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Risk stratification for bipolar disorder using polygenic risk scores among young high-risk adults
(2020)
Objective: Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD.
Methods: A final sample of 185 young, high-risk German adults (aged 18–35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes.
Results: We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls.
Conclusions: In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification.
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.
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.
TOR1A is the most common inherited form of dystonia with still unclear pathophysiology and reduced penetrance of 30–40%. ∆ETorA rats mimic the TOR1A disease by expression of the human TOR1A mutation without presenting a dystonic phenotype. We aimed to induce dystonia-like symptoms in male ∆ETorA rats by peripheral nerve injury and to identify central mechanism of dystonia development. Dystonia-like movements (DLM) were assessed using the tail suspension test and implementing a pipeline of deep learning applications. Neuron numbers of striatal parvalbumin+, nNOS+, calretinin+, ChAT+ interneurons and Nissl+ cells were estimated by unbiased stereology. Striatal dopaminergic metabolism was analyzed via in vivo microdialysis, qPCR and western blot. Local field potentials (LFP) were recorded from the central motor network. Deep brain stimulation (DBS) of the entopeduncular nucleus (EP) was performed. Nerve-injured ∆ETorA rats developed long-lasting DLM over 12 weeks. No changes in striatal structure were observed. Dystonic-like ∆ETorA rats presented a higher striatal dopaminergic turnover and stimulus-induced elevation of dopamine efflux compared to the control groups. Higher LFP theta power in the EP of dystonic-like ∆ETorA compared to wt rats was recorded. Chronic EP-DBS over 3 weeks led to improvement of DLM. Our data emphasizes the role of environmental factors in TOR1A symptomatogenesis. LFP analyses indicate that the pathologically enhanced theta power is a physiomarker of DLM. This TOR1A model replicates key features of the human TOR1A pathology on multiple biological levels and is therefore suited for further analysis of dystonia pathomechanism.
Background: Understanding which factors influence dietary intake, particularly in daily life, is crucial given the impact diet has on physical as well as mental health. However, a factor might influence whether but not how much an individual eats and vice versa or a factor’s importance may differ across these two facets. Distinguishing between these two facets, hence, studying dietary intake as a dual process is conceptually promising and not only allows further insights, but also solves a statistical issue. When assessing the association between a predictor (e.g. momentary affect) and subsequent dietary intake in daily life through ecological momentary assessment (EMA), the outcome variable (e.g. energy intake within a predefined time-interval) is semicontinuous. That is, one part is equal to zero (i.e. no dietary intake occurred) and the other contains right-skewed positive values (i.e. dietary intake occurred, but often only small amounts are consumed). However, linear multilevel modelling which is commonly used for EMA data to account for repeated measures within individuals cannot be applied to semicontinuous outcomes. A highly informative statistical approach for semicontinuous outcomes is multilevel two-part modelling which treats the outcome as generated by a dual process, combining a multilevel logistic/probit regression for zeros and a multilevel (generalized) linear regression for nonzero values. Methods: A multilevel two-part model combining a multilevel logistic regression to predict whether an individual eats and a multilevel gamma regression to predict how much is eaten, if an individual eats, is proposed. Its general implementation in R, a widely used and freely available statistical software, using the R-package brms is described. To illustrate its practical application, the analytical approach is applied exemplary to data from the Eat2beNICE-APPetite-study. Results: Results highlight that the proposed multilevel two-part model reveals process-specific associations which cannot be detected through traditional multilevel modelling. Conclusions: This paper is the first to introduce multilevel two-part modelling as a novel analytical approach to study dietary intake in daily life. Studying dietary intake through multilevel two-part modelling is conceptually as well as methodologically promising. Findings can be translated to tailored nutritional interventions targeting either the occurrence or the amount of dietary intake.
