety, depression | Attention problems, depression, anxiety, avolition, social difficulties, disorganization, sleep disturbances |
| Proximala symptoms | Subthreshold (hypo)manic symptoms | Subthreshold psychotic symptoms |
| Neurodevelopmental profile | Superior or low premorbid cognitive functioning | Verbal memory and processing speed deficits |
a
Proximal symptoms are those that appear closer to conversion to full symptomatic episode.
Prodromal Symptoms
Results from longitudinal studies indicate that bipolar offspring are at a higher risk of developing bipolar disorder than the general population (46, 49–51), but they are equally at risk of developing other psychopathology, such as major depressive disorder, anxiety disorders, or psychotic disorders (28, 38, 44, 45, 52–54) (Table 2). Similarly, adolescents from community cohort studies who developed bipolar disorder also exhibited significantly high rates of comorbid anxiety disorders and disruptive behavior disorders (55).
| Authors | Mean Follow-Up | N at Baseline (M/F) | Mean Age at Baseline (years) | Offspring Sample Description | Main Objectives | Assessment Tools | Conversion Rate to BSD | Main Findings | Offspring Exclusion Criteria | Limitations | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Pittsburgh Bipolar Offspring Study | |||||||||||
| Axelson et al., 2015 (49) | 6, 8 years | BO: 391 (200/191), CO: 248 (114/134) | BO: 11.9 (SD 3.7), CO: 11.8 (SD 3.6) | Offspring of patients with BD I or II | To study diagnostic differences between BO and CO; to describe the developmental trajectory of mood episodes and identify diagnostic precursors of full-threshold BD in BO | SCID, K-SADS-PL; subthreshold (hypo)mania was diagnosed using the BDNOS criteria from the COBY study, FH-RDC, the Hollingshead scale (SES) | 9.2% | There was a higher prevalence of BSD and MDE in BO as compared with CO. Nearly all non-mood axis I disorders were more prevalent in BO than in CO. Mania/hypomania was almost always preceded by mood and non-mood disorders. Distinct subthreshold episodes of (hypo)mania were the strongest predictor of progression to full threshold mania/hypomania in BO. | Mental retardation | The information collected for the interval between evaluations was retrospective; most offspring were not through the age of risk for onset of BD at their last assessment; low conversion rate; only a small proportion of the biological coparents had direct diagnostic interviews. | |
| Levenson et al., 2015 (56) | Not stated | BO: 386 (190/196), CO: 301 (144/157) | BO: 11.4 (SD 3.6), CO: 11.0 (SD 3.5) | Offspring of patients with BD I or II | To evaluate baseline sleep and circadian phenotypes in BO and CO; to evaluate whether baseline sleep and circadian phenotypes in the BO are associated with future conversion to BD | SCID, K-SADS-PL; subthreshold (hypo)mania was diagnosed using the BDNOS criteria from the COBY study, PDS, Tanner stages, Hollingshead scale (SES), SSHS | Not stated | Conversion to BSD among BD was significantly predicted by parental and child ratings of child frequent nighttime awakenings, by parental ratings of inadequate sleep, and by child report of time to fall asleep on weekends. | Mental retardation | Use of questionnaire-based proxy measures of sleep and circadian phenotypes; long average time span between baseline SSHS and conversion to BD; low conversion rate; cross-sectional nature of the analyses. | |
| Hafeman et al., 2016 (45) | Not stated | BO: 359 (176/183), CO: 220 (99/121) | BO: 11.6 (SD 3.6), CO: 11.7 (SD 3.4) | Offspring of patients with BD I or II | To assess dimensional symptomatic predictors of new-onset BSD in BO | FH-RDC, SCID, K-SADS-PL, CALS, CBCL, CADS, CHI, DBD, MFQ, SCARED, Hollingshead Four-Factor Index (SES); subthreshold (hypo)mania was diagnosed using the BDNOS criteria from the COBY study | 14.7% | The most important prospective dimensional predictors of new-onset BSD disorders were anxiety/depressive symptoms (baseline), affective lability (baseline and proximal), and subthreshold manic symptoms (proximal). There was an increased risk of new-onset BSD with earlier parental age at mood disorder onset. | Mental retardation | Follow-up visits every 2 years; low conversion rates; not all offspring were through the age of risk for onset of bipolar illness at their last assessment. | |
| Dutch Bipolar Offspring Study | |||||||||||
| Mesman et al., 2013 (52) | 12 years | BO: 108 (58/50) | BO: 16.5 (SD 2.00) | Offspring of parents with BD I or II and age 12 to 21 years | To provide data on the onset and developmental trajectories of mood disorders and other psychopathology in BO | K-SADS-PL, SCID | 13% (3% BD I) | 72% of BO developed psychopathology. In 88% of offspring with a BSD, the index episode was an MDE. In total, 24% of offspring with a UMD developed a BSD. Mood disorders were often recurrent, with high comorbidity rates, and started before age 25. | Children with a severe physical disease or disability or with an IQ below 70 | Small sample size; low generalizability to populations without familial risk; no control group; no data on prepubertal and early adolescent disorders or episodes; not assessing for BDNOS. | |
| Mesman et al., 2017 (61) | 12 years | BO: 107 (57/50) | BO: 16 (range 12–21) | Offspring of parents with BD I or II and age 12 to 21 years | To identify early symptomatic signs of BD in BO with a history of mood disorder (any mood disorder group); to explore the early symptomatic signs of first mood episode development in BO without a history of mood disorder (no mood disorder group) | K-SADS-PL | 2.6% (BD II) in the no mood disorder group, 34% (BD I and II) in the any mood disorder group | Subthreshold manic symptoms were the strongest predictor of BD onset in the any mood disorder BO group. Subthreshold depressive symptomatology was associated with first mood disorder onset. | Children with a severe physical disease or disability or with an IQ below 70 | Small sample size; low generalizability to populations without familial risk; only the baseline screen items of the K-SADS-PL were used. | |
| Canadian Bipolar Offspring | |||||||||||
| Duffy et al., 2013 (54) | 6.23 years | BO: 229 (93/136), CO: 86 (36/50) | BO: 16.35 (SD 5.34), CO: 14.71 (SD 2.26) | Offspring with only one parent with a diagnosis of BD I or II and no other major psychiatric comorbidity | To describe the cumulative incidence of anxiety disorders in BO compared with CO; to identify predictors of anxiety disorders in BO; to determine the association between antecedent anxiety disorders and subsequent mood disorders in BO | K-SADS-PL, HARS, SCAS, Hollingshead SES scale, EAS, CECA.Q | 14% | The cumulative incidence of anxiety disorders was higher and occurred earlier in BO compared with CO. High emotionality and shyness increased the risk of anxiety disorders. Anxiety disorders increased the adjusted risk of mood disorders. | Not stated | Low conversion rate; anxiety disorders preceded mainly MDD; some BO were affected with a mood diagnosis before completing the temperament measure; some offspring were not through the age of risk for onset of bipolar illness at their last assessment. | |
| Duffy et al., 2014 (38) | 6.29 years | BO: 229 (92/137), CO: 86 (36/50) | BO: 16.35 (SD 5.34), CO: 14.71 (SD 2.25) | Offspring with only one parent with a diagnosis of BD I or II and no other major psychiatric comorbidity | To estimate the differential risk of lifetime psychopathology between BO and CO; to compare the clinical course of mood disorders between BO subgroups (defined by the lithium response of the parent) | K-SADS-PL, HARS, SCAS, Hollingshead SES scale, EAS, CECA.Q | 13.54% | The adjusted cumulative incidence of BD was higher in BO than CO. There were no differences in lifetime risk of mood disorders between BO subgroups, except for schizoaffective disorder (all cases occurred in BO of lithium nonresponder parents). | Not stated | Retrospective data collection in some offspring; difficult to mask to family affiliation. | |
| Other offspring cohorts | |||||||||||
| Akiskal et al., 1985 (53) | 3 years | BO: 68 (39/29) | Not stated | Individuals with a parent or older sibling with BD I, less than 24 years old at intake, and looking for clinical attention within approximately 1 year of onset of psychopathologic manifestations | To chart the prospective course of early manifestations in the referred juvenile relatives of known bipolar adults | MCDQ, the Washington University schema | 57% | Acute depressive episodes and dysthymic–cyclothymic disorders are the most common psychopathologic features in the BO. Manic onset occurred after age 13. | Any first-degree relative with schizophrenia | No control group; influence of age at onset of parental illness and type of parental illness not assessed. | |
| Carlson et al., 1993 (51) | 3 years | BO: 125, CO: 108 | BO: 7–16 years | Children of parents with BD | To examine the relationship between attention and behavioral disorders in childhood and subsequent BD | Pupil Evaluation Inventory–Peers, ASSESS-Peers, DBRS- Teachers, DBRS- Mother and Father, the distractibility index of the digit span task, SADS-L, SCID (DSM-III), Social and Occupational Competence, GAS, bipolarity rating, SUD rating | 4.8% | In childhood, mild to moderate attention and behavior problems were significantly more frequent in BO than in CO. In young adulthood, fewer than half of the BO were free of significant psychopathology. | Not stated | Not stated | |
| Egeland et al., 2012 (50) | 16 years | BO: 115, CO: 106 | Not stated | BO from the CARE study in preschool or in school (younger than 14 years old) | To identify the pattern and frequency of prodromal symptoms/behaviors associated with onset of BD I during childhood or adolescence | CARE Interview instrument | 7.8% (BD I) | Higher conversion rates among BO. BO who converted to BD I showed a higher frequency of sensitivity, crying, hyperalertness, anxiety/worry, and somatic complaints during preschool years and of mood and energy changes, decreased sleep, and fearfulness during school years. | Small sample size; nonstandard interview instrument | ||
a
BD=bipolar disorder; BDNOS=bipolar disorder not otherwise specified; BO=bipolar offspring; BSD=bipolar spectrum disorder; CADS=Childhood Affective Dysregulation Scale; CALS=Child Affective Lability Scale; CARE=Children and Adolescent Research Evaluation; CBCL=Child Behavior Checklist; CECA.Q=Childhood Experiences of Care and Abuse Questionnaire; CHI=Children’s Hostility Inventory; CO=control offspring; COBY=Course and Outcome of Bipolar Youth; DBD=Disruptive Behavioral Disorders Rating Scale; DBRS=Devereux School Behavior Rating Scales; EAS=Early Adolescent Temperament Scale; FH-RDC=Family History–Research Diagnostic Criteria; GAS=Global Assessment Scale; HARS=Hamilton Anxiety Rating Scale; K-SADS-PL=Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version; MCDQ=Mood Clinic Data Questionnaire; MDD=major depressive disorder; MDE=major depressive episode; MFQ=Mood and Feelings Questionnaire; PDS=Petersen Pubertal Developmental Scale; SADS-L=Schedule for Affective Disorders–Present and Lifetime; SCARED=Screen for Child Anxiety Related Disorders; SCAS=Spence Children’s Anxiety Rating Scale; SCID=Structured Clinical Interview for DSM-IV Axis I Disorders; SES=socioeconomic status; SSHS=School Sleep Habits Survey; SUD=substance use disorder; UMD=unipolar mood disorder.
