From a performance stand point, excess body fat lowers your work to weight ratio, This means that a heavier person would consume more energy per minute of work resulting in a lower energy economy during activity.

In addition, excess body fat can lead to additional loads placed on joint during weight bearing activities such as running, causing joint distress. Healthy or athletic body fat percentages typically allow for more optimal performances, due to the improved economy and reduced injuries.

The immune system is often impaired when body fat stores are too low. A reduced ability to fight infections means more interruptions in training and more chance of being sick on race day.

For female athletes, there are some very immediate consequences of a low body fat level, including a fall in circulating oestrogen levels.

This in turn can lead to a loss of bone mass, causing problems for women in later life through an increased risk of bone fracture. Assessing body fat can be done using the following methodologies: Hydrostatic weighing, skinfold assessment and bio-electrical impedance.

Of these methods, one that is both accurate and practical is skinfold measurement. The measurements are taken with calipers, which gauge the skinfold thickness in millimeters of areas where fat typically accumulates i. Once the measurements are recorded, the numbers are inserted into an equation that calculates a body fat percentage and alternatively body lean mass.

Skinfold is a preferred method of body fat measurement for non-clinical settings because it is easy to administer with proven accuracy and is not obtrusive with regards to the patient. It also provides much more data than just the final composition measurement - it also yields the thickness of many sites, which can be used as bases of comparison with future results.

For example, an abdominal skinfold improvement from 35mm to 24mm would show a significant improvement in that site even if the overall body fat percentage may have only reduced minimally.

BMI is often mistaken as measurable guide to body fat. However, BMI is simply a weight to height ratio. It is a tool for indicating weight status in adults and general health in large populations. BMI correlates mildly with body fat but when used in conjunction with a body fat measurement gives a very accurate presentation of your current weight status.

With that being said, an elevated BMI above 30 significantly increases your risk of developing long-term and disabling conditions such as hypertension, diabetes mellitus, gallstones, stroke, osteoarthritis, and some forms of cancer. For adults over 20 years old, BMI typically falls into one of the above categories see table above.

UC Davis Health School of Medicine Betty Irene Moore School of Nursing News Careers Giving. menu icon Menu. Sports Medicine. Enter search words 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.

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Inserm — UA07 — Rayonnement Synchrotron pour la Recherche Biomédicale STROBE ID17 Installation Européenne du Rayonnement Synchrotron ESRF , Grenoble, France.

UM Sports Pathologies, Hôpital Sud, Avenue Kimberley, CS , , Echirolles-Cedex, France. You can also search for this author in PubMed Google Scholar. Me: Conceptualization, Methodology. E: Conceptualization, Methodology, Supervision, Pictures Acquisition, Writing—Original Draft, Data.

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The body composition results yielded by hydrostatic weighing are pretty accurate. BUT, the space requirements are impractical. Often, third-party companies will drive around trucks with a dunk tank inside and charge a fee for the assessment.

And on top of that, trying to convince members to get wet is not an easy challenge to overcome. An individual is weighed on dry land and then is asked to sit submerged entirely underwater. The individual will then expel as much air from their lungs while their underwater weight is taken.

The underwater weight is then compared to dry land weight and, using a specific formula, determines a person's body composition. Hydrostatic weighing is based upon the Archimedes Principle , which says that the buoyant force on a submerged object is equal to the weight of the fluid displaced by the thing.

In other words, denser material i. Pretty smart, eh? Pros: Hydrostatic weighing is an incredibly accurate technique that yields precise body composition results if done correctly. Always a plus! Cons: Ah yes, the downsides. The results of hydrostatic weighing may be compromised if an individual does not literally breath out every last breath gah!

User Experience: Good for Pisces who loves water, but bad for everyone else who doesn't. ROI: If you and your customers are serious about results, then reaching out to a service that provides hydrostatic weighing is a good investment. It's a great value that adds to a memorable experience.

Bottom Line: Accurate, and not a ton of room for error. But the user experience is not great. Moreover, it's not something you can include in your facility on a permanent basis. There may be better options for you. With ADP, it is important to breathe literally and not be intimidated by the name!

