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Predictive Equation to Estimate Resting Metabolic Rate in Older Chilean Women. Nutrients 2022; 14:nu14153199. [PMID: 35956375 PMCID: PMC9370421 DOI: 10.3390/nu14153199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/27/2022] [Accepted: 08/02/2022] [Indexed: 12/10/2022] Open
Abstract
Resting metabolic rate (RMR) depends on body fat-free mass (FFM) and fat mass (FM), whereas abdominal fat distribution is an aspect that has yet to be adequately studied. The objective of the present study was to analyze the influence of waist circumference (WC) in predicting RMR and propose a specific estimation equation for older Chilean women. This is an analytical cross-sectional study with a sample of 45 women between the ages of 60 and 85 years. Weight, height, body mass index (BMI), and WC were evaluated. RMR was measured by indirect calorimetry (IC) and %FM using the Siri equation. Adequacy (90% to 110%), overestimation (>110%), and underestimation (<90%) of the FAO/WHO/UNU, Harris−Benedict, Mifflin-St Jeor, and Carrasco equations, as well as those of the proposed equation, were evaluated in relation to RMR as measured by IC. Normal distribution was determined according to the Shapiro−Wilk test. The relationship of body composition and WC with RMR IC was analyzed by multiple linear regression analysis. The RMR IC was 1083.6 ± 171.9 kcal/day, which was significantly and positively correlated with FFM, body weight, WC, and FM and inversely correlated with age (p < 0.001). Among the investigated equations, our proposed equation showed the best adequacy and lowest overestimation. The predictive formulae that consider WC improve RMR prediction, thus preventing overestimation in older women.
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Ocagli H, Lanera C, Azzolina D, Piras G, Soltanmohammadi R, Gallipoli S, Gafare CE, Cavion M, Roccon D, Vedovelli L, Lorenzoni G, Gregori D. Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population. Nutrients 2021; 13:458. [PMID: 33573101 PMCID: PMC7912404 DOI: 10.3390/nu13020458] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/21/2021] [Accepted: 01/26/2021] [Indexed: 11/16/2022] Open
Abstract
Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69-0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome.
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Affiliation(s)
- Honoria Ocagli
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy; (H.O.); (C.L.); (D.A.); (G.P.); (R.S.); (M.C.); (L.V.); (G.L.)
| | - Corrado Lanera
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy; (H.O.); (C.L.); (D.A.); (G.P.); (R.S.); (M.C.); (L.V.); (G.L.)
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy; (H.O.); (C.L.); (D.A.); (G.P.); (R.S.); (M.C.); (L.V.); (G.L.)
- Department of Translational Medicine, University of Piemonte Orientale, Via Solaroli 17, 28100 Novara, Italy
| | - Gianluca Piras
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy; (H.O.); (C.L.); (D.A.); (G.P.); (R.S.); (M.C.); (L.V.); (G.L.)
| | - Rozita Soltanmohammadi
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy; (H.O.); (C.L.); (D.A.); (G.P.); (R.S.); (M.C.); (L.V.); (G.L.)
| | - Silvia Gallipoli
- ZETA Research Incorporation, Via A. Caccia 8, 34122 Trieste, Italy;
| | - Claudia Elena Gafare
- Department of Nutrition, University of Buenos Aires and Food and Diet Therapy Service, Acute General Hospital Juan A. Fernandez, Av. Cerviño 3356, Buenos Aires C1425, Argentina;
| | - Monica Cavion
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy; (H.O.); (C.L.); (D.A.); (G.P.); (R.S.); (M.C.); (L.V.); (G.L.)
| | - Daniele Roccon
- Nursing Home “A. Galvan”, Via Ungheria 340, Pontelongo, 35029 Padova, Italy;
| | - Luca Vedovelli
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy; (H.O.); (C.L.); (D.A.); (G.P.); (R.S.); (M.C.); (L.V.); (G.L.)
| | - Giulia Lorenzoni
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy; (H.O.); (C.L.); (D.A.); (G.P.); (R.S.); (M.C.); (L.V.); (G.L.)
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Via Loredan 18, 35121 Padova, Italy; (H.O.); (C.L.); (D.A.); (G.P.); (R.S.); (M.C.); (L.V.); (G.L.)
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Abstract
Estimation of RMR using prediction equations is the basis for calculating energy requirements. In the present study, RMR was predicted by Harris–Benedict, Schofield, Henry, Mifflin–St Jeor and Owen equations and measured by indirect calorimetry in 125 healthy adult women of varying BMI (17–44 kg/m2). Agreement between methods was assessed by Bland–Altman analyses and each equation was assessed for accuracy by calculating the percentage of individuals predicted within ± 10 % of measured RMR. Slopes and intercepts of bias as a function of average RMR (mean of predicted and measured RMR) were calculated by regression analyses. Predictors of equation bias were investigated using univariate and multivariate linear regression. At group level, bias (the difference between predicted and measured RMR) was not different from zero only for Mifflin–St Jeor (0 (sd 153) kcal/d (0 (sd 640) kJ/d)) and Henry (8 (sd 163) kcal/d (33 (sd 682) kJ/d)) equations. Mifflin–St Jeor and Henry equations were most accurate at the individual level and predicted RMR within 10 % of measured RMR in 71 and 66 % of participants, respectively. For all equations, limits of agreement were wide, slopes of bias were negative, and intercepts of bias were positive and significantly (P < 0⋅05) different from zero. Increasing age, height and BMI were associated with underestimation of RMR, but collectively these variables explained only 15 % of the variance in estimation bias. Overall accuracy of equations for prediction of RMR is low at the individual level, particularly in women with low and high RMR. The Mifflin–St Jeor equation was the most accurate for this dataset, but prediction errors were still observed in about one-third of participants.
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