New specific equation to estimate resting energy expenditure in severely obese patients

Adequacy of basal metabolic rate prediction equations in individuals with severe obesity: A systematic review and meta-analysis

The determination of energy requirements in clinical practice is based on basal metabolic rate (BMR), frequently predicted by equations that may not be suitable for individuals with severe obesity. This systematic review and meta-analysis examined the accuracy and precision of BMR prediction equations in adults with severe obesity. Four databases were searched in March 2021 and updated in May 2023. Eligible studies compared BMR prediction equations with BMR measured by indirect calorimetry. Forty studies (age: 28-55 years, BMI: 40.0-62.4 kg/m2) were included, most of them with a high risk of bias. Studies reporting bias (difference between estimated and measured BMR) were included in the meta-analysis (n = 20). Six equations were meta-analyzed: Harris & Benedict (1919); WHO (weight) (1985); Owen (1986); Mifflin (1990); Bernstein (1983); and Cunningham (1980). The most accurate and precise equations in the overall analysis were WHO (-12.44 kcal/d; 95%CI: -81.4; 56.5 kcal/d) and Harris & Benedict (-18.9 kcal/d; 95%CI -73.2; 35.2 kcal/d). All the other equations tended to underestimate BMR. Harris & Benedict and WHO were the equations with higher accuracy and precision in predicting BMR in individuals with severe obesity. Additional analyses suggested that equations may perform differently according to obesity BMI ranges, which warrants further investigation.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

BACKGROUND

Resting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.

OBJECTIVE

The aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).

DATA SOURCES

A systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.

ELIGIBILITY CRITERIA

Randomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.

ANALYSIS

A narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).

RESULTS

Twenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I2 range: 80-93%) except the Ten-Haaf (p = 0.48, I2 = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.

CONCLUSION

Many RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.

PROSPERO REGISTRATION

CRD42020218212.

Estimates of Resting Energy Expenditure and Total Energy Expenditure Using Predictive Equations for Individuals After Bariatric Surgery: a Systematic Review with Meta-analysis

PURPOSE

Patients after metabolic bariatric surgery (MBS) require attention to maintain energy balance and avoid weight regain. Predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) are needed since gold standard methods like calorimetry and doubly labeled water are rarely available in routine clinical practice. This study aimed to determine which predictive equation for REE and TEE has the lowest bias in subjects after MBS.

METHODS

MEDLINE, Embase, Web of Science, and CENTRAL searches were performed. Meta-analyses were performed with the data calculated by the predictive equations and measured by the gold standard methods for those equations that had at least two studies with these data. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist.

RESULTS

Seven studies were included. The present study found that the Mifflin St. Jeor (1990) equation had the lowest bias (mean difference =  - 39.71 kcal [95%CI =  - 128.97; 49.55]) for calculating REE in post-BS individuals. The Harris-Benedict (1919) equation also yielded satisfactory results (mean difference =  - 54.60 kcal [95%CI =  - 87.92; - 21.28]).

CONCLUSION

The predictive equation of Mifflin St. Jeor (1990) was the one that showed the lowest bias for calculating the REE of patients following MBS.

Multimodal prehabilitation in patients with non-small cell lung cancer undergoing anatomical resection: protocol of a non-randomised feasibility study

BACKGROUND

The preoperative period can be used to enhance a patient's functional capacity with multimodal prehabilitation and consequently improve and fasten postoperative recovery. Especially, non-small cell lung cancer (NSCLC) surgical patients may benefit from this intervention, since the affected and resected organ is an essential part of the cardiorespiratory fitness. Drafting a prehabilitation programme is challenging, since many disciplines are involved, and time between diagnosis of NSCLC and surgery is limited. We designed a multimodal prehabilitation programme prior to NSCLC surgery and aimed to conduct a study to assess feasibility and indicative evidence of efficacy of this programme. Publication of this protocol may help other healthcare facilities to implement such a programme.

METHODS

The multimodal prehabilitation programme consists of an exercise programme, nutritional support, psychological support, smoking cessation, patient empowerment and respiratory optimisation. In two Dutch teaching hospitals, 40 adult patients with proven or suspected NSCLC will be included. In a non-randomised fashion, 20 patients follow the multimodal prehabilitation programme, and 20 will be assessed in the control group, according to patient preference. Assessments will take place at four time points: baseline, the week before surgery, 6 weeks postoperatively and 3 months postoperatively. Feasibility and indicative evidence of efficacy of the prehabilitation programme will be assessed as primary outcomes.

DISCUSSION

Since the time between diagnosis of NSCLC and surgery is limited, it is a challenge to implement a prehabilitation programme. This study will assess whether this is feasible, and evidence of efficacy can be found. The non-randomised fashion of the study might result in a selection and confounding bias. However, the control group may help putting the results of the prehabilitation group in perspective. By publishing this protocol, we aim to facilitate others to evaluate and implement a multimodal prehabilitation programme for surgical NSCLC patients.

TRIAL REGISTRATION

The current study is registered as NL8080 in the Netherlands Trial Register on the 10th of October 2019, https://www.trialregister.nl/trial/8080 . Secondary identifiers: CCMO (Central Committee on Research Involving Human Subjects) number NL70578.015.19, reference number of the Medical Ethical Review Committee of Máxima MC W19.045.

Positive Effects of Extra-Virgin Olive Oil Supplementation and DietBra on Inflammation and Glycemic Profiles in Adults With Type 2 Diabetes and Class II/III Obesity: A Randomized Clinical Trial

Background

Evidence on the effects of dietary interventions on inflammatory markers in individuals with obesity and type 2 diabetes mellitus (T2DM) is scarce. Our study evaluated the effects of extra-virgin olive oil alone and in combination with a traditional Brazilian diet on inflammatory markers and glycemic profiles in adults with both T2DM and class II/III obesity.

Methods

Adults aged 18-64 years with T2DM and class II/III obesity were randomized into two intervention groups: 1) extra-virgin olive oil only and 2) extra-virgin olive oil + a traditional Brazilian diet (OliveOil+DietBra). Data on sociodemographic characteristics, lifestyle, anthropometry, biochemical markers and inflammatory markers were collected. The primary outcomes were glycemic parameters and inflammatory markers. The body mass index (BMI) and weight were the secondary outcomes.

