Factors associated with muscle mass in community-dwelling older people in Singapore: Findings from the SHIELD study
Aging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height2), and prevalence of lo...
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| Published in | PloS one Vol. 14; no. 10; p. e0223222 |
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| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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United States
Public Library of Science
09.10.2019
Public Library of Science (PLoS) |
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| Abstract | Aging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height2), and prevalence of low ASMI in older people with normal nutritional status (Malnutrition Universal Screening Tool score = 0); (2) to determine factors associated with ASMI, and odds ratios of having low ASMI.
SHIELD is a study of community-dwelling older people aged 65 years and above in Singapore. ASMI was determined using bioelectrical impedance analysis and low ASMI was defined as <7.0 kg/m2 for males and <5.7 kg/m2 for females (Asian Working Group for Sarcopenia, 2014).
A total of 400 older people (183 males and 217 females) took part in this study. The overall prevalence of low ASMI was 20.6% (15.5% in males and 24.9% in females). Females had significantly lower ASMI than males (P < 0.0001), age was inversely associated with ASMI (P = 0.0024) while BMI and calf circumference were positively associated with ASMI (both P < 0.0001) in the total cohort. In addition, ASMI was positively associated with bone mass in both genders (both P < 0.0001). After adjusting for covariates, the odds ratios of having low ASMI with every 1 year and 10 years increase in age were 1.13 (95% CI: 1.06, 1.20) and 3.36 (95% CI: 1.82, 6.21) respectively.
The high prevalence of low ASMI in community-dwelling older people with normal nutritional status highlights the need for early screening. There was a strong inverse association between age and ASMI while BMI, calf circumference and bone mass were positively associated with ASMI. These findings will give further weight to the importance and development of public health strategies in maintaining and improving muscle health in this population group. |
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| AbstractList | Objectives Aging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height2), and prevalence of low ASMI in older people with normal nutritional status (Malnutrition Universal Screening Tool score = 0); (2) to determine factors associated with ASMI, and odds ratios of having low ASMI. Methods SHIELD is a study of community-dwelling older people aged 65 years and above in Singapore. ASMI was determined using bioelectrical impedance analysis and low ASMI was defined as <7.0 kg/m2 for males and <5.7 kg/m2 for females (Asian Working Group for Sarcopenia, 2014). Results A total of 400 older people (183 males and 217 females) took part in this study. The overall prevalence of low ASMI was 20.6% (15.5% in males and 24.9% in females). Females had significantly lower ASMI than males (P < 0.0001), age was inversely associated with ASMI (P = 0.0024) while BMI and calf circumference were positively associated with ASMI (both P < 0.0001) in the total cohort. In addition, ASMI was positively associated with bone mass in both genders (both P < 0.0001). After adjusting for covariates, the odds ratios of having low ASMI with every 1 year and 10 years increase in age were 1.13 (95% CI: 1.06, 1.20) and 3.36 (95% CI: 1.82, 6.21) respectively. Conclusions The high prevalence of low ASMI in community-dwelling older people with normal nutritional status highlights the need for early screening. There was a strong inverse association between age and ASMI while BMI, calf circumference and bone mass were positively associated with ASMI. These findings will give further weight to the importance and development of public health strategies in maintaining and improving muscle health in this population group. Aging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height2), and prevalence of low ASMI in older people with normal nutritional status (Malnutrition Universal Screening Tool score = 0); (2) to determine factors associated with ASMI, and odds ratios of having low ASMI. SHIELD is a study of community-dwelling older people aged 65 years and above in Singapore. ASMI