Pdf Factors in Resting Metabolic Rate Peer Review

ABSTRACT

Groundwork: Some previous studies have indicated that a low basal metabolic rate (BMR) is an contained predictor of future weight gain, but low rates of follow-upwards and highly select populations may limit the ability to generalize the results.

Objective: Nosotros assessed whether adults with a low BMR gain more than weight than do adults with a high BMR who are living in a typical Western environment.

Blueprint: We extracted BMR, trunk-composition, demographic, and laboratory data from electronic databases of 757 volunteers who were participating in our research protocols at the Mayo Clinic between 1995 and 2012. Inquiry report volunteers were e'er weight stable, had no acute illnesses and no confounding medication apply, and were nonsmokers. The top and lesser 15th percentiles of BMR, adjusted for fat-complimentary mass (FFM), fat mass, age, and sex activity, were identified. Follow-up electronic medical tape organisation information were available for 163 subjects, which allowed us to determine their subsequent weight changes for ≥3 y (mean: ∼nine.seven y).

Results: Past definition, the BMR was unlike in the high-BMR group (2001 ± 317 kcal/d; northward = 86) than in the low-BMR group (1510 ± 222 kcal/d; north = 77), but they were comparable with respect to age, body mass index, FFM, and fat mass. Rates of weight gain were not greater in the lesser BMR group (0.3 ± i.0 kg/y) than in the top BMR group (0.v ± 1.5 kg/y) (P = 0.17).

Conclusion: Adults with low BMRs did not gain more weight than did adults with loftier BMRs, implying that habitual differences in nutrient intake or activity counterbalance variations in BMR equally a hazard cistron for weight gain in a typical Western population.

INTRODUCTION

Obesity is a consequence of a persistent imbalance between energy intake and energy expenditure (EE).ⁱⁱⁱ Body size is a main determinant of 24-h EE and the basal metabolic rate (BMR), which is likely to be somewhat stable over relatively brief periods of time, whereas free energy intake tin vary widely from day to twenty-four hour period. For many adults in Western cultures, the BMR is the unmarried largest component of daily EE. Therefore, the BMR is of interest in the context of energy balance. Much of the interindividual variability in the BMR is predicted by body composition, age, sexual practice, and ethnicity (i, ii). Some (iii–vi) but not all (7, viii) investigators study that a depression BMR, adjusted for torso composition, sex, and age, is an independent predictor of futurity weight gain. However, the participants in the prominent, positive studies were somewhat selected populations (3–5). Our objective was to sympathise whether a reduced resting metabolic rate is an independent predictor of weight gain in a more typical Western population. Nosotros collated our well-controlled sets of BMR and body-composition data from studies that were conducted betwixt 1995 and 2012 in Rochester, Minnesota. We used the electronic medical record (EMR) system to track the subsequent body weight of the research volunteers. These 2 resource allowed us to accost whether a depression BMR independently predisposes individuals to a weight proceeds.

METHODS

We extracted the BMR, body-composition, demographic, and laboratory data from our databases of volunteers who participated in 30 different institutional review board-canonical protocols that were conducted betwixt 1995 and 2012 at the Mayo Clinic. If volunteers participated in >1 protocol, we used data from the get-go study. From this database, we identified individuals in the elevation and bottom 15th percentiles of BMR after adjustment for fat-free mass (FFM), fat mass, age, and sex. We used the Mayo Clinic EMR to appraise subsequent body-weight changes. Enrollment criteria for all studies were such that the volunteers were complimentary of acute illnesses, weight stable, and nonsmokers. The studies systematically excluded volunteers who regularly took sympathomimetic medications or who had thyroid disease (unless rendered euthyroid with a normal thyroid-stimulating hormone). We included the available follow-up weight information only if volunteers had ≥1 weight recorded ≥3 y later the appointment of the research study in which they participated. If >1 weight/y was available, we recorded the beginning weight in each twelvemonth after the original enquiry study for equally many years as were available. All participants provided informed written consent, and we collected medical tape data but if patients consented that their records could be used for research.

