Fracture risk and assessment in adults with cancer

Fracture Risk Prediction Using the Fracture Risk Assessment Tool in Individuals With Cancer

Importance

The Fracture Risk Assessment Tool (FRAX) is a fracture risk prediction tool for 10-year probability of major osteoporotic fracture (MOF) and hip fracture in the general population. Whether FRAX is useful in individuals with cancer is uncertain.

Objective

To determine the performance of FRAX for predicting incident fractures in individuals with cancer.

Design, Setting, and Participants

This retrospective population-based cohort study included residents of Manitoba, Canada, with and without cancer diagnoses from 1987 to 2014. Diagnoses were identified through the Manitoba Cancer Registry. Incident fractures to March 31, 2021, were identified in population-based health care data. Data analysis occurred between January and March 2023.

Main Outcomes and Measures

FRAX scores were computed for those with bone mineral density (BMD) results that were recorded in the Manitoba BMD Registry.

Results

This study included 9877 individuals with cancer (mean [SD] age, 67.1 [11.2] years; 8693 [88.0%] female) and 45 877 individuals in the noncancer cohort (mean [SD] age, 66.2 [10.2] years; 41 656 [90.8%] female). Compared to individuals without cancer, those with cancer had higher rates of incident MOF (14.5 vs 12.9 per 1000 person-years; P < .001) and hip fracture (4.2 vs 3.5 per 1000 person-years; P = .002). In the cancer cohort, FRAX with BMD results were associated with incident MOF (HR per SD increase, 1.84 [95% CI, 1.74-1.95]) and hip fracture (HR per SD increase, 3.61 [95% CI, 3.13-4.15]). In the cancer cohort, calibration slopes for FRAX with BMD were 1.03 for MOFs and 0.97 for hip fractures.

Conclusions and Relevance

In this retrospective cohort study, FRAX with BMD showed good stratification and calibration for predicting incident fractures in patients with cancer. These results suggest that FRAX with BMD can be a reliable tool for predicting incident fractures in individuals with cancer.

Prediction of Osteoporotic Hip Fracture Outcome: Comparative Accuracy of 27 Immune-Inflammatory-Metabolic Markers and Related Conceptual Issues

Objectives: This study, based on the concept of immuno-inflammatory-metabolic (IIM) dysregulation, investigated and compared the prognostic impact of 27 indices at admission for prediction of postoperative myocardial injury (PMI) and/or hospital death in hip fracture (HF) patients. Methods: In consecutive HF patient (n = 1273, mean age 82.9 ± 8.7 years, 73.5% females) demographics, medical history, laboratory parameters, and outcomes were recorded prospectively. Multiple logistic regression and receiver-operating characteristic analyses (the area under the curve, AUC) were used to establish the predictive role for each biomarker. Results: Among 27 IIM biomarkers, 10 indices were significantly associated with development of PMI and 16 were indicative of a fatal outcome; in the subset of patients aged >80 years with ischaemic heart disease (IHD, the highest risk group: 90.2% of all deaths), the corresponding figures were 26 and 20. In the latter group, the five strongest preoperative predictors for PMI were anaemia (AUC 0.7879), monocyte/eosinophil ratio > 13.0 (AUC 0.7814), neutrophil/lymphocyte ratio > 7.5 (AUC 0.7784), eosinophil count < 1.1 × 109/L (AUC 0.7780), and neutrophil/albumin × 10 > 2.4 (AUC 0.7732); additionally, sensitivity was 83.1-75.4% and specificity was 82.1-75.0%. The highest predictors of in-hospital death were platelet/lymphocyte ratio > 280.0 (AUC 0.8390), lymphocyte/monocyte ratio < 1.1 (AUC 0.8375), albumin < 33 g/L (AUC 0.7889), red cell distribution width > 14.5% (AUC 0.7739), and anaemia (AUC 0.7604), sensitivity 88.2% and above, and specificity 85.1-79.3%. Internal validation confirmed the predictive value of the models. Conclusions: Comparison of 27 IIM indices in HF patients identified several simple, widely available, and inexpensive parameters highly predictive for PMI and/or in-hospital death. The applicability of IIM biomarkers to diagnose and predict risks for chronic diseases, including OP/OF, in the preclinical stages is discussed.

Developing a Nomogram for Predicting Colorectal Cancer and Its Precancerous Lesions Based on Data from Three Non-Invasive Screening Tools, APCS, FIT, and sDNA

Purpose

This study aimed to develop and validate a nomogram for predicting positive colonoscopy results using the data from non-invasive screening strategies.

