Predictor Of Resting Metabolic Rate Research Articles

Revised Harris-Benedict Equation: New Human Resting Metabolic Rate Equation.

This paper contains a revision of the Harris-Benedict equations through the development and validation of new equations for the estimation of resting metabolic rate (RMR) in normal, overweight, and obese adult subjects, taking into account the same anthropometric parameters. A total of 722 adult Caucasian subjects were enrolled in this analysis. After taking a detailed medical history, the study enrolled non-hospitalized subjects with medically and nutritionally controlled diseases such as diabetes mellitus, cardiovascular disease, and thyroid disease, excluding subjects with active infections and pregnant or lactating women. Measurement of somatometric characteristics and indirect calorimetry were performed. The values obtained from RMR measurement were compared with the values of the new equations and the Harris-Benedict, Mifflin-St Jeor, FAO/WHO/UNU, and Owen equations. New predictive RMR equations were developed using age, body weight, height, and sex parameters. RMR males: (9.65 × weight in kg) + (573 × height in m) - (5.08 × age in years) + 260; RMR females: (7.38 × weight in kg) + (607 × height in m) - (2.31 × age in years) + 43; RMR males: (4.38 × weight in pounds) + (14.55 × height in inches) - (5.08 × age in years) + 260; RMR females: (3.35 × weight in pounds) + (15.42 × height in inches) - (2.31 × age in years) + 43. The accuracy of the new equations was tested in the test group in both groups, in accordance with the resting metabolic rate measurements. The new equations showed more accurate results than the other equations, with the equation for men (R-squared: 0.95) showing better prediction than the equation for women (R-squared: 0.86). The new equations showed good accuracy at both group and individual levels, and better reliability compared to other equations using the same anthropometric variables as predictors of RMR. The new equations were created under modern obesogenic conditions, and do not exclude individuals with regulated (dietary or pharmacological) Westernized diseases (e.g., cardiovascular disease, diabetes, and thyroid disease).

Vitamin D Status and Resting Metabolic Rate May Modify through Expression of Vitamin D Receptor and Peroxisome Proliferator-Activated Receptor Gamma Coactivator-1 Alpha Gene in Overweight and Obese Adults

Background: Resting metabolic rate (RMR) used to prognosticate and measure the amount of energy required. Vitamin D is known as a new predictor of RMR. The aim of this study is to investigate the relationship between vitamin D effects on RMR in connection with the vitamin D receptor (VDR) and peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) gene expression. Methods: We enrolled 298 overweight and obese adults in this cross-sectional study. Body mass index (BMI), fat mass, fat-free mass, insulin level, visceral fat, and vitamin D status were assessed. RMR was measured by means of indirect calorimetry. The real-time polymerase chain reaction using specific primer pairs for VDR and PGC-1α was performed. Results: There were significant differences in terms of fat free mass, fat percentage, insulin levels, RMR/kg body weight, and RMR/BMI, VDR, and PGC-1α among participants were categorized based on the vitamin D status. But after using general linear model for adjusting, all significant results missed their effectiveness except RMR/kg body weight and VDR. Linear regression analysis used to show the mediatory role of VDR and PGC-1α on the RMR/kg body weight and vitamin D status relationship. Our results showed that VDR had a mediatory effect on the relationship between RMR/kg body weight and vitamin D status (β = 0.38, 95% CI –0.48 to 1.60; β = –1.24, 95% CI –5.36 to 1.70). However, PGC-1α did not affect the relationship between RMR/kg body weight and vitamin D status (β = 0.50, 95% CI = –0.02 to 3.42; β = 0.59, 95% CI 0.14–3.90). Conclusion: Our study showed the mediatory effect of VDR gene expression in the association of 25(OH)₂D plasma levels and resting metabolic rate among obese individuals.

