Anyonya Dampatyam Download Pdf
Lord Krishna gives advice to Uddhava and prescribes an easy way for reaching Him. Various sorts of evils are propagated. When a men gets this love, he sees love everywhere he hears love everywhere, he talks love everywhere, he thinks love everywhere. He has passed his I. Notes and Commentary 'Anyonya-asraya' is mutual dependence.
Biological processes utilize energy and therefore must be prioritized based on fuel availability. Bone is no exception to this, and the benefit of remodeling when necessary outweighs the energy costs. Bone remodeling is important for maintaining blood calcium homeostasis, repairing micro cracks and fractures, and modifying bone structure so that it is better suited to withstand loading demands. Osteoclasts, osteoblasts, and osteocytes are the primary cells responsible for bone remodeling, although bone marrow adipocytes and other cells may also play an indirect role. There is a renewed interest in bone cell energetics because of the potential for these processes to be targeted for osteoporosis therapies. In contrast, due to the intimate link between bone and energy homeostasis, pharmaceuticals that treat metabolic disease or have metabolic side effects often have deleterious bone consequences. In this brief review, we will introduce osteoporosis, discuss how bone cells utilize energy to function, evidence for bone regulating whole body energy homeostasis, and some of the unanswered questions and opportunities for further research in the field.
Osteoporosis is a major public health condition commonly related to aging. It is a skeletal disorder characterized by reduced bone mineral density (BMD) and microarchitectural deterioration of bone tissue (Kanis et al. 2013). The reduction in BMD is caused by an imbalance between osteoclast-mediated bone resorption and osteoblast-mediated bone formation (i.e., uncoupled remodeling). Osteoporosis compromises bone strength, leading to an increased susceptibility to fractures. In the United States, it is expected that the number and cost of fractures will increase almost 50% by the year 2025 (Burge et al. 2007). In addition to occurring with aging and after menopause, osteoporosis can be attributed to many clinical conditions that impact whole-body energy metabolism, such as diabetes mellitus (DM) and anorexia nervosa (AN).
Postmenopausal osteoporosis is associated with low BMD and changes in body composition. Postmenopausal women are predisposed to fracture, in particular at the hip and wrist regions (Burge et al. 2007). During menopausal transition, women usually experience accelerated bone loss, mostly due to changes in sex hormone levels. Estrogen deficiency is an important factor that contributes to postmenopausal osteoporosis development. The drop in estrogen levels induces osteoclast recruitment and activation by increasing receptor activator of nuclear factor κ β ligand production by osteoblastic cells, which favors bone resorption activity and consequently causes rapid bone loss (Clarke and Khosla 2010). The onset of postmenopausal osteoporosis is also associated with age-related changes in body composition. Lean mass, which exerts a positive influence on the skeleton, decreases with age and becomes more evident in women over 65 years (Zhang et al. 2013; Chen et al. 2015). On the other hand, there is an increase in body fat with advancing age (Ho et al. 2010; Sornay-Rendu et al. 2012). However, the increase in adipose tissue may have different effects on bone mass (Compston 2015). Fat mass can have a positive association with BMD in postmenopausal women (Chain et al. 2017; Ho-Pham et al. 2010), suggesting that a slight accumulation of body fat may be advantageous for these women and it would not induce bone loss (Chain et al. 2017). However, low trauma fracture is observed in obese postmenopausal women with normal BMD (Premaor et al. 2010; Compston 2015). Obesity is associated with the production of a specific set of cytokines and adipokines by visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) that may have a negative or positive effect on bone mass (Russell et al. 2010). Thus, it needs to be taken into consideration that not only the amount of fat mass but also VAT/SAT proportion is important to postmenopausal women bone health, as it will determine the type of cytokines and adipokines secreted having a diverse impact on bone mass.
AN is a psychiatric disease characterized by a voluntary food restriction leading to severe weight loss and, consequently, bone loss with increased fracture risk. Bone loss is caused in part by decrease in mechanical loading, as these patients exhibit a loss of lean and fat mass compartments. Concomitantly, marrow adipose tissue (MAT) expands in AN and has a negative association with bone mass. This increase in MAT is an evident feature of AN, and it might represent a compensatory mechanism related to the lack of peripheral fat or a shift toward fat lineage instead of bone cell as a result of impaired osteoblastogenesis (Bredella et al. 2009). Curiously, MAT content is decreased in women who had begun to recover their nutritional status. Therefore, this may indicate that MAT is more susceptible to nutritional changes than the other fat depots, and its reduction can induce a bone mass improvement in these patients (Fazeli et al. 2012).