Results

Forty individuals with T2DM and class II/III obesity were enrolled, and 34 (85%) completed the intervention course. The intake of olive oil was 37.88 ± 12.50 mL/day in the olive oil group and 37.71 ± 12.23 mL/day in the OliveOil+DietBra group, with no significant difference between groups (p = 0.484). Compared to the olive oil only group, the OliveOil+DietBra group had significantly lower levels of fasting insulin (p = 0.047) at the end of the intervention, whereas the other glycemic parameters were not altered. In the OliveOil+DietBra group, serum levels of inflammatory cytokines, IL-1α (p = 0.006) and adiponectin (p = 0.049) were lower and those of TNFα were higher (p = 0.037). There was a significant reduction in BMI and weight compared to the baseline values in the OliveOil+DietBra group (p = 0.015).

Conclusions

The intervention with OliveOil+DietBra effectively decreased the levels of fasting insulin, IL-1α and adiponectin, suggesting its beneficial role in improving the inflammatory profiles and fasting insulin levels in adults with class II/III obesity and T2DM.

Clinical Trial Registration

ClinicalTrials.gov, identifier: NCT02463435.

The Effectiveness of Extra Virgin Olive Oil and the Traditional Brazilian Diet in Reducing the Inflammatory Profile of Individuals with Severe Obesity: A Randomized Clinical Trial

We analyzed the effectiveness of two nutritional interventions alone and together, EVOO and the DieTBra, on the inflammatory profile of severely obese individuals. This study was an RCT with 149 individuals aged from 18 to 65 years, with a body mass index ≥ 35 kg/m2, randomized into three intervention groups: (1) 52 mL/day of EVOO (n = 50); (2) DieTBra (n = 49); and (3) DieTBra plus 52 mL/day of EVOO (DieTBra + EVOO, n = 50). The primary outcomes we measured were the-neutrophil-to-lymphocyte ratio (NLR) and the secondary outcomes we measured were the lymphocyte-to-monocyte ratio (LMR); leukocytes; and C reactive protein (CRP). After 12 weeks of intervention, DieTBra + EVOO significantly reduced the total leucocytes (p = 0.037) and LMR (p = 0.008). No statistically significant differences were found for the NLR in neither the intra-group and inter-group analyses, although a slight reduction was found in the DieTBra group (-0.22 ± 1.87). We observed reductions in the total leukocytes and LMR in the three groups, though without statistical difference between groups. In conclusion, nutritional intervention with DietBra + EVOO promotes a significant reduction in inflammatory biomarkers, namely leukocytes and LMR. CRP was reduced in EVOO and DieTBra groups and NLR reduced in the DieTBra group. This study was registered at ClinicalTrials.gov under NCT02463435.

Reduction in Pain and Pain Intensity with Nonpharmacological Treatment in Severely Obese Patients: A Randomized Clinical Trial

The objective of this study was to analyze the effectiveness of two nonpharmacological interventions-traditional Brazilian diet (DieTBra), and extra-virgin olive oil (EVOO)-in terms of the reduction in pain and pain intensity in individuals with severe obesity. We conducted a 12-week parallel randomized clinical trial with 149 individuals (body mass index (BMI) ≥ 35 kg/m2) who were randomized into three groups: supplementation with EVOO (n = 50), DieTBra (n = 49), and EVOO + DieTBra (n = 50). Of the total, 133 individuals with a mean BMI of 46.04 kg/m2 completed the study. By the end of the follow-up, there was a reduction in severe pain in the EVOO + DieTBra group (p = 0.003). There was a significant reduction in severe pain in the EVOO + DieTBra group (-22.7%); pain in the wrist and hand (-14.1%), upper back (-26.9%), and knees (-18.4%) in the DieTBra group; and reduction in hip pain (-11.1%) with EVOO consumption. We conclude that EVOO and DieTBra, either alone or in combination, are effective interventions to reduce pain intensity and pain in different regions in individuals with severe obesity, and have great potential for clinical application.

Accuracy of resting metabolic rate prediction equations among healthy adults in Trinidad and Tobago

BACKGROUND

Over 50% of adults in Latin America and the Caribbean have a body mass index (BMI) ≥ 25 suggesting excess energy intakes relative to energy expenditure. Accurate estimation of resting metabolic rate (RMR), the largest component of total energy requirements, is crucial to strategies aimed at reducing the prevalence and incidence of overweight and obesity.

AIM

We evaluated the accuracies of established and locally developed RMR prediction equations (RMR_P) among adults.

METHODS

Four hundred adult volunteers ages 20 to 65 years had RMR measured (RMR_M) with a MedGem® indirect calorimeter according to recommended procedures. RMR_P were compared to RMR_M with values ± 10% of RMR_M deemed accurate. Anthropometry was measured using standard procedure. Linear regression with bootstrap analyses was used to develop local RMR_P equations based on anthropometric and demographic variables. The University of the West Indies Ethics Committee approved the study.

RESULTS

Males had higher mean absolute RMR (p < 0.001) but similar mean age-adjusted measured RMR per kg of body (20.9 vs. 21.5 kcals/day; p = 0.1) to females. The top performing established anthropometry-based RMR_P among participants by sex, physical activity (PA) level and BMI status subgroups were Mifflin-St Jeor, Owen, Korth, Harris-Benedict, and Livingston, while Johnstone, Cunningham, Müller (body composition (BC)), Katch and McArdle, Mifflin-St Jeor (BC) were the most accurate BC-based RMR_P. Locally developed RMR_P had accuracies comparable to their top-ranked established RMR_P counterparts.

CONCLUSIONS

Accuracies of established RMR_P depended on habitual PA level, BMI status, BC and sex. Furthermore, locally developed RMR_P provide useful alternatives to established RMR_P.

Accuracy of the MedGem® portable indirect calorimeter for measuring resting energy expenditure in adults with class II or III obesity

BACKGROUND AND AIMS

The accuracy of some portable indirect calorimeters in estimating resting energy expenditure (REE) - and hence energy recommendations - among pre-bariatric patients is unknown. Our objective was to assess the accuracy of the MedGem® among individuals with class II or III obesity awaiting bariatric surgery.

