Division of Endocrinology and Molecular Medicine, Department of Medicine, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, USA.
Nutr Diabetes. 2019 Jul 29;9(1):21. doi: 10.1038/s41387-019-0087-8.
Resting metabolic rate (RMR) is a key determinant of daily caloric needs. Respirometry, a form of indirect calorimetry (IC), is considered one of the most accurate methods to measure RMR in clinical and research settings. It is impractical to measure RMR by IC in routine clinical practice; therefore, several formulas are used to predict RMR. In this study, we sought to determine the accuracy of these formulas in determining RMR and assess additional factors that may determine RMR.
We measured RMR in 114 subjects (67% female, 30% African American [AA]) using IC. Along with standard anthropometrics, dual-energy X-ray absorptiometry was used to obtain fat-free mass(FFM) and total fat mass. Measured RMR (mRMR) by respirometry was compared with predicted RMR (pRMR) generated by Mifflin-St.Joer, Cunningham, and Harris-Benedict (HB) equations. Linear regression models were used to determine factors affecting mRMR.
Mean age, BMI, and mRMR of subjects were 46 ± 16 years (mean ± SD), 35 ± 10 kg/m, and 1658 ± 391 kcal/day, respectively. After adjusting for age, gender, and anthropometrics, the two largest predictors of mRMR were race (p < 0.0001) and FFM (p < 0.0001). For every kg increase in FFM, RMR increased by 28 kcal/day (p < 0.0001). AA race was associated with 144 kcal/day (p < 0.0001) decrease in mRMR. The impact of race on mRMR was mitigated by adding in truncal FFM to the model. When using only clinically measured variables to predict mRMR, we found race, hip circumference, age, gender, and weight to be significant predictors of mRMR (p < 0.005). Mifflin-St.Joer and HB equations that use just age, gender, height, and weight overestimated kcal expenditure in AA by 138 ± 148 and 242 ± 164 (p < 0.0001), respectively.
We found that formulas utilizing height, weight, gender, and age systematically overestimate mRMR and hence predict higher calorie needs among AA. The lower mRMR in AA could be related to truncal fat-free mass representing the activity of metabolically active intraabdominal organs.
静息代谢率(RMR)是每日热量需求的关键决定因素。呼吸测量法,一种间接量热法(IC),被认为是在临床和研究环境中测量 RMR 的最准确方法之一。在常规临床实践中,通过 IC 测量 RMR 是不切实际的;因此,使用了几种公式来预测 RMR。在这项研究中,我们试图确定这些公式在确定 RMR 方面的准确性,并评估可能决定 RMR 的其他因素。
我们使用 IC 测量了 114 名受试者(67%为女性,30%为非裔美国人[AA])的 RMR。除了标准人体测量学外,双能 X 射线吸收法还用于获得去脂体重(FFM)和总脂肪量。通过呼吸测量法测量的静息代谢率(mRMR)与米夫林-斯捷尔(Mifflin-St.Joer)、坎宁安(Cunningham)和哈里斯-本尼迪克特(HB)公式生成的预测静息代谢率(pRMR)进行比较。使用线性回归模型确定影响 mRMR 的因素。
受试者的平均年龄、BMI 和 mRMR 分别为 46±16 岁(平均值±标准差)、35±10kg/m 和 1658±391kcal/天。在调整年龄、性别和人体测量学因素后,mRMR 的两个最大预测因素是种族(p<0.0001)和 FFM(p<0.0001)。FFM 每增加 1kg,RMR 就会增加 28kcal/天(p<0.0001)。AA 种族与 mRMR 减少 144kcal/天(p<0.0001)相关。将躯干 FFM 加入模型可减轻种族对 mRMR 的影响。当仅使用临床测量变量预测 mRMR 时,我们发现种族、臀围、年龄、性别和体重是 mRMR 的重要预测因素(p<0.005)。仅使用年龄、性别、身高和体重的米夫林-斯捷尔和 HB 公式高估了 AA 中的热量消耗,分别高估了 138±148 和 242±164(p<0.0001)。
我们发现,使用身高、体重、性别和年龄的公式系统地高估了 mRMR,因此预测 AA 中的热量需求更高。AA 中较低的 mRMR 可能与代表代谢活跃的腹腔内器官的躯干去脂体重有关。