Functional Outcomes in Bipolar Disorder: Cross-Cohort Analyses from the Global Bipolar Cohort

Functional Outcomes in Bipolar Disorder: Cross-Cohort Analyses from the Global Bipolar Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Functional Outcomes in Bipolar Disorder: Cross-Cohort Analyses from the Global Bipolar Cohort *Anastasia K. Yocum, *Janice M. Fullerton, Melanie M. Ashton, and 209 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9390347/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background The Global Bipolar Cohort (GBC) was established to identify existing bipolar disorder (BD) cohorts worldwide and foster collaborations focused on descriptive and analytic outcomes relevant to BD. A distributed analytic framework has been implemented to engage multiple sites without the need for central data pooling. This report describes the GBC endeavor and global functional impairment patterns. Cross-cohort comparisons of functional correlates are limited by heterogeneous measures and data-sharing constraints. Large, culturally diverse comparisons are needed to distinguish broadly reproducible correlates from cohort-specific effects. Participating sites completed a 28-item descriptive survey covering diagnostic methods, cognition, genetics, treatment, functioning, and follow-up strategies. We implemented a harmonized local logistic regression model of dichotomized functional outcome and shared summary statistics only. Results We identified 69 cohorts across five continents. Thirty-seven cohorts contributed functional outcome analyses from 17,130 participants. Outcome measures included clinician-rated disability scales and social indicators such as employment and marital status. The proportion classified with poor functioning ranged from 16% to 77% (mean 50%). In 32 of 37 cohorts, the overall regression model significantly explained variance in functioning. Current depressive symptoms were the most robust and reproducible correlate of poor functional outcome: they were assessed in 29 cohorts, significant in 22 (75.8%), ranked among the top three correlates in 22, and were the top-ranked correlates in 19. Associations between depressive burden and poor functioning were observed across clinician-rated disability scales and work or social indicators, and across geographically diverse cohorts. Comorbid substance use disorder and medication-related variables were associated with poorer functioning in subsets of cohorts, whereas sex, ancestry, bipolar subtype, psychosis history, and premorbid IQ showed weak or inconsistent associations. Cognitive measures, available in a minority of regression models, showed modest and non-uniform effects. Conclusions Across heterogeneous international cohorts, current depressive symptom burden emerged as the most consistent correlate of poor functioning in bipolar disorder. These findings replicate earlier multisite work at a larger scale, show that protocol-based distributed analyses can identify reproducible clinical signals without sharing individual-level data, and support prioritizing detection and treatment of depressive symptoms when aiming to improve real-world functioning. Future work should expand longitudinal harmonization and representation of under-studied populations. Bipolar Disorder Functional Outcomes Depression Mania Distributed Analysis Secondary Data Analysis Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Bipolar Disorder (BD) is characterized by recurrent episodes of depression, mania, or hypomania and is among the leading global causes of disability, ranking approximately 17th worldwide, with lifetime prevalence estimates around 2 - 2.5 % across populations (Carvalho et al. 2020). Its clinical course is complex, dynamic, and heterogeneous, often resulting in substantial and persistent functional impairment that extends well beyond acute affective episodes (Berk et al. 2017; McIntyre et al. 2022; Sanchez-Moreno et al. 2009). Even during euthymic periods, 65% of patients report work-related difficulties, 42% experience relationship impairments (MacQueen et al. 2001; Ustün and Kennedy 2009), and up to two-thirds experience disruptions in personal, social, and occupational functions (Correll et al. 2025; Goetz et al. 2007; Suppes 2019; Tohen et al. 2017). Our earlier multi-center study (Burdick et al. 2022), consistent with prior reports (McIntyre and Calabrese 2019), highlighted the strong association of depressive symptom burden and functional impairment. Additional correlates of poor functional outcomes include executive dysfunction, processing speed and lower educational attainment (O’Donnell et al. 2023; O’Donnell et al. 2017), and a meta-analysis indicated that neurocognitive deficits across multiple domains play a critical role in shaping everyday functioning in BD (Depp et al. 2014). The complexity and heterogeneity of BD require large, diverse, and deeply phenotyped cohorts to achieve sufficient statistical power to identify reliable predictors of illness progression and functional outcomes (Cai et al. 2026). Although the term ‘heterogeneity’ is widely used in psychiatry, it is rarely precisely defined; conceptually, it reflects the extent to which clinical presentations deviate from the state of “perfect conformity”, and encompasses variation in symptoms, illness trajectories, comorbidities, underlying biology (including genetics), and contextual factors (Nunes et al. 2020b). Categorical diagnostic systems amplify this complexity by permitting vast combinatorial variations of symptom configurations within a single diagnosis, complicating effect-size estimation, replication, and cross-cohort comparison (van Os et al. 2013). BD exemplifies these challenges: depressive symptoms may result from the disorder itself, associated with life events, or its comorbidities such as personality disorder or anxiety disorders. Symptoms additionally can span diverse cognitive, affective, and somatic dimensions, each potentially exerting distinct influences on functional impairment. Further, polygenic risk score (PRS) analyses show that specific clinical sub-phenotypes of BD (e.g. psychosis, rapid cycling, early onset, or suicidality) map onto partially distinct genetic architectures, indicating that biological heterogeneity also contributes directly to clinical variability (Coombes et al. 2020). This variation highlights the need for coordinated global efforts and harmonized phenotyping to better understand how different features of BD shape outcomes. Collaborative consortia such as the Psychiatric Genomics Consortium (PGC) (Agrawal et al. 2025; Andreassen et al. 2023) and ENIGMA (Thompson et al. 2020) have been very productive by analyzing large data sets from multiple sources. However, these efforts remain constrained by their reliance on categorical diagnoses and a limited set of clinical descriptors, e.g. BD subtype or psychosis history (O’Connell et al. 2025). In recognition of these limitations, the PGC has initiated a substantial effort to collect and harmonize phenotypic subtype data from 57 European-ancestry cohorts, comprising 23,819 individuals across 16 target subphenotypes (van der Veen et al. 2025). Parallel PGC efforts in major depressive disorder have demonstrated the value of inclusive, globally diverse sampling, identifying shared risk loci across populations (Flint 2023). Yet, the retrospective analysis of clinical data from multiple independently designed studies remains challenging due to inconsistently defined phenotypes which limit the ability to resolve heterogeneity. As a consequence, much of the existing literature is anchored in cross-sectional snapshots of cohorts, diagnosed by current standards, value-laden clinical judgements that create binary categories. Within large clinical consortia, available symptom data are typically limited in depth and longitudinal content, with little capacity to analyze chronological patterns or functional trajectories. Although research in low- and middle-income countries is expanding and contributes to global representation (Agrawal et al. 2025), many of these studies, e.g. A-BIG-Net, are similarly cross-sectional or based on single time-point assessments (Giusti-Rodríguez et al. 2025). Without sustained, prospective clinical data collection, the dynamic course of BD, including the evolution of early psychopathology, the interaction of comorbidities, and intra-individual symptom fluctuations, will remain difficult to delineate. These limitations underscore the importance of coordinated, ongoing, and globally distributed efforts to harmonize clinical phenotypes, thereby enabling more equitable, generalizable, and mechanistically informative models of illness course and functional outcomes. The Global Bipolar Cohort (GBC) was established in 2019 as an international consortium of researchers and clinicians dedicated to coordinating and harmonizing clinical data across global cohorts, thereby addressing many of the persistent challenges that have limited prior collaborative efforts. A preliminary proof-of-concept study by Burdick et al. integrated data analyses from 13 cohorts across seven countries (n = 5,882) and demonstrated widespread though variable functional impairment ranging from 41% to 75% (Burdick et al. 2022). Depressive symptom burden, lower educational attainment, and a greater number of prior mood episodes emerged as key correlates of poorer functioning. A subsequent GBC study involving 10,351 individuals from 11 cohorts evaluated regional differences in treatment patterns and identified notable geographic variation, including lower lithium use in North America and higher antipsychotic use in Europe (Yocum and Singh 2025). Cross-cohort comparisons are accomplished within the GBC through a decentralized analytic framework modeled on meta-analytic principles. A comprehensive global survey identified and characterized existing global BD cohorts and documented available measures relevant to outcomes and functioning, including social and occupational domains. Each participating site then received a harmonized analysis protocol and implemented all statistical models locally, generating site-specific summary statistics for functional outcome correlates based on their own data. These outputs were subsequently aggregated using an approach adapted from meta-analytic methodology (not a traditional meta-analysis) to facilitate structured synthesis and comparison of results across cohorts while preserving local data governance. This distributed approach supports transparent, protocol-based harmonization across geographically and culturally diverse samples and enables nuanced evaluation of how clinical features, cognitive profiles, treatment patterns, and contextual factors jointly relate to functional outcomes in BD. We hypothesized that this global, harmonized framework would identify shared and region-specific determinants of functional outcomes, providing meaningful geographic and cultural differences in the strength and patterning of associations across cohorts. MATERIALS and METHODS Cohort Identification and Data Collection A comprehensive survey was developed to identify existing BD cohorts worldwide and assess their suitability for collaborative research within the Global Bipolar Cohort (GBC). The 28-item survey (see Supplemental information S2) collected information on cohort characteristics, clinical instruments, demographics, and the availability of biospecimens and cognitive assessments. Invitations were sent to 691 researchers identified through mailing lists from major international consortia, including the Bipolar Disorder Working Group of the Psychiatric Genomics Consortium (O’Connell et al. 2025), ENIGMA-BD (Thompson et al. 2020), and ConLiGen (Schulze et al. 2010). Additional dissemination occurred via network coordinators from groups such as the World Psychiatric Association, A-BIG-NET (Kuo et al. 2023), and the Genetics of ECT initiative (Soda et al. 2020), who circulated the survey link through mailing lists and social media. The distribution list was intentionally inclusive, many recipients were involved in one or more studies, the emphasis was on unique representation of the studies rather than the individual response rates. Principal investigators (PIs) were eligible to participate in the functional outcomes analysis if they completed the survey prior to January 2023, and reported the use of at least one measure of functional outcome (Supplemental Table 1 & 2). Distributed Analysis Framework Cohorts meeting the inclusion criteria were invited to participate in a functional outcome analysis using a distributed analytic framework designed to balance two competing needs: (i) cross-cohort harmonization of analytic procedures, and (ii) preservation of local data governance, privacy protections, and site-specific data structures. Rather than pooling individual-level data or conducting a traditional meta-analysis, the GBC implemented a protocol-based distributed analysis model (Supplemental information), one that specified core analytic decisions, including outcome definition principles, eligible predictor domains, minimum power requirements, regression modeling strategy, and required outputs. Each participating site implemented the same analytic framework locally, using their own data and statistical resources. Sites conducted logistic regression analyses predicting dichotomized functional outcome (“good” vs. “poor” functioning) following standardized instructions, example code, and output templates (Supplemental information). Model specifications were fixed a priori : all available correlates were entered simultaneously using an “enter” method, with a minimum participant-to-variable ratio of 10:1 to ensure model stability (Peduzzi et al. 1996). Sites reported a predefined set of model-level and predictor-level summary statistics (e.g., omnibus tests, odds ratios, confidence intervals, p-values), which were subsequently aggregated centrally for cross-cohort synthesis. Each site selected a single primary functional outcome measure based on prespecified criteria: (1) completeness of data, (2) level of granularity (e.g., FAST preferred over GAF), and (3) cross-site comparability. Functional outcomes were dichotomized using either established clinical cutoffs or sample-based thresholds (e.g., mean-based z-score splits), with consistent coding (0 = good functioning; 1 = poor functioning). Sites documented their outcome definitions and any context-specific coding decisions (e.g., treatment of retirement or marital status) to ensure transparency and interpretability. Definition and Harmonization of Functional Outcome Measures Functional outcome measures varied across cohorts and included clinician-rated scales (e.g., FAST, WHODAS, GAF), structured self-report instruments, and social indicators (e.g., employment and marital status). For scale-based measures with established clinical thresholds (e.g., FAST, WHODAS), sites applied validated cutoffs when available. In cohorts lacking validated thresholds or using non-standardized functional indicators, sample-based thresholds were applied using z-score normalization, with dichotomization at the sample mean unless otherwise justified. Coding conventions were standardized across sites (0 = good functioning; 1 = poor functioning), and all outcome definitions were documented. Social indicators required additional contextual consideration. Employment status classifications varied by cohort and were coded according to local norms, with students and retirees handled explicitly. Marital status coding similarly accounted for sociocultural and demographic context, including treatment of widowed participants. Although this approach permitted variation in operational definitions across sites, the analytic framework emphasized cross-cohort replication and consistency of associations rather than direct comparison of effect magnitudes. This