Comparison of predictive equations and measured resting energy expenditure among obese youth attending a pediatric healthy weight clinic: one size does not fit all

Resting Energy Expenditure Equations Have Lower Accuracy for Adolescents with Overweight/Obesity Versus Healthy-Weight Adolescents

Objective: The objectives of the study were (1) to assess whether resting energy expenditure (REE) equations have comparable validity for adolescents with overweight/obesity vs. adolescents with healthy weight and (2) to examine determinants of measured REE in adolescents with overweight/obesity vs. adolescents with healthy weight. Methods: Ten equations were used to predict REE for 109 adolescents (70% males; 36.7% with overweight/obesity); 95% equivalence testing was used to assess how well each equation agreed with the criterion measure of indirect calorimetry. Linear regression models were fitted to examine how much REE variance was accounted for by age, sex, race, fat-free mass (FFM), and fat mass. Results: For adolescents with healthy weight, all ten equations were significantly equivalent to the criterion measure within ±8.4% (p < 0.05), whereas for participants with overweight/obesity, only three equations were equivalent within the same range (p < 0.05). Controlling for age, sex, race, fat mass, and FFM accounted for 74% of REE variance. FFM explained the greatest amount (26%) of variance in REE, while weight status itself explained an additional 22%. Conclusions: Prediction equations tend to be more accurate for adolescents with healthy weight than adolescents with overweight/obesity unless the original sample specifically included participants with overweight/obesity. Determinants of REE are similar regardless of weight status.

Prediction of resting energy expenditure for adolescents with severe obesity: A multi-centre analysis

BACKGROUND AND OBJECTIVES

Resting energy expenditure (REE) assessments can help inform clinical treatment decisions in adolescents with elevated body mass index (BMI), but current equations are suboptimal for severe obesity. We developed a predictive REE equation for youth with severe obesity and obesity-related comorbidities and compared results to previously published predictive equations.

METHODS

Data from indirect calorimetry, clinical measures, and body composition per Dual x-ray absorptiometry (DXA) were collected from five sites. Data were randomly divided into development (N = 438) and validation (N = 118) cohorts. A predictive equation was developed using Elastic Net regression, using sex, race, ethnicity, weight, height, BMI percent of the 95th%ile (BMIp95), waist circumference, hip circumference, waist/hip ratio, age, Tanner stage, fat and fat-free mass. This equation was verified in the validation cohort and compared with 11 prior equations.

RESULTS

Data from the total cohort (n = 556, age 15 ± 1.7 years, 77% female, BMIp95 3.3 ± 0.94) were utilized. The best fit equation was REE = -2048 + 18.17 × (Height in cm) - 2.57 × (Weight in kg) + 7.88 × (BMIp95) + 189 × (1 = male, 0 = female), R2 = 0.466, and mean bias of 23 kcal/day.

CONCLUSION

This new equation provides an updated REE prediction that accounts for severe obesity and metabolic complications frequently observed in contemporary youth.

Energy Expenditure in Free-Living Physical Activities and Sedentary Behaviors in Mexican Schoolchildren

Purpose: Metabolic equivalents for youth (METy) are derived using the estimated basal metabolic rate (BMR). However, is unknown whether this METy can be different when measured resting energy expenditure (REE) is used. The purposes of this article are to: (a) To determine whether there is equivalence between METy values calculated using measured REE (METy-mea) and METy using predicted BMR (METy-est). (b) To determine whether METy values of different activities are dependent on age, sex, and body composition. Methods: A cross-sectional study with a sample of 122 Mexican children (5-11 years old) was conducted. With indirect calorimetry, energy expenditure was measured at rest and during 16 sedentary- to vigorous-intensity physical activities. METy were obtained in two forms: METy-mea and METy-est. Equivalence testing was used to compare METy-mea and METy-est. To examine the individual-level agreement, Bland-Altman plots were graphed, and intra-class correlation coefficients (ICC) were calculated. Linear regression models were estimated having METy as the outcome. Results: For 15 activities, there was equivalence between METy-mea and METy-est (t > 2.05, p < 0.050). However, at the individual-level, for 7 activities the agreement between METy-eREE and METy-mREE was inadequate (ICC<0.75). In the Bland-Altman plots was evident that in 8 activities METy-est was biased compared to METy-mea, METy-est had more correlations with body mass index and body fat. Conclusions: In conclusion, at group-level, for most activities METy-mea and METy-est were equivalent. However, at the individual level, METy-est of many activities is not a valid estimate of METy-mea. METy-est of many physical activities were dependent on adiposity, which could be an artifact introduced when BMR is predicted.

