Laurence Genton

Single Prediction Equation for Bioelectrical Impedance Analysis in Adults Aged 20 -94 Years

Existing equations for bioelectrical impedance analysis (BIA) are of limited use when subjects age or become overweight because these equations were developed in young, normal-weight subjects and are not valid in elderly or overweight people. The purpose of this study was to validate a single BIA equation in healthy white subjects aged 22--94 y with a body mass index between 17.0 and 33.8 kg/m(2). Healthy subjects (202 men and 141 women) aged 20--94 y were measured by two methods: fat-free mass (FFM) was measured by dual-energy x-ray absorptiometry (Hologic QDR-4500) and by a bioelectrical impedance analyzer (Xitron 4000B). Validity of BIA was assessed by double cross validation. Because correlations were high (r = 0.986--0.987) and prediction errors low, a single equation was developed using all subjects, as follows: FFM = -4.104 + (0.518 x height(2)/resistance) + (0.231 x weight) + (0.130 x reactance) + (4.229 x sex: men = 1, women = 0). FFM predicted with dual-energy x-ray absorptiometry was 54.0 +/- 10.7 kg. BIA-predicted FFM was 54.0 +/- 10.5 kg (r = 0.986, standard error of the estimate = 1.72 kg, technical error = 1.74 kg). In conclusion, the new Geneva BIA equation was valid for prediction of FFM in healthy adults aged 22--94 y with body mass indexes between 17.0 and 33.8 kg/m(2). Inclusion of reactance in the single prediction equation appeared to be essential for use of BIA equations in populations with large variations in age or body mass.

Fat-free and fat mass percentiles in 5225 healthy subjects aged 15 to 98 years

OBJECTIVES Fat-free mass (FFM) and fat mass (FM) are important in the evaluation of nutritional status. Bioelectrical impedance analysis (BIA) is a simple, reproducible method used to determine FFM and FM. Because normal values for FFM and FM have not yet been established in adults aged 15 to 98 y, its use is limited in the evaluation of nutritional status. The aims of this study were to determine reference values for FFM, FM, and percentage of FM by BIA in a white population of healthy adults, observe their differences with age, and develop percentile distributions for these parameters between ages 15 and 98 y. METHODS Whole-body resistance and reactance of 2735 healthy white men and 2490 healthy white women, aged 15 to 98 y, was determined by 50-kHz BIA, with four skin electrodes on the right hand and foot. FFM and FM were calculated by a previously validated, single BIA formula and analyzed for age decades. RESULTS Mean FFM peaked in 35- to 44-y-old men and 45- to 54-y-old women and declined thereafter. Mean FFM was 8.9 kg or 14.8% lower in men older than 85 y than in men 35 to 44 y old and 6.2 kg or 14.3% lower in women older than 85 y than in women 45 to 54 y old. Mean FM and percentage of FM increased progressively in men and women between ages 15 and 98 y. The results suggested that the greater weight noted in older subjects is due to larger FM. CONCLUSIONS The percentile data presented serve as reference to evaluate deviations from normal values of FFM and FM in healthy adult men and women at a given age.

Total body mass, fat mass, fat-free mass, and skeletal muscle in older people: cross-sectional differences in 60-year-old persons.

OBJECTIVES: To evaluate body composition parameters, including fat‐free mass (FFM), appendicular skeletal muscle mass (ASMM), relative skeletal muscle mass (RSM) index, body cell mass (BCM), BCM index, total body potassium (TBK), fat mass, percentage fat mass (FM), and their differences between age groups and to evaluate the frequency of sarcopenia in healthy older subjects

Dual-energy X-ray absorptiometry and body composition: differences between devices and comparison with reference methods.

Body composition measurements provide essential information for assessing and monitoring nutrition state.1 Some researchers use underwater weighing or potassium counting as reference methods for total body composition. However, these methods assume a constant density and potassium content of lean body mass (LBM), which may not be true, and measure only two compartments, fat (FM) and LBM.2 A multicompartment approach combining different technologies that measure bone, mineral, muscle and water is preferable and currently considered the gold standard.3 Nevertheless, its high costs, long duration, and the potential intolerance of patients limit its use in clinical routine. Thus, other methods, especially dual-energy x-ray absorptiometry (DXA), have been investigated. Although the original purpose of DXA was to determine bone mineral density, recent devices measure total and regional body composition of three compartments, fat and lean soft tissues and bone mineral.4 This editorial focuses on the advantages and limitations of DXA and the differences in total and regional body compositions. In this article, the combination of lean soft tissue and bone mineral is referred to as LBM.

