Unlabelled: We evaluated the prevalence of osteoporosis and annual changes in bone mineral density (BMD) over 10 years post-liver transplantation. BMD in the lumbar spine improved significantly post-transplantation, reaching a 12% increase at year 10. In contrast, BMD in the femoral neck and hip deteriorated and did not return to baseline levels.
Introduction: This study (1) evaluated the prevalence of osteoporosis, and the annual changes in bone mineral density (BMD) over 10 years, and (2) identified the risk factors for worsened BMD in stable liver transplant recipients (LTRs).
Methods: LTRs who underwent liver transplantation (LT) at Singapore General Hospital between February 2006 and Mar 2019 were included. Demographic, clinical data, and BMD in the lumbar spine (LS), femoral neck (FN), and total hip (TH) were collected retrospectively from the medical records.
Results: Eighty-three patients (mean age: 55 ± 8 years) with a median follow-up of 80 months were included. The prevalence of osteoporosis increased significantly from 18.1% pre-LT to 34.3% post-LT (p = 0.021), and the incidence of osteoporosis was 18.2%. Worsened BMD (normal to osteopenia/osteopenia to osteoporosis) was found in 27.2% of LTRs. No significant risk factors were associated with worsened BMD, but females had a trend towards a higher odd (adjusted odds ratio: 3.54, 95%CI (0.61-20.5), p = 0.159). The LS BMD increased within 6-month post-LT and continued to improve throughout the entire follow-up period. In contrast, BMD in the FN and TH deteriorated and did not return to baseline levels post-LT.
Conclusion: Prevalence of osteoporosis increased significantly post-LT. Over a 10-year follow-up, 27.2% of LTRs had worsened BMD status, and a possible risk factor may be female gender. While the LS BMD improved with time, the BMD in the FN and TH persisted below baseline throughout the follow-up period. Future studies should explore long-term therapies to improve BMD in the FN and TH post-LT.
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http://dx.doi.org/10.1007/s11657-021-01037-x | DOI Listing |
BMC Infect Dis
January 2025
Department of Infectious Diseases, School of Medicine, Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High Risk Behaviors, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
Background: Reduced Bone Mineral Density (BMD) has been linked to Human Immunodeficiency Virus (HIV) infection and treatment. There is a lack of information regarding the osteoporosis status of middle-aged patients with HIV in Iran, despite the fact that Antiretroviral Therapy (ART) is widely accessible.
Objective: The purpose of this cross-sectional study was to assess the BMD status and low BMD risk factors in patients with HIV under ART living in Iran.
Sci Rep
January 2025
Department of Orthopedic Surgery at the First Affiliated Hospital, Harbin Medical University, Harbin, China.
Osteoporosis (OP) is a prevalent age-related bone metabolic disease. Aging and mitochondrial dysfunction are involved in the onset and progression of OP, but the specific mechanisms have not been elucidated. The aim of this study was to identify novel potential biomarkers associated with aging and mitochondria in OP.
View Article and Find Full Text PDFAnn Intern Med
January 2025
Department of Pharmacoepidemiology, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan (K.K.).
Background: Dialysis patients have high rates of fracture morbidity, but evidence on optimal management strategies for osteoporosis is scarce.
Objective: To determine the risk for cardiovascular events and fracture prevention effects with denosumab compared with oral bisphosphonates in dialysis-dependent patients.
Design: An observational study that attempts to emulate a target trial.
Endocrinol Diabetes Metab
January 2025
Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran.
Introduction: In Iran, the assessment of osteoporosis through tools like dual-energy X-ray absorptiometry poses significant challenges due to their high costs and limited availability, particularly in small cities and rural areas. Our objective was to employ a variety of machine learning (ML) techniques to evaluate the accuracy and precision of each method, with the aim of identifying the most accurate pattern for diagnosing the osteoporosis risks.
Methods: We analysed the data related to osteoporosis risk factors obtained from the Fasa Adults Cohort Study in eight ML methods, including logistic regression (LR), baseline LR, decision tree classifiers (DT), support vector classifiers (SVC), random forest classifiers (RF), linear discriminant analysis (LDA), K nearest neighbour classifiers (KNN) and extreme gradient boosting (XGB).
J Clin Orthop Trauma
January 2025
Resident, Department of Community Medicine, Mahatma Gandhi Medical College and Research Institute, SBVU (Deemed to be University), Pondicherry, India.
Introduction: Osteoporosis is a silent disease that is more prevalent among postmenopausal women (PMW) due to hormonal transition. Various toolkits, including the Osteoporosis Knowledge Assessment Tool (OKAT), were available for the knowledge assessment. The Osteoporosis-related knowledge is crucial for preventing osteoporosis, but there is no validated, reliable questionnaire in Tamil to measure this knowledge.
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