METHODS

Male and female adults who were awaiting bariatric surgery were recruited to this cross-sectional study. MedGem-derived oxygen consumption (O₂) and REE were compared to O₂ and REE from a metabolic cart. REE was also calculated using several common equations to assess the clinical utility of this portable tool. Body composition was assessed by dual energy X-Ray absorptiometry. Paired t-test and Bland-Altman analysis (expressed as bias [average difference] and limits of agreement [bias ± 2 standard deviations]) evaluated O₂ and REE accuracy.

RESULTS

Twenty-six individuals were included (34 ± 9 years old; n = 20, 76.9% female), with the majority (n = 15, 57.7%) having class II obesity. Neither O₂ (MedGem: 283 ± 63 vs. metabolic cart: 293 ± 64 ml/min, p = 0.114) nor REE (MedGem: 1963 ± 437 vs. metabolic cart: 2047 ± 440 kcal/day, p = 0.057) were different between devices. Body composition did not relate to bias. The MedGem had wide limits of agreement for REE (-504 to 336 kcal/day), which was similar to predictive equations.

CONCLUSIONS

Although REE from the MedGem was not different than REE from the metabolic cart, individual-level accuracy was poor and similar to predictive equations in patients with class II or III obesity. Nonetheless, efficacy of repeated assessments during weight loss warrant future investigation.

Predictive equations for estimating resting energy expenditure in women with overweight and obesity at three postpartum stages

The objective was to investigate which predictive equations provide the best estimates of resting energy expenditure (REE) in postpartum women with overweight and obesity. Lactating women with overweight or obesity underwent REE measurement by indirect calorimetry, and fat-free mass (FFM) was assessed by dual-energy X-ray absorptiometry at three postpartum stages. Predictive equations based on body weight and FFM were obtained from the literature. Performance of the predictive equations were analysed as the percentage of women whose REE was accurately predicted, defined as a predicted REE within ±10 % of measured REE. REE data were available for women at 10 weeks (n 71), 24 weeks (n 64) and 15 months (n 57) postpartum. Thirty-six predictive equations (twenty-five weight-based and eleven FFM-based) were validated. REE was accurately predicted in ≥80 % of women at all postpartum visits by six predictive equations (two weight-based and four FFM-based). The weight-based equation with the highest performance was that of Henry (weight, height, age 30-60 years) (Henry_WH30-60), with an overall mean of 83 % accurate predictions. The Henry_WH30-60 equation was highly suitable for predicting REE at all postpartum visits (irrespective of the women's actual age), and the performance was sustained across changes in weight and lactation status. No FFM-based equation was remarkably superior to Henry_WH30-60 for the total postpartum period.

Agreement between equations-estimated resting metabolic rate and indirect calorimetry-estimated resting metabolic rate in low-income obese women

Objectives Indirect calorimetry is established as a gold standard to determine the resting metabolic rate (RMR), however, its clinical use is limited, especially in low-income settings. Thus, the use of predictive equations appear as an alternative to estimate the RMR, but its precision is debatable, especially in obese individuals and in populations without specifically developed equations. To evaluate the agreement between the RMR estimated by equations and by indirect calorimetry in low-income obese women. Subjects and methods A cross-sectional study with adult and obese women, which estimated the RMR by indirect calorimetry and compared with 13 predictive equations using the concordance correlation coefficient, root mean square error (RMSE) and Bland-Altman methods. The maximum allowed differences were predefined as 10%. Results No equation presented its confidence intervals for the Bland-Altman limits of agreement inside the predefined acceptable range. The Harris-Benedict equation achieved better agreement (bias of 2.9% and RMSE of 274.3kcal) whereas the Henry-Rees equation achieved better precision (42.3% of the sample within the 10% maximum allowed difference). Conclusion None of the studied equations satisfactorily estimated the RMR estimated by indirect calorimetry. In the absence of specific equations for this population, the use of the Harris-Benedict and Henry-Rees equations could be considered.

Traditional Brazilian diet and extra virgin olive oil reduce symptoms of anxiety and depression in individuals with severe obesity: Randomized clinical trial

RATIONAL & OBJECTIVE

Although the relationship between obesity, anxiety and depression is known, the clinical benefits of nutrition therapy in individuals with obesity have been scarcely studied. This study assessed the effectiveness of a traditional Brazilian diet (DieTBra) and extra virgin olive oil (EVOO) on the symptoms of anxiety and depression in severe obese participants.

METHOD

This was a parallel randomized clinical trial in adults with severe obesity (Body Mass Index - BMI ≥ 35 kg/m²), DietBra Trial. Participants were randomized into three intervention groups in a ratio of 1: 1: 1 and followed up for 12 weeks. The three intervention groups were: 1) DieTBra, 2) 52 mL/day EVOO, and 3) DieTBra +52 mL/day EVOO. The symptoms of anxiety and depression were assessed by the Hospital Anxiety and Depression Scale. The outcome variables were: anxiety, depression and simultaneous anxiety and depression in the same individual.

RESULTS

149 severe obese individuals were randomized. The mean BMI was 46.3 ± 6.5 kg/m² with a mean age of 38.9 ± 8.7 years. After randomization, individuals on medication for depression and anxiety were excluded, totaling 129 at baseline, with 44:43:42 per group. At the end of 12 weeks 113 individuals completed the study. There was a significant reduction of symptoms for the three groups: EVOO, DieTBra and DieTBra + EVOO, respectively: anxiety 38.23% (p = 0.019), 45.83% (p=<0.014), 33.33% (p < 0.013) depression 55.17% (p=<0.001), 50.00% (p=<0.007), 34.61% (p=<0.035), and anxiety/depression 57.69% (p=<0.001), 66.67% (p=<0.014), 27.78% (p=<0.013).

CONCLUSIONS

Both DieTBra and olive oil interventions were effective in reducing symptoms of anxiety and depression in severe obese adults. These interventions can be integrated with clinical protocols for treatment of symptoms of anxiety and depression in severe obese individuals.

TRIAL REGISTRATION

Clinicaltrials.gov NCT02463435.

