Influence of the type of electrodes in the assessment of body composition by bioelectrical impedance analysis in the supine position.

BACKGROUND & AIMS: The main source of error in body composition assessment of bedridden patients by bioelectrical impedance analysis (BIA) is the electrode inadequacy and placement. As electrocardiogram (ECG) electrodes are often used for BIA measurements, this study aimed to compare three of them with a reference BIA electrode. METHODS: BIA was performed sequentially on 24 healthy subjects in the supine position, using 3 different ECG electrodes (3M® Red Dot® 2330; Ambu® BlueSensor 2300; Ambu® BlueSensor SU-00-C) and the reference electrode (Bianostic AT®) for the BIA device (Nutriguard-M®, Data Input, Germany). Resistance (R), reactance (Xc), phase angle (PhA), appendicular skeletal muscle index (ASMI), fat-free mass index (FFMI) and fat mass percentage (FM%) obtained with the different electrodes were compared using Bland-Altman plots, repeated measures one-way ANOVA and paired t-test. Patient characteristics potentially involved in BIA measurement differences were assessed using linear regression analysis. RESULTS: The study population consisted of 9 men and 15 women, 33% and 47% of whom were overweight, respectively. The measured R was within the physiological range for all men (428-561 Ω) and women (472-678 Ω), regardless of the type of electrodes used. Compared to the reference electrode, the 3M® Red Dot® 2330 and Ambu® BlueSensor SU-00-C electrodes gave significantly different Xc and PhA values, but only the Ambu® BlueSensor SU-00-C gave significantly different ASMI, FFMI and FM% at 50 kHz, with biases of -0.2 kg/m2, -0.3 kg/m2 and +1.4%, respectively. The higher the current frequency, the lower was the Xc and PhA measured by the Ambu® BlueSensor SU-00-C compared to the reference electrode. These measurement differences seemed mainly due to the too small gel area of the Ambu® BlueSensor SU-00-C (154 mm2) compared to the reference electrode (1311 mm2). CONCLUSIONS: The use of electrodes with small gel area affects BIA measurement in the supine position, especially when PhA is used as an indicator of the nutritional status. Therefore, it is essential to specify the type of electrodes and carry out comparative tests before changing consumables for body composition assessment, to ensure BIA measurement reliability in clinical and research settings.

Association of phase angle and running performance.

BACKGROUND: A high phase angle derived from bioelectrical impedance analysis has been linked to a high level of physical activity. However, it is unknown whether a high phase angle is related to running performance. METHODS: We included all subjects who participated for the first time to the Course de l'Escalade between 1999 and 2016, a yearly city run occurring in Geneva. The subjects underwent a measurement by 50-kHz tetrapolar bioelectrical impedance analysis (Nutriguard®). Running time was converted to running speed in km/h. Results are shown as mean (SD) and as frequencies. We performed sex-specific univariate and multivariate regressions, adjusted for age, body mass index, categories of running distance and year of measurement, to evaluate whether the phase angle is associated with running speed. RESULTS: We analyzed 2264 subjects (1025 women and 1239 men). In univariate regressions, phase angle was significantly related to running speed in women (coeff 0.52, 95% CI 0.35-0.67, p < 0.001, adjusted R2 0.037) and men (coeff 0.57, 95% CI 0.42-0.73, p < 0.001, adjusted R2 0.039). Multivariate regressions showed that the phase angle was still significantly associated with running speed in women and men (p < 0.001 for both models), with an adjusted R2 of 0.262 and 0.282, respectively. CONCLUSIONS: The phase angle is positively associated with running performance in men and women. It remains to be demonstrated if this association reflects the benefit of regular training and whether the phase angle might be suitable to monitor improvements in running performance. CLINICAL TRIAL REGISTRY: clinicaltrials.gov, identifier: NCT03400761.

An Increase in Fat Mass Index Predicts a Deterioration of Running Speed.