approach differs from both pooled individual-level analyses and conventional meta-analyses. Individual-level data were not centralized, and effect sizes were not statistically pooled. Instead, consistency and robustness of associations were evaluated through structured comparison of site-specific results, including rank-based summaries and replication patterns across cohorts with diverse measures, populations, and sociocultural contexts. Four cohorts (Maritime, GAIN, Houston, and Cagliari) provided de-identified individual-level data for centralized analysis, enabling validation of distributed results; however, these data were analyzed using the same analytic specifications to maintain comparability. The distributed framework enabled large-scale international participation, reduced administrative and legal barriers associated with data transfer agreements, minimized privacy and security risks, and facilitated protocol-driven harmonization of analyses across otherwise heterogeneous cohorts. Statistical Analysis Sites were encouraged to consider relevant contextual factors when assigning participants to functional outcome categories (e.g., employment or marital status), taking into account local demographic characteristics and sociocultural norms. These decisions were documented to ensure that site-level coding remained transparent, interpretable, and broadly comparable across cohorts. Analyses adhered to a minimum participant-to-variable ratio of 10:1, with priority given to core predictor variables specified a priori (Supplemental information). Each participating site conducted logistic regression analyses to identify correlates of functional outcome (good vs. poor functioning), including demographic, clinical, cognitive, and medication-related variables, following the standardized protocol detailed in the Supplemental Information. All independent variables were entered simultaneously using the “enter” method, rather than stepwise selection, to preserve comparability of model structure across sites [ref -. advanced statistics ] Sites returned a predefined set of model-level and predictor-level summary statistics, including omnibus tests of model fit, odds ratios, 95% confidence intervals, and p-values, to the coordinating team for structured cross-cohort synthesis. Where cognitive data were available, general cognitive ability (“g”) was derived locally using unrotated principal component analysis (PCA), following standardized instructions (Burdick et al. 2019). Up to two measures per cognitive domain were included, subject to the same 10:1 subject-to-variable ratio constraint. When a global cognitive index (e.g., full-scale IQ) was available, it could be substituted for g; however, premorbid IQ estimates were explicitly excluded from g derivation to avoid conflation with illness-related cognitive effects. Cross-Cohort Synthesis and Predictor Ranking Results from site-specific logistic regression analyses were combined using a structured summary-based synthesis rather than pooled statistical estimation (Table 2). For each predictor variable, the coordinating team collated site-level outputs and quantified cross-cohort consistency using three complementary metrics: (1) availability, defined as the number of cohorts in which the predictor was included in the regression model; (2) statistical significance, defined as the number and proportion of cohorts in which the predictor was significantly associated with the functional outcome (p < 0.05); and (3) relative importance, operationalized as the frequency with which the predictor ranked first or among the top three correlates within each site-specific model based on statistical strength (−log₁₀[p-value]). These metrics were summarized across cohorts in Table 2, which provides an overview of the associations across heterogeneous samples, outcome measures, and social contexts. Correlates were grouped according to demographic, clinical, cognitive, and treatment-related categories. Table 2 suggests cross-site convergence rather than magnitude of effect, which was not directly comparable due to differences in outcome measures, covariate availability, and local coding decisions. Because functional outcomes were operationalized using different instruments, cutoff strategies, and scaling approaches across cohorts, effect sizes (e.g., odds ratios) were not directly comparable across sites. For each site-specific regression model, correlates were ranked according to the −log₁₀(p-value), which reflects the strength of evidence for association. These within-cohort rankings were then summarized across cohorts to quantify how frequently each correlate ranked first or among the top three. This approach was used to assess cross-cohort reproducibility of associations rather than an absolute effect size. Rankings were only compared within models and were not used to compare effect magnitude between cohorts. This strategy aligns with the distributed analytic framework, which emphasizes consistency of findings across heterogeneous samples rather than pooled estimation of effect sizes. This differs from conventional meta-analysis in that effect sizes were not pooled and no assumption of measurement equivalence across cohorts was imposed. Instead, consistency of direction, statistical support, and relative explanatory contribution across independent models was used as the primary indicator of robustness. Integration with Prior Analyses Regression outputs generated through the current distributed analysis were combined with previously published results from the initial GBC functional outcomes study (Burdick et al. 2022). For clarity, the earlier analyses are designated as “Wave 1,” while the newly collected site-level regression summaries constitute “Wave 2.” Together, these two waves form the expanded GBC functional outcomes dataset, enabling broader cross-cohort evaluation of correlates of functioning in bipolar disorder across a larger number of cohorts, countries, and outcome measures, while maintaining consistent analytic principles. RESULTS Global Survey of Bipolar Disorder Cohorts The global survey identified 69 studies investigating BD across five continents. Geographic representation was strongest in Australia, the United States, multiple European countries, and South Africa, with participation from South and East Asia through A-BIG-NET, a consortium advancing genetic research in Asian populations (Kuo et al. 2023) (Figure 1a). Of these, 24 cohorts met inclusion criteria for the functional outcomes analyses and were able to implement the distributed analytic protocol, yielding broad international representation (Figure 1b). Most cohorts recruited participants through hospital-based settings (59.7%), including primary hospitals (n = 12), specialized clinics (n = 11), and tertiary referral services (n = 4). The remaining cohorts were drawn from mixed community–clinical sources (28.4%, n = 19) or community-based samples (11.9%, n = 8). As anticipated, participating cohorts had substantial heterogeneity in assessment practices and data structures. The Structured Clinical Interview for DSM Disorders (SCID) was the most frequently used diagnostic instrument (47.1%), followed by the Mini International Neuropsychiatric Interview (MINI; 22.1%) and the Diagnostic Interview for Genetic Studies (DIGS; 13.2%) (Figure 2a). Cognitive data availability varied across cohorts: approximately 30% collected no cognitive data, 40% collected cognitive data in a subset of participants, and 25% collected cognitive data on all participants (Figure 2a). In contrast, genetic data were available in over half of cohorts, particularly in North America and Europe, and approximately one third of these cohorts had contributed data to the PGC (O’Connell et al. 2025). Longitudinal data relevant to functional outcomes were available in 55% of cohorts, most commonly in subsets of participants (averaging ~50% within cohort), reflecting ongoing challenges in sustained longitudinal follow-up (Figure 2a). Across cohorts, ancestry composition was predominantly European, with smaller representation of African, South Asian, and other ancestry groups (Figure 3), underscoring both the global reach of the GBC and persistent gaps in representation. Functional Outcomes Across Cohorts A summary of functional outcomes is presented in Table 1, integrating previously published results from 13 cohorts (Wave 1; 7 countries) (Burdick et al. 2022) with new analyses from 24 cohorts (Wave 2; 14 countries). The combined dataset includes 17,130 participants from 17 countries. Female participants were overrepresented in 33 of the 37 cohorts contributing functional outcome data; 35 cohorts had female representation exceeding 60% (Figure 2b). Functional outcome measures varied across cohorts, reflecting differences in study design and available instruments. In Wave 2, over three-quarters of cohorts collected indicators of social functioning, including employment status (~76%) and marital status (~71%). Other cohorts employed validated functional scales, including the Functioning Assessment Short Test (FAST; 30%) (Chen et al. 2019), the World Health Organization Disability Assessment Schedule (WHODAS; 27%) (Federici et al. 2017), and the Global Assessment of Functioning (GAF; 27%) (Chen et al. 2019). Less than one-fifth of cohorts used alternative measures (e.g., SOFAS, QOL.BD, LIFE-RIFT, CGI), and only 6% reported no functional outcome measure. Across cohorts, functional outcomes were dichotomized using either validated clinical cutoffs or sample-based thresholds, as detailed in the Supplemental Information. As a result, cohorts using the same functional instrument sometimes applied different cutoffs, reflecting differences in instrument versions or population characteristics. The proportion of participants classified as having poor functioning ranged from 16% to 77% across sites (Table 1), with a mean of 50% (SD = 14%). Overall, 9,231 individuals (53.2% of the combined sample) were classified as having poor functional outcomes, and 18 of 37 cohorts reported impairment in more than half of participants, highlighting the substantial functional burden associated with BD across settings. Logistic regression omnibus tests were statistically significant in 32 of 37 cohorts (p < 0.05; Table 1), indicating that the included correlate collectively explained meaningful variance in functional outcomes in most samples. The five cohorts with non-significant omnibus tests were relatively small (n < 110, with 8–13 cases per correlate), consistent with limited statistical power despite adherence to minimum case-to-correlate ratios. Predictors of Functional Outcome Using site-specific functional outcome definitions, regression analyses across 37 cohorts evaluated a common set of demographic, clinical, cognitive, and treatment-related correlates. Despite heterogeneity in outcome measures and cutoff strategies, a consistent pattern emerged. Current depressive symptoms were the most robust and reproducible correlates of poor functional outcome across cohorts (Figure 4; Supplemental Table 3). Associations were observed across diverse outcome types, including clinician-rated disability scales (FAST, WHODAS, GAF, MSIF) and work–social indicators (e.g., employment status), but were less consistently observed for marital status, a longer-term social outcome influenced by broader contextual factors (Robards et al. 2012) (Figure 4a). The association between current depressive symptoms and poor functioning was observed across geographically and ancestrally diverse cohorts (Figure 4b), underscoring the central role of depressive burden in functional impairment in BD regardless of sociocultural context. Rankings provided in Supplemental Table 4 further reinforce this pattern, with current depressive symptoms frequently ranking among the top correlates within individual cohort models based on within-model statistical support, regardless of the functional outcome measure employed. These rankings reflect the reproducibility of associations across independent cohorts rather than comparative effect magnitude across variables or sites. Other predictive correlates showed more context-dependent associations. Comorbid substance use disorders and comorbid anxiety disorders were significant in multiple cohorts, particularly those using broader global measures such as the GAF or employment status. Medication-related variables, including antipsychotic use, antidepressant use, and total psychotropic medication load, were associated with poorer outcomes in a subset of cohorts, likely reflecting confounding by illness severity or treatment complexity. In contrast, demographic variables such as age, sex, and ancestry showed weaker and less consistent associations across cohorts. Cross-Cohort Predictors Predictive correlates across cohorts are presented in Table 2. Among ‘Level One’ variables, age and sex were assessed in all 37 cohorts. Age was significantly associated with functional outcome in 12 cohorts (32.4%) and ranked among the top three in 12 cohorts, particularly in analyses using employment or marital status outcomes, which are intrinsically age-sensitive. Sex was less consistently associated with functional outcome, reaching significance in only 13.5% of cohorts. Race, assessed in 20 cohorts, showed no significant associations; this may reflect limited within-cohort diversity and underpowered subgroup analyses. ‘Level Two’ and ‘Level Three’ variables encompass clinical and cognitive features. Current depressive symptoms stood out as the strongest correlate overall: assessed in 29 cohorts, significant in 22 (75.8%), ranked among the top three in 22, and the top correlate in 19 cohorts. In contrast, manic symptoms, BD subtype, and psychosis history, although frequently assessed, showed limited association with functional outcomes. Cognitive measures were assessed in approximately one third of cohorts and demonstrated modest and inconsistent associations, while premorbid IQ showed no significant effects. Among treatment-related variables, lithium and antipsychotic use were most frequently associated with poorer functioning, whereas anticonvulsants, antidepressants, and total medication load showed weaker and less consistent patterns. Interpretation of medication effects is limited by confounding by indication and cohort-specific prescribing practices; they should be interpreted as markers of illness complexity or severity, not treatment effects. DISCUSSION This study represents a large and geographically diverse assessment of functional outcomes in BD, integrating data from 37 cohorts across 17 countries and over 17,000 individuals. Despite heterogeneity in available measures, sample characteristics, and analytic capabilities across sites, a clear pattern emerged: current depressive symptoms were the most consistent correlate of poor functioning. This finding replicates earlier work from 13 cohorts (Burdick et al. 2022) and remained stable across a wide range of outcome types, measurement approaches, and sociocultural settings, reaffirming depressive burden as a central driver of psychosocial disability in bipolar disorder (Sanchez-Moreno et al. 2009). Beyond depression, the study reveals the heterogeneous and context-dependent nature of functional impairment in BD. Effects of several variables, e.g. comorbid substance use or anxiety disorders, were not uniform across sites. Their variability most likely reflects differences in sample composition, local demographics, assessment practices, and regional sociocultural norms. The types of measures are of consequence. Marital or employment status may be subjectively valued variably according to differing social expectations. Clinician-rated functional scales, capturing short-term, objective symptom functioning, showed more consistent associations with mood symptoms. This aligns with a causally pluralistic framework (Stein et