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Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley, Malaysia

Background: The current osteoporosis screening instruments are not optimized to be used among the Malaysian population. This study aimed to develop an osteoporosis screening algorithm based on risk factors for Malaysians. Methods: Malaysians aged ≥50 years (n = 607) from Klang Valley, Malaysia were...

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Autores principales: Subramaniam, Shaanthana, Chan, Chin-Yi, Soelaiman, Ima-Nirwana, Mohamed, Norazlina, Muhammad, Norliza, Ahmad, Fairus, Ng, Pei-Yuen, Jamil, Nor Aini, Abd Aziz, Noorazah, Chin, Kok-Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177333/
https://www.ncbi.nlm.nih.gov/pubmed/32272697
http://dx.doi.org/10.3390/ijerph17072526
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author Subramaniam, Shaanthana
Chan, Chin-Yi
Soelaiman, Ima-Nirwana
Mohamed, Norazlina
Muhammad, Norliza
Ahmad, Fairus
Ng, Pei-Yuen
Jamil, Nor Aini
Abd Aziz, Noorazah
Chin, Kok-Yong
author_facet Subramaniam, Shaanthana
Chan, Chin-Yi
Soelaiman, Ima-Nirwana
Mohamed, Norazlina
Muhammad, Norliza
Ahmad, Fairus
Ng, Pei-Yuen
Jamil, Nor Aini
Abd Aziz, Noorazah
Chin, Kok-Yong
author_sort Subramaniam, Shaanthana
collection PubMed
description Background: The current osteoporosis screening instruments are not optimized to be used among the Malaysian population. This study aimed to develop an osteoporosis screening algorithm based on risk factors for Malaysians. Methods: Malaysians aged ≥50 years (n = 607) from Klang Valley, Malaysia were interviewed and their bone health status was assessed using a dual-energy X-ray absorptiometry device. The algorithm was constructed based on osteoporosis risk factors using multivariate logistic regression and its performance was assessed using receiver operating characteristics analysis. Results: Increased age, reduced body weight and being less physically active significantly predicted osteoporosis in men, while in women, increased age, lower body weight and low-income status significantly predicted osteoporosis. These factors were included in the final algorithm and the optimal cut-offs to identify subjects with osteoporosis was 0.00120 for men [sensitivity 73.3% (95% confidence interval (CI) = 54.1%–87.7%), specificity 67.8% (95% CI = 62.7%–85.5%), area under curve (AUC) 0.705 (95% CI = 0.608–0.803), p < 0.001] and 0.161 for women [sensitivity 75.4% (95% CI = 61.9%–73.3%), specificity 74.5% (95% CI = 68.5%–79.8%), AUC 0.749 (95% CI = 0.679–0.820), p < 0.001]. Conclusion: The new algorithm performed satisfactorily in identifying the risk of osteoporosis among the Malaysian population ≥50 years. Further validation studies are required before applying this algorithm for screening of osteoporosis in public.
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spelling pubmed-71773332020-04-28 Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley, Malaysia Subramaniam, Shaanthana Chan, Chin-Yi Soelaiman, Ima-Nirwana Mohamed, Norazlina Muhammad, Norliza Ahmad, Fairus Ng, Pei-Yuen Jamil, Nor Aini Abd Aziz, Noorazah Chin, Kok-Yong Int J Environ Res Public Health Article Background: The current osteoporosis screening instruments are not optimized to be used among the Malaysian population. This study aimed to develop an osteoporosis screening algorithm based on risk factors for Malaysians. Methods: Malaysians aged ≥50 years (n = 607) from Klang Valley, Malaysia were interviewed and their bone health status was assessed using a dual-energy X-ray absorptiometry device. The algorithm was constructed based on osteoporosis risk factors using multivariate logistic regression and its performance was assessed using receiver operating characteristics analysis. Results: Increased age, reduced body weight and being less physically active significantly predicted osteoporosis in men, while in women, increased age, lower body weight and low-income status significantly predicted osteoporosis. These factors were included in the final algorithm and the optimal cut-offs to identify subjects with osteoporosis was 0.00120 for men [sensitivity 73.3% (95% confidence interval (CI) = 54.1%–87.7%), specificity 67.8% (95% CI = 62.7%–85.5%), area under curve (AUC) 0.705 (95% CI = 0.608–0.803), p < 0.001] and 0.161 for women [sensitivity 75.4% (95% CI = 61.9%–73.3%), specificity 74.5% (95% CI = 68.5%–79.8%), AUC 0.749 (95% CI = 0.679–0.820), p < 0.001]. Conclusion: The new algorithm performed satisfactorily in identifying the risk of osteoporosis among the Malaysian population ≥50 years. Further validation studies are required before applying this algorithm for screening of osteoporosis in public. MDPI 2020-04-07 2020-04 /pmc/articles/PMC7177333/ /pubmed/32272697 http://dx.doi.org/10.3390/ijerph17072526 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Subramaniam, Shaanthana
Chan, Chin-Yi
Soelaiman, Ima-Nirwana
Mohamed, Norazlina
Muhammad, Norliza
Ahmad, Fairus
Ng, Pei-Yuen
Jamil, Nor Aini
Abd Aziz, Noorazah
Chin, Kok-Yong
Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley, Malaysia
title Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley, Malaysia
title_full Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley, Malaysia
title_fullStr Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley, Malaysia
title_full_unstemmed Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley, Malaysia
title_short Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley, Malaysia
title_sort development of osteoporosis screening algorithm for population aged 50 years and above in klang valley, malaysia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177333/
https://www.ncbi.nlm.nih.gov/pubmed/32272697
http://dx.doi.org/10.3390/ijerph17072526
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