Type 2 Diabetes Mellitus in Class II and III Obesity: Prevalence, Associated Factors, and Correlation between Glycemic Parameters and Body Mass Index

Despite the worldwide growth of class II and III obesity, the factors associated with type 2 diabetes mellitus (T2DM) in these obese individuals are not widely understood. Moreover, no study has investigated these associations in South America. Our study aimed to investigate the prevalence of T2DM and its associated factors, with an emphasis on biochemical parameters and eating habits, in class II and III obese individuals. We also aimed to analyze the correlation between glycemic parameters and body mass index (BMI). Baseline data from a randomized clinical trial (DieTBra Trial) of 150 class II and III obese individuals (BMI > 35 kg/m2) was used. An accelerometer, Food Frequency Questionnaire, and bioimpedance analysis were used to assess physical activity levels, eating habits, and body composition, respectively. Blood was collected after 12 h of fasting. Hierarchical multivariate Poisson regression was performed, and prevalence ratios (PRs) were calculated. Correlations between glycemic parameters (fasting blood glucose, glycosylated hemoglobin, homeostasis model assessment of insulin resistance (HOMA-IR), and insulin) and BMI were also analyzed. The prevalence of T2DM was 40.0% (95% CI, 32.1-48.3), high fasting blood glucose level was 19.33% (95% CI, 13.3-26.6), and high glycosylated hemoglobin was 32.67% (95% CI, 25.2-40.8). Age ≥ 50 years (PR = 3.17, 95% CI, 1.26-7.98) was significantly associated with T2DM; there was a positive linear trend between age and T2DM (p = 0.011). Multivariate analysis showed an association with educational level (PR = 1.49, 1.07-2.09, p = 0.018), nonconsumption of whole grains daily (PR = 1.67, 1.00-2.80, p = 0.049), and high HOMA-IR (PR = 1.54, 1.08-2.18, p = 0.016). We found a high prevalence of T2DM and no significant correlations between BMI and glycemic parameters.

Effects of Extra Virgin Olive Oil (EVOO) and the Traditional Brazilian Diet on Sarcopenia in Severe Obesity: A Randomized Clinical Trial

BACKGROUND

Nutritional interventions may have positive effects on sarcopenia and body composition.

OBJECTIVE

to evaluate the effectiveness of extra virgin olive oil (EVOO) consumption and a healthy traditional Brazilian diet (DieTBra) on improving sarcopenia indicators and reducing total body fat in severe obesity.

METHODS

A randomized controlled trial registered at ClinicalTrials.gov (NCT02463435) conducted with 111 severely obese participants randomized into three treatment groups-(1) EVOO (52 mL/day), (2) DieTBra, (3) DieTBra + EVOO (52 mL/day)-for 12 weeks. Body composition was assessed by dual-energy X-ray absorptiometry and sarcopenia by walking speed and handgrip strength.

RESULTS

Significant reductions in total body fat (p = 0.041) and body weight (p = 0.003) were observed in the DieTBra group. In the DietBra + olive oil group there was also a significant reduction in body weight (0.001) compared to the olive oil-only group. ANCOVA analyses showed reductions in total body fat in the DieTBra (p = 0.016) and DieTBra + olive oil (p = 0.004) groups. Individuals in the DieTBra group had significant improvements in their walking speed (p = 0.042) and handgrip strength (p = 0.044).

CONCLUSIONS

DieTBra contributes to improvements in handgrip strength, walking speed, and total body fat in severely obese adults. The major study was registered at ClinicalTrials.gov (NCT02463435).

Traditional Brazilian Diet and Olive Oil Reduce Cardiometabolic Risk Factors in Severely Obese Individuals: A Randomized Trial

Cardioprotective effects associated with extra virgin olive oil (EVOO) have been studied within the Mediterranean diet. However, little is known about its consumption in the traditional Brazilian diet (DieTBra) or without any dietary prescription, particularly in severely obese individuals. This study aimed to assess the effectiveness of DieTBra and EVOO in cardiometabolic risk factor (CMRF) reduction in severely obese individuals. We conducted a parallel randomized clinical trial with 149 severely obese individuals (body mass index ≥ 35.0 kg/m2) aged 18-65 years, assigned to three groups: 52 mL/day of EVOO (n = 50), DieTBra (n = 49), and DieTBra + 52 mL/day of EVOO (n = 50). Participants were followed up for 12 weeks. Low-density lipoprotein cholesterol (LDL-c) was the primary endpoint and several cardiometabolic parameters were secondary endpoints. Endpoints were compared at baseline and at the end of the study using analysis of variance, the Kruskal-Wallis test, and Student's t-test. The TC/High-density lipoprotein (HDL) ratio (-0.33 ± 0.68, p = 0.002) and LDL/HDL ratio (-0.26 ± 0.59, p = 0.005) decreased in the EVOO group. Delta values for all variables showed no significant statistical difference between groups. However, we highlight the clinical significance of LDL-c reduction in the EVOO group by 5.11 ± 21.79 mg/dL and in the DieTBra group by 4.27 ± 23.84 mg/dL. We also found a mean reduction of around 10% for Castelli II (LDL/HDL) and homocysteine in the EVOO group and TG and the TG/HDL ratio in the DieTBra group. EVOO or DieTBra when administered alone lead to reduction in some cardiometabolic risk parameters in severely obese individuals.

Effect of Extra Virgin Olive Oil and Traditional Brazilian Diet on the Bone Health Parameters of Severely Obese Adults: A Randomized Controlled Trial

Dietary interventions can stabilize and/or reverse bone mass loss. However, there are no reports on its effects on bone mineral density (BMD) in severely obese people, despite the vulnerability of this group to bone loss. We examine the effect of extra virgin olive oil supplementation and the traditional Brazilian diet (DieTBra) on BMD and levels of calcium, vitamin D, and parathyroid hormone (PTH) in severely obese adults. A randomized controlled trial followed-up with severely obese adults (n = 111, with mean body mass index 43.6 kg/m2 ± 4.5 kg/m2) for 12 weeks. Study participants received either olive oil (52 mL/day), DieTBra, or olive oil + DieTBra (52 mL/day + DieTBra). BMD was assessed by total spine and hip dual-energy X-ray absorptiometry. After interventions, BMD means for total spine (p = 0.016) and total hip (p = 0.029) were higher in the DieTBra group than in the olive oil + DieTBra group. Final mean calcium levels were higher in the olive oil group compared to the olive oil + DieTBra group (p = 0.026). Findings suggest that DieTBra and extra virgin olive oil have positive effects on bone health in severely obese adults. The major study was registered at ClinicalTrials.gov (NCT02463435).

