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Background: By 2030, over 50% of individuals living with bipolar disorder (BD) are expected to be aged ≥50 years. However, older age bipolar disorder (OABD) remains understudied. There are limited large-scale prospectively collected data organized in key dimensions capable of addressing several fundamental questions about BD affecting this subgroup of patients., Methods: We developed initial recommendations for the essential dimensions for OABD data collection, based on (1) a systematic review of measures used in OABD studies, (2) a Delphi consensus of international OABD experts, (3) experience with harmonizing OABD data in the Global Aging & Geriatric Experiments in Bipolar Disorder Database (GAGE-BD, n ≥ 4500 participants), and (4) critical feedback from 34 global experts in geriatric mental health., Results: We identified 15 key dimensions and variables within each that are relevant for the investigation of OABD: (1) demographics, (2) core symptoms of depression and (3) mania, (4) cognition screening and subjective cognitive function, (5) elements for BD diagnosis, (6) descriptors of course of illness, (7) treatment, (8) suicidality, (9) current medication, (10) psychiatric comorbidity, (11) psychotic symptoms, (12) general medical comorbidities, (13) functioning, (14) family history, and (15) other. We also recommend particular instruments for capturing some of the dimensions and variables., Conclusion: The essential data dimensions we present should be of use to guide future international data collection in OABD and clinical practice. In the longer term, we aim to establish a prospective consortium using this core set of dimensions and associated variables to answer research questions relevant to OABD., (© 2023 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background: The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established., Objectives: To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators., Methods: We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668)., Results: The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators., Conclusion: We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field., (© 2022 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.)

Aims: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry., Methods: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles., Results: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex., Conclusions: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: Clinical staging is widely used in medicine to map disease progression, inform prognosis, and guide treatment decisions; in psychiatry, however, staging remains a hypothetical construct. To facilitate future research in bipolar disorders (BD), a well-defined nomenclature is needed, especially since diagnosis is often imprecise with blurred boundaries, and a full understanding of pathophysiology is lacking., Methods: Under the auspices of the International Society of Bipolar Disorders, a Task Force of international experts was convened to review, discuss, and integrate findings from the scientific literature relevant to the development of a consensus staging model and standardize a terminology that could be used to advance future research including staging of BD and related disorders., Results: Consensus opinion and areas of uncertainty or difference were identified in regard to terms referring to staging as it may apply to BD, to at-risk status and subthreshold stages, and to various clinical stages of BD as it is currently diagnosed., Conclusion: The use of a standardized nomenclature about the clinical stages of BD will facilitate communication about research on clinical and pathological components of this heterogeneous group of disorders. The concepts presented are based on current evidence, but the template provided allows for further refinements as etiological advances come to light., (© 2021 The Authors. Bipolar Disorders published by John Wiley & Sons Ltd.)