As there is strong evidence that the index (hypo)manic episode in both bipolar offspring and community cohorts is frequently preceded by other affective or nonaffective symptoms (38, 49, 52, 55), longitudinal studies have tried to disentangle whether any of these conditions can be considered as early symptoms of bipolar disorder and help to predict future bipolar disorder onset. For instance, in the Dutch bipolar offspring cohort, 88% of the offspring who developed a bipolar spectrum disorder initially presented with a depressive episode, with an average time to bipolar conversion of 5.1 years (52) (Table 2). Subjective sleep problems also may be related to the development of bipolar disorder (56) (Table 2), but more evidence is needed before any firm conclusions can be drawn. Childhood anxiety disorder has been described as a prodromal symptom of major mood disorders, but it seems more related to unipolar depression than to bipolar disorder (44, 54). Anxiety disorders, in turn, seem to be predicted by the temperamental traits of shyness and emotionality (54) (Table 2). In contrast, subthreshold (hypo)manic symptoms have emerged as a key predictor of the development of (hypo)mania in community (37, 57, 58), high-risk (59), and bipolar offspring (45, 49, 50, 60, 61) cohorts (Table 2), even after adjusting for risk factors associated with psychopathology, such as parental psychiatric morbidity (49, 58). Moreover, greater intensity of hypomanic symptoms or earlier age at onset is associated with an increased risk of progressing to bipolar I or II disorder among children and adolescents initially meeting operationalized criteria for bipolar disorder not otherwise specified (62, 63).
Some studies have focused on the predictive value of several dimensional factors and not only in categorical predictors (45, 50, 61). Data emerging from the Pittsburg Bipolar Offspring youth cohort (45) show that offspring of parents with bipolar disorder with significant symptoms of anxiety/depression, affective lability, and subsyndromal manic symptoms were at increased risk of developing bipolar spectrum disorders. While affective lability and anxiety/depression were elevated throughout follow-up in those who later developed bipolar disorder, manic symptoms increased up to the point of conversion. Offspring with all the above risk factors, and particularly those with parents with early-onset bipolar disorder, had a 49% risk of developing bipolar disorder. Similarly, in an Amish cohort of bipolar offspring (50), converters to bipolar disorder showed a higher prevalence of sensitivity, hyperalertness, anxiety, and somatic complaints during the preschool period and more mood and energy fluctuations, tearfulness, sleep disturbances, and fearfulness during school years. However, a meta-analysis reporting data on prodromal symptoms in pediatric and adult samples with bipolar disorder pointed out that even if there are some highly reported prodromal symptoms, the prodromal stage tends to differ between individuals (64).
As bipolar disorder usually first presents with a depressive episode (65), longitudinal studies have assessed the presence of prodromal symptoms of conversion from unipolar depression to bipolar disorder (Figure 1). The main replicated finding is the relationship between diagnosis of psychotic depression and switching to (hypo)mania (66–69). A recent meta-analysis identified a family history of bipolar disorder, an earlier age at depression onset, and the presence of psychotic symptoms as most robustly predicting conversion from depression to bipolar disorder (70). When focusing only on patients diagnosed with psychotic depression, it has been found that conversion to bipolar disorder is mainly related to early age at onset (67, 68), functional impairment (67), mixed features (69, 71), and previous hypomanic symptoms (72).
In summary, parental bipolar disorder, especially early-onset (e.g., <21 years old) parental bipolar disorder, is the most important single risk factor for developing bipolar disorder. In addition, if the youth has subsyndromal manic symptoms, which is the most consistent prodromal factor, and ongoing mood lability or irritability, anxiety, and depression, there is increased likelihood that this youth will develop bipolar disorder (Figure 2). However, the onset and severity of these symptoms are heterogeneous.
FIGURE 2. Putative Risk Factors and Prodromal Symptoms of Bipolar Disordera
a Several environmental risk factors for bipolar disorder have been proposed, such as stressful life events including sexual abuse, antidepressant use, or substance misuse like cocaine or alcohol misuse. Biological risk factors include family history of bipolar disorder or neurodevelopmental factors such as child developmental delay. Family history of bipolar disorder is one of the strongest risk factors for bipolar disorder, while sexual abuse has been consistently related to a worse illness course. Prodromal symptoms of bipolar disorder can be heterogeneous. Dimensional factors predictive of bipolar disorder include anxiety and depressive symptoms, mood lability, and psychosis or subjective sleep problems, but the most robust predictive factor is the presence of subthreshold (hypo)manic symptoms. Depressive episodes with an early onset and/or psychotic symptoms also seem to predict conversion to bipolar disorder. The interaction between risk factors and prodromal symptoms may lead to bipolar disorder, but the exact mechanisms remain unknown.
Helping Prediction of Bipolar Disorder Onset Through Screening Tools
The above predictors are based on studies that focus on groups as a whole, but they do not inform about the individual risk of developing bipolar disorder. Moreover, the prodromal symptoms are heterogeneous, requiring the assessment of each individual risk (64). Building on accumulated knowledge about early bipolar disorder symptoms, researchers have striven to design reliable screening tests and screening criteria that could help to predict conversion to bipolar disorder. However, reliable clinical scales to assess prodromal symptoms are still lacking. To date, the predictive value of four clinical scales has been tested in longitudinal studies: the General Behavior Inventory, a self-report measure useful to discriminate between mood and behavioral disorders; the Child Behavior Checklist–Pediatric Bipolar Disorder, a profile consisting of severe aggression, inattention, and mood instability; the Hypomanic Personality Scale; and the Hypomania Checklist–32 Revised scale (73–78). Higher scores on the depression scale of the General Behavior Inventory (74), higher scores on the Hypomanic Personality Scale (75, 76), and positive subthreshold hypomanic symptoms identified by the Hypomania Checklist–32 (77) were related to an increased risk of future mood disorder among bipolar offspring. In turn, the Child Behavior Checklist–Bipolar seems useful for predicting comorbid and impairing psychopathology rather than any one specific DSM-IV diagnosis (73, 78). It is worth mentioning that most participants without the Child Behavior Checklist–Bipolar phenotype did not manifest bipolar disorder, attention deficit hyperactivity disorder (ADHD), cluster B personality disorder, or multiple psychiatric comorbid conditions at young-adult follow-up assessment (negative predictive values of 86% to 95%) (78). An abbreviated version of the General Behavior Inventory, the Seven Up Seven Down, has also been proposed, but it failed to predict new onset of bipolar disorder (79).