DM is a metabolic disease characterized by a hyperglycemic state. Bone loss is one of the classical complications of type 1 DM (McCabe 2007; Starup-Linde et al. 2016; Vestergaard 2007). Conversely, type 2 DM patients usually have an adequate or increased BMD (Vestergaard 2007; Starup-Linde et al. 2016; de Araujo et al. 2017). However, fracture risk is increased in both types of diabetes even though there are discrepancies in bone phenotype (Starup-Linde et al. 2016). Nutritional status is one factor that differs between types of diabetes: unlike type 1 DM, type 2 DM patients are overweight or obese with visceral fat accumulation (de Araujo et al. 2017). Higher body weight exerts a greater mechanical loading on the skeleton (Reid 2010), and it can explain the absence of bone loss in these individuals. On the other hand, adipose tissue produces certain cytokines and adipokines, which have a deleterious effect on bone (Russell et al. 2010) and predispose obese patients to fracture. In type 1 DM animal models, as in caloric restriction models, trabecular bone mass reduction is associated with an increase in MAT (Botolin and McCabe 2007). However, MAT expansion is not observed in type 1 DM condition in humans (Slade et al. 2012). Likewise, some have reported that increased MAT is not a feature in obese type 2 DM subject (de Araujo et al. 2017). Yet, others have described a greater MAT content in lumbar spine and femoral metaphysis of morbid obese subjects with type 2 DM using insulin therapy or antidiabetic oral agents (Yu et al. 2017). This highlights that MAT function and its determinants in diabetes condition need to be better understood. In addition to aging, MAT was related to serum lipid levels in type 1 diabetes subjects (Slade et al. 2012), and hyperglycemia has been shown to be a determinant factor of MAT in type 2 diabetes condition (Yu et al. 2017). Moreover, available data in the literature did not observe any association between MAT and homeostatic model assessment of insulin resistance (HOMA-IR; Yu et al. 2017; de Paula et al. 2015; de Araujo et al. 2017). Another important aspect to be considered is the relationship between saturated and unsaturated lipids of the MAT and bone mass. Saturated lipids are higher in type 2 DM subjects with previous fractures (Patsch et al. 2013), suggesting that the lipid profile of MAT might be a predictive marker of bone health (Patsch et al. 2013; Yeung et al. 2005).
Due to the impact of metabolic diseases on bone health, it is not surprising that changes in bone cell energy metabolism modify differentiation and function. Osteoblasts, osteocytes, and marrow adipocytes all originate from common progenitor mesenchymal stem cells. Osteoclasts, which are bone-resorbing cells, have a monocytic origin (Bianco and Robey 2004; Xiao et al. 2015). The fuel sources and pathways that are utilized by these cells to generate adenosine 5′-triphosphate (ATP) are currently under intense scrutiny. ATP is the most widely used nucleoside triphosphate to provide chemical energy for biochemical reactions. It is generated in the cytoplasm through glycolysis and in the mitochondria through oxidative phosphorylation. The specialized cells that make up bone need to generate substantial amounts of ATP to maintain a normal healthy skeleton. During development, osteoblasts produce and secrete α-1 type 1 collagen and mineralize bone (Rodan 1992). Osteocytes, which are embedded in mineralized bone, secrete a number of osteokines like sclerostin and function in a highly hypoxic environment as mechanical sensors (Weivoda, Youssef, and Oursler 2017). Osteoclasts, on the other hand, need to generate H+ to acidify and help resorb bone, hydrolyzing ATP to generate resorption pits. In this section, we will review the current understanding of the exogenous fuel sources and bioenergetics pathways that are utilized by cells in the skeleton to meet this ATP need.
Recent studies have shown that bone marrow stromal cells utilize oxidative phosphorylation to generate ATP preferably over glycolysis, while concomitantly increasing antioxidant enzymes generated to combat the detrimental effects of reactive oxygen species (Chen et al. 2008; Shum et al. 2016). In contrast to some of these data, Wnt3a, a known osteogenic factor specifically induces glycolysis through an mTORC2-dependent pathway (Esen et al. 2013). There is an increase in Glut1, the insulin-independent glucose transporter, along with several glycolytic pathway enzymes, in a number of precalvarial osteoblasts and bone marrow stem cell lines at the later stages of osteoblast differentiation. The preferential use of glycolysis to generate ATP is akin to the Warburg effect that has been described in a variety of cancer cells (Diaz-Ruiz, Rigoulet, and Devin 2011). The reasons for this seem to be multifold, namely, need for (1) generating nicotinamide dinucleotide (NAD+) when lactate is generated from pyruvate, (2) generating ribose precursors and nicotinamide adenine dinucleotide phosphate (NADPH) through pentose phosphate pathway, and (3) generating ATP at a faster rate. However, which of these are relevant for osteoblasts is currently not clear.
Early studies using ex vivo techniques to measure bone cell energetics in the 1950s identified that parathyroid hormone (PTH) induces lactate via aerobic glycolysis. More recently, it was shown that this mechanism is through induction of insulin-like growth factor 1 (IGF-1) and glycolytic gene upregulation (Neuman, Neuman, and Brommage 1978; Esen et al. 2015). However, others have shown increases in both oxidative phosphorylation and glycolysis during the late stages of in vitro osteoblast differentiation in calvarial-derived osteoblast cells (Guntur et al. 2014).
The substrates used in these studies are exogenously added glucose, pyruvate, and glutamine (Guntur et al. 2014; Komarova, Ataullakhanov, and Globus 2000). All of these substrates are metabolized and processed through glycolysis and Krebs cycle. Glutamine, used by osteoblasts as an alternate fuel source, is converted to α-ketoglutarate to enter the Krebs cycle through the process of glutaminolysis. Karner et al. (2015) have shown that this process is also Wnt signaling mediated and is essential for osteoblast differentiation. However, the transporters and enzymes involved in this pathway in osteoblasts still need to be identified. Furthermore, more recent studies have also identified that early osteoblast differentiation in bone marrow stromal cells (BMSCs) requires intracellular fatty acids as energy sources. Other studies have shown that oxidation of fatty acids by osteoblasts for generating energy is controlled by Wnt-Lrp5 signaling, and a loss in this process will result in decreased bone mass along with increases in whole-body fat mass (Rendina-Ruedy, Guntur, and Rosen 2017; Frey et al. 2015).
