Therapeutic potential of Wnt/β-catenin signaling pathway in osteoporosis: from molecular pathways to future clinical challenges

Strategies for the prevention and treatment of osteoporosis during early postmenopause

Osteoporosis in Men Treated With Androgen Deprivation Therapy for Prostate Cancer

Osteoporosis After Orchiectomy for Prostate Cancer

Bisphosphonates are the current mainstay of treatment for postmenopausal osteoporosis. Although daily oral dosing is effective, it is associated with poor compliance, partly because of the pre and postdose fasting and posture requirements. This negatively impacts treatment outcomes, leading to a reduced clinical benefit. Improved, yet still suboptimal adherence has been noticed with less frequent bisphosphonate dosing e.g. once-weekly and once-monthly oral regimens. The recently approved quarterly intravenous (i.v.) injection regimen of ibandronate and yearly i.v. infusion of zoledronic acid are attractive options in the management of postmenopausal osteoporosis. These regimens may assure quarterly and year long compliance.

Bone remodeling is a continuous process that maintains the homeostasis of the skeletal system, and it depends on the homeostasis between bone-forming osteoblasts and bone-absorbing osteoclasts. A large number of studies have confirmed that the Smad signaling pathway is essential for the regulation of osteoblastic and osteoclastic differentiation during skeletal development, bone formation and bone homeostasis, suggesting a close relationship between Smad signaling and bone remodeling. It is known that Smads proteins are pivotal intracellular effectors for the members of the transforming growth factor-β (TGF-β) and bone morphogenetic proteins (BMP), acting as transcription factors. Smad mediates the signal transduction in TGF-β and BMP signaling pathway that affects both osteoblast and osteoclast functions, and therefore plays a critical role in the regulation of bone remodeling. Increasing studies have demonstrated that a number of Smad signaling regulators have potential functions in bone remodeling. Therefore, targeting Smad dependent TGF-β and BMP signaling pathway might be a novel and promising therapeutic strategy against osteoporosis. This article aims to review recent advances in this field, summarizing the influence of Smad on osteoblast and osteoclast function, together with Smad signaling regulators in bone remodeling. This will facilitate the understanding of Smad signaling pathway in bone biology and shed new light on the modulation and potential treatment for osteoporosis.

Programmed death-1 (PD-1) and its ligand PD-L1 inhibitors have become pivotal agents in cancer immunotherapy, demonstrating significant efficacy across multiple malignancies. However, beyond regulating T cell activation, the PD-1/PD-L1 axis also exerts complex and critical effects on bone metabolism. Notably, both clinical observations and mechanistic studies have revealed a paradox: on one hand, PD-1/PD-L1 blockade appears to confer bone-protective benefits; on the other hand, it has been associated with bone-related adverse events (AEs) in up to 69% of patients, including pathological fractures and vertebral compression fractures. This review comprehensively explores the bidirectional regulatory effects of the PD-1/PD-L1 pathway on bone metabolism and investigates the underlying mechanisms contributing to these contradictory findings. The discrepancies may be attributed to a combination of clinical variables, microenvironmental conditions, cell-specific responses, and intricate interactions among multiple signaling pathways, including the Wnt/β-Catenin pathway and the PD-L1-PKM2 axis. We further examine the pathophysiological basis of osteoporosis and fragility fractures occurring during PD-1/PD-L1 inhibitor therapy, and argue for their recognition as a subclass of immune-related adverse events (irAEs). Finally, we propose a framework for bone health surveillance and stratified prevention strategies aimed at preserving antitumor efficacy while improving skeletal health and quality of life-offering novel insights into osteoporosis prevention and management in the context of immune checkpoint inhibition.

