Equation to estimate resting energy expenditure in adolescents with sickle cell anemia

BACKGROUND: Basal energy requirements are higher in adolescents with sickle cell anemia (SCA) than in healthy control subjects. However, no equation is available to accurately predict their energy needs. OBJECTIVE: Our objective was to develop a clinically useful equation to estimate resting energy expenditure (REE) in adolescents with SCA. DESIGN: REE and other components of total energy expenditure were measured in adolescents with SCA (n = 37) and in control subjects (n = 23) for 24 h in a whole-room indirect calorimeter. Multiple linear regression analysis was used to describe the relations of REE with independent variables such as sex, weight, height, fat-free mass, fat mass, age, and hemoglobin concentration in adolescents with SCA. The Bland-Altman comparison technique was used to compare values predicted by existing equations with measured REE values. RESULTS: Mean (+/-SD) measured REEs were 7746 +/- 974 and 6332 +/- 869 kJ/d in the male and female subjects with SCA, respectively, and these values were 16% higher than those in the healthy control subjects. Standard equations underestimated REE by 12% (P 相似文献   

An evaluation of resting energy expenditure in hospitalized, severely underweight patients.

A prospective trial was conducted with 14 hospitalized patients who were severely underweight with a mean weight of 40.9+/-5.1 kg and 70.7+/-7.8% of ideal body weight, to compare estimates of resting energy expenditure (REE) with measured values. The 9 women and 3 men, whose mean age was 66.5+/-13.9 y, underwent nutritional assessment and measurement of their REE by indirect calorimetry using the Sensormedics Deltatrac MBM100 indirect calorimeter. Their REE was also estimated by the Harris-Benedict formula (mean 1032+/-66 kcal/d) as well as a previously established empirical formula where REE = 25 x body weight in kg (mean 1023+/-129 kcal/d). Results by both estimates were significantly lower than the measured resting energy expenditure (MREE) in this group of patients (P<0.0001). The percentage difference between MREE and estimated REE by the Harris-Benedict formula was 18.4+/-9.4% and 20.9+/-7.5% by the empirical formula. The MREE exceeded the estimated REE in each individual. The correlation between MREE and body weight (r2 = 0.558, r = 0.005) was better than that between MREE and estimated REE by Harris-Benedict formula (r2 = 0.275, P = 0.08) suggesting that weight was the principal determinant rather than the other components (height, age, sex) of the Harris-Benedict formula. Our data shows that commonly employed formulae routinely underestimate the energy needs of severely underweight patients below 50 kg in body weight. The Harris-Benedict equation had limited predictive value for the individual, explaining approximately 25% of the variance in energy expenditure. Given the particular importance of matching energy intake to needs in this group of patients with limited reserves, many of whom are critically ill, we suggest an empirical equation using 30-32 kcal/kg be used to estimate the energy requirements of severely underweight patients when direct measurements are unavailable or clinically less imperative.     

Resting energy expenditure and nutritional state in patients with liver cirrhosis before and after liver transplantation

Resting energy expenditure (REE), body composition, and the biochemical parameters of liver function were measured in 26 patients before and 432 days (range: 103-1022 days) after liver transplantation (LTX). PreLTX REE was variable (mean: 1638 +/- 308 kcal/day, range: 1220-2190 kcal/day or +10 +/- 11% of Harris Benedict = HB prediction, range: -19 - +33%) and was closely related to body cell mass (r = 0.66, p < 0.0003). PostLTX REE was variable (mean: 1612 +/- 358 kcal/day, range: 1010-2490 kcal/day or +5 +/- 15% of HB prediction, range: -20 - +37%) and was closely related to body cell mass (r = 0.65, p < 0.0006). When compared with preLTX values only small changes in mean REE (-71 +/- 43 kcal/day) and a close correlation between pre and postLTX REE (r = 0.82, p < 0.001) were observed. In contrast to REE, changes in body weight were highly variable (-16.5 - +32.7 kg/year). This variance was not explained by the number of postoperative complications, pre and postLTX liver function, possible graft rejection and/or hepatitis reinfection. Pre-operative hypermetabolism (i.e. REE >+20% of HB prediction) was associated with postoperative hypermetabolism and a reduced liver function before and after LTX. Hypermetabolic patients had a poorer nutritional outcome after LTX (weight change: 0 +/- 8.4 kg/year) when compared with normometabolic controls (weight change: +5.7 +/- 7.4 kg/year; p < 0.05). There was no significant association between deviations in pre and postLTX REE and changes in body weight. When corrected for changes in the nutritional state our data provide evidence for the persistence of resting energy expenditure in liver transplant patients.     

