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Factors Affecting Bone Mineral Density Among Snowy Region Residents in Japan: Analysis Using Multiple Linear Regression and Bayesian Network Model

BACKGROUND: As the onset of osteoporosis leads to reduced activities of daily living and may result in patients being bedridden, efforts to prevent decreased bone density are necessary. Various studies on the relationship between sex, age, nutrients, and exercise habits and bone mineral density have...

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Autores principales: Suzuki, Teppei, Shimoda, Tomoko, Takahashi, Noriko, Tsutsumi, Kaori, Samukawa, Mina, Yoshimura, Sadako, Ogasawara, Katsuhiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283262/
https://www.ncbi.nlm.nih.gov/pubmed/29789280
http://dx.doi.org/10.2196/ijmr.8555
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author Suzuki, Teppei
Shimoda, Tomoko
Takahashi, Noriko
Tsutsumi, Kaori
Samukawa, Mina
Yoshimura, Sadako
Ogasawara, Katsuhiko
author_facet Suzuki, Teppei
Shimoda, Tomoko
Takahashi, Noriko
Tsutsumi, Kaori
Samukawa, Mina
Yoshimura, Sadako
Ogasawara, Katsuhiko
author_sort Suzuki, Teppei
collection PubMed
description BACKGROUND: As the onset of osteoporosis leads to reduced activities of daily living and may result in patients being bedridden, efforts to prevent decreased bone density are necessary. Various studies on the relationship between sex, age, nutrients, and exercise habits and bone mineral density have been conducted to date. However, for snowy region residents, the magnitude of influence of various factors affecting bone mineral density and the influence level have not been clarified. OBJECTIVE: This study aimed to clarify the degree of influence and factors influencing bone mineral density based on survey results on health conditions and lifestyle habits in heavy snow areas. METHODS: A total of 354 citizens who visited a drugstore in the target area were included in a study that included using the brief-type self-administered diet history questionnaire on lifestyle and exercise habits. Height, weight, body composition, and bone densitometer values were analyzed using multiple regression to calculate their association with bone mineral density. In addition, a Bayesian network model was used to determine the influence level of each factor as a conditional probability. RESULTS: Multiple regression analysis revealed that age, sex, fracture, and calcium intake significantly influenced bone mineral density. In addition, the result of Bayesian network analysis suggested that age and sex affected bone mineral density, whereas nutrients and exercise habits might not have a direct impact. However, calcium intake and the T-score were significant factors affecting the presence or absence of fracture experiences, suggesting that adequate calcium intake is essential for preventing fractures. CONCLUSIONS: In the multiple regression analysis, age, sex, fracture, and calcium intake were selected as factors; however, in the Bayesian analysis, only age and sex affected bone mineral density while nutrients did not. In addition, the fact that calcium intake and the T-score were shown to affect bone fracture history suggests that calcium intake is an important measure that can prevent bone fractures. Overall, these results suggest that measures such as ensuring a bone fracture–free environment and providing nutritional advice for calcium intake can be effective in preventing bone loss.
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spelling pubmed-62832622019-01-03 Factors Affecting Bone Mineral Density Among Snowy Region Residents in Japan: Analysis Using Multiple Linear Regression and Bayesian Network Model Suzuki, Teppei Shimoda, Tomoko Takahashi, Noriko Tsutsumi, Kaori Samukawa, Mina Yoshimura, Sadako Ogasawara, Katsuhiko Interact J Med Res Original Paper BACKGROUND: As the onset of osteoporosis leads to reduced activities of daily living and may result in patients being bedridden, efforts to prevent decreased bone density are necessary. Various studies on the relationship between sex, age, nutrients, and exercise habits and bone mineral density have been conducted to date. However, for snowy region residents, the magnitude of influence of various factors affecting bone mineral density and the influence level have not been clarified. OBJECTIVE: This study aimed to clarify the degree of influence and factors influencing bone mineral density based on survey results on health conditions and lifestyle habits in heavy snow areas. METHODS: A total of 354 citizens who visited a drugstore in the target area were included in a study that included using the brief-type self-administered diet history questionnaire on lifestyle and exercise habits. Height, weight, body composition, and bone densitometer values were analyzed using multiple regression to calculate their association with bone mineral density. In addition, a Bayesian network model was used to determine the influence level of each factor as a conditional probability. RESULTS: Multiple regression analysis revealed that age, sex, fracture, and calcium intake significantly influenced bone mineral density. In addition, the result of Bayesian network analysis suggested that age and sex affected bone mineral density, whereas nutrients and exercise habits might not have a direct impact. However, calcium intake and the T-score were significant factors affecting the presence or absence of fracture experiences, suggesting that adequate calcium intake is essential for preventing fractures. CONCLUSIONS: In the multiple regression analysis, age, sex, fracture, and calcium intake were selected as factors; however, in the Bayesian analysis, only age and sex affected bone mineral density while nutrients did not. In addition, the fact that calcium intake and the T-score were shown to affect bone fracture history suggests that calcium intake is an important measure that can prevent bone fractures. Overall, these results suggest that measures such as ensuring a bone fracture–free environment and providing nutritional advice for calcium intake can be effective in preventing bone loss. JMIR Publications 2018-05-22 /pmc/articles/PMC6283262/ /pubmed/29789280 http://dx.doi.org/10.2196/ijmr.8555 Text en ©Teppei Suzuki, Tomoko Shimoda, Noriko Takahashi, Kaori Tsutsumi, Mina Samukawa, Sadako Yoshimura, Katsuhiko Ogasawara. Originally published in the Interactive Journal of Medical Research (http://www.i-jmr.org/), 22.05.2018. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Interactive Journal of Medical Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.i-jmr.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Suzuki, Teppei
Shimoda, Tomoko
Takahashi, Noriko
Tsutsumi, Kaori
Samukawa, Mina
Yoshimura, Sadako
Ogasawara, Katsuhiko
Factors Affecting Bone Mineral Density Among Snowy Region Residents in Japan: Analysis Using Multiple Linear Regression and Bayesian Network Model
title Factors Affecting Bone Mineral Density Among Snowy Region Residents in Japan: Analysis Using Multiple Linear Regression and Bayesian Network Model
title_full Factors Affecting Bone Mineral Density Among Snowy Region Residents in Japan: Analysis Using Multiple Linear Regression and Bayesian Network Model
title_fullStr Factors Affecting Bone Mineral Density Among Snowy Region Residents in Japan: Analysis Using Multiple Linear Regression and Bayesian Network Model
title_full_unstemmed Factors Affecting Bone Mineral Density Among Snowy Region Residents in Japan: Analysis Using Multiple Linear Regression and Bayesian Network Model
title_short Factors Affecting Bone Mineral Density Among Snowy Region Residents in Japan: Analysis Using Multiple Linear Regression and Bayesian Network Model
title_sort factors affecting bone mineral density among snowy region residents in japan: analysis using multiple linear regression and bayesian network model
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283262/
https://www.ncbi.nlm.nih.gov/pubmed/29789280
http://dx.doi.org/10.2196/ijmr.8555
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