Osteocytes make up close to 95% of the cells in the adult skeleton (Bonewald 2011). There are currently no studies that have identified the bioenergetic pathways in these cells. Osteocytes are embedded in a hypoxic environment. This, combined with the fact that they are terminally differentiated osteoblasts leads to the hypothesis that they would be highly glycolytic in their energy production. These cells have been shown to generate protons and acidify their microenvironment (Jahn et al. 2017), though there are currently no published studies that have examined energy metabolism in osteocytes.
The origin of the osteoclast is the bone marrow macrophage. The fully differentiated osteoclast forms a sealing zone between its ruffled membrane and the area it needs to resorb. It then proceeds to generate an acidic environment within the sealing zone containing cathepsin K and matrix metalloproteases to initiate remodeling (Boyle, Simonet, and Lacey 2003). There are two studies that have identified the bioenergetic pathways in osteoclasts. The first study identified the need for glycolysis for normal osteoclast differentiation, which seems to be an underlying theme for all the different cells in the skeleton (Indo et al. 2013). The authors observed an increase in Glut1 and other glycolytic enzymes with osteoclast differentiation. The same study also identified an increase in glutaminolysis as seen with osteoblast differentiation in response to Wnt signaling. Modulating either pathway leads to a decrease in osteoclast differentiation and function. In a more recent study, Lemma et al. (2016) showed that there is increased oxidative phosphorylation with differentiation in osteoclasts with an increase in mitochondrial mass and biogenesis. This is followed by a need for glycolysis to properly function during resorption.
The sequence of events described in these studies suggests that the need to shift to glycolysis might be based on the function of these specialized cells. The generation of ATP for pumping protons into the sealing zone may need to occur at a rate that exceeds the ability of the cells to promote mitochondrial biogenesis. Thus, glycolysis in the cytoplasm might be a more readily available source of ATP than if it is generated through oxidative phosphorylation in the mitochondria and transported out through ADP/ATP transporters.
Metabolic pathways can control gene expression through epigenetic modifications as most of the substrates and cofactors that are necessary for epigenetic modifications are generated through bioenergetic pathways. For example, NAD+ (oxidized form) is an essential electron acceptor for a number of dehydrogenases to generate nicotinamide adenine dinucleotide (NADH). In the mitochondria, NADH functions as the electron donor in the electron transport chain to generate a ΔpH and the mitochondrial membrane potential necessary for ATP generation. NAD+ is also absolutely essential for the enzymatic activity as a cofactor for sirutinin enzyme–mediated deacetylations (Imai et al. 2000). The effects of NAD+ on bone metabolism are currently not known though there is a keen interest in using nicotinamide mononucleotide (NAM) or nicotinamide riboside which are precursors that have been shown to affect the aging process. More importantly, long-term treatment of wild-type C57BL/6J mice with NAM showed significantly higher BMD compared to age-matched controls, suggesting that there could be a beneficial effect of these compounds on bone mass (Mills et al. 2016).
Acetyl CoA generated in the Krebs cycle in the mitochondria is also a crucial cofactor and substrate for acetylation reactions. It is used by histone acetyltransferases as an acetyl donor and known to exist in two separate pools: one in the mitochondria and the other in the cytoplasm. Karner et al. (2015) have recently shown that reduced nuclear acetyl CoA leads to suppression of osteoblast gene expression. This could be one potential explanation for the need for the cells to switch to glycolysis with osteoblast differentiation (Mills et al. 2016). Therefore, control of metabolic pathways and the flux through which these substrates can be increased or decreased has far-reaching consequences in controlling not only ATP generation but also gene expression. These are in turn controlled by substrate availability and cellular ATP demand, setting the tone for which pathway needs to be upregulated or downregulated.
One of the crucial pieces of data that are lacking from most of these studies is the status of mitochondrial respiration and mitochondrial membrane potential, which at any point of time is a good indicator of cellular energetic pathways. The switch to glycolysis that is observed with osteoblasts and osteoclasts would indicate that there is a defect or insufficiency in mitochondrial metabolism, but whether this is because of some dysfunction that occurs with differentiation or due to specific programming is not clear. Studying ATP generation and mitochondrial dynamics with differentiation and identifying the amount of ATP that is generated from these pathways will be crucial for obtaining a complete bioenergetic picture.
Current studies have focused on identifying the pathways during normal osteoblast differentiation, so there needs to be a push to study the role of these energetic pathways in pathophysiological conditions. More careful study of bioenergetic pathways at the molecular level may identify novel mechanisms of pathological bone loss and subsequently novel pathways to target for therapies.