Hospital length of stay and nutritional status.

Purpose of reviewThis review looks at the recent medical literature on the association between hospital length of stay and nutritional status. Recent findingsSimple anthropometric parameters underestimate the nutritional risk in hospitalized patients. The Malnutrition Universal Screening Tool and Nutritional Risk Screening are simple screening tools that identify patients who require further monitoring. Recent weight loss appears to be the most important single indicator of nutritional status. Body composition measurements identify patients with muscle mass depletion and excess body fat, both of which are significantly associated with increased length of stay. The Subjective Global Assessment is useful at detecting patients with established malnutrition and the Mini Nutritional Assessment for the elderly is useful at detecting patients who need preventive nutritional measures. The Nutritional Risk Index, which incorporates albumin and weight loss, appears to capture both nutritional risk and poor clinical outcome. SummaryNutritional risk is associated with the length of stay in hospital. The choice of nutritional screening and assessment tools depends on the type of institution (university hospital versus community hospital), the patient populations (acute care versus intermediary care; general hospital versus elderly population) and the resources available.

Body composition: Why, when and for who?

Body composition reflects nutritional intakes, losses and needs over time. Undernutrition, i.e. fat-free mass (FFM) loss, is associated with decreased survival, worse clinical outcome and quality of life, as well as increased therapy toxicity in cancer patients. In numerous clinical situations, such as sarcopenic obesity and chronic diseases, the measurement of body composition with available methods, such as dual-X ray absorptiometry, computerized tomography and bioelectrical impedance analysis, quantifies the loss of FFM, whereas body weight loss and body mass index only inconstantly reflect FFM loss. The measurement of body composition allows documenting the efficiency of nutrition support, tailoring the choice of disease-specific and nutritional therapies and evaluating their efficacy and putative toxicity. Easy-to-use body composition methods integrated to the routine of care allow sequential measurements for an initial nutritional assessment and objective patients follow-up. By allowing an earlier and objective management of undernutrition, body composition assessment could contribute to reduce undernutrition-induced morbidity, worsening of quality of life, and global health care costs by a timely nutrition intervention.

Assessment of food intake in hospitalised patients: a 10-year comparative study of a prospective hospital survey

BACKGROUND & AIMS A food quality control and improvement permanent process was initiated in 1999. To evaluate the food service evolution, protein-energy needs coverage were compared in 1999 and 2008 with the same structure survey in all hospitalized patients receiving 3 meals/day. METHODS Nutritional values of food provided, consumed and wasted over 24h including non-exclusive nutritional support were calculated individually. Nutritional needs were estimated as 110% of Harris-Benedict formula for energy and 1.2 or 1.0 g protein/kg/day for patients <65 or ≥65 years old, respectively. Multivariate analysis identified factors associated with low nutritional intake in both populations standardized to body mass index (BMI) of 1999s patients. RESULTS Out of 1677 patients, 1291 were included. Mean BMI was higher in 2008 than 1999 (P<0.001). The proportion of underfed patients was unchanged (69 vs. 70%, NS). The consumption of ≥1 oral nutritional supplements (ONS) daily increased the protein needs coverage from 80% to 115% (P<0.001). The year 1999, high BMI, 1st week of hospital stay, specific diet, ONS absence and low meal quality were associated with low nutritional intakes. CONCLUSION The nutritional needs coverage could have improved in 2008 if BMI was similar to 1999s. ONS consumption is associated with a lower risk of underfeeding in hospitalized patients.