Data collection

The quantitative variables nosotros collected from the research-study data included historic period, sex, height, weight, BMI (in kg/m²), BMR, and body composition. Historic period, sexual activity, fat mass, and FFM were nerveless because these variables contribute to most of the variance in the BMR. We also collected data on fasting plasma glucose and insulin when available. All patient scales at the Mayo Clinic undergo regular calibrations that include an annual visual inspection and verification of the overall functioning of the calibration; every 2 y, scales are calibrated with the use of Grip-handle and Nesting Slab Weights (Rice Lake Weighing Systems). The calibration weights were at least three-quarters of the maximum capacity of the scale.

BMR

The BMR was ever measured in the overnight postabsorptive state with the employ of calorimetry with a ventilated hood (DeltaTrack Metabolic Monitor; Sensor Medics). The extensive scale of these metabolic carts has been reported (ix). In brief, the metabolic carts were calibrated each morning and underwent extra quality command including monthly pressure and gas calibrations together with biannual alcohol-fire test calibrations. The test-retest difference was <three% for indistinguishable measures of oxygen uptake for adults in the same environment on sequential days with the apply of our instruments. In addition, each day, we starting time checked the calibration with a known gas mixture, and if the variance from the known mixture was >1.25%, the instrument was reset. If the ambience CO₂ concentrations exceeded those that are known to interfere with CO₂ measurements, the room ventilation was increased to lower ambient CO₂ to acceptable concentrations. We also measured oxygen uptake and CO_two each month in one of our personnel; if the O₂ consumption rate was >xx-mL/min dissimilar from the hateful, we remeasured that person with another instrument to examination for biological compared with musical instrument issues.

All volunteers were admitted as inpatients to the Mayo Clinic General Clinical Research Centre in the evening before the study twenty-four hour period and consumed their evening meals at a standardized time (1800). An indirect calorimetry measurement was performed in the fasting state earlier the participant arose from bed the adjacent morning, which was typically between 0700 and 0800.

Body limerick

Total body fat and FFM were measured with the use of dual-energy X-ray absorptiometry (DXA). All DXA measurements were performed with the use of Lunar/GE equipment. To ensure consistency over time and between instruments, we used 4 contained calibration phantoms that were composed of a range of known fat and nonfat contents (Hormel Institute). Each musical instrument was calibrated to the phantoms such that we could identify any discrepancies between DXA-predicted and -known percentages of fat. The DXA-reported body fatty was corrected for any calibration variations as assessed past the phantoms. Every new DXA instrument was tested with the use of an institutional review board–approved protocol whereby volunteers and the meat-block phantoms (not just visitor phantoms) were scanned on sometime and new DXA instruments. Software updates were tested by analyzing scan data with the use of both former and new versions to correct for possible variations that could have been introduced by software changes. These procedures assured that we maintained consistent trunk-composition measures over long periods of time and with different instruments.

Assays

Fasting insulin concentrations were measured with the use of chemiluminescent sandwich assays (Sanofi Diagnostics Pasteur).

Calculations and statistical analysis

All values are given as means ± SDs. The approach to identifying subjects in the everyman and highest 15th percentiles of BMR is depicted in Supplemental Figure 1. The predicted BMR for our population was determined with the use of the measured BMR as the dependent variable and each individual's FFM, fat mass, age, and sexual practice every bit independent variables in a multiple linear regression formula. Each individual'due south variation from the predicted BMR (observed minus predicted) was used to sympathize the total variation in our population and, thus, to identify subjects in the top xv% and bottom 15% of BMR. Nosotros identified follow-up weight data from 86 and 77 volunteers in the high- and low-BMR groups, respectively. The a priori hypothesis was that there would exist greater rates of body-weight proceeds in the low-BMR group than in the high-BMR group. Statistical ability calculations were based on the following information: one) publications have indicated that the mean annual weight gain of US adults is ∼0.6 (10); ii) we and other authors (5) have shown that the SD of weight gain averages 1.2 kg/y; and 3) our depression- and high-BMR groups differed by ∼500 kcal/d, which, co-ordinate to Piaggi et al. (v), should accept resulted in a mean greater weight gain of one kg/y in the low-BMR group. This estimate was based on an observed changed relation between the sleeping metabolic charge per unit and 24-h EE (both of which were adjusted for historic period, sex activity, ethnicity, FFM, and FM) and the rate of the percent of weight change (5). With the use of this data, we showed that, if we had 75 persons/group, we would have had a ability of 0.99 to detect a difference of 1 kg/y of weight gain between the high-and low-BMR groups with the use of a 2-sided t exam and P = 0.05.