Regulation of energy metabolism by peripheral tissues
Energy balance is defined by the regulation of food intake, nutrient storage, and energy expenditure. These processes are all controlled by central mechanisms, and feedback from peripheral tissues is an essential part of these regulatory networks. Leptin, for example, is secreted from adipocytes that are storing lipids and acts in the hypothalamus to reduce food intake and increase energy expenditure (Roh, Song, and Kim 2016). On the other hand, during exercise, interleukin 6 (IL-6) is secreted from skeletal muscle and promotes sympathetic nervous system (SNS)-mediated mobilization of fat stores. Intracerebroventricular administration of IL-6 increases energy expenditure, and deletion of IL-6 promotes obesity (Febbraio and Pedersen 2002; Roh, Song, and Kim 2016). Alternately, low glucose levels in the blood (lowered by utilization or storage by tissues) stimulate the hypothalamic–pituitary–adrenal axis and the SNS to mobilize glucose and fatty acids (Routh et al. 2014). The latter example, however, is a more passive way in which peripheral tissues modify energy metabolism (i.e., simply by using energy). Bone will certainly use energy, causing a reduction in available fuel and further stimulation to eat or mobilize fuel stores, but active mechanisms through which bone regulates energy metabolism are currently being investigated. Although we have not currently identified ways in which the bone can feedback directly to the brain, there is evidence that bone can regulate insulin secretion and sensitivity, which we will summarize below.
Involvement of Bone in the Regulation of Energy Metabolism
Osteocalcin (OC) is the second most abundant protein in bone, after type 1 collagen, and its serum levels have long been used as a marker for bone turnover. However, a role of OC in regulating insulin sensitivity has been established and is well reviewed (Booth et al. 2013; Motyl, McCabe, and Schwartz 2010; Wei and Karsenty 2015). These findings were initiated by the observation OC knockout mice were overweight, with high blood glucose (Lee et al. 2007). However, these mice also had lower insulin levels, suggesting they were not simply prone to the classical obesity-induced insulin resistance, in which insulin levels are generally high (Lee et al. 2007). Instead, the authors did indeed find insulin resistance (marked by impaired insulin tolerance test and reduced glucose infusion rate in hyperinsulinemic euglycemic clamp studies) in the OC knockout mice, along with the intriguing finding that OC protein itself could stimulate insulin secretion from pancreatic β cells (Lee et al. 2007). More recently, Ferron et al. (2010) demonstrated that undercarboxylated OC release from bone during resorption stimulates insulin production in the pancreas. This combined with the known role of insulin in promoting bone remodeling suggests a bone–pancreas feed-forward loop regulating bone and insulin secretion. Although studies in humans point to the carboxylated form of OC as having a more prominent role in glucose metabolism, these studies nonetheless suggest that metabolic homeostasis can be modified by hormones released from bone (Booth et al. 2013).
More recently, non-OC-mediated mechanisms have been proposed for bone in the regulation of energy homeostasis. Ablation of osteoblasts with diphtheria toxin expression under control of the OC-Cre promoter resulted in hypoinsulinemia, hyperglycemia, and impaired insulin sensitivity (Yoshikawa et al. 2011). This finding is similar to the metabolic phenotype of the OC knockout mice; however, the authors also observed increased energy expenditure and reduced gonadal fat mass, which could not be rescued with OC administration. This suggests that osteoblasts or osteocytes produce another hormone(s) that contributes to the regulation of energy metabolism. Furthermore, increasing evidence suggests that neuropeptide Y expressed from osteoblasts is one of these essential molecules mediating changes in energy expenditure (reviewed in this issue; Rodriguez-Carballo et al. 2015; Baldock et al. 2007; Lundberg et al. 2007; Lee et al. 2015). The above examples modulated energy expenditure and glucose homeostasis, but it is becoming evident that bone can have impacts on brown/beige adipose tissue as well. In a recent study by Brun et al. (2017), deletion of peroxisome proliferator activated receptor gamma (PPARγ) in osteocytes (utilizing the Dmp1-Cre mouse) resulted in high bone mass, improved glucose tolerance and insulin sensitivity, and increased beiging of white adipose tissue. These mice were also resistant to high-fat diet–induced obesity and steatosis and could not be explained by changes in OC. The authors postulated that osteokines under the control of PPARγ were responsible for the effects, and although not tested directly, they identified bone morphogenetic protein 7 (BMP7) as a candidate molecule (Brun et al. 2017).
In addition to bone cells themselves regulating energy metabolism, bone marrow adipocytes within the bone marrow niche have been demonstrated to be important contributors to glucose control. In particular, marrow adipocytes expand in size and number during metabolic diseases, such as AN and DM. In part, this is thought to be due to altered mesenchymal lineage selection toward the adipogenic lineage at the expense of the osteogenic lineage. However, MAT is the major source of adiponectin in calorie restriction models, suggesting it too has a role in modifying insulin sensitivity (Cawthorn et al. 2014). Furthermore, stimulation of the SNS through cold exposure, or treatment with antipsychotic drugs, promotes marrow fat loss, suggesting fat is lipolyzed for fuel in these cases (Scheller et al. 2015; Motyl et al. 2015). How such acute changes in marrow fat globally modulate energy metabolism, however, is unknown.
