Examining Variations of Resting Metabolic Rate of Adults
We perused scientific articles published between 1980 and 2011 to identify studies that measured RMR using PubMed, BIOSIS Previews, NTIS, EMBASE, MEDLINE, and Pascal databases. Several search terms were used, including RMR, resting energy expenditure, resting oxygen uptake (or V̇O2), healthy adults, and older healthy adults. To be included, the studies had to have directly assessed RMR using either oxygen uptake or a metabolic chamber in healthy adults. For purposes of this review, RMR was required to be measured in an awake adult, at least 3 h postprandial, in a thermoneutral state with no exercise for the previous 8 h. These criteria, although general, seemed to adequately represent normal daily life for an adult. Studies that examined cohorts having specific maladies were not included, unless they offered separate data for a healthy adult control group, which we could use separately in our analyses. RMR in units of kilocalories per kilograms per hour had to be available or be able to be computed from the data presented. For example, studies that reported their data in kilocalories per day, kilocalories per kilograms, or kilocalories per kilogram of fat-free mass (FFM) per day that also reported data for weight and body fat allowed us to compute RMR in kilocalories per kilograms per hour. In addition, the studies had to be published in peer-reviewed journals in English. While reading many of the papers, particularly their "methods and materials" sections, it became obvious that RMR data from the same subjects were frequently reported in two or more papers. Therefore, we used the first reported results and excluded the redundant paper reporting the same information. We made no attempt to find and use nonpublished data that may be available to avoid positive publication bias, nor did we attempt to contact authors to answer questions we might have about the data that was presented. When intervention studies reported baseline and outcome measures of RMR, we chose only to abstract the former estimates to facilitate comparisons with all other nonintervention studies under review. Differing specific methods for estimating RMR were used by studies (e.g., prior rest of 15–60 min, postprandial state of 3–12 h, refraining from exercise 8–24 h, and supine versus sitting position). We recognize this as an inherent potential problem in making study-to-study comparisons, for which we could not control and we did not endeavor such comparisons with the groups of estimates we examined.
If a paper cited previous publications that provided unique information on RMR, we went back to that study to assess the relevance of its data for our review. In this manner, we identified publications from as far back as 1921, which we added to our systematic survey (dated before 1980 = 7 studies or 12 [<3%] of the publication estimates). The low number of studies was a result of 1) methods that measured basal and not RMR and 2) an incomplete search of all studies as mentioned previously. The search identified more than 600 publications, but once the previously mentioned criteria were applied, only 197 studies remained. The 197 studies resulted in 410 publication estimates of RMR that could represent a specific cohort or population subgroup. Of the 410 estimates, 13 did not provide standard errors and were eliminated yielding 397 population estimates. The studies included 11,951 subjects, and the reported sex distribution was as follows: 52% as women, 39% as men, and 9% with no indication of sex status. The ages of the participants ranged from 18 to older than 80 yr. We found limited studies particularly for oldest adult groups (≥75 yr) among whom RMR information for 80- to 90-yr-old adults was virtually absent. We did not consider the existing studies for this latter age group because they focused only on BMR and not RMR; hence, they failed to meet the inclusion criteria for our review. For a complete listing of all articles used for the analyses, contact the corresponding author.
The studies included a wide range of ages and sample sizes; therefore, we used several differing approaches to examine the data. To partition RMR publication estimates to examine the influence of sex and age, we first stratified all publication estimates by sex and then by 10-yr incremental age groupings using the mean age reported by a study. The relationship of relative weight with RMR was examined by stratifying publication estimates according to standard World Health Organization categories of BMI (kg·m): <25, normal; 25–29.9, overweight; and ≥30, obese, and by sex. To examine the combinations of sex, age, and obesity status, we first stratified publication estimates by sex and BMI categories and then divided them into three classifications based on the mean age (yr) of the publication estimates: young (20–39 yr), middle age (40–54 yr), and older adult (55–74 yr). These age groupings seemed logical based on activity/life stages known to occur among adults (e.g., active early career and family, declining activity and more established career and family, and periretirement age, and postretirement or senescence). We acknowledge that this is an artificially crude set of distinctions compared with simply dividing the sample by decade of age, or many other potential approaches. However, we attempted to achieve a balance of meaningful distinctions to reflect population subgroups by age against partitioning the publication estimates so fine that error variances would render comparisons moot. As such, our process and final inclusion of studies should not be considered an "exhaustive," fully standardized effort, and we readily acknowledge any unintended selection bias resulting from our decisions. Finally, because methodology and equipment have changed over the decades, we explored possible trends in the data by decade of study publication.
We used the Comprehensive Meta-Analysis statistical software to estimate weighted means and 95% confidence intervals (95% CI) for each of the subgroups using the inverse variance weighting technique.Q-statistics were computed to evaluate the heterogeneity between sets of study estimates for contrasts of interest (e.g., RMR differences between all men and women). In the presence of significant heterogeneity, we used a random effects model, which occurred for all of the contrasts we explored. We used 95% CI to compare subgroups and conservatively inferred that when CI did not overlap, the mean estimates were significantly different. Doing so has the advantage of partially controlling for the overinterpretation that occurs when making multiple comparisons.
