Annemiek Dols

Objectives: The prevalence of cognitive impairment is high in older age bipolar disorder (OABD). It remains unclear whether the course of cognitive functioning in these individuals follows a course of cognitive decline, whether this is different among subgroups, and if so, which factors are associated with cognitive decline. Methods: Our clinical sample included 206 patients aged over 50 years from the Dutch Older Bipolar (DOBi) cohort with a total range of included measures of 306 months (1997–2022). Growth Mixture Modeling (GMM) was used to find homogeneous subgroups based on their longitudinal trajectories of the Mini‐Mental State Examination (MMSE) scores. Patients were categorized in classes based on similar trajectories, and these classes were compared based on their performance on nine cognitive tests using mixed models. Results: Three classes could be identified through GMM, each demonstrating a modest, parallel decline in MMSE scores: one class showed consistently high cognitive performance; another with moderate cognitive functioning; and a third group with significant cognitive impairment. Multivariate analyses showed that an advanced age at baseline and an earlier onset of the disorder were predictive of a more impaired cognitive trajectory. Furthermore, nine supplementary cognitive assessments demonstrated pronounced disparities across these three groups, with the most substantial cognitive deficits in individuals with the most unfavorable MMSE trajectories. Discussion: Our findings underscore the heterogeneous nature of cognitive aging in OABD, revealing a spectrum of cognitive longitudinal trajectories. Findings indicate that the MMSE is a valid screener in order to broadly predict cognitive functioning and its course in OABD. Identifying individuals who are particularly susceptible to cognitive decline is crucial for initiating tailored rehabilitation strategies.

Objectives: To identify preliminary insights into the perceived utility and risks of commercially available AI chatbots such as ChatGPT used by individuals with severe mental illness (SMI), namely bipolar disorder, for self‐reflection and mental health support. Methods: Data were collected via semi‐structured interviews from two individuals diagnosed with bipolar disorder who independently used ChatGPT for self‐reflection and to explore diagnosis, symptom management, and treatment. Both individuals presented to psychiatric services for clinical reasons unrelated to chatbot use. The interviews were audio‐recorded, transcribed verbatim, and thematically analyzed. Results: We identified a few key themes in the data. First, both participants perceived ChatGPT as a versatile tool, providing educational, emotional, and social support, with use evolving from information seeking to more personal and relational functions. Second, distinct attachment patterns reflected their clinical and psychological profiles: one engaged in reflective, intellectualized conversations, whereas the other developed emotionally intimate interactions. Third, both described therapeutic benefits, including nonjudgmental support and reassurance, sometimes perceived as more accessible or understanding than professional care. Conclusions: These cases suggest that ChatGPT can shift trust from mental health professionals to AI, fulfilling multiple roles resulting in role blurring and fluid boundaries. Engagement patterns varied with clinical stability, highlighting greater reliance and less distancing in more vulnerable individuals. Although perceived benefits were reported, risks include overreliance, diminished trust in clinicians, and limited awareness of AI limitations, underscoring ethical and clinical considerations for integrating AI in psychiatric care. These cases highlight persistent challenges in psychiatric care that AI alone cannot address.

Introductions: Lithium remains the first‐line maintenance treatment for bipolar disorder (BD), yet its safety in older‐age BD (OABD) is uncertain due to limited large‐scale data. OABD patients often experience more severe physical comorbidities compared to younger BD patients, but systematic evidence regarding lithium's association with these conditions is lacking. This study examined the association between lithium use and physical comorbidities across age groups using the international Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE‐BD) dataset. Methods: A cross‐sectional analysis was conducted using combined Wave 1 and 2 data from GAGE‐BD project, encompassing 37 studies from 20 sites worldwide. Participants aged ≥ 18 years with BD were classified according to current lithium use. Physical comorbidities were harmonised into eight organ‐system domains. Sociodemographic and clinical variables were compared between individuals receiving lithium treatment and those not prescribed lithium. Generalised linear mixed models adjusted for age, site and lithium × age interaction were applied to evaluate associations with physical comorbidities. Results: Of the 2873 total participants included in the study, 1069 were currently receiving lithium treatment and 1804 were not prescribed lithium. Participants treated with lithium showed a lower overall prevalence of physical comorbidities compared with those not receiving lithium. Regression analyses revealed significant age‐by‐lithium interactions for cardiovascular (χ ² = 9.27, p = 0.002), respiratory (χ ² = 7.56, p = 0.006), genitourinary (χ ² = 8.66, p = 0.003) and endocrine (χ ² = 16.96, p < 0.001) comorbidities. Model‐estimated curve crossings occurred at approximately 43 years (cardiovascular), 32 years (respiratory), 51 years (genitourinary) and 59 years (endocrine), above which predicted prevalence was lower among participants treated with lithium, whereas no differences were observed for gastrointestinal, hepatic, renal or musculoskeletal systems. Conclusions: Lithium use in OABD was associated with a lower burden of specific physical comorbidities, particularly cardiovascular, respiratory and endocrine conditions. Although prescription bias cannot be excluded, the findings challenge the perception that lithium exacerbates physical health risks in older adults. Instead, they support lithium's continued use—with appropriate monitoring—as a safe and effective treatment option for OABD, and highlight the need for prospective studies to further clarify the relationship between lithium exposure and physical health outcomes.