was determined using bioelectrical impedance analysis and low ASMI was defined as <7.0 kg/m2 for males and <5.7 kg/m2 for females (Asian Working Group for Sarcopenia, 2014). A total of 400 older people (183 males and 217 females) took part in this study. The overall prevalence of low ASMI was 20.6% (15.5% in males and 24.9% in females). Females had significantly lower ASMI than males (P < 0.0001), age was inversely associated with ASMI (P = 0.0024) while BMI and calf circumference were positively associated with ASMI (both P < 0.0001) in the total cohort. In addition, ASMI was positively associated with bone mass in both genders (both P < 0.0001). After adjusting for covariates, the odds ratios of having low ASMI with every 1 year and 10 years increase in age were 1.13 (95% CI: 1.06, 1.20) and 3.36 (95% CI: 1.82, 6.21) respectively. The high prevalence of low ASMI in community-dwelling older people with normal nutritional status highlights the need for early screening. There was a strong inverse association between age and ASMI while BMI, calf circumference and bone mass were positively associated with ASMI. These findings will give further weight to the importance and development of public health strategies in maintaining and improving muscle health in this population group. Objectives Aging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height2), and prevalence of low ASMI in older people with normal nutritional status (Malnutrition Universal Screening Tool score = 0); (2) to determine factors associated with ASMI, and odds ratios of having low ASMI. Methods SHIELD is a study of community-dwelling older people aged 65 years and above in Singapore. ASMI was determined using bioelectrical impedance analysis and low ASMI was defined as <7.0 kg/m2 for males and <5.7 kg/m2 for females (Asian Working Group for Sarcopenia, 2014). Results A total of 400 older people (183 males and 217 females) took part in this study. The overall prevalence of low ASMI was 20.6% (15.5% in males and 24.9% in females). Females had significantly lower ASMI than males (P < 0.0001), age was inversely associated with ASMI (P = 0.0024) while BMI and calf circumference were positively associated with ASMI (both P < 0.0001) in the total cohort. In addition, ASMI was positively associated with bone mass in both genders (both P < 0.0001). After adjusting for covariates, the odds ratios of having low ASMI with every 1 year and 10 years increase in age were 1.13 (95% CI: 1.06, 1.20) and 3.36 (95% CI: 1.82, 6.21) respectively. Conclusions The high prevalence of low ASMI in community-dwelling older people with normal nutritional status highlights the need for early screening. There was a strong inverse association between age and ASMI while BMI, calf circumference and bone mass were positively associated with ASMI. These findings will give further weight to the importance and development of public health strategies in maintaining and improving muscle health in this population group. Aging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height.sup.2 ), and prevalence of low ASMI in older people with normal nutritional status (Malnutrition Universal Screening Tool score = 0); (2) to determine factors associated with ASMI, and odds ratios of having low ASMI. SHIELD is a study of community-dwelling older people aged 65 years and above in Singapore. ASMI was determined using bioelectrical impedance analysis and low ASMI was defined as <7.0 kg/m.sup.2 for males and <5.7 kg/m.sup.2 for females (Asian Working Group for Sarcopenia, 2014). A total of 400 older people (183 males and 217 females) took part in this study. The overall prevalence of low ASMI was 20.6% (15.5% in males and 24.9% in females). Females had significantly lower ASMI than males (P < 0.0001), age was inversely associated with ASMI (P = 0.0024) while BMI and calf circumference were positively associated with ASMI (both P < 0.0001) in the total cohort. In addition, ASMI was positively associated with bone mass in both genders (both P < 0.0001). After adjusting for covariates, the odds ratios of having low ASMI with every 1 year and 10 years increase in age were 1.13 (95% CI: 1.06, 1.20) and 3.36 (95% CI: 1.82, 6.21) respectively. The high prevalence of low ASMI in community-dwelling older people with normal nutritional status highlights the need for early screening. There was a strong inverse association between age and ASMI while BMI, calf