A low fat mass is associated with a good running performance. This study explores whether modifications in body composition predicted changes in running speed. We included people who underwent several measurements of body composition by bioelectrical impedance analysis between 1999 and 2016, at the "Course de l'Escalade", taking place yearly in Geneva. Body composition was reported as a fat-free mass index (FFMI) and fat mass index (FMI). Running distances (men: 7.2 km; women: 4.8 km) and running times were used to calculate speed in km/h. We performed multivariate linear mixed regression models to determine whether modifications of body mass index, FFMI, FMI or the combination of FFMI and FMI predicted changes in running speed. The study population included 377 women (1419 observations) and 509 men (2161 observations). Changes in running speed were best predicted by the combination of FFMI and FMI. Running speed improved with a reduction of FMI in both sexes (women: ß -0.31; 95% CI -0.35 to -0.27, p < 0.001. men: ß -0.43; 95% CI -0.48 to -0.39, p < 0.001) and a reduction of FFMI in men (ß -0.20; 95% CI -0.26 to -0.15, p < 0.001). Adjusted for body composition, the decline in running performance occurred from 50 years onward, but appeared earlier with a body mass, FFMI or FMI above the median value at baseline. Changes of running speed are determined mostly by changes in FMI. The decline in running performance occurs from 50 years onward but appears earlier in people with a high body mass index, FFMI or FMI at baseline.

Running performance in a timed city run and body composition: A cross-sectional study in more than 3000 runners.

OBJECTIVE: The importance of body composition for running performance is unclear in the general population. The aim of this study was to evaluate whether body composition influences running speed and whether it is a better predictor of running speed than body mass index (BMI). METHODS: The study included 1353 women (38.2 ± 12.1 y of age) and 1771 men (39.6 ± 12.1 y of age) who underwent, for the first time, a measurement of body composition by bioelectrical impedance analysis between 1999 and 2016, before a timed run occurring annually in Geneva. The running distances and times were converted to average speed (km/h). Body composition was expressed as sex-specific quartiles, where quartile 1 (lowest values) was the reference quartile. The relationships between speed and BMI or body composition were analyzed by multivariate linear regressions. RESULTS: Multivariate regressions showed that the higher the fat mass index (FMI) quartile, the lower the running speed in women and men (all P < 0.001). In men, a fat-free mass index (FFMI) in quartile 4 (>20 kg/m2) was associated with a poor running performance (r = -0.50, P < 0.001), whereas in women, an FFMI in quartile 2 or 3 (15-16.4 kg/m2) was associated with a higher running speed (r = 0.23, P = 0.04; r = 0.28, P = 0.01, respectively). Body composition predicted speed better than BMI in women (R2 = 26.8% versus 14.4%) and men (R2 = 29.8% versus 25.4%). CONCLUSIONS: Running speed is negatively associated with BMI and FMI in both sexes. Body composition is a better predictor of running performance than BMI.

Bioimpedance-Derived Phase Angle and Mortality Among Older People.

BACKGROUND: Phase angle measured by bioelectrical impedance analysis (BIA) may be a marker of health state. OBJECTIVE: This historical cohort study of prospectively collected BIA measurements aims to investigate the link between phase angle and mortality in older people and evaluate whether a phase angle cutoff can be defined. DESIGN: We included all adults aged ≥65 years who underwent a BIA measurement by the Nutriguard® device at the Geneva University Hospitals. We retrieved retrospectively the phase angle and comorbidities at the last BIA measurement and mortality until December 2012. We calculated phase angle standardized for sex, age, and body mass index (BMI), using reference values determined with the same brand of BIA device. Sex-specific and standardized phase angle were categorized into quartiles. The association of mortality with sex-specific or standardized phase angle was evaluated through univariate and multivariate Cox regression models, Kaplan-Meier curves, and receiver operating characteristic (ROC) curves. RESULTS: We included 1307 (38% women) participants, among whom 628 (44% women) died. In a multivariate Cox regression model adjusted for comorbidities and setting of measurement (ambulatory vs. hospitalized), the protective effect against mortality increased progressively as the standardized phase angle quartile increased (HR 0.71 [95% CI 0.58, 0.86], 0.53 [95% CI 0.42, 0.67], and 0.32 [95% CI 0.23, 0.43]). The discriminative value of continuous standardized phase angle, assessed as the area under the ROC curve, was 0.72 (95%CI 0.70, 0.75). We could not define an acceptable phase angle cutoff for individual prediction of mortality (LK), based on sensibility and specificity values. CONCLUSIONS: This study shows the association of phase angle and mortality in older patients, independent of age, sex, comorbidities, BMI categories, and setting of measurement.

