Weight change adjusted equations for assessing resting metabolic rate in overweight and obese adults

BackgroundAlthough over one hundred equations have been developed to predict the energy expenditure of individuals, none are sensitive to weight change in assessment of resting metabolic rate (RMR) before and after weight loss.ObjectiveTo formulate adjusted equations for overweight and obese individuals and to compare their accuracy with existing prediction RMR equations before and after weight loss.Subjects/materialsThis is historical prospective study. Participants included 39 overweight and obese men and women before and after losing 10–20% from baseline weight on a diet and physical activity regimen for at least three months. Pre and post weight loss measured RMR results were compared to estimated RMR using several existing prediction equations: Harris and Benedict, Ravussin and Bogardus, and Mifflin et al. To improve the accuracy of these prediction equations, we suggest new equations adjusted for weight loss, based on measured RMR and evaluated their accuracy.ResultsPre and post weight loss data indicated: significant fat reduction in both genders; reduction in free-fat mass only in men, and a significant decrease in measured RMR only in women. Our suggested equations were the most accurate and closest to measured RMR in both genders, in comparison to the Harris and Benedict, Ravussin and Bogardus, and Mifflin et al equation results. Estimated RMR using the latter equations was significantly lower than measured RMR in both genders at pre and post weight loss (P < 0.01).ConclusionsThis study highlights the need for adjusting RMR equations before and after weight loss in overweight and obese individuals. Further research is needed to validate our suggested equations.     

Another step in the search for better oral rehydration solutions.

Objective: To compare resting metabolic rate (RMR) measured by indirect calorimetry versus RMR predicted by several published formulas in a sample of healthy young women.

Methods: RMR was measured using indirect calorimetry and predicted using 6 commonly used equations (Nelson, 1992; Mifflin, 1990; Owen, 1986; Schofield_Weight, 1985; Schofield_{Weight and Height}, 1985; Harris-Benedict, 1919) in 47 reportedly healthy young females (age = 22.8 ± 2.9 years; body mass index = 21.8 ± 2.1 kg/m²). Comparisons between measured versus predicted RMR were conducted using paired t tests, and agreement using Pearson's correlation coefficient, analysis of variance, and the method of Bland-Altman.

Results: All 6 equations overestimated measured RMR by 140–738 kcal/d (all p < 0.001). The proportion of subjects for whom measured versus predicted RMR differed by ±10% ranged from 74% (Nelson) to 100% (Harris-Benedict). The adjusted coefficients of determination (R²) between measured and predicted RMR ranged from 0.13 to 0.19 (all p < 0.05). Bland-Altman analysis R² values ranged from 0.03 (p = 0.233; Harris-Benedict) to 0.72 (p = 0.000; Owen). Given its continued popularity, we modified the Harris-Benedict equation (RMR_{modified Harris-Benedict} (kcal/d) = 738 / (RMR_{Harris-Benedict} ? 738)). Doing so reduced the mean difference between measured and predicted RMR from +738 kcal/d to ?0.53 kcal/d (p = 0.984).

Conclusion: No equation performed well, and none should be used interchangeably with measured RMR. We recommend that a new equation be validated for, and prospectively tested in, young women. In the interim, RMR should be measured in this population or predicted using the modified Harris-Benedict equation that we developed.     

Measured and predicted resting metabolic rate in Italian males and females, aged 18-59 y

