Literature DB >> 8704349

Screening for osteopenia and osteoporosis: selection by body composition.

K Michaëlsson1, R Bergström, H Mallmin, L Holmberg, A Wolk, S Ljunghall.   

Abstract

There is a great need for simple means of identifying persons at low risk of developing osteoporosis, in order to exclude them from screening with bone mineral measurements, since this procedure is too expensive and time-consuming for general use in the unselected population. We have determined the relationships between body measure (weight, height, body mass index, lean tissue mass, fat mass, waist-to-hip ratio) and bone mineral density (BMD) in 175 women of ages 28-74 years in a cross-sectional study in a county in central Sweden. Dual-energy X-ray absorptiometry was performed at three sites: total body, L2-4 region of lumbar spine, and neck region of the proximal femur. Using multiple linear regression models, the relationship between the dependent variable, BMD, and each of the body measures was determined, with adjustment for confounding factors. Weight alone, in a multivariate model, explained 28%, 21% and 15% of the variance in BMD of total body, at the lumbar spine and at the femoral neck according to these models. The WHO definition of osteopenia was used to dichotomize BMD, which made it possible, in multivariate logistic regression models, to estimate the risk of osteopenia with different body measures categorized into tertiles. Weight of over 71 kg was associated with a very low risk of being osteopenic compared with women weighing less than 64 kg, with odds ratios (OR) of 0.01 (95% confidence interval (CI) 0.00-0.09), 0.06 (CI 0.02-0.22) and 0.13 (CI 0.04-0.42) for osteopenia of total body, lumbar spine and femoral neck, respectively. Furthermore a sensitivity/specificity analysis revealed that, in this population, a woman weighing over 70 kg is not likely to have osteoporosis. Test specifics of a weight under 70 kg for osteoporosis (BMD less than 2.5 SD compared with normal young women) of femoral neck among the postmenopausal women showed a sensitivity of 0.94, a specificity of 0.36, positive predictive value (PPV) of 0.21, and negative predictive value (NPV) of 0.97. Thus, exclusion of the 33% of women with the highest weight meant only that 3% of osteoporotic cases were missed. The corresponding figures for lumbar spine were sensitivity 0.89, specificity 0.38, PPV 0.33, and NPV 0.91. All women who were defined as being osteoporotic of total body weighed under 62 kg. When the intention was to identify those with osteopenia of total body among the postmenopausal women we attained a sensitivity of 0.92 and a NPV of 0.91 for a weight under 70 kg, whereas we found that weight could not be used as an exclusion criterion for osteopenia of femoral neck and lumbar spine. Our data thus indicate that weight could be used to exclude women from a screening program for postmenopausal osteoporosis.

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Year:  1996        PMID: 8704349     DOI: 10.1007/BF01623934

Source DB:  PubMed          Journal:  Osteoporos Int        ISSN: 0937-941X            Impact factor:   4.507


  22 in total

1.  Familial resemblance of radial bone mass between premenopausal mothers and their college-age daughters.

Authors:  F A Tylavsky; A D Bortz; R L Hancock; J J Anderson
Journal:  Calcif Tissue Int       Date:  1989-11       Impact factor: 4.333

2.  Black-white differences in serum sex hormones and bone mineral density.

Authors:  J A Cauley; J P Gutai; L H Kuller; J Scott; M C Nevitt
Journal:  Am J Epidemiol       Date:  1994-05-15       Impact factor: 4.897

3.  Bone mineral normative data in Malmö, Sweden. Comparison with reference data and hip fracture incidence in other ethnic groups.

Authors:  M K Karlsson; P Gärdsell; O Johnell; B E Nilsson; K Akesson; K J Obrant
Journal:  Acta Orthop Scand       Date:  1993-04

4.  The diagnosis of osteoporosis.

Authors:  J A Kanis; L J Melton; C Christiansen; C C Johnston; N Khaltaev
Journal:  J Bone Miner Res       Date:  1994-08       Impact factor: 6.741

5.  Effects of high-intensity strength training on multiple risk factors for osteoporotic fractures. A randomized controlled trial.

