Publications

468 Publications visible to you, out of a total of 468

Abstract (Expand)

Clinical and epidemiological studies are commonly used in medical sciences. They typically collect data by using different input forms and information systems. Metadata describing input forms, database schemas and input systems are used for data integration but are typically distributed over different software tools; each uses portions of metadata, such as for loading (ETL), data presentation and analysis. In this paper, we describe an approach managing metadata centrally and consistently in a dedicated Metadata Repository (MDR). Metadata can be provided to different tools. Moreover, the MDR includes a matching component creating schema mappings as a prerequisite to integrate captured medical data. We describe the approach, the MDR infrastructure and provide algorithms for creating schema mappings. Finally, we show selected evaluation results. The MDR is fully operational and used to integrate data from a multitude of input forms and systems in the epidemiological study LIFE.

Authors: Toralf Kirsten, A. Kiel, M. Rühle, J.Wagner

Date Published: 2nd Mar 2017

Publication Type: Not specified

Abstract (Expand)

BACKGROUND: The Generalized Anxiety Disorder Scales GAD-7 and GAD-2 are instruments for the assessment of anxiety. The aims of this study are to test psychometric properties of these questionnaires, to provide normative values, and to investigate associations with sociodemographic factors, quality of life, psychological variables, and behavioral factors. METHODS: A German community sample (n=9721) with an age range of 18-80 years was surveyed using the GAD-7 and several other questionnaires. RESULTS: Confirmatory factor analyses confirmed the unidimensionality and measurement invariance of the GAD-7 across age and gender. Females were more anxious than males (mean scores: M=4.07 vs. M=3.01; effect size: d=0.33). There was no linear age trend. A total of 5.9% fulfilled the cut-off criterion of 10 and above. Anxiety was correlated with low quality of life, fatigue, low habitual optimism, physical complaints, sleep problems, low life satisfaction, low social support, low education, unemployment, and low income. Cigarette smoking and alcohol consumption were also associated with heightened anxiety, especially in women. When comparing the GAD-7 (7 items) with the ultra-short GAD-2 (2 items), the GAD-7 instrument was superior to the GAD-2 regarding several psychometric criteria. LIMITATIONS: The response rate (33%) was low. Because of the cross-sectional character of the study, causal conclusions cannot be drawn. A further limitation is the lack of a gold standard for diagnosing anxiety. CONCLUSIONS: The GAD-7 can be recommended for use in clinical research and routine.

Authors: A. Hinz, A. M. Klein, E. Brahler, H. Glaesmer, T. Luck, S. G. Riedel-Heller, K. Wirkner, A. Hilbert

Date Published: 1st Mar 2017

Publication Type: Not specified

Human Diseases: generalized anxiety disorder

Abstract (Expand)

The LIFE Child study is a large population-based longitudinal childhood cohort study conducted in the city of Leipzig, Germany. As a part of LIFE, a research project conducted at the Leipzig Research Center for Civilization Diseases, it aims to monitor healthy child development from birth to adulthood and to understand the development of lifestyle diseases such as obesity. The study consists of three interrelated cohorts; the birth cohort, the health cohort, and the obesity cohort. Depending on age and cohort, the comprehensive study program comprises different medical, psychological, and sociodemographic assessments as well as the collection of biological samples. Optimal data acquisition, process management, and data analysis are guaranteed by a professional team of physicians, certified study assistants, quality managers, scientists and statisticians. Due to the high popularity of the study, more than 3000 children have already participated until the end of 2015, and two-thirds of them participate continuously. The large quantity of acquired data allows LIFE Child to gain profound knowledge on the development of children growing up in the twenty-first century. This article reports the number of available and analyzable data and demonstrates the high relevance and potential of the study.

Authors: T. Poulain, R. Baber, M. Vogel, D. Pietzner, T. Kirsten, A. Jurkutat, A. Hiemisch, A. Hilbert, J. Kratzsch, J. Thiery, M. Fuchs, C. Hirsch, F. G. Rauscher, M. Loeffler, A. Korner, M. Nuchter, W. Kiess

Date Published: 2nd Feb 2017

Publication Type: Journal article

Abstract (Expand)

A novel method for the automated detection of the outer choroid boundary within spectral-domain optical coherence tomography image data, based on an image model within the space of functions of bounded variation and the application of quadratic measure filters, is presented. The same method is used for the segmentation of retinal layer boundaries and proves to be suitable even for data generated without special imaging modes and moderate line averaging. Based on the segmentations, an automated determination of the central fovea region and choroidal thickness measurements for this and two adjacent 1-mm regions are provided. The quality of the method is assessed by comparison with manual delineations performed by five trained graders. The study is based on data from 50 children of the ages 8 to 13 that were obtained in the framework of the LIFE Child study at Leipzig University.