Digital Tools to Support Family-Based Weight Management for Children: Mixed Methods Pilot and Feasibility Study

BACKGROUND

Family-based behavioral therapy is an efficacious approach to deliver weight management counseling to children and their parents. However, most families do not have access to in-person, evidence-based treatment. We previously developed and tested DRIVE (Developing Relationships that Include Values of Eating and Exercise), a home-based parent training program to maintain body weight among children at risk for obesity, with the intent to eventually disseminate it nationally alongside SafeCare, a parent support program that focuses on parent-child interactions. Currently the DRIVE program has only been tested independently of SafeCare. This study created the "mHealth DRIVE" program by further adapting DRIVE to incorporate digital and mobile health tools, including remotely delivered sessions, a wireless scale that enabled a child-tailored weight graph, and a pedometer. Telehealth delivery via mHealth platforms and other digital tools can improve program cost-effectiveness, deliver long-term care, and directly support both families and care providers.

OBJECTIVE

The objective of this study was to examine preliminary acceptability and effectiveness of the mHealth DRIVE program among children and parents who received it and among SafeCare providers who potentially could deliver it.

METHODS

Study 1 was a 13-week pilot study of a remotely delivered mHealth family-based weight management program. Satisfaction surveys were administered, and height and weight were measured pre- and post-study. Study 2 was a feasibility/acceptability survey administered to SafeCare providers.

RESULTS

Parental and child satisfaction (mean of 4.9/6.0 and 3.8/5.0, respectively) were high, and children's (N=10) BMI z-scores significantly decreased (mean -0.14, SD 0.17; P=.025). Over 90% of SafeCare providers (N=74) indicated that SafeCare families would benefit from learning how to eat healthily and be more active, and 80% of providers reported that they and the families would benefit from digital tools to support child weight management.

CONCLUSIONS

Pediatric mHealth weight management interventions show promise for effectiveness and acceptability by families and providers.

TRIAL REGISTRATION

Clinicaltrials.gov NCT03297541, https://clinicaltrials.gov/ct2/show/NCT03297541.

Resting Energy Expenditure Prediction Equations in the Pediatric Population: A Systematic Review

Background and Aims: The determination of energy requirements is necessary to promote adequate growth and nutritional status in pediatric populations. Currently, several predictive equations have been designed and modified to estimate energy expenditure at rest. Our objectives were (1) to identify the equations designed for energy expenditure prediction and (2) to identify the anthropometric and demographic variables used in the design of the equations for pediatric patients who are healthy and have illness. Methods: A systematic search in the Medline/PubMed, EMBASE and LILACS databases for observational studies published up to January 2021 that reported the design of predictive equations to estimate basal or resting energy expenditure in pediatric populations was carried out. Studies were excluded if the study population included athletes, adult patients, or any patients taking medications that altered energy expenditure. Risk of bias was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: Of the 769 studies identified in the search, 39 met the inclusion criteria and were analyzed. Predictive equations were established for three pediatric populations: those who were healthy (n = 8), those who had overweight or obesity (n = 17), and those with a specific clinical situation (n = 14). In the healthy pediatric population, the FAO/WHO and Schofield equations had the highest R ² values, while in the population with obesity, the Molnár and Dietz equations had the highest R ² values for both boys and girls. Conclusions: Many different predictive equations for energy expenditure in pediatric patients have been published. This review is a compendium of most of these equations; this information will enable clinicians to critically evaluate their use in clinical practice. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=226270, PROSPERO [CRD42021226270].

Resting Energy Expenditure of Physically Active Boys in Southeastern Poland-The Accuracy and Validity of Predictive Equations

Optimization of energy intake in the diet of young athletes is of primary importance. In addition to the energy expenditure associated with their body development, the demand resulting from intensive physical activity also increases. The aim of this study was to compare the accuracy of formulas commonly used for resting energy expenditure (REE) calculations with values obtained from measurements using indirect calorimetry among male children and adolescents practicing football. The study was conducted among 184 boys aged 9 to 17 using a calorimeter and a device for assessing body composition by means of electrical bioimpedance using a segment analyzer. The mean error ranged from −477 kcal/d by the Maffeis formula to −182 kcal/d for the Institute of Medicine of the National Academies (IMNA) formula. A statistically significant difference was found for all formulas in the calculated value in relation to the measured REE value (p < 0.0001). Most “ready-to-use” formulas underestimate REE, which can be a risk in determining the total energy demand in a group that requires more calories, especially when due to intensive growth and development and the expenditure associated with regular training and increased physical activity.

