不同体重指数青年男性的静息能量消耗特点及预测方程评价

doi:10.19723/j.issn.1671-167X.2024.02.007

摘要/Abstract

摘要：

目的: 探讨不同体重指数(body mass index，BMI)青年男性静息能量消耗(resting energy expenditure，REE)的特点，比较常见静息代谢率(resting metabolic rate, RMR)预测方程与间接测热法的差异。方法: 以2017年12月至2021年6月于北京大学第三医院运动医学科进行静息代谢测试的30名青年男性为研究对象，采用间接测热法测定RMR，采用生物电阻抗法测定体成分。分析研究对象REE的特点，并与11个常见预测方程的推算值进行比较，通过配对t检验和组内相关系数(intra-class correlation coefficient，ICC)评估两者差异。结果: 30名青年男性的平均年龄为(26.93±4.16)岁，整体RMR为(1 960.17±463.11) kcal/d (1 kcal=4.186 8 kJ)，其中BMI正常者的RMR为(1 744.33±249.62) kcal/d，显著低于超重或肥胖者[(2 104.06±520.32) kcal/d](P < 0.01)，但体质量校正后，BMI正常者的RMR显著高于超重或肥胖者[(24.02±2.61) kcal/(kg·d) vs. (19.98±4.38) kcal/(kg·d)，P < 0.01]；不同BMI受试者的RMR与体质量、脂肪量、去脂体重、体表面积、细胞外液呈显著正相关(P均 < 0.05)。11个预测方程的预测值与实测值的一致性均不佳(ICC均 < 0.75)，其中，超重或肥胖青年男性采用世界卫生组织(World Health Organization, WHO)推荐使用的RMR预测方程的预测值与实测值的一致性相对较高(ICC=0.547，P < 0.01)。结论: 不同BMI青年男性的RMR存在显著差异，超重或肥胖者要考虑体质量矫正后的RMR情况。不同预测方程的预测值与RMR的实测值一致性较差，建议通过间接测热法准确测定RMR。对于超重和肥胖的青年男性可以考虑采用WHO推荐使用的预测方程计算RMR，但有必要建立适用于不同BMI人群的RMR预测方程。

关键词: 静息代谢率, 人体质量指数, 青年人, 男性, 超重或肥胖

Abstract:

Objective: To compare the resting energy expenditure (REE) characteristics among young men with different body mass indexes (BMI). Methods: Thirty young men [average age was (26.93±4.16) years] were enrolled in this study. They underwent resting metabolism tests in the Department of Sports Medicine of Peking University Third Hospital from December 2017 to June 2021. The resting metabolic rate (RMR) was measured by indirect calorimetry, the body composition was measured by bioresistance antibody component analyzer. The REE characteristics were analyzed, and 11 predictive equations were used to estimate RMR and compared with the measured value. The differences were analyzed by paired t-test and intra-class correlation coefficient (ICC). Results: The RMR of the overall 30 young men was (1 960.17±463.11) kcal/d (1 kcal=4.186 8 kJ). Including (1 744.33±249.62) kcal/d in those with normal BMI, which was significantly lower than that in those who were overweight or obese [(2 104.06± 520.32) kcal/d, P < 0.01], but the weight-corrected RMR in those with normal BMI was significantly higher than that in those who were overweight or obese [(24.02±2.61) kcal/(kg·d) vs. (19.98±4.38) kcal/(kg·d), P < 0.01]. The RMR was significantly and positively correlated with body weight, adiposity, lean body mass, body surface area, and extracellular fluid in the subjects with diffe-rent BMI (all P < 0.05). The predicted values of the 11 prediction equations were not in good agreement with the measured values (all ICC < 0.75), with relatively high agreement between the predicted and measured values of the World Health Organization (WHO) equation in overweight obese young men (ICC=0.547, P < 0.01). Conclusion: There were significant differences in RMR among young men with different BMI, and the RMR after weight correction should be considered for those who were overweight or obese. The consistency between the predicted values of different prediction equations and the actual measured values of RMR was relatively poor, and it is recommended to accurately measure RMR by indirect calorimetry. For overweight or obese young men, the WHO prediction equation can be considered to calculate RMR, but it is necessary to establish an RMR prediction equation applicable to different BMI populations.