Authors:  M E Nelson; M A Fiatarone; C M Morganti; I Trice; R A Greenberg; W J Evans
Journal:  JAMA       Date:  1994-12-28       Impact factor: 56.272

6.  Bone loss accompanying voluntary weight loss in obese humans.

Authors:  L B Jensen; F Quaade; O H Sørensen
Journal:  J Bone Miner Res       Date:  1994-04       Impact factor: 6.741

7.  Relation between body size and bone mineral density in elderly men and women.

Authors:  S L Edelstein; E Barrett-Connor
Journal:  Am J Epidemiol       Date:  1993-08-01       Impact factor: 4.897

Review 8.  Can vigorous exercise play a role in osteoporosis prevention? A review.

Authors:  B Gutin; M J Kasper
Journal:  Osteoporos Int       Date:  1992-03       Impact factor: 4.507

9.  Evidence for alteration of the vitamin D-endocrine system in obese subjects.

Authors:  N H Bell; S Epstein; A Greene; J Shary; M J Oexmann; S Shaw
Journal:  J Clin Invest       Date:  1985-07       Impact factor: 14.808

Review 10.  Ethnic and genetic differences in bone mass: a review with a hereditary vs environmental perspective.

Authors:  W S Pollitzer; J J Anderson
Journal:  Am J Clin Nutr       Date:  1989-12       Impact factor: 7.045

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  38 in total

1.  Dental panoramic radiograph as a tool to detect postmenopausal women with low bone mineral density: untrained general dental practitioners' diagnostic performance.

Authors:  Takashi Nakamoto; Akira Taguchi; Masahiko Ohtsuka; Yoshikazu Suei; Minoru Fujita; Keiji Tanimoto; Mikio Tsuda; Mitsuhiro Sanada; Koso Ohama; Junichiro Takahashi; Madeleine Rohlin
Journal:  Osteoporos Int       Date:  2003-06-24       Impact factor: 4.507

Review 2.  Superiority of age and weight as variables in predicting osteoporosis in postmenopausal white women.

Authors:  Manfred Wildner; Andrea Peters; Vibhavendra S Raghuvanshi; Jörg Hohnloser; Uwe Siebert
Journal:  Osteoporos Int       Date:  2003-09-16       Impact factor: 4.507

3.  Bone mineral density of the spine and femur in healthy Saudis.

Authors:  M Salleh M Ardawi; Abdulraouf A Maimany; Talal M Bahksh; Hasan A N Nasrat; Waleed A Milaat; Raja M Al-Raddadi
Journal:  Osteoporos Int       Date:  2004-05-27       Impact factor: 4.507

4.  Bone mineral mass in males and females with and without Down syndrome.

Authors:  Fatima Baptista; Ana Varela; Luis B Sardinha
Journal:  Osteoporos Int       Date:  2004-09-09       Impact factor: 4.507

Review 5.  Prescreening tools to determine who needs DXA.

Authors:  Elliott N Schwartz; Dee M Steinberg
Journal:  Curr Osteoporos Rep       Date:  2006-12       Impact factor: 5.096

6.  Decision rules for bone mineral density testing.

Authors:  Katherine A Kovacs
Journal:  CMAJ       Date:  2002-09-03       Impact factor: 8.262

Review 7.  Risk factors for low bone mass in healthy 40-60 year old women: a systematic review of the literature.

Authors:  E J Waugh; M-A Lam; G A Hawker; J McGowan; A Papaioannou; A M Cheung; A B Hodsman; W D Leslie; K Siminoski; S A Jamal
Journal:  Osteoporos Int       Date:  2008-06-04       Impact factor: 4.507

8.  Artificial neural networks in prediction of bone density among post-menopausal women.

Authors:  M Sadatsafavi; A Moayyeri; A Soltani; B Larijani; M Nouraie; S Akhondzadeh
Journal:  J Endocrinol Invest       Date:  2005-05       Impact factor: 4.256

9.  Body mass index and disease burden in elderly men and women: the Tromsø Study.

Authors:  Jan-Magnus Kvamme; Tom Wilsgaard; Jon Florholmen; Bjarne K Jacobsen
Journal:  Eur J Epidemiol       Date:  2010-01-20       Impact factor: 8.082

10.  Development and validation of osteoporosis risk-assessment model for Korean postmenopausal women.

Authors:  Sun Min Oh; Byung-Ho Nam; Yumie Rhee; Seong-Hwan Moon; Deog Young Kim; Dae Ryong Kang; Hyeon Chang Kim
Journal:  J Bone Miner Metab       Date:  2013-02-19       Impact factor: 2.626

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