OBJECTIVES: To determine the resting metabolic rate in a sample of the Italian population, and to evaluate the validity of predictive equations for resting metabolic rate (RMR) from the literature in normal and obese subjects. DESIGN: Cross-sectional observational study. SETTINGS: Department of Human Physiology and Nutrition, University 'Tor Vergata', Rome. SUBJECTS: A total of 320 healthy subjects, 127 males and 193 females, aged 18-59 y. METHODS: Weight, height and resting metabolic rate by indirect calorimetry were measured. Resting metabolic rate was also predicted using equations from the literature. RESULTS: Resting metabolic rate (mean s.d.) in normal weight subjects was 7983+/-1007 kJ/24 h (males) and 6127 907 kJ/24h (females). Measured RMR and predicted RMR values using various equations from the literature were significantly different in males and females, except for the Harris-Benedict equation and the Schofield equations. Also, in overweight and obese subjects the prediction error was generally larger compared to normal-weight subjects for all formulas except for the Harris-Benedict and Schofield formulas. In overweight and obese males but not in females, RMR was lower than in normal-weight subjects after correcting for weight and age differences. Stepwise multiple regression of resting metabolic rate against weight, height and age in males and females did not reveal a prediction formula with a lower prediction error than the Harris-Benedict or Schofield formulas and thus was not further explored. CONCLUSIONS: The Harris-Benedict formula and the Schofield formula provide a valid estimation of resting metabolic rate at a group level in both normal-weight and overweight Italians. However, the individual error can be so high that for individual use a measured value has to be preferred over an estimated value.     

Simple kcal/kg formula is comparable to prediction equations for estimating resting energy expenditure in older cognitively impaired long term care residents

Objective

Assessment of energy needs is a critical step in developing the nutrition care plan, especially for individuals unable to modulate their own energy intakes. The purpose of this study was to assess precision and accuracy of commonly used prediction equations in comparison to measured resting energy expenditure in a sample of “oldest old” adults residing in long term care (LTC).

Subjects and Design

Resting energy expenditure (mREE) was measured by indirect calorimetry in 45 residents aged 86.1 ± 7.3 years, and compared to frequently used prediction equations (pREE): Mifflin St.Jeor, Harris Benedict, World Health Organization and Owen. Precision and accuracy were determined by concordance correlation coefficients and number of individuals within ± 10% of mREE. Bland Altman plots with linear dependence trends were constructed to visualize agreement. To complete analyses, the common 25 kcal/kg formula was assessed and alternative formulas were determined for best fit by regressing adjusted mREE on body weight.

Results

mREE averaged 976.2 ± 190.3 kcal/day for females and 1260.0 ± 275.9 kcal/d for males. The strength of the relationships between pREE and mREE were only moderate (r = 0.41–0.72). In examining linear trends in the Bland Altman plots, significant systematic deviation from mREE was detected for all pREE. Two kcal/kg formulas were generated: 20.6 kcal/kg for females and 22.7 kcal/kg for males, which were not significantly different.

Conclusion

None of the prediction equations adequately estimated energy needs in this sample of the “oldest old.” A simple formula using 21–23 kcal/kg may be a more practical and reliable method to determine energy needs in the LTC setting.     

Validity of predictive equations for resting energy expenditure in US and Dutch overweight and obese class I and II adults aged 18-65 y

BACKGROUND: Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry. OBJECTIVE: The objective was to analyze which resting energy expenditure (REE) predictive equation was the best alternative to indirect calorimetry in US and Dutch adults aged 18-65 y with a body mass index (in kg/m(2)) of 25 to 40. DESIGN: Predictive equations based on weight, height, sex, age, fat-free mass, and fat mass were tested. REE in Dutch adults was measured with indirect calorimetry, and published data from the Institute of Medicine were used for US adults. The accuracy of the equations was evaluated on the basis of the percentage of subjects predicted within 10% of the REE measured, the root mean squared prediction error (RMSE), and the mean percentage difference (bias) between predicted and measured REE. RESULTS: Twenty-seven predictive equations (9 of which were based on FFM) were included. Validation was based on 180 women and 158 men from the United States and on 154 women and 54 men from the Netherlands aged <65 y with a body mass index (in kg/m(2)) of 25 to 40. Most accurate and precise for the US adults was the Mifflin equation (prediction accuracy: 79%; bias: -1.0%; RMSE: 136 kcal/d), for overweight Dutch adults was the FAO/WHO/UNU weight equation (prediction accuracy: 68%; bias: -2.5%; RMSE: 178), and for obese Dutch adults was the Lazzer equation (prediction accuracy: 69%; bias: -3.0%; RMSE: 215 kcal/d). CONCLUSIONS: For US adults aged 18-65 y with a body mass index of 25 to 40, the REE can best be estimated with the Mifflin equation. For overweight and obese Dutch adults, there appears to be no accurate equation.     

