Browse > Article
http://dx.doi.org/10.4162/nrp.2019.13.3.256

Validity of the dietary reference intakes for determining energy requirements in older adults  

Ndahimana, Didace (Department of Food and Nutrition, Gangneung-Wonju National University)
Go, Na-Young (Department of Food and Nutrition, Gangneung-Wonju National University)
Ishikawa-Takata, Kazuko (Department of Nutrition and Metabolism, National Institutes of Biomedical Innovation, Health and Nutrition)
Park, Jonghoon (Department of Physical Education, Korea University)
Kim, Eun-Kyung (Department of Food and Nutrition, Gangneung-Wonju National University)
Publication Information
Nutrition Research and Practice / v.13, no.3, 2019 , pp. 256-262 More about this Journal
Abstract
BACKGROUND/OBJECTIVES: The objectives of this study were to evaluate the accuracy of the Dietary Reference Intakes (DRI) for estimating the energy requirements of older adults, and to develop and validate new equations for predicting the energy requirements of this population group. MATERIALS/METHODS: The study subjects were 25 men and 23 women with a mean age of $72.2{\pm}3.9\;years$ and $70.0{\pm}3.3\;years$, and mean BMI of $24.0{\pm}2.1$ and $23.9{\pm}2.7$, respectively. The total energy expenditure (TEE) was measured by using the doubly labeled water (DLW) method, and used to validate the DRI predictive equations for estimated energy requirements (EER) and to develop new EER predictive equations. These developed equations were cross-validated by using the leave-one-out technique. RESULTS: In men, the DRI equation had a -7.2% bias and accurately predicted the EER (meaning EER values within ${\pm}10%$ of the measured TEE) for 64% of the subjects, whereas our developed equation had a bias of -0.1% and an accuracy rate of 84%. In women, the bias was -6.6% for the DRI equation and 0.2% for our developed equation, and the accuracy rate was 74% and 83%, respectively. The predicted EER was strongly correlated with the measured TEE, for both the DRI equations and our developed equations (Pearson's r = 0.915 and 0.908, respectively). CONCLUSIONS: The DRI equations provided an acceptable prediction of EER in older adults and these study results therefore support the use of these equations in this population group. Our developed equations had a better predictive accuracy than the DRI equations, but more studies need to be performed to assess the performance of these new equations when applied to an independent sample of older adults.
Keywords
Nutritional requirements; energy metabolism; elderly;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 de la Torre CL, Ramirez-Marrero FA, Martinez LR, Nevarez C. Predicting resting energy expenditure in healthy Puerto Rican adults. J Am Diet Assoc 2010;110:1523-6.   DOI
2 United Nations. World Population Ageing 2017: Highlights (ST/ESA/SER.A/397). New York, NY: United Nations; 2017. p.1.
3 Arredondo A, Aviles R. Costs and epidemiological changes of chronic diseases: implications and challenges for health systems. PLoS One 2015;10:e0118611.   DOI
4 Scommegna P. Noncommunicable Diseases among Older Adults in Low- and Middle-Income Countries [Internet]. Washington, D.C.: Population Reference Bureau; 2012 [cited 2018 February 26]. Available from: http://www.prb.org/Publications/Reports/2012/non communicable-diseases-older-adults.aspx.
5 Kennedy BK, Berger SL, Brunet A, Campisi J, Cuervo AM, Epel ES, Franceschi C, Lithgow GJ, Morimoto RI, Pessin JE, Rando TA, Richardson A, Schadt EE, Wyss-Coray T, Sierra F. Geroscience: linking aging to chronic disease. Cell 2014;159:709-13.   DOI
6 Sherman SE, D'Agostino RB, Cobb JL, Kannel WB. Does exercise reduce mortality rates in the elderly? Experience from the Framingham Heart Study. Am Heart J 1994;128:965-72.   DOI
7 Shahar DR, Yu B, Houston DK, Kritchevsky SB, Lee JS, Rubin SM, Sellmeyer DE, Tylavsky FA, Harris TB; Health, Aging and Body Composition Study. Dietary factors in relation to daily activity energy expenditure and mortality among older adults. J Nutr Health Aging 2009;13:414-20.   DOI
8 Qiu S, Cai X, Schumann U, Velders M, Sun Z, Steinacker JM. Impact of walking on glycemic control and other cardiovascular risk factors in type 2 diabetes: a meta-analysis. PLoS One 2014;9:e109767.   DOI
9 Schuler G, Adams V, Goto Y. Role of exercise in the prevention of cardiovascular disease: results, mechanisms, and new perspectives. Eur Heart J 2013;34:1790-9.   DOI
10 Friedenreich CM, Neilson HK, Farris MS, Courneya KS. Physical activity and cancer outcomes: a precision medicine approach. Clin Cancer Res 2016;22:4766-75.   DOI
11 Normal human aging: the Baltimore Longitudinal Study of Aging. J Gerontol 1985;40:767.
12 Middleton LE, Manini TM, Simonsick EM, Harris TB, Barnes DE, Tylavsky F, Brach JS, Everhart JE, Yaffe K. Activity energy expenditure and incident cognitive impairment in older adults. Arch Intern Med 2011;171:1251-7.   DOI
13 Milanovic Z, Pantelic S, Trajkovic N, Sporis G, Kostic R, James N, Jorgic B. Age-related decrease in physical activity and functional fitness among elderly men and women. Clin Interv Aging 2013;8:549-56.