Objectives: The association of bipolar disorder with early and excessive cardiovascular disease was identified over a century ago. Nonetheless, the vascular-bipolar link remains underrecognized, particularly with regard to how this link can contribute to our understanding of pathogenesis and treatment., Methods: An international group of experts completed a selective review of the literature, distilling core themes, identifying limitations and gaps in the literature, and highlighting future directions to bridge these gaps., Results: The association between bipolar disorder and vascular disease is large in magnitude, consistent across studies, and independent of confounding variables where assessed. The vascular-bipolar link is multifactorial and is difficult to study given the latency between the onset of bipolar disorder, often in adolescence or early adulthood, and subsequent vascular disease, which usually occurs decades later. As a result, studies have often focused on risk factors for vascular disease or intermediate phenotypes, such as structural and functional vascular imaging measures. There is interest in identifying the most relevant mediators of this relationship, including lifestyle (eg, smoking, diet, exercise), medications, and systemic biological mediators (eg, inflammation). Nonetheless, there is a paucity of treatment studies that deliberately engage these mediators, and thus far no treatment studies have focused on engaging vascular imaging targets., Conclusions: Further research focused on the vascular-bipolar link holds promise for gleaning insights regarding the underlying causes of bipolar disorder, identifying novel treatment approaches, and mitigating disparities in cardiovascular outcomes for people with bipolar disorder., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Seasonal peaks in hospitalizations for mood disorders and schizophrenia are well recognized and often replicated. The within-subject tendency to experience illness episodes in the same season, that is, seasonal course, is much less established, as certain individuals may temporarily meet criteria for seasonal course purely by chance., Aims: In this population, prospective cohort study, we investigated whether between and within-subject seasonal patterns of hospitalizations occurred more frequently than would be expected by chance., Methods: Using a compulsory, standardized national register of hospitalizations, we analyzed all admissions for mood disorders and schizophrenia in the Czech Republic between 1994 and 2013. We used bootstrap tests to compare the observed numbers of (a) participants with seasonal/regular course and (b) hospitalizations in individual months against empirical distributions obtained by simulations., Results: Among 87 184 participants, we found uneven distribution of hospitalizations, with hospitalization peaks for depression in April and November (X 2 (11) = 363.66, P < .001), for mania in August (X 2 (11) = 50.36, P < .001) and for schizophrenia in June (X 2 (11) = 70.34, P < .001). Significantly more participants than would be expected by chance, had two subsequent rehospitalizations in the same 90 days in different years (7.36%, bootstrap P < .01) or after a regular, but non-seasonal interval (6.07%, bootstrap P < .001). The proportion of participants with two consecutive hospitalizations in the same season was below chance level (7.06%)., Conclusions: Psychiatric hospitalizations were unevenly distributed throughout the year (cross-sectional seasonality), with evidence for regularity, but not seasonality of hospitalizations within subjects. Our data do not support the validity of seasonal pattern specifier. Season may be a general risk factor, which increases the risk of hospitalizations across psychiatric participants., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD., Method: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD., Results: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding., Conclusion: Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings., (© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objective: This study aimed to examine differences in the clinical presentation of very-early-onset (VEO) and early-onset (EO) bipolar disorder (BD) not fully explored previously., Methods: We selected two groups of subjects with BD from the Maritime Bipolar Registry based on age at onset of first major mood episode (VEO with onset prior to age 15 years; EO ranging from 15 to 18 years) and compared them with a reference group (onset after 18 years of age). There were 363 subjects (240 with bipolar I disorder and 123 with bipolar II disorder; mean age 44.2 ± 12.8 (SD) years), with 41 subjects in the VEO and 95 in the EO groups., Results: In comparison with the EO and reference groups, more subjects in the VEO group developed major depression as an index episode (88% for the VEO group versus 61% for the EO group and 54% for the reference group), and had an unremitting clinical course (65% versus 42% and 42%, respectively), rapid cycling (54% versus 34% and 28%, respectively), and comorbid attention-deficit hyperactivity disorder (17% versus 1% and 3%, respectively); a higher proportion of the VEO group had first-degree relatives with affective disorders compared with the EO and reference groups (0.41 versus 0.32 and 0.29, respectively), and they had lower scores on the Global Assessment of Functioning scale (mean scores of 64 versus 70 and 70). Overall, the EO group was similar to the reference group on most measures, except for increased suicidal behavior VEO 53%, EO 44% and reference group 25%). The results of polychotomous logistic regression also support the view that VEO BD represents a rather specific subtype of BD., Conclusions: Our results suggest the recognized correlates of early-onset BD may be driven by subjects at the lowest end of the age at onset spectrum., (© 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: Suicide is a significant cause of mortality in patients with major affective disorders (MAD), and suicidal behavior and MAD co-aggregate in families. However, the transmission of suicidal behavior is partially independent from that of MAD. We analyzed the lifetime prevalence of completed and attempted suicides in a large sample of families with bipolar disorder (BD), its relation to family history of MAD and BD, and the contribution of clinical and treatment factors to the risk of suicidal behavior., Methods: We studied 737 families of probands with MAD with 4919 first-degree relatives (818 affected, 3948 unaffected, and 153 subjects with no information available). Lifetime psychiatric diagnoses and suicidal behavior in first-degree relatives were assessed using semi-structured interviews, family history methods, and reviews of clinical records. Cox proportional hazard and logistic regression models were used to investigate the role of clinical covariates in the risk of suicidal behavior, and in the prevalence of MAD and BD., Results: The estimated lifetime prevalence of suicidal behavior (attempted and completed suicides) in 737 probands was 38.4 ± 3.0%. Lithium treatment decreased suicide risk in probands (p = 0.007). In first-degree relatives, a family history of suicidal behavior contributed significantly to the joint risk of MAD and suicidal behavior (p = 0.0006)., Conclusions: The liability to suicidal behavior is influenced by genetic factors (particularly family history of suicidal behavior and MAD). Even in the presence of high genetic risk for suicidal behavior, lithium treatment decreases suicide rates significantly., (© 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objective: Hippocampal volume decrease associated with illness burden is among the most replicated findings in unipolar depression. The absence of hippocampal volume changes in most studies of individuals with bipolar disorder (BD) may reflect neuroprotective effects of lithium (Li)., Methods: We recruited 17 BD patients from specialized Li clinics, with at least two years of regularly monitored Li treatment (Li group), and compared them to 12 BD participants with < 3 months of lifetime Li exposure and no Li treatment within two years prior to the scanning (non-Li group) and 11 healthy controls. All BD patients had at least 10 years of illness and five episodes. We also recruited 13 Li-naïve, young BD participants (15-30 years of age) and 18 sex- and age-matched healthy controls. We compared hippocampal volumes obtained from 1.5-T magnetic resonance imaging (MRI) scans using optimized voxel-based morphometry with small volume correction., Results: The non-Li group had smaller left hippocampal volumes than controls (corrected p < 0.05), with a trend for lower volumes than the Li group (corrected p < 0.1), which did not differ from controls. Young, Li-naïve BD patients close to the typical age of onset had comparable hippocampal volumes to controls., Conclusions: Whereas patients with limited lifetime Li exposure had significantly lower hippocampal volumes than controls, patients with comparable illness burden, but with over two years of Li treatment, or young Li-naïve BD patients, showed hippocampal volumes comparable to controls. These results provide indirect support for neuroprotective effects of Li and negative effects of illness burden on hippocampal volumes in bipolar disorders., (© 2012 John Wiley and Sons A/S.)