Comparison of predictive equations for resting metabolic rate in obese psychiatric patients taking olanzapine

ObjectiveThe prediction of resting metabolic rate (RMR) is important to determine the energy expenditure of obese patients with severe mental illnesses (SMIs). However, there is lack of research concerning the most accurate RMR predictive equations. The purpose of this study was to compare the validity of four RMR equations on patients with SMIs taking olanzapine.MethodsOne hundred twenty-eight obese (body mass index >30 kg/m²) patients with SMIs (41 men and 87 women) treated with olanzapine were tested from 2005 to 2008. Measurements of anthropometric parameters (height, weight, body mass index, waist circumference) and body composition (using the BodPod) were performed at the beginning of the study. RMR was measured using indirect calorimetry. Comparisons between measured and estimated RMRs from four equations (Harris-Benedict adjusted and current body weights, Schofield, and Mifflin-St. Jeor) were performed using Pearson's correlation coefficient and Bland-Altman analysis.ResultsSignificant correlations were found between the measured and predicted RMRs with all four equations (P < 0.001), with the Mifflin-St. Jeor equation demonstrating the strongest correlation in men and women (r = 0.712, P < 0.001). In men and women, the Bland-Altman analysis revealed no significant bias in the RMR prediction using the Harris-Benedict adjusted body weight and the Mifflin equations (P > 0.05). However, in men and women, the Harris-Benedict current body weight and the Schofield equations showed significant overestimation error in the RMR prediction (P < 0.001).ConclusionWhen estimating RMR in men and women with SMIs taking olanzapine, the Mifflin-St. Jeor and Harris-Benedict adjusted body weight equations appear to be the most appropriate for clinical use.     

Estimation of Resting Energy Expenditure: Validation of Previous and New Predictive Equations in Obese Children and Adolescents

Objective: Accurate estimation of resting energy expenditure (REE) in childrenand adolescents is important to establish estimated energy requirements. The aim of the present study was to measure REE in obese children and adolescents by indirect calorimetry method, compare these values with REE values estimated by equations, and develop the most appropriate equation for this group.

Methods: One hundred and three obese children and adolescents (57 males, 46 females) between 7 and 17 years (10.6 ± 2.19 years) were recruited for the study. REE measurements of subjects were made with indirect calorimetry (COSMED, FitMatePro, Rome, Italy) and body compositions were analyzed.

Results: In females, the percentage of accurate prediction varied from 32.6 (World Health Organization [WHO]) to 43.5 (Molnar and Lazzer). The bias for equations was ?0.2% (Kim), 3.7% (Molnar), and 22.6% (Derumeaux-Burel). Kim's (266 kcal/d), Schmelzle's (267 kcal/d), and Henry's equations (268 kcal/d) had the lowest root mean square error (RMSE; respectively 266, 267, 268 kcal/d). The equation that has the highest RMSE values among female subjects was the Derumeaux-Burel equation (394 kcal/d). In males, when the Institute of Medicine (IOM) had the lowest accurate prediction value (12.3%), the highest values were found using Schmelzle's (42.1%), Henry's (43.9%), and Müller's equations (fat-free mass, FFM; 45.6%). When Kim and Müller had the smallest bias (?0.6%, 9.9%), Schmelzle's equation had the smallest RMSE (331 kcal/d). The new specific equation based on FFM was generated as follows: REE = 451.722 + (23.202 * FFM). According to Bland-Altman plots, it has been found out that the new equations are distributed randomly in both males and females.

Conclusion: Previously developed predictive equations mostly provided unaccurate and biased estimates of REE. However, the new predictive equations allow clinicians to estimate REE in an obese children and adolescents with sufficient and acceptable accuracy.     