Really, ADP is much like the dunk tank method minus the scuba diver experience and will have you noticing "something in the air". An individual enters a small chamber-like structure.

At this time, changes in chamber pressure are recorded to determine body volume. ADP uses one's body mass and volume to gauge body density.

The method uses air instead of water to measure volume. Using those density readings, ADP then calculates the proportions of lean to fat mass i. Pros: ADP's easily accessible chamber and non-invasive method make it a great option for the more vulnerable communities such as the elderly, children, and those with physical handicaps.

Cons: There are many variables including hydration and body temperature that can cloud ADP's body composition results; these variables can make it challenging to receive an accurate reading, especially for the active gym population who are often being tested pre and post-workout.

The last thing we want is for our workouts to interfere with our readings. It's why we're being evaluated in the first place!

User Experience: Claustrophobic much? ADP is definitely not good for those who hate enclosed spaces. However, if you have people willing to "brave the cocoon," the process is pretty straightforward and non-invasive. However, there's also the not-so-fun hair cap you need to wear to get consistent results.

ROI: Very accurate, but much better for an academic or medical setting. You might have more trouble trying to sell it as a value-add in a commercial setting like a health club. Bottom Line: Quite expensive takes a lot of space, but it can be pretty accurate if controlled correctly.

Bioelectrical impedance BIA puts the buzz in body composition measurements with internal electrical calculations. BIA devices are incredibly mobile and can be used in various spaces, which makes them optimal for small gyms or wellness centers.

However, beware of accuracy after chugging that Gatorade post-workout! An individual stands on a platform and wraps their hands around the two available handles.

To get bioelectrical impedance measurements, the individual is asked to hold their breath and relax. At this relaxed state, the BIA device sends a small electrical pulse through the body.

For about 20 seconds, tiny electrical impulses run up both legs and arms. Some systems, though, only have arm holds. Bioelectrical impedance runs a small current of electricity through the whole body to gauge body composition. The method relies on the currents to easily permeate a cell's membrane.

Resistance to the current from water is a function of how hydrated your body is and is correlated with your body fat percentage. Like other methods, BIA doesn't explicitly measure fat, but rather it infers body fat from a direct measurement of something else, which in this case is water.

Pros: The number of total body water TBW is determined using a BIA machine, which is significantly quicker than the DEXA test 15 minutes and repeated "dunks" of hydrostatic weighing. Additionally, the device is easily movable, as the 3D body scanning method.

It is also reasonably simple to run a BIA machine without the assistance of a professional. Cons: Like Air Displacement Plethysmography, variables such as hydration amount can compromise the accuracy and precision of results. This again makes BIA difficult for athletes or gym-goers who are looking for the most optimal body composition results.

In addition, electrical currents also make BIA unsuitable for pregnant and pacemaker populations. User Experience: For those who like a quick and painless process aka all of us , this is a great option.

Just don't down that massive electrolyte water before hopping in if you want accurate results! ROI: Portable. Easy to use. It can be easily monetized as a value-added service. Overall, a great investment.

Bottom Line: A great option for mobility, size, and relatively accurate body composition results. However, be aware of the variables that can skew results and set expectations with members or clients inappropriately.

Moreover, there's a big range of devices in this category with a relatively high price tag, so choose wisely. Aside from sounding like a character from X-men, the DEXA scan is definitely a medical standard in measuring body composition.

The DEXA scan exposes patients to minimal amounts of ionizing radiation, which makes it different from other radio waves or ultrasound methods. The individual lies down on the exam table as the DEXA scanner moves over them. This process takes about 15 minutes, and it is exactly what a cool Sci-Fi movie looks like.

The DEXA duel-energy X-ray absorptiometry scan, originally focused on measuring bone mineral density, is now widely used as a tool to measure body composition as well. DEXA accomplishes this by running two beams of light over the bone and gauging how much light is absorbed.

The denser the material i. This reading is then converted into a body fat percentage. Pros: Accurate results; measures bone density as well as body composition. It's probably the most well-validated body composition method in the academic world, other than the MRI.

