SCL-90: An Outpatients Psychiatric Rating Scale—Preliminary Report

Psychopharmacology bulletin (Impact Factor: 0.5). 02/1973; 9(1):13-28.
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    • "All of the participants underwent an initial baseline assessment that included collection of clinical, dietary, and psychological parameters. The Symptoms Checklist-90-R (SCL-90-R), a self-report checklist inquiring about symptoms during the preceding week, was used as a measure of general psychopathology (Derogatis et al. 1973). Depressive symptoms and eating behaviors were determined using the Beck Depression Inventory (BDI) (Beck et al. 1961) and the Binge Eating Scale (BES) (Gormally et al. 1982), respectively . "
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    ABSTRACT: Objective: The aim of this nested case-control study was to compare the effectiveness of cognitive-behavioral treatment (CBT) for treatment-resistant obese (body mass index [BMI] ≥30 kg/m2) women compared with standard dietary treatment. The main outcome measures were attrition and weight loss success. Methods: We designed a 6-month case-control study, nested within a cohort of adult (age ≥18 years) treatment-resistant (history of at least two previous diet attempts) obese women. Cases were 20 women who were offered CBT sessions. Controls (n=39) were randomly selected from the source population and matched to cases in terms of baseline age, BMI, and number of previous diet attempts. Results: Compared with controls, cases were significantly more likely to complete the 6-month program in both age-adjusted (odds ratio [OR]=2.94, 95% confidence interval [CI]=1.05-8.97) and multivariate-adjusted (OR=2.77, 95% CI=1.02-8.34) analyses. In contrast, cases were not more likely to achieve weight loss success in age-adjusted (OR=1.32, 95% CI=0.86-1.67) and multivariate-adjusted (OR=1.21, 95% CI=0.91-1.44) analyses. Conclusions: Compared with a standard dietary treatment, CBT was significantly more effective in reducing attrition in treatment-resistant obese women, without differences in terms of weight loss success.
    Neuro endocrinology letters 10/2015; 36(4):368-373. · 0.80 Impact Factor
    • "age, gender, income, education level and working status) were assessed at baseline. In addition, the Symptoms Checklist-90 (SCL-90: Derogatis et al. (1973)), the Neuroticism-Extraversion-Openness-Five-Factor Inventory (NEO-FFI: Costa and McCrae (1989)) and the Medical Outcomes Study 36-item Short Form (MOS-SF-36: Ware and Sherbourne (1992)) were administered at baseline. At 11-year follow-up, medication use between 3-and 11-year follow-up (yes/ no) was documented retrospectively. "
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    ABSTRACT: Background: Depression heterogeneity has hampered development of adequate prognostic models. Therefore, more homogeneous clinical entities (e.g. dimensions, subtypes) have been developed, but their differentiating potential is limited because neither captures all relevant variation across persons, symptoms and time. To address this, three-mode Principal Component Analysis (3MPCA) was previously applied to capture person-, symptom- and time-level variation in a single model (Monden et al., 2015). This study evaluated the added prognostic value of such an integrated model for longer-term depression outcomes. Methods: The Beck Depression Inventory (BDI) was administered quarterly for two years to major depressive disorder outpatients participating in a randomized controlled trial. A previously developed 3MPCA model decomposed the data into 2 symptom-components ('somatic-affective', 'cognitive'), 2 time-components ('recovering', 'persisting') and 3 person-components ('severe non-persisting depression', 'somatic depression' and 'cognitive depression'). The predictive value of the 3MPCA model for BDI scores at 3-year (n=136) and 11-year follow-up (n=145) was compared with traditional latent variable models and traditional prognostic factors (e.g. baseline BDI component scores, personality). Results: 3MPCA components predicted 41% and 36% of the BDI variance at 3- and 11-year follow-up, respectively. A latent class model, growth mixture model and other known prognostic variables predicted 4-32% and 3-24% of the BDI variance at 3- and 11-year follow-up, respectively. Limitations: Only primary care patients were included. There was no independent validation sample. Conclusion: Accounting for depression heterogeneity at the person-, symptom- and time-level improves longer-term predictions of depression severity, underlining the potential of this approach for developing better prognostic models.
    Journal of Affective Disorders 09/2015; 189:1-9. DOI:10.1016/j.jad.2015.09.018 · 3.38 Impact Factor
    • "Childhood trauma was assessed with the 27-item Dutch version of the Early Trauma Inventory-self report (ETI-SR) (Bremner et al. 2007), assessing early traumatic experiences before the age of 18 years which include general trauma, physical abuse, emotional abuse and sexual abuse (Hovens et al. 2000; Witteveen et al. 2006; Hovens et al. 2002; Witteveen et al. 2006). In order to investigate the specificity of changes in PTSD symptoms we also investigated SCL-90 total score (psychoneuroticism) as well as the depression, somatisation, agoraphobia and anxiety sub scales (Derogatis et al. 1973). Differences in PTSD symptoms between time points were log-transformed in order to improve the distribution. "
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    ABSTRACT: Genomic variation in the SKA2 gene has recently been identified as a promising suicide biomarker. In light of its role in glucocorticoid receptor transactivation, we investigated whether SKA2 DNA methylation influences cortisol stress reactivity and is involved in the development of post traumatic stress disorder (PTSD). Increased SKA2 methylation was significantly associated with lower cortisol stress reactivity in 85 healthy individuals exposed to the Trier Social Stress Test (B=-173.40, t=-2.324, p=0.023). Next, we observed that longitudinal decreases in SKA2 methylation after deployment were associated with the emergence of post-deployment PTSD symptoms in a Dutch military cohort (N=93) (B=-0.054, t=-3.706, p=3.66 × 10(-4)). In contrast, exposure to traumatic stress during deployment by itself resulted in longitudinal increases in SKA2 methylation (B=0.037, t=4.173, p=6.98 × 10(-5)). Using pre-deployment SKA2 methylation levels and childhood trauma exposure, we found that the previously published suicide prediction rule significantly predicted post-deployment PTSD symptoms (AUC=0.66, 95%CI: 0.53-0.79) with an optimal sensitivity of 0.81 and specificity of 0.91. Permutation analysis using random methylation loci supported these findings. Together, these data establish the importance of SKA2 for cortisol stress responsivity and the development of PTSD and provide further evidence that SKA2 is a promising biomarker for stress-related disorders including PTSD.Neuropsychopharmacology accepted article preview online, 11 September 2015. doi:10.1038/npp.2015.286.
    Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 09/2015; DOI:10.1038/npp.2015.286 · 7.05 Impact Factor
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