To compare high- and low-BMR groups, including the difference in rates of body-weight changes (kilograms per twelvemonth) and percentages of body-weight changes per yr, an unpaired Student's t test was performed. The rate of trunk-weight change (kilograms and percentage) per year for each individual was obtained with the use of a linear regression model for all collected weight and date time points. For all analyses, two-tailed P values were reported. The departure was considered significant at P < 0.05 for the principal result variables. For secondary effect variables that were not part of our a priori hypothesis, we reported the unadjusted P values but also noted which values would not have been <0.05 if the values had been adjusted with the use of a Bonferroni test. Statistical analyses were performed with JMP statistical software (version x.0.0).

RESULTS

Data from a total of 757 unique subjects were identified to create the database. From this database, nosotros identified cohorts in the pinnacle and lesser 15th percentiles of BMR (adjusted for FFM, fat mass, age, and sex) who had BMRs that were >169 and <−173 kcal/d to a higher place and below predicted BMRs, respectively (n = 114 for the peak grouping, and due north = 113 for the lesser group). We were able to collect ≥1 (mean: seven) EMR weight for 163 of 227 volunteers. Baseline data from subjects from whom we were able to collect follow-up weight are provided in Table 1. The two groups were comparable with respect to age, BMI, FFM, fatty mass, and fasting plasma glucose and insulin concentrations. By definition, subjects in the top-fifteen% group had BMRs that were much higher than those of subjects in the bottom-15% group (2001 ± 317 compared with 1510 ± 222 kcal/d); because the BMR was non a random variable, these information were not appropriate for statistical testing.

TABLE 1

Characteristics of study participants ¹

Baseline data	Total population	Top fifteen% of BMR	Bottom 15% of BMR	P
n	757	86	77
Male person sex activity, %	49	59	62	0.lxx
Age, y	xl ± 17 ⁱⁱ	39 ± 15	43 ± 17	0.16
Acme, cm	171.9 ± 9.8	174.0 ± 9.ii	175.0 ± 9.7	0.53
Weight, kg	80.2 ± 17.3	83.3 ± 18.4	86.4 ± 16.0	0.26
BMI, kg/m²	27.1 ± 5.1	27.four ± 5.2	28.3 ± five.4	0.28
FFM, kg	54.iii ± 12.4	56.9 ± eleven.nine	59.ane ± 11.two	0.23
Fatty mass, kg	25.5 ± 12.3	25.seven ± 12.ane	27.i ± 13.5	0.51
BMR, kcal/d	1665 ± 322	2001 ± 317	1510 ± 222	—
Fasting glucose, mg/dL	91 ± 10 [615] ³	92 ± 18 [70]	91 ± vii [62]	0.71
Insulin, μU/mL	vi.2 ± eight.1 [715]	vi.3 ± 5.2 [82]	5.iii ± three.5 [74]	0.17

Baseline data	Total population	Top 15% of BMR	Lesser 15% of BMR	P
north	757	86	77
Male sex, %	49	59	62	0.lxx
Historic period, y	40 ± 17 ²	39 ± xv	43 ± 17	0.16
Top, cm	171.9 ± ix.8	174.0 ± nine.2	175.0 ± nine.7	0.53
Weight, kg	eighty.ii ± 17.3	83.3 ± 18.iv	86.iv ± xvi.0	0.26
BMI, kg/m²	27.1 ± 5.1	27.4 ± 5.2	28.three ± 5.iv	0.28
FFM, kg	54.three ± 12.4	56.9 ± 11.9	59.1 ± xi.2	0.23
Fatty mass, kg	25.5 ± 12.iii	25.7 ± 12.ane	27.1 ± xiii.5	0.51
BMR, kcal/d	1665 ± 322	2001 ± 317	1510 ± 222	—
Fasting glucose, mg/dL	91 ± 10 [615] ⁱⁱⁱ	92 ± eighteen [70]	91 ± 7 [62]	0.71
Insulin, μU/mL	6.2 ± 8.one [715]	6.three ± 5.two [82]	five.3 ± iii.5 [74]	0.17

P values are nonpaired t exam comparisons betwixt the top 15% and lesser xv% of BMR. Because BMR was the criteria for selecting the populations, it was non a random variable and thus not subject to statistical testing. BMR, basal metabolic charge per unit; FFM, fat-free mass.