Key words: Resting metabolic rate, Body mass index, Young adult, Male, Overweight or obese

中图分类号:

R151.44

吴一凡,玉应香,谢岚,张志达,常翠青. 不同体重指数青年男性的静息能量消耗特点及预测方程评价[J]. 北京大学学报（医学版）, 2024, 56(2): 247-252.

Yifan WU,Yingxiang YU,Lan XIE,Zhida ZHANG,Cuiqing CHANG. Characteristics of resting energy expenditure and evaluation of prediction formulas in young men with different body mass indexes[J]. Journal of Peking University (Health Sciences), 2024, 56(2): 247-252.

图/表 6

表1

表2

表3

表4

表5

表6

参考文献 27

1	Speakman JR , Selman C .Physical activity and resting metabolic rate[J].Proc Nutr Soc,2003,62(3):621-634. doi: 10.1079/PNS2003282
2	中国营养学会.中国居民膳食营养素参考摄入量(2013版)[M].北京: 科学出版社,2014:83.
3	Mtaweh H , Tuira L , Floh AA , et al.Indirect calorimetry: History, technology, and application[J].Front Pediatr,2018,6,257. doi: 10.3389/fped.2018.00257
4	McMurray RG , Soares J , Caspersen CJ , et al.Examining variations of resting metabolic rate of adults: A public health perspective[J].Med Sci Sports Exerc,2014,46(7):1352-1358. doi: 10.1249/MSS.0000000000000232
5	Ocagli H , Lanera C , Azzolina D , et al.Resting energy expenditure in the elderly: Systematic review and comparison of equations in an experimental population[J].Nutrients,2021,13(2):458. doi: 10.3390/nu13020458
6	中国医疗保健国际交流促进会营养与代谢管理分会, 中国营养学会临床营养分会, 中华医学会糖尿病学分会, 等.中国超重/肥胖医学营养治疗指南(2021)[J].中国医学前沿杂志: 电子版,2021,13(11):1-55.
7	赵松山, 刘友梅, 姚家邦, 等.中国成年男子体表面积的测量[J].营养学报,1984,6(2):87-95.
8	Compher C , Frankenfield D , Keim N , et al.Best practice methods to apply to measurement of resting metabolic rate in adults: A systematic review[J].J Am Diet Assoc,2006,106(6):881-903. doi: 10.1016/j.jada.2006.02.009
9	Weir JB .New methods for calculating metabolic rate with special reference to protein metabolism[J].J Physiol,1949,109(1/2):1-9.
10	Harris JA , Benedict FG .A biometric study of human basal meta-bolism[J].Proc Natl Acad Sci USA,1918,4(12):370-373. doi: 10.1073/pnas.4.12.370
11	Schofield WN .Predicting basal metabolic rate, new standards and review of previous work[J].Hum Nutr Clin Nutr,1985,39(Suppl 1):5-41.
12	Energy and protein requirements .Report of a joint FAO/WHO/UNU expert consultation[J].World Health Organ Tech Rep Ser,1985,724,1-206.