World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: generation of a new reference standard from a retrospective analysis of a German database of resting energy expenditure

BACKGROUND: Reference standards for resting energy expenditure (REE) are widely used. Current standards are based on measurements made in the first part of the past century in various races and locations. OBJECTIVE: The aim of the present study was to investigate the application of the World Health Organization (WHO) equations from 1985 in healthy subjects living in a modern, affluent society in Germany and to generate a new formula for predicting REE. DESIGN: The study was a cross-sectional and retrospective analysis of data on REE and body composition obtained from 2528 subjects aged 5-91 y in 7 different centers between 1985 and 2002. RESULTS: Mean REE varied between 5.63 and 8.07 MJ/d in males and between 5.35 and 6.46 MJ/d in females. WHO prediction equations systematically overestimated REE at low REE values but underestimated REE at high REE values. There were significant and independent effects of sex, age, body mass or fat-free mass, and fat mass on REE. Multivariate regression analysis explained up to 75% of the variance in REE. Two prediction formulas including weight, sex, and age or fat-free mass, fat mass, sex, and age, respectively, were generated in a subpopulation and cross-validated in another subpopulation. Significant deviations were still observed for underweight and normal-weight subjects. REE prediction formulas for specific body mass index groups reduced the deviations. The normative data for REE from the Institute of Medicine underestimated our data by 0.3 MJ/d. CONCLUSIONS: REE prediction by WHO formulas systematically over- and underestimates REE. REE prediction from a weight group-specific formula is recommended in underweight subjects.     

A practical approach to estimate resting energy expenditure in frail elderly people

Objectives: Some prediction equations of resting energy expenditure (REE) are available and can be used in clinical wards to determine energy requirements of patients. The aim of the present study was to assess the accuracy of those equations in sick elderly patients, using the Bland & Altman methods with our database of 187 REE measurements.Design: The 3 equations tested were Harris & Benedict equation of 1919, WHO/FAO/UNU equation of 1985 and Fredrix et al. equation of 1990. In addition, three models developed from the present data were tested.Results: The present study shows that the Fredrix et al equation gave an accurate prediction of REE without significant bias along the whole range of REE. It also shows that under-weight sick elderly patients (BMI ≤ 21 kg/m²) had a greater weight-adjusted REE than their normal weight counterparts.Conclusion: A simple formula using a factor multiplying body weight, i.e. 22 kcal/kg/d in under-weight and 19 kcal/kg/d in normal weight sick elderly was accurate to predicting REE and bias was not influenced by the level of REE. This model included half of the group in the range of ±10% of the difference between predicted REE and measured REE, but the confidence interval of the bias was ±400 kcal/d. Conversely, the Harris & Benedict and WHO formulae did accurately predict REE.     