Methods

The volunteers participated in primary colorectal cancer (CRC) screenings using Asia-Pacific colorectal screening (APCS) scoring, faecal immunochemical testing (FIT) and stool deoxyribonucleic acid (sDNA) testing and underwent a colonoscopy. The positive colonoscopy results included CRC, advanced adenoma (AA), high-grade intraepithelial neoplasia (HGIN), and low-grade intraepithelial neoplasia (LGIN). The enrolled participants were randomly selected for training and validation sets in a 7:3 ratio. A model for predicting positive colonoscopy results was virtualized by the nomogram using logistic regression analysis.

Results

Among the 179 enrolled participants, 125 were assigned to training set, while 54 were assigned to validation set. After multivariable logistic regression was done, APCS score, FIT result, and sDNA result were all identified as the predictors for positive colonoscopy results. A model that incorporated the above independent predictors was developed and presented as a nomogram. The C-index of the nomogram in the validation set was 0.768 (95% CI, 0.644-0.891). The calibration curve demonstrated a good agreement between prediction and observation. The decision curve analysis (DCA) curve showed that the model achieved a net benefit across all threshold probabilities. The AUC of the prediction model for predicting positive colonoscopy results was much higher than that of the FIT + sDNA test scheme.

Conclusion

The nomogram for predicting positive colonoscopy results was successfully developed based on 3 non-invasive screening tools (APCS scoring, FIT and sDNA test).

Insights and implications of sexual dimorphism in osteoporosis

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.

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

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.

Osteoporotic fracture risk in women with breast cancer treated with aromatase inhibitors: a health insurance claims database study in Japan

BACKGROUND

Hormone therapy with aromatase inhibitors (AIs) for estrogen receptor-dependent breast cancer may expose patients to an increased osteoporosis risk. This study was performed to estimate fracture risk in women with breast cancer to whom AIs were prescribed in Japan.

METHODS

This retrospective study used data from the Japanese Medical Data Vision database. Women with breast cancer prescribed AIs over a 12-month period were identified and matched to women not prescribed AIs using a propensity score. Fracture rates were estimated by a cumulative incidence function and compared using a cause-specific Cox hazard model. The proportion of women undergoing bone density tests was retrieved.

RESULTS

For all fractures sites combined, cumulative fracture incidence at 10 years was 0.19 [95%CI: 0.16-0.22] in women prescribed AIs and 0.18 [95%CI: 0.15-0.21] without AIs. AI prescription was not associated with any changes in risk (adjusted hazard ratio: 1.08 [95%CI: 0.99-1.17] p = 0.08). Women prescribed AI more frequently underwent bone density testing (31.9% [95% CI: 31.2%; 32.6%] versus 2.2% [95% CI: 2.0%; 2.4%]).

CONCLUSIONS

The anticipated association between AI exposure and osteoporotic fracture risk in Japanese women with breast cancer was not seen clearly.

C-C Motif Chemokine Ligand 5 (CCL5) Promotes Irradiation-Evoked Osteoclastogenesis

The imbalance that occurs in bone remodeling induced by irradiation (IR) is the disruption of the balance between bone formation and bone resorption. In this study, primary osteocytes (OCYs) of femoral and tibial origin were cultured and irradiated. It was observed that irradiated OCY showed extensive DNA damage, which led to the initiation of a typical phenotype of cellular senescence, including the secretion of senescence-associated secretory phenotype (SASP), especially the C-C motif chemokine ligand 5 (CCL5). In order to explore the regulation of osteoclastogenic potential by IR-induced senescent OCYs exocytosis factor CCL5, the conditioned medium (CM) of OCYs was co-cultured with RAW264.7 precursor cells. It was observed that in the irradiated OCY co-cultured group, the migration potential increased compared with the vehicle culture group, accompanied by an enhancement of typical mature OCs; the expression of the specific function of enzyme tartrate-resistant acid phosphatase (TRAP) increased; and the bone-destructive function was enhanced. However, a neutralizing antibody to CCL5 could reverse the extra-activation of osteoclastogenesis. Accordingly, the overexpression of p-STAT3 in irradiated OCY was accompanied by CCL5. It was concluded that CCL5 is a potential key molecule and the interventions targeting CCL5 could be a potential strategy for inhibiting osteoclastogenesis and restoring bone remodeling.

Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

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.