al. 2024), in which biological, psychological, and social determinants jointly contribute to functional outcomes, and in which no single causal pathway can fully account for the heterogeneity observed across global cohorts. It depends, in part, on what measures are a priority and assessed at any given time. Cognitive performance and educational attainment demonstrated modest and less consistent relationships with functioning. Although cognitive deficits are widely viewed as central to disability in BD (Barbosa et al. 2018), their predictive influence varied across cohorts, likely reflecting true differences in cognitive impairment across populations as well as measurement bias limitations, including brief or uneven cognitive batteries in some sites. In contrast, static demographic or early clinical attributes, such as age at onset, sex, and bipolar subtype, showed limited predictive value for real-world functioning. Taken together, these results suggest that malleable, clinically relevant domains, particularly those capturing symptom burden, comorbidity, and psychosocial capacity, may be more informative of functional outcome than fixed demographic or historical illness characteristics. This study also provides an overview of global BD research resources, highlighting both strengths and gaps. While the participating cohorts spanned multiple continents, representation remained skewed toward Europe, North America, and Oceania, with limited inclusion from low- and middle-income countries and historically underrepresented ancestry groups. Several additional cohorts identified in the global survey were not included in the present analyses due to lack of systematically collected functional outcome measures. These sites nevertheless represent valuable research assets and will be prioritized for inclusion as harmonized functional assessments become more widely adopted. Limitations Several limitations warrant consideration. First, although the distributed analytic approach enabled global participation, it restricted standardization of predictors and correlates, outcome definitions, and modeling strategies. Site-level differences in data quality, available variables, and sample characteristics likely contributed to residual heterogeneity. The majority of the cohorts had more female than male patients with BD, possibly reflecting sex-based differences in research participation and/or genuine sex-bias in incidence. The reliance on binary functional outcome classifications improved comparability across sites but reduced granularity relative to continuous scales such as FAST (Chen et al. 2019) or WHODAS (Federici et al. 2017), potentially obscuring subtle variation in functional impairment or recovery. Similarly, associations between medication variables and functioning must be interpreted cautiously: they likely reflect confounding by indication, as individuals with more severe or treatment-resistant illness are more likely to receive complex pharmacotherapy. A further limitation is the predominance of cross-sectional data (Vieta and De Prisco 2024). Only a minority of cohorts had longitudinal assessments, and variation in timing and design precluded unified trajectory analyses. Longitudinal data are essential for examining how functional outcomes evolve, how they interact with symptom fluctuations and treatment, and how they align with life events or environmental exposures. Addressing this gap will require expanded participation in longitudinal frameworks, including emerging initiatives such as the BD² Integrative Network (Savitz et al. 2025), the Atlas of Longitudinal Datasets sponsored by the Wellcome Trust (Arseneault), and FACE-BD (Godin et al. 2024). These initiatives offer a foundation for integrating longitudinal clinical, genomic, cognitive, and digital phenotypes within a learning health system approach. The distributed analytic framework, while preserving data autonomy, did not allow for formal meta-analytic pooling of effect sizes, necessitating reliance on rank-based summaries rather than aggregated quantitative estimates. However, the consistency of key correlates, particularly depressive symptoms, across cohorts employing different functional outcome measures and cutoff strategies supports the robustness of these associations. Rather than relying on pooled effect sizes, the distributed analytic framework emphasizes replication and convergence across heterogeneous and independent samples. The persistence of similar rankings despite variation in outcome operationalization suggests that core drivers of functional impairment in BD are detectable across diverse measurement strategies. Future waves of the GBC will incorporate standardized meta-analytic procedures that maintain the benefits of distributed computation while improving statistical precision. A limitation of the rank-based synthesis is that rankings reflect statistical support within heterogeneous models rather than directly comparable effect sizes; future phases of the GBC will address this by implementing harmonized outcome scaling to enable effect-size–based synthesis across cohorts. Finally, several potentially important contributors, such as social determinants of health, trauma exposure, environmental conditions, and treatment adherence, were not consistently available across sites, reflecting broader gaps in psychiatric data infrastructure. These domains remain essential targets for future research. In summary, the GBC initiative demonstrates both the feasibility and the importance of coordinated research efforts in bipolar disorder across diverse global settings. The findings reaffirm the role of depressive symptoms in functional impairment, yet highlights the multifaceted and context-dependent influences of biological, psychological, and social factors, and identify critical opportunities for expanding longitudinal and cross-institutional research capacity. A major priority for future phases of the GBC will be systematic harmonization of clinical and functional data across cohorts. The Human Phenotype Ontology offers a rigorous semantic framework for standardizing phenotypic descriptors (Gargano et al. 2024; McInnis et al. 2025), enabling interoperability across heterogeneous datasets, facilitating integration with genomic, cognitive, and digital phenotyping resources, and providing the structured, mechanistically interpretable foundation required for next-generation, AI-ready analytic pipelines. Leveraging harmonized measures, longitudinal trajectories, and multimodal phenotypes, future work aims to advance mechanistic insight into functional outcomes and support more precise, individualized strategies for promoting recovery and resilience worldwide. Abbreviations SCID Structured Clinical Interview for DSM (First MB, Williams JBW, Karg RS, Spitzer RL. 2015) MINI Mini-International Neuropsychiatric Interview (Sheehan et al. 1998) DIGS Diagnostic Interview for Genetics Studies(Nurnberger et al. 1994) DSM Diagnostic and Statistical Manual (American Psychiatric, Association 2013) SADS-L Schedule for Affective Disorders and Schizophrenia - Lifetime version (Endicott and Spitzer 1978) HDRS Hamilton Depression Rating Scale(Hamilton 1960) YMRS Young Mania Rating Scale (Young et al. 1978) S-R Self-Report MADRS Montgomery Asberg Depression Rating Scale (Montgomery and Åsberg 1979) BDRS Bipolar Depression Rating Scale (Berk et al. 2007) CGI-BP Clinical Global Impression scale - Bipolar (Spearing et al. 1997) MDQ Mood Disorders Questionnaire (Hirschfeld et al. 2000) CDI - SF Child Depression Inventory short form (Smucker et al. 1986) SPHERE-12 Somatic and Psychological HEalth Report -12 item (Clarke and McKenzie 2003) TEMPS-A Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Auto questionnaire (Akiskal et al. 2005) IDS-C Inventory of Depression Symptoms - Clinical (Rush et al. 1996) IDS-30 Inventory of Depression Symptoms - 30 item (Rush et al. 1996) PANSS Positive and Negative Symptom Scale (Kay et al. 1987) BDI Beck Depression Inventory (Beck et al. 1961) PHQ-9 Patient Health Questionnaire - 9 item (Kroenke et al. 2001) HARS Hamilton Anxiety Rating Scale (Hamilton 1959) BPI Bipolar Phenotype Inventory TCI-125 Temperament and Character Inventory (125 item) (Cloninger CR, Przybeck TR, Svrakic DM, Wetzel RD. 1994) CASES Cognitive, Affective, and Somatic Empathy Scale (Raine et al. 2022) SHAPS Snaith-Hamilton Pleasure Scale (Snaith et al. 1995) HCP -32 Hypomania Symptom Checklist (Angst et al. 2005) BSS Beck Scale for Suicide Ideation (Beck et al. 1979) QIDS-C Quick Inventory of Depressive Symptoms - Clinical version (Rush et al. 2003) ASRM Altman Self-Rating Mania Scale (Altman et al. 1997) CES-D Center for Epidemiological Studies Depression Scale (Radloff 1977) SCL-90 Symptom Checklist -9 (Derogatis et al. 1973) MAS or BRMS Bech-Rafaelsen mania scale (Bech et al. 1978) BISS Bipolar Inventory of Signs and Symptoms (Gonzalez et al. 2008) GAS Global Assessment Scale (Endicott et al. 1976) UKU-SERS UKU Side Effects Rating Scale (Lingjaerde et al. 1987) PROMIS Patient-Reported Outcomes Measurement Information System (Cella et al. 2010) Declarations Dedication: We dedicate this manuscript to the memory of Professor Dan Stein (1962–2025), friend, mentor, and colleague who was a driving force for clinical research and academic collaboration in South Africa. Dan died on 6 December 2025. He was a champion of cross-continental partnerships that included African cohorts in the pursuit of knowledge for the benefit of mankind. Dan provided edits and guidance on earlier drafts of this work; we hope the final manuscript reflects the values he embodied: rigor, compassion, and inclusion. FUNDING This work was independently funded at each collaborating institution. The Heinz C Prechter Bipolar Research Program supported the efforts of AK Yocum and MG McInnis. Consortia grants supported efforts of Tilo Kircher from the German Research Foundation (DFG) FOR 2107, SFB/TRR 393 (“Trajectories of Affective Disorders”, project grant no 521379614), and the Germany’s Excellence Strategy (EXC 3066/1 “The Adaptive Mind”, Project No. 533717223), as well as the DYNAMIC center, funded by the LOEWE program of the Hessian Ministry of Science and Arts (grant number: LOEWE1/16/519/03/09.001(0009)/98). Biosamples and corresponding data were sampled, processed and stored in the Marburg Biobank CBBMR. TVR was supported by an Al and Val Rosentrauss Fellowship from the Rebecca L Cooper Medical Research Foundation. The Unimelb cohort was supported by the NHMRC (1060664), Henry Freeman Trust, Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation and the Society of Mental Health Research. The Imaging Genetics in Psychosis (IGP) study was supported by Project Grants from the Australian National Health and Medical Research Council (NHMRC; APP630471 and APP1081603), and the Macquarie University’s ARC Centre of Excellence in Cognition and its Disorders (CE110001021). MB is supported by a NHMRC Leadership 3 Investigator grant (GNT2017131). Biju Viswanath is funded by the Intermediate (Clinical and Public Health) Fellowship (IA/CPHI/20/1/505266) of the DBT/Wellcome Trust India Alliance. OAA is funded by the Research Council of Norway ( 324499, 324252) , Nordforsk (#164218) and KG Jebsen Stiftelsen. The UNSW and FUP cohorts were funded, in part, by grants from the Australian Government administered through The Mindgardens Neuroscience Network, Australian National Health and Medical Research Council (NHMRC) Program Grant 1037196, NHMRC & Medical Research Futures Fund (MRFF) Grant 1200428, NHMRC Investigator Grants 1176716 and 1177991, and the NSW Office of Health and Medical Research. JMF was supported by philanthropic donations from Janette Mary O’Neil, Betty C. Lynch OAM (dec), and The Aberdeen Fund directors. We recognize and thank the members of the Global Bipolar Cohort (GBC) collaborative who have attended monthly conferences and provided comments on this work: Mark Ackerman, Frances Adiukwu, Ana Andreazza, Gerard Anmella, Ji Hyun Baek, Eduard Bakštein, Michael Bauer, Harriet Birabwa-Oketcho, Olav Bjerkehagen Smeland, David Bodenstein, David Bond, Emre Bora, Ben Coleman, Susan K. Conroy, Emma Corley, Alfredo Cuéllar-Barboza, Chad Daversa, Cemal Demirlek, Ray DePaulo, Denis Duagi, Howard Edenberg, Maria Faurholt-Jepsen, Giovanna Fico, Kate Freeman, Michael Gitlin, David Glahn, Fernando Goes, Roberto Goya-Maldonado, Melissa Haendel, Georgina Hosang, Rebekah Huber, Leslie Hulvershorn, Eric Hurwitz, Michael Kalfas, Tadafumi Kato, James (Jim) Kennedy, Kamyar Kermatian, Marián Kolenič, Jennifer L. Kruse, Yunna Kwan, Tsuo-Hung (Lawrence) Lan, Scott Langenecker, Marzieh Majd, Kathleen Merikangas, Daniel Mueller, Julie A. McMurry, Janardhanan Narayanaswamy, Dost Öngür, Abigail Ortiz, Tania “Abby” Ortiz Dominguez, Lauren Rekerle, János Réthelyi, Peter Robinson, Kelly Ryan, Sara Sadat-Afjeh, Eva C. Schulte, Marylou Selo, Alessandro Serretti, Tian-Mei Si, Eric Simon, Balwinder Singh, Sarah Sperry, Emma Stapp, Piper Svenson-Ranallo, Holly Swartz, Yujia Tian, Evangelia Eirini (Eva) Tsermpini, Rudolf Uher, Nasanbayar Ulzii-Orshikh, Norma Verdolini, Holly Wilcox, Stephanie Witt, Yan-Kun Wu, Nefize Yalin, Jessica Yang, Justine Zhang Ethics Approvals All study cohorts were subject to ethics approval at the respective sites and the respective results integrated for comparative analyses. Availability of Data The availability of data is subject to the regulations and policies of the respective sites, contact information for the cohorts is provided in the supplemental tables. Authors Contributions As a large collaborative the vision is inclusivity. 