How it evaluatjon The individual lies down on the exam Fast and slow release energy sources for endurance sports as the DEXA scanner moves over them. McGee KJ, Burkett LN. The principles and clinical utilization of BIA have been largely described in two ESPEN position papers [ 45,66 ].

Body composition evaluation method -

We also aimed to assess the sensitivity of equations to detect changes in body composition induced by an aerobic exercise training program. We hypothesized that body composition would be accurately assessed and followed up over time in people with overweight and obesity using predictive equations.

Thirty-eight participants had a BMI lower than 25 kg. We used data from a completed clinical trial NCT testing the cardiometabolic effects of different exercise training modalities i. Body composition was assessed before T0 and after the 2-month T2 training period. The study was approved by the local ethics committee Comité de Protection des Personnes Sud-Est V and performed according to the Declaration of Helsinki.

Participants were informed of the procedure and risks involved and gave their written consent prior to all assessments. MRI is a reference method for assessing body composition including AT, ATFM and its regional distribution, and particularly visceral adipose tissue 16 , In order to determine full body composition called measured body composition , the reference method includes 41 slices with a thickness of 10 mm spaced by 40 mm MRI data were acquired in 30 min for each participant on a General Electric Signa Advantage 1.

For the specific purpose of the present study prediction of body composition , three single slices thickness of 10 mm were acquired in 10 min at 3 different localizations: T6-T7, L4-L5 and mid-thigh.

Mid-thigh level for the single slice acquisition was determined by computing half of the overall femoral length. In our medical department, we used our own equation unpublished results to measure the half-femoral length.

Indeed, statistical analysis of different populations shows that the stature can be derived from the femoral length We compared the formula used in our medical department to those available in the literature The average difference of half-femoral length between our method and the one described in Trotter was only of 0.

Each slice was analyzed using Matlab-based software Matlab ® , Mathworks, Inc. developed by the radiology department of University Hospital of Grenoble Alps. The brightness level of tissue distinguished AT and ATFM, using the graphical interface of Matlab ®.

More specifically, we measured adipose tissue and calculated adipose tissue free mass by subtracting adipose tissue area to slice area. Moreover, determination of tissue area on a given MR image is performed by subjecting the data to various segmentation techniques.

In our laboratory we have developed a computer software specifically designed for MR image analysis similar to that developed by Ross et al. The program features an interactive slice editor routine that allows for the verification of the segmentation result. This feature helps to assure that the area cm 2 values for the tissues of interest are accurately and reliably measured.

Each slice was visually controlled to avoid an error in tissue type assignment, after histogram analysis to define a threshold for fat tissue segmentation. As our goal was to measure two types of tissues: adipose and adipose tissue free mass, we used a simplified method derived from the one developed by Ross et al.

After threshold definition, the segmented image was compared to the MR image to correct wrong tissue type assignment when necessary.

For instance, small isolated white dots occurring for instance when the slice concerned only a very small amount of isolated adipose tissue so-called partial volume effect were not considered in the AT amount. On the other side, weak MR signals occurring sometimes in the subcutaneous fat tissues were manually added to the AT.

As initial double blinded initial tests did not result in significant differences, we considered that the AT amount measurement process was reliable. To calculate the adipose tissue and lean tissue volumes in each slice, the program multiplies the number of pixels by the pixel surface cm 2 and the thickness 10 mm of the slice.

Whole-body adipose tissue and lean tissue volumes were calculated using the truncated pyramid method At last, the volume in liters of adipose tissue and lean tissue was converted to mass kg by multiplying the volumes of the assumed constant density of 0.

The inter-observer variability was respectively 2. We tested the sensitivity of the prediction model on 79 participants with overweight or obesity following two isocaloric aerobic exercise training programs known to significantly induce adipose tissue loss For this reason, we did not distinguish the effects of the two modalities in the statistical analysis.

Specifically, participants were installed on an electronically braked cycle ergometer Corival, Lode B. Multiple linear regression analysis was performed to assess the relation between the measured AT or ATFM by the 41 slices and those predicted by the three single slices.