DEXA scans you with X-rays, which can be harmful to your health if exposure is too frequent. User Experience: Let's face it. We all love lying down and relaxing while being assessed.

But X-rays are dangerous, and there's some convincing to do to get people scanned. ROI: The most expensive choice on this list might not be the most practical investment for a facility due to its relatively high price tag.

Nonetheless, third-party assessment services are offering DEXA scans as a service, which is becoming more common. Hiring a third party for DEXA scans and building it into your wellness program, fat-loss shred, inch-loss competition, or simply as a general as an added value could be a great way to increase membership sales and retention.

Bottom Line: Amazing medical-grade device and technology. It is not so practical for most gyms and wellness centers given their size, price, and assessment process, but not a bad idea to provide a service to members through a third-party service. We love the way that it engages clients both visually and numerically.

One of the key reasons why body scanning took first place is the fact that it builds on the power of the caliper and circumference methods without human error. Like calipers and the circumference methods, 3D body scanners body surface imaging technology are the most direct ways to measure body fat that you can have without taking an MRI.

However, not all body scanners are built the same. Therefore, be careful and ensure that the digital tape measurements your body scanner of choice uses are precise and accurate.

An individual stands on a rotating platform with arms stretched out and hands in fists. The scan takes about 40 seconds one full rotation while a 3D image of the individual is being rendered.

Using non-invasive 3D cameras, body scanning captures surface data of an individual and renders an exact 3D model.

This is accomplished with harmless infrared light that reflects off the whole body but is invisible to the eye. Digital measurements on the body's surface are then taken that replicate a tape measure but are without the imprecision of human sizes.

Additional measurements, such as volume and surface area, can be calculated as well from the 3D model. These measurements are then used to calculate body composition.

The research is so promising that the NIH just approved a multi-million-dollar grant to two prominent body composition institutions to further investigate these body scanners, with the goal of commercial viability and dependability as a major motivation.

Pros: The 3D Scanner is easily transportable and, like BIA, only takes a short amount of time. The 3D visuals are extremely engaging and become a great conversation starter. Many gyms will use 3D body scanning as a customer acquisition tool to demonstrate the sophistication of their services.

That's especially important if their total body weight is not changing, but their shape is transforming.

Cons: Because 3D body scanning uses a non-invasive and surface-only camera, individuals wearing baggy or loose-fitting clothing will not elicit precise results.

Therefore, subjects must wear form-fitting clothing. Men, however, don't usually own compression shorts. User Experience: It definitely ranks high on the "personal space" scale as users do not have to be touched.

Also, we love the quick assessment time and ability to see the body composition output in 3D form. It gave a better estimation of energy expenditure than did the Schofield predictive equation [ 36 ].

However, in 9 anorexia nervosa patients with a mean BMI of In overweight or obese patients, the muscle catabolism in response to inflammation was the same as that observed in patients with normal BMI.

Indeed, despite a higher BMI, the FFM of overweight or obese individuals is similar or slightly increased to that of patients with normal BMI. Thus, the use of actual weight for the assessment of the energy needs of obese patients would result in overfeeding and its related complications.

Thus, follow-up of FFM by BIA could help optimize nutritional intakes when indirect calorimetry cannot be performed. Body composition evaluation allows a qualitative assessment of body weight variations.

Body composition evaluation could be used for the follow-up of healthy elderly subjects [ 90 ]. Body composition evaluation allows characterization of the increase in body mass in terms of FFM and FM [ 81,91 ].

After hematopoietic stem cell transplantation, the increase in BMI is the result of the increase in FM, but not of the increase in FFM [ 81 ]. By identifying the patients gaining weight but reporting no or insufficient FFM, body composition evaluation could contribute to influencing the medical decision of continuing nutritional support that would have been stopped in the absence of body composition evaluation.

In summary, body composition evaluation is of the utmost interest for the follow-up of nutritional support and its impact on body compartments. This point has been recently illustrated in oncology patients with sarcopenic obesity. FFM loss was determined by CT as described above. In cancer patients, some therapies could affect body composition by inducing muscle wasting [ 92 ].