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Tables Table 1: Cohort-level summary of functional outcome analyses (waves 1 & 2) Cohort Region Country N % poor function Variable: case ratio Omnibus χ 2 df Sig. FACE-BD (Leboyer et al. 2022) Europe France 1547 59 % 81.42 488 19 <0.0001 MICHIGAN (Yocum et al. 2023) N. America USA 553 75 % 26.33 66.6 21 <0.0001 PROMPT (Hepgul et al. 2016) Europe England 66 64 % 8.25 22.3 8 0.004 CRiB (Strawbridge et al. 2016) Europe England 69 51 % 8.63 19.4 8 0.013 UBC (Burdick et al. 2022) Europe England 83 47 % 8.30 9.2 10 0.51 Barcelona (Burdick et al. 2022) Europe Spain 104 43 % 8.67 39.6 12 0.001 Deakin (Williams et al. 2020) Oceania Australia 129 50 % 14.33 40.4 9 <0.0001 LITMUS (Nierenberg et al. 2013) N. America USA 281 54 % 20.07 111.5 14 <0.001 CHOICE (Nierenberg et al. 2014) N. America USA 445 61 % 23.42 147.5 19 <0.001 MAYO (Burdick et al. 2022) N. America USA 1908 74 % 190.80 106.3 10 <0.001 Oslo (Burdick et al. 2022) Europe Norway 265 59 % 15.59 88.6 17 <0.001 GAGE-BD (Sajatovic et al. 2019) N. America USA 212 45 % 30.29 63 7 <0.001 ISMMS/BWH (Burdick et al. 2022) N. America USA 220 70 % 10.48 66.7 21 <0.001 UTHealth (Diaz et al. 2021) N. America USA 326 64 % 17.16 55.41 19 2.01E-05 GAIN (Smith et al. 2009) N. America USA 1451 44 % 111.62 195.8 13 9.94E-35 BDRN (Gordon-Smith et al. 2017) Europe UK 2042 45 % 120.12 420.06 17 1.18E-78 BIPLONG (Reininghaus et al. 2014) Europe Austria 223 53 % 11.15 38.81 20 0.00705 FOR2107 (Kircher et al. 2019) Europe Germany 142 47 % 10.14 48.71 14 1.00E-05 ATLADIS (Ferentinos et al. 2017) Europe Greece 275 56 % 14.47 53.85 19 3.48E-05 DDBC^ Europe Netherlands 73 35 % 9.13 38.99 11 5.32E-05 DOBi (Dols et al. 2014) Europe Netherlands 106 40 % 13.25 11.57 8 0.1714 Cagliari (Manchia et al. 2019) Europe Italy 266 50 % 15.65 67.9 17 4.94E-08 TAULI (Navarra-Ventura et al. 2021) Europe Spain 60 53 % 10 11.61 6 0.07128 BIPOGENT-IPM Europe Spain 68 29 % 11.33 9.42 6 0.1515 MadManic^ Europe Spain 124 27 % 13.78 60.45 15 2.11E-07 FIDMAG (Alonso-Lana et al. 2019) Europe Spain 129 55 % 12.9 111 10 3.35E-19 NIMHANS (Sreeraj et al. 2021) Asia India 390 61 % 26 296.14 15 3.54E-54 GREAT (Tsai et al. 2012) Asia Taiwan 501 33 % 55.66 30.19 13 0.0044 SelcukU^ Asia Turkey 134 35 % 11.17 94.16 12 7.71E-15 NeuroGAP (Stevenson et al. 2019) Africa South Africa 615 76 % 87.86 107.46 7 3.09E-20 COFAMS (Knight and Baune 2019) Oceania Australia 70 52 % 10 7.81 7 0.34955 COGSBD (Lemvigh et al. 2022) Oceania Australia 96 42 % 10.67 32.24 9 0.00018 FUP (Dols et al. 2014) Oceania Australia 1972 35 % 123.25 135.42 16 5.65E-21 GBP (Lind et al. 2023) Oceania Australia 1598 61 % 84.11 189.28 19 4.56E-30 UNSW (Mitchell et al. 2009) Oceania Australia 235 40 % 14.69 57.55 16 1.35E-06 IGP (Shepherd et al. 2015) Oceania Australia 74 37 % 10.57 29.28 7 0.00013 Maritime (Nunes et al. 2020a) N. America Canada 278 16% 30.88 23.24 12 0.025 Table 2. Summary of variable-level associations across combined cohorts (N=37) providing functional outcomes regression analyses. For each correlate, the table reports cohorts with available data, number and percentage with significant associations (p < 0.05), and ranking based on the size of the p-value, not on the size of the effect (top correlate or among top three). N Cohort with Measure N Cohorts with Significant Association p < 0.05; N (%) N Cohorts with Top Associations; RANK = 1; N (%) N Cohorts with Top Associations; RANK = 1-3; N (%) LEVEL ONE MEASURES --- --- --- --- *Age 37 12 (32%) 7 (18%) 12 (32%) *Sex 37 5 (13%) 2 (5%) 7 (19%) *Race 20 0 (0%) 0 (0%) 2 (10%) *Education level 29 8 (28%) 2 (7%) 8 (28%) LEVEL TWO MEASURES --- --- --- --- *BD subtype 27 2 (7%) 0 (0%) 4 (16%) *Psychosis Hx 33 3 (9%) 2 (6%) 6 (18%) *Current depression 29 22 (76%) 19 (66%) 22 (76%) *Current Mania 26 5 (19%) 0 (0%) 8 (31%) Age at onset depression 8 0 (0%) 0 (0%) 1 (13%) Age at onset mania 15 2 (13%) 1 (7%) 3 (20%) # prior manias 8 1 (13%) 1 (13%) 2 (25%) #prior depressions 9 2 (22%) 0 (0%) 3 (33%) *# total episodes 15 3 (20%) 0 (0%) 4 (27%) #hospitalizations 10 3 (30%) 0 (0%) 2 (20%) #suicide attempts 9 2 (22%) 0 (0%) 2 (22%) Comorbid substance dx 19 5 (26%) 0 (0%) 3 (16%) Comorbid anxiety dx 16 2 (13%) 0 (0%) 1 (6%) LEVEL THREE MEASURES --- --- --- --- Global cognition 10 2 (20%) 0 (0%) 3 (30%) Premorbid IQ 9 0 (0%) 0 (0%) 1 (11%) MEDICATIONS --- --- --- --- None 8 2 (25%) 0 (0%) 1 (13%) Lithium 20 5 (25%) 0 (0%) 3 (15%) Anticonvulsants 18 2 (11%) 1 (6%) 2 (11%) Antidepressants 17 4 (24%) 1 (6%) 4 (24%) Antipsychotics 19 6 (32%) 1 (5%) 3 (16%) Total # psychotropic medicines 18 3 (17%) 0 (0%) 2 (11%) Additional Declarations No competing interests reported. Supplementary Files SupplementalTable4F.xlsx SupplementalTable3F.xlsx SupplementalTable2F.xlsx SupplementalTable1Final.xlsx SupplementalInformationF.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Submission checks completed at journal 27 Apr, 2026 First submitted to journal 11 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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The cumulative sample size of Bipolar patients in cohorts from each country are color coded, with larger sample sizes indicated with darker colors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9390347/v1/79881d789fb167bc184d3e95.png"},{"id":108978042,"identity":"8a57d575-912d-45dd-8dbd-daf67d588465","added_by":"auto","created_at":"2026-05-11 11:33:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea:\u003c/strong\u003e \u003cem\u003eSummary of cohort characteristics from respondents to the GBC Global survey (69 cohorts), relative to number of study participants (bipolar cases; y-axis) and colored by world region. Panels indicate the diagnostic instrument used, as well as the availability of genetic, cognitive, and longitudinal data.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e: \u003cem\u003eBiological sex distribution (%) of participants in the 24 cohorts that completed functional outcomes analyses.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9390347/v1/ce977f0349d23e165a06b53e.png"},{"id":108978225,"identity":"a1d5f3a8-25f8-40f0-a2a3-fa9d5192d44b","added_by":"auto","created_at":"2026-05-11 11:35:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTreemap of all 69 cohorts, organized by country of origin and region, and colored by predominant genetic ancestry.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9390347/v1/8bd2dbacb0ba33c87d663519.png"},{"id":108979859,"identity":"256c0473-31bf-4fe5-9e10-2535f0a54876","added_by":"auto","created_at":"2026-05-11 12:02:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1256212,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9390347/v1/81abf25a-b10c-4671-8ed8-151c2366eb82.pdf"},{"id":108941347,"identity":"cf8def68-3dcc-4194-8df7-4c5e530999b5","added_by":"auto","created_at":"2026-05-11 05:34:44","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14897,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable4F.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9390347/v1/0ca248a80fd7f2915c638cdd.xlsx"},{"id":108941349,"identity":"2d4fd58f-90b7-486b-9203-7a50dfb418e1","added_by":"auto","created_at":"2026-05-11 05:34:44","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18466,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable3F.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9390347/v1/006a02b298142fe912972f1d.xlsx"},{"id":108978106,"identity":"5fc6a20e-9d39-46e1-8cb0-76ea2f19b338","added_by":"auto","created_at":"2026-05-11 11:34:05","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16281,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable2F.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9390347/v1/ccf3672fe0066dc81f55197c.xlsx"},{"id":108941353,"identity":"2a531463-622e-404c-b6a0-9e9c6cba1983","added_by":"auto","created_at":"2026-05-11 05:34:44","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":32654,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1Final.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9390347/v1/81c0d5172316de05772b1678.xlsx"},{"id":108941355,"identity":"45602fe6-0708-45b9-a3ab-63c96b33e1b6","added_by":"auto","created_at":"2026-05-11 05:34:44","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6031618,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalInformationF.docx","url":"https://assets-eu.researchsquare.com/files/rs-9390347/v1/36cdc49a4876509a181d65c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Functional Outcomes in Bipolar Disorder: Cross-Cohort Analyses from the Global Bipolar Cohort","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBipolar Disorder (BD) is characterized by recurrent episodes of depression, mania, or hypomania and is among the leading global causes of disability, ranking approximately 17th worldwide, with lifetime prevalence estimates around 2 - 2.5 % across populations (Carvalho et al. 2020). Its clinical course is complex, dynamic, and heterogeneous, often resulting in substantial and persistent functional impairment that extends well beyond acute affective episodes (Berk et al. 2017; McIntyre et al. 2022; Sanchez-Moreno et al. 2009). Even during euthymic periods, 65% of patients report work-related difficulties, 42% experience relationship impairments (MacQueen et al. 2001; Ustün and Kennedy 2009), and up to two-thirds experience disruptions in personal, social, and occupational functions (Correll et al. 2025; Goetz et al. 2007; Suppes 2019; Tohen et al. 2017). Our earlier multi-center study (Burdick et al. 2022), consistent with prior reports (McIntyre and Calabrese 2019), highlighted the strong association of depressive symptom burden and functional impairment. Additional correlates of poor functional outcomes include executive dysfunction, processing speed and lower educational attainment (O’Donnell et al. 2023; O’Donnell et al. 2017), and a meta-analysis indicated that neurocognitive deficits across multiple domains play a critical role in shaping everyday functioning in BD (Depp et al. 2014).\u003c/p\u003e\n\u003cp\u003eThe complexity and heterogeneity of BD require large, diverse, and deeply phenotyped cohorts to achieve sufficient statistical power to identify reliable predictors of illness progression and functional outcomes (Cai et al. 2026). Although the term ‘heterogeneity’ is widely used in psychiatry, it is rarely precisely defined; conceptually, it reflects the extent to which clinical presentations deviate from the state of “perfect conformity”, and encompasses variation in symptoms, illness trajectories, comorbidities, underlying biology (including genetics), and contextual factors (Nunes et al. 2020b). \u0026nbsp;Categorical diagnostic systems amplify this complexity by permitting vast combinatorial variations of symptom configurations within a single diagnosis, complicating effect-size estimation, replication, and cross-cohort comparison (van Os et al. 2013). BD exemplifies these challenges: depressive symptoms may result from the disorder itself, associated with life events, or its comorbidities such as personality disorder or anxiety disorders. Symptoms additionally can span diverse cognitive, affective, and somatic dimensions, each potentially exerting distinct influences on functional impairment. Further, polygenic risk score (PRS) analyses show that specific clinical sub-phenotypes of BD (e.g. psychosis, rapid cycling, early onset, or suicidality) map onto partially distinct genetic architectures, indicating that biological heterogeneity also contributes directly to clinical variability (Coombes et al. 2020). This variation highlights the need for coordinated global efforts and harmonized phenotyping to better understand how different features of BD shape outcomes.\u003c/p\u003e\n\u003cp\u003eCollaborative consortia\u0026nbsp;such as\u0026nbsp;the Psychiatric Genomics Consortium\u0026nbsp;(PGC)\u0026nbsp;(Agrawal et al. 2025; Andreassen et al. 2023) and ENIGMA (Thompson et al. 2020) have\u0026nbsp;been very productive by analyzing large data sets from multiple sources. \u0026nbsp;However,\u0026nbsp;these efforts\u0026nbsp;remain constrained by their reliance on categorical diagnoses and a limited set of clinical descriptors, e.g. BD subtype or\u0026nbsp;psychosis history (O’Connell et al. 2025).\u0026nbsp;In\u0026nbsp;recognition of these limitations, the PGC has\u0026nbsp;initiated a substantial effort to collect and\u0026nbsp;harmonize\u0026nbsp;phenotypic subtype data from 57 European-ancestry cohorts, comprising 23,819 individuals across 16 target subphenotypes (van der Veen et al. 2025).\u0026nbsp;Parallel PGC efforts in major depressive disorder have demonstrated the value of inclusive, globally diverse sampling, identifying shared risk loci across populations (Flint 2023). Yet, the retrospective analysis of clinical data from multiple independently designed studies remains challenging due to inconsistently defined phenotypes which limit the ability to resolve heterogeneity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs a consequence, much of the existing literature is anchored in cross-sectional snapshots of cohorts, diagnosed by current standards, value-laden clinical judgements that create binary categories. Within large clinical consortia, available symptom data are typically limited in depth and longitudinal content, with little capacity to analyze chronological patterns or functional trajectories. Although research in low- and middle-income countries is expanding and contributes to global representation (Agrawal et al. 2025), many of these studies, e.g. A-BIG-Net, are similarly cross-sectional or based on single time-point assessments (Giusti-Rodríguez et al. 2025). Without sustained, prospective clinical data collection, the dynamic course of BD, including the evolution of early psychopathology, the interaction of comorbidities, and intra-individual symptom fluctuations, will remain difficult to delineate. These limitations underscore the importance of coordinated, ongoing, and globally distributed efforts to harmonize clinical phenotypes, thereby enabling more equitable, generalizable, and mechanistically informative models of illness course and functional outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Global Bipolar Cohort (GBC) was established in 2019 as an international consortium of researchers and clinicians\u0026nbsp;dedicated to coordinating and harmonizing clinical data across global cohorts, thereby addressing many of the persistent challenges that have limited prior collaborative efforts. A preliminary proof-of-concept study by Burdick et al. integrated data analyses from 13 cohorts across seven countries (n = 5,882) and demonstrated widespread though variable functional impairment ranging from 41% to 75% (Burdick et al. 2022). Depressive symptom burden, lower educational attainment, and a greater number of prior mood episodes emerged as key correlates of poorer functioning. A subsequent GBC study involving 10,351 individuals from 11 cohorts evaluated regional differences in treatment patterns and identified notable geographic variation, including lower lithium use in North America and higher antipsychotic use in Europe (Yocum and Singh 2025).\u003c/p\u003e\n\u003cp\u003eCross-cohort comparisons are accomplished within the GBC through a decentralized analytic framework modeled on meta-analytic principles. A comprehensive global survey identified and characterized existing global BD cohorts and documented available measures relevant to outcomes and functioning, including social and occupational domains. Each participating site then received a harmonized analysis protocol and implemented all statistical models locally, generating site-specific summary statistics for functional outcome correlates based on their own data. \u0026nbsp;These outputs were subsequently aggregated using an approach adapted from meta-analytic methodology (not a traditional meta-analysis) to facilitate structured synthesis and comparison of results across cohorts while preserving local data governance. This distributed approach supports transparent, protocol-based harmonization across geographically and culturally diverse samples and enables nuanced evaluation of how clinical features, cognitive profiles, treatment patterns, and contextual factors jointly relate to functional outcomes in BD. \u0026nbsp;We hypothesized that this global, harmonized framework would identify shared and region-specific determinants of functional outcomes, providing meaningful geographic and cultural differences in the strength and patterning of associations across cohorts.\u003c/p\u003e"},{"header":"MATERIALS and METHODS","content":"\u003ch3\u003e\u003cstrong\u003eCohort Identification and Data Collection\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA comprehensive survey was developed to identify existing BD cohorts worldwide and assess their suitability for collaborative research within the Global Bipolar Cohort (GBC). The 28-item survey (see Supplemental information S2) collected information on cohort characteristics, clinical instruments, demographics, and the availability of biospecimens and cognitive assessments.\u003c/p\u003e\n\u003cp\u003eInvitations were sent to 691 researchers\u0026nbsp;identified through mailing lists from major international consortia, including the Bipolar Disorder Working Group of the Psychiatric Genomics Consortium (O\u0026rsquo;Connell et al. 2025), ENIGMA-BD (Thompson et al. 2020), and ConLiGen (Schulze et al. 2010).\u0026nbsp;Additional dissemination occurred via network coordinators from groups such as the World Psychiatric Association, A-BIG-NET (Kuo et al. 2023),\u0026nbsp;and the Genetics of ECT initiative (Soda et al. 2020),\u0026nbsp;who circulated the survey link through mailing lists and social media. The\u0026nbsp;distribution list was intentionally inclusive, many recipients were involved in one or more studies, the emphasis was on unique representation of the studies rather than the individual response rates. Principal investigators (PIs) were eligible to participate in the functional outcomes analysis if they completed the survey prior to January 2023, and reported the use of at least one measure of functional outcome (Supplemental Table 1 \u0026amp; 2).