Body composition and resting metabolic rate in obese adolescents

Objective: Analyze body composition and resting metabolic rate (RMR) in obese adolescents. Methods: 58 obese adolescents were assessed between 12 and 16 years (13.7 ± 1.2 years). The body composition was measured dual-energy x-ray absorptiometry (DEXA). The resting metabolic rate and oxidation of glucose and lipids were determined by measuring the oxygen consumption and the production of carbon dioxide. Student t test was used for independent data, multiple linear regression and Pearson correlation, significance was set p <5%. Results: Significant differences were observed in the percentage of body fat (p = 0.01), percentage of lean body mass (LBM) (p = 0.01), percentage body fat (p = 0.01), lower limb lean mass (p = 0.01) and resting metabolic rate (p = 0.04) between males and females. The LBM is highlighted as the best predictor of RMR (p = 0.01). There was a positive correlation between RMR and LBM, as observed association between lipid oxidation and LBM. Conclusion: Total body composition and segment influence the resting metabolic rate and substrate oxidation. In addition, the LBM can be considered a factor of increased resting energy expenditure in obese adolescents.

RMR Estimation Model Accuracy Using Air Displacement Plethysmography–Derived Body Composition Measures in Young Adults

Objective: Predictive equations derived from regression techniques based on large samples are extensively utilized in estimating resting metabolic rate (RMR). Body composition assessments utilize model equations to estimate RMR. However, the agreement of these predictive models with indirect calorimetery (IC) has come into question. Our aim is to investigate the agreement of RMR estimation models using air displacement plethysmography (ADP) measures against a gas exchange IC system (RMR-C).Methods: Sixty-six participants (25 men, 41 women) completed the study. RMR measurements were obtained from IC and ADP within 10 minutes of one another. IC RMR estimates were tested against 9 other validated models using ADP measures via analysis of variance (ANOVA) techniques with multiple comparisons testing and Bland-Altman analysis.Results: Based on the ANOVA, the Nelson (1992) model underpredicted RMR compared to IC (p < 0.001). The Doré et al. (1982) model was the best predictor of RMR compared to the IC measures (p = 0.907).Discussion: The current RMR estimation model using ADP measures underpredicts total caloric needs. The Doré et al. (1982) model more accurately predicted RMR in the entire sample.

Differences in resting metabolic rate between paraplegic and able-bodied subjects are explained by differences in body composition

Little is known about the relation between body composition and energy metabolism in paraplegia. We investigated the relation between body composition and energy metabolism in healthy paraplegics as compared with able-bodied control subjects. We hypothesized that paraplegics would have lower fat-free mass (FFM), body cell mass (BCM), resting metabolic rate (RMR), and thermic effect of feeding (TEF). This cross-sectional study included 34 control subjects and 28 paraplegics (mean age: 29.1 +/- 7.6 and 33.9 +/- 9.2 y, respectively) with body mass indexes (in kg/m(2)) of 23.5 +/- 1.8 and 24.3 +/- 6.0, respectively. We measured RMR and TEF with indirect calorimetry, total body water with deuterium dilution, and extracellular water with corrected bromide space. We calculated FFM (total body water/0.732) and BCM [(total body water - extracellular water)/0.732)]. FFM was higher in control subjects than in paraplegics (77.2 +/- 7.2% and 69.2 +/- 8.7%, respectively; P = 0.0002), as were BCM (47.4 +/- 6.7% and 35.9 +/- 8.1%, respectively; P < 0.0001) and RMR (7016 +/- 935 and 6159 +/- 954 kJ/d, respectively; P = 0.0007). FFM was the single best predictor of RMR in both groups (r(2) = 0.83 for control subjects and 0.70 for paraplegics, P < 0.0001 for both). RMR adjusted for FFM did not differ significantly between control subjects and paraplegics (6670 +/- 504 and 6588 +/- 501 kJ/d, respectively). TEF also did not differ significantly between control subjects and paraplegics (6.25 +/- 2.2% and 5.53 +/- 1.8% of energy intake, respectively). FFM, BCM, and RMR, but not obligatory TEF, are lower in paraplegics than in control subjects. RMR does not differ between control and paraplegic subjects after adjustment for FFM, indicating similar metabolic activity in the fat-free compartment of the body.

A new equation especially developed for predicting resting metabolic rate in the elderly for easy use in practice.