Agreement between different methods and predictive equations for resting energy expenditure in overweight and obese Brazilian men

Predictive equations and methods tend to overestimate or underestimate resting energy expenditure (REE) compared with indirect calorimetry (IC). This cross-sectional study aimed to evaluate the agreement between methods and equations for REE estimation of overweight and obese Brazilian men. Data from 48 healthy volunteers, ages 20 to 43 years and with body mass index ranging from 26.4 to 35.2, were collected between October 2008 and October 2009. REE was measured by IC, using Deltatrac (IC1) and KORR-MetaCheck (IC2) devices. It was estimated by bioelectrical impedance analysis (BIA) using tetrapolar (BIA1) and bipolar (BIA2) devices, and by the equations of Mifflin, World Health Organization/Food and Agriculture Organization/United Nations University, Fleisch, Horie-Waitzberg and Gonzalez, and Ireton-Jones. The association and agreement among the methods and equations were assessed by the interclass correlation coefficient, Bland-Altman analysis, and by the percentage of the difference between values obtained from the standard method and alternative methods and equations. Most methods showed high agreement with IC1. The highest agreements were found for Mifflin (-2.14%), Fleisch (-3.05%), Horie-Waitzberg and Gonzalez (4.41%), and BIA2 (5.25%). Similar results were shown by the Bland-Altman analyses. BIA2, followed by BIA1, Ireton-Jones, Mifflin, and Fleisch, showed the highest association with IC1. Thus, the Mifflin, Fleisch, Horie-Waitzberg and Gonzalez equations, and BIA2, were the most accurate methods for REE estimation in this study. However, because those equations have shown considerable variability, they should be used cautiously. In addition, the IC2 was not found to be an accurate method for REE estimation in overweight and obese men included in this study.     

A new equation especially developed for predicting resting metabolic rate in the elderly for easy use in practice

Summary Background Equations published in the literature for predicting resting metabolic rate (RMR) in older individuals were exclusively derived from studies with small samples of this age group. Aim of the present investigation was therefore to compare the measured RMR of a relatively large group of older females and males with values for RMR calculated from the most commonly used WHO [1] equations. Furthermore, on the basis of the data collected by our study group a new equation for calculating RMR in the elderly was to be developed. Variables used in this equation should be easily and exactly determinable in practice. Subjects and methods RMR was measured by indirect calorimetry after an overnight fast in a sample of 179 female (age 67.8 ± 5.7 y, BMI 26.4 ± 3.7 kg/m²) and 107 male (age 66.9 ± 5.1 y, BMI 26.3 ± 3.1 kg/m²) participants in the longitudinal study on nutrition and health status in an aging population of Giessen, Germany. The subjects were at least 60 years old, did not suffer from thyroid dysfunction, and were not taking thyroid hormones. Stepwise multiple linear regression analysis was used to estimate the best predictors of RMR. Results In females there was no significant difference between our measured RMR (5504 ± 653 kJ/d) and RMR predicted with the WHO [1] equation (5458 ± 440 kJ/d), whereas in males measured RMR (6831 ± 779 kJ/d) was significantly higher than calculated RMR (6490 ± 550 kJ/d). Results of regression analysis, considering body weight, body height, age, and sex, showed that RMR is best calculated by the following equation: RMR [kJ/d]= 3169 + 50.0 · body weight [kg] − 15.3 · age [y] + 746 · sex [female = 0, male = 1]. The variables of this equation accounted for 74 % (R²) of the variance in RMR and predicted RMR within ± 486 kJ/d (SEE). Conclusion On the basis of the data determined in a large group of older individuals, we offer a new equation for calculating RMR in the elderly that is both easy and accurate for use in practice. Received: 5 November 2001, Accepted: 28 February 2002     

Nutritional Status in Sick Children and Adolescents Is Not Accurately Reflected by BMI-SDS

Objective: Nutritional status provides helpful information of disease severity and treatment effectiveness. Body mass index standard deviation scores (BMI-SDS) provide an approximation of body composition and thus are frequently used to classify nutritional status of sick children and adolescents. However, the accuracy of estimating body composition in this population using BMI-SDS has not been assessed. Thus, this study aims to evaluate the accuracy of nutritional status classification in sick infants and adolescents using BMI-SDS, upon comparison to classification using percentage body fat (%BF) reference charts.