circumference and bone mass were positively associated with ASMI. These findings will give further weight to the importance and development of public health strategies in maintaining and improving muscle health in this population group. Objectives Aging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height.sup.2 ), and prevalence of low ASMI in older people with normal nutritional status (Malnutrition Universal Screening Tool score = 0); (2) to determine factors associated with ASMI, and odds ratios of having low ASMI. Methods SHIELD is a study of community-dwelling older people aged 65 years and above in Singapore. ASMI was determined using bioelectrical impedance analysis and low ASMI was defined as <7.0 kg/m.sup.2 for males and <5.7 kg/m.sup.2 for females (Asian Working Group for Sarcopenia, 2014). Results A total of 400 older people (183 males and 217 females) took part in this study. The overall prevalence of low ASMI was 20.6% (15.5% in males and 24.9% in females). Females had significantly lower ASMI than males (P < 0.0001), age was inversely associated with ASMI (P = 0.0024) while BMI and calf circumference were positively associated with ASMI (both P < 0.0001) in the total cohort. In addition, ASMI was positively associated with bone mass in both genders (both P < 0.0001). After adjusting for covariates, the odds ratios of having low ASMI with every 1 year and 10 years increase in age were 1.13 (95% CI: 1.06, 1.20) and 3.36 (95% CI: 1.82, 6.21) respectively. Conclusions The high prevalence of low ASMI in community-dwelling older people with normal nutritional status highlights the need for early screening. There was a strong inverse association between age and ASMI while BMI, calf circumference and bone mass were positively associated with ASMI. These findings will give further weight to the importance and development of public health strategies in maintaining and improving muscle health in this population group. Aging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height2), and prevalence of low ASMI in older people with normal nutritional status (Malnutrition Universal Screening Tool score = 0); (2) to determine factors associated with ASMI, and odds ratios of having low ASMI.OBJECTIVESAging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height2), and prevalence of low ASMI in older people with normal nutritional status (Malnutrition Universal Screening Tool score = 0); (2) to determine factors associated with ASMI, and odds ratios of having low ASMI.SHIELD is a study of community-dwelling older people aged 65 years and above in Singapore. ASMI was determined using bioelectrical impedance analysis and low ASMI was defined as <7.0 kg/m2 for males and <5.7 kg/m2 for females (Asian Working Group for Sarcopenia, 2014).METHODSSHIELD is a study of community-dwelling older people aged 65 years and above in Singapore. ASMI was determined using bioelectrical impedance analysis and low ASMI was defined as <7.0 kg/m2 for males and <5.7 kg/m2 for females (Asian Working Group for Sarcopenia, 2014).A total of 400 older people (183 males and 217 females) took part in this study. The overall prevalence of low ASMI was 20.6% (15.5% in males and 24.9% in females). Females had significantly lower ASMI than males (P < 0.0001), age was inversely associated with ASMI (P = 0.0024) while BMI and calf circumference were positively associated with ASMI (both P < 0.0001) in the total cohort. In addition, ASMI was positively associated with bone mass in both genders (both P < 0.0001). After adjusting for covariates, the odds ratios of having low ASMI with every 1 year and 10 years increase in age were 1.13 (95% CI: 1.06, 1.20) and 3.36 (95% CI: 1.82, 6.21) respectively.RESULTSA total of 400 older people (183 males and 217 females) took part in this study. The overall prevalence of low ASMI was 20.6% (15.5% in males and 24.9% in females). Females had significantly lower ASMI than males (P < 0.0001), age was inversely associated with ASMI (P = 0.0024) while BMI and calf circumference were positively associated with ASMI (both P < 0.0001) in the total cohort. In addition, ASMI was positively associated with bone mass in both genders (both P < 0.0001). After adjusting for covariates, the odds ratios of having low ASMI with every 1 year and 10 years increase in age were 1.13 (95% CI: 1.06, 1.20) and 3.36 (95% CI: 1.82, 6.21) respectively.The high prevalence of low ASMI in