Body composition patterns and breast cancer risk in Chinese women with breast diseases: A latent class analysis.
J Adv Nurs
; 75(11): 2638-2646, 2019 Nov.
Article
em En
| MEDLINE
| ID: mdl-31016759
ABSTRACT
AIMS:
To identify unobserved body composition patterns among Chinese women with breast diseases using latent class analysis (LCA) and to examine the relationship between body composition patterns and breast cancer (BC) risk.DESIGN:
A descriptive, cross-sectional study.METHODS:
Female patients (N = 1816) with breast diseases were included in the study from April 2016 - March 2017. Body composition measures were done by the bioelectrical impedance analysis. The LCA models were estimated using Mplus 8.1.RESULTS:
Four latent classes were identified based on water, protein, minerals and body fat mass Class 1 - Low Muscle Mass class; Class 2 - High Body Composition class; Class 3 - High Fat class; and Class 4 - Normal Body Composition Class. Classes 2 and 3 are higher risk classes for developing BC compared with the other two classes (p < 0.05). Overall, age is positively associated with the odds of BC development (p < 0.001). However, age effect depends on the body composition patterns. Age effect on the odds of BC was statistically significant only for women who had least body fat mass (Class 1, OR = 1.110, 95% C.I. 1.084-1.136) or had normal body composition (Class 4, OR = 1.090, 95% C.I. 1.036-1.147). The effect of age was not statistically significant if women had higher risk body composition (e.g., in Classes 2 or 3).CONCLUSION:
Latent Class Analysis is a useful person-centred analytical approach for identification of unobserved patterns of body composition and it could be used to predict the risk of BC and develop personalized interventions for body composition studies.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Composição Corporal
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Doenças Mamárias
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Neoplasias da Mama
Tipo de estudo:
Etiology_studies
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Observational_studies
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Prevalence_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adolescent
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Adult
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Aged
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Aged80
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Female
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Humans
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Middle aged
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article