Elevated tissue sodium deposition in patients with type 2 diabetes on hemodialysis detected by 23Na magnetic resonance imaging - Kopp, Christoph; Linz, Peter; Maier, Carolin; Wabel, Peter; Hammon, Matthias; Nagel, Armin Michael; Rosenhauer, Daniela; Horn, Stephan; Uder, Michael; Luft, Friedrich C.; Titze, Jens; Dahlmann, Anke (Kidney International, 2018)
Long-term elevated blood sugar levels result in tissue matrix compositional changes in patients with diabetes mellitus type 2 (T2DM). We hypothesized that hemodialysis patients with T2DM might accumulate more tissue sodium than control hemodialysis patients. To test this, 23Na magnetic resonance imaging (23Na MRI) was used to estimate sodium in skin and muscle tissue in hemodialysis patients with or without T2DM. Muscle fat content was estimated by 1H MRI and tissue sodium content by 23Na MRI pre- and post-hemodialysis in ten hemodialysis patients with T2DM and in 30 matched control hemodialysis patients. We also assessed body fluid distribution with the Body Composition Monitor. 1H MRI indicated a tendency to higher muscle fat content in hemodialysis patients with T2DM compared to non-diabetic hemodialysis patients. 23Na MRI indicated increased sodium content in muscle and skin tissue of hemodialysis patients with T2DM compared to control hemodialysis patients. Multi-frequency bioimpedance was used to estimate extracellular water (ECW), and excess ECW in T2DM hemodialysis patients correlated with HbA1c levels. Sodium mobilization during hemodialysis lowered muscle sodium content post-dialysis to a greater degree in T2DM hemodialysis patients than in control hemodialysis patients. Thus, our findings provide evidence that increased sodium accumulation occurs in hemodialysis patients with T2DM and that impaired serum glucose metabolism is associated with disturbances in tissue sodium and water content.
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Interleukin-1 beta activates essential checkpoints in the formation of lung-resident memory T cell - Lapuente, D.; Fuchs, J.; Maier, Carolin; Bonsmann, M. Storcksdieck Genannt; Maaske, A.; Stab, V.; Heinecke, V.; Liedtke, K.; Hess, R.; Westendorf, A.; Bayer, W.; Ehrhardt, C.; et al. (EUROPEAN JOURNAL OF IMMUNOLOGY, 2019)
CRIS-Team WoS Importer:2019-10-08

Towards Implementation of OMOP in a German University Hospital Consortium - Maier, Carolin; Lang, L.; Storf, H.; Vormstein, P.; Bieber, R.; Bernarding, J.; Herrmann, T.; Haverkamp, C.; Horki, P.; Laufer, J.; Berger, F.; Hoening, G.; et al. (Applied Clinical Informatics, 2018)
In 2015, the German Federal Ministry of Education and Research initiated a large data integration and data sharing research initiative to improve the reuse of data from patient care and translational research. The Observational Medical Outcomes Partnership (OMOP) common data model and the Observational Health Data Sciences and Informatics (OHDSI) tools could be used as a core element in this initiative for harmonizing the terminologies used as well as facilitating the federation of research analyses across institutions. To realize an OMOP/OHDSI-based pilot implementation within a consortium of eight German university hospitals, evaluate the applicability to support data harmonization and sharing among them, and identify potential enhancement requirements. The vocabularies and terminological mapping required for importing the fact data were prepared, and the process for importing the data from the source files was designed. For eight German university hospitals, a virtual machine preconfigured with the OMOP database and the OHDSI tools as well as the jobs to import the data and conduct the analysis was provided. Last, a federated/distributed query to test the approach was executed. While the mapping of ICD-10 German Modification succeeded with a rate of 98.8% of all terms for diagnoses, the procedures could not be mapped and hence an extension to the OMOP standard terminologies had to be made.Overall, the data of 3 million inpatients with approximately 26 million conditions, 21 million procedures, and 23 million observations have been imported.A federated query to identify a cohort of colorectal cancer patients was successfully executed and yielded 16,701 patient cases visualized in a Sunburst plot. OMOP/OHDSI is a viable open source solution for data integration in a German research consortium. Once the terminology problems can be solved, researchers can build on an active community for further development.
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