title: Assessment of determinants for osteoporosis in elderly men.
abstract: CONCLUSIONS
  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.
  
  
  BACKGROUND
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Public Health, Epidemiology and Health Economics, University of Liège, Sart-Tilman, Belgium. s.scholtissen@ulg.ac.be
authors.forename: S
authors.initials: S
authors.lastname: Scholtissen
authors.forename: F
authors.initials: F
authors.lastname: Guillemin
authors.forename: O
authors.initials: O
authors.lastname: Bruyère
authors.forename: J
authors.initials: J
authors.lastname: Collette
authors.forename: B
authors.initials: B
authors.lastname: Dousset
authors.forename: C
authors.initials: C
authors.lastname: Kemmer
authors.forename: S
authors.initials: S
authors.lastname: Culot
authors.forename: D
authors.initials: D
authors.lastname: Crémer
authors.forename: H
authors.initials: H
authors.lastname: Dejardin
authors.forename: G
authors.initials: G
authors.lastname: Hubermont
authors.forename: D
authors.initials: D
authors.lastname: Lefebvre
authors.forename: V
authors.initials: V
authors.lastname: Pascal-Vigneron
authors.forename: G
authors.initials: G
authors.lastname: Weryha
authors.forename: J Y
authors.initials: JY
authors.lastname: Reginster
mesh_terms: D015502:Absorptiometry, Photon / Q000379:methods; D000368:Aged; D000375:Aging; D000465:Algorithms; D001530:Belgium / Q000453:epidemiology; D015519:Bone Density; D001851:Bone Diseases, Metabolic / Q000453:epidemiology; D024042:Collagen Type I; D003430:Cross-Sectional Studies; D005272:Femur Neck / Q000000981:diagnostic imaging; D050723:Fractures, Bone / Q000453:epidemiology; D005602:France / Q000453:epidemiology; D006621:Hip Joint / Q000000981:diagnostic imaging; D006801:Humans; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D010024:Osteoporosis / Q000175:diagnosis* / Q000453:epidemiology / Q000235:genetics; D010446:Peptide Fragments / Q000097:blood; D010455:Peptides; D011347:Procollagen / Q000097:blood; D012372:ROC Curve; D012044:Regression Analysis; D012307:Risk Factors
chemical_list: D024042:Collagen Type I; D010446:Peptide Fragments; D010455:Peptides; D011347:Procollagen; C052929:collagen type I trimeric cross-linked peptide