Accuracy of Predictive Resting-Metabolic-Rate Equations in Chinese Mainland Adults

Accurate measurement of the resting metabolic rate (RMR) is necessary when we make energy requirements and nutrition suggestions in clinical. However, indirect calorimetry is not always available. The objectives of this study were to make a comparison between RMR measured by indirect calorimetry and RMR predicted by different kinds of equations, and to develop new predictive equations for Chinese mainland adults. In this study, 315 Chinese mainland adults from different provinces all over China were recruited. Subjects underwent half a day of testing, which consisted of anthropometric assessment and RMR measurement. Measured and predicted RMR were compared; new optimal equations for Chinese mainland adults were developed and tested by splitting the subjects into a development and validation group. The measured RMR was in the range of 831–2776 kcal/day (mean 1651 ± 339 kcal/day). Our findings indicated that, except for the Harris–Benedict and Schofield equations, three Chinese equations and two fat-free mass (FFM) modeling equations all significantly underestimated RMR compared to the measured value (all p < 0.01). There were no significant differences between predicted and measured RMR using the new equations for females and males. Of the pre-existing equations, Schofield’s is the most suitable for Chinese mainland adults. However, the two new equations developed in this study seem to be more effective for predicting the RMR of Chinese mainland adults, and need to be validated by a larger independent sample with different physiological and anthropometric characteristics.

The validity of resting energy expenditure predictive equations in adults with central obesity: A sub-sample of the RaNCD cohort study

BACKGROUND

There are different equations for estimating Resting Energy Expenditure (REE). However, these equations were mainly developed based on populations of western countries.

AIM

The present study was conducted to determine the validity of REE predictive equations in adults with central obesity.

METHODS

This study was conducted with 129 adults with central obesity aged 35-65 years, a sub-sample from a large cohort study (Western Iran), Kurdish population. REE was measured by indirect calorimetry (IC) and REE predictive equations. Data were analysed using Pearson correlation, paired t-test, concordance correlation coefficient (CCC), mean squared deviation (MSD), level of agreement (LOA) and Bland-Altman plot.

RESULTS

All REE predictive equations had low CCC and high LOA. Although there was no statistically significant difference in the REE measured with IC and the REE predicted with the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), FAO/WHO/UNU (Height), Muller and revised Harris-Benedict equations (P = 0.874, 0.113, 0.619, 0.143 and P = 0.121), other equations had statistically significant differences with IC (P<0.001). In addition, the highest correlation was found between the IC (r = 0.682). The least difference was related to the FAO/WHO/UNU equation, with an agreement limit of -507.96 to 500.79 Kcal/day, with a 95% confidence interval.

CONCLUSIONS

The results of this study showed that the FAO/WHO/UNU, Muller, revised Harris-Benedict equations and Mifflin St Jeor equations are relatively acceptable for estimating REE. However, these prediction equations are not good at predicting REE; more precise equations are needed to apply for different ethnic groups.

Evaluating the predictive factors of resting energy expenditure and validating predictive equations for Chinese obese children

BACKGROUND

To study the predictive factors of resting energy expenditure (REE) and evaluate the accuracy of predicted equations with indirect calorimeter (IC) in Chinese school-age children, particularly for the obese population.

METHODS

Recruited children were from the department of child healthcare in Nanjing children's hospital during July 2014-September 2015. Anthropometric parameters and body composition were measured by bioelectrical impedance. Measured REE was assessed by IC. Predicted REE was estimated using ten published equations.

RESULTS

248 children aged 7-13 years were recruited, including 148 obese [body mass index standard deviation score (BMISDS) = 2.48 ± 0.91] and 100 non-obese (BMISDS = - 0.96 ± 1.08). The unit mass of REE (REE/kg) in obese group (29.06 ± 5.74) was lower than that in non-obese group (37.51 ± 6.56). The stepwise regression showed that age, BMISDS and fat-free mass (FFM) had a major impact on REE/kg as the regression equation: Y = 54.41 - 1.36 × X₁ - 2.25 × X₂ - 0.16 × X₃ (Y REE/kg, X₁ age, X₂ BMISDS, X₃ FFM; R = 0.633, R² = 0.401, P < 0.01). The accuracy of predicted REE in obese subjects was 62.16% by the new predictive equations.