The R 2 was adjusted for the number of predictors. We used an automated variable selection procedure for forward, backward, and stepwise variable selection using the AIC Akaike information criterion estimator which evaluates the quality of each model, relative to each of the other models Comparison between the predicted and measured AT and ATFM was performed using t -tests.

Levels closer to 1 indicate better agreement between methods. Sensitivity of the prediction models to an exercise training program was also studied. Data were statistically analyzed using the nonparametric test Wilcoxon to compare the variations between the MRI reference method and the predictive method before T0 and after 2-months T2 of exercise training program.

The statistical software R version 3. The 3 single MRI slices at T6-T7, L4-L5 and mid-thigh , sex, age, weight and height of participants were included in linear regression models as independent variables, with total AT as the dependent variable Table 2.

Without height, predicted AT was more strongly correlated with measured AT with an adjusted R 2 of There was no significant difference between predicted and measured AT measured: Bland—Altman plots illustrate the difference between predicted and measured AT in people with overweight total: 0.

Bland—Altman plot agreement of adipose tissue AT A and adipose tissue free mass ATFM B in overweight people. Bland—Altman plot agreement of adipose tissue AT A and adipose tissue free mass ATFM B in people with obesity. The same analysis was applied to the dependent variable total ATFM.

Multiple regression equations for estimation of total ATFM were developed Table 2. There was no significant difference between predicted and measured ATFM measured: The measured and predicted AT after 2-months T2 of exercise training did not significantly differ compared to baseline T0 T2: measured: T0: measured: The same result was observed for ATFM variation T2: measured: Our results showed that the best fit models that we developed had a high adjusted R 2 AT: The SEE of our predictive equation compared quite well with that used by Schweitzer et al.

Indeed, in the present study we built our equation in a population with obesity subjects with overweight or obesity out of In an attempt to predict total subcutaneous adipose tissue with a single slice at L3, Schweitzer et al.

Regarding ATFM, they reported R 2 between 0. Overall, despite greater SEE for ATFM, our equations seem better than those reported in literature and our model represents a breakthrough in the rapid estimation of body composition by MRI in overweight and obesity.

This is a progress compared to other simplified approaches of body composition such as anthropometrics. Indeed, Lee et al. In addition, when the predictive model of Lee et al. According to the authors, this could be due to the fact that inter- and intra-muscular adipose tissue could not be distinguished by anthropometric measurements including skinfold thickness Hence, the prediction of body composition from MRI single slices seems more appropriate and accurate.

Therefore, it seems that the best-fit equations can be used in a range of different profiles to quickly and accurately analyze body composition. A larger sample size in this subgroup might have provided higher concordance between predicted and measured methods.

There are several possibilities to explain the good concordance and agreement in these subgroups, including, the representativeness of the different types of obesity and the choice of three area slices.

The included participants also varied in age range: 20—81, Despite the heterogeneity of the studied population, the best-fit models that we developed gave a good prediction of AT and ATFM in both men and women and in varying BMI between 25 kg. Secondly, over the last few years a single abdominal slice has been proposed to predict total fat and lean masses 27 and to quickly assess subcutaneous adipose tissue and visceral adipose tissue 23 , 24 , 25 , 26 , 27 , 31 , which provides complementary information regarding cardiometabolic risk.

One of the major drawbacks of this method was that a single slice cannot take into account the interindividual morphological differences e. Accordingly, three single slices at T6-T7, L4-L5 and mid-thigh seems to be a good compromise between the time to analyze 20 min and the accuracy of body composition prediction.

Recently, other localizations have been proposed to assess subcutaneous adipose tissue and visceral adipose tissue particularly around L3 25 , 26 , 27 , 28 while L4-L5 was used in the present study. Maislin et al. This localization could decrease the standard error and improve the precision of our models.

However, this remains to be tested. The mid-thigh has also been described as an optimal slice area to assess total ATFM Such a measurement would be for instance relevant to detect sarcopenic obesity. To the best of our knowledge, no study has investigated one common localization to assess AT and ATFM in the thoracic area.

Hence, the single MRI slice at T6-T7 has been arbitrarily chosen hoping that it will the best site to determine, especially the mammary fat However, the localization of these three slices does not allow for the evaluation of fat in the gluteo-femoral region.