Mean ± SD (all such values).

Mean ± SD; n in brackets (all such values).

TABLE ane

Characteristics of written report participants ¹

Baseline information	Total population	Height fifteen% of BMR	Bottom 15% of BMR	P
n	757	86	77
Male sexual activity, %	49	59	62	0.lxx
Age, y	40 ± 17 ^two	39 ± 15	43 ± 17	0.16
Height, cm	171.9 ± ix.8	174.0 ± 9.2	175.0 ± nine.vii	0.53
Weight, kg	80.ii ± 17.3	83.3 ± 18.four	86.four ± 16.0	0.26
BMI, kg/m²	27.1 ± five.ane	27.iv ± 5.2	28.three ± 5.iv	0.28
FFM, kg	54.3 ± 12.4	56.9 ± eleven.9	59.one ± eleven.ii	0.23
Fatty mass, kg	25.5 ± 12.iii	25.7 ± 12.1	27.1 ± 13.5	0.51
BMR, kcal/d	1665 ± 322	2001 ± 317	1510 ± 222	—
Fasting glucose, mg/dL	91 ± ten [615] ³	92 ± xviii [70]	91 ± 7 [62]	0.71
Insulin, μU/mL	6.two ± 8.1 [715]	6.3 ± 5.2 [82]	v.3 ± iii.5 [74]	0.17

Baseline data	Full population	Elevation 15% of BMR	Lesser 15% of BMR	P
n	757	86	77
Male sex, %	49	59	62	0.70
Age, y	xl ± 17 ²	39 ± xv	43 ± 17	0.sixteen
Height, cm	171.nine ± ix.8	174.0 ± 9.2	175.0 ± ix.vii	0.53
Weight, kg	fourscore.2 ± 17.iii	83.3 ± 18.four	86.4 ± 16.0	0.26
BMI, kg/chiliadⁱⁱ	27.ane ± 5.1	27.4 ± five.2	28.3 ± 5.four	0.28
FFM, kg	54.three ± 12.4	56.9 ± 11.nine	59.1 ± 11.2	0.23
Fat mass, kg	25.5 ± 12.3	25.7 ± 12.1	27.1 ± 13.5	0.51
BMR, kcal/d	1665 ± 322	2001 ± 317	1510 ± 222	—
Fasting glucose, mg/dL	91 ± 10 [615] ³	92 ± xviii [70]	91 ± seven [62]	0.71
Insulin, μU/mL	half-dozen.2 ± viii.i [715]	half-dozen.3 ± 5.ii [82]	5.3 ± 3.5 [74]	0.17

one

P values are nonpaired t test comparisons between the meridian fifteen% and bottom 15% of BMR. Because BMR was the criteria for selecting the populations, it was not a random variable and thus not subject to statistical testing. BMR, basal metabolic rate; FFM, fat-free mass.

Hateful ± SD (all such values).

iii

Hateful ± SD; north in brackets (all such values).

The 64 participants for whom we could non collect follow-up weight data differed from subjects with whom we could follow upward in that they were younger (32 ± 13 compared with 41 ± sixteen y; P < 0.0001). Otherwise, in that location were no differences in the characteristics of subjects for whom we could and could not obtain follow-upward weight data. There were no significant differences in the characteristics of subjects who were lost to follow-upward between low- and high-BMR groups.