Impact of body composition changes on risk of all-cause mortality in older adults.

PURPOSE: This study evaluates the relationship between body mass index (BMI), fat mass index (FMI) and fat-free mass index (FFMI) changes and mortality in persons ≥65 years. METHODS: Adults ≥65 years with at least two body composition measurements (BCM) between 1990 and 2011 were included. We excluded persons who died within one month of the second BCM and who had two single BCM in a one-month timeframe. Mortality data was retrieved until December 2012. For each person, we calculated the regression slopes for BMI, FMI and FFMI changes. Significant positive slopes were categorized as "gain", negative slopes as "loss" and the others as "maintenance". The impact of body composition changes was evaluated by Cox regression models while adjusting for sex, age, co-morbidities and body composition at the last measurement. RESULTS: We included 791 persons with 3049 BCM. After adjustment for sex, and age and co-morbidities, a loss of FFMI, but not of FMI or BMI, increased the risk of mortality (HR 2.02, 95%CI 1.28-3.19). The prediction of mortality with FFMI loss remained significant when further adjusting for FMI loss and the last available body composition (HR 1.68, 95%CI 1.04-2.70). CONCLUSIONS: FFMI loss is related to increased mortality in older persons.

Body composition and all-cause mortality in subjects older than 65 y.

BACKGROUND: A low or high body mass index (BMI) has been associated with increased mortality risk in older subjects without taking fat mass index (FMI) and fat-free mass index (FFMI) into account. This information is essential because FMI is modulated through different healthcare strategies than is FFMI. OBJECTIVE: We aimed to determine the relation between body composition and mortality in older subjects. DESIGN: We included all adults ≥65 y old who were living in Switzerland and had a body-composition measurement by bioelectrical impedance analysis at the Geneva University Hospitals between 1990 and 2011. FMI and FFMI were divided into sex-specific quartiles. Quartile 1 (i.e., the reference category) corresponded to the lowest FMI or FFMI quartile. Mortality data were retrieved from the hospital database, the Geneva death register, and the Swiss National Cohort until December 2012. Comorbidities were assessed by using the Cumulative Illness Rating Scale. RESULTS: Of 3181 subjects included, 766 women and 1007 men died at a mean age of 82.8 and 78.5 y, respectively. Sex-specific Cox regression models, which were used to adjust for age, BMI, smoking, ambulatory or hospitalized state, and calendar time, showed that body composition did not predict mortality in women irrespective of whether comorbidities were taken into account. In men, risk of mortality was lower with FFMI in quartiles 3 and 4 [HR: 0.78 (95% CI: 0.62, 0.98) and 0.64 (95% CI: 0.49, 0.85), respectively] but was not affected by FMI. When comorbidities were adjusted for, FFMI in quartile 4 (>19.5 kg/m(2)) still predicted a lower risk of mortality (HR: 0.72; 95% CI: 0.54, 0.96). CONCLUSIONS: Low FFMI is a stronger predictor of mortality than is BMI in older men but not older women. FMI had no impact on mortality. These results suggest potential benefits of preventive interventions with the aim of maintaining muscle mass in older men. This trial was registered at clinicaltrials.gov as NCT01472679.

Low fat-free mass as a marker of mortality in community-dwelling healthy elderly subjects.