13	Mifflin MD , St Jeor ST , Hill LA , et al.A new predictive equation for resting energy expenditure in healthy individuals[J].Am J Clin Nutr,1990,51(2):241-247. doi: 10.1093/ajcn/51.2.241
14	Henry CJ .Basal metabolic rate studies in humans: measurement and development of new equations[J].Public Health Nutr,2005,8(7A):1133-1152. doi: 10.1079/PHN2005801
15	Cunningham JJ .A reanalysis of the factors influencing basal metabolic rate in normal adults[J].Am J Clin Nutr,1980,33(11):2372-2374. doi: 10.1093/ajcn/33.11.2372
16	de Lorenzo A , Tagliabue A , Andreoli A , et al.Measured and predicted resting metabolic rate in Italian males and females, aged 18-59 y[J].Eur J Clin Nutr,2001,55(3):208-214. doi: 10.1038/sj.ejcn.1601149
17	Owen OE , Holup JL , D'Alessio DA , et al.A reappraisal of the caloric requirements of men[J].Am J Clin Nutr,1987,46(6):875-885. doi: 10.1093/ajcn/46.6.875
18	Liu HY , Lu YF , Chen WJ .Predictive equations for basal metabolic rate in Chinese adults: A cross-validation study[J].J Am Diet Assoc,1995,95(12):1403-1408. doi: 10.1016/S0002-8223(95)00369-X
19	Camps SG , Wang NX , Tan WS , et al.Estimation of basal metabolic rate in Chinese: Are the current prediction equations applicable?[J].Nutr J,2016,15(1):79.
20	Xue J , Li S , Zhang Y , et al.Accuracy of predictive resting-metabolic-rate equations in Chinese mainland adults[J].Int J Environ Res Public Health,2019,16(15):2747. doi: 10.3390/ijerph16152747
21	Mao Q , Wu JH , Huang CY , et al.Basal Energy Expenditure of Chinese healthy adults: Comparison of measured and predicted values[J].Biomed Environ Sci,2020,33(8):566-572.
22	刘燕萍, 陈伟, 毛德倩, 等.间接测热法测定北京成年居民基础代谢率及与身体成分的相关性[J].协和医学杂志,2013,4(1):11-14.
23	张莹, 吴景欢, 洪平, 等.北京市超重和肥胖成人基础代谢率的研究[J].卫生研究,2016,45(5):739-742.
24	中华人民共和国中央人民政府. 国务院新闻办就《中国居民营养与慢性病状况报告(2020年)》有关情况举行发布会[EB/OL]. (2020-12-24)[2021-12-22]. http://www.gov.cn/xinwen/2020-12/24/content_5572983.htm.
25	许晓丽, 李淑娟, 卓勤, 等.2010—2012年中国不同工作强度在职人员能量和宏量营养素摄入状况[J].卫生研究,2021,50(3):415-420.
26	谢海雁, 于康, 安奇志, 等.代谢车间接测热法、公式推测法及人体成分测定法用于测定静息能量消耗的比较[J].中华临床营养杂志,2018,26(2):90-93.
27	汪锡, 王远, 许杨, 等.非肥胖健康成人静息代谢率预测公式准确性研究[J].北京生物医学工程,2017,36(6):607-613.