The major depressive disorder is one of the most common mental illnesses worldwide. Current treatment standards recommend a combined therapy with medication and psychotherapy. As an additive component and to further improvements in treatment, physical activity such as yoga may be integrated into conventional treatment. This study investigates the impact of a 3-month body-oriented yoga in patients with major depressive disorder (MDD). In total, n = 83 patients were included. An intervention group received a vigorous Ashtanga-Yoga three times a week. The waiting-list control group obtained a treatment as usual (TAU). As a primary outcome depression scores (Beck Depression Inventory-II (BDI-II), Montgomery Asberg Depression Rating Scale (MADRS)) were tested at three time points. Secondary outcome was the positive and negative affect [Positive and Negative Affect Scale (PANAS)] and remission rates. To analyze the data, multilevel models and effect sizes were conducted. The results showed an improvement in BDI-II scores for both groups over time [γ = − 3.46, t(165) = − 7.99, p < 0.001] but not between groups [γ = 0.98, t(164) = 1.12, p = 0.263]. An interaction effect (time x group) occurred for MADRS [γ = 2.10, t(164) = 2.10, p < 0.038]. Positive affects improved over time for both groups [γ = 1.65, t(165) = 4.03, p < 0.001]. Negative affects decreased for all over time [γ = − 1.00, t(165) = − 2.51, p = 0.013]. There were no significant group differences in PANAS. Post hoc tests revealed a greater symptom reduction within the first 6 weeks for all measurements. The effect sizes for depression scores showed a positive trend. Remission rates indicated a significant improvement in the yoga group (BDI-II: 46.81%, MADRS: 17.02%) compared to the control group (BDI: 33.33%, MADRS: 8.33%). The findings suggest that there is a trendsetting additive effect of Ashtanga-Yoga after 3 months on psychopathology and mood with a greater improvement at the beginning of the intervention. Further research in this field can help to achieve more differentiated results.
Physical inactivity is discussed as one of the most detrimental influences for lifestyle-related medical complications such as obesity, heart disease, hypertension, diabetes and premature mortality in in- and outpatients with major depressive disorder (MDD). In contrast, intervention studies indicate that moderate-to-vigorous-intensity physical activity (MVPA) might reduce complications and depression symptoms itself. Self-reported data on depression [Beck-Depression-Inventory-II (BDI-II)], general habitual well-being (FAHW), self-esteem and physical self-perception (FAHW, MSWS) were administrated in a cross-sectional study with 76 in- and outpatients with MDD. MVPA was documented using ActiGraph wGT3X + ® accelerometers and fitness was measured using cardiopulmonary exercise testing (CPET). Subgroups were built according to activity level (low PA defined as MVPA < 30 min/day, moderate PA defined as MVPA 30–45 min/day, high PA defined as MVPA > 45 min/day). Statistical analysis was performed using a Mann–Whitney U and Kruskal–Wallis test, Spearman correlation and mediation analysis. BDI-II scores and MVPA values of in- and outpatients were comparable, but fitness differed between the two groups. Analysis of the outpatient group showed a negative correlation between BDI-II and MVPA. No association of inpatient MVPA and psychopathology was found. General habitual well-being and self-esteem mediated the relationship between outpatient MVPA and BDI-II. The level of depression determined by the BDI-II score was significantly higher in the outpatient low- and moderate PA subgroups compared to outpatients with high PA. Fitness showed no association to depression symptoms or well-being. To ameliorate depressive symptoms of MDD outpatients, intervention strategies should promote habitual MVPA and exercise exceeding the duration recommended for general health (≥ 30 min/day). Further studies need to investigate sufficient MVPA strategies to impact MDD symptoms in inpatient settings. Exercise effects seem to be driven by changes of well-being rather than increased physical fitness.
Background: Diet and physical activity (PA) have a major impact on physical and mental health. However, there is a lack of effective strategies for sustaining these health-protective behaviors. A shift to a microtemporal, within-person approach is needed to capture dynamic processes underlying eating behavior and PA, as they change rapidly across minutes or hours and differ among individuals. However, a tool that captures these microtemporal, within-person processes in daily life is currently not present.
Objective: The APPetite-mobile-app is developed for the ecological momentary assessment of microtemporal, within-person processes of complex dietary intake, objectively recorded PA, and related factors. This study aims to evaluate the feasibility and usability of the APPetite-mobile-app and the validity of the incorporated APPetite-food record.