The goals of this multiauthored book, the first published on the subject, were “to summarize the current state of the art and to identify directions for needed research” and importantly “to examine bone biology and osteoporosis in men in light of how they differ from similar events in women.” The field of bone biology, at the bedside but particularly in the laboratory, has grown tremendously in the last decade, and there is a clear need for a reference source. The editor assembled a panel of experts to expound on a variety of topics, and the book in some respects succeeded in achieving its goals. However, the emphasis of the book is very clinical. It devoted too many clinical chapters to epidemiology, risk factors, and descriptive measurements of bone mass and morphology (which nonetheless are very important) and too few to basic bone biology. Further, a number of chapters were somewhat repetitious and covered similar material such as those on age-related changes in bone remodeling and trabecular architecture and aging and risk factors for low bone mass and risk factors for fractures. It is surprising that a book on bone disease in this day and age would not have one or more introductory chapters on basic bone cell biology that would include descriptions of and methods for evaluating osteoblast and osteoclast function and the microenvironment of the bone marrow, on animal models of osteoporosis including gene knockouts, and on the latest use of techniques of cell and molecular biology to investigate pathogenesis that are being used and how they can be used to expand knowledge in the field. Especially, there is no mention of the Human Genome Project that will shortly make available the structure of all human genes and how this information might be used to make discoveries about the pathogenesis and possibly new treatments for different kinds of osteoporosis. In the 21st century, molecular medicine will no doubt become the standard of care and include new methods for identifying patients at risk for developing bone disease. The goal of investigators will be to discern the role of proteins produced by newly described genes in normal and disease states. Another major omission was the lack of discussion or even mention of the recently discovered osteoclastogenesis stimulating factor/osteoprotegerin ligand and osteoclast inhibitory factor/osteoprotegerin system that has revolutionized our understanding of regulation of bone resorption and has provided a new rationale for developing new and possibly more effective treatments for osteoporosis, Paget's disease, and other diseases. This is especially important in view of the central role of bone resorption in determining bone mass and the rate of age-related bone loss in normal individuals and in individuals with osteoporosis. Despite these criticisms, the chapters that deal with epidemiology, growth, aging, bone mass and morphology, and clinical evaluation are comprehensive and well organized and provide useful comparisons between men and women. There are excellent chapters on exercise, insulin-like growth factors, age-related changes in mineral metabolism, calcium and vitamin D nutrition, androgens, estrogens, idiopathic osteoporosis, glucocorticoids, alcohol, and hypercalciuria. When provided, summaries and directions for future research at the ends of chapters are valuable. In summary, this book provides a useful, well-referenced review of current knowledge of what is known and what is not known relative to osteoporosis in men. If one proposes to use the latest techniques of cell and molecular biology for future investigations, details and description of many of these approaches must be sought elsewhere.

Outcomes associated with hypogonadism in women with chronic kidney disease

Physical cues, such as extracellular matrix stiffness, direct cell differentiation and support tissue-specific function. Perturbation of these cues underlies diverse pathologies, including osteoarthritis, cardiovascular disease and cancer. However, the molecular mechanisms that establish tissue-specific material properties and link them to healthy tissue function are unknown. We show that Runx2, a key lineage-specific transcription factor, regulates the material properties of bone matrix through the same transforming growth factor-β (TGFβ)-responsive pathway that controls osteoblast differentiation. Deregulated TGFβ or Runx2 function compromises the distinctly hard cochlear bone matrix and causes hearing loss, as seen in human cleidocranial dysplasia. In Runx2+/⁻ mice, inhibition of TGFβ signalling rescues both the material properties of the defective matrix, and hearing. This study elucidates the unknown cause of hearing loss in cleidocranial dysplasia, and demonstrates that a molecular pathway controlling cell differentiation also defines material properties of extracellular matrix. Furthermore, our results suggest that the careful regulation of these properties is essential for healthy tissue function.

