Age-at-onset subsets of bipolar I disorders: A critical insight into admixture analyses

Definition of early age at onset in bipolar disorder according to distinctive neurodevelopmental pathways: insights from the FACE-BD study

BACKGROUND

Converging evidence suggests that a subgroup of bipolar disorder (BD) with an early age at onset (AAO) may develop from aberrant neurodevelopment. However, the definition of early AAO remains unprecise. We thus tested which age cut-off for early AAO best corresponds to distinguishable neurodevelopmental pathways.

METHODS

We analyzed data from the FondaMental Advanced Center of Expertise-Bipolar Disorder cohort, a naturalistic sample of 4421 patients. First, a supervised learning framework was applied in binary classification experiments using neurodevelopmental history to predict early AAO, defined either with Gaussian mixture models (GMM) clustering or with each of the different cut-offs in the range 14 to 25 years. Second, an unsupervised learning approach was used to find clusters based on neurodevelopmental factors and to examine the overlap between such data-driven groups and definitions of early AAO used for supervised learning.

RESULTS

A young cut-off, i.e. 14 up to 16 years, induced higher separability [mean nested cross-validation test AUROC = 0.7327 (± 0.0169) for ⩽16 years]. Predictive performance deteriorated increasing the cut-off or setting early AAO with GMM. Similarly, defining early AAO below 17 years was associated with a higher degree of overlap with data-driven clusters (Normalized Mutual Information = 0.41 for ⩽17 years) relatively to other definitions.

CONCLUSIONS

Early AAO best captures distinctive neurodevelopmental patterns when defined as ⩽17 years. GMM-based definition of early AAO falls short of mapping to highly distinguishable neurodevelopmental pathways. These results should be used to improve patients' stratification in future studies of BD pathophysiology and biomarkers.

Clinical correlates of late-onset versus early-onset bipolar disorder in a global sample of older adults

OBJECTIVES

Late-onset bipolar disorder (LOBD) represents a significant subgroup of bipolar disorder (BD). However, knowledge for this group is mostly extrapolated from small studies in subjects with early/mixed age of illness onset. In this global sample of older adults with BD (OABD: ≥50 years old) we aim to characterize the sociodemographic and clinical presentation of LOBD (≥40 years at BD onset) compared to early-onset BD (EOBD: <40 years at BD onset).

METHODS

The Global Aging and Geriatric Experiments in Bipolar Disorder consortium provided international data on 437 older age bipolar disorder participants. We compared LOBD versus EOBD on depression, mania, functionality, and physical comorbidities. Exploratory analyses were performed on participants with BD onset ≥50 years old.

RESULTS

LOBD (n = 105) did not differ from EOBD (n = 332) on depression, mania, global functioning, nor employment status (p > 0.05). Late-onset bipolar disorder was associated with higher endocrine comorbidities (odds ratio = 1.48, [95%CI = 1.0,12.1], p = 0.03). This difference did not remain significant when subjects with BD onset ≥50 years old were analyzed.

LIMITATIONS

This study is limited by the retrospective nature of the variable age of onset and the differences in evaluation methods across studies (partially overcame by harmonization processes).

CONCLUSION

The present analysis is in favor of the hypothesis that LOBD might represent a similar clinical phenotype as classic EOBD with respect to core BD symptomatology, functionality, and comorbid physical conditions. Large-scale global collaboration to improve our understanding of BD across the lifespan is needed.

Bipolar disorder: Trimodal age-at-onset distribution

OBJECTIVE

Bipolar disorder (BD) is a chronic mental health disorder with significant morbidity and mortality. Age at onset (AAO) may be a key variable in delineating more homogeneous subgroups of BD patients. However, no known research has systematically assessed how BD age-at-onset subgroups should be defined.

