Reference : Assessment of determinants for osteoporosis in elderly men.
Scientific journals : Article
Human health sciences : General & internal medicine
http://hdl.handle.net/2268/20339
Assessment of determinants for osteoporosis in elderly men.
English
Scholtissen, Sophie mailto [Université de Liège - ULg > Département des sciences de la santé publique > Epidémiologie et santé publique >]
Guillemin, F. [> > > >]
Bruyère, Olivier mailto [Université de Liège - ULg > Département des sciences de la santé publique > Epidémiologie et santé publique - Département des sciences de la santé publique >]
Collette, Julien mailto [Centre Hospitalier Universitaire de Liège - CHU > > Chimie médicale >]
Dousset, B. [> > > >]
Kemmer, C. [> > > >]
Culot, S. [> > > >]
Cremer, D. [> > > >]
Dejardin, H. [> > > >]
Hubermont, G. [> > > >]
Lefebvre, D. [> > > >]
Pascal-Vigneron, V. [> > > >]
Weryha, G. [> > > >]
Reginster, Jean-Yves mailto [Université de Liège - ULg > Département des sciences de la santé publique > Epidémiologie et santé publique >]
2009
Osteoporosis International
Springer Science & Business Media B.V.
20
7
1157-66
Yes (verified by ORBi)
International
0937-941X
1433-2965
Godalming
United Kingdom
[en] SUMMARY: The aim of this cross-sectional study was to determine and quantify some determinants associated to low bone mineral density (BMD) in elderly men. This study showed that ageing, a lower body mass index (BMI), a higher blood level of C-terminal cross-linking telopeptides of type I collagen (CTX-1), family history of osteoporosis, and/or fracture and prior fracture were associated with bone mineral density. INTRODUCTION: Our aims were to identify some determinants associated to low bone mineral density in men and to develop a simple algorithm to predict osteoporosis. METHODS: A sample of 1,004 men aged 60 years and older was recruited. Biometrical, serological, clinical, and lifestyle determinants were collected. Univariate, multivariate, and logistic regression analyses were performed. Receiver operating characteristic analysis was used to assess the discriminant performance of the algorithm. RESULTS: In the multiple regression analysis, only age, BMI, CTX-1, and family history of osteoporosis and/or fracture were able to predict the femoral neck T-score. When running the procedure with the total hip T-score, prior fracture also appeared to be significant. With the lumbar spine T-score, only age, BMI, and CTX-1 were retained. The best algorithm was based on age, BMI, family history, and CTX-1. A cut-off point of 0.25 yielded a sensibility of 78%, a specificity of 59% with an area under the curve of 0.73 in the development and validation cohorts. CONCLUSION: Ageing, a lower BMI, higher CTX-1, family history, and prior fracture were associated with T-score. Our algorithm is a simple approach to identify men at risk for osteoporosis.
http://hdl.handle.net/2268/20339
10.1007/s00198-008-0789-6

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