Sclerostin: A gem from the genome leads to bone-building antibodies

PURPOSE: This study examined the effects of different types of exercise on bone metabolism and bone mineral density (BMD) in elderly individuals. Additionally, we sought to comprehensively analyze the physiological mechanisms underlying the prevention and management of osteoporosis through exercise.METHODS: This review included previous publications found in PubMed, Google Scholar, and Science Direct databases.RESULTS: Aerobic exercise was found to stimulate osteoblast activity and enhance bone formation by activating the Wnt/β-catenin signaling pathway, suppressing sclerostin expression, reducing pro-inflammatory cytokines, and promoting vitamin D metabolism. In contrast, resistance exercise increased the expression of genes associated with osteoblast differentiation and bone formation by applying a mechanical load to the bone, while simultaneously inhibiting osteoclast-related markers, thereby reducing bone resorption. Moderateintensity exercise showed the most significant improvements in bone metabolism across both types of exercise.CONCLUSIONS: Both aerobic and resistance exercises are effective non-pharmacological interventions for maintaining bone health and preventing osteoporosis in elderly individuals. Combined exercise, incorporating both types of exercise, appears to be more effective in optimizing the balance between bone formation and resorption, leading to improved BMD. In conclusion, aerobic and resistance exercises serve as beneficial nonpharmacological strategies for enhancing bone microarchitecture, maintaining BMD, and improving bone metabolism, thereby contributing positively to bone health in the elderly population.