In turn, muscle wasting in patients with BMI less than 25 was significantly associated with sorafenib toxicity in patients with metastatic renal cancer [ 8 ]. In metastatic breast cancer patients receiving capecitabine treatment, and in patients with colorectal cancer receiving 5-fluoro-uracile, using the convention of dosing per unit of body surface area, FFM loss was the determinant of chemotherapy toxicity [ 9,10 ] and time to tumor progression [ 10 ].

In colorectal cancer patients administered 5-fluoro-uracil, low FFM is a significant predictor of toxicity only in female patients [ 9 ]. The variation in toxicity between women and men may be partially explained by the fact that FFM was lower in females.

Indeed, FFM represents the distribution volume of most cytotoxic chemotherapy drugs. In 2, cancer patients, the individual variations in FFM could change by up to three times the distribution volume of the chemotherapy drug per body area unit [ 5 ].

Thus, administering the same doses of chemotherapy drugs to a patient with a low FFM compared to a patient with a normal FFM would increase the risk of chemotherapy toxicity [ 5 ]. These data suggest that FFM loss could have a direct impact on the clinical outcome of cancer patients.

These findings justify the systematic evaluation of body composition in all cancer patients in order to detect FFM loss, tailor chemotherapy doses according to FFM values, and then improve the efficacy-tolerance and cost-efficiency ratios of the therapeutic strategies [ 93 ].

corticosteroids, immunosuppressors infliximab, azathioprine or methotrexate , or sedatives propofol. In summary, measurement of FFM should be implemented in cancer patients treated with chemotherapy.

Clinical studies are needed to demonstrate the importance of measuring body composition in patients treated with other medical treatments. The implementation of body composition evaluation in routine care presents a challenge for the next decades. Indeed the concomitant increases in elderly subjects and patients with chronic diseases and cancer, and in the prevalence of overweight and obesity in the population, will increase the number of patients nutritionally at risk or undernourished, particularly those with sarcopenic obesity.

Body composition evaluation should be used to improve the screening of undernutrition in hospitalized patients. The results could be expressed according to previously described percentiles of healthy subjects [ 95,96 ].

Body composition evaluation should be performed at the different stages of the disease, during the course of treatments and the rehabilitation phase.

BIA, L3-targeted CT, and DEXA represent the techniques of choice to evaluate body composition in clinical practice fig.

In the setting of cost-effective and pragmatic use, these three techniques should be alternatively chosen. In cancer, undernourished, and nutritionally at-risk patients, an abdominal CT should be completed by the analysis of L3-targeted images for the evaluation of body composition.

In other situations, BIA appears to be the simplest most reproducible and less expensive method, while DEXA, if feasible, remains the reference method for clinical practice.

By allowing earlier management of undernutrition, body composition evaluation can contribute to reducing malnutrition-induced morbidity and mortality, improving the quality of life and, as a consequence, increasing the medico-economic benefits fig. The latter needs to be demonstrated.

Moreover, based on a more scientific approach, i. allowing for printing reports, objective initial assessment and follow-up of nutritional status, and the adjustment of drug doses, body composition evaluation would contribute to a better recognition of the activities related to nutritional evaluation and care by the medical community, health care facilities, and health authorities fig.

Screening of undernutrition is insufficient to allow for optimal nutrition care. This is in part due to the lack of sensitivity of BMI and weight loss for detecting FFM loss in patients with chronic diseases.

Methods of body composition evaluation allow a quantitative measurement of FFM changes during the course of disease and could be used to detect FFM loss in the setting of an objective, systematic, and early undernutrition screening.

FFM loss is closely related to impaired clinical outcomes, survival, and quality of life, as well as increased therapy toxicity in cancer patients. Thus, body composition evaluation should be integrated into clinical practice for the initial assessment, sequential follow-up of nutritional status, and the tailoring of nutritional and disease-specific therapies.

