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Osteoporosis is the most common metabolic bone disease and goes largely undiagnosed throughout the world, due to the inaccessibility of DXA machines. Multivariate analyses of serum bone turnover markers were evaluated in 226 Orange County, California, residents with the intent to determine if serum osteocalcin and serum pyridinoline cross-links could be used to detect the onset of osteoporosis as effectively as a DXA scan. Descriptive analyses of the demographic and lab characteristics of the participants were performed through frequency, means and standard deviation estimations. We implemented logistic regression modeling to find the best classification algorithm for osteoporosis. All calculations and model building steps were carried out using R statistical language. Through these analyses, a mathematical algorithm with diagnostic potential was created. This algorithm showed a sensitivity of 1.0 and a specificity of 0.83, with an area under the Receiver Operating Characteristic curve of 0.93, thus demonstrating a high predictability for osteoporosis. Our intention is for this algorithm to be used to evaluate osteoporosis in locations where access to DXA scanning is scarce.


This article was originally published in Bone and Muscle, volume 1, in 2018.

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.



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