1. Introduction
Osteoporosis is a prevalent skeletal disorder that exerts a profound impact on specific populations, especially postmenopausal women, the elderly, and individuals with certain lifestyles or medical conditions. Characterized by reduced bone density, degeneration of bone tissue, and disruption of the bone's microstructure, this disease leads to increased bone fragility and a heightened risk of fractures. The global prevalence of osteoporosis is substantial, affecting millions of people. Approximately 10 million people over the age of 50 in the United States suffer from the disease. In the United Kingdom, about half of women and one-fifth of men over the age of 50 are likely to experience osteoporotic fractures. The annual economic cost of fractures caused by osteoporosis is substantial, reaching approximately $17.9 billion and £4 billion annually in the United States and the United Kingdom, representing a considerable public health challenge, especially given an aging population [1].
In recent decades, the incidence of osteoporosis has been on the rise, mainly due to the increasing proportion of elderly individuals in the population. This trend is expected to continue in the future, placing a growing burden on healthcare systems worldwide. Osteoporosis is not only a medical concern but also has far-reaching social and economic implications. Fractures resulting from osteoporosis can cause severe pain, disability, and a significant decline in the quality of life for patients. They often require long-term medical care, rehabilitation, and, in some cases, hospitalization, leading to a heavy economic burden on families and society.
Moreover, osteoporosis is often a silent disease in its early stages, with no obvious symptoms until a fracture occurs. This makes early detection and prevention crucial. Understanding the causes, risk factors, and effective treatment methods of osteoporosis is essential for healthcare professionals, patients, and the public. It is the key to reducing the occurrence of osteoporosis-related fractures and improving the overall health and well-being of at-risk populations.
2. Pathogenesis of bone resorption
Bone resorption stands as a pivotal mechanism in the progression of osteoporosis. The cells termed osteoclasts, which are tasked with the degradation of bone tissue, are instrumental in this physiological process. Normally, bone remodeling involves balanced osteoclast and osteoblast activity to maintain bone homeostasis. However, osteoporosis disrupts this balance, leading to excessive osteoclast-mediated resorption [2].
The RANKL/RANK/OPG signaling axis plays a pivotal role in regulating osteoclast differentiation and bone resorption processes. Osteoblasts and various other cell types secrete RANKL, which interacts with the RANK receptor on osteoclast precursors, thereby facilitating their maturation into functional osteoclasts. Osteoprotegerin (OPG), functioning as a decoy receptor, sequesters RANKL and impedes its engagement with RANK, consequently dampening osteoclastogenesis and its resorptive activity. In the context of osteoporosis, the equilibrium between RANKL and OPG is perturbed, leading to an upsurge in RANKL-driven osteoclast function and augmented bone resorption [1].
Bone resorption is significantly impacted by the production of cytokines and inflammatory compounds. Pro-inflammatory cytokines, such as TNF-α, IL-1, and IL-6, can stimulate osteoclast activity and intensify the bone resorption process. These cytokines are typically elevated in conditions associated with osteoporosis, such as rheumatoid arthritis and chronic inflammatory diseases [3]. Additionally, oxidative stress contributes to bone resorption by not only fostering the activation of osteoclasts but also by diminishing the function of osteoblasts.
3. Prevalence of osteoporosis in specific groups
Osteoporosis is a common skeletal disorder that has a profound impact on specific populations, particularly postmenopausal women and the elderly.
3.1. Osteoporosis in women
The likelihood of developing osteoporosis escalates markedly in women following menopause, as the decline in estrogen levels post-menopausal phase accelerates. Estrogen is pivotal in sustaining bone integrity by suppressing bone resorption. In postmenopausal women, the decline in estrogen levels leads to an imbalance in bone remodeling, where bone resorption predominates over bone formation, increasing the risk of osteoporosis [2]. Consequently, the diminished estrogen levels disturb the equilibrium of the bone remodeling cycle, leading to a situation where bone resorption outpaces bone formation. This imbalance manifests as expedited bone loss in postmenopausal females, leaving them acutely vulnerable to osteoporotic fractures. For instance, in Europe, a considerable population of postmenopausal women grapple with osteoporosis, and this demographic is confronted with a notably elevated risk of fractures [3].
3.2. Osteoporosis in elderly population
Osteoporosis frequently impacts the elderly population, regardless of gender [4]. The aging process is inherently linked to a gradual decrease in bone density and robustness. As one ages, the efficiency of bone remodeling diminishes, causing an imbalance between bone breakdown and formation. Moreover, a decline in physical exercise, suboptimal dietary habits, and hormonal fluctuations play pivotal roles in the onset of osteoporosis among senior citizens. In the United States, a considerable proportion of older individuals suffer from this condition, and the resultant fractures significantly compromise their quality of life and increase mortality rates [5].
