Fracture risk and assessment in adults with cancer

Ye, Carrie; Leslie, William D.

doi:10.1007/s00198-022-06631-4

Cited by 13 publications

(9 citation statements)

References 188 publications

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“…Several factors, including low insulin-like factor 3 (INSL3), VD deficiency, reduced estrogen level, unfavorable fat/muscle ratio, and high follicle-stimulating hormone (FSH), may contribute to osteoporosis in KS patients. 151 …”

Section: Molecular Mechanisms Underlying Sexual Dimorphism In Osteopo...mentioning

confidence: 99%

“… 505 , 519 Patients with cancer were also recommended to undergo fracture risk assessment since they have unique risk factors for osteoporosis. 151 However, it is still inconclusive whether the benefits of screening for osteoporosis to prevent osteoporotic fractures are superior to the potential hazards caused by bone measurement tests in men. 10 …”

Section: Screening Approaches For Osteoporosis and Osteoporotic Fract...mentioning

confidence: 99%

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Insights and implications of sexual dimorphism in osteoporosis

Zhang,

Xie,

Sun

et al. 2024

Bone Res

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Osteoporosis, a metabolic bone disease characterized by low bone mineral density and deterioration of bone microarchitecture, has led to a high risk of fatal osteoporotic fractures worldwide. Accumulating evidence has revealed that sexual dimorphism is a notable feature of osteoporosis, with sex-specific differences in epidemiology and pathogenesis. Specifically, females are more susceptible than males to osteoporosis, while males are more prone to disability or death from the disease. To date, sex chromosome abnormalities and steroid hormones have been proven to contribute greatly to sexual dimorphism in osteoporosis by regulating the functions of bone cells. Understanding the sex-specific differences in osteoporosis and its related complications is essential for improving treatment strategies tailored to women and men. This literature review focuses on the mechanisms underlying sexual dimorphism in osteoporosis, mainly in a population of aging patients, chronic glucocorticoid administration, and diabetes. Moreover, we highlight the implications of sexual dimorphism for developing therapeutics and preventive strategies and screening approaches tailored to women and men. Additionally, the challenges in translating bench research to bedside treatments and future directions to overcome these obstacles will be discussed.

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Section: Molecular Mechanisms Underlying Sexual Dimorphism In Osteopo...mentioning

confidence: 99%

Section: Screening Approaches For Osteoporosis and Osteoporotic Fract...mentioning

confidence: 99%

Insights and implications of sexual dimorphism in osteoporosis

Zhang,

Xie,

Sun

et al. 2024

Bone Res

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“…Importantly, the combined use of diverse MR analysis methods and multiple sensitivity analysis methods bolstered the reliability of the results. Additionally, musculoskeletal disorders are associated with various broadly defined cirrhosis [ 70 , 71 ] and diverse types of cancer [ 72 , 73 ]. To mitigate potential bias arising from these associations, the controls for liver cirrhosis and HCC in this study were defined to exclude broadly defined cirrhosis and all types of cancer, respectively, which ensured a higher degree of accuracy in the results of the causal analysis.…”

Section: Discussionmentioning

confidence: 99%

Genetic evidence of the causal relationship between chronic liver diseases and musculoskeletal disorders

Lu,

Li,

et al. 2024

J Transl Med

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Background Chronic liver diseases constitute a major global public health burden, posing a substantial threat to patients’ daily lives and even survival due to the potential development of musculoskeletal disorders. Although the relationship between chronic liver diseases and musculoskeletal disorders has received extensive attention, their causal relationship has not been comprehensively and systematically investigated. Methods This study aimed to assess the causal relationships between viral hepatitis, primary biliary cholangitis, primary sclerosing cholangitis (PSC), liver cirrhosis, and hepatocellular carcinoma (HCC) with osteoporosis, osteoarthritis, and sarcopenia through bidirectional Mendelian randomization (MR) research. The traits related to osteoporosis and osteoarthritis included both overall and site-specific phenotypes, and the traits linked to sarcopenia involved indicators of muscle mass and function. Random-effect inverse-variance weighted (IVW), weighted median, MR-Egger, and Causal Analysis Using the Summary Effect Estimates were used to evaluate causal effects, with IVW being the main analysis method. To enhance robustness, sensitivity analyses were performed using Cochran’s Q test, MR-Egger intercept, MR-PRESSO global test, funnel plots, leave-one-out analyses, and latent causal variable model. Results The forward MR analysis indicated that PSC can reduce forearm bone mineral density (beta = − 0.0454, 95% CI − 0.0798 to − 0.0110; P = 0.0098) and increase the risk of overall osteoarthritis (OR = 1.012, 95% CI 1.002–1.022; P = 0.0247), while HCC can decrease grip strength (beta = − 0.0053, 95% CI − 0.008 to − 0.0025; P = 0.0002). The reverse MR analysis did not find significant causal effects of musculoskeletal disorders on chronic liver diseases. Additionally, no heterogeneity or pleiotropy was detected. Conclusions These findings corroborate the causal effects of PSC on osteoporosis and osteoarthritis, as well as the causal impact of HCC on sarcopenia. Thus, the implementation of comprehensive preventive measures is imperative for PSC and HCC patients to mitigate the risk of musculoskeletal disorders, ultimately improving their quality of life.

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“…In addition, many cancer treatments can have detrimental effects on bone health. In particular, hormone deprivation therapies for breast cancer and prostate cancer adversely affect bone turnover, resulting in decreases in BMD and bone quality, which can lead to fractures [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

Lipnicka

Studniarski

Irzmański

2023

Life

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Background: Osteoporosis is a socio-economic problem of modern aging societies. Bone fractures and the related treatments generate the highest costs. The occurrence of osteoporotic fractures is a cause of chronic disability, many complications, reduced quality of life, and often premature death. Aim of the study: The aim of the study was to determine which of the patient’s potential risk factors pertaining to various diseases and lifestyle have an essential impact on the occurrence of low-energy fractures and the hierarchy of these factors. Methods: The study was retrospective. The documentation of 222 patients (206 women and 16 men) from an osteoporosis treatment clinic in Łódź, Poland was analyzed. Each patient was described by a vector consisting of 27 features, where each feature was a different risk factor. Using artificial neural networks, an attempt was made to create a model that, based on the available data, would be able to predict whether the patient would be exposed to low-energy fractures. We developed a neural network model that achieved the best result for the testing data. In addition, we used other methods to solve the classification problem, i.e., correctly dividing patients into two groups: those with fractures and those without fractures. These methods were logistic regression, k-nearest neighbors and SVM. Results: The obtained results gave us the opportunity to assess the effectiveness of various methods and the importance of the features describing patients. Using logistic regression and the recursive elimination of features, a ranking of risk factors was obtained in which the most important were age, chronic kidney disease, neck T-score, and serum phosphate level. Then, we repeated the learning procedure of the neural network considering only these four most important features. The average mean squared error on the test set was about 27% for the best variant of the model. Conclusions: The comparison of the rankings with different numbers of patients shows that the applied method is very sensitive to changes in the considered data (adding new patients significantly changes the result). Further cohort studies with more patients and more advanced methods of machine learning may be needed to identify other significant risk factors and to develop a reliable fracture risk system. The obtained results may contribute to the improved identification patients at risk of low-energy fractures and early implementation of comprehensive treatment.

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Fracture risk and assessment in adults with cancer

Cited by 13 publications

References 188 publications

Insights and implications of sexual dimorphism in osteoporosis

Insights and implications of sexual dimorphism in osteoporosis

Genetic evidence of the causal relationship between chronic liver diseases and musculoskeletal disorders

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

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