Background: Mentally ill patients die on average 10 years earlier than the general population, largely due to general medical disorders. This study is the first to explore in a large German sample the prevalence, mortality, and medical comorbidity in pa- tients with severe mental illness (SMI). The patients were affected by borderline personality disorder (BPD), psychotic disorders, bipolar disorder, or severe unipolar depression.
Methods: Our database consists of billing data from all adults with statutory health insurance in Germany. Twelve-month administrative SMI prevalence and medical comorbidity were estimated using cross-sectional data from 2016 (age ≥ 18; N = 59 561 310). Two-year mortality was established longitudinally in a randomly selected subset of the billing data (most recent mortality information available for 2012 to 2014; 2012: n = 15 590 107).
Results: Severe unipolar depression had the highest prevalence (2.01%), followed by psychotic disorders (1.25%), BPD (0.34%), and bipolar disorder (0.29%). While the prevalence of malignant neoplasms showed moderate deviations from reference values [severe unipolar depression: OR = 1.30 (95% CI = 1.29; 1.31), BPD: OR = 1.11 (1.09; 1.14), psychotic dis- orders: OR = 0.90 (0.89; 0.90), bipolar disorder: OR = 1.07 (1.06; 1.09)], other disease groups (infectious, endocrine/nutritional/ metabolic, circulatory, respiratory) were substantially elevated in all categories of SMI. Mortality rates for psychotic disorders, BPD, bipolar disorder, and severe unipolar depression were increased (OR = 2.38 [95% CI=2.32; 2.44], 2.30 [2.08; 2.54], 1.52 [1.42; 1.62], and 1.40 [1.37; 1.44], respectively), with a loss of 2.6 to 12.3 years, depending on age, sex, and SMI.
Conclusion: Mortality is substantially elevated in all SMI patients. The results underline the need to remove barriers to adequate general medical care, both on the patient and the provider side, to reduce excess mortality.
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http://dx.doi.org/10.3238/arztebl.2019.0405 | DOI Listing |
Neuroradiology
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Introduction: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.
Methods: A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging.
Transl Psychiatry
January 2025
Department of Psychology, Goldsmiths University of London, London, UK.
Bipolar disorder (BD) involves altered reward processing and decision-making, with inconsistencies across studies. Here, we integrated hierarchical Bayesian modelling with magnetoencephalography (MEG) to characterise maladaptive belief updating in this condition. First, we determined if previously reported increased learning rates in BD stem from a heightened expectation of environmental changes.
View Article and Find Full Text PDFNeuropharmacology
January 2025
Department of Psychiatry and Center for Circadian Biology, University of California San Diego, La Jolla, CA, USA; VA San Diego Healthcare System, San Diego, CA, USA. Electronic address:
Bipolar disorder (BD) is a severe mental illness characterized by recurrent episodes of depression and mania. Lithium is the gold standard pharmacotherapy for BD, but outcomes are variable, and the relevant therapeutic mechanisms underlying successful treatment response remain uncertain. To identify synaptic markers of BD and lithium response, we measured the effects of lithium on induced pluripotent stem cell-derived neurons from BD patients and controls.
View Article and Find Full Text PDFFront Psychiatry
January 2025
Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
Introduction: Unipolar and bipolar mood disorders in older adults are accompanied by cognitive impairment, including executive dysfunction, with a severe impact on daily life. Up and till now, strategies to improve cognitive functioning in late-life mood disorders (LLMD) are sparse. Therefore, we aimed to assess the efficacy of adaptive, computerized cognitive training (CT) on executive and subjective cognitive functioning in LLMD.
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