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Background: Epidemiological studies suggest that an increased red meat intake is associated with a higher risk of type 2 diabetes, whereas an increased fiber intake is associated with a lower risk. Objectives: We conducted an intervention study to investigate the effects of these nutritional factors on glucose and lipid metabolism, body-fat distribution, and liver fat content in subjects at increased risk of type 2 diabetes. Methods: This prospective, randomized, and controlled dietary intervention study was performed over 6 mo. All groups decreased their daily caloric intake by 400 kcal. The "control" group (N = 40) only had this requirement. The "no red meat" group (N = 48) in addition aimed to avoid the intake of red meat, and the "fiber" group (N = 44) increased intake of fibers to 40 g/d. Anthropometric parameters and frequently sampled oral glucose tolerance tests were performed before and after intervention. Body-fat mass and distribution, liver fat, and liver iron content were assessed by MRI and single voxel proton magnetic resonance spectroscopy. Results: Participants in all groups lost weight (mean 3.3 +/- 0.5 kg, P < 0.0001). Glucose tolerance and insulin sensitivity improved (P < 0.001), and body and visceral fat mass decreased in all groups (P < 0.001). These changes did not differ between groups. Liver fat content decreased significantly (P < 0.001) with no differences between the groups. The decrease in liver fat correlated with the decrease in ferritin during intervention (r(2) = 0.08, P = 0.0021). This association was confirmed in an independent lifestyle intervention study (Tuebingen Lifestyle Intervention Program, N = 229, P = 0.0084). Conclusions: Our data indicate that caloric restriction leads to a marked improvement in glucose metabolism and body-fat composition, including liver-fat content. The marked reduction in liver fat might be mediated via changes in ferritin levels. In the context of caloric restriction, there seems to be no additional beneficial impact of reduced red meat intake and increased fiber intake on the improvement in cardiometabolic risk parameters. This trial was registered at clinicaltrials.gov as NCT03231839.
The model-driven software development paradigm requires that appropriate model transformations are applicable in different stages of the development process. The transformations have to consistently propagate changes between the different involved models and thus ensure a proper model synchronization. However, most approaches today do not fully support the requirements for model synchronization and focus only on classical one-way batch-oriented transformations. In this paper, we present our approach for an incremental model transformation which supports model synchronization. Our approach employs the visual, formal, and bidirectional transformation technique of triple graph grammars. Using this declarative specification formalism, we focus on the efficient execution of the transformation rules and how to achieve an incremental model transformation for synchronization purposes. We present an evaluation of our approach and demonstrate that due to the speedup for the incremental processing in the average case even larger models can be tackled.