Effects of Endocrine-Disrupting Chemicals on Bone Health.

This comprehensive review critically examines the detrimental impacts of endocrine-disrupting chemicals (EDCs) on bone health, with a specific focus on substances such as bisphenol A (BPA), per- and polyfluoroalkyl substances (PFASs), phthalates, and dioxins. These EDCs, by interfering with the endocrine system's normal functioning, pose a significant risk to bone metabolism, potentially leading to a heightened susceptibility to bone-related disorders and diseases. Notably, BPA has been shown to inhibit the differentiation of osteoblasts and promote the apoptosis of osteoblasts, which results in altered bone turnover status. PFASs, known for their environmental persistence and ability to bioaccumulate in the human body, have been linked to an increased osteoporosis risk. Similarly, phthalates, which are widely used in the production of plastics, have been associated with adverse bone health outcomes, showing an inverse relationship between phthalate exposure and bone mineral density. Dioxins present a more complex picture, with research findings suggesting both potential benefits and adverse effects on bone structure and density, depending on factors such as the timing and level of exposure. This review underscores the urgent need for further research to better understand the specific pathways through which EDCs affect bone health and to develop targeted strategies for mitigating their potentially harmful impacts.

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. OBJECTIVE: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. METHODS: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. RESULTS: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). CONCLUSIONS: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

Bone Loss after Solid Organ Transplantation: A Review of Organ-Specific Considerations.

This review article investigates solid organ transplantation-induced osteoporosis, a critical yet often overlooked issue, emphasizing its significance in post-transplant care. The initial sections provide a comprehensive understanding of the prevalence and multifactorial pathogenesis of transplantation osteoporosis, including factors such as deteriorating post-transplantation health, hormonal changes, and the impact of immunosuppressive medications. Furthermore, the review is dedicated to organ-specific considerations in transplantation osteoporosis, with separate analyses for kidney, liver, heart, and lung transplantations. Each section elucidates the unique challenges and management strategies pertinent to transplantation osteoporosis in relation to each organ type, highlighting the necessity of an organ-specific approach to fully understand the diverse manifestations and implications of transplantation osteoporosis. This review underscores the importance of this topic in transplant medicine, aiming to enhance awareness and knowledge among clinicians and researchers. By comprehensively examining transplantation osteoporosis, this study contributes to the development of improved management and care strategies, ultimately leading to improved patient outcomes in this vulnerable group. This detailed review serves as an essential resource for those involved in the complex multidisciplinary care of transplant recipients.

Temporal trends in clinical features of patients with primary aldosteronism over 20 years.

Primary aldosteronism (PA) accounts for approximately 5-10% of hypertension cases. Over the past 20 years, the reported incidence of PA has increased due to widespread screening for secondary hypertension and imaging studies. We aimed to evaluate the temporal trends in the clinical characteristics and subtypes of PA. A total of 1064 patients with PA in two tertiary hospitals between 2000 and 2021 were categorized into three groups according to the year of diagnosis: 2000-2009, 2010-2015, and 2016-2021. The clinical characteristics of the patients over the three time periods were compared using a trend analysis. The age at diagnosis and sex of patients with PA did not change over 20 years. The proportion of patients with bilateral hyperaldosteronism (BHA) increased (11%, 25%, and 40%, P for trend <0.001). The proportion of hypokalemia (87%, 61%, and 40%) and plasma aldosterone concentration (36.0, 30.8, and 26.6 ng/dL) decreased (all P for trend <0.001). There was a trend toward an increased proportion of incidentally detected patients compared to clinically symptomatic patients (36%, 55%, and 61%, P for trend <0.001). The concordance rate of imaging and adrenal venous sampling results decreased (91%, 70%, and 57% P for trend <0.001). However, the proportion of patients with resistant hypertension and comorbidities did not differ. In conclusion, among patients with PA, patients with BHA and incidental detection have increased over 20 years, and more patients are likely to present with milder clinical symptoms and biochemical profiles.

Protein Signatures of Parathyroid Adenoma according to Tumor Volume and Functionality.

BACKGRUOUND: Parathyroid adenoma (PA) is a common endocrine disease linked to multiple complications, but the pathophysiology of the disease remains incompletely understood. The study aimed to identify the key regulator proteins and pathways of PA according to functionality and volume through quantitative proteomic analyses. METHODS: We conducted a retrospective study of 15 formalin-fixed, paraffin-embedded PA samples from tertiary hospitals in South Korea. Proteins were extracted, digested, and the resulting peptides were analyzed using liquid chromatography-tandem mass spectrometry. Pearson correlation analysis was employed to identify proteins significantly correlated with clinical variables. Canonical pathways and transcription factors were analyzed using Ingenuity Pathway Analysis. RESULTS: The median age of the participants was 52 years, and 60.0% were female. Among the 8,153 protein groups analyzed, 496 showed significant positive correlations with adenoma volume, while 431 proteins were significantly correlated with parathyroid hormone (PTH) levels. The proteins SLC12A9, LGALS3, and CARM1 were positively correlated with adenoma volume, while HSP90AB2P, HLA-DRA, and SCD5 showed negative correlations. DCPS, IRF2BPL, and FAM98A were the main proteins that exhibited positive correlations with PTH levels, and SLITRK4, LAP3, and AP4E1 had negative correlations. Canonical pathway analysis demonstrated that the RAN and sirtuin signaling pathways were positively correlated with both PTH levels and adenoma volume, while epithelial adherence junction pathways had negative correlations. CONCLUSION: Our study identified pivotal proteins and pathways associated with PA, offering potential therapeutic targets. These findings accentuate the importance of proteomics in understanding disease pathophysiology and the need for further research.

A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture.

Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term subsequent risk have not been developed. Purpose To develop and validate a deep learning prediction model for subsequent fracture risk using digitally reconstructed radiographs from hip CT in patients with recent hip fractures. Materials and Methods This retrospective study included adult patients who underwent three-dimensional hip CT due to a fracture from January 2004 to December 2020. Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs were generated and assembled to construct an ensemble model. DenseNet modules were used to calculate risk probability based on extracted image features and fracture-free probability plots were output. Model performance was assessed using the C index and area under the receiver operating characteristic curve (AUC) and compared with other models using the paired t test. Results The training and validation set included 1012 patients (mean age, 74.5 years ± 13.3 [SD]; 706 female, 113 subsequent fracture) and the test set included 468 patients (mean age, 75.9 years ± 14.0; 335 female, 22 subsequent fractures). In the test set, the ensemble model had a higher C index (0.73) for predicting subsequent fractures than that of other image-based models (C index range, 0.59-0.70 for five of six models; P value range, < .001 to < .05). The ensemble model achieved AUCs of 0.74, 0.74, and 0.73 at the 2-, 3-, and 5-year follow-ups, respectively; higher than that of most other image-based models at 2 years (AUC range, 0.57-0.71 for five of six models; P value range, < .001 to < .05) and 3 years (AUC range, 0.55-0.72 for four of six models; P value range, < .001 to < .05). Moreover, the AUCs achieved by the ensemble model were higher than that of a clinical model that included known risk factors (2-, 3-, and 5-year AUCs of 0.58, 0.64, and 0.70, respectively; P < .001 for all). Conclusion In patients with recent hip fractures, the ensemble deep learning model using digital reconstructed radiographs from hip CT showed good performance for predicting subsequent fractures in the short term. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Li and Jaremko in this issue.