The role of obesity in sarcopenia and the optimal body composition to prevent against sarcopenia and obesity

Abstract

Background: Elderly people with low lean and high fat mass, are diagnosed with sarcopenic obesity (SO), and often have poor clinical outcomes. This study aimed to explore the relationship between obesity and sarcopenia, and the optimal proportion of fat and muscle for old individuals.

Methods: Participants aged 60 years or above were instructed to perform bioelectrical impedance analysis to obtain the muscle and fat indicators, and handgrip strength was also performed. Sarcopenia was diagnosed according to predicted appendicular skeletal muscle mass and function. Body mass index (BMI) and body fat percentage (BF%) were used to define obesity. The association of muscle and fat indicators were analyzed by Pearson's correlation coefficient. Pearson Chi-Square test was utilized to estimate odds ratios (OR) and 95% confidence intervals (CI) on the risk of sarcopenia according to obesity status.

Results: 1637 old subjects (74.8 ± 7.8 years) participated in this study. Not only fat mass, but also muscle indicators were positively correlated to BMI and body weight (p < 0.05). Absolute muscle and fat mass in different positions had positive associations (p < 0.05). Muscle mass and strength were negatively related to appendicular fat mass percentage (p < 0.05). When defined by BMI (OR = 0.69, 95% CI [0.56, 0.86]; p = 0.001), obesity was a protective factor for sarcopenia, whilst it was a risk factor when using BF% (OR = 1.38, 95% CI [1.13, 1.69]; p = 0.002) as the definition. The risk of sarcopenia reduced with the increase of BMI in both genders. It was increased with raised BF% in males but displayed a U-shaped curve for females. BF% 26.0-34.6% in old females and lower than 23.9% in old males are recommended for sarcopenia and obesity prevention.

Conclusion: Skeletal muscle mass had strong positive relationship with absolute fat mass but negative associations with the percentage of appendicular fat mass. Obesity was a risk factor of sarcopenia when defined by BF% instead of BMI. The management of BF% can accurately help elderly people prevent against both sarcopenia and obesity.

Keywords: aging; body fat percentage; body mass index; fat; muscle; sarcopenic obesity.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2023 Liu, Cheng, Tong, Cheung, Chow, Law and Wong.

Figures

Figure 1

The correlation between muscle and fat indicators and the differences between normal, sarcopenic, obese, and sarcopenic obese groups. In (A), the dark blue showed the strong positive correlation (correlation coefficient = 1), while the dark red showed the strong negative correlation (correlation coefficient = –1). Black cross was shown if there was no statistical significance (P > 0.05). The correlation coefficient was displayed in the lower half of the square. Female=0, male=1 for gender. (B, C) showed the differences of SMM and BFM in four groups according to BMI- and BF%-defined obesity. The post-hoc results were shown as a, b, c, d on the bars with same color; the results in groups with inconsistent letters were significantly different (P < 0.05). AFM%, arm fat mass percentage; LFM%, leg fat mass percentage; BF%, body fat percentage; TFM%, trunk fat mass percentage; BMI, body fat index; WHR, waist to hip ratio; LFM, leg fat mass; FMI, fat mass index; AFM, arm fat mass; BFM, body fat mass; TFM, trunk fat mass; AFFM%, arm fat-free mass percentage; LFFM%, leg fat-free mass percentage; SMI, skeletal muscle mass index; ASMI, appendicular skeletal muscle mass index; SMM, skeletal muscle mass; AFFM, arm fat-free mass; HGS, handgrip strength; LFFM, leg fat-free mass; N, normal group; S, only sarcopenic group; O, only obese group; SO, sarcopenic obese group.

Figure 2

Linear regression model to show the annual rate of ASMI and HGS decline in females with (blue) or without (red) obesity. (A, B) showed the changes of ASMI and HGS according to age in females with or without obesity when obesity defined by BMI ≥ 25 kg/m2. (C, D) showed the changes of the above variables when obesity defined by body fat percentage > 35% in female. All p-value of regression models is ≤ 0.05. F, female; O, obese; NO, non-obese; BMI, body mass index; BF%, body fat percentage; ASMI, appendicular skeletal muscle mass index; HGS, handgrip strength.

Figure 3

Linear regression model to show the annual rate of ASMI and HGS decline in males with (blue) or without (red) obesity. (A, B) showed the changes of ASMI and HGS according to age in males with or without obesity when obesity defined by BMI ≥ 25 kg/m2. (C, D) showed the changes of the above variables when obesity defined by body fat percentage > 27% in male. All p-value of regression models is < 0.05. M, male; O, obese; NO, non-obese; BMI, body mass index; BF%, body fat percentage; ASMI, appendicular skeletal muscle mass index; HGS, handgrip strength.

Figure 4

The risk of sarcopenia in males and females with different BMI and BF%. (A) showed that BMI was classified into 5 intervals based on the recommendation from WHO, the normal BMI (18.5–22.9) was regarded the reference group with OR=1.00. Blue points as OR values and blue shade as 95% CI represented male, and red represented female. (B) showed that BF% was classified into 5 intervals by quintile, the group with the lowest value of BF% was reference group. The specific interval of male (blue) was shown on the upper horizontal axis and female (red) on the lower horizontal axis.