Identifying neurocognitive heterogeneity in Older Adults with Bipolar Disorder: a cluster analysis

Montejo, Laura; Jiménez, Esther; Solé, Brisa; Murru, Andréa; Arbelo, Néstor; Benabarre, Antonio; Valentí, Marc; Clougher, Derek; Rodríguez, Manuel Arturo; Borràs, Roger; Martínez‐Arán, Anabel; Vieta, Eduard; Bonnín, Caterina del Mar; Torrent, Carla

doi:10.1016/j.jad.2021.11.028

Cited by 9 publications

(6 citation statements)

References 52 publications

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“…We also observed the presence of a group of individuals with entirely preserved cognitive functions, where protective and resiliency factors related to cognition may be studied (Laird et al, 2019a(Laird et al, , 2019b. This preserved group has also been shown in age diverse (Martin et al, 2020) and geriatric depression samples (Morin et al, 2019), as well as in bipolar disorder (Lima et al, 2019;Montejo et al, 2022).…”

Section: Discussionsupporting

confidence: 54%

Cognitive phenotypes in late-life depression

Szymkowicz

Ryan²,

Elson³

et al. 2022

Int. Psychogeriatr.

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Objective: To identify cognitive phenotypes in late-life depression (LLD) and describe relationships with sociodemographic and clinical characteristics. Design: Observational cohort study Setting: Baseline data from participants recruited via clinical referrals and community advertisements who enrolled in two separate studies. Participants: Non-demented adults with LLD (n = 120; mean age = 66.73 ± 5.35 years) and non-depressed elders (n = 56; mean age = 67.95 ± 6.34 years). Measurements: All completed a neuropsychological battery, and individual cognitive test scores were standardized across the entire sample without correcting for demographics. Five empirically derived cognitive domain composites were created, and cluster analytic approaches (hierarchical, k-means) were independently conducted to classify cognitive patterns in the depressed cohort only. Baseline sociodemographic and clinical characteristics were then compared across groups. Results: A three-cluster solution best reflected the data, including “High Normal” (n = 47), “Reduced Normal” (n = 35), and “Low Executive Function” (n = 37) groups. The “High Normal” group was younger, more educated, predominantly Caucasian, and had fewer vascular risk factors and higher Mini-Mental Status Examination compared to “Low Executive Function” group. No differences were observed on other sociodemographic or clinical characteristics. Exploration of the “High Normal” group found two subgroups that only differed in attention/working memory performance and length of the current depressive episode. Conclusions: Three cognitive phenotypes in LLD were identified that slightly differed in sociodemographic and disease-specific variables, but not in the quality of specific symptoms reported. Future work on these cognitive phenotypes will examine relationships to treatment response, vulnerability to cognitive decline, and neuroimaging markers to help disentangle the heterogeneity seen in this patient population

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Section: Discussionsupporting

confidence: 54%

Cognitive phenotypes in late-life depression

Szymkowicz

Ryan²,

Elson³

et al. 2022

Int. Psychogeriatr.

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“…In the middle‐aged BD population on the other hand, a meta‐analysis of 48 reports could not find distinctive differences in cognitive performance between younger BD‐I and BD‐II patients 18 . Recent studies have identified cognitive subgroups within the OABD population that may explain the cognitive heterogeneity 43 . In particular, Martino et al (2017) identified three subgroups based on the number of cognitive areas affected (intact, selective deficits, and globally impaired), but also these three groups also did not significantly differ in terms of BD subtype 44 …”

Section: Discussionmentioning

confidence: 91%

“…18 Recent studies have identified cognitive subgroups within the OABD population that may explain the cognitive heterogeneity. 43 In particular, Martino et al (2017) identified three subgroups based on the number of cognitive areas affected (intact, selective deficits, and globally impaired), but also these three groups also did not significantly differ in terms of BD subtype. 44 In our GAGE-BD sample, somatic burden was similar in BD-I and BD-II older-aged patients.…”