New and Emerging Therapies for Osteoporosis

Effects of imatinib mesylate in osteoblastogenesis

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Results Discussion Methods Appendix 1 Appendix 2 Appendix 3 Data availability References Decision letter Author response Article and author information Abstract For the treatment of postmenopausal osteoporosis, several drug classes with different mechanisms of action are available. Since only a limited set of dosing regimens and drug combinations can be tested in clinical trials, it is currently unclear whether common medication strategies achieve optimal bone mineral density gains or are outperformed by alternative dosing schemes and combination therapies that have not been explored so far. Here, we develop a mathematical framework of drug interventions for postmenopausal osteoporosis that unifies fundamental mechanisms of bone remodeling and the mechanisms of action of four drug classes: bisphosphonates, parathyroid hormone analogs, sclerostin inhibitors, and receptor activator of NF-κB ligand inhibitors. Using data from several clinical trials, we calibrate and validate the model, demonstrating its predictive capacity for complex medication scenarios, including sequential and parallel drug combinations. Via simulations, we reveal that there is a large potential to improve gains in bone mineral density by exploiting synergistic interactions between different drug classes, without increasing the total amount of drug administered. Editor's evaluation The authors have developed a mathematical framework of drug interventions for postmenopausal osteoporosis using bisphosphonates, parathyroid hormone analogs, romosozumab, and denosumab. After calibrating and validating the model, authors demonstrated a predictive ability for complex clinical scenarios including sequential and parallel drug combinations. These data may be of great help in clinical practice. https://doi.org/10.7554/eLife.76228.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest Our bones are constantly being renewed in a fine-tuned cycle of destruction and formation that helps keep them healthy and strong. However, this process can become imbalanced and lead to osteoporosis, where the bones are weakened and have a high risk of fracturing. This is particularly common post-menopause, with one in three women over the age of 50 experiencing a broken bone due to osteoporosis. There are several drug types available for treating osteoporosis, which work in different ways to strengthen bones. These drugs can be taken individually or combined, meaning that a huge number of drug combinations and treatment strategies are theoretically possible. However, it is not practical to test the effectiveness of all of these options in human trials. This could mean that patients are not getting the maximum potential benefit from the drugs available. Jörg et al. developed a mathematical model to predict how different osteoporosis drugs affect the process of bone renewal in the human body. The model could then simulate the effect of changing the order in which the therapies were taken, which showed that the sequence had a considerable impact on the efficacy of the treatment. This occurs because different drugs can interact with each other, leading to an improved outcome when they work in the right order. These results suggest that people with osteoporosis may benefit from altered treatment schemes without changing the type or amount of medication taken. The model could suggest new treatment combinations that reduce the risk of bone fracture, potentially even developing personalised plans for individual patients based on routine clinical measurements in response to different drugs. Introduction Osteoporosis, a disease characterized by porous bone prone to fractures, affects hundreds of millions of people worldwide (Cooper and Ferrari, 2019; Hernlund et al., 2013). Most recent estimates place the global annual incidence of bone fragility fractures at 9 million in the year 2000 (Cooper and Ferrari, 2019); projections for the year 2050 suggest between 7 and 21 million annual hip fractures (Gullberg et al., 1997). Osteoporosis-associated bone fractures lead to disabilities, pain, and increased mortality (Cooper and Ferrari, 2019). In the United States, medical cost for osteoporosis, including inpatient, outpatient, and long-term care costs, has been estimated at US$17 billion in 2005 (Burge et al., 2007); in the European Union, the total cost of osteoporosis, including pharmacological interventions and loss of quality-adjusted life-years (QALYs), is projected to rise from about €100 billion in 2010 to €120 billion in 2025 (Odén et al., 2013). Osteoporotic bone is the consequence of an imbalance of continuous bone resorption and bone formation, which—under close to homeostatic conditions—has the function to remove microfractures and renew the structural integrity of bone. Postmenopausal women are particularly at risk of osteoporosis: the rapid decline of systemic estrogen levels after menopause and other aging-related effects such as increased oxidative stress contribute to or drive the development of osteoporosis (Riggs et al., 1998; Manolagas, 2010). Moreover, osteoporosis can be a sequela of diseases affecting bone metabolism and remodeling such as primary hyperparathyroidism or gastrointestinal diseases (Painter et al., 2006). Osteoporosis can also be a side effect of treatments for other diseases; as a prime example, glucocorticoid administration is the most common cause of secondary osteoporosis (Weinstein, 2012). Over the last decades, an array of different osteoporosis treatments have emerged, from simple dietary supplementations such as calcium and vitamin D to specialized drugs targeting bone-forming and -resorbing cells and related signaling pathways (Tu et al., 2018). This entails a plethora of different medication options, including a large number of possible dosing schemes and combinations of drugs, administered in sequence or in parallel. Due to the huge number of such treatment schemes and the required time from study inception to completion, very few of them have been clinically tested so far when compared to the total number of available options. Concomitant with the development of new osteoporosis drugs, mathematical and biophysical modeling approaches capturing bone-related physiology have advanced our quantitative understanding