Wei sheng yan jiu = Journal of hygiene research · 2026
[Basal metabolic rate of adults aged 40-60 years using indirect calorimetry].
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Guo Minghang, Feng Jie, Mao Deqian, Wen Jian, Gan Ping, Yao Xiangjie, Lu Jiayu, Li Rongzhen, Huang Chunli, Zheng Xiangyi, Lin Junyuan, Yang Lichen, Ding Gangqiang
Аннотация
OBJECTIVE: To measure the basal metabolic rate(BMR) of Chinese adults aged 40-60 years using indirect calorimetry, analyze its variation characteristics and influencing factors with respect to age, gender, and body weight status, and compare the consistency between measured BMR values and those calculated by commonly used predictive equations in this population.
METHODS: Participants were recruited in Tianjin and Guangzhou according to the inclusion and exclusion criteria and divided into overweight/obesity and normal-weight groups based on body mass index. BMR was measured using a portable cardiopulmonary function device. Multiple linear regression was used to analyze the main influencing factors, and the Bland-Altman method combined with paired-sample t-tests was applied to evaluate the agreement between four commonly used BMR predictive equations and the measured values.
RESULTS: A total of 61 participants with valid BMR data were included in the analysis, mean age(49.9±6.2) years; 33 males, 28 females. BMR was significantly higher in the overweight/obesity group than in the normal-weight group of the same gender(male:(1712.9±162.4) vs. (1525.3±104.4) kcal/d, P<0.001; female:(1416.3±127.7) vs. (1143.6±123.6) kcal/d, P<0.001). Multiple linear regression showed that when fat-free mass(β=30.89, P<0.001) was included in the BMR prediction model instead of body weight, the influence of gender became non-significant, suggesting that fat-free mass was a key variable explaining the effects of body weight status and gender differences. Bland-Altman analysis indicated that current commonly used BMR equations, both domestic and international, exhibited varying degrees of bias when applied to calculate BMR in this age group.
CONCLUSION: In Chinese adults aged 40-60 years, BMR is higher in overweight/obese individuals, and fat-free mass may be a core factor explaining individual and gender differences in BMR. The application of existing predictive equations in this population shows varying degrees of bias, underscoring the need for developing more targeted BMR formulas in the future. For individuals requiring precise assessment, direct measurement method such as indirect calorimetry remain the recommended priority.
METHODS: Participants were recruited in Tianjin and Guangzhou according to the inclusion and exclusion criteria and divided into overweight/obesity and normal-weight groups based on body mass index. BMR was measured using a portable cardiopulmonary function device. Multiple linear regression was used to analyze the main influencing factors, and the Bland-Altman method combined with paired-sample t-tests was applied to evaluate the agreement between four commonly used BMR predictive equations and the measured values.
RESULTS: A total of 61 participants with valid BMR data were included in the analysis, mean age(49.9±6.2) years; 33 males, 28 females. BMR was significantly higher in the overweight/obesity group than in the normal-weight group of the same gender(male:(1712.9±162.4) vs. (1525.3±104.4) kcal/d, P<0.001; female:(1416.3±127.7) vs. (1143.6±123.6) kcal/d, P<0.001). Multiple linear regression showed that when fat-free mass(β=30.89, P<0.001) was included in the BMR prediction model instead of body weight, the influence of gender became non-significant, suggesting that fat-free mass was a key variable explaining the effects of body weight status and gender differences. Bland-Altman analysis indicated that current commonly used BMR equations, both domestic and international, exhibited varying degrees of bias when applied to calculate BMR in this age group.
CONCLUSION: In Chinese adults aged 40-60 years, BMR is higher in overweight/obese individuals, and fat-free mass may be a core factor explaining individual and gender differences in BMR. The application of existing predictive equations in this population shows varying degrees of bias, underscoring the need for developing more targeted BMR formulas in the future. For individuals requiring precise assessment, direct measurement method such as indirect calorimetry remain the recommended priority.