Nevertheless, the combination of self-reports and clinical semistructured interviews might be a more accurate approach for clinical decision making than the use of a single scale. Moreover, the assessment of subsyndromal manic symptoms requires trained professionals, as subsyndromal symptoms are difficult to ascertain when assessing children or if comorbid disorders are present. When considering self-report measures, much discussion of the ideal informant has taken place (i.e., parents, offspring, or both), but findings consistently show the greatest validity for parent report, regardless of whether the parent has a diagnosis of mood disorder—one reason being that the degree of awareness of one’s own symptoms can influence youth self-report (80).
Besides these proposed screening tests, a set of ultra-high-risk criteria for bipolar disorder exists: the bipolar at-risk criteria developed by Bechdolf et al. (81). They comprise general criteria, such as being in the peak age range for the onset of the disorder, as well as subthreshold clinical and behavioral data and genetic risk. In a sample of help-seeking youths, individuals meeting the bipolar at-risk criteria transitioned significantly more to first-episode (hypo)mania than the group screening negative for the criteria (81). However, important potentially differentiating features such as Mitchell’s bipolar signature, including features such as psychomotor-retarded melancholia and atypical depression, are not explored in many risk indices (82). The Early Phase Inventory for Bipolar Disorders criteria (83) and the Bipolar Prodrome Symptom Scale, based on the At Risk for Mania Syndrome criteria (84), are promising screening tools, but they still need to be prospectively tested.
Similar to the existing risk calculators in medicine, the Pittsburgh Bipolar Offspring Study developed a risk calculator to predict the 5-year risk of developing bipolar disorder in offspring of parents with bipolar disorder (85). Including dimensional measures of mania, depression, anxiety, and mood lability; psychosocial functioning; and parental age of mood disorder, the model predicted onset of bipolar disorder with an area under the curve of 0.76. If replicated, in the future the risk calculator will be instrumental in the development of preventive treatments as well as for biological studies.
Helping Prediction of Bipolar Disorder Onset Through Biomarkers
Biological and behavioral biomarkers hold promise as objective and useful tools for identifying patients at higher risk of developing bipolar disorder (86). Although biomarkers and staging have not yet had an impact on the official classificatory systems for mental disorders, this is a stated goal of the DSM series (87).
Neuroimaging Biomarkers
In a sample of 98 young unaffected individuals at high familial risk of bipolar disorder and 58 healthy control subjects, the presence of maintained increased insula activation during a task involving executive and language processing could differentiate individuals at high risk of bipolar disorder who later develop depression from healthy control subjects and from those at high familial risk who did not develop a psychiatric disorder (88). Mourão-Miranda et al. (89) showed that the combination of machine learning techniques and functional MRI data collected during an emotional face gender-labeling task could not only discriminate control adolescents from bipolar offspring but also could be helpful in predicting which at-risk adolescents would eventually develop psychiatric disorders. Regarding differences between offspring of parents with schizophrenia and offspring of parents with bipolar disorder, Sugranyes et al. (90) found through repeated neuroimaging measures that schizophrenia offspring displayed cross-sectional reductions in surface area on the occipital lobe compared with bipolar offspring and community control subjects.
Peripheral Biomarkers
Antithyroid peroxidase antibody positivity (91), salivary cortisol levels (92), and cerebral metabolite concentrations measured by proton magnetic resonance spectroscopy (93) could not differentiate high-risk offspring from control offspring or predict conversion to bipolar disorder. However, preliminary findings from the Dutch Bipolar Offspring Study indicate that the monocytes of a large proportion of bipolar patients and their offspring, particularly those who later develop a mood disorder, aberrantly express messenger RNAs of inflammatory, trafficking, survival, and mitogen-activated protein kinase pathway genes compared with healthy control subjects (94). This aberrant neuroimmune state in bipolar offspring was found to be independent of lifetime or future mood disorders; hence, it might reveal a vulnerability for mood disorders rather than being a direct predictor (95, 96). In a prospective general-population U.K. birth cohort childhood study, higher levels of the systemic inflammatory marker IL-6 in childhood were associated with hypomanic symptoms in young adulthood, even after adjusting for sociodemographic variables, past psychological and behavioral problems, body mass index, and maternal postnatal depression (97).
Nevertheless, most of the identified alterations in peripheral blood in ultra-high-risk populations are shared between different psychiatric disorders, potentially predicting the onset of bipolar disorder, depression, or schizophrenia, but alone they are not able to reliably predict occurrence of bipolar disorder over another disorder. One study proposed a blood-based biomarker panel for diagnosing bipolar disorder, employing several different biomarkers. This panel, consisting mostly of immune-related biomarkers, was able to discriminate between recently diagnosed (less than 30 days) bipolar disorder and both recently diagnosed schizophrenia and healthy control subjects (60). This suggests that a single blood biomarker will likely not be useful for ascertaining diagnosis, but that a composite of several biomarkers, and probably other sources of information, will be required in order to achieve sufficient diagnostic properties for clinical utility.
Behavioral Biomarkers
A newly emerging biomarker in the form of ecological momentary assessment is arising from the ability to track behavioral data through mobile devices (98, 99). Hence, big data, such as geolocation, activity, Internet use, calls, and payments, can be analyzed and provide algorithms that might be used through machine learning techniques (100) as sources for risk surveillance and hence early personalized interventions (101).
Exploring Early Treatment Strategies in Bipolar Disorder
The underlying tenet of early diagnosis is to implement early treatment in order to prevent or delay progression to more advanced stages of the disease associated with greater disability (102). However, there are critical ethical issues pertaining to preventive interventions in at-risk individuals. Potential benefits need to be balanced against risks of preonset interventions. Key considerations include the individual’s knowledge, autonomy, and right to choose, ideally in an environment of stigma-free treatment (103).
Effective psychotherapeutic interventions, usually better received by patients and with a more favorable benefit–risk profile, can be an attractive first step in early intervention, although their effectiveness at these early stages needs to be determined (83). Post hoc analysis of many psychosocial interventions for bipolar disorder suggested greater efficacy if used earlier in the illness course (104). Psychoeducation programs have proven effective in preventing relapse in patients with established bipolar disorder and may be more useful earlier in the disorder (105, 106), but they have not been assessed in at-risk populations or pediatric bipolar disorder. Hence, group psychoeducation may be particularly indicated in patients with an established diagnosis of bipolar disorder but with a limited number of recurrences (107). Family-focused therapy, which combines psychoeducation sessions and training in communication and problem-solving skills, is the only psychological intervention tested in these populations. Results on this therapy are still controversial, although they suggest that it is related to longer affective stability and milder symptoms during follow-up (108, 109) when assessed in youths at high familial risk for bipolar disorder and diagnosis of bipolar disorder not otherwise specified, major depressive disorder, or cyclothymic disorder, or in adolescents with bipolar I or II disorder. Other interventions, such as multifamily psychoeducational psychotherapy (110) or interpersonal and social rhythm therapy (111), have shown some preliminary but promising results in reducing conversion rates and symptom severity among high-risk adolescents with a positive family history of bipolar disorder. Psychotherapies are not free of side effects (112); at early stages, when the diagnosis is not established, emphasis on diagnoses should be avoided, and it is more useful to target identified symptoms and helpful strategies (113). A number of online psychosocial interventions that are increasingly available have tentative data regarding their effectiveness (114, 115).
Choosing preventive pharmacological treatments in at-risk individuals is particularly complex. In the at-risk stage, we may be treating a population that might not convert to bipolar disorder, and the treatment of prodromal symptoms or comorbid conditions may involve medications with a potential risk of precipitating a manic episode, such as psychostimulants or notably antidepressants. Hence, even though some pharmacological treatments, such as lithium, are known to be more effective when started early in the course of the disease (116), the long- and short-term tolerability of each treatment and its potential to prevent bipolar disorder need to be carefully weighed against the individual risk of developing bipolar disorder. Some pilot studies have assessed the protective properties of valproate sodium and quetiapine against the onset of mania, with mixed results (117–119). Moreover, treatment with mood stabilizers or antipsychotics has known short- and long-term adverse effects (120), so their use as first-line treatment in at-risk youths might not be recommendable (121). For instance, valproate sodium has been associated with reproductive–endocrine abnormalities and should be used with caution in women of childbearing age (122, 123). Another scenario is posed when it comes to youths with bipolar disorder not otherwise specified. These youths have as much psychosocial impairment, as many comorbid disorders, and as much risk for suicide and substance abuse as those with bipolar disorder I, and they are at high risk of converting to bipolar I or II disorder (62, 124). Thus, until further research is available, we recommend treating them with the existing psychological and pharmacological treatments for youths with bipolar disorder, depending on factors such as the impact of the symptoms on the youth’s functioning and well-being and the individual risk of converting to bipolar I or II disorder.