Relationship between blood adipocytokines and resting energy expenditure in young and elderly women

It has been demonstrated in a previous study that resting energy expenditure (REE) is associated with adiponectin levels in the blood. However, body composition was not taken into consideration in that study. The purpose of the present study was to again investigate the relationship between blood adipocytokines and REE, adjusted by body composition, in both young and elderly women. REE and blood adipocytokines were measured in 115 young (age: 22.3+/-2.1 y, BMI: 21.3+/-1.9 kg/m(2)) and 71 elderly (63.4+/-6.5 y, 22.9+/- 2.3 kg/m(2)) women. Dual energy X-ray absorptiometry was used to measure percent body fat. Fat mass and fat free mass (FFM) were calculated. REE (kcal/d and kcal/kg BW/d) was lower in elderly women than in young women, but no significant difference was observed in REE, expressed as kcal/kg FFM/d, between the two groups. Although elderly women had a higher percent body fat and higher serum leptin concentrations than young women, plasma adiponectin concentrations did not differ between young and elderly women. In elderly women, REE (kcal/d) was significantly and inversely correlated with plasma adiponectin concentration (r=-0.386, p<0.001), but REE expressed per kilogram of BW or FFM was not significantly correlated. Furthermore, no significant correlation was observed between REE (kcal/d) and concentrations of plasma adiponectin or serum leptin, after adjusting for potential confounders such as body composition and hormones, in either age group. These results suggest that adipocytokines do not influence REE in adult women.     

Measured versus predicted energy expenditure in children with inactive Crohn's disease

INTRODUCTION: The aim of the study was to assess if the estimated average requirements for energy for normal children (EAR) and the Schofield equation could reliably predict energy requirements in children with inactive Crohn's disease (CD). METHODS: Twenty-three children with inactive CD were studied, median age 14.3 years (range 7.8-16.9). Resting energy expenditure (REE) was measured by indirect calorimetry and compared with that predicted using the Schofield equation (BMR). Total energy expenditure (TEE) was measured using REE and a 3-day activity diary and compared with EAR. RESULTS: REE ranged from 79% to 136% of BMR. Mean REE was not significantly greater than mean BMR (P=0.25 2-tailed t-test). TEE ranged from 72% to 163% of estimated average requirements for energy for children of that weight (EARw). EARw tended to underestimate TEE in large children and overestimate TEE in small children (Bland-Altman plot R=0.5, P=0.002). EARw was a poor predictor of TEE (R=0.35, P=0.1). EAR underestimated energy requirements by >500 kcal/day in 40% of the children. CONCLUSIONS: The Schofield equation and EAR are unreliable methods of predicting total energy requirements in children with inactive CD with a significant potential to underestimate energy needs. When energy requirements were greater than EAR it was due to physical activity and body habitus rather than raised REE.     

Energy balance in elderly patients after surgery for a femoral neck fracture

To study energy and protein balances in elderly patients after surgery, spontaneous energy and protein intake and resting energy expenditure (REE) were measured in 20 elderly female patients with a femoral neck fracture (mean age 81 +/- 4, SD, range 74-87 years; weight 53 +/- 8, range 42-68 kg) during a 5-6 day period following surgery. REE, measured over 20-40 min by indirect calorimetry using a ventilated canopy, averaged 0.98 +/- 0.15 kcal/min on day 3 and decreased to 0.93 +/- 0.15 kcal/min on day 8-9 postsurgery (p less than 0.02). REE was positively correlated with body weight (r = 0.69, p less than 0.005). Mean REE extrapolated to 24 hr (24-REE) was 1283 +/- 194 kcal/day. Mean daily food energy intake measured over the 5-day follow-up period was 1097 +/- 333 kcal/day and was positively correlated with 24-REE (r = 0.50, p less than 0.05). Daily energy balance was -235 +/- 351 kcal/day on day 3 (p less than 0.01 vs zero) and -13 +/- 392 kcal/day on day 8-9 postsurgery (NS vs zero) with a mean over the study period of -185 +/- 289 kcal/day (p less than 0.01 vs zero). When an extra 100 kcal/day was allowed for the energy cost of physical activity, mean daily energy balance over the 5-day study period was calculated to be -285 +/- 289 kcal/day (p less than 0.01 vs zero). Measurements of total 24-hr urinary nitrogen (N) excretion were obtained in a subgroup of 14 patients.(ABSTRACT TRUNCATED AT 250 WORDS)     