Methods: The APPetite-mobile-app captures dietary intake event-contingently through a food record, captures PA continuously through accelerometers, and captures related factors (eg, stress) signal-contingently through 8 prompts per day. Empirical data on feasibility (n=157), usability (n=84), and validity (n=44) were collected within the Eat2beNICE-APPetite-study. Feasibility and usability were examined in healthy participants and psychiatric patients. The relative validity of the APPetite-food record was assessed with a subgroup of healthy participants by using a counterbalanced crossover design. The reference method was a 24-hour recall. In addition, the energy intake was compared with the total energy expenditure estimated from accelerometry.
Results: Good feasibility, with compliance rates above 80% for prompts and the accelerometer, as well as reasonable average response and recording durations (prompt: 2.04 min; food record per day: 17.66 min) and latencies (prompts: 3.16 min; food record: 58.35 min) were found. Usability was rated as moderate, with a score of 61.9 of 100 on the System Usability Scale. The evaluation of validity identified large differences in energy and macronutrient intake between the two methods at the group and individual levels. The APPetite-food record captured higher dietary intakes, indicating a lower level of underreporting, compared with the 24-hour recall. Energy intake was assessed fairly accurately by the APPetite-food record at the group level on 2 of 3 days when compared with total energy expenditure. The comparison with mean total energy expenditure (2417.8 kcal, SD 410) showed that the 24-hour recall (1909.2 kcal, SD 478.8) underestimated habitual energy intake to a larger degree than the APPetite-food record (2146.4 kcal, SD 574.5).
Conclusions: The APPetite-mobile-app is a promising tool for capturing microtemporal, within-person processes of diet, PA, and related factors in real time or near real time and is, to the best of our knowledge, the first of its kind. First evidence supports the good feasibility and moderate usability of the APPetite-mobile-app and the validity of the APPetite-food record. Future findings in this context will build the foundation for the development of personalized lifestyle modification interventions, such as just-in-time adaptive interventions.
Multiple Sklerose (MS) ist die häufigste entzündliche Erkrankung des zentralen Nervensystems (ZNS) im jungen Erwachsenenalter. Weltweit sind mehr als 2,3 Mio. Menschen betroffen – Frauen doppelt so häufig wie Männer. Die Erkrankung ist gekennzeichnet durch eine autoimmunvermittelte Demyelinisierung im ZNS einhergehend mit motorischen, sensorischen und neuropsychiatrischen Defiziten.
Bereits Charcot beschrieb im 19. Jahrhundert auch psychiatrische Syndrome als Teil der Erkrankung. Am häufigsten treten Depressionen und Angststörungen auf [1]. Im Krankheitsverlauf können auch kognitive Defizite und organische Persönlichkeitsveränderungen hinzukommen [8]. Psychotische Symptome sind selten und spielen eher eine Rolle als Nebenwirkung der MS-Therapie mit Kortikosteroiden und seltener β‑Interferonen [5].
Bei unserer Patientin trat die akute psychotische Störung als erste klinische Manifestation der MS auf und führte zur Diagnosestellung.
ADHD is a neurodevelopmental disorder with a long trajectory into adulthood where it is often comorbid with depression, substance use disorder (SUD) or obesity. Previous studies described a dysregulated dopaminergic system, reflected by abnormal reward processing, both in ADHD as well as in depression, SUD or obesity. No study so far however tested systematically whether pathologies in the brain’s reward system explain the frequent comorbidity in adult ADHD. To test this, we acquired MRI scans from 137 participants probing the reward system by a monetary incentive delay task (MIDT) as well as assessing resting-state connectivity with ventral striatum as a seed mask. No differences were found between comorbid disorders, but a significant linear effect pointed toward less left intrastriatal connectivity in patients depending on the number of comorbidities. This points towards a neurobiologically impaired reward- and decision-making ability in patients with more comorbid disorders. This suggests that less intrastriatal connectivity parallels disorder severity but not disorder specificity, while MIDT abnormalities seem mainly to be driven by ADHD.