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDistributed Analysis Framework\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eCohorts meeting the inclusion criteria were invited to participate in a functional outcome analysis using a distributed analytic framework designed to balance two competing needs: (i) cross-cohort harmonization of analytic procedures, and (ii) preservation of local data governance, privacy protections, and site-specific data structures.\u003c/p\u003e\n\u003cp\u003eRather than pooling individual-level data or conducting a traditional meta-analysis, the GBC implemented a protocol-based distributed analysis model (Supplemental information), one that specified core analytic decisions, including outcome definition principles, eligible predictor domains, minimum power requirements, regression modeling strategy, and required outputs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach participating site implemented the same analytic framework locally, using their own data and statistical resources. Sites conducted logistic regression analyses predicting dichotomized functional outcome (\u0026ldquo;good\u0026rdquo; vs. \u0026ldquo;poor\u0026rdquo; functioning) following standardized instructions, example code, and output templates (Supplemental information). Model specifications were fixed \u003cem\u003ea priori\u003c/em\u003e: all available correlates were entered simultaneously using an \u0026ldquo;enter\u0026rdquo; method, with a minimum participant-to-variable ratio of 10:1 to ensure model stability (Peduzzi et al. 1996). Sites reported a predefined set of model-level and predictor-level summary statistics (e.g., omnibus tests, odds ratios, confidence intervals, p-values), which were subsequently aggregated centrally for cross-cohort synthesis.\u003c/p\u003e\n\u003cp\u003eEach site selected a single primary functional outcome measure based on prespecified criteria: (1) completeness of data, (2) level of granularity (e.g., FAST preferred over GAF), and (3) cross-site comparability. Functional outcomes were dichotomized using either established clinical cutoffs or sample-based thresholds (e.g., mean-based z-score splits), with consistent coding (0 = good functioning; 1 = poor functioning). Sites documented their outcome definitions and any context-specific coding decisions (e.g., treatment of retirement or marital status) to ensure transparency and interpretability.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDefinition and Harmonization of Functional Outcome Measures\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eFunctional outcome measures varied across cohorts and included clinician-rated scales (e.g., FAST, WHODAS, GAF), structured self-report instruments, and social indicators (e.g., employment and marital status).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor scale-based measures with established clinical thresholds (e.g., FAST, WHODAS), sites applied validated cutoffs when available. In cohorts lacking validated thresholds or using non-standardized functional indicators, sample-based thresholds were applied using z-score normalization, with dichotomization at the sample mean unless otherwise justified. Coding conventions were standardized across sites (0 = good functioning; 1 = poor functioning), and all outcome definitions were documented.\u003c/p\u003e\n\u003cp\u003eSocial indicators required additional contextual consideration. Employment status classifications varied by cohort and were coded according to local norms, with students and retirees handled explicitly. Marital status coding similarly accounted for sociocultural and demographic context, including treatment of widowed participants. Although this approach permitted variation in operational definitions across sites, the analytic framework emphasized cross-cohort replication and consistency of associations rather than direct comparison of effect magnitudes.\u003c/p\u003e\n\u003cp\u003eThis approach differs from both pooled individual-level analyses and conventional meta-analyses. Individual-level data were not centralized, and effect sizes were not statistically pooled. Instead, consistency and robustness of associations were evaluated through structured comparison of site-specific results, including rank-based summaries and replication patterns across cohorts with diverse measures, populations, and sociocultural contexts. Four cohorts (Maritime, GAIN, Houston, and Cagliari) provided de-identified individual-level data for centralized analysis, enabling validation of distributed results; however, these data were analyzed using the same analytic specifications to maintain comparability.\u003c/p\u003e\n\u003cp\u003eThe distributed framework enabled large-scale international participation, reduced administrative and legal barriers associated with data transfer agreements, minimized privacy and security risks, and facilitated protocol-driven harmonization of analyses across otherwise heterogeneous cohorts.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eSites were encouraged to consider relevant contextual factors when assigning participants to functional outcome categories (e.g., employment or marital status), taking into account local demographic characteristics and sociocultural norms. These decisions were documented to ensure that site-level coding remained transparent, interpretable, and broadly comparable across cohorts. Analyses adhered to a minimum participant-to-variable ratio of 10:1, with priority given to core predictor variables specified a priori (Supplemental information).\u003c/p\u003e\n\u003cp\u003eEach participating site conducted logistic regression analyses to identify correlates of functional outcome (good vs. poor functioning), including demographic, clinical, cognitive, and medication-related variables, following the standardized protocol detailed in the Supplemental Information. All independent variables were entered simultaneously using the \u0026ldquo;enter\u0026rdquo; method, rather than stepwise selection, to preserve comparability of model structure across sites \u0026nbsp;[ref -. advanced statistics ] Sites returned a predefined set of model-level and predictor-level summary statistics, including omnibus tests of model fit, odds ratios, 95% confidence intervals, and p-values, to the coordinating team for structured cross-cohort synthesis.\u003c/p\u003e\n\u003cp\u003eWhere cognitive data were available, general cognitive ability (\u0026ldquo;g\u0026rdquo;) was derived locally using unrotated principal component analysis (PCA), following standardized instructions (Burdick et al. 2019). Up to two measures per cognitive domain were included, subject to the same 10:1 subject-to-variable ratio constraint. When a global cognitive index (e.g., full-scale IQ) was available, it could be substituted for g; however, premorbid IQ estimates were explicitly excluded from g derivation to avoid conflation with illness-related cognitive effects.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCross-Cohort Synthesis and Predictor Ranking\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eResults from site-specific logistic regression analyses were combined using a structured summary-based synthesis rather than pooled statistical estimation (Table 2). For each predictor variable, the coordinating team collated site-level outputs and quantified cross-cohort consistency using three complementary metrics: (1) availability, defined as the number of cohorts in which the predictor was included in the regression model; (2) statistical significance, defined as the number and proportion of cohorts in which the predictor was significantly associated with the functional outcome (p \u0026lt; 0.05); and (3) relative importance, operationalized as the frequency with which the predictor ranked first or among the top three correlates within each site-specific model based on statistical strength (\u0026minus;log₁₀[p-value]).\u003c/p\u003e\n\u003cp\u003eThese metrics were summarized across cohorts in Table 2, which provides an overview of the associations across heterogeneous samples, outcome measures, and social contexts. Correlates were grouped according to demographic, clinical, cognitive, and treatment-related categories. Table 2 suggests cross-site convergence rather than magnitude of effect, which was not directly comparable due to differences in outcome measures, covariate availability, and local coding decisions.\u003c/p\u003e\n\u003cp\u003eBecause functional outcomes were operationalized using different instruments, cutoff strategies, and scaling approaches across cohorts, effect sizes (e.g., odds ratios) were not directly comparable across sites.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each site-specific regression model, correlates were ranked according to the \u0026minus;log₁₀(p-value), which reflects the strength of evidence for association. These within-cohort rankings were then summarized across cohorts to quantify how frequently each correlate ranked first or among the top three. This approach was used to assess cross-cohort reproducibility of associations rather than an absolute effect size.\u003c/p\u003e\n\u003cp\u003eRankings were only compared \u003cem\u003ewithin\u003c/em\u003e models and were not used to compare effect magnitude \u003cem\u003ebetween\u003c/em\u003e cohorts. This strategy aligns with the distributed analytic framework, which emphasizes consistency of findings across heterogeneous samples rather than pooled estimation of effect sizes.\u003c/p\u003e\n\u003cp\u003eThis differs from conventional meta-analysis in that effect sizes were not pooled and no assumption of measurement equivalence across cohorts was imposed. Instead, consistency of direction, statistical support, and relative explanatory contribution across independent models was used as the primary indicator of robustness.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eIntegration with Prior Analyses\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eRegression outputs generated through the current distributed analysis were combined with previously published results from the initial GBC functional outcomes study (Burdick et al. 2022). For clarity, the earlier analyses are designated as \u0026ldquo;Wave 1,\u0026rdquo; while the newly collected site-level regression summaries constitute \u0026ldquo;Wave 2.\u0026rdquo; Together, these two waves form the expanded GBC functional outcomes dataset, enabling broader cross-cohort evaluation of correlates of functioning in bipolar disorder across a larger number of cohorts, countries, and outcome measures, while maintaining consistent analytic principles.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eGlobal Survey of Bipolar Disorder Cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe global survey identified 69 studies investigating BD across five continents. Geographic representation was strongest in Australia, the United States, multiple European countries, and South Africa, with participation from South and East Asia through A-BIG-NET, a consortium advancing genetic research in Asian populations (Kuo et al. 2023) (Figure 1a). Of these, 24 cohorts met inclusion criteria for the functional outcomes analyses and were able to implement the distributed analytic protocol, yielding broad international representation (Figure 1b).\u003c/p\u003e\n\u003cp\u003eMost cohorts recruited participants through hospital-based settings (59.7%), including primary hospitals (n = 12), specialized clinics (n = 11), and tertiary referral services (n = 4). The remaining cohorts were drawn from mixed community\u0026ndash;clinical sources (28.4%, n = 19) or community-based samples (11.9%, n = 8). As anticipated, participating cohorts\u0026nbsp;had substantial heterogeneity in assessment practices and data structures. The Structured Clinical Interview for DSM Disorders (SCID) was the most frequently used diagnostic instrument (47.1%), followed by the Mini International Neuropsychiatric Interview (MINI; 22.1%) and the Diagnostic Interview for Genetic Studies (DIGS; 13.2%) (Figure 2a).\u003c/p\u003e\n\u003cp\u003eCognitive data availability varied across cohorts: approximately 30% collected no cognitive data, 40% collected cognitive data in a subset of participants, and 25% collected cognitive data on all participants (Figure 2a). In contrast, genetic data were available in over half of cohorts, particularly in North America and Europe, and approximately one third of these cohorts had contributed data to the PGC (O\u0026rsquo;Connell et al. 2025). Longitudinal data relevant to functional outcomes were available in 55% of cohorts, most commonly in subsets of participants (averaging ~50% within cohort), reflecting ongoing challenges in sustained longitudinal follow-up (Figure 2a).\u003c/p\u003e\n\u003cp\u003eAcross cohorts, ancestry composition was predominantly European, with smaller representation of African, South Asian, and other ancestry groups (Figure 3), underscoring both the global reach of the GBC and persistent gaps in representation.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFunctional Outcomes Across Cohorts\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA summary of functional outcomes is presented in Table 1, integrating previously published results from 13 cohorts (Wave 1; 7 countries) (Burdick et al. 2022) with new analyses from 24 cohorts (Wave 2; 14 countries). The combined dataset includes 17,130 participants from 17 countries. Female participants were overrepresented in 33 of the 37 cohorts contributing functional outcome data; 35 cohorts had female representation exceeding 60% (Figure 2b).\u003c/p\u003e\n\u003cp\u003eFunctional outcome measures varied across cohorts, reflecting differences in study design and available instruments. In Wave 2, over three-quarters of cohorts collected indicators of social functioning, including employment status (~76%) and marital status (~71%). Other cohorts employed validated functional scales, including the Functioning Assessment Short Test (FAST; 30%) (Chen et al. 2019), the World Health Organization Disability Assessment Schedule (WHODAS; 27%) (Federici et al. 2017), and the Global Assessment of Functioning (GAF; 27%) (Chen et al. 2019). Less than one-fifth of cohorts used alternative measures (e.g., SOFAS, QOL.BD, LIFE-RIFT, CGI), and only 6% reported no functional outcome measure.\u003c/p\u003e\n\u003cp\u003eAcross cohorts, functional outcomes were dichotomized using either validated clinical cutoffs or sample-based thresholds, as detailed in the Supplemental Information. As a result, cohorts using the same functional instrument sometimes applied different cutoffs, reflecting differences in instrument versions or population characteristics. The proportion of participants classified as having poor functioning ranged from 16% to 77% across sites (Table 1), with a mean of 50% (SD = 14%). Overall, 9,231 individuals (53.2% of the combined sample) were classified as having poor functional outcomes, and 18 of 37 cohorts reported impairment in more than half of participants, highlighting the substantial functional burden associated with BD across settings.\u003c/p\u003e\n\u003cp\u003eLogistic regression omnibus tests were statistically significant in 32 of 37 cohorts (p \u0026lt; 0.05; Table 1), indicating that the included correlate collectively explained meaningful variance in functional outcomes in most samples. The five cohorts with non-significant omnibus tests were relatively small (n \u0026lt; 110, with 8\u0026ndash;13 cases per correlate), consistent with limited statistical power despite adherence to minimum case-to-correlate ratios.