of the biological principles governing bone mineral metabolism, bone turnover, and development of osteoporosis. Pioneering work by Lemaire et al., 2004 describes the dynamics of bone-forming and -resorbing cell populations coupled through signaling pathways and could qualitatively reproduce the effects of senescence, glucocorticoid excess, and estrogen and vitamin D deficiency on bone turnover. Since then, compartment-based descriptions of the mineral metabolism, bone-forming and -resorbing cell populations, and related signaling factors have elucidated the role of essential regulatory mechanisms underlying mineral balance and bone turnover (Komarova et al., 2003; Lemaire et al., 2004; Pivonka et al., 2008; Pivonka et al., 2010; Peterson and Riggs, 2010; Zumsande et al., 2011; Schmidt et al., 2011; Graham et al., 2013; Tanaka et al., 2014; Komarova et al., 2015; Berkhout et al., 2015). Coarse-grained as well as detailed spatially extended descriptions of bone geometry have also addressed the effects of mechanical forces and the propagation of the multicellular units responsible for bone turnover (Ryser et al., 2009; Buenzli et al., 2011; Scheiner et al., 2013; Buenzli et al., 2014; Pivonka et al., 2013), as well as the influence of secondary diseases such as multiple myeloma (Ayati et al., 2010). Detailed models of bone remodeling and calcium homeostasis have become versatile and widely used tools in hypothesis testing, such as the seminal model by Peterson and Riggs, 2010, which includes submodels for various organs such as gut, kidney, and the parathyroid gland. Pharmacokinetic and pharmacodynamic (PK/PD) models of therapeutic interventions have mostly focused on capturing the mechanisms of action of a single or a few drugs and testing their dosing regimens (Marathe et al., 2008; Marathe et al., 2011; Ross et al., 2012; Scheiner et al., 2014; Eudy et al., 2015; Lisberg et al., 2017; Martínez-Reina and Pivonka, 2019; Zhang and Mager, 2019). Recent modeling efforts have also started addressing the effects of drug combinations on bone-forming and -resorbing cells, pointing out the need for corresponding model frameworks to include clinically relevant variables like bone mineral density (BMD) and bone turnover biomarkers (BTMs) (Lemaire and Cox, 2019), as well as combination therapies of physical exercise and drug treatment (Lavaill et al., 2020). An integrated mathematical framework for multiple drugs, which can also be used to quantitatively predict the effects of drug combinations in sequence and in parallel is not yet available. Building on established mechanisms of bone turnover, we here present a quantitative model of bone turnover and postmenopausal osteoporosis treatment, unifying the description of multiple classes of drugs with different mechanisms of action, namely, bisphosphonates, parathyroid hormone (PTH) analogs, sclerostin antibodies, and receptor activator of NF-κB ligand (RANKL) antibodies. We calibrate the model using published population-level data from several clinical trials and assess its ability to predict the outcome of previously conducted clinical studies based on the medication scheme alone. We then use the model to demonstrate how medication schemes involving drug combinations can be optimized for a given medication load and discuss future model extensions. Mechanisms of bone turnover and its regulation Our model is based on a small set of key principles of bone turnover, which we briefly recapitulate here (Figure 1). As a composite tissue comprising hydroxyapatite, collagen, other proteins, and water (Boskey, 2013), bone is constantly turned over to renew its integrity and remove microdamage, at an average rate of about 4% per year in cortical bone and about 30% per year in trabecular bone (Manolagas, 2000). Figure 1 Download asset Open asset Schematic of the osteoporosis model describing the cell dynamics and signaling pathways within a 'representative bone remodeling unit (BRU)'. Regulatory interactions between different model components are indicated by colored boxes (see legend). TGFβ, transforming growth factor beta; BMP, bone morphogenetic protein; PDGF, platelet-derived growth factor; IGF, insulin-like growth factor; FGF, fibroblast growth factor. Bone-resorbing and -forming cells Bone resorption is performed by osteoclasts, multinucleated cells formed through the differentiation and fusion of their immediate precursors (pre-osteoclasts), which are derived from pluripotent hematopoietic stem cells via the myeloid lineage (Boyce and Xing, 2008). Osteoclasts attach to bone tissue and resorb it through the secretion of hydrogen ions and bone-degrading enzymes (Fuller and Chambers, 1995), which leads to the release of minerals and signaling factors stored in the bone matrix. New bone is formed by osteoblasts, a cell type derived from mesenchymal stem cells via several intermediate states that give rise to pre-osteoblasts and finally osteoblasts (Eriksen, 2010). Groups of osteoblasts organize into cell clusters (osteons) and collectively lay down an organic matrix (osteoid), which subsequently becomes mineralized over the course of months. Osteoblasts that are enclosed in the newly secreted bone matrix become osteocytes, nondividing cells with an average life span of up to several decades. Osteoclasts and osteoblasts organize into spatially defined local clusters termed 'bone remodeling units' (BRUs) (Figure 1), in which osteoblasts replenish the bone matrix previously resorbed by osteoclasts with a delay of several weeks. In cortical bone, the outer protective bone layer, BRUs migrate as a whole in 'tunnels,' whereas within the inner cancellous bone, BRUs propagate on the surfaces of the trabeculae, renewing the bone matrix in the process (Eriksen, 2010). Signaling pathways The differentiation and activity of osteoclasts and osteoblasts are regulated through several signaling pathways and hormones; recent reviews provide comprehensive descriptions of the various pathways (Siddiqui and Partridge, 2016). Osteoclast formation and activity are prominently regulated by RANKL and macrophage colony-stimulating factor (M-CSF) synthesized by bone marrow stromal cells. RANKL binds to receptor activator of NF-κB (RANK) on osteoclast precursors and promotes their differentiation into mature osteoclasts; osteoprotegerin (OPG) acts as a decoy receptor for RANKL and thus inhibits bone resorption (Boyce and