The duration of follow-up was 8.nine ± 5.0 and 10.7 ± iii.eight y for the groups in the top and bottom 15th percentiles, respectively (P = 0.08; Bonferroni correction). Rates of weight change were not different between subjects in the peak and bottom 15th percentiles of BMR (0.5 ± 1.v compared with 0.3 ± 1.0 kg/y, respectively; P = 0.16) (Figure 1). As well, there were no differences in the percentage of weight modify per year between groups (0.7% ± 1.seven%/y in the meridian 15th percentile of BMR, and 0.3% ± i.0%/y in the lesser 15th percentile of BMR; P = 0.08). To provide data in a manner that was consequent with previous reports (4), nosotros also identified participants who gained >10 kg by 3 y after the initial study date. There was no significant deviation in the percentages of subjects who gained 10 kg between top- and bottom-BMR groups (4.7% compared with 3.ix%, respectively). We also tested post hoc whether subjects with and without obesity might differ with regard to the relation between BMRs and rates of weight gain. For subjects with normal BMI, the loftier-BMR grouping (northward = 61) had a greater charge per unit of weight gain than did the low-BMR group (n = 52) (0.67 ± i.04 compared with 0.23 ± 0.76 kg/y, respectively; P = 0.04 with Bonferroni aligning). For subjects with BMI ≥thirty, there were no significant differences between weight changes in the high-BMR grouping (n = 25) and low-BMR group (n = 25) (0.20 ± 2.19 compared with. 0.31 ± 1.33 kg/y, respectively; P = 0.seventy with Bonferroni aligning).

Figure 1

Relation between initial residual BMR (measured minus predicted) and the rate of BW modify (ρ = 0.12, P = 0.13; nonpaired t test) is depicted. Black diamonds stand for information from subjects in the superlative xv% of residual BMR, and gray diamonds stand for data from subjects in the bottom 15% of residue BMR. BMR, basal metabolic rate; BW, body weight.

To assess the combined biological and technical variation in these measurements, nosotros queried our unabridged database of 757 volunteers for subjects who had participated in 2 unlike studies. Nosotros found 33 subjects who had participated in 2 studies with separate trunk-composition and BMR measurements. The mean fourth dimension betwixt the ii studies was ane.0 ± 0.9 y and occurred between 1996 and 2009. For each participant in each study, nosotros assessed the deviation of the BMR from that predicted for the entire accomplice on the basis of FFM, fat mass, age, and sex. We compared the deviation from the predicted BMR between the first and second studies with the use of a paired t test. For these 33 participants, the BMR deviation from the predicted BMR was −33 ± 143 kcal/d (−2% ± 9%) and −61 ± 190 kcal/d (−4% ± 11%) for the outset and second studies, respectively (P = 0.twoscore).

We also examined rates of weight proceeds in the top and bottom 10th percentiles of BMR. We had 56 and 50 participants with follow-upwardly data in the top and bottom groups, respectively. The groups were well matched for body limerick, age, and sex; the groups differed by ∼600 kcal/d in BMRs (2061 ± 316 compared with 1480 ± 204 kcal/d, respectively), and the rates of weight gain were likewise not different between groups (0.5 ± 1.5 compared with. 0.3 ± ane.0 kg/y, respectively). The smaller groups of superlative and bottom fifth percentiles (<40 subjects/group merely with 670-kcal/d betwixt-grouping differences in BMRs) likewise did not differ with respect to weight gain. Although between-group difference in BMRs was greater, the number of subjects with satisfactory follow-up was less, such that the statistical power to detect differences was not improved.

Word

Because a BMR is a substantial portion of daily EE for many adults in mod societies, we assessed whether adults with low BMRs (bottom xv% adjusted for body composition, age, and sex) are more predisposed to proceeds weight than are adults with high BMRs (top fifteen%). By collating data from 757 volunteers studied in the Mayo Dispensary Full general Clinical Research Eye nether strict protocol conditions, we identified ii cohorts of adults with mean BMRs that differed by 500 kcal/d. With the use of comprehensive EMR data at our institution, we were able to collect follow-up weight data on ∼72% of these research participants at time points ≥3 y afterward the original study. We showed that the rates of body-weight change (kilograms per year and percentage per year) were not greater and were numerically lower in adults with depression BMRs than in adults with high BMRs. These findings indicate that adults with low BMRs are not uniquely predisposed to future weight gains.

