Determinants of Resting Energy Expenditure in Very Old Nursing Home Residents.

OBJECTIVES: This study aimed to measure resting energy expenditure (REE) in institutionalized old persons and to determine factors possibly related to change in REE as a basis for estimating energy requirements. DESIGN AND SETTINGS: A monocentric cross-sectional study was conducted. Statistical approaches were conducted to determine independent factors associated with REE. Various published predictive equations of REE were compared to our population. PARTICIPANTS: 72 residents of a nursing home, mostly women (80.5%) aged 87.4±6.6 years were included. MEASUREMENTS: REE (indirect calorimetry), body composition (bio-impedance analysis), biological and anthropometric data were collected. RESULTS: Mean REE was 1006±181 kcal/d and was higher in men than in (1227±195 vs. 953±131 kcal/d, p<0.05). According to criteria adapted from the Global Leadership Initiative on Malnutrition consensus, 65.3 % of the institutionalized population were malnourished. In multivariate analysis adjusted on gender and age, REE was positively associated with calorie intake, fat-free mass (FFM), functional abilities (French Autonomie Gérontologie Groupe Iso Ressources scale), and elevated CRP level (> 25 mg/l). Significant differences (p<0.05) appeared between measured REE and predicted REE by using various published equations. CONCLUSION: REE of very old nursing home residents is influenced by FFM, calorie intake, functional abilities, and CRP levels and is poorly predicted by classical equations based on age, gender, height, and weight. This suggests a metabolic adaptation to caloric restriction and inflammation and prompts to consider the level of physical activity and muscle loss when assessing caloric requirements in this population.

Hypermetabolism in ALS patients: an early and persistent phenomenon.

The malnutrition common among patients with ALS can be attributed in some cases to increased resting energy expenditure (REE). However, the origins and evolution of this hypermetabolism have yet to be fully elucidated. The aim of the present study was to monitor REE over time in patients with ALS and to identify factors that may explain any variation observed. ALS patients underwent nutritional, neurological and respiratory assessment every 6 months for 2 years (or until they died or became physically incapable of being examined). Sixty-one patients were studied. At inclusion, 47.5% exhibited hypermetabolism, with a mean measured REE (mREE) 19.7 +/- 6.4% higher than the mean calculated REE (cREE) (P < 0.0001). The hypermetabolism persisted when mREE was normalized for fat free mass (FFM): 35.1 +/- 4.2 versus 32.3 +/- 4.7 kcal/kg day(-1) (P = 0.02) in hypermetabolic and normometabolic patients, respectively. In univariate analysis, mREE was negatively correlated with age and positively correlated with BMI, FFM, energy and protein intakes, and albumin level. No correlation was found with neurological scores, disease characteristics, respiratory function and survival. Multivariate analysis revealed no significant factors. Only 10 of 45 patients in whom REE was measured at least twice changed their metabolic status. Neither mREE nor mREE/cREE varied significantly over time, despite deteriorating neurological, nutritional and respiratory parameters (P < 0.0001), and an increase in mREE/FFM (P = 0.01). This study confirms that about 50% of ALS patients are hypermetabolic, and 80% show no change in metabolic status over time. Thus, metabolic status (a clinically useful indicator of the need for nutritional support) can be determined early in the evolution of the disease. The origin of hypermetabolism in this context remains unknown, but growing evidence points to mitochondria as having an important role.

Prediction of resting energy expenditure in severely obese Italian women.

The aims of the present study were to develop and cross-validate new equations for predicting resting energy expenditure (PREE) in severely obese Italian women, and to compare their accuracy with those of the Harris-Benedict, Bernstein, WHO/FAO/UNU, Owen, Mifflin, Nelson, Siervo, Huang and Livingston equations to predict REE, using the Bland-Altman method. One hundred and eighty two women [mean body mass index (BMI) 45.6 kg/m2; 56.7% fat mass (FM)], aged 19 to 60 yr participated in this study. REE was measured by indirect calorimetry and body composition by bioelectrical analysis. Equations were derived by stepwise multiple regression analysis, using a calibration group and tested against the validation group. Two new specific equations based on anthropometric REE=Weightx0.042+Heightx3.619-2.678 (R2=0.66, SE=0.56 MJ) or body composition parameters REE=FFMx0.067+FMx0.046+1.568 (R2=0.63, SE=0.58 MJ) were generated. Mean PREE were no different from the mean measured resting energy expenditure (MREE) (<1%, p>0.800) and REE was predicted accurately (95-105% of MREE) in 60% of subjects. The WHO/FAO/UNU, Harris-Benedict and Siervo equations showed mean differences <2% and PREE was accurate in <44% of subjects. The Huang, Mifflin and Livingston equations showed a mean PREE underestimation (>5.0%, p<0.001) and PREE was accurate in <38% of subjects. The Owen, Bernstein and Nelson equations showed a greater PREE underestimation (>14%, p<0.001) in >90% of subjects. The new prediction equations allow an accurate estimation of REE in groups of severely obese women and result in lower mean differences and lower limits of agreement between PREE and MREE than commonly used equations.