Mechanisms Involved in the Relationship between Vitamin D and Insulin Resistance: Impact on Clinical Practice

Recent evidence has revealed anti-inflammatory properties of vitamin D as well as extra-skeletal activity. In this context, vitamin D seems to be involved in infections, autoimmune diseases, cardiometabolic diseases, and cancer development. In recent years, the relationship between vitamin D and insulin resistance has been a topic of growing interest. Low 25-hydroxyvitamin D (25(OH)D) levels appear to be associated with most of the insulin resistance disorders described to date. In fact, vitamin D deficiency may be one of the factors accelerating the development of insulin resistance. Vitamin D deficiency is a common problem in the population and may be associated with the pathogenesis of diseases related to insulin resistance, such as obesity, diabetes, metabolic syndrome (MS) and polycystic ovary syndrome (PCOS). An important question is the identification of 25(OH)D levels capable of generating an effect on insulin resistance, glucose metabolism and to decrease the risk of developing insulin resistance related disorders. The benefits of 25(OH)D supplementation/repletion on bone health are well known, and although there is a biological plausibility linking the status of vitamin D and insulin resistance supported by basic and clinical research findings, well-designed randomized clinical trials as well as basic research are necessary to know the molecular pathways involved in this association.

3D Skeletal Human Action Recognition Using a CNN Fusion Model

Smart homes have become central in the sustainability of buildings. Recognizing human activity in smart homes is the key tool to achieve home automation. Recently, two-stream Convolutional Neural Networks (CNNs) have shown promising performance for video-based human action recognition. However, such models cannot act directly on the 3D skeletal sequences due to its limitation to the 2D image video inputs. Considering the powerful effect of 3D skeletal data for describing human activity, in this study, we present a novel method to recognize the skeletal human activity in sustainable smart homes using a CNN fusion model. Our proposed method can represent the spatiotemporal information of each 3D skeletal sequence into three images and three image sequences through gray value encoding, referred to as skeletal trajectory shape images (STSIs) and skeletal pose image (SPI) sequences, and build a CNNs’ fusion model with three STSIs and three SPI sequences as input for skeletal activity recognition. Such three STSIs and three SPI sequences are, respectively, generated in three orthogonal planes as complementary to each other. The proposed CNN fusion model allows the hierarchical learning of spatiotemporal features, offering better action recognition performance. Experimental results on three public datasets show that our method outperforms the state-of-the-art methods.

Conclusions

This chapter concludes with the major theme that ran through the previous chapters; how much of each of these skeletal activity reconstruction features are a result of environmental influences (i.e., activities) and how much of the variation in these features are a result of genes. Biological confounds, which are largely genetic, have been found in all of the skeletal features covered in the previous chapters. For example, evolutionary body type rules (i.e., Bergmann’s and Allen’s Rules) affect measures of cross-sectional geometries. Plus, age is known to increase entheseal change scores. Furthermore, twin studies have revealed hereditary etiologies for osteoarthritis and Schmorl’s nodes. Yet, not all of the variance is genetic and, thus, the question remains whether skeletal indicators of activity can still be used to reconstruct activity patterns. Methods that avoid circular reasoning and aim to use only skeletal features with predictive validity should be the ultimate goal for those studying skeletal remains. If skeletal indicators of activity cannot be used to reconstruct what people did in the past, then perhaps these skeletal features can help in other ways, such as improving age estimates or drawing better conclusions about biological relatedness.

Reading the Bones

Do skeletal indicators used to reconstruct past people’s activity patterns actually reflect biological differences? This book reviews the literature on the most commonly utilized activity pattern indicators in bioarchaeology to answer this genes versus environment question. Chapter 2, for example, focuses on cross-sectional geometries, which have been used to look at mobility, and asks whether these measures of bone may also be influenced by climate-driven body shape adaptions. Chapters 3 and 4 look at entheseal changes, which are locations of muscle attachments, and osteoarthritis, which is also known as degenerative joint disease, to determine whether these features can be applied by bioarchaeologists to reconstruct activity patterns, especially when one considers that the best predictors for these features is age. Stress fractures (such as spondylolysis), which are covered in chapter 5, and activity indicator facets (such as kneeling facets), which are discussed in chapter 6, are more likely related to anatomical variation and other hereditary factors than activities previously linked to these skeletal features. After looking at all the evidence, which comes from research by bioarchaeologists, medical and sports studies, experimental animal research, genetic twin studies, and occupational studies on the living and the deceased, it appears that not all skeletal activity indicators will prove fruitful when reconstructing past people’s activity patterns.