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Validation of bioelectrical impedance analysis for body composition assessment in children with obesity aged 8-14y.
Gutiérrez-Marín, D, Escribano, J, Closa-Monasterolo, R, Ferré, N, Venables, M, Singh, P, Wells, JC, Muñoz-Hernando, J, Zaragoza-Jordana, M, Gispert-Llauradó, M, et al
Clinical nutrition (Edinburgh, Scotland). 2021;(6):4132-4139
Abstract
BACKGROUND & AIMS The aim was to generate a predictive equation to assess body composition (BC) in children with obesity using bioimpedance (BIA), and avoid bias produced by different density levels of fat free mass (FFM) in this population. METHODS This was a cross-sectional validation study using baseline data from a randomized intervention trial to treat childhood obesity. Participants were 8 to 14y (n = 315), underwent assessments on anthropometry and BC through Air Displacement Plethysmography (ADP), Dual X-Ray Absorptiometry and BIA. They were divided into a training (n = 249) and a testing subset (n = 66). In addition, the testing subset underwent a total body water assessment using deuterium dilution, and thus obtained results for the 4-compartment model (4C). A new equation to estimate FFM was created from the BIA outputs by comparison to a validated model of ADP adjusted by FFM density in the training subset. The equation was validated against 4C in the testing subset. As reference, the outputs from the BIA device were also compared to 4C. RESULTS The predictive equation reduced the bias from the BIA outputs from 14.1% (95%CI: 12.7, 15.4) to 4.6% (95%CI: 3.8, 5.4) for FFM and from 18.4% (95%CI: 16.9, 19.9) to 6.4% (95% CI: 5.3, 7.4) for FM. Bland-Altman plots revealed that the new equation significantly improved the agreement with 4C; furthermore, the observed trend to increase the degree of bias with increasing FM and FFM also disappeared. CONCLUSION The new predictive equation increases the precision of BC assessment using BIA in children with obesity.
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A novel approach to assess body composition in children with obesity from density of the fat-free mass.
Gutiérrez-Marín, D, Escribano, J, Closa-Monasterolo, R, Ferré, N, Venables, M, Singh, P, Wells, JCK, Muñoz-Hernando, J, Zaragoza-Jordana, M, Gispert-Llauradó, M, et al
Clinical nutrition (Edinburgh, Scotland). 2021;(3):1102-1107
Abstract
BACKGROUND & AIMS Assessment of Fat Mass (FM) and fat-free mass (FFM) using Air-displacement plethysmography (ADP) technique assumes constant density of FFM (DFFM) by age and sex. It has been recently shown that DFFM further varies according to body mass index (BMI), meaning that ADP body composition assessments of children with obesity could be biased if DFFM is assumed to be constant. The aim of this study was to validate the use of the calculations of DFFM (rather than constant density of the FFM) to improve accuracy of body composition assessment in children with obesity. METHODS cross-sectional validation study in 66 children with obesity (aged 8-14 years) where ADP assessments of body composition assuming constant density (FFMBODPOD and FMBODPOD) were compared to those where DFFM was adjusted in relation to BMI (FFMadjusted and FMadjusted), and both compared to the gold standard reference, the 4-component model (FFM4C and FM4C). RESULTS FFMBODPOD was overestimated by 1.50 kg (95%CI -0.68 kg, 3.63 kg) while FFMadjusted was 0.71 kg (-1.08 kg, 2.51 kg) (percentage differences compared to FFM4C were 4.9% (±2.9%) and 2.8% (±2.1%), respectively (p < 0.001)). Consistently, FM was underestimated by both methods, representing a mean difference between methods of 4.0% (±2.9%) and 6.8% (±3.8%), respectively, when compared to the reference method. The agreement and reliability of body composition assessments were improved when adjusted using calculations (adjusted models) rather than assuming constant DFFM. CONCLUSIONS The use of constant values for fat-free mass properties may increase bias when assessing body composition (FM and FFM) in children with obesity by two-component techniques such as ADP. Using adjusted corrections as proposed in the present work may reduce the bias by half.
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The Obemat2.0 Study: A Clinical Trial of a Motivational Intervention for Childhood Obesity Treatment.
