Age-at-onset and comorbidity may separate depressive disorder subtypes along a descending gradient of bipolar propensity

Azorin, Jean‐Michel; Belzeaux, Raoul; Adida, M.

doi:10.1016/j.bbr.2015.01.014

Cited by 4 publications

(2 citation statements)

References 82 publications

(99 reference statements)

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“…In addition, young subjects with early-onset BD (before the age of 21 years) are more likely to have more severe psychopathology (PD comorbidity, mixed episodes, incongruent delusions, and longer duration of illness). These results are largely consistent and corroborate those of some previous studies [7][8][9][26][27][28][29].…”

Section: Discussionsupporting

confidence: 93%

Admixture Analysis of Age of Onset in Bipolar Disorder and Impact of Anxiety Comorbidity

Pini,

Carpita,

Nardi

et al. 2024

Cureus

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Background: The present study aimed to examine clinical differences between subjects with early-onset (<21 years) and adult-onset (>30 years) bipolar I disorder, in particular, in relation to anxiety comorbidity. Method: Subjects were selected from a cohort of 161 consecutive patients with bipolar disorder type I as diagnosed by the Structured Clinical Interview for DSM Disorder (SCID-I). Clinical characteristics and axis I comorbidity were compared between those whose illness first emerged before the age of 21 years (n=58) and those whose first episode occurred after the age of 30 years (n=27). Psychopathology was assessed using the 18-item version of the Brief Psychiatric Rating Scale (BPRS). The frequency of delusions, hallucinations, and formal thought disorders was evaluated with the SCID-I. Overall, social and occupational functioning was assessed by the Global Assessment of Functioning (GAF).Results: Most subjects with early-onset bipolar disorder were males, had panic disorder and substance abuse comorbidity, longer duration of illness, exhibited mood-incongruent delusions, and presented with a mixed episode at onset more frequently than the later adult-onset subjects. Mixed mania at the first episode of illness and lifetime panic disorder comorbidity predicted mixed polarity at the first hospitalization episode in the early-onset subjects.Conclusions: Overall, early age at onset seems to delineate a distinct bipolar I disorder subtype characterized by a greater likelihood of mixed episodes, lifetime panic disorder comorbidity, and schizophrenia-like delusions.

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Section: Discussionsupporting

confidence: 93%

Admixture Analysis of Age of Onset in Bipolar Disorder and Impact of Anxiety Comorbidity

Pini,

Carpita,

Nardi

et al. 2024

Cureus

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“…Most studies focus on the differences and commonalities between BD and schizophrenia (García-Bueno et al, 2014 ), and depression (Azorin et al, 2015 ); however, some recent studies report comparisons between BD and AD patients (Berridge, 2013 ) due to either late onset or BD aging population. Inflammation and oxidative stress have been found as common pathophysiological processes underlying AD (Akiyama et al, 2000 ; Kamer et al, 2008 ; Sardi et al, 2011 ) and LOBD (Goldstein et al, 2009 ; Konradi et al, 2012 ; Leboyer et al, 2012 ; Lee et al, 2013 ; Bauer et al, 2014a ; Hope et al, 2015 ), as well as many other neuropsychological illness, such as depression and mania (Brydon et al, 2009 ; Dickerson et al, 2013 ; Castanon et al, 2014 ; Singhal et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder Using Multivariate Analysis

Besga

Gonzalez

Echeburúa

et al. 2015

Front. Aging Neurosci.

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BackgroundLate onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment.ObjectiveThe aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables.MaterialsA sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time.MethodsWe applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch’s t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance.ResultsWelch’s t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%.ConclusionIt is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance.

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Developmental trajectories in psychiatric disorders: does substance/alcohol use moderate the effects of affective temperaments as moderators of age at onset? A study in post-acute, hospitalized patients with psychotic or DSM-5 bipolar or major depressive disorders

Perrini¹,

Matrone

Bartolomeis

et al. 2021

Journal of Addictive Diseases

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Age-at-onset and comorbidity may separate depressive disorder subtypes along a descending gradient of bipolar propensity

Cited by 4 publications

References 82 publications

Admixture Analysis of Age of Onset in Bipolar Disorder and Impact of Anxiety Comorbidity

Admixture Analysis of Age of Onset in Bipolar Disorder and Impact of Anxiety Comorbidity

Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder Using Multivariate Analysis

Developmental trajectories in psychiatric disorders: does substance/alcohol use moderate the effects of affective temperaments as moderators of age at onset? A study in post-acute, hospitalized patients with psychotic or DSM-5 bipolar or major depressive disorders

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