BACKGROUND: low fat-free mass has been related to high mortality in patients. This study evaluated the relationship between body composition of healthy elderly subjects and mortality. METHODS: in 1999, 203 older subjects underwent measurements of body composition by bioelectrical impedance analysis, Charlson co-morbidity index and estimation of energy expenditure through physical activity by a validated questionnaire. These measurements were repeated in 2002, 2005 and 2008 in all consenting subjects. Mortality data between 1999 and 2010 were retrieved from the local death registers. The relationship between mortality and the last indexes of fat and fat-free masses was analysed by multiple Cox regression models. RESULTS: women's and men's data at last follow-up were: age 81.1 ± 5.9 and 80.9 ± 5.8 years, body mass index 25.3 ± 4.6 and 26.1 ± 3.4 kg/m(2), fat-free mass index 16.4 ± 1.8 and 19.3 ± 1.9 kg/m(2) and fat mass index 9.0 ± 3.2 and 6.8 ± 2.0 kg/m(2). Fifty-eight subjects died between 1999 and 2010. The fat-free mass index (hazard ratio 0.77; 95% confidence interval 0.63-0.95) but not the fat mass index, predicted mortality in addition to sex and Charlson index. The multiple Cox regression model explained 31% of the variance of mortality. CONCLUSION: a low fat-free mass index is an independent risk factor of mortality in elderly subjects, healthy at the time of body composition measurement.

Body composition changes over 9 years in healthy elderly subjects and impact of physical activity.

BACKGROUND & AIMS: Age-related changes of body composition affect health status. This study aims at clarifying body composition changes in healthy elderly subjects, and evaluating the impact of physical activity on these changes. METHODS: In 1999, 213 subjects ≥ 65 years recruited through advertisements underwent assessment of health state, energy expenditure by physical activity, body composition by bioimpedance analysis and body cell mass by total body potassium. In 2008, 112 of them repeated these assessments with additional determination of Barthel index, Mini Mental State Examination and Geriatric Depression Score. RESULTS: Lean tissues decreased in both genders (p < 0.05). Compared to subjects aged 65-74 years at baseline, those aged ≥75 years lost more body weight (men: -3.7 ± 5.4 vs. 0.4 ± 5.4 kg, women: -3.6 ± 5.5 vs. 0.3 ± 5.2 kg, both p < 0.05), and fat-free mass (men: -3.6 ± 3.3 vs. -0.4 ± 2.7 kg, women: -1.8 ± 2.3 vs. -0.1 ± 2.5 kg, both p < 0.05). Plotting of fat-free mass evolution against age at baseline showed an exponential loss of fat-free mass. Increased physical activity limited lean tissue loss in men but not in women. CONCLUSION: Loss of lean tissues occurs exponentially with aging. Further research should confirm these changes in subjects over 80 years. Increasing physical activity limits fat-free mass loss in men but not women.

Oral nutritional supplements and taste preferences: 545 days of clinical testing in malnourished in-patients.

BACKGROUND & AIMS: Acceptability and intake of oral nutritional supplements are often suboptimal, partly because patients dislike flavour, texture or smell. We assessed the taste preferences about milk-based and fruit-juice typed supplements in malnourished in-patients. METHODS: One hundred and nine in-patients requiring oral nutritional support were assigned to consume four given supplements on four consecutive days, to answer a questionnaire based on a 10-point visual analogue scale (VAS) on acceptance/tolerance, and to choose their preferred product for the fifth day. RESULTS: Overall pleasantness was significantly better for milk-based supplements than for sweet and salty fruit-juice typed products (on VAS: 6.2+/-3.1 versus 4.4+/-3.9, p<0.01 and 3.5+/-3.4, p<0.0001, respectively, when 1 meant "not at all" and 10 "very much"), whereas digestive tolerance was comparable. When offered together on day 5, milk-based products were more frequently preferred (81.6%) than fruit-juice typed supplements (18.4%, p<0.001). Among milk-based products, vanilla, coffee and strawberry had comparable good results, whereas chocolate was less chosen and neutral never. For fruit-juice typed products, tomato obtained better results than orange or apple. CONCLUSIONS: Oral nutritional supplements are globally well-accepted and tolerated, but with variations according to categories and flavours that must be considered to improve compliance.