Xing, 2008; Clarke, 2008). When laying down new bone, osteoblasts store signaling factors in the bone matrix, including transforming growth factor beta (TGFβ), bone morphogenetic protein (BMP), insulin growth factors (IGFs), platelet-derived growth factor (PDGF), and fibroblast growth factors (FGFs) (Solheim, 1998). Upon bone resorption, these factors are released and regulate cell fates and activity of osteoblasts and osteoclasts, thereby coupling bone resorption and formation (Houde et al., 2009; Eriksen, 2010). Osteocytes secrete sclerostin, a Wnt inhibitor interfering with extracellular binding of Wnt ligands (Li et al., 2005). Sclerostin inhibits bone formation and promotes resorption via downregulation of osteoblastogenesis and upregulation of osteoclastogenesis (Delgado-Calle et al., 2017; Maré et al., 2020). Since bone also acts as a mineral reservoir for the body, regulators of calcium homeostasis such as PTH and vitamin D also strongly affect the balance of bone formation and resorption alongside the intestinal absorption and renal reabsorption of calcium (Mundy and Guise, 1999). Estrogen The sex hormone estrogen inhibits bone resorption by inducing apoptosis of osteoclasts (Kameda et al., 1997) and lowering circulating sclerostin levels (Mödder et al., 2011). The rapid decline of estrogen levels after menopause is one known cause of postmenopausal osteoporosis (Riggs et al., 1998). Results Model overview The primary purpose of our model is to provide an efficient representation of bone turnover on multiple time scales from weeks to decades that allows for the quantitative description of drug interventions. Of particular interest are the consequences of pharmacological therapies on long-term dynamics of the BMD in specific bone sites and biochemical markers of bone formation and resorption. To this end, we considered a minimal set of physiologically relevant dynamic components (Figure 1) that are sufficient to capture a large range of clinically observed population-level data on drug interventions. Thus, our model describes a 'representative BRU' that abstracts from the vast set of intricate regulatory mechanisms underlying calcium homeostasis or the complex bone geometry. Our model comprises the following dynamic components to describe the bone turnover through a representative BRU: cell densities of (i) pre-osteoclasts, (ii) osteoclasts, (iii) pre-osteoblasts, (iv) osteoblasts, (v) osteocytes, (vi) sclerostin concentration, (vii) total bone density, and (viii) bone mineral content (BMC). The BMD is given by the product of bone density and BMC. Osteoblasts and osteoclasts can undergo apoptosis and are derived from pre-osteoblasts and pre-osteoclasts, respectively, with differentiation rates that depend on regulatory factors such as estrogen and sclerostin (Figure 1). Pre-osteoblasts and pre-osteoclasts are formed at constant rates and undergo apoptosis. These progenitor populations provide a dynamic reservoir for rapid differentiation and activation of osteoblasts and osteoclasts, respectively, which can be temporarily depleted if stimulated by a drug intervention. Osteocytes are derived from osteoblasts and provide a source of sclerostin, which has a regulatory effect on osteoblasts, osteoclasts, and thus, bone density change. The gain and loss rates of bone density are proportional to the density of osteoblasts and osteoclasts, respectively. The BMC has a steady state whose level can be temporarily shifted through drug administration, effectively accounting for more complex underlying dynamics such as promotion of secondary mineralization. All rates of cell formation, differentiation, apoptosis, and bone formation and resorption generally depend on the concentration of sclerostin, estrogen, and a 'resorption signal.' These dependencies also implicitly account for regulation of bone remodeling via other routes, for example, the RANK–RANKL–OPG pathway. The effects of aging and the onset of menopause are represented through an age-dependent serum estrogen concentration, which has been determined from the literature (Sowers et al., 2008; Appendix 1). The resorption signal corresponds to the melange of signaling factors stored in the bone matrix. Therefore, its release is proportional to the rate of bone resorption. The serum concentration of BTMs such as the resorption marker C-terminal telopeptide (CTX), the formation markers procollagen type 1 amino-terminal propeptide (P1NP), and bone-specific alkaline phosphatase (BSAP) were identified with elementary functions of the bone resorption and formation rates in the model (Appendix 1). We extended this core model of long-term bone turnover by a dynamic description of the mechanisms of action of several drug classes used in osteoporosis treatment: RANKL antibodies (denosumab), sclerostin antibodies (romosozumab), bisphosphonates (alendronate and others), and PTH analogs (teriparatide) (Appendix 2). We also included blosozumab, another sclerostin inhibitor, which was investigated in osteoporosis trials but not approved for osteoporosis treatment at the time the present work was conducted. PTH is known to exert anabolic or catabolic effects depending on whether administration is intermittent or continuous (Tam et al., 1982; Hock and Gera, 1992); PTH description in our model is restricted to the anabolic administration regimes relevant for osteoporosis treatment. A schematic overview of all model components, mechanisms, and regulatory interactions is given in Figure 1; a detailed formal description of the model and its extensions is provided in Appendix 1 and Appendix 2. Capturing clinical study results with the model The model and the corresponding medication modules rely on an array of physiological parameters (rates of cell formation, differentiation and death, concentration thresholds for signaling activity, medication efficacies and half-lives, etc.) many of which are not directly measureable. However, clinical measurements on physiological responses to medications with different mechanisms of action provide a wealth of indirect information about time scales of bone turnover and regulatory feedbacks. We calibrated the model using published clinical data from various seminal studies on both (i) long-term BMD age dependence and (ii) the response of BMD and BTMs to the administration of different drugs (see Appendix 3—table 2 for a comprehensive list of data sources). Although