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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.
Characterization of freeze-fractured epithelial plasma membranes on nanometer scale with ToF-SIMS
(2015)
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to characterize the freeze-fracturing process of human epithelial PANC-1 and UROtsa cells. For this purpose, phosphatidylcholine, sphingomyelin, phosphatidylethanolamine, and phosphatidylserine standard samples were investigated to find specific signals with both high specificity and signal intensity. The results were used to investigate single cells of subconfluent cell layers prepared with a special silicon wafer sandwich preparation technique. This freeze-fracturing technique strips cell membranes off the cells, isolating them on opposing silicon wafer substrates. Criteria were found for defining regions with stripped off cell membranes and, on the opposing wafer, complementary regions with the remaining cells. Measured ethanolamine/choline and serine/choline ratios in these regions clearly showed that in the freeze-fracturing process, the lipid bilayer of the plasma membrane is split along its central zone. Accordingly, only the outer lipid monolayer is stripped off the cell, while the inner lipid monolayer remains attached to the cell on the opposing wafer, thus allowing detailed analysis of a single lipid monolayer. Furthermore, it could be shown that using different washing procedures did not influence the transmembrane lipid distribution. Under optimized preparation conditions, it became feasible to detect lipids with a lateral resolution of approximately 100 nm. The data indicate that ToF-SIMS would be a very useful technique to study with very high lateral resolution changes in lipid composition caused, for example, by lipid storage diseases or pharmaceuticals that interfere with the lipid metabolism.
Aims/hypothesis Polycystic ovary syndrome (PCOS) is a risk factor of type 2 diabetes. Screening for impaired glucose metabolism (IGM) with an OGTT has been recommended, but this is relatively time-consuming and inconvenient. Thus, a strategy that could minimise the need for an OGTT would be beneficial. Materials and methods Consecutive PCOS patients (n=118) with fasting glucose < 6.1 mmol/l were included in the study. Parameters derived from medical history, clinical examination and fasting blood samples were assessed by decision tree modelling for their ability to discriminate women with IGM (2-h OGTT value >= 7.8 mmol/l) from those with NGT. Results According to the OGTT results, 93 PCOS women had NGT and 25 had IGM. The best decision tree consisted of HOMA-IR, the proinsulin:insulin ratio, proinsulin, 17-OH progesterone and the ratio of luteinising hormone:follicle-stimulating hormone. This tree identified 69 women with NGT. The remaining 49 women included all women with IGM (100% sensitivity, 74% specificity to detect IGM). Pruning this tree to three levels still identified 53 women with NGT (100% sensitivity, 57% specificity to detect IGM). Restricting the data matrix used for tree modelling to medical history and clinical parameters produced a tree using BMI, waist circumference and WHR. Pruning this tree to two levels separated 27 women with NGT (100% sensitivity, 29% specificity to detect IGM). The validity of both trees was tested by a leave-10%-out cross-validation. Conclusions/interpretation Decision trees are useful tools for separating PCOS women with NGT from those with IGM. They can be used for stratifying the metabolic screening of PCOS women, whereby the number of OGTTs can be markedly reduced.