Accelerometry calibration in people with class II-III obesity: Energy expenditure prediction and physical activity intensity identification

BACKGROUND

Almost all accelerometer calibration studies were developed for non-obese people, which hampers an accurate prediction of energy expenditure (EE) and induces a misclassification of sedentary activity (SA) and physical activity intensities (PAI) in class II-III obese people.

RESEARCH QUESTION

The purpose of this study was to develop regression equations to predict EE and cut-points to classify SA and PAI in severe obese people based on several metrics obtained from hip and back accelerometer placement data.

METHODS

43 class II-III obese participants performed a protocol that included sitting and standing positions and walking at several speeds. During the protocol participants wore an accelerometer at hip and back, and respiratory gas exchange was measured by indirect calorimetry. Accelerometer metrics analyzed were: activity counts, mean amplitude deviation and euclidean norm minus one. EE was predicted through linear mixed models while cut-points to classify SA and PAI were obtained applying receiver operating characteristic curves. Leave-one-out cross-validation data was used to calculate Bland-Altman plots, prediction accuracy, Kappa statistic and percent agreement.

RESULTS

All prediction models presented a quadratic equation that had as predictors body mass and one of the accelerometer metrics. Predicted EE indicated a good agreement and a root mean square error below 1.02 kcal min^-1. Global classification agreement from developed cut-points was categorized as almost perfect with a percent agreement above 84 %. Prediction accuracy and classification agreement were similar among accelerometer metrics in each position and between them in hip and back placement.

SIGNIFICANCE

Hip and back accelerometer data collected in severe obese people allow to accurately estimate EE and to correctly classify SA and PAI. These results enable future studies to adopt appropriate regression equations and cut-points developed for class II-III obese people rather than those established for non-obese people.

Predictive Equations Based on Body Composition for Resting Energy Expenditure Estimation in Adults with Obesity

BACKGROUND AND AIM

An accurate estimation of Resting Energy Expenditure (REE) in patients with obesity is crucial. Therefore, our aim was to assess the validity of REE predictive equations based on body composition variables in treatment-seeking Arab adults with obesity.

METHODS

Body composition and REE were measured by Tanita BC-418 bioimpedance and Vmax Encore 229 IC, respectively, and predictive equations based on fat mass and fat-free mass were used in REE estimations among 87 adults of both genders, in the Outpatient Clinic in the Department of Nutrition and Dietetics at Beirut Arab University (Lebanon). The mean differences between the measured and estimated REE values were calculated to assess the accuracy, and the Bland-Altman method was used to assess the level of agreement.

RESULTS

Ten predictive equations were included. In males, all the predictive equations gave significantly different estimates of REE when compared to that measured by IC. On the other hand, in females, the mean difference between the REE value estimated by Huang and Horie-Waitzberg equations and that measured using IC was not significant, and the agreement was confirmed using Bland-Altman plots.

CONCLUSION

Huang and Horie-Waitzberg equations are suggested for accurate REE estimation in females; however, new validated REE estimation equations for males in this population are still needed.

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.

Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity?

This study aimed to develop and validate new predictive equations for resting energy expenditure (REE) in a large sample of subjects with obesity also considering raw variables from bioimpedance-analysis (BIA). A total of 2225 consecutive obese outpatients were recruited and randomly assigned to calibration (n = 1680) and validation (n = 545) groups. Subjects were also split into three subgroups according to their body mass index (BMI). The new predictive equations were generated using two models: Model 1 with age, weight, height, and BMI as predictors, and Model 2 in which raw BIA variables (bioimpedance-index and phase angle) were added. Our results showed that REE was directly correlated with all anthropometric and raw-BIA variables, while the correlation with age was inverse. All the new predictive equations were effective in estimating REE in both sexes and in the different BMI subgroups. Accuracy at the individual level was high for specific group-equation especially in subjects with BMI > 50 kg/m². Therefore, new equations based on raw-BIA variables were as accurate as those based on anthropometry. Equations developed for BMI categories did not substantially improve REE prediction, except for subjects with a BMI > 50 kg/m². Further studies are required to verify the application of those formulas and the role of raw-BIA variables for predicting REE.

PPARG2 Pro12Ala polymorphism influences body composition changes in severely obese patients consuming extra virgin olive oil: a randomized clinical trial

BACKGROUND

Previous intervention studies have reported the association of the PPARG2 Pro12Ala (rs1801282) and IL6 -174G > C (rs1800795) polymorphisms with weight loss; however, their results are inconsistent. We aimed to investigate the effect of the PPARG2 Pro12Ala and IL6 -174G > C polymorphisms on body weight, body composition and metabolic parameters after a 12-week nutritional intervention with a traditional Brazilian diet and extra virgin olive oil supplementation in severely obese patients.

METHODS

A total of 149 severely obese individuals [body mass index (BMI) ≥ 35 kg/m2] were randomized into three 12-week nutritional intervention groups - the extra virgin olive oil supplementation (OO) group (n = 50), the traditional Brazilian diet (DieTBra) group (n = 49), and the DieTBra plus extra virgin olive oil supplementation (DieTBra+OO) group (n = 50). Anthropometric measurements, body composition, metabolic parameters, physical activity practise and dietary intake were assessed. The associations were tested using generalized linear models adjusted for confounders.

RESULTS

The PPARG2 Pro12Ala polymorphism influenced body composition changes. Ala carriers in the intervention groups with extra virgin olive oil supplementation had greater reductions in the percentage of body fat (%BF) (OO: p = 0.049, DietBra+OO: p = 0.004) and greater increases in both fat-free mass (FFM) (OO: p = 0.020, DieTBra: p = 0.007) and lean mass (LM) (OO: p = 0.020, DieTBra+OO: p = 0.007) than did ProPro homozygotes. No association was found for the IL6 -174G > C polymorphism.

CONCLUSIONS

Extra virgin olive oil intake may modulate favourable body composition changes, promoting a decrease in the %BF and increases in the LM and FFM of severely obese individuals, even without weight loss, in the presence of the Ala allele of the Pro12Ala polymorphism.