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003ePredictors of Functional Outcome\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eUsing site-specific functional outcome definitions, regression analyses across 37 cohorts evaluated a common set of demographic, clinical, cognitive, and treatment-related correlates. Despite heterogeneity in outcome measures and cutoff strategies, a consistent pattern emerged. Current depressive symptoms were the most robust and reproducible correlates of poor functional outcome across cohorts (Figure 4; Supplemental Table 3). Associations were observed across diverse outcome types, including clinician-rated disability scales (FAST, WHODAS, GAF, MSIF) and work\u0026ndash;social indicators (e.g., employment status), but were less consistently observed for marital status, a longer-term social outcome influenced by broader contextual factors (Robards et al. 2012) (Figure 4a).\u003c/p\u003e\n\u003cp\u003eThe association between current depressive symptoms and poor functioning was observed across geographically and ancestrally diverse cohorts (Figure 4b), underscoring the central role of depressive burden in functional impairment in BD regardless of sociocultural context. Rankings provided in Supplemental Table 4 further reinforce this pattern, with current depressive symptoms frequently ranking among the top correlates within individual cohort models based on within-model statistical support, regardless of the functional outcome measure employed. These rankings reflect the reproducibility of associations across independent cohorts rather than comparative effect magnitude across variables or sites.\u003c/p\u003e\n\u003cp\u003eOther predictive correlates showed more context-dependent associations. Comorbid substance use disorders and comorbid anxiety disorders were significant in multiple cohorts, particularly those using broader global measures such as the GAF or employment status. Medication-related variables, including antipsychotic use, antidepressant use, and total psychotropic medication load, were associated with poorer outcomes in a subset of cohorts, likely reflecting confounding by illness severity or treatment complexity. In contrast, demographic variables such as age, sex, and ancestry showed weaker and less consistent associations across cohorts.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCross-Cohort Predictors\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003ePredictive correlates across cohorts are presented in Table 2. Among \u0026lsquo;Level One\u0026rsquo; variables, age and sex were assessed in all 37 cohorts. Age was significantly associated with functional outcome in 12 cohorts (32.4%) and ranked among the top three in 12 cohorts, particularly in analyses using employment or marital status outcomes, which are intrinsically age-sensitive. Sex was less consistently associated with functional outcome, reaching significance in only 13.5% of cohorts. Race, assessed in 20 cohorts, showed no significant associations; this may reflect limited within-cohort diversity and underpowered subgroup analyses.\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Level Two\u0026rsquo; and \u0026lsquo;Level Three\u0026rsquo; variables encompass clinical and cognitive features. Current depressive symptoms stood out as the strongest correlate overall: assessed in 29 cohorts, significant in 22 (75.8%), ranked among the top three in 22, and the top correlate in 19 cohorts. In contrast, manic symptoms, BD subtype, and psychosis history, although frequently assessed, showed limited association with functional outcomes. Cognitive measures were assessed in approximately one third of cohorts and demonstrated modest and inconsistent associations, while premorbid IQ showed no significant effects.\u003c/p\u003e\n\u003cp\u003eAmong treatment-related variables, lithium and antipsychotic use were most frequently associated with poorer functioning, whereas anticonvulsants, antidepressants, and total medication load showed weaker and less consistent patterns. Interpretation of medication effects is limited by confounding by indication and cohort-specific prescribing practices; they should be interpreted as markers of illness complexity or severity, not treatment effects.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study represents a large and geographically diverse assessment of functional outcomes in BD, integrating data from 37 cohorts across 17 countries and over 17,000 individuals. Despite heterogeneity in available measures, sample characteristics, and analytic capabilities across sites, a clear pattern emerged: current depressive symptoms were the most consistent correlate of poor functioning. This finding replicates earlier work from 13 cohorts (Burdick et al. 2022) and remained stable across a wide range of outcome types, measurement approaches, and sociocultural settings, reaffirming depressive burden as a central driver of psychosocial disability in bipolar disorder (Sanchez-Moreno et al. 2009).\u003c/p\u003e\n\u003cp\u003eBeyond depression, the study reveals the heterogeneous and context-dependent nature of functional impairment in BD. \u0026nbsp;Effects of several variables, e.g. comorbid substance use or anxiety disorders, were not uniform across sites. Their variability most likely reflects differences in sample composition, local demographics, assessment practices, and regional sociocultural norms. The types of measures are of consequence. Marital or employment status may be subjectively valued variably according to differing social expectations. \u0026nbsp;Clinician-rated functional scales, capturing short-term, objective symptom functioning, showed more consistent associations with mood symptoms. This aligns with a causally pluralistic framework (Stein et al. 2024), in which biological, psychological, and social determinants jointly contribute to functional outcomes, and in which no single causal pathway can fully account for the heterogeneity observed across global cohorts. It depends, in part, on what measures are a priority and assessed at any given time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCognitive performance and educational attainment demonstrated modest and less consistent relationships with functioning. Although cognitive deficits are widely viewed as central to disability in BD (Barbosa et al. 2018), their predictive influence varied across cohorts, likely reflecting true differences in cognitive impairment across populations as well as measurement bias limitations, including brief or uneven cognitive batteries in some sites. In contrast, static demographic or early clinical attributes, such as age at onset, sex, and bipolar subtype, showed limited predictive value for real-world functioning. Taken together, these results suggest that malleable, clinically relevant domains, particularly those capturing symptom burden, comorbidity, and psychosocial capacity, may be more informative of functional outcome than fixed demographic or historical illness characteristics.\u003c/p\u003e\n\u003cp\u003eThis study also provides an overview of global BD research resources, highlighting both strengths and gaps. While the participating cohorts spanned multiple continents, representation remained skewed toward Europe, North America, and Oceania, with limited inclusion from low- and middle-income countries and historically underrepresented ancestry groups. Several additional cohorts identified in the global survey were not included in the present analyses due to lack of systematically collected functional outcome measures. These sites nevertheless represent valuable research assets and will be prioritized for inclusion as harmonized functional assessments become more widely adopted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant consideration. First, although the distributed analytic approach enabled global participation, it restricted standardization of predictors and correlates, outcome definitions, and modeling strategies. Site-level differences in data quality, available variables, and sample characteristics likely contributed to residual heterogeneity.\u0026nbsp;The majority of the cohorts had more female than male patients with BD, possibly reflecting sex-based differences in research participation and/or genuine sex-bias in incidence.\u0026nbsp;The reliance on binary functional outcome classifications improved comparability across sites but reduced granularity relative to continuous scales such as FAST (Chen et al. 2019) or WHODAS (Federici et al. 2017), potentially obscuring subtle variation in functional impairment or recovery. Similarly, associations between medication variables and functioning must be interpreted cautiously: they likely reflect confounding by indication, as individuals with more severe or treatment-resistant illness are more likely to receive complex pharmacotherapy.\u003c/p\u003e\n\u003cp\u003eA further limitation is the predominance of cross-sectional data (Vieta and De Prisco 2024). Only a minority of cohorts had longitudinal assessments, and variation in timing and design precluded unified trajectory analyses. Longitudinal data are essential for examining how functional outcomes evolve, how they interact with symptom fluctuations and treatment, and how they align with life events or environmental exposures. Addressing this gap will require expanded participation in longitudinal frameworks, including emerging initiatives such as the BD\u0026sup2; Integrative Network (Savitz et al. 2025), the Atlas of Longitudinal Datasets sponsored by the Wellcome Trust (Arseneault), and FACE-BD (Godin et al. 2024). These initiatives offer a foundation for integrating longitudinal clinical, genomic, cognitive, and digital phenotypes within a learning health system approach.\u003c/p\u003e\n\u003cp\u003eThe distributed analytic framework, while preserving data autonomy, did not allow for formal meta-analytic pooling of effect sizes, necessitating reliance on rank-based summaries rather than aggregated quantitative estimates. However, \u0026nbsp; the consistency of key correlates, particularly depressive symptoms, across cohorts employing different functional outcome measures and cutoff strategies supports the robustness of these associations. Rather than relying on pooled effect sizes, the distributed analytic framework emphasizes replication and convergence across heterogeneous and independent samples. The persistence of similar rankings despite variation in outcome operationalization suggests that core drivers of functional impairment in BD are detectable across diverse measurement strategies.\u003c/p\u003e\n\u003cp\u003eFuture waves of the GBC will incorporate standardized meta-analytic procedures that maintain the benefits of distributed computation while improving statistical precision. A limitation of the rank-based synthesis is that rankings reflect statistical support within heterogeneous models rather than directly comparable effect sizes; future phases of the GBC will address this by implementing harmonized outcome scaling to enable effect-size\u0026ndash;based synthesis across cohorts.\u003c/p\u003e\n\u003cp\u003eFinally, several potentially important contributors, such as social determinants of health, trauma exposure, environmental conditions, and treatment adherence, were not consistently available across sites, reflecting broader gaps in psychiatric data infrastructure. These domains remain essential targets for future research.\u003c/p\u003e\n\u003cp\u003eIn summary, the GBC initiative demonstrates both the feasibility and the importance of coordinated research efforts in bipolar disorder across diverse global settings. The findings reaffirm the role of depressive symptoms in functional impairment, yet highlights the multifaceted and context-dependent influences of biological, psychological, and social factors, and identify critical opportunities for expanding longitudinal and cross-institutional research capacity. A major priority for future phases of the GBC will be systematic harmonization of clinical and functional data across cohorts. The Human Phenotype Ontology offers a rigorous semantic framework for standardizing phenotypic descriptors (Gargano et al. 2024; McInnis et al. 2025), enabling interoperability across heterogeneous datasets, facilitating integration with genomic, cognitive, and digital phenotyping resources, and providing the structured, mechanistically interpretable foundation required for next-generation, AI-ready analytic pipelines. Leveraging harmonized measures, longitudinal trajectories, and multimodal phenotypes, future work aims to advance mechanistic insight into functional outcomes and support more precise, individualized strategies for promoting recovery and resilience worldwide.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSCID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eStructured Clinical Interview for DSM (First MB, Williams JBW, Karg RS, Spitzer RL. 2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMINI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;Mini-International Neuropsychiatric Interview (Sheehan et al. 1998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDIGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDiagnostic Interview for Genetics Studies(Nurnberger et al. 1994)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDiagnostic and Statistical Manual (American Psychiatric, Association 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSADS-L\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSchedule for Affective Disorders and Schizophrenia - Lifetime version (Endicott and Spitzer 1978)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHDRS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHamilton Depression Rating Scale(Hamilton 1960)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYMRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eYoung Mania Rating Scale (Young et al. 1978)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eS-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSelf-Report\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMADRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMontgomery Asberg Depression Rating Scale (Montgomery and Åsberg 1979)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBDRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBipolar Depression Rating Scale (Berk et al. 2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCGI-BP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eClinical Global Impression scale - Bipolar (Spearing et al. 1997)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMDQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMood Disorders Questionnaire (Hirschfeld et al. 2000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCDI - SF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eChild Depression Inventory short form (Smucker et al. 1986)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPHERE-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSomatic and Psychological HEalth Report -12 item (Clarke and McKenzie 2003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTEMPS-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTemperament Evaluation of Memphis, Pisa, Paris, and San Diego Auto questionnaire (Akiskal et al. 2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIDS-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eInventory of Depression Symptoms - Clinical (Rush et al. 1996)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eIDS-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eInventory of Depression Symptoms - 30 item (Rush et al. 1996)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePANSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePositive and Negative Symptom Scale (Kay et al. 1987)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBeck Depression Inventory (Beck et al. 1961)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePHQ-9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePatient Health Questionnaire - 9 item (Kroenke et al. 2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHARS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHamilton Anxiety Rating Scale (Hamilton 1959)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBipolar Phenotype Inventory\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTCI-125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTemperament and Character Inventory (125 item) (Cloninger CR, Przybeck TR, Svrakic DM, Wetzel RD. 1994)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCASES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCognitive, Affective, and Somatic Empathy Scale (Raine et al. 