Cluster analysis of nutritional factors associated with low muscle mass index in middle-aged and older adults

Abstract

Background & aims: Sarcopenia, or age-related muscle loss, is an enormous health problem in an aging world because of its many clinical and societal adverse effects. The uncovering of healthy dietary patterns is an important strategy to prevent or delay sarcopenia. We used K-means clustering to identify subgroups of men and women based on nutritional and health-related factors and investigated risk factors for low muscle mass in the subgroups and in the study population as a whole.

Methods: We analyzed a total 10,863 participants over 40 years of age who participated in the Korea National Health and Nutrition Survey from 2008 to 2011. Dual energy X-ray absorptiometry was used to determine the appendicular lean mass (ALM) of the participants. Participants with low ALM adjusted BMI (ALM/BMI) were then identified using the criteria of the Foundation for the National Institutes of Health sarcopenia project. K-means clustering and multivariate logistic regression were used to analyze associations between nutritional and health-related variables and low ALM/BMI in the population as a whole and in the individual clusters.

Results: A total of 712 (15.8%) men and 869 (13.7%) women had low ALM/BMI. Five clusters were identified in men and women, respectively. Two clusters of men and one cluster of women exhibited an increased risk of low ALM/BMI. Old age, low total energy intake, low levels of physical activity, and a high number of chronic diseases were consistent risk factors for low ALM/BMI in all Korean men and women. Low protein was a common risk factor for low ALM/BMI in men. After dividing all subjects by the K-means clustering algorithm, two risk factors (high fat intake and smoking) and four factors (low intakes of carbohydrate, protein and fat, and high alcohol consumption) were additionally proposed in Korean men and women, respectively.

Conclusions: Age, low total energy intake, low level of physical activity, and an increased number of chronic diseases were consistent risk factors for low ALM/BMI in men and women. Cluster-specific risk factors were also noted in men and women.

Keywords: Aging; Nutrition; Sarcopenia; Unsupervised machine learning.