Background: Older adults with bipolar disorder ( BD ) are at high risk for severe mood symptoms, cognitive difficulties, and early mortality; however, underlying brain alterations have received little study. Gray matter decreases in ventral prefrontal cortex ( VPFC ) seen in younger persons with BD , and additionally in dorsal PFC ( DPFC ), are implicated. Here, cortical thickness was examined in an international sample. Methods: Cortical thickness in 34 regions was measured from structural magnetic resonance imaging data using enhancing neuroImaging genetics through meta‐analysis pipelines for individuals with BD ( n = 308) and non‐psychiatric comparison participants ( n = 287), ages 40–70y, from six Global Aging and Geriatric Experiments in BD ( GAGE ‐ BD ) consortium sites. Group differences were assessed for VPFC , DPFC and remaining cortical regions, covarying for scanner, age and sex. Results were considered significant corrected for false discovery fate ( FDR ) at p _FDR < 0.05. Relationships with demographic and clinical factors were explored. Results: Cortical thickness was significantly lower among BD in VPFC and DPFC , and additional distributed regions ( p _FDR < 0.05) and negatively associated with age ( p < 0.05); group and age did not interact significantly. Relationships with BD subtype and medications were observed; lithium was associated with higher frontal pole and anterior cingulate thickness ( p _FDR < 0.05). Conclusions: In this international middle and older aged adult BD sample, lower VPFC cortical thickness was observed, suggesting these deficits persist from younger age. DPFC and additional regions of reduced cortical thickness could contribute to the older adult BD phenotype. Cortical thinning was more pronounced among older individuals regardless of diagnosis. Lithium may lessen cortical thinning.

Objectives: Lithium is recommended as the first‐line maintenance treatment for older adults with bipolar disorder (OABD). However, age‐related reduction in renal clearance, heightened sensitivity to adverse effects, and drug interactions increase the risk of toxicity and necessitate lower therapeutic serum levels in this population. Despite expert consensus supporting age‐adjusted targets, most clinical laboratories still do not report age‐specific therapeutic ranges. We conducted a systematic review on recommended serum lithium levels in OABD to update evidence supporting age‐specific therapeutic ranges for safe and effective lithium management. Methods: We searched Medline, PsycINFO, Embase, and Cochrane Central Register of Controlled Trials since 2017 for studies analyzing lithium serum levels in individuals with bipolar disorder ≥ 60 years. Two reviewers independently screened titles, abstracts, and full texts. We used the National Institutes of Health Quality Assessment Tool to assess methodological quality. Results: Fourteen studies, including 8808 older adults using lithium, met inclusion criteria. All studies were observational with cross‐sectional or cohort designs. Mean or median lithium levels ranged from 0.47–0.67 mmol/L. Levels of 0.82–1.00 mmol/L were associated with increased side effects or early toxicity signs, while levels of 1.20–1.25 mmol/L were overtly toxic. Impaired renal function and concomitant use of diuretics, ACE inhibitors, and NSAIDs required closer monitoring for toxicity. Conclusions: These findings are consistent with age‐adjusted lithium therapeutic targets in older adults and provide additional observational support for clinical laboratories to adopt age‐specific therapeutic ranges. Clinicians should consider individualized monitoring accounting for age‐related physiological changes and medication interactions to ensure safer and effective lithium therapy.

Background: Cognitive impairment is common in bipolar disorder (BD), but the underlying pathophysiology remains unclear. This systematic review aimed to (1) summarize all literature describing relationships of biofluid biomarkers and cognition in BD and (2) identify which biofluid biomarkers correlate most consistently with cognition in BD. Methods: This systematic review followed procedures of the PRISMA statement. PubMed, EMBASE, and PsycINFO were searched from inception until July 2023. Original studies assessing the relationships between biofluid biomarkers and cognitive functioning in adults with BD were included. Studies on neuroimaging markers and genetic biomarkers were excluded. Results: We identified 60 studies, together describing 184 biofluid biomarkers that were measured in relation to cognitive functioning in BD. Biomarkers were organized into ten categories: oxidative stress markers ( n = 14); growth factors ( n = 13); neurotransmitters ( n = 14); neuropeptides and hormones ( n = 14); neurodegenerative markers ( n = 11); inflammatory/immune markers ( n = 59); serostatus to infectious agents ( n = 11); amino acids, vitamins, and minerals ( n = 7); metabolic factors ( n = 23); hemogram, coagulation, and fibrinolysis markers ( n = 18). Preliminary evidence for a significant relationship with cognition appeared for HSV‐1 IgG, CRP, and homocysteine (Hcy); higher biomarker levels were associated with worse cognition. Discussion: Included studies were heterogeneous and many were deemed to be of low quality following risk of bias assessment. The identified three biofluid biomarkers represent history of previous infections, current inflammation, and/or physical or psychological stress. Poor physical health, possibly represented by a broad range of biomarker aberrations, may play a role in the pathophysiology of cognitive impairment in BD. Trial Registration: PROSPERO registration number: CRD42021224226

Background and ObjectivesThe diagnosis of behavioral variant frontotemporal dementia is often difficult because behavioral change has a broad differential diagnosis. Genetic testing may aid in the ...