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSHAPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSnaith-Hamilton Pleasure Scale (Snaith et al. 1995)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHCP -32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHypomania Symptom Checklist (Angst et al. 2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBeck Scale for Suicide Ideation (Beck et al. 1979)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQIDS-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQuick Inventory of Depressive Symptoms - Clinical version (Rush et al. 2003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eASRM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAltman Self-Rating Mania Scale (Altman et al. 1997)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCES-D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCenter for Epidemiological Studies Depression Scale (Radloff 1977)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSCL-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSymptom Checklist -9 (Derogatis et al. 1973)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMAS or BRMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBech-Rafaelsen mania scale (Bech et al. 1978)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBISS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBipolar Inventory of Signs and Symptoms (Gonzalez et al. 2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGlobal Assessment Scale (Endicott et al. 1976)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUKU-SERS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUKU Side Effects Rating Scale (Lingjaerde et al. 1987)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePROMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePatient-Reported Outcomes Measurement Information System (Cella et al. 2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eDedication: We dedicate this manuscript to the memory of Professor Dan Stein (1962\u0026ndash;2025), friend, mentor, and colleague who was a driving force for clinical research and academic collaboration in South Africa. Dan died on 6 December 2025. He was a champion of cross-continental partnerships that included African cohorts in the pursuit of knowledge for the benefit of mankind. Dan provided edits and guidance on earlier drafts of this work; we hope the final manuscript reflects the values he embodied: rigor, compassion, and inclusion.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFUNDING\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was independently funded at each collaborating institution. \u0026nbsp;The Heinz C Prechter Bipolar Research Program supported the efforts of AK Yocum and MG McInnis. Consortia grants supported efforts of Tilo Kircher from the German Research Foundation (DFG) FOR 2107, SFB/TRR 393 (“Trajectories of Affective Disorders”, project grant no 521379614), and the Germany’s Excellence Strategy (EXC 3066/1 “The Adaptive Mind”, Project No. 533717223), as well as the DYNAMIC center, funded by the LOEWE program of the Hessian Ministry of Science and Arts (grant number: LOEWE1/16/519/03/09.001(0009)/98). Biosamples and corresponding data were sampled, processed and stored in the Marburg Biobank CBBMR. TVR was supported by an Al and Val Rosentrauss Fellowship from the Rebecca L Cooper Medical Research Foundation. The Unimelb cohort was supported \u0026nbsp;by the NHMRC (1060664), Henry Freeman Trust, Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation and the Society of Mental Health Research. The Imaging Genetics in Psychosis (IGP) study was supported by Project Grants from the Australian National Health and Medical Research Council (NHMRC; APP630471 and APP1081603), and the Macquarie University’s ARC Centre of Excellence in Cognition and its Disorders (CE110001021). MB is supported by a NHMRC Leadership 3 Investigator grant (GNT2017131). Biju Viswanath is funded by the Intermediate (Clinical and Public Health) Fellowship (IA/CPHI/20/1/505266) of the DBT/Wellcome Trust India Alliance. OAA is funded by the Research Council of Norway (\u003cem\u003e324499, 324252)\u003c/em\u003e, Nordforsk (#164218) and KG Jebsen Stiftelsen. The UNSW and FUP cohorts were funded, in part, by grants from the Australian Government administered through The Mindgardens Neuroscience Network, Australian National Health and Medical Research Council (NHMRC)\u0026nbsp;Program Grant 1037196,\u0026nbsp;NHMRC \u0026amp; Medical Research Futures Fund (MRFF) Grant 1200428, NHMRC Investigator Grants 1176716\u0026nbsp;and 1177991, and the NSW Office of Health and Medical Research. JMF was supported by philanthropic donations from Janette Mary O’Neil, Betty C. Lynch OAM (dec), and The Aberdeen Fund directors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe recognize and thank the members of the Global Bipolar Cohort (GBC) collaborative who have attended monthly conferences and provided comments on this work: Mark Ackerman, Frances Adiukwu, Ana Andreazza, Gerard Anmella, Ji Hyun Baek, Eduard Bakštein, Michael Bauer, Harriet Birabwa-Oketcho, Olav Bjerkehagen Smeland, David Bodenstein, David Bond, Emre Bora, Ben Coleman, Susan K. Conroy, Emma Corley, Alfredo Cuéllar-Barboza, Chad Daversa, Cemal Demirlek, Ray DePaulo, Denis Duagi, Howard Edenberg, Maria Faurholt-Jepsen, Giovanna Fico, Kate Freeman, Michael Gitlin, David Glahn, Fernando Goes, Roberto Goya-Maldonado, Melissa Haendel, Georgina Hosang, Rebekah Huber, Leslie Hulvershorn, Eric Hurwitz, Michael Kalfas, Tadafumi Kato, James (Jim) Kennedy, Kamyar Kermatian, Marián Kolenič, Jennifer L. Kruse, Yunna Kwan, Tsuo-Hung (Lawrence) Lan, Scott Langenecker, Marzieh Majd, Kathleen Merikangas, Daniel Mueller, Julie A. McMurry, Janardhanan Narayanaswamy, Dost Öngür, Abigail Ortiz, Tania “Abby” Ortiz Dominguez, Lauren Rekerle, János Réthelyi, Peter Robinson, Kelly Ryan, Sara Sadat-Afjeh, Eva C. Schulte, Marylou Selo, Alessandro Serretti, Tian-Mei Si, Eric Simon, Balwinder Singh, Sarah Sperry, Emma Stapp, Piper Svenson-Ranallo, Holly Swartz, Yujia Tian, Evangelia Eirini (Eva) Tsermpini, Rudolf Uher, Nasanbayar Ulzii-Orshikh, Norma Verdolini, Holly Wilcox, Stephanie Witt, Yan-Kun Wu, Nefize Yalin, Jessica Yang, Justine Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approvals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll study cohorts were subject to ethics approval at the respective sites and the respective results integrated for comparative analyses. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe availability of data is subject to the regulations and policies of the respective sites, contact information for the cohorts is provided in the supplemental tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs a large collaborative the vision is inclusivity. Each site was asked to provide a list of investigators who contributed meaningfully to the acquisition, stewardship. and analyses of the cohort data. They are included in the author listings. \u0026nbsp;Contributions to the conceptualization and writing of the manuscript are also recognized with authorship.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgrawal A, Bulik CM, Abebe DS, Andreassen OA, Atkinson EG, Choi KW, et al. The Psychiatric Genomics Consortium: discoveries and directions. Lancet Psychiatry. Elsevier BV; 2025 Aug;12(8):600\u0026ndash;10.\u003c/li\u003e\n \u003cli\u003eAkiskal HS, Akiskal KK, Haykal RF, Manning JS, Connor PD. TEMPS-A: progress towards validation of a self-rated clinical version of the Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire. J. Affect. Disord. 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The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J. Clin. Epidemiol. Elsevier BV; 2010 Nov;63(11):1179\u0026ndash;94.\u003c/li\u003e\n \u003cli\u003eChen M, Fitzgerald HM, Madera JJ, Tohen M. Functional outcome assessment in bipolar disorder: A systematic literature review. Bipolar Disord. Wiley; 2019 May 1;21(3):194\u0026ndash;214.\u003c/li\u003e\n \u003cli\u003eClarke DM, McKenzie DP. An examination of the efficiency of the 12-item SPHERE questionnaire as a screening instrument for common mental disorders in primary care. Aust. N. Z. J. Psychiatry. SAGE Publications; 2003 Apr;37(2):236\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eCloninger CR, Przybeck TR, Svrakic DM, Wetzel RD. The Temperament and Character Inventory (TCI): A Guide to Its Development and Use. St Louis, MO: Washington University, Center for Psychobiology of Personality; 1994.\u003c/li\u003e\n \u003cli\u003eCoombes BJ, Markota M, Mann JJ, Colby C, Stahl E, Talati A, et al. Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores. Transl. Psychiatry. Springer Science and Business Media LLC; 2020 Sep 18;10(1):314.\u003c/li\u003e\n \u003cli\u003eCorrell CU, Cortese S, Solmi M, Boldrini T, Demyttenaere K, Domschke K, et al. Beyond symptom improvement: transdiagnostic and disorder-specific ways to assess functional and quality of life outcomes across mental disorders in adults. World Psychiatry. 2025 Oct;24(3):296\u0026ndash;318.\u003c/li\u003e\n \u003cli\u003eDepp CA, Harmell AL, Savla GN, Mausbach BT, Jeste DV, Palmer BW. A prospective study of the trajectories of clinical insight, affective symptoms, and cognitive ability in bipolar disorder. J. Affect. 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Elsevier BV; 2012 Jan;136(1-2):81\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eUst\u0026uuml;n B, Kennedy C. What is \u0026ldquo;functional impairment\u0026rdquo;? Disentangling disability from clinical significance. World Psychiatry. World Psychiatry; 2009 Jun;8(2):82\u0026ndash;5.\u003c/li\u003e\n \u003cli\u003evan der Veen T, Tesfaye M, Yang JMK, Boltz T, David FS, Crinion S, et al. Immune, developmental, and synaptic pathways define bipolar disorder clinical heterogeneity [Internet]. medRxiv. 2025 [cited 2025 Nov 24]. p. 2025.06.23.25330155. Available from:\u0026nbsp;\u003cu\u003ehttp://dx.doi.org/10.1101/2025.06.23.25330155\u003c/u\u003e\u003c/li\u003e\n \u003cli\u003eVieta E, De Prisco M. Cross-sectional studies: Is pressing the pause button worth it in research? Eur. Neuropsychopharmacol. Elsevier BV; 2024 Aug;85:32\u0026ndash;3.\u003c/li\u003e\n \u003cli\u003eWilliams LJ, Stuart AL, Berk M, Brennan-Olsen SL, Hodge JM, Cowdery S, et al. Bone health in bipolar disorder: a study protocol for a case-control study in Australia. BMJ Open. BMJ; 2020 Feb 12;10(2):e032821.\u003c/li\u003e\n \u003cli\u003eYocum AK, Anderau S, Bertram H, Burgess HJ, Cochran AL, Deldin PJ, et al. Cohort profile update: the heinz C. Prechter longitudinal study of bipolar disorder. Int. J. Epidemiol. Oxford University Press; 2023;52(6):e324\u0026ndash;31.\u003c/li\u003e\n \u003cli\u003eYocum AK, Singh B. Global trends in the use of pharmacotherapy for the treatment of bipolar disorder. Curr. Psychiatry Rep. Springer Science and Business Media LLC; 2025 May;27(5):239\u0026ndash;47.\u003c/li\u003e\n \u003cli\u003eYoung RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br. J. Psychiatry. 1978 Nov;133:429\u0026ndash;35.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Cohort-level summary of functional outcome analyses\u0026nbsp;\u003c/em\u003e\u003cem\u003e(waves 1 \u0026amp; 2)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e% poor function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVariable: case ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 179px;\"\u003e\n \u003cp\u003eOmnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFACE-BD\u0026nbsp;\u003c/strong\u003e(Leboyer et al. 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e59 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e81.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMICHIGAN\u0026nbsp;\u003c/strong\u003e(Yocum et al. 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN. America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e75 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e26.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e66.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePROMPT\u0026nbsp;\u003c/strong\u003e(Hepgul et al. 2016) \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e64 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRiB\u0026nbsp;\u003c/strong\u003e(Strawbridge et al. 2016)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e51 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e8.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUBC\u003c/strong\u003e(Burdick et al. 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e47 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarcelona\u003c/strong\u003e(Burdick et al. 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e43 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeakin\u0026nbsp;\u003c/strong\u003e(Williams et al. 2020)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e50 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e14.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLITMUS\u0026nbsp;\u003c/strong\u003e(Nierenberg et al. 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN. America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e54 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e20.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e111.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHOICE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(Nierenberg et al. 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN. America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e61 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e23.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e147.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAYO\u003c/strong\u003e(Burdick et al. 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN. America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e74 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e190.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e106.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOslo\u003c/strong\u003e(Burdick et al. 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e59 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e15.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e88.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAGE-BD\u003c/strong\u003e(Sajatovic et al. 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN. America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e45 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e30.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eISMMS/BWH\u003c/strong\u003e(Burdick et al. 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN. America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e70 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUTHealth\u003c/strong\u003e(Diaz et al. 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN. America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e64 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e17.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e55.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2.01E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAIN\u0026nbsp;\u003c/strong\u003e(Smith et al. 2009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN. America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e44 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e111.