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Updated: Jun 14, 2025

Fragility Assessment of Bovine Cortical Bone Using Scratch Tests
Published on: November 30, 2017
Skeletal fragility in pituitary disease: how can we predict fracture risk?
Fabio Bioletto1, Alessandro Maria Berton2, Marco Barale3
1Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, Turin, 10126, Italy. fabio.bioletto@unito.it.
Abstract:
Pituitary hormones play a crucial role in regulating skeletal physiology, and skeletal fragility is a frequent complication of pituitary diseases. The ability to predict the risk of fracture events is crucial for guiding therapeutic decisions; however, in patients with pituitary diseases, fracture risk estimation is particularly challenging. Compared to primary osteoporosis, the evaluation of bone mineral density by dual X-ray absorptiometry is much less informative about fracture risk. Moreover, the reliability of standard fracture risk calculators does not have strong validations in this setting. Morphometric vertebral assessment is currently the cornerstone in the assessment of skeletal fragility in patients with pituitary diseases, as prevalent fractures remain the strongest predictor of future fracture events. In recent years, new tools for evaluating bone quality have shown promising results in assessing bone impairment in patients with pituitary diseases, but most available data are cross-sectional, and evidence regarding the prediction of incident fractures is still scarce. Of note, apart from measures of bone density and bone quality, the estimation of fracture risk in the context of pituitary hyperfunction or hypofunction cannot ignore the evaluation of factors related to the underlying disease, such as its severity and duration, as well as the specific therapies implemented for its treatment. Aim of this review is to provide an up-to-date overview of all major evidence regarding fracture risk prediction in patients with pituitary disease, highlighting the need for a tailored approach that critically integrates all clinical, biochemical, and instrumental data according to the specificities of each disease.
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