Considering the feasibility of using nutritional supplements for primary prevention and the reported link between folate deficiency or omega-3 fatty acids and mood symptoms, these compounds have been proposed as a possible treatment in at-risk samples (121, 125). However, in a double-blind parallel-group placebo-controlled trial, Sharpley et al. (125) did not find any impact of folic acid supplementation on the incidence of mood disorder in a youth sample at increased familial risk of mood disorder. Post hoc analysis, though, suggested that folic acid might help to delay the time to onset of mood disorder (125). A recent study reported that omega-3 fatty acids failed to prevent conversion from at-risk mental state to threshold psychosis (126), yet results are limited by the low conversion rate in the placebo group. Thus, the efficacy of omega-3 fatty acids in high-risk populations needs further investigation (127). Anti-inflammatory strategies such as aspirin have demonstrated potential to reduce risk of depression in epidemiological studies. Aspirin is being explored as a potential preventive strategy for depression in a very large clinical trial of over 19,000 people (128). Hence, examining the potential protective effects of nutritional and tolerable pharmacological supplements remains a promising line of research (121). Potential treatments for cognitive dysfunction (cognitive enhancers) might come up in the near future and pose new ethical questions as to when and in whom to use them (129).
Regarding predictors of treatment response, there are no solid results yet (130, 131), but reported results do suggest a number of regions meriting further investigation, such as the gene coding for a subunit of the ligand-gated ionotropic glutamate receptor, GluR2/GLURB (131). A recent genome-wide study from the International Consortium on Lithium Genetics of 2,563 patients found a single locus of four linked single-nucleotide polymorphisms on chromosome 21 that met genome-wide significance criteria for association with lithium response (132). Furthermore, in an independent prospective study of 73 patients treated with lithium monotherapy for a period of up to 2 years, carrying the response-associated alleles was associated with a significantly lower rate of relapse (132). The pharmacogenetics of pharmacodynamic pathways, such as P450 enzymes and blood-brain barrier polymorphisms, is being explored as a predictor of antidepressant response (133), although not yet for mood stabilizers. However, sensitivity and specificity limitations mean that these genetic findings are not yet robust enough to guide treatment decisions.
Summary and Future Directions
The findings of this review support the notion of a prodromal state and a progressive course in bipolar disorder. This dynamic course fits in the model of clinical staging proposed by several authors (14, 134–137), which assumes that illnesses progress from an at-risk stage to a late and resistant stage.
A positive family history of bipolar disorder, particularly if the parents have early-onset bipolar disorder, is the most significant risk factor for developing a bipolar spectrum disorder. Regarding prodromal symptoms, the most robust result is that subthreshold (hypo)manic symptoms are the strongest predictor of bipolar conversion, both in studies focusing on bipolar offspring and in community youths. Consequently, this translates into a need for screening for attenuated mania-like symptoms when assessing young patients seeking help for mood lability, anxiety, depression, or behavioral disorders, especially among bipolar offspring (138). Moreover, preliminary findings indicate that bipolar offspring with an aberrant inflammatory state or changes in the volume or activity of the amygdala may be more vulnerable to developing a mood disorder, suggesting a potential role for these alterations as putative biomarkers for disease prediction in individuals at genetic risk (121, 139).
However, even if there is a promising emerging set of putative prodromal symptoms, biomarkers, and environmental risk factors, the possible additive or synergistic associations between all these factors remains a mystery (121). Therefore, more studies are needed to build a clear picture of high-risk bipolar states that can help clinicians differentiate genuinely at-risk individuals from persons with benign and self-limiting states (140, 141). Moreover, since prodromal symptoms are highly heterogeneous and particular to each individual, individualized risk assessment is needed. Novel bioinformatic techniques, such as machine learning approaches, present a valuable ally in the field of early intervention to overcome such limitations (142, 143).
Early intervention is an ideal breeding ground for new randomized clinical trials looking for the most efficacious treatment strategy for early stages. Currently there are no specific treatments for symptomatic youths who do not fulfill diagnostic criteria for bipolar disorder not otherwise specified but are at very high risk of developing bipolar disorder because one or both of the parents are diagnosed with bipolar disorder, particularly early-onset bipolar disorder. Since these children already present psychopathology in the form of symptoms of depression, anxiety, mood lability, or subsyndromal mania, they require existing treatments to target these symptoms—either pharmacotherapy or psychological therapies such as cognitive-behavioral therapies, family-focused therapy, self-help programs, or mental health first aid. What we do not know yet is whether these treatments will also prevent the onset of bipolar disorder. Thus, the need to perform studies to prevent or at least delay the onset of bipolar disorder should be considered a priority in psychiatry, especially in countries with a higher prevalence of pediatric bipolar disorder (144). Furthermore, as pointed out by Lambert et al. (145), once the most efficacious therapy is identified, further efforts should be made to ensure that at-risk populations have access to these interventions. Providing specialty care in youth mental health clinical services may be preferable to standard outpatient care, as evidence suggests that specialized treatment is more clinically effective and cost-effective (146, 147). Very gradual dosage escalation and cautious use of pharmacotherapy, potentially augmented by pharmacogenetics if positive data emerge, may help treatment choice when pharmacological treatment becomes necessary. In the early stages, prevention of potential side effects is paramount, since an initial adverse experience primes beliefs about medication and powerfully influences future adherence and engagement (148). In the case of bipolar disorder, there is some indication that critical factors for illness outcome, such as cognitive impairment, are heavily influenced by illness course and morbidity (149, 150). Hence, the early implementation of prevention strategies as appropriate according to illness stage and clinical phenotype may already help in preventing functional impairment. For very early stages, promoting and enhancing cognitive reserve may be one way to go (151–154).
Early intervention strategies in bipolar disorder face the lack of specificity of prodromal symptoms, as evidence emerging from studies performed in populations at ultra-high risk for psychosis indicate that there might be a common risk syndrome for diverse major psychiatric illnesses prior to the development of full symptoms more characteristic of any particular disease (141). Fernandes and Berk (142) hypothesized that this might also be true for biomarkers, with biomarkers useful for staging being common across different psychiatric disorders. Indeed, many of the biomarkers found in populations at risk for bipolar disorder are predictive for major psychiatric disorders in general and are common across commonly comorbid noncommunicable medical disorders, such as diabetes and cardiovascular disorders. This raises the question of whether more general interventions oriented toward enhancing stress-management strategies or reducing the proinflammatory state identified in at-risk individuals should be preferable. Findings concerning neurodevelopment, though, indicate that there may already be subtle potential differences between some psychiatric diseases at the earliest stages (155). In any case, this highlights the urgency of performing studies to test whether any given early intervention would help reduce vulnerability to psychiatric illnesses in general and not only to bipolar disorder, as bipolar offspring are at high risk of developing a wide range of psychiatric illnesses. As mentioned before, testing the protective potential of a variety of psychological interventions such as cognitive-behavioral therapies, family-focused therapy, self-help programs, or mental health first aid, or compounds such as omega-3 fatty acids, N-acetylcysteine, or minocycline, might be a feasible line of research. Lifestyle modifications such as smoking cessation, encouragement of physical activity, and healthy diet are indicated across the psychiatric spectrum and commonly comorbid medical disorders as well (156).
Overall, this review supports the idea of the existence of an at-risk state in bipolar disorder, thereby laying the foundations for bringing early intervention to life. However, we cannot deny that further efforts are required to advance on the difficult road of primary prevention. Given that psychiatric and commonly comorbid medical disorders share common risk determinants and operative biological pathways, a shared framework for disease prevention and control is warranted. A cross-disciplinary, multitarget approach is essential for wide-scale implementation in real-world settings (156). The need for new prospective studies with a larger sample size and standardized recruitment criteria and assessment tools is unquestionable. These studies should assess the validity of the proposed predictive factors to better determine which individuals are at highest risk for conversion and therefore more likely to benefit from early interventions. Further studies on early psychological and pharmacological interventions, either alone or in combination, are equally warranted.
In conclusion, considering that the onset of bipolar disorder usually occurs during adolescence—a period of personal, social, and professional development that is often truncated by the illness—introducing early interventions in psychiatry is imperative. By the time The American Journal of Psychiatry commemorates its 200th year of publication, we look forward to seeing that early intervention in psychiatry has been integrated in common clinical practice.
References
1.
Mayo WJ:The aims and ideals of the American Medical Association. Proceedings and Addresses of the National Education Association 1928; 66:163
2.
Jacka FN, Reavley NJ, Jorm AF, et al: Prevention of common mental disorders: what can we learn from those who have gone before and where do we go next? Aust N Z J Psychiatry 2013; 47:920–929
3.