community-dwelling older people with normal nutritional status highlights the need for early screening. There was a strong inverse association between age and ASMI while BMI, calf circumference and bone mass were positively associated with ASMI. These findings will give further weight to the importance and development of public health strategies in maintaining and improving muscle health in this population group.CONCLUSIONSThe high prevalence of low ASMI in community-dwelling older people with normal nutritional status highlights the need for early screening. There was a strong inverse association between age and ASMI while BMI, calf circumference and bone mass were positively associated with ASMI. These findings will give further weight to the importance and development of public health strategies in maintaining and improving muscle health in this population group. ObjectivesAging is associated with low muscle mass and has been linked to adverse health outcomes. The objectives of this cross-sectional study were: (1) to describe anthropometry, body composition, appendicular skeletal muscle mass index (ASMI; appendicular skeletal muscle mass/height2), and prevalence of low ASMI in older people with normal nutritional status (Malnutrition Universal Screening Tool score = 0); (2) to determine factors associated with ASMI, and odds ratios of having low ASMI.MethodsSHIELD is a study of community-dwelling older people aged 65 years and above in Singapore. ASMI was determined using bioelectrical impedance analysis and low ASMI was defined as <7.0 kg/m2 for males and <5.7 kg/m2 for females (Asian Working Group for Sarcopenia, 2014).ResultsA total of 400 older people (183 males and 217 females) took part in this study. The overall prevalence of low ASMI was 20.6% (15.5% in males and 24.9% in females). Females had significantly lower ASMI than males (P < 0.0001), age was inversely associated with ASMI (P = 0.0024) while BMI and calf circumference were positively associated with ASMI (both P < 0.0001) in the total cohort. In addition, ASMI was positively associated with bone mass in both genders (both P < 0.0001). After adjusting for covariates, the odds ratios of having low ASMI with every 1 year and 10 years increase in age were 1.13 (95% CI: 1.06, 1.20) and 3.36 (95% CI: 1.82, 6.21) respectively.ConclusionsThe high prevalence of low ASMI in community-dwelling older people with normal nutritional status highlights the need for early screening. There was a strong inverse association between age and ASMI while BMI, calf circumference and bone mass were positively associated with ASMI. These findings will give further weight to the importance and development of public health strategies in maintaining and improving muscle health in this population group. |
| Audience | Academic |
| Author | Chew, Samuel Teong Huang Yalawar, Menaka Kwan, Shuyi Charmaine Baggs, Geraldine How, Choon How Huynh, Dieu Thi Thu Tan, Cynthia Yan Ling Cheong, Magdalin Ong, Rebecca Hui San Chow, Wai Leng Tey, Siew Ling Husain, Farah Safdar Tan, Ngiap Chuan Low, Yen Ling |
| AuthorAffiliation | 3 Care and Health Integration, Changi General Hospital, Singapore 8 Department of Dietetic & Food Services, Changi General Hospital, Singapore 6 Abbott Nutrition Research and Development, Columbus, Ohio, United States of America 5 Statistical Services, Cognizant Technologies Solution Pvt. Ltd., Bangalore, India 7 Health Services Research, Changi General Hospital, Singapore 9 SingHealth Polyclinics, Singapore Ritsumeikan University, JAPAN 2 Department of Geriatric Medicine, Changi General Hospital, Singapore 1 Abbott Nutrition Research and Development, Asia-Pacific Center, Singapore 4 SingHealth-Duke NUS Family Medicine Academic Clinical Program, Singapore |
| AuthorAffiliation_xml | – name: 7 Health Services Research, Changi General Hospital, Singapore – name: 4 SingHealth-Duke NUS Family Medicine Academic Clinical Program, Singapore – name: 6 Abbott Nutrition Research and Development, Columbus, Ohio, United States of America – name: 8 Department of Dietetic & Food Services, Changi General Hospital, Singapore – name: 9 SingHealth Polyclinics, Singapore – name: 3 Care and Health Integration, Changi General Hospital, Singapore – name: 1 Abbott Nutrition Research and Development, Asia-Pacific Center, Singapore – name: 5 Statistical Services, Cognizant Technologies Solution Pvt. Ltd., Bangalore, India – name: Ritsumeikan University, JAPAN – name: 2 Department of Geriatric Medicine, Changi General Hospital, Singapore |