Hospitalized mechanically ventilated patients are at higher risk of enteral underfeeding than non-ventilated patients.

BACKGROUND & AIMS: Enteral nutrition (EN) is the preferred method of nutrition support in hospitalized patients but only 50-90% of the required calories are actually delivered. In order to identify where our nutrition support team (NST) should focus its activity, we prospectively evaluated the level of coverage of energy and protein needs during the first 5 days of EN in intensive care unit (ICU) and non-ICU patients and the relationship of energy and protein coverage with serum albumin, transthryretin, insulin-like growth factor-1 (IGF-1) and C-reactive protein (CRP). METHODS: Subjects (n=183) who required nutrition support and received EN were prospectively recruited. Calorie prescription was 20 and 25, 25 and 30 kcal/kg BW for women and men 60 years and <60 years, respectively. Protein needs were estimated as 1.2g protein/kg BW. Logistic regression analysis was used to estimate odds ratios (OR) for energy and protein delivery 66.6% and <66.6% and albumin, transthryretin, IGF-1 (low vs. normal) and CRP (high vs. normal) in ventilated vs. non-ventilated patients. RESULTS: Significantly more mechanically ventilated than non-ventilated patients received <66.6% of energy (71% vs. 48%) and protein (96% vs. 65%). The ventilated patients were more likely to be energy (OR 2.1, CI 1.1-4.0) and protein (OR 15.7, CI 4.9-50.8) underfed than non-ventilated patients. There was a significant association on day 5 between low protein delivery and low albumin (OR 2.9, CI 1.3-6.5), low transthyretin (OR 3.0, CI 1.4-6.5), low IGF-1 (OR 2.8, CI 1.2-6.7) and high CRP (OR 3.5, CI 1.6-7.8). CONCLUSIONS: The energy and protein needs of hospitalized patients are not met during the first 5 days of EN. Ventilated patients are more likely to be energy and protein underfed than non-ventilated patients and to have low plasma protein level. These findings support our decision to intensify EN monitoring by our NST in ventilated patients to optimize their nutritional coverage.

Comparison of body weight and composition measured by two different dual energy X-ray absorptiometry devices and three acquisition modes in obese women.

BACKGROUND AND AIMS: Weight measured by dual-energy X-ray (DXA) was shown to be increasingly underestimated in subjects over 75 kg compared to an electronic scale. This study compares body weight and composition measured by balance beam scale and three DXA acquisition modes in obese subjects. METHODS: In 39 obese, body weight was measured by balance beam scale, and body weight and composition by DXA Hologic QDR4500A in normal (NPM) and high power mode (HPM) (Enhanced v8.26 and v8.26* software) and DXA GE-Lunar Prodigy (v6.5 software). To ensure linearity of body weight and composition measured by the different DXA acquisitions, we also measured 13 women with a body mass index (BMI) of 25-30 kg/m(2). RESULTS: While QDR4500A HPM overestimates scale weight by about 2 kg over the whole BMI spectrum, QDR4500A NPM underestimates scale weight as a weight-dependent response (-1.7+/-1.8 kg overall, -4.1+/-1.6 kg in morbidly obese women). These results suggest switching from one mode to the other at a specific threshold, i.e. in our study a weight of 90 kg or a BMI of 34 kg/m(2). Prodigy gives weight about similar to scale (+0.5+/-0.8 kg). Both Hologic acquisition modes underestimate fat mass but overestimate lean body mass compared to Prodigy. CONCLUSIONS: The QDR4500A NPM is inappropriate in women over 90 kg. Unfortunately, the QDR4500A HPM overestimates body weight in the range of 90-150 kg. The difference between scale and Prodigy weight remains stable throughout weight ranges. To better assess their accuracies in terms of body composition, QDR4500A NPM, HPM and Prodigy should be tested against phantoms or in vivo multi-compartment models.

Comparison of tools for nutritional assessment and screening at hospital admission: a population study.