Luque, V, Feliu, A, Escribano, J, Ferré, N, Flores, G, Monné, R, Gutiérrez-Marín, D, Guillen, N, Muñoz-Hernando, J, Zaragoza-Jordana, M, et al
Nutrients. 2019;11(2)
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Multicomponent interventions consisting of dietary modification, physical activity, behavioural therapy, and education have shown to improve body mass index, blood pressure, and lipids profile. The Obemat2.0 trail was designed and conducted to implement and to test the efficacy of a structured multicomponent motivational therapy to treat childhood obesity. The study is a randomised clustered clinical trial with a treatment on children with obesity lasting 12 months. The study had two arms: a control group and an intervention group. The recruitment started in June 2016 and the fieldwork is expected to end in June 2019. The study results will show whether a multicomponent program, including a bundle of motivational strategies conducted in primary centres by therapists with 12h of specific training could be more effective than usual care. Authors expect this clinical trial to open a window of opportunity to support professionals at the primary care level to treat childhood obesity.
Abstract
The primary aim of the Obemat2.0 trial was to evaluate the efficacy of a multicomponent motivational program for the treatment of childhood obesity, coordinated between primary care and hospital specialized services, compared to the usual intervention performed in primary care. This was a cluster randomized clinical trial conducted in Spain, with two intervention arms: motivational intervention group vs. usual care group (as control), including 167 participants in each. The motivational intervention consisted of motivational interviewing, educational materials, use of an eHealth physical activity monitor and three group-based sessions. The primary outcome was body mass index (BMI) z score increments before and after the 12 (+3) months of intervention. Secondary outcomes (pre-post intervention) were: adherence to treatment, waist circumference (cm), fat mass index (z score), fat free mass index (z score), total body water (kg), bone mineral density (z score), blood lipids profile, glucose metabolism, and psychosocial problems. Other assessments (pre and post-intervention) were: sociodemographic information, physical activity, sedentary activity, neuropsychological testing, perception of body image, quality of the diet, food frequency consumption and foods available at home. The results of this clinical trial could open a window of opportunity to support professionals at the primary care to treat childhood obesity. The clinicaltrials.gov identifier was NCT02889406.
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Application of Bayesian analysis to the doubly labelled water method for total energy expenditure in humans.
Ruan, Y, Bluck, LCJ, Smith, J, Mander, A, Singh, P, Venables, M
Rapid communications in mass spectrometry : RCM. 2018;(1):23-32
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Abstract
RATIONALE The doubly labelled water (DLW) method is the reference method for the estimation of free-living total energy expenditure (TEE). In this method, where both 2 H and 18 O are employed, different approaches have been adopted to deal with the non-conformity observed regarding the distribution space for the labels being non-coincident with total body water. However, the method adopted can have a significant effect on the estimated TEE. METHODS We proposed a Bayesian reasoning approach to modify an assumed prior distribution for the space ratio using experimental data to derive the TEE. A Bayesian hierarchical approach was also investigated. The dataset was obtained from 59 adults (37 women) who underwent a DLW experiment during which the 2 H and 18 O enrichments were measured using isotope ratio mass spectrometry (IRMS). RESULTS TEE was estimated at 9925 (9106-11236) [median and interquartile range], 9646 (9167-10540), and 9,638 (9220-10340) kJ·day-1 for women and at 13961 (12851-15347), 13353 (12651-15088) and 13211 (12653-14238) kJ·day-1 for men, using normalized non-Bayesian, independent Bayesian and hierarchical Bayesian approaches, respectively. A comparison of hierarchical Bayesian with normalized non-Bayesian methods indicated a marked difference in behaviour between genders. The median difference was -287 kJ·day-1 for women, and -750 kJ·day-1 for men. In men there is an appreciable compression of the TEE distribution obtained from the hierarchical model compared with the normalized non-Bayesian methods (range of TEE 11234-15431 kJ·day-1 vs 10786-18221 kJ·day-1 ). An analogous, yet smaller, compression is seen in women (7081-12287 kJ·day-1 vs 6989-13775 kJ·day-1 ). CONCLUSIONS The Bayesian analysis is an appealing method to estimate TEE during DLW experiments. The principal advantages over those obtained using the classical least-squares method is the generation of potentially more useful estimates of TEE, and improved handling of outliers and missing data scenarios, particularly if a hierarchical model is used.