CONCLUSIONS

The REE/kg in obese children was lower and closely correlated with age, BMISDS and FFM. It is necessary to validate the new predictive equation in a larger sample to estimate energy requirements, particularly for children with obesity.

Pilot Study Measuring the Novel Satiety Hormone, Pro-Uroguanylin, in Adolescents With and Without Obesity

OBJECTIVE

Disruption of satiety signaling may lead to increased caloric intake and obesity. Uroguanylin, the intestinal hormone, travels as a precursor to the central nervous system where it activates guanylyl cyclase C and stimulates pro-satiety neurons. Rodent studies have demonstrated that guanylyl cyclase C-knockout mice overeat and have increased weight gain versus wild-type mice and hyper-caloric obesity diminishes uroguanylin expression. We measured circulating plasma pro-uroguanylin, along with other gastrointestinal peptides and inflammatory markers, in human adolescents with and without obesity, as a pilot study. We hypothesized that adolescents with obesity would have less circulating pro-uroguanylin than adolescents without obesity have.

METHODS

We recruited 24 adolescents (age 14-17 years) with and without obesity (body mass index >95% or body mass index <95%) and measured plasma pro-uroguanylin at fasting and successive time points after a meal. We measured 3 other satiety hormones and 2 inflammatory markers to characterize overall satiety signaling and highlight any link between uroguanylin and inflammation.

RESULTS

Female adolescents with obesity had lower circulating pro-uroguanylin levels than female adolescents without obesity; we observed no difference in males. Other measured gastrointestinal peptides varied in their differences between cohorts. Inflammatory markers were higher in female participants with obesity.

CONCLUSIONS

In adolescents with and without obesity, we can measure circulating pro-uroguanylin levels. In female adolescents without obesity, levels are particularly higher. Pro-uroguanylin secretion patterns differ from other circulating gastrointestinal peptides. In female adolescents with obesity, inflammation correlates with decreased pro-uroguanylin levels.

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.

Association between circulating CCL2 levels and modifiable behaviors in overweight and obese adolescents: a cross-sectional pilot study

BACKGROUND

We evaluated the relationship between an early inflammatory biomarker, chemokine (C-C motif) ligand 2 (CCL2), and other clinical biomarkers and lifestyle behaviors, in overweight/obese adolescents at high risk of developing cardiometabolic derangements.

METHODS

We collected anthropometric measurements, clinical biomarkers, and three 24-h dietary recalls from 21 vocational high school students (91% male), 14-19 years, with body mass index (BMI) ≥25 kg/m2. Pearson's or Spearman's correlation coefficients were used to examine relationships.

RESULTS

Mean BMI was 33.2 kg/m2 (range 25.7-45.6) and 38% were prediabetic by fasting glucose. Mean CCL2 was 512.9 pg/mL (range 220-917) and positively correlated with triglycerides (r=0.45; p=0.04) and TNF-α (r=0.57; p=0.007) and marginally negatively correlated with fruit/vegetable intake (r=-0.42, p=0.06) and omega-3 fatty acids (r=-0.41, p=0.07).

CONCLUSIONS

CCL2 was positively associated with pro-inflammatory biomarkers and negatively associated with some anti-inflammatory dietary factors.

Do Overweight Adolescents Adhere to Dietary Intervention Messages? Twelve-Month Detailed Dietary Outcomes from Curtin University's Activity, Food and Attitudes Program

Dietary components of adolescent obesity interventions are rarely evaluated with comprehensive reporting of dietary change. The objective was to assess dietary change in overweight adolescents, including adherence to dietary intervention. The dietary intervention was part of a multi-component intervention (CAFAP) targeting the physical activity, sedentary and healthy eating behaviors of overweight adolescents (n = 69). CAFAP was a staggered entry, within-subject, waitlist controlled clinical trial with 12 months of follow up. Diet was assessed using three-day food records and a brief eating behavior questionnaire. Changes in dietary outcomes were assessed using linear mixed models, adjusted for underreporting. Food record data suggested reduced adherence to dietary intervention messages over time following the intervention, despite conflicting information from the brief eating behavior questionnaire. During the intervention, energy intake was stable but favorable nutrient changes occurred. During the 12 month maintenance period; self-reported eating behaviors improved, energy intake remained stable but dietary fat and saturated fat intake gradually returned to baseline levels. Discrepancies between outcomes from brief dietary assessment methods and three-day food records show differences between perceived and actual intake, highlighting the need for detailed dietary reporting. Further, adherence to dietary intervention principles reduces over time, indicating a need for better maintenance support.