4. Risk factors and assessment
4.1. Risk factors
Age serves as a pivotal determinant in the onset of osteoporosis. With advancing years, there is a progressive decline in bone density, heightening the susceptibility to this condition. Particularly, women are disproportionately impacted, particularly in the postmenopausal phase, as hormonal fluctuations exacerbate their vulnerability.
Moreover, lifestyle is also an important factor influencing the prevalence of osteoporosis. The choices we make in our day-to-day lives significantly impact the onset of osteoporosis. Habits such as smoking, overindulgence in alcohol, and a lifestyle devoid of physical activity are all linked to a heightened susceptibility to this condition. The act of smoking has the potential to diminish estrogen levels and disrupt normal bone function, whereas excessive drinking hinders calcium absorption and accelerates bone breakdown. Furthermore, an inactive lifestyle results in reduced bone stimulation, which may lead to bone density loss [4].
Specific health issues like rheumatoid arthritis, Cushing's syndrome, and diabetes elevate the likelihood of developing osteoporosis. These diseases have the potential to directly influence bone metabolism or indirectly affect it by altering other bodily systems. Additionally, the use of medications like glucocorticoids, commonly prescribed for a spectrum of illnesses, may lead to bone density reduction and a heightened risk of fractures [2].
4.2. Risk assessment index
Bone mineral density (BMD) measurement, employing dual-energy X-ray absorptiometry (DXA), stands as the benchmark methodology for the diagnosis of osteoporosis and the evaluation of fracture propensity. The T-score, a comparative metric that aligns an individual's BMD with that of a youthful adult reference group, serves as the criterion for categorizing bone health status. A T-score of -2.5 or below signifies the presence of osteoporosis, whereas a score bracketed between -1.0 and -2.5 denotes osteopenia [3].
Fracture Risk Assessment Tools: Fracture risk assessment instruments, such as FRAX, are designed to gauge an individual's likelihood of sustaining a hip fracture or experiencing a significant osteoporotic fracture within the next decade. These tools take into account a spectrum of risk factors, encompassing age, gender, bone mineral density (BMD), history of past fractures, family history of hip fractures, tobacco use, alcohol consumption, and the utilization of glucocorticoids. Through the integration of these variables, FRAX affords a more holistic evaluation of the risk of fractures compared to reliance on BMD measurement alone [3].
Clinical Risk Factors: Apart from BMD and FRAX, clinical risk factors are instrumental in evaluating the likelihood of developing osteoporosis [6]. Such factors encompass a past of fragility fractures, a low body mass index (BMI), a family history of hip fractures, ongoing glucocorticoid therapy, and the occurrence of specific health conditions like rheumatoid arthritis. The existence of these indicators aids in pinpointing those who may be more susceptible to osteoporosis, thereby necessitating further assessment and therapeutic intervention.
We can predict osteoporosis risk using machine learning techniques. Germany has initiated the development of machine learning models based on the analysis of chronic disease data from the German disease analysis database. By screening for individuals at high risk of osteoporosis, it becomes possible to achieve early detection and personalized management, thereby reducing the disease burden [7].
5. Treatments for osteoporosis
5.1. Lifestyle modifications
Adhering to a diet replete with calcium and vitamin D is crucial for maintaining robust bone health. Such a diet includes an array of calcium-rich foods such as dairy products, lush green leafy vegetables, and an assortment of fortified foods. Apart from sunlight exposure, one can obtain vitamin D through the ingestion of oily fish and fortified milk products. In cases where dietary intake falls short, supplementation with calcium and vitamin D could be recommended [5].
Regular weight-bearing and muscle-strengthening exercises are beneficial for bone health. Weight-bearing exercises, such as walking, running, and weightlifting, stimulate bone formation and increase bone density. Muscle-strengthening exercises, such as resistance training, can also help improve bone strength and reduce the risk of falls.
The harm caused by smoking and excessive alcohol consumption cannot be ignored. Quitting smoking and reducing alcohol intake are important lifestyle modifications for preventing and treating osteoporosis. Smoking cessation can help improve bone health by reducing the negative effects of smoking on bone metabolism. Moderating alcohol intake can also help prevent bone loss and reduce the risk of fractures.
5.2. Pharmacological treatments
Antiresorptive agents can be used to treat osteoporosis. Bisphosphonates are the most used antiresorptive agents for the treatment of osteoporosis. They work by inhibiting osteoclast activity, thereby reducing bone resorption. Alendronate, risedronate, and zoledronic acid are some of the commonly used bisphosphonates. Now I will list the therapeutic examples and clinical trials of these three drugs.