Equations published in the literature for predicting resting metabolic rate (RMR) in older individuals were exclusively derived from studies with small samples of this age group. of the present investigation was therefore to compare the measured RMR of a relatively large group of older females and males with values for RMR calculated from the most commonly used WHO [1] equations. Furthermore, on the basis of the data collected by our study group a new equation for calculating RMR in the elderly was to be developed. Variables used in this equation should be easily and exactly determinable in practice. RMR was measured by indirect calorimetry after an overnight fast in a sample of 179 female (age 67.8 +/- 5.7 y, BMI 26.4 +/- 3.7 kg/m(2)) and 107 male (age 66.9 +/- 5.1 y, BMI 26.3 +/- 3.1 kg/m(2)) participants in the longitudinal study on nutrition and health status in an aging population of Giessen, Germany. The subjects were at least 60 years old, did not suffer from thyroid dysfunction, and were not taking thyroid hormones. Stepwise multiple linear regression analysis was used to estimate the best predictors of RMR. In females there was no significant difference between our measured RMR (5504 +/- 653 kJ/d) and RMR predicted with the WHO [1] equation (5458 +/- 440 kJ/d), whereas in males measured RMR (6831 +/- 779 kJ/d) was significantly higher than calculated RMR (6490 +/- 550 kJ/d). Results of regression analysis, considering body weight, body height, age, and sex, showed that RMR is best calculated by the following equation: RMR [kJ/d]= 3169 + 50.0 x body weight [kg] - 15.3 x age [y] + 746 x sex [female = 0, male = 1]. The variables of this equation accounted for 74 % (R(2)) of the variance in RMR and predicted RMR within +/- 486 kJ/d (SEE). On the basis of the data determined in a large group of older individuals, we offer a new equation for calculating RMR in the elderly that is both easy and accurate for use in practice.

Influence of body composition on the metabolic rate of nestling European shags ( Phalacrocorax aristotelis )

During the early development of avian nestlings, their mass-specific resting metabolic rate (RMR) changes in a biphasic pattern with the peak value often being much higher than that expected for an adult bird of similar body mass. In the present study we examined the possible influence of variations in the size of internal organs in “setting” the high RMR and peak metabolic rate (PMR) during development in a large altricial species, the European shag (Phalacrocorax aristotelis). Thermoneutral RMR and cold-exposure induced PMR were measured in nestlings 15 days old, the age at which the highest RMR occurred during development. Body mass averaged 414 g. Mean values of RMR and PMR were 5.75 W and 9.08 W, respectively; the RMR value corresponds to approximately 250% of the expected value for an adult non-passerine bird of similar body mass. The masses of all the organs measured (breast and leg muscles, heart, liver, intestine, and kidney) varied isometrically with total body mass. However, large chicks had a significantly lower fractional water content than small chicks, suggesting that the former had achieved a higher level of functional maturity. In contrast to what has been suggested for adult birds in general, the heart and kidney masses of shag nestlings were not significantly correlated with the metabolic rates. The intestine length, in contrast, was highly and positively correlated with both the RMR and the PMR, i.e. intestine length was a better predictor of RMR and PMR than was total body mass. In addition, liver mass was positively correlated with RMR. The results of the present study suggest that the liver in particular may play a key role in establishing the high, mass-specific RMR which is attained during development in bird chicks. Our results also support previous suggestions that early in their development, altricial chicks mainly allocate energy to the growth of `energy-processing' organs (such as the intestine and liver) rather than to `energy-consuming' organs.

Resting energy expenditure in reduced-obese subjects in the National Weight Control Registry

