TY - GEN A1 - Hische, Manuela A1 - Larhlimi, Abdelhalim A1 - Schwarz, Franziska A1 - Fischer-Rosinský, Antje A1 - Bobbert, Thomas A1 - Assmann, Anke A1 - Catchpole, Gareth S. A1 - Pfeiffer, Andreas F. H. A1 - Willmitzer, Lothar A1 - Selbig, Joachim A1 - Spranger, Joachim T1 - A distinct metabolic signature predictsdevelopment of fasting plasma glucose T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 850 KW - prediction KW - fasting glucose KW - type 2 diabetes KW - metabolomics KW - plasma KW - random forest KW - metabolite KW - regression KW - biomarker Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427400 SN - 1866-8372 IS - 850 ER - TY - JOUR A1 - Stadler, Waltraud A1 - Ott, Derek V. M. A1 - Springer, Anne A1 - Schubotz, Ricarda I. A1 - Schütz-Bosbach, Simone A1 - Prinz, Wolfgang T1 - Repetitive TMS suggests a role of the human dorsal premotor cortex in action prediction JF - Frontiers in human neuroscienc N2 - Predicting the actions of other individuals is crucial for our daily interactions. Recent evidence suggests that the prediction of object-directed arm and full-body actions employs the dorsal premotor cortex (PMd). Thus, the neural substrate involved in action control may also be essential for action prediction. Here, we aimed to address this issue and hypothesized that disrupting the PMd impairs action prediction. Using fMRI-guided coil navigation, rTMS (five pulses, 10Hz) was applied over the left PMd and over the vertex (control region) while participants observed everyday actions in video clips that were transiently occluded for 1s. The participants detected manipulations in the time course of occluded actions, which required them to internally predict the actions during occlusion. To differentiate between functional roles that the PMd could play in prediction, rTMS was either delivered at occluder-onset (TMS-early), affecting the initiation of action prediction, or 300 ms later during occlusion(TMS-late), affecting the maintenance of anongoing prediction. TMS-early over the left PMd produced more prediction errors than TMS-early over the vertex. TMS-late had no effect on prediction performance, suggesting that the left PMd might be involved particularly during the initiation of internally guided action prediction but may play a subordinate role in maintaining ongoing prediction. These findings open a new perspective on the role of the left PMd in action prediction which is in line with its functions in action control and in cognitive tasks. In the discussion, there levance of the left PMd for integrating external action parameters with the observer's motor repertoire is emphasized. Overall, the results are in line with the notion that premotor functions are employed in both action control and action observation. KW - action observation KW - prediction KW - occlusion KW - premotor KW - PMd KW - transcranial magnetic stimulation Y1 - 2012 U6 - https://doi.org/10.3389/fnhum.2012.00020 SN - 1662-5161 VL - 6 IS - 2 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Kissling, W. D. A1 - Dormann, Carsten F. A1 - Groeneveld, Juergen A1 - Hickler, Thomas A1 - Kühn, Ingolf A1 - McInerny, Greg J. A1 - Montoya, Jose M. A1 - Römermann, Christine A1 - Schiffers, Katja A1 - Schurr, Frank Martin A1 - Singer, Alexander A1 - Svenning, Jens-Christian A1 - Zimmermann, Niklaus E. A1 - O'Hara, Robert B. T1 - Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents JF - Journal of biogeography N2 - Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities. KW - Community ecology KW - ecological networks KW - global change KW - guild assembly KW - multidimensional complexity KW - niche theory KW - prediction KW - species distribution model KW - species interactions KW - trait-based community modules Y1 - 2012 U6 - https://doi.org/10.1111/j.1365-2699.2011.02663.x SN - 0305-0270 VL - 39 IS - 12 SP - 2163 EP - 2178 PB - Wiley-Blackwell CY - Hoboken ER -