Clarke EA: What is preventive medicine? Can Fam Physician 1974; 20:65–68
4.
Berk M, Berk L, Dodd S, et al: Stage managing bipolar disorder. Bipolar Disord 2014; 16:471–477
5.
Kapczinski NS, Mwangi B, Cassidy RM, et al: Neuroprogression and illness trajectories in bipolar disorder. Expert Rev Neurother 2017; 17:277–285
6.
Vieta E, Popovic D, Rosa AR, et al: The clinical implications of cognitive impairment and allostatic load in bipolar disorder. Eur Psychiatry 2013; 28:21–29
7.
Baldessarini RJ, Bolzani L, Cruz N, et al: Onset-age of bipolar disorders at six international sites. J Affect Disord 2010; 121:143–146
8.
Chengappa KN, Kupfer DJ, Frank E, et al: Relationship of birth cohort and early age at onset of illness in a bipolar disorder case registry. Am J Psychiatry 2003; 160:1636–1642
9.
Perlis RH, Dennehy EB, Miklowitz DJ, et al: Retrospective age at onset of bipolar disorder and outcome during two-year follow-up: results from the STEP-BD study. Bipolar Disord 2009; 11:391–400
10.
Bellivier F, Golmard JL, Rietschel M, et al: Age at onset in bipolar I affective disorder: further evidence for three subgroups. Am J Psychiatry 2003; 160:999–1001
11.
Merikangas KR, Akiskal HS, Angst J, et al: Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication. Arch Gen Psychiatry 2007; 64:543–552
12.
Kraepelin E: Manic-depressive insanity and paranoia. Edinburgh, Livingstone, 1921
13.
Tohen M, Hennen J, Zarate CM Jr, et al: Two-year syndromal and functional recovery in 219 cases of first-episode major affective disorder with psychotic features. Am J Psychiatry 2000; 157:220–228
14.
Duffy A, Alda M, Hajek T, et al: Early stages in the development of bipolar disorder. J Affect Disord 2010; 121:127–135
15.
Bolman WM, Westman JC: Prevention of mental disorder: an overview of current programs. Am J Psychiatry 1967; 123:1058–1068
16.
Eaton WW, Badawi M, Melton B: Prodromes and precursors: epidemiologic data for primary prevention of disorders with slow onset. Am J Psychiatry 1995; 152:967–972
17.
Baethge C, Baldessarini RJ, Khalsa HM, et al: Substance abuse in first-episode bipolar I disorder: indications for early intervention. Am J Psychiatry 2005; 162:1008–1010
18.
Schooler N, Rabinowitz J, Davidson M, et al: Risperidone and haloperidol in first-episode psychosis: a long-term randomized trial. Am J Psychiatry 2005; 162:947–953
19.
Goi PD, Bucker J, Vianna-Sulzbach M, et al: Pharmacological treatment and staging in bipolar disorder: evidence from clinical practice. Rev Bras Psiquiatr 2015; 37:121–125
20.
Tohen M, Vieta E, Gonzalez-Pinto A, et al: Baseline characteristics and outcomes in patients with first episode or multiple episodes of acute mania. J Clin Psychiatry 2010; 71:255–261
21.
Joyce K, Thompson A, Marwaha S: Is treatment for bipolar disorder more effective earlier in illness course? A comprehensive literature review. Int J Bipolar Disord 2016; 4:19
22.
McGuffin P, Rijsdijk F, Andrew M, et al: The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Arch Gen Psychiatry 2003; 60:497–502
23.
Bortolato B, Köhler CA, Evangelou E, et al: Systematic assessment of environmental risk factors for bipolar disorder: an umbrella review of systematic reviews and meta-analyses. Bipolar Disord 2017; 19:84–96
24.
Post RM, Altshuler L, Leverich G, et al: More stressors prior to and during the course of bipolar illness in patients from the United States compared with the Netherlands and Germany. Psychiatry Res 2013; 210:880–886
25.
Post RM, Altshuler LL, Kupka R, et al: Age of onset of bipolar disorder: Combined effect of childhood adversity and familial loading of psychiatric disorders. J Psychiatr Res 2016; 81:63–70
26.
Doucette S, Levy A, Flowerdew G, et al: Early parent-child relationships and risk of mood disorder in a Canadian sample of offspring of a parent with bipolar disorder: findings from a 16-year prospective cohort study. Early Interv Psychiatry 2016; 10:381–389
27.
Kemner SM, Mesman E, Nolen WA, et al: The role of life events and psychological factors in the onset of first and recurrent mood episodes in bipolar offspring: results from the Dutch Bipolar Offspring Study. Psychol Med 2015; 45:2571–2581
28.
Wals M, Hillegers MH, Reichart CG, et al: Stressful life events and onset of mood disorders in children of bipolar parents during 14-month follow-up. J Affect Disord 2005; 87:253–263
29.
Birmaher B, Gill MK, Axelson DA, et al: Longitudinal trajectories and associated baseline predictors in youths with bipolar spectrum disorders. Am J Psychiatry 2014; 171:990–999
30.
Jiménez E, Solé B, Arias B, et al: Impact of childhood trauma on cognitive profile in bipolar disorder. Bipolar Disord 2017; 19:363–374
31.
Greenfield SF, Strakowski SM, Tohen M, et al: Childhood abuse in first-episode psychosis. Br J Psychiatry 1994; 164:831–834
32.
Leverich GS, McElroy SL, Suppes T, et al: Early physical and sexual abuse associated with an adverse course of bipolar illness. Biol Psychiatry 2002; 51:288–297
33.
Berk M, Moylan S, Jacka FN: A royal gift to prevention efforts. Aust N Z J Psychiatry 2014; 48:110–111
34.
Martin A, Young C, Leckman JF, et al: Age effects on antidepressant-induced manic conversion. Arch Pediatr Adolesc Med 2004; 158:773–780
35.
Barbuti M, Pacchiarotti I, Vieta E, et al: Antidepressant-induced hypomania/mania in patients with major depression: evidence from the BRIDGE-II-MIX study. J Affect Disord 2017; 219:187–192
36.
Tohen M, Greenfield SF, Weiss RD, et al: The effect of comorbid substance use disorders on the course of bipolar disorder: a review. Harv Rev Psychiatry 1998; 6:133–141
37.
Ratheesh A, Cotton SM, Betts JK, et al: Prospective progression from high-prevalence disorders to bipolar disorder: Exploring characteristics of pre-illness stages. J Affect Disord 2015; 183:45–48
38.
Duffy A, Horrocks J, Doucette S, et al: The developmental trajectory of bipolar disorder. Br J Psychiatry 2014; 204:122–128
39.
Goldstein BI, Strober M, Axelson D, et al: Predictors of first-onset substance use disorders during the prospective course of bipolar spectrum disorders in adolescents. J Am Acad Child Adolesc Psychiatry 2013; 52:1026–1037
40.
Tohen M, Zarate CA Jr, Hennen J, et al: The McLean-Harvard First-Episode Mania Study: prediction of recovery and first recurrence. Am J Psychiatry 2003; 160:2099–2107
41.
Pasco JA, Williams LJ, Jacka FN, et al: Tobacco smoking as a risk factor for major depressive disorder: population-based study. Br J Psychiatry 2008; 193:322–326
42.
Quinn PD, Rickert ME, Weibull CE, et al: Association between maternal smoking during pregnancy and severe mental illness in offspring. JAMA Psychiatry 2017; 74:589–596
43.
Moylan S, Gustavson K, Øverland S, et al: The impact of maternal smoking during pregnancy on depressive and anxiety behaviors in children: the Norwegian Mother and Child Cohort Study. BMC Med 2015; 13:24
44.
Salvatore P, Baldessarini RJ, Khalsa HM, et al: Antecedents of manic versus other first psychotic episodes in 263 bipolar I disorder patients. Acta Psychiatr Scand 2014; 129:275–285
45.
Hafeman DM, Merranko J, Axelson D, et al: Toward the definition of a bipolar prodrome: dimensional predictors of bipolar spectrum disorders in at-risk youths. Am J Psychiatry 2016; 173:695–704
46.
Preisig M, Strippoli MF, Castelao E, et al: The specificity of the familial aggregation of early-onset bipolar disorder: A controlled 10-year follow-up study of offspring of parents with mood disorders. J Affect Disord 2016; 190:26–33
47.
Betts KS, Williams GM, Najman JM, et al: Predicting spectrums of adult mania, psychosis and depression by prospectively ascertained childhood neurodevelopment. J Psychiatr Res 2016; 72:22–29
48.
MacCabe JH, Lambe MP, Cnattingius S, et al: Excellent school performance at age 16 and risk of adult bipolar disorder: national cohort study. Br J Psychiatry 2010; 196:109–115
49.
