Universidad de Castilla-La Mancha, Facultad de Fisioterapia y Enfermería, Toledo, Spain; Universidad de Castilla-La Mancha, Health and Social Research Center, Cuenca, Spain.
Universidad de Castilla-La Mancha, Health and Social Research Center, Cuenca, Spain; Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca, Chile.
Clin Nutr. 2023 Jul;42(7):1161-1167. doi: 10.1016/j.clnu.2023.05.006. Epub 2023 May 12.
Lean mass is considered the best predictor of bone mass, as it is an excellent marker of bone mechanical stimulation, and changes in lean mass are highly correlated with bone outcomes in young adults. The aim of this study was to use cluster analysis to examine phenotype categories of body composition assessed by lean and fat mass in young adults and to assess how these body composition categories are associated with bone health outcomes.
Cluster cross-sectional analyses of data from 719 young adults (526 women) aged 18-30 years from Cuenca and Toledo, Spain, were conducted. Lean mass index (lean mass (kg)/height (m)), fat mass index (fat mass (kg)/height (m)), bone mineral content (BMC) and areal bone mineral density (aBMD) were assessed by dual-energy X-ray absorptiometry.
A cluster analysis of lean mass and fat mass index z scores resulted in a classification of a five-category cluster solution that could be interpreted according to the body composition phenotypes of individuals as follows: high adiposity-high lean mass (n = 98), average adiposity-high lean mass (n = 113), high adiposity-average lean mass (n = 213), low adiposity-average lean mass (n = 142), and average adiposity-low lean mass (n = 153). ANCOVA models showed that individuals in clusters with a higher lean mass had significantly better bone health (z score: 0.764, se: 0.090) than their peers in other cluster categories (z score: -0.529, se: 0.074) after controlling for sex, age, and cardiorespiratory fitness (p < 0.05). Additionally, subjects belonging to the categories with a similar average lean mass index but with high or low-adiposity levels (z score: 0.289, se: 0.111; z score: 0.086, se: 0.076) showed better bone outcomes when the fat mass index was higher (p < 0.05).
This study confirms the validity of a body composition model using a cluster analysis to classify young adults according to their lean mass and fat mass indices. In addition, this model reinforces the main role of lean mass on bone health in this population and that in phenotypes with high-average lean mass, factors associated with fat mass may also have a positive effect on bone status.
瘦体重被认为是骨量的最佳预测因子，因为它是骨骼机械刺激的良好标志物，瘦体重的变化与年轻人的骨量结果高度相关。本研究的目的是使用聚类分析来检查通过瘦体重和脂肪质量评估的年轻成年人身体成分的表型类别，并评估这些身体成分类别如何与骨骼健康结果相关联。
对来自西班牙昆卡和托莱多的 719 名年龄在 18-30 岁的年轻成年人（526 名女性）的数据进行聚类横断面分析。使用双能 X 射线吸收法评估瘦体重指数（瘦体重（kg）/身高（m））、脂肪质量指数（脂肪质量（kg）/身高（m））、骨矿物质含量（BMC）和面积骨矿物质密度（aBMD）。
对瘦体重和脂肪质量指数 z 分数的聚类分析产生了一个五类聚类解决方案的分类，可以根据个体的身体成分表型进行解释，如下所示：高肥胖-高瘦体重（n=98）、平均肥胖-高瘦体重（n=113）、高肥胖-平均瘦体重（n=213）、低肥胖-平均瘦体重（n=142）和平均肥胖-低瘦体重（n=153）。协方差分析模型显示，在控制性别、年龄和心肺适能后，与其他聚类类别（z 分数：-0.529，se：0.074）相比，聚类中瘦体重较高的个体的骨骼健康状况明显更好（z 分数：0.764，se：0.090）（p<0.05）。此外，属于平均瘦体重指数相似但脂肪质量指数较高或较低的类别（z 分数：0.289，se：0.111；z 分数：0.086，se：0.076）的受试者，当脂肪质量指数较高时，骨量结果更好（p<0.05）。
本研究通过聚类分析确认了一种身体成分模型的有效性，该模型可根据瘦体重和脂肪质量指数对年轻成年人进行分类。此外，该模型强调了瘦体重对该人群骨骼健康的主要作用，并且在具有高平均瘦体重的表型中，与脂肪质量相关的因素也可能对骨骼状态产生积极影响。