Resting Energy Expenditure and Related Factors in 6- to 9-Year-Old Southern African Children of Diverse Population Groups

Lower resting energy expenditure (REE) may partially explain the disproportionate prevalence of overweight/obesity among black African women. As no previous studies have investigated the REE of Southern African (South. Afr.) children, we aimed to determine, by sex and population group, the REE of 6- to 9-year-old urban school children. In a cross-sectional study with quota sampling, REE was measured with indirect calorimetry (IC). Confounders considered were: body composition (BC) (fat-free mass (FFM), FFM index, fat mass (FM), FM index), assessed using multifrequency bioelectrical impedance analysis, and physical activity (PA) measured with a pedometer. Multivariate regression was used to calculate REE adjusted for phenotypes (BC, z-scores of weight-for-age, height-for-age, body mass index-for-age) and PA. Sex and population differences in REE were determined with two-way ANOVA. Ninety-four healthy children (59.6% girls; 52.1% black) with similar socioeconomic status and PA opportunities participated. Despite BC variations, sex differences in REE were not significant (41 kcal/day; P = 0.375). The REE of black participants was lower than of white (146 kcal/day; P = 0.002). When adjusted for FFM and HFA z-score, the differences in REE declined but remained clinically meaningful at 91 kcal/day (P = 0.039) and 82 kcal/day (P = 0.108), respectively. We recommend the development of population-specific REE prediction equations for South. Afr. children.     

Estimating energy requirements in burned children: a new approach derived from measurements of resting energy expenditure

We examined the determinants of resting energy expenditure (REE) in 127 observations in 56 burned children. Predicted basal energy expenditure (PBEE), body surface area (BSA), and body weight correlated significantly with REE (r2 = 0.76). Days postburn and burn size (% BSA burned) only accounted for 21%, and 24% of the variation in the elevation in REE above PBEE. The single most powerful predictor of REE was PBEE (REE = 1.29 x PBEE); addition of other variables did not improve the prediction. When our recently described activity factor of 1.2 for burn patients is used, the data predict that the average energy requirement to maintain energy balance is 1.55 x PBEE, which is significantly lower than commonly used recommendations, especially for larger burns. The energy required to ensure that 95% of patients achieve energy balance was (1.55 x PBEE) + (2.39 xoff+PBEE0.75), approximately equal to 2 x PBEE. Because the equations presented are derived from measurements of energy expenditure, they represent the most valid approach to estimating energy requirements.     

Reliability of energy expenditure prediction equations in the weight management clinic

Background:  Few weight management clinics have access to indirect calorimetry with which to measure energy expenditure. Instead, they use energy expenditure prediction equations, which were not designed for use in obesity. We aimed to establish the extent to which such equations overestimate and underestimate resting energy expenditure (REE) in overweight and obese individuals. Methods:  We compared the Schofield, Harris & Benedict, James & Lean and World Health Organisation (WHO) REE prediction equations with the clinical gold standard of indirect calorimetry in 28 males and 168 females, with a mean (SD) age of 28.9 (6.4) years and body mass index (BMI) of 19–67 kg m^?2. Results:  The mean REE estimated by indirect calorimetry, and the Schofield, Harris & Benedict, James & Lean and WHO equations were 8.09, 8.30, 8.09, 8.37 and 8.23 MJ day^?1 (1934, 1983, 1933, 2001 and 1966 kcal day^?1), respectively. Although rising BMI exerted only a small effect on the mean differences between indirect calorimetry and the predicted REE [Schofield: +272 kJ (+65 kcal)/10 units BMI, P = 0.02; Harris & Benedict: +42 kJ (+10 kcal)/10 units BMI, P = 0.69; James & Lean: +217 kJ (+52 kcal) 10 units BMI, P = 0.06 and WHO: +42 kJ (+10 kcal) BMI, P = 0.11], the variance among overweight and obese patients of BMI >25 was substantially higher compared to that among normal weight subjects of BMI <25, on whom the equations were based. The estimated REE by Schofield for an individual of BMI 35 kg m^?2, for example, could lie anywhere from 2.78 MJ (661 kcal) above the indirect calorimetry value to 2.59 MJ (618) kcal below it. Conclusions:  Prediction equations offer a quick assessment of energy needs for hypocaloric diets although, in reality, they run the random risk of excessive restriction or further weight gain.     