Objective: Several studies have reported higher prevalence of obesity in patients suffering from bipolar disorder (BD). To study the relation of elevated body mass index (BMI) in patients with BD more closely, we investigated differences in sociodemographic, clinical, and medical characteristics with respect to BMI, with the hypothesis that BMI is related to prognosis and outcome., Methods: We measured the BMI of 276 subjects of a tertiary care sample from the Maritime Bipolar Registry. Subjects were 16 to 83 years old, with psychiatric diagnoses of bipolar I disorder (n = 186), bipolar II disorder (n = 85), and BD not otherwise specified (n = 5). The registry included basic demographic data and details of the clinical presentation. We first examined the variables showing a significant association with BMI; subsequently, we modeled the relationship between BMI and psychiatric outcome using structural equation analysis., Results: The prevalence of obesity in our sample was 39.1%. We found higher BMI in subjects with a chronic course (p < 0.001) and longer duration of illness (p = 0.02), lower scores on the Global Assessment of Functioning Scale (p = 0.02), and on disability (p = 0.002). Overweight patients had more frequent comorbid subthreshold social (p = 0.02) and generalized anxiety disorders (p = 0.05), diabetes mellitus type II (p < 0.001), and hypertension (p = 0.001). Subjects who achieved complete remission of symptoms on lithium showed significantly lower BMI (p = 0.01)., Conclusions: Our findings suggest that BMI is associated with the prognosis and outcome of BD. Whether this association is causal remains to be determined.

Objective: Rapid cycling (RC) affects 13-30% of bipolar patients. Most of the data regarding RC have been obtained in tertiary care research centers. Generalizability of these findings to primary care populations is thus questionable. We examined clinical and demographic factors associated with RC in both primary and tertiary care treated populations., Method: Clinical data were obtained by interview from 240 bipolar I disorder (BDI) or bipolar II disorder (BDII) community-treated patients and by chart reviews from 119 bipolar patients treated at an outpatient clinic of a teaching hospital., Results: Lifetime history of rapid cycling was present in 33.3% and 26.9% of patients from the primary and tertiary care samples, respectively. Among community-treated patients, lifetime history of RC was significantly associated with history of suicidal behavior and higher body mass index. There was a trend for association between RC and BDII, psychiatric comorbidity, diabetes mellitus, as well as lower age of onset of mania/hypomania. In the tertiary care treated sample there was a trend for association between lifetime history of RC and suicidal behavior. Tertiary versus primary care treated subjects with lifetime history of RC demonstrated markedly lower response to mood stabilizers., Conclusions: Lifetime history of RC is highly prevalent in both primary and tertiary settings. Even primary care treated subjects with lifetime history of RC seem to suffer from a more complicated and less treatment-responsive variant of bipolar disorder. Our findings further suggest relatively good generalizability of data from tertiary to primary care settings.