Weight loss in obese subjects is associated with a reduction in resting metabolic rate (RMR). Whether the reduction can be explained solely by a reduction in lean body mass remains controversial. Our objective was to determine whether the reduction in RMR after weight loss was proportional to the decrease in lean mass alone or was greater than could be explained by body composition. We measured the RMR, fasting respiratory quotient (RQ), and body composition in 40 reduced-obese subjects [ie, 7 men and 33 women who had lost > or = 13.6 kg (30 lb) and maintained the loss for > or = 1 y] enrolled in the National Weight Control Registry and 46 weight-matched control subjects (9 men, 37 women). A stepwise multiple regression found lean mass, fat mass, age, and sex to be the best predictors of RMR in both groups. After adjusting RMR for these variables, we found no significant difference in RMR (5926 +/- 106 and 6015 +/- 104 kJ/d) between the 2 groups (P = 0.35). When we adjusted fasting RQ for percentage body fat and age, the reduced-obese group had a slightly higher (0.807 +/- 0.006) RQ than the control group (0.791 +/- 0.005, P = 0.05). This may have been due to the consumption of a diet lower in fat or to a reduced capacity for fat oxidation in the reduced-obese group. These results show that in at least some reduced-obese individuals there does not seem to be a permanent obligatory reduction in RMR beyond the expected reduction for a reduced lean mass.

Predicted and Measured Resting Metabolic Rate of Male and Female Endurance Athletes

Objective To measure the resting metabolic rate (RMR) of a group of endurance-trained male and female athletes and to compare it with values predicted using published equations. Design RMR was measured twice: 1 week apart for the men and approximately 1 month apart for the women. RMR was predicted using equations of Harris and Benedict, Owen et al, Mifflin et al, and Cunningham. Subjects/Setting Subjects were 37 trained endurance athletes (24 men, 13 women) who had participated in studies previously completed in our laboratory. Main outcome measures The primary outcome measure was the comparison of predicted RMR with measured RMR. An exploratory procedure for the determination of predictive variables in these athletes was also performed. Statistical analyses performed The Root Mean Squared Prediction Error method was used to compare predicted RMR with measured RMR. The maximum R 2 procedure method was used to determine the best possible combination of four variables that explained the largest amount of variance in RMR. Results The Cunningham equation was found to predict measured RMR most accurately (within 158 kcal/d for men and 103 kcal/d for women). Fat-free mass was the best predictor of RMR in men, whereas energy intake was the best predictor in women. Applications/conclusions The Cunningham equation provides an accurate estimate of RMR when determining energy needs of highly active people. Equations specific to athletes need to be developed. Factors in addition to body weight, height, and age should be investigated as possible predictor variables in athletes. J Am Diet Assoc. 1996; 96:30-34.

Accuracy of standardized equations for predicting metabolic rate in premenarcheal girls

Several different equations based on height, weight, sex, and age are available to predict metabolic rate in children and adolescents. The purpose of this study was to determine which of the published standardized equations most accurately predicts metabolic rate in premenarcheal girls. Resting metabolic rate (RMR) was measured in 186 premenarcheal girls by using open-circuit indirect calorimetry with a ventilated hood. Measured RMR was compared with equations provided by the FAO/WHO/UNU, the Mayo Clinic, and Robertson and Reid for girls of the same age. The accuracy of each equation differed for each Tanner stage. The Mayo Clinic standards significantly differed from measured RMR among girls in Tanner stages 1 and 2. The Robertson and Reid standards differed from measured RMR for girls in Tanner stage 1. Only the FAO/WHO/UNU equation predictions did not differ from measured RMR for all Tanner stages studied. These results indicate that the FAO/WHO/UNU equation is the best predictor of RMR in premenarcheal girls at different stages of development. Furthermore, because RMR measured under inpatient and outpatient conditions did not differ significantly, an overnight measurement is not necessary for a reliable determination of RMR.

Weight loss and change in resting metabolic rate

The relation between change in resting metabolic rate (RMR) and change in fat-free mass (FFM) after weight loss is not well understood and is often inappropriately expressed in kilocalories per unit of FFM. We measured RMR and FFM in 35 obese patients enrolled in a conservative weight-loss program. RMR per kilogram FFM was not different after weight loss. However, the regression of delta RMR on delta FFM revealed that the decline in RMR tended to be greater than could be accounted for by loss of FFM. At initial test and retest, body fat (Fat) was not a predictor of RMR after FFM had been taken into account but delta Fat significantly contributed to the prediction of delta RMR when added to the equation after delta FFM. Thus, people losing larger amounts of weight had declines in RMR greater than could be accounted for by loss of FFM. Self-reported age of onset of obesity was not related to delta RMR.