Validity of predictive equations for resting energy expenditure in US and Dutch overweight and obese class I and II adults aged 18-65 y

BACKGROUND: Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry. OBJECTIVE: The objective was to analyze which resting energy expenditure (REE) predictive equation was the best alternative to indirect calorimetry in US and Dutch adults aged 18-65 y with a body mass index (in kg/m(2)) of 25 to 40. DESIGN: Predictive equations based on weight, height, sex, age, fat-free mass, and fat mass were tested. REE in Dutch adults was measured with indirect calorimetry, and published data from the Institute of Medicine were used for US adults. The accuracy of the equations was evaluated on the basis of the percentage of subjects predicted within 10% of the REE measured, the root mean squared prediction error (RMSE), and the mean percentage difference (bias) between predicted and measured REE. RESULTS: Twenty-seven predictive equations (9 of which were based on FFM) were included. Validation was based on 180 women and 158 men from the United States and on 154 women and 54 men from the Netherlands aged <65 y with a body mass index (in kg/m(2)) of 25 to 40. Most accurate and precise for the US adults was the Mifflin equation (prediction accuracy: 79%; bias: -1.0%; RMSE: 136 kcal/d), for overweight Dutch adults was the FAO/WHO/UNU weight equation (prediction accuracy: 68%; bias: -2.5%; RMSE: 178), and for obese Dutch adults was the Lazzer equation (prediction accuracy: 69%; bias: -3.0%; RMSE: 215 kcal/d). CONCLUSIONS: For US adults aged 18-65 y with a body mass index of 25 to 40, the REE can best be estimated with the Mifflin equation. For overweight and obese Dutch adults, there appears to be no accurate equation.     

Energy requirements in frail elderly people: a review of the literature

This review collates studies of healthy, sick, underweight (BMI < or = 21 kg/m2) and very elderly people (> or = 90 yr), in whom resting energy expenditure (REE) was measured using indirect calorimetry. We have observed the following: (1) REE, when adjusted for differences in both body weight and fat-free mass (FFM), is similar in healthy and in sick elderly people being 20 and 28 kcal/kg of FFM per day, respectively, (2) their nutritional status influences their energy requirements given that weight-adjusted REE increases in line with a decrease in BMI, (3) total energy expenditure is lower in sick elderly people given that their physical activity level, i.e. the ratio of total energy expenditure to REE, is reduced during disease averaging at 1.36, (4) energy intake (EI) being only 1.23 x REE is insufficient to cover energy requirements in sick elderly patients, whereas the EI of healthy elderly people appears sufficient to cover requirements, and finally, (5) gender ceases to be a determinant of REE in people aged 60 yr or over, with the Harris & Benedict equation capable of accurately predicting mean REE in this population, whether healthy or sick.     

Twenty-four hour urinary creatinine: A simple technique for estimating resting energy expenditure in normal population and the hospitalised patients

This study was designed to determine the relationship between resting energy expenditure (REE) and 24h urinary creatinine excretion. A total of 61 normal males and females and hospital patients had their REE and 24 h urinary creatinine measured. A statistically significant correlation between measured REE (in kcal) and 24 h urinary creatinine (in mg) was noted, (y = 0.488 × + 964; r = 0.81, P 0.0001, SD of the residuals 156). If 24 h urinary creatinine is in mmol, then the equation would be 55 × + 964. Creatinine coefficients (creatinine excretion in mg/day/kg body weight) of 25 (.221 mmol) for males and 20 (.176 mmol) for females were found.These findings suggest that an equation based on 24 h urinary creatinine excretion will be a more accurate estimate of energy expenditure than conventional methods based on height, weight, age and sex and will be applicable to a normal population as well as hospitalised patients.     