A number of previous studies take shown that the BMR, sleeping metabolic rate, and 24-h EE, adjusted for FFM, fat mass, age, and sexual practice, are independently predictive of hereafter weight gain (3–6). Although one of the studies reported a significant relation between low BMR and weight gain in Italian Caucasians (half-dozen), the other statistically significant associations were shown in studies of the Pima Indian population (3–5). In add-on to the somewhat homogeneous genetic background of the Pima Indian population, information technology is possible that the environment in the Indian communities is such that they are more sensitive to a reduced BMR as a predisposition to weight gain than are inhabitants of Southwestern Minnesota. Virtually studies enrolled obese participants (hateful BMI: 32–36) (3–5), and some of studies included relatively small numbers of subjects (6) or had shorter durations of follow-up (3, four). The percentage of subjects in whom nosotros were able to collect follow-up weight data was comparable with that in other studies (3, 5) and much greater than the 30% follow-up reported in a study of Italian adults (6). Ane report of lean Nigerians showed that higher resting energy expenditures (REEs) were positively associated with weight gain (8), merely the REE was not measured under as carefully controlled weather condition, and body composition was measured by bioelectrical impedance, either of which could have affected the results. The authors suggested that in that location may be a differential regulation of torso-weight proceeds between lean and overweight populations (8). The previous studies that showed no relation between the REE and weight change included relatively small numbers of subjects (11) or used skinfold thicknesses to measure body composition (which is a suboptimal trunk-limerick approach to adjusting BMR) in men simply (7). For these reasons, neither study definitively addressed the question of BMR and weight proceeds.

The strength of our study is our access to information from a large number of adults in a typical Western population who had robust trunk-composition assessments and BMRs measured under very standardized conditions. We included participants who were lean and obese, young and onetime adults, metabolically normal and insulin resistant. We excluded acutely ill participants and participants with illnesses or who were taking medications that could take affected their metabolic phenotypes. These people are exactly the population who are nigh likely to be characterized as at hazard of weight gain because of their constitutionally low BMRs. To exaggerate potential differences, we compared these adults with a grouping of high-BMR adults who were supposedly protected from weight gain. Over the years, we accept used rigorous methods to maintain accurate and consistent indirect calorimetry and trunk-composition measurements, thereby ensuring that the betwixt-subject differences in BMRs were biological and not technical. We tested this association by examining information from subjects who participated in 2 dissimilar studies over the course of thirteen y to ameliorate define the combined biological and technical variability of our methods. The absolute differences in intraindividual exam-retest BMRs from predicted BMRs was small (mean of 7%), and there was no trend for positive or negative changes over the years of the study. In contrast, BMRs were a mean of 33% greater in the high-BMR grouping than in the low-BMR group. Finally, our sample size was more than sufficient to notice the previously reported associations (v). We suggest that our efforts to ensure consistent BMR and trunk-limerick measurements under standardized conditions, our practical approach to real-earth follow-up with multiple weight observations for virtually subjects, and the big sample size of both men and women clearly addressed whether BMR is an independent predictor of futurity trunk-weight gain in free-living adults.

However, there are limitations to this study. The retrospective cohort written report pattern meant that we could not collect data from people who moved away. All the same, our follow-up rates were comparable with or better than those of studies that reported significant associations betwixt BMR and subsequent weight proceeds (3, 5, 6). Because it was not possible to collect comprehensive data regarding the nutrition and activity habits for the ∼9 y after the original studies, we cannot know why adults with low BMRs gained no more weight than did adults with high BMRs. Even so, note that previous investigators concluded that a low BMR is an independent predictor of greater weight gain (e.g., persons with a depression BMR will proceeds more than weight irrespective of lifestyle factors). Although some participants were given information regarding their BMR results, we did not indicate to them whether their BMRs deviated from expected BMRs. Thus, it is unlikely that participation in our studies resulted in substantial, lifelong changes in diet or action habits. None of our participants with follow-upward EMR data developed cancer, and thus we do not believe that weight changes that could have been due to cancer or cancer treatments confounded our results.