Body composition evaluation could contribute to strengthening the role and credibility of nutrition in the global medical management, reducing the negative impact of malnutrition on the clinical outcome and quality of life, thereby increasing the overall medico-economic benefits.

Thibault and C. Pichard are supported by research grants from the public foundation Nutrition Plus. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest.

filter your search All Content All Journals Annals of Nutrition and Metabolism. Advanced Search. Skip Nav Destination Close navigation menu Article navigation. Volume 60, Issue 1. Rationale for a New Strategy for the Screening of Undernutrition.

Body Composition Evaluation for the Assessment of Nutritional Status. Body Composition Evaluation for the Calculation of Energy Needs. Body Composition Evaluation for the Follow-Up and Tailoring of Nutritional Support. Body Composition Evaluation for Tailoring Medical Treatments.

Towards the Implementation of Body Composition Evaluation in Clinical Practice. Disclosure Statement. Article Navigation. Review Articles December 16 The Evaluation of Body Composition: A Useful Tool for Clinical Practice Subject Area: Endocrinology , Further Areas , Nutrition and Dietetics , Public Health.

Ronan Thibault ; Ronan Thibault. a Centre de Recherche en Nutrition Humaine Auvergne, UMR Nutrition Humaine, INRA, Clermont Université, Service de Nutrition Clinique, CHU de Clermont-Ferrand, Clermont-Ferrand, France;. This Site. Google Scholar. Claude Pichard Claude Pichard. b Nutrition Unit, Geneva University Hospital, Geneva, Switzerland.

Ann Nutr Metab 60 1 : 6— Article history Received:. Cite Icon Cite. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. View large Download slide.

Table 1 Main reasons for the lack of nutritional screening at hospitals. View large. View Large. Table 2 Mean values of body composition compartments adapted from Pichard and Kyle [ 19 and Wang et al.

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Stenholm S, Harris TB, Rantanen T, Visser M, Kritchevsky SB, Ferrucci L: Sarcopenic obesity: definition, cause and consequences. Curr Opin Clin Nutr Metab Care ;— Pichard C, Kyle UG: Body composition measurements during wasting diseases.

Wang ZM, Pierson RN Jr, Heymsfield SB: The five-level model: a new approach to organizing body-composition research. Schols AM, Broekhuizen R, Weling-Scheepers CA, Wouters EF: Body composition and mortality in chronic obstructive pulmonary disease.

Slinde F, Gronberg A, Engstrom CP, Rossander-Hulthen L, Larsson S: Body composition by bioelectrical impedance predicts mortality in chronic obstructive pulmonary disease patients. Respir Med ;— Vestbo J, Prescott E, Almdal T, Dahl M, Nordestgaard BG, Andersen T, Sorensen TI, Lange P: Body mass, fat-free body mass, and prognosis in patients with chronic obstructive pulmonary disease from a random population sample: findings from the Copenhagen City Heart Study.

Am J Respir Crit Care Med ;— Segall L, Mardare NG, Ungureanu S, Busuioc M, Nistor I, Enache R, Marian S, Covic A: Nutritional status evaluation and survival in haemodialysis patients in one centre from Romania.

Nephrol Dial Transplant ;— Beddhu S, Pappas LM, Ramkumar N, Samore M: Effects of body size and body composition on survival in hemodialysis patients. J Am Soc Nephrol ;— Fürstenberg A, Davenport A: Assessment of body composition in peritoneal dialysis patients using bioelectrical impedance and dual-energy X-ray absorptiometry.

Am J Nephrol ;— Futter JE, Cleland JG, Clark AL: Body mass indices and outcome in patients with chronic heart failure. Eur J Heart Fail ;— Marin B, Desport JC, Kajeu P, Jesus P, Nicolaud B, Nicol M, Preux PM, Couratier P: Alteration of nutritional status at diagnosis is a prognostic factor for survival of amyotrophic lateral sclerosis patients.

J Neurol Neurosurg Psychiatry ;— Janiszewski PM, Oeffinger KC, Church TS, Dunn AL, Eshelman DA, Victor RG, Brooks S, Turoff AJ, Sinclair E, Murray JC, Bashore L, Ross R: Abdominal obesity, liver fat, and muscle composition in survivors of childhood acute lymphoblastic leukemia.