Section: General Functioning Cognitive Performance and Somatic Burdenmentioning

confidence: 99%

Bipolar I and bipolar II subtypes in older age: Results from the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE‐BD) project

et al. 2022

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Objectives The distinction between bipolar I disorder (BD‐I) and bipolar II disorder (BD‐II) has been a topic of long‐lasting debate. This study examined differences between BD‐I and BD‐II in a large, global sample of OABD, focusing on general functioning, cognition and somatic burden as these domains are often affected in OABD. Methods Cross‐sectional analyses were conducted with data from the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE‐BD) database. The sample included 963 participants aged ≥50 years (714 BD‐I, 249 BD‐II). Sociodemographic and clinical factors were compared between BD subtypes including adjustment for study cohort. Multivariable analyses were conducted with generalized linear mixed models (GLMMs) and estimated associations between BD subtype and (1) general functioning (GAF), (2) cognitive performance (g‐score) and (3) somatic burden, with study cohort as random intercept. Results After adjustment for study cohort, BD‐II patients more often had a late onset ≥50 years (p = 0.008) and more current severe depression (p = 0.041). BD‐I patients were more likely to have a history of psychiatric hospitalization (p < 0.001) and current use of anti‐psychotics (p = 0.003). Multivariable analyses showed that BD subtype was not related to GAF, cognitive g‐score or somatic burden. Conclusion BD‐I and BD‐II patients did not differ in terms of general functioning, cognitive impairment or somatic burden. Some clinical differences were observed between the groups, which could be the consequence of diagnostic definitions. The distinction between BD‐I and BD‐II is not the best way to subtype OABD patients. Future research should investigate other disease specifiers in this population.

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“…Therefore, our exploratory hypothesis that a MCI may more likely accompany a LOBD was not sufficiently confirmed. However, further larger longitudinal studies should be conducted to explore this hypothesis, also including brain structural MRI studies, with volumetric analyses of white matter hyperintensities and gray matter volume, Diffusion Tensor Imaging (DTI) studies, and a full neurocognitive assessment ( 16 , 62 ). In line with existing literature, our findings reported a significantly higher prevalence of medical comorbidities in the non-LOBD group, particularly metabolic and endocrinological diseases ( 63 , 64 ).…”

Section: Discussionmentioning

confidence: 99%

Depressive and Anxious Temperaments as Predictors of Late Onset Bipolar Disorder? Preliminary Results of a “Real World” Exploratory Study

et al. 2022

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IntroductionBipolar disorder (BD) onset typically occurs between 15 and 30 years, being diagnosed under the age of 50 in 90% of cases, named “non-late onset BD” (non-LOBD). However, clinical observation of late-onset BD (LOBD) raised some concern regarding a differential psychopathological pattern, outcomes and treatment, including a specific affective temperament vulnerability. Therefore, an exploratory study in the “real world” was carried out by investigating psychopathological and temperamental features of a psychogeriatric cohort of LOBD and non-LOBD subjects.MethodsA total of 180 patients affected with BD-I, BD-II, and Cyclothymic Disorder were screened in a Mood Disorder Outpatient Service, during the timeframe January 2019-August 2021. Out of 78 enrolled outpatients, 66 (33 non-LOBD, 33 LOBD) were recruited, by the retrospective collection of sociodemographic, cognitive, psychopathological and clinical assessment, including the short-version of the Temperament Evaluation of Memphis, Pisa, and San Diego (TEMPS-M).ResultsLOBD is significantly associated with higher rates of BD-II diagnosis (χ2 = 27.692, p < 0.001), depressive episodes (p = 0.025), mixed states (p = 0.009), predominant depressive and anxious affective temperaments (p < 0.001). Non-LOBD is significantly associated with higher endocrinological (χ2 = 6.988, p = 0.008) and metabolic comorbidity (χ2 = 5.987, p = 0.014), a diagnosis of BD-I, manic episodes, and predominant hyperthymic affective temperaments (p = 0.001). GDS (p < 0.001) and MSRS (p = 0.005) scores were significantly higher in LOBD.ConclusionFurther longitudinal studies with larger sample sizes and a control group are needed to determine whether LOBD may represent a distinct psychopathological entity from non-LOBD and evaluate differences (if any) in terms of prognosis and treatment between non-LOBD and LOBD.

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Identifying neurocognitive heterogeneity in Older Adults with Bipolar Disorder: a cluster analysis

Cited by 9 publications

References 52 publications

Cognitive phenotypes in late-life depression

Cognitive phenotypes in late-life depression

Bipolar I and bipolar II subtypes in older age: Results from the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE‐BD) project

Depressive and Anxious Temperaments as Predictors of Late Onset Bipolar Disorder? Preliminary Results of a “Real World” Exploratory Study

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