Postmenopausal females afflicted with osteoporosis may experience a notable decrease in vertebral fracture incidence when treated with a daily dose of 10 mg of alendronate over a period of three to four years, particularly if they have a history of fractures or a femoral T-score below -2.5. The Fracture Intervention Trial's long-term extension revealed that women who persisted with alendronate therapy for an additional five years after an initial five-year treatment exhibited a significantly diminished risk of clinical vertebral fractures, in contrast to those who ceased the therapy. Nevertheless, no significant variation was observed in the risk of non-vertebral fractures between the two groups.
In postmenopausal women with osteoporosis, a daily regimen of risedronate at 5 mg can substantially diminish the likelihood of vertebral and non-vertebral fractures. For the elderly feminine demographic, this medication has been shown to notably lower the incidence of hip fractures, with an even more marked impact on those with osteoporosis.
Administered annually in a 5-milligram intravenous dose, zoledronic acid has been shown to significantly diminish the occurrence of vertebral, non-vertebral, and hip fractures in postmenopausal women suffering from osteoporosis. Moreover, when administered promptly after a patient's initial hip fracture, it effectively lowers the likelihood of subsequent clinical fractures and mortality [5]. These medications have been shown to increase bone density and reduce the risk of fractures in postmenopausal women and men with osteoporosis.
Denosumab, a sophisticated monoclonal antibody, specifically targets RANKL to suppress the development and function of osteoclasts. This intervention has demonstrated remarkable efficacy in mitigating the likelihood of vertebral, non-vertebral, and hip fractures among postmenopausal women suffering from osteoporosis. Nonetheless, it bears a potential risk of infrequent yet severe adverse effects, such as osteonecrosis of the jaw and atypical femoral fractures.
Teriparatide, a synthetic derivative of human parathyroid hormone, serves as a potent anabolic compound that promotes bone growth. It is sanctioned for the management of osteoporosis in both postmenopausal females and males who are at elevated risk for fractures. This therapeutic agent has demonstrated its efficacy in enhancing bone mineral density and mitigating the likelihood of fractures. However, its utilization is restricted to a maximum duration of two years because of the associated risk of developing osteosarcoma [6].
6. Limitations and prospects
6.1. Limitations of current research
Risk assessment tools such as FRAX have notable limitations, including limited applicability to specific populations (e.g., ethnic minorities or patients undergoing osteoporosis treatment) and insufficient consideration of important risk factors such as a history of falls, potentially resulting in inaccurate fracture risk predictions.
Current osteoporosis treatments, despite their effectiveness, have significant side effects. Bisphosphonates may induce severe adverse reactions, such as osteonecrosis of the jaw and atypical femoral fractures. Calcitonin use is restricted due to increased risks of cardiovascular diseases and breast cancer. These side effects limit long-term medication adherence and therapeutic outcomes. Additionally, low disease awareness and poor treatment adherence remain substantial challenges, contributing to a significant “treatment gap.” Many high-risk individuals are not diagnosed promptly, and adherence to prescribed therapies is often inadequate.
6.2. Prospects for the future
Future research should aim to improve risk assessment tools like FRAX by incorporating additional factors influencing fracture risk, such as fall history, detailed lifestyle factors, and ethnicity-specific data, to enhance prediction accuracy across diverse populations and strengthen clinical decision-making. Machine learning techniques may further refine osteoporosis risk prediction, offering advanced analytical approaches to fracture assessment.
In-depth investigations into the pathogenesis of osteoporosis are needed to identify novel therapeutic targets and develop safer, more effective medications. For example, studies on cathepsin K inhibitors and antisclerostin therapies hold promise for bringing new breakthroughs in osteoporosis treatment by addressing unmet needs in modulating bone remodeling and reducing side effects associated with current therapies.
Moreover, it is imperative to elevate the awareness and understanding of osteoporosis among both the general public and healthcare professionals to address the existing knowledge disparities. By enhancing the dialogue between doctors and patients, making treatment plans more accessible, and furnishing comprehensive medication instructions, we can markedly boost patient compliance with therapeutic regimens. This, in turn, will be instrumental in managing the progression of the disease more effectively and alleviating the enduring repercussions of osteoporosis on the quality of life and the economic strain on healthcare systems.
7. Conclusion
Osteoporosis, a prevalent and crippling condition, afflicts millions globally, especially postmenopausal women and the elderly. Grasping its etiology, susceptibility elements, and therapeutic alternatives is vital for its efficacious deterrence and handling. Adopting lifestyle adjustments, including a nutritious diet, consistent physical activity, quitting smoking, and temperate alcohol consumption, alongside resorting to suitable medicinal interventions when required, can markedly lower the likelihood of osteoporosis and its concomitant fractures.) Additional scientific inquiry is imperative to refine more potent therapeutic approaches and to enhance the premature identification and management of this ailment.