J Clin Endocrinol Metab ;— Wagner D, Adunka C, Kniepeiss D, Jakoby E, Schaffellner S, Kandlbauer M, Fahrleitner-Pammer A, Roller RE, Kornprat P, Müller H, Iberer F, Tscheliessnigg KH: Serum albumin, subjective global assessment, body mass index and the bioimpedance analysis in the assessment of malnutrition in patients up to 15 years after liver transplantation.

Clin Transplant ;E—E Kimyagarov S, Klid R, Levenkrohn S, Fleissig Y, Kopel B, Arad M, Adunsky A: Body mass index BMI , body composition and mortality of nursing home elderly residents. Arch Gerontol Geriatr ;— J Nutr Health Aging ;— Schols AM, Wouters EF, Soeters PB, Westerterp KR: Body composition by bioelectrical-impedance analysis compared with deuterium dilution and skinfold anthropometry in patients with chronic obstructive pulmonary disease.

Thibault R, Le Gallic E, Picard-Kossovsky M, Darmaun D, Chambellan A: Assessment of nutritional status and body composition in patients with COPD: comparison of several methods in French.

Rev Mal Respir ;— Kyle UG, Janssens JP, Rochat T, Raguso CA, Pichard C: Body composition in patients with chronic hypercapnic respiratory failure. Rieken R, van Goudoever JB, Schierbeek H, Willemsen SP, Calis EA, Tibboel D, Evenhuis HM, Penning C: Measuring body composition and energy expenditure in children with severe neurologic impairment and intellectual disability.

Kidney Int Suppl ;S37—S Frisancho AR: New norms of upper limb fat and muscle areas for assessment of nutritional status. Caregaro L, Alberino F, Amodio P, Merkel C, Bolognesi M, Angeli P, Gatta A: Malnutrition in alcoholic and virus-related cirrhosis.

Am J Clin Nutr l;— Alberino F, Gatta A, Amodio P, Merkel C, Di Pascoli L, Boffo G, Caregaro L: Nutrition and survival in patients with liver cirrhosis. Nutrition ;— Liu E, Spiegelman D, Semu H, Hawkins C, Chalamilla G, Aveika A, Nyamsangia S, Mehta S, Mtasiwa D, Fawzi W: Nutritional status and mortality among HIV-infected patients receiving antiretroviral therapy in Tanzania.

J Infect Dis ;— Soler-Cataluna JJ, Sanchez-Sanchez L, Martinez-Garcia MA, Sanchez PR, Salcedo E, Navarro M: Mid-arm muscle area is a better predictor of mortality than body mass index in COPD. Marquis K, Debigaré R, Lacasse Y, LeBlanc P, Jobin J, Carrier G, Maltais F: Midthigh muscle cross-sectional area is a better predictor of mortality than body mass index in patients with chronic obstructive pulmonary disease.

Am J Respir Crit Care Med ;15;— Kyle UG, Pirlich M, Lochs H, Schuetz T, Pichard C: Increased length of hospital stay in underweight and overweight patients at hospital admission: a controlled population study. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, Heitmann BL, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols AM, Pichard C, Composition of the ESPEN Working Group.

Bioelectrical impedance analysis. Review of principles and methods. Santarpia L, Marra M, Montagnese C, Alfonsi L, Pasanisi F, Contaldo F: Prognostic significance of bioelectrical impedance phase angle in advanced cancer: preliminary observations.

Gupta D, Lammersfeld CA, Vashi PG, King J, Dahlk SL, Grutsch JF, Lis CG: Bioelectrical impedance phase angle in clinical practice: implications for prognosis in stage IIIB and IV non-small cell lung cancer. BMC Cancer ; Gupta D, Lis CG, Dahlk SL, Vashi PG, Grutsch JF, Lammersfeld CA: Bioelectrical impedance phase angle as a prognostic indicator in advanced pancreatic cancer.

