Characterization of bone disease in cystic fibrosis

被引:0
|
作者
Contreras-Bolivar, Victoria [1 ,2 ,3 ]
Andreo-Lopez, Maria Carmen [1 ,2 ]
Munoz-Torres, Manuel [1 ,2 ,3 ,4 ]
机构
[1] Univ Hosp Clin San Cecilio, Endocrinol & Nutr Unit, Granada 18016, Spain
[2] Inst Invest Biosanit Granada ibs GRANADA, Granada 18014, Spain
[3] Inst Salud Carlos III, CIBER Frailty & Hlth Aging CIBERFES, Granada 18012, Spain
[4] Univ Granada, Dept Med, Granada 18016, Spain
来源
MEDICINA CLINICA | 2025年 / 164卷 / 01期
关键词
Cystic fibrosis; Cystic fibrosis bone disease (CFBD); Densitometry (DXA); Vertebral fracture assessment (VFA); Quantitative computed tomography (qCT); QUANTITATIVE ULTRASOUND; MINERAL DENSITY; YOUNG-ADULTS; CHILDREN; FRACTURES; HEALTH; MICROARCHITECTURE; INFECTION; STRENGTH; MARKERS;
D O I
10.1016/j.medcli.2024.05.014
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
With the increased life expectancy of people with cystic fibrosis (CF), clinical attention has focused on prevention and treatment of non-pulmonary comorbidities. CF-related bone disease (CFBD) is a common complication and leads to increased fracture rates. Dual energy X-ray absorptiometry (DXA) is the recommended and gold standard technique to identify and monitor bone health. However, DXA has limitations because of its two-dimensional nature. Complementary tools to DXA are available, such as trabecular bone score (TBS) and vertebral fracture assessment (VFA). Quantitative computed tomography (QCT), magnetic resonance imaging (MRI) and quantitative ultrasound (QUS) may also be useful. (c) 2024 Elsevier Espana, S.L.U. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:23 / 29
页数:7
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