Dataset Information

Predictive Equations Overestimate Resting Metabolic Rate in Survivors of Chronic Stroke.

ABSTRACT:

Objective

To measure resting metabolic rate (RMR) in survivors of chronic (>3 months prior) stroke (mean ± SEM age, 61±7.5 years) and to compare to predicted RMR using predictive equations in adults without stroke.

Design

Cross-sectional study.

Setting

Hospital.

Participants

Survivors of stroke (N=71).

Intervention

Not applicable.

Main outcome measures

RMR was measured by indirect calorimetry. Participants underwent a total body dual-energy x-ray absorptiometry scan and treadmill test for peak oxygen consumption (V̇o₂peak). RMR was calculated using 9 established equations.

Results

RMR measured (1552±319 kcal/d) was significantly lower than 9 predicted RMR values (all P<.001), with the best being McArdle-Katch (1652±233 kcal/d), Livingston (1677±230 kcal/d), and Mifflin (1707±242 kcal/d). The Institute of Medicine of the National Academies (2437±386 kcal/d) had the largest discrepancy with measured RMR. Predicted RMR determined with 8 of 9 equations was between 9% and 18% greater than measured RMR. Appendicular lean mass (r=0.64, P<.001), total lean mass (r=0.64, P<.001), and V̇o₂peak (r=0.41, P<.001) were associated with measured RMR.

Conclusions

RMR predictive equations established in adults without stroke are not appropriate for the population with stroke population, indicating the need to measure RMR until a more accurate predictive equation is developed. This could support modifications to nutritional intake guidelines in patients with conditions of muscle atrophy. If measurement of RMR is not feasible, the Katch-McArdle equation should be used to estimate RMR in a patient with stroke because on average it provides the lowest percentage overestimate compared with other equations.

SUBMITTER: Ryan AS

PROVIDER: S-EPMC9246861 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predictive Equations Overestimate Resting Metabolic Rate in Survivors of Chronic Stroke.

Ryan Alice S AS Novitskaya Maria M Treuth Alice L AL

Archives of physical medicine and rehabilitation 20220219 7

<h4>Objective</h4>To measure resting metabolic rate (RMR) in survivors of chronic (>3 months prior) stroke (mean ± SEM age, 61±7.5 years) and to compare to predicted RMR using predictive equations in adults without stroke.<h4>Design</h4>Cross-sectional study.<h4>Setting</h4>Hospital.<h4>Participants</h4>Survivors of stroke (N=71).<h4>Intervention</h4>Not applicable.<h4>Main outcome measures</h4>RMR was measured by indirect calorimetry. Participants underwent a total body dual-energy x-ray absorp ...[more]

PMID: 35192798

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Project description:BackgroundResting 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.MethodsWe 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.ResultsMean age, BMI, and mRMR of subjects were 46 ± 16 years (mean ± SD), 35 ± 10 kg/m2, 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.ConclusionWe 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.

Project description:Accurate estimation of glomerular filtration rate (GFR) is important for diagnosis and risk stratification in chronic kidney disease and for selection of living donors. Ethnic differences have required correction factors in the originally developed creatinine-based GFR estimation equations for populations around the world. Existing equations have not been validated in the vegetarian Indian population. We examined the performance of creatinine and cystatin-based GFR estimating equations in Indians.GFR was measured by urinary clearance of inulin. Serum creatinine was measured using IDMS-traceable Jaffe's and enzymatic assays, and cystatin C by colloidal gold immunoassay. Dietary protein intake was calculated by measuring urinary nitrogen appearance. Bias, precision and accuracy were calculated for the eGFR equations.A total of 130 participants (63 healthy kidney donors and 67 with CKD) were studied. About 50% were vegetarians, and the remainder ate meat 3.8 times every month. The average creatinine excretion were 14.7 mg/kg/day (95% CI: 13.5 to 15.9 mg/kg/day) and 12.4 mg/kg/day (95% CI: 11.2 to 13.6 mg/kg/day) in males and females, respectively. The average daily protein intake was 46.1 g/day (95% CI: 43.2 to 48.8 g/day). The mean mGFR in the study population was 51.66?±?31.68 ml/min/1.73m2. All creatinine-based eGFR equations overestimated GFR (p?<?0.01 for each creatinine based eGFR equation). However, eGFR by CKD-EPICys was not significantly different from mGFR (p?=?0.38). The CKD-EPICys exhibited lowest bias [mean bias: -3.53?±?14.70 ml/min/1.73m2 (95% CI: -0.608 to -0.98)] and highest accuracy (P30: 74.6%). The GFR in the healthy population was 79.44 ± 20.19 (range: 41.90-134.50) ml/min/1.73m2.Existing creatinine-based GFR estimating equations overestimate GFR in Indians. An appropriately powered study is needed to develop either a correction factor or a new equation for accurate assessment of kidney function in the Indian population.

Project description:BackgroundResting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.ObjectiveThe aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).Data sourcesA systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.Eligibility criteriaRandomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.AnalysisA narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).ResultsTwenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I2 range: 80-93%) except the Ten-Haaf (p = 0.48, I2 = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.ConclusionMany RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.Prospero registrationCRD42020218212.

Dataset Information

Predictive Equations Overestimate Resting Metabolic Rate in Survivors of Chronic Stroke.

Objective

Design

Setting

Participants

Intervention

Main outcome measures

Results

Conclusions

Publications

Predictive Equations Overestimate Resting Metabolic Rate in Survivors of Chronic Stroke.

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