Design: BMI-SDS was calculated from anthropometric measurements and %BF was measured using dual-energy x-ray absorptiometry (DXA) for 393 sick children and adolescents (5 months–18 years). Subjects were classified by nutritional status (underweight, normal weight, overweight, and obese), using 2 methods: (1) BMI-SDS, based on age- and gender-specific percentiles, and (2) %BF reference charts (standard). Linear regression and a correlation analysis were conducted to compare agreement between both methods of nutritional status classification. %BF reference value comparisons were also made between 3 independent sources based on German, Canadian, and American study populations.

Results: Correlation between nutritional status classification by BMI-SDS and %BF agreed moderately (r ² = 0.75, 0.76 in boys and girls, respectively). The misclassification of nutritional status in sick children and adolescents using BMI-SDS was 27% when using German %BF references. Similar rates observed when using Canadian and American %BF references (24% and 23%, respectively).

Conclusions: Using BMI-SDS to determine nutritional status in a sick population is not considered an appropriate clinical tool for identifying individual underweight or overweight children or adolescents. However, BMI-SDS may be appropriate for longitudinal measurements or for screening purposes in large field studies. When accurate nutritional status classification of a sick patient is needed for clinical purposes, nutritional status will be assessed more accurately using methods that accurately measure %BF, such as DXA.     

Determination of Nutrition and Physical Activity Status of Early Adolescents in Secondary Schools in Anatolian Side of Istanbul

ABSTRACT

This study was conducted to determine the nutritional and physical activity statuses of adolescents and to examine the relationship between their nutritional and physical activity levels and anthropometric measurements. The sample of this study was composed of 2.000 students from 20 secondary schools in Istanbul, Turkey. The physical activity levels were also examined using the survey, food consumption levels were determined by applying the 24-h Dietary Recall. The height and weight were measured and evaluated by World Health Organization (WHO) growth references. Among the male students (n = 888), 50.9% were normal weighted, 25.8% were overweight, 19.2% were obese, and 2.2% were severely obese. The percentage of those who were normal weight was higher (65.5%) among the female adolescents (n = 852), and 21.8% of the females were overweight, 10.2% were obese, and 0.9% were severely obese. The differences in terms of intake values of all food items were found to be statistically significant (p < .05). There was no statistically significant difference in the food consumption of male and female adolescents in terms of the level of intake of macronutrients. Children and adolescents need to be provided an adequate, balanced nutrition and physical activity to help them grow and develop healthily.     

Influence of Purine Intake on Uric Acid Excretion in Infants Fed Soy Infant Formulas

Objective: These studies tested the hypothesis that increasing intake of purines, delivered as RNA from soy protein-based infant formula, would increase urinary uric acid excretion in infants.

Methods: Study One examined the influence of feeding on serum uric acid in a total of 178 infants from four separate trials with infants fed commercial and experimental soy-based and milk-based infant formulas or human milk. Studies Two and Three compared the effect of a standard purine soy formula (STD Purine; 180 mg purines/L from RNA) and a reduced purine soy formula (Reduced Purine; 65 mg purines/L; 26 mg/L from RNA and 39 mg/L from ribonucleotides) on urinary uric acid excretion in infants. In Study Two, 11 infants ranging in age from 16 to 128 days of age were fed both formulas in a random crossover design. Complete 72-hour urine collections were done at the end of each 11-day feeding period. Urinary uric acid excretion was expressed as mmol/day. In Study Three, 33 infants were enrolled before eight days of age and randomized to one of the formulas one week later. Spot urine samples were collected at 28 and/or 56 days of age and urinary uric acid concentration was expressed as mmol/mmol creatinine.