相关文章 15

[1]	薛子璇,唐世英,邱敏,刘承,田晓军,陆敏,董靖晗,马潞林,张树栋. 青年肾肿瘤伴瘤栓的临床病理特征及预后分析[J]. 北京大学学报（医学版）, 2023, 55(5): 802-811.
[2]	林浩,李菁华,杨潇,陈晓婷,史宇晖,常春,郝元涛,曹望楠. 中国成都男男性行为人群HIV暴露前预防用药行为-认知偏差现状及其影响因素[J]. 北京大学学报（医学版）, 2023, 55(3): 511-520.
[3]	杜强,洪锴,潘伯臣. 两种检测男性生殖道沙眼衣原体和解脲支原体方法的对比[J]. 北京大学学报（医学版）, 2021, 53(4): 785-788.
[4]	李远骋, 崔闻心, 郭雪儿, 朱璠, 刘思辰, 贾碧波, 汪培, 马迎华. 青年学生男男性行为人群中人类免疫缺陷病毒阳性与阴性者获得性免疫缺陷综合征(艾滋病)相关知识与行为比较[J]. 北京大学学报（医学版）, 2021, 53(3): 511-517.
[5]	王涛,许克新,张维宇,胡浩,张晓威,王焕瑞,刘献辉,陈京文,张晓鹏. 男性膀胱过度活动症的尿动力学分型及临床疗效随访[J]. 北京大学学报（医学版）, 2019, 51(6): 1048-1051.
[6]	代晓微，徐影，郑连文，李凌云，李丹丹，谭鑫，高飞，王艳，吴桂杰. 1 324例少精子症和无精子症患者的染色体核型分析[J]. 北京大学学报(医学版), 2018, 50(5): 774-777.
[7]	吴桂杰，马帅，郑连文，徐影，孟繁鹤，代晓微. 1例复杂染色体易位伴男性不育患者的家系核型分析及文献复习[J]. 北京大学学报(医学版), 2018, 50(4): 729-731.
[8]	赵连明，姜辉，洪锴，林浩成，唐文豪，刘德风，毛加明，廉颖，马潞林. Y染色体AZFc区缺失患者的治疗结局分析[J]. 北京大学学报(医学版), 2016, 48(4): 607-611.
[9]	王立芳，周虹,张妍，王燕. 孕前体质指数与早产的关系[J]. 北京大学学报(医学版), 2016, 48(3): 414-417.
[10]	叶海云，许清泉，黄晓波，马凯，王晓峰. 卡介苗膀胱灌注治疗致结核性前列腺脓肿1例[J]. 北京大学学报(医学版), 2015, 47(6): 1039-1041.
[11]	韦冬梅, 吴丽晶, 高爱钰, 李钦, 程兰, 王海俊. 儿童视屏行为和家庭相关因素与儿童体重指数的关系[J]. 北京大学学报（医学版）, 2015, 47(3): 390-394.
[12]	姚林, 张雷, 范宇, 张政, 徐燕鹏, 刘茗洁, 何志嵩, 龚侃, 李学松, 张争, 张崔健, 周利群. 前瞻随机对照研究：新技术对硬性膀胱镜患者舒适度的提高[J]. 北京大学学报（医学版）, 2014, 46(4): 635-637.
[13]	陈亮, 付杰, 陈菲, 潘虹, 张娜, 王玲, 王晟, 鞠慧岩, 薛晴, 贺占举, 左文莉, 徐阳, 杨慧霞. Y染色体长度变异对男性生育功能的影响[J]. 北京大学学报（医学版）, 2014, 46(2): 211-214.
[14]	黄锦, 廉颖, 乔杰, 刘平. 男性罗氏易位的临床特点及其胚胎着床前遗传学诊断[J]. 北京大学学报（医学版）, 2012, 44(4): 544-546.
[15]	许克新, 张晓鹏, 王栋, 胡浩, 黄晓波, 王晓峰. AdVance经闭孔吊带治疗男性中、重度压力性尿失禁的疗效[J]. 北京大学学报（医学版）, 2012, 44(4): 555-557.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Equation source	Age/years	Equation/(kcal/d)	Data source
Harris, et al^[10]	All	66+13.7×weight (kg)+5×height (cm)-6.8×age (years)	136 men, American
Schofield^[11]	18-30	15.057×weight (kg)+692.2	Meta analysis of Western countries data
WHO^[12]	18-30	15.3×weight (kg)+679	Meta analysis of Western countries data
Mifflin, et al^[13]	NA	9.99×weight (kg)+6.25×height (cm)-4.92×age (years)+166×gender (male, 1; female, 0)-161	498 healthy subjects, American
Henry^[14]	18-30	14.4×weight (kg)+3.13×height (cm)+113	Include 10 552 subjects, meta analysis
Cunningham^[15]	All	21.6×FFM (kg)+501.6	Meta analysis
de Lorenzo, et al^[16]	18-59	[53.284×weight (kg)+20.957×height (cm)-23.859×age (years)+487]×0.239	320 healthy subjects, Italian
Owen, et al^[17]	18-82	290+22.3×FFM (kg)	60 men (include obese subjects), American
Liu, et al^[18]	NA	13.88×weight (kg)+4.16×height (cm)-3.43×age (years)-112.40×gender (male=0; female=1)+54.34	223 healthy subjects, Chinese
Camps, et al^[19]	All	[52.6×weight (kg)+2 788]×0.239	232 subjects (BMI: 16-41 kg/m²), Singaporean Chinese subjects
Xue, et al^[20]	NA	13.9×weight (kg)+247-5.39×age (years)+855	315 healthy subjects, Chinese