INTRODUCTION: This population study aimed to test the sensitivity and specificity of nutritional risk index (NRI), malnutrition universal screening tool (MUST) and nutritional risk screening tool 2002 (NRS-2002) compared to subjective global assessment (SGA) and to evaluate the association between nutritional risk determined by these screening tools and length of hospital stay (LOS). METHODS: Patients (n=995) were assessed at hospital admission by four screening tools (SGA, NRI, MUST and NRS-2002). Sensitivity, specificity and predictive values were calculated to evaluate NRI, MUST and NRS-2002 compared to SGA. Multiple logistic regressions, adjusted for age, were used to estimate odds ratios (OR) and confidence interval (CI) for medium and high, compared to low risk in patients hospitalized >11, compared to 1-10 days LOS. RESULTS: The sensitivity was 62%, 61% and 43% and specificity was 93%, 76% and 89% with the NRS-2002, MUST and NRI, respectively. NRS-2002 had higher positive (85%) and negative predictive values (79%) than the MUST (65% and 76%) or NRI (76% and 66%, respectively). Patients who were severely malnourished or at high nutritional risk by SGA (OR 2.4, CI 1.5-3.9), MUST (OR 3.1, CI 2.1-4.7) and NRS-2002 (OR 2.9, CI 1.7-4.9) were significantly more likely to be hospitalized >11 days, compared to 1-10 days, than patients assessed as low risk. CONCLUSION: NRS-2002 had higher sensitivity and specificity than the MUST and NRI, compared to SGA. There was a significant association between LOS and nutritional status and risk by SGA, NRS-2002, MUST and NRI. Nutritional status and risk can be assessed by SGA, NRS-2002 and MUST in patients at hospital admission.

[Simplified malnutrition screening tool: Malnutrition Universal Screening Tool (MUST)]. / Outil de dépistage simplifié de la dénutrition: Malnutrition Universal Screening Tool (MUST).

MUST (Malnutrition Universal Screening Tool) is a nutritional screening tool easy to use by any trained care-giver and valid for any adult patient. It considers body mass index, weight change and acute disease effect equally and determines a malnutrition risk score. If necessary, anthropometric measures may be simpliyfied by alternative methods. MUST is reliable between different healthcare settings et promotes detection and management of malnutrition during the patient medical course.

[Role of impedance measurement in nutritional screening]. / Rôle de l'impédancemétrie dans le dépistage de la dénutrition.

Nutritional status has a prognostic value in the clinical evolution of patients who are malnourished, are becoming malnourished or are in process of being rehabilitated. The evaluation of nutritional status is based on a comprehensive approach, and includes body composition measurement by bio-impedance analysis (BIA). BIA determines the quantity of body fat-free and fat mass and has a precision around 4%. The reliability of BIA depends on the use of body composition prediction equations that are adapted to the subjects studied and on the inclusion of various anthropometric parameters (weight, height, sex, age, race, etc). BIA remains imprecise in the presence of abnormal distribution of body compartments (ascites, dialysis, lipodystrophy) or of extreme weights (cachexia, severe obesity). Multi-frequency or segmental BIA were developed to overcome hydration abnormalities and variations in body geometry. However, these techniques require further validation. This review discusses the indications and limitations of BIA.

L-ornithine alpha-ketoglutarate in HIV infection: effects on muscle, gastrointestinal, and immune functions.