| Author_xml | – sequence: 1 givenname: Siew Ling orcidid: 0000-0002-5064-5283 surname: Tey fullname: Tey, Siew Ling organization: Abbott Nutrition Research and Development, Asia-Pacific Center, Singapore – sequence: 2 givenname: Samuel Teong Huang surname: Chew fullname: Chew, Samuel Teong Huang organization: Department of Geriatric Medicine, Changi General Hospital, Singapore – sequence: 3 givenname: Choon How surname: How fullname: How, Choon How organization: SingHealth-Duke NUS Family Medicine Academic Clinical Program, Singapore – sequence: 4 givenname: Menaka surname: Yalawar fullname: Yalawar, Menaka organization: Statistical Services, Cognizant Technologies Solution Pvt. Ltd., Bangalore, India – sequence: 5 givenname: Geraldine surname: Baggs fullname: Baggs, Geraldine organization: Abbott Nutrition Research and Development, Columbus, Ohio, United States of America – sequence: 6 givenname: Wai Leng surname: Chow fullname: Chow, Wai Leng organization: Health Services Research, Changi General Hospital, Singapore – sequence: 7 givenname: Magdalin surname: Cheong fullname: Cheong, Magdalin organization: Department of Dietetic & Food Services, Changi General Hospital, Singapore – sequence: 8 givenname: Rebecca Hui San surname: Ong fullname: Ong, Rebecca Hui San organization: Health Services Research, Changi General Hospital, Singapore – sequence: 9 givenname: Farah Safdar surname: Husain fullname: Husain, Farah Safdar organization: SingHealth Polyclinics, Singapore – sequence: 10 givenname: Shuyi Charmaine surname: Kwan fullname: Kwan, Shuyi Charmaine organization: SingHealth Polyclinics, Singapore – sequence: 11 givenname: Cynthia Yan Ling surname: Tan fullname: Tan, Cynthia Yan Ling organization: SingHealth Polyclinics, Singapore – sequence: 12 givenname: Yen Ling surname: Low fullname: Low, Yen Ling organization: Abbott Nutrition Research and Development, Asia-Pacific Center, Singapore – sequence: 13 givenname: Ngiap Chuan surname: Tan fullname: Tan, Ngiap Chuan organization: SingHealth Polyclinics, Singapore – sequence: 14 givenname: Dieu Thi Thu surname: Huynh fullname: Huynh, Dieu Thi Thu organization: Abbott Nutrition Research and Development, Asia-Pacific Center, Singapore |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31596873$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2019 Public Library of Science 2019 Tey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2019 Tey et al 2019 Tey et al |
| Copyright_xml | – notice: COPYRIGHT 2019 Public Library of Science – notice: 2019 Tey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2019 Tey et al 2019 Tey et al |
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| DOI | 10.1371/journal.pone.0223222 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: SLT, GB, YLL, and DTTH are paid employees of Abbott and own Abbott stocks. YM is a paid employee of Cognizant Technologies Solution Pvt. Ltd., a Contract Research Organization, which provides statistical services to Abbott Nutrition and has no competing interests. STHC reports receiving honoria for speaking engagement and travel grant from Abbott. All other authors have declared that no competing interests exist. The specific roles of the authors are articulated in the 'author contributions' section. There are no patents pending or related with the data presented. There are no products in development or marketed products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials. |
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| SubjectTerms | Age Aged Aging Analysis Anthropometry Bioelectric impedance Bioelectricity Biology and Life Sciences Body composition Bone density Bone mass Cohort Studies Communities Cross-sectional studies Elderly Exercise Female Females Health aspects Humans Independent Living Linear Models Male Males Malnutrition Medicine and Health Sciences Muscle, Skeletal - anatomy & histology Muscles Nutritional status Older people Organ Size People and Places Physical sciences Physiological aspects Public health Sarcopenia Singapore Skeletal muscle Systematic review Type 2 diabetes Vitamin D |
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| Title | Factors associated with muscle mass in community-dwelling older people in Singapore: Findings from the SHIELD study |
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