TRIAL REGISTRATION

Registered under ClinicalTrials.gov Identifier No. NCT02463435.

Validity of Predictive Equations for Resting Energy Expenditure Developed for Obese Patients: Impact of Body Composition Method

Predictive equations have been specifically developed for obese patients to estimate resting energy expenditure (REE). Body composition (BC) assessment is needed for some of these equations. We assessed the impact of BC methods on the accuracy of specific predictive equations developed in obese patients. REE was measured (mREE) by indirect calorimetry and BC assessed by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). mREE, percentages of prediction accuracy (±10% of mREE) were compared. Predictive equations were studied in 2588 obese patients. Mean mREE was 1788 ± 6.3 kcal/24 h. Only the Müller (BIA) and Harris & Benedict (HB) equations provided REE with no difference from mREE. The Huang, Müller, Horie-Waitzberg, and HB formulas provided a higher accurate prediction (>60% of cases). The use of BIA provided better predictions of REE than DXA for the Huang and Müller equations. Inversely, the Horie-Waitzberg and Lazzer formulas provided a higher accuracy using DXA. Accuracy decreased when applied to patients with BMI ≥ 40, except for the Horie-Waitzberg and Lazzer (DXA) formulas. Müller equations based on BIA provided a marked improvement of REE prediction accuracy than equations not based on BC. The interest of BC to improve REE predictive equations accuracy in obese patients should be confirmed.

New equations to estimate resting energy expenditure in obese adults from body composition

AIMS

The aims of this study were: to develop new equations for predicting resting energy expenditure (REE) in obese Italian subjects according to body composition parameters; to compare them with predicted values estimated by other REE prediction equations; and to cross-validate our equations using a validation set cohort.

METHODS

Four hundred patients were enrolled and divided into three groups. Besides anthropometry and REE (indirect calorimetry), total body fat and lean were evaluated by dual X-ray absorptiometry, and fat mass and fat-free mass by bioelectrical impedance analysis.

RESULTS

The subjects eligible to participate were 330. Group 1 (n = 174) was used to develop (R ² = 0.79) and (R ² = 0.77). Group 2 (n = 115) was used to generate (R ² = 0.85) and (R ² = 0.81). Group 3 (n = 41) was used to cross-validate the equations.

CONCLUSION

Equations 1 and 3 are reliable to measure REE from calorimetry and better than other equations that use anthropometric variables as predictors of REE. Further analysis in different populations is required before it can be applied in clinical practice.

Prediction and evaluation of resting energy expenditure in a large group of obese outpatients

Background/Objectives:

The aim of this study was to compare resting energy expenditure (REE) measured (MREE) by indirect calorimetry (IC) and REE predicted (PREE) from established predictive equations in a large sample of obese Caucasian adults.

Subjects/Methods:

We evaluated 1851 obese patients (body mass index (BMI)>30 kg m⁻²) aged between 18a and 65 years. Data were obtained by comparing MREE with PREE, derived from different equations, within and between normal weight and obese groups. The mean differences between PREE and MREE as well as the accuracy prediction within ±10% level were investigated in the whole sample and in three subgroups, classified by BMI (Group 1=30–39.9 kg m⁻²; Group 2=40–49.9 kg m⁻²; Group 3>50 kg m⁻²).

Results:

We observed that FAO, Henry and Muller3 (body composition (BC)) equations provided good mean PREE–MREE (bias −0.7, −0.3 and 0.9% root mean standard error (RMSE) 273, 263 and 269 kcal per day, respectively); HB and Henry equations were more accurate individually (57 and 56.9%). Only the Muller1 (BC) equation gave the lowest PREE–MREE difference (bias −1.7% RMSE 228 kcal per day) in females, while Johnstone and De Lorenzo equations were the most accurate (55.1 and 54.8%). When the sample was split into three subgroups according to BMI, no differences were found in males; however, the majority of the equations included in this study failed to estimate REE in severely obese females (BMI>40 kg m⁻²). Overall, prediction accuracy was low (~55%) for all predictive equations, regardless of BMI.

Conclusions:

Different established equations can be used for estimating REE at the population level in both sexes. However, the accuracy was very low for all predictive equations used, particularly among females and when BMI was high, limiting their use in clinical practice. Our findings suggest that the validation of new predictive equations would improve the accuracy of REE prediction, especially for severely obese subjects (BMI>40 kg m⁻²).

Resting Energy Expenditure of Black Overweight Women in South Africa Is Lower than of White Women

BACKGROUND

Overweight affects 65% of black South African women. Effective weight management requires accurate measurement or estimation of energy expenditure.

AIMS

The study aimed to determine, among overweight women, whether measured resting energy expenditure (REE) differs between black and white participants, and the performance of REE estimation equations.

METHODS

The REE of 44 black (age 39.6 ± 9.7 years, body mass index (BMI) 35.1 ± 6.2 kg/m2) and 41 white (age 38.0 ± 11.6 years, BMI 33.9 ± 7.6 kg/m2) women was measured with indirect calorimetry and estimated with equations. Body composition was assessed with multi-frequency bioelectrical impedance analysis. Differences in REE were determined with t tests (Welch), and included adjustment for fat free mass (FFM) and BMI, and for FFM index (FFMI).

RESULTS

Measured REE was 585 kJ/day (95% CI 264-905; p = 0.0005) and 861 kJ/day (95% CI 499-1,221; p < 0.0001) lower in black than in white women when adjusted for FFM and BMI, and FFMI, respectively. Out of 14 equations, 13 underestimated REE (error range 2,261 ± 727 kJ/day (Bernstein equation, white women) to 8 ± 782 kJ/day (BMI equation, black women)).

CONCLUSIONS

Black overweight women have significantly lower REE than their white counterparts. No tested estimation equation provided satisfactory results across race/ethnicity. REE measurements or development of overweight- or race/ethnicity-specific estimation equations are recommended.