Objectives: The risk of suicidal behavior is substantially elevated in major affective disorders (AD). In bipolar disorder (BD), as many as 15% of patients may commit suicide and family history of suicide is recognized as one of the most important risk factors. Lithium reduces the rates of suicidal behavior in BD, especially in patients who achieve full mood stabilization. Yet even patients who continue experiencing mood episodes do benefit from anti-suicidal properties of lithium. These observations raise questions about the nature of the relationship between the neurobiological mechanisms of BD and suicide, namely whether they are shared or independent., Methods: We studied the distribution of suicides and suicide attempts in 539 subjects from 78 families of probands with major AD, all responders to lithium prophylaxis. A Cox proportional hazard regression model was used to assess the contribution of several independent variables to the risks of AD, BD, and suicidal behavior., Results: The lifetime prevalence of BD was significantly greater among first-degree relatives of suicide than non-suicide probands (22% versus 11%) and the prevalence of BD in families was associated with an increased risk of developing mood disorder and subsequently committing or attempting suicide (p = 0.003). Families fell into 1 of 3 groups, corresponding to a low (<0.1%), intermediate (17.8%), and high (87.8%) risk for suicide in affectively ill subjects., Conclusions: Suicidal behavior is distributed unevenly in families of probands with BD, aggregating in a subset of families. Our results also suggest that partially overlapping sets of genetic factors may underlie BD and suicide.

Objective: Neuroimaging studies show structural brain abnormalities in bipolar patients. Some of the abnormalities may represent biological risk factors conveying vulnerability for the disease. This paper aims to identify neuroanatomical risk factors for bipolar disorder (BD)., Methods: We reviewed magnetic resonance imaging (MRI) findings in populations in which the effects of the disease or treatment are minimal or where the chances of finding genetically coded risk factors shared within the families are increased. Such populations include unaffected relatives of bipolar patients, first-episode patients, children or adolescents with BD and patients with familial BD., Results: MEDLINE search revealed 30 relevant scientific papers. Abnormalities in the volume of the striatum, left hemispheric white matter, thalamus and anterior cingulate as well as quantitative MRI signal hyperintensities were identified already in unaffected relatives of bipolar patients. Subjects in the early stages of the disease showed volume changes of the ventricles, white matter, caudate, putamen, amygdala, hippocampus and the subgenual prefrontal cortex. Reduction in the subgenual prefrontal cortex volume was replicated in three of four studies in patients with familial BD., Conclusions: Possible candidates for neuroanatomical risk factors for BD are volumetric abnormalities of the subgenual prefrontal cortex, striatum, white matter, and probably also the hippocampus and amygdala. Qualitative finding of white matter hyperintensities was already utilized as an endophenotype.

Objectives: In this study we examined general assessment of functioning (GAF), and its relation to clinical and demographic factors in bipolar patients. A number of studies, mostly from specialized programs, show that bipolar disorder often leads to occupational and social impairment. Here we report data from patients in a primary care setting., Methods: A total of 252 patients from the Maritime Bipolar Registry with DSM-IV diagnoses of bipolar I or bipolar II disorder participated in the study. GAF ratings during maintenance treatment were compared across clinical and demographic variables., Results: The mean GAF score in this sample was 67 +/- 17 (range 10-100). The GAF scores followed bimodal distribution with mean values of 50.5 +/- 10.3 and 79.0 +/- 10.3. Decreased functioning was found in patients with chronic illness course, history of rapid cycling, suicidal behaviour, psychiatric comorbidity, hypothyroidism, and diabetes mellitus, regardless of treatment of these conditions. There were no differences in the level of functioning between men and women, bipolar I and II patients, those with and without psychotic episodes, hypertension, treatment with antidepressants or antipsychotics., Conclusions: Functioning in primary care-treated bipolar patients in maintenance phase of treatment is decreased not only due to specific disorder-related variables, but also due to frequent comorbidity with other psychiatric and medical conditions.