Authors: M. Wagner, P. Scheibe, M. Francke, B. Zimmerling, K. Frey, M. Vogel, S. Luckhaus, P. Wiedemann, W. Kiess, F. G. Rauscher

Date Published: 1st Feb 2017

Publication Type: Journal article

Abstract (Expand)

OBJECTIVE/METHODS: DNA methylation plays an important role in obesity and related metabolic complications. We examined genome-wide DNA promoter methylation along with mRNA profiles in paired samples of human subcutaneous adipose tissue (SAT) and omental visceral adipose tissue (OVAT) from non-obese vs. obese individuals. RESULTS: We identified negatively correlated methylation and expression of several obesity-associated genes in our discovery dataset and in silico replicated ETV6 in two independent cohorts. Further, we identified six adipose tissue depot-specific genes (HAND2, HOXC6, PPARG, SORBS2, CD36, and CLDN1). The effects were further supported in additional independent cohorts. Our top hits might play a role in adipogenesis and differentiation, obesity, lipid metabolism, and adipose tissue expandability. Finally, we show that in vitro methylation of SORBS2 directly represses gene expression. CONCLUSIONS: Taken together, our data show distinct tissue specific epigenetic alterations which associate with obesity.

Authors: M. Keller, L. Hopp, X. Liu, T. Wohland, K. Rohde, R. Cancello, M. Klos, K. Bacos, M. Kern, F. Eichelmann, A. Dietrich, M. R. Schon, D. Gartner, T. Lohmann, M. Dressler, M. Stumvoll, P. Kovacs, A. M. DiBlasio, C. Ling, H. Binder, M. Bluher, Y. Bottcher

Date Published: 27th Jan 2017

Publication Type: Not specified

Human Diseases: obesity

Abstract (Expand)

Background and objectives: Obesity has been associated with increased risk of dementia. Grey and white matter (WM) of the brain are commonly used as biomarkers for early detection of dementia. However, considering WM, available neuroimaging studies had mainly small sample size and yielded less conclusive results (Kullmann et al., 2015). Recently, a positive correlation between obesity and fractional anisotropy (FA) in a middle age group was reported (Birdsill et al. 2017). Furthermore, obesity is related to many medical problems such as diabetes and hypertension. Diabetes and hypertension were found to be correlated with brain structures independently (de Leeuw et al., 2002; Weinstein et al., 2015). Yet, studies rarely investigated non-lesion WM microstructure and its association with diabetes and blood pressure. Therefore we aim to investigate the relation between abdominal obesity, diabetes, blood pressure and WM microstructural variability in a large cohort of community-dwelling healthy adults. Methods: The sample included dementia-free participants (mean age 55 ± 16 years; 50.7% women) of the LIFE cohort with brain MRI scans (n = 1255). WM microstructure was measured with diffusion tensor imaging (DTI). Mean FA was derived from the individual WM skeleton processed by tract-based-spatial-statistic method. Linear regression models were used to assess the relationships between diabetes, blood pressure, waist to hip ratio (WHR) and DTI parameters. Adjustments were made for age, sex, education and Apoe4. Results: The preliminary result indicated diabetes, systolic blood pressure and WHR were independently associated with lower FA, and diabetes explained the most variance besides age. Subgroup analysis revealed both systolic blood pressure and WHR were negatively associated with mean FA in the non-diabetes group (n=1101). Conclusions: The preliminary result of our study indicates that diabetes accelerated brain aging on directional diffusion of WM. Abdominal fat and blood pressure were associated with WM variabilities independently from age, sex and diabetes. With subsequent analysis of additional DTI measures, blood parameters, WM hyperintensity maps and voxel-based microstructural WM “integrity”, we aim to further characterize the associations between obesity, diabetes, blood pressure and WM microstructure. This will contribute to the existing literature and help to disentangle the underlying mechanism.

Authors: Rui Zhang, Frauke Beyer, L. Lampe, T. Luck, S. G. Riedel-Heller, M. Stumvoll, Markus Löffler, M. L. Schroeter, A. Villringer, A. V. Witte

Date Published: 2017

Publication Type: Not specified

Human Diseases: diabetes mellitus, obesity, hypertension

Abstract (Expand)

Application of new high-throughput technologies in molecular medicine collects massive data for hundreds to thousands of persons in large cohort studies by characterizing the phenotype of each individual on a personalized basis. The chapter aims at increasing our understanding of disease genesis and progression and to improve diagnosis and treatment. New methods are needed to handle such "big data." Machine learning enables one to recognize and to visualize complex data patterns and to make decisions potentially relevant for diagnosis and treatment. The authors address these tasks by applying the method of self-organizing maps and present worked examples from different disease entities of the colon ranging from inflammation to cancer.

Authors: Hans Binder, Lydia Hopp, K. Lembcke, Henry Löffler-Wirth

Date Published: 2017

Publication Type: Not specified

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