Gupta D, Lammersfeld CA, Burrows JL, Dahlk SL, Vashi PG, Grutsch JF, Hoffman S, Lis CG: Bioelectrical impedance phase angle in clinical practice: implications for prognosis in advanced colorectal cancer.

Paiva SI, Borges LR, Halpern-Silveira D, Assunção MC, Barros AJ, Gonzalez MC: Standardized phase angle from bioelectrical impedance analysis as prognostic factor for survival in patients with cancer. Schwenk A, Beisenherz A, Römer K, Kremer G, Salzberger B, Elia M: Phase angle from bioelectrical impedance analysis remains an independent predictive marker in HIV-infected patients in the era of highly active antiretroviral treatment.

Desport JC, Marin B, Funalot B, Preux PM, Couratier P: Phase angle is a prognostic factor for survival in amyotrophic lateral sclerosis. Amyotroph Lateral Scler ;— Wirth R, Volkert D, Rösler A, Sieber CC, Bauer JM: Bioelectric impedance phase angle is associated with hospital mortality of geriatric patients.

Mushnick R, Fein PA, Mittman N, Goel N, Chattopadhyay J, Avram MM: Relationship of bioelectrical impedance parameters to nutrition and survival in peritoneal dialysis patients.

Kidney Int Suppl ;S53—S Selberg O, Selberg D: Norms and correlates of bioimpedance phase angle in healthy human subjects, hospitalized patients, and patients with liver cirrhosis.

Eur J Appl Physiol ;— Shah S, Whalen C, Kotler DP, Mayanja H, Namale A, Melikian G, Mugerwa R, Semba RD: Severity of human immunodeficiency virus infection is associated with decreased phase angle, fat mass and body cell mass in adults with pulmonary tuberculosis infection in Uganda.

J Nutr ;— Barbosa-Silva MC, Barros AJ: Bioelectric impedance and individual characteristics as prognostic factors for post-operative complications.

Durnin JV, Womersley J: Body fat assessed from total body density and its estimation from skinfold thickness: measurements on men and women aged from 16 to 72 years. Hill GL: Body composition research: implications for the practice of clinical nutrition.

JPEN J Parenter Enter Nutr ;— Pierson RN Jr, Wang J, Thornton JC, Van Itallie TB, Colt EW: Body potassium by four-pi 40K counting: an anthropometric correction. Am J Physiol ;F—F Sohlström A, Forsum E: Changes in total body fat during the human reproductive cycle as assessed by magnetic resonance imaging, body water dilution, and skinfold thickness: a comparison of methods.

Leonard CM, Roza MA, Barr RD, Webber CE: Reproducibility of DXA measurements of bone mineral density and body composition in children. Pediatr Radiol ;— Genton L, Karsegard VL, Zawadynski S, Kyle UG, Pichard C, Golay A, Hans DB: Comparison of body weight and composition measured by two different dual energy X-ray absorptiometry devices and three acquisition modes in obese women.

Jaffrin MY: Body composition determination by bioimpedance: an update. Kyle UG, Pichard C, Rochat T, Slosman DO, Fitting JW, Thiebaud D: New bioelectrical impedance formula for patients with respiratory insufficiency: comparison to dual-energy X-ray absorptiometry.

Eur Respir J ;— Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel Gómez J, Lilienthal Heitmann B, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols AMWJ, Pichard C, ESPEN: Bioelectrical impedance analysis.

Utilization in clinical practice. Mourtzakis M, Prado CM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE: A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab ;— Bolton CE, Ionescu AA, Shiels KM, Pettit RJ, Edwards PH, Stone MD, Nixon LS, Evans WD, Griffiths TL, Shale DJ: Associated loss of fat-free mass and bone mineral density in chronic obstructive pulmonary disease.

Kyle UG, Genton L, Karsegard L, Slosman DO, Pichard C: Single prediction equation for bioelectrical impedance analysis in adults aged 20—94 years. Kyle UG, Genton L, Slosman DO, Pichard C: Fat-free and fat mass percentiles in 5, healthy subjects aged 15 to 98 years.