Results: In Study One, each of the feedings resulted in mean serum uric acid levels within normal reference ranges. Soy formula led to higher serum uric acid levels than human milk, and human milk to levels indistinguishable from cow milk-based formulas. In Study Two, infants excreted significantly more uric acid in the urine when fed the STD Purine formula compared to the Reduced Purine formula (0.86±.04 vs. 0.57±.04 mmol/d) (p=0.006). In Study Three, infants fed the STD Purine formula had a significantly higher concentration of uric acid in their urine compared to those fed the Reduced Purine formula (2.1±0.2 vs. 1.4±0.1 mmol uric acid/mmol creatinine) (p=0.0001).

Conclusion: These data indicate that healthy infants can digest RNA and subsequently absorb the liberated purine ribonucleotides as determined by urinary uric acid concentration.     

A comparative study of indirect calorimetry and prediction equations in overweight and obese Chinese adults

ObjectivesTo determine the resting energy expenditure (REE) in Chinese adults using indirect calorimetry (IC), and evaluate the validity of published predictive equations for estimating REE in overweight and obese subjects comparing to IC.Material and methods181 subjects with different body mass index (BMI) were recruited. Body composition was determined by bio-electrical impedance analysis (BIA). REE was measured by IC and estimated by 29 REE prediction equations to analyze the accuracy of REE prediction equations.ResultsFor all participants combined, the Bernstein et al. and Bernestin et al. (BC) equations similarly predicted REE values between -4% and 8% (P > 0.05) of measured REE. When participant data were stratified by sex, BMI, the accuracy of each regression equation is different. In male overweight and obese group, the highest accuracy of the Bernestin et al. (BC) was 65.5% and 70.4%. In female overweight and obese group, the highest accuracy of the Bernestin et al. (BC) and Bernestin et al. was 79.6% and 69.4%.ConclusionThe Bernestin et al. (BC) was closest to IC in male overweight/obese and female overweight, and the Bernestin et al was closest to IC in female obese. However, because those equations have shown considerable variability, they should be used cautiously when estimating the individual REE. When the IC method cannot be used, the Bernestin et al. (BC) and Bernestin et al are more accurate.     

成都地区不同BMI健康男性的能量代谢研究

目的 研究四川省成都市不同体质指数(Body Mass Index, BMI)健康男性的能量代谢情况及预测公式估计值与测量值之间的差异。方法 选取四川成都地区的健康男性成年人,并以BMI为依据进行分组,采用不同预测公式计算能量代谢情况,同时采用间接测热法确定研究对象能量代谢水平,比较不同组间能量代谢水平及预测值与实测值的差异,分析身高、体重等因素的与能量代谢的相关程度。结果 本研究共招募研究对象33名,其中,正常体重组12名,超重组11名,肥胖组10名。正常组的基础能量消耗(basal energy expenditure, BEE)、静息能量消耗(resting energy expenditure, REE)均低于超重和肥胖组,但经体重校正后,基础代谢率(basal metabolism rate, BMR)、静息代谢率(resting metabolic rate, RMR)由高到低依次为:正常组>超重组>肥胖组; Lazzer 公式能较好地反映能量消耗,准确率为84.85%,其次是Schofield 和WHO/FAO/UNU公式,准确率均为81.82%; 身高、体重、BMI、腰围和体表面积与BEE呈正相关关系,相关程度最高的是体重与体表面积。结论 BMI正常人群的能量代谢率明显高于超重与肥胖人群,其中体重、体表面积与基础能量消耗呈正相关; Lazzer公式能较好地预测本地区的基础能量消耗。     

Diet and gene interaction.

Objective: To investigate the effect of chromium picolinate (CP) supplementation on body composition, resting metabolic rate (RMR), selected biochemical parameters and iron and zinc status in moderately obese women participating in a 12-week exercise program.