In decision, we show that adults with BMRs that are well below predicted BMRs do not gain more than weight than do adults with BMRs that are well above predicted BMRs despite a 500-kcal/d difference between the 2 groups. These information point that, in a typical majority Caucasian, Western population, variations in BMR are not responsible for tendencies toward weight gain. In the real world, people start and cease diets, practice programs, and medications that may affect weight. Nosotros advise that these factors are far more effective than BMR is in predisposing individuals to weight gain. The implication of our findings is that adults with depression BMRs either eat less or expend more energy in physical activity than practise those with high BMRs under free-living atmospheric condition.

The authors' responsibilities were as follows—MDJ: was the guarantor of this work and, as such, had total access to all of the information in the study and took responsibleness for the integrity of the information and accuracy of the information analysis; and both authors: designed and performed the research, analyzed the data, wrote the manuscript, and read and approved the final manuscript. Neither author reported a disharmonize of involvement related to the study.

REFERENCES

Weyer

Snitker

Southward

Rise

Bogardus

Ravussin

Determinants of energy expenditure and fuel utilization in man: effects of body composition, age, sex, ethnicity and glucose tolerance in 916 subjects

Int J Obes Relat Metab Disord

1999

;

715

–

ii.

Toubro

Due south

Sorensen

Ronn

Christensen

Astrup

20-four-60 minutes energy expenditure: the role of body composition, thyroid status, sympathetic activeness, and family membership

J Clin Endocrinol Metab

1996

;

2670

–

iii.

Tataranni

Harper

Information technology

Snitker

Del Parigi

Vozarova

Bunt

Bogardus

Ravussin

Body weight gain in free-living Pima Indians: effect of energy intake vs expenditure

Int J Obes Relat Metab Disord

2003

;

1578

–

iv.

Ravussin

East

Lillioja

Due south

Knowler

Christin

Freymond

Abbott

Boyce

Howard

Bogardus

Reduced rate of energy expenditure as a risk gene for body-weight gain

N Engl J Med

1988

;

318

467

–

Piaggi

Thearle

Bogardus

Krakoff

Lower energy expenditure predicts long-term increases in weight and fat mass

J Clin Endocrinol Metab

2013

;

E703

–

Buscemi

Southward

Verga

Caimi

Cerasola

Low relative resting metabolic rate and body weight gain in adult Caucasian Italians

Int J Obes (Lond)

2005

;

287

–

seven.

Seidell

Muller

Sorkin

Andres

Fasting respiratory substitution ratio and resting metabolic rate as predictors of weight gain: the Baltimore Longitudinal Study on Aging

Int J Obes Relat Metab Disord

1992

;

sixteen

667

–

eight.

Luke

Durazo-Arvizu

Cao

Adeemo

Tayo

Cooper

Positive association betwixt resting free energy expenditure and weight gain in a lean adult population

Am J Clin Nutr

2006

;

1076

–

nine.

Jensen

Bajnarek

Lee

Nielsen

Due south

Koutsari

Relationship between postabsorptive respiratory commutation ratio and plasma free fat acid concentrations

J Lipid Res

2009

;

1863

–

10.

Kahn

Cheng

Longitudinal changes in BMI and in an index estimating excess lipids among white and blackness adults in the United States

Int J Obes (Lond)

2008

;

136

–

11.

Weinsier

Nelson

Hensrud

Darnell

Exist

Hunter

Schutz

Metabolic predictors of obesity. Contribution of resting energy expenditure, thermic effect of food, and fuel utilization to 4-year weight gain of post-obese and never-obese women

J Clin Invest

1995

;

980

–

five

ABBREVIATIONS

BMR
DXA

dual-energy X-ray absorptiometry
EE
EMR

electronic medical record
FFM
REE

resting energy expenditure.

Author notes

Supported by NIH grant NCRR UL1 TR000135 and by the NIH (grants DK-45343, DK-40484, and DK-50456). PA was a postdoctoral research fellow, which was supported by Thammasat Academy.

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Source: https://academic.oup.com/ajcn/article-abstract/104/4/959/4557125

Pdf Factors in Resting Metabolic Rate Peer Review

ABSTRACT

INTRODUCTION

METHODS

Data collection

BMR

Body limerick

Assays

Calculations and statistical analysis

RESULTS

Word

REFERENCES

ABBREVIATIONS

Author notes

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