Bone remodeling is a dynamic process that, when uncoupled, leads to osteoporosis. Although osteoporosis occurs with conditions such as aging and menopause, it can also occur secondary to metabolic diseases such as DM and secondary to treatment with pharmaceuticals that impact metabolism. Bone requires a significant portion of the available fuel of an organism; thus, it is not surprising that glucose homeostasis, adipose tissue metabolism, and bone remodeling appear to be tightly linked. There is increasing evidence that bone can regulate organismal energy metabolism through pathways other than just OC-mediated alone. However, we have not yet seen strong evidence that bone regulates central control of energy balance. The importance of studying these pathways is at least 2-fold. First, treatments that modulate energy metabolism may have unexpected impacts on bone and vice versa. For example, atypical antipsychotic drugs have known metabolic side effects, and these lead to trabecular bone loss in rodent models. Alternately, osteoporotic therapies may in turn have deleterious metabolic side effects. Second, studying the energetics of bone cells themselves will lead to a better understanding of the etiology of pathologic conditions like diabetes and the mechanism of action of bone therapies like PTH. In conclusion, understanding the energy metabolism of bone may lead to novel treatment regimens and/or therapeutics with improved efficacy and reduced side effects.
Authors’ Note
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Authors’ Contribution
Authors contributed to conception or design (KM); data acquisition, analysis, or interpretation (KM, AG, AC, CR); drafting the manuscript (KM, AG, AC); and critically revising the manuscript (KM, AG, AC, CR). All authors gave final approval and agreed to be accountable for all aspects of work in ensuring that questions relating to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Adriana Carvalho received financial support from the São Paulo Research Foundation (2013/09853-6 and 2014/14505-0). Additional financial support was from the following National Institutes of Health awards: National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) award number K01 AR067858 to Katherine Motyl, National Institute of General Medical Sciences award number P20 GM121301 to Katherine Motyl, and NIAMS award number R03 AR068095 to Anyonya Guntur.
| Baldock, P. A., Allison, S. J., Lundberg, P., Lee, N. J., Slack, K., Lin, E. J., Enriquez, R. F.. (2007). Novel role of Y1 receptors in the coordinated regulation of bone and energy homeostasis. J Biol Chem 282, 19092–102. Google Scholar Medline ISI |
| Bianco, P., Robey, P. G. (2004). 39—Skeletal stem cells. In Handbook of Stem Cells (Lanza, R., Gearhart, J., Hogan, B., Melton, D., Pedersen, R., Thomson, J., West, M., eds.), pp. 415–24. Academic Press, Burlington, MA. Google Scholar |
| Bonewald, L. F. (2011). The amazing osteocyte. J Bone Miner Res 26, 229–38. Google Scholar Medline ISI |
| Booth, S. L., Centi, A., Smith, S. R., Gundberg, C. (2013). The role of osteocalcin in human glucose metabolism: Marker or mediator? Nat Rev Endocrinol 9, 43–55. Google Scholar Medline ISI |
| Botolin, S., McCabe, L. R. (2007). Bone loss and increased bone adiposity in spontaneous and pharmacologically induced diabetic mice. Endocrinology 148, 198–205. Google Scholar Medline ISI |
| Boyle, W. J., Simonet, W. S., Lacey, D. L. (2003). Osteoclast differentiation and activation. Nature 423, 337–42. Google Scholar Medline ISI |
| Bredella, M. A., Fazeli, P. K., Miller, K. K., Misra, M., Torriani, M., Thomas, B. J., Ghomi, R. H., Rosen, C. J., Klibanski, A. (2009). Increased bone marrow fat in anorexia nervosa. J Clin Endocrinol Metab 94, 2129–36. Google Scholar Medline ISI |
| Brun, J., Berthou, F., Trajkovski, M., Maechler, P., Foti, M., Bonnet, N. (2017). Bone regulates browning and energy metabolism through mature osteoblast/osteocyte PPARgamma expression. Diabetes 66, 2541–54. Google Scholar Medline ISI |
| Burge, R., Dawson-Hughes, B., Solomon, D. H., Wong, J. B., King, A., Tosteson, A. (2007). Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J Bone Miner Res 22, 465–75. Google Scholar Medline ISI |
| Cawthorn, W. P., Scheller, E. L., Learman, B. S., Parlee, S. D., Simon, B. R., Mori, H., Ning, X.. (2014). Bone marrow adipose tissue is an endocrine organ that contributes to increased circulating adiponectin during caloric restriction. Cell Metab 20, 368–75. Google Scholar Medline ISI |
| Chain, A., Crivelli, M., Faerstein, E., Bezerra, F. F. (2017). Association between fat mass and bone mineral density among Brazilian women differs by menopausal status: The Pró-Saúde Study. Nutrition 33, 14–19. Google Scholar Medline ISI |