OBJECTIVES: There have been claims that l-ornithine alpha-ketoglutarate (OKG) exerts anticatabolic, anabolic, and immunomodulating properties. This study aimed at quantifying the effects of OKG on muscle force, body composition, and immune function in outpatients infected with the human immunodeficiency virus (HIV) and presenting weight loss. METHODS: Forty-six HIV(+) patients were included in a double-blind, prospective, randomized, controlled trial for 12 wk (10 g/d of OKG or isonitrogenous placebo and nutritional counseling). Podometry, handgrip strength, step test, triceps skinfold thickness, 50-kHz bioelectrical impedance, 3-d diet record, CD4 cell count, HIV-1 RNA concentration (viral load), and gastrointestinal symptoms were assessed at 0, 4, 8, and 12 wk. RESULTS: At baseline, patients (OKG, n = 22; placebo, n = 24) has similar CD4 counts (338 +/- 172 and 310 +/- 136 cells/mL), viral load (3.6 +/- 1.3 and 3.5 +/- 1.3 log(10) copies/mL), body mass index (20.0 +/- 2.4 and 20.6 +/- 3.0 kg/m(2)), weight loss (9.0 +/- 3.12 and 9.4 +/- 3.0 kg), and food intake (2509 +/- 962 and 2610 +/- 808 kcal/d). Twenty-nine patients completed the protocol. Both groups increased their body mass index (P = 0.02 versus baseline) and triceps skinfold thickness (P < 0.01 versus baseline). They showed a similar positive correlation between handgrip strength and fat-free mass. Frequency of gastrointestinal symptoms increased in the OKG group (86% versus 54% in the placebo group, P = 0.025). No other differences were observed between groups. CONCLUSIONS: All patients increased their body mass index and triceps skinfold thickness due to food supplementation and diet counseling. Oral OKG failed to improve nutritional, functional, or immunologic status in these weight-losing HIV(+) patients and had important gastrointestinal side effects.

[ Evaluation of the nutritional state of patients in emergency services: contribution in measurement of body composition]. / Evaluation de l'état nutritionnel des patients admis aux urgences: contribution de la mesure de la composition corporelle.

Weight and body mass index are used in clinical routine to detect malnourished patients. However, a study performed at the emergency admission of the University Hospital of Geneva shows that the prevalence of malnutrition is very underestimated when based on body mass index instead of fat-free mass. Indeed, a third of the patients with normal body mass index, i.e. between 20 and 24.9 kg/m2, present a fat-free mass under percentile 10. This suggests that the body mass index alone cannot be used as diagnostic criteria for malnutrition. This review describes the prevalence of malnutrition at the emergency admission of the University Hospital of Geneva, the prevalence of malnutrition in the literature and its relationship with length of hospital stay and mortality.

Body composition in 995 acutely ill or chronically ill patients at hospital admission: a controlled population study.

OBJECTIVE: To determine if fat-free mass and fat mass in acutely ill and chronically ill patients differed from healthy controls at hospital admission and if prevalence of malnutrition differed by body mass index (BMI) or fat-free mass percentile. SUBJECTS/SETTING: 995 consecutive patients 15 to 100 years of age admitted to the hospital were measured in the hospital admission center and compared with 995 healthy age- and height-matched subjects DESIGN: Cross-sectional study. Fat-free mass, fat mass, and percentage fat mass were determined by 50 kHz bioelectrical impedance analysis. Prevalence of malnutrition was determined by BMI < or = 20 kg/m2 or fat-free mass in the 10th percentile. STATISTICAL ANALYSIS: Analysis of variance was used to examine differences between acutely ill and chronically ill patients and controls and between age groups. RESULTS: Fat-free mass was significantly lower in patients than controls (P< or = .05), and the difference with age in fat-free mass in patients was greater than the age-related difference in the controls. A higher percentage fat mass was found in spite of lower BMI in chronically ill patients older than 55 years. Among participants, 25% of acutely ill and 37.3% of chronically ill patients fell below fat-free mass in the 10th percentile, compared with 15.6% of acutely ill and 18.9% of chronically ill patients falling below BMI < or = 20 kg/m2. APPLICATIONS/CONCLUSION: Weight and BMI do not evaluate body compartments and therefore do not reveal if weight changes result in loss of fat-free mass or gain in fat mass. In spite of minimal differences in BMI between patients and controls, we found that fat-free mass was lower and fat mass was higher in acutely ill and chronically ill patients than controls. The objective measurement of body composition, as part of a comprehensive nutritional assessment, helps to identify subjects who have low fat-free mass or high fat mass.