BMD constitutes the major target variable of our model, the dynamics of BTM concentrations carry important complementary information about the mode of action of the administered drugs (antiresorptive, anabolic, and combinations) that crucially informs the calibration procedure. To allow the model to capture the effects of medications as physiologically sensible modulations of the age-dependent bone mineral metabolism, we created hybrid datasets each of which comprised both aging-related BMD changes and the response to a treatment (see 'Methods' and Appendix 1—figure 1D). We then determined a single set of model parameters through a simultaneous fit of the free 31 model parameters to capture a specified set of hybrid aging/treatment datasets containing different drug responses (Appendix 3). Without constraining the average rate of skeletal bone turnover, model calibration yielded an inferred value of about 6% per year on average, of the same order of for cortical bone, which constitutes about of the (Manolagas, 2000). The model was to capture the BMD and BTM dynamics all calibration datasets with (Appendix 1), the structural To the of the we the mean between model and clinical the for BMD was for all calibration datasets (Appendix 3—table an between model and The of BTMs the of their from was in all calibration an description of the mode of action in the in the total of BTM observed for datasets were mostly due to in the of and and of the BTM as by different (Appendix 3 and Appendix 3—table 3). After the we to validate the calibrated model by its ability to predict the effects of drug dosing schemes that had not been used for Model included complex sequential and parallel drug combinations and the model to predict the effects of treatment schemes used in calibration (Appendix 3—table 2). To this end, the model only drug dosing information used in the clinical trials but was not by BMD or BTM which it had to the single set of previously determined the model showed a capacity to quantitatively the effects of a of medication both treatment and (Figure Figure 1). in scenarios including sequential treatments with up to three different drug types and parallel treatments with different drugs, respectively, the model was to predict the complex of both BMD and levels with a high of (Figure 2). all for BMD were (Appendix 3—table an predictive capacity of the In this provided a of the capacity to capture the physiological dynamics of bone turnover and the mechanisms of action of various drugs relevant to osteoporosis treatment using a single set of model Figure 2 with 1 all Download asset Open asset a single set of the calibrated model can quantitatively predict the effects of various drugs in different dosing and in of total hip bone mineral density and clinical data including aging and treatment of various sequential drug including romosozumab, and aging/treatment datasets were created data from et al., in in total and as well as et al., et al., and et al., in A and as into the treatment in including BMD and changes of the bone resorption marker C-terminal telopeptide and the bone formation marker procollagen type 1 amino-terminal propeptide the the medication scheme (see legend). Data average types and types as in the In both BMD is as a of its value at alternative treatment schemes established the predictive capacity of the model for the considered we to the model to study and drug dosing As an example, we considered a sequential treatment with three drugs of different the the sclerostin inhibitor romosozumab, and the RANKL inhibitor denosumab. In a clinical by et al., the sequence per for 1 by per for 1 by per for 1 had been (Figure 2). However, in there are different in which these drugs can be and A it is not whether synergistic or interactions between these drugs and the physiological state in which they the may lead to a and long-term of BMD and biomarkers between different medication all in a clinical present a and and of the study to treatment we these different treatment options using the present model (Figure To assess the clinical of different we compared clinically relevant different (i) the maximum BMD compared to at treatment of when it (Figure and (ii) the long-term effects of treatment on BMD as by the BMD after treatment (Figure Figure 3 Download asset Open asset The model for different of the same drugs at constant total medication of bone mineral density (BMD) and C-terminal telopeptide and procollagen type 1 amino-terminal propeptide concentrations for different of the three drugs and as treatment at age The total amount of drug administered is results on the sequence were in et al., also Figure 2. BMD to at treatment the course of treatment for different drug BMD after treatment to at treatment for different drug we that the of different medication were different the same total amount of drug administered (Figure as and a maximum BMD the course of the treatment, which to treatments to maximum BMD gain (Figure were to as by the maximum BMD treatment, they performed compared and with to long-term BMD as by model (Figure This that BMD gains may be limited as a for the clinical benefit of a treatment as a our modeling the for this is in effects after treatment of osteoclastogenesis leads to a of an osteoclast After treatment end, this becomes to and rise to a large osteoclast leading to resorption of the bone matrix that had been up treatment. In this specific drug lead to an of this for example, by osteoclast apoptosis such a thereby bone turnover in the In our model considerable potential in the of dosing regimens and drug in osteoporosis treatment, combination to achieve an optimal effect for a given medication These are possible because the mechanisms of action of one drug may or on the state of the bone mineral metabolism by the treatment with another Discussion of osteoporosis is and in many bone physiology as our we have a mathematical modeling framework that can quantitatively capture and predict the of osteoporosis in postmenopausal women with and without medical Our model is on a small set of essential mechanisms of bone turnover. The of this the of the bone mineral dynamics relevant for osteoporosis medications can be into only a few These components describe the of osteoblasts, osteoclasts, and osteocytes, as well as their cell populations and a few essential regulatory through and signaling factors such a