| Chen, C. T., Shih, Y. R., Kuo, T. K., Lee, O. K., Wei, Y. H. (2008). Coordinated changes of mitochondrial biogenesis and antioxidant enzymes during osteogenic differentiation of human mesenchymal stem cells. Stem Cells (Dayton, OH) 26, 960–68. Google Scholar Medline ISI |
| Chen, Y., Xiang, J., Wang, Z., Xiao, Y., Zhang, D., Chen, X., Li, H., Liu, M., Zhang, Q. (2015). Associations of bone mineral density with lean mass, fat mass, and dietary patterns in postmenopausal Chinese women: A 2-year prospective study. PLoS One 10, e0137097. Google Scholar Medline ISI |
| Clarke, B. L., Khosla, S. (2010). Physiology of bone loss. Radiol Clin North Am 48, 483–95. Google Scholar Medline ISI |
| Compston, J. (2015). Obesity and fractures in postmenopausal women. Curr Opin Rheumatol 27, 414–19. Google Scholar Medline ISI |
| de Araujo, I. M., Salmon, C. E., Nahas, A. K., Nogueira-Barbosa, M. H., Elias, J., de Paula, F. J. (2017). Marrow adipose tissue spectrum in obesity and type 2 diabetes mellitus. Eur J Endocrinol 176, 21–30. Google Scholar Medline ISI |
| de Paula, F. J., de Araújo, I. M., Carvalho, A. L., Elias, J., Salmon, C. E., Nogueira-Barbosa, M. H. (2015). The relationship of fat distribution and insulin resistance with lumbar spine bone mass in women. PLoS One 10, e0129764. Google Scholar Medline ISI |
| Diaz-Ruiz, R., Rigoulet, M., Devin, A. (2011). The Warburg and Crabtree effects: On the origin of cancer cell energy metabolism and of yeast glucose repression. Biochim Biophys Acta 1807, 568–76. Google Scholar Medline ISI |
| Esen, E., Chen, J., Karner, C. M., Okunade, A. L., Patterson, B. W., Long, F. (2013). WNT-LRP5 signaling induces Warburg effect through mTORC2 activation during osteoblast differentiation. Cell Metab 17, 745–55. Google Scholar Medline ISI |
| Esen, E., Lee, S. Y., Wice, B. M., Long, F. (2015). PTH promotes bone anabolism by stimulating aerobic glycolysis via IGF signaling. J Bone Miner Res 30, 2137. Google Scholar Medline ISI |
| Fazeli, P. K., Bredella, M. A., Freedman, L., Thomas, B. J., Breggia, A., Meenaghan, E., Rosen, C. J., Klibanski, A. (2012). Marrow fat and preadipocyte factor-1 levels decrease with recovery in women with anorexia nervosa. J Bone Miner Res 27, 1864–71. Google Scholar Medline ISI |
| Febbraio, M. A., Pedersen, B. K. (2002). Muscle-derived interleukin-6: Mechanisms for activation and possible biological roles. FASEB J 16, 1335–47. Google Scholar Medline ISI |
| Ferron, M., Wei, J., Yoshizawa, T., Del Fattore, A., DePinho, R. A., Teti, A., Ducy, P., Karsenty, G. (2010). Insulin signaling in osteoblasts integrates bone remodeling and energy metabolism. Cell 142, 296–308. Google Scholar Medline ISI |
| Frey, J. L., Li, Z., Ellis, J. M., Zhang, Q., Farber, C. R., Aja, S., Wolfgang, M. J., Clemens, T. L., Riddle, R. C. (2015). Wnt-Lrp5 signaling regulates fatty acid metabolism in the osteoblast. Mol Cell Biol 35, 1979–91. Google Scholar Medline ISI |
| Guntur, A. R., Le, P. T., Farber, C. R., Rosen, C. J. (2014). Bioenergetics during calvarial osteoblast differentiation reflect strain differences in bone mass. Endocrinology 155, 1589–95. Google Scholar Medline ISI |
| Ho, S. C., Wu, S., Chan, S. G., Sham, A. (2010). Menopausal transition and changes of body composition: A prospective study in Chinese perimenopausal women. Int J Obes (Lond) 34, 1265–74. Google Scholar Medline ISI |
| Ho-Pham, L. T., Nguyen, N. D., Lai, T. Q., Nguyen, T. V. (2010). Contributions of lean mass and fat mass to bone mineral density: A study in postmenopausal women. BMC Musculoskelet Disord 11, 59. Google Scholar Medline ISI |
| Imai, S., Armstrong, C. M., Kaeberlein, M., Guarente, L. (2000). Transcriptional silencing and longevity protein Sir2 is an NAD-dependent histone deacetylase. Nature 403, 795–800. Google Scholar Medline ISI |
| Indo, Y., Takeshita, S., Ishii, K. A., Hoshii, T., Aburatani, H., Hirao, A., Ikeda, K. (2013). Metabolic regulation of osteoclast differentiation and function. J Bone Miner Res 28, 2392–99. Google Scholar Medline ISI |
| Jahn, K., Kelkar, S., Zhao, H., Xie, Y., Tiede-Lewis, L. M., Dusevich, V., Dallas, S. L., Bonewald, L. F. (2017). Osteocytes acidify their microenvironment in response to PTHrP in vitro and in lactating mice in vivo. J Bone Miner Res 32, 1761–72. Google Scholar Medline ISI |
| Kanis, J. A., McCloskey, E. V., Johansson, H., Cooper, C., Rizzoli, R., Reginster, J. Y., and (IOF), S. A. B. o. t. E. S. f. C. a. E. A. o. O. a. O. E. a. t. C. o. S. A. o. t. I. O. F (2013). European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int 24, 23–57. Google Scholar Medline ISI |
| Karner, C. M., Esen, E., Okunade, A. L., Patterson, B. W., Long, F. (2015). Increased glutamine catabolism mediates bone anabolism in response to WNT signaling. J Clin Invest 125, 551–62. Google Scholar Medline ISI |
