Brain-derived neurotrophic factor in bipolar disorder: Associations with age at onset and illness duration

Baykara, Burak; Koç, Doğukan; Resmi, Halil; Akan, Pınar; Tunca, Zeliha; Özerdem, Ayşegül; Ceylan, Deniz; Yalcın, Neslihan Gurz; Binici, Nagihan Cevher; Güney, Sevay Alşen; Mesman, Esther; Hillegers, Manon H. J.; Emiroğlu, Neslihan İnal

doi:10.1016/j.pnpbp.2020.110075

Cited by 10 publications

(8 citation statements)

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“…Studies have increasingly suggested that adolescent- and adult-onset bipolar disorder may be distinct subtypes in terms of their pathophysiology, psychiatric comorbidity and clinical outcomes (Baldessarini et al, 2012; Baykara et al, 2021; Frahm Laursen et al, 2019; Goldstein and Levitt, 2006). However, few studies have investigated the initiation and progression of medical illnesses between these two subtypes during the early stage of bipolar disorder.…”

Section: Discussionmentioning

confidence: 99%

Prevalence and 5-year trend of incidence for medical illnesses after the diagnosis of bipolar disorder: A nationwide cohort study

Chen

Tsai

Pan

et al. 2021

Aust N Z J Psychiatry

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Objective: Medical comorbidities are prevalent in patients with bipolar disorder. Evaluating longitudinal trends of the incidence of medical illnesses enables implementation of early prevention strategies to reduce the high mortality rate in this at-risk population. However, the incidence risks of medical illnesses in the early stages of bipolar disorder remain unclear. This study investigated the incidence and 5-year trend of medical illnesses following bipolar disorder diagnosis. Methods: We identified 11,884 patients aged 13–40 years who were newly diagnosed as having bipolar disorder during 1996–2012 and 47,536 age- and sex-matched controls (1:4 ratio) who represented the general population from Taiwan’s National Health Insurance Research Database. We estimated the prevalence and incidence of individual medical illnesses yearly across the first 5 years after the index date. The adjusted incidence rate ratio was calculated to compare the occurrence of specific medical illnesses each year between the bipolar disorder group and control group using the Poisson regression model. Results: Apart from the prevalence, the adjusted incidence rate ratios of most medical illnesses were >1.00 across the first 5-year period after bipolar disorder diagnosis. Cerebrovascular diseases, ischaemic heart disease, congestive heart failure, other forms of heart disease, renal disease and human immunodeficiency virus infection exhibited the highest adjusted incidence rate ratios during the first year. Except for that of renal disease, the 5-year trends of the adjusted incidence rate ratios decreased for cerebrovascular diseases, cardiovascular diseases (e.g. ischaemic heart disease, other forms of heart disease, and vein and lymphatic disease), gastrointestinal diseases (e.g. chronic hepatic disease and ulcer disease) and communicable diseases (e.g. human immunodeficiency virus infection, upper respiratory tract infection and pneumonia). Conclusion: Incidence risks of medical illnesses are increased in the first year after bipolar disorder diagnosis. Clinicians must carefully evaluate medical illnesses during this period because the mortality rates from medical illnesses are particularly high in people with bipolar disorder.

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Section: Discussionmentioning

confidence: 99%

Prevalence and 5-year trend of incidence for medical illnesses after the diagnosis of bipolar disorder: A nationwide cohort study

Chen

Tsai

Pan

et al. 2021

Aust N Z J Psychiatry

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“…Studying subgroups that share similar risk factors or pathophysiological processes may reduce the magnitude of clinical heterogeneity. Due to the differences in the trajectory of EOBD and LOBD, age at onset could be considered a potential biomarker for more homogeneous BD subgroups (Baykara et al, 2020; Coryell et al, 2013; Jauhar et al, 2019). Evidence has suggested that EOBD is associated with an inaccurate diagnosis, poorer treatment and more adverse outcomes—as more psychiatric comorbidities, number of mood episodes, severe long‐term functional impairment and high suicidality and risk of hospitalization for suicide and self‐harm (Connor et al, 2017; Liu et al, 2020; Yatham et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Social support in patients with bipolar disorder and differing ages at onset