Sex specific differences in the determinants, occurrence, and distribution of osteoporosis and osteoporotic fractures play a pivotal role in the implementation of timely surveillance, prevention and effective treatment approaches. This review is aimed at synthesizing recently published scientific evidence on disparities in the epidemiology and management of osteoporosis and related fragility fractures in men. Several studies have identified race-, sex-, geographic-, socioeconomic-, and comorbidity-based disparities in osteoporosis care. Over the last decade, the awareness and imperative for identifying osteoporosis in men have increased. Nonetheless, the treatment gap is increasing and osteoporosis in men remains severely underappreciated and undertreated. Fewer studies in men have focused on the factors beyond osteoporosis awareness. Recent data of individuals aged 50 years and older show an alarmingly greater increase in hip fractures in men compared to women. This coupled with the existing knowledge of greater disability burden and excess mortality due to fragility fractures in men is a cause of public health concern. This review offers a comprehensive examination of widespread and profound disparities across the spectrum of osteoporosis care and post-fracture management in men and highlights the considerable health economic aspect of this burden. We call for targeted multifaceted interventions: develop novel methods to engage patients and health professionals, increase screening and treatment of osteoporosis in men, conduct epidemiological studies focused on disease phenotyping and risk factor assessment, studies on the identification of perceptions and barriers to effective screening and treatment, expansion and further evaluation of cost-effective therapies and primary prevention strategies for fractures, implementation of fracture liaison services to address the treatment gap in secondary prevention, and promote inclusivity in outcome studies and therapeutic trials. These interventions are paramount to reduce inequities in osteoporosis care and post-fracture care in men.