References
[1]. Adejuyigbe, B., Kallini, J., Chiou, D., & Kallini, J. R. (2023). Osteoporosis: Molecular pathology, diagnostics, and therapeutics. International Journal of Molecular Sciences, 24(19), 14583.
[2]. Aibar-Almazán, A., Voltes-Martínez, A., Castellote-Caballero, Y., Afanador-Restrepo, D. F., Carcelén-Fraile, M. d. C., & López-Ruiz, E. (2022). Current Status of the Diagnosis and Management of Osteoporosis. International Journal of Molecular Sciences, 23(16), 9465.
[3]. Compston, J., Cooper, A., Cooper, C., Gittoes, N., Gregson, C., Harvey, N., Hope, S., Kanis, J. A., McCloskey, E. V., Poole, K. E. S., Reid, D. M., Selby, P., Thompson, F., Thurston, A., Vine, N., & The National Osteoporosis Guideline Group (NOGG). (2017). UK clinical guideline for the prevention and treatment of osteoporosis. Archives of Osteoporosis, 12(1), 43.
[4]. Sözen, T., Özışık, L., & Başaran, N. Ç. (2017). An overview and management of osteoporosis. European Journal of Rheumatology, 4(1), 46-56. Adejuyigbe, B., Kallini, J., Chiou, D., & Kallini, J. R. (2023). Osteoporosis: Molecular pathology, diagnostics, and therapeutics. International Journal of Molecular Sciences, 24(19), 14583.
[5]. Tu, K. N., Lie, J. D., Wan, C. K., Cameron, M., Austel, A. G., Nguyen, J. K., Van, K., & Hyun, D. (2018). Osteoporosis: A review of treatment options. Pharmacy and Therapeutics, 43(2), 92–104.
[6]. Clynes, M. A., Harvey, N. C., Curtis, E. M., Fuggle, N. R., Dennison, E. M., & Cooper, C. (2020). The epidemiology of osteoporosis. British Medical Bulletin, 133(1), 105–117.
[7]. Tu, J.-B., Liao, W.-J., Liu, W.-C., & Gao, X.-H. (2024). Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data. Scientific Reports, 14(5245),
Cite this article
Chen,P. (2025). The Pathogenesis, Risk Factors, and Treatment of Osteoporosis. Theoretical and Natural Science,111,23-28.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Adejuyigbe, B., Kallini, J., Chiou, D., & Kallini, J. R. (2023). Osteoporosis: Molecular pathology, diagnostics, and therapeutics. International Journal of Molecular Sciences, 24(19), 14583.
[2]. Aibar-Almazán, A., Voltes-Martínez, A., Castellote-Caballero, Y., Afanador-Restrepo, D. F., Carcelén-Fraile, M. d. C., & López-Ruiz, E. (2022). Current Status of the Diagnosis and Management of Osteoporosis. International Journal of Molecular Sciences, 23(16), 9465.
[3]. Compston, J., Cooper, A., Cooper, C., Gittoes, N., Gregson, C., Harvey, N., Hope, S., Kanis, J. A., McCloskey, E. V., Poole, K. E. S., Reid, D. M., Selby, P., Thompson, F., Thurston, A., Vine, N., & The National Osteoporosis Guideline Group (NOGG). (2017). UK clinical guideline for the prevention and treatment of osteoporosis. Archives of Osteoporosis, 12(1), 43.
[4]. Sözen, T., Özışık, L., & Başaran, N. Ç. (2017). An overview and management of osteoporosis. European Journal of Rheumatology, 4(1), 46-56. Adejuyigbe, B., Kallini, J., Chiou, D., & Kallini, J. R. (2023). Osteoporosis: Molecular pathology, diagnostics, and therapeutics. International Journal of Molecular Sciences, 24(19), 14583.
[5]. Tu, K. N., Lie, J. D., Wan, C. K., Cameron, M., Austel, A. G., Nguyen, J. K., Van, K., & Hyun, D. (2018). Osteoporosis: A review of treatment options. Pharmacy and Therapeutics, 43(2), 92–104.
[6]. Clynes, M. A., Harvey, N. C., Curtis, E. M., Fuggle, N. R., Dennison, E. M., & Cooper, C. (2020). The epidemiology of osteoporosis. British Medical Bulletin, 133(1), 105–117.
[7]. Tu, J.-B., Liao, W.-J., Liu, W.-C., & Gao, X.-H. (2024). Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data. Scientific Reports, 14(5245),