Indicator	Normal BMI (n=12), $\bar x \pm s$	Overweight or obese (n=18), $\bar x \pm s$	P
Age/years	26.50±2.32	27.22±5.08	0.649
Height/cm	175.66±3.56	174.79±6.39	0.674
Weight/kg	72.52±4.66	107.68±27.30	< 0.001
BMI/(kg/m²)	23.51±1.43	35.19±8.44	< 0.001
Fat mass/kg	12.70±1.91	39.92±21.24	< 0.001
Body fat rate/%	17.44±1.84	35.13±9.18	< 0.001
FFM/kg	59.83±3.25	67.76±8.09	0.003
TBW/kg	41.70±2.98	44.94±6.60	0.123
Body water/%	57.51±2.23	43.06±7.27	< 0.001
Intracellular fluid/kg	26.32±2.21	27.09±4.37	0.579
Extracellular fluid/kg	15.38±0.82	17.85±2.50	0.001
BMR/(kcal/d)	1 705.42±98.02	2 022.39±296.13	< 0.001
mRMR/(kcal/d)	1 744.33±249.62	2 104.06±520.32	< 0.001
Weight-corrected RMR/[kcal/(kg·d)]	24.02±2.61	19.98±4.38	< 0.001
FFM-corrected RMR/[kcal/(kg·d)]	29.11±3.45	30.96±6.24	0.359

Indicator	Normal BMI (n=12), $\bar x \pm s$	Overweight or obese (n=18), $\bar x \pm s$	P
EE/(kcal/d)	1 744.33±249.62	2 104.06±520.32	< 0.001
Carbohydrate
g	196.10±136.52	157.17±56.20	0.416
kcal/d	819.80±571.04	657.00±234.69	0.416
%	37.50±15.69	38.00±13.76	0.937
Fat
g	85.10±32.48	82.17±24.87	0.813
kcal/d	804.50±307.59	777.75±234.04	0.819
%	41.60±12.89	44.17±11.33	0.629
Protein
g	92.20±26.91	71.83±25.98	0.088
kcal/d	397.80±115.99	309.75±112.72	0.088
%	21.00±8.61	17.83±6.19	0.346
npRER	0.84±0.05	0.84±0.04	0.917
UN/(g/d)	14.73±4.29	11.47±4.18	0.089

Indicator	Normal BMI (n=12)	Overweight or obese (n=18)
Height	0.231	0.140
Weight	0.670^*	0.560^*
Body fat	0.617^*	0.503^*
FFM	0.600^*	0.569^*
BSA	0.630^*	0.554^*
Body water	0.523	0.529
Intracellular fluid	0.480	0.438
Extracellular fluid	0.603^*	0.631^**

Predictive equation	Normal BMI (n=12)		Overweight or obese (n=18)
Predictive equation	Value/(kcal/d), $\bar x \pm s$	P	Value/(kcal/d), $\bar x \pm s$	P
mRMR	1 744.33±249.62		2 104.06±520.32
BMR	1 705.42±98.02	0.529	2 022.39±296.13	0.433
Harris, et al^[10]	1 762.84±71.30	0.771	2 236.99±385.60	0.230
Schofield^[11]	1 784.21±70.20	0.523	2 313.50±411.09	0.063
WHO^[12]	1 788.63±71.33	0.477	2 326.47±417.73	0.051
Mifflin, et al^[13]	1 697.01±58.47	0.476	2 039.23±286.89	0.542
Henry^[14]	1 707.17±72.44	0.551	2 210.67±399.88	0.324
Cunningham^[15]	1 793.91±70.24	0.441	1 965.18±174.68	0.203
de Lorenzo, et al^[16]	1 768.70±66.98	0.704	2 207.92±358.02	0.337
Owen, et al^[17]	1 624.19±72.52	0.078	1 801.01±180.34	0.010
Liu, et al^[18]	1 700.83±70.55	0.493	2 182.68±387.24	0.466
Camps, et al^[19]	1 578.11±58.61^**	0.021	2 020.04±343.24	0.423
Xue, et al^[20]	1 967.26±61.84^**	0.004	2 451.99±378.67^**	0.004