Evaluation of bone marrow glucose uptake and adiposity in male rats after diet and exercise interventions.

Objectives: Obesity impairs bone marrow (BM) glucose metabolism. Adult BM constitutes mostly of adipocytes that respond to changes in energy metabolism by modulating their morphology and number. Here we evaluated whether diet or exercise intervention could improve the high-fat diet (HFD) associated impairment in BM glucose uptake (BMGU) and whether this associates with the morphology of BM adipocytes (BMAds) in rats. Methods: Eight-week-old male Sprague-Dawley rats were fed ad libitum either HFD or chow diet for 24 weeks. Additionally after 12 weeks, HFD-fed rats switched either to chow diet, voluntary intermittent running exercise, or both for another 12 weeks. BMAd morphology was assessed by perilipin-1 immunofluorescence staining in formalin-fixed paraffin-embedded tibial sections. Insulin-stimulated sternal and humeral BMGU were measured using [18F]FDG-PET/CT. Tibial microarchitecture and mineral density were measured with microCT. Results: HFD rats had significantly higher whole-body fat percentage compared to the chow group (17% vs 13%, respectively; p = 0.004) and larger median size of BMAds in the proximal tibia (815 µm2 vs 592 µm2, respectively; p = 0.03) but not in the distal tibia. Switch to chow diet combined with running exercise normalized whole-body fat percentage (p < 0.001) but not the BMAd size. At 32 weeks of age, there was no significant difference in insulin-stimulated BMGU between the study groups. However, BMGU was significantly higher in sternum compared to humerus (p < 0.001) and higher in 8-week-old compared to 32-week-old rats (p < 0.001). BMAd size in proximal tibia correlated positively with whole-body fat percentage (r = 0.48, p = 0.005) and negatively with humeral BMGU (r = -0.63, p = 0.02). HFD significantly reduced trabecular number (p < 0.001) compared to the chow group. Switch to chow diet reversed this as the trabecular number was significantly higher (p = 0.008) than in the HFD group. Conclusion: In this study we showed that insulin-stimulated BMGU is age- and site-dependent. BMGU was not affected by the study interventions. HFD increased whole-body fat percentage and the size of BMAds in proximal tibia. Switching from HFD to a chow diet and running exercise improved glucose homeostasis and normalized the HFD-induced increase in body fat but not the hypertrophy of BMAds.

Mechanisms Leading to Increased Insulin-Stimulated Cerebral Glucose Uptake in Obesity and Insulin Resistance: A High-Fat Diet and Exercise Training Intervention PET Study with Rats (CROSRAT).

Recent studies have shown that obesity and insulin resistance are associated with increased insulin-stimulated glucose uptake (GU) in the brain. Thus, insulin sensitivity seems to work differently in the brain compared to the peripheral tissues like skeletal muscles, but the underlying mechanisms remain unknown. Regular exercise training improves skeletal muscle and whole-body insulin sensitivity. However, the effect of exercise on glucose metabolism in the brain and internal organs is less well understood. The CROSRAT study aims to investigate the effects of exercise training on brain glucose metabolism and inflammation in a high-fat diet-induced rat model of obesity and insulin resistance. Male Sprague Dawley rats (n = 144) are divided into nine study groups that undergo different dietary and/or exercise training interventions lasting 12 to 24 weeks. Insulin-stimulated GU from various tissues and brain inflammation are investigated using [18F]FDG-PET/CT and [11C]PK11195-PET/CT, respectively. In addition, peripheral tissue, brain, and fecal samples are collected to study the underlying mechanisms. The strength of this study design is that it allows examining the effects of both diet and exercise training on obesity-induced insulin resistance and inflammation. As the pathophysiological changes are studied simultaneously in many tissues and organs at several time points, the study provides insight into when and where these pathophysiological changes occur.

Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering.

Clustering time activity curves of PET images have been used to separate clinically relevant areas of the brain or tumours. However, PET image segmentation in multiorgan level is much less studied due to the available total-body data being limited to animal studies. Now, the new PET scanners providing the opportunity to acquire total-body PET scans also from humans are becoming more common, which opens plenty of new clinically interesting opportunities. Therefore, organ-level segmentation of PET images has important applications, yet it lacks sufficient research. In this proof of concept study, we evaluate if the previously used segmentation approaches are suitable for segmenting dynamic human total-body PET images in organ level. Our focus is on general-purpose unsupervised methods that are independent of external data and can be used for all tracers, organisms, and health conditions. Additional anatomical image modalities, such as CT or MRI, are not used, but the segmentation is done purely based on the dynamic PET images. The tested methods are commonly used building blocks of the more sophisticated methods rather than final methods as such, and our goal is to evaluate if these basic tools are suited for the arising human total-body PET image segmentation. First, we excluded methods that were computationally too demanding for the large datasets from human total-body PET scanners. These criteria filtered out most of the commonly used approaches, leaving only two clustering methods, k-means and Gaussian mixture model (GMM), for further analyses. We combined k-means with two different preprocessing approaches, namely, principal component analysis (PCA) and independent component analysis (ICA). Then, we selected a suitable number of clusters using 10 images. Finally, we tested how well the usable approaches segment the remaining PET images in organ level, highlight the best approaches together with their limitations, and discuss how further research could tackle the observed shortcomings. In this study, we utilised 40 total-body [18F] fluorodeoxyglucose PET images of rats to mimic the coming large human PET images and a few actual human total-body images to ensure that our conclusions from the rat data generalise to the human data. Our results show that ICA combined with k-means has weaker performance than the other two computationally usable approaches and that certain organs are easier to segment than others. While GMM performed sufficiently, it was by far the slowest one among the tested approaches, making k-means combined with PCA the most promising candidate for further development. However, even with the best methods, the mean Jaccard index was slightly below 0.5 for the easiest tested organ and below 0.2 for the most challenging organ. Thus, we conclude that there is a lack of accurate and computationally light general-purpose segmentation method that can analyse dynamic total-body PET images.