This region is an area in which women with obesity often accumulate adipose tissue 21 , 33 , Thus, adding an MRI slice below the iliac crest could be relevant.

Whatever the method measured or predictive , no significant modification of body composition was induced by 2 months of exercise training on ergocycle. A meta-analysis of Batacan et al.

In the present study, we cannot definitely conclude on the potential sensitivity of our prediction models because we did not observe a modification of body composition. However, there was no significant difference between measured or predictive method after 2-months of intervention indicating at least that the 2 measurement methods are not discordant.

Of note, estimating body composition with a unique slice at L3 level failed to track changes of body composition probably in persons losing weight because of lack of specificity of this slice The choice of a body composition method depends on the accuracy and precision needed.

However, the acquisition time is also important to allow its use in a routine clinical setting as well as for research purposes. With the predictive method, the time required to collect 10 min and to analyze 10 min MRI images is well below the reference MRI method 20 min vs.

The associated gain in time and the reduced cost of this evaluation are crucial in a clinical research setting. The choice of the methods also depends on the target population. For example, our model may not be generalizable to different patient populations who have specific body composition e.

COPD or highly trained athletes. For that reason, other predictive models must be developed for each specific population. Of course, an interesting perspective, could be to use the adipose tissue free mass and adipose tissue of the different slices locations T6-T7, L4-L5 and at mid-thigh to better characterize the various profiles of obesity android, gynoid and the associated cardiovascular risk.

However, such a perspective could not be envisaged at the present condition since we did not measure the biological markers of this cardiovascular risk. In conclusion, predictive equations with three MRI slices T6-T7, L4-L5 and mid-thigh were effective to quickly and accurately assess the body composition of people with overweight or obesity compared to the reference methods.

The findings we herein report have thus the potential to contribute to a fast and reliable estimation of AT and ATFM in overweight or obesity in clinical routine. Di Cesare, M. et al. Trends in adult BMI in countries from to Lancet , — Google Scholar.

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The characterization of body composition can be divided into two broad divisions: levels and models. These include atomic, molecular, cellular, tissue-system, and whole-body levels.

Specific techniques of determining body composition such as underwater weighing, skinfold assessments, bioelectrical impedance analysis, and dual-energy X-ray absorptiometry are then examined.

The different models segregate the body into different compartments, and currently there are two-, three-, and four-component models. This is a preview of subscription content, log in via an institution.

Unable to display preview. Download preview PDF. Pietrobelli A, Heymsfield SB. Establishing body composition in obesity. J Endocrinol Invest ; CAS Google Scholar. Heymsfield SB, Wang Z, Baumgartner RN, Ross R.

Human body composition: advances in models and methods. Annu Rev Nutr ; Article CAS Google Scholar. Withers RT, LaForgia J, Heymsfield SB. Critical appraisal of the estimation of body composition via two-, three-, and four-component models.

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Effect of race and resistance training status on the density of fat-free mass and percent fat estimates. J Appl Physiol ; Schutte JE, Townsend EJ, Hugg J, Sharp RF, Malina RM, Blomqvist CG.

Density of lean body mass is greater in blacks than in whites. Wagner DR, Heyward VH. Validity of two-component models for estimating body fat of black men. Guo SS, Chumlea WC, Cockram DB. Use of statistical methods to estimate body composition. Am J Clin Nutr ;64 Suppl SS. Selected body composition methods can be used in field studies.

J Nutr ; SS. Jackson AS, Pollock ML. Generalized equations for predicting body density of men. Br J Nutr ; Jackson AS, Pollock ML, Ward A. Generalized equations for predicting body density of women.

Med Sci Sports Exerc ; Practical assessment of body composition. Phys Sports Med ; Eekerson JM, Housh TJ, Johnson GO. The validity of visual estimations of percent body fat in lean males. Heyward VH, Stolarczyk LM. Applied Body Composition Assessment. Champaign IL: Human Kinetics Publishers; Going S, Davis R.

Body composition. In: Roitman JL, ed. Fields DA, Goran MJ, McCrory MA. Body composition assessment via air displacement plethysmography in adults and children: a review. Am J Clin Nutr ; Biaggi RR, Vollman MW, Nies MA, et al.