| Komarova, S. V., Ataullakhanov, F. I., Globus, R. K. (2000). Bioenergetics and mitochondrial transmembrane potential during differentiation of cultured osteoblasts. Am J Physiol Cell Physiol 279, C1220–29. Google Scholar Medline ISI |
| Lee, N. J., Nguyen, A. D., Enriquez, R. F., Luzuriaga, J., Bensellam, M., Laybutt, R., Baldock, P. A., Herzog, H. (2015). NPY signalling in early osteoblasts controls glucose homeostasis. Mol Metab 4, 164–74. Google Scholar Medline ISI |
| Lee, N. K., Sowa, H., Hinoi, E., Ferron, M., Ahn, J. D., Confavreux, C., Dacquin, R.. (2007). Endocrine regulation of energy metabolism by the skeleton. Cell 130, 456–69. Google Scholar Medline ISI |
| Lemma, S., Sboarina, M., Porporato, P. E., Zini, N., Sonveaux, P., Di Pompo, G., Baldini, N., Avnet, S. (2016). Energy metabolism in osteoclast formation and activity. Int J Biochem Cell Biol 79, 168–80. Google Scholar Medline ISI |
| Lundberg, P., Allison, S. J., Lee, N. J., Baldock, P. A., Brouard, N., Rost, S., Enriquez, R. F.. (2007). Greater bone formation of Y2 knockout mice is associated with increased osteoprogenitor numbers and altered Y1 receptor expression. J Biol Chem 282, 19082–91. Google Scholar Medline ISI |
| McCabe, L. R. (2007). Understanding the pathology and mechanisms of type I diabetic bone loss. J Cell Biochem 102, 1343–57. Google Scholar Medline ISI |
| Mills, K. F., Yoshida, S., Stein, L. R., Grozio, A., Kubota, S., Sasaki, Y., Redpath, P.. (2016). Long-term administration of nicotinamide mononucleotide mitigates age-associated physiological decline in mice. Cell Metab 24, 795–806. Google Scholar Medline ISI |
| Motyl, K. J., DeMambro, V. E., Barlow, D., Olshan, D., Nagano, K., Baron, R., Rosen, C. J., Houseknecht, K. L. (2015). Propranolol attenuates risperidone-induced trabecular bone loss in female mice. Endocrinology 156, 2374–83. Google Scholar Medline ISI |
| Motyl, K. J., McCabe, L. R., Schwartz, A. V. (2010). Bone and glucose metabolism: A two-way street. Arch Biochem Biophys 503, 2–10. Google Scholar Medline ISI |
| Neuman, W. F., Neuman, M. W., Brommage, R. (1978). Aerobic glycolysis in bone: Lactate production and gradients in calvaria. Am J Physiol 234, C41–50. Google Scholar Medline ISI |
| Patsch, J. M., Li, X., Baum, T., Yap, S. P., Karampinos, D. C., Schwartz, A. V., Link, T. M. (2013). Bone marrow fat composition as a novel imaging biomarker in postmenopausal women with prevalent fragility fractures. J Bone Miner Res 28, 1721–28. Google Scholar Medline ISI |
| Premaor, M. O., Pilbrow, L., Tonkin, C., Parker, R. A., Compston, J. (2010). Obesity and fractures in postmenopausal women. J Bone Miner Res 25, 292–97. Google Scholar Medline ISI |
| Reid, I. R. (2010). Fat and bone. Arch Biochem Biophys 503, 20–27. Google Scholar Medline ISI |
| Rendina-Ruedy, E., Guntur, A. R., Rosen, C. J. (2017). Intracellular lipid droplets support osteoblast function. Adipocyte, 1–9. Google Scholar Medline ISI |
| Rodan, G. A. (1992). Introduction to bone biology. Bone 13, S3–6. Google Scholar Medline ISI |
| Rodriguez-Carballo, E., Gamez, B., Mendez-Lucas, A., Sanchez-Freutrie, M., Zorzano, A., Bartrons, R., Alcantara, S., Perales, J. C., Ventura, F. (2015). p38alpha function in osteoblasts influences adipose tissue homeostasis. FASEB J 29, 1414–25. Google Scholar Medline ISI |
| Roh, E., Song, D. K., Kim, M. S. (2016). Emerging role of the brain in the homeostatic regulation of energy and glucose metabolism. Exp Mol Med 48, e216. Google Scholar Medline ISI |
| Routh, V. H., Hao, L., Santiago, A. M., Sheng, Z., Zhou, C. (2014). Hypothalamic glucose sensing: Making ends meet. Front Syst Neurosci 8, 236. Google Scholar Medline |
| Russell, M., Mendes, N., Miller, K. K., Rosen, C. J., Lee, H., Klibanski, A., Misra, M. (2010). Visceral fat is a negative predictor of bone density measures in obese adolescent girls. J Clin Endocrinol Metab 95, 1247–55. Google Scholar Medline ISI |
| Scheller, E. L., Doucette, C. R., Learman, B. S., Cawthorn, W. P., Khandaker, S., Schell, B., Wu, B.. (2015). Region-specific variation in the properties of skeletal adipocytes reveals regulated and constitutive marrow adipose tissues. Nat Commun 6, 7808. Google Scholar Medline ISI |
| Shum, L. C., White, N. S., Mills, B. N., Bentley, K. L., Eliseev, R. A. (2016). Energy metabolism in mesenchymal stem cells during osteogenic differentiation. Stem Cells Dev 25, 114–22. Google Scholar Medline ISI |
| Slade, J. M., Coe, L. M., Meyer, R. A., McCabe, L. R. (2012). Human bone marrow adiposity is linked with serum lipid levels not T1-diabetes. J Diabetes Complications 26, 1–9. Google Scholar Medline ISI |
| Sornay-Rendu, E., Karras-Guillibert, C., Munoz, F., Claustrat, B., Chapurlat, R. D. (2012). Age determines longitudinal changes in body composition better than menopausal and bone status: The OFELY study. J Bone Miner Res 27, 628–36. Google Scholar Medline ISI |