Methods: Forty-four women, 27 to 51 years of age, were randomly assigned to two groups based on their body mass index. Subjects received either 400 μg/day of chromium as a CP supplement or a placebo in double-blind fashion and participated in a supervised weight-training and walking program two days per week for 12 weeks. Body composition and RMR were measured at baseline, 6 and 12 weeks. Selected biochemical parameters and iron and zinc status were measured at baseline and 12 weeks.

Results: Body composition and RMR were not significantly changed by CP supplementation. No significant differences in fasting plasma glucose, serum insulin, plasma glucagon, serum C-peptide and serum lipid concentrations or in iron and zinc indices were found between the two groups over time. Serum total cholesterol concentration significantly decreased (p = 0.0016) over time for all subjects combined, probably as a result of the exercise training. Exercise training significantly reduced total iron binding capacity (TIBC) by 3% for all subjects combined (p = 0.0011).

Conclusions: Twelve weeks of 400 μg/day of chromium as a CP supplement did not significantly affect body composition, RMR, plasma glucose, serum insulin, plasma glucagon, serum C-peptide and serum lipid concentrations or iron and zinc indices in moderately obese women placed on an exercise program. The changes in serum total cholesterol levels and TIBC were a result of the exercise program.     

Depression and Anxiety in Adolescent Females: The Impact of Sleep Preference and Body Mass Index

PurposeTo examine the differences in depressive symptoms and anxiety between (a) normal weight and overweight, and (b) morning type and evening type (sleep chronotype) adolescent girls. The interaction of sleep chronotype and weight and depressive symptoms and anxiety were also examined.MethodThe design consisted of a cross-sectional study of 264 adolescent females (mean age = 14.9 ± 2.2, range 11–17 years). Sleep chronotype, depressive symptoms, and anxiety were obtained by self-report questionnaire. The mean of three measurements of height and weight was used to calculate the body mass index (BMI). BMI was plotted on the CDC BMI-for-age growth charts to obtain percentile ranking. Participants were categorized into two groups according to BMI percentile: normal weight (<85th percentile) and overweight (≥85th percentile).ResultsCompared with normal-weight females, overweight females were more likely to be non-Caucasian, lower socioeconomic status, have more advanced pubic hair and breast stages, and earlier age at menarche. No differences were observed with respect to sleep chronotype, depressive symptoms, and trait anxiety between normal weight and overweight females. Evening chronotype was associated with more depressive symptoms (β = ?.65, p < .01) and higher trait anxiety (β = ?.22, p < .05). Evening chronotype was associated with more depressive symptoms in both normal-weight and overweight females. However, the association was stronger in overweight females.ConclusionsIndividually, sleep and weight impact physical and mental health during adolescence. The combination of evening chronotype and overweight appears to have the strongest association on the emotional health of adolescent females. Further investigations are needed to provide potential biological mechanisms for this relationship.     

Comparison of equations for estimating resting metabolic rate in healthy subjects over 70 years of age

INTRODUCTION: There is a lack of validation studies of formulas for estimating resting metabolic rate (RMR) in healthy subjects over 70 years of age. Indirect calorimetry allows measuring RMR (RMRm), but is time consuming and costly and therefore formula are generally used to estimate RMR (RMRe). We assessed the degree of agreement between RMRm and RMRe predicted by five popular equations: Harris-Benedict (HB), Mifflin-St Jeor (MJ), Owen (OW), World Health Organization (WHO/FAO/UNU) and Lührmann (LM) in a cohort of elderly subjects. METHODS: In 119 healthy subjects, aged 70-98 yr, RMRm was obtained by indirect calorimetry and RMRe by the HB, MJ, OW, WHO/FAO/UNU and LM equations. Means were compared by paired t-test. The Bland and Altman method was used to assess agreement between RMRm and RMRe. Accuracy was defined as the % of individuals whose RMRe was within +/-10% of RMRm. RESULTS: The HB showed the lowest mean RMRe-RMRm difference (-40.9 kcal/day), followed by LM (+44.8 kcal/day) and WHO/FAO/UNU (+53 kcal/day). The HB performed the best of the five equations, having 72.4% of the cases within+/-10% of RMRm. In 18.7% of male subjects and 20% of female subjects HB underestimated the measured values. CONCLUSIONS: Large discrepancies exist between RMRm and RMRe in subjects above 70 years of age. HB performs best, but still tends to underestimate in both sexes. In order to develop more accurate equations to estimate RMR in elderly subjects it would be worthwhile to examine whether additionally specific markers of body composition should be taken into consideration.     

