Changes in resting metabolic rate in an elderly German population: Cross-sectional and longitudinal data

Lührmann, Petra; Edelmann-Schafer, B.; Neuhäuser‐Berthold, Monika

doi:10.1007/s12603-009-0186-7

Cited by 8 publications

(10 citation statements)

References 29 publications

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“…A limitation of Fredrix's 10 equation is the very small sample size (n ¼ 40) and the age range (age: 51e82 y) which may introduce some reservations in regards to the statistical robustness of the equation and the applicability in the oldest old, respectively. More recently, Luhrmann et al 12 has validated a new equation in older aged subjects. The study included a large sample size (n ¼ 286) and was characterised by an age range between 60 and 85 years old.…”

Section: Discussionmentioning

confidence: 99%

“…The measured REE was then compared to the following published REE prediction equations: 1) HarriseBenedict, 6 2) Owen, 7 3) Mifflin, 8 4) Bernstein, 20 5) World Health Organization (WHO), 21 6) Fredrix, 10 7) Livingston, 22 8) Muller, 9 9) Luhrmann, 12 10) Schofield, 15 11) European Community, 11 12) Henry, 23 13) Korth, 14 14) De Lorenzo. 13 In addition two recent computational approaches based on metaregression of data from 47 published studies and on the calculation of the Aggregate estimates of REE were also evaluated.…”

Section: Predicted Reementioning

confidence: 99%

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Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects

Siervo¹,

Bertoli²,

Battezzati³

et al. 2014

Clinical Nutrition

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Section: Discussionmentioning

confidence: 99%

Section: Predicted Reementioning

confidence: 99%

Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects

Siervo¹,

Bertoli²,

Battezzati³

et al. 2014

Clinical Nutrition

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“…Physiologically, the metabolic rate decreases with increasing age. However, while the influence of age on EE has been studied in a large healthy population [16][17][18], publications on measured EE in the critically ill mostly included a relatively small subgroup of elderly patients. Furthermore, the epidemics of obesity may complicate the application of prediction equations [3].…”

Section: Introductionmentioning

confidence: 99%

The effect of age and body mass index on energy expenditure of critically ill medical patients

2020

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Background Data on the influence of age and body mass index (BMI) on energy metabolism of the critically ill are heterogeneous. Due to the increasingly aging critically ill population, investigation on age-and BMI-specific energy metabolism is relevant. Methods A total of 394 indirect calorimetry measurements were conducted on 348 critically ill adult medical patients, including 46 repeat measurements after 3.6 ± 4.3 days. Measured resting energy expenditure (MREE) was compared for age groups, BMI, and gender. Predicted energy expenditure (PEE) using the Penn State, Swinamer, and Ireton-Jones equations and the ACCP recommendations was also compared with MREE. Results The patients were 65.6 ± 14.5 years old. Their mean Acute Physiology and Chronic Health Evaluation II score was 27.6 ± 7.8. Mean BMI was 27.8 ± 8.4 kg/m 2 , and 25.6% were obese. MREE adjusted for ideal body weight decreased with increasing age, while it increased with increasing BMI. Age, BMI, and gender are independent determinants of MREE after adjusting for clinical factors (R 2 = 0.34). All four prediction equations showed a proportional bias, with the Penn State equation performing acceptably. In 46 patients with repeat indirect calorimetry, there was no significant difference between the first and second MREE (p = 0.62). Conclusions Age, BMI, and gender are independent determinants of resting energy expenditure in critically ill adults. Variations between measured and predicted energy expenditure are considerable. Should prediction equations be used, their performance in the specific population should be taken into consideration. Repeat indirect calorimetry may not always be necessary. However, this may depend on the length of stay and the extent of stress.

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“…Gallagher et al (29) showed that the age-related reduction in skeletal muscle mass was greater in men than in women, which may lead to overestimates of BMR in men. By contrast, Luhrmann et al (30) suggested that the decline in RMR with advancing age could not be due only to changes in body composition. Further studies are needed in order to clarify the reason why BMR decreases with aging.…”

Section: Discussionmentioning

confidence: 93%

Validity of One-Day Physical Activity Recall for Estimating Total Energy Expenditure in Elderly Residents at Long-Term Care Facilities: CLinical EValuation of Energy Requirements Study (CLEVER Study)

Nishida

Nakae

Yamada

et al. 2019

J Nutr Sci Vitaminol

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Malnutrition is a severe problem among elderly residents living in long-term care facilities. A simple and accurate estimation for total daily energy expenditure (TEE) is required in order to provide them with an adequate amount of food. The purpose of this study was to validate a physical activity assessment tool for estimating TEE among elderly residents. The data of 58 subjects aged 69-99 y were analyzed in this study. The one-day physical activity recall was filled out by facility staff for a typical day. TEE was measured by the doubly labeled water (DLW) method (TEEDLW), and predicted by one-day physical activity recall using the basal metabolic rate (pTEEBMR) and body weight (pTEEBW). The TEEDLW, pTEEBMR and pTEEBW were 1,1296196 kcal/d, 1,1866251 kcal/d and 1,3266236 kcal/d, respectively. In a regression model, body weight, movement means and sex explained 39.0% of the variance in the difference between pTEEBMR and TEEDLW (p,0.05). Percentage of fat mass, body weight, and Barthel Index except mobility explained 47.3% of the variance in the difference between pTEEBW and TEEDLW (p,0.05). The current results suggested that pTEEBW is not recommended to estimate TEE because the error depends on body size, and pTEEBMR was significantly correlated with TEEDLW but also included systematic errors in elderly residents. In order to improve the accuracy of the present assessment to estimate TEE, it is necessary to establish a new equation of basal metabolic rate for elderly residents and consider sex and movement means to estimate TEE.

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Changes in resting metabolic rate in an elderly German population: Cross-sectional and longitudinal data

Cited by 8 publications

References 29 publications

Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects

Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects

The effect of age and body mass index on energy expenditure of critically ill medical patients

Validity of One-Day Physical Activity Recall for Estimating Total Energy Expenditure in Elderly Residents at Long-Term Care Facilities: CLinical EValuation of Energy Requirements Study (CLEVER Study)

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