Total Energy Expenditure in Healthy Ambulatory Older Adults Aged ≥80 Years: A Doubly Labelled Water Study

Introduction: The life expectancy of older adults continues to increase; however, knowledge regarding their total energy requirements is lacking. This study aimed to compare the total energy expenditure (TEE) of older adults ≥80 years measured using doubly labelled water (DLW), with estimated TEE. The hypothesis was that the Mifflin, Ikeda, and Livingston equations will more closely estimate energy requirements than the commonly used Schofield equation. Methods: Resting metabolic rate (RMR) and TEE were measured using the reference methods of indirect calorimetry and DLW, respectively. Bland-Altman plots compared measured RMR and TEE with predicted RMR using equations (Mifflin, Ikeda, Livingston, Schofield) and predicted TEE (predicted RMR × physical activity level). Results: Twenty-one older adults (age range 80.7–90.1 years, BMI 26.1 ± 5.5 kg/m2) were included. The Schofield equation demonstrated the greatest bias from measured RMR, overestimating approximately up to double the mean difference (865 ± 662 kJ/day) compared with the three other equations. The Schofield equation exhibited the greatest bias (overestimation of 641 ± 1,066 kJ/day) compared with measured TEE. The other three equations underestimated TEE, with the least bias from Ikeda (37 ± 1,103 kJ/day), followed by Livingston (251 ± 1,108 kJ/day), and Mifflin (354 ± 1,140 kJ/day). Data are mean ± SD. Conclusions: In older adults ≥80 years, the Ikeda, Mifflin, and Livingston equations provide closer estimates of TEE than the widely used Schofield equation. The development of nutrition guidelines therefore should consider the utilization of equations which more accurately reflect age-specific requirements.

[1]  C. Earthman,et al.  Doubly labelled water for determining total energy expenditure in adult critically ill and acute care hospitalized inpatients: a scoping review , 2021, Clinical Nutrition.

[2]  Corby K. Martin,et al.  Daily energy expenditure through the human life course , 2021, Science.

[3]  E. Ferriolli,et al.  Total Energy Expenditure and Functional Status in Older Adults: A Doubly Labelled Water Study , 2020, The journal of nutrition, health & aging.

[4]  D. Schoeller,et al.  Total energy expenditure measured using doubly labeled water compared with estimated energy requirements in older adults (≥65 y): analysis of primary data , 2019, The American journal of clinical nutrition.

[5]  N. Kellow,et al.  Total energy expenditure in adults aged 65 years and over measured using doubly-labelled water: international data availability and opportunities for data sharing , 2018, Nutrition Journal.

[6]  J. Richardson,et al.  Deriving population norms for the AQoL-6D and AQoL-8D multi-attribute utility instruments from web-based data , 2016, Quality of Life Research.

[7]  D. Schoeller,et al.  Special Considerations for Measuring Energy Expenditure with Doubly Labeled Water under Atypical Conditions , 2015, Journal of obesity & weight loss therapy.

[8]  Megan E. Rollo,et al.  Evaluation of a Mobile Phone Image-Based Dietary Assessment Method in Adults with Type 2 Diabetes , 2015, Nutrients.

[9]  H. Truby,et al.  Accuracy of Self-Reported Physical Activity Levels in Obese Adolescents , 2014, Journal of nutrition and metabolism.

[10]  M. Ferguson,et al.  Malnutrition and poor food intake are associated with prolonged hospital stay, frequent readmissions, and greater in-hospital mortality: results from the Nutrition Care Day Survey 2010. , 2013, Clinical nutrition.

[11]  Takashi Kawamura,et al.  A new equation to estimate basal energy expenditure of patients with diabetes. , 2013, Clinical nutrition.

[12]  J. Ratcliffe,et al.  Measuring and valuing quality of life for public health research: application of the ICECAP-O capability index in the Australian general population , 2012, International Journal of Public Health.

[13]  Tanvir Ahmed,et al.  Assessment and management of nutrition in older people and its importance to health , 2010, Clinical interventions in aging.

[14]  D. Schoeller,et al.  Prediction of fat-free mass by bioelectrical impedance analysis in older adults from developing countries: A cross-validation study using the deuterium dilution method , 2010, The journal of nutrition, health & aging.

[15]  T. Peters,et al.  Valuing the ICECAP capability index for older people. , 2008, Social science & medicine.

[16]  C. Compher,et al.  Best practice methods to apply to measurement of resting metabolic rate in adults: a systematic review. , 2006, Journal of the American Dietetic Association.

[17]  Sandra Capra,et al.  Nutrient reference values for Australia and New Zealand: Including recommended dietary intakes , 2006 .

[18]  J. Speakman,et al.  Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. , 2005, The American journal of clinical nutrition.

[19]  C J K Henry,et al.  Basal metabolic rate studies in humans: measurement and development of new equations. , 2005, Public health nutrition.

[20]  Edward H Livingston,et al.  Simplified resting metabolic rate-predicting formulas for normal-sized and obese individuals. , 2005, Obesity research.

[21]  Christopher M Callahan,et al.  Six-Item Screener to Identify Cognitive Impairment Among Potential Subjects for Clinical Research , 2002, Medical care.

[22]  D. Schoeller,et al.  Influence of delayed isotopic equilibration in urine on the accuracy of the (2)H(2)(18)O method in the elderly. , 2002, Journal of applied physiology.

[23]  H. Dodge,et al.  Random versus volunteer selection for a community-based study. , 1998, The journals of gerontology. Series A, Biological sciences and medical sciences.

[24]  M. Soares,et al.  The validity of predicting the basal metabolic rate of young Australian men and women , 1997, European Journal of Clinical Nutrition.

[25]  C. Henry,et al.  A re-examination of basal metabolic rate predictive equations: the importance of geographic origin of subjects in sample selection. , 1994, European journal of clinical nutrition.

[26]  M. Mifflin,et al.  A new predictive equation for resting energy expenditure in healthy individuals. , 1990, The American journal of clinical nutrition.

[27]  S. Badylak,et al.  Resting metabolic rate and postprandial thermogenesis in highly trained and untrained males. , 1988, The American journal of clinical nutrition.

[28]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[29]  W. Schofield Predicting basal metabolic rate, new standards and review of previous work. , 1985, Human nutrition. Clinical nutrition.

[30]  D A Schoeller,et al.  Measurement of energy expenditure in humans by doubly labeled water method. , 1982, Journal of applied physiology: respiratory, environmental and exercise physiology.

[31]  J. B. Weir New methods for calculating metabolic rate with special reference to protein metabolism , 1949, The Journal of physiology.