Mental disorders contribute significantly to the disease burden in Europe. Brain stimulation treatments such as electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS) are supported by strong evidence and are recommended in clinical guidelines in many countries. However, access to these interventions remains highly inconsistent across Europe. Reimbursement by public health services plays a critical role in enabling equitable access to these therapies. In this study, we examined disparities in national reimbursement policies for ECT and rTMS for the treatment of mental disorders in both inpatient and outpatient settings across 28 European countries, leveraging information from public databases and expertise from a multinational expert group within the ECNP Neuromodulation Network. Our results show that ECT is reimbursed for inpatient care in all 28 studied countries, while outpatient ECT is reimbursed in 14 of 28 countries (50.0%), with a geographic trend toward higher availability in Scandinavian and Western European countries. rTMS reimbursement was reported in 14 of 28 countries (50.0%) for inpatient care and in 8 of 28 countries (28.6%) for outpatient care, with no consistent geographic pattern. Subsequent analyses examining reimbursement policies in relation to the national Human Development Index (HDI), the Gini coefficient, and healthcare infrastructure metrics revealed no statistically significant differences between countries that do and do not reimburse outpatient ECT. Although countries reimbursing outpatient ECT showed numerically fewer hospital beds and slightly higher HDI values, these differences did not reach statistical significance. Similarly, no significant associations were observed between rTMS reimbursement and indicators of socioeconomic development, income inequality, or healthcare infrastructure. These findings highlight a striking disparity in national reimbursement policies across Europe, despite the solid evidence base for ECT and rTMS in treating mental health disorders. The current landscape suggests that many patients in Europe experience limited access to these effective treatments that could alleviate individual suffering and reduce societal disease burden. We call for urgent policy action to align health system reimbursement with clinical evidence for treatment efficacy and to improve access to evidence-based brain stimulation therapies across Europe.

BACKGROUND Rigorous clinical research on conditions such as older-age bipolar disorder (OABD) patients is imperative to guide prevention and treatment efforts. Large-scale prospective studies are challenging and resource-intensive; combining existing, smaller datasets allows for powerful analyses that are globally representative. The Global Aging & Geriatric Experiments in Bipolar Disorder (GAGE-BD) consortium, which integrates and harmonizes archival data from sites across the world, is one such effort. This requires development of approaches to aid in classifying the domains covered by variables contained in contributed datasets and to set the stage for optimal and efficient harmonization of variables across instruments and data structures. OBJECTIVE To develop a domain-specific ontology for systematically classifying variables in older-age bipolar disorder (OABD) research. To design and implement an computational tool (VariCat) to standardize variable categorization and capture metadata crucial for integration and harmonization across heterogeneous datasets. To demonstrate the capability of this framework via a case study in enabling scalable data integration and harmonization of archival clinical datasets across multiple study cohorts and sites. METHODS Based upon our systematic review of measures used in 53 studies of OABD, and upon data dictionaries and numeric data from 19 cohorts comprising over 1400 individuals with and without bipolar disorder (BD), we developed an ontology of data comprising 20 domains, 102 subdomains, and 129 sub-subdomains for classifying OABD-relevant variables. Subsequently, we created a computer-based interactive program, VariCat, implemented in both Python and REDCap, for data analysts to classify each contributed variable according to its domains/subdomains and to input harmonization-relevant characteristics. RESULTS VariCat provides a way to systematically classify and harmonize heterogeneous variables across datasets by generating a structured metadata repository. Across 37 studies, this approach identified domain coverage (e.g., cognitive variables present in 19 studies) and enabled harmonization of 212 variables into an integrated dataset spanning 20 sites. In a single-site application (UCSD Inflammaging dataset), VariCat categorized all variables and revealed domain distributions (e.g., physical health, illness severity, and cognition as the most represented domains). Across datasets, the framework identified commonly used instruments (e.g., Trail Making Test Part B) and enabled systematic identification of data gaps and harmonization opportunities. CONCLUSIONS We developed and employed a bipolar disorder-specific ontology based on existing datasets and literature and incorporated this into a variable categorization program available in two different formats. This work will help better characterize OABD and also benefit the growing number of international consortia that study psychiatric disorders using archival and/or prospective data. CLINICALTRIAL NA