| Starup-Linde, J., Lykkeboe, S., Gregersen, S., Hauge, E. M., Langdahl, B. L., Handberg, A., Vestergaard, P. (2016). Bone structure and predictors of fracture in type 1 and type 2 diabetes. J Clin Endocrinol Metab 101, 928–36. Google Scholar Medline ISI |
| Vestergaard, P. (2007). Discrepancies in bone mineral density and fracture risk in patients with type 1 and type 2 diabetes—A meta-analysis. Osteoporos Int 18, 427–44. Google Scholar Medline ISI |
| Wei, J., Karsenty, G. (2015). An overview of the metabolic functions of osteocalcin. Curr Osteoporos Rep 13, 180–85. Google Scholar Medline ISI |
| Weivoda, M. M., Youssef, S. J., Oursler, M. J. (2017). Sclerostin expression and functions beyond the osteocyte. Bone 96, 45–50. Google Scholar Medline ISI |
| Xiao, Y., Zijl, S., Wang, L., de Groot, D. C., van Tol, M. J., Lankester, A. C., Borst, J. (2015). Identification of the common origins of osteoclasts, macrophages, and dendritic cells in human hematopoiesis. Stem Cell Rep 4, 984–94. Google Scholar Medline ISI |
| Yeung, D. K., Griffith, J. F., Antonio, G. E., Lee, F. K., Woo, J., Leung, P. C. (2005). Osteoporosis is associated with increased marrow fat content and decreased marrow fat unsaturation: A proton MR spectroscopy study. J Magn Reson Imaging 22, 279–85. Google Scholar Medline ISI |
| Yoshikawa, Y., Kode, A., Xu, L., Mosialou, I., Silva, B. C., Ferron, M., Clemens, T. L., Economides, A. N., Kousteni, S. (2011). Genetic evidence points to an osteocalcin-independent influence of osteoblasts on energy metabolism. J Bone Miner Res 26, 2012–25. Google Scholar Medline ISI |
| Yu, E. W., Greenblatt, L., Eajazi, A., Torriani, M., Bredella, M. A. (2017). Marrow adipose tissue composition in adults with morbid obesity. Bone 97, 38–42. Google Scholar Medline ISI |
| Zhang, H., Chai, X., Li, S., Zhang, Z., Yuan, L., Xie, H., Zhou, H.. (2013). Age-related changes in body composition and their relationship with bone mineral density decreasing rates in central south Chinese postmenopausal women. Endocrine 43, 643–50. Google Scholar Medline ISI |
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Continuous flow of love without any break even for a second is 'avicchinna-anuragam.
That is the reason why Narada Rishi says: It cannot be used to fill any desire, itself being the check to all desires. There are those, the worldly-minded, who kill the Self in them.
Just as is observed in the case of a royal palace, food and other things. Chapter 6 verses explains the importance of seeking holy company. The devotee gets parama-vyakulata when he forgets his Beloved. When lust manifests, the intellect becomes blind and the most intelligent man becomes an easy stra to passion.
It must be constant. So the term 'sukshmataram' xutra used. Musk, saffron, radium, sandalwood, learned persons, virtuous persons, heroes, philanthropists, etc. But Bhakti is Suddha Prem. He is worse than a prisoner.
narada-bhakti-sutras
Commenting on Sutra 5, Srila Prabhupada compared enthusiasm to a powerful engine that has to be used properly. He never believes in devotion, concentration and meditation. He ran with ghee in a cup to the dog. The three Gunas are the waves.
The full name of Aaruni is Uddalaki Aruni. That is the reason why the Gita II says: But Devarshi Narada then is of opinion, however, that the sign of devotion is surrendering all actions to God and feeling severe grief in forgetting Him.
Narada Bhakti Sutra
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Lord Krishna gives advice to Uddhava and prescribes an easy way for reaching Him. Various sorts of evils are propagated. When a men gets this love, he sees love everywhere he hears love everywhere, he talks love everywhere, he thinks love everywhere. He has passed his I. Notes and Commentary 'Anyonya-asraya' is mutual dependence.
Nowadays people want to lead a life of ease and expect the grace of Mahatmas. Emotion is not devotion. Sutra means an aphorism or terse saying impregnated with deep significance. Sattva, Rajas, and Tamas bhatki according to the distinction of the aspirants the suffering, the seeker for knowledge and the self-interested.
The devotee will have to be very vigilant and cautious. It is Hari who is playing the part of prostitute, thief, dacoit, scavenger'. Abandoning attachment to sensual objects means not allowing the mind to dwell bhakkti memories of those objects.
But, eating, drinking, dressing, etc. This is their horrible mistake. And among all Nqrada, he who, full of faith, with the inner Self abiding in Me, adoreth Me, he is considered by Me to be the most completely harmonised.
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He who renounces the fruits of his actions, and who renounces, goes beyond the pairs of opposites such as heat and cold, pleasure and pain.