Studart‐Bottó

Bezerra-Filho

Sarmento

et al. 2021

Clin Psychology and Psychoth

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Objective:The main purpose of this study was to evaluate the association between age at onset and social support in outpatients with bipolar disorder who were in the recovery phase. We also investigated the association between age at onset and disability.Methods: A total of 180 bipolar disorder I outpatients, of whom 50 had early onset with age at onset ≤18 years old, 108 had middle onset with age at onset between 19 and 39 years old, and 22 had late onset with age at onset ≥40 years old, were assessed with the Medical Outcomes Study Social Support Scale and Sheehan Disability Scale. Results:The early onset group had lower tangible social support, longer length of illness, more childless participants, lower income and more suicide attempters than the late onset group. Conclusions:Early onset seems to have inferior outcomes in tangible social support than late onset, but this trend should be considered as a starting point for future studies.

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“…At the same time, MDD is more inclined to later ages onset ( 55 ). On the whole, BD patients are of earlier onset age, longer illness duration ( 56 ), with more severe clinical presentation and poorer outcomes (such as more episodes of depression, greater severity of depression) ( 51 , 56 ).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram based on lymphocyte subsets to distinguish bipolar depression from major depressive disorder

Shuai

Mou

et al. 2022

Front. Psychiatry

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ObjectiveBipolar depression (BD) and major depressive disorder (MDD) are both common affective disorders. The common depression episodes make it difficult to distinguish between them, even for experienced clinicians. Failure to properly diagnose them in a timely manner leads to inappropriate treatment strategies. Therefore, it is important to distinguish between BD and MDD. The aim of this study was to develop and validate a nomogram model that distinguishes BD from MDD based on the characteristics of lymphocyte subsets.Materials and methodsA prospective cross-sectional study was performed. Blood samples were obtained from participants who met the inclusion criteria. The least absolute shrinkage and selection operator (LASSO) regression model was used for factor selection. A differential diagnosis nomogram for BD and MDD was developed using multivariable logistic regression and the area under the curve (AUC) with 95% confidence interval (CI) was calculated, as well as the internal validation using a bootstrap algorithm with 1,000 repetitions. Calibration curve and decision curve analysis (DCA) were used to evaluate the calibration and clinical utility of the nomogram, respectively.ResultsA total of 166 participants who were diagnosed with BD (83 cases) or MDD (83 cases), as well as 101 healthy controls (HCs) between June 2018 and January 2022 were enrolled in this study. CD19+ B cells, CD3+ T cells, CD3–CD16/56+ NK cells, and total lymphocyte counts were strong predictors of the diagnosis of BD and MDD and were included in the differential diagnosis nomogram. The AUC of the nomogram and internal validation were 0.922 (95%; CI, 0.879–0.965), and 0.911 (95% CI, 0.838–0.844), respectively. The calibration curve used to discriminate BD from MDD showed optimal agreement between the nomogram and the actual diagnosis. The results of DCA showed that the net clinical benefit was significant.ConclusionThis is an easy-to-use, repeatable, and economical nomogram for differential diagnosis that can help clinicians in the individual diagnosis of BD and MDD patients, reduce the risk of misdiagnosis, facilitate the formulation of appropriate treatment strategies and intervention plans.

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Brain-derived neurotrophic factor in bipolar disorder: Associations with age at onset and illness duration

Cited by 10 publications

References 59 publications

Prevalence and 5-year trend of incidence for medical illnesses after the diagnosis of bipolar disorder: A nationwide cohort study

Prevalence and 5-year trend of incidence for medical illnesses after the diagnosis of bipolar disorder: A nationwide cohort study

Social support in patients with bipolar disorder and differing ages at onset

Development and validation of a nomogram based on lymphocyte subsets to distinguish bipolar depression from major depressive disorder

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