Prevalence and factors affecting underweight,overweight and obesity using Asian and World Health Organization cutoffs among adults in Nepal: Analysis of the Demographic and Health Survey 2016

ObjectiveThere is a little published data on prevalence and determinants of underweight, overweight and obesity among adults in Nepal. This study analysed the cross-sectional Nepal Demographic and Health Survey (NDHS) 2016 to obtain these using the World Health Organization (WHO) and Asian-specific cutoffs of body mass index (BMI).MethodsThe 2016 NDHS used a multistage cluster-sampling design to obtain data on major health indicators in Nepal. The BMI cutoffs for underweight was <18.5 kg/m². The BMI cutoffs for overweight/obesity as per the Asian and WHO classifications were ≥23, and ≥25 kg/m², respectively. After reporting the prevalence according to sex and background characteristics, multilevel logistic regression was conducted to estimate odds ratios.SubjectsThis analysis included 12,652 adults (5283 males and 7369 females) with a median age of 40 years (interquartile range [IQR]: 28–54).ResultsThe overall median BMI was 21.5 kg/m² (IQR:19.3–24.3). The overall prevalence of underweight was 16.7% (15.1% among males and 17.1% among females). The Asian-specific BMI cutoffs found the prevalence of overweight and obesity as 26.4% (27.4% among males and 25.6% among females) and 11.0% (7.7% among males and 13.3% among females), respectively. The WHO-recommended BMI cutoffs found 18.2% people overweight (16.7% among males and 19.3% among females) and 4.3% (2.5% among males and 5.6% among females) people obese. The prevalence and odds of extreme body weight categories (i.e., underweight, overweight and obesity) varied according to age, sex, education level, household wealth status, place, ecological zone and provinces of residence as per both recommended cutoffs. Overall, higher education level and wealth status were positively associated with overweight/obesity and inversely associated with underweight as per both cutoffs.ConclusionA large proportion Nepalese adults have either underweight, overweight or obesity, and could be at a greater risk of mortality and morbidity due to these extreme body weight categories. It is essential to address the factors or characteristics that are associated with the higher prevalence and likelihood of these extreme body weight categories to reduce the overall burden of underweight and overweight/obesity in Nepal.     

双能X线吸收法和生物电阻抗法测量成年超重/肥胖人群体脂率一致性分析

目的 分析生物电阻抗(MF-BIA)法和双能X线吸收(DXA)法测量成年超重/肥胖人群体脂率的一致性,并建立MF-BIA法校正预测模型。方法 招募志愿成年超重/肥胖者1 323人,分别采用MF-BIA法和DXA法测定受试者的体脂率,分析两方法测量结果的一致性,并建立MF-BIA法校正预测模型。结果 成年男女性超重/肥胖的MF-BIA法与DXA法测量体脂率差值分别为-6.5%、-4.3%和-2.5%、0.5%,差异均有统计学意义(均P<0.01),其体脂率的组内相关系数分别为0.746、0.807和0.628、0.674,差异均有统计学意义(均P<0.01)。MF-BIA法校正预测模型包括超重男性人群:体脂率(DXA法)=13.425+0.719×体脂率(MF-BIA法);肥胖男性人群:体脂率(DXA法)=12.572+0.741×体脂率(MF-BIA法);超重女性人群:体脂率(DXA法)=9.785+0.802×体脂率(MF-BIA法);肥胖女性人群:体脂率(DXA法)=20.348+0.532×体脂率(MF-BIA法)。结论 MF-BIA法和DXA法测量我国成年超重/肥胖人群体脂率一致性较差,使用MF-BIA法测量体脂率需进行校正。