Metabolic syndrome in patients with bipolar disorder: Comparison with major depressive disorder and non-psychiatric controls

Objective: We aimed to investigate the prevalence of the metabolic syndrome (MetS) and its individual components in subjects with bipolar disorder (BD) compared to those with major depressive disorder (MDD) and non-psychiatric controls. Methods: We examined 2431 participants (mean age 44.3. ±. 13.0, 66.1 female), of whom 241 had BD; 1648 had MDD; and 542 were non-psychiatric controls. The MetS was ascertained according to NCEP ATP III criteria. Multivariable analyses were adjusted for age, sex, ethnicity, level of education, smoking status and severity of depressive symptoms, and in the case of BD subjects, also for psychotropic medication use. Results: Subjects with BD had a significantly higher prevalence of MetS when compared to subjects with MDD and non-psychiatric controls (28.4 vs. 20.2 and 16.5, respectively, p<. 0.001), also when adjusted for sociodemographic and lifestyle factors (OR 1.52, 95 CI: 1.09-2.12, p= 0.02 compared to MDD; OR 1.79, 95 CI: 1.20-2.67, p= 0.005 compared to non-psychiatric controls). The differences between BD subjects with controls could partly be ascribed to a higher mean waist circumference (91.0. cm vs. 88.8, respectively, p= 0.03). In stratified analysis, the differences in the prevalence of MetS between patients with BD and MDD were found in symptomatic but not in asymptomatic cases. Conclusion: This study confirms a higher prevalence of MetS in patients with BD compared to both MDD patients and controls. Specifically at risk are patients with a higher depression score and abdominal obesity. © 2015 Elsevier Inc

Splint: the efficacy of orthotic management in rest to prevent equinus in children with cerebral palsy, a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>Range of motion deficits of the lower extremity occur in about the half of the children with spastic cerebral palsy (CP). Over time, these impairments can cause joint deformities and deviations in the children's gait pattern, leading to limitations in moblity. Preventing a loss of range of motion is important in order to reduce secondary activity limitations and joint deformities. Sustained muscle stretch, imposed by orthotic management in rest, might be an effective method of preventing a decrease in range of motion. However, no controlled study has been performed.</p> <p>Methods</p> <p>A single blind randomised controlled trial will be performed in 66 children with spastic CP, divided over three groups with each 22 participants. Two groups will be treated for 1 year with orthoses to prevent a decrease in range of motion in the ankle (either with static or dynamic knee-ankle-foot-orthoses) and a third group will be included as a control group and will receive usual care (physical therapy, manual stretching). Measurements will be performed at baseline and at 3, 6, 9 and 12 months after treatment allocation. The primary outcome measure will be ankle dorsiflexion at full knee extension, measured with a custom designed hand held dynamometer. Secondary outcome measures will be i) ankle and knee flexion during gait and ii) gross motor function. Furthermore, to gain more insight in the working mechanism of the orthotic management in rest, morphological parameters like achilles tendon length, muscle belly length, muscle fascicle length, muscle physiological cross sectional area length and fascicle pennation angle will be measured in a subgroup of 18 participants using a 3D imaging technique.</p> <p>Discussion</p> <p>This randomised controlled trial will provide more insight into the efficacy of orthotic management in rest and the working mechanisms behind this treatment. The results of this study could lead to improved treatments.</p> <p>Trial Registration Number</p> <p>Nederlands Trial Register <a href="http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=2091">NTR2091</a></p

Disturbances in Hypothalamic-Pituitary-Adrenal Axis and Immunological Activity Differentiating between Unipolar and Bipolar Depressive Episodes

Differentiating bipolar depression (BD) from unipolar depression (UD) is difficult in clinical practice and, consequently, accurate recognition of BD can take as long as nine years. Research has therefore focused on the discriminatory capacities of biomarkers, such as markers of the hypothalamic-pituitary-adrenal (HPA) axis or immunological activity. However, no previous study included assessments of both systems, which is problematic as they may influence each other. Therefore, this study aimed to explore whether cortisol indicators and inflammatory markers were a) independently associated with and/or b) showed effect modification in relation to a lifetime (hypo)manic episode in a large sample of depressed patients.Data were derived from the Netherlands Study of Depression and Anxiety and comprised 764 patients with a DSM-IV depressive disorder at baseline, of which 124 (16.2%) had a lifetime (hypo)manic episode at the 2-year assessment, or a more recent episode at the 4-year or 6-year assessment. Baseline cortisol awakening response, evening cortisol and diurnal cortisol slope were considered as cortisol indicators, while baseline C-reactive Protein (CRP), Interleukin-6 (IL-6), and Tumor Necrosis Factor Alpha (TNF-α) were included as inflammatory markers.In depressed men and women, none of the cortisol indicators and inflammatory markers were (independently) associated with a (hypo)manic episode. However, effect modification was found of diurnal cortisol slope and CRP in relation to a (hypo)manic episode. Further analyses showed that depressed men with high levels of diurnal cortisol slope and CRP had an increased odds (OR=10.99, p=.001) of having a (hypo)manic episode. No significant differences were found in women.Our findings suggest that the combination of high diurnal cortisol slope and high CRP may differentiate between UD and BD. This stresses the importance of considering HPA-axis and immunological activity simultaneously, but more research is needed to unravel their interrelatedness