Axelson D, Goldstein B, Goldstein T, et al: Diagnostic precursors to bipolar disorder in offspring of parents with bipolar disorder: a longitudinal study. Am J Psychiatry 2015; 172:638–646
50.
Egeland JA, Endicott J, Hostetter AM, et al: A 16-year prospective study of prodromal features prior to BPI onset in well Amish children. J Affect Disord 2012; 142:186–192
51.
Carlson GA, Weintraub S: Childhood behavior problems and bipolar disorder: relationship or coincidence? J Affect Disord 1993; 28:143–153
52.
Mesman E, Nolen WA, Reichart CG, et al: The Dutch bipolar offspring study: 12-year follow-up. Am J Psychiatry 2013; 170:542–549
53.
Akiskal HS, Downs J, Jordan P, et al: Affective disorders in referred children and younger siblings of manic-depressives: mode of onset and prospective course. Arch Gen Psychiatry 1985; 42:996–1003
54.
Duffy A, Horrocks J, Doucette S, et al: Childhood anxiety: an early predictor of mood disorders in offspring of bipolar parents. J Affect Disord 2013; 150:363–369
55.
Lewinsohn PM, Klein DN, Seeley JR: Bipolar disorders in a community sample of older adolescents: prevalence, phenomenology, comorbidity, and course. J Am Acad Child Adolesc Psychiatry 1995; 34:454–463
56.
Levenson JC, Axelson DA, Merranko J, et al: Differences in sleep disturbances among offspring of parents with and without bipolar disorder: association with conversion to bipolar disorder. Bipolar Disord 2015; 17:836–848
57.
Boschloo L, Spijker AT, Hoencamp E, et al: Predictors of the onset of manic symptoms and a (hypo)manic episode in patients with major depressive disorder. PLoS One 2014; 9:e106871
58.
Papachristou E, Oldehinkel AJ, Ormel J, et al: The predictive value of childhood subthreshold manic symptoms for adolescent and adult psychiatric outcomes. J Affect Disord 2017; 212:86–92
59.
Ratheesh A, Cotton SM, Davey CG, et al: Pre-onset risk characteristics for mania among young people at clinical high risk for psychosis. Schizophr Res (Epub ahead of print, July 30, 2017)
60.
Haenisch F, Cooper JD, Reif A, et al: Towards a blood-based diagnostic panel for bipolar disorder. Brain Behav Immun 2016; 52:49–57
61.
Mesman E, Nolen WA, Keijsers L, et al: Baseline dimensional psychopathology and future mood disorder onset: findings from the Dutch Bipolar Offspring Study. Acta Psychiatr Scand 2017; 136:201–209
62.
Axelson DA, Birmaher B, Strober MA, et al: Course of subthreshold bipolar disorder in youth: diagnostic progression from bipolar disorder not otherwise specified. J Am Acad Child Adolesc Psychiatry 2011; 50:1001–1016.e3
63.
Alloy LB, Urošević S, Abramson LY, et al: Progression along the bipolar spectrum: a longitudinal study of predictors of conversion from bipolar spectrum conditions to bipolar I and II disorders. J Abnorm Psychol 2012; 121:16–27
64.
Van Meter AR, Burke C, Youngstrom EA, et al: The bipolar prodrome: meta-analysis of symptom prevalence prior to initial or recurrent mood episodes. J Am Acad Child Adolesc Psychiatry 2016; 55:543–555
65.
Grande I, Berk M, Birmaher B, et al: Bipolar disorder. Lancet 2016; 387:1561–1572
66.
Goldberg JF, Harrow M, Whiteside JE: Risk for bipolar illness in patients initially hospitalized for unipolar depression. Am J Psychiatry 2001; 158:1265–1270
67.
Østergaard SD, Straszek S, Petrides G, et al: Risk factors for conversion from unipolar psychotic depression to bipolar disorder. Bipolar Disord 2014; 16:180–189
68.
Strober M, Carlson G: Bipolar illness in adolescents with major depression: clinical, genetic, and psychopharmacologic predictors in a three- to four-year prospective follow-up investigation. Arch Gen Psychiatry 1982; 39:549–555
69.
Tohen M, Khalsa HK, Salvatore P, et al: Two-year outcomes in first-episode psychotic depression the McLean-Harvard First-Episode Project. J Affect Disord 2012; 136:1–8
70.
Ratheesh A, Davey C, Hetrick S, et al: A systematic review and meta-analysis of prospective transition from major depression to bipolar disorder. Acta Psychiatr Scand 2017; 135:273–284
71.
Verdolini N, Perugi G, Samalin L, et al: Aggressiveness in depression: a neglected symptom possibly associated with bipolarity and mixed features. Acta Psychiatr Scand 2017; 136:362–372
72.
Salvatore P, Baldessarini RJ, Khalsa HM, et al: Predicting diagnostic change among patients diagnosed with first-episode DSM-IV-TR major depressive disorder with psychotic features. J Clin Psychiatry 2013; 74:723–731, quiz 731
73.
Biederman J, Petty CR, Monuteaux MC, et al: The Child Behavior Checklist-Pediatric Bipolar Disorder profile predicts a subsequent diagnosis of bipolar disorder and associated impairments in ADHD youth growing up: a longitudinal analysis. J Clin Psychiatry 2009; 70:732–740
74.
Reichart CG, van der Ende J, Wals M, et al: The use of the GBI as predictor of bipolar disorder in a population of adolescent offspring of parents with a bipolar disorder. J Affect Disord 2005; 89:147–155
75.
Blechert J, Meyer TD: Are measures of hypomanic personality, impulsive nonconformity and rigidity predictors of bipolar symptoms? Br J Clin Psychol 2005; 44:15–27
76.
Kwapil TR, Miller MB, Zinser MC, et al: A longitudinal study of high scorers on the hypomanic personality scale. J Abnorm Psychol 2000; 109:222–226
77.
Goodday SM, Preisig M, Gholamrezaee M, et al: The association between self-reported and clinically determined hypomanic symptoms and the onset of major mood disorders. BJPsych Open 2017; 3:71–77
78.
Meyer SE, Carlson GA, Youngstrom E, et al: Long-term outcomes of youth who manifested the CBCL-Pediatric Bipolar Disorder phenotype during childhood and/or adolescence. J Affect Disord 2009; 113:227–235
79.
Mesman E, Youngstrom EA, Juliana NK, et al: Validation of the Seven Up Seven Down Inventory in bipolar offspring: screening and prediction of mood disorders. Findings from the Dutch Bipolar Offspring Study. J Affect Disord 2017; 207:95–101
80.
Youngstrom EA, Freeman AJ, Jenkins MM: The assessment of children and adolescents with bipolar disorder. Child Adolesc Psychiatr Clin N Am 2009; 18:353–390, viii–ix [viii-ix.]
81.
Bechdolf A, Ratheesh A, Cotton SM, et al: The predictive validity of bipolar at-risk (prodromal) criteria in help-seeking adolescents and young adults: a prospective study. Bipolar Disord 2014; 16:493–504
82.
Mitchell PB, Wilhelm K, Parker G, et al: The clinical features of bipolar depression: a comparison with matched major depressive disorder patients. J Clin Psychiatry 2001; 62:212–216, quiz 217
83.
Leopold K, Ritter P, Correll CU, et al: Risk constellations prior to the development of bipolar disorders: rationale of a new risk assessment tool. J Affect Disord 2012; 136:1000–1010
84.
Correll CU, Olvet DM, Auther AM, et al: The Bipolar Prodrome Symptom Interview and Scale-Prospective (BPSS-P): description and validation in a psychiatric sample and healthy controls. Bipolar Disord 2014; 16:505–522
85.
Hafeman DM, Merranko J, Goldstein TR, et al: Assessment of a person-level risk calculator to predict new-onset bipolar spectrum disorder in youth at familial risk. JAMA Psychiatry 2017; 74:841–847
86.
Fernandes BS, Williams LM, Steiner J, et al: The new field of ‘precision psychiatry’. BMC Med 2017; 15:80
87.
Vieta E: DSM-5.1. Acta Psychiatr Scand 2016; 134:187–188
88.
Whalley HC, Sussmann JE, Romaniuk L, et al: Prediction of depression in individuals at high familial risk of mood disorders using functional magnetic resonance imaging. PLoS One 2013; 8:e57357
89.
Mourão-Miranda J, Oliveira L, Ladouceur CD, et al: Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents. PLoS One 2012; 7:e29482
90.
Sugranyes G, Solé-Padullés C, de la Serna E, et al: Cortical morphology characteristics of young offspring of patients with schizophrenia or bipolar disorder. J Am Acad Child Adolesc Psychiatry 2017; 56:79–88
91.