Accuracy of predictive equations for estimating resting energy expenditure in obese adolescents

OBJECTIVE

To compare resting energy expenditure (REE) measured by indirect calorimetry with REE predicted using different equations in obese adolescents.

STUDY DESIGN

We recruited 264 obese patients (body mass index ranging from 30.0-70.0 kg/m(2)) between 14 and 18 years of age. Data were obtained comparing measured and predicted REE derived from published equations for normal weight and obese adolescents. The average differences between measured and predicted REE, as well as the accuracy at ± 10% level, were evaluated.

RESULTS

Evaluating the mean REE in 109 males (1938 ± 271 kcal/d) and 155 females (2569 ± 459 kcal/d), we found that the Lazzer equation in males had the smallest difference between measured and predicted REE; in females the Henry-1, Food and Agriculture Organization/World Health Organization/United Nations University, Schmelze, and Lazzer equations were the most accurate. The prediction accuracy was considered adequate within ± 10%.

CONCLUSIONS

REE predictive equations developed in obese patients and for specific age groups are more suitable than those for the general population. Inaccuracy of predicted REE could affect dietary prescription appropriateness and, consequently, dietary compliance in this age group.

Nutrition Considerations in Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a serious degenerative muscular disease affecting males. Diagnosis usually occurs in childhood and is confirmed through genetic testing and/or muscle biopsy. Accompanying the disease are several nutrition-related concerns: growth, body composition, energy and protein requirements, constipation, swallowing difficulties, bone health, and complementary medicine. This review article addresses the nutrition aspects of DMD.

Accurate estimation of energy requirements of young patients

The provision of optimal nutritional care is based on accurate estimations of patients' resting energy expenditure. The latter can be calculated with the use of predictive equations or measured with indirect calorimetry (IC). Owing to their ease of use, mathematical equations have largely replaced IC in clinical practice. This article examines the limitations and predictive inaccuracy of commonly used equations in pediatrics, which may contribute to the provision of poor nutritional care and directly affect patient outcomes. In addition, the role of IC is discussed and the physiology of nutrient metabolism, in terms of energy expenditure, is reviewed.

The impact of Curtin University's activity, food and attitudes program on physical activity, sedentary time and fruit, vegetable and junk food consumption among overweight and obese adolescents: a waitlist controlled trial

Background

To determine the effects of participation in Curtin University's Activity, Food and Attitudes Program (CAFAP), a community-based, family-centered behavioural intervention, on the physical activity, sedentary time, and healthy eating behaviours of overweight and obese adolescents.

Methods

In this waitlist controlled clinical trial in Western Australia, adolescents (n = 69, 71% female, mean age 14.1 (SD 1.6) years) and parents completed an 8-week intervention followed by 12 months of telephone and text message support. Assessments were completed at baseline, before beginning the intervention, immediately following the intervention, and at 3-, 6-, and 12- months follow-up. The primary outcomes were physical activity and sedentary time assessed by accelerometers and servings of fruit, vegetables and junk food assessed by 3-day food records.

Results

During the intensive 8-week intervention sedentary time decreased by −5.1 min/day/month (95% CI: −11.0, 0.8) which was significantly greater than the rate of change during the waitlist period (p = .014). Moderate physical activity increased by 1.8 min/day/month (95% CI: −0.04, 3.6) during the intervention period, which was significantly greater than the rate of change during the waitlist period (p = .041). Fruit consumption increased during the intervention period (monthly incidence rate ratio (IRR) 1.3, 95% CI: 1.10, 1.56) and junk food consumption decreased (monthly IRR 0.8, 95% CI: 0.74, 0.94) and these changes were different to those seen during the waitlist period (p = .004 and p = .020 respectively).

Conclusions

Participating in CAFAP appeared to have a positive influence on the physical activity, sedentary and healthy eating behaviours of overweight and obese adolescents and many of these changes were maintained for one year following the intensive intervention.

Trial Registration

Australia and New Zealand Clinical Trials Registry ACTRN12611001187932