T Cell Deficits and Overexpression of Hepatocyte Growth Factor in Anti-inflammatory Circulating Monocytes of Middle-Aged Patients with Bipolar Disorder Characterized by a High Prevalence of the Metabolic Syndrome

FSW – Publicaties zonder aanstelling Universiteit Leide

The Mood Disorder Questionnaire (MDQ) for detecting (hypo)manic episodes:Its validity and impact of recall bias

<p>Background: Bipolar disorders often remain unrecognized in clinical practice, which may be a consequence of imprecise recall of manic symptoms earlier in life. This study will therefore examine the validity oldie widely used Mood Disorder Questionnaire (MDQ) in detecting a (hypo)manic episode and explore the impact of recall bias.</p><p>Methods: As an indication of impairments in recalling manic symptoms, we examined the long-term reliability of the MDQ after two years of follow-up in a sample of 2087 persons. Then, the validity of he MDQ was tested against the gold standard of a CIDI-based DSM-IV (hypo)manic episode Its performance was compared for detecting a lifetime episode (at T1) versus a recent episode in the past two years (at T2).</p><p>Results: The long term reliability of the MDQ was limited as the coned recall of individual items ranged horn 44.6% to 68.8% after two years. The overall validity of the MDQ in detecting a lifetime (hypo)manic episode was limited and no adequate cut-off point with acceptable sensitivity and specificity could be identified. However, the MDQ accurately detected a recent episode with a sensitivity of 0.83 and a specificity of 0.82 for the standard and optimal cut-off point of Taking into account two additional MDQ questions on clustering in time and severity of problems decreased its validity.</p><p>Limitations: Patients with a primary, clinical diagnosis of bipolar disorder were excluded.</p><p>Conclusions: The MDQ accurately detected recent (hypo)manic episodes, but imprecise recall may result in a limited performance for episodes earlier in life. (C) 2013 Elsevier B.V. All rights reserved.</p>

Predictors of the Onset of Manic Symptoms and a (Hypo)Manic Episode in Patients with Major Depressive Disorder

Objective: One third of patients with a major depressive episode also experience manic symptoms or, even, a (hypo) manic episode. Retrospective studies on the temporal sequencing of symptomatology suggest that the majority of these patients report depressive symptoms before the onset of manic symptoms. However, prospective studies are scarce and this study will, therefore, prospectively examine the onset of either manic symptoms or a (hypo) manic episode in patients with a major depressive disorder. In addition, we will consider the impact of a large set of potential risk factors on both outcomes. Methodology: Four-year follow-up data were used to determine the onset of manic symptoms as well as a CIDI-based (hypo) manic episode in a large sample (n = 889, age: 18-65 years) of outpatients with a major depressive disorder and without manic symptoms at baseline. Baseline vulnerability (i.e., sociodemographics, family history of depression, childhood trauma, life-events) and clinical (i.e., isolated manic symptoms, depression characteristics, and psychiatric comorbidity) factors were considered as potential risk factors. Results: In our sample of depressed patients, 15.9% developed manic symptoms and an additional 4.7% developed a (hypo) manic episode during four years. Baseline isolated manic symptoms and comorbid alcohol dependence predicted both the onset of manic symptoms and a (hypo) manic episode. Low education only predicted the onset of manic symptoms, whereas male gender, childhood trauma and severity of depressive symptoms showed strong associations with, especially, the onset of (hypo) manic episodes. Conclusions: A substantial proportion (20.6%) of patients with a major depressive disorder later developed manic symptoms or a (hypo) manic episode. Interestingly, some identified risk factors differed for the two outcomes, which may indicate that pathways leading to the onset of manic symptoms or a (hypo) manic episode might be different. Our findings indirectly support a clinical staging model

Preliminary effects of a digital mental health intervention for depression and anxiety

Background: Digital mental healthcare interventions (DMHIs) have been repeatedly mentioned as a possible solution for the growing demand for accessible treatment for patients suffering from common mental health problems, i.e. depression and anxiety disorders. However, structural implementation of DMHI is sparse and results on outcome seems inconclusive. To enrich the body of evidence, this paper compares a need-driven digital mental healthcare intervention (DMHI) for patients diagnosed with depression or anxiety disorders with traditional face-to-face treatment. The digital treatment is provided using a smartphone app which provides videoconferencing, chat, calendar- and registration functions. Method: In a naturalistic retrospective cohort study patients who received DMHI are compared to patients who received traditional face-to-face treatment. Furthermore three illustrative cases were selected to demonstrate how personalization is expressed in individual treatments. Results: The first results of the DMHI compare favorably with traditional face-to-face treatment, showing comparable satisfaction rates, equal effectiveness, and a significant decrease in treatment duration in weeks. Conclusion: The DMHI has the potential to be as effective, but more efficient than traditional face-to-face treatment. Furthermore the digital treatment opens up options to fine-tune the frequency, duration, and content of care contacts to align with patients' individual situations and personal preferences

Pearson’s correlations between cortisol indicators and inflammatory markers.

<p>Abbreviations: even., evening cortisol; diurn., diurnal cortisol slope; AUCg, area under the curve with respect to the ground; AUCi area under the curve with respect to increase; CRP, C-reactive Protein; IL-6, Interleukin-6; TNF-α, Tumor Necrosis Factor Alpha.</p><p>* = p <.05.</p><p>Pearson’s correlations between cortisol indicators and inflammatory markers.</p