Methods
We perused scientific articles published between 1980 and 2011 to identify studies that measured RMR using PubMed, BIOSIS Previews, NTIS, EMBASE, MEDLINE, and Pascal databases. Several search terms were used, including RMR, resting energy expenditure, resting oxygen uptake (or V̇O2), healthy adults, and older healthy adults. To be included, the studies had to have directly assessed RMR using either oxygen uptake or a metabolic chamber in healthy adults. For purposes of this review, RMR was required to be measured in an awake adult, at least 3 h postprandial, in a thermoneutral state with no exercise for the previous 8 h. These criteria, although general, seemed to adequately represent normal daily life for an adult. Studies that examined cohorts having specific maladies were not included, unless they offered separate data for a healthy adult control group, which we could use separately in our analyses. RMR in units of kilocalories per kilograms per hour had to be available or be able to be computed from the data presented. For example, studies that reported their data in kilocalories per day, kilocalories per kilograms, or kilocalories per kilogram of fat-free mass (FFM) per day that also reported data for weight and body fat allowed us to compute RMR in kilocalories per kilograms per hour. In addition, the studies had to be published in peer-reviewed journals in English. While reading many of the papers, particularly their "methods and materials" sections, it became obvious that RMR data from the same subjects were frequently reported in two or more papers. Therefore, we used the first reported results and excluded the redundant paper reporting the same information. We made no attempt to find and use nonpublished data that may be available to avoid positive publication bias, nor did we attempt to contact authors to answer questions we might have about the data that was presented. When intervention studies reported baseline and outcome measures of RMR, we chose only to abstract the former estimates to facilitate comparisons with all other nonintervention studies under review. Differing specific methods for estimating RMR were used by studies (e.g., prior rest of 15–60 min, postprandial state of 3–12 h, refraining from exercise 8–24 h, and supine versus sitting position). We recognize this as an inherent potential problem in making study-to-study comparisons, for which we could not control and we did not endeavor such comparisons with the groups of estimates we examined.
If a paper cited previous publications that provided unique information on RMR, we went back to that study to assess the relevance of its data for our review. In this manner, we identified publications from as far back as 1921, which we added to our systematic survey (dated before 1980 = 7 studies or 12 [<3%] of the publication estimates). The low number of studies was a result of 1) methods that measured basal and not RMR and 2) an incomplete search of all studies as mentioned previously. The search identified more than 600 publications, but once the previously mentioned criteria were applied, only 197 studies remained. The 197 studies resulted in 410 publication estimates of RMR that could represent a specific cohort or population subgroup. Of the 410 estimates, 13 did not provide standard errors and were eliminated yielding 397 population estimates. The studies included 11,951 subjects, and the reported sex distribution was as follows: 52% as women, 39% as men, and 9% with no indication of sex status. The ages of the participants ranged from 18 to older than 80 yr. We found limited studies particularly for oldest adult groups (≥75 yr) among whom RMR information for 80- to 90-yr-old adults was virtually absent. We did not consider the existing studies for this latter age group because they focused only on BMR and not RMR; hence, they failed to meet the inclusion criteria for our review. For a complete listing of all articles used for the analyses, contact the corresponding author.
The studies included a wide range of ages and sample sizes; therefore, we used several differing approaches to examine the data. To partition RMR publication estimates to examine the influence of sex and age, we first stratified all publication estimates by sex and then by 10-yr incremental age groupings using the mean age reported by a study. The relationship of relative weight with RMR was examined by stratifying publication estimates according to standard World Health Organization categories of BMI (kg·m): <25, normal; 25–29.9, overweight; and ≥30, obese, and by sex. To examine the combinations of sex, age, and obesity status, we first stratified publication estimates by sex and BMI categories and then divided them into three classifications based on the mean age (yr) of the publication estimates: young (20–39 yr), middle age (40–54 yr), and older adult (55–74 yr). These age groupings seemed logical based on activity/life stages known to occur among adults (e.g., active early career and family, declining activity and more established career and family, and periretirement age, and postretirement or senescence). We acknowledge that this is an artificially crude set of distinctions compared with simply dividing the sample by decade of age, or many other potential approaches. However, we attempted to achieve a balance of meaningful distinctions to reflect population subgroups by age against partitioning the publication estimates so fine that error variances would render comparisons moot. As such, our process and final inclusion of studies should not be considered an "exhaustive," fully standardized effort, and we readily acknowledge any unintended selection bias resulting from our decisions. Finally, because methodology and equipment have changed over the decades, we explored possible trends in the data by decade of study publication.
We used the Comprehensive Meta-Analysis statistical software to estimate weighted means and 95% confidence intervals (95% CI) for each of the subgroups using the inverse variance weighting technique.Q-statistics were computed to evaluate the heterogeneity between sets of study estimates for contrasts of interest (e.g., RMR differences between all men and women). In the presence of significant heterogeneity, we used a random effects model, which occurred for all of the contrasts we explored. We used 95% CI to compare subgroups and conservatively inferred that when CI did not overlap, the mean estimates were significantly different. Doing so has the advantage of partially controlling for the overinterpretation that occurs when making multiple comparisons.
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