@misc{GroopCooperPerkovicetal.2015, author = {Groop, Per-Henrik and Cooper, Mark E. and Perkovic, Vlado and Sharma, Kumar and Schernthaner, Guntram and Haneda, Masakazu and Hocher, Berthold and Gordat, Maud and Cescutti, Jessica and Woerle, Hans-Juergen and von Eynatten, Maximilian}, title = {Dipeptidyl peptidase-4 inhibition with linagliptin and effects on hyperglycaemia and albuminuria in patients with type 2 diabetes and renal dysfunction}, series = {Diabetes \& vascular disease research}, journal = {Diabetes \& vascular disease research}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-404460}, pages = {8}, year = {2015}, abstract = {Efficacy, Safety \& Modification of Albuminuria in Type 2 Diabetes Subjects with Renal Disease with LINAgliptin (MARLINA-T2D), a multicentre, multinational, randomized, double-blind, placebo-controlled, parallel-group, phase 3b clinical trial, aims to further define the potential renal effects of dipeptidyl peptidase-4 inhibition beyond glycaemic control. A total of 350 eligible individuals with inadequately controlled type 2 diabetes and evidence of renal disease are planned to be randomized in a 1:1 ratio to receive either linagliptin 5mg or placebo in addition to their stable glucose-lowering background therapy for 24weeks. Two predefined main endpoints will be tested in a hierarchical manner: (1) change from baseline in glycated haemoglobin and (2) time-weighted average of percentage change from baseline in urinary albumin-to-creatinine ratio. Both endpoints are sufficiently powered to test for superiority versus placebo after 24weeks with =0.05. MARLINA-T2D is the first of its class to prospectively explore both the glucose- and albuminuria-lowering potential of a dipeptidyl peptidase-4 inhibitor in patients with type 2 diabetes and evidence of renal disease.}, language = {en} } @misc{HischeLarhlimiSchwarzetal.2012, author = {Hische, Manuela and Larhlimi, Abdelhalim and Schwarz, Franziska and Fischer-Rosinsk{\´y}, Antje and Bobbert, Thomas and Assmann, Anke and Catchpole, Gareth S. and Pfeiffer, Andreas F. H. and Willmitzer, Lothar and Selbig, Joachim and Spranger, Joachim}, title = {A distinct metabolic signature predictsdevelopment of fasting plasma glucose}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, number = {850}, issn = {1866-8372}, doi = {10.25932/publishup-42740}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427400}, pages = {12}, year = {2012}, abstract = {Background High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. Methods We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. Results We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. Conclusions We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.}, language = {en} }