METHODS

We systematically searched the following databases: Cochrane Central Register of Controlled Trials, PsycINFO, MEDLINE, Embase, CINAHL, Scopus, Proquest Dissertations and Theses, Google Scholar and BIOSIS Previews. Original quantitative English language studies investigating AAO in BD were sought.

RESULTS

A total of 9454 unique publications were identified. Twenty-one of these were included in data analysis (n = 22981 BD participants). Fourteen of these studies (67%, n = 13626 participants) found a trimodal AAO distribution: early-onset (µ = 17.3, σ = 1.19, 45% of sample), mid-onset (µ = 26.0, σ = 1.72, 35%), and late-onset (µ = 41.9, σ = 6.16, 20%). Five studies (24%, n = 1422 participants) described a bimodal AAO distribution: early-onset (µ = 24.3, σ = 6.57, 66% of sample) and late-onset (µ = 46.3, σ = 14.15, 34%). Two studies investigated cohort effects on BD AAO and found that when the sample was not split by cohort, a trimodal AAO was the winning model, but when separated by cohort a bimodal distribution fit the data better.

CONCLUSIONS

We propose that the field conceptualises bipolar disorder age-at-onset subgroups as referring broadly to life stages. Demarcating BD AAO groups can inform treatment and provide a framework for future research to continue to investigate potential mechanisms of disease onset.

Age-at-onset subsets of bipolar I disorders: A critical insight into admixture analyses

Gaussian mixture analysis is frequently used to model the age-at-onset (AAO) in bipolar I disorder and identify homogeneous subsets of patients. This study aimed to examine whether, using admixture analysis of AAO, cross-sectional designs (which cause right truncation), unreliable diagnosis for individuals younger than 10 years old (which causes left truncation) and the selection criterion used for admixture analysis impact the number of identified subsets. A simulation study was performed. Different criteria - the likelihood ratio test (LRT), the Akaike information criterion (AIC), and the Bayesian information criterion (BIC) - were compared using no, left and/or right truncation simulated data. The error rate of each criterion (percentage of erroneous number of detected subsets) was estimated. An application to two real databases, including 2,876 and 1,393 patients, is provided. Without data truncation and regardless of the distribution of AAO, the LRT and the AIC had much higher error rates (12% and 33%, respectively) than the BIC (1%). For a homogeneous population, the error rate increased with the introduction of left truncation. This study shows that the number of subsets identified using admixture analysis may depend on the sample size, the selection criterion, and the study design.

Clinical correlates of age at onset distribution in bipolar disorder: a comparison between diagnostic subgroups

Background

Admixture analysis of age at onset (AAO) has helped delineating the clinical profile of early onset (EO) bipolar disorder (BD). However, there is scarce evidence comparing the distributional properties of AAO as well as the clinical features of EO BD type 1 (BD1) with EO BD type 2 (BD2). To this end, we studied 515 BD patients (224 BD1, 279 BD2, and 12 BD not otherwise specified [NOS]) diagnosed according to DSM-IV-TR criteria.

Methods

AAO was defined as the first reliably diagnosed hypo/manic or depressive episode according to diagnostic criteria. We used normal distribution mixture analysis to identify subgroups of patients according to AAO. Models were chosen according to the Schwarz’s Bayesian information criteria (BIC). Clinical correlates of EO were analysed using univariate tests and multivariate logistic regression models.

Results

A two normal components model best fitted the observed distribution of AAO in BD1 (BIC = −1599.3), BD2 (BIC = −2158.4), and in the whole sample (BIC = −3854.9). A higher number of EO BD2 patients had a depression-(hypo)mania-free interval (DMI) course, while a higher rate of (hypo)mania-depression-free interval (MDI) course was found in EO BD1. EO BD2 had also a higher rate of comorbidity with alcohol dependence compared to EO BD1. The latter finding was confirmed by multivariate logistic regression analysis.

Conclusions

In conclusion, both BD1 and BD2 had bimodal AAO distributions, but EO subgroups had a diagnostic-specific clinical delineation.