Osteo Criteria

[Guest guest]

Hi folks:

I have been trying to find **definitive** specific numerical data for

the levels of BMD regarded as defining osteopenia and osteoporosis.

I have not been able to locate any such indications for males - there

is much less osteo data for males than for females, of course - but

the following study abstract does indicate the cutoffs used for the

purposes of this study. If anyone has definitive data from another

source for either males or females it would be much appreciated if a

reference could be posted.

In the following abstract, L2-L4 of 0.970 g/cm^2 is the cutoff for

low BMD, and 0.800 represents a diagnosis of osteoporosis. The worst

of my numbers is appreciably above even the higher of these cutoffs,

so, if similar data are applicable to males, then perhaps I was a bit

hasty in drawing conclusions. We shall see. But without doubt my

data are appreciably below the relevant **averages**, so corrective

action is certainly called for. Here is the abstract:

" Construction of an algorithm for quick detection of patients with

low bone mineral density and its applicability in daily general

practice

Authors: van der Voort, D.J.M.a; Dinant, G.J.a; Rinkens, P.E.L.M.a;

van der Voort-Duindam, C.J.M.a; van Wersch, J.W.J.b; Geusens, P.P.c

Affiliations: a. University Maastricht, Department of General

Practice and Research Institute for Extramural and Transmural Health

Care, P.O. Box 616, 6200 MD, Maastricht, The Netherlands

b. Department of Clinical Chemistry, De Wever Hospital, Heerlen, The

Netherlands

c. Department of Internal Medicine, Academic Hospital, Maastricht,

The Netherlands

Keyword: Osteoporosis, Diagnostic study, Bone mineral density,

General practice

Abstract (English): Objective: To construct a quick algorithm to

detect patients with low bone mineral density (BMD) and osteoporosis

and determine its applicability in daily general practice. Design:

Cross-sectional study in all 9107 postmenopausal women, aged 50–80,

registered at 12 general practice centers. Subjects and measurements:

All healthy women (5303) and 25% of the remaining group (943/3804)

were invited to participate. Of 6246 invited women, 4725 (76%)

participated. The women were questioned (state of health, medical

history, family history, and food questionnaire) and examined

[weight, height, body mass index (BMI), and BMD of the lumbar spine].

Statistics: Multivariable, stepwise backward and forward logistic

regression analyses were performed, with BMD of the lumbar spine (L2-

L4, cut-off points at 0.800 g/cm2 for osteoporosis and 0.970 g/cm2

for low BMD) as the dependent variable. An algorithm was constructed

with those variables that correlated statistically significantly and

clinically relevant with the presence of both osteoporosis and low

BMD. Results: The prevalence of osteoporosis was 23%, that of low BMD

was 65%. Only three variables (age, BMI, and fractures) were

statistically significant and clinically relevant correlated with the

presense of both osteoporosis and low BMD. Age (OR 2.70 for

osteoporosis and OR 1.77 for low BMD) and fractures during the past

five years (OR 3.60 for osteoporosis and OR 2.85 for low BMD) were

found to be the key predictors. From the algorithm the absolute risks

varied from 9% to 51% for osteoporosis and from 48% to 84% for low

BMD. The corresponding relative risks varied from 1.0 to 5.7 and from

1.0 to 1.8. Conclusions: Using an algorithm with age, BMI, and

fracture history subgroups at high risk could be identified. However,

in whatever combination, many women with osteoporosis could not be

identified. Despite the differences in methods, we found predictors

for osteoporosis which were comparable with the results of other

cross-sectional studies, meaning that the first selection of patients

at high risk for low BMD can be done adequately by both specialists

and general practitioners. "

This study does not appear to be listed in Pubmed.

Rodney.