Validation of a bioelectrical impedance analysis equation to predict appendicular skeletal muscle mass (ASMM)

RATIONALE Appendicular skeletal muscle mass (ASMM) is useful in the evaluation of nutritional status because it reflects the body muscle protein mass. The purpose of this study was to validate, against dual-energy X-ray absorptiometry (DEXA), a BIA equation to predict ASMM to be used in volunteers and patients. METHOD Healthy men (n = 246 men, BMI 25.3+/-2.9 kg/m(2)) and women (n =198, 24.1+/-3.6 kg/m(2)), and heart, lung and liver transplant patients (213 men, BMI of 24.6+/-4.4 kg/m(2); 113 women, BMI 23.0+/-5.2 kg/m(2)) were measured by BIA (Xitron Technologies) and DEXA (Hologic QDR 4500). A BIA equation to predict ASMM (kg) that included height(2)/resistance, weight, gender, age and reactance, was developed by means of multiple regressions. [table: see text] Mean difference (Bland-Altman) for volunteers was 0.1+/-1.1 kg, r =0.95, SEE 1.12 kg and for patients -0.4+/-1.5 kg, r =0.91, SEE 1.5 kg. Best fitted multiple regression equation was -4.211 + (0.267 x height2 / resistance) + (0.095 x weight)+(1.909 x sex (men = 1, women = 0)) + (-0.012 x age) + (0.058 x reactance). CONCLUSIONS BIA permits the prediction of ASMM in healthy volunteers and patients between 22 and 94 years of age. A slightly larger, though clinically not significant, error was noted in patients.

Comparison of Four Bioelectrical Impedance Analysis Formulas in Healthy Elderly Subjects

Background: Changes of body composition occur with aging and influence health status. Thus accurate methods for measuring fat-free mass (FFM) in the elderly are essential. Objective: The purpose of this study was to compare FFM obtained by three bioelectrical impedance analysis (BIA) published formulas specific for the elderly and one equation intended for all age groups, with FFM derived from dual-energy X-ray absorptiometry (FFMDXA), in healthy elderly subjects. Methods: Healthy Caucasian subjects over 65 years (106 women, age 75 ± 6.2, body mass index 25.2 ± 4.1 and 100 men, age 74.6 ± 6.6, body mass index 25.8 ± 3.0) were measured by DXA (Hologic QDR-4500) and BIA (Xitron, 50 kHz). FFMBIA was calculated by the published formulas of Deurenberg, Baumgartner, Roubenoff and Kyle and compared to FFMDXA by a Bland-Altman analysis. Results: The Deurenberg and Roubenoff BIA formulas underestimated FFM compared to DXA by –7.1 and –2.9 kg in women and –6.7 and –2.3 kg in men, respectively. The Baumgartner formula overestimated FFM by 4.3 kg in women and 1.4 kg in men. The Kyle formula showed differences of 0.0 kg in women and 0.2 kg in men, and the limits of agreement of FFMBIA (Kyle) relative to FFMDXA were –3.3 and +3.3 kg for women and –3.8 and +4.3 kg for men. Conclusion: The Kyle BIA formula accurately predicts FFM in elderly Swiss subjects between 65 and 94 years, with a body mass index of 17 to 34.9 kg/m2. The other BIA formulas developed especially for the elderly are not valid in this population.

Nutrition status in patients younger and older than 60 y at hospital admission: a controlled population study in 995 subjects

OBJECTIVE Body weight and body mass index are easily obtainable indicators of nutrition status but do not provide information on changes in fat-free mass (FFM) and fat mass with age. In this prospective controlled study, we investigated whether body composition measurements were useful in identifying moderately or severely depleted patients, as judged by the Subjective Global Assessment at hospital admission. In addition, the subjects were grouped by age (< or =60 and >60 y) to determine whether there was an effect of aging on the prevalence of malnutrition. METHODS Nine hundred ninety-five consecutive patients were evaluated for malnutrition by body mass index, serum albumin, Subjective Global Assessment, and 50-kHz bioelectrical impedance analysis and compared with 995 age- and height-matched healthy volunteers for FFM and fat mass. RESULTS A body mass index less than 20 kg/m(2) was found in 17.3% of patients. Low albumin (< or =34.9 g/L) was found in 14.9% of all patients and 23.7% of those older than 60 y. In contrast, 23.1% and 38.3% of all patients were severely and moderately depleted, respectively, according to the Subjective Global Assessment. FFM was significantly lower in severely depleted men and women and moderately depleted women (P < or = 0.001), and fat mass was significantly higher (P < or = 0.05) in well-nourished patients than in volunteers. Patients older than 60 y had lower FFM and higher fat mass than did patients 60 y or younger or volunteers (P < or = 0.001). CONCLUSION The prevalence of malnutrition was greater in patients older than 60 y than in those 60 y and younger. Patients classified as severely depleted according to the Subjective Global Assessment were depleted of FFM. Body composition measurement can help to identify patients with low FFM and high fat mass.