Measurement of body composition as a surrogate evaluation of energy balance in obese patients

In clinical practice obesity is primarily diagnosed through the body mass index. In order to characterize patients affected by obesity the use of traditional anthropometric measures appears misleading. Beyond the body mass index, there are overwhelming evidences towards the relevance of a more detailed description of the individual phenotype by characterizing the main body components as free-fat mass, muscle mass, and fat mass. Among the numerous techniques actually available, bioelectrical impedance analysis seems to be the most suitable in a clinical setting because it is simple, inexpensive, noninvasive, and highly reproducible. To date, there is no consensus concerning the use of one preferred equation for the resting energy expenditure in overweight and/or obese population. Energy restriction alone is an effective strategy to achieve an early and significant weight loss, however it results in a reduction of both fat and lean mass therefore promoting or aggravating an unfavourable body composition (as sarcobesity) in terms of mortality and comorbidities. Therefore the implementation of daily levels of physical activity should be simultaneously promoted. The major role of muscle mass in the energy balance has been recently established by the rising prevalence of the combination of two condition as sarcopenia and obesity. Physical exercise stimulates energy expenditure, thereby directly improving energy balance, and also promotes adaptations such as fiber type, mitochondrial biogenesis, improvement of insulin resistance, and release of myokines, which may influence different tissues, including muscle.

Validity of predictive equations for resting energy expenditure according to the body mass index in a population of 1726 patients followed in a Nutrition Unit

BACKGROUND & AIMS

The resting energy expenditure (REE) predictive formulas are often used in clinical practice to adapt the nutritional intake of patients or to compare to REE measured by indirect calorimetry. We aimed to evaluate which predictive equations was the best alternative to REE measurements according to the BMI.

METHODS

28 REE prediction equations were studied in a population of 1726 patients without acute or chronic high-grade inflammatory diseases followed in a Nutrition Unit for malnutrition, eating disorders or obesity. REE was measured by indirect calorimetry for 30 min after a fasting period of 12 h. Some formulas requiring fat mass and free-fat mass, body composition was measured by bioelectrical impedance analysis. The percentage of accurate prediction (±10%/REE measured) and Pearson r correlations were calculated.

RESULTS

Original Harris & Benedict equation provided 73.0% of accurate predictions in normal BMI group but only 39.3% and 62.4% in patients with BMI < 16 kg m(-2) and BMI ≥ 40 kg m(-2), respectively. In particularly, this equation overestimated the REE in 51.74% of patients with BMI < 16 kg m(-2). Huang equation involving body composition provided the highest percent of accurate prediction, 42.7% and 66.0% in patients with BMI < 16 and >40 kg m(-2), respectively.

CONCLUSION

Usual predictive equations of REE are not suitable for predicting REE in patients with extreme BMI, in particularly in patients with BMI <16 kg m(-2). Indirect Calorimetry may still be recommended for an accurate assessment of REE in this population until the development of an adapted predictive equation.

BMR in a Brazilian adult probability sample: the Nutrition, Physical Activity and Health Survey

OBJECTIVE

To measure BMR in a probability sample of adults from an urban city of Brazil and to compare indirectly measured BMR (BMRi) with BMR predicted from different equations.

DESIGN

BMR data were obtained by indirect calorimetry and estimated by different predictive equations (Schofield; Harris and Benedict; Henry and Rees). Anthropometric and body composition measures were also obtained.

SETTING

The Nutrition, Physical Activity and Health Survey (PNAFS), a household survey conducted in Niterói, Rio de Janeiro state, Brazil.

SUBJECTS

Representative sample of 529 adults (aged ≥20 years; 339 females) living in Niterói, Rio de Janeiro state, Brazil.

RESULTS

Mean BMRi values were 5839.7 (se 73.9) kJ/d and 4758.1 (se 39.5) kJ/d for men and women, respectively. Predicted BMR by all equations was significantly higher (difference between means and 95% CI did not include zero) than BMRi in both men and women of all ages. Overall bias in BMR (predicted BMR minus BMRi) using the Schofield equations (overestimation of about 20%) was higher than when using the Henry and Rees equations (13% and 16% overestimation for males and females, respectively). The percentage of individuals whose BMR predicted by the Schofield equations fell within 10% of BMRi was very low (7.8% and 14.1% of males nd females, respectively).

CONCLUSIONS

Current available predictive equations of BMR are not adequate to estimate BMR in Brazilians living in Niterói, Rio de Janeiro, Brazil.

Manganese-mitigation of cadmium toxicity to seedling growth of Phytolacca acinosa Roxb. is controlled by the manganese/cadmium molar ratio under hydroponic conditions

Manganese (Mn) can interact with cadmium (Cd) in environments and influence the toxic effect of Cd on plants. However, few studies have investigated the relationship between the Mn/Cd ratio and plant Cd-toxicity along Cd concentrations. In this paper, we studied the effects of external Mn/Cd molar ratios (0, 10, 30, 50 and 60) on Cd toxicity in the Mn hyperaccumulator and Cd tolerant plant, Phytolacca acinosa Roxb., at three Cd levels (50, 100 and 200 μM) under hydroponic conditions. Our result showed that seedling growth (y) under Cd stress was strongly positively related to the solution Mn/Cd molar ratio (SMCR). The relationship between the two variables under solution Cd concentrations was well explained by the linear regression model y=a+b1 (SMCR)+b2 (Solution-Cd). Increasing SMCR significantly reduced the Cd concentration and increased the Mn concentration in plant tissues. However, seedling growth was consistent with the shoot Mn/Cd molar ratio rather than with the Mn or Cd concentrations in plant tissues. At low levels of SMCR (e.g. 0 and 10), elevation of Mn distribution in shoot tissues might be a mechanism in P. acinosa seedlings to defend against Cd-toxicity. In comparison with low levels of SMCR, high levels of SMCR (e.g. 50 and 60) greatly alleviated lipid peroxidation and plant water-loss, and enhanced photosynthesis. However, the alleviated lipid peroxidation in the Mn-mitigation of Cd toxicity was likely to be the secondary effect resulting from the antagonism between Mn and Cd in the plant.

An approach to quantifying abnormalities in energy expenditure and lean mass in metabolic disease

Background/objectives:

The objective of this study was to develop approaches to expressing resting energy expenditure (REE) and lean body mass (LM) phenotypes of metabolic disorders in terms of Z-scores relative to their predicted healthy values.