14 Sun F, Norman IJ, While AE. Physical activity in older people: a systematic review. BMC Public Health 2013;13:449.   DOI
15 Manini TM, Everhart JE, Anton SD, Schoeller DA, Cummings SR, Mackey DC, Delmonico MJ, Bauer DC, Simonsick EM, Colbert LH, Visser M, Tylavsky F, Newman AB, Harris TB. Activity energy expenditure and change in body composition in late life. Am J Clin Nutr 2009;90:1336-42.   DOI
16 Speakman JR, Westerterp KR. Associations between energy demands, physical activity, and body composition in adult humans between 18 and 96 y of age. Am J Clin Nutr 2010;92:826-34.   DOI
17 Institute of Medicine of the National Academies (US). Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. Washington, D.C.: National Academies Press; 2002.
18 Roberts SB, Fuss P, Heyman MB, Young VR. Influence of age on energy requirements. Am J Clin Nutr 1995;62:1053S-1058S.   DOI
19 Park J, Kazuko IT, Kim E, Kim J, Yoon J. Estimating free-living human energy expenditure: Practical aspects of the doubly labeled water method and its applications. Nutr Res Pract 2014;8:241-8.   DOI
20 Roberts SB, Dallal GE. Energy requirements and aging. Public Health Nutr 2005;8:1028-36.   DOI
21 Lazzer S, Agosti F, Resnik M, Marazzi N, Mornati D, Sartorio A. Prediction of resting energy expenditure in severely obese Italian males. J Endocrinol Invest 2007;30:754-61.   DOI
22 Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 1949;109:1-9.   DOI
23 Huang KC, Kormas N, Steinbeck K, Loughnan G, Caterson ID. Resting metabolic rate in severely obese diabetic and nondiabetic subjects. Obes Res 2004;12:840-5.   DOI
24 Lazzer S, Agosti F, Silvestri P, Derumeaux-Burel H, Sartorio A. Prediction of resting energy expenditure in severely obese Italian women. J Endocrinol Invest 2007;30:20-7.   DOI
25 Ndahimana D, Kim EK. Measurement methods for physical activity and energy expenditure: a review. Clin Nutr Res 2017;6:68-80.   DOI
26 Black AE, Prentice AM, Coward WA. Use of food quotients to predict respiratory quotients for the doubly-labelled water method of measuring energy expenditure. Hum Nutr Clin Nutr 1986;40:381-91.
27 Marra M, Montagnese C, Sammarco R, Amato V, Della Valle E, Franzese A, Contaldo F, Pasanisi F. Accuracy of predictive equations for estimating resting energy expenditure in obese adolescents. J Pediatr 2015;166:1390-1396.e1.   DOI
28 Drehmer DE, Morris GW. Cross-validation with small samples: An algorithm for computing Gollob's estimator. Educ Psychol Meas 1981;41:195-200.   DOI
29 Weijs PJ. Validity of predictive equations for resting energy expenditure in US and Dutch overweight and obese class I and II adults aged 18-65 y. Am J Clin Nutr 2008;88:959-70.   DOI
30 Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc 2005;105:775-89.   DOI
31 Okubo H, Sasaki S, Rafamantanantsoa HH, Ishikawa-Takata K, Okazaki H, Tabata I. Validation of self-reported energy intake by a self-administered diet history questionnaire using the doubly labeled water method in 140 Japanese adults. Eur J Clin Nutr 2008;62:1343-50.   DOI
32 Tooze JA, Schoeller DA, Subar AF, Kipnis V, Schatzkin A, Troiano RP. Total daily energy expenditure among middle-aged men and women: the OPEN Study. Am J Clin Nutr 2007;86:382-7.   DOI
33 Rolfes SR, Pinna K, Whitney EN. Understanding Normal and Clinical Nutrition. 9th ed. Belmont, CA: Wadsworth, Cengage Learning; 2012.
34 Nahikian-Nelms M, Sucher KP, Lacey K, Roth SL. Nutrition Therapy and Pathophysiology. 2nd ed. Belmont, CA: Wadsworth, Cengage Learning; 2011. p. 240.
35 Kim EK, Kim JH, Kim MH, Ndahimana D, Yean SE, Yoon JS, Kim JH, Park J, Ishikawa-Takata K. Validation of dietary reference intake equations for estimating energy requirements in Korean adults by using the doubly labeled water method. Nutr Res Pract 2017;11:300-6.   DOI