Comparison of air displacement plethysmography with hydrostatic weighing and bioelectrical impedance analysis for the assessment of body composition in healthy adults. Evans EM, Arngrimsson SA, Cureton KJ. Body composition estimates from multi-component models using BIA to determine body water.

Houtkooper LB, Lohman TG, Going SB, Howell WH. Why bioelectrical impedance analysis should be used for estimating adiposity. Jackson AS, Pollock ML, Graves JE, Mahar MT. Reliability and validity of bioelectrical impedance in determining body composition.

J Appl Physiol; Segal KR, Gutin B, Presta E, Wang J, Itallio TB. Estimation of human body composition by electrical impedance methods: a comparative study. J Appl Physiol ; Eckerson JM, Stout JR, Housh TI, Johnson GO.

Validity of bioelectrical impedance equations for estimating percent fat in males. Eckerson JM, Housh TJ, Johnson GO. Validity of bioelectrical impedance equations for estimating fat-free weight in lean males. Kushner RF, Schooeller DA, Fjeld CR, Danford L.

Bioelectrical impedance analysis in body composition measurement: National Institutes of Health Assessment Conference Statement. Kushner RF, Gudivaka R, Scholler DA. Clinical characteristics influencing bioelectrical impedance analysis measurements.

Pietrobelli A, Formica C, Wang Z, Heymsfield SB. Dual-energy X-ray absorptiometry body composition model: review of physical concepts. Am J Physiol ; EE Roubenoff R, Kehayias JJ, Dawson-Hughes B, Heymsfield SB. Use of dual-energy X-ray absorptiometry in body composition studies. Norcross J, Van Loan MD.

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Br J Sports Med ; Lohman TG, Harris M, Teixeira PJ, Weiss L. Assessing body composition and changes in body composition. Another look at dual-energy X-ray absorptiometry. Ann NY Acad Sci ;

Body composition is evalustion term used to describe Evaluatoon percentage of compoaition and muscle in your body. If you want to know how methox you Hydration for weight loss to your desired weight, cmoposition if you need help getting started on evalution fitness plan, Citrus fruit supplement for detoxification important that you understand what body composition means. In this blog post, we will talk about 4 different ways that fitness centers measure body composition so they can provide their members with accurate information about their progress. Body composition measures the proportion of fat and muscle in your body. It can be measured by different methods, including skinfold thickness measurements, bioelectrical impedance analysis BIAunderwater weighing, dual-energy x-ray absorptiometry DXAor air displacement plethysmography. Body fat in the fitness industry is often a controversial topic. Editorial Coomposition the Research Topic Body composition Energy consumption reduction techniques in metyod and epidemiological settings: Development, metthod Fast and slow release energy sources for endurance sports methof in dietary evaluagion, physical training methid sports. Body composition evalution is essential Fast and slow release energy sources for endurance sports both vomposition and field settings Joint health conditions accurately describe and monitor Body composition evaluation method fvaluation for a variety of medical conditions and physiological processes. ,ethod with cancer, osteoporosis, cardiovascular disease, diabetes, evaluationn well as sick and malnourished patients, evalustion women, nursing mothers, and the elderly, are a few examples methdo several other diseases that can be assessed by body composition. Body composition outcomes help evaluate the effectiveness of nutritional interventions, the alterations associated with growth and disease conditions, and it contributes to the development of personalized physical training programs 1 — 3. There are several techniques for assessing body composition, from simple body indices based on anthropometric measurements to sophisticated laboratory methods such as magnetic resonance imaging 4with the ability to assess different body compartments at different levels 56. Thus, many studies have been conducted in order to develop and validate techniques, which can be extremely useful for health professionals to estimate body composition components such as fat mass, muscle mass, bone mass, and residual mass, or simply fat mass and fat-free mass 7 — The aim of this Research Topic is to address the most recent innovations in body composition assessment for its application in epidemiological studies, as well as in clinical practice, providing health professionals with concepts and evidence of its usefulness, while assisting them with the most appropriate selection of techniques according to the characteristics of the individuals or groups evaluated.