Snijders G, de Witte L, Mesman E, et al: The seroprevalence of antithyroid peroxidase antibodies in bipolar families and bipolar twins: results from two longitudinal studies. Int J Bipolar Disord 2017; 5:1
92.
Goodday SM, Horrocks J, Keown-Stoneman C, et al: Repeated salivary daytime cortisol and onset of mood episodes in offspring of bipolar parents. Int J Bipolar Disord 2016; 4:12
93.
Singh MK, Jo B, Adleman NE, et al: Prospective neurochemical characterization of child offspring of parents with bipolar disorder. Psychiatry Res 2013; 214:153–160
94.
Padmos RC, Hillegers MH, Knijff EM, et al: A discriminating messenger RNA signature for bipolar disorder formed by an aberrant expression of inflammatory genes in monocytes. Arch Gen Psychiatry 2008; 65:395–407
95.
Mesman E, Hillegers MH, Ambree O, et al: Monocyte activation, brain-derived neurotrophic factor (BDNF), and S100B in bipolar offspring: a follow-up study from adolescence into adulthood. Bipolar Disord 2015; 17:39–49
96.
Snijders G, Schiweck C, Mesman E, et al: A dynamic course of T cell defects in individuals at risk for mood disorders. Brain Behav Immun 2016; 58:11–17
97.
Hayes JF, Khandaker GM, Anderson J, et al: Childhood interleukin-6, C-reactive protein and atopic disorders as risk factors for hypomanic symptoms in young adulthood: a longitudinal birth cohort study. Psychol Med 2017; 47:23–33
98.
Hidalgo-Mazzei D, Murru A, Reinares M, et al: Big Data in mental health: a challenging fragmented future. World Psychiatry 2016; 15:186–187
99.
W Adams Z, McClure EA, Gray KM, et al: Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research. J Psychiatr Res 2017; 85:1–14
100.
Luo W, Phung D, Tran T, et al: Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 2016; 18:e323
101.
Vieta E: The bipolar maze: a roadmap through translational psychopathology. Acta Psychiatr Scand 2014; 129:323–327
102.
Rosa AR, Magalhães PV, Czepielewski L, et al: Clinical staging in bipolar disorder: focus on cognition and functioning. J Clin Psychiatry 2014; 75:e450–e456
103.
Ratheesh A, Cotton SM, Davey CG, et al: Ethical considerations in preventive interventions for bipolar disorder. Early Interv Psychiatry 2017; 11:104–112
104.
Berk M, Brnabic A, Dodd S, et al: Does stage of illness impact treatment response in bipolar disorder? Empirical treatment data and their implication for the staging model and early intervention. Bipolar Disord 2011; 13:87–98
105.
Colom F, Vieta E, Sánchez-Moreno J, et al: Group psychoeducation for stabilised bipolar disorders: 5-year outcome of a randomised clinical trial. Br J Psychiatry 2009; 194:260–265
106.
Morriss R, Lobban F, Riste L, et al: Clinical effectiveness and acceptability of structured group psychoeducation versus optimised unstructured peer support for patients with remitted bipolar disorder (PARADES): a pragmatic, multicentre, observer-blind, randomised controlled superiority trial. Lancet Psychiatry 2016; 3:1029–1038
107.
Vieta E, Morilla I: Early group psychoeducation for bipolar disorder. Lancet Psychiatry 2016; 3:1000–1001
108.
Miklowitz DJ, Schneck CD, George EL, et al: Pharmacotherapy and family-focused treatment for adolescents with bipolar I and II disorders: a 2-year randomized trial. Am J Psychiatry 2014; 171:658–667
109.
Miklowitz DJ, Schneck CD, Singh MK, et al: Early intervention for symptomatic youth at risk for bipolar disorder: a randomized trial of family-focused therapy. J Am Acad Child Adolesc Psychiatry 2013; 52:121–131
110.
Nadkarni RB, Fristad MA: Clinical course of children with a depressive spectrum disorder and transient manic symptoms. Bipolar Disord 2010; 12:494–503
111.
Goldstein TR, Fersch-Podrat R, Axelson DA, et al: Early intervention for adolescents at high risk for the development of bipolar disorder: pilot study of Interpersonal and Social Rhythm Therapy (IPSRT). Psychotherapy (Chic) 2014; 51:180–189
112.
Berk M, Parker G: The elephant on the couch: side-effects of psychotherapy. Aust N Z J Psychiatry 2009; 43:787–794
113.
Vieta E: Staging and psychosocial early intervention in bipolar disorder. Lancet Psychiatry 2015; 2:483–485
114.
Hidalgo-Mazzei D, Mateu A, Reinares M, et al: Internet-based psychological interventions for bipolar disorder: Review of the present and insights into the future. J Affect Disord 2015; 188:1–13
115.
Lauder S, Chester A, Castle D, et al: A randomized head to head trial of MoodSwings.net.au: an Internet based self-help program for bipolar disorder. J Affect Disord 2015; 171:13–21
116.
Kessing LV, Vradi E, Andersen PK: Starting lithium prophylaxis early v. late in bipolar disorder. Br J Psychiatry 2014; 205:214–220
117.
Chang KD, Dienes K, Blasey C, et al: Divalproex monotherapy in the treatment of bipolar offspring with mood and behavioral disorders and at least mild affective symptoms. J Clin Psychiatry 2003; 64:936–942
118.
DelBello MP, Adler CM, Whitsel RM, et al: A 12-week single-blind trial of quetiapine for the treatment of mood symptoms in adolescents at high risk for developing bipolar I disorder. J Clin Psychiatry 2007; 68:789–795
119.
Findling RL, Frazier TW, Youngstrom EA, et al: Double-blind, placebo-controlled trial of divalproex monotherapy in the treatment of symptomatic youth at high risk for developing bipolar disorder. J Clin Psychiatry 2007; 68:781–788
120.
Jerrell JM, McIntyre RS, Tripathi A: Childhood treatment with psychotropic medication and development of comorbid medical conditions in adolescent-onset bipolar disorder. Hum Psychopharmacol 2011; 26:451–459
121.
McNamara RK, Nandagopal JJ, Strakowski SM, et al: Preventative strategies for early-onset bipolar disorder: towards a clinical staging model. CNS Drugs 2010; 24:983–996
122.
Joffe H, Cohen LS, Suppes T, et al: Longitudinal follow-up of reproductive and metabolic features of valproate-associated polycystic ovarian syndrome features: A preliminary report. Biol Psychiatry 2006; 60:1378–1381
123.
Joffe H, Cohen LS, Suppes T, et al: Valproate is associated with new-onset oligoamenorrhea with hyperandrogenism in women with bipolar disorder. Biol Psychiatry 2006; 59:1078–1086
124.
Birmaher B, Axelson D, Goldstein B, et al: Four-year longitudinal course of children and adolescents with bipolar spectrum disorders: the Course and Outcome of Bipolar Youth (COBY) study. Am J Psychiatry 2009; 166:795–804
125.
Sharpley AL, Hockney R, McPeake L, et al: Folic acid supplementation for prevention of mood disorders in young people at familial risk: a randomised, double blind, placebo controlled trial. J Affect Disord 2014; 167:306–311
126.
McGorry PD, Nelson B, Markulev C, et al: Effect of ω-3 polyunsaturated fatty acids in young people at ultrahigh risk for psychotic disorders: the NEURAPRO Randomized Clinical Trial. JAMA Psychiatry 2017; 74:19–27
127.
Pusceddu MM, Kelly P, Stanton C, et al: N-3 polyunsaturated fatty acids through the lifespan: implication for psychopathology. Int J Neuropsychopharmacol 2016; 19:pyw078
128.
Williams LJ, Pasco JA, Mohebbi M, et al: Statin and aspirin use and the risk of mood disorders among men. Int J Neuropsychopharmacol 2016; 19:pyw008
129.
Miskowiak KW, Carvalho AF, Vieta E, et al: Cognitive enhancement treatments for bipolar disorder: A systematic review and methodological recommendations. Eur Neuropsychopharmacol 2016; 26:1541–1561
130.
Drago A, Serretti A, Smith R, et al: No association between genetic markers in BDNF gene and lithium prophylaxis in a Greek sample. Int J Psychiatry Clin Pract 2010; 14:154–157
131.
Perlis RH, Smoller JW, Ferreira MA, et al: A genomewide association study of response to lithium for prevention of recurrence in bipolar disorder. Am J Psychiatry 2009; 166:718–725
132.
Hou L, Heilbronner U, Degenhardt F, et al: Genetic variants associated with response to lithium treatment in bipolar disorder: a genome-wide association study. Lancet 2016; 387:1085–1093
133.
Singh AB, Bousman CA: Antidepressant pharmacogenetics. Am J Psychiatry 2017; 174:417–418
134.