Subjects/methods:

Body composition and REE were measured in 135 healthy participants. Prediction equations for LM and REE were obtained from linear regression and the range of normality by the standard deviation of residuals. Application is demonstrated in patients from three metabolic disorder groups (lipodystrophy, n=7; thyrotoxicosis, n=16; and resistance to thyroid hormone (RTH), n=46) in which altered REE and/or LM were characterised by departure from the predicted healthy values, expressed as a Z-score.

Results:

REE (kJ/min)=−0.010 × age (years)+0.016 × FM (kg)+0.054 × fat-free mass (kg)+1.736 (R²=0.732, RSD=0.36 kJ/min).

LM (kg)=5.30 × bone mineral content (kg)+10.66 × height² (m)+6.40 (male).

LM (kg)=0.20 × fat (kg)+14.08 × height² (m)−2.93 (female).

(male R²=0.55, RSD=3.90 kg; female R²=0.59, RSD=3.85 kg).

We found average Z-scores for REE and LM of 1.77 kJ/min and −0.17 kg in the RTH group, 5.82 kJ/min and −1.23 kg in the thyrotoxic group and 2.97 kJ/min and 4.20 kg in the LD group.

Conclusion:

This approach enables comparison of data from individuals with metabolic disorders with those of healthy individuals, describing their departure from the healthy mean by a Z-score.

Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations

Basal metabolic rate (BMR) represents the largest component of total energy expenditure and is a major contributor to energy balance. Therefore, accurately estimating BMR is critical for developing rigorous obesity prevention and control strategies. Over the past several decades, numerous BMR formulas have been developed targeted to different population groups. A comprehensive literature search revealed 248 BMR estimation equations developed using diverse ranges of age, gender, race, fat-free mass, fat mass, height, waist-to-hip ratio, body mass index and weight. A subset of 47 studies included enough detail to allow for development of meta-regression equations. Utilizing these studies, meta-equations were developed targeted to 20 specific population groups. This review provides a comprehensive summary of available BMR equations and an estimate of their accuracy. An accompanying online BMR prediction tool (available at http://www.sdl.ise.vt.edu/tutorials.html) was developed to automatically estimate BMR based on the most appropriate equation after user-entry of individual age, race, gender and weight.

Detection of thermogenesis in rodents in response to anti-obesity drugs and genetic modification

Many compounds and genetic manipulations are claimed to confer resistance to obesity in rodents by raising energy expenditure. Examples taken from recent and older literature, demonstrate that such claims are often based on measurements of energy expenditure after body composition has changed, and depend on comparisons of energy expenditure divided by body weight. This is misleading because white adipose tissue has less influence than lean tissue on energy expenditure. Application of this approach to human data would suggest that human obesity is usually due to a low metabolic rate, which is not an accepted view. Increased energy expenditure per animal is a surer way of demonstrating thermogenesis, but even then it is important to know whether this is due to altered body composition (repartitioning), or increased locomotor activity rather than thermogenesis per se. Regression analysis offers other approaches. The thermogenic response to some compounds has a rapid onset and so cannot be due to altered body composition. These compounds usually mimic or activate the sympathetic nervous system. Thermogenesis occurs in, but may not be confined to, brown adipose tissue. It should not be assumed that weight loss in response to these treatments is due to thermogenesis unless there is a sustained increase in 24-h energy expenditure. Thyroid hormones and fibroblast growth factor 21 also raise energy expenditure before they affect body composition. Some treatments and genetic modifications alter the diurnal rhythm of energy expenditure. It is important to establish whether this is due to altered locomotor activity or efficiency of locomotion. There are no good examples of compounds that do not affect short-term energy expenditure but have a delayed effect. How and under what conditions a genetic modification or compound increases energy expenditure influences the decision on whether to seek drugs for the target or take a candidate drug into clinical studies.

Metabolic profile of clinically severe obese patients

BACKGROUND

Since low basal metabolic rate (BMR) is a risk factor for weight regain, it is important to measure BMR before bariatric surgery. We aimed to evaluate the BMR among clinically severe obese patients preoperatively. We compared it with that of the control group, with predictive formulas and correlated it with body composition.

METHODS

We used indirect calorimetry (IC) to collect BMR data and multifrequency bioelectrical impedance to collect body composition data. Our sample population consisted of 193 patients of whom 130 were clinically severe obese and 63 were normal/overweight individuals. BMR results were compared with the following predictive formulas: Harris-Benedict (HBE), Bobbioni-Harsch (BH), Cunningham (CUN), Mifflin-St. Jeor (MSJE), and Horie-Waitzberg & Gonzalez (HW & G). This study was approved by the Ethics Committee for Research of the University of Brasilia. Statistical analysis was used to compare and correlate variables.

RESULTS

Clinically severe obese patients had higher absolute BMR values and lower adjusted BMR values (p < 0.0001). A positive correlation between fat-free mass and a negative correlation between body fat percentage and BMR were found in both groups. Among the clinically severe obese patients, the formulas of HW & G and HBE overestimated BMR values (p = 0.0002 and p = 0.0193, respectively), while the BH and CUN underestimated this value; only the MSJE formulas showed similar results to those of IC.

CONCLUSIONS

The clinically severe obese patients showed low BMR levels when adjusted per kilogram per body weight. Body composition may influence BMR. The use of the MSJE formula may be helpful in those cases where it is impossible to use IC.

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.

Comparison of predictive equations for resting energy expenditure in overweight and obese adults

Objective. To compare values from predictive equations of resting energy expenditure (REE) with indirect calorimetry (IC) in overweight and obese adults. Methods. Eighty-two participants aged 30 to 60 years old were retrospectively analyzed. The persons had a body mass index ≥25 kg/m(2). REE was estimated by IC and other five equations of the literature (Harris and Benedict, WHO1, WHO2, Owen, Mifflin). Results. All equations had different values when compared to those of IC. The best values were found by Harris and Benedict, WHO1, and WHO2, with high values of intraclass correlation coefficient and low values of mean difference. Furthermore, WHO1 and WHO2 showed lower systematic error and random. Conclusion. No predictive equations had the same values of REE as compared to those of indirect calorimetry, and those which least underestimated REE were the equations of WHO1, WHO2, and Harris and Benedict. The next step would be to validate the new equation proposed.