{"title":"发展新的预测方程来估计伊朗成年人的基础代谢率:一项研究方案","authors":"B. Nikooyeh, T. Neyestani","doi":"10.29252/NFSR.6.2.1","DOIUrl":null,"url":null,"abstract":"Background and Objectives: Studies indicate over-estimation of basal metabolic rate (BMR) using common equations for the Asian people. The present study aims to develop new predictive equations for the Iranian people and to compare these equations with commonly used formulas. Materials and Methods: Total, 150healthy subjects aged 18-60 yrare invited to the Laboratory of Nutrition Research, National Nutrition and Food Technology Research Institute. Demographic data are gathered using a questionnaire. Then, anthropometric measures are taken and blood sampling is done for thyroid function tests. If the subject merits all the inclusion criteria, indirect calorimetry will be performed. The value of BMR will be predicted using common equations (Harris-Benedict, FAO/WHO/UNU, Miffilin). Differences between predicted (using equations) and measured (using indirect calorimetry) values are estimated. Correlations between the two sets of data is performed using Pearson or Spearman coefficients. Between-method agreement is checked using Bland-AltmanPlot. Accuracy of the predicted values using equations isconsidered as the proportion of participants whose calculated BMR is 90-110% of their measured BMR. Multiple regression analysis is employed to develop new predictive equations for the BMR based on the independent variables. Conclusions: Since facilities for the measurement of BMR may not be accessible in many clinical or research settings, BMR is usually estimated using predictive equations. However, several studies have reported inaccuracy of these equations for certain populations. Therefore, development of new population-specific predictive equations seems reasonable. These equations could hopefully reduce the energy estimation errors both in clinical nutritional interventions and community-based nutrition researches.","PeriodicalId":325113,"journal":{"name":"Nutrition and Food Sciences Research","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Development of New Predictive Equations to Estimate Basal Metabolic Rrates in Iranian Adults: A Study Protocol\",\"authors\":\"B. Nikooyeh, T. Neyestani\",\"doi\":\"10.29252/NFSR.6.2.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background and Objectives: Studies indicate over-estimation of basal metabolic rate (BMR) using common equations for the Asian people. The present study aims to develop new predictive equations for the Iranian people and to compare these equations with commonly used formulas. Materials and Methods: Total, 150healthy subjects aged 18-60 yrare invited to the Laboratory of Nutrition Research, National Nutrition and Food Technology Research Institute. Demographic data are gathered using a questionnaire. Then, anthropometric measures are taken and blood sampling is done for thyroid function tests. If the subject merits all the inclusion criteria, indirect calorimetry will be performed. The value of BMR will be predicted using common equations (Harris-Benedict, FAO/WHO/UNU, Miffilin). Differences between predicted (using equations) and measured (using indirect calorimetry) values are estimated. Correlations between the two sets of data is performed using Pearson or Spearman coefficients. Between-method agreement is checked using Bland-AltmanPlot. Accuracy of the predicted values using equations isconsidered as the proportion of participants whose calculated BMR is 90-110% of their measured BMR. Multiple regression analysis is employed to develop new predictive equations for the BMR based on the independent variables. Conclusions: Since facilities for the measurement of BMR may not be accessible in many clinical or research settings, BMR is usually estimated using predictive equations. However, several studies have reported inaccuracy of these equations for certain populations. Therefore, development of new population-specific predictive equations seems reasonable. These equations could hopefully reduce the energy estimation errors both in clinical nutritional interventions and community-based nutrition researches.\",\"PeriodicalId\":325113,\"journal\":{\"name\":\"Nutrition and Food Sciences Research\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nutrition and Food Sciences Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.29252/NFSR.6.2.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nutrition and Food Sciences Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.29252/NFSR.6.2.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development of New Predictive Equations to Estimate Basal Metabolic Rrates in Iranian Adults: A Study Protocol
Background and Objectives: Studies indicate over-estimation of basal metabolic rate (BMR) using common equations for the Asian people. The present study aims to develop new predictive equations for the Iranian people and to compare these equations with commonly used formulas. Materials and Methods: Total, 150healthy subjects aged 18-60 yrare invited to the Laboratory of Nutrition Research, National Nutrition and Food Technology Research Institute. Demographic data are gathered using a questionnaire. Then, anthropometric measures are taken and blood sampling is done for thyroid function tests. If the subject merits all the inclusion criteria, indirect calorimetry will be performed. The value of BMR will be predicted using common equations (Harris-Benedict, FAO/WHO/UNU, Miffilin). Differences between predicted (using equations) and measured (using indirect calorimetry) values are estimated. Correlations between the two sets of data is performed using Pearson or Spearman coefficients. Between-method agreement is checked using Bland-AltmanPlot. Accuracy of the predicted values using equations isconsidered as the proportion of participants whose calculated BMR is 90-110% of their measured BMR. Multiple regression analysis is employed to develop new predictive equations for the BMR based on the independent variables. Conclusions: Since facilities for the measurement of BMR may not be accessible in many clinical or research settings, BMR is usually estimated using predictive equations. However, several studies have reported inaccuracy of these equations for certain populations. Therefore, development of new population-specific predictive equations seems reasonable. These equations could hopefully reduce the energy estimation errors both in clinical nutritional interventions and community-based nutrition researches.