Energy prediction equations are inadequate for obese Hispanic youth

Assessing energy requirements is a fundamental activity in clinical dietetics practice. A study was designed to determine whether published linear regression equations were accurate for predicting resting energy expenditure (REE) in fasted Hispanic children with obesity (aged 7 to 15 years). REE was measured using indirect calorimetry; body composition was estimated with whole-body air displacement plethysmography. REE was predicted using four equations: Institute of Medicine for healthy-weight children (IOM-HW), IOM for overweight and obese children (IOM-OS), Harris-Benedict, and Schofield. Accuracy of the prediction was calculated as the absolute value of the difference between the measured and predicted REE divided by the measured REE, expressed as a percentage. Predicted values within 85% to 115% of measured were defined as accurate. Participants (n=58; 53% boys) were mean age 11.8±2.1 years, had 43.5%±5.1% body fat, and had a body mass index of 31.5±5.8 (98.6±1.1 body mass index percentile). Measured REE was 2,339±680 kcal/day; predicted REE was 1,815±401 kcal/day (IOM-HW), 1,794±311 kcal/day (IOM-OS), 1,151±300 kcal/day (Harris-Benedict), and, 1,771±316 kcal/day (Schofield). Measured REE adjusted for body weight averaged 32.0±8.4 kcal/kg/day (95% confidence interval 29.8 to 34.2). Published equations predicted REE within 15% accuracy for only 36% to 40% of 58 participants, except for Harris-Benedict, which did not achieve accuracy for any participant. The most frequently accurate values were obtained using IOM-HW, which predicted REE within 15% accuracy for 55% (17/31) of boys. Published equations did not accurately predict REE for youth in the study sample. Further studies are warranted to formulate accurate energy prediction equations for this population.     

Prediction of resting energy expenditure in a large population of obese children

BACKGROUND: Recommendations for energy intake in obese children rely on accurate methods for measuring energy expenditure that cannot be assessed systematically. OBJECTIVE: The aim was to establish and validate new equations for predicting resting energy expenditure (REE), specifically in obese children. DESIGN: REE (indirect calorimetry) and body composition (bioelectrical impedance analysis) were measured in 752 obese subjects aged 3-18 y. The first cohort (n=471) was used to establish predictive equations, the second (and independent) cohort (n=211) was used to validate these equations, and the third cohort, a follow-up group of children who lost weight (n=70), was used to examine predictive REE in the postobese period. REE values predicted with the use of various published equations and the new established equation were compared with measured REE by using the Bland-Altman method and Student's t tests. RESULTS: In cohort 1, significant determinants of the new prediction equations were fat-free mass in boys (model R2=0.79) and age and fat-free mass in girls (model R2=0.76). External validation conducted by using the Bland-Altman method and Student's t tests, in cohort 2, showed no significant difference between measured REE and predicted REE with the new equation. When already published equations were applied, systematical bias appeared with all published equations except for that of the World Health Organization. In cohort 3, the children who lost weight, almost all equations significantly underestimated REE. CONCLUSIONS: These new predictive equations allow clinicians to estimate REE in an obese pediatric population with sufficient and acceptable accuracy. This estimation may be a strong basis for energy recommendations in childhood obesity.     