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e195.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e9.94E-35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBDRN\u0026nbsp;\u003c/strong\u003e(Gordon-Smith et al. 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e45 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e120.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e420.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.18E-78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIPLONG\u0026nbsp;\u003c/strong\u003e(Reininghaus et al. 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAustria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e53 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e38.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.00705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOR2107\u0026nbsp;\u003c/strong\u003e(Kircher et al. 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e47 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e48.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eATLADIS\u0026nbsp;\u003c/strong\u003e(Ferentinos et al. 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e56 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e14.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e53.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.48E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDDBC^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e35 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e9.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e38.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e5.32E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDOBi\u0026nbsp;\u003c/strong\u003e(Dols et al. 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e40 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e11.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.1714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCagliari\u0026nbsp;\u003c/strong\u003e(Manchia et al. 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e50 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e15.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e4.94E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAULI\u003c/strong\u003e(Navarra-Ventura et al. 2021)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e53 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e11.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.07128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIPOGENT-IPM \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e29 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.1515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMadManic^\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e27 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e13.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e60.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2.11E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFIDMAG\u0026nbsp;\u003c/strong\u003e(Alonso-Lana et al. 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e55 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.35E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNIMHANS\u0026nbsp;\u003c/strong\u003e(Sreeraj et al. 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e61 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e296.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.54E-54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGREAT\u0026nbsp;\u003c/strong\u003e(Tsai et al. 2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e33 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e55.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e30.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelcukU^\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e35 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e11.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e94.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e7.71E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeuroGAP\u0026nbsp;\u003c/strong\u003e(Stevenson et al. 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e76 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e87.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e107.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.09E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOFAMS\u0026nbsp;\u003c/strong\u003e(Knight and Baune 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e52 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.34955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOGSBD\u0026nbsp;\u003c/strong\u003e(Lemvigh et al. 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e42 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e32.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.00018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFUP\u0026nbsp;\u003c/strong\u003e(Dols et al. 2014)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e35 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e123.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e135.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e5.65E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGBP\u003c/strong\u003e(Lind et al. 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e61 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e84.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e189.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e4.56E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUNSW\u0026nbsp;\u003c/strong\u003e(Mitchell et al. 2009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e40 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e14.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e57.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.35E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIGP\u0026nbsp;\u003c/strong\u003e(Shepherd et al. 2015)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e37 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e29.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.00013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaritime\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(Nunes et al. 2020a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN. America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e30.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e23.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e \u003cem\u003eSummary of variable-level associations across\u0026nbsp;\u003c/em\u003e\u003cem\u003ecombined\u0026nbsp;cohorts (N=37) providing functional outcomes regression analyses. For each\u0026nbsp;correlate, the table reports cohorts with available data, number and percentage with significant associations (p \u0026lt; 0.05), and ranking based on the size of the p-value, not on the size of the effect (top\u0026nbsp;correlate\u0026nbsp;or among top three).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003eN Cohort with Measure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN Cohorts with Significant Association\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep \u0026nbsp;\u0026lt; 0.05; N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003eN Cohorts with Top Associations; RANK = 1;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003eN Cohorts with Top Associations; RANK = 1-3; N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLEVEL ONE MEASURES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e*Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e12 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e7 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e12 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e*Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e7 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e*Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e*Education level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e8 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLEVEL TWO MEASURES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e*BD subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e4 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e*Psychosis Hx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e6 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e*Current depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e19 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e22 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e*Current Mania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e8 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eAge at onset depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eAge at onset mania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e# prior manias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e#prior depressions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e*# total episodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e4 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e#hospitalizations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e#suicide attempts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eComorbid substance dx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eComorbid anxiety dx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLEVEL THREE MEASURES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eGlobal cognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003ePremorbid IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMEDICATIONS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eLithium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eAnticonvulsants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eAntidepressants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e4 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eAntipsychotics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e6 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003eTotal # psychotropic medicines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-bipolar-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbd","sideBox":"Learn more about [International Journal of Bipolar Disorders](http://journalbipolardisorders.springeropen.com/)","snPcode":"40345","submissionUrl":"https://submission.nature.com/new-submission/40345/3","title":"International Journal of Bipolar Disorders","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bipolar Disorder, Functional Outcomes, Depression, Mania, Distributed Analysis, Secondary Data Analysis","lastPublishedDoi":"10.21203/rs.3.rs-9390347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9390347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Global Bipolar Cohort (GBC) was established to identify existing bipolar disorder (BD) cohorts worldwide and foster collaborations focused on descriptive and analytic outcomes relevant to BD. A distributed analytic framework has been implemented to engage multiple sites without the need for central data pooling. This report describes the GBC endeavor and global functional impairment patterns. Cross-cohort comparisons of functional correlates are limited by heterogeneous measures and data-sharing constraints. Large, culturally diverse comparisons are needed to distinguish broadly reproducible correlates from cohort-specific effects. Participating sites completed a 28-item descriptive survey covering diagnostic methods, cognition, genetics, treatment, functioning, and follow-up strategies. We implemented a harmonized local logistic regression model of dichotomized functional outcome and shared summary statistics only.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 69 cohorts across five continents. Thirty-seven cohorts contributed functional outcome analyses from 17,130 participants. Outcome measures included clinician-rated disability scales and social indicators such as employment and marital status. The proportion classified with poor functioning ranged from 16% to 77% (mean 50%). In 32 of 37 cohorts, the overall regression model significantly explained variance in functioning. Current depressive symptoms were the most robust and reproducible correlate of poor functional outcome: they were assessed in 29 cohorts, significant in 22 (75.8%), ranked among the top three correlates in 22, and were the top-ranked correlates in 19. Associations between depressive burden and poor functioning were observed across clinician-rated disability scales and work or social indicators, and across geographically diverse cohorts. Comorbid substance use disorder and medication-related variables were associated with poorer functioning in subsets of cohorts, whereas sex, ancestry, bipolar subtype, psychosis history, and premorbid IQ showed weak or inconsistent associations. Cognitive measures, available in a minority of regression models, showed modest and non-uniform effects.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAcross heterogeneous international cohorts, current depressive symptom burden emerged as the most consistent correlate of poor functioning in bipolar disorder. These findings replicate earlier multisite work at a larger scale, show that protocol-based distributed analyses can identify reproducible clinical signals without sharing individual-level data, and support prioritizing detection and treatment of depressive symptoms when aiming to improve real-world functioning. Future work should expand longitudinal harmonization and representation of under-studied populations.\u003c/p\u003e","manuscriptTitle":"Functional Outcomes in Bipolar Disorder: Cross-Cohort Analyses from the Global Bipolar Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:34:38","doi":"10.21203/rs.3.rs-9390347/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T18:21:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211338220422657764723930583553221632759","date":"2026-05-18T16:46:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296340857807475962226741591670524983302","date":"2026-05-18T10:09:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T21:17:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25580421804878013121089178907539955902","date":"2026-04-29T20:04:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T17:50:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T07:03:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-27T07:02:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Bipolar Disorders","date":"2026-04-11T19:51:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-bipolar-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbd","sideBox":"Learn more about [International Journal of Bipolar Disorders](http://journalbipolardisorders.springeropen.com/)","snPcode":"40345","submissionUrl":"https://submission.nature.com/new-submission/40345/3","title":"International Journal of Bipolar Disorders","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f68a24dc-bbe1-499c-b689-05de6ecd7835","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T18:21:34+00:00","index":18,"fulltext":""},{"type":"reviewerAgreed","content":"211338220422657764723930583553221632759","date":"2026-05-18T16:46:52+00:00","index":17,"fulltext":""},{"type":"reviewerAgreed","content":"296340857807475962226741591670524983302","date":"2026-05-18T10:09:04+00:00","index":15,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T21:17:44+00:00","index":9,"fulltext":""},{"type":"reviewerAgreed","content":"25580421804878013121089178907539955902","date":"2026-04-29T20:04:00+00:00","index":8,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T05:34:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:34:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9390347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9390347","identity":"rs-9390347","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}