Body composition patterns and breast cancer risk in Chinese women with breast diseases: A latent class analysis. (8th August 2019)

• Record Type: Journal Article
• Authors: Zhang, Jinyu; Wang, Jichuan; Zhou, Jie; Fang, Qiong; Zhang, Nan; Yuan, Changrong
• Is Part Of: Journal of advanced nursing. Volume 75:Number 11(2019)
• Page Start: 2638
• Page End: 2646
• Publication Date: 2019-08-08
• Subjects: body composition patterns -- breast cancer -- latent class analysis -- nursing -- risk; Nursing -- Periodicals; 610.7305
• DOI: 10.1111/jan.14040
• Languages: English
• ISSNs: 0309-2402

Abstract

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. Abstract : 目的: 使用潜在等级分析(LCA)对中国患乳腺疾病的女性未被观察到的身体成分模式进行识别, 并探讨身体成分模式与患乳腺癌(BC)之间的关系。 设计: 描述性横断面研究。 方法: 本研究于2016年至2017年三月进行, 研究对象包含了患有乳腺疾病的女性患者(数量为1816名)。身体成分测量是通过生物抗电阻分析法来完成的。采用Mplus 8.1对LCA模式进行估计。 结果: 根据水、蛋白质、矿物质和体脂肪量确定了四个潜在等级:等级1——低肌肉含量等级;等级2——高身体成分等级;等级3——高脂肪含量等级;等级4——正常身体成分等级。和其他两个等级( p < 0.05)相比, 等级2和等级3的人群患乳腺癌的风险更高。总的来说, 年龄与患乳腺癌的几率呈正相关( p < 0.001)。然而, 年龄影响取决于身体成分模式。从统计学上来讲, 年龄只对脂肪量最低(等级1, OR = 1.110, 95% C.I.: 1.084–1.136)的女性或身体成分正常(等级4, OR = 1.090, 95% C.I.: 1.036–1.147)的女性有影响。如果女性含有更高风险的身体成分(例如, 等级2或等级3), 那么在统计学上来讲, 年龄影响不是很大。 结论: 潜在等级分析法是一种很有用的以人为中心的分析方法, 用于识别未被观察到的身体成分模式。并且它可以用来预测患乳腺癌的风险和为身体成分研究制定个性化干预措施。