Resting energy expenditure is lower than predicted in people taking atypical antipsychotic medication

Resting energy expenditure (REE) is lower than predicted in persons taking atypical antipsychotic medication, and weight management is a significant clinical challenge for some of them. However, to date there have been no published guidelines to assist clinicians in choosing appropriate prediction equations to estimate energy expenditure in persons taking atypical antipsychotic medications. The objectives of this study were to measure REE in a group of men taking the atypical antipsychotic clozapine and to determine whether REE can be accurately predicted for this population using previously published regression equations. REE was measured using indirect calorimetry via a ventilated hood on eight men who had completed at least 6 months of treatment with clozapine. Comparisons between measured REE and predicted REE using five different equations were undertaken. The commonly-used Harris-Benedict and Schofield equations systematically overestimated REE. Predictions of REE from other equations were too variable for clinical use. When estimating energy requirements as part of a weight-management program in men who have been taking clozapine for 6 months, predictions of REE from the equations of Harris-Benedict and Schofield should be reduced by 280 kcal/day.     

Assessment of resting energy expenditure in mechanically ventilated patients

BACKGROUND: Usual equations for predicting resting energy expenditure (REE) are not appropriate for critically ill patients, and indirect calorimetry criteria render its routine use difficult. OBJECTIVE: Variables that might influence the REE of mechanically ventilated patients were evaluated to establish a predictive relation between these variables and REE. DESIGN: The REE of 70 metabolically stable, mechanically ventilated patients was prospectively measured by indirect calorimetry and calculated with the use of standard predictive models (Harris and Benedict's equations corrected for hypermetabolism factors). Patient data that might influence REE were assessed, and multivariate analysis was conducted to determine the relations between measured REE and these data. Measured and calculated REE were compared by using the Bland-Altman method. RESULTS: Multivariate analysis retained 4 independent variables defining REE: body weight (r(2) = 0.14, P < 0.0001), height (r(2) = 0.11, P = 0.0002), minute ventilation (r(2) = 0.04, P = 0.01), and body temperature (r(2) = 0.07, P = 0.002): REE (kcal/d) = 8 x body weight + 14 x height + 32 x minute ventilation + 94 x body temperature - 4834. REE calculated with this equation was well correlated with measured REE (r(2) = 0.61, P < 0.0001). Bland-Altman plots showed a mean bias approaching zero, and the limits of agreement between measured and predicted REE were clinically acceptable. CONCLUSION: Our results suggest that REE estimated on the basis of body weight, height, minute ventilation, and body temperature is clinically more relevant than are the usual predictive equations for metabolically stable, mechanically ventilated patients.     

健康老年人静息能量消耗

目的 :　探讨老年人 REE与性别、年龄 ,人体测量学指标的相关性。方法 :　用间接能量测定仪测试 82名 (男 3 0、女 5 2 )平均年龄 80岁的中国健康汉族老年人的静息能量消耗 (rest-ing energy expenditure,REE)的水平 ,并将 REE测试值与根据 Harris- Benedict公式算出的基础能量消耗值 (basal energy expenditure,BEE)进行比较。同时应用生物电阻抗分析法 (bioelectricalimpedance analysis,BIA)测定去脂体重 (fatfree mass,FFM)和体脂重量 (fat mass,FM)等人体测量学数据。结果 :　 82名健康老人的 REE平均值为 (4.44± 0 .5 2 ) MJ/2 4 h,与公式计算的 BEE比无统计学差异 ,但比 FAO/WHO/UNU(1 985 )公式值低 9% ,比 Owen公式值低 1 9%。本研究观察到我国健康老年人的 REE与去脂体重、体重、体表面积 (body surface area,BSA)、年龄、身高、性别和体重指数 (body mass index,BMI)之间有相关性。老年男女的每公斤体重、每公斤去脂体重和单位体表面积所产生的 REE间无统计学差异。结论 :　 Harris- Benedict公式、FAO/WHO/UNU(1 985 )公式与 Owen公式都过高估计了我国健康老年人的基础能量消耗。由于老年人的REE存在较大的个体差异 ,其 REE值宜实测而不宜用公式预测。我国健康老年人的 REE与去脂体重、体?