Berk M, Conus P, Lucas N, et al: Setting the stage: from prodrome to treatment resistance in bipolar disorder. Bipolar Disord 2007; 9:671–678
135.
Kapczinski F, Dias VV, Kauer-Sant’Anna M, et al: Clinical implications of a staging model for bipolar disorders. Expert Rev Neurother 2009; 9:957–966
136.
Vieta E, Reinares M, Rosa AR: Staging bipolar disorder. Neurotox Res 2011; 19:279–285
137.
Grande I, Magalhães PV, Chendo I, et al: Staging bipolar disorder: clinical, biochemical, and functional correlates. Acta Psychiatr Scand 2014; 129:437–444
138.
Diler RS, Goldstein TR, Hafeman D, et al: Characteristics of depression among offspring at high and low familial risk of bipolar disorder. Bipolar Disord 2017; 19:344–352
139.
Bechdolf A, Wood SJ, Nelson B, et al: Amygdala and insula volumes prior to illness onset in bipolar disorder: a magnetic resonance imaging study. Psychiatry Res 2012; 201:34–39
140.
Berk M, Hallam K, Malhi GS, et al: Evidence and implications for early intervention in bipolar disorder. J Ment Health 2010; 19:113–126
141.
McGorry PD: Risk syndromes, clinical staging and DSM V: new diagnostic infrastructure for early intervention in psychiatry. Schizophr Res 2010; 120:49–53
142.
Fernandes BS, Berk M. Staging in bipolar disorder: one step closer to precision psychiatry. Rev Bras Psiquiatr 2017; 39:88–89
143.
Salvador R, Radua J, Canales-Rodríguez EJ, et al: Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis. PLoS One 2017; 12:e0175683
144.
Post RM, Altshuler LL, Kupka R, et al.: More childhood onset bipolar disorder in the United States than Canada or Europe: Implications for treatment and prevention. Neurosci Biobehav Rev 2017; 74(Pt A):204–213
145.
Lambert M, Niehaus V, Correll C: Pharmacotherapy in children and adolescents at clinical-high risk for psychosis and bipolar disorder. Pharmacopsychiatry 2016; 49:229–244
146.
Kessing LV, Hansen HV, Hvenegaard A, et al: Treatment in a specialised out-patient mood disorder clinic v. standard out-patient treatment in the early course of bipolar disorder: randomised clinical trial. Br J Psychiatry 2013; 202:212–219
147.
Garrido JM, Sánchez-Moreno J, Vázquez M, et al: Evaluation of patient satisfaction in a state reference center of bipolar disorder. J Behav Health Serv Res (Epub ahead of print, July 13, 2017)
148.
Mas S, Gasso P, Torra M, et al. Intuitive pharmacogenetic dosing of risperidone according to CYP2D6 phenotype extrapolated from genotype in a cohort of first episode psychosis patients. Eur Neuropsychopharmacol 2017; 27:647–656
149.
Solé B, Vieta E, Martinez-Aran A. Thinking ahead: executive dysfunction in bipolar disorder. Eur Neuropsychopharmacol 2016; 26:1348–1349
150.
Solé B, Jiménez E, Torrent C, et al: Cognitive impairment in bipolar disorder: treatment and prevention strategies. Int J Neuropsychopharmacol 2017; 20:670–680
151.
Forcada I, Mur M, Mora E, et al: The influence of cognitive reserve on psychosocial and neuropsychological functioning in bipolar disorder. Eur Neuropsychopharmacol 2015; 25:214–222
152.
Anaya C, Torrent C, Caballero FF, et al: Cognitive reserve in bipolar disorder: relation to cognition, psychosocial functioning and quality of life. Acta Psychiatr Scand 2016; 133:386–398
153.
Amoretti S, Bernardo M, Bonnin CM, et al.: The impact of cognitive reserve in the outcome of first-episode psychoses: 2-year follow-up study. Eur Neuropsychopharmacol 2016; 26:1638–1648.
154.
Grande I, Sanchez-Moreno J, Solé B, et al: High cognitive reserve in bipolar disorders as a moderator of neurocognitive impairment. J Affect Disord 2017; 208:621–627
155.
Passos IC, Mwangi B, Vieta E, et al: Areas of controversy in neuroprogression in bipolar disorder. Acta Psychiatr Scand 2016; 134:91–103
156.
O’Neil A, Jacka FN, Quirk SE, et al: A shared framework for the common mental disorders and non-communicable disease: key considerations for disease prevention and control. BMC Psychiatry 2015; 15:15
157.
Woodberry KA, Shapiro DI, Bryant C, et al: Progress and future directions in research on the psychosis prodrome: a review for clinicians. Harv Rev Psychiatry 2016; 24:87–103
Information & Authors
Information
Published In
History
Received: 8 September 2017
Revision received: 26 October 2017
Accepted: 30 October 2017
Published online: 24 January 2018
Published in print: May 01, 2018
Keywords
Authors
Funding Information
Dr. Vieta has received support from the Instituto de Salud Carlos III, Ministry of Economy and Competitiveness of Spain (PI 12/00912), integrated into the Plan Nacional de I+D+I and cofunded by ISCIII-Subdirección General de Evaluación and Fondo Europeo de Desarrollo Regional (FEDER); Centro para la Investigación Biomédica en Red de Salud Mental (CIBERSAM), Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement (2014_SGR_398), Seventh European Framework Programme (ENBREC), and the Stanley Medical Research Institute. Dr. Grande is supported by the Instituto de Salud Carlos III, Ministry of Economy and Competitiveness of Spain (Juan Rodés Contract) (JR15/00012) and a grant (PI16/00187) integrated into the Plan Nacional de I+D+I and cofunded by ISCIII-Subdirección General de Evaluación and FEDER. Dr. Carvalho is supported by a research fellowship award from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; Brazil). Dr. Fernandes is supported by a postdoctoral fellowship from Deakin University, Australia, and by a research grant MCTI/CNPQ/Universal 14/2014461833/2014-0 from CNPq, Brazil. Dr. Berk is supported by a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship (grant 1059660).Dr. Vieta has received grants and honoraria from AstraZeneca, Ferrer, Forest Research Institute, Gedeon Richter, GlaxoSmithKline, Janssen, Lundbeck, Otsuka, Pfizer, Sanofi-Aventis, Sunovion, and Takeda as well as from the CIBERSAM, Grups Consolidats de Recerca 2014 (SGR 398), the Seventh European Framework Programme (ENBREC), and the Stanley Medical Research Institute. Dr. Grande has consulted for Ferrer and has been a speaker for Ferrer and Janssen-Cilag. Dr. Berk has received grant/research support from the Stanley Medical Research Foundation, MBF, NHMRC, NHMRC Senior Principal Research Fellowship grant 1059660, Cooperative Research Centre, Simons Autism Foundation, Cancer Council of Victoria, Rotary Health, Meat and Livestock Board, Woolworths, BeyondBlue, Geelong Medical Research Foundation, Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline, Organon, Novartis, Mayne Pharma, and Servier; has been a speaker for AstraZeneca, Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline, Lundbeck, Pfizer, Sanofi Synthelabo, Servier, Solvay, and Wyeth; has been a consultant for AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Bioadvantex, Merck, GlaxoSmithKline, Lundbeck, Janssen-Cilag, and Servier; and is a co-inventor of two provisional patents regarding the use of NAC and related compounds for psychiatric indications, which, while assigned to the Mental Health Research Institute, could lead to personal remuneration upon a commercialization event. Dr. Birmaher has received research support from NIMH and royalties from Random House, Lippincott Williams & Wilkins, and UpToDate. Dr. Tohen has been a consultant for AstraZeneca, Abbott, Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline, Johnson & Johnson, Otsuka, Roche, Lundbeck, Elan, Allergan, Alkermes, Merck, Minerva, Neuroscience, Pamlab, Alexza, Forest, Teva, Sunovion, Gedeon Richter, and Wyeth; he was a full-time employee at Eli Lilly (1997–2008); and his spouse is a former employee at Lilly (1998–2013). Dr. Suppes has received grants from NIMH, the VA Cooperative Studies Program, Pathway Genomics, the Stanley Medical Research Institute, Elan Pharma International, and Sunovion; consulting fees from Lundbeck, Sunovion, and Merck; CME and honoraria from Medscape Education, Global Medical Education, and CMEology; royalties from Jones and Bartlett and UpToDate; and travel reimbursement from Lundbeck, Sunovion, and Merck. The other authors report no financial relationships with commercial interests.
Metrics & Citations
Metrics
Citations
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
View Options
View options
PDF/EPUB
View PDF/EPUBLogin options
Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.
Personal login Institutional Login Open Athens loginNot a subscriber?
PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.
Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).