Refine
Year of publication
- 2020 (13) (remove)
Document Type
- Article (13)
Language
- English (13) (remove)
Has Fulltext
- yes (13)
Is part of the Bibliography
- no (13)
Keywords
- ADHD (6)
- bipolar disorder (3)
- attention (2)
- continuous performance test (2)
- hyperactivity (2)
- impulsivity (2)
- polygenic risk score (2)
- 14-3-3 gene family (1)
- A2BP1 (1)
- ADHD differential diagnosis (1)
- Amisulpride (1)
- Attention deficit (1)
- Copy number (1)
- Dopamine (1)
- GHQ-28 (1)
- Genetic syndromes (1)
- Genetics (1)
- Hyperactivity (1)
- L-DOPA (1)
- Monetary incentive delay (1)
- Nesplora Aquarium (1)
- PARK2 (1)
- Polygenic risk score (1)
- Qb-Test (1)
- QbTest® (1)
- RBFOX1 (1)
- Reward (1)
- Striatum (1)
- UPPS (1)
- YWHAE (1)
- YWHAZ (1)
- affective disorder (1)
- affective disorders (1)
- aggressiveness (1)
- alcohol use disorder (1)
- autism (1)
- biomarker (1)
- blood (1)
- body mass index (1)
- common variants (1)
- depression (1)
- diabetes mellitus (1)
- disease modelling (1)
- early recognition (1)
- ex-Gaussian analysis (1)
- extraversion (1)
- gender (1)
- genetic phenotypes (1)
- glucose metabolism (1)
- hiPSC (1)
- machine learning (1)
- major depression (MD) (1)
- major depressive disorder (MDD) (1)
- metabolic syndrome (1)
- mitochondria (1)
- naturalistic sample (1)
- neuropsychology (1)
- neuroticism (1)
- obesity (1)
- prediabetes (1)
- proteome (1)
- rare variants (1)
- schizophrenia (1)
- substance abuse disorder (1)
- transcriptomics (1)
- venturesomeness (1)
Institute
- Medizin (13)
Introduction: Bipolar disorder (BD) is characterized by recurrent episodes of depression and mania and affects up to 2% of the population worldwide. Patients suffering from bipolar disorder have a reduced life expectancy of up to 10 years. The increased mortality might be due to a higher rate of somatic diseases, especially cardiovascular diseases. There is however also evidence for an increased rate of diabetes mellitus in BD, but the reported prevalence rates vary by large.
Material and Methods: 85 bipolar disorder patients were recruited in the framework of the BiDi study (Prevalence and clinical features of patients with Bipolar Disorder at High Risk for Type 2 Diabetes (T2D), at prediabetic state and with manifest T2D) in Dresden and Würzburg. T2D and prediabetes were diagnosed measuring HBA1c and an oral glucose tolerance test (oGTT), which at present is the gold standard in diagnosing T2D. The BD sample was compared to an age-, sex- and BMI-matched control population (n = 850) from the Study of Health in Pomerania cohort (SHIP Trend Cohort).
Results: Patients suffering from BD had a T2D prevalence of 7%, which was not significantly different from the control group (6%). Fasting glucose and impaired glucose tolerance were, contrary to our hypothesis, more often pathological in controls than in BD patients. Nondiabetic and diabetic bipolar patients significantly differed in age, BMI, number of depressive episodes, and disease duration.
Discussion: When controlled for BMI, in our study there was no significantly increased rate of T2D in BD. We thus suggest that overweight and obesity might be mediating the association between BD and diabetes. Underlying causes could be shared risk genes, medication effects, and lifestyle factors associated with depressive episodes. As the latter two can be modified, attention should be paid to weight changes in BD by monitoring and taking adequate measures to prevent the alarming loss of life years in BD patients.
Attention-Deficit/Hyperactivity Disorder (ADHD) and obesity are frequently comorbid, genetically correlated, and share brain substrates. The biological mechanisms driving this association are unclear, but candidate systems, like dopaminergic neurotransmission and circadian rhythm, have been suggested. Our aim was to identify the biological mechanisms underpinning the genetic link between ADHD and obesity measures and investigate associations of overlapping genes with brain volumes. We tested the association of dopaminergic and circadian rhythm gene sets with ADHD, body mass index (BMI), and obesity (using GWAS data of N = 53,293, N = 681,275, and N = 98,697, respectively). We then conducted genome-wide ADHD–BMI and ADHD–obesity gene-based meta-analyses, followed by pathway enrichment analyses. Finally, we tested the association of ADHD–BMI overlapping genes with brain volumes (primary GWAS data N = 10,720–10,928; replication data N = 9428). The dopaminergic gene set was associated with both ADHD (P = 5.81 × 10−3) and BMI (P = 1.63 × 10−5); the circadian rhythm was associated with BMI (P = 1.28 × 10−3). The genome-wide approach also implicated the dopaminergic system, as the Dopamine-DARPP32 Feedback in cAMP Signaling pathway was enriched in both ADHD–BMI and ADHD–obesity results. The ADHD–BMI overlapping genes were associated with putamen volume (P = 7.7 × 10−3; replication data P = 3.9 × 10−2)—a brain region with volumetric reductions in ADHD and BMI and linked to inhibitory control. Our findings suggest that dopaminergic neurotransmission, partially through DARPP-32-dependent signaling and involving the putamen, is a key player underlying the genetic overlap between ADHD and obesity measures. Uncovering shared etiological factors underlying the frequently observed ADHD–obesity comorbidity may have important implications in terms of prevention and/or efficient treatment of these conditions.
Introduction: Affective disorders are a major global burden, with approximately 15% of people worldwide suffering from some form of affective disorder. In patients experiencing their first depressive episode, in most cases it cannot be distinguished whether this is due to bipolar disorder (BD) or major depressive disorder (MDD). Valid fluid biomarkers able to discriminate between the two disorders in a clinical setting are not yet available.
Material and Methods: Seventy depressed patients suffering from BD (bipolar I and II subtypes) and 42 patients with major MDD were recruited and blood samples were taken for proteomic analyses after 8 h fasting. Proteomic profiles were analyzed using the Multiplex Immunoassay platform from Myriad Rules Based Medicine (Myriad RBM; Austin, Texas, USA). Human DiscoveryMAPTM was used to measure the concentration of various proteins, peptides, and small molecules. A multivariate predictive model was consequently constructed to differentiate between BD and MDD.
Results: Based on the various proteomic profiles, the algorithm could discriminate depressed BD patients from MDD patients with an accuracy of 67%.
Discussion: The results of this preliminary study suggest that future